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journal.pntd.0007649

A high-throughput and multiplex microsphere immunoassay based on non-structural protein 1 can discriminate three flavivirus infections

Despite a marked decrease of Zika virus ( ZIKV ) infection since late 2017 , the specter of congenital Zika syndrome ( CZS ) and its re-emergence in flavivirus-endemic regions highlight the need for sensitive and specific diagnostic tests 1–4 ., Similar to the laboratory diagnosis for other flaviviruses , detection of nucleic acid as soon as possible post-symptom onset ( PSO ) is considered as the gold standard to confirm ZIKV infection , 5 , 6 ., Since many individuals test for ZIKV infection beyond the period when RNA is detectable and most ( ~80% ) of ZIKV infections are asymptomatic , serological tests remain as a key component of ZIKV confirmation 5 , 6 ., Furthermore , ZIKV can be transmitted sexually or following asymptomatic infection 7–9 ., ZIKV is a member of the genus Flavivirus of the family Flaviviridae , which includes several pathogenic mosquito-borne viruses in different serocomplexes ., The four serotypes of dengue virus ( DENV ) belong to the DENV serocomplex; West Nile virus ( WNV ) and Japanese encephalitis virus ( JEV ) to the JEV serocomplex; yellow fever virus ( YFV ) as a single member; and ZIKV10 ., Given that the envelope ( E ) protein is the major target of antibody response after flavivirus infection , different E antigens such as recombinant E protein , inactivated virions or virus-like particles have been developed for serological tests 10–13 ., Due to the presence of several highly conserved residues of flavivirus E proteins , anti-E antibodies in serum are commonly cross-reactive to different flaviviruses 13–17 ., The guidelines of Centers for Disease Control and Prevention ( CDC ) recommend that positive or equivocal results of E protein-based IgM tests require further testing with time-consuming plaque reduction neutralization tests ( PRNT ) 5 , 6 ., However , PRNT can confirm ZIKV-infected individuals who acquire ZIKV as the first flavivirus infection , known as primary ZIKV ( pZIKV ) infection , but often can only be interpreted as unspecified flavivirus infections for those who have experienced previous DENV or other flavivirus infections , limiting its application for ZIKV serodiagnosis in flavivirus-endemic regions ., When 795 sera that were IgM positive for ZIKV antigen by ELISA were tested for flavivirus neutralizing antibodies by PRNT , 45% were positive for ZIKV and at least one other flavivirus 18 ., This non-specificity may be an inherent property of the early post-infection response to ZIKV or reflect prior flavivirus experience ., A large number of Americans ( 7 million ) have experienced a WNV infection since 1999 19 and ~8 million traveled to yellow fever endemic countries in 2015 20 , 21 ., Thus , a sensitive , specific and multiplex serological test that can distinguish ZIKV and other flavivirus infections is needed in both U . S . and flavivirus-endemic countries 18 ., Moreover , several studies have shown that anti-DENV or WNV antibodies can enhance ZIKV infection in vitro 22–26 and in small animals , in which administration of DENV-immune plasma resulted in increased viremia and mortality in stat2 knock out mice 27 ., This is known as antibody-dependent enhancement , in which antibody at suboptimal concentration for neutralization can enhance DENV , ZIKV or other flavivirus entry and replication in Fcγ receptor-bearing cells such as monocytes and is believed to contribute to disease pathogenesis 28 ., Despite ADE of ZIKV by previous DENV immunity was not supported by two studies in non-human primates 29 , 30 , more in-depth studies of DENV immunity on ZIKV disease outcome and complication in humans are warranted 31–33 ., Thus , serological tests that can distinguish pZIKV infection ( p = primary ) from ZIKV infection with previous DENV ( ZIKVwprDENV , wpr = with previous ) infection are crucial to understand the pathogenesis of ZIKV and CZS in regions where ZIKV and DENV co-circulate ., Compared with traditional E protein-based assays , several enzyme-linked immunosorbent assays ( ELISAs ) based on ZIKV nonstructural protein 1 ( NS1 ) , including a recently reported blockade of binding ELISA , have shown improved specificity 34–39 ., However , secondary DENV ( sDENV ) and ZIKVwprDENV infections , of which both were common in endemic regions , cannot be discriminated 34–39 ., Moreover , none can detect and distinguish ZIKV , DENV and other flavivirus in a single assay ., With its high-throughput and multiplex ( up to 100-plex ) capacity , microsphere immunoassay ( MIA ) has been employed in the detection of cytokines , transplantation and transfusion antigens , and various bacterial and viral pathogens 40–43 ., Previously , we reported that a combination of ELISAs based on the NS1 proteins of DENV and ZIKV can distinguish various DENV and ZIKV infections 44 , 45 ., In this study , we developed a high-throughput and multiplex IgG MIA using NS1 proteins of DENV1 to DENV4 , ZIKV and WNV , and showed that the NS1 IgG MIA can detect and distinguish not only primary DENV , ZIKV and WNV infections but also sDENV and ZIKVwprDENV infections ., The Institutional Review Boards ( IRB ) of the University of Hawaii approved this study ( CHS #17568 , CHS#23786 ) ., S1 Table summarizes the numbers , serotypes , sampling time and sources of different panels of serum or plasma samples , including those from primary DENV1 ( pDENV1 ) , primary DENV2 ( pDENV2 ) , primary DENV3 ( pDENV3 ) , primary WNV ( pWNV ) , pZIKV , sDENV and ZIKVwprDENV infections as well as flavivirus-naïve individuals ., Samples collected <3 months or ≥3 months PSO were designated as convalescent- or post-convalescent-phase samples , respectively ., Samples from reverse transcription-PCR ( RT-PCR ) confirmed Zika cases were from the Pediatric Dengue Cohort Study ( PDCS ) and the Pediatric Dengue Hospital-based Study in Managua , Nicaragua between July 2016 and March 2017 46 , 47 ., The Zika cases that were DENV-naïve or previously DENV-exposed were defined as pZIKV ( p = primary ) or ZIKVwprDENV ( wpr = with previous ) panels , respectively ., The DENV-immune status was based on anti-DENV antibody testing by an inhibition ELISA at entry and annually of the PDCS 44–47 ., Parents or legal guardians of all participants provided written informed consents , and participants ≥6-year old provided assents ., These studies were approved by the IRBs of the University of California , Berkeley , and Nicaraguan Ministry of Health ., Thirty-six plasma samples from blood donors , who were tested WNV-positive by the transcription-mediated amplification ( a sensitive nucleic acid detection method used in blood bank ) , IgM and IgG antibodies between 2006 and 2015 , designated as pWNV infection , were provided by the American Red Cross at Gaithersburg , Maryland 48 ., Pre-2015-16 ZIKV epidemic convalescent- and post-convalescent-phase samples from RT-PCR confirmed cases with different primary DENV infections ( pDENV1 , pDENV2 , and pDENV3 ) or sDENV infection were from Taiwan , Hawaii and Nicaragua; 53 flavivirus-naïve samples from a seroprevalence study in Taiwan were included as control in this study 44 , 45 , 49–52 ., Samples from cases with primary DENV4 infection were not available ., Primary DENV or sDENV infection was determined by IgM/IgG ratio or focus-reduction neutralization tests as described previously 49–51 ., The NS1 gene ( corresponding to amino acid residues 1–352 ) of ZIKV ( HPF2013 strain ) with a His-tag at the C-terminus was codon-optimized ( Integrated DNA Technologies , Skokie , IL ) and cloned into pMT-Bip vector to establish a Drosophila S2-cell stable clone 44 ., ZIKV-NS1 protein from supernatants of the stable clone was purified by fast purification chromatography system ( AKTA Pure , GE Health Care Bio-Science , Pittsburg , PA ) 44 ., Purified DENV1-4 and WNV NS1 proteins were purchased from The Native Antigen ( Oxford , UK ) ., Ten μg each of the 6 purified NS1 proteins , bovine serum albumin ( BSA ) and PBS ( as negative antigen control ) were coupled individually onto 8 types of magnetic carboxylated miscrosphere beads ( 1 . 25 X 106 each ) containing different fluorophores ( MagPlexTM-C ) ( Luminex , TX , Austin ) using two-step carbodiimide process at room temperature 53 , 54 ., The antigen-conjugated microspheres were stored in 250 uL PBN buffer ( PBS with 1% BSA and 0 . 05% sodium azide , Sigma Aldrich ) at 4°C until use ., Eight types of microsphere beads coupled with different NS1 proteins , BSA or PBS were combined and diluted in PBS-1% BSA ., Fifty μL of the mixture ( containing ~1250 beads of each type ) were added to each well of a flat-bottom 96-well plate , and incubated with 50 μL diluted serum or plasma ( 1:100 dilution in PBS-1% BSA ) at 37°C for 30 min in the dark , followed by wash with 200 μL of PBS-1% BSA twice , incubation with 50 μL of red phycoerythrin-conjugated anti-human or anti-mouse IgG ( Jackson Immune Research Laboratory , West Grove , PA ) at 37°C for 45 min in the dark , and wash with 200 μl of PBS-1% BSA twice 54 ., Microspheres were then resuspended in 100 μl of PBS-1% BSA , incubated for 5 min and read by Luminex 200 machine ( Austin , TX ) ., All incubations were performed on a plate shaker at 700 rpm and all wash steps used a 96-well magnetic plate separator ( Millipore Corp . , Billerica , MA ) 54 ., Each plate includes two positive controls ( confirmed-ZIKV or DENV infection ) , four negative controls ( flavivirus-naïve samples ) , samples , and mouse anti-His mAb ( all in duplicates ) ., The median fluorescence intensity ( MFI ) was determined for 100 microspheres for each well ., The MFI values for each antigen were divided by the mean MFI value of one positive control ( MFI~104 ) and multiplied by 104 to calculate to rMFI for comparison between plates ( S1 Fig ) ., The cutoff rMFI for each antigen was defined by the mean rMFI value of 19 flavivirus-naïve samples plus 5 standard deviations , which gave a confidence level higher than 99 . 9% from 4 negatives 55 ., Each MIA was performed twice ( each in duplicate ) ., New batch of conjugated antigens was tested with flavivirus-naïve panel to determine the cutoff rMFI ., DENV1- , DENV2- , DENV3- , and ZIKV-NS1 IgG ELISAs have been described previously 44 , 45 ., Briefly , purified NS1 proteins ( 16 ng for individual NS1 protein per well ) were coated on 96-well plates at 4°C overnight , followed by blocking ( StartingBlock blocking buffer , Thermo Scientific , Waltham , MA ) , incubation with primary antibody ( serum or plasma at 1:400 dilution ) and secondary antibody ( anti-human IgG conjugated with horseradish peroxidase , Jackson Immune Research Laboratory , West Grove , PA ) , and wash 44 , 45 ., After adding tetramethylbenzidine substrate ( Thermo Scientific , Waltham , MA ) followed by stop solution , the optical density ( OD ) at 450 nm was read with a reference wavelength of 630 nm ., Each ELISA plate included two positive controls ( confirmed-ZIKV or DENV infection ) , four negative controls ( flavivirus-naïve sample ) , and samples ( all in duplicate ) ., The OD values were divided by the mean OD value of one positive control ( OD close to 1 ) in the same plate to calculate the relative OD ( rOD ) values for comparison between plates 44 , 45 ., The cutoff rOD was defined by the mean rOD value of negatives plus 12 standard deviations , which gave a confidence level of 99 . 9% from 4 negatives 55 ., Each ELISA was performed twice ( each in duplicate ) ., Two-tailed Mann-Whitney test was used to determine the P values between two groups , the two-tailed Spearman correlation test the relationship between the rOD and rMFI values , and the receiver-operating characteristics ( ROC ) analysis the cutoffs of the rMFI and rOD ratios ( GraphPad Prism 6 ) ., The 95% confidence interval ( CI ) was calculated by Excel ., We first employed the multiplex NS1 IgG MIA to test samples from primary DENV ( pDENV1 , pDENV2 and pDENV3 ) , pZIKV and pWNV infection panels ., Compared with flavivirus-naïve panel , the pDENV1 panel recognized the NS1 proteins of DENV1 ( 100% ) and other DENV serotypes ( 33 . 3 to 61 . 9% ) , but not those of different serocomplexes ( ZIKV and WNV NS1 proteins ) ( Fig 1A and 1B ) ., Similarly , the pDENV2 and pDENV3 panels recognized the NS1 protein of the homologous serotype ( DENV2 , DENV3 ) better than those of other serotypes ( Fig 1C and 1D ) , but did not recognize ZIKV or WNV NS1 protein except two samples ( recognizing WNV , 2/13 ) ., The pZIKV panel recognized ZIKV NS1 protein but not those of WNV and DENV except two sample recognizing DENV2 ( 2/38 ) , whereas the pWNV panel recognized WNV proteins rather than those of ZIKV and DENV except one sample ( recognizing DENV4 , 1/36 ) ( Fig 1E and 1F ) ., Taken together , these findings suggested that primary infection panels recognized the homologous ( infecting serotype ) NS1 protein better than other NS proteins within the same serocomplex , and in general did not recognize an NS protein of different serocomplexes ( Fig 1G ) ., We next tested samples from sDENV and ZIKVwprDENV panels ., For convalescent-phase samples , sDENV panel not only recognized NS1 proteins of DENV1-4 ( 66 . 7 to 100% ) but also those of ZIKV and WNV ( 45 . 8 to 54 . 2% ) ( Fig 2A ) ., The ZIKVwprDENV panel recognized ZIKV NS1 protein ( 100% ) as well as DENV1-4 and WNV NS1 proteins ( 60 . 0 to 90 . 0% ) ( Fig 2B ) ., A similar trend was observed for post-convalescent-phase samples ( Fig 2C and 2D ) ., These findings were in agreement with our previous reports based on NS1 IgG ELISAs 44 , 45 , and suggested that after repeated flavivirus infections , such as sDENV and ZIKVwprDENV infections , anti-NS1 antibodies cross-reacted to multiple NS1 proteins , including those from prior exposure or sometimes those with no prior exposure ., Previously we reported that sDENV panel not only recognized DENV1 NS1 protein but also ZIKV NS1 protein in IgG ELISA ( 95 . 8 and 66 . 7% , respectively ) ; similarly the ZIKVwprDENV panel recognized both ZIKV and DENV1 NS1 proteins ( 95 . 0 and 85 . 0% , respectively ) 44 ., Using the rOD ratio of ZIKV NS1 to DENV1 NS1 with a cutoff at 0 . 24 , we can distinguish ZIKVwprDENV and sDENV panels ., Since the same sDENV and ZIKVwprDENV panels recognized both DENV1 and ZIKV NS1 proteins in IgG MIA ( Fig 2A and 2B ) , we calculated the ratio of relative median fluorescence intensity ( rMFI ) of ZIKV NS1 to that of DENV1 NS1 and found that a cutoff of the rMFI ratio at 0 . 62 , as determined by ROC analysis , can distinguish these two panels with a sensitivity of 88 . 9% and specificity of 91 . 7% ( Fig 2E ) ., Since both panels also recognized DENV2 NS1 protein , we further calculated the ratio of rMFI of ZIKV NS1 to DENV2 NS1; interestingly a cutoff of the rMFI ratio at 0 . 62 was able to distinguish these two panels with a sensitivity of 94 . 4% and specificity of 90 . 9% ( Fig 2F ) ., Similar observations were found for post-convalescent-phase sDENV and ZIKVwprDENV panels; these two panels can be distinguished by a cutoff ( 0 . 62 ) of the rMFI ratio for ZIKV NS1 to DENV1 NS1 or DENV2 NS1 with a sensitivity/specificity of 90 . 0/100% or 83 . 3/100% , respectively ( Fig 2G and 2H ) ., Since these panels have been tested with individual DENV1 to DENV4 and ZIKV NS1 IgG ELISAs previously 45 , we compared the detection rates for each NS1 protein between ELISA and MIA ., For the pZIKV panel , ZIKV NS1 ELISA had a detection rate of 100% , comparable to that of MIA , for the post-convalescent-phase samples , but only 5% for the convalescent-phase samples , which was much lower than that of MIA ( 100% ) ( Fig 3A and 3B ) ., Although 19 convalescent-phase pZIKV samples were tested negative by ZIKV NS1 IgG ELISA , the relative optical density ( rOD ) values were positively correlated with the rMFI values ( correlation coefficient r = 7464 , P = 0 . 0002 ) ( Fig 3C ) , suggesting that ZIKV NS1 MIA was more sensitive than ELISA ., A positive correlation was also found between rOD and rMFI values for the post-convalescent-phase samples ( r = 8922 , P<0 . 0001 ) ( Fig 3D ) ., For pDENV1 panel , DENV1 NS1 ELISA and MIA had comparable detection rates ( 100% ) for both convalescent and post-convalescent-phase samples ( Fig 3E and 3F ) ., Similarly , a positive correlation was found between rOD and rMFI values ( Fig 3G and 3H ) ., For ZIKVwprDENV panels , ZIKV NS1 IgG ELISA and MIA had comparable detection rates for both convalescent and post-convalescent-phase samples ( Fig 4A and 4B ) ., A positive correlation was found between rOD and rMFI values for ZIKV NS1 as well as DENV1 , DENV2 , DENV3 and DENV4 NS1 tested ( Fig 4C–4E ) ., Similar observations were found for sDENV panels ( S2 Fig ) ., Table 1 summarizes the results of all samples tested with different NS1 proteins ( DENV1 , DENV2 , DENV3 , DENV4 , DENV1 , 2 , 3 or 4 , ZIKV and WNV ) in the IgG MIA ., For statistical analysis comparing different panels , one sample from each participant was included ( S2 Table ) ., The overall sensitivity of each DENV ( DENV1 , DENV2 , DENV3 ) NS1 IgG MIA to detect different DENV infections ranged from 73 . 6 to 90 . 1% and specificity from 98 . 1 to 100% ( Table 2 ) ., Interestingly , combination of four DENV NS1 IgG MIA increased the sensitivity to 94 . 5% , while maintaining the specificity of 97 . 2% , suggesting that this multiplex assay can be applied to detect DENV infections rather than distinguish different DENV serotypes ., For the ZIKV NS1 IgG MIA , the overall sensitivity was 100% and specificity 87 . 9% ., For the WNV NS1 IgG MIA , the overall sensitivity was 86 . 1% and specificity 78 . 4% ( Table 2 ) ., In this study , we developed a high-throughput and multiplex IgG MIA using NS1 proteins of DENV1 to DENV4 , ZIKV and WNV to detect and distinguish various DENV , ZIKV and WNV infections ., Based on the results , we propose an algorithm to discriminate primary DENV , pZIKV and pWNV infections , sDENV infection and ZIKVwprDENV infection ( Fig 5 ) ., Previous studies of flavivirus serodiagnosis mainly focused on two flaviviruses ., Compared with a recent study of IgG MIA containing ZIKV and DENV antigens , our multiplex IgG MIA consists of 6 antigens ( DENV1 to DENV4 , WNV and ZIKV NS1 proteins ) plus two controls ( BSA and PBS ) 56 ., To our knowledge , this is the first report of a single serological test to detect three flavivirus infections ., Our findings that combination of DENV1 to DENV4 NS1 IgG MIA increased the sensitivity to 94 . 3% while maintaining a specificity of 97 . 2% and that the rMFI ratio of ZIKV NS1 to DENV1 or DENV2 NS1 can distinguish ZIKVwprDENV and sDENV infections with a sensitivity of 83 . 3–94 . 4% and specificity of 90 . 9–100 . 0% have important applications to serodiagnosis and serosurveillance of DENV and ZIKV infections in regions where both viruses co-circulate ., Generally in agreement with our recent study of individual DENV NS1 ELISAs 45 , we found that DENV1 and DENV3 NS1 IgG MIAs can detect primary DENV infection of the homologous serotype with a sensitivity ( 100% ) higher than that for heterologous serotypes ( 25 . 0 to 100% ) ( Table 2 ) ., DENV1 , DENV2 and DENV3 NS1 IgG MIAs can detect secondary DENV infection with a sensitivity of 95 . 5 to 100% ., This was also consistent with our previous study using Western blot analysis , in which anti-NS1 antibodies recognized NS1 protein predominantly of the infecting serotype after primary DENV infection and multiple NS1 proteins after secondary infection 13 ., Taken together , due to the variable and extensive cross-reactivity of anti-NS1 antibodies after primary and secondary DENV infections , respectively , it is difficult to use a single NS1 IgG MIA or ELISA to identify the infecting DENV serotype ., Notably , the combination of four DENV NS1 IgG MIA can detect different primary and secondary DENV infections with a sensitivity of 94 . 3% and specificity of 97 . 2% ( Table 2 ) , suggesting the feasibility and application of this multiplex NS1 IgG MIA to detect DENV infection rather than distinguish DENV serotypes ., The overall sensitivity of the ZIKV NS1 IgG MIA was 100% and the specificity was 87 . 9% , primarily due to the cross-reactivity of the sDENV panel ( Table 2 ) ., The sensitivity ( 100% ) was higher than or comparable with those previously reported ( 79 to 100% ) using the Euroimmun ZIKV NS1 IgG ELISA kit 34–37 ., The ZIKV NS1 blockade of binding ELISA had an overall specificity of 91 . 4–92 . 6% , which reduced to 77 . 6–90 . 5% when comparing with sDENV panel 38 , 39 ., A recently reported ZIKV NS1 IgG3 ELISA had a sensitivity of 97% based on samples from Salvador , but it reduced to 83% when comparing with samples outside of Salvador 32 ., A previous study of multiplex IgG MIA including ZIKV NS1 reported a sensitivity of 100% and specificity of 78% for pZIKV panel based on PRNT results , however , the sDENV and ZIKVwprDENV panels were not distinguished 56 ., For the WNV NS1 IgG MIA , the overall sensitivity was 86 . 1% probably due to sampling during the early convalescent-phase for this pWNV panel ( S1 Table ) , and the specificity was 78 . 4% , mainly due to the cross-reactivity from the sDENV and ZIKVwprDENV panels ( Table 2 ) ., Using the rMFI ratio of ZIKV NS1 to DENV1 or DENV2 NS1 , we can distinguish ZIKVwprDENV and sDENV panels with a sensitivity of 83 . 3–94 . 4% and specificity of 90 . 9–100 . 0% ., This was consistent with our previous reports of IgG ELISAs using the rOD ratio of ZIKV NS1 to DENV1 NS1 or mixed DENV1-4 NS1 to distinguish these two panels with a sensitivity of 91 . 7–94 . 1% and specificity of 87 . 0–95 . 0% 44 , 45 ., It is worth noting since DENV3 and DNV4 NS1 proteins were not recognized by several samples from the sDENV and ZIKVwprDENV panels ( Fig 2A and 2D ) , they were not included in the analysis of the rMFI ratio ., Comparing the results of individual NS1 IgG MIA in this study and those of NS1 IgG ELISA reported previously 45 , we found comparable detection rates between MIA and ELISA , and positive correlations between the rMFI and rOD values for both convalescent-phase and post-convalescent-phase samples of most panels tested including pDENV1 , sDENV and ZIKVwprDENV panels except pZIKV panel ( Figs 3 and 4 and S2 Fig ) ., Of note , the IgG MIA detection rates for DENV1-4 for the post-convalescent-phase ZIKVwprDENV panel were much lower than those for the sDENV panel ( Fig 4E and S2E Fig ) , suggesting that prior DENV exposure of the ZIKVwprDENV panel may have been only to a single DENV serotype ., For the convalescent-phase pZIKV panel , the higher detection rate of ZIKV NS1 IgG MIA ( 100% ) than that of ELISA ( 5% ) and the positive correlation between rOD and rMFI values suggest that MIA was more sensitive than ELISA ( Fig 3A–3C ) ., Thus , we did not observe a trend of increased detection rates of NS1 IgG MIA from convalescent to post-convalescent phases for primary infection panels ( pZIKV , pDENV1 ) ( Fig 3B and 3F ) as previously reported for NS1 IgG ELISA and blockade of binding of NS1 ELISA 38 , 45 ., Notably we incubated 16 ng antigen coated on each well with 50 μL of serum ( 1:400 ) in ELISA , whereas we incubated ~10 ng antigen ( in 1250 beads ) with serum ( final dilution 1:200 ) per well in MIA ., The higher concentration of serum and more surface area of antigen coupled on beads may account for the higher sensitivity of the IgG MIA compared with IgG ELISA for the pZIKV convalescent-phase panel ., Although neutralization tests are still considered a confirmatory assay , they are time-consuming and can be performed only in reference laboratories ., Compared with PRNT and ELISA , the multiplex MIA requires less time ( 2 . 5 h vs . 7 h for ELISA and 5–6 days for PRNT ) and less sample volume ( 1 μL vs . 8 μL for ELISA and 144 μL for PRNT for 8 antigens or viruses ) ., The newly developed multiplex NS1 IgG MIA could have wide-ranging applications , such as serodiagnosis , blood screening , serosurveillance of ZIKV , DENV and WNV infections , and retrospective study of ZIKV infection among pregnant women with CZS 57 , 58 ., The current octaplex ( 6 NS1 antigens plus PBS and BSA controls ) IgG MIA serves as a “proof-of-concept” assay to demonstrate that NS1-based MIA can distinguish three flavivirus infections; incorporation of other antigens would increase the detection capacity for different clinical settings and studies ., These together would further our understanding of the epidemiology , pathogenesis and complications of ZIKV in regions where multiple flaviviruses co-circulate 1–4 ., There are several limitations of this study ., First , due to limited samples of < 3 months PSO from patients with primary DENV infection ( S1 Table ) , the study focused on NS1 IgG MIA ., Future studies on NS1-based IgM MIA are warranted ., Second , despite the availability of two-time point samples for the pZIKV and ZIKVwprDENV panels , future studies involving more sequential samples are needed to validate these observations ., Additionally , the sample size in each panel with well-documented infection is small ., Third , although this multiplex assay can distinguish various panels of samples with three flavivirus infections , future tests that can distinguish other pathogenic flaviviruses such as JEV , YFV and tick-borne encephalitis virus ( TBEV ) remain to be exploited 59 , 60 ., Moreover , samples with well-documented repeated flavivirus infections such as DENV with previous ZIKV infection and sequential DENV and WNV infections are lacking and remain to be investigated in the future ., In light of the successful implementation of several flavivirus vaccines and vaccine trials in flavivirus-endemic regions , serological tests that can distinguish ZIKV infection from vaccinations with DENV , JEV , YFV and TBEV vaccines are warranted 59 , 60 .

Introduction, Methods, Results, Discussion

The explosive spread of Zika virus ( ZIKV ) and associated complications in flavivirus-endemic regions underscore the need for sensitive and specific serodiagnostic tests to distinguish ZIKV , dengue virus ( DENV ) and other flavivirus infections ., Compared with traditional envelope protein-based assays , several nonstructural protein 1 ( NS1 ) -based assays showed improved specificity , however , none can detect and discriminate three flaviviruses in a single assay ., Moreover , secondary DENV infection and ZIKV infection with previous DENV infection , both common in endemic regions , cannot be discriminated ., In this study , we developed a high-throughput and multiplex IgG microsphere immunoassay ( MIA ) using the NS1 proteins of DENV1-DENV4 , ZIKV and West Nile virus ( WNV ) to test samples from reverse-transcription-polymerase-chain reaction-confirmed cases , including primary DENV1 , DENV2 , DENV3 , WNV and ZIKV infections , secondary DENV infection , and ZIKV infection with previous DENV infection ., Combination of four DENV NS1 IgG MIAs revealed a sensitivity of 94 . 3% and specificity of 97 . 2% to detect DENV infection ., The ZIKV and WNV NS1 IgG MIAs had a sensitivity/specificity of 100%/87 . 9% and 86 . 1%/78 . 4% , respectively ., A positive correlation was found between the readouts of enzyme-linked immunosorbent assay and MIA for different NS1 tested ., Based on the ratio of relative median fluorescence intensity of ZIKV NS1 to DENV1 NS1 , the IgG MIA can distinguish ZIKV infection with previous DENV infection and secondary DENV infection with a sensitivity of 88 . 9–90 . 0% and specificity of 91 . 7–100 . 0% ., The multiplex and high-throughput assay could be applied to serodiagnosis and serosurveillance of DENV , ZIKV and WNV infections in endemic regions .

Although there was a decrease of Zika virus ( ZIKV ) infection since late 2017 , the specter of congenital Zika syndrome and its re-emergence in flavivirus-endemic regions emphasize the need for sensitive and specific serological tests to distinguish ZIKV , dengue virus ( DENV ) and other flaviviruses ., Compared with traditional tests based on envelope protein , several nonstructural protein 1 ( NS1 ) -based assays had improved specificity , however , none can discriminate three flaviviruses in a single assay ., Moreover , secondary DENV infection and ZIKV infection with previous DENV infection , both common in endemic regions , cannot be distinguished ., Herein we developed a high-throughput and multiplex IgG microsphere immunoassay using the NS1 proteins of four DENV serotypes , ZIKV and West Nile virus to test samples from laboratory-confirmed cases with different primary and secondary flavivirus infections ., Combination of four DENV NS1 assays revealed a sensitivity of 94 . 3% and specificity of 97 . 2% ., The ZIKV and WNV NS1 assays had a sensitivity/specificity of 100%/87 . 9% and 86 . 1%/78 . 4% , respectively ., Based on the signal ratio of ZIKV NS1 to DENV1 NS1 , the assay can distinguish ZIKV infection with previous DENV infection and secondary DENV infection with a sensitivity of 88 . 9–90 . 0% and specificity of 91 . 7–100 . 0% ., This has applications to serodiagnosis and serosurveillance in endemic regions .

dengue virus, medicine and health sciences, enzyme-linked immunoassays, immune physiology, pathology and laboratory medicine, pathogens, immunology, microbiology, viruses, rna viruses, antibodies, immunologic techniques, research and analysis methods, immune system proteins, serology, proteins, medical microbiology, microbial pathogens, immunoassays, biochemistry, west nile virus, flaviviruses, viral pathogens, physiology, biology and life sciences, organisms, zika virus

journal.pntd.0004831

The Burden of Zoonoses in Kyrgyzstan: A Systematic Review

Zoonoses are diseases in humans , which are naturally transmissible directly or indirectly from vertebrate animals ., Of 1415 species of infectious organisms know to be human pathogens , 61% are zoonotic 1 ., The Food and Agriculture Organization of the United Nations ( FAO ) , the World Health Organization ( WHO ) and the World Organisation for Animal Health ( OIE ) have underlined the important socioeconomic impact of these diseases , yet in many low income countries the burden of zoonoses remains unknown 2 ., The lack of information often results in a vicious circle of underestimation and limited incentive to quantify the problem 3 , 4 ., Kyrgyzstan is a country in Central Asia , neighboured by China in the west , Kazakhstan in the north , and Tajikistan and Uzbekistan in the southeast ( Fig 1 ) ., Because of a poor functioning veterinary and sanitation system , emerging zoonoses are an increasing problem 5 ., Since independence in 1991 , veterinary services deteriorated , causing an increase in zoonotic disease ( ZD ) 6 , 7 ., At particular risk are the 64% of the inhabitants who live in rural areas , where livestock farming plays an important role ., Seventy-six percent of these rural dwellers are considered to be poor 8 ., The small-scale farming , which is often nomadic , allows intensive contact between humans and animals 9 ., Furthermore , in Kyrgyzstan , an estimated 100 DALYs per 100 , 000 were lost due to inadequate hygiene in 2012 10 , which ranks the country in the 61th place out of 146 low/middle-income countries on disease burden due to poor hygiene ., The combination of poor healthcare , poverty , inadequate hygiene , and the close interaction between humans , livestock and other animals , leaves a large share of the population at risk of being infected with zoonoses ., Another difficulty in assessing the burden of the diseases , is the low scientific output from Kyrgyzstan which is often published in Russian 11 , 12 ., The World Bank and the OIE have advised Kyrgyzstan to develop national animal disease control strategies 2 ., A quantification of the impact of zoonoses helps prioritizing these diseases ., The aim of this study was to quantify the burden of ZD in Kyrgyzstan using disability-adjusted life years ( DALYs ) a standardized approach to increase comparability of disease impact 13–19 ., In this review , we have assessed the available data on zoonoses in Kyrgyzstan with special attention to the potential underreporting using stochastic disease modelling ., We have comprehensively summarised the burden of the most important zoonoses that are endemic in Kyrgyzstan and addressed the underestimation in officially reported cases ., The ZDs described in this systematic review ( Table 1 ) are regarded as the most important in terms of socioeconomic impact based on the WHO report on neglected tropical diseases , the World Bank report on Kyrgyzstan of 2011 and other systematic reviews of neighbouring or overlapping regions 2 , 5 , 20 , 21 ., We assembled all the available evidence regarding prevalence or incidence of the selected ZDs in Kyrgyzstan since the country became independent ., Therefore , the time period for the search was January 1991-January 2016 ., Both formal , peer-reviewed scientific literature , and informal sources , grey literature , were considered ., A full list of sources can be found in S1 Supporting Information ., We conducted a systematic review by following guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses ( PRISMA guidelines , Moher , Liberati , Tetzlaff , Altman , & The PRISMA Group , 2009 ) 31 ( S2 Supporting Information– Prisma checklist ) ., A list of synonyms for the ZDs was constructed using the pathogen’s name and alternative ( popular ) names of the disease ., The computer search was constructed by combining these terms with the Boolean OR and the term ‘Kyrgyzstan’ with the AND Boolean ., The databases of PubMed , Google Scholar , Web of Science , OVID , Scopus , the WHO Global Health Library , Food and Agriculture Organization of the United Nations ( FAO ) and ProMED-mail were searched using English search terms and Google Scholar using Russian search terms ., For each database , the search construct was adapted to the specific modus operandi of the search engine ., S1 Supporting Information lists the search terms as well as the search constructs for the different databases ., Furthermore , we searched the internet for published reports on demographic surveillance sites in the English and the Russian language ., Data from government sources was contributed by the co-authors ., Following retrieval , studies were selected by critically appraising the titles and abstracts ., A study was excluded when it did not address prevalence or incidence for the specific disease , when it was not from the defined period , when it did not address the disease in humans or when it did not address Kyrgyzstan ., Secondly , the full text was screened and for each retrieved result the list of references was inspected for additional sources ( backward searching ) ., Forward searching was performed by entering the titles in google scholar using the ‘cited by’ function ., Searches were executed until no new results were found ., Additional results were screened according to the same methodology ., Finally , the selected studies were summarized based on study design , study area , disease measure ( prevalence/incidence ) and the reported margin of error ( S3 Supporting Information ) ., Each study was critically appraised on methodology , selectivity in reporting and assumptions made by the authors ., Fig 2 displays a flow diagram of the used selection strategy ., The Disability-Adjusted Life Year ( DALY ) was used as the burden-of-disease metric ., It is a health gap measure which quantifies health loss ., DALY calculation is a standardized method developed by the World Bank , Harvard School of Public Health and the World Health Organization for the Global Burden of Disease and injury ( GBD ) study and the global burden of foodborne diseases 13 , 32–35 ., It allows the comparison of health conditions across countries and across diseases 13 , 32 ., In this study , an incidence-based DALY calculation was applied ., This allows us to include all sequelae resulting from infection 36 , 37 ., The DALYs are calculated as the sum of the healthy years lost to disability ( YLD ) and the years of life lost due to premature death ( YLL ) ., The YLD is the sum of the different outcomes that result in disability , where an outcome is defined as sequelae of the disease or another categorisation of the disease , e . g . chronic vs . acute ., The YLD per outcome is the product of the duration , the incidence , and the disability weight of the outcome ., The YLL is the residual life expectancy at the age of death ., YLLs were calculated based on the life table from 38 ., YLDs with a lifelong duration were calculated based on a local life table from the WHO for Kyrgyzstan for 2013 39 ., Based on the recommendations and methodology of the GBD 2010 and FERG 33 , 36 , we have used a non-discounted and non-weighted approach in calculating DALYs ., If a disease and its outcomes were quantifiable , a corresponding disease outcome model was constructed based on literature ., When data on the incidence , the outcome of a disease , or other parameters for the DALY calculation were missing , these gaps were filled using data from neighbouring countries or overlapping regions ., The disease model or outcome-tree model was constructed per ZD using health outcomes with an evidence-based causal relationship between infection and outcome ., Disagreement in inputs of the disease model between different sources was modelled using distributions ( pert , triangular , and uniform ) accounting for this uncertainty ., A full description of the disease models , the input parameters and its uncertainties used to calculate the DALYs can be found in S3 and S4 Supporting Information ., Where available , disability weights from the GBD 2010 study were used ., Age distributions of outcomes were , when possible , based on data collected from Kyrgyzstan ., Furthermore , the total population size , age and sex distribution was obtained from census data by the National Statistical Committee of the Kyrgyz Republic and the United Nations Demographic Yearbook 40 , 41 ., The uncertainty in the estimates was modelled using Monte Carlo analysis ., We generated in this simulation 10 , 000 draws from the probability distributions ., All analyses were performed using R version 3 . 2 . 2 ( R Foundation for Statistical Computing , Vienna , Austria ) 42 ., Additional information on the analyses and disease models is provided in S4 Supporting Information ., The burden of disease was calculated for the reference year 2013 , a result of a trade-off between data availability and as recent as possible ., The data collected by the Department of State Sanitary and Epidemiological Supervision of the Ministry of Health of the Kyrgyz Republic provided the officially reported cases for notifiable diseases which includes a number of zoonoses ., This is available online 41 , 43 ., We have used the data retrieved from literature to evaluate these reported figures and assess the level of underestimation ., As reported in S3 Supporting Information , the availability of published disease data is scarce in Kyrgyzstan; we were not able to assess the burden of anthrax since not enough was known about the outcome of the cases ., In 2013 , 16 human cases of anthrax were reported ., The majority of the cases were the cutaneous form , Zoldoshev reviewed 217 cases of cutaneous anthrax with no fatalities 44 ., For alveolar echinococcosis ( AE ) , cystic echinococcosis ( CE ) , brucellosis and rabies incidence data for Kyrgyzstan was available from the Department of State Sanitary and Epidemiological Supervision of the Ministry of Health of the Kyrgyz Republic 41 , 43 ., Prevalence data on AE , CE and brucellosis was used to address the underestimation in the official data; to address the potential underestimation in rabies we have used data from overlapping regions ( Eurasia ) 27 ., The incidence of congenital toxoplasmosis was not formally recorded , but Minbaeva et al . ( 2013 ) provided estimates for Kyrgyzstan 45 ., For campylobacteriosis and non-typhoidal salmonellosis , no specific disease prevalence or incidence data was recorded for Kyrgyzstan ., The incidence estimates are based on the number of acute gastrointestinal infections reported in 2013 41 and the assumed etiological fraction as described by 21 , 22 ., This conservative estimate was used as the lower limit for the number of cases; the upper limit was formed by the etiologic proportion of the diarrhoea incidence multiplied by the gastroenteritis incidence from the European region based on Walker et al . and Lanata et al . 24–27 ., Invasive non-typhoidal salmonellosis ( iNTS ) forms an important outcome of non-typhoidal salmonellosis infection since mortality is much higher compared to the gastro-enteric manifestation of the disease 35 , 46 ., However limited data is available on the true incidence of iNTS since only few population-based incidence studies have been conducted ., Therefore , we have used the ratio between iNTS:NTS as described by Ao et al . 46 who classified Kyrgyzstan in the Asia/Oceania region where the proportion iNTS:NTS was 1:3 , 851 compared to 1:7 in European region which included Russia; the global average ratio was 1:28 46 ., We identified 438 unique citations and excluded 411 by title and abstract screening ., Of the remaining 28 potential eligible citations with relevant abstracts , 10 were eligible for full text review ., The PRISMA flowchart summarizing the data collection process is presented in Fig 2 ., Reports published during January 1991-January 2016 were searched ., The last search was performed on 19-02-2016 ., All collected data are summarised in S3 Supporting Information ., In 2013 seven ZDs were quantifiable in Kyrgyzstan ., AE , brucellosis , campylobacteriosis , CE , congenital toxoplasmosis , NTS and rabies ., These were responsible for an estimated total of 141 , 583 33 , 912–250 , 924 new cases resulting in 35 , 209 13 , 413–83 , 777 DALYs and 576 279–1 , 168 deaths ( Table 2 , Fig 3 ) ., Both Rabies and AE contribute a large number of DALYs per case , 70 . 1 10 . 0–90 . 0 DALYs/case and 50 . 3 20 . 7–78 . 3 DALYs/case respectively , due to high mortality ( Fig 4 ) ., Campylobacteriosis and NTS had relatively low mortality but a high incidence; most of the mortality was due to the sequelae Guillain Barre Syndrome ( GBS ) and invasive non-typhoidal salmonellosis ( iNTS ) respectively , see S4 Supporting Information ., Infections with salmonellosis and AE were responsible for the majority of deaths , respectively 254 66–571 and 236 153–466 ., Although only 5 . 1% ( 11/216 ) of the cases of congenital toxoplasmosis was fatal , the DALY/case is high ( 6 . 69 ) due to early onset of the sequelae and the lifelong duration ., Table 2 provides the estimates for the number of cases , the DALY , the number of deaths and the DALY per case per disease and their 95% uncertainty range ., Fig 3 displays a graphical representation of the per annum burden per disease and its uncertainty , plotting the DALY estimates from Table 2 per disease ., Table 3 displays the sensitivity analysis with different iNTS:NTS ratios ., Fig 4 provides the percentage of YLD and YLL per disease ., Premature mortality , or YLL , ( in blue in Fig 4 ) contributes for all diseases most to the DALY , ranging from 67% for CE to 100% for Rabies ., This work provides a first attempt at quantifying the burden of ZD in Kyrgyzstan ., It underlines the lack of published data on many zoonoses in this region ., However , the estimates of the impact of the ZDs help to break the vicious circle of underreporting by providing estimates of the true incidence and burden of these diseases ., Because of the scarcity of data we did not exclude information based on methodology; we analysed it using conservative assumptions and stochastic modelling to handle uncertainty 47 ., We have used official Kyrgyz data and addressed its underestimation ., The total burden of the seven quantified ZDs ( 35 , 209 13 , 413–83 , 777 DALYs in 2013 ) is slightly less than the yearly burden of HIV , which was attributable for 38 , 870 21 , 261–64 , 297 DALYs in 2010 in Kyrgyzstan 48 ., This burden is based on prevalence based DALYs , as used in the GBD 2010 studies 33 ., Forty-three percent of the estimated burden of zoonoses or 14 , 967 6 , 213–32 , 319 DALYs in 2013 , in Kyrgyzstan is caused by echinococcosis ( both AE and CE ) ., Torgerson et al . estimated in 2010 that in China 16 , 629 new cases of AE per year arose among 22 . 6 million people at risk 17 ., In Kyrgyzstan we estimate for 2013 that 236 cases arose among 5 . 7 million people ., However AE is characterized by a clustered distribution and some regions have a much higher incidence rate 49 ., The officially reported incidence of AE has increased since 2004 at an alarming rate ., Where before 2004 only 0–3 cases per year were reported , in 2013 148 cases were officially reported 49 , 50 ., We assume that , corrected for underestimation , the incidence is likely to be approximately 236 153–466 cases in 2013 ., Although the goal of this study was to provide an estimate of the burden of zoonoses in 2013 , the collected data allows us to reflect on temporal trends ., Raimkylov & Kuttubaev , and Usubalieva et al . describe an increasing trend over the last decade in the incidence of echinococcosis 49 , 50 , although increased awareness might lead to more diagnoses ., The yearly incidence of brucellosis , on the other hand , seems to decline over time ( S3 Supporting Information ) ., The application of the ocular Rev-1 vaccination over several years has most likely resulted in this decreased incidence 51 ., Over time , the improvement of diagnostics and the application of novel treatments may cause changes in the outcome of the disease and thus the DALY per case ., For example , in our analysis , we assumed that all cases of AE are eventually fatal due to insufficient treatment ., However , as illustrated in Switzerland , adequate treatment of the disease will lead to an increased survival 52 ., This illustrates the need for periodic updating of the burden assessment ., We choose an incidence-based approach in the DALY modelling because it allows us to include all sequelae resulting from infection ., However , one of the consequences of using the incidence-based DALY approach , is that deaths in the future are attributed to the year of the infection ., Careful interpretation of the mortality rate is therefore advised ., For example , AE did not cause the reported number of deaths in 2013 since its incidence is increasing and it has a long latency; the deaths that will be caused by the infections diagnosed in 2013 are attributed to that year ., For a disease with a short incubation time and a relative constant incidence rate , such as rabies , the difference is not so striking ., Using a prevalence based DALY approach in diseases with a trend over time and a long latent phase or incubation period , might lead to under or overestimation as it reflects past infection rather than a present day event 37 ., Another limitation is the assumption , in common with other burden studies , that the outcome of diseases can be extrapolated to different countries ., Regional differences in pathogens might change the tropism of the causative agent or cause a shift towards certain sequelae ., The same holds true for other spatially fluctuating factors , such as co-infection; The incidence of iNTS , for example , is correlated with malaria and HIV infection 46 ., This underlines the importance of not only reporting incident cases , but also of documenting disease outcome ., Other factors such as ethnicity might also have an influence on disease outcome , as for example has been postulated in tuberculosis and Plasmodium falciparum malaria 53 , 54 ., Even more striking are the vast differences in treatment according to region , as illustrated for AE ., Likewise , brucellosis , with inadequate treatment is more likely to become chronic or relapse 55 which increases the burden ., Underestimation of disease is caused by under-ascertainment and underreporting of cases ., A disease might not be severe enough for the patient to visit a medical facility ., In addition , patients might have limited access to care or the disease are not accurately diagnosed ., Underreporting is the result of incomplete registration of cases ., Even in countries with a high standard of medical care , such as WHO high-income countries , reported cases form only the tip of the iceberg of the true incidence ., For example , it is estimated that only 1/30 . 3-1/86 cases of campylobacteriosis are reported in the USA 56 , 57; the estimate in the European Union is that on average 1/47 cases of campylobacteriosis are reported 58 ., CE is often substantially underreported ., In Uzbekistan the official case numbers appear reported were 1 , 435 cases reported in 2000 and 819 cases reported in 2001 59 ., However , Nazirov and others undertook a detailed study of hospital records throughout Uzbekistan and found a total of 4 , 430 cases in 2000 and 4 , 089 cases in 2001 ., Likewise in Chile official notifications between 2001 and 2009 were a mean of 311 cases per annum , whilst a detailed audit of hospital records revealed a mean of 1 , 009 cases per annum 60 ., The assumption we made on the underestimation of the incidence of ZDs is conservative ., For most ZDs we have used either a uniform or a pert distribution and included the officially reported incidence as minimum and the with a multiplication factor corrected value as maximum ., The assumption for the mode in AE , CE and brucellosis are also conservative ., The multiplication factor we used to correct for underestimation in brucellosis ( 4 . 6 ) lies close to the mean multiplication factor ( 5 . 4 1 . 6–15 . 4 ) Kirk et al . used in 35 ., Hampson et al . reviewed the global burden of Rabies and estimated for 2010 and estimated 14 rabies deaths in Kyrgyzstan contributing to 887 DALYs 27 ., Since rabies is a fatal disease , which often affects the young , it is possible that some cases go unreported in Kyrgyzstan ., We believe that our estimate and its 95% uncertainty range represent the true incidence ., The burden consists only of the estimated fatal cases , and not the disability caused by dog bites and the burden of the treatment ., This indirect burden is highest in countries where crude nerve-tissue vaccines are used 61 , which is not the case in Kyrgyzstan ., Other carnivores than dogs , are assumed not be relevant in contributing to the transmission risk 62 , however , there is a steep increase described in the wolf population and an increasing contact rate between humans and these wild carnivores in mainly in the south of Kyrgyzstan 63 ., In this study we have explored the proportion of diarrhoea attributable to Campylobacter and non-typhoidal Salmonella in Kyrgyzstan ., We assumed etiologic proportion of diarrhoea of both pathogens based on literature 64 ., Close inspection of the reported incidence of acute intestinal infections , reveals an approximate two-fold increase in cases between 2004 and 2007 ., However , the change was likely because the funding of hospitals was modified to a case-based system 65 ., This illustrates that the variance in reported data does not always represent epidemiological change; it can be merely a reflection of an alteration in policy ., The estimates we present are based on conservative extrapolates from overlapping regions ., However , more accurate incidence data on salmonellosis and campylobacteriosis in Kyrgyzstan are lacking ., Estimates of overlapping regions often lacked nuance and tend to group heterogeneous countries ., Our median incidence estimates for both NTS ( 1 , 101/100 , 000 ) and campylobacteriosis ( 1 , 305/100 , 000 ) are higher than the estimated yearly incidence by Havelaar et al . of campylobacteriosis ( 802/100 , 000 cases ) and of NTS ( 318/100 , 000 cases ) in the EUR B region 66 ., However earlier estimates by the same author are higher; ranging from 1 , 800–11 , 800 cases/100 . 000 for salmonellosis and 2 , 240–13 , 500 cases/100 . 000 for campylobacteriosis 58 ., The data presented in 58 shows a correlation between Gross Domestic Product ( GDP ) and both salmonellosis and campylobacteriosis; both diseases have a higher estimated true incidence in countries with a lower GDP ., There seems to be no clear relation between the quantity of consumed protein ( egg , chicken , and pork ) according to the FAO and the estimated true incidence of the two diseases in the different European countries ( EU-27 ) 58 ., We believe that although chicken , egg and pork consumption in Kyrgyzstan are lower than in EU-27 countries , the lower GDP and the lower hygiene standard in Kyrgyzstan justify our estimates ., To obtain more reliable burden estimates of both campylobacteriosis and salmonellosis , it would be advisable to undertake a community-based incidence study in Kyrgyzstan ., Both diarrhoea incidence and aetiology are important inputs to narrow the uncertainty around our estimates ., Furthermore , a longitudinal study on the aetiology of febrile illness might provide a reliable estimate of the burden of different zoonoses or sequelae ( brucellosis , iNTS , listeriosis , Q-fever , leptospirosis ) ., A small scale investigation in Bishkek showed that part of the undiagnosed febrile illness was due to brucellosis and Q-fever 67 ., To date , the exact burden of leptospirosis in Kyrgyzstan is unknown ., Although occurrence of leptospirosis in cattle in Kyrgyzstan has been reported 68 , no data on the occurrence of this ZD in humans in Kyrgyzstan is available ., Torgerson et al . estimated that the burden of leptospirosis in Kyrgyzstan was 927 355–1629 DALYs per year 69 ., Costa et al . clearly illustrate a lack of data on the occurrence of leptospirosis in Central Asia; the estimates of incidence for Kyrgyzstan were based on extrapolation using a multivariable regression model 70 ., Likewise , the role of cryptosporidium and giardia as causative agent for ZD in Kyrgyzstan has not been established ., These parasites have zoonotic potential 71 , however the incidence of the disease caused by these parasites has not been investigated in Kyrgyzstan , nor has the role of animals in the transmission of these ZDs been quantified ., Therefore , burden assessment at this moment is not feasible ., Only sequelae that have a solid proven causal relationship with the pathogen have been included in the disease models we used ., Reactive arthritis , irritable bowel syndrome and GBS are evidence based sequelae of campylobacteriosis 72 ., However , we followed the conservative assumption of Kirk et al . that the relation between some sequelae were not sufficiently proven in middle and high-mortality countries 35 ., Most of the burden of salmonellosis is due to YLLs , mainly deaths caused by iNTS ., The sensitivity analysis ( Table 3 ) illustrates the influence of the proportion of iNTS:NTS ., This underlines the importance of investigating the incidence of iNTS in Kyrgyzstan and is in line with the findings of Ao et al . 46 conclude that there is a lack of population-based incidence data on iNTS ., In a limited-means setting such as Kyrgyzstan it is inevitable for policy makers to prioritize health care needs ., The DALY provides one tool to do so , but is by itself not sufficient 73 ., In the application of the DALY by healthcare legislators , it is important to look at the presented figures in a wider context ., DALYs should be combined with for example , economic parameters in cost-utility analyses 74 ., It is also important to realize that the DALY might not capture the full effect of the disease and that a disease might have bigger impact than just on the ones directly affected 75 ., Especially ZDs often cause economic loss in livestock production as well 21 , 76 ., Where in this paper we have only quantified the human burden , it makes sense to extend the work with the assessment of the economic impact of the disease in both humans and animals ., Furthermore , it is advised to conduct an integrated approach in disease intervention and prevention where both veterinary and human health officials work together 3 .

Introduction, Materials and Methods, Results, Discussion

Zoonotic disease ( ZD ) pose a serious threat to human health in low-income countries ., In these countries the human burden of disease is often underestimated due to insufficient monitoring because of insufficient funding ., Quantification of the impact of zoonoses helps in prioritizing healthcare needs ., Kyrgyzstan is a poor , mountainous country with 48% of the population employed in agriculture and one third of the population living below the poverty line ., We have assessed the burden of zoonoses in Kyrgyzstan by conducting a systematic review ., We have used the collected data to estimate the burden of ZDs and addressed the underestimation in officially reported disease incidence ., The estimated incidences of the ZDs were used to calculate incidence-based Disability Adjusted Life Years ( DALYs ) ., This standardized health gap measure enhances comparability between injuries and diseases ., The combined burden for alveolar echinococcosis , cystic echinococcosis , brucellosis , campylobacteriosis , congenital toxoplasmosis , non-typhoidal salmonellosis and rabies in Kyrgyzstan in 2013 was 35 , 209 DALYs 95% Uncertainty interval ( UI ) :13 , 413–83 , 777; 576 deaths 95% UI: 279–1 , 168 were attributed to these infections ., We estimate a combined median incidence of ZDs of 141 , 583 cases 95% UI: 33 , 912–250 , 924 in 2013 ., The highest burden was caused by non-typhoidal Salmonella and Echinococcus multilocularis , respectively 14 , 792 DALYs 95% UI: 3 , 966–41 , 532 and 11 , 915 DALYs 95% UI: 4 , 705–27 , 114 per year ., The health impact of zoonoses in Kyrgyzstan is substantial , comparable to that of HIV ., Community-based surveillance studies and hospital-based registration of all occurrences of zoonoses would increase the accuracy of the estimates .

Zoonoses are diseases transmitted from vertebrate animals to humans ., They can cause a variety of symptoms ranging from mild gastrointestinal complaints to debilitating illness and even death ., Especially in low-income countries where animals play an important role for many , the burden of these diseases can be substantial ., However , there is often little attention for these diseases , thus they remain under-researched and underfunded ., In this review , we present estimates of the burden of the most important zoonotic diseases in Kyrgyzstan for the reference year 2013 ., We estimated the burden by calculating the incidence-based disability adjusted life years ( DALYs ) , allowing comparison between diseases and injuries ., Disease frequency data is scarce and hospital-based incidence data often underestimates the true incidence of the disease ., By addressing the underestimation in officially reported incidence using data from our systematic review , we estimated the true incidence of the most important zoonoses in Kyrgyzstan ., We quantified the substantial impact these diseases have on the wellbeing of people in Kyrgyzstan in 2013 ., The results underline the need for more intensive monitoring and surveillance of zoonotic diseases .

medicine and health sciences, tropical diseases, parasitic diseases, salmonellosis, brucellosis, bacterial diseases, rabies, neglected tropical diseases, veterinary science, campylobacteriosis, echinococcosis, public and occupational health, infectious diseases, veterinary diseases, zoonoses, helminth infections, biology and life sciences, viral diseases

journal.pcbi.1000611

Distributed Dynamical Computation in Neural Circuits with Propagating Coherent Activity Patterns

To understand brain function , it is essential to study the collective electrical activity of neural circuits 1 ., This activity typically exhibits intriguing spatiotemporally organized patterns: they are commonly observed in multi-unit electrophysiological recording , EEG local field potential recording , MEG , optical imaging and fMRI imaging , both in spontaneous activity 2–5 and evoked responses 6–21 ., In space , these patterns often take the form of localized patches or clusters of activity 2–16 ., Recordings over large populations of neurons have shown that several of such localized patterns can occur simultaneously across cortical regions 2–16 ., Over time , these patterns often do not remain at specific locations ., As self-sustained entities , they propagate or move about in space 4–8 , 10–16 ., In doing so , they interact with each other , resulting in dynamical collective behavior ., Here we will consider what kind of functional role this behavior may have ., Propagating coherent patterns have been registered in the experimental literature as “spreading” or “drifting” activity 4–8 or as “traveling waves” 13–24 ., The simultaneous presence of several of these patterns has been observed in the spontaneous activity of cat visual cortex 4 , 5; see 25 for a corresponding model study , in evoked response patterns in turtle olfactory bulb 14 , and visual cortex of various species 9 , 15 , as well as in sensorimotor cortex of behaving mice 7 ., When several localized , moving patterns occur together , they are likely to interact ., Indeed , interactions have been shown to occur in rat somatosensory cortex 13 ., To describe the collective activity in olfactory , visual , auditory and somatosensory cortices of behaving rabbits , the term “interacting wave packets” was explicitly used 11 , 12 , which nicely captures the relevance of propagations and interactions of these patterns ., Despite the ubiquity of these patterns and their interactions , their fundamental functional role has remained unknown ., Although some authors have speculated on the role of propagating waves 26 , the functional implications of other aspects such as the simultaneous presence of multiple propagating patterns or their interactions have remained completely unclear ., Current theoretical frameworks describe neural activity either in computational or dynamical systems perspectives ., Conventional computational theory is based on the manipulation and representation of static symbols 27 ., This perspective contradicts the temporal variability of brain activity , which calls for a dynamical systems approach ., When dynamical systems theories are applied to neuroscience , the prevailing concept is that of stable low-dimensional attractors 28 ., This notion , although it has provided many important insights , is less suitable to capture the functional role of brain activity in its actual spatiotemporal complexity ., We need to resolve the restrictions of conventional computation and standard dynamical systems theories , in order to describe neural activity and understand its fundamental function ., This study is based on the consideration that neural circuits are spatially-extended , pattern-forming systems , containing large numbers of simple neurons with spatially restricted connectivity 29 , 30 , 31 ., In spatially extended physical systems composed of large numbers of simple interacting elements , such as reaction-diffusion systems and fluidic systems , localized propagating coherent patterns are a common feature known under different names , including wave packets , spots , breathers and soliton waves , amongst others 32 , 33 , 34 ., They are an emergent , collective property of these systems ., Using these systems as analogy , we construct a simple , spatially extended neural circuit model to represent the gross architecture within the cerebral cortex ., As an emergent , collective property of the system , the circuit exhibits dynamical activity patterns , reproducing some of the complexities observed in empirical studies ., In particular , the circuit provides simultaneous propagation of multiple locally coherent patterns and their interactions ., By revealing how their ongoing collective behavior can naturally embody computation , we demonstrate what fundamental function these patterns can serve ., Propagating coherent spiking patterns can support several essential aspects of a computational processing ., As self-sustained objects , these patterns can signal information by propagating across neural circuits ., Information processing , or computation , occurs when they interact or , specifically , collide with each other ., Collectively , these patterns perform distributed , parallel and cascaded computational operations , thereby enabling neural systems to work in an efficient and flexible way ., We shall call this distributed dynamical computation , which is proposed as a framework for understanding spatiotemporal propagating activity patterns in neural circuits ., This understanding links their dynamics with a form of non-conventional , abstract computation ., The importance of spatiotemporal dynamical patterns in the brain has been proposed in 29 , with an emphasis on spatial modes and their coupling ., Here , we have focused on propagating coherent activity patterns , which are ubiquitous in the brain ., These dynamical patterns are neither random nor stable; rather they are characterized by rich dynamical behaviors ., We have used a simple , stereotypical spiking neural circuit to generate spatially localized propagating patterns ., The patterns capture some of the key features of real pattern complexities: a distribution of multiple localized activity patterns , their propagations and their mutual interactions ., To understand their fundamental functional role , we propose the notion of distributed dynamical computation ., Localized propagating patterns are the underling primitives of dynamical computation; over time they transfer information across space and process information through their interactions ., Collisions distributed over different locations and occurring at different time moments can be connected to each other by propagating patterns ., This mechanism enables elementary computations to occur in a cascaded fashion , resulting in more complex computations ., In addition , several interactions distributed across different locations can occur simultaneously , resulting in parallel processing ., Dynamical computation emerges on the basis of activity in neural circuits; they enable and sustain propagating localized patterns and their interactions ., In this framework , the propagation and processing of signals are fluid; signals do not rely on the fixed physical lines of neural circuits to guide their propagation trajectories ., The computations are not confined to specific anatomical sites; rather they occur wherever moving patterns collide with each other ., With respect to real-brain architecture , this is clearly a simplification ., We may consider the neural architecture as biasing the trajectories of propagating patterns to various extents ., Nevertheless , a certain independence of fixed connectivity structure must be at the basis of how flexibility in brain functions is achieved ., Propagating coherent activity patterns implement logical operations in a manner reminiscent of the collisions in the classical billiard ball model 45 , 46 , 47 ., In this model , however , collisions are elastic and reversible , whereas in our model they are inelastic and therefore irreversible ., This allows the exchange of information between the interacting patterns ., The corresponding computations are equally irreversible and therefore context-dependent ., An additional essential difference with the billiard ball model is that computation in our model is naturally embedded in the ongoing behavior of a circuit ., Computation based on the propagations and interactions of coherent spiking patterns in neural systems is definitely a non-conventional form of computation ., Conventional computation requires information to be represented and manipulated in the form of static symbols 27 ., As the longstanding debate between computationalists and dynamicists 51 has pointed out , static symbols are less suitable to describe the temporal variability in the way the brain executes its functions and how it achieves flexibility ., Dynamical computation can capture the spatiotemporal characteristics of brain activity patterns and provide them with an underlying computational interpretation ., By synthesizing dynamics and computation , the present approach offers a starting point for a comprehensive understanding of the working mechanisms of the brain ., The collective propagation of activity patterns through a substrate of neurons can be portrayed as spatiotemporal spike chains ., Our current emphasis on propagating patterns bears a similarity to the paradigm of synfire chains 52 , 53 , in which sequential spike chains play a central role ., They are obtained by setting up feed-forward networks , designed to support wave-like spikes propagation through them ., These networks perform information processing by synchronizing different spike chains 52 , 53 ., In our model , spatiotemporal spike chains are an emergent property of recurrent networks 54 ., Rather than synchrony , their nonlinear pattern-forming capacities and transient interactions are the essential mechanisms for dynamical computation ., In the current study we have mainly focused on general-purpose computation based on ongoing , autonomous dynamics of neural circuits ., We have also found that external perturbations can modulate the ongoing patterns , which include their propagations and interactions ., Hence , propagating activity patterns could enable neural systems carry out some specific computations when actual sensory inputs are given ., Indeed , propagating coherent patterns such as propagating waves have been found in evoked activity 7 , 9 , 11 , 12 , 14 , 55 ., Furthermore , during whole computing processes based on propagating coherent patterns , internal synaptic modifications and external feedbacks from other parts of the brain can be used to shape or control dynamical wave pattern to generate specific propagating patterns as required outputs or behavior sequences ., The effect from feedback activity is analogous to use feedback signals to control waves patterns in spatially-extended non-equilibrium physical systems 56 ., Instead of focusing on multiple , stationary patch patterns 57 , 58 or single propagating wave pattern as in the most studies about neural fields 58 , 59 , the current model generates dynamical spiking activity patterns that can capture some of the complexities of empirically observed patterns ., Therefore , the current study provides specific , experimentally testable predictions ., In particular , the collective behavior of interacting , propagating coherent patterns belongs to the class of anomalous super-diffusion ., As a process with underlying long-range coherence , collective anomalous super-diffusion is an important indicator of complicated , nontrivial interactions between propagating patterns ., Its presence can be tested experimentally in a straightforward way ., First qualitative indications that this process may occur in real neural circuits are the seeming randomness of the points of origin of neural activity patterns and the variability of their propagating directions 11 , 22 , 23 ., More conclusive evidence can be obtained through calculating the MSD of the collective motions in the same way as for the current model data ., In the current dynamical computational framework , propagating coherent activity patterns are the fundamental primitives for signaling information and for processing information through their interactions ., Indeed , at the level of neural circuits , signaling information by propagating coherent patterns has been clearly and very well documented as an important component of the function of the cortex 18–21 ., Interactions between multiple active patterns , however , have merely been registered in experimental studies without considering their importance 11 , 12 , 13 ., Our current work shows in an abstract , principled way how these interactions could play a key role in dynamical computation ., For instance , in the visual cortex of ferrets , top-down influences have been found to be evident in terms of localized wave patterns 17 , which could have collisions with wave patterns evoked by external visual inputs; such collisions might reflect “attention guided” processing of visual stimuli ., It is , therefore , of crucial importance to study interactions between different propagating wave patterns experimentally and sow how they relate to the functions of the cortex .

Introduction, Discussion

Activity in neural circuits is spatiotemporally organized ., Its spatial organization consists of multiple , localized coherent patterns , or patchy clusters ., These patterns propagate across the circuits over time ., This type of collective behavior has ubiquitously been observed , both in spontaneous activity and evoked responses; its function , however , has remained unclear ., We construct a spatially extended , spiking neural circuit that generates emergent spatiotemporal activity patterns , thereby capturing some of the complexities of the patterns observed empirically ., We elucidate what kind of fundamental function these patterns can serve by showing how they process information ., As self-sustained objects , localized coherent patterns can signal information by propagating across the neural circuit ., Computational operations occur when these emergent patterns interact , or collide with each other ., The ongoing behaviors of these patterns naturally embody both distributed , parallel computation and cascaded logical operations ., Such distributed computations enable the system to work in an inherently flexible and efficient way ., Our work leads us to propose that propagating coherent activity patterns are the underlying primitives with which neural circuits carry out distributed dynamical computation .

The brain processes information with extraordinary efficiency , and can perform feats such as effortlessly recognizing objects from among thousands of possibilities within a fraction of a second ., This is accomplished because the brain represents and processes information in a distributed fashion and in a dynamical way ., This processing is manifested in spatiotemporal neural activity patterns of great complexities within the brain ., Here , we construct a spiking neural circuit that can reproduce some of the complexities , which are evident in terms of multiple wave patterns with interactions between each other ., We show that their dynamics can support propagating pattern-based computation; spiking wave patterns signal information by propagating across neural circuits , and computational operations occur when they collide with each other ., Such dynamical computation contrasts sharply with that done by static and physically fixed logic gates operating in other computing machines such as computers ., Moreover , we elucidate that the collective dynamics of multiple , interacting wave patterns enable computation processing implemented in a fundamentally distributed and parallel manner in the neural circuit .

biophysics/theory and simulation, computational biology/computational neuroscience

journal.pgen.0030147

Adaptive Evolution of Conserved Noncoding Elements in Mammals

Phenotypic evolution proceeds both by changes in protein coding sequences and by changes in gene expression that determine when , where , and how much genes are expressed 1–3 ., Although recent genome-wide studies have begun the process of identifying genes that show signals of adaptive evolution in coding sequences 4 , much less is known about the adaptation of regulatory sequences ., One avenue to studying adaptation of gene regulation is to identify regulatory elements that show rapid evolution at the DNA sequence level 2 ., However , a challenge for this approach is that at present we have only limited knowledge of the DNA sequence elements that drive gene expression and regulation ., One possible way forward is to study the evolution of conserved noncoding elements ( CNCs ) 5–7 ., In recent years it has been shown that ∼3 . 5% of noncoding DNA sequence is substantially conserved across diverse mammals 8–10 , and that a smaller amount of noncoding sequence is also shared with more distant vertebrates , including chicken and even fish 9 , 11–13 ., Some CNCs show extremely high levels of conservation; for example , Bejerano et al . 9 identified 481 segments longer than 200 bp that are absolutely conserved among the human , rat , and mouse genomes ., Recent studies of CNCs , using varied definitions , have reported that most CNCs are segments of around 100–300 bp , and that they are widely distributed across the human genome 9 , 10 , 14–18 ., CNCs are not preferentially located near genes 18 ., In some cases , clusters of CNCs are found in gene deserts and a subset of these CNCs have been shown to play functional roles as enhancers 19–21 ., It has been shown repeatedly that screening for CNCs is an effective method for identifying cis-regulatory modules of gene expression 18–25 ., CNCs that are shared among humans and distant outgroups such as Fugu are heavily overrepresented near developmental regulator genes , and many serve as highly conserved regulators of these functionally conserved genes 13 ., That said , there is still considerable uncertainty about the function of most CNCs , and it has been suggested that some CNCs may serve other kinds of functions , perhaps including roles in chromatin structure or structural connections between chromosomes 26 ., In principle , another possibility might be that many CNCs could simply be regions of the genome with low mutation rates ., However , two kinds of evidence argue convincingly that the low evolutionary rates of CNCs are indeed due to selective constraint ., First , the allele frequency spectrum of human SNPs that lie within CNCs is skewed towards rare variants , consistent with the action of weak purifying selection 27 , 28 ., Second , the rate of evolutionary change of CNCs is closer to the neutral rate in primates than in rodents 28 , 29 ., The latter observation is probably due to reduced efficiency of weak purifying selection in primates , which have smaller effective population sizes ., Hence , in this study , in view of the likely functional importance of CNCs , we set out to describe the patterns of evolutionary sequence change in these elements ., We start with a simple null model in which the evolution of each CNC is characterized by a single substitution rate parameter r that accounts for varying levels of constraint and local mutation rate across CNCs ., For each CNC we compare the null model to a hierarchy of alternative models that allow the CNC to have different evolutionary rates in different parts of the phylogeny ., In the simplest alternative model , the CNC evolves at a single rate across the phylogeny except for one branch , which shows a change in rate ( Figure 1 ) ., More complex alternative models allow multiple changes in rate ., Increases in rate can be interpreted as evidence for positive adaptation or relaxation of functional constraint for the element in question ., Decreases in rate are consistent with a tightening of selective constraint ., Two recently published papers 5 , 7 have taken similar approaches to identify nongenic regions that show accelerated evolution specifically in the human lineage ., Both studies concluded that human lineage-selection signals are enriched near neurological genes ., In the study of Pollard et al . 5 , the most dramatically accelerated region was found to be part of a novel RNA gene that is expressed during cortical development ., Here , we expand this kind of approach to look more broadly at evolutionary patterns of CNCs across the mammals ., To identify CNCs that have been targets of selection , we introduce a likelihood ratio test that we call the “Shared Rates Test” ( SRT ) ., Under the null model , the divergence times of lineages are shared across CNCs , but each CNC may evolve faster or slower according to its local mutation rate and level of evolutionary constraint ., For each CNC , we test whether any branches are surprisingly long or short compared to the others , indicating speed-ups or slow-downs of the substitution rate ., For example , in Figure 1 , the first two trees evolve at different rates , but with the same tree “shape” ( i . e . , the ratios of branch lengths are the same ) ., In contrast , the third tree has a longer-than-expected branch on the human lineage , suggesting the action of natural selection ., In our model , each branch of the mammalian tree has a branch-length parameter vb , defined as the average number of substitutions per site on branch b for CNCs evolving under a constant level of constraint ., ( Here , vb is defined as the average number of substitutions per site on branch b across all CNCs . ), In addition , under the null hypothesis , each CNC is associated with a single rate parameter r0 ( h ) ( where h indicates a particular CNC ) ., Then the number of substitutions that occur in CNC h , on branch b has an expectation at each site of Nb , h , where, Under the null model , there are seven branch length parameters for the tree that we consider , and one additional rate parameter for each CNC ., As described in the Methods and Text S1 , we obtain a joint maximum likelihood estimate for all the parameters , assuming the Felsenstein 84 model of sequence evolution 31 ., Our model is designed so that all CNCs have the same expected tree shape ( i . e . , the ratios of expected branch lengths are the same ) ., However the total size of the tree is allowed to vary according to r0 ( h ) , in order to reflect variation in mutation rates and the level of selective constraint across CNCs ., In addition , we place no constraints on the relative values of the vb , so that lineage-specific variation in mutation rates ( such as the higher substitution rate in rodents ) is reflected in longer estimates for those branch lengths ( Figures 1 and S1 ) ., In summary , the null model allows mutation rates and levels of constraint to vary across CNCs , and it allows for the property that broad-scale mutation rates may vary across lineages ., In addition to the basic null model , we consider a family of alternative models that allow additional rate parameters for particular CNCs ., In the simplest alternative , a single branch on the tree evolves at a rate that is different from the background rate shared by the remaining lineages ( as for the third tree in Figure 1 ) ., In the extreme alternative , each of the seven branches evolves with its own rate ri ( h ) , giving a total of seven rate parameters for the CNC in question ., ( For simplicity of notation , we will henceforth drop the notation h on the rate parameters . ), In the extreme case , to test the hypotheses H0: r1 = r2 =, ···= r7 ( = r0 ) versus HA: r1 ≠ r2 ≠, ···≠ r7 at a particular CNC , we compute the SRT as, where L is the likelihood of the sequence data for the five mammalian species , maximized with respect to the rate parameters , and with the fixed estimate of branch lengths parameters (, ) and the sequence evolution model ., Large values of the SRT indicate a substantially better fit of the alternative than the null model ., Another example of alternative model is the case in which branches 2 and 3 have distinct rates r2 and r3 , while the other branches have a single “background” rate r0 , −2 , −3 ., In this case , to test the hypotheses H0: r1 = r2 =, ···= r7 ( = r0 ) versus HA: r2 ≠ r3 ≠ r1 = r4 =, ···= r7 ( = r0 , −2 , −3 ) , we can compute the likelihood ratio statistic as, In this paper , we perform two kinds of analyses ., One analysis performs model selection using the SRT , while the other tests for individual branches with rate changes ., When testing for a rate change on the ith branch only , it is convenient to transform the likelihood ratio statistic as follows ., In this case , we will use special notation , denoted by SRTi:, where sign ( x ) = 1 if x > 0 and otherwise sign ( x ) = −1 ., Rewriting the SRT in this way provides the convenient property that SRTi > 0 implies that ri is larger than the background rate r0 , −i , and hence branch i shows a rate speed-up relative to the rest of the tree; conversely , SRTi < 0 implies a slow-down on branch i ., As a convention , when we subscript SRT by a character or number , it will represent the signed likelihood ratio statistic testing for rate changes on the indicated branch ., Otherwise , the notation SRT without subscripts will be used to indicate use of an unsigned test statistic , in the form of Equations 2 and 3 ., Our SRT is a likelihood ratio test and , as such , standard theory suggests that under the null hypothesis the test statistic should asymptotically follow a chi-square distribution with degrees of freedom equal to the difference in the number of estimated parameters between the constrained ( null ) and less-constrained ( alternative ) models ., Similarly , the signed root of this statistic for a one-dimensional parameter of interest is asymptotically standard normal ., Therefore , when the null hypothesis is true and the number of sites in a CNC is large enough , the unsigned SRT might be expected to follow the chi-square distribution with the degrees of freedom equal to the difference in the number of rate parameters between the two models ., For example there are six degrees of freedom in the global test ( Equation, 2 ) and two degrees of freedom in the example in Equation 3 ., Similarly , under the null , the signed test SRTi is constructed to have a standard normal distribution as the CNC size goes to infinity ., Our simulation studies show that the asymptotic theory is reasonably accurate for both versions of the test statistic , except in the cases in which the lineages tested for selection are relatively short and are expected to accumulate few substitutions ( namely , the human and chimpanzee lineages; Figure S3 ) ., Hence , to reduce computational burden , we calculate p-values using the asymptotic chi-square or normal approximations , except for tests on the human and chimpanzee branches for which , except where stated , we compute p-values based on the empirical null distribution in simulated data ( see Methods ) ., An additional consideration is that we do not want the estimated null branch lengths ( vb ) to be heavily influenced by outlier CNCs with evidence for selection ., To mitigate the impact of such CNCs , we first identify CNCs with clear overall departures from the null model ( SRT > 25 in the global six degrees of freedom test , corresponding to p < 0 . 00034 ) , and then reestimate the branch lengths after dropping those nonneutral CNCs , which represent 2 . 8% and 3 . 8% of the total mammalian and amniotic CNCs , respectively ., In summary , then , our analysis performs the following steps: ( 1 ) Estimate maximum likelihood branch lengths and rates under the null; ( 2 ) identify outlier CNCs that have SRT > 25 comparing the seven- and one-parameter models; ( 3 ) drop outlier CNCs and recalculate the null branch lengths and rates; and ( 4 ) compute the shared rates test statistics for each CNC according to a range of alternative models ., For reasons discussed below , in practice these analyses were performed in a sliding window of 50 consecutive CNCs , as defined by position in the human physical map ., All analyses considered the mammalian and amniotic CNCs separately ., It is well established that the extent of divergence among mammalian species varies substantially across large genomic regions 33–38 ., For example , Gaffney and Keightley 38 showed that divergence between the mouse and rat genomes varied between and within chromosomes ., While the causes and the scales of this type of variation are not completely understood , it has been shown that divergence correlates with various genomic features , including GC and CpG content , simple-repeat structures , and recombination rate , suggesting that these genomic features drive variation in mutation rates 35 , 37 ., Variation in mutation rates or levels of CNC conservation across genomic regions should not be problematic for our method , provided that the substitution rate in any given region maintains a constant ratio to the average across the mammalian phylogeny ., If a CNC is in a region with a higher , or lower , mutation rate than average , this effect should simply be absorbed into the rate parameter that we estimate for each CNC as part of our null model ., However , if mutation rate variation is not stable across the phylogeny , this might produce false signals for our method ., Therefore , we looked at whether the average tree shapes are significantly variable across chromosomes ( according to the human physical map ) as well as within chromosomes ., We found that in fact there is nontrivial variation in tree shape , both at the chromosome level , and across genomic regions within chromosomes ., For example , within Chromosome 2 there is a highly significant autocorrelation in the fraction of the tree occupied by the mouse lineage ( Figure 2 ) ., This result implies that local variation in large-scale mutation rates is not conserved across evolutionary time; for example , genomic regions that evolve faster than average on some lineages may evolve slower than average elsewhere on the tree ., If average tree shapes were constant across the genome , we could use CNCs from across the genome to estimate the tree shape for our null model ., However , the observation that tree shape is not constant suggests that instead our model should allow for variation in tree shape across the genome ., After some experimentation , we settled on using a sliding window of 50 consecutive CNCs to estimate the tree shape ., That is , we test each CNC for significant departures from the tree shape in a 50-CNC window that , in the human physical map , is centered near the CNC in question ( see Methods ) ., On average , this window size corresponds to 525 kb and 1 . 3 Mb ( median ) for mammalian CNCs and amniotic CNCs , respectively ., Overall , we find that using the sliding window method produces only a modest impact on the rate of significant CNCs , but it should improve our inferences by taking into account the local variation in tree shapes ( Figures 2 and S4 ) ., An obvious concern about using a sliding window based on the locations of CNCs in humans is that due to chromosomal rearrangements , CNCs that are close together in humans may not be close together in other mammals ., Consequently , a sliding window based on the human map might not provide a suitable correction ., Fortunately , our window size is relatively small compared to the typical size of syntenic blocks 8 , 39 and in Figure 3 , we show that the results of tests on the human lineage are highly concordant whether we use windows based on the human or mouse physical maps and , indeed , are only modestly different from the results using all CNCs together ., Consequently , all subsequent results use 50-CNC windows based on the human map ., Another plausible concern about our model stems from the prediction that selection against weakly deleterious mutations is more efficient in species with large populations than in small populations ., This means that weakly constrained sites in CNCs are likely to evolve more quickly in primates than in rodents ( which have larger effective population sizes ) ., This effect has been observed in a comparison between the evolutionary rates of CNCs and putatively neutral flanking sequences 29 ., Hence—in contrast to our null model—one might expect the overall tree shape for a CNC to depend on its level of selective constraint ., To investigate this issue , we classified CNCs into four different levels of conservation , according to their substitution rates on the dog lineage ., We then separately compared the average human-to-chimpanzee divergence against the average mouse-to-rat divergence , within each of the four conservation levels ( Table S15 ) ., We find that that as the level of constraint increases , the divergence in rodents indeed decreases faster than divergence in hominids , consistent with the results of Keightley et al . 29 ., However , we find that the variation across CNCs is relatively small ( less than 11% change across different classes of CNCs ) and much less than when CNCs are compared to neutral sequences ( Table S3 ) ., As shown below , we do not have the power to detect such small variations in tree shape at individual CNCs , so we conclude that it is not necessary to control for overall conservation level more carefully for the current study ., For each CNC , we calculated SRTi for each of the seven branches of the mammalian tree to identify CNCs that have experienced a speed-up or slow-down on a particular branch ., Figure 4A shows the histogram of p-values on the mouse lineage ( SRTm ) for the mammalian CNCs ., The p-values are defined as P ( SRTi > srti ) where srti is the observed value ., Hence , p-values near 0 indicate increased rates , and near 1 indicate decreased rates ., The histogram is flat for intermediate p-values with peaks at both ends , suggesting that most CNCs fit the null distribution of SRTm , but with a substantial number of outliers ., At the significance level of 0 . 001 , 1027 ( 1 . 2% ) and 503 ( 0 . 6% ) mammalian CNCs show speed-ups and slow-downs , respectively ., Among amniotic CNCs , 228 ( 1 . 4% ) and 106 ( 0 . 6% ) show speed-ups and slow-downs , respectively on the mouse lineage ., Figure 4B plots the expected and observed branch lengths on the mouse lineage for the CNCs that are significant at p < 0 . 001 in each tail ., ( Similar plots for other lineages are shown in Figure S5 . ), The red points above the diagonal indicate CNCs with rate speed-ups ., For the central 95% of the significantly fast-evolving CNCs , the observed branch lengths are between 0 . 04 to 0 . 13 substitutions per site , and are 2–4-fold higher than the expected branch lengths ., The blue points below the diagonal are CNCs with reduced branch lengths ., Nearly half of these CNCs accumulated no substitutions on the mouse lineage ., The other long lineages show similar p-value histograms though with some variability in the proportion of significant CNCs ., The dog lineage is the most enriched for signals , with 2 . 3% and 1 . 9% of mammalian CNCs showing speed-ups and slow-downs , respectively , at p < 0 . 001 ( in each tail ) ., Even after a stringent Bonferroni correction , 186 and 46 CNCs , respectively , are still significant at p = 0 . 001 in the dog lineage ., The overall results for amniotic CNCs are similar , but the fraction of significant results is slightly higher on each branch ( Table S8 ) ., For most lineages , our significance threshold ( one-sided p-value < 0 . 001 on each end ) corresponds to a genome-wide false discovery rate ( FDR ) between 0 . 05 and 0 . 1 ( Table S9 ) ., Since the distribution of SRTi on the human and chimpanzee lineages does not follow the standard asymptotic distribution , we simulated data under the null over a range of substitution rates that cover the observed range over all 50-CNC windows ( see Methods ) ., We account for heterogeneity in the distribution of SRTi across bins of CNCs with different numbers of expected substitutions on the tested lineage by computing p-values based on the empirical null distribution of SRTi constructed in each bin ( unpublished data ) ., At a significance level of 0 . 001 , 256 mammalian CNCs and 59 amniotic CNCs , respectively , show rate speed-ups on the human lineage ( Table S8 ) ., Note that there is little power to detect rate reductions on these very short lineages ., To better understand these SRTi results , we performed power simulations under a range of models ., The simulation results , summarized in Figure S6 , show considerably greater power to detect speed-ups than slow-downs on all lineages , consistent with the results of Siepel et al . 40 ., Thus , the fact that we detect more speed-ups than slow-downs does not necessarily imply that speed-ups are actually more common , and it is likely that many slow-down events are simply not detected by our analysis ., Our human results allow a comparison to the human accelerated regions ( HARs ) identified by Pollard et al . 5 using a similar type of approach , based on regions that were highly conserved ( at least 96% identity ) across chimpanzee , mouse , and rat ., Among the top 49 HARs , which include two coding regions , 34 overlap with CNCs in our dataset; however , generally , the HARs are considerably shorter and more conserved and lie within our CNCs ., Perhaps not surprisingly , since the HARs are the top genome-wide hits in their data , the signals in our overlapping CNCs tend to be weaker ., Among the 34 CNCs , just five CNCs are significant in our analysis at a genome-wide FDR less than 0 . 05 ., Nonetheless , our CNCs that overlap HARs do show a strong enrichment of modest signals ., Our human lineage p-values are <0 . 01 for 26 of the 34 CNCs overlapping HARs , and are <0 . 1 for 33 of the 34 ( Table S10 ) ., Within our dataset , one of the most significant CNCs on the human lineage is a 144-bp amniotic CNC located on human Chromosome 21 starting at 33481809 ( q22 . 11 , NCBI Build 35 ) ., It was not detected by Pollard et al . 5 because it fails their filtering threshold for similarity between chimpanzee , mouse , and rat ., As illustrated in Figure 5 , the posterior expected number of substitutions ( see Methods for details ) on the human lineage is 5 . 2 , which is 26-fold higher than the value of 0 . 2 expected under the null model ., The corresponding SRTh is 4 . 84 ., The p-value for this CNC is so small that it is difficult to evaluate by simulation; however , the standard normal approximation suggests that p ≈ 6 × 10−7 ( our simulations indicate that this is conservative ) ., In addition to the five nucleotide substitutions , there is also a 2-bp insertion on the human lineage that was not included in the statistical inference ., Since the UCSC genome browser database was recently updated , we were able to inspect an alignment of 17 vertebrate species for this region ., Manual inspection confirmed that all six of these substitutions occurred on the human lineage ., The function of this CNC is unclear but the two nearest genes are C21orf54 , 17 kb upstream , and IFNAR2 , 42 kb downstream of the CNC ., Not much is known about C21orf54 , but IFNAR2 codes for a type I membrane protein that forms one of the two chains of a receptor for interferons alpha and beta 41 ., This CNC is strongly conserved among the other mammalian species and chicken but does not appear to be present in the fugu genome ., In addition to the rapid evolution on the human lineage , there is weak evidence for slower evolution of this CNC on the mouse and dog lineages ( one-sided p-values = 0 . 011 and 0 . 023 , respectively; see Figure 5B ) ., Thus far , we have focused on the simplest class of alternative models , in which a CNC changes substitution rate on a single branch only and has a constant background rate elsewhere on the tree ., We now extend this approach in order to classify each CNC according to a family of more complicated models of evolutionary patterns ., Our data are connected by a tree containing seven branches ., The simplest model ( our “null” ) has a single rate parameter , and the most complicated alternative model has seven different rate parameters ., In between , there are 876 ways of partitioning the seven branches into two or more different substitution rate groups ., However , considering all of these partitions does not seem biologically meaningful or necessary , and here we focus on a reduced set of 126 alternative candidate models ., The alternative models we consider can be divided into two distinct classes of models ., In one class of models , each tree is assumed to have a “background” rate parameter ., Then , each CNC may have between one and six “selected” lineages , and each selected lineage evolves at its own rate ., In the other class of models , each tree may be split into subtrees that share a single rate , while the rest of the tree has a single background rate ( for full details , see Table S11 ) ., We use a modified Akaike Information Criterion ( AIC ) procedure to classify each CNC into its best model ., In brief , the method attempts to account for multiplicities of alternative models as well as the number of estimable parameters in each model ( see Methods ) ., We have performed simulations to test the performance of this method , and we find that it provides suitable control over the rate of “false positives” ( i . e . , accepting models with more parameters than used to simulate the data ) ., That said , our simulations show that it is often difficult to correctly classify complex models with multiple rate changes ( see Methods; Figure S7 ) ., The results of our data analysis are summarized in Figure 6 and Table S12 ., We estimate that ∼68% ( 54 , 643/81 , 957 ) of the mammalian CNCs evolve at a single rate ., The remaining nonneutral CNCs show rate changes on at least one lineage ., The number of CNCs assigned to each model category decreases with increasing model complexity ., Among the 32% of CNCs with more than one rate , ∼75% ( 20 , 420/27 , 314 ) exhibit rate changes on a single lineage but not on the remaining lineages and ∼9% ( 2 , 419/27 , 314 ) exhibit rate changes on the primate or the rodent lineage that are inherited across all branches below ., For the two-parameter models , the rate change events are easily classified as speed-ups or slow-downs ., Counts for both types of event are shown in Figure 6B ., For most lineages , there are slightly more speed-up events than slow-downs ( ∼55% versus ∼45% ) ., However , there are 638 and 530 CNCs that show rate speed-ups on the human and chimpanzee lineages , respectively , far more than the four and eight CNCs , respectively , showing slow-downs ., Presumably , these results are due in large part to the greater power to detect speed-ups , as well as differences in power across lineages ( Figure S6 ) ., It is notable that the dog lineage shows a very large number of rate changes , which may not be fully explained by the long length of this lineage ( second longest among the seven ) ., Since there is no strong tendency towards an excess of speed-ups over slow-downs on this lineage , it is unlikely that this can be explained by occasional CNCs with low-quality dog sequence ., Perhaps a hint is that we have observed greater variation in the dog-lineage substitution rates at neutral sites than on other lineages ., Perhaps there is greater fine-scale variation on the dog lineage that is not well captured by our 50-CNC window method ( see Methods; Figure S8 ) ., As discussed above , we have identified many CNCs with significantly accelerated rates on one or more branches ., However , it is unclear a priori whether these speed-ups reflect positive adaptation or relaxation of functional constraint ., In order to address this issue , we estimated substitution rates in unconserved sequences near each CNC to estimate local neutral rates ( see Methods ) ., We then determined how many of the CNCs showing rate speed-ups have an accelerated rate that actually exceeds the corresponding lineage-specific neutral rate ., If the rate in a CNC actually exceeds the local neutral rate , this is strong evidence for adaptive evolution ., However , a negative result here is difficult to interpret , since adaptive evolution in an otherwise slow-evolving sequence may not necessarily bring the total rate above the neutral background rate ., Our results are summarized in Table 1 ., We observe that most CNCs showing accelerations on the human and chimpanzee branches indeed have rate estimates exceeding the neutral rates; of these , more than half are actually significantly faster than the neutral rate at p < 0 . 05 ., Meanwhile , the other branches of the mammalian tree all show smaller fractions of CNCs with rates that exceed the neutral rate , and very few of these are significantly faster than the neutral rate ., One plausible explanation might be that if there is sufficiently rapid evolution on a long branch , this might cause an otherwise conserved element not to be classified as a “most conserved” region by the HMM 10 ., However , some simple calculations suggest that this is likely to be a modest effect in practice ., Moreover , we see the same effect for both the mammalian and amniotic CNCs ( Table 1 ) , even though the HMM data for the latter include the relatively long branch to chicken , and should therefore be much less susceptible to this effect ., Instead , to explain these observations , we hypothesize that the rate speed-ups that we detect may often reflect rapid bursts of adaptation in which a CNC accumulates a series of sequence changes , thus modifying its function ., A single burst of adaptation may produce enough sequence changes to exceed the neutral rate on a short branch , but not on a longer branch ., In this model , we would have the most power to detect adaptive events on short branches ., Our data argue strongly against a model in which a CNC adapts continuously over extended periods of evolutionary time , as such a model should also produce signals on the long branches ., We have also performed analyses of the locations of CNCs showing branch-specific speed-ups , with respect to nearby genes ., A recent report by Drake et al . 27 found that the frequency spectrum in CNCs is most skewed towards rare variants ( indicating weak purifying selection ) in introns and near genes , and is less skewed in CNCs that are far from genes ., To test whether CNCs showing speed-ups on particular branches occur at higher rates near to or far from genes , we divided all our CNCs into four classes: intronic , within 10 kb of a gene , between 10 kb and 100 kb , and greater than 100 kb from any gene ., We found that on the mouse and rat lineages , CNCs showing speed-ups ( p < 0 . 001 on the branch-specific test SRTi ) occur at higher rates in introns and within 10 kb of genes than among CNCs further from genes ., However , this trend was not replicated on the other lineages of the tree ( Table S13 ) ., We next looked at whether CNCs showing significant rate speed-ups are more likely to be in the proximity of particular kinds of genes 17 , using the PANTHER GO database 32 ., A significant difficulty in this sort of analysis is that even for those CNCs that act as cis-regulators , it is unknown which of the nearby genes is being regulated ., However , as a rather imperfect proxy for this we simply used , for each CNC , the nearest gene ( in either orientation ) ., For each branch of the mammalian tree , we divided the CNCs into those with increased rate on that branch ( by AIC ) and used CNCs evolving under the null model as “neutral” controls ., We looked at whether particular biological process categories were enriched among the nearest genes of the selected CNCs compared to the neutral CNCs ., For mammalian CNCs , there is significant enrichment of the process categories “amino acid activation” and “other coenzyme and prosthetic group metabolism” on the dog and the lineage leading to the common anc

Introduction, Results, Discussion, Methods, Supporting Information

Conserved noncoding elements ( CNCs ) are an abundant feature of vertebrate genomes ., Some CNCs have been shown to act as cis-regulatory modules , but the function of most CNCs remains unclear ., To study the evolution of CNCs , we have developed a statistical method called the “shared rates test” to identify CNCs that show significant variation in substitution rates across branches of a phylogenetic tree ., We report an application of this method to alignments of 98 , 910 CNCs from the human , chimpanzee , dog , mouse , and rat genomes ., We find that ∼68% of CNCs evolve according to a null model where , for each CNC , a single parameter models the level of constraint acting throughout the phylogeny linking these five species ., The remaining ∼32% of CNCs show departures from the basic model including speed-ups and slow-downs on particular branches and occasionally multiple rate changes on different branches ., We find that a subset of the significant CNCs have evolved significantly faster than the local neutral rate on a particular branch , providing strong evidence for adaptive evolution in these CNCs ., The distribution of these signals on the phylogeny suggests that adaptive evolution of CNCs occurs in occasional short bursts of evolution ., Our analyses suggest a large set of promising targets for future functional studies of adaptation .

Conservation of DNA sequences across evolutionary history is a highly informative signal for identifying regions with important biological functions ., In particular , conserved noncoding regions have been shown to be good candidates for containing regulatory elements that have roles in gene regulation ., Recent studies have found that there are many thousands of conserved noncoding elements ( CNCs ) in vertebrate genomes and have suggested possible functions for some of these elements , but the function of most CNCs remains unknown ., To study the evolution of CNCs , we developed a statistical method to identify CNCs that show changes in evolutionary rates on particular branches of the mammalian phylogenetic tree ., Those rate changes may indicate changes in the function of a CNC ., We applied our method to CNCs of five mammalian genomes , and found that , indeed , many CNCs have experienced rate changes during their evolution ., We also found a subset of CNCs showing accelerations in evolutionary rate that actually exceed the neutral rates , suggesting that adaptive evolution has shaped the evolution of those elements .

evolutionary biology, genetics and genomics, mammals, computational biology

journal.pgen.1002236

A Comprehensive Map of Mobile Element Insertion Polymorphisms in Humans

Retrotransposons are endogenous genomic sequences that copy and paste into locations throughout host genomes 1–3 ., Most mobile elements annotated in the human reference genome are remnants of ancient retrotransposition events and are no longer capable of active retrotransposition ., However , a fraction of mobile elements remain active and contribute to variation between individuals in the human population ., These active elements belong almost exclusively to the Alu , L1 , and SVA families of non-LTR retrotransposons 4 ., The Alu family is the most common mobile element in primate genomes , with more than 1 . 1 million copies in Homo sapiens 5–7 ., The sequence of a full-length Alu element is 300 bp long ., Alu elements are classified into a range of sub-families which have different propensities for retrotransposition , and are identified according to sequence alterations ., Several AluY sub-families are currently active and are responsible for the bulk of mobile element insertion variation in Homo sapiens ., The human reference genome contains over 140 , 000 annotated AluY elements ., After Alus , L1 insertions are the next most prevalent family of mobile elements ., There are over 500 , 000 L1 elements annotated in Homo sapiens ., A full-length L1 is a sequence of roughly 6 kb in length and the most active L1 sub-family in the human lineage is L1HS 8 , 9 ., There are a little more than 1 , 500 L1HS annotated elements in the human reference ., A third family of mobile element are SVA retrotransposons 10 ., SVAs are hybrid elements of SINE , VNTR and Alu components that range in size up to several Kb , with more than 3 , 600 annotated SVA elements in the human reference genome ., SVA elements are thought to be the youngest family of retrotransposons in primates 11 ., Other less common classes of mobile elements , such as DNA transposons , and endogenous retroviruses are not the focus in this study ., Mobile element insertions ( MEI ) are known to generate significant structural variation within Homo sapiens 12 , 13 and have diverse functional impacts 14–16 ., In vitro experiments identified key features of Alu 17 and L1 18 elements responsible for retrotransposon activity ., The identification of MEI variant loci in humans initially began with disease-causing insertion events ( e . g . hemophilia 19 , breast cancer 20 ) ., Experimental approaches were based upon library screening and small-scale PCR based display assays 21 ., These approaches have been augmented by comparisons of the NCBI and the HuRef genomes 22 , 23 , large scale fosmid-end sequences 24 , and targeted sequencing of element-specific PCR products 25–28 ., The dbRIP database of MEI polymorphisms 29 currently contains 2 , 691 polymorphic loci , enabling early estimates for the total number of segregating events 25 and per-generation mutation rates 23 ., MEI polymorphisms can be detected either as insertions or as deletions in samples relative to the reference genome ., Mechanistically , however , both types of observations are due to retrotransposon insertion; precise excisions of mobile elements are essentially non-existent 1 ., Therefore MEI detected as deletions are , in fact , retrotransposon insertions in the reference DNA and can be verified as such by comparison with ancestral genomes ., Detection and genotyping properties of MEI detected as insertions ( “non-reference MEI” ) and as deletions ( “reference MEI” ) are substantially different ., We present their respective properties separately before combining the two detection modes into a unified MEI analysis ., The deletion detection methods and properties of the full set of 1000GP deletions have been extensively described in the 1000GP CNV companion paper 30 ., This allows us to focus on specific properties of the reference MEI subset of those deletions ., Effective computational algorithms using second-generation sequencing data exist for identifying deletions 27 , 31 , 32 , and have been used to find MEI in particular 33 ., Detecting non-reference MEI directly as insertions from whole genome shotgun sequence data poses a more challenging problem , owing to the inherent difficulties associated with accurate mapping of sequenced reads derived from highly repetitive regions of the genome ., Only recently have methods been developed for the purpose of non-reference MEI detection from second-generation whole genome shotgun data including published studies of L1 element insertions 34 and of Alu insertions 35 ., These studies adopted similar computational approaches to one of our insertion detection methods ( the read pair method , see Materials and Methods ) and have different detection properties ( Text S2 Comparisons , Figures S8 , S9 , S10 ) ., Relative to previous studies , we present a broad analysis of MEI variation in the human population; with more variant loci detected , from the three major mobile element families , using multiple detection methods , each with comprehensive experimental validation ( Table 1 ) ., The present study represents the combined efforts of the MEI sub-group of the 1000 Genomes Project and has been prepared as a companion to previous 1000GP pilot publications 30 , 36 ., The MEI analyzed in this study were included the 1000GP variant call release of July 2010 ( ftp://ftp-trace . ncbi . nih . gov/1000genomes/ftp/pilot_data/release/2010_07 ) , also provided as Table S1 ) ., The specific purpose here is to provide a more detailed description of the methods , validation experiments , and properties of the 1000GP catalog of MEI events , and to extend the analysis by adding genotype information , population allele frequencies , and population specific mutation rates ., We analyzed two whole-genome datasets produced by the 1000GP , the low coverage pilot dataset consisting of 179 individuals sequenced to ∼1–3X coverage and the trio pilot dataset consisting of two family trios sequenced to high , ∼15–40X coverage ( Table S2 , Figure S4 ) ., These datasets included samples from three continental population groups , 60 samples of European origin ( CEU ) , 59 African ( YRI ) , and 60 Asian samples from Japan and China ( CHBJPT ) ., The two pilot datasets were produced and analyzed for complementary purposes ., The trio dataset was used for assessing detection methods in high coverage samples and for the purpose of finding candidate de novo insertions in the trio children ., The high coverage dataset was used to assess population properties of MEI ., Both datasets contributed to the overall catalog of events ., We developed two complementary methods for the detection of non-reference MEI , a read-pair constraint ( RP ) method applied to Illumina paired-end short read data , and a split-read ( SR ) method applied to the longer read data from Roche/454 pyrosequencing ( Materials and Methods: non-reference MEI detection ) ., Figure 1a and 1b shows the respective detection signatures and examples of event displays ., Candidate MEI events were formed as clusters of supporting fragments ., A limitation specific to RP detection arises from annotated elements within a characteristic read pair fragment length of candidate MEI ( Figure 1a ) ., Read pairs spanning from a uniquely mapped anchor into an annotated mobile element with a fragment length consistent with the given library fragment length distribution ( Figure S5 ) are characteristic of the reference allele and are not evidence for non-reference MEI ., These “background” read pairs occasionally have fragment lengths on the extreme tails of the library distribution and can potentially be misclassified as evidence for non-reference MEI ., For this reason RP detection criteria required at least two supporting fragments spanning into the insertions from both sides of the insertion ., We also masked insertion positions within a fragment length around each annotated element of the corresponding family from RP detection in order to achieve a low false detection rate ., The SR method was not dependent on the fragment length distribution in the 454 data so these additional detection criteria were not required ., We applied the two methods to both 1000GP pilot datasets ( Table 1 ) separately , yielding a total of 5 , 370 distinct genomic MEI loci , 33% of which were found by both SR and RP methods ( Figure 1c ) ., The overall level of detection overlap between SR and RP methods is limited by detection sensitivity and specificity ( see below ) and the number of samples sequenced by both 454 and by Illumina read pairs ., In addition to the 5 , 370 non-reference MEI , we identified 2 , 010 reference MEI detected as deletions of mobile elements in samples ., The reference MEI events were selected from the full release set of 1000GP pilot deletions ( n\u200a=\u200a22025 ) 30 , 36 based on matching deletion coordinates to RepeatMasker 3 . 27 Alu , L1 , and SVA annotations 6 , and the requirement that the mobile element is absent in the chimpanzee genome 37 ( 6x pan Trogodytes-2 . 1 assembly ) at the corresponding positions in hg18 ( Materials and Methods: Reference MEI selection ) ., Figure 1e shows an example event display of an AluYb8 reference MEI , detected as a deletion in the trio pilot data ., All but one of the reference MEI were found by one or more of the RP or SR deletion detection algorithms that were part of the released 1000GP deletion call set 30 , 38–42 with a small overlapping contribution from algorithms based on assembly or read depth methods 43 , 44 ( Figure 1d , Table S3 ) ., The complete set of 7 , 310 MEI calls is simply the combined set of reference and non-reference MEI over both pilot datasets ( summarized in Table 1 , complete list in Table S1 ) ., Insertions occurring at the same locus from different call sets were merged using a 100 bp window for matching positions , choosing the SR insertion coordinate when available to represent the merged event ., Similarly for reference MEI , deletion merging was accomplished among the 23 separate 1000GP call sets using a precision-aware algorithm described in detail in the 1000GP SV companion paper 30 ., The full catalog of MEI loci appear to be distributed randomly across the genome ( Figure 2b ) with a characteristic spacing of 0 . 4 Mb between MEI loci , except for an apparent MEI hotspot in the HLA region of chromosome 6 where 19 MEI loci are clustered in a 1 Mb region ( 8 times the genomic average density for MEI , Figure S11 ) ., Accurate read mapping in the HLA region is complicated by a high density of variation 36 , however , we see no evidence of falsely detected MEI here ., The balance between reference and non-reference MEI , proportions of RP and SR detected loci , the fraction of previously identified MEI loci , and the validation rate are all consistent with genomic averages; only the density of MEI is significantly increased ., The genomic proportions of the three mobile element families are 85±2% Alu , 12±2% L1 , and 2 . 5±1% SVA ( Figure 2b ) for both reference and non-reference MEI ., Most non-reference MEI loci were detected from the low coverage pilot data ( Figure 2c ) while the reference MEI were more evenly distributed between the low coverage and trio pilot data ( Figure 2d ) ., As described in the 1000GP main pilot paper 36 , more than 80% of the non-reference MEI were newly identified loci not detected by previous studies 23–28 , 34 , 35 , 45 ., However , in the mean time , several published studies have produced new lists of non-reference MEI loci including L1 insertions 34 and Alu insertions 28 , 35 ., Half of the non-reference MEI loci from this study have not yet been reported elsewhere ( Figure 2e , Figure S8 ) ., Table 1 of the 1000GP paper lists 5 , 371 MEI , two of these events were subsequently merged into one to form the present count of 5 , 370 MEI detected as insertions ., For reference MEI , we find that 76% of our events matched deletion coordinates listed in the dbVAR ( 28 January 2011 ) structural variation database or a deletion identified in the HuRef genome 22 , 46 , leaving 24% of the reference MEI unreported prior to 1000GP publications ., The 1000GP catalog of MEI variant sites includes all 7 , 310 detected loci , including those matching MEI from other publications ., Further comparisons among the recent MEI studies are provided in Text S2 ., We benchmarked each of the four non-reference MEI call sets ( separate SR and RP call sets for the low coverage and trio pilot datasets ) to assess detection sensitivity and specificity ., As MEI are currently not suitable for microarray validation due to their highly repetitive sequence , all validations were done by locus-specific PCR ., 200 loci were randomly selected from each of the four insertion call sets ., Using an automated pipeline 32 , primer design was possible for 746 loci ( Table S4 ) ., In addition to the randomly selected loci , other candidate loci were selected for validation experiments in order to confirm SVA insertions ( n\u200a=\u200a7 ) , to test potential de novo insertions from the pilot 2 trio ( n\u200a=\u200a1 ) , and gene-interrupting events ( n\u200a=\u200a86 attempted ) , as well as for algorithm training and testing purposes ( n\u200a=\u200a386 ) ., These additional PCR results ( Table S4a ) were not used to assess false detection rates , except for the special case of SVA insertions , which were under-represented in the random loci selection since SVA insertions are relatively rare ., All candidate loci with successful primer design were tested on two different population genetic panels ( Materials and Methods: Validation ) one with DNA of 25 individuals from the low coverage pilot , and one with DNA from all samples of the trio pilot dataset ., In addition to other human samples from populations not represented by the pilot datasets , DNA of a chimpanzee was also included on the panel to confirm that the identified insertion is indeed human-specific ., An example of typical results for a low coverage locus is shown in Figure 3a ., Through additional primer design for loci with inconclusive results and PCRs using a primer residing within the 3′ end of a retrotransposon , in particular within SVA elements , more than 98% of the tested candidate loci were successfully genotyped ., The validation experiments revealed overall insertion false discovery rates for each dataset of less than 5% ( Table 1 ) ., Among the different retrotransposon families ( L1 , SVA , and Alu elements ) , false discovery rates varied noticeably ( Figure 3b ) , with Alu insertions showing the lowest false-positive rate ( 2 . 0 1 . 1–3 . 4 % , followed by L1s ( 17 10–27 % ) , and SVAs ( 27 8–55 % ) with 95% confidence intervals ., This is not entirely unexpected as polymorphic Alu insertions tend to be low divergence full-length AluY elements , unlike L1 or SVA insertions which tend to be truncated and may be accompanied by adjacent transduced genomic DNA sequences ., Although the SR and RP detection methods are very different , the overall detection specificities were remarkably consistent ., Following the validation of non-reference MEI , we assessed detection sensitivity ., The primary challenge here was to find suitable gold standard non-reference MEI that should be present in our samples from which to assess sensitivity ., We estimated sensitivity in three different ways , as a consistency check ., First , we estimated sensitivity by using the high quality non-reference MEI from HuRef 23 as a gold standard and found that 74% of the 650 Alu , L1 , or SVA insertions in HuRef matched MEI insertion loci in our catalog ( Table S5 ) ., This represents a lower limit for insertion detection sensitivity since not all MEI in the HuRef genome are necessarily present in the 1000GP pilot samples ., Next we looked at the overlapping insertion detection between the RP and SR methods in the trio children samples ( Figure 3c , Figure S6 ) , which were the samples sequenced to the highest depth for both Illumina and 454 data ., Based on the detected loci overlap ( see Materials and Methods: Detection sensitivity ) , we estimate 67%±3% and 70%±7% sensitivities respectively for RP and SR insertion detection in the trio children ( Table S6 ) , with a combined SR+RP detection sensitivity exceeding 90% in the CEU trio child ( see Materials and Methods , Eq . 4 ) with high coverage data from both 454 and Illumina reads ., A third approach to estimate for the non-reference MEI detection sensitivity is based on the validation PCR genotypes in the low coverage dataset ., Since the PCR loci were selected as random subsets for each RP and SR call set independently , the validated sites selected from SR events can be used as a gold standard to assess RP detection sensitivity , and vice-versa ., Detection sensitivity as a function of allele frequency ( Figure 3d ) was estimated for each method from PCR genotypes at those loci randomly selected for validation of the complementary method ., PCR genotypes provided the allele frequency estimate on the abscissa ., Statistical errors at high allele frequency are large because the limited number of tested MEI loci at higher allele frequencies ., Detection sensitivity of the RP method saturates close to 70% at high coverage and the SR method sensitivity exceeds 70% at high coverage ( Figure S6 ) ., The corresponding trend is apparent in Figure 3d ., The combined detection sensitivity approaches 90% for common alleles ( Materials and Methods , Eq . 4 ) ., However , since relatively few of the low coverage samples were sequenced with 454 , a realistic estimate for the detection sensitivity to common MEI insertions is between 70% and 80% ., This is consistent with 75% derived from the HuRef gold standard comparison and the sensitivity estimate from the trio pilot overlaps ., Equivalent estimates for Alu , L1 , and SVA specific sensitivities for common MEI alleles are 75%±10% , 50%±10% , and 50%±20% respectively ( Table S9 ) ., Regarding reference MEI detected as deletions , the overall validation rate from PCR and local assembly for the MEI component of deletions was 96% ., This does not imply that the remaining 4% were false , only that the released set of deletions contained reference MEI detected by two high specificity algorithms with characteristic false detection rates less than 10% ., These algorithms did not require additional validation evidence in the 1000GP release ., A rough estimate for the false detection rate for the MEI component of deletions is therefore 0 . 4% ., The number of algorithms supporting a given call is another indicator of call quality ., The average number of separate deletion calls ( out of a maximum of 23 call sets ) supporting events in the MEI subset was 7 . 8 while the average over all other deletions was 2 . 3 ( Figure S2 ) ., The high validation rate and high consensus among detection algorithms indicate that this subset of deletions is relatively free of detection artifact ., The practical limitation on the specificity of these events as reference MEI is the subsequent MEI selection criteria ., Only a small fraction the 2 , 010 selected events were ambiguous in terms of matching coordinates to an annotated mobile element with corresponding gap in the chimpanzee genome assembly ( e . g . Figure S3 , bottom panel ) ., The 1000GP CNV paper identified 2029 reference MEI variants using the BreakSeq algorithm ., Overlap between the respective lists is 89% ., We estimate 10% as an upper limit on the false discovery rate for reference MEI ., Detection sensitivity for reference MEI was estimated from the fractions of gold standard reference MEI identified by Xing et al . from HuRef 22 , 23 , 46 , and reference MEI identified by Mills et al . 4 , 47 from 1000GP samples NA12878 and NA12156 matched to any of our 2 , 010 reference MEI ( Table S5 ) ., In each case the fraction of those MEI deletions found in this study exceeded 90% ., This level of detection sensitivity is considerably higher than the bulk deletion detection sensitivity reported in the SV companion paper 30 , indicating that the RP and SR deletion detection methods developed for the 1000GP were particularly well suited for reference MEI detection ., We characterized each detected MEI event ( Table S1 ) by the insertion position , which algorithm ( s ) detected the event , number of fragments supporting the insertion and reference alleles , insertion length ( Figure S12 ) , element family , bracketing homology ( Figure 4a ) , and assembled sequence ., MEI have a characteristic “target site duplication” region of homology bracketing the insertion ., The target site duplication length distributions for the MEI detected by different methods , as well as for different element families , peaked at 15 bp with a standard deviation of 7 bp ( Figure 4a ) ., The full insertion sequence from reference MEI is readily extracted from the reference , but non-reference MEI require local de novo assembly to reconstruct the inserted sequence ., For this we used 454 data to reconstruct 1 , 105 Alu insertions ( Tables S1 and S7 ) from our event list based on the PHRAP assembly program 48 ., We then used BLAT 49 to map assembled contigs back to the build 36 . 3 human reference to identify the boundaries of the inserted sequence ., The inserted sequence was then mapped back to the RepeatMasker mobile element sequences using the RepeatMasker web server ( http://www . repeatmasker . org ) to identify the sub-family ( Figure 4b ) ., The accuracy of Alu sub-family classification was assessed by comparison to matched 359 Alu insertions from dbRIP 29 and nine fully sequenced Alu insertions from PCR validation experiments ., 272 of the assembled Alu sub-family classes were identical ( 74% ) ., The most active Alu sub-families are AluYa5 and AluYb8 ., AluY sub-families account for essentially all Alu variation ., The relative proportions among Alu sub-families are consistent among reference and non-reference MEI , as well as consistent with the Alu sub-families observed in HuRef 23 ., The Alu sub-family breakdown differs from that reported by Hormozdiari et . al . 35 who identified more than 10% of their set of insertions from AluJ or AluS sub-families ., The authors of that study point out that these ‘older’ Alu events could arise from mechanisms other than retrotransposon insertions ., Genotyping of non-reference MEI ( Materials and Methods: Genotyping ) was based on counts of fragments supporting the reference allele and fragments supporting the insertion allele at each locus for each sample ., Heterozygous MEI sites are identified by roughly equal amounts of reference and alternate allele supporting fragments spanning an insertion locus , while homozygous sites should have all fragments supporting one or the other allele ., For reference MEI , we used genotypes produced by the Genome STRiP package 39 , which was developed for 1000GP deletion genotyping 30 , 39 and incorporates Beagle 50 imputation based on linkage with local SNPs ., Both genotyping methods provide phred-scaled 51 genotype quality ( GQ ) metrics at each site that reflect confidence in the given call based on supporting evidence , GQ\u200a=\u200a0 to a total lack of genotype evidence and GQ\u200a=\u200a10 indicating that the genotype should be 90% accurate ., The GQ metric depends on the number of fragments found to support the MEI and non-MEI alleles for a given locus and sample ( Text S2: Genotyping methods ) ., As in most issues of sensitivity vs . specificity , there is a trade-off between high genotype efficiency and genotyping accuracy ., The drop-off in genotyping efficiency vs . GQ threshold is more severe for non-reference MEI ( Figure S13 ) ., For subsequent genotype-based analysis of non-reference MEI sites and samples we required GQ≥7 , which corresponds to roughly 40% genotyping efficiency in the low coverage pilot data ., For reference MEI we required GQ≥10 , which corresponds to an efficiency of 80% ., Genotyping efficiency improves with increased sample read coverage ( Figure S13 , bottom panel ) , particularly for non-reference MEI ., Genotyping accuracy for non-reference MEI is assessed by direct comparison to PCR validation genotypes in the same samples , and by testing for Mendelian errors in the trios and violations of Hardy-Weinberg Equilibrium in the low coverage data ( Text S2 Genotyping tests , Figures S13 and S14 ) ., Validation genotypes are listed in Table S4 ( also as the “VG” field of the released MEI insertion genotyped VCF files ) ., Genotype contingency tables for the low coverage data ( Table 3 ) show an 87% agreement between sequenced genotypes and PCR genotypes for sites with GQ≥7 ., Genotyping accuracy improves with increasing GQ threshold ( Figure S13 ) but never exceeds 90% in the low coverage data ., Non-reference MEI genotyping performance for high coverage trio data ( Table 3 , Table S8 ) was considerably better than for the low coverage data ., However , for population analyses we used only low coverage data in order to minimize the potential for coverage biases ., The accuracy of GenomeSTRiP genotypes ( for reference MEI events ) with GQ≥10 was estimated at 99% in the full 1000GP deletion call set 30 , 36 , 39 ., We estimated MEI allele frequencies from the count of high quality ( GQ≥7 non-reference and GQ≥10 for reference MEI ) genotyped insertion alleles for each MEI locus ., Allele frequencies were estimated from loci with at least 25 high quality genotypes for each continental population group ., The two MEI detection modes ( i . e . reference and non-reference insertions ) have very different allele frequency spectra ( Figure 5a–5c ) ., Since the non-reference MEI and reference MEI components have very different powers of detection and genotyping , the two components were corrected separately ( Materials and Methods: Allele frequency spectra ) before being combined into the full MEI spectrum ( Figure 5d–5f ) ., We estimated correction factors for each population group , each element type , and each detection mode ( Table S9 ) ., Non-reference MEI correction factors are larger than reference MEI factors because of the lower detection sensitivity and genotyping efficiency ., The allele count spectra were compared to the standard neutral model 52–54 , θ/i , where θ is an MEI diversity parameter and i is the allele count in a fixed number of samples ., The value of θ is fit from the MEI allele count spectrum for each population group and the fitted model is the gray dotted line appearing in Figure 5d–5f ., Only allele count bins in the range 0 . 15<frequency<0 . 95 were used in the fit ( bins marked with error bars in Figure 5d–5f ) to avoid regions of poor detection sensitivity ., The corresponding gray dashed lines superimposed on Figures 5a–5c also represent the neutral model expectation , modified to account for the respective ascertainment conditions , ( θ/2N ) for reference MEI , ( θ/i ) ( 2N−i ) / ( 2N ) for non-reference MEI , where N\u200a=\u200a25 is the number of samples in the spectra ., These ascertainment condition expressions are based on the assumption that the reference genome represents a random sample from the given population , which is admittedly simplistic but nevertheless explains much of the difference between the allele spectra of reference and non-reference MEI ., A coalescent simulation ( Text S2 Coalescent , Figure S17 ) for MEI variation also shows this behavior using standard population history parameters 55 ., Fitted values of the diversity parameter θ for each of three population groups and each element family are listed in Table 4 , along with rough estimates for the corresponding MEI mutation rates based on the neutral model ( μ\u200a=\u200aθ/ ( 4·Ne ) ) with an effective population size Ne of 10 , 000 56 , 57 ., Confidence intervals for μ and θ ( Table 4 ) take into account Poisson noise and uncertainties in the correction factors , but do not reflect the degree to which the model assumptions are valid ., All three element families have been combined into the allele count spectra shown in Figure 5 , although the Alu family is the dominant component ., Allele frequency spectra for different element families have similar shapes ( Figure 6a ) ., We know from SNP studies that the shape of the allele frequency spectrum is modulated by demographic history , and that this shape is characteristically different for European , African , and Asian populations 56 , 57 ., When compared to SNP allele frequency spectra from the same datasets ( Figure 6b ) , the MEI and SNP frequency spectra show similar trends among the corresponding populations ., Among the three population groups , the CHBJPT spectrum shows relatively few low frequency allele loci ., This was also apparent in comparison with the neutral model ( Figure 5e ) ., We also analyzed population differentiation by applying principal component analysis to the matrix of allele counts across the low coverage pilot samples and loci ( Figures S15 and S16 ) ., Some structure is immediately apparent in the matrix of allele counts , e . g . increased heterozygosity in the YRI samples , but PCA reveals population specific patterns of MEI that result in tight clusters of samples according to geographic origin ( Figure 6c ) ; again similar to population patterns for SNPs 58 , CNVs 59 and deletions 30 ., As few as 39 of the 5 , 370 non-reference MEI loci were located in exonic sequence , mostly in untranslated regions , and only 3 were found in coding exons ( Table 2 ) ., These numbers are much lower than expected from random placement ( Materials and Methods: Functional calculation ) , indicating strong selection against MEI disrupting gene function ., The suppression factor for an MEI to occur in a coding region compared to the genome-averaged rate is 46x , a much stronger suppression than is observed for coding SNPs ( Table S10 , suppression factor\u200a=\u200a3 . 9x ) , and is similar to SNPs that cause the loss of a stop codon ( 42x , derived from Table 2 of 36 ) ., Two of the MEI interrupting coding regions were PCR-validated ., These two MEI appear to be of little functional consequence: ZNF404 is a member of a highly paralogous zinc finger gene family and C14orf166B is a predicted gene without functional annotation ., These findings suggest very strong negative selection against MEI interrupting coding regions ., Although it is obvious from first principles that insertions in functional regions should be deleterious , the observed suppression factor in a large catalog of MEI in populations quantifies the effect ., The high-coverage trio data allows for the most precise estimates of the total number of MEI variants between pairs of individuals because of the high detection sensitivity ., The number of pair-wise variant loci is calculated as the presence or absence of an insertion at a given locus , combining reference and non-reference MEI ., We selected the two trio children ( NA12878 and NA19240 ) for comparison between CEU and YRI individuals and the trio parents for comparison of individuals within the CEU and the YRI population groups ., After corrections for detection sensitivity and false detection ( Text S2 and Table S6 ) , we found 2 , 034±120 MEI variant loci between the African and the European trio children , 1 , 442±120 between the YRI parents , and 663±140 MEI between the CEU parents ., The pair-wise event numbers scale linearly with coalescent time derived from SNPs ( Figure 6d ) in these samples ( Text S2: Coalescent 60–64 ) ., Previous estimates for the de novo mobile element insertion rate and our own estimate of the MEI mutation rate are one event per 20 births in the human population 23 ., Accordingly , we did not expect to find de novo insertions in our sample of two trio children ., Among all MEI events detected in the trio offspring against the reference ( 1 , 778 in NA12878 and 1 , 971 in NA19240 ) , we did identify a single de novo candidate insertion in NA12878 , not detected in either parent or in any other sample ( Table S6 , de Novo ) ., A

Introduction, Results, Discussion, Materials and Methods

As a consequence of the accumulation of insertion events over evolutionary time , mobile elements now comprise nearly half of the human genome ., The Alu , L1 , and SVA mobile element families are still duplicating , generating variation between individual genomes ., Mobile element insertions ( MEI ) have been identified as causes for genetic diseases , including hemophilia , neurofibromatosis , and various cancers ., Here we present a comprehensive map of 7 , 380 MEI polymorphisms from the 1000 Genomes Project whole-genome sequencing data of 185 samples in three major populations detected with two detection methods ., This catalog enables us to systematically study mutation rates , population segregation , genomic distribution , and functional properties of MEI polymorphisms and to compare MEI to SNP variation from the same individuals ., Population allele frequencies of MEI and SNPs are described , broadly , by the same neutral ancestral processes despite vastly different mutation mechanisms and rates , except in coding regions where MEI are virtually absent , presumably due to strong negative selection ., A direct comparison of MEI and SNP diversity levels suggests a differential mobile element insertion rate among populations .

We embarked on this study to explore the 1000 Genomes Project ( 1000GP ) pilot dataset as a substrate for Mobile Element Insertion ( MEI ) discovery and analysis ., MEI is already well known as a significant component of genetic variation in the human population ., However the full extent and effects of MEI can only be assessed by accurate detection in large whole-genome sequencing efforts such as the 1000GP ., In this study we identified 7 , 380 distinct genomic locations of variant MEI and carried out rigorous validation experiments that confirmed the high accuracy of the detected events ., We were able to measure the frequency of each variant in three continental population groups and found that inherited MEI variants propagate through populations in much the same way as single nucleotide polymorphisms , except that MEI are more strongly suppressed in protein coding parts of the genome ., We also found evidence that the MEI mutation rate has not been constant over human population history , rather that different populations appear to have different characteristic MEI mutation rates .

functional genomics, genetic mutation, genome evolution, genome scans, neutral theory, population genetics, genome sequencing, mutation, genome analysis tools, genome databases, mutation types, mutation databases, genetic polymorphism, biology, genetics, genomics, computational biology, genetics and genomics, human genetics

journal.pcbi.1006142

Systematic interrogation of diverse Omic data reveals interpretable, robust, and generalizable transcriptomic features of clinically successful therapeutic targets

More than half of drug candidates that advance beyond phase I clinical trials fail due to lack of efficacy 1 , 2 ., One possible explanation for these failures is sub-optimal target selection 3 ., Many factors must be considered when selecting a target for drug discovery 4 , 5 ., Intrinsic factors include the likelihood of the target to be tractable ( can the target’s activity be altered by a compound , antibody , or other drug modality ? ) , safe ( will altering the target’s activity cause serious adverse events ? ) , and efficacious ( will altering the target’s activity provide significant benefit to patients ? ) ., Extrinsic factors include the availability of investigational reagents and disease models for preclinical target validation , whether biomarkers are known for measuring target engagement or therapeutic effect , the duration and complexity of clinical trials required to prove safety and efficacy , and the unmet need of patients with diseases that might be treated by modulating the target ., Over the past decade , technologies have matured enabling high-throughput genome- , transcriptome- , and proteome-wide profiling of cells and tissues in normal , disease , and experimentally perturbed states ., In parallel , researchers have made substantial progress curating or text-mining biomedical literature to extract and organize information about genes and proteins , such as molecular functions and signaling pathways , into structured datasets ., Taken together , both efforts have given rise to a vast amount of primary , curated , and text-mined data about genes and proteins , which are stored in online repositories and amenable to computational analysis 6 , 7 ., To improve the success rate of drug discovery projects , researchers have investigated whether any features of genes or proteins are useful for target selection ., These computational studies can be categorized according to whether the researchers were trying to predict tractability 8 , 9 , safety 10–13 , efficacy ( no publications to our knowledge ) , or overall success ( alternatively termed “drug target likeness” ) 8 , 13–26 ., Closely related efforts include disease gene prediction , where the goal is to predict genes mechanistically involved in a given disease 27–32 , and disease target prediction , where the goal is to predict genes that would make successful drug targets for a given disease 33–35 ., To our knowledge , we report the first screen for features of genes or proteins that distinguish targets of approved drugs from targets of drug candidates that failed in clinical trials ., In contrast , related prior studies have searched for features that distinguish targets of approved drugs from the rest of the genome ( or a representative subset ) 13 , 15–25 ., Using the remainder of the genome for comparison has been useful for finding features enriched among successful targets , but it is uncertain whether these features are specific to successful targets or are enriched among targets of failed drug candidates as well ., Our study aims to fill this knowledge gap by directly testing for features that separate targets by clinical outcome , expanding the scope of prior studies that have investigated how genetic disease associations 36 and publication trends 37 of targets correlate with clinical outcome ., Our work has five additional innovative characteristics ., First , we included only targets of drugs that are presumed to be selective ( no documented polypharmacology ) to reduce ambiguity in assigning clinical trial outcomes to targets ., Second , we included only phase III failures to enrich for target efficacy failures , as opposed to safety and target engagement failures , which are more common in phase I and phase II 2 ., Third , we excluded targets of assets only indicated for cancer , as studies have observed that features of successful targets for cancer differ from features of successful targets for other indications 22 , 23 , moreover , cancer trials fail more frequently than trials for other indications 2 ., Fourth , we interrogated a diverse and comprehensive set of features , over 150 , 000 features from 67 datasets covering 16 feature types , whereas prior studies have examined only features derived from protein sequence 16–18 , 24 , 25 , protein-protein interactions 13 , 15 , 18–23 , Gene Ontology terms 13 , 15 , 16 , and gene expression profiles 15 , 19 , 21 , 25 ., Fifth , because targets of drugs and drug candidates do not constitute a random sample of the genome , we implemented a suite of tests to assess the robustness and generalizability of features identified as significantly separating successes from failures in the biased sample ., A handful of the initial 150 , 000+ features passed our tests for robustness and generalizability to new targets or target classes ., Interestingly , these features were predominantly derived from gene expression datasets ., Notably , two significant features were discovered repeatedly in multiple datasets: successful targets tended to have lower mean mRNA expression across tissues and higher expression variance than failed targets ., We also trained a classifier to predict phase III success probabilities for untested targets ( no phase III clinical trial outcomes reported for drug candidates that selectively modulate these targets ) ., We identified 943 targets with sufficiently unfavorable expression characteristics to be predicted twice as likely to fail in phase III clinical trials as past phase III targets ., Furthermore , we identified 2 , 700 , 856 target pairs predicted with 99% consistency to have a 2-fold difference in success probability ., Such pairwise comparisons may be useful for prioritizing short lists of targets under consideration for a therapeutic program ., We conclude this paper with a discussion of the biases and limitations faced when attempting to analyze , model , or interpret data on clinical trial outcomes ., We extracted phase III clinical trial outcomes reported in Pharmaprojects 38 for drug candidates reported to be selective ( single documented target ) and tested as treatments for non-cancer diseases ., We grouped the outcomes by target , scored targets with at least one approved drug as successful ( NS = 259 ) , and scored targets with no approved drugs and at least one documented phase III failure as failed ( NF = 72 ) ( S1 Table ) ., The target success rate ( 77% ) appears to be inflated relative to typically reported phase III success rates ( 58% ) 2 because we scored targets by their best outcome across multiple trials ., We obtained target features from the Harmonizome 39 , a recently published collection of features of genes and proteins extracted from over 100 Omics datasets ., We limited our analysis to 67 datasets that are in the public domain or GSK had independently licensed ( Table 1 ) ., Each dataset in the Harmonizome is organized into a matrix with genes labeling the rows and features such as diseases , phenotypes , tissues , and pathways labeling the columns ., We included the mean and standard deviation calculated along the rows of each dataset as additional target features ., These summary statistics provide potentially useful and interpretable information about targets , such as how many pathway associations a target has or how variable a target’s expression is across tissues ., The datasets contained a total of 174 , 228 features covering 16 feature types ( Table 1 ) ., We restricted our analysis to 44 , 092 features that had at least three non-zero values for targets assigned a phase III outcome ., Many datasets had strong correlations among their features ., To reduce feature redundancy and avoid excessive multiple hypothesis testing while maintaining interpretability of features , we replaced each group of highly correlated features with the group mean feature and assigned it a representative label ( Fig 1 , S2 Table ) ., The number of features shrunk to 28 , 562 after reducing redundancy ., We performed permutation tests 40 , 41 on the remaining 28 , 562 target features to find features with a significant difference between the successful and failed targets , and we corrected p-values for multiple hypothesis testing using the Benjamini-Yekutieli method 42 ( Fig 1 , S2 Table ) ., We used permutation testing to apply the same significance testing method to all features , since they had heterogeneous data distributions ., We detected 19 features correlated with clinical outcome at a within-dataset false discovery rate of 0 . 05 ( Table 2 ) ., The significant features were derived from 7 datasets , of which 6 datasets were gene expression atlases: Allen Brain Atlas adult human brain tissues 43 , 44 , Allen Brain Atlas adult mouse brain tissues 43 , 45 , BioGPS human cell types and tissues 46–48 , BioGPS mouse cell types and tissues 46–48 , Genotype-Tissue Expression Project ( GTEx ) human tissues 49 , 50 , and Human Protein Atlas ( HPA ) human tissues 51 ., The remaining dataset , TISSUES 52 , was an integration of experimental gene and protein tissue expression evidence from multiple sources ., Two correlations were significant in multiple datasets: successful targets tended to have lower mean expression across tissues and higher expression variance than failed targets ., Because targets of drugs and drug candidates do not constitute a random sample of the genome , features that separate successful targets from failed targets in our sample may perform poorly as genome-wide predictors of success versus failure ., We performed three analyses to address this issue ( Fig 1 ) ., Statistical significance did not guarantee the remaining features would be useful in practice for discriminating between successes and failures ., To test their utility , we trained a classifier to predict target success or failure , using cross-validation to select a model type ( Random Forest or logistic regression ) and a subset of features useful for prediction ., Because we used all targets with phase III outcomes for the feature selection procedure described above , simply using the final set of features to train a classifier on the same data would yield overly optimistic performance , even with cross-validation ., Therefore , we implemented a nested cross-validation routine to perform both feature selection and model selection 58 ., We searched over 150 , 000 target features from 67 datasets covering 16 feature types for predictors of target success or failure in phase III clinical trials ( Table 1 , Fig 1 ) ., We found several features significantly correlated with phase III outcome , robust to re-sampling , and generalizable across target classes ( Table 2 ) ., To assess the usefulness of such features , we implemented a nested cross-validation routine to select features , train a classifier to predict the probability a target will succeed in phase III clinical trials , and estimate the stability and generalization performance of the model ( Figs 2 and 3 , Tables 3 , 4 and 5 ) ., Ultimately , we found two features useful for predicting success or failure of targets in phase III clinical trials ., Successful targets tended to have low mean mRNA expression across tissues and high standard deviation of mRNA expression across tissues ( Fig 3F ) ., These features were significant in multiple gene expression datasets , which increased our confidence that their relationship to phase III outcome was real , at least for the targets in our sample , which included only targets of selective drugs indicated for non-cancer diseases ., One interpretation of why the gene expression features were predictive of phase III outcome is that they are informative of the specificity of a target’s expression across tissues ., A target with tissue specific expression would have a high standard deviation relative to its mean expression level ., Tissue specific expression has been proposed by us and others as a favorable target characteristic in the past 4 , 14 , 60–62 , but the hypothesis had not been evaluated empirically using examples of targets that have succeeded or failed in clinical trials ., For a given disease , if a target is expressed primarily in the disease tissue , it is considered more likely that a drug will be able to exert a therapeutic effect on the disease tissue while avoiding adverse effects on other tissues ., Additionally , specific expression of a target in the tissue affected by a disease could be an indicator that dysfunction of the target truly causes the disease ., The distribution of the success and failure examples in feature space ( Fig 3F ) partially supports the hypothesis that tissue specific expression is a favorable target feature ., Successes were enriched among targets with low mean expression and high standard deviation of expression ( tissue specific expression ) , and failures were enriched among targets with high mean expression and low standard deviation of expression ( ubiquitous expression ) ., However , it does not hold in general that , at any given mean expression level , targets with high standard deviation of expression tend to be more successful than targets with low standard deviation of expression ., To further investigate the relationship between these features and phase III clinical trial outcomes , we re-ran the entire modeling pipeline ( Fig, 2 ) with gene expression entropy , a feature explicitly quantifying specificity of gene expression across tissues 21 , appended to each tissue expression dataset ( S1 Text ) ., Model performance was unchanged ( S1 Fig ) ; gene expression entropy across tissues became the dominant selected feature , appearing in 610 models over 1000 train-test cycles; and mean gene expression across tissues remained an important feature , appearing in 381 models ( S6 Table ) ., To find concrete examples illustrating when tissue expression may be predictive of clinical trial outcomes , we pulled additional information from the Pharmaprojects database about targets at the two extremes of tissue expression ( tissue specific or ubiquitous ) ., We found examples of:, 1 ) successful tissue specific targets where the target is specifically expressed in the tissue affected by the disease ( Table 6 ) ,, 2 ) failed tissue specific targets with plausible explanations for failure despite tissue specific expression ( Table 7 ) ,, 3 ) failed ubiquitously expressed targets ( Table 8 ) , and, 4 ) successful ubiquitously expressed targets with plausible explanations for success despite ubiquitous expression ( Table 9 ) ., Our results encourage further investigation of the relationship between tissue specific expression and clinical trial outcomes ., Deeper insight may be gleaned from analysis of clinical trial outcomes of target-indication pairs using gene expression features explicitly designed to quantify specificity of a target’s expression in the tissue ( s ) affected by the disease treated in each clinical trial ., Latent factors ( variables unaccounted for in this analysis ) could confound relationships between target features and phase III outcomes ., For example , diseases pursued vary from target to target , and a target’s expression across tissues may be irrelevant for diseases where drugs can be delivered locally or for Mendelian loss-of-function diseases where treatment requires systemic replacement of a missing or defective protein ., Also , clinical trial failure rates vary across disease classes 2 ., Although we excluded targets of cancer therapeutics from our analysis , we otherwise did not control for disease class as a confounding explanatory factor ., Modalities ( e . g . small molecule , antibody , antisense oligonucleotide , gene therapy , or protein replacement ) and directions ( e . g . activation or inhibition ) of target modulation also vary from target to target and could be confounding explanatory factors or alter the dependency between target features and outcomes ., The potential issues described above are symptoms of the fact that our analysis ( and any analysis of clinical trial outcomes ) attempts to draw conclusions from a small ( 331 targets with only 72 failures ) and biased sample 63 , 64 ., The large uncertainty in the performance of the classifier across 200 repetitions of 5-fold cross-validation is evidence of the difficulty in finding robust signal in such a small dataset ( Fig 3 ) ., For example , in the region where the model predicts highest probability of success ( low mean expression and high standard deviation of expression ) , there are no failed phase III targets ( Fig 3F ) , which is why the median PPV rises nearly to 1 ( Fig 3C ) , but targets with phase III outcomes sparsely populate this region , so the PPV varies widely depending upon how targets happen to fall into training and testing sets during cross-validation ., The small sample issue is compounded by latent factors , such as target classes , disease classes , modalities , and directions of target modulation , that are not uniformly represented in the sample ., Correlations between target features and clinical trial outcomes likely depend on these factors , but attempts to stratify , match , or otherwise control for these factors are limited by the sample size ., ( The number of combinations of target class , disease class , modality , and direction of modulation exceeds the sample size . ), We employed several tests to build confidence that our findings generalize across target classes , but did not address other latent factors ., Consequently , we cannot be sure that conclusions drawn from this study apply equally to targets modulated in any direction , by any means , to treat any disease ., For specific cases , expert knowledge and common sense should be relied upon to determine whether conclusions from this study ( or similar studies ) are relevant ., Another limitation is selection bias 63 , 64 ., Targets of drugs are not randomly selected from the genome and cannot be considered representative of the population of all possible targets ., Likewise , diseases treated by drugs are not randomly chosen; therefore , phase III clinical trial outcomes for each target cannot be considered representative of the population of all possible outcomes ., Although we implemented tests to build confidence that our findings can generalize to new targets and new target classes , ultimately , no matter how we dissect the sample , a degree of uncertainty will always remain about the relevance of any findings for new targets that lack a representative counterpart in the sample ., Additionally , data processing and modeling decisions have introduced bias into the analysis ., For example , we restricted the analysis to phase III clinical trial outcomes because failures in phase III are more likely to be due to lack of target efficacy than failures in earlier phases , but factors unrelated to target efficacy still could explain many of the phase III failures , such as poor target engagement , poorly defined clinical trial endpoints , and a poorly defined patient population ., Also , we scored each target as successful or failed by its best outcome in all applicable ( selective drug , non-cancer indication ) phase III clinical trials ., This approach ignores nuances ., A target that succeeded in one trial and failed in all others is treated as equally successful as a target that succeeded in all trials ., Also , the outcome of a target tested in a single trial is treated as equally certain as the outcome of a target tested in multiple trials ., Representing target outcomes as success rates or probabilities may provide better signal for discovering features predictive of outcomes ., Another decision was to use datasets of features as we found them , rather than trying to reason about useful features that could be derived from the original data ., Because of the breadth of data we interrogated , the effort and expertise necessary to hand engineer features equally well across all datasets exceeded our resources ., Others have had success hand engineering features for similar applications in the past , particularly with respect to computing topological properties of targets in protein-protein interaction networks 18 , 20 , 21 ., This analysis could benefit from such efforts , potentially changing a dataset or feature type from yielding no target features correlated with phase III outcomes to yielding one or several useful features 22 ., On a related point , because we placed a priority on discovering interpretable features , we performed dimensionality reduction by averaging groups of highly correlated features and concatenating their ( usually semantically related ) labels ., Dimensionality reduction by principal components analysis 65 or by training a deep auto-encoder 66 could yield more useful features , albeit at the expense of interpretability ., We also employed a stringent univariate feature selection step ( Fig 2 , Step, 2 ) to bias our analysis toward yielding a simple and interpretable model ., In doing so , we diminished the chance of the multivariate feature selection step ( Fig 2 , Step, 4 ) finding highly predictive combinations of features that individually were insignificantly predictive ., We addressed this concern by re-running the entire modeling pipeline ( Fig, 2 ) with the threshold for the univariate feature selection step made less stringent by eliminating the multiple hypothesis testing correction and accepting features with nominal p-values less than 0 . 05 ( S2 Text ) ., This allowed hundreds of features to pass through to the multivariate feature selection step ( Random Forest with incremental feature elimination ) and ultimately dozens of features ( median of 73 ) were selected for each of the final models in the 1000 train-test cycles ( S7 Table ) ., Despite this increase in number of features , the mean expression and standard deviation of expression features were still robustly selected , appearing in 958 and 745 models , respectively , and the models had a median AUROC of 0 . 56 and AUPRC of 0 . 75 , performing no better than the simple models ( S2 Fig ) ., This finding suggests that our sample size was not large enough to robustly select predictive combinations of features from a large pool of candidate features 67 , 68 ., We cannot stress enough the importance of taking care not to draw broad conclusions from our study , particularly with respect to the apparent dearth of features predictive of target success or failure ., We examined only a specific slice of clinical trial outcomes ( phase III trials of selective drugs indicated for non-cancer diseases ) summarized in a particular way ( net outcome per target , as opposed to outcome per target-indication pair ) ., Failure of a feature to be significant in our analysis should not be taken to mean it has no bearing on target selection ., For example , prior studies have quantitatively shown that genetic evidence of disease association ( s ) is a favorable target characteristic 3 , 36 , but we did not find a significant correlation between genetic evidence and target success in phase III clinical trials ., Our finding is consistent with the work of Nelson et al . 36 , who investigated the correlation between genetic evidence and drug development outcomes at all phases and found a significant correlation overall and at all phases of development except phase III ., As a way of checking our work , we applied our methods to test for features that differ between targets of approved drugs and the remainder of the druggable genome ( instead of targets of phase III failures ) , and we recovered the finding of Nelson et al . that targets of approved drugs have significantly more genetic evidence than the remainder of the druggable genome ( S8 Table ) ., This example serves as a reminder to be cognizant of the domain of applicability of research findings ., Though we believe we have performed a rigorous and useful analysis , we have shed light on only a small piece of a large and complex puzzle ., Advances in machine learning enable and embolden us to create potentially powerful predictive models for target selection ., However , as described in the limitations , scarce training data are available , the data are far from ideal , and we must be cautious about building models with biased data and interpreting their predictions ., For example , many features that appeared to be significantly correlated with phase III clinical trial outcomes in our primary analysis did not hold up when we accounted for target class selection bias ., This study highlights the need for both domain knowledge and modeling expertise to tackle such challenging problems ., Our analysis revealed several features that significantly separated targets of approved drugs from targets of drug candidates that failed in phase III clinical trials ., This suggested that it is feasible to construct a model integrating multiple interpretable target features derived from Omics datasets to inform target selection ., Only features derived from tissue expression datasets were promising predictors of success versus failure in phase III , specifically , mean mRNA expression and standard deviation of expression across tissues ., Although these features were significant at a false discovery rate cut-off of 0 . 05 , their effect sizes were too small to be useful for classification of the majority of untested targets , however , even a two-fold improvement in target quality can dramatically increase R&D productivity 69 ., We identified 943 targets predicted to be twice as likely to fail in phase III clinical trials as past phase III targets , and , therefore , should be flagged as having unfavorable expression characteristics ., We also identified 2 , 700 , 856 target pairs predicted with 99% consistency to have a 2-fold difference in success probability , which could be useful for prioritizing short lists of targets with attractive disease relevance ., It should be noted that our analysis was not designed or powered to show that specific datasets or data types have no bearing on target selection ., There are many reasons why a dataset may not have yielded any significant features in our analysis ., In particular , data processing and filtering choices could determine whether or not a dataset or data type has predictive value ., Also , latent factors , such as target classes , disease classes , modalities , and directions of target modulation , could confound or alter the dependency between target features and clinical trial outcomes ., Finally , although we implemented tests to ensure robustness and generalizability of the target features significantly correlated with phase III outcomes , selection bias in the sample of targets available for analysis is a non-negligible limitation of this study and others of its kind ., Nevertheless , we are encouraged by our results and anticipate deeper insights and better models in the future , as researchers improve methods for handling sample biases and learn more informative features ., Our goals in performing dimensionality reduction were to identify groups of highly correlated features , avoid excessive multiple hypothesis testing , and maintain interpretability of features ., For each dataset , we computed pair-wise feature correlations ( r ) using the Spearman correlation coefficient 72–74 for quantitative , filled-in datasets , and the cosine coefficient 73 , 74 for sparse or categorical datasets ., We thresholded the correlation matrix at r2 = 0 . 5 ( for the Spearman correlation coefficient , this corresponds to one feature explaining 50% of the variance of another feature , and for the cosine coefficient , this corresponds to one feature being aligned within 45 degrees of another feature ) and ordered the features by decreasing number of correlated features ., We created a group for the first feature and its correlated features ., If the dataset mean was included in the group , we replaced the group of features with the dataset mean ., Otherwise , we replaced the group of features with the group mean and assigned it the label of the first feature ( to indicate that the feature represents the average of features correlated with the first feature ) , while also retaining a list of the labels of all features included in the group ., We continued through the list of features , repeating the grouping process as described for the first feature , except excluding features already assigned to a group from being assigned to a second group ., We performed permutation tests 40 , 41 to find features with a significant difference between successful and failed targets ., We used permutation testing in order to apply the same significance testing method to all features ., The features in our collection had heterogeneous shapes of their distributions and varying degrees of sparsity , and therefore no single parametric test would be appropriate for all features ., Furthermore , individual features frequently violated assumptions required for parametric tests , such as normality for the t-test ( for continuous-valued features ) or having at least five observations in each entry of the contingency table for the Chi-squared test ( for categorical features ) ., For each feature , we performed 105 success/failure label permutations to obtain a null distribution for the difference between the means of successful and failed targets , and then calculated an empirical two-tailed p-value as the fraction of permutations that yielded a difference between means at least as extreme as the actual observed difference ., We used the Benjamini-Yekutieli method 42 to correct for multiple hypothesis testing within each dataset and accepted features with corrected p-values less than 0 . 05 as significantly correlated with phase III clinical trial outcomes , thus controlling the false discovery rate at 0 . 05 within each dataset ., We trained a classifier to predict target success or failure in phase III clinical trials , using a procedure like the above for initial feature selection , then using cross-validation to select a model type ( Random Forest or logistic regression ) and subset of features useful for prediction ., We used an outer cross-validation loop with 5-folds repeated 200 times , yielding a total of 1000 train-test cycles , to estimate the generalization performance and stability of the feature selection and model selection procedure 58 ., Each train-test cycle had five steps:, 1 ) splitting examples into training and testing sets ,, 2 ) univariate feature selection on the training data ,, 3 ) aggregation of significant features from different datasets into a single feature matrix ,, 4 ) model selection and model-based ( multivariate ) feature selection on the training data , and, 5 ) evaluation of the classifier on the test data ., Computational analyses were written in Python 3 . 4 . 5 and have the following package dependencies: Fastcluster 1 . 1 . 20 , Matplotlib 1 . 5 . 1 , Numpy 1 . 11 . 3 , Requests 2 . 13 . 0 , Scikit-learn 0 . 18 . 1 , Scipy 0 . 18 . 1 , and Statsmodels 0 . 6 . 1 ., Code , documentation , and data have been deposited on GitHub at https://github . com/arouillard/omic-features-successful-targets .

Introduction, Results, Discussion, Conclusion, Methods

Target selection is the first and pivotal step in drug discovery ., An incorrect choice may not manifest itself for many years after hundreds of millions of research dollars have been spent ., We collected a set of 332 targets that succeeded or failed in phase III clinical trials , and explored whether Omic features describing the target genes could predict clinical success ., We obtained features from the recently published comprehensive resource: Harmonizome ., Nineteen features appeared to be significantly correlated with phase III clinical trial outcomes , but only 4 passed validation schemes that used bootstrapping or modified permutation tests to assess feature robustness and generalizability while accounting for target class selection bias ., We also used classifiers to perform multivariate feature selection and found that classifiers with a single feature performed as well in cross-validation as classifiers with more features ( AUROC = 0 . 57 and AUPR = 0 . 81 ) ., The two predominantly selected features were mean mRNA expression across tissues and standard deviation of expression across tissues , where successful targets tended to have lower mean expression and higher expression variance than failed targets ., This finding supports the conventional wisdom that it is favorable for a target to be present in the tissue ( s ) affected by a disease and absent from other tissues ., Overall , our results suggest that it is feasible to construct a model integrating interpretable target features to inform target selection ., We anticipate deeper insights and better models in the future , as researchers can reuse the data we have provided to improve methods for handling sample biases and learn more informative features ., Code , documentation , and data for this study have been deposited on GitHub at https://github . com/arouillard/omic-features-successful-targets .

Drug discovery often begins with a hypothesis that changing the abundance or activity of a target—a biological molecule , usually a protein—will cure a disease or ameliorate its symptoms ., Whether a target hypothesis translates into a successful therapy depends in part on the characteristics of the target , but it is not completely understood which target characteristics are important for success ., We sought to answer this question with a supervised machine learning approach ., We obtained outcomes of target hypotheses tested in clinical trials , scoring targets as successful or failed , and then obtained thousands of features ( i . e . properties or characteristics ) of targets from dozens of biological datasets ., We statistically tested which features differed between successful and failed targets , and built a computational model that used these features to predict success or failure of targets in clinical trials ., We found that successful targets tended to have more variable mRNA abundance from tissue to tissue and lower average abundance across tissues than failed targets ., Thus , it is probably favorable for a target to be present in the tissue ( s ) affected by a disease and absent from other tissues ., Our work demonstrates the feasibility of predicting clinical trial outcomes from target features .

medicine and health sciences, tissue proteins, phase iii clinical investigation, protein expression, clinical medicine, mathematics, artificial intelligence, pharmacology, molecular biology techniques, discrete mathematics, combinatorics, research and analysis methods, computer and information sciences, proteins, gene expression, artificial genetic recombination, gene targeting, molecular biology, molecular biology assays and analysis techniques, gene expression and vector techniques, biochemistry, permutation, drug research and development, phenotypes, clinical trials, genetics, biology and life sciences, physical sciences, machine learning

journal.pcbi.1005389

Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction

Bayesian phylogeography has emerged as a powerful approach to analyzing virus spread ., It utilizes sequence data to perform ancestral reconstruction and estimate the most likely lineages of the viruses in rooted , time-measured phylogenies 1 using nucleotide substitution models , molecular clocks , and coalescent priors under a probabilistic Bayesian framework known as Bayesian stochastic search variable selection ( BSSVS ) 1–3 ., This framework has improved ancestral state reconstruction and has recently been used to analyze human and animal influenza viruses both globally 4–5 and nationally 6–7 ., By identifying the relationship between geospatial origins and genetic lineages , much can be learned about the complex process in which these viruses spread ., Phylodynamic analyses that aim to combine immunological , epidemiological , and evolutionary biology techniques 8 also enhance our understanding of virus transmission dynamics and their relationship to a phylogeny ., These studies have unveiled novel properties of several influenza viruses , including pdm09 9 , H3N2 10 and highly pathogenic avian influenza H5N1 11 ., Building upon the benefits of a BSSVS framework , recent work by Lemey et al . 12 utilized a phylogeographic generalized linear model ( GLM ) approach to identify environmental , genetic , demographic , and geographic predictors that contributed to the global spread of H3N2 influenza viruses ., In the GLM , the BSSVS on the discrete location variable is instead used to estimate the posterior inclusion probability of potential predictors in a log-linear combination to model the transition rate matrix ., Similarly , studies have followed this approach to uncover the predictors associated with the spread of H5N1 in Egypt 13 and for HIV in Brazil 14 ., Such studies have demonstrated the utility of combining genetic and geospatial inferences from phylogeography with surveillance data in epidemiological studies like Yang et al . 15 ., These analyses may enable actionable solutions for public health officials once consistent identification of contributing predictors is achieved ., Although the GLM appears to show promise with its simultaneous ability to perform ancestral state reconstruction and also assess the contribution of predictor variables of interest , there has yet to be an assessment of how a standard BSSVS approach and a GLM approach differ in reconstructing a phylogeny ., Specifically , no study has yet compared root state probabilities in a phylogeny constructed via BSSVS to the same probabilities using the GLM approach ., Such information may inform researchers of differences in phylogeographic trends that may be experienced by choosing one framework over the other ., In this work we analyze the 2014–15 H3N2 flu season within the U . S . by performing ancestral state reconstruction of a discrete location variable via the following three frameworks: an asymmetric substitution model without BSSVS ( –BSSVS ) , an asymmetric substitution model with BSSVS ( +BSSVS ) 1 , and a GLM 12 ., For the BSSVS framework , we analyze separate versions that place both a Poisson distribution ( +BSSVS ( P ) ) and a uniform distribution ( +BSSVS ( U ) ) on the number of rate parameters that achieve a point-mass on 1 . 0 in order to determine the influence of location priors ., For the GLM framework , we analyze separate versions that include and do not include sample size predictors , which we denote as GLM ( +SS ) and GLM ( –SS ) , respectively , in order to directly quantify the effect of sampling bias on GLM-constructed rate matrices and potential suppression of the signal of other predictors ., This brings us to a total of five methods that encompass the three frameworks ., We refer readers to Materials and methods for full details on the methods ., These selections allow us to empirically evaluate differences in phylogenies obtained via each method and to determine whether one framework provides more accurate posterior estimates given a fixed set of data ., We demonstrate these trends using multiple random samples from a large collection of flu sequences to show reproducibility as well as analyze several coalescent tree priors to show consistency among the reconstruction methods across varying parameters ., Finally , we show that support for GLM predictors can change given the tree priors and sequence sets , but that trends among specific predictors will emerge to allow accurate determination of their impact on viral diffusion ., In Fig 1A , we show mean log marginal likelihood estimates among the six samples obtained by path sampling ( PS ) and stepping stone sampling ( SSS ) for each prior and reconstruction method ., For PS , the two best-performing mean methods are the GLM ( +SS ) and GLM ( -SS ) , respectively , under each prior ., The mean +BSSVS ( U ) outperforms the mean +BSSVS ( P ) under each prior as well , although the mean -BSSVS exceeds both under the constant and exponential priors ., For SSS , the log marginal likelihood increases in a near-linear manner for the +BSSVS ( P ) , +BSSVS ( U ) , GLM ( –SS ) , and GLM ( +SS ) methods ., The -BSSVS method , however , finds the largest posterior support under the constant , expansion , exponential , logistic , and Skyline priors ., In S1 Fig , we present log marginal likelihood estimates for each individual model ., From S1 Fig , we show that each GLM ( +SS ) and GLM ( –SS ) unanimously finds more posterior support than their corresponding +BSSVS ( P ) for both PS and SSS ., The +BSSVS ( P ) method demonstrates consistently poor performance , as its posterior estimates are the worst of the five methods in 25 of 36 PS analyses and 32 of 36 PS analyses ( 79% overall ) across all priors , while no GLM ( +SS ) or GLM ( –SS ) yields the lowest posterior estimate of model support among the three methods for either PS or SSS under any prior , although no pairwise t-test shows a significant difference ., Each of the 180 models show statistically significant differences between the null and observed means for the association index ( S2 Fig ) ., These data suggest stronger support for the phylogeny-trait association 16 and , as all p < 0 . 01 , suggest the evolution of influenza during this flu season was structured by geography ., The support of the sampling location-phylogeny associations observed in S2 Fig can be explained , in part , by the amount of genetic diversity observed within and across each region ., In Fig 1B we show the average genetic distances between intra-region and inter-region sequences ., Here , we calculated the genetic distances among all ( 2852 ) pairwise sequences and present the mean distance of sequences sampled in the same region ( e . g . Region 1-Region 1 ) to those sampled in different regions ( e . g . Region 1-Region 2 ) ., From Fig 1B , the pairwise intra-region sequences ( n = 4 , 496 per sample ) have a lesser amount of genetic diversity than the pairwise inter-region sequences ( n = 35 , 974 per sample ) in each our six sequence sets ., A two-tailed t-test shows p < 0 . 01 for each sample , indicating that sequences from within the same region demonstrate significantly lower amounts of genetic diversity than those from external regions ., The average intra- and inter-region distances in the full set of 1 , 163 sequences are 0 . 872% ( 95% CI = 0 . 867 , 0 . 878 ) , and 0 . 929% ( 95% CI = 0 . 926 , 0 . 932 ) , respectively ( p < 0 . 0001 ) ., These data demonstrate that our method of downsampling maintained representative levels of genetic diversity across the six samples ., In Fig 2 , we show four root state metrics obtained from the maximum clade credibility ( MCC ) trees of each of the 180 models ., In Fig 2A , we show the mean root state posterior probability ( RSPP ) ., Aside from the constant coalescent prior , the mean GLM ( –SS ) and GLM ( +SS ) methods consistently show the largest mean RSPP of the five methods ., The mean GLM ( –SS ) finds significantly greater RSPPs under each coalescent prior than the mean -BSSVS ( p < 0 . 03 for each coalescent prior ) and significantly greater RSPPs than both the mean +BSSVS ( P ) and +BSSVS ( U ) for the expansion and exponential coalescent priors ., Similarly , the GLM ( +SS ) shows a mean RSPP significantly greater than the -BSSVS and +BSSVS ( U ) methods for all coalescent priors except constant , and significantly greater RSPP than the +BSSVS ( P ) for the constant , expansion , Skygrid , and Skyline coalescent priors ., Across all coalescent priors , the mean RSPP for the -BSSVS , +BSSVS ( P ) , +BSSVS ( U ) , GLM ( –SS ) , and GLM ( +SS ) methods are 0 . 48 , 0 . 56 , 0 . 49 , 0 . 81 , and 0 . 74 respectively , These differences per method could be influenced by the sample size per discrete state , so we show the Pearson’s r correlation coefficient between the sample size at each discrete state and its corresponding posterior probability at the root in Fig 2B ., Here we observe that the +BSSVS ( P ) shows a correlation coefficient less than 0 . 4 for the constant , expansion , Skygrid , and Skyline coalescent priors but for the exponential and logistic coalescent priors the coefficient is nearly doubled ., Meanwhile , the +BSSVS ( U ) , -BSSVS , GLM ( –SS ) , and GLM ( +SS ) methods are generally consistent under all priors ., The mean +BSSVS ( P ) shows significantly less correlation than each of the other four methods for the constant , expansion , and Skyline coalescent priors ( p < 0 . 02 for each ) while the +BSSVS ( U ) , -BSSVS , and GLM methods do not show any significant differences under any coalescent prior ., Fig 2C and 2D show the Kullback-Leibler ( KL ) divergence between the prior and posterior probabilities at the root states calculated using two different prior assumptions ( see Materials and methods for details ) ., KL values indicate the extent to which a model is able to generate different posterior probabilities at the root state from the prior probabilities at the root state ., That is , high KL values indicate strong divergence from the prior probabilities and , thus , strong posterior information gain , while low KL values indicate the opposite ., From Fig 2C and 2D , the mean GLM ( –SS ) and GLM ( +SS ) KL divergences demonstrate a marked increase over the -BSSVS , +BSSVS ( P ) , and +BSSVS ( U ) methods under the expansion , exponential , logistic , Skygrid , and Skyline coalescent priors ( p < 0 . 02 for all two-tailed t-tests ., Under the constant coalescent prior , both the mean GLM ( –SS ) and GLM ( +SS ) KL divergences exceed the mean KL under both assumptions of the -BSSVS , +BSSVS ( P ) , and +BSSVS ( U ) methods , but none of these values are significant ., The +BSSVS ( P ) method , in turn , shows significantly greater KL divergences under both assumptions than the -BSSVS method under all coalescent priors and than the +BSSVS ( U ) method under the constant , exponential , and logistic coalescent priors ., We show data for each of the four metrics in Fig 2 by individual model in S3 and S4 Figs ., We summarize the identified root states of the four methods in Table 1 ., Here , we can see that the -BSSVS method identified three different regions , with the majority occurring in Region 4 , while Region 5 is identified in over 30% of -BSSVS models ., The +BSSVS ( P ) method identified six different regions as the root state , with Regions 6 and 4 representing the most frequently-identified ., The +BSSVS ( U ) method identified Region 4 in nearly half of the models while Regions 5 and 6 account for the remainder of models ., Comparatively , 35 of the 36 GLM ( –SS ) runs identified Region 4 as the root state , with the lone exception being Sample 2 using the Skygrid coalescent prior , which identified Region 8 ., For the GLM ( +SS ) analyses , Region 4 is identified as the root state in 33 of 36 models while Region 5 accounts for the remaining three ., The root heights and corresponding Bayesian credible intervals are similar between the three methods for each sample and each coalescent prior ( S5 Fig ) ., As influenza viruses rarely persist for more than one season , except in tropical areas 17 , 18 , we obtained the geographic distribution of the number of internal nodes with a height of at least one year ( NH1s ) from the MCC tree of each model and show these data in Fig 3A ., From Fig 3A , we can see that the -BSSVS method indicates that Region 4 contains the highest volume of NH1s under each prior , while Region 5 contains the second-largest volume of NH1s ., The +BSSVS ( P ) method shows Region 4 containing the most NH1s for the exponential , logistic , Skyline , and Skygrid coalescent priors , with Region 6 accounting for the next largest volume in the latter three priors ., Under the constant coalescent prior , a nearly equal amount of NH1s are observed in Regions 4 , 6 , and 8 , while the expansion prior shows Region 5 containing the largest number of NH1s ., For the +BSSVS ( U ) method , the NH1s are most commonly observed in Region 4 under each coalescent prior , with Regions 5 and 6 primarily accounting for the remaining nodes ., The frequency of NH1s in Region 8 are low under this method , but do occur under the constant , expansion , and Skygrid coalescent priors ., Finally , the NH1s are largely concentrated in Region 4 for both the GLM ( –SS ) and GLM ( +SS ) methods under each coalescent prior ., The frequent identification of Region 4 as the root state ( Table 1 ) and location of NH1 events ( Fig 3A ) indicates that there is likely at least one local variable playing a role in the tree topologies ., Given this , from Fig 3B we note that Region 4 exhibits both the highest expected temperature and precipitation during a typical flu season as we compare the posterior support of all predictors for both the GLM ( –SS ) and GLM ( +SS ) methods in Fig 4 ., From Fig 4 , we can see that sample size at the region of origin ( SS ( O ) ) is strongly supported for the GLM ( +SS ) runs with Bayes factor ( BF ) > 69 for each coalescent prior and with each corresponding mean regression coefficient greater than 1 . 33 ., The predictor with the second largest support for inclusion in the GLM ( +SS ) runs is temperature at the region of origin ( BF > 5 and regression coefficient > 0 . 75 for each prior except constant size ) , followed by glycoprotein at the region of origin ( 3 . 0 < BF < 4 . 5 for the expansion , exponential , Skyline , and Skygrid coalescent priors ) although the respective mean regression coefficients for glycoprotein remain near zero ., For the GLM ( –SS ) runs , temperature at the region of origin yields the largest mean posterior inclusion probability across all coalescent priors ( BF > 20 for each prior , BF > 400 for the expansion , exponential , logistic , and Skyline priors ) followed by precipitation at the region of origin ( 5 . 0 < BF < 8 . 5 for all priors ) ., Mean posterior estimates of the corresponding regression coefficients and their standard errors , shown as E ( β|δ = 1 ) , indicate strictly positive values for these two predictors in the GLM ( –SS ) runs , although the 95% highest posterior density ( HPD ) of the regression coefficient for precipitation at the region of origin spans zero for each model ( S6 Fig ) ., If the entire HPD lies on the positive side of zero , this suggests that the predictor is driving the diffusion of the virus ., Conversely , if the entire HPD lies on the negative side of zero , this suggests that the predictor is rather preventing the diffusion ., Thus , we show the proportion of GLMs in which the absolute value of the HPD is positive in Table 2 ., The 95% HPDs of temperature at the region of origin are strictly positive in 26 of the 36 GLM ( –SS ) runs and span zero in the remaining ten ., The glycoprotein predictor at the region of origin finds the highest mean support for the constant prior ( BF = 1 . 1 ) , which is a sharp turn from the GLM ( +SS ) runs ., See Materials and methods for more information on metrics of support and interpretations of our predictors ., We show the posterior regression coefficients and inclusion probabilities of every predictor from each of the 36 GLM ( –SS ) runs in S6 and S7 Figs , respectively , and corresponding data for the 36 GLM ( +SS ) runs in S8 and S9 Figs , respectively ., In this paper , we compared three ancestral state reconstruction frameworks and five total methods using six randomly-drawn sequence samples and six coalescent priors for a total of 180 models while fixing the nucleotide substitution process for each ., We compared each of our analyses with established model selection techniques 19 , 20 and compared features of each model’s MCC tree to identify posterior statistical support and discrepancies in the phylogeographic reconstructions ., Regarding model selection , we found that PS shows the most posterior support for either the GLM ( –SS ) or GLM ( +SS ) in 34 of 36 runs ( with one -BSSVS and one +BSSVS ( U ) accounting for the remaining two ) , while SSS shows the most support for 29 of 36 –BSSVS models , five GLM ( +SS ) , one GLM ( –SS ) , and one +BSSVS ( U ) ., Each GLM ( –SS ) and GLM ( +SS ) outperformed its corresponding +BSSVS ( P ) under both PS and SSS ., Both statistics agree that +BSSVS ( P ) models offered the poorest posterior support , as 72% of PS analyses and 89% of SSS analyses ( 81% combined ) show the +BSSVS ( P ) model as the least-supported among the five frameworks ( Fig 1A and S1 Fig ) , although we note that no framework shows significantly more support than any other framework for PS or SSS via t-tests ., Although the -BSSVS method is highly supported under SSS , the method fails to find strong support regarding both RSPP and KL divergence ( Fig 2C , 2D and S4 Fig ) ., The RSPPs using the -BSSVS method are significantly lower than those obtained via the GLM ( –SS ) method ( p = 0 . 03 for the constant coalescent prior , p < 0 . 001 for the expansion , exponential , logistic , Skygrid , and Skyline coalescent priors ) , while the GLM ( –SS ) also show a significant increase for KL divergence for both the uniform and sample size assumptions over the -BSSVS models under each coalescent prior except for constant size ., Similarly , the GLM ( +SS ) method shows significantly greater RSPPs and both KL divergences than the -BSSVS models ( p < 0 . 03 for all coalescent priors except constant ) ., Meanwhile , the +BSSVS ( P ) method finds significantly greater RSPPs than the -BSSVS method under only the constant coalescent prior ( p < 0 . 001 ) and significantly greater KL divergences over the -BSSVS method under each coalescent prior , each with p < 0 . 03 ., The +BSSVS ( P ) method also found significantly greater KL divergences for the constant , exponential , and logistic coalescent priors ., The +BSSVS ( U ) method only found significantly greater support over the -BSSVS method via KL with the sample size assumption for the expansion coalescent prior ., While these results show that the -BSSVS method finds poor statistical support at the identified root state , we also found that both the GLM ( –SS ) and GLM ( +SS ) methods in turn significantly outperformed both the +BSSVS ( P ) and +BSSVS ( U ) models both for both KL divergences under five of the six coalescent priors ( excluding constant ) ., The GLM ( –SS ) runs also found significantly greater RSPPs than the +BSSVS ( P ) and +BSSVS ( U ) under each coalescent prior except constant , while the GLM ( +SS ) runs found significantly greater RSPPs than the +BSSVS ( P ) and +BSSVS ( U ) methods for the expansion , Skygrid , and Skyline priors ., The association index of each model obtained via BaTS ( S2 Fig ) demonstrate a strong association between sampling location and the phylogeny for each of the 180 models , which suggests that the diffusion was spatially-structured ., Some of the phylogeny-location association can be attributed to the smaller amount of genetic diversity in sequences from the same region ( Fig 1B ) , however the statistical significance of the intra- and inter-region genetic distances could not fully account for the differences in RSPP and KL divergence , regardless of the coalescent prior ., Furthermore , Region 4 was the most frequently-identified root state for the -BSSVS , +BSSVS ( U ) , GLM ( –SS ) , and GLM ( +SS ) methods , the second most frequently identified root state for +BSSVS ( P ) method ( Table 1 ) , and was also the location of the most NH1s ( Fig 3A ) ., These NH1s are biologically important for seasonal influenza , as these viruses typically experience bottlenecking at this height as part of a sink-source ecological dynamic 17 , 21 , 22 ., As Region 4 experiences the highest temperature and most precipitation during flu season , at 6 . 9°C warmer and 10 . 3 cm wetter , respectively , than the remaining nine regions ( Fig 3B ) we describe it as the most “tropical” in the U . S . during a typical flu season ., This provides a well-supported explanation for the observed trends in Region 4 , especially under both GLM methods ., As the data for the GLM ( –SS ) and GLM ( +SS ) runs indicate strong support for temperature at the region of origin ( Fig 4 ) , our results would suggest that Region 4 is the most likely origin of each of the six samples using those two methods ., This conclusion , however , is hindered by the strong sampling bias exhibited by the GLM ( –SS ) , and GLM ( +SS ) methods ., These two methods ( as well as the -BSSVS and +BSSVS ( U ) ) demonstrate consistently strong , positive Pearson’s r correlation coefficients between the root state posterior probability and sample size at each discrete state , regardless of coalescent prior ( Fig 2B and S3B Fig ) ., Furthermore , the inclusion of the sample size predictors in the GLM ( +SS ) runs shows that sample size at the region of origin is strongly influencing its posterior estimates , with 35 of 36 runs showing BF > 3 and 22 of 36 showing a positive 95% HPD on the regression coefficient ( Table 2 , S8 and S9 Figs ) ., The mean posterior inclusion probability for the sample size predictor at the region of origin corresponds to BFs of 1317 . 9 , 70 . 0 , 122 . 9 , 102 . 7 , 92 . 6 , and 101 . 8 for the constant , expansion , exponential , logistic , Skygrid , and Skyline priors , respectively ., Given the similarities in RSPP , Pearson’s r , and KL data between the GLM ( –SS ) and GLM ( +SS ) runs ( Fig 2 , S3 and S4 Figs ) , we believe that sample size is influencing the GLM ( –SS ) runs to a similar degree , although its BF support cannot be measured ., Thus , although it would appear that both GLM methods presented in this paper are providing biologically justifiable and statistically supported evidence regarding the diffusion of this influenza virus over our selected time period , the strong sampling biases give us pause ., Instead , the significant decrease in Pearson’s r for the +BSSVS ( P ) models from the other four methods under the constant , expansion , and Skyline coalescent priors provide more confidence in those data , despite its poor performance with respect to log marginal likelihoods via PS and SSS ( Fig 1A and S1 Fig ) ., We compared the -BSSVS , +BSSVS ( P ) , +BSSVS ( U ) , GLM ( –SS ) , and GLM ( +SS ) methods for modeling a single discrete trait , sampling location , which highlighted differences in diffusion of seasonal influenza in the U . S . Our results collectively indicate that the GLMs provide the strongest posterior support for MCC metrics of the three ancestral state reconstruction frameworks used in this study , however the strong sampling bias exhibited by that method marginalizes confidence in their reconstructions ., As mentioned , the strong support for sample size is consistent with previous studies that used the phylogeographic GLMs 12 , 13 ., Air travel was previously shown to be a driver of the global diffusion of H3N2 using a GLM 12 , but none of the GLM ( –SS ) or GLM ( +SS ) runs showed support for this predictor ., However , our study was performed within a single country and aggregated all air travel data from each individual state into a matrix of region-to-region passenger flux , which perhaps limits its contribution to these models ., Furthermore , the paper by Lemey et al . 12 discretized by “air communities” ( p ., 2 ) to better reflect trends in air travel , while we partitioned strictly based on pre-defined , arbitrary geographic regions ., We also assumed a single introduction into the U . S . and did not include incoming travel from international flights that could certainly have introduced strains with more genetic diversity than those used in this study ., We recognize several limitations with this study including the omission of international air travel ., In addition , our assumption of a single introduction into the U . S . could also have limited inference regarding the contribution of air travel and may explain the lack of BF support for that predictor from both region of origin and destination when a previous study has implicated these data as a driver of the diffusion 12 ., Also , the transportation predictor fails to incorporate inter-region travel via ground transportation , which certainly could have implications within a single country ., Furthermore , we only analyzed hemagglutinin sequences in this study and did not investigate neuraminidase or any other segments of the influenza genome ., We arbitrarily selected 25% of samples from each region for our subsampling in order to better reflect the observed sampling frequencies , but it is possible that larger subsample sizes or an alternative sampling approach could have resulted in stronger or weaker support for the predictors in the GLM as well as the RSPPs via the three reconstruction approaches ., However , our use of Pearson’s correlation coefficient between sample size and root state posterior probability ( Figs 2B and 3B ) and comparison of GLMs that include and do not include sample size predictors aim to outline the impact of sampling bias within our dataset ., We plan to conduct similar research on additional influenza seasons and using alternative sampling methods in order to further study whether this sampling bias is a systematic function in the GLMs or is limited to the dataset used in this study ., Sampling bias is a known issue in phylodynamics 23 , 24 and may not be possible to eliminate , although varying approaches may differ in their sensitivity to such biases ., Finally , we limited our study to a single influenza season which prevents seasonality comparisons and impacts from local persistence ., Overall , this study aimed to investigate the phylogeography of the H3N2 influenza viruses that circulated in the U . S . during the 2014–15 flu season and to also investigate three established methods of ancestral state reconstruction ., While our GLM results provide superior posterior support than either +BSSVS method or the -BSSVS framework , these results appear to be dominated by a strong sampling bias ., Although these results are not necessarily incorrect , the investigation of additional frameworks reveals that the +BSSVS ( P ) is likely the “best” approach for this dataset to minimize such concerns , depending on the selection of coalescent prior , if given the choice among the five presented in our work for this particular virus and time frame ., Furthermore , we demonstrate that our approach of subsampling to compare multiple models may not only reflect subtle changes to the phylogeny but also to the contribution of the predictor variables in the GLMs ., Although we do not believe that the GLM provides an ideal , unbiased reconstruction framework for our dataset , this type of assessment could be valuable for understanding the true nature of the phylogeny-sampling location association in future work ., Such studies may also encourage researchers to utilize the GLM framework as a means of obtaining more information-driven variables into their phylogeographic studies and to unlock the potential for more accurate ancestral state reconstructions to better aid epidemiological and public health efforts .

Introduction, Results, Discussion

Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection ( BSSVS ) framework ., Recently , this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model ( GLM ) of genetic , geographic , demographic , and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor ., Although the latter appears to enhance the biological validity of ancestral state reconstruction , there has yet to be a comparison of phylogenies created by the two methods ., In this paper , we compare these two methods , while also using a primitive method without BSSVS , and highlight the differences in phylogenies created by each ., We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014–15 flu season in the U . S ., We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors , and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors ., Furthermore , the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state , which exhibits the most tropical climate during a typical flu season in the U . S ., The GLM , however , appears to be highly susceptible to sampling bias compared with the other methods , which casts doubt on whether its reconstructions should be favored over those created by alternate methods ., We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models , and believe that the connection between reconstruction method and sampling bias warrants further investigation .

For the better part of the last decade , epidemiological researchers have employed a Bayesian framework to reconstruct phylogenetic trees and determine the spatiotemporal relationships between clades of viruses ., Recently , an extension of this framework has enabled direct assessment of how various demographic , geographic , genetic , and environmental variables play a role in these relationships , but there has yet to be a comparison between the former and the latter ., Here , we aim to assess the differences between the two reconstruction techniques , as well as an additional primitive method , using the 2014–15 influenza season in the U . S . as a case study under a variety of population growth scenarios ., We highlight how the new method demonstrates significant increases in commonly-reported trends in phylogenies and that the method identifies climate predictors that appear to be consistent with known trends in seasonal trends in influenza ., However , we found that this method appears to be the most heavily influenced by the locations at which the viruses were obtained ., Our work offers valuable insight for researchers wishing to study the evolutionary history of viruses and also may prove useful in determining the correct method to choose for a given application of virus phylogeography .

biogeography, taxonomy, ecology and environmental sciences, medicine and health sciences, pathology and laboratory medicine, influenza, atmospheric science, pathogens, population genetics, immunology, microbiology, orthomyxoviruses, viruses, preventive medicine, seasons, phylogenetics, data management, rna viruses, population biology, glycoproteins, vaccination and immunization, public and occupational health, infectious diseases, geography, computer and information sciences, medical microbiology, microbial pathogens, phylogeography, evolutionary systematics, biochemistry, meteorology, influenza viruses, earth sciences, viral pathogens, genetics, biology and life sciences, viral diseases, evolutionary biology, glycobiology, organisms

journal.pgen.1004037

Intrasubtype Reassortments Cause Adaptive Amino Acid Replacements in H3N2 Influenza Genes

The genome of influenza A virus consists of 8 segments , each represented by an RNA molecule ., Coinfection of a cell by viruses of different genotypes occasionally leads to reassortments , i ., e ., formation of genotypes containing molecules from different sources ., Most of the major Influenza A pandemics during the last century were caused by reassortant strains 1 ., Indeed , reassortments , especially those creating novel combinations of hemagglutinin ( HA ) and neuraminidase ( NA ) genes , may lead to radical changes in antigenic properties and give rise to viral types that escape the herd immunity ., Still , after a reassortment event , the viral segments find themselves in a novel genetic environment , which may lead to disruption of coadaptations that previously existed between them and reduce viral fitness 2 , 3 ., Thus , it is likely that only a small proportion of reassortment events lead to creation of novel , successful viral genotypes ., Reassortments between different Influenza A subtypes gave rise to the major pandemics of the 1957 , 1968 , 2009 , and possibly 1918 1 , 4 ., Reassortments between strains belonging to a single subtype likely occur much more frequently than inter-subtype reassortments; however , they leave a less pronounced phylogenetic signal , and are therefore harder to study 5 ., In theory , reassortments can be detected through the incongruencies between phylogenies of different segments of a viral genome ., Indeed , after a reassortment , the segments obtained from the same viral isolate will occupy conflicting phylogenetic positions , due to the differences in their evolutionary histories ., In practice , however , detecting reassortments is difficult ., The influenza sequence databases are subject to ascertainment biases , with recent sequences being oversampled , and some countries sampled better than others ., Reassortment events are prone to be missed when one or both parental strains are not sampled properly , or when they are closely related ., Conversely , spurious reassortments may be inferred due to differences in phylogenies caused by phylogenetic noise ., Multiple reassortments nested within a single clade compound the difficulties ., In most of the early studies , reassortments were inferred via manual detection of incongruencies between phylogenies of different viral segments 6–9 ., However , this approach is impractical for systematic analyses of large datasets of influenza genomes with complex reassortment histories ., Recently , several methods for automatic detection of reassortments have been proposed ., These methods can be broadly categorized into two groups ., The distance methods 10 , 11 measure , for each viral segment , the degree of similarity between all pairs of viral genomes , and infer reassortments from the differences between the distance matrices obtained from different segments ., The phylogenetic methods 4 , 12–14 make explicit use of the evolutionary histories of individual segments , comparing their phylogenies and detecting incompatibilities between them ., In general , phylogenetic methods are more robust than the distance methods 12 , 14 , particularly in detecting reassortments that became fixed or reached high frequencies within the population ., Comparisons of relative frequencies of different progeny coming from coinfecting strains in experiments 3 , 15–20 as well as phylogenetic analyses of circulating reassortant strains 10 , 21 demonstrate that reassortments between different segments are not equiprobable ., Such differences likely arise , in part , from variance in the extent of epistatic interactions between pairs of genes ., For example , a recent analysis of experimental data suggests that polymerase genes ( PB1 and PA ) tend to be inherited together , and that reassortment preferences for HA depend on the subtypes of the parental strains , while NA and matrix protein ( MP ) have no preferences in their reassortments with other segments 19 ., Analysis of distributions of coalescent times for different segments suggests , however , that reassortments between HA and NA are particularly frequent 22 ., Besides radical shifts in antigenic properties , reassortments are associated with a reduction in genetic diversity of circulating strains , indicative of positive selection favoring the spread of the reassortant strain 22 ., Several observations also suggest that , subsequent to the “antigenic shift” , reassortants tend to undergo increased “antigenic drift” , i . e . , elevated rate of amino acid replacements , perhaps due to follow-up coadaptation of genes that find themselves in new genetic environments 5 , 23 ., For example , a reassortment associated with host change has led to short-term positive selection in NS gene of swine Influenza A 24 ., Still , the phenomenon of coevolution of viral genes subsequent to reassortments has not yet been studied systematically ., Here , we purport to close this gap ., To study the within-subtype reassortments in the influenza H3N2 virus , we first used GiRaF 12 to automatically infer the reassortant taxa in the dataset of 1376 complete influenza H3N2 genotypes ., GiRaF compares large pools of Monte Carlo-sampled phylogenetic trees constructed for each viral segment separately , inferring the topological incongruencies between them ., Assuming that an incongruence between phylogenies of two segments , observed in a high fraction of comparisons , reflects an ancestral reassortment event , GiRaF then predicts the subsets of taxa that are descendant to such reassortments ., The number of reassortments predicted by GiRaF in which a particular segment was involved was similar for all segments except M1 and NS1 ( Table 1 ) ; in M1 and NS1 , fewer reassortments were detected , probably because their shorter sequences and/or higher conservation ( Table 1 ) lead to a weaker phylogenetic signal ., We saw no preferences for particular pairs of segments to be reassorted ( Table S1 ) ., We then mapped the reassortments inferred by GiRaF onto the reconstructed phylogenies of each of the segments involved ., We assumed that each reassortment event had happened on the phylogenetic branch leading to the last common ancestor of the reassortant taxa ( reassortment-carrying branch , RCB ) ., This way of mapping reassortments was self-evident for monophyletic sets of taxa ., However , when the histories of reassortment events are complex ( which is the case for Influenza A; 7 , 9 ) , the subsets of genotypes resulting from a single reassortment predicted by GiRaF are not necessarily monophyletic 12 ., Indeed , we found that in our dataset , many of the inferred sets of reassortant taxa were not monophyletic ( Figure 1 ) ., To test the robustness of our conclusions , we therefore also tried alternative ways of mapping reassortment events , which also supported our key findings ( see below ) ., We asked whether reassortments affect subsequent accumulation of amino acid-changing replacements ., We hypothesized that subsequently to RCBs , the rate of accumulation of amino acid replacements will be temporarily elevated as the reassorted sets of genes coadapt to each other ., To address this , we inferred , for the genes coded by each of the 8 segments , the phylogenetic positions of all amino acid replacements , and studied those replacements that were descendant to at least one RCB ., When a reassortment and a replacement occurred on the same phylogenetic branch , it was impossible to deduce which came first ., To avoid this ambiguity , we considered a replacement to be descendant to a particular reassortment if it occurred on a phylogenetic branch descendant to an RCB , but not on the RCB itself ., ( Including the substitutions on the RCBs as descendants to reassortments gave similar results; see below . ), Between 33% and 50% ( depending on the segment ) of the RCBs were terminal branches , and thus their effect on subsequent replacements could not be studied ., 14% to 33% more of the RCBs were pre-terminal branches ., Amino acid replacements on the terminal branches , especially in RNA viruses , tend to be more deleterious , and may follow evolutionary patterns distinct from the replacements elsewhere on the phylogeny 25–30; thus , we chose to exclude such replacements from our analyses ., Therefore , the RCBs on pre-terminal branches could also have no effect on replacements in our dataset ., The remaining 33% to 42% of RCBs could be followed by amino acid replacements , and in fact , most of the replacements occurred in reassortant clades ., In NA , for example , 320 out of 411 ( 78% ) observed non-terminal amino acid replacements were descendant to at least one RCB ( Table 1 ) ., To assess the effect of reassortments on subsequent accumulation of amino acid replacements , we measured the phylogenetic distance between each replacement and its most recent ancestral RCB ., We then compared these distances to those expected if the phylogenetic positions of post-reassortment replacements were random in respect to reassortments ., This approach is conservative , in that it ignores any possible long-term effect of reassortments spanning phylogenetic distances comparable with the height of the phylogenetic tree ., In two of the genes , NA and PB1 , the mean distance was significantly lower than expected ( Table 1 ) , indicating that RCBs were followed by a transient increase in the rate of amino acid replacements ., Since the inferred number of RCBs for each gene is moderate ( e . g . , 20 for NA , only 14 of which could have descendant replacements ) , individual RCBs could have a disproportionate effect on the distribution of distances ., For the NA segment , we asked whether the observed accelerated evolution after reassortments is due to some single reassortment event ., To this end , we repeated the analysis 14 times , each time excluding one of the RCBs , and comparing the observed and expected distances between the amino acid replacements and the remaining RCBs ., In all 14 comparisons , the results remained significant , indicating that the acceleration of replacements is a phenomenon to which multiple reassortments contribute ( Table 2 ) ., To further test its robustness , we repeated the analyses using alternative methods of mapping reassortments onto the tree ( Tables S2 , S3 , S4 , S5 , S6 , S7 ) ., These methods differ in the strength of the required statistical evidence for inference of reassortments , and in the ways non-monophyletic sets of reassortant taxa are treated ( see below ) ., For NA , acceleration of evolution after reassortments was significant in 5 out of 6 analyses; in the sixth analysis , it was marginally significant ( p\u200a=\u200a0 . 09 ) ., For PB1 , acceleration was significant in 3 out of 6 analyses ., Moreover , in a number of analyses , acceleration was also observed in several other genes for which no significant result is observed in Table 1: M1 in 4 tests , and HA and NS1 in 3 tests each ., Thus , 5 out of 8 genes show evidence for acceleration at least in half of the tests ., Still , the results for NA are the most robust , and this is the gene for which the evidence for reassortments-caused-acceleration of evolution is the strongest ., When the substitutions on the RCB itself were also counted as descendant to the reassortment , the results were similar ( Tables S8 , S9 , S10 , S11 , S12 , S13 ) ., In PB1 , the increase in the rate of amino acid replacements after the RCB is rather long-lived: it spans a phylogenetic distance of ∼0 . 04 ds units , although the excess is not significant for most individual distance bins ( Figure 2 ) ., In contrast , in NA , this increase is very brief , with most of the excess replacements observed on the very short phylogenetic branches that immediately follow the RCB ( Figure 3 ) ., In particular , we observe 30 such replacements at phylogenetic distances up to ∼0 . 003 ds units ( which is just above the time it takes the NA gene to obtain a single synonymous replacement , and is therefore the highest phylogenetic resolution we can achieve; leftmost bin in Figure 3 ) ., Because virtually no such replacements would be expected to occur in such a short period of time if they had been independent of reassortments , all these excess 30 replacements are reassortment-provoked ., Therefore , at least ∼9% ( 30/320 ) of all amino acid replacements in NA were caused by reassortments; since some of the later replacements descendant to the RCBs could also be reassortment-provoked , the actual number is probably higher ., Moreover , the fact that these replacements occur so fast implies that most of them are facilitated by positive selection , and thus comprise a post-reassortment adaptive walk 31 , 32 ., What is the length of such adaptive walks , i . e . , the characteristic number of amino acid replacements provoked by an individual reassortment ?, The 30 “fast” replacements were descendant to 3 individual RCBs ( Table 2 ) ; 11 more RCBs were not followed by replacements so soon , although they could be followed by reassortment-associated replacements later ., Therefore , an average reassortment provoked ∼2 . 1 ( 30/14 ) amino acid replacements in its descendant clade ., However , these replacements could occur in multiple independent lineages , so that the number of post-reassortment replacements per lineage was lower ., Indeed , by the time the phylogenetic distance of 0 . 003 ds units after an RCB was reached , the descendant subtree had often multifurcated , so that these 3 RCBs gave rise to a total of 33 individual descendant lineages; 36 more lineages originated from the remaining 11 RCBs ., In 2 out of the 3 RCBs that provoked replacements , different post-RCB lineages accumulated replacements independently ( Table 2 ) ., As a result , over the evolutionary time of 0 . 003 ds units , an average post-RCB lineage attained 0 . 43 ( 30/69 ) reassortment-provoked replacements ( Table 2 ) ., This is the lower boundary for the length of the “adaptive walk” per lineage associated with a reassortment event , as it excludes any effect of reassortments over longer timescales ., We asked whether the post-RCB amino acid replacements are enriched in particular classes of mutations , compared to the rest of the replacements ., In this analysis , we considered the NA gene , because in it , the effect of post-reassortment adaptive walk is the most robust; and also the HA gene , because it is the other primary determinant of antigenic properties , is known to evolve under continuous positive selection , and is highly epidemiologically relevant ., Several categories of mutations had biased phylogenetic distances from the RCBs , compared with the complementary sets ( Table 3 ) ., Firstly , in NA , the replacements at amino acid sites experiencing positive selection tended to be farther from RCBs , while no such difference was observed for HA ., Secondly , the replacements at sites that distinguish the antigenic clusters 33 of HA tended to occur farther from RCBs ., Thirdly , parallel replacements had a strong tendency to occur soon after the RCBs both in NA and HA ., Fourthly , reversions in NA , but not in HA , occurred soon after the RCBs ., Fifthly , the sites previously shown to be involved in intragenic epistatic interactions 34 as “leading” occurred father from RCBs both in NA and in HA , while the “trailing” sites occurred soon after the RCBs ., Neither the NA nor the HA genes showed any bias for replacements at the epitopic sites ., As was the case with the previous analysis , most of these results were also supported by alternative methods of inferring RCBs ( Tables S14 , S15 , S16 , S17 , S18 , S19 ) ; the exception were reversions in NA , which gave discordant results in different tests ., Our study provides the first systematic analysis of association between reassortments and amino acid-level changes in influenza A . We show that a reassortment involving a particular segment provokes a transient increase in the rate of amino acid replacements at the gene encoded on this segment; these replacements tend to occur at sites that do not normally experience positive selection , and often involve parallel replacements ., One way to estimate the effect of RCBs on subsequent accumulation of amino acid replacements would be to directly compare the replacement rates on branches that had descended from reassortments and those that had not ., However , this is not feasible in Influenza A , because in most segments , the majority of branches ( 50–85% ) descend from at least one RCB ( Table 1 ) ; the remaining branches tended to be deep-lying , and thus likely have biased patterns of replacements 29 , 30 ., Instead , we searched for a transient increase in the rate of amino acid changes on the branches descendant to the internal RCBs ., This analysis could miss some of the very rapid reassortment-provoked changes that had happened on the same branches as the reassortments themselves ., We compared the phylogenetic distances between the amino acid replacements and the preceding RCBs to the null distribution expected under the assumption that the amino acid replacements were distributed over post-RCB , non-terminal branches randomly , with probability of a replacement to fall onto a particular branch proportional to the branch length ., In NA and PB1 genes , the replacements occur sooner after the RCBs than in the null model ., When alternative strategies of mapping reassortments were used , three other genes – M1 , HA and NS1 – also show the same pattern in at least half of the tests ., Since it is hard to validate the reassortment-mapping algorithms , the results of each individual analysis for each individual gene should be taken with some caution ., Together , however , they provide strong support for the reassortment-caused accumulation of changes , especially in the NA gene ., The post-RCB excess of amino acid replacements was not exclusively dependent on a single particular RCB ( Table 2 ) ; thus , it seems to be a universal phenomenon ., Non-uniformity of the substitution rate has been described previously for Influenza , and has been attributed to episodic action of positive selection 35 , to simultaneous fixation of multiple interacting advantageous mutations 36 or to frequent selective sweeps under clonal interference ., There is no obvious mechanistic association between reassortments and either of these factors ., If reassortments themselves are adaptive , they can spread through population rapidly by means of positive selection 22 ., Strong positive selection favoring an allelic variant may also drive to fixation neutral and even mildly deleterious point mutations linked with it 37; this phenomenon is prevalent in Influenza A which evolves , on the within-segment level , under nearly complete linkage 38 , and where clonal dynamics is largely determined by linkage with beneficial alleles 38 , 39 ., This phenomenon could cause an excess of replacements on the same branches as the reassortment events , which is , however , not observed in our data ( data not shown ) ., There is no way hitchhiking can cause accumulation of replacements on the branches descendant to RCBs ., Therefore , the only feasible explanation for the post-reassortment increase of the rate of amino acid replacements seems to be that they constitute an adaptive walk 31 , 32 , i . e . , a burst of positively selected adaptive changes provoked by a shift in the fitness landscape ., In NA , we observe a radical increase in the rate of amino acid replacements immediately after reassortments ., This excess spans only a short period of time: it is mostly over by 0 . 003 ds units , i . e . , by the time a single synonymous replacement occurs somewhere in the gene ( which takes less than a year; 40 ) ., Thus , at least NA , and very likely other genes , experience transient positive selection after reassortment events ., The excess of replacements at phylogenetic distances up to 0 . 003 ds ( i . e . , up to the time a single neutral replacement is expected to be accumulated at the gene ) in NA suggest that a total of ∼30 amino acid replacements in the entire phylogeny , or ∼0 . 43 replacements per lineage , were facilitated by preceding RCBs ., Therefore , reassortments are responsible for at least ∼9% of all amino acid replacements in this gene ., In fact , this may be an underestimate for at least two reasons ., First , some of the reassortments were likely to have been undetected ., Second , a fraction of the adaptive walks could have spanned longer phylogenetic distances than this threshold ( Figures 2 , 3 ) ., What is the cause of the post-reassortment accumulation of positively selected replacements ?, Influenza A is a model system for studying positive selection , with most of the selective pressure exerted by the host immune system ., Positive selection is most pronounced in the genes coding for the surface glycoproteins ( NA and HA ) , and within these genes , at the epitopic sites which are most involved in the immune response ., Conceivably , the immunity-driven positive selection could increase after a reassortment ., We observe , however , that the post-RCB adaptive walk is mostly manifested at sites other than those under constant positive selection , or responsible for antigenic properties , and is not affected by the epitopic vs . non-epitopic location of the site ( Table 3 ) ., This suggests that the post-reassortment adaptive walks are not driven by the pressure to evade the host immune system ., Rather , these replacements are probably associated with epistatic interactions between genes 41 ., In general , reassortments and host shifts lead to changes in the patterns of both synonymous and nonsynonymous substitutions , probably due to joint effects of changes in the mutation and selection patterns 29 , 42–44 ., After a reassortment event , a gene finds itself in a novel genetic environment which may , through epistatic interactions , exert novel selective pressures on its amino acid sites , facilitating further amino acid changes ., An adaptive walk could compensate for the loss of fitness associated with the preceding reassortment 2; however , as the reassortments themselves tend to be adaptive 22 , it seems more likely that these replacements could exploit novel fitness peaks that have become newly accessible after the reassortments ., The evidence for the post-reassortment adaptive walk is the most robust for the NA gene ., Sequence evolution of influenza NA does not always lead to changes in antigenic properties 45 , and may be caused by other forces instead ., Indeed , antibody-driven affinity-changing mutations in HA can be compensated by substitutions changing the activity of NA 46 , 47; this indicates that the choice of the optimal NA genotype is dependent at least on HA , and possibly on other genes as well ., Furthermore , reassortments often involve a currently circulating strain and an older strain 48 , and a reassortment between HA and NA frequently involves an up-to-date variant of HA and an older variant of NA , as suggested by less discordance between sampling time and phylogenetic position of HA sequences than of NA sequences ( 7 and our data ) ., Therefore , while the immune escape is the primary factor of evolution of HA , much of the NA evolution may be epistatic and , in particular , compensatory ., Parallel replacements are overrepresented after reassortments ( Table 3 ) ., Overall , the rate of parallelism in Influenza A evolution is high 49 , 50 , probably due to similar selective pressures exerted on different strains ., The high parallelism observed in this study suggests that the replacements involved in a post-reassortment adaptive walk may also be adaptive in other contexts ., Epistatic interactions both within 51–54 and between segments 55 are wide-spread in Influenza A . One evolutionary manifestation of this phenomenon is positive epistasis between replacements: a replacement can facilitate subsequent replacements at different sites of the same protein 34 ., The sites involved in such epistatic interactions can be classified as “leading” or “trailing” , depending on whether replacements in them tend to come as first or second in epistatic pairs; for example , replacements at leading sites can introduce radical changes to protein structure , while replacements at trailing sites may compensate those changes 34 ., We find that , while the replacements at leading sites are remote from reassortments , the replacements at trailing sites occur sooner after reassortments than expected ., Therefore , the sites experiencing post-reassortment replacements are the same sites that also react to the change of the protein structure due to replacements elsewhere in the protein ., This suggests that the class of sites denoted as “trailing” in 34 , and involved in post-adaptive walk in this study , may be responsible for adaptation to novel genetic environment that stems from changes in the same gene as well as in other genes ., Association between reassortments and the rate of subsequent accumulation of amino acid mutations may be important for predicting future pandemic strains ., For example , the avian H5N1 influenza is among the most likely candidates for the agent of a future pandemia 56–58 ., Naturally occurring strains of A/H5N1 are not transmittable between mammals; however , to become transmittable , they require just five additional mutations 59 or a reassortment with just four additional mutations 60 ., Two of these mutations are already frequent among the A/H5N1 viruses 61 ., If a reassortment commonly leads to accelerated accumulation of amino acid replacements , gaining the remaining mutations and evolving a natural mammalian-transmittable H5N1 strain may take less time than predicted by simple models 61 ., We downloaded all complete human H3N2 influenza A genotype sequences ( N\u200a=\u200a2205 ) available on 27 . 10 . 2011 from the flu database 62 ., Nucleotide sequences for each segment were aligned using muscle 63 , 64 ., Genotypes containing truncated sequences , multiple unidentified nucleotides , or indels were discarded ., We used CD-HIT 65 to cluster genotypes that had identical sequences of NA segments , and retained one random sequence from each cluster , thus retaining 1379 genotypes for further analysis ., For segments encoding PB1 , M1 and NS1 that contain overlapping ORFs , we excluded the overlapping regions , and analyzed the longest remaining ORF ., All alignments are available at http://makarich . fbb . msu . ru/flu_walks/ ., For each segment , we Bayes-sampled the 1 , 000 phylogenetic trees using MrBayes MPI version 66–68 with the following settings: GTR+I+G model , 22 million iterations , sampling each 22 , 000th iteration ., Three isolates: A/Ontario/RV123/2005 , A/Ontario/1252/2007 and A/Indiana/08/2011 were excluded from analyses , because we found the branch leading to the clade formed by them to be , for several segments of non-human origin ( NP , M , NS , PB2 and PA ) , too long for a meaningful estimation of evolutionary parameters; these isolates are SOIV triple reassortants ( see also 69 ) ., Each phylogenetic tree was rooted by the isolate A/Albany/18/1968 ., These 1 , 000 trees were used to infer the reassortment events ( see below ) ., For each segment , the consensus tree of the 1 , 000 MrBayes-sampled trees was used as input for HyPhy 70 to estimate the evolutionary parameters and to restore the ancestral sequences ., The branch-specific dS values were estimated using the local MG94xHKY85 71 model ., The ancestral sequences were reconstructed using the GTR+I+G global nucleotide model ., As an alternative approach , we also repeated our analyses using maximum likelihood trees constructed with PhyML 72 instead on consensus Bayesian trees , and obtained similar results ., For subsequent analyses , we rescaled the lengths of all branches in the consensus trees in the units of dS; this allowed us to study the distribution of nonsynonymous replacements independently of branch lengths ., To obtain the gene-specific dN/dS values , we used global MG94xHKY85 model in HyPhy ., For phylogenetic mapping of reassortments , we used a two-step procedure ., First , we computationally predicted the subsets of taxa that occupied incompatible positions in phylogenies of different segments using GIRAF software 12 running on a cluster node with 512 Gb of RAM ., GiRaF automatically predicted the subsets of taxa originating from each ancestral reassortment event on the basis of the MrBayes sampled trees for all eight segments ., Importantly , not all segments were necessarily involved in each inferred reassortment; although in reality each reassortment splits all segments into two subsets ( the retained and the acquired segments ) , for some of the segments , the phylogenetic signal was often too weak to allow GiRaF to ascribe them to one of the two mutually reassorting sets of segments 12 ., For such segments , no reassortment event could then be inferred; as a result , although the same sets of taxa were considered for each segment , the number of reassortment events per segment varied ( Table 1 ) , and the number of segments involved in each reassortment ( on either side ) was usually under 8 , with some of the segments “abstaining” ., We used two approaches to quality filtering of the predicted reassortments ., In the first approach ( “joint reassortments” , recommended in 12 and used in the main text ) , we acquired the subsets of taxa involved in reassortments from the “catalog file” produced by GiRaF ., This file includes only those reassortments that involved inconsistencies between at least 3 pairs of segments; therefore , each reassortment involved between 4 and 8 segments ., In the second approach ( “high-confidence reassortments” ) , we used the reassortment subsets involving any number of pairs of segments ( i . e . , between 2 and 8 segments ) , but required the GiRaF-predicted confidence level for the reassortments of 1 . 0 ., Second , we inferred , on the basis of these lists of reassortant taxa , the phylogenetic positions of the RCBs ., In theory , each reassortment event should give rise to a monophyletic set of taxa; the last common ancestral branch to this clade is then the RCB ., In reality , however , many of the predicted subsets of taxa were not monophyletic ., This occurs because , under complex histories of sequential reassortments , GIRAF can either split the taxa descendant to a common reassortment event into multiple sets , or join the taxa descendent from multiple reassortment events into a single set 12 ., In such cases , the inference of RCBs is ambiguous ., We used three different approaches to infer the phylogenetic position of the RCBs ., While these approaches produced identical results for monophyletic sets of reassortants , they differed in the way they treated non-monophyletic sets ., For each set of reassortants , we inferred as RCB ( s ), ( i ) the ( single ) branch leading to the most recent common ancestor of all reassortants ( “one-point inference” , used in the main text ) ;, ( ii ) the set of branches leading to the most recent common ancestors of each clade involving only reassortants ( “two-point inference” ) ; or, ( iii ) the union of, ( i ) and, ( ii ) ( “three-point inference” ) ., Arguably , each approach has its merits ., Under, ( i ) , the number of inferred RCBs is minimal ( and equal to the number of sets of reassortant taxa ) , and so this approach is most parsimonious; conversely , under, ( ii ) and, ( iii ) , a single subset of reassortant taxa could give rise to multiple RCBs on the same phylogenetic tree ., Under, ( ii ) , only reassortant taxa are descendants to RCBs; finally ,, ( iii ) may be best for inference of sequences of nested reassortments such that a later reassortment affects a subset of lineages that were also involved in an earlier reassortment , and GIRAF underpredicts the set of reassortant taxa for the earlier reas

Introduction, Results, Discussion, Methods

Reassortments and point mutations are two major contributors to diversity of Influenza A virus; however , the link between these two processes is unclear ., It has been suggested that reassortments provoke a temporary increase in the rate of amino acid changes as the viral proteins adapt to new genetic environment , but this phenomenon has not been studied systematically ., Here , we use a phylogenetic approach to infer the reassortment events between the 8 segments of influenza A H3N2 virus since its emergence in humans in 1968 ., We then study the amino acid replacements that occurred in genes encoded in each segment subsequent to reassortments ., In five out of eight genes ( NA , M1 , HA , PB1 and NS1 ) , the reassortment events led to a transient increase in the rate of amino acid replacements on the descendant phylogenetic branches ., In NA and HA , the replacements following reassortments were enriched with parallel and/or reversing replacements; in contrast , the replacements at sites responsible for differences between antigenic clusters ( in HA ) and at sites under positive selection ( in NA ) were underrepresented among them ., Post-reassortment adaptive walks contribute to adaptive evolution in Influenza A: in NA , an average reassortment event causes at least 2 . 1 amino acid replacements in a reassorted gene , with , on average , 0 . 43 amino acid replacements per evolving post-reassortment lineage; and at least ∼9% of all amino acid replacements are provoked by reassortments .

Influenza A is a rapidly evolving virus with genome composed of eight distinct RNA molecules called segments ., This genetic structure allows formation of new combinations of segments when a cell is coinfected by multiple viral strains , in a process called reassortment ., While “antigenic drift” – the process of continuous accumulation of point mutations that change the antigenic properties of the viral proteins – is mainly responsible for the seasonal flu , the heaviest pandemics were caused by spread of novel reassortant strains and the associated radical “antigenic shift” ., However , the association between these two types of processes has not been studied systematically ., Here , we use the extensive available complete-genome sequencing data for Influenza A H3N2 subtype to infer the evolutionary timings of within-subtype reassortment events , and study the patterns of point amino acid-changing replacements that followed reassortments ., We find that reassortments were often rapidly followed by replacements , which possibly compensated for the loss of fitness associated with the reassortment or explored newly accessible fitness peaks ., These findings may be relevant for prediction of future pandemic strains of Influenza A .

organismal evolution, genome evolution, population genetics, microbiology, parallel evolution, mutation, microbial evolution, forms of evolution, comparative genomics, biology, evolutionary theory, evolutionary genetics, viral evolution, natural selection, virology, co-infections, genomics, evolutionary biology, genomic evolution, evolutionary processes

journal.pcbi.1000927

Synaptic Plasticity Controls Sensory Responses through Frequency-Dependent Gamma Oscillation Resonance

Synchronous oscillations 1–3 in neural networks are thought to be important to sensory and cognitive functions 4 , 5 ., In particular , gamma band oscillations ( 30∼70Hz ) have been observed in various neural circuits 6 , 7 , and their role has been intensively studied 8–11 ., Gamma oscillations synchronize the response of neural populations 12 , selectively amplify local sensory signals 13 , enhance signal transmission by reducing noise 14 , and regulate information processing by phase-dependent gating 15 ., However , little is known about the mechanism by which the nervous system controls or takes advantage of these gamma oscillation effects ., Here we suggest that sensory response can be precisely controlled by the synaptic plasticity of a neural circuit , through the dynamic modulation of spontaneous gamma oscillations ., Using a model neural network of the primary visual cortex ( V1 ) , we show that, ( i ) the resonance between spontaneous and stimulus-driven oscillations regulates sensory responses and synchrony in a neural population;, ( ii ) the synaptic plasticity of thalamocortical neurons modulates the frequency of spontaneous oscillation in V1; and, ( iii ) this change of spontaneous oscillation regulates gamma resonance , thus controlling the afferent-downstream synchrony ., We found that this synaptic modulation can either facilitate or depress the response of the network to stimuli , by changing gamma resonance conditions ., Our results suggest that the brain can readily control its synchrony condition for the proper processing of sensory information ., We performed our simulations with a model cortical network of excitatory ( E ) and inhibitory ( I ) neurons ( 1mm by 1mm , consisting of 3341 neurons ) adapted from our previous study 13 ( Fig . 1A , top ) ., When feedforward input spikes ( generated by random Poisson process ) were injected into the model visual cortex network , neurons generated spontaneous gamma rhythms in their firing pattern ( Fig . 1B and C ) ., The spontaneous oscillations were detectable almost whenever the connections between E and I cells were allowed and the input spike rate was above a certain level ( ∼10spikes/s ) that can drive a measurable amount of cortical responses ( for detailed parameter tests , see ref . 13 ) ., As we reported previously , the frequency of oscillation was modulated by changes in the thalamocortical synaptic strength parameter that controls the excitatory postsynaptic conductance ( EPSC ) in cortical neurons induced by a feedforward input spike ., In the first part of this study , we fixed this thalamocortical synaptic strength and examined the effect of temporal changes in input spike rate only ., Subsequently we studied how variations in thalamocorical synaptic strength affect cortical responses ., We first controlled input firing rate patterns to examine how gamma oscillation is regulated when feedforward input spike rate varies temporally ( Fig . 1A , bottom ) ., For static input , mean input firing rate was set to 40spike/s , and the input spike correlogram indicated no temporal correlation between input spikes ( Fig . 1B ) ., Responding to this input , cortical neurons generated an oscillatory output spikes pattern ., The oscillation power spectrum showed one strong spontaneous gamma peak at fsout\u200a=\u200a38Hz; inter-spike interval ( ISI ) distribution showed that most I cells fired in every gamma cycle , while E cells fired , on average , less than once in a cycle ( Fig . 1D ) ., In addition , I cells were synchronized more sharply than E cells in each gamma cycle , indicating that this gamma rhythm was induced by fast-spiking I cells ., For oscillating input , in contrast , we drove the network with sinusoidally oscillating input ( mean firing rate 40 spikes/s , mean oscillation amplitude ±20 spikes/s ) , at the same frequency as the spontaneous gamma oscillation frequency for static input ( fsout ) ., In this instance , output oscillation frequency was the same as input frequency ( Fig . 1E ) , but the correlation of output spikes became stronger , and the output rate pattern was phase-locked to input oscillation cycle ( Fig . 1C ) ., The ISI distributions of E and I cells were also sharpened , showing that the “gating” or “temporal sharpening” effect 15 of sensory responses is enhanced by coherent input oscillations 16 , 17 ( Fig . 1E ) ., We refer to this modulation as “resonance” between spontaneous and stimulus-driven oscillations ., To further examine the resonance condition between spontaneous and driven oscillations , we injected various frequencies of oscillating inputs to the network ., Input frequency was varied within the range fin\u200a=\u200a25∼55Hz , similar to the boundary of spontaneous oscillation frequency observed in our previous study 13 ., When fin was markedly different from the spontaneous oscillation frequency ( fsout\u200a=\u200a38Hz ) for static input , the network displayed two separate peaks in its spectrum ( Fig . 2A , red and blue arrows ) , showing that two different types of oscillations coexist in the cortical response ., The spontaneous oscillation peak ( blue arrows ) remained the same ( 38Hz ) but became weaker than in the previous cases ( Fig . 1D , E ) ., The driven oscillation peak ( red arrows ) appeared at fin , confirming that this oscillation was driven by the input spikes pattern ., Thus , in this case , spontaneous and driven oscillations existed independently ., In contrast , when fin was relatively close but not identical to fsout , the spontaneous oscillation peak at fsout disappeared , and the power spectrum displayed only one peak near fin ( Fig . 2A , purple arrows ) , demonstrating the resonance between spontaneous and driven oscillations ., In addition , ISI distributions became sharper than in “irresonant” cases ( Fig . 2B ) ., Therefore , when fin is close enough to fsout , spontaneous oscillation frequency is adjusted close to driven oscillation frequency , and the resonance between two oscillations strengthens cortical gamma rhythm , which enhances the synchronization of cortical spike activities ., Next , we examined how cortical responsiveness is modulated by gamma oscillation resonance ., We varied input frequencies ( fin\u200a=\u200a25∼55Hz ) for different input oscillation strengths ., The oscillation amplitude of the input spike rate was set to ±10 spikes/s for weak oscillation and ±20 spikes/s for strong oscillation; mean input rate was 40 spikes/s for all static and oscillating inputs ., We measured the output spike probability to a single input spike as the response probability 13 of the cortical neurons ., When fin was close to spontaneous oscillation frequency ( fsout\u200a=\u200a38Hz ) , where the gamma oscillation resonance was strong , response probability was significantly enhanced ( Fig . 2C ) , and response delay was decreased ( Fig . 2D ) ., Response modulations were larger for the stronger oscillation ., We further investigated whether these modulations might have resulted from temporal correlation changes in the input spike pattern 18 , simply due to input frequency variation ., In order to remove any influence from input correlation , we sampled temporally “unpaired” input spikes and again measured cortical responses to them ., Input spikes were chosen only if there were no other input spikes within 20ms before and after in the same neuron ., Even in this case , the response probability was noticeably higher around the gamma resonance area ( Fig . 2E ) , confirming a significant change in cortical responsiveness ., However , response delay did not change as much as in the previous result ( Fig . 2F ) , suggesting that this value strongly depends on the temporal correlation of input spikes ., We also examined the dependence of response modulation on the oscillation phase in each cycle ., A normalized efficacy of each unpaired input spike was defined as a relative probability to generate a cortical spike , and was measured as a function of input spike phase ( Fig . 2G ) in each gamma cycle ., In Fig . 2H , the input spike efficacy plot shows a “pass” band before 0° phase with a peak value around −90° , and a “block” band after 0° phase , which is known as the mechanism of temporal regulation ( or selective gating ) of sensory signals in gamma oscillation 15 ., In other words , input spikes within the pass band phase have a much higher probability of generating cortical spikes than those within the block band ., We found that the pass band was sharpened by gamma resonance ., During resonance , the pass band amplitude grew higher and the width narrowed ( Fig . 2H , fin\u200a=\u200a35Hz and 40Hz ) ., The increase of the maximum in the normalized efficacy means that the pass band is sharpened by gamma resonance ( Fig . 2I ) ., As a result , the networks ability to synchronize cortical responses was enhanced by gamma resonance ., This result suggests that sensory responses can be manipulated by controlling gamma resonance ., It was previously reported that the frequency of gamma oscillation can be rapidly modulated by instantaneous changes in synaptic excitation-inhibition balance 13 , 19 , 20 ., Based on these findings , we hypothesized that the synaptic plasticity of thalamocortical connections can control the frequency of spontaneous cortical gamma oscillation , and therefore regulate sensory responses ., To test this idea , we first examined how the frequency of spontaneous cortical oscillation was regulated by the change of thalamocortical synaptic strength ., In our simulations , we controlled the amplitude ( gmax ) of EPSC driven by each input spike , as a simulation of the synaptic plasticity of LGN-V1 connections ( Fig . 3A ) ., We confirmed that the frequency of spontaneous cortical gamma oscillation ( fsout ) increased as gmax increased ( Fig . 3B ) , a conclusion that was qualitatively observed in our previous simulations 13 ., This frequency variation can be explained by the modulation of synaptic excitation-inhibition 19 , 20 and response delay 13 ., In our simulations , fsout varied from 37Hz to 61Hz ( Fig . 3C ) ., Based on these results , we assumed that selective response regulation ( depending on input pattern ) can be achieved by synaptic plasticity through the control of gamma oscillation resonance , because the resonance frequency of the system is shifted by the changes in gmax ., To further test this assumption , we varied gmax under different input frequencies ( fin\u200a=\u200a40 , 45 , 50Hz ) and measured the response probability of the network ( Fig . 4A ) ., As we expected , response probability increased near the gamma resonance region , but the resonance point changed noticeably depending on input frequency ., For example , when fin\u200a=\u200a40Hz , the response enhancement was largest at gmax\u200a=\u200a40µS/cm2 , where gmax corresponds to spontaneous oscillation frequency fsout∼40Hz ( Fig . 3C ) ., When fin\u200a=\u200a50Hz , the resonance point shifted to gmax\u200a=\u200a60µS/cm2 , where fsout is close to 50Hz ., The response delay of cortical neurons was similarly modulated ( Fig . 4B ) ., Gamma resonance thus occurs at different points , depending on input frequency , fin , and the amplitude of EPSC , gmax ., In other words , when gmax is changed through synaptic plasticity , gamma oscillation regulates the cortical network response selectively , depending on fin ., Synchronized oscillations of various frequencies are observed in the visual pathway 21–23 and are thought to convey information about the visual scene 5 , 24 ., Previously , Castelo-Branco and colleagues reported a strong correlation of oscillatory responses between the retina , LGN , and the visual cortex in an anesthetized cat 25 ., Their observations are in good agreement with our model ., First , cortical oscillation frequencies are clustered as two distinct bands ( low-frequency 30–60Hz , high-frequency 60–120Hz ) ., These low- and high- frequency oscillations can coexist in the cortex ., High-frequency oscillations in the cortex are shown to be the result of feedforward synchronization with the retina and LGN activity whose oscillation frequencies are in this range ., Low-frequency oscillations are shown to be spontaneous gamma oscillations in the cortical circuit ., In our model simulation , cortical spikes could be driven by feedforward oscillations of various frequencies , from above spontaneous gamma frequency to over 120Hz ( not shown here ) ., Therefore , as shown in our results , cortical responses can be synchronized by two different activities: spontaneous and driven oscillations ., Second , cortical oscillation frequency strongly depends on stimulus condition ., For stationary stimuli , cortical neurons are synchronized with high frequency ( 60–120 Hz ) feedforward oscillations ., For dynamic stimuli , slow cortical oscillations ( 30–60Hz ) dominate , and subcortical high frequency oscillations become transient ., In our model , these two cases are different “resonance” modes; the mode can switch , depending on whether the resonant point is closer to low-frequency cortical gamma oscillation or high-frequency feedforward oscillation ., Since the temporal correlation of a feedforward spike train can vary according to stimulus condition , thalmocortical synaptic strength can be modified by activity-dependent short-term plasticity 26 ., If this modification arises differently for two stimuli types , the observed variation of cortical oscillation frequency is readily explained ., The strengthened synapses increase spontaneous cortical gamma oscillation frequency , resulting in cortical synchronization with the high-frequency feedforward oscillations ., On the other hand , if thalamocortical synapses dont change , low-frequency spontaneous gamma oscillations dominate , and the feedforward oscillation becomes transient ., As a result of this stimulus-specific synchronization mechanism , cortical activity can be tuned to various oscillation frequencies ., When cortical spontaneous oscillations are strong , LGN activity can be synchronized to cortical oscillations with a retarded phase ., Because this situation requires a corticothalamic feedback loop 25 , it cannot be fully described by our current model ., Generally , this feedback is assumed as a mechanism of population activity normalization or gain control 27 ., In our model , the addition of a corticothalamic feedback loop could work as another possible controller of thalamocortical synaptic strength ., This mechanism will be further studied in future work ., Another relevant example is that information in the hippocampus is differentially routed , depending on its fast and slow gamma frequency components 28 ., This might be a slightly different version of the above mechanism , and we suggest that our gamma resonance model can be a promising candidate for this type of input-specific neuronal synchronization ., Short-term synaptic plasticity 26 , 29 has been observed at various places in the nervous system and is thought to be important to the precise tuning of sensory responses depending on stimuli conditions 30 , 31 ., At the thalamocortical synapses of the somatosensory system , plasticity usually appears as a form of short-term depression 32–34 ., Similar thalamocortical synaptic depression is also found in the visual system of the cat in vitro 35 , 36 and in vivo 37 ., However , the effect of thalamocortical synaptic plasticity on visual cortex response is still open to question , because it seems to work both ways: the cortical response can be either depressed or facilitated 18 , 38 , 39 ., From the observations above , it seems possible that stimuli-dependent short-term plasticity at the thalamocortical synapse controls the resonance between feedforward and cortical activities ., Rapid changes in synaptic excitation can modulate the frequency and amplitude of gamma oscillations of a neural network 19 ., In our simulations , spontaneous gamma frequency varied from 37Hz to 61Hz , which is comparable to measured gamma peak frequency variation in humans 40 caused by excitation-inhibition balance modulation ., Since oscillation frequency changes very rapidly in this way , cortical activity can be modulated cycle-by-cycle in gamma rhythms ., Thus it can be an effective method of regulating dynamic sensory response to rapidly varying stimuli ., In addition , the gamma modulation effect can be spatially localized fairly tightly: a small neural population can be tuned selectively by well-localized feedforward inputs 13 ., In this way , a neural network can suitably control its sensory response to complicated ( spatially and temporally ) visual stimuli patterns ., Although there is no direct experimental evidence yet , it is also possible to relate our model to developing visual systems in young animals , as a tuning mechanism of thalamocortical and corticothalamic synaptic strength ., In this case , long-term plasticity 41 becomes important ., Long-term potentiation and depression ( LTP and LTD ) are observed at thalamocortical synapses in the developing somatosensory cortex 42 and visual cortex 43 and are thought to contribute to the stimulus-dependent enhancement of sensory responses ., As with the short-term plasticity described above , activity-dependent synaptic plasticity can differentially tune the thalamocortical circuit , depending on the stimulus condition ., As a result , the resonance between feedforward and cortical oscillations is modified accordingly , which may contribute to the experience-dependent development of the sensory system ., For example , if thalamocortical EPSC varies differentially depending on the visual stimulus pattern by spike timing dependent plasticity ( STDP ) 44 , 45 , this will change spontaneous cortical oscillation frequency ., LGN-cortex resonance , accordingly , will alternate between two modes: “resonance” and “irresonance . ”, An experimental observation that cortical plasticity can be driven by different thalamic activity patterns 46 also suggests the possibility of such a resonance control mechanism ., Recently it was shown that a single neuron equipped with STDP can robustly detect input spike patterns 47 , and that this downstream learning is noticeably facilitated by oscillatory drive with “phase-of-firing coding ( PoFC ) . ”, Considering that synchronized oscillations are commonly observed in the visual pathway 21–23 , thalamocortical synapses might be properly learned by oscillations in an earlier pathway , or by activities in the corticothalamic feedback loop ., In conclusion , we demonstrate that the resonance between spontaneous and driven gamma oscillations can significantly regulate sensory responses in a neural population ., The synaptic plasticity of thalamocortical neurons can readily control gamma resonance by varying the frequency of spontaneous oscillation , and therefore can selectively enhance or degrade the networks processing of information ., Our results suggest a general model of how the nervous system can make use of its internal plasticity for the effective control of sensory responses under various conditions ., The simplicity and the wide applicability of our model make it a serious candidate for further experimental tests ., A two-dimensional layer model of the cortex neural network was used in our simulations , slightly adapted from our previous work 13 ., The network size is 1mm by 1mm , including 3341 neurons ., The network consists of simplified E ( 75% ) and I ( 25% ) model neurons with Hodgkin-Huxley type Na+ and K+ ion channels and synaptic conductance channels ., The membrane potential of an individual neuron , , is determined by , where σ is the type of neuron ( E or I ) , C is the membrane capacitance , and gL is the leakage conductance ., gσE and gσI are the synaptic conductances , providing the cortical E and I inputs ., We used the commonly accepted values for physiological parameters ( C\u200a=\u200a10−6 Fcm−2 , VL\u200a=\u200a−70mV , VNa\u200a=\u200a55mV , VK\u200a=\u200a−80mV , VE\u200a=\u200a0mV , VI\u200a=\u200a−80mV and gL\u200a=\u200a50*10−6 Scm−2 ) ., The Hodgkin-Huxley ion channel conductance GNa and GK takes the generally known form 48 , 49 as in our previous work 13 ., We have assumed spatially isotropic local cortico-cortical connections ., A neurons synaptic conductance is given by , , where s are input spike timings ., The spatial connection factor takes the form , where r is the cortical distance , and σ and σ′ are the type of connected neurons ( E or I ) ., The spatial connection decay constants were set as =\u200a200µm , =\u200a100µm ., The excitatory and inhibitory postsynaptic conductance fluctuations were set as ., The time constants in milliseconds were set as ( 3 , 1 ) for σ\u200a=\u200aE and ( 7 , 1 ) for σ\u200a=\u200aI ., The contribution of each cortical interaction was controlled by weighting factor Wσσ′ for the type of neuron pair ( σ , σ′ ) ., The EPSC driven by thalamocortical feedforward input spikes was given by ., sets the maximum fluctuation amplitude and was varied within 30∼70 µS/cm2 as a simulation of synaptic plasticity ., Our simulations were performed using the GENESIS 2 . 3 environment ( Text S1 ) 49 ., Simulation outputs were analyzed using Matlab scripts .

Introduction, Results, Discussion, Methods

Synchronized gamma frequency oscillations in neural networks are thought to be important to sensory information processing , and their effects have been intensively studied ., Here we describe a mechanism by which the nervous system can readily control gamma oscillation effects , depending selectively on visual stimuli ., Using a model neural network simulation , we found that sensory response in the primary visual cortex is significantly modulated by the resonance between “spontaneous” and “stimulus-driven” oscillations ., This gamma resonance can be precisely controlled by the synaptic plasticity of thalamocortical connections , and cortical response is regulated differentially according to the resonance condition ., The mechanism produces a selective synchronization between the afferent and downstream neural population ., Our simulation results explain experimental observations such as stimulus-dependent synchronization between the thalamus and the cortex at different oscillation frequencies ., The model generally shows how sensory information can be selectively routed depending on its frequency components .

In the nervous system , a network of neurons shows interesting population activities ., One example is a various frequency of synchronized oscillations which are thought to be important to sensory functions ., In particular , it has been reported that gamma frequency rhythms ( 30∼70Hz ) in the cortex can significantly regulate the responses to visual stimuli ., In this study , we further investigate the mechanism by which the nervous system can control the effect of gamma oscillation on the modulation of neural responses ., We found that the sensory response of the visual cortex strongly depends on the extent of synchronization between external stimulus rhythms and spontaneous gamma oscillations in the cortical network ., Furthermore , the simulation results show that the plasticity of the neural circuit can modulate the frequency of spontaneous gamma oscillations , thus readily controlling neural population responsiveness ., This finding is related to the question of how the brain efficiently interprets external input signals under various conditions , using its internal neural connectivity ., Our study provides insight into this question .

neuroscience/theoretical neuroscience, biophysics/theory and simulation, computational biology/systems biology, computational biology/computational neuroscience

journal.pcbi.1002879

An Integrative Model of Ion Regulation in Yeast

A feature of fungal physiology is the ability to adapt successfully to a variety of environmental perturbations , including ionic 1 , osmotic 2 and pH stress 3 ., All of these stresses have a substantial impact on intracellular K+/Na+ concentrations and other important physiological parameters of the cell such as cell volume , plasma membrane potential and intracellular pH 4 ., These physiological parameters are fundamental for the proper function of cellular processes , such as protein synthesis and cell cycle progression , and often , they are interlinked with each other 5 ., For example , cell volume determines the concentration of molecules , including K+ , Na+ and H+ , and the levels of intracellular and extracellular K+/Na+ have large impacts on intracellular pH , plasma membrane potential and enzyme activities 6 ., Therefore , one of the key aspects of cellular adaptation to environmental stresses is the maintenance of these parameters within a narrow range ., This is achieved through the orchestrated activity of monovalent ion transporters , regulatory enzymes , signaling pathways and cellular osmolyte metabolism ( see Ref . 5 for a comprehensive review ) ., The major monovalent ion transporters at the plasma membrane in budding yeast , Saccharomyces cerevisiae , include Pma1p , Tok1p , Nha1p , Ena1p and the Trk1 , 2p ( Trk ) transporter system 5 ( Fig . 1 ) ., Pma1p is an H+-ATPase , which extrudes intracellular H+ produced by cellular metabolism 7 ., This extrusion creates a proton gradient across the plasma membrane , which serves as an energy source for molecular transport ., K+ is essential for maintaining normal cellular functions ., It is taken up primarily through the Trk transporter system 8 ., Due to its similarity with K+ and its abundance in natural environment , Na+ enters the cell through different K+ transporters ., High intracellular Na+ is toxic to the cell ., To extrude excessive Na+ and sometimes K+ , the yeast cell uses three different transporters: Nha1p , Ena1p and Tok1p ., Nha1p is a Na+ , K+/H+ antiporter that extrudes Na+ and K+ by taking in H+ across the plasma membrane 9 ., Ena1p is a Na+/K+-ATPase that extrudes Na+ and K+ using the energy released through ATP hydrolysis 10 ., Tok1p is a voltage-gate channel that extrudes K+ exclusively 11 ., Under ionic stress conditions , cellular adaptation has been shown to involve both immediate and long-term regulation of these monovalent cation transporters 6 , 12 , 13 ., This includes post-translational regulation of Nha1p , Tok1p and the Trk system and transcriptional regulation of Ena1p through the activations of the HOG pathway and the calcineurin pathway ., Because of the importance of these transporters , and the regulatory mechanisms that control their function , in maintenance of cellular homeostasis , they have been subjected to intensive study ., Substantial progress has been made in characterizing the response of individual transporters 14 , 15 and individual regulatory signaling events 16 , 17 ., These studies revealed that each transporter has distinct response characteristics and is regulated by different pathways ., However , the exact role that each transporter plays in maintaining cellular physiological parameters , such as intracellular K+/Na+ concentration , intracellular pH and the plasma membrane potential , is still not well understood ., This is partly because a large number of transporters are involved in transporting relative few types of ions ( K+ , Na+ and H+ ) , and consequently , the impact of one transporter is dependent on the activity of other related transporters ., Therefore , investigations focusing on a single transporter or pathway may not be sufficient to reveal how transporters and regulatory mechanisms collectively contribute to the maintenance of the physiological parameters , and how they are coordinated to ensure a robust response to stress conditions ., Therefore , a systems-level approach that integrates available data on individual components to understand the system-level properties of ion regulation process is needed 5 ., To attempt to address these questions , we have constructed an integrative model of ion regulation in S . cerevisiae ., Mathematical modeling has proven to be a promising tool for the study of the complex processes of environmental stress adaptation 16 , 17 and especially , molecular transporter and ion regulation 18 , 19 ., This approach has been fruitful in revealing how the system level properties emerged from collected activities of individual components and identifying the role each component plays in the system 18 , 19 , 20 , 21 ., This approach seems particularly well suited to understanding the coordinated activities of various components , in this case ion transporters and their regulatory mechanisms , and allows the impact of other factors such as cell volume , intracellular ion concentration and membrane potential to be included in analyses ., Using the integrative model , we specifically investigate:, 1 ) the different roles of the three K+/Na+ exporters , i . e . Tok1p , Nha1p and Ena1p , in maintaining cation homeostasis;, 2 ) the role of transcriptional regulation , specifically via activation of the HOG and calcineurin pathways;, 3 ) how they are interconnected to maintain fundamental physiological parameters such as membrane potential and intracellular pH and, 4 ) the overall strategies employed by the cell to ensure a robust response to four stress conditions that are most relevant to ion homeostasis and have been under extensive study previously , i . e . NaCl , osmotic , KCl and alkaline pH stresses ., The integrative mathematical model describes S . cerevisiae cellular physiological parameters , including intracellular cation concentrations ( H+ , Na+ and K+ ) ; plasma membrane potential; cell volume and regulatory responses ( post-translational and transcriptional ) to external osmotic , ionic and alkaline pH perturbations ( Fig . 1 ) ., This integrated model can be categorized into three linked modules: the ‘transporter’ module , the ‘signaling’ module and the ‘volume’ module ., The ‘transporter’ module incorporates the dynamics of intracellular cation concentration , the characteristics of cation flux through transporters at the plasma membrane and the activities of enzymes regulating these transporters ., Six transporters were considered explicitly in this module: Pma1p , Tok1p , Nha1p , Ena1p , the Trk1 , 2p ( Trk ) transporter system , and the as yet genetically uncharacterized non-selective cation channel NSC1 ( Table 1 ) ., In addition , H+ uptake through other secondary transporters was also considered ., These secondary transporters take up external nutrient molecules into the cell using electro-chemical energy stored in the proton gradient ., The ‘signaling’ module keeps track of the activities of stress response signaling pathways ( the HOG pathway and the calcineurin pathway ) , regulatory enzymes ( Nrg1p ) , the expression levels of ENA1 and the changes in the intracellular concentration of the osmostablizer , glycerol ., The ‘volume’ module describes the volume changes of the cell depending on internal and external osmotic pressures ., An inherent challenge with large models that integrate several cellular processes is that it is usually very difficult to estimate parameter values simultaneously from a limited number of data sets in a statistically meaningful way ., However , by ensuring consistency with experimental data at each step of model construction , several integrative models constructed previously have shown notable successes in predicting biological mechanisms ( for example , see refs . 17 , 22 ) ., Here , we have adopted a similar approach ( Fig . S1 ) ., In this section we describe the model construction , integration and validation briefly ., The detailed model for each module is described in the Methods section , Table S1 , S2 , S3 , S4 , S5 and Text S1 ., The ‘transporter’ module was composed of sub-modules that describe each ion transporter involved in monovalent ion homeostasis , whereas the ‘signaling’ module was composed of sub-modules that describe the signaling pathways that regulate those transporters ., We first constructed models for each of the sub-module such that they are consistent with published experimental data or adapted from previously published models with minimal modification ., For example , the sub-modules for the transporter Tok1p , the Hog1p pathway and cell volume were adapted from works by Loukin and Saimi , Klipp et al . and Zi et al . 15 , 17 , 23 ., The models for each sub-module were determined based on data measuring the activity of individual components ., To integrate these sub-modules and ensure that they are linked coherently in the integrated model , we surveyed the literature and chose three data sets to further constrain the parameters in the integrated model ( Fig . 2 ) ., A subset of parameter values that determine the total activity of each sub-module was then adjusted such that the simulation results of the integrative model are consistent with these three data sets ., Specifically , these parameters were the rate constants determining the total flux through each transporter and those parameters that determine the extent of ENA1 expression in response to activations of different signaling pathways ( see Table S2 and S3 ) ., This subset of parameters was chosen such that the relative contribution of each sub-module to overall ion homeostasis was adjusted while the response characteristics of each sub-module were kept the same ., By adjusting the model based on the three data sets , we show that the integrated model is in a good agreement with the experimental measurements ( Fig . 2 ) ., The first two data sets compare the contribution of different signaling pathways to the regulation of ENA1 gene expression in different genetic backgrounds i . e . wild-type cells , calcineurin knock-out ( Fig . 2A ) and ppz ( ppz1 , 2 ) mutants ( Fig . 2B ) ., By comparing model simulations to these two experiments , we ensure that the relative contributions of the Ppz phosphatases , the HOG pathway and the calcineurin pathway ( under NaCl stress ) and Nrg1p ( under alkaline pH stress ) to the transcriptional up-regulation of ENA1 are correctly integrated in the model ., The third data set measures the dynamics of intracellular K+ and Na+ concentrations under NaCl stress , in the presence or absence of the calcineurin inhibitor , FK506 ( Fig . 2C ) ., By comparing the model with this data set , we ensure that the models describing the activities of those transporters involved in the K+/Na+ regulation , i . e . the Trk system , Nha1p , Ena1p , Tok1p and NSC1 , are correctly integrated in the model ., To validate and test the predictive power of the integrated model , we compared the simulation results with the other three data sets that had not been considered during the model building process ., The comparison allows us to test whether the relative contributions of those transporters are correctly integrated in the model ( Fig . 3A ) and whether the dynamics of calcineurin pathway and the transporters responsible for K+/Na+ homeostasis is well approximated under stress conditions in both wild-type and mutant cells ( Fig . 3B , C ) ., In the first experiment ( Fig . 3A ) , the membrane potentials were measured in wild-type cells and mutant cells where ENA1-4 , NHA1 , TOK1 and combinations of these genes were knocked out 24 ., Simulation of the model for the wild-type and the mutant cells agreed well with experimental measurements ( Fig . 3A ) , suggesting the relative activities of these three pumps are well approximated in the model ( note that in the model we only consider the effect of Ena1p among proteins encoded by ENA1-4 since Ena1p is the primary Na+-ATPase responsible for Na+ extrusion in budding yeast 25 ) ., The second and the third experiments measure the impact of combinations of stress conditions for both wild-type and calcineurin mutant cells 26 ( Fig . 3B ) and the intracellular K+/Na+ concentrations in both HAL3 over-expression and deletion mutants during NaCl stress 27 ( Fig . 3C ) , respectively ., The halotolerance protein Hal3p binds to Ppz phosphatases , and over-expression and deletion of HAL3 in a cell decreases and increases the activity of Ppz proteins , respectively , which in turn affects a variety of cellular components including the Trk K+ uptake system and calcineurin activity 6 ., The model simulation in general agreed with experimental data , confirming the accuracy of the model in predicting the regulatory activity of calcineurin and the K+/Na+ homeostasis ., To gain further insight into how well the parameters in our model are constrained , we evaluated the sensitivity of each sub-module model to the data shown in Fig . 2C , 3B and 3C where quantitative measurements are available ( see Methods for detail ) ., In general , the activities of three transporters , i . e . Ena1p , Tok1p and the Trk system , and the calcineurin pathway are tightly constrained by the experimental data shown in Fig . 2C as well as the data shown in Fig . 3B and 3C ( Table S6 ) ., This suggests the integrative model serves as a good approximation to the dynamics of each cellular component ., Note that although the activity of Pma1p , the dynamics of the HOG pathway and cell volume change are not constrained by these three data sets , we think our model describes those processes well ., This is because the model for the Pma1p activity is calibrated with previous estimates and the model for the HOG pathway and the cell volume change was adopted from a previously established model which has been shown to be consistent with a wide range of experimental measurements 23 ( see Text S1 for details ) ., Yeast cells are able to adapt to a remarkably wide range of stress conditions by employing both short-term and long-term regulatory responses ., Among those stress conditions that yeast cells frequently encounter , the ones that are most relevant to ion regulation include NaCl , osmotic , KCl and alkaline pH stresses 5 ., Therefore , in the following sections we use this model to investigate how different transporters , such as Ena1p , Tok1p and Nha1p , the HOG pathway and the calcineurin pathway are coordinated to achieve both immediate and longer-term adaptation to these stress conditions ., We first used the model to determine the effect of Hog1p phosphorylation on Tok1p at the plasma membrane upon NaCl stress ., Higher external Na+ imposes strong stresses on normal cellular processes ., In particular , high intracellular Na+ concentration at the beginning of NaCl stress can cause transcription factors to dissociate from DNA ., Proft and Struhl have suggested that the interaction of phosphorylated Hog1p ( P-Hog1p ) with the membrane localized Na+/H+ antiporter Nha1p and the potassium channel Tok1p decreases intracellular Na+ , and thereby facilitates transcription factor rebinding to DNA in the first 10 to 30 minutes after initiation of stress 13 ., It has been shown that phosphorylation of Nha1p by Hog1p increases the rate of Na+ efflux 13 , 28 ., However , it is unclear how the activity of Tok1p changes upon phosphorylation by P-Hog1p and how this change affects intracellular Na+ ., To focus on the impact of the interaction of P-Hog1p with Tok1p on the initial adaptation process , we simulated the model with three scenarios for 50 minutes under 0 . 4M NaCl stress: ( 1 ) cytosolic P-Hog1p activates Tok1p; ( 2 ) no interaction occurs between P-Hog1p and Tok1p and ( 3 ) that cytosolic P-Hog1p inhibits Tok1p ., Simulations for the scenario that Tok1p is inhibited by cytosolic P-Hog1p resulted in the lowest level of intracellular Na+ during the first 50 minutes of adaptation ( Fig . 4A ) ., The reason is that the membrane potential is highly dependent on the activity of Tok1p; inhibition of Tok1p activity resulted in a depolarized membrane , which in turn , reduces Na+ influx into the cell , whereas increasing Tok1p activity polarized the plasma membrane , thereby , resulting in around 10 mM higher intracellular Na+ concentration during the first 50 minutes ( Fig . 4C ) ., Therefore , by considering the collective activities of the monovalent transporters and the HOG pathway , our model predicts the inhibitory action of Hog1p on Tok1p ., In the simulations below , we set this interaction as inhibition ., In the study of Proft and Struhl 13 , the nha1 , tok1 and hog1 deletion mutants showed different extents of delayed transcription factor rebinding to DNA under 0 . 4M NaCl stress ., To further examine the impact of this interaction of P-Hog1p with Nha1p and Tok1p in the immediate adaptation to NaCl stress in a quantitative way and give insights into the experimental observation as to why transcription factor rebinding to DNA is delayed in these mutants , we simulated the model for the wild-type strain , the nha1 , tok1 and hog1deletion mutant strains plus a hypothetical strain ( mut1 ) lacking interactions between P-Hog1p and Nha1p or Tok1p ., Higher intracellular Na+ concentrations were observed in simulations for the nha1 , hog1 and mut1 mutants under 0 . 4MNaCl stress ( Fig . 4B ) ., More than 20 mM Na+ accumulated in nha1 mutant cells even before NaCl stress onset , probably due to reduced Na+ efflux capacity ., This difference resulted in a much higher intracellular Na+ concentration in nha1 mutant cells ( 140 mM ) than in wild-type cells ( 100 mM ) at the beginning of the stress ., The hog1 mutant also showed a notably higher intracellular Na+ level throughout the simulation , due to the inability to restore cell volume ., These results offered explanations as to why it takes longer for hog1 and nha1 strains to restore transcriptional activity at the beginning of NaCl stress 13 ., There was also an approximately 10 mM higher Na+ concentration seen in the hypothetical strain where the interaction of Hog1p with Nha1p and Tok1p was abolished than in the wild-type strain during the first 120 minutes of stress , demonstrating the importance of this interaction ., The tok1 mutant had a lower intracellular Na+ level in the simulation due to the inability of the cell to establish the membrane potential under both unstressed growth conditions and under NaCl stress ( Fig . 4B ) ., This is not consistent with the observation of the delayed transcription factor rebinding 13 ., We reason that disruption of Tok1p may induce defects that are associated with membrane depolarization or other aspects of cellular adaptation that are not investigated in this study ., The simulation predicts that intracellular Na+ concentration in wild type cells reached a higher level after 120 minutes , than seen at the onset of the stress ., It has been shown that the vacuole plays an important role in NaCl stress adaptation and there are active Na+ transports from the cytosol to the vacuole under NaCl stress 29 ., Therefore , it is likely that the Na+ concentration is kept at a low level in the cytosol and the nueclues under NaCl stress through sequestration of Na+ into the vacuole 30 ., Both NaCl and sorbitol are frequently used to investigate the osmotic stress response in experimental studies ., However , NaCl imposes saline stress as well as osmotic stress ., Here , we investigate the impact of NaCl stress on a cell in this section and the impact of osmotic stress in the next section ., We focus on the roles of the Na+ exporters , i . e . Nha1p , Ena1p and the calcineurin pathway and how they are coordinated during the adaptation processes ., In the simulations below , the external Na+ and K+ concentrations and the external pH are assumed to be 5 mM , 1 mM and pH 6 . 5 , respectively , under unstressed condition ., Changes in these external conditions do not alter the results qualitatively ., High osmolarity and high external Na+ concentration imposes substantial changes of several cellular physiological conditions ., Simulation of the model shows that , upon NaCl stress , intracellular Na+ and K+ concentrations increased suddenly ( Fig . 5A ) , concomitant with a decrease of cell volume ( Fig . 5D ) ., At the same time , due to high external Na+ concentration , Na+ influxes through Trk1p and NSC1 increased to a high level ( Fig . S2 ) ., As examined in the section above , the immediate responses to these changes are mediated , at least in part , by activation of Hog1p ., Phosphorylation of Hog1p led to increased glycerol biosynthesis , which acts as an osmostabiliser and restores cell volume 16 ., At the same time , the interaction between cytosolic P-Hog1p with Nha1p and Tok1p decreased intracellular Na+ concentration ., Another consequence of inhibition of Tok1p was a depolarized membrane ( Fig . 5E , F ) ., This lowered membrane potential led to a reduced secondary transporter activity ( as represented by H+ uptake shown in Fig . S2 ) ., During the long-term cellular adaption to NaCl stress , the simulation showed a switch in transporter use for Na+ export at around 50 minutes after stress onset ( Fig . S2 ) ., Upon initial imposition of NaCl stress , intracellular Na+ was extruded primarily by Nha1p , this rate decreased over time as a result of dephosphorylation of P-Hog1p , once cell volume was restored ., Concurrently , the activity of Ena1p increased due to transcriptional up-regulation of ENA1 expression ( Fig . S2 ) , maintaining Na+ extrusion ., This is consistent with the major role of Ena1p during NaCl stress adaptation 12 ., To examine the role of calcineurin activation , we simulated the model for knock-out mutant strains where calcineurin activity is abolished ., This setting is also equivalent to cells treated with the immunosuppressant FK506 , which inhibits calcineurin activity ., We , therefore , refer to this set of simulations as simulations for cells treated with FK506 below ., In the simulation , the expression of ENA1 returned to the unstressed level after 100 minutes after stress initiation in cells treated with FK506 ., Consequently , Na+ concentrations approached 220 mM , in stark contrast to ∼150 mM Na+ in untreated cells ( Fig . 5A ) ., Notably , other consequences of the low ENA1 expression in FK506 treated cells are a lower membrane potential , and consequently , a lower H+ uptake rate and a higher intracellular pH compared to wild-type cells ( Fig . 5E , F and S2 ) ., Therefore , our simulation results give insight into the experimental observation that activation of calcineurin and consequently a high expression of ENA1 is critical for the cell to maintain a relatively low intracellular Na+ concentration 26 ., To consider osmotic stress alone , we interrogated the model with an equivalent osmolarity environment lacking an ionic component , i . e . 1 . 6M sorbitol ., This resulted in the same decrease in cell volume in the simulation , leading to a higher intracellular Na+ concentration ( Fig . S3 and S4 ) ., The intermediate cellular response ( in the first 0–30 minutes ) to high osmolarity was very similar to the response to NaCl stress ., However , Na+ influxes were at a low level in the long term in contrast to the high Na+ influx under NaCl stress ( Fig . S3B ) ., Consequently , Na+ concentration returned to unstressed levels once cellular volume was restored ( Fig . S3A , D ) ., High ENA1 expression is not required for long term adaptation to osmotic stress , and FK506 had little effect on the adaptation response to osmotic stress , and in stark contrast to what is seen in cells treated with osmoequivalent levels of NaCl ., Therefore , our model suggests that the role of calcineurin activation is to respond to the ionic stress induced by high external Na+ but not pure osmotic stress , consistent with previous studies 26 , 31 , Yeast cells grow well under KCl stress 9 ., In our simulation , however , we found that 0 . 8M KCl stress led to a highly depolarized plasma membrane ( the membrane potential was up to +20 mV ) ( Fig . S5 ) , as a result of high K+ flux into the cell ( Fig . S6 ) ., It is known that positive membrane potential is detrimental to the cell and induces severe nutrient limitation 4 , 32 ., We therefore reasoned that it is likely that unidentified regulatory mechanisms , not yet included in the model , are essential for restoring the membrane potential and thus adaptation to KCl ., To identify candidate regulatory targets , we systematically searched for transporters of which varying the activity resulted in increased protection against KCl stress ., We found that , in slightly acidic environment ( pH 6 . 5 ) , increases in the activities of Nha1p and Ena1p had high impacts on the plasma membrane potential ( Fig . 6 ) ., Nine-fold increase in Ena1p activity or four-fold increase in Nha1p activity restored the membrane potential to the unstressed level ( around −94 mV ) ., In contrast , in neutral environment ( pH 7 . 0 ) , only increases in the activity of Ena1p restored the plasma membrane potential , because the activity of Nha1p was much lower at pH 7 . 0 where the proton gradient is disrupted ( Fig . 6 ) ., Interestingly , Banuelos et al . showed that , under KCl stress , Nha1p is required for growth at pH 6 . 5 and Ena1p is required for growth at pH 7 . 0 , but currently the mechanism is unknown ., 9 ., Taken together , our results suggest that Nha1p and/or Ena1p are activated ( through either post-translational or transcriptional regulation ) under KCl stress conditions ( although differently depending on external pH ) by unidentified mechanisms , and we predict that this activation or upregulation is essential to restore the plasma membrane potential in the adaptation to KCl stress ., Further experiments are needed to verify the predicted increases in Nha1p and Ena1p activities under KCl stress at different external pH and test the molecular mechanism that regulates these increases ., We further used this model to investigate the impact of external alkaline pH on intracellular physiological parameters and how cells respond to such changes ., Increase in external pH disrupts the H+ gradient across the plasma membrane , and therefore , the chemical potential across the plasma membrane is drastically reduced ., Our simulation predicts that intracellular pH increased from 7 . 14 to 7 . 25 upon pH 8 . 0 stress ( Fig . 7E ) , due to reductions in the activities of those secondary transporters that utilize the H+ gradient , including Nha1p and other H+ uptake transporters ( Fig . S7 ) ., It has been shown that ENA1 gene expression is highly up-regulated ( up to 20 fold ) , as a result of the activation of several signaling pathways including the calcineurin pathway and the Rim pathway , which negatively act upon Nrg1p 14 , 33 ., The simulation result agrees with these previous studies and shows that increase in Ena1p activity resulted in an elevated plasma membrane potential ( Fig . 7F ) ., The intracellular Na+ did not show notable difference in cells treated with and without FK506 ( Fig . 7A ) , suggesting that calcineurin activation is not required for K+/Na+ homeostasis for alkaline pH adaptation ., Since previous work has shown that calcineurin activation is essential for the overall adaptation to alkaline pH stress 34 , 35 , it is possible that the main function of calcineurin activation in alkaline adaptation is to activate other cellular programs , such as nutrient and cell wall stress responses 34 , 35 , rather than maintaining K+/Na+ homeostasis ., To understand the role that each transporter plays in response to different stress conditions , we varied the activity of each transporter in the presence of no stress or four different stresses ( NaCl , sorbitol , KCl and alkaline pH ) ., Varying Pma1p activity had a substantial impact on intracellular pH and membrane potential under all five conditions investigated ( Fig . S8 ) , which highlights the importance of this pump in ion homeostasis 36 , 37 ., In addition to Pma1p , intracellular pH was also sensitive to Nha1p activity except under alkaline pH stress , which suggests a major role for Nha1p in maintaining intracellular pH . This is consistent with previous experimental work 9 ., The Trk system was identified as the other transporter with a significant impact on membrane potential , intracellular pH and Na+/K+ ratio under all five conditions ( Fig . S8 ) ., Surprisingly , increases in the activity of the Trk system resulted in an increase in intracellular Na+/K+ ratio in the model under Na+ abundant conditions , which contradicts its role of discriminating against Na+ uptake ., The reason is that in the model , the increase in the activity of Trk system depolarizes the plasma membrane , and thus , triggers K+ efflux through Tok1p ., Since both K+ and Na+ enter the cell via the Trk system and only K+ is pumped out through Tok1p , higher activity of Trk system causes Na+ to accumulate ., Hence , this result is likely due to the over-simplified representation of the Trk system in describing the ratio of K+/Na+ influxes in our model ., Further experiments examining the response characteristics of the Trk transporter system are needed to better describe this transporter ., Despite this shortcoming , our conclusions of the role of Nha1p , Tok1p , Ena1p , the HOG and the calcineurin pathways are robust to changes in the sub-module describing the Trk system ., Because these conclusions depend on the overall dynamics of the intracellular Na+/K+ , which are well described in our integrative model , rather than the ratio of K+ and Na+ fluxes through the Trk system ., Ion regulation is fundamental to physiology and function across eukaryotic cells ., Diverse ion transporters are tightly regulated in a cell to accomplish cellular function 5 , 20 , 38 ., In this study , we have developed an integrative model for ion regulation in budding yeast ., Using this model , we investigated the system level properties and predict the role of individual components in the highly coordinated cellular adaptation responses to NaCl , sorbitol , KCl and alkaline pH stress conditions ., In particular , we have shown how the Na+/K+ exporters ( Nha1p , Ena1p and Tok1p ) , the HOG pathway and the calcineurin pathway are coordinated to maintain fundamental physiological parameters , such as K+/Na+ concentration , the plasma membrane potential and intracellular pH under diverse stress conditions ., High ( >0 . 2 M ) external NaCl imposes hyper-osmotic stress as well as Na+ ionic stress , while high external sorbitol only imposes hyper-osmotic stress ., Our results suggest that the immediate cellular response to NaCl stress and sorbitol stress are similar ., High external osmolarity leads to drastic decreases in cell volume and consequently an increase in Na+ concentration ., In addition to restoring cell volume , an important aspect of immediate adaptation is to decrease intracellular Na+ ., This is mediated by the phosphorylation of Hog1p ( P-Hog1p ) , and its interaction with Nha1p and Top1p at the plasma membrane 13 ., Our model analysis predicts that , P-Hog1p inhibits Tok1p activity and this inhibition leads to a depolarized plasma membrane and thus a lower rate of Na+ influx ., We speculate that the membrane depolarization mediated by the HOG pathway has important implications at the onset of stress when the nature of the external challenge is unknown to the cell: a depolarized membrane leads to reduced activities of secondary transporters and consequently the molecular import of the cell ., In cases where the cell is challenged by osmotic stress induced by toxic compounds , membrane depolarization serves as a protective response to prevent uptake of these toxic compounds ., This would allow the cell to survive the challenge and respond to the particular stress condition properly through transcriptional regulatory programs ., The model simulations showed marked differences in the intracellular Na+ concentration during the long-term adaptations to NaCl stress and sorbitol stress ., Under sorbitol stress , intracellular Na+ concentration returned to unstressed lev

Introduction, Results, Discussion, Methods

Yeast cells are able to tolerate and adapt to a variety of environmental stresses ., An essential aspect of stress adaptation is the regulation of monovalent ion concentrations ., Ion regulation determines many fundamental physiological parameters , such as cell volume , membrane potential , and intracellular pH . It is achieved through the concerted activities of multiple cellular components , including ion transporters and signaling molecules , on both short and long time scales ., Although each component has been studied in detail previously , it remains unclear how the physiological parameters are maintained and regulated by the concerted action of all components under a diverse range of stress conditions ., In this study , we have constructed an integrated mathematical model of ion regulation in Saccharomyces cerevisiae to understand this coordinated adaptation process ., Using this model , we first predict that the interaction between phosphorylated Hog1p and Tok1p at the plasma membrane inhibits Tok1p activity and consequently reduces Na+ influx under NaCl stress ., We further characterize the impacts of NaCl , sorbitol , KCl and alkaline pH stresses on the cellular physiology and the differences between the cellular responses to these stresses ., We predict that the calcineurin pathway is essential for maintaining a non-toxic level of intracellular Na+ in the long-term adaptation to NaCl stress , but that its activation is not required for maintaining a low level of Na+ under other stresses investigated ., We provide evidence that , in addition to extrusion of toxic ions , Ena1p plays an important role , in some cases alongside Nha1p , in re-establishing membrane potential after stress perturbation ., To conclude , this model serves as a powerful tool for both understanding the complex system-level properties of the highly coordinated adaptation process and generating further hypotheses for experimental investigation .

Ion regulation is fundamental to cell physiology ., The concentrations of monovalent ions , such as H+ , K+ and Na+ , determine many physiological parameters such as cell volume , plasma membrane potential and intracellular pH . In yeast cells , these parameters are maintained within a narrow range during the adaptation to external perturbations , including ionic , osmotic and alkaline pH stress ., This is achieved by the remarkably coordinated activities of ion transporters , regulatory molecules and signaling pathways ., The response characteristics of individual components in adaptation have been studied extensively ., However , a coherent understanding of the coordinated adaptation process is lacking ., In this study , we address this gap by constructing a mathematical model that integrates the characteristics of the ion transporters , regulatory molecules , signaling pathways and changes in cell volume ., Using this model , we characterize the impact of ionic , osmotic and alkaline pH stresses on cellular physiology and analyze the role that individual components play in the cellular adaptation processes ., Our results also reveal system level properties achieved by the concerted regulatory responses ., Therefore , this integrated model serves as a suitable tool to understand the coordinated processes of ion regulation in response to environmental stresses , and to make predictions that are experimentally testable .

systems biology, biochemical simulations, regulatory networks, biology, computational biology

journal.pbio.0060146

Dynamics and Design Principles of a Basic Regulatory Architecture Controlling Metabolic Pathways

In the past several years , genomic approaches have dramatically accelerated the discovery of biological regulatory networks ., Combined with detailed biochemical and genetic studies , these approaches have yielded the intricate wiring diagrams for many biological systems ., Although revealing , such wiring diagrams are usually drawn as arrows representing activation or repression that link regulators with the genes they regulate , and typically , one can only make qualitative statements ( such as whether a gene is activated or repressed ) based on network architecture ., Genetic and biochemical studies traditionally focus on gene regulation under steady growth conditions , and it is often difficult to rationalize the complex design of a regulatory system based only on these tasks ., To understand the functional significance and design principles of complex regulatory networks , it is essential to analyze the dynamical output quantitatively ., In engineering systems such as feedback control , specifications for dynamics—such as speed of the response and settling time—strongly constrain the possible choices of network architecture and control parameters 1 ., It is expected that the dynamic properties of the cellular response are important determinants for its fitness in an ever-changing environment ., Consequently , certain features of the network architecture and parameters may be selected for their relevance to dynamics , as opposed to the steady-state behavior 2 ., A number of recent studies have explored the connection between architecture , dynamics , and fitness; examples include rationalizing simple network motifs by their dynamical response properties 3 , 4 , connecting just-in-time expression with fitness advantage 5 , and justifying seemingly redundant regulatory mechanisms by their contributions to the different aspects of the dynamical response 6 ., We explored the significance of dynamics in the regulation of metabolic pathways ., Regulation of metabolic activity is of central importance for single cell organisms such as Escherichia coli and yeast , since they must respond to a constantly changing nutritional environment ., Previous genetic and biochemical studies have elucidated the structure of various metabolic pathways and the associated regulatory circuits ., Emerging from these studies is a basic architecture that regulates a linear branch of a biosynthetic pathway 7 , 8 ., This architecture is characterized by a dual-feedback mechanism to control the metabolic flux and the synthesis of the enzymes in the pathway ., The metabolic flux is generally controlled by end product feedback inhibition of the first enzyme unique to the pathway , and the expression of enzymes is regulated by transcription factors that can sense either the end product ( e . g . , tryptophan biosynthesis in E . coli 9 ) , or an intermediate ( e . g . , leucine biosynthesis in yeast ) ., Transcriptional activation of a pathway by an intermediate is particularly widespread ( e . g . , lysine and adenine biosynthesis in yeast 10 , 11 , lysine and methionine biosynthesis in E . coli 12 , 13 ) , which influenced our choice of the leucine pathway as a case study ., Although all of these pathways have been studied extensively , so far data for the quantitative dynamical response have been scarce ., The leucine biosynthetic pathway in the yeast Saccharomyces cerevisiae is summarized in Figure 1 ., This pathway converts pyruvate to leucine by the sequential reactions catalyzed by nine different enzymes ., Part of the pathway is shared by valine biosynthesis , and several enzymes are also shared by the isoleucine biosynthetic pathway ., There are three major regulatory features ., First , leucine can bind to Leu4 , inhibiting its catalytic activity 14 ., Second , the branch-specific transcription factor Leu3 is known to be able to regulate the expression of all the genes in the pathway 15 , 16 ., The activation domain of Leu3 is shielded when the pathway is inactive , and it is the binding of the metabolic intermediate α-isopropylmalate ( αIPM ) to Leu3 that unmasks its activation domain and allows it to activate the transcription of its targets ., Finally , the pathway is also regulated by the transcription factor Gcn4 , which is responsible for the general amino acid starvation response ., Gcn4 controls a few hundred targets , including most of the genes in the leucine biosynthesis pathway , under amino acid starvation conditions 8 , 17 ., It is known that combinatorial regulation by Gcn4 and branch-specific regulators such as Leu3 is a general scheme for controlling the synthesis of different amino acids ., However , the effect of multiple regulators on the dynamics of gene expression remains uncharacterized ., To study gene induction with high accuracy and high temporal resolution , we built an automated system to monitor protein abundance in single cells ., Although similar systems have been built before to acquire time-dependent population data 18–20 , they have not been applied to systematic analysis of the dynamics of a genetic network ., Our system consists of parallel batch cultures , in which yeast strains with different genes tagged by green fluorescent protein ( GFP ) are grown , connected to a flow cytometer by a syringe pump ., Both sample delivery and data collection are controlled automatically by a computer with software written by the authors ( see Figure S3 for a schematic of the design ) ., The automated system we have built allows us to monitor up to six different strains simultaneously for several hours ., Since a large number of cells ( ∼105 to 106 ) are sampled in short time intervals ( 1 to 7 min per sample ) , we obtain accurate and highly reproducible induction profiles ., Previously , it had been shown in a genome-wide study that measuring protein abundance by GFP tagging and flow cytometry yields highly reproducible data and is less susceptible to experimental variation compared with other techniques such as Western blotting or mRNA microarray measurements 21 ., A major advantage of single cell measurement by flow cytometry is that it gives complete information on the distribution of protein abundance in a large population , instead of an average number obtained from a bulk measurement ., This is particularly important when the cell population is inhomogeneous , where different cohorts might behave differently ., Shown in Figure 2A is the time course of LEU2-GFP induction after switching from synthetic complete media ( SCD ) to synthetic media without leucine ( SCD-Leu ) ., Following media transfer , LEU2 is induced and the distribution of LEU2-GFP levels in the population moves upward smoothly and gradually evolves into a bimodal shape ., We find that such a bimodal distribution is due to continuous cell division ., The population with the lower GFP level consists of the newly formed daughter cells , which receive less protein than the mother cells due to asymmetric cell division 22 ., Despite the fact that many previous studies have used the population distribution of GFP level as a measure of gene expression , no satisfactory solution has been found for separating the effects due to the inhomogeneous population from those due to gene regulation at the single-cell level ., The conventional approach is to sample cells with more uniform size , selected from within a narrow range close to the median of the forward-scattering channel ( FSC ) and the side-scattering channel ( SSC ) ., Nevertheless , this method would still show an artificial decrease in gene expression due to cell division ( Figure 2D ) ., To help resolve this problem , we use a dye to distinguish the newly formed daughter cells from the mother cells , in a procedure similar to that used by Porro and coworkers 23–26 ., The cells are stained with Cy5 before inoculation ., Since the cell wall of the newly formed cells is synthesized after inoculation , it carries few Cy5 dye molecules ., By monitoring the GFP channel and the Cy5 channel simultaneously , we were able to decompose the overall distribution into the distributions of the mother and daughter subpopulations ., Such decomposition allows us to accurately reconstruct the gene induction profile for the original mother subpopulation , which is more homogeneous and less sensitive to the effects of cell division ( Figure 2 ) ( see Materials and Methods for more details ) ., As seen in Figure 2D , this approach eliminates the artifact of falling GFP levels , found either by looking at the whole population or by using conventional gating methods ., Using the system described above , we first measured the induction profiles of all the genes in the leucine biosynthesis pathway after transfer from SCD to SCD-Leu media ., The high accuracy and high temporal resolution allow us to follow the change of the distribution and to calculate the rate of change of the GFP levels in the population ( see Materials and Methods ) , which closely reflects the rate of protein production because the lifetimes of the GFP tagged proteins are much longer than the typical response time 27 , whereas the maturation time is shorter 28 ., A striking feature we observed from these induction profiles is that the genes upstream of the intermediate αIPM ( which serves as a key control point ) and those that are immediately downstream display drastically different responses ., The upstream genes ( ILV2 , ILV6 , ILV3 , ILV5 , LEU4 , and LEU9 ) were quickly induced but displayed a small-fold change ranging from 2- to 4-fold ., The specific rates of change ( defined as, ) for these genes was at the maximum ( about 1% per min ) immediately following the transfer of media and then dropped to nearly zero in less than 50 min ( Figure 3A and 3B , i–vi ) ., In contrast , the two downstream genes ( LEU1 and LEU2 ) showed a relatively slower induction profile but much higher-fold changes ., The rate of change for LEU1 and LEU2 both started low and reached their maximum ( 2% per min for LEU1 and 5% per min for LEU2 ) in approximately 50 min ., The overall fold changes for both genes after 400 min exceed 20-fold ( Figure 3A and 3B , vii–viii ) ., Interestingly , the third downstream gene , BAT2 , whose product catalyzes the last step of leucine biosynthesis , displayed an induction profile similar to upstream enzymes , with a quick induction and an overall 2-fold change ( Figure 3A and 3B , ix ) ., Bat2 is a multi-functional enzyme shared by the valine and isoleucine biosynthetic pathways ( see Figure 1 ) ., To investigate the mechanism underlying the different responses for genes in the pathway , we measured their expression time courses under different genetic perturbations ., Previous studies demonstrated that several genes in the leucine pathway are coregulated by the general regulator Gcn4 and the branch-specific transcription factor Leu3 7 ., Gcn4 is activated by general amino acid starvation and is controlled by translational regulation 29 ., Under general amino acid starvation , uncharged tRNA activates the kinase Gcn2 , which phosphorylates the translation initiation factor eIF2-α , leading to the translation of Gcn4 ., To analyze the relative role of the general signal ( uncharged tRNA ) through Gcn2/Gcn4 and the pathway specific signal ( αIPM ) through Leu3 in determining the dynamical response , we measured gene induction profiles in LEU3 and GCN2 knockout strains ., Figure 4 compares the detailed induction profiles of all the genes in the leucine pathway in wild-type background to the profiles of the same genes in leu3Δ and gcn2Δ background ., We again observed qualitatively different behaviors for Leu1 and Leu2 in comparison with the rest of the pathway ., The deletion of LEU3 almost completely abolished the induction of these two downstream enzymes , making it clear that Leu3 is the major inducer of Leu1 and Leu2 ., The effect of LEU3 deletion on the upstream enzymes and Bat2 is less pronounced ., Leu4 and Ilv3 show a slight increase in gene expression level in the leu3Δ background ., The other upstream enzymes , as well as Bat2 , have dynamic profiles almost indistinguishable from wild type , except that the basal level of Ilv5 in leu3Δ background is much higher than that in the wild-type background ., There are several possible explanations for the slight increase in expression of some of the upstream genes ., As reported previously 30 , Leu3 without αIPM can act as a repressor ., It is therefore possible that in the wild type , Leu3 acts to repress upstream genes and these genes are derepressed in the leu3Δ strains ., Another possibility is that in the leu3 deletion strains , Leu1 and Leu2 are not expressed , which leads to a severe leucine deficit in SCD-Leu media , activating a general starvation response ., In contrast to the LEU3 knockout , the GCN2 deletion has a much less pronounced effect on either the upstream or downstream genes ., The basic dynamic profiles of all genes are comparable to the wild-type strains ., Since many studies have suggested that the upstream genes are inducible by Gcn4 under general amino acid starvation 8 , 29 , this result suggests that Gcn2-dependent activation is weak under our experimental conditions , where only leucine is scarce in the medium ., Most likely , the leucine-specific induction is efficient enough that the intracellular leucine concentration never drops to a sufficiently low level to activate the general amino acid starvation responses ., To further analyze the initial fast induction dynamics of the upstream genes , we also used a different approach to quickly induce the pathway genes under a strictly controlled environment ., Instead of subjecting the cells to spinning , washing , and staining , we simply diluted the SCD medium with SCD-Leu , lowering the concentration of leucine by about 10-fold in a few seconds ., We continuously monitored the protein abundance before and after the dilution and observed that genes in the pathway are visibly induced within minutes ., This approach eliminates the dead time ( ∼15 min ) due to media transfer/staining ., It also eliminates potential artifacts in gene induction due to possible stress caused by the media transfer/staining protocol ., The dilution method allowed us to consistently detect small inductions ( less than 20% change ) within minutes after the change of environment ., While it is no longer possible to separate mother and daughter populations with this method , that separation is most useful for the analysis of the later part of the time course when cell division effect becomes significant ., Using the dilution method , we have analyzed the fast induction profiles of one of the upstream genes under different genetic and media perturbations ., The resulting induction profiles for LEU4 are shown in Figure 5 ., It is clear that the fast induction of LEU4 is due to the depletion of leucine , since there are no observable changes in expression under the controls in which we add the same media or a media lacking lysine and methionine ., Deletion of the branch-specific factor LEU3 does not seem to affect the initial fast induction , since the induction curves for the wild-type and mutant strain are nearly identical for the first 70 min or so ., The mutant strain eventually has higher induction , possibly due to a severe leucine deficit that triggers a stronger general starvation response ., Deletion of GCN2 leads to a minor but observable effect , resulting in a slightly slower induction profile ., In contrast , deletion of GCN4 has a much stronger effect , with the initial fast induction almost completely abolished ., Taken together , these data suggests that the fast response of the upstream genes is specifically induced by leucine depletion , is independent of the branch-specific regulator Leu3 , and that the general regulator Gcn4 is involved , possibly via a Gcn2-independent pathway ., Our experimental observations lead to the following model for the dynamics of the pathway when the leucine level in the growth media is reduced ., The response of the upstream genes consists of transient and minor inductions ., This induction , combined with the release of Leu4 from leucine inhibition leads to a quick increase in αIPM synthesis ., The accumulation of αIPM and its binding to the transcription factor Leu3 activates the sustainable and large amplitude expression of Leu1 and Leu2 to reinforce the branch specific response ., The induction of the downstream genes leads to more effective conversion of αIPM to the end product and the rebalance of the intracellular leucine concentration ., To test whether the above intuitive picture captures the essential features of the response , we built a simplified mathematical model to quantitatively describe the observed dynamics and make new predictions ., To minimize the number of parameters and simplify the model , we make the assumption that the enzymes not specific to the leucine pathway ( Ilv2 , −3 , −5 , −6 , and Bat2 ) are not rate limiting , i . e . , they are operating far below Vmax ., There are several justifications for this—first , none of these genes are induced in media lacking isoleucine and valine even though they comprise part of the corresponding biosynthetic pathway ( Figure 6 ) ., Furthermore , their induction is transient and an order of magnitude lower than that of Leu1 and Leu2 in media lacking leucine ., These observations suggest that the change of the flux through these enzymes is mainly due to the change of the metabolite concentration ., A simple calculation indicates that the flux through each of these enzymes can quickly balance the flux upstream of the enzyme in a time much shorter than the typical pathway response time ( see Text S2 ) ., As such , we will ignore these enzymes in our kinetic model ., In our model , the pathway response is dominated by the release of inhibition on the upstream enzyme Leu4 and the transcriptional up-regulation of the downstream enzymes Leu1 and Leu2 ., Assuming that the metabolite upstream of Leu4 is always abundant , the equations describing the essential dynamics of the pathway can be written as:, A schematic of the model is shown in Figure 7 ., The model consists of a set of differential equations describing the dynamics of the two intermediates I1 ( αIPM ) and I2 ( product of Leu1 , β-isopropylmalate , abbreviated as βIPM ) ; the downstream enzymes E1 and E2 , representing Leu1 and Leu2 respectively; the upstream enzyme Eu , representing Leu4 ( treated as constant ) ; and the end product P ( leucine ) ., The rates of protein production ( the first two terms in Equations 1 and 2 ) are assumed to be proportional to the mRNA level ., Since the half-lives of the mRNAs of the downstream genes are short ( ∼10 to 20 min 31 ) , we simply assume that the mRNA level is proportional to the rate of transcription , which has a low basal level ( b1 and b2 terms ) in the absence of αIPM and a much higher level when αIPM is present ( c1 and c2 terms ) ., It is known that Leu3 is constitutively bound to its DNA binding sites 32 ., We thus assume that the transcriptional induction by αIPM is proportional to the probability that αIPM is bound to the preformed Leu3–DNA complex , modeled by a sigmoid function of the αIPM concentration ., The dynamics for the intermediates ( Equations 3 and 4 ) are governed by the standard Michaelis-Menten kinetics ., The activity of the upstream gene is controlled by the feedback inhibition by the end product ( first term in Equation 3 ) ., The dynamics of the end product are determined by the rate of synthesis and the rate of usage ( d5P ) , which is assumed to be proportional to the leucine concentration ., The Fext term reflects the external leucine flux ., It is presumed to be positive in the rich media condition , and set to zero when leucine is missing in the environment ., The remaining terms ( d1–4 ) are used to model dilution effects due to cell growth ( see Materials and Methods for details ) ., We first tested whether the model can reproduce the quantitative dynamics we observed for the downstream enzymes ., By adjusting the free parameters in Equations 1–5 within specified bounds ( based on previous knowledge and physical estimates , see Materials and Methods for details ) , the model is capable of producing downstream enzyme induction profiles ( E1 and E2 in our model ) that fit the observed Leu1 and Leu2 induction profiles ( Figure 8A and 8B ) ., The Leu1 profile is produced by taking into account the slower growth of the Leu1-GFP strain , which is done by scaling the dilution and usage terms by the relative growth rate ., Given the fitting of the downstream enzyme levels , the model also predicts the dynamics of the intermediate αIPM and the intracellular leucine level ( Figure 8C and 8D ) , which are difficult to measure with high temporal resolution ., In particular , the model predicts that the αIPM concentration starts at a low level , reaches its maximum around 50 min , and then decreases to the new steady-state level ., The intracellular leucine level gets depleted with a characteristic time of ∼15 min , reaches minimum around 50 min , and then recovers to steady state after a period of overshooting ( Figure 8D ) ., The fitting of the gene expression profiles puts constraints on the choice of the parameters but does not yield a unique solution ., Similar quality fitting can be achieved by different sets of parameters ., Part of this parameter degeneracy is intrinsic to the nonlinear model , which exhibits “soft modes” in the parameter space , where the output depends only on certain combinations of the parameters 33 ., While the exact parameter values cannot be uniquely inferred from our model and experimental data , there are robust features of the dynamics that are independent of the choice of the parameters , which gives our model predictive power ., To test the predictive power of the model , we considered several environmental and genetic perturbations and used the model to predict how these perturbations would alter the pathway response ., We then compared the predicted induction profiles to those measured by experiment ., Because αIPM is the key control point of the pathway , we considered two different perturbations that would affect the dynamics of αIPM concentration ., For the first perturbation , we increased the external flux of αIPM by adding exogenous αIPM to the media at a specific time after the transfer from SCD to SCD-Leu media ., This is modeled by the addition of a constant external flux term ϕext in Equation 3 ., The second is a genetic perturbation in which we constitutively overexpress the enzyme Leu1 ., This is modeled by the addition of a constant term bext in Equation 1 , where bext represents the constant production of additional Leu1 due to overexpression ., As one would expect , the model predicts that increasing the αIPM flux leads to increased induction of Leu2 ., In contrast , overexpressing Leu1 leads to more efficient depletion of αIPM , leading to decreased Leu2 induction ., The predicted induction profiles for several different values of ϕext and bext are plotted in Figure S1 ., We performed both perturbations experimentally ., In the αIPM addition experiment , we started with two identically prepared cell cultures in two different reactors , added 7 . 5 mM αIPM into one of the reactors 110 min after inoculation , and monitored the induction profiles of the two cultures simultaneously ., The two induction profiles were nearly identical before the addition of αIPM , and started to deviate from each other in less than 10 min after the addition of αIPM ., As expected , the addition of αIPM leads to higher induction of Leu2 ., By allowing the parameter ϕext to vary ( representing the fact that we do not know exactly how much αIPM from the medium is absorbed by the cell ) we can fit the induction profile accurately ( Figure 8A , green lines ) ., The remaining parameters are fixed from fitting the wild-type data ., Notice that while we have to fit one additional parameter , the parameter is sufficient to fit the timing , amplitude , and shape of the extra induction due to the addition of αIPM ., The Leu1 overexpression experiment was performed by transfecting the wild-type cell with a plasmid containing a galactose-inducible GAL1/GAL10 promoter driving Leu1 expression ., When galactose is used as the carbon source , this strain expresses Leu1 at a high level constitutively ., As predicted by our model , Leu2 expression was not as high as in wild type , and again , by fitting only bext ( the extra Leu1 produced by the GAL1/GAL10 promoter ) , we can reproduce the temporal profile ( Figure 8A , red lines ) ., Again , the model correctly predicts the effect of the perturbation ., We have observed contrasting dynamical responses for enzymes upstream and downstream of the control point αIPM and showed that the differential dynamics are caused by differential regulation by Leu3 ., In addition , we have observed that the enzymes immediately downstream of the intermediate have high-fold induction ., Some of these features are also present in other metabolic pathways controlled by a similar regulatory architecture ( e . g . , Lys9 in lysine biosynthesis and Ade17 in adenine biosynthesis ( VC , CC , and HL , unpublished data ) ) ., Are these features of gene expression linked to the dynamics of the systems recovery ?, To address this question , we explored the connection between gene expression and the intracellular leucine level , which we assume to be a limiting factor for the recovery of cell growth ., Using the mathematical model , we analyzed how changing properties of the network may affect the dynamics of gene expression and intracellular leucine recovery ., We first explored the connection between basal expression of the downstream enzymes and leucine dynamics ., Our model predicts that elevating the basal expression level of Leu1 ( by overexpression ) reduces αIPM levels and consequently Leu2 expression , as confirmed by the experiments ., The model also predicts that constitutively overexpressing Leu1 leads to a significant delay in the intracellular leucine recovery ( Figure 8D , red line ) , since Leu2 becomes rate limiting ., A possible solution is to express both downstream enzymes at high levels constitutively ., However , this strategy is not optimal when leucine is abundant in the environment , and the enzymes are not needed ., A better strategy for improving the dynamics of the response is to tune the strength of the induction ( fold change ) for the downstream enzymes , instead of increasing the basal expression level ., This may speed up the recovery of intracellular leucine during the transition to leucine-poor media while minimizing the cost in leucine-rich media ., We implemented this perturbation to the system by changing the parameters c1 and c2 in our model , which correspond to the rates of transcription from the LEU1 and LEU2 promoters when bound by the Leu3-αIPM complex ., Figure 9 shows that when the induction rates of the downstream enzymes are increased , the model predicts that the speed of leucine recovery is improved ., Interestingly , although changing the kinetic parameters modifies the dynamic response , the leucine concentration in the steady state remains the same ., This turns out to be a general property of the model as a consequence of flux conservation , as long as the effective upstream enzyme level only depends on the end product and the dilution due to cell growth can be neglected ( which is generally true based on the model parameters we inferred , see Text S1 ) ., These observations lead us to speculate that the regulatory architecture of the system makes it possible to separately tune steady state and dynamics , and that certain features are chosen for their influence on efficient dynamics , even though they do not contribute to the maintenance of steady-state nutrient level ., We have analyzed the dynamics of a regulatory module that controls the leucine biosynthetic pathway in yeast , and explored the connection between dynamics and network architecture ., Using an automated system for monitoring protein expression level in single cells , we have systematically measured the changes in expression level for the genes in the pathway quantitatively , with high temporal resolution ., Compared to past studies—e . g . , RNA expression based on microarray experiments 34—we can distinguish the temporal expression profiles of enzymes on the same pathway with much higher accuracy ., Our approach observes features that are not seen when the network is at steady state and can provide a key to rationalizing aspects of the regulation that may not be necessary for maintaining growth in a constant environment ., One remarkable feature we observed is the differential response ( both in terms of amplitude and timing ) of the enzymes in the pathway ., For enzymes shared by the isoleucine and valine pathways ( Ilv2 , −6 , −5 , −3 , and Bat2 ) , the induction is fast and transient with a small amplitude ., For the four enzymes specific to the leucine branch ( Leu4 , −9 , −1 , and −2 ) , we observed two different responses separated by the key control point: the intermediate αIPM , which couples metabolic feedback to transcriptional regulation ., While the two enzymes upstream of the control point ( Leu4 and Leu9 ) displayed a fast transient induction with small amplitude , the two enzymes downstream of the control point ( Leu1 and Leu2 ) showed a slow but sustained induction with large amplitudes ., These differential responses are caused by differential regulation by the branch-specific regulator Leu3 and the general regulator Gcn4: Leu1 and Leu2 are strictly controlled by Leu3 , thus their activation requires the accumulation of αIPM , which is the slow step; the fast transient induction of other enzymes seems to be dependent on Gcn4 but not Leu3 ., These observations are in accord with the previous genetic analysis that showed that all the enzymes in the pathway , except Leu1 and Leu2 , can be induced by the general amino acid starvation response 8 , and that basal expression of Leu2 is suppressed by a DNA-bound but inactive Leu3 35 ., However , the vastly different effect of Leu3 on upstream and downstream genes and its consequence on the kinetics of gene induction had not been explored before ., Mechanistically , it is unclear how Leu3 acts differently at the promoters of the upstream and downstream genes ., We speculate that the different regulation might be achieved by the positioning of the Leu3 binding site in the promoter , and in particular its positioning relative to the Gcn4 binding site ., We have observed a correlation between the response profiles and the binding site arrangement ., At the LEU1 and LEU2 promoters , the Leu3 binding sites are close to the transcription start site , whereas the Gcn4 binding sites are further upstream ., At most of the other promoters , the Gcn4 sites are closer to the transcription start site ., Why are there two types of qualitatively different regulation and consequently different dynamical responses ?, For the enzymes shared by valine and isoleucine pathways , it can be rationalized that they should not be strictly controlled by the branch-specific regulator Leu3 , since the cell needs to turn on valine and isoleucine synthesis in environments lacking valine and isoleucine but with leucine abundant ( which keeps Leu3 inactive ) ., From this perspective , the leucine biosynthetic pathway provides an interesting model system to investigate cross regulation of pathways that share a subset of enzymes ., Our preliminary study with media lacking all possible combinations of the three amino acids indicates that all the enzymes of the pathway adjust their expression level only in response to leucine depletion ( Figure 6 ) ., Thus with the depletion of valine and/or isoleucine but not leucine , the metabolic flux is turned on by the release of end product inhibition without changing the enzyme levels ., This suggests that these shared enzymes are operating far below saturation

Introduction, Results, Discussion, Materials and Methods

The dynamic features of a genetic networks response to environmental fluctuations represent essential functional specifications and thus may constrain the possible choices of network architecture and kinetic parameters ., To explore the connection between dynamics and network design , we have analyzed a general regulatory architecture that is commonly found in many metabolic pathways ., Such architecture is characterized by a dual control mechanism , with end product feedback inhibition and transcriptional regulation mediated by an intermediate metabolite ., As a case study , we measured with high temporal resolution the induction profiles of the enzymes in the leucine biosynthetic pathway in response to leucine depletion , using an automated system for monitoring protein expression levels in single cells ., All the genes in the pathway are known to be coregulated by the same transcription factors , but we observed drastically different dynamic responses for enzymes upstream and immediately downstream of the key control point—the intermediate metabolite α-isopropylmalate ( αIPM ) , which couples metabolic activity to transcriptional regulation ., Analysis based on genetic perturbations suggests that the observed dynamics are due to differential regulation by the leucine branch-specific transcription factor Leu3 , and that the downstream enzymes are strictly controlled and highly expressed only when αIPM is available ., These observations allow us to build a simplified mathematical model that accounts for the observed dynamics and can correctly predict the pathways response to new perturbations ., Our model also suggests that transient dynamics and steady state can be separately tuned and that the high induction levels of the downstream enzymes are necessary for fast leucine recovery ., It is likely that principles emerging from this work can reveal how gene regulation has evolved to optimize performance in other metabolic pathways with similar architecture .

Single-cell organisms must constantly adjust their gene expression programs to survive in a changing environment ., Interactions between different molecules form a regulatory network to mediate these changes ., While the network connections are often known , figuring out how the network responds dynamically by looking at a static picture of its structure presents a significant challenge ., Measuring the response at a finer time scales could reveal the link between the networks function and its structure ., The architecture of the system we studied in this work—the leucine biosynthesis pathway in yeast—is shared by other metabolic pathways: a metabolic intermediate binds to a transcription factor to activate the pathway genes , creating an intricate feedback structure that links metabolism with gene expression ., We measured protein abundance at high temporal resolution for genes in this pathway in response to leucine depletion and studied the effects of various genetic perturbations on gene expression dynamics ., Our measurements and theoretical modeling show that only the genes immediately downstream from the intermediate are highly regulated by the metabolite , a feature that is essential to fast recovery from leucine depletion ., Since the architecture we studied is common , we believe that our work may lead to general principles governing the dynamics of gene expression in other metabolic pathways .

biochemistry, cell biology, computational biology, biophysics, genetics and genomics

A quantitative, high-temporal resolution study of gene induction in a metabolic pathway reveals an intricate connection between the regulatory architecture and the dynamic response of the system, pointing to possible principles underlying the design of these pathways.

journal.pcbi.1005815

Automated deconvolution of structured mixtures from heterogeneous tumor genomic data

Tumor heterogeneity is now recognized as a pervasive feature of cancer biology with implications for every step of cancer development , progression , metastasis , and mortality ., Most solid tumors exhibit some form of hypermutability phenotype 1 , leading to extensive genomic variability as tumor cell populations expand 2 ., Studies of single cells by fluorescence in situ hybridization ( FISH ) 3 , 4 have long revealed extensive cell-to-cell variability in single tumors , an observation that has since been shown , by single-cell sequencing technologies , to occur with a far greater scale and variety of mechanisms than previously suspected ( e . g . , 5 , 6 ) ., Furthermore , studies of clonal populations across progression stages have revealed that it is often rare cell populations that underlie progression , rather than the dominant clones 4 ., Indeed , heterogeneity itself has been shown to be predictive of progression and patient outcomes 7 ., All of these observations have suggested the importance of having ways of accurately profiling tumor heterogeneity , for both basic cancer research and translational applications ., Experimental technologies for profiling tumor heterogeneity are constantly improving , but are so far impractical for systematically profiling variability genome-wide in large patient populations ., FISH and related imaging technologies can profile many thousands of cells , but only at limited sets of preselected markers 4 ., Single-cell sequencing can derive genome-wide profiles of hundreds to thousands of cells in single tumors 5 , 8 , 9 , but is so far cost-prohibitive for doing so in more than very small patient populations ., Furthermore , technical challenges make it difficult to develop accurate profiles of structural variations , such as copy number variations ( CNVs ) , which are the major drivers of progression in most solid tumors 10 ., Bulk regional sequencing can profile small numbers of tumor sites per patient in large patient populations 11 but provides only a coarse picture of the heterogeneity within each site ., RNA sequencing ( RNA-Seq ) provides a measure of the quantity of RNA expression and is practical on substantially larger numbers of single-cells than DNA-Seq 9; however , it is subject to greater noise than DNA-Seq 12 and provides a more indirect measure of clonal heterogeneity ., These technical challenges to assessing heterogeneity experimentally have led to enormous interest in computational deconvolution ( also known as mixed membership modeling or unmixing ) methods as a way of computationally separating cell populations from mixed samples ., Originally proposed as a way of correcting for stromal contamination in genomic measurements 13 , such methods were later extended to reconstructing clonal substructure 14 and subclonal evolution 15 among tumor cell populations ., The past few years have seen an explosion of such methods for deconvolution of numerous forms of genomic data sources ( e . g . , 16–31 ) ., All such methods , however , are limited in accuracy and capable of resolving at best a few major clonal subpopulations , a small fraction of the heterogeneity revealed by single-cell experimental studies ., These limits result from an inherent difficulty of separating high-dimensional mixtures , especially from sparse , noisy data ., The gap between the heterogeneity we know to be present and what we can resolve by deconvolution is enormous , suggesting a need for further methodological advances ., Genomic deconvolution is a burgeoning field in which many different approaches are now available , often differing in models , algorithms , and the kinds of data or study design for which they are well suited ., Leading contemporary approaches include TITAN 19 , THetA 18 , THetA2 32 , PhyloWGS 33 , SPRUCE 34 , Canopy 35 , BitPhylogeny 36 , and PyClone 37 , each of which we briefly discuss here ., TITAN uses a graphical model to estimate subpopulations based on copy number alterations and loss of heterozygosity events for whole genome or whole exome sequencing data , assuming as input read depths and allelic ratios at single nucleotide variant ( SNV ) sites ., THetA and its follow-up version THetA2 perform tumor composition estimation using both SNV and copy number data derived from sequence read depths ., PhyloWGS uses a probabilistic model to perform deconvolution jointly with phylogeny inference specifically on low-coverage whole genome sequencing data , making use of copy number estimates and variant allele frequencies ( VAFs ) of simple somatic variants ., SPRUCE uses SNV and CNV data similar to that of THetA/THetA2 to make inferences as to the composition of heterogeneous tumor samples , but via a combinatorial enumeration strategy to explore the space of possible phylogenies consistent with a data set ., Canopy optimizes for a probabilistic model to perform joint phylogenetic inference and tumor deconvolution from a data set based on several data sources , including VAFs and allele-specific copy numbers ., BitPhylogeny similarly performs joint phylogenetics and deconvolution using Markov chain Monte Carlo ( MCMC ) sampling , but is unusual among methods in this domain in making use of DNA methylation data ., PyClone performs tumor deconvolution for multiple samples from a single patient using SNV data , CNV data , and combinations thereof as input and is designed to work specifically with targeted deep sequencing data ( >1000X coverage ) ., In prior work , we proposed that one could better resolve genomic mixtures by taking account of extensive substructure we would expect such mixtures to exhibit 21 ., That is , an individual tumor or tumor site is not likely to be a uniform mixture of all cell types observed across all tumor samples in a study ., Rather , one can expect distinct samples to group into subsets that share more or fewer cells depending on how closely related they are to one another ., For example , all tumor samples can be expected to share some contamination by normal cells while tumors with common subtypes can be expected to share both normal cells and cell states characteristic of those subtypes ., Likewise , tumor regions might be expected to share more similarity with those nearby than those more distant in a single patient ., This kind of substructure is in principle exploitable to improve our ability to reconstruct accurate mixed membership models ., Specifically , by deconstructing tumor samples into subgroups with similar mixtures , one can decompose the problem of reconstructing a high-dimensional mixture into the easier problem of reconstructing several overlapping lower-dimensional mixtures ., We previously showed how to implement such an approach to substructured mixture deconvolution , adapting an earlier deconvolution strategy for uniform mixtures that was based on identifying geometric structures ( simplices ) of tumor point clouds in genomic space 15 , 38 but subdividing these point clouds into low-dimensional subsimplices that collectively constitute a higher-level object known as a simplicial complex ., This prior work used a pipeline of several sequential steps to transform a genomic point cloud into a structured mixed membership model 21: The resulting pipeline established a proof-of-concept for the approach , but also introduced several difficult computational challenges ., For example , it required accurately pre-specifying the number of partitions and the dimensionality of each of the partitions , both difficult inference problems in themselves that require significant knowledge of the system under study ., In the present work , we improve on this proof-of-concept method by tackling several subproblems on the path to more completely automating inference of substructured genomic mixtures from populations of tumor samples ., We have eliminated several nuisance parameters from the prior work , most notably by introducing methods for automated dimensionality estimation of subsimplicies and automated maximum likelihood inference of other previously user-defined parameters ., We also improve upon our earlier work by proposing a model better suited to capture the uncertainty in cluster assignments through use of a fuzzy clustering representation of data points ( samples ) with respect to the inferred simplicial complex ( and therefore the tumor phylogeny ) , allowing tumor samples to exhibit partial or uncertain membership in multiple phylogenetic branches ., This flexibility is of particular importance when a sample is near a branch point in the simplicial structure , which corresponds biologically to a sample having a genomic profile similar to a most recent common ancestor of multiple tumor lineages ., In addition , we develop a more comprehensive likelihood function , allowing us to optimize over and thus eliminate nuisance parameters from prior work ., Although the approach we introduce makes inferences as to intraturmor heterogeneity , we use information present across multiple patients ( that is , intertumor heterogeneity ) to make those inferences ., This application assumes that commonalities in progression processes can be observed across subgroups of patients , even if the exact presentation is unique for each tumor ., Because the model presumes common subgroups of tumors proceeding along similar evolutionary trajectories , an inferred mixture vertex will correspond to a coarse-grained model of a shared progression stage among a subset of tumors ., That is , the vertex , would be interpreted as an approximate representation of a recurring cell type appearing in the course of progression of multiple samples ., Since no two samples have exactly the same evolutionary history , however , it would be expected to reflect the common features of a cluster of similar cell types while averaging out their differences ., The overall simplicial complex structure will correspond to a model of the space of evolutionary trajectories among all of these progressions stages across all observed tumor subgroups ., Paths in the evolutionary tree will correspond to the recurring evolutionary pathways between the averaged progression stages represented by the vertices ., Based on those reconstructions , we can then make inferences for each sample as to the relative amounts of each progression stage represented in that tumor , providing a coarse-grained inference of intratumor heterogeneity ., We validate the approach through application to breast tumor data from The Cancer Genome Atlas ( TCGA ) 39 and comparison with the widely-cited PyClone software 37 ., We also compare with a more recent deconvolution method using DNA methylation data , providing an independent basis for comparison to the DNA copy number and RNA expression-derived deconvolution of our method 28 ., We conceptually model input data as a matrix M ∈ R s × g , where the s ∈ N rows correspond to distinct samples ( which might be biopsies of tumors in a patient population , tumor sites in a single patient , or regions of a single tumor ) and the g ∈ N columns correspond to probes along a genome ( typically one per gene , although potentially at lower or higher resolution ) ., Note , however , that as the underlying data types input to the method are changed , the interpretation of output is changed correspondingly ., For instance , if the features used as input are not gene copy numbers , but rather SNV sites , then the components of the matrix M will be SNV VAFs for the given samples ., Similarly , if samples are different regions from a single patient , the inferred phylogeny is for a single patient , rather than across a patient panel ., For ease of exposition , we refer to rows as samples and columns as genes below ., We use this generic matrix format because data from many sources can be preprocessed into such a matrix ( e . g . , array-based CNV , SNV , or expression data or whole-genome or whole-exome sequence-derived CNVs , SNVs , or expression levels ) ., Although the basic strategy is intended to be generic with respect to platform and genomic datatype , we specifically consider here three scenarios: 1 ) CNV data as might be derived from array comparative genomic hybridization ( aCGH ) or DNA-Seq read depths , 2 ) RNA expression data as might be derived from expression microarrays or RNA-Seq , and 3 ) a heterogeneous combination of DNA CNV and RNA expression data ., Our goal is to decompose the rows of M into an approximately convex combination of a smaller set of unknown mixture components ( putative cell populations ) ., More formally , we seek a decomposition, M = F V + ϵ ( 1 ), where F ∈ R s × k are mixture proportions , V ∈ R k × g are unmixed subpopulations , k ∈ N is the number of inferred cell subpopulations , and ϵ ∈ R s × g is an error matrix ., F is interpreted as the mixture fractions of the pure subpopulations , also called mixing proportions , and V as the inferred genomic profiles of the pure subpopulations , also called mixture components ., This interpretation leads to natural constraints on the problem: 1 ) ∑i Fij = 1 for a fixed j and 2 ) ∀i , j: 0 ≤ Fi , j ≤ 1 . Given these constraints , the formal goal of the method is to compute F and V given M , with an intermediate step of determining the mixture dimension k ., Our approach to performing this deconvolution involves constructing a more involved simplicial complex mixed membership model , which will imply F and V , through a series of discrete inference steps ., While most aspects of model inference are automated , as detailed in the remainder of Materials and Methods , the following parameters and hyperparameters still require manual selection: To begin analysis , we first pre-process M into a matrix of Z-scores:, M z = M − μ M σ M ( 2 ), where μM is a vector of the mean copy numbers of each gene across all samples , and σM is a vector of the standard deviations of the copy numbers ., This process is altered slightly to accommodate heterogeneous DNA and RNA data that have been concatenated as features ., We assume that the distributions of read counts will differ for DNA and RNA data , so instead of μ and σ for all samples column-wise , we use a μ and σ for pools of all data for each data type ., That is , we evaluate the mean and standard deviation for Z-score computation for all samples and for all DNA features , and separately for all samples and all RNA features ., In the RNA only case , we use the framework outlined in Eq 2 . Next , to facilitate analysis of genomic point clouds , we reduce the dimension of the data using principal components analysis ( PCA ) 40 ., While there are more sophisticated dimensionality reconstruction strategies available , we favor PCA as a simple , standard method that has relatively modest data needs ., We identify a total of kupper PCs , using the Matlab pca routine in economy mode , where k u p p e r ∈ N < g is an upper bound on the number of cell subpopulations we will infer ., In the present work , we use kupper = 12 , intended to be approximately an upper limit on the number of distinct mixture components a method of this class might be able to infer ., We denote the PCA scores , corresponding to amounts of each PC in each tumor , as S M ∈ R s × k u p p e r ., Then , in order to fine-tune the automated dimensionality detection , we implement the sliver method of dimensionality estimation described in 41 ., The core model proposed by that work relies on testing for the presence of “slivers” , geometric objects with poor aspect ratios , which occur when the following expression , which we call Assertion 3 , is satisfied:, ν < δ j r wherer = L j j !, ( 3 ), where, ν represents the volume of some enclosing structure ,, j represents the current estimate of dimension , increasing for each time Assertion 3 is false, up until the limit of 12 , and, δ represents a tolerance factor between 0 and 1 ., For a quick estimate of an enclosing structure , we use the algorithm proposed in 15 ., We then use the top j − 1 PCs after the algorithm terminates ., To automate the selection of the δ parameter , we use all values spaced 0 . 05 apart between 0 and 1 . The range of possible δ values is 0 to 1 for this parameter based on the approach outlined by 41 ., Because some values of the parameter lead to the same estimate of the dimensionality of the dataset , we choose one representative value from each partition of the range of dimension estimate values , then choose the model that has the highest likelihood ., Lastly , we normalize the scores for each PC to a 0 , 1 range , which is then assumed by the pre-clustering technique applied in the next section 42 ., We compute the 0–1 normalized version of SM as, S 0 , 1 = S M − min S M max S M − min S M ( 4 ), where the minimums and maximums are computed for each PC , taken over all samples ., We next pre-cluster data to identify initial candidate subsets of samples inferred to have drawn from the same set of mixture components ., Each such subset will correspond to a distinct subsimplex of the full simplicial complex to be inferred ., While this is a clustering problem , it is a non-standard one in that we seek to cluster data into distinct low-dimensional subspaces of a contiguous higher-dimensional point cloud , rather than into disjoint subclouds as is in conventional clustering ., We developed a specialized clustering method for this purpose 42 , based on a two-stage variant of medoidshift clustering 43 ., We initially cluster in Euclidean PC space to reduce the raw data to a smaller set of representative data points ., We then cluster these representatives under a negative-weight exponential kernel function using the ISOMAP distance measure 44 , a form of geodesic metric measuring distance between data points through a k-nearest-neighbor graph of the input point cloud , which collectively draws on features of manifold learning and related technologies ., The combination of ISOMAP distance and negative exponential kernel produces a clustering in which cluster representatives are approximately extremal points of the simplicial complex that serve to pull apart distinct subspaces of the point cloud ., The initial Euclidean clustering suppresses noise , which otherwise makes the negative exponential kernel highly sensitive to outlier data ., We refer the reader to 42 for full details ., At the end of this process , we are left with a small set of cluster representatives M2stage , defined as the union over clusters i of a neighborhood N ( xi ) of points associated with each cluster representative xi:, M 2 s t a g e = ∪ i M N ( x i ) , ( 5 ), where each representative is itself a point in S0 , 1 , and a corresponding clustering of all samples C = {C1 , … , Cr} , S0 , 1 = ⋃Ci∈CCi ., We further assess uncertainty of the cluster assignments by determining a relative statistical weight of each data point in each cluster ., We use a weight function based on a folded multivariate normal distribution , where the mean of the function is a 0 vector , the covariance matrix is the identity multiplied by the distance from each cluster center to the mean of all cluster centers , and the value at which the density function is evaluated is the distance from xi to Cj in ISOMAP space ., After these relative weights have been derived , we convert them to probabilities of assignment of each point to each cluster ., If we denote the raw weight of the ith data point as a vector Ri , then we can define the normalized weight vector:, W i = R i − min C j ∈ C R i max C j ∈ C R i − min C j ∈ C R i ( 6 ), In the above formula , Cj refers to an arbitrary cluster in the clustering C , over which we maximize or minimize ., The clustering in principle depends on a chosen neighborhood size for the k-nearest-neighbors graph , although a scan over all possible neighborhood sizes found no sensitivity of the final model likelihood to this parameter ., We next seek to estimate the dimension of each cluster , which will correspond to the number of mixture components inferred for that cluster ., The major challenge of this step is distinguishing a genuine axis of variation from random noise stemming from biological and technical limitations , particularly when working with sparse , noisy genomic measurements ., Intuitively , we identify dimension by iteratively adding axes of variation via PCA until we can no longer reject the hypothesis that variance in the next dimension is distinguishable from noise ., We first build a model of expected noise per dimension by randomly sampling data points of pure Gaussian noise with mean 0 and identity covariance ., We then perform PCA on this random point cloud and estimate the mean μG ( i ) and standard deviation σG ( i ) of the point cloud for each PC i ∈ 1 , … , kupper ., We then identify the smallest i ≤ kupper such that the standard deviation of the true data in PC i is smaller than μG ( i ) + κσG ( i ) , where κ defines a significance threshold in standard deviations ., In the present work , we set κ = 3 to yield effectively a significance threshold of < 0 . 001 for rejecting the hypothesis that the next dimension can be explained by Gaussian noise ., The result of this module , then , is a vector of inferred dimensions of each of the clusters: D ∈ {1 , … , kupper}r ., We would expect this test to be conservative ( underestimate true dimension ) , although less so as the size of the data set and its precision increases ., We found it necessary to use a custom-made conservative dimensionality estimator , as opposed to a more standard technique ( e . g . , 41 ) , because the number of data points available in this application is much smaller than is typically assumed by methods in this problem domain ., We use the approach outlined in 41 in the initial phase , as it is prior to the pre-clustering , and therefore typically has a several-fold increase in the minimum number of data points considered , bringing it better in line with the data needs of that method ., We next seek to establish an initial mixed membership model by separately unmixing each cluster , using the inferred dimension from the previous step as the number of mixture components ., We establish the model by minimizing an objective function based on the noise-tolerant geometric unmixing method of 38:, P ( θ | X ) ∝ ∏ i = 1 r ( e x p ( − ∑ j = 1 s ( | x i − F j i V j i | W j i ) ) M S T ( V j , A j ) − γ β ) ( 7 ) Where, γ is a regularization penalty set based on an estimated signal-to-noise ratio ( SNR ) of the data source 21 ,, V are the inferred vertices ,, A is the adjacency matrix ,, MST is a minimum spanning tree cost ,, W is the relative weight function computed above ,, F are the inferred mixture components ,, xi is the ith data point ,, β is a BIC penalty for model complexity 45 and ,, |⋅| is L1 distance ., The first term penalizes data points outside the bounding simplex via an exponentially-weighted L1 penalty ., The MST term captures a form of minimum evolution model on the simplex itself intended to penalize the amount of mutation from a common source needed to explain the simplex vertices ( mixture components ) 21 ., We optimize for the objective function via the Matlab fmincon function , fitting V and F to assign mixture components and mixture fractions to each cluster independently ., In practice , we use a transformed version of the equation into negative log space , as the optimization packages are built for minimization rather than maximization , and log domain better handles underflow for small likelihoods while preserving the ordering of solutions ., We next seek to join the discrete simplices , each modeling a subset of samples as a uniform mixture , into a unified simplicial complex ., We accomplish this by merging simplex vertices if we cannot reject the hypothesis that they represent distinct points in genomic space ., We first establish a probability model using the k-nearest-neighbors graph on samples and vertices by modeling the set of overlapping neighbors between two vertices via a hypergeometric distribution ., On the assumption two vertices draw their neighbor sets independently from the pool of all samples , the expected number of data points in common would be, | N 1 | | N 2 | N ( 8 ), where there are N data points , |N1| nearest neighbors of the first vertex , and |N2| neighbors of the second vertex ., We merge two vertices when the number of observed overlapping nearest neighbors is above expectation ., We empirically determined on our synthetic data that the method is insensitive to the number of nearest neighbors for choices between 2 and N and chose k = 15 nearest neighbors arbitrarily within this range for the real data ., This approach replaces computationally costly bootstrap estimates used in our prior work 21 ., For those instances in which the process above does not result in a single connected simplicial complex , we add a step of post-processing to reconcile the geometric body into a single , connected simplicial complex ., For those collections of bodies that do not consist of one connected component after the hypergeometric distribution correction , we iterate over all pairs of simplex vertices , merge the two vertices by creating a new vertex from the mean of the previous two vertices in all features , set the adjacency matrix to the union of the adjacency matrices of the two previous vertices , and compute the value of the objective function outlined in Cluster-wise Unmixing ., We continue to merge points until there is at least one candidate consisting of a single connected component ., If there are multiple such candidates , the candidate with the lowest objective function value , corresponding to the maximum of the likelihood function , is chosen ., Pseudocode for this algorithm is provided in Fig 2 . To demonstrate the efficacy of the algorithm , we use breast cancer ( BRCA ) CNV and RNA-Seq data from The Cancer Genome Atlas ( TCGA ) 39 ., We downloaded level 4 DNA CNV data on 2 Jun 2016 ( 1 , 080 samples ) and RNA-SeqV2 data on 1 Jun 2016 ( 1 , 041 samples ) , of which 1 , 022 samples were in common , along with clinical data for this cohort ., For copy number data at level 4 , gene features are extracted and a list of genes is provided , in contrast to the blocking procedure required by earlier work 42; however , the platform is flexible to represent more or less granular data ., We ran the pipeline using the following parameters: maximum number of dimensions supplied to the pre-processing sliver method: 12; number of bootstrapped replicates for pre-clustering: 1000; neighborhood size for pre-clustering: 1; number of nearest neighbors for vertex merger: 15; cutoff for dimensionality estimation: 3 standard deviations; maximum number of iterations of fmincon per simplex: 1000 ., The choices reflect computational resource limitations , as well as a stable number of bootstrapped replicates , and choices to ensure convergence of the methods ., The neighborhood size was chosen based on assumptions implicit in our normalization technique—for full details , see 42 ., The number of nearest neighbors was chosen based on the test of simulated data similar to 42 demonstrating insensitivity to this parameter up to approximately N neighbors ., The 3 standard deviations chosen correspond to a p-value of approximately 0 . 001 ., The runtime of the experiments depends largely on the dimension of the maximally likely clusters ( i . e . , the number of subpopulations in the tumor dataset that our model chooses as most likely ) and the number of iterations in the minimization phase ( iterations of fmincon ) ., In order to assess the consistency of our method with respect to outlier data points , we conducted a sensitivity analysis using the TCGA CNV data ., The sensitivity analysis was structured in an analogous fashion to 10-fold cross validation ., For each of ten iterations , we excluded 10% of the data set , selected by a random uniform distribution ., For the remaining data , the model was run to completion to produce a simplicial complex and assignment of mixture components and mixture fractions to the data points in that set of replicates ., We then compared inferences by several measures to assess consistency across subsamples of the data ., We assessed similarity of the inferred component sets between replicates ., To assess similarity of two sets of inferred vertex components A and B , we first identified for each component in A the closest matching component B , based on normalized Euclidean distance in PC space ., We likewise identified for each component in B , the closest matching component in A . We assigned a score for the similarity of two vertex sets based on the mean distance between each component and its closest match relative to the mean distance between pairs of distinct components within A and within B ., RNA-Seq data was downloaded from TCGA ., The data consists of lists of gene expression in normalized counts , as well as gene name lists identifying each feature ., Data from each of the samples were concatenated into a matrix of samples by genes ., Using the parameters described above , the weighted unmixing procedure produces a tetrahedral simplex ., Although other simplicies and simplicial complexes were considered by our algorithm , the tetrahedron was determined to be the maximum likelihood model ., The results are illustrated in Fig 3 , which shows the true point cloud as well as our inferred structure , where samples are colored by the clinical subtype ., The DNA level 4 data consists of log2 ( ⋅ ) copy number ratios , which are exponentiated and Z-scored prior to unmixing following the methods outlined above ., We also considered application to DNA CNV data from TCGA ., The results are visualized in Fig 4 ., The decreased noise of DNA CNV technology relative to RNA-Seq technology results in a more sharply defined simplicial complex structure than was apparent with RNA-Seq data , consisting of three lines connected at a shared fulcrum ., We attribute the clearer structure to the lower inherent stochasticity of DNA versus RNA data , which would be expected to better approximate the assumption that mixtures of cells will behave as linear combinations of their underlying cell types ., We note that the central vertex , labeled 4 , appears skewed away from the apparent junction of the three subsimplices ., We attribute this skew in the position of the junction to the difficulty of accurately clustering samples near such subsimplicial boundaries , leading to imprecise positioning of the shared vertex in the distinct subsimplices that is only partly corrected when the vertices are merged ., Lastly , we considered a combination of DNA and RNA features ., Because of the varying noise profiles of the data types 12 , we adjusted the normalization procedure as outlined above ., We have plotted the results of the unmixing below in Fig 5 , using the same color code for tumor subtypes as with the RNA-only and DNA-only data ., The combined data leads to a somewhat more complex structure than either individual data type alone , consisting of a tetrahedron and triangle connected at a point ., The higher dimension compared to the individual data types may reflect changes in the overall noise profile or to the complementary aspects of progression that are revealed by the two data types in isolation ., We further used the TCGA CNV data to assess sensitivity of the method to subsamples of the data ., We assessed reproducibility across ten replicates of 90% subsamples of the TCGA data and quantified reproducibility of inferred mixture component sets based on the ratio of Euclidean distances between best matching component pairs between replicates versus Euclidean distances within replicate sets ., A score below one would then indicate general consistency between vertex sets relative to variability within each set , while a higher score would then be interpreted to mean that vertex components are highly distinct between runs relative to the variability among components within a set ., Across all 45 comparisons among pairs of replicates , we fo

Introduction, Materials and methods, Results, Discussion

With increasing appreciation for the extent and importance of intratumor heterogeneity , much attention in cancer research has focused on profiling heterogeneity on a single patient level ., Although true single-cell genomic technologies are rapidly improving , they remain too noisy and costly at present for population-level studies ., Bulk sequencing remains the standard for population-scale tumor genomics , creating a need for computational tools to separate contributions of multiple tumor clones and assorted stromal and infiltrating cell populations to pooled genomic data ., All such methods are limited to coarse approximations of only a few cell subpopulations , however ., In prior work , we demonstrated the feasibility of improving cell type deconvolution by taking advantage of substructure in genomic mixtures via a strategy called simplicial complex unmixing ., We improve on past work by introducing enhancements to automate learning of substructured genomic mixtures , with specific emphasis on genome-wide copy number variation ( CNV ) data , as well as the ability to process quantitative RNA expression data , and heterogeneous combinations of RNA and CNV data ., We introduce methods for dimensionality estimation to better decompose mixture model substructure; fuzzy clustering to better identify substructure in sparse , noisy data; and automated model inference methods for other key model parameters ., We further demonstrate their effectiveness in identifying mixture substructure in true breast cancer CNV data from the Cancer Genome Atlas ( TCGA ) ., Source code is available at https://github . com/tedroman/WSCUnmix

One of the major challenges in making sense of cancer genomics is high heterogeneity cell-to-cell , as a tumor is typically made up of multiple cell populations with distinct genomes and gene expression patterns ., The difficulty of working with such data has led to interest in computationally inferring the components of genomic mixtures ., We develop a new approach to this problem designed to take better advantage of the fact that mixtures of cells across tumors or tumor regions can be expected to be highly non-uniform; samples that share greater common ancestry or progression mechanisms are likely to have more similar mixtures of cell types ., We present new work on reconstructing mixtures from multiple genomic samples where the samples can be presumed to share such a pattern of similarity ., Our methods automate the process of reconstructing these mixtures and the relationships between samples ., We demonstrate their effectiveness on tumor genomic data in comparison to alternative methods in the literature .

cancer genomics, taxonomy, medicine and health sciences, breast tumors, cancers and neoplasms, basic cancer research, oncology, phylogenetics, data management, materials science, copy number variation, materials by structure, genome complexity, computer and information sciences, breast cancer, comparative genomics, evolutionary systematics, genetics, biology and life sciences, physical sciences, genomics, evolutionary biology, mixtures, computational biology, genomic medicine

journal.pntd.0006983

TIM-1 serves as a receptor for Ebola virus in vivo, enhancing viremia and pathogenesis

Zaire ebolavirus ( EBOV ) is one of five species of ebolaviruses within the Filoviridae family ., EBOV continues to cause significant outbreaks in sub-Saharan Africa with case fatality rates as high as 90% 1 ., All filoviruses have a broad species and cellular tropism ., With the exception of lymphocytes , most cells within the body are thought to support EBOV infection and replication 2 , 3 ., Histopathological studies of EBOV infected humans and non-human primates ( NHPs ) have demonstrated viral antigen in many different organs including: liver , spleen , lymph nodes , kidney , adrenal glands , lungs , gastrointestinal tract , skin , brain and heart 3–7 ., Numerous cell surface receptors are appreciated to mediate filovirus binding and internalization into the endosomal compartment of cells , including phosphatidylserine ( PS ) receptors 8 , 9 and C-type lectin receptors 10–14 ., PS receptors do not interact with the viral glycoprotein ( GP ) , but bind to PS on the surface of the virion lipid membrane , causing internalization of viral particles into the endosomal compartment 9 , 15 ., This mechanism of viral entry has been termed apoptotic mimicry 16 ., Following endosomal uptake of filovirions , proteolytic GP processing occurs , thereby allowing GP to interact with its endosomal cognate receptor , Niemann Pick C1 17–21 ., One important family of PS receptors is the T-cell immunoglobulin mucin domain ( TIM ) family ., TIM family members , encoded by the Havcr family of genes , contribute to the uptake of apoptotic bodies to clear dying cells from tissues and the circulation 22–24 ., TIM proteins are type 1 , cell surface glycoproteins ., Three family members are present in humans ( hTIM-1 , hTIM-3 and hTIM-4 ) and four in mice ( TIM-1 , TIM-2 , TIM-3 and TIM-4 ) 25 ., hTIM-1 was identified through a bioinformatics-based screen to be important for filovirus entry 8 ., Subsequent studies demonstrated that hTIM-1 and hTIM-4 , but not hTIM-3 , enhance entry of a broad range of viruses including members of the alphavirus , arenavirus , baculovirus , filovirus , and flavivirus families 9 , 15 , 26–29 ., Murine TIM-1 and TIM-4 also enhance enveloped virus uptake into the endosomal compartment 9 , 27 , 29 ., The molecular interactions between TIM family members and enveloped viruses are well defined ., The amino terminal IgV domain binds to PS on the outer leaflet of the viral membrane through a IgV domain binding pocket that is conserved across the TIM family of receptors 9 , 26 , 27 , 29 ., We have reported that the ability of PS on Ebola virus like particles and EBOV glycoprotein pseudotyped vesicular stomatitis virus to bind to TIM-1 is equivalent , suggesting similar levels of PS present on the surface of these virions 15 ., Aspartic acid and asparagine residues within the IgV binding pocket are essential for virion binding 9 , 15 , 27; these same TIM residues are required for apoptotic body binding and uptake 30 ., The IgV domain is extended from the plasma membrane by a mucin like domain ( MLD ) that is anchored to the cell surface with by a transmembrane domain connected to a short intracellular cytoplasmic tail ., The length , but not the specific sequence , of the MLD is required for TIMs to serve as enveloped virus receptors 29 ., Surprisingly , neither the TIM transmembrane domain nor cytoplasmic tail is required as a GPI-anchored TIM-1 construct is completely functional as a viral receptor 26 , 29 ., These findings indicate that the TIM-1 cytoplasmic tail , which contains a tyrosine phosphorylation site that initiates signaling events 31–33 , is not essential for TIM-1-mediated virus uptake ., While it is well established that TIM proteins serve as cell surface receptors for a number of enveloped viruses during infection of cultured cells , the importance of these family members for in vivo filovirus infection and pathogenesis has not been extensively examined ., With the wide variety of cell surface receptors able to mediate filovirus uptake into endosomes , it is possible that sufficient receptor redundancy exists in vivo , such that the loss of any one of the PS receptors may have little or no effect on EBOV viremia , tissue virus load or pathological consequence ., Alternatively , specific cell surface receptors , such as TIM-1 , might be critical for in vivo infection and pathogenesis ., As PS receptors have been reported to mediate both immunomodulatory and proinflammatory responses 34–37 , an additional impact of TIM proteins on virus infection may be due to alterations in innate immune responses ., A recent study demonstrated that TIM-1-deficient mice have lower morbidity and mortality than wild-type mice when challenged intravascularly ( i . v . ) with mouse-adapted EBOV ( maEBOV ) 38 ., This study highlighted the role of TIM-1 in non-permissive T lymphocytes , reporting that EBOV interaction with TIM-1 on CD4+ T cells enhanced proinflammatory cytokine dysregulation in purified CD4+ T cells ., The authors conclude that an enhanced TIM-1-dependent cytokine storm in T cells significantly contributes to EBOV pathogenesis ., However , the impact of TIM-1 on viremia in mice was examined in the plasma at a single time point during infection , leaving open the possibility that TIM-1 may also serve as an important receptor for EBOV entry in vivo ., Here , we examined the in vivo importance of TIM-1 for virus replication and pathogenesis using a highly tractable BSL2 model virus of EBOV ., Our BSL2 virus model is recombinant vesicular stomatitis virus ( VSV ) encoding either full length EBOV glycoprotein or mucin domain deleted EBOV glycoprotein in place of the native VSV G protein ( EBOV GP/rVSV or EBOV GPΔO/rVSV ) ., Our use of these viruses allowed us to conduct detailed studies focused , on the role of TIM-1 virus entry , host responses , and pathogenesis ., As reported for maEBOV , we observed that both EBOV GP/rVSV and EBOV GPΔO/rVSV were less pathogenic in TIM-1-deficient mice compared to TIM-1-sufficient mice ., The impact of the loss of TIM-1 was specific for EBOV GP-expressing viruses since wild-type VSV was equally virulent in TIM-1-deficient and TIM-1-sufficient mice over a wide range of challenge doses ., Importantly , reduced mortality observed in the EBOV GP encoding virus-infected TIM-1-/- mice was associated at late times during infection with lower viremia and virus loads in multiple tissues previously appreciated to be important in EBOV pathogenesis ., Consistent with reduced overall virus loads , proinflammatory chemokine profiles were lower in the infected TIM-1-deficient mice at late times during infection ., Finally , to directly evaluate whether we observed enhanced pathogenesis in TIM-1-sufficient mice associated with T cell activation as previously reported 38 , we depleted the T cell compartment of TIM-1-sufficient or -deficient mice and challenge them with EBOV GP/rVSV ., T cell-depleted , TIM-1-sufficient mice succumbed to EBOV GP/rVSV more readily than T cell-depleted , TIM-1-deficient mice , suggesting that in our model system a TIM-1-dependent T cell cytokine storm was not responsible for virus pathogenesis ., In total , our studies provide evidence that TIM-1-associated pathogenesis correlated with enhanced virus load at late times during infection , consistent with TIM-1 having an important role as a receptor for EBOV in vivo ., This study was conducted in strict accordance with the Animal Welfare Act and the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health ( University of Iowa ( UI ) Institutional Assurance Number: #A3021-01 ) ., All animal procedures were approved by the UI Institutional Animal Care and Use Committee ( IACUC ) which oversees the administration of the IACUC protocols and the study was performed in accordance with the IACUC guidelines ( Protocol #8011280 , Filovirus glycoprotein/cellular protein interactions ) ., BALB/c TIM-1-deficient mice have been previously described 39 and were a kind gift from Dr . Paul Rothman ( Johns Hopkins University ) ., Briefly , exons 4 and 5 of the TIM-1 gene , Havcr1 , were replaced with a LacZ gene , generating a TIM-1-null mouse ( TIM-1-/- ) ., BALB/c IFN-αβ receptor-deficient ( Ifnar-/- ) mice were a kind gift from Dr . Joan Durbin , NYU Langone Medical Center ., Mice were bred at the University of Iowa ., BALB/c Ifnar-/- and BALB/c Havcr1-/- ( TIM-1-/- ) mice were crossed for the creation of heterozygous progeny ., Progeny were interbred and mice screened for the correct BALB/c Ifnar-/-/Havcr1-/- genotype ( referred to as TIM-1-/- throughout this study ) ., Genomic DNA from mouse tail-clips was assessed by PCR for genotypes ., All expected genotypes were produced in normal Mendelian ratios ., The primers and protocol for Ifnar-/- genotyping has been previously described 40 ., Havcr1 primer sequences included: shared forward , 5 GTTTGCTGCCTTATTTGTGTCTGG 3; WT reverse , 5 CAGACATCA-ACTCTACAAGGTCCAAGAC 3; knockout reverse , 5 GTCTGTCCTAGCTTCCTCACTG 3 ., PCR amplification was performed for 30 cycles at 94°C for 30 sec , 60°C for 30 sec , and 72°C for 1 min ., These studies used recombinant , replication-competent vesicular stomatitis virus ( VSV ) expressing GFP and either full length EBOV GP ( EBOV GP/rVSV-GFP ) 41 ( kind gift of Dr . Kartik Chandran ) , EBOV GP lacking the mucin domain of GP1 ( EBOV GPΔO/rVSV-GFP ) 8 , 15 or rVSV-GFP encoding its native glycoprotein , G ( G/rVSV ) ( kind gift of Dr . Sean Whelan ) ., Virus stocks were produced by infecting Vero cells , an African green monkey kidney epithelial cell line , at a low multiplicity of infection ( MOI ) of ~0 . 001 and collecting supernatants 48 hours following infection ., Virus stocks were concentrated by centrifugation at 7 , 000 rpm at 4°C overnight ., The virus pellet was resuspended and centrifuged through a 20% sucrose cushion by ultracentrifugation at 26 , 000 rpm for 2 hours at 4°C in a Beckman Coulter SW32Ti rotor ., The pellet was resuspended in PBS , treated with endotoxin removal agent ( ThermoScientific #20339 ) , aliquoted , and frozen at -80°C until use ., Five- to eight-week-old female BALB/c Ifnar-/- ( control ) and BALB/c Ifnar-/-/Havcr1-/- ( TIM-1-/- ) mice were infected i . v . with recombinant , infectious VSV that encoded GFP and EBOV GP , EBOV ΔO or the native VSV G glycoprotein ( EBOV GP/rVSV-GFP , EBOV GPΔO/rVSV-GFP and G/rVSV-GFP , respectively ) using concentrations of virus noted in the figure legends ., The dose of EBOV GP/rVSV or EBOV GPΔO/rVSV-GFP administered was dependent upon the stock ., The dose of each stock was titered in vivo to identify stock concentrations that gave predictably high ( 75% or greater of challenged mice ) levels of mortality of Ifnar-/- ( control ) mice in 5–7 days ., For studies with G/rVSV-GFP , either 101 or 105 iu of VSV virus was administered by i . v . injection ., Survival was tracked; mice were weighed and scored for sickness daily ., Clinical assessment of sickness was scored as follows: 0 , no apparent illness; 1 , slightly ruffled fur; 2 , ruffled fur , active; 3 , ruffled fur , inactive; 4 , ruffled fur , inactive , hunched posture; 5 , moribund or dead ., Mice were humanely euthanized if they reached a score of 4 ., All mouse infection studies were concluded at 10 or 12 days following infection due to surviving mice regaining any lost weight and having no signs of clinical illness ., Organs were harvested from control and TIM-1-/- mice at 1 , 3 or 5 days following infection from with EBOV GPΔO/rVSV ., Prior to euthanasia , mice were anesthetized with isoflurane to perform retro-orbital bleeds for serum ., Mice were euthanized and perfused with 10 mL of PBS through the heart and organs harvested , weighed and frozen at -80°C ., To determine virus titers , organs or sera were thawed and organs were homogenized in PBS and filtered through a 0 . 45 μm syringe filter ., Viral titers were determined by end-point dilution on Vero cell as previously described 8 ., Infection was scored 5 days following infection for GFP positivity using an inverted fluorescent microscope ., Virus titers were calculated as 50% tissue culture infective dose ( TCID50 ) /mL by the Spearman-Karber method ., All organ titers were normalized according to the weight of the organ at harvest ., Quantitative reverse transcriptase polymerase chain reaction ( qRT-PCR ) was used to detect proinflammatory cytokine and chemokines levels from organs of mice challenged with EBOV GPΔO/rVSV ., At time of harvest , organs were placed in Trizol and frozen at -80°C until further use ., Total RNA was isolated using TRIzol LS reagent ( Life Technologies ) according to manufacturer’s tissue RNA isolation procedure ., RNA was quantified by Nanodrop ( Thermo Scientific ) ., Total RNA ( 2 μg ) was reverse transcribed into cDNA using random primers and the High-Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) ., SYBR Green based quantitative PCR reactions ( Applied Biosystems ) were performed using 1 . 5μL of a 1:100 dilution of cDNA from each reaction and specific primers for murine cytokines and chemokines ., Primer sequences are found in S1 Table ., Expression levels of the cytokine/ chemokines of interest were defined as a ratio between threshold cycle ( Ct ) values for the gene of interest and the endogenous control , mouse hypoxanthine guanine phosphoribosyl transferase ( HPRT ) , and is displayed as the log2 value of this ratio ., Five- to eight-week-old female BALB/c Ifnar-/- and BALB/c Ifnar-/-/TIM-1-/- mice were injected with 200μg of anti-CD4 ( clone GK1 . 5 ) and 200μg anti-CD8 ( clone 2 . 43 ) depleting monoclonal antibodies both one day prior to retro-orbital infection with EBOV GP/rVSV-GFP and two days post infection ., Survival was tracked; mice were weighed and scored for sickness daily as described above to assess euthanasia criteria for each infected mouse ., Prior to infection with EBOV GP/rVSV-GFP , depletion was validated by isolating peripheral blood mononuclear cells from both depleted and non-depleted animals and staining of PBMCs with anti-CD90 antibody ( clone 30-H12 ) ., Staining was done by incubating with anti-CD90 antibody in FACS buffer and Fc block ( clone 2 . 4G2 ) for 30 minutes , washing 3 times to remove excess antibody , and detecting fluorescence on a BD FACSCalibur ., Statistical analyses were performed using GraphPad Prism software ( GraphPad Software , Inc . ) ., Results are shown as means or geometric means and standard error of the means ( s . e . m . ) or geometric s . e . m . , respectively , is shown where appropriate ., Log-rank ( Mantel-Cox ) tests were used to analyze differences in survival ., In vivo experiments were performed at least in duplicate with at least 8 mice total per treatment group ., Mice or samples were randomly assigned to various treatment groups ., All data points and animals were reported in results and statistical analyses ., For the nonparametric viral titer data , Mann-Whitney U-test was used ., P values less than 0 . 05 were considered significant ., For two way comparisons between control and experimental values , a Student’s t-test was performed ., To create a TIM-1 deficient mouse , exons 4 and 5 of the Havcr1 gene encoding TIM-1 were replaced with the LacZ gene by homologous recombination as previously described 39 ., This mouse strain was used to study the role of TIM-1 in allergic airway diseases and Th2 responses 39 ., Phenotypic characterization of TIM-1-/- mice revealed no differences in immune cell numbers , immune system development , or immunological homeostasis compared to WT mice 39 ., BALB/c TIM-1-/- mice were bred onto a BALB/c interferon αβ receptor ( Ifnar-/- ) knock out background since type I interferon abrogates replication of the BSL2 recombinant EBOV GP/rVSV used in these studies 41 , 42 ., Homozygous BALB/c Ifnar-/-/TIM-1-/- and Ifnar-/- mice ( called TIM-1-/- and control mice , respectively , throughout the remainder of this study ) were used for all infections ., Challenge virus was administered intravenously to mimic a primary route of EBOV transmission , blood-to-blood contact ., Mice were challenged with the lowest dose of virus that produced predictable death in control mice in 5–7 days ( S1 Fig ) ., Minor titer variations were observed between virus stocks and dosages were adjusted accordingly ., We challenged the TIM-1-/- and control mice with full length EBOV GP/rVSV or EBOV GPΔO/rVSV , which has the GP1 mucin like domain ( MLD ) deleted ., EBOV GPΔO pseudovirions and recombinant viruses have the same tropism as virus bearing EBOV GP 8 , 43–45 ., Use of both viruses in these studies allowed us to determine if the elimination of the mucin domain altered the pathogenesis associated with in vivo challenge with these viruses ., As expected , TIM-1-sufficient control mice succumbed to EBOV GP/rVSV or EBOV GPΔO/rVSV between days 4–7 of infection ( Fig 1A and 1B ) ., By contrast , TIM-1-/- mice challenged with the same dose had significantly reduced mortality following EBOV GP/rVSV or EBOV GPΔO/rVSV infection and delayed time-to-death of those that did succumb to infection ., These findings indicate that TIM-1-/- mice had improved survival when infected with EBOV GP/rVSV compared to controls and that survival was not affected by the presence of the GP1 MLD ., In tissue culture studies , we have shown that hTIM-1 does not mediate WT VSV entry 8 , presumably because the cognate receptor for VSV , LDL receptor , is abundantly present on target cells and mediates VSV entry 46 ., However , the relevance of TIM-1 in vivo for VSV infection has not been examined ., Further , WT VSV serves as an excellent control for in vivo studies with EBOV GP-bearing viruses ., We challenged TIM-1-/- and control mice with 105 iu of VSV by i . v . injection ., In contrast to our EBOV GP/rVSV findings , we observed no difference in the survival curve between the two strains of mice ( Fig 1C ) ., Since it is likely that VSV bearing it native GP is more pathogenic than a recombinant VSV containing a different viral GP , we also evaluated mortality associated with different doses of VSV and found that administration of as little as 101 iu of VSV was lethal to Ifnar-/- mice ( S2 Fig ) ., Thus , we repeated VSV infections in control and TIM-1-/- mice at a challenge dose of 101 iu to determine if subtle changes in virus pathogenesis could be discerned ., Even at this low dose , there was no difference in the survival in the TIM-1-/- mice versus the control mice ( Fig 1D ) ., These results provide evidence that the difference in EBOV GP/rVSV pathogenesis in BALB/c Ifnar-/- and TIM-1-/- mice was due to the presence of EBOV GP expressed in the recombinant VSV rather than other VSV genes ., The reduced pathogenesis of EBOV GP expressing virus in TIM-1-/- mice was consistent with findings described by Younan et al . using maEBOV 38 ., The effect of TIM-1 expression on viremia and organ viral loads following i . v . EBOV GPΔO/rVSV infection was examined in serum and organs harvested 1 , 3 or 5 days following infection ( Fig 2 ) ., Viremia and infectious virus in various organs were quantified by endpoint dilution titering on Vero cells , a highly permissive cell line for EBOV GPΔO/rVSV ., At early times during infection , no difference in viremia or virus load was observed in most organs of TIM-1-/- versus control mice ., However , by day 5 of EBOV GPΔO/rVSV infection , TIM-1-/- mice had a 100-fold reduction in viremia compared to control mice ( Fig 2 ) and a similar trend was observed during infections with full length EBOV GP/rVSV ( S3 Fig ) ., In parallel , levels of infectious virus in liver , kidney , and adrenal gland were also significantly reduced ., Studies at day 5 of infection also indicated that EBOV GPΔO/rVSV loads were much reduced in the brain of TIM-1-/- mice and trended lower in the testis ( S4A and S4B Fig ) , consistent with an overall reduction in virus load in the TIM-1-/- mice at late times during infection ., Thus , reduced virus replication in a number of organs was associated with the survival observed in TIM-1-/- mice ., These findings provide evidence that TIM-1 expression is important for the generation of high viral load in some organs at late times in infection ., Viral loads in the spleen and lungs were not affected by the loss of TIM-1 ( Fig 2 and S4C Fig ) ., The viral burden in the spleen was significantly higher at day 1 than in any other organ assessed and remained high in both mouse strains throughout the course of infection with a peak in titers occurring at day 3 ., These results are consistent with previous studies that implicate spleen in early and sustained EBOV replication 47–49 ., Lung titers were not significantly different between the control and TIM-1-/- mice at 5 days following infection ., This result was somewhat unexpected as we had previously demonstrated robust hTIM-1 expression on the apical surface airway epithelial cells 8 ., As TIM-1 was not observed to be expressed on the basolateral side of lung epithelium , TIM-1 may be important for entry of aerosolized EBOV entry into a host , but may not influence basolateral infection of lung via the circulation ., Elevated proinflammatory and immunomodulatory cytokines and chemokines are evident in serum and infected organs during EBOV infection of animal models and patients 50–56 ., To determine if reduced virus load in TIM-1-deficient mice at late time points was associated with lower RNA expression profiles of selected , well-characterized cytokines , levels in the spleen , liver and kidney were examined prior to and following EBOV GPΔO/rVSV infection ., Organs were harvested at day 3 and 5 of infection and total RNA was isolated and amplified for the mRNA of the housekeeping gene , HPRT , and the cytokines TNF , IL-6 , IL-12 and IL-10 ., Cytokine expression levels were normalized against mouse HPRT expression ., Overall , baseline values of the organ cytokine expression from uninfected control and TIM-1-/- mice were similar ( Fig 3 ) ., While at day 5 of infection TNF was significantly higher in spleen of control mice , in general during infection the expression of cytokine was variable within groups and levels were not significantly different between the two strains of mice ., Elevated levels of several chemokines and growth factors have been implicated in fatal EBOV disease outcomes including MIP-1α , MIP-1β , MCP-1 , M-CSF , MIF , IP-10 , GRO-α and eotaxin 54 ., Therefore , we analyzed control and TIM-1-/- organs following EBOV GPΔO/rVSV infection for the chemokines , CXCL10 ( IP-10 ) and CCL2 ( MCP-1 ) ., At least one of the two transcripts for these proinflammatory chemokines in all three organs was elevated in the control mice at both day 3 and/or 5 of infection compared to the TIM-1-/- mouse tissues ( Fig 4 ) ., In combination with our survival and viral burden results , these observations suggest that the presence of TIM-1 in mice contributes to EBOV GP/rVSV pathogenesis through increased infection of cells in several organs at late times during infection and that this is associated with increased expression of proinflammatory chemokines ., TIM-1 is expressed by a number of different hematopoietic and non-hematopoietic cells 57 ., Our findings indicate that virus load in spleen , an organ rich in hematopoietic cells , was not affected by the loss of TIM-1 expression , suggesting that it might be TIM-1 expression on non-hematopoietic cells late during infection that affects EBOV GP/rVSV load and survival ., As others have suggested that TIM-1 on T cell subsets contribute to enhanced EBOV pathogenesis 38 , we depleted T cells in control and TIM-1-/- mice to assess outcomes during EBOV GP/rVSV infection ., Mice were intraperitoneally administered α-CD8 mAb , 2 . 43 , and α-CD4 mAb , GK1 . 5 , at days -1 and 2 ., We verified that T cells within peripheral blood were profoundly depleted at day 5 of infection by flow cytometry following immunostaining with an α-CD90 mAb ( Fig 5A ) ., As observed for the T cell-competent mice in above studies , T cell-depleted control mice challenged with EBOV GP/rVSV succumbed to infection between 4–6 days , whereas T cell-depleted TIM-1-/- mice had significantly better survival ( Fig 5B ) ., These data do not provide support for the contention that TIM-1 on T cells contributes to pathogenesis associated with our viral infection model ., Instead , in total , our findings are consistent with TIM-1 expression on non-T cell populations contributing to pathogenesis ., Here , we show that loss of TIM-1 expression decreased overall mortality and delayed time-to-death of those mice that did succumb when challenged with EBOV GP/rVSV ., The impact on survival of TIM-1 expression was similar with rVSV bearing MLD-deleted EBOV GP , indicating that the presence of the MLD did not affect the observed pathogenesis ., Consistent with the enhanced survival of the TIM-1-deficient mice following virus challenge , we show that these mice also had reduced infectious virus in liver , kidney and adrenal gland at late times during infection ., EBOV replication in these organs is well established and thought to contribute to overall EBOV load 47 , 58–61 ., The lower virus load in these organs of the TIM-1-/- mice was also reflected in a ~100-fold reduction in viremia at day 5 of infection ., The reduced pathology in our TIM-1-deficient mice was EBOV GP-dependent since survival associated with G/rVSV infection was unaffected by TIM-1 expression ., Thus , our studies indicate that the glycoprotein present on the virions was responsible for the TIM-1-dependent changes in virus load and mouse survival ., The correlation between enhanced survival and reduced viral loads in the TIM-1-/- mice suggests that TIM-1 serves as a virus receptor for EBOV in some organs ., However , this role of TIM-1 must be late in infection since viremia and organ virus loads do not differ between the two mouse strains at days 1–3 of infection ., A number of studies have shown at early times of infection EBOV antigens are primarily , if not exclusively , found in cells of the myeloid compartment 49 , 61 , 62 , cells that do not express TIM-1 ., However , as infection progresses , additional cell types become EBOV antigen positive , suggesting a spread of virus to other cell types 50 , 62 ., Our data suggest that TIM-1 on some of this later group of cells contributes to virus infection and pathogenesis ., Likely , late cell targets that express TIM-1 would include kidney epithelial cells 63 , 64 and epithelial populations 8 in adrenal gland , eye , liver , brain and testis ., Interestingly , we did not observe that all organs previously implicated as important in EBOV infection had lower virus load in TIM-1-/- mice ., Splenic viral loads were high throughout infection in both control and TIM-1-/- mice ., These data suggested that TIM-1 expressing cells do not appreciably contribute to splenic virus loads and that splenic loads can be high in mice without those animals necessarily succumbing to infection ., While the TIM-1 does not interact directly with EBOV GP , the binding of TIM-1 to virion-associated PS has been shown to elicit viral particle entry into the endosomal compartment 9 , 15 where EBOV GP is proteolytically processed , binds to NPC1 and mediates membrane fusion 17–21 ., Filoviral particle entry into endosomes occurs through interactions with a number of cell surface receptors in tissue culture ., However , these studies and those by Younan , et al 38 provide support that TIM-1 is important for in vivo infection and contributes to EBOV pathogenesis ., Future studies to evaluate the role of additional cell surface receptors implicated in EBOV entry would provide valuable insights to the potential receptor redundancy ., These receptors include other TIM family members , TAM tyrosine kinase receptors and C-type lectins ., Our studies and those performed by Younan et al . 38 delivered EBOV GP/rVSV intravenously ., In other studies , we observed that intraperitoneal ( i . p . ) delivery of EBOV GP/rVSV or maEBOV into WT versus TIM-1-/- mice was equally pathogenic ., This finding may be explained by the previous observation that another TIM family member , TIM-4 , is highly expressed on resident peritoneal macrophages 65 and is used as a receptor for EBOV 27 ., Likely , the use of TIM-4 as a receptor within this compartment usurps the need for TIM-1 expression during i . p . challenge , even late during infection ., Surprisingly , Younan et al . did not observe that TIM-1-/- mice had decreased maEBOV virema 38 ., The authors reported that the genome copy number in plasma did not significantly differ in TIM-1-sufficient and -deficient mice at day 6 of infection ., The discrepancy between our findings and the previous study may be due to the tissues examined , the virus administered , the quantity of virus administered and/or the timing of the sampling ., One notable difference between the studies is Younan , et al . administered a very large dose of maEBOV ( 30 , 000 LD50 ) to the mice , whereas the dose of virus given to mice in our study was the minimal lethal dose determined in preliminary titration studies ., The physiological role of TIM-1 has been extensively studied ., Agonistic monoclonal antibody binding to TIM-1 on CD4+ T , iNKT and splenic B cells induces cellular activation in a wide range of organisms from zebrafish to humans 24 , 32 , 33 , 63 , 66 , 67 ., This observation has led to the understanding that TIM-1 serves as a costimulatory molecule on these cells and leads to upregulation of cytokines in T and NKT cells 24 , 63 , as well as antibody production by B cells 67 ., In contrast , transient TIM-1 expression on injured kidney epithelial cells serves an anti-inflammatory role through its uptake and clearance of apoptotic bodies 64 ., Younan , et al . described the role of TIM-1 in EBOV pathology to TIM-1 stimulation of T cell cytokine and chemokine dysregulation 38 ., In general , we did not observe significant differences of proinflammatory cytokines in TIM-1+ and TIM-1- mice even at late times during infection when titer differences were notable ., It is certainly possible that other cytokines , not evaluated here , might be more dramatically altered ., We did observe elevated levels of the proinflammatory chemokine transcripts , CCL2 and CXCL10 , in the TIM-1-sufficient mice compared to the deficient mice ., We postulate that the higher levels of chemokines in TIM-1+ mice may reflect the innate immune responses stimulated by the higher virus load ., Alternatively , as postulated by Younan , et al . , the elevated chemokine profile and associated mortality in the TIM-1+ mice might be due to a TIM-1-dependent cytokine storm elicited by T cells 38 ., We tested this latter possibility by virus challenge of T cell-depleted mice ., T cell depletion did not alter EBOV GP/rVSV pathology ., We found significantly greater mortality associated with virus infection of TIM-1-sufficient mice which were depleted for T cells than T cell-depleted , TIM-1-deficient mice , suggesting that T cells are not responsible for the reduced survival of TIM-1-sufficient mice ., Hence , our findings do not support the conclusion that TIM-1 expression on T cells plays a significant role in the pathology associated with this acute infection ., A caveat to our studies is that our infections were performed in mice on an Ifnar-/- background ., It is possible that the levels of cytokines and chemokines observed in our studies are influenced by the genetic background of the mice ., Others have looked at the effect of an Ifnar-/- background on immune responses ., Studies have shown that a number of cytokines and chemokines are suppressed by the absence of type I interferon ( IFN ) responses in the first 24 hours of virus infection 68 , 69 ., However , by 24 hours of viral infection , the burst of production of these transcripts and proteins in wild-type mice is reported to subside and levels in wild-type and Ifnar-/- animals

Introduction, Materials and methods, Results, Discussion

T cell immunoglobulin mucin domain-1 ( TIM-1 ) is a phosphatidylserine ( PS ) receptor , mediating filovirus entry into cells through interactions with PS on virions ., TIM-1 expression has been implicated in Ebola virus ( EBOV ) pathogenesis; however , it remains unclear whether this is due to TIM-1 serving as a filovirus receptor in vivo or , as others have suggested , TIM-1 induces a cytokine storm elicited by T cell/virion interactions ., Here , we use a BSL2 model virus that expresses EBOV glycoprotein to demonstrate the importance of TIM-1 as a virus receptor late during in vivo infection ., Infectious , GFP-expressing recombinant vesicular stomatitis virus encoding either full length EBOV glycoprotein ( EBOV GP/rVSV ) or mucin domain deleted EBOV glycoprotein ( EBOV GPΔO/rVSV ) was used to assess the role of TIM-1 during in vivo infection ., GFP-expressing rVSV encoding its native glycoprotein G ( G/rVSV ) served as a control ., TIM-1-sufficient or TIM-1-deficient BALB/c interferon α/β receptor-/- mice were challenged with these viruses ., While G/rVSV caused profound morbidity and mortality in both mouse strains , TIM-1-deficient mice had significantly better survival than TIM-1-expressing mice following EBOV GP/rVSV or EBOV GPΔO/rVSV challenge ., EBOV GP/rVSV or EBOV GPΔO/rVSV in spleen of infected animals was high and unaffected by expression of TIM-1 ., However , infectious virus in serum , liver , kidney and adrenal gland was reduced late in infection in the TIM-1-deficient mice , suggesting that virus entry via this receptor contributes to virus load ., Consistent with higher virus loads , proinflammatory chemokines trended higher in organs from infected TIM-1-sufficient mice compared to the TIM-1-deficient mice , but proinflammatory cytokines were more modestly affected ., To assess the role of T cells in EBOV GP/rVSV pathogenesis , T cells were depleted in TIM-1-sufficient and -deficient mice and the mice were challenged with virus ., Depletion of T cells did not alter the pathogenic consequences of virus infection ., Our studies provide evidence that at late times during EBOV GP/rVSV infection , TIM-1 increased virus load and associated mortality , consistent with an important role of this receptor in virus entry ., This work suggests that inhibitors which block TIM-1/virus interaction may serve as effective antivirals , reducing virus load at late times during EBOV infection .

T cell immunoglobulin mucin domain-1 ( TIM-1 ) is one of a number of phosphatidylserine ( PS ) receptors that mediate clearance of apoptotic bodies by binding PS on the surface of dead or dying cells ., Enveloped viruses mimic apoptotic bodies by exposing PS on the outer leaflet of the viral membrane ., While TIM-1 has been shown to serve as an adherence factor/receptor for filoviruses in tissue culture , limited studies have investigated the role of TIM-1 as a receptor in vivo ., Here , we sought to determine if TIM-1 was critical for Ebola virus glycoprotein-mediated infection using a BSL2 model virus ., We demonstrate that loss of TIM-1 expression results in decreased virus load late during infection and significantly reduced virus-elicited mortality ., These findings provide evidence that TIM-1 serves as an important receptor for Ebola virus in vivo ., Blocking TIM-1/EBOV interactions may be effective antiviral strategy to reduce viral load and pathogenicity at late times of EBOV infection .

blood cells, cell motility, innate immune system, medicine and health sciences, vesicular stomatitis virus, immune cells, pathology and laboratory medicine, immune physiology, cytokines, spleen, pathogens, immunology, microbiology, viruses, animal models, developmental biology, model organisms, rna viruses, experimental organism systems, molecular development, kidneys, research and analysis methods, white blood cells, animal cells, animal studies, medical microbiology, microbial pathogens, t cells, mouse models, pathogenesis, chemotaxis, immune system, rhabdoviruses, cell biology, anatomy, viral pathogens, physiology, chemokines, biology and life sciences, cellular types, renal system, organisms

journal.ppat.1002583

Preferential Entry of Botulinum Neurotoxin A Hc Domain through Intestinal Crypt Cells and Targeting to Cholinergic Neurons of the Mouse Intestine

Botulinum neurotoxins ( BoNTs ) are responsible for a severe nervous disease in man and animals known as botulism , characterized by skeletal muscle flaccid paralysis and respiratory arrest , resulting from inhibition of acetylcholine ( ACh ) release in peripheral cholinergic nerve terminals ., BoNTs are produced by Clostridium botulinum as single chain proteins ( ap . 150 kDa ) , which are divided into 7 toxinotypes ( A to G ) according to their immunogenic properties ., The toxins are exported outside the bacteria and are proteolytically cleaved into a heavy chain ( H; ap . 100 kDa ) and a light chain ( L; ap . 50 kDa ) , which remain linked by a disulfide bridge ., The di-chain molecule constitutes the active neurotoxin ., The half C-terminal domain of the H-chain ( Hc ) is involved in binding to specific receptors on target neuronal cells and in driving the toxin entry pathway into cells , whereas the N-terminal part permits the translocation of the L chain into the cytosol ., The L chain catalyzes a zinc-dependent proteolysis of one or two of the three proteins of the SNARE complex , which play an essential role in evoking neurotransmitter exocytosis ., The BoNT/A L-chain cleaves the synaptosomal associated protein SNAP25 at the neuromuscular junction 1–4 ., The highly specific binding of BoNTs to target nerve endings involves protein and ganglioside receptors that localize at the neuronal plasma membrane 5 ., Gangliosides of GD1b and GT1b series are involved in binding and functional entry into cells of BoNT/A and BoNT/B 6–9 ., The protein receptors on neuronal cells have been identified as synaptotagmin I and II for both BoNT/B and BoNT/G , and synaptic vesicle protein SV2 ( isoforms A , B and C ) for BoNT/A 8 , 10–14 , BoNT/E 15 , and BoNT/F 16 , 17 ., SV2C is the preferred BoNT/A neuronal receptor 14 , whereas BoNT/E recognizes glycosylated SV2A and SV2B 15 ., BoNT/D also uses SV2 proteins as receptor in association with gangliosides for its entry into neuronal cells , but binds to SV2 via a distinct mechanism than BoNT/A and BoNT/E 18 ., In addition , SV2A and SV2B have also been evidenced to mediate the entry of tetanus toxin ( TeNT ) into the central target neurons including hippocampal and spinal cord neurons 19 ., Botulism usually results from the ingestion of preformed neurotoxin in contaminated food , or ingestion of spores or bacteria , which under certain circumstances , may colonize the gut and produce the neurotoxin in situ 20 ., In either case , BoNT escapes the gastro-intestinal tract to reach the target cholinergic nerve endings , possibly through the blood and lymph circulation 21 ., Indeed , previous observations have shown that after oral administration of BoNT in experimental animals , the toxin enters the blood and lymph circulation ., The upper small intestine was found to be the primary site of absorption 22–25 , but BoNT can also be absorbed from the stomach 21 ., Penetration of BoNT through an epithelial cell barrier and its subsequent migration to cholinergic nerve endings are the essential first steps of botulinum intoxication ., In in vitro models , BoNTs have been found to bind to polarized epithelial cells and to undergo receptor-mediated endocytosis and transcytosis from apical to basolateral sides 24 , 26–30 ., However , little is known about the precise pathway of BoNT migration from the intestinal lumen to the target nerve endings ., The digestive tract contains its own independent nervous system , the enteric nervous system ( ENS ) , which is as complex as the central nervous system , and it is also referred as the “brain of the gut” ., ENS controls and coordinates motility , exocrine and endocrine secretions , and blood microcirculation of the gastrointestinal tract ., Nerve cell bodies of ENS are clustered into small ganglia which are organized in two major plexuses: the myenteric plexus between the longitudinal and circular muscle layers , and the submucosal plexus associated with the mucosal epithelium between the circular muscles and the muscularis mucosa ., Ganglia also contain glial cells and their extensions ., ENS neurons can be classified as afferent sensory neurons , interneurons , and motor neurons , which are connected to the central autonomic nervous system through both sensory and motor pathways ., More than 20 types of neurotransmitters have been identified in ENS , and most enteric neurons may produce and release several of them ., However , neurotransmitter functions have not been fully identified ., Secretory and motor neurons are cholinergic , these latter also contain substance P . Myenteric neurons are connected to the cholinergic parasympathetic neurons through nicotinic , and in some areas , muscarinic receptors 31 , 32 ., Vasoactive intestinal protein ( VIP ) and serotonin are also major neurotransmitters in the regulation of normal gut function and interconnection with the central nervous system 33 ., In this study , we used fluorescent Hc fragment from BoNT/A to monitor the trafficking of the toxin into the mouse intestinal mucosa ., It has been previously shown that the Hc domain from TeNT , which shares similar structural organization and catalytic activity with BoNTs , is a useful tool to investigate the intracellular trafficking of the neurotoxin 34–36 ., Although , it cannot be ruled out that a cross interplay between the BoNT domains may modify the toxin routing driven by Hc 37 , recombinant HcA has been reported to retain the same structure than that of the receptor binding domain of the BoNT/A holotoxin and to enter hippocampal neurons similarly to the whole neurotoxin 16 , 38 ., In addition , HcA has been found to bind and transcytose through intestinal cells as well as the holotoxin 26 , 39 , validating its use to investigate the intestinal trafficking of the toxin ., In striated muscle , BoNT/A is known to inhibit ACh release from motor nerve terminals by cleaving the synaptosomal associated protein SNAP-25 , leading to the inability of synaptic vesicles containing ACh to undergo transmitter release for a review , see 3 ., In the gastrointestinal smooth muscle , BoNT/A also impairs cholinergic transmission by inhibiting ACh release from postganglionic cholinergic nerve endings in vitro and in vivo 40 , 41 ., The first aim of this study was to determine whether BoNT/A affected smooth muscle contractility when applied intra-luminally on isolated mouse ileum segments ., In preparations that were equilibrated in the standard oxygenated solution for about 30 min , spontaneous contractile responses were usually observed at a frequency rate of about 6–10 min−1 ( n\u200a=\u200a4 ) ., These spontaneous contractions had a peak force that was variable from preparation to preparation , but comprised between 0 . 5 and 1 . 2 g ( n\u200a=\u200a4 ) ., BoNT/A reduced their frequency after 2 , 3 and 4 h of the intralumenal injection ( Figure 1A ) ., It is worth noting that for control preparations maintained for 4 h in the same conditions as the ones treated with BoNT/A , not only there was no reduction of the spontaneous contractions , but a small increase in their frequency ( 10% after 2 and 3, h ) ( Figure 1A ) ., The contraction pattern changed also after exposure to BoNT/A , from very regular oscillations during the first hour to very irregular contractions after more than 2 h ( Figure 1B and 1C ) ., Electric field stimulation evoked contractile responses that attained a peak force comprised between 2 and 7 mN ( n\u200a=\u200a3 ) under control conditions ( Figure 1E ) , with little rundown of the responses when stimulations were applied every 50–60 min ( Figure 1D ) ., As shown in Figure 1D , BoNT/A reduced the electrically-evoked contractile response in a time-dependent manner ., The time to decrease to 50% the evoked tension-time integral response was about 110 min , and the toxin reduced to about 90% electrically-evoked contractions within 240 min ., Although it is difficult to exclude the possibility of direct muscle stimulation by the applied field-stimuli , several lines of evidence indicate that this would represent no more than 10–15% of the evoked tension-time integral response in our experimental conditions ., Most of this evidence comes from:, ( i ) Data obtained with BoNT/A showing that blockade of the evoked contraction attains a maximum around 86–90% of control values , and was never complete ., The remaining tension can be suspected to be due to direct muscle stimulation unaffected by BoNT/A ., ( ii ) If direct stimulation of the muscle would occur , one would expect that tension levels would be sustained and maintained during the field stimulation , which is not the case ( Figure 1E ) ., ( iii ) Spontaneous contractions occurring during the falling phase of the evoked-contractile response were not enhanced in amplitude , which is consistent with the low influence of direct muscle stimulation under the experimental conditions used ., Interestingly , after BoNT/A has blocked the evoked response by field stimulation , the addition of carbachol ( 20 µM ) or ACh ( data not shown ) to the standard solution evoked a contractile response ( Figure 1F ) ., These results suggest that under the conditions used BoNT/A is able to exert an action on cholinergic terminals that leads to a blockade of the contractile responses evoked by electric field stimulation , while spontaneous myogenic contractions were reduced in frequency , but not completely abolished ., The fact that carbachol could induce contractile activity after BoNT/A-induced blockade of contraction evoked by electric field stimulation , strongly suggests that the sensitivity of ACh receptors ( muscarinic and nicotinic ) is not affected by the toxin ( Figure 1F ) ., In a previous report , we have found that BoNT/A transcytosis through intestinal cell monolayers grown on filters is mediated by SV2C or , at least , an immunologically related protein 30 ., To address whether SV2C might be a functional receptor in the ex vivo intestinal tract model , BoNT/A was preincubated with the intravesicular domain segment L4 of SV2C prior to injection into ligated intestinal loop ., As shown in Figure 1A , preincubation with SV2C/L4 significantly prevented the BoNT/A inhibitory effects on spontaneous intestinal smooth muscle contractions , suggesting a partially SV2C-dependent BoNT/A uptake through the intestinal mucosa ., However , these results do not rule out that SV2C/L4 also passed , independently or associated with BoNT/A , through the intestinal barrier and impaired BoNT/A uptake by nerve terminals ., We first investigated the potential binding sites for HcA in mouse intestine mucosa and submucosa , as well as in the musculosa ., For that , cryosections of mouse small intestine , fixed on glass slides , were incubated with fluorescent HcA and analyzed by confocal microscopy ., Since BoNT has been reported to be preferentially absorbed from the upper small intestine 23 , 24 , sections from ileum were analyzed ., Only a faint staining was observed in brush border of enterocytes along intestinal villi ( Figure 2A ) ., However , a strong HcA binding was observed on intestinal crypt localized at the bottom of villi ., Paneth cells , which are characterized by their numerous secretory-granule content , are spatially restricted to intestinal crypts and were used as a marker of these regions ., Staining of Paneth cells with the TRITC-labeled lectin Urex europaeus agglutinin type 1 ( UEA1 ) 42 did not significantly colocalize with HcA ( Figure 2B ) ., This may indicate that some cells from intestinal crypts , distinct from Paneth cells , exhibit preferential binding sites for HcA ., Moreover , small cells scattered along the villi were stained with HcA ( Figure 2A ) and some of them co-stained with UEA1 ( Figure 2B ) ., These UEA1 positive cells likely correspond to goblet cells 43 ., It is worth noting that HcA stained neuronal cell bodies and neuronal structures in the submucosa and musculosa ., However , only a low proportion of neuronal structures were recognized and labeled by HcA , as revealed by immunolabeling neurofilaments and co-staining with the fluorescent toxin fragment ( Figure 2C ) ., Hence , the binding domain of BoNT/A potentially targets epithelial cells in intestinal crypts , and neuronal structures of intestinal plexuses ., Note that anti-neurofilament antibodies are not specific of the nerve endings , where BoNT/A is assumed to bind , but recognize neuronal structures all along the neuronal cells ., This probably accounts for the irregular co-staining between HcA and anti-neurofilament antibodies ., In addition , the irregular pattern of HcA staining might also be related to the variability in orientation and size of the cryosections ., Moreover , cryosections might artificially expose certain antigens which are buried in intact tissues ., Thus , immunostaining pattern in cryosections has to be considered with caution and confirmed in ex vivo experiments with intact tissues as shown in the following figures ., To analyze HcA entry into the intestinal mucosa and submucosa , ex vivo experiments were performed , as previously described with the whole toxin ., For this , excised small intestine loops were washed , ligated at both extremities , and incubated in oxygenated Krebs-Ringer solution at 37°C ., Fluorescent HcA was inoculated into the intestinal lumen , and at various time intervals , intestinal loops were washed , fixed and processed for dissection , and immunostaining ., A competition assay between HcA-Cy3 and native BoNT/A injected into an ileum loop and monitored by fluorescence analysis of the intestinal mucosa , supported that fluorescent HcA follows the same entry pathway than native BoNT/A ( Figure 3A ) ., Fluorescent HcA entered similarly ileum , duodenum or jejunum segments , as tested by mucosal fluorescence analysis ( Figure 3A ) ., After 30–60 min incubation , labeled HcA was detected inside the lumen of intestinal crypts , and in some crypt cells , but not or with a low intensity in enterocytes or other cells in the villi ( Figure 3 B and C ) ., HcA also labeled long cell extensions in the submucosa , which correspond to nerve fibers or neuronal extensions ( Figure 3D; arrow head ) since they were co-stained with antibodies against neurofilaments ( not shown ) ., Longer incubation periods ( 90–120 min ) permitted to visualize HcA staining of long filaments in the musculosa , ( Figure 3E ) , which were identified as nerve fibers from the myenteric plexus ( see below ) , but with a weaker intensity ., This suggests a progressive entry of HcA from the intestinal lumen through the mucosa , preferentially through intestinal crypts , to certain neuronal cell and extensions in the submucosa , and then in the musculosa ., To identify the intestinal crypt cells targeted by HcA , fluorescent HcA was injected into the lumen of an intestinal loop ., After an incubation of 15–30 min , the intestinal mucosa was prepared for microscopy observation ., Only few numbers ( 1 or, 2 ) of small cells from each intestinal crypt were stained with HcA ( Figure 4 ) ., Cells stained with HcA were distinct from Paneth cells , which were easily detectable by their numerous granules ( Figure 4 ) , and by their staining with UEA1 ( not shown ) ., Chromogranin-A antibodies , a common marker of neuroendocrine cells in the gastrointestinal tract 44 , colocalized with HcA ( Figure 4A ) ., All cells stained with HcA were also stained with chromogranin-A antibodies , indicating that HcA specifically entered neuroendocrine cells from intestinal crypts ., However , not all chromogranin-A positive cells were stained with HcA , but only about 80% ., Serotonin-producing cells , which are abundant in the ENS , were investigated for their colocalization with HcA ., In the intestinal crypts , all the cells stained with HcA were also immunolabeled with serotonin antibodies ( Figure 4B and C ) ., Interestingly , HcA accumulated in the basal pole of neuroendocrine cells , which is wider than the apical pole exposed to the intestinal crypt lumen ., This strongly supports that HcA uses neuroendocrine cells , mostly serotonin-producing cells , from intestinal crypts for its transport through the intestinal mucosa ., In addition , we checked whether BoNT/A can be transcytosed through the mouse neuroendocrine intestinal cell line STC-1 45 ., As shown in Figure 5A , the passage of biologically active BoNT/A was monitored from apical to basolateral side of STC-1 cell monolayers ., The transcytotic passage of BoNT/A through STC-1 cells was not statistically different from that through Caco-2 enterocytes , but it was lower than through the mouse intestinal crypt cell line m-ICcl2 as shown in Figure 5A ( p<0 . 05 ) and 30 ., It is noteworthy that the passage yield through STC-1 cells was more difficult to assess ( high standard deviation values ) , since these cells do not form tight junctions as epithelial cells ., Since epithelial cells such as Caco-2 and HT29 cells express at the cell surface and secrete from apical and basolateral sides several types of proteases 46–48 , we tested whether these proteases degrade BoNT/A , thus impairing or decreasing the transcytosis level ., As shown in Figure 5A , a 2 to 4 fold higher level of BoNT/A transcytosis was observed in Caco-2 and m-ICcl2 cells incubated with a cocktail of anti-proteases ., However , even in the presence of anti-proteases , BoNT/A transport was more efficient ( 20-fold ) in m-ICcl2 than in Caco-2 cell monolayers ., Thus , the decreased BoNT/A transcytosis through Caco-2 cells is not likely due to a higher protease degradation of BoNT/A before and/or after transport ., STC-1 cells possibly also secrete proteases , and a higher level of BoNT/A transcytosis through this cell type might be expected ., However , since STC-1 cells do not form tightly organized cell monolayers , the results of experiments with anti-proteases were inconclusive ., As we have previously found that m-ICcl2 express SV2C or an imunologically related protein as a putative BoNT/A receptor 30 , we investigated the presence of SV2 proteins and chromogranin A in STC-1 and intestinal cells by Western blotting ( Figure 5B ) ., Chromogranin A was strongly expressed in STC-1 confirming its neuroendocrine type , and to a lower extent in m-ICcl2 and even less in Caco-2 cells , but not in Vero cells used as negative control ., Antibodies against SV2A and SV2B showed no protein related to the expected size of SV2 ( 82 kDa ) in intestinal and STC-1 cells ., In contrast , specific SV2C antibodies directed against the N-terminal part or the intravesicular loop L4 , which is assumed to be the receptor binding domain of BoNT/A 11 , 14 , recognized a protein with the expected size in the intestinal cells and STC-1 , but not in Vero cells ( Figure 5B ) ., The specificity of SV2 antibodies is shown in Figure S1 ., Next , we investigated whether SV2C was involved in the entry of HcA into m-ICcl2 and STC-1 cells by a competition assay between fluorescent HcA and SV2C/L4 ., Cells grown on glass cover slides were exposed to HcA-Cy3 or a combination of fluorescent Hc with a 10-fold higher molar concentration of SV2C/L4 for 10 min at 37°C and then processed for microscopic observation ., As shown in Figure 5C , HcA entered into m-ICcl2 and STC-1 cells , and SV2C-L4 greatly impaired the entry of HcA into both cell types by 97 and 94% , respectively , as determined by counting the number of fluorescent HcA patches per µm2 of cell area ( Figure 5D ) , supporting the view that SV2C participates in the entry mechanism of HcA into cells ., After 30–60 min incubation of fluorescent HcA into an intestinal loop lumen , HcA was detected in the intestinal submucosa , where it stained certain neuronal structures ( Figure 6A and data not shown ) ., Antibodies against neurofilaments allowed visualizing a complex and abundant network of neuronal cell bodies and neuronal extensions in the submucosal plexus immediately underneath intestinal villi and crypts ., Only some of these neuronal structures were stained with HcA ( Figure 6A ) ., BoNTs are well known to interact with cholinergic neurons and to specifically block spontaneous and evoked quantal acetylcholine release 4 , 49 ., However , it has been shown that BoNT/A and BoNT/E are also able to enter other neuronal cell types such as glutamatergic and gamma-aminobutyric acid ( GABA ) -ergic neurons , as well as astrocytes 50 , 51 ., Since ENS contains a large variety of neuronal cell types , we investigated the most representative types as putative targets of HcA in the mouse small intestine ., Cholinergic neurons were monitored by immunostaining with antibodies for choline acetyltransferase ( ChAT ) ., ChAT-immunoreactive neurons are abundant ( about 55% ) in the submucosal plexus , where they are involved in various gut functions including the control of evoked anion secretion by the jejunal and ileal epithelium , and they also interact with Peyers patch follicles 52 , 53 ., Most of ChAT-immunoreactive nerve terminals from the intestinal submucosa were labeled with fluorescent HcA ( Figure 6B and G ) ., Vasoactive intestinal peptide ( VIP ) -immunoreactive neurons are known to be located in jejunum and ileum submucosal plexus , as well as in other organs ., VIP modulates several basic functions including blood flow , smooth muscle relaxation , and exocrine secretion 54 ., VIP-immunoreactive neurons are estimated to represent about 45% of neurons from the submucosal plexus 53 , 55 ., Numerous cells were immunostained with anti-VIP antibodies in mouse intestinal submucosa , but a colocalization between VIP immunoreactivity and HcA staining was only observed in a few of them ( less than 3% ) ( Figure 6C and G ) ., Note that some of the filaments and cell bodies stained with anti-VIP antibodies did not colocalize with neurofilament staining , and may probably represent glial structures ., Glutamatergic neurons , which are the major neurons from the central nervous system involved in excitatory responses , are also present in ENS ., Glutamate receptors have been detected in enteric neurons and glutamatergic enteric neurons where they have been found to mediate excitatory synaptic transmission , whereas only a subset of them are involved in sensory responses 56 ., In mouse intestinal submucosa , only a low number of glutamatergic neurons ( less than 1% ) , as evidenced by anti-glutamate antibodies , colocalized with HcA ( Figure 6D and G ) ., Serotonin is also an important neurotransmitter in ENS , where it is involved in the control of motility , secretion and sensory functions ., Serotonin is produced by 2 to 20% of all enteric neurons 33 ., In our analysis only a few neuronal cell extensions were stained with anti-serotonin antibodies in the submucosal plexus , and some of them were also labeled with HcA , as shown in Figure 6E ., Glial cells were neither immunostained with anti-neurofilament antibodies nor with HcA , but exhibited a clear immunolabelling with GFAP antibodies ( data not shown ) ., These results support the view that HcA binding is mostly specific of nerve endings in the intestinal submucosa ., Also , we investigated the intestine musculosa , which is the predicted target tissue of BoNT/A for its inhibitory activity on intestinal motility ., No significant HcA staining was observed in the musculosa 30 or 60 min after incubation with the fluorescent probe in the intestinal loop , and only a few cell bodies or extensions were stained after a longer incubation period ( 90–120 min ) in our experimental conditions ., Cell extensions stained with HcA colocalized with neurofilament staining ( Figure 7 ) ., However , only some of the nerve endings were labeled with HcA ., Almost all cell bodies labeled with HcA exhibited ChAT-immunoreactivity ., Similar results of colocalization with ChAT-immunoreactive neurons were obtained using full length BoNT/A injected into the intestinal loop lumen and detected with anti-HcA antibodies ( Figure S2 ) ., This is consistent with the fact that a large majority of neurons in the myenteric plexus are immunoreactive for ChAT , albeit many of them produce additional neurotransmitters 55 ., No significant colocalization was observed between HcA staining and glutamate- or serotonin-producing neurons in the myenteric plexus ( data not shown ) ., The protein receptor of BoNT/A on neuronal cells has been identified as SV2 ., Among the three SV2 isoforms , SV2C shows the highest affinity to BoNT/A in vitro , whereas BoNT/A binds to SV2A and SV2B with a lower strength 11 , 14 , 57 ., SV2A is present in almost all neurons whatever their neurotransmitter type is , while SV2B shows a more restricted distribution ., SV2C is reported to be present in a subset of neurons 58 ., However , SV2 proteins are expressed not only in neuronal cells , but also in other cell types such as neuroendocrine cells , in particular in the gastrointestinal tract 59 ., We investigated the distribution of SV2 proteins in mouse intestinal mucosa with our in toto tissue model ., Numerous cell extensions in the submucosal plexus were stained with anti-SV2C antibodies ( Figure 6F ) , whereas no specific staining was observed with anti-SV2B antibodies , and only a diffuse staining in certain crypt cells and cell extensions in the submucosa was evidenced with anti-SV2A antibodies ( data not shown ) ., The mouse intestinal crypt cell line m-ICcl2 was found to express SV2C at a higher level than in enterocyte-type cell lines ( Figure 5B and 30 ) ., However , in our ex vivo conditions intestinal crypt cells were not , or only weakly immunoreactive with anti SV2C antibodies ., This does not preclude that a subset of crypt cells express a significant level of SV2C that was not detected in our conditions ., Most of SV2C-immunoreative filaments in the submucosa , were also labeled with anti-neurofilament antibodies , indicating that SV2C is widely distributed in neuronal cells and neuronal extensions from the small intestine ., However , HcA signal was observed in only some neuronal endings bearing SV2C , but not along the neuronal extensions stained with anti-SV2C antibodies ( Figure 6F ) ., Only a low proportion of SV2C-immunoreactive cells were labeled with HcA ( Figure 6G ) ., It is worth noting that a distinct network of thin filaments around small blood vessels was stained with anti-SV2C , and only weakly with anti-neurofilament antibodies ., No specific binding of HcA was observed on these structures ( Figure S3A and B ) ., In the intestinal submucosa and musculosa , large cells with wide cellular bodies and short extensions were stained with anti-SV2C antibodies , but not with anti-neurofilament antibodies ., Co-labelling with anti-GFAP indicated that they were glial cells ( Figure S3C ) ., However , HcA , as reported above , was not found to label glial cells ., The standard scheme of botulism intoxication includes BoNT transit through the digestive tract , passage across the intestinal epithelial barrier and subsequent delivery to the blood circulation and dissemination to the target motor nerve endings ., Indeed , BoNT has been found to be absorbed preferentially from the upper small intestine , but also from the stomach in experimental rodents , and to be delivered in the blood and lymph circulation 21 , 23 , 24 , 60 ., The aim of this study was to identify the entry pathway and target cells of BoNT/A in the mouse intestinal wall ., For that , we checked the activity of BoNT/A in mouse intestine following intralumenal administration and we used the fluorescent Hc domain , which is the functional binding domain of BoNT , to monitor the trafficking of BoNT/A ., First , we tested whether BoNT/A injected into intestinal lumen was able to enter intestinal mucosa and to induce local effects on the intestine ., In vitro studies have already shown that BoNT/A reduces cholinergic transmission in gastrointestinal smooth muscles as well as pylori and Oddi sphincter muscles by inhibiting ACh release 40 , 41 , 61–63 ., In our experimental conditions , BoNT/A passed through the epithelial intestinal barrier and by diffusion through the extracellular space , locally targeted intestinal neurons independently of the blood circulation ., BoNT/A reduced the frequency of spontaneous contractions of small intestine and inhibited the contractile response evoked by electric field stimulation within 2–4 h after intralumenal administration ., Since carbachol was still able to stimulate muscle contraction after BoNT/A treatment , this supports a toxin-dependent inhibition of ACh release ., To the best of our knowledge , this is the first report demonstrating a local intestinal effect of botulism after intralumenal administration of purified BoNT/A ., Constipation is often ( about 70% ) , but not always , associated with food-borne botulism and its participation to the progression of the disease is unknown 64 , 65 ., However , constipation is a major and early symptom of botulism resulting from an intestinal colonization by C . botulinum such as during infant botulism 66 , 67 ., This digestive symptom might result from a local effect of BoNT after crossing the intestinal barrier instead of toxin dissemination through the general circulation ., BoNT locally synthesized in the intestine is possibly absorbed in a higher local concentration able to induce an intestinal muscle paralysis , than toxin orally ingested which disseminates more broadly through the digestive tract ., Interestingly , BoNT/A-dependent inhibition of evoked smooth muscle contraction was significantly prevented by preincubation with the intravesicular domain of SV2C ., This indicates that a protein related to SV2C/L4 might impair the BoNT/A passage through the intestinal barrier and/or toxin uptake by the underlying nerve terminals ., We have previously found that SV2C , or a related protein , is part of BoNT/A receptor mediating toxin transcytosis through cultured intestinal cell monolayers 68 ., In addition , SV2C/L4 , which is expressed by intestinal and neuroendocrine STC-1 cells ( Figure 5B ) , significantly prevented HcA entry into the intestinal crypt m-ICcl2 and STC-1 cells ( Figure 5C , D ) ., Taken together , these data suggest that SV2C , or a related protein , facilitates BoNT/A uptake through the mouse intestinal barrier ., BoNT/A trafficking in mouse intestinal wall was investigated with fluorescent HcA as already used 38 ., First , we investigated the potential binding sites of HcA by using mouse small intestine cryosections overlaid with fluorescent probes ., Thereby , HcA was found to bind only to certain cell types of the intestinal mucosa , preferentially from intestinal crypts , whereas enterocytes showed only a weak staining of the brush border ., In contrast , it was reported that the botulinum complexes type A or type C ( BoNT and associated non-toxin proteins , ANTPs ) , strongly bind to the epithelia cell surface and goblet cells of guinea pig small intestine ., Moreover , this binding was shown to be mediated by the hemagglutinins HA1 and HA3b , which interact with distinct gangliosides and/or glycoproteins 24 , 29 , 60 , 69 from those recognized by the neurotoxin alone 11 , 14 ., This certainly accounts for the differential binding between progenitor toxin and BoNT to intestinal epithelial cells ., The functions of ANTPs are still controversial ., Ancillary proteins probably participate in BoNT protection from degradation inside the digestive tract in a dose-dependent manner , particularly in the stomach 21 ., In addition , it is assumed that HAs are involved in the internalization of progenitor type C toxin into intestinal cells and subsequently in the small intestine 24 , 29 , 70 , and that they disrupt the intestinal epithelial barrier facilitating toxin absorption via the paracellular route 71–74 ., However , since BoNT/A absorption from mouse stomach or small intestine was found to occur independently of the presence of ANTPs , HAs might have not a

Introduction, Results, Discussion, Materials and Methods

Botulism , characterized by flaccid paralysis , commonly results from botulinum neurotoxin ( BoNT ) absorption across the epithelial barrier from the digestive tract and then dissemination through the blood circulation to target autonomic and motor nerve terminals ., The trafficking pathway of BoNT/A passage through the intestinal barrier is not yet fully understood ., We report that intralumenal administration of purified BoNT/A into mouse ileum segment impaired spontaneous muscle contractions and abolished the smooth muscle contractions evoked by electric field stimulation ., Entry of BoNT/A into the mouse upper small intestine was monitored with fluorescent HcA ( half C-terminal domain of heavy chain ) which interacts with cell surface receptor ( s ) ., We show that HcA preferentially recognizes a subset of neuroendocrine intestinal crypt cells , which probably represent the entry site of the toxin through the intestinal barrier , then targets specific neurons in the submucosa and later ( 90–120 min ) in the musculosa ., HcA mainly binds to certain cholinergic neurons of both submucosal and myenteric plexuses , but also recognizes , although to a lower extent , other neuronal cells including glutamatergic and serotoninergic neurons in the submucosa ., Intestinal cholinergic neuron targeting by HcA could account for the inhibition of intestinal peristaltism and secretion observed in botulism , but the consequences of the targeting to non-cholinergic neurons remains to be determined .

Botulism is a severe and often fatal disease in man and animals characterized by flaccid paralysis ., Clostridium botulinum produces a potent neurotoxin ( botulinum neurotoxin ) responsible for all the symptoms of botulism ., Botulism is most often acquired by ingesting preformed botulinum neurotoxin in contaminated food or after intestinal colonization by C . botulinum under certain circumstances , such as in infant botulism , and toxin production in the intestine ., The first step of the disease consists in the passage of the botulinum neurotoxin through the intestinal barrier , which is still poorly understood ., We investigated the trafficking of the botulinum neurotoxin in a mouse intestinal loop model , using fluorescent HcA ( half C-terminal domain of the heavy chain ) ., We observed that HcA preferentially recognizes neuroendocrine intestinal crypt cells , which likely represent the entry site of the toxin through the intestinal barrier , then targets specific neurons , mainly cholinergic neurons , in the submucosa , and later ( 90–120 min ) in the musculosa leading to local paralytic effects such as inhibition of intestinal peristaltism ., These results represent an important advance in the understanding of the initial steps of botulism intoxication and can be the basis for the development of new specific countermeasures against botulism .

medicine, biology, microbiology, molecular cell biology, toxicology

journal.pgen.1005053

Phenotype Specific Analyses Reveal Distinct Regulatory Mechanism for Chronically Activated p53

The TP53 ( p53 ) tumor suppressor , a stress-responsive transcription factor ( TF ) , is somatically mutated in more than 50% of human cancers , with a range between 10% and nearly 100% depending on the tumor type ., Furthermore , germ line mutations of p53 , in both humans and mice , predispose individuals to malignant tumor development 1 , 2 ., p53 plays critical roles in the induction of cell death and cell cycle arrest in response to stress , including DNA damage , oncogenic stress , and metabolic stress ., Hence p53 is implicated in a wide range of cellular processes , such as cell cycle checkpoint , apoptosis , senescence and quiescence 3–5 ., Despite increasing knowledge about p53 target genes , however , it is not entirely clear which aspects of p53 function are attributable to each of these p53-associated phenotypes and its tumor suppressor activity 6 ., p53 is typically regulated at the protein level through post-translational modification ., In normal conditions , p53 is under the regulation of a strong negative feedback loop , where MDM2 , a direct p53 target , serves as the E3 ubiquitin ligase , leading to the constant proteasomal degradation of p53 7 ., Thus p53 is highly unstable in non-stress conditions but upon stress induction , such as DNA damage , it can be rapidly stabilized through its dissociation from MDM2 ., However , whether or not the prevailing model of acute p53 induction represents the major program of p53’s tumor suppressive functions is under debate 8 ., For example , studying whole body irradiated p53 inducible knock-in mice , Christophorou et al . showed that a late restoration of p53 function , rather than the usual acute p53-mediated pathological response , led to a reduced lymphoma burden 9 ., In addition , Brady et al . recently showed that p53 differentially regulates specific transcriptional programs of the acute DNA damage response ( DDR ) and its more chronic tumor suppression functions through its use of different transactivation domains ., Their data indicate a close correlation between p53 activities in driving tumor suppression and senescence 10 ., Notably , senescence has been shown to be largely dependent on a persistent , rather than an acute , DDR 11 ., Thus these studies suggest that the downstream effects of acutely activated p53 and p53-mediated tumor suppression may well be separable processes ., Several studies of p53 genomic binding profile have recently been published , revealing a number of new p53 targets , which include genes potentially associated with its tumor suppressor functions ., An early study found p53 targets that potentially suppress metastasis 12 ., A number of autophagy genes were recently identified as direct p53 targets and p53-induced autophagy was shown to be important for DNA damage-induced apoptosis and the anti-transformation activity of p53 13 ., In addition , in ES cells , p53 regulates self-renewal and pluripotency upon DNA damage 14 , and early-differentiation p53 targets include many developmental transcription factors 15 ., Currently , however , efforts at genome-wide p53 mapping have mostly been focused on acutely or dynamically activated p53 ., Thus comprehensive analyses of the persistent activities of p53 , which may be more relevant to its tumor suppressor function , are still missing ., Here we show distinct regulatory mechanisms for p53-targets between acute and more persistent modes of p53 activation ., In addition to the classical DDR , where p53 is acutely induced ( ‘acute’ p53 ) , we have determined profiles of genome-wide p53 binding and p53-responsive genes in two distinct cellular conditions , where p53 is persistently activated ( ‘chronic’ p53 ) in normal human diploid fibroblasts ( HDFs ) : oncogene-induced senescence ( OIS ) ; and transformed pro-apoptotic conditions ., In contrast to acute p53 , chronic p53 was closely associated with CpG island ( CGI ) type promoters ., Although the binding profiles of p53 in the OIS and pro-apoptotic conditions were similar , the p53-responsive genes were distinct , suggesting that downstream gene regulation by chronic p53 is highly context dependent ., Interestingly , our integrative p53 networks and pathway modeling , combined with external high-throughput datasets , suggest that p53 can be functionally and/or physically associated with many of its own targets , thus forming extensive self-regulatory p53 hubs in the chronic conditions examined in this study ., Finally , together with external clinical datasets , our data reinforce the evidence for the anti-lipogenic functions for p53 ., Our study not only extends our knowledge of phenotype-associated gene regulation by p53 , but also provides unique and widely useful resources for the targets of persistently activated p53 ., To gain a comprehensive understanding of p53 biology , we established phenotypes that are associated with p53 either acutely activated by DNA damage or persistently activated by oncogenic stress in a single cell type ( IMR90 HDFs ) ( Fig . 1A ) ., During the acute DNA damage response ( acDDR ) phase induced by etoposide treatment ( d1 ) , the cells were viable and had stopped proliferating but were not yet fully senescent , whereas most cells became senescent seven days after etoposide treatment ( Fig . 1B-1C ) ., Of note , although acDDR cells showed a modest increase in senescence-associated ß-galactosidase activity ( Fig . 1B ) , it was not accompanied by up-regulation of other functional markers of senescence , such as HMGA proteins and p16 ( Fig . 1C ) ., As expected , p53 was transiently stabilized in the acDDR phase with a parallel up-regulation of p53 targets , such as p21 and MDM2 , in total cell lysates ( Fig . 1C ) ., Interestingly , in chromatin-enriched fractions , p53 levels were comparable between the acute ( d1 ) and senescence phases ( d7 ) ., This is perhaps in part due to the enlarged cellular phenotype of senescent cells , the p53 level then being more diluted in total cell lysates of senescent cells ., To establish the conditions for the sustained activation of p53 , we used the well-established models of oncogenic stress 16 ., Ectopic oncogenic HRASG12V induces senescence ( RAS-induced senescence , RIS ) , a state of irreversible cell cycle arrest , where p53 plays a major role 16 ., In contrast , E1A , the ‘immortalizing’ adenoviral oncoprotein , transforms HDFs when used in combination with oncogenic HRASG12V ., At the same time , E1A stabilizes p53 and thereby sensitizes cells to apoptosis ( Figs . 1A , 1D , and S1A ) 17 ., Thus E1A/RAS-expressing cells are highly proliferative , yet sensitive to apoptosis due to sustained p53 activation ( here we call this condition ‘pro-apoptotic’ , pApo ) ., In both cases , a stable accumulation of p53 was readily detectable in chromatin fractions without additional stimuli ( Fig . 1D ) , and again the elevated levels of p53 , particularly in the RIS condition , were more clearly detected in chromatin fractions than in total lysates ( Fig . 1D ) ., These data suggest that comparable amounts of p53 can be responsible for the distinct phenotypes ., Having established highly distinct p53-associated phenotypes—acDDR , RIS , and pApo—we performed microarray analysis , with and without sh-p53 , for each condition using a miR30 RNAi design in the lentiviral backbone 18 ., To reduce secondary effects of p53 knockdown , we introduced sh-p53 after each phenotype was established in the chronic conditions ., The efficiency of p53 knockdown was confirmed in the chromatin fractions ( Fig . 1E ) ., The set of differentially expressed ( DE ) genes upon sh-p53 introduction in each phenotype differed greatly between all conditions , with only a small number of well-characterized p53 targets in common ( Figs . 1F and S1B , and S1 Table ) ., Regulation of three representative core p53 targets was validated using a different sh-p53 ( S1C Fig ) ., Pathway analyses of DE gene sets confirmed distinct transcriptional signatures in each phenotype ( Figs . 1G and S1D ) , indicating that p53 can , directly or indirectly , regulate gene sets unique in terms of both their context and phenotype , i . e . in either the ‘acute’ or ‘chronic’ p53 condition , and the RIS or pApo condition ., We next examined whether this phenotype-associated gene regulation was achieved through a specific p53 binding profile by using p53 ChIP-seq analyses of the acDDR , RIS and pApo conditions compared with normal-growing cells ( S2 Table ) ., We used at least three replicates for each condition ( except the growing condition , with two replicates ) to define high-confidence ( HC ) peak sets ( see materials and methods ) ., In contrast to the strong induction of p53 during acDDR , actual peak numbers were substantially lower than in the other conditions ( Fig . 2A ) ., The number of HC peaks in the acDDR condition was comparable to peak sets described in earlier reports 12 , 13 , 19–21 ., Notably , as in our acDDR condition , these studies were performed on cells treated for less than 24h ., Our data suggest that the mode of p53 exposure , acute or chronic , affects the affinity of p53 binding and therefore the outcome ., The genomic features of the HC p53 binding sites in the acDDR condition differed from those in RIS and pApo ., The proportion of p53 peaks that mapped to transcription start site ( TSS ) proximal regions ( core promoter ) was substantially higher in the RIS and pApo conditions at 64% and 50% , respectively , compared with only 26% in the acDDR condition , where the majority of peaks ( >70% ) were in introns , exons or up-stream distal regions ( Fig . 2B ) ., The preferential association of p53 with promoter regions in the chronic conditions is not due to varied numbers of p53 peaks between conditions , because the association was conserved when we selected the same numbers of peaks from each condition for the analysis ( S3 Table ) ., Through visual inspection of our ChIP-seq data using a genome browser , we noticed that p53 peaks tended to be either sharp or broad , with acDDR peaks being substantially narrower than in the other conditions ( Fig . 2C ) ., There are two major types of core promoter: ‘focused’ with a single or a few densely aggregated TSSs , and ‘dispersed’ with many TSSs ., In vertebrates , these ‘focused’ and ‘dispersed’ promoters typically correspond to non-CGI-promoters containing core promoter elements ( e . g . TATA-boxes ) and CGI-type promoters , which are generally TATA-less , respectively 22 ., We examined the co-occurrence of p53 peaks and CGIs in each condition ., p53 peaks in the chronic ( RIS and pApo ) conditions overlapped substantially more with CGIs than in the acDDR condition ., The acDDR-associated peaks in our and other published p53 datasets were mostly of the non-CGI type ( Fig . 2D ) ., The higher frequency of CGI-type p53 peaks in chronic conditions is not simply due to their preferential distribution in the promoter regions ( Fig . 2B ) , since this tendency was retained when examining promoter regions only ( Fig . 2E ) ., These data show the distinct genomic binding profiles of p53 between the acute and chronic conditions , revealing extensive usage of CGI promoters in the latter ., Gene ontology ( GO ) enrichment of non-CGI p53 peaks mapped to genes showed that the functional groups involved in the typical p53-associated functions , such as cell cycle , DNA damage and apoptosis , were overrepresented particularly in the chronic conditions , whereas for the CGI p53 peaks , we observed the most significant enrichment for functional groups involved in RNA metabolism and processing ( S4 Table ) ., These data suggest that the outcome of p53 binding in the chronic conditions is different from that of the acute condition , which has been a commonly used experimental system , and thus our data substantially extend not only the list of candidate p53-targets and but also their mode of regulation ., We next examined whether these p53-bound regions contained the p53 consensus motif ., Using position weight matrices , searching for known canonical p53 responsive elements ( p53REs ) , we identified their enrichment in both types of peaks ( Fig . 2F , see Materials and Methods ) ., Reflecting the peak shapes , p53-binding motifs were dispersed throughout the CGI-type peaks , whereas p53REs were focused around the peak center of non-CGI-type peaks ( Fig . 2G ) ., Such CGI-type p53 peaks have not been reported even in the promoter of CDKN1A ( p21 ) , the best-characterized p53 target ( Fig . 2H ) ., The well-established view is that p21 has two major canonical p53REs at around-2 . 3 kb ( the distal p53RE ) and-1 . 4 kb ( the proximal p53RE ) ., The distal p53RE is bound more strongly by p53 than the proximal site 23 ., We consistently observed sharp p53 peaks at the distal site in all conditions ( #1 in Fig . 2H ) ., In addition , the p21 locus contained prominent p53 enrichment at the major CGI , which encompasses the classic p21 TSS , in chronic conditions only ., p53 binding to the CGI , which contained various potential p53REs ( Figs . 2 and S2A ) , coincided with enrichment for H3K4me3 ( a marker of CGI-promoters ) and a downstream spreading of H3K36me3 ( a marker for transcription elongation ) 24 ( Fig . 2H ) , suggesting that this CGI is a promoter for the classic p21 transcript variant 1 ( v1 ) ., Both the classic v1 and the alternative transcripts—represented by variant 2 , whose TSSs are located in direct proximity to the distal p53RE ( #2 in Fig . 2H ) —were up-regulated in all conditions , therefore the relative contribution of the distal p53RE and the CGI promoter to p21 v1 is not yet clear ( Figs . 2 and S2B-S2C ) ., Nevertheless these data reinforce the unexpected association between chronic p53 and CGI promoters ., We next compared our p53-dependent expression data with our p53 binding data ., In contrast to the expression profiles ( Fig . 1F ) , the overlap in the p53 binding profiles between conditions was substantially larger , and the similarity was even more striking for the peaks within the promoter regions ( S3A Fig ) ., To better predict phenotype-associated p53 function , we developed the “R-based analysis of ChIP-seq And Differential Expression” ( Rcade ) package , integrating genome-wide binding profiles of TFs with their responsive gene expression profiles ., Briefly , we coupled the expression analysis to a TSS-local read-based ChIP-seq analysis , thereby circumventing ‘peak-calling’ and thus reducing false-positives and bias issues inherent with peak-calling methods ., However , because most of the acDDR peaks failed to fulfill the localization criteria specified ( S3B Fig ) , in our further analyses we only focused on the pApo and RIS chronic conditions , where Rcade identified 1487 and 563 genes , respectively , which included both established and many previously unknown , ‘putative’ p53 targets ( S3C Fig and S5 Table ) ., GO analysis of the Rcade-derived genes showed that various biological processes were represented in both conditions , including typical p53-related functions ( cell cycle , DNA damage response , and apoptosis ) ; functions of membrane-bound organelles and metabolism; and gene expression and RNA metabolism/processing ( S3D Fig ) ., The Rcade-derived genes include both previously known as well as many unknown/uncharacterized genes as direct targets of p53 ., For example , ANKRA2 and HSPA4L , which are poorly characterized , were identified as putative direct p53-inducible targets in both RIS and pApo conditions ., Significant down-regulation of ANKRA2 and HSPA4L upon p53 knockdown was confirmed by qPCR in at least two different conditions in IMR90 cells ( S3E Fig ) ., Similar results were obtained using the second sh-p53 ( S3E Fig ) ., Interestingly , tumor-specific , disruptive mutations of ANKRA2 were previously identified in oral squamous cell carcinoma 25 , and mutations in ANKRA2 are also reported in the Catalogue Of Somatic Mutations In Cancer ( COSMIC , http://www . sanger . ac . uk/genetics/CGP/cosmic/ ) ., In addition , methylation of the CGI promoter of HSPA4L as well as the methylation-associated down-regulation of HSPA4L in acute lymphocytic leukemia ( ALL ) have been reported previously 26 , thus underlining the usefulness of our Rcade datasets ., Using a PiggyBac transposon system 27 , we established a tetracycline-inducible p53 system in H1299 cells ( a p53-null lung cancer cell line ) and confirmed that ectopic wild type p53 could induce expression of ANKRA2 and HSPA4L ( S3F Fig ) ., To gain a comprehensive understanding of the p53 regulome , we first generated integrative networks of the Rcade-derived p53-targets , taking advantage of numerous external high-throughput datasets ., Since co-regulated genes are likely to be ‘connected’ , we measured connectivity within the Rcade-derived p53-targets , taking into account topological measures of local ( ‘Degree’ ) and global ( ‘Between-ness centrality’ ) connectivity ( see materials and methods ) ., This largely unbiased network approach revealed that the ( putative ) p53-targets were highly inter-connected , providing evidence for the validity of our Rcade gene lists ( Fig . 3 , compare to the random gene set ) ., p53 was identified as the most globally ( pApo ) and locally ( both conditions ) connected gene in the networks , indicating the importance of p53 to the integrity of entire networks ., To gain insight into the functional relationship between putative p53 targets , we next constructed phenotype-specific , ‘knowledge-based’ pathway models of the p53 regulome ( see materials and methods ) ( S4 and S5 Figs , high resolution figures are available at http://australian-systemsbiology . org/tp53 ) ., These revealed a highly complex network in the pro-apoptotic condition and provided the first detailed p53 regulome of senescence ., p53 appeared to regulate multiple components within the same pathways or biochemical complexes , but often with distinct aspects depending on the cellular context ., Thus many p53-related phenomena fragmented throughout the literature could be seen in a single biological context , and yet each context may involve distinct p53 functions ., For example , Rcade genes associated with mitochondria in the pApo condition were largely distinct from those in the RIS condition and included , in addition to apoptotic genes , genes involved in mitochondrial metabolism and homeostasis ( oxidative phosphorylation , fatty acid and lipid metabolism , mitochondrial biogenesis ) ., Consistent with a recent study , which showed an extensive transcriptional regulation of autophagy by p53 in response to acute DNA damage in mouse embryonic fibroblasts 13 , we also found that the autophagy program was regulated by p53 in the chronic conditions ( pApo in particular ) but through largely distinct genes compared to the previous report 13 ( S4 Fig ) , extending the role for p53 in autophagy regulation ., One striking notion from our pathway modeling is that a subset of the p53 regulome formed a ‘p53 hub’: p53 has been reported to interact with , or be modified by , the components of this hub in diverse experimental conditions , thus suggesting that many of the direct targets of p53 in turn regulate p53 in the chronic conditions ( Figs . 4A , S4 , and S5 , and S6 Table ) ., This is in accordance with the high local connectivity of p53 in the networks ., Information specifically about protein-protein interactions between the p53 hub components highlighted that many of them can interact with each other ( Fig . 4B ) ., The components within the self-regulatory network of p53 are best exemplified by MDM2 , the E3 ubiquitin ligase , which negatively regulates p53 stability , thereby conferring a strong negative feedback loop 7 ., However , an MDM2-independent negative feedback loop has been shown in a senescence context 28 ., Moreover , additional mechanisms for modulating the MDM2-p53 loop are suspected to exist in the cancer context 29 , 30 ., Of note , consistent with the high connectivity of MDM2 in our p53 networks ( Fig . 3 ) , MDM2 itself formed a prominent ‘sub-hub’ within the p53 hub ( Fig . 4A ) , reinforcing the existence of multiple levels of mechanisms for regulating p53 and the p53-MDM2 loop in the chronic conditions ., Together , our data suggest that intensive and multi-level fine-tuning of p53 function may be an important mode of phenotype regulation ., Finally , to test the clinical relevance of our datasets for chronic p53 targets , we performed recursive partitioning analysis ( RPA ) of each Rcade component for survival in four publicly available cancer datasets ( Fig . 5A ) 31–33 ., For example , the RPA identified an association between high levels of MDM2 , a bona fide oncogene , and poor prognosis in two datasets ( Fig . 5A ) ., On the other hand , we observed a mixed association between prognosis and p21 ( CDKN1A ) levels , whose clinical relevance in human tumors is controversial , supporting the validity of this method 34 ( Figs . 5A and S6 ) ., Interestingly , several autophagy genes were identified in the pApo condition , where high levels of these genes were mostly associated with better prognosis in multiple clinical datasets ( S6 Fig , left ) ., Implications of autophagy in cancer are complex and thus careful interpretation is necessary , but these data support the recent study that showed the contribution of autophagy to p53-dependent tumor suppression 13 ., Using this method we went on to validate clinically relevant p53 putative targets ., We prioritized p53-repressive targets , since p53 mutations are common in cancers where p53-repressed genes are likely to be up-regulated , and if those gene products contribute to tumorigenesis , they may provide good candidates for therapeutic targets in p53-deficient cancers ., Of the p53-repressive targets whose expression levels were significantly correlated with prognosis in at least two different datasets , we chose the lipogenic enzyme stearoyl-CoA desaturase ( SCD ) for further validation , for the following reasons ( Fig . 5A ) : ‘lipid metabolism’ was featured in our pathway modeling in both chronic conditions ( S4 and S5 Figs . ) ; the ‘lipogenic phenotype’ is a hallmark of cancer 35; high SCD expression has been correlated with a transformation phenotype , tumor cell survival , and poor outcome in many cancers , and SCD has been implicated as potential targets for cancer therapy 36 ., Although several lipogenic TFs , such as SREBFs and PPARs , have been implicated in the regulation of SCD expression , it is not clear how SCD is regulated under stress as well as in cancer 37 ., SCD catalyzes the rate-limiting reaction in the biosynthesis of the major monounsaturated fatty acids ( oleate and palmitoleate ) , which are components of essential building blocks of rapidly proliferating cells 37 ., Consistently , SCD was initially up-regulated in response to hyperactive RAS , and then it reduced to an almost undetectable level after the full establishment of senescence , where p16 , a marker of senescence , is highly up-regulated ( Fig . 6A ) ., In E1A/RAS expressing transformed pApo cells , SCD levels were relatively high , supporting the role of SCD in rapidly proliferating transformed cells ( Fig . 6B ) ., In both cases , however , when we introduced sh-p53 to RIS or pApo cells , SCD levels were up-regulated , suggesting that SCD is regulated by multiple mechanisms , whereby p53 counteracts the positive control of SCD by pro-tumorigenic signals ., To lend further support to the finding that SCD is repressed by p53 in cancer , we analyzed a publicly available breast cancer dataset that contains gene expression and p53 sequencing data 38 ., In contrast to p21 and MDM2 , SCD levels were significantly higher in tumors with p53 mutations than with wild-type p53 ( Fig . 6C ) ., We also examined the relationship between p53 and Scd1 ( a mouse homologue of SCD ) in Kras-driven mouse pancreatic ductal adenocarcinoma ( mPDA ) cell lines established from KrasLSL-G12D; Pdx1-cre , or KrasLSL-G12D; P48-cre mice ( KC cell lines ) and KrasLSL-G12D; p53lox/+; Pdx1-cre compound mutant mice ( KPΔC cell lines ) 39 ., In KC cell lines ( p53-wild type ) , p53 was readily up-regulated by DNA damage treatment , whereas p53 was undetectable in KPΔC cell lines ( p53-null ) ( Fig . 6D ) ., Scd1 was down-regulated in the KC , but not in the KPΔC , cell lines ( Fig . 6D ) ., Furthermore , repression of SCD by p53 was confirmed in a tetracycline-inducible p53 system in H1299 cells ., Upon doxycycline addition , the endogenous SCD level was repressed in a dose- and time-dependent manner ( Figs . 6E and S7A ) ., In the SCD locus , chronic p53 accumulation was observed mainly on the CGI ( Fig . 6F ) ., Although an early study showed that overexpressed wild type p53 can bind the upstream canonical p53RE in the SCD promoter ( Fig . 6F ) 40 , our data indicate that endogenous p53 preferentially accumulates on a distinct region in the CGI promoter when it is chronically activated ., This p53-bound region , containing several p53 motifs ( Figs . 6 and S7B ) , was sufficient for p53 to repress downstream luciferase expression ( Fig . 6G ) ., Taken together , our data suggest that SCD expression , which is associated with poor prognosis in some cancers , is directly repressed by chronic p53 through the CGI promoter , providing direct mechanistic insight into the anti-lipogenic role of p53 ., Here we present an extensive study of p53 regulation in different phenotypes using normal human cells ., We compared p53 binding profiles in three settings; acDDR , RIS , and E1A and RAS-expressing pApo conditions ., In the acDDR condition , which has been the commonly used model for genome-wide mapping of p53 binding sites , p53 peaks were primarily of a sharp non-CGI type , exhibiting a wide distribution in the genome ., Interestingly , increasing evidence for distant gene regulation by p53 has been shown using systems where p53 is acutely activated 14 , 41 ., This may explain , in part , the diverse locations of non-CGI p53 peaks in the acDDR condition ., In contrast , both RIS and pApo conditions were associated with sustained accumulation of p53 on chromatin , where p53 preferentially associated with CGI promoters ., In one of the previous p53 ChIP-seq studies , Botcheva et al . identified a substantial number of CGI-type p53 peaks in an acute condition ( Fig . 2D ) 21 ., We reanalyzed these external data and found 1811 p53 CGI-peaks , 50% and 52% of which were included in our HC p53 CGI-peaks in the RIS ( 6148 CGI-peaks ) and pApo ( 6566 CGI-peaks ) conditions , respectively ., Although the relatively high frequency of CGI-peaks in this external dataset ( compared to 846 HC p53 CGI-peaks in our acDDR condition ) may be an overestimate due to their lack of biological replicates , it reinforces the significance of the connection between p53 and CGI promoters ., It is not clear why their study identified many CGI peaks in their acDDR condition ., Both studies used HDFs ( IMR90 cells ) , which are highly sensitive to senescence induction by oxidative stress ., Notably , we maintained our cells in a physiological ( 5% ) O2 condition to minimize the amount of oxidative stress derived from routine cell culture ., Thus the basal levels of p53 and the background senescence phenotype might be different between the studies ., The molecular mechanism for the unique profile of chronic p53 seen in our study is unclear ., The levels of global chromatin bound p53 were comparable between the acute and chronic ( at least RIS ) conditions ( Fig . 1E ) ., Furthermore , p53 binding profiles at promoter regions were almost identical between the RIS and pApo conditions , but the Rcade gene sets were distinct ( compare S3A and S3C Figs ) ., Thus , quantitative differences in the global levels of p53 or its genomic distribution alone cannot explain the differential p53 activities ., Generally , CGIs are ‘open’ , enriched for the binding sites of many TFs , including Sp1 , which can recruit the TATA-binding general TF complex to TATA-less CGI promoters 22 ., Thus in CGI regions , it is conceivable that complex interactions between transcription ( co ) factors can occur depending on cellular contexts ., The consensus p53 binding site consists of two decameric half-sites separated by 0–13 nucleotides , but the ‘non-canonical’ half-sites can also function as a p53RE 42 , 43 ., Our analysis of two CGI promoters , which are p53-activated ( p21 ) and p53-repressive ( SCD ) , suggests that both CGI-promoters contain multiple ‘weak’ p53REs ( including many half-sites ) , which somehow favor persistent accumulation of p53 ( S2A and S7B Figs ) ., These weak p53 associations might well be reinforced by other factors ., It is also possible that p53 might associate with DNA through its binding partners ., Indeed , our motif enrichment analyses identified known p53-cofactors , including Sp1 ( S7 Table ) within p53 CGI-peaks ., Therefore , it is possible that persistent cellular stress creates distinct contexts , where the quality of p53 ( e . g . its post-translational modifications , PTMs ) and the sets of p53 binding proteins are different from acute conditions , thereby facilitating the p53-CGI association ., Indeed , p53 can be modified by a multitude of diverse PTMs , including phosphorylation , acetylation , methylation , ubiquitilation , neddylation , sumolyation , and poly-ribosylation 44 ., Although the functional roles of these PTMs are not fully understood , some PTMs such as phosphorylation and acetylation typically contribute to stabilization and activation of p53 44 ., Interestingly , as shown in Fig . 4A , many factors involved in PTMs of p53 were included in the p53 self-regulatory hubs derived from the Rcade gene sets ( Fig . 4A ) ., This might provide a mechanism for context-dependent fine-tuning of PTMs of p53 at least at a global level ., It will be important to determine phenotype-specific genome-wide profiling of individual PTMs of p53 ., In addition , a recent study has shown that a genome-wide redistribution of DNA methylation occurs during replicative senescence , where persistent p53 plays a key role 45 ., Thus it would also be interesting to examine the structural alterations in CGI regions during RIS and pApo conditions ., Notably , these two chronic phenotypes are highly distinct; RIS cells are stably arrested and resistant to apoptosis , whereas pApo cells are rapidly proliferating and sensitive to apoptosis , yet both are largely dependent on p53 16 , 17 ., Such distinct p53-associated phenotypes were not achieved through differential p53 binding alone , since both conditions exhibited highly similar p53-binding profiles , where CGI-type genes are over-represented ( S3A and S3C Fig ) ., The unique feature of CGIs , such as their relatively open configuration and their enriched TF binding motifs , might also provide environments that allow for diverse downstream regulation upon p53 binding in conjunction with other ( co ) factors 46 ., In addition , our integrated network analyses in chronic conditions identified the extensive capability of p53 for physical interaction with its own targets , further reinforcing the diverse results of p53 binding to the same target promoters ., Although the dynamic regulation of p53 through the MDM2-p53 negative feedback loop was readily detected in the DDR condition ( Fig . 1C ) , its relevance in the chronic conditions was not so obvious ., In pApo transformed cells , MDM2 was highly up-regulated compared to other conditions , whereas the chromatin bound p53 levels were comparable , or even slightly higher in the pApo condition ( Fig . 1D ) ., Although this may be in part due to E1A-induced p14ARF , which inhibits the E3 ligase activity of MDM2 47 , this is also reminiscent of the tumor specific escape of mutant p53 from Mdm2 degradation in mice harboring germ line p53 mutations , an observation that suggests the existence of additional mechanisms for modulating the p53-MDM2 loop during tumorigenesis 29 , 30 ., It has also been shown that the p53-repressive target ,

Introduction, Results, Discussion, Materials and Methods

The downstream functions of the DNA binding tumor suppressor p53 vary depending on the cellular context , and persistent p53 activation has recently been implicated in tumor suppression and senescence ., However , genome-wide information about p53-target gene regulation has been derived mostly from acute genotoxic conditions ., Using ChIP-seq and expression data , we have found distinct p53 binding profiles between acutely activated ( through DNA damage ) and chronically activated ( in senescent or pro-apoptotic conditions ) p53 ., Compared to the classical ‘acute’ p53 binding profile , ‘chronic’ p53 peaks were closely associated with CpG-islands ., Furthermore , the chronic CpG-island binding of p53 conferred distinct expression patterns between senescent and pro-apoptotic conditions ., Using the p53 targets seen in the chronic conditions together with external high-throughput datasets , we have built p53 networks that revealed extensive self-regulatory ‘p53 hubs’ where p53 and many p53 targets can physically interact with each other ., Integrating these results with public clinical datasets identified the cancer-associated lipogenic enzyme , SCD , which we found to be directly repressed by p53 through the CpG-island promoter , providing a mechanistic link between p53 and the ‘lipogenic phenotype’ , a hallmark of cancer ., Our data reveal distinct phenotype associations of chronic p53 targets that underlie specific gene regulatory mechanisms .

The p53 transcription factor is a frequently mutated tumour suppressor that contributes to repairing or eliminating damaged cells ., Levels of p53 are typically regulated through its stability; it is constantly produced and degraded , so that upon stress , p53 is up-regulated quickly ., This acutely induced p53 has been used as a major model system for studying genome-wide p53 targets ., However , emerging evidence suggests that persistently activated p53 is involved in cancer-associated phenotypes , such as cellular senescence ., We investigate genome-wide gene regulation by acutely induced p53 through DNA damage as well as chronically activated p53 in oncogene-induced senescence and pro-apoptotic states ., Interestingly , acute and chronic p53 DNA binding profiles are highly distinctive , the latter being preferentially associated with larger and relatively open promoters called CpG islands ., Furthermore , our integrative analyses of both p53-dependent gene expression and p53-binding genomic DNA profiles reveal that p53 and many of its targets in chronic conditions form extensive self-regulatory hubs , where they can physically interact ., The data not only substantially extend the list of direct p53 targets but highlight unique gene regulation by chronic p53 ., Finally we show that the cancer-associated lipogenic enzyme , stearoyl-CoA desaturase , is a bona fide p53-repressive target through its CpG island promoter in chronic conditions .

journal.pbio.1001352

Reversing the Outcome of Synapse Elimination at Developing Neuromuscular Junctions In Vivo: Evidence for Synaptic Competition and Its Mechanism

Physiological evidence that axons completely lose connections with some postsynaptic cells as part of naturally occurring development was first observed at the neuromuscular junction in mammals more than 40 years ago 1 ., Since then analogous axonal loss has been seen in many parts of the central and peripheral nervous systems 2 , 3 ., While the underlying mechanism is still unclear anywhere , evidence suggests that in the neuromuscular system local events at or near the synapse regulate the process ., Evidence for local regulation includes the following: ( 1 ) the axonal inputs that are eliminated from neuromuscular junctions do so by gradually vacating their synaptic contact sites 4 rather than suddenly undergoing degeneration , as occurs when axons are damaged 5; ( 2 ) the axon that ultimately is maintained increases its synaptic contact area by gradually occupying many of the synaptic sites that were previously occupied by other motor axons 4; ( 3 ) the loss and acquisition of synaptic sites is paralleled by a local reduction and strengthening in synaptic efficacy 6; ( 4 ) the loss of axonal branches from one axon that projects to many muscle fibers occurs asynchronously , suggesting that the timing of elimination is not set by a signal from the cell soma but regulated independently at each neuromuscular junction site 7; ( 5 ) local differences between the synaptic activity of axons converging at the same neuromuscular junction have the ability to cause synapses to be eliminated 8 , 9; ( 6 ) local changes in target cell signaling can affect synapse maintenance 10; and ( 7 ) once an axon has vacated all of its synaptic territory at a neuromuscular junction , it locally sheds cytoplasm that is internalized by glia associated with the neuromuscular junction entry zone 4 , 11 ., Collectively , these data argue that the ultimate identity of the one permanent presynaptic input to a muscle fiber is determined by events occurring at the level of individual neuromuscular junctions ., Other data suggest that neuronal properties ( as opposed to synaptic properties ) such as an axons biochemical identity or its firing pattern play a role in determining the outcome of synapse elimination , but even these may operate through local synaptic mechanisms 12 ., Several different local mechanisms have been proposed to explain what drives this process forward ., One idea is that individual axon branch removal occurs randomly from a motor unit and is related to an intrinsic requirement that neurons scale back their initially exuberant arbors 13 ., A second idea is that the fate of axons is predetermined by positional or perhaps other molecular cues that specify which axon is the best match for each muscle fiber 14–16 ., A third possibility is that axons converging at a neuromuscular junction compete with each other causing all but the ultimate victor to be removed ., It is also possible that some combination of these forces is at play ., The idea that synapse elimination is primarily the result of a competitive interaction between the innervating axons was originally proposed because in many muscles the loss of inputs results in exactly one axon remaining at each junction 17 ., A competitive mechanism is also suggested by the fact that increases in the size and strength of one input are related to the shrinkage and weakening of other axons 4 , 6 ., But there is no direct evidence supporting such a mechanism at the synaptic level , and while a number of studies have suggested inter-axonal competition as the likely mechanism , to our knowledge none have shown a direct reciprocal causal relationship between the fates of the surviving and eliminated axons during developmental synapse elimination 8 , 18–21 ., Moreover , in some circumstances multiple axons can remain at the same neuromuscular junction 22–24 indicating that in some circumstances either competition can be overridden by other factors or that the whole process is not competitive in the first place ., Understanding what drives the process forward is important because this mechanism seems to be one of the strategies at play more generally in the developing mammalian nervous system to help shape it to the particular environment in which it finds itself ., Thus we felt that it would be worthwhile to directly test whether or not synapse elimination is driven by interaxonal competition ., We reasoned that if competition between two axons vying for the same postsynaptic site was causing the elimination of one of them , then that axon should not ever be removed if its putative competitor was no longer present ., We therefore ablated the axon that had the greatest likelihood of being maintained at a neuromuscular junction to see if the weaker input would have a reversal of fate and now be maintained ., If this outcome did occur , we were interested to know when the decision for an axon to be eliminated finally becomes irreversible ., For example , it was possible that axons compete and set into motion a program of elimination that is irreversible even many days before the axonal loss finally occurs ., The cascade that leads to neuronal cell death has such points of no return 25 , which imply that the downstream events are irreversible ., Might the same be true for the program leading to synapse elimination ?, If conversely the synapse elimination program were readily reversible , even at late stages , it would argue that axons remain viable in an ongoing effort to maintain access to the target muscle fiber ., In this latter case , the synaptic reorganization events might be played out with little lag between the competitive actions and their consequences on axonal growth or retraction ., For example , if one input “pushed” another off a synaptic site , axon withdrawal would be temporally coordinated with near simultaneous axonal takeover , allowing for a highly dynamic process where axonal territory might wax and wane on timescales of minutes or hours ., Indeed time-lapse imaging shows that an axons synaptic territory can be increased and decreased in a dynamic manner 4 ., Despite the relatively high temporal and spatial resolution images in many previous studies , however , the motive force for growth and retraction and the details of these behaviors for interacting axons remain obscure ., The experiments reported here allowed us to examine how developing axons respond to vacated synaptic sites ., We developed a laser-based technique with which we could remove one of two closely spaced axons that innervated the same neuromuscular junction ., This technique showed that axons readily grew to occupy vacant sites even when they appeared to be in the process of withdrawing at the time the sites were vacated ., In addition we observed that axons were stimulated to grow even in situations when the muscle fiber was still active ., This combination of synaptic vacancy and the axonal takeover it induces allows us to explain a range of complex phenomena associated with synapse elimination ., In order to selectively remove one axonal branch without damaging any neighboring axons in vivo , we used a diode-pumped mode-locked Ti:Sapphire laser oscillator to cause localized phototoxicity in fluorescent protein containing motor axons in living mice ., By taking advantage of non-linear aspects of multi-photon excitation , we could damage one axon and leave immediately adjacent axons unscathed ., The focused laser spot was positioned over an axon branch using a modified scanning microscope system ( see Materials and Methods and Figure S1 ) , and the axons fluorescence was bleached at one location ., One hundred and seventy-three axons were irradiated ( 71 in adult neuromuscular junctions and 102 in 1-wk-old neonates ) ., Damage to axons typically evolved over 30–45 min and the whole process of axon removal required many hours ., Even though we observed bleaching of the axon segment at the time of irradiation , evidence for structural damage only became apparent within 10–20 min ( see Figures 1–3 ) ., Signs of axon damage included dramatic swelling of the axon distal to the site of laser focus and a progressive widening of the region of non-fluorescence both distal and proximal to the laser irradiation site ., Presumably this loss of fluorescence is secondary to leakage of proteins from the cytoplasm at the damage site ., This phase which typically lasted up to several hours was followed by the complete disappearance of the distal axon save for occasionally a few small disconnected fluorescent fragments that ultimately all disappeared by 10 h ., In the proximal direction the damage initiated a die-back that was reminiscent both in time course and scale of “acute axonal degeneration” of damaged central axons 26 ., Typically , the die-back stopped at the proximal branch point ( Figure 2 ) , although sometimes it extended anterogradely from the branch point to cause the disappearance of other terminal branches ., If the fluorescence at the laser spot recovered after several minutes , that was an indication that the fluorescence in the axonal branch had been bleached but the axon was not seriously damaged because no subsequent changes were noted over the next half hour to hour , or the following day ( see Materials and Methods for details ) ., We attempted to remove one of the axons converging at multiply innervated neuromuscular junction in early postnatal life ., In anesthetized mice that were 7–8 d old we located neuromuscular junctions in the superficial ( ventral ) surface of the sternomastoid muscle that were innervated by two axons ., In the sternomastoid muscle about half the neuromuscular junctions are multiply innervated at 1 wk of age , whereas at 2 wk the number of multiply innervated neuromuscular junctions is very small ( <0 . 1% ) 8 ., At 1 wk nearly all of the multiply innervated junctions are contacted by only two axons 4 , 7 , indicating that each of these junctions will lose one input over the next several days ., Using multi-photon laser irradiation we successfully removed one axon from each of 15 multiply innervated neuromuscular junctions ., At an additional 87 neonatal muscle fibers , axons were damaged but the experiment failed for other reasons including connective tissue buildup and muscle fiber rotation that obscured details when we returned to the muscle the next day; inadvertent muscle , nerve , or blood vessel damage; or occasional animal mortality post-surgery ., After confirming that the axon was damaged during the imaging session ( up to 3 h ) we sutured the neck wound and allowed the animals to recover ., In most cases ( 10/15 ) we intentionally irradiated the axon with the larger caliber ., In 4/15 junctions the two axons had nearly the same caliber , but based on their appearance at the site of entry into the junction , we could identify the axon that we thought had less territory; we then irradiated the larger input ., In the remaining case we intentionally laser irradiated the axon with the smaller caliber ., In all of these junctions the selectivity of the laser damage was apparent because whereas the damaged axon disappeared , in no case did the non-targeted axon show any swelling , fragmentation , bleaching , or loss ( Figures 1–3 ) ., Because in this experiment the two axons were labeled with the same fluorescent protein and in most cases their territories coalesced at light microscopic resolutions , it was not possible to know precisely how much territory the remaining axon occupied except retrospectively ., Once the laser irradiated axon had disappeared , it was easy to see the extent of the territory occupied by the remaining axon ( Figure 3B ) ., In the 14 cases where we attempted to remove the stronger input , the remaining axon occupied half or less of the junctional acetylcholine receptor ( AChR ) sites ( mean 12% ) , and in the one case where we irradiated the thinner axon , the remaining axon occupied 80% of junction ( Table 1 ) ., The territories occupied by these axons were consistent with previous work showing that terminal axon caliber correlated with synaptic territory 7 ., Because the axon occupying the majority of the territory at postnatal day 7 or 8 ( P7 or P8 ) was more than twice as likely to remain at a junction than the axon occupying the minority of the territory 4 , we would anticipate that in most of the 14 cases where we targeted the stronger axon , the remaining undamaged axon would have been eliminated had we not perturbed the system ., Nonetheless , when we re-anesthetized the mice a day later and returned to the same muscle fibers , in none of the 14 cases had the remaining axon withdrawn ., Nor in any case did we see any evidence of regrowth of the damaged axon ., We were certain that the axon remaining at the junction was the axon that was not irradiated on the previous day because its site of entry into the junction was in each case the same as the site where the thin axon was situated on the day of laser irradiation ( see Figure 3 ) ., In each case , however , the axon that remained changed in a striking way ., Each of these thin axons now extended branches throughout the postsynaptic area to fully occupy the territory formerly overlain by the laser irradiated axon ., In all but one case the growth response appeared complete within the first 24 h after laser exposure ., The axonal expansion of territory was reminiscent of the “takeover” seen during normal synapse elimination at the neuromuscular junction: the advancing axon specifically enlarged its coverage of the adjacent postsynaptic AChR sites without extending sprouts to new sites 4 ., Because ordinarily smaller axons were twice as likely to leave a multiply innervated junction than larger ones 4 , the probability that none of them ( 14 cases ) would have withdrawn is very low ( probability <0 . 0000003 ) ., Therefore , the fate of the weaker axon was changed by removing the stronger input , a result that argues that competition is the cause of the elimination of the weaker input ., We were interested to know what occurred in the immediate aftermath of removing the other input ., In particular , did the remaining axon continue to be eliminated for some time , suggesting , for example , that competitive effects have some momentum and a certain amount of time is required before an axon can change its fate ?, We found , however , that at no time after axon removal did the remaining axon show any evidence of continued elimination ., In three cases we reimaged junctions less than 24 h after the initial view ( one at 6 h , one at 12 h , and one at 17 h ) ., At the 6 h and 12 h views , the remaining axons had not lost any territory ( see Figure 2 ) ., The junction viewed at 17 h had already shown signs of expansion ., Unfortunately , it was not possible for us to anesthetize the same animal for imaging more than once per day and have it survive , so the exact time axons began to grow following laser damage of the other synaptic occupant remains unclear ., We do know , however , that at some point after a period of quiescence that lasted up to 12 h , the territory occupied by the remaining axon changed rapidly ., In 13/13 junctions imaged at 24 h after laser induced removal of the stronger input , the remaining axon had expanded dramatically ., In all but one case the axon occupied the entire postsynaptic site , and in the one other case , it occupied 75% of it ( Table 1A , Figures 1 and 3 ) ., The increase in territory by the remaining axon was often matched by a thickening of the caliber of its preterminal branch ( Table 1A , Figures 1 and 3 ) ., In the one junction we studied in which the weaker input was intentionally eliminated , we also noted complete takeover of its territory by the stronger axon at 24 h ( Table 1A ) ., Thus by 1 d after the laser removal of one axon at dually innervated neuromuscular junctions , the remaining input had grown and now appeared identical to the axons that survive naturally occurring synapse elimination and singly innervate neuromuscular junctions ., In each case the undamaged axon now occupied all or nearly all the postsynaptic territory and possessed a thick preterminal axon ., Importantly , in 6 of the 15 cases , the axon that remained had occupied less than 5% of the junction at the time of axon removal ., These axons were as effective in taking over the remaining territory as axons that had a larger footprint at the time of axon removal ( Figure 3B and Table 1A ) ., We thus conclude that once a competing axon is removed the remaining axon , within hours , and irrespective of the contact area of its terminal arbor , changes its fate to take the position and characteristics of the dominant axon ., Once an axon has lost all territory at a neuromuscular junction it undergoes a stereotyped process of withdrawal in which the bulb tipped axon branch sheds some of its cytoplasm and appears to retract away from the junction 11 , 18 , 27 ., These “retraction bulbs” are seen frequently in developing muscles at the time of synapse elimination but are not seen at all in adult muscles ., Might these structures be irreversibly committed to retraction ?, In anesthetized mice we successfully damaged 18 strong axonal inputs of singly innervated junctions where a second axon had recently retracted but was still visible nearby ., Previous time lapse studies indicate that retracted axons which were within ∼200 µm of a junction had disconnected at some point over the previous 48 h 4 , 11 ., After damaging the axonal input that innervated the junction , we allowed the animals to recover and waited to see if nearby retracted inputs ever attempted to return to the junction over the following days ., To our surprise , in 55% of the cases ( n\u200a=\u200a10/18 ) , the axon stopped retracting , grew back to the junction , and occupied the entire junctional area ( Table 1B ) ., The laser-irradiated axon typically died back to the proximal branch point ., Although in most cases given the position of the two axons it was unambiguous that the damaged axon did not reinnervate the junction , a potential ambiguity could occur if the damaged axon rapidly reinnervated the junction while at the same time the retracting axon completely disappeared ., In order to directly identify the re-growing axon , we used a doubly transgenic mouse in which individual neurons expressed different concentrations of cyan fluorescent protein ( CFP ) and yellow fluorescent protein ( YFP ) in each axon ( see Materials and Methods ) ., In this case , we found that the damaged axon ( identified by its color ) did not return over the next 48 h , whereas the retracted axon ( unambiguously identified by its color and its site of exit from the nerve fascicle ) reinnervated the junction within 24 h and increased in caliber over the next day ( Figure 4A ) ., In order to determine why some retracting axons succeeded in regrowth whereas others did not , we compared the fates of retracting axons at various distances from their previous neuromuscular junction ., In three of the cases , we infer that the retracting axons had only recently been eliminated because at the time of laser irradiation of the dominant axon they still had a fine filamentous process connecting them to the junctional site ., All of these retracting axons reoccupied the junction 24 h later ., But such a tendril was not required for reinnervation: 7/16 of the remaining retracting axons also reoccupied the junctions despite not being connected ., Generally , we found that retracting axons close to the junction were significantly more likely to grow back than ones farther away ( Figure 4B ) ., These results indicate that once an axon has disconnected from a junction , there still may be a window of 1–2 d before it has transitioned to a mode where retraction is irreversible ., In summary , the ability of retracted axons to return to a junction suggests that growth stimulating signals and/or signals that disinhibit the retraction process are activated following laser axotomy ., The delay in initiation of growth appears to be nearly the same whether or not the undamaged input was in direct contact with the laser damaged axons at the junction , suggesting that the onset of growth response is not a result of the loss of contact inhibition ., It is , however , clear that the time required to completely reinnervate a neuromuscular junction is a bit slower for axons that have completely disconnected and have a longer distance to grow ( see Table 1B ) ., The results described above show that axons that are in the midst of withdrawing from a synapse can be stimulated to grow and occupy the recently vacated sites of a damaged axonal competitor ., We were interested in learning if this method of axonal takeover also underlies the way synaptic competition occurs in normal development ., In particular there are two alternative ways axons might enlarge their territory ., One way is that an axon expands its territory by “pushing off” a competing axon ., If this were the case , each AChR region is sequentially occupied first by one axon and then by another with no lag in takeover and no interval when the AChRs are unoccupied ., An alternative way an axon might enlarge its territory is if the stimulus for an axon to take over a site is generated in response to that sites vacancy following withdrawal of another axon ., This latter mechanism is what likely stimulates the growth of axon terminals following laser axotomy of a competing axon ., Previous studies did not have the necessary spatial resolution to resolve this question at sites of synaptic takeover; however , in one situation where the two axons territories were widely separated , sites that were vacated by one axon were not taken over by the other ., That result rules out the idea that in all cases synapse elimination requires one axon to displace another from a synaptic site 4 ., In this work we wanted to extend our analysis to sites of takeover ., We reasoned that if synaptic vacancy were the proximate cause for synaptic takeover in naturally occurring synapse elimination , then it should be possible to observe instances of transiently vacated AChR sites at the boundaries of the territories occupied by different inputs ., Whereas if one axon only lost its territory when another invaded its site , then no lag should ever be seen ., In two neuromuscular junctions we did observe synaptic territories that were unoccupied at the first imaging session and then occupied a day later ., The presence of unoccupied receptors suggests but does not prove that one axon had withdrawn and the other axon was responding by taking over those sites ., More direct evidence would require seeing one axon withdraw from a site that would then appear vacant before another axon would later reoccupy it ., Our previous experience suggested that finding examples , were they to exist , would be difficult because in most cases competing axons overlap in some regions with one sitting on top of the other , meaning that if withdrawal precedes takeover , the axon branches are likely lifting but not retracting before takeover occurs ., The distances involved are below the resolution limit of the microscope , making it difficult to know if AChRs are transiently unoccupied or not 12 ., In addition , we know that takeover following laser axotomy occurs rapidly , but previous time lapse images of retraction suggest that removal of a detached branch occurs more slowly , meaning that the takeover may be happening before the other branch disappears 4 ., We thus devised a photo-bleach method to unambiguously see the extent of overlap at neuromuscular junctions as a way to screen for multiple innervated junctions where the two axons abutted each other but had minimal overlap ( see Materials and Methods ) ., Such junctions would be most likely to reveal the transient presence of a vacant AChR site ., In one case out of hundreds of attempts we did see the withdrawal of one axon leading to AChR vacancy and then the takeover of that site by a second axon ( Figure 5 ) over the course of 3 d ., In the first view the two inputs appear to overlap in some areas but not at the site of axon entry ., After 1 d , the smaller input has withdrawn completely from the junction ., A vacated AChR region is seen at the site of axon entry ., By the following day , this region has become occupied by the remaining input ., It is clear in this example that withdrawal preceded takeover and that the takeover occurred after a delay of many hours ., The timing of takeover is similar to what we found following removal of an input by laser irradiation ., Because we did not observe takeover in the first hours after removal of an axonal input by laser irradiation , we think it is likely that takeover is stimulated by the withdrawal ( i . e . , vacated sites ) and that withdrawal before takeover is possibly the general mechanism by which synaptic contacts are rearranged at junctions ., However , based on some of the data in this article ( see section below ) and previous studies 4 , it seems likely that a vacant site is a necessary prerequisite for axonal growth but not a sufficient stimulus ., For example , when a vacant site is nearby other innervated sites , it is very likely to be reoccupied ., But when the site is off by itself at the edge of a junction or at the nerve entry site , then the vacant site is sometimes not reinnervated and the receptors eventually disappear ., The results above suggest that a signal from a vacant site may stimulate the growth response during naturally occurring synapse elimination ., In many situations axonal growth is thought to be stimulated by signals that emanate from denervated , and thus inactive , target cells 28 ., However , in the cases of normal takeover in development and in the case where we targeted the laser to a weak input , the loss of an axon was unlikely to give rise to target inactivity because we had denervated a minuscule portion of a large synapse ., We were thus interested to know if small partial denervations of target cells are generally sufficient to activate an axonal regeneration response ., In particular if only a small synaptic bouton is removed and the muscle fiber is not functionally denervated , will sprouting be stimulated ?, As described above we found one case in development where a small site became unoccupied and then later reoccupied by the remaining axon ( see Figure 5 ) , but we wanted to see if this was a general trend that occurred if vacant sites were present at any age ., We were also interested to know whether the proximate cause for the sprouting within a neuromuscular junction could be explained by release of contact inhibition ., We thus did focal laser axotomies in adult neuromuscular junctions to denervate small isolated synaptic boutons while retaining innervation to the rest of the junction ., Surprisingly in adult animals more than half ( 55% , n\u200a=\u200a12/22 ) of these small laser-targeted axonal surgeries which denervated between 5% and 30% of the AChR sites still induced reinnervation ( Figure 6 ) ., The reinnervation started after a delay of at least 1 d following laser exposure and was typically complete by 2 d but sometimes longer ( see Figure 6B ) ., In all cases reinnervation occurred by sprouting from adjacent branches in the terminal ., The sprouts appeared to be directed specifically to unoccupied AChR sites and not elsewhere , yet the original branching pattern ( pre-irradiation ) was not necessarily preserved , suggesting that regrowth was not necessarily guided by preexisting glial sheaths but by highly localized signaling originating at the vacated sites ., This study was undertaken to better understand the sequence of events that occur during development underlying the transition from multiple to single innervation in skeletal muscle ., This phenomenon , which has analogs in other parts of the developing nervous system , occurs by one axons takeover of most of the postsynaptic sites that were earlier occupied by other axons ., However , a number of questions about the underlying mechanism remain unanswered ., First , what drives the exchange of territory such that when one axon loses sites another typically gains those sites 4 ?, Second , what determines the identity of the eventual surviving input given that an axon that loses territory at one time point sometimes gains it back at a later time 4 ?, And third , why do the contacts of an axon within a neuromuscular junction tend over time to cluster to occupy a contiguous segregated territory 29 ?, In this work we focused on answering the first question ., In so doing we think we have also uncovered explanations for the other questions and believe we now have a framework to interpret many aspects of this form of synaptic plasticity ., We show that axons rapidly respond to vacant synaptic sites by growth ., In multiply innervated neuromuscular junctions an axon whose elimination appears imminent will , within 1 d , occupy all the sites of an axon that was experimentally removed ., Moreover , axons that have recently withdrawn completely from a neuromuscular junction will reverse their fate and reoccupy it if the innervating axon is caused to disappear ., These results strongly support the idea that the process leading to single innervation is competitive: an axon destined for elimination always survives if the other innervating axon is removed ., This growth response of one terminal axon branch to the damage of another terminal branch is in some ways reminiscent of the reinnervation response following partial denervation of a muscle where an axon that is undamaged sprouts to occupy neuromuscular junctions on denervated muscle fibers 30 ., However , these two phenomena seem to be dissimilar in several important respects and may have different underlying mechanisms ., First , a number of studies support the idea that sprouting following partial denervation is stimulated by muscle fiber inactivity 30 , 31; however , several of our results show axons growing into vacated synaptic sites even when the muscle fiber is functionally innervated ., It is also clear that in naturally occurring synapse elimination , an axon continues to take over vacated sites even when it already occupies the vast majority of the terminal area so that its growth is not being stimulated by inactivity of the muscle 4 ., A second difference between partial denervation of muscle and the growth response described here is that the following partial denervation axons grow through vacated Schwann cell tubes 32 or extend along new Schwann cell processes 33 ., Neither of these paths is available within neuromuscular junctions ., Third , the growth response following laser axotomy in neonatal animals is fast compared to the response of axons in adults to partial denervation ., Another difference is that many of the axons undergoing branch loss in development were atrophic and had to transition from a withdrawing state to a growing state , whereas the axons responding by growth following partial denervation in adults are in a healthy quiescent state before being induced to grow ., These differences suggest that the local growth response to synaptic vacancy within a neuromuscular junction is different from the growth response of axons to the loss of all innervation to a subset of muscle fibers ( i . e . , partial denervation ) ., Because muscle fiber inactivity is unlikely to be the stimulus that induces axonal growth into vacant synaptic sites in our studies , what then is the signal ?, One idea is that Schwann cell processes that no longer are associated with an axon become activated ., Previous studies have shown that Schwann cell activation following nerve damage is a potent stimulus for axon growth 34 , 35 ., Thus , it is possible that focal loss of nerve-glial contact leads to the release of a glial-derived signal that causes axons to grow ., Interestingly glial cell-derived neurotrophic factor ( GDNF ) , a glial based growth factor , is one of the strongest known stimuli for mammalian motor nerve growth 36

Introduction, Results, Discussion, Materials and Methods

During mammalian development , neuromuscular junctions and some other postsynaptic cells transition from multiple- to single-innervation as synaptic sites are exchanged between different axons ., It is unclear whether one axon invades synaptic sites to drive off other inputs or alternatively axons expand their territory in response to sites vacated by other axons ., Here we show that soon-to-be-eliminated axons rapidly reverse fate and grow to occupy vacant sites at a neuromuscular junction after laser removal of a stronger input ., This reversal supports the idea that axons take over sites that were previously vacated ., Indeed , during normal development we observed withdrawal followed by takeover ., The stimulus for axon growth is not postsynaptic cell inactivity because axons grow into unoccupied sites even when target cells are functionally innervated ., These results demonstrate competition at the synaptic level and enable us to provide a conceptual framework for understanding this form of synaptic plasticity .

Early in development , neurons make multiple synaptic connections with their target cells ., Over time , many of these connections disappear , leaving behind a fraction of the original connections ., Because this pruning occurs when mammals first leave the uterus , its thought that this type of remodeling may serve to sculpt the nervous system to match a particular environment ., However , what causes synapse elimination is not well understood ., In this study , we use in vivo imaging to study the connections between motor neuron axons and their target muscle cells , at the neuromuscular junction ( NMJ ) , during a developmental stage when each NMJ has multiple connections ., We find that synapse loss is driven by competition between nerve cells vying to remain in contact with the same target cell ., We show that an axon that would have been eliminated can always be spared by removing ( with laser microsurgery ) another axon converging on the same synaptic site ., The remaining axon not only survives but rapidly grows to occupy the synaptic sites vacated by the removed axon ., These results provide a framework for understanding synaptic rearrangements in the developing nervous system .

motor systems, developmental neuroscience, synaptic plasticity, biology, neuroscience

Competition between neurons for the same synaptic sites at the developing neuromuscular junction drives synaptic rearrangements.

journal.ppat.1006413

Genomic fossils reveal adaptation of non-autonomous pararetroviruses driven by concerted evolution of noncoding regulatory sequences

Similar to virus–host interactions , virus–virus interactions , especially those occurring during mixed plant virus infections in nature , have complex outcomes ranging from antagonism to synergism 1 , 2 ., Such interactions between different virus species affect their adaptation 1 , 2 ., Numerous virus-derived sequences , referred to as endogenous viral elements ( EVEs ) , have recently been discovered in various eukaryotic genomes 3–6 ., In addition to EVEs derived from retroviruses , EVEs originating from viruses without active reverse-transcription or integration abilities have been identified 4 , 7–10 ., Because these elements are vertically inherited viral sequences integrated into the germline genome of a host , they are viral genomic fossils and hence serve as invaluable historical records 3 , 11 , 12 ., Although EVEs may provide an unprecedented opportunity to advance our understanding of evolutionary-scale virus–virus interactions , these records have rarely been exploited to explore such interactions ., Pararetroviruses ( PRVs ) , including Caulimoviridae and Hepadnaviridae families , are reverse-transcribing double-stranded DNA viruses that lack an integrase and a process for integration 5 , 13 ., PRVs also possess EVEs called endogenous PRVs that originated from the incidental integration of PRV DNA into host genomes through non-homologous end-joining 14 , 15 ., Endogenous PRVs have been identified in an increasing number of plant genomes and have also been recently discovered in bird and reptile genomes 4 , 5 , 11 , 16–18 ., PRVs are thought to be distantly related to long terminal repeat ( LTR ) retrotransposons 19 ., Interestingly , many LTR retrotransposons are non-autonomous with respect to their parasitic life cycle in host cells , i . e . , they have lost most or all of their coding capability but can amplify themselves by using the protein machinery of autonomous LTR retrotransposons that are functionally and structurally intact 20–22 ., A hallmark of the parasitism of non-autonomous LTR retrotransposons on their autonomous partners is the substantial sequence similarity of their LTRs—the location of noncoding regulatory sequences ( NRSs ) 22–24 ., Plant PRVs have open circular genomes and encode a movement protein ( MP ) , a capsid protein ( CP ) harboring a zinc finger motif , a protease ( PR ) , and a reverse transcriptase with RNase H activity ( RT/RH ) 25 ., In addition to the domains encoding these essential proteins , diverse non-standard domains or open reading frames ( ORFs ) have frequently been found in plant PRV genomes , the protein products of which generally play roles in vector transmission or immune suppression 26 , 27 ., The intergenic region ( IGR ) of plant PRVs , a highly diverse noncoding region containing multiple NRSs , is crucial for viral transcription , translation , and replication 25 , 27 ., All known PRVs encode all essential proteins and are thus autonomous PRV species during their parasitic life cycle in host cells ., Limited cases of non-autonomous virus species have been previously documented ., One well-known example is adeno-associated virus ( Dependoparvovirus , a single-stranded DNA virus ) , which has been applied as a gene therapy vector 28 ., No non-autonomous PRV species have been reported from nature to date ., In this study , we uncovered paleogenomic evidence for non-autonomous PRVs and revealed their interplay with different PRV species through an analysis of endogenous PRVs in grass family ( Poaceae ) genomes ( S1 Table ) ., We discovered two examples of virus–virus interactions: a possible commensal partnership between a non-autonomous PRV and an autonomous PRV species , and a possible mutualistic partnership between two functionally complementary non-autonomous PRV species ., Unexpectedly , we found that the two partners in each interplaying system have frequently exchanged ( >18 estimated major recombination events ) their NRSs with each other via region-specific recombination to maintain partnership and coevolution ., The NRS homogenization between partner viruses led by such recombination events suggests that concerted evolution has occurred in these proposed partnerships ., Our results provide paleoviral insights into the genesis and adaptation of complex virus systems ., We previously identified the first known endogenous PRV family in the genome of rice ( Oryza sativa ) 29 ., This family , derived from a sister species of rice tungro bacilliform virus ( RTBV ) —an autonomous PRV that infects O . sativa—has been designated as endogenous RTBV-like ( eRTBVL ) 14 , 29 , 30 ., In the present study , we observed domain reshuffling in at least 13 eRTBVL segments in the O . sativa genome , 7 of which formed a long cluster on chromosome 8 with segments of eRTBVL-X ( the youngest group of eRTBVL 30 ) ( S1 Fig ) ., These reshuffled sequences exhibited a consensus pattern among the 13 segments ( S2 Fig ) , which suggests that the domain reshuffling must have occurred in the corresponding viral genome prior to integration ., We named this reshuffled eRTBVL as endogenous RTBV-like 2 ( eRTBVL2 ) and reconstructed its ancestral virus circular genome ( Fig 1A ) ., Instead of an RT/RH domain and a third ORF , this eRTBVL2 possessed a functionally unknown domain , henceforth referred to as the SFKTE domain ( for the conserved five-residue SFKTE present in all homologous sequences ) ( Fig 1A and 1B ) ., A BLAST search for the SFKTE domain sequence in the O . sativa genome identified 15 loci ( e-value < 4 . 00 × 10−44 ) that have recently been annotated as endogenous PRVs similar to petunia vein clearing virus ( PVCV ) sequences; these PRVs are hereafter referred to as endogenous PVCV-like ( ePVCVL ) ( Fig 2A; 18 ) ., By aligning the regions around the identified sequences , we constructed the ancestral virus circular genome for these ePVCVL segments ( Fig 1A; details in S3 Fig ) ., The results of a detailed sequence comparison using consensus sequences of viral genomes imply a possible recombination event between the viruses of eRTBVL and ePVCVL that may have generated a recombinant virus responsible for eRTBVL2 ( Fig 1B ) ., Recombination analyses with multiple methods statistically validated this recombination event ( P = 7 . 18 × 10−309; S2 Fig ) ., Examination of presumed recombination breakpoints revealed no obvious sequence similarity between the parent sequences; instead , we detected a small microhomologous region at the left breakpoint ( S2 Fig ) , which suggests an illegitimate recombination event ., Three predicted essential domains ( MP , CP , and PR ) were confirmed by conserved motif alignment , but the RT/RH domain indispensable for replication was not detected in eRTBVL2 or ePVCVL ( S2 Table and S4 Fig ) ., Despite the absence of the RT/RH domain , the presence of multiple genomic fossils of these viruses ( 13 eRTBVL2 and 24 ePVCVL segments in the O . sativa genome; S2 Fig and S3 Table ) suggests the success of their proliferation ., We therefore propose that the viruses of eRTBVL2 and ePVCVL are non-autonomous PRV species ., To achieve replication , non-autonomous PRVs of eRTBVL2 and ePVCVL should require an autonomous partner virus or other related elements ., Considering the high sequence similarity of IGRs carrying NRSs ( Fig 1B; predicted NRSs in S5 Fig ) , we hypothesized that the virus of eRTBVL2 may depend on the protein machinery of the virus of eRTBVL ( an autonomous PRV ) for proliferation , similar to the case of parasitic interactions between non-autonomous and autonomous LTR retrotransposon pairs 20–22 ., We thus tested the spatio-temporal likelihood of this proposed interplay ., In a phylogenetic tree of IGR sequences of eRTBVL and eRTBVL2 ( Fig 1C ) , most eRTBVL2 sequences were placed within or close to the eRTBVL-X clade , with three other eRTBVL2 sequences each falling into one of three older eRTBVL clades ( -A1 , -A2 and -B ) 30 ., Phylogenetic trees of other homologous regions ( ORF1 , MP , CP , and PR domains ) between eRTBVL and eRTBVL2 had topologies similar to the IGR-based tree ( see S6 Fig for these four ORF/domains ) ., The results of these phylogenetic analyses suggest that recombination may have occurred between the viruses of eRTBVL and eRTBVL2 at IGRs and other homologous regions , implying their spatio-temporal coexistence ., Detailed recombination analyses confirmed the contribution of the virus of eRTBVL to the recombination of the viruses of the three eRTBVL2 sequences phylogenetically close to eRTBVL-A1 , -A2 , and -B clades , and also supported recombination events between the viruses of eRTBVL-X and other eRTBVL2 sequences ( P = 1 . 37 × 10−9 to 1 . 44 × 10−181; S7A Fig ) ., We next analyzed the temporal relationship of eRTBVL2 segments based on a phylogeny of the SFKTE domain ( S8 Fig ) ., We rooted the phylogenetic tree of SFKTE amino acid sequences of eRTBVL2 and ePVCVL ( S8 Fig ) using the oldest ePVCVL segment , where the relative antiquity of the latter was determined by a bidirectional genome-wide orthology analysis of ePVCVL loci in Oryza species ( see Materials and Methods and S4 and S5 Tables; PCR and Sanger sequencing validation in S9 Fig ) ., In the generated SFKTE domain tree ( S8 Fig ) , the eRTBVL2 segments related to the eRTBVL-X group ( Fig 1C ) were the latest branching sequences , whereas the three eRTBVL2 segments related to eRTBVL-A1 , -A2 , and -B groups ( Fig 1C ) branched earlier ( S8 Fig ) ., Because the eRTBVL-X group is the youngest eRTBVL group and eRTBVL-A1 , -A2 , and -B groups are older 30 , the SFKTE phylogeny indicates that the evolution of the virus of eRTBVL2 is temporally consistent with that of eRTBVL ., Taken together , these results strongly support the coexistence and coevolution of the viruses of eRTBVL2 and eRTBVL and provide evidence for a possible partnership between the two viruses during mixed infection ., The virus of eRTBVL2 did not seem to be a parasite on the virus of eRTBVL , because we observed no higher magnitude of proliferation in the former relative to the latter ( Fig 1C and S6 Fig ) ., Taking into account the observation that the replication dependence of the virus of eRTBVL2 on the virus of eRTBVL had no recognizable deleterious effect on the latter , we suggest a possible commensal partnership between the viruses of eRTBVL2 and eRTBVL ., Although our search for the autonomous partner of the virus of ePVCVL revealed no such candidate in the genomes of O . sativa or other Oryza species , we noticed another endogenous PVCV-like family ( hereafter ePVCVL2 ) showing defective structures ( Fig 2B and 2C; 18 ) ., We successfully reconstructed the ancestral virus circular genome of ePVCVL2; this ancestral genome possessed MP , PR , and RT/RH domains but the CP domain was absent ( Fig 3A; details in S2 Table , S3 and S4 Figs ) ., The composition of this genome suggests that the virus of ePVCVL2 is structurally and functionally complementary to the virus of ePVCVL ., Given the existence of the naturally defective genome as well as multiple fossils of the virus of ePVCVL2 ( 11 segments in the O . sativa genome; S3 Table ) , we suggest that this virus is another non-autonomous PRV species ., Detailed comparison of ePVCVL and ePVCVL2 consensus sequences revealed a high degree of local similarity between their IGRs as well as their MP domains ( 97 . 2% nucleotide identity: 99 . 3% for IGR and 95 . 3% for MP ) ( Fig 3B ) ., Given that IGR sequence identities between eRTBVL groups ( intraspecies level ) ranged from 72 . 6% to 92 . 8% , this interspecies similarity of IGRs is exceptionally high ., Both ePVCVL and ePVCVL2 encode a PR domain , but the nucleotide sequence of this region was very dissimilar between these two types of endogenous PRVs ( Fig 3B ) ., This dissimilarity of PR domains , extraordinarily high IGR sequence similarity ( identical NRSs between IGRs; predicted NRSs in S5 Fig ) , and observed functional complementarity between the viruses of ePVCVL and ePVCVL2 all suggest a possible mutualistic partnership in which the two viruses mutually compensate to facilitate proliferation ., To confirm the proposed partnership , we performed a bidirectional genome-wide orthology analysis of ePVCVL2 loci in Oryza genomes ( the same analysis of ePVCVL loci mentioned above ) ., This analysis revealed that ePVCVL and ePVCVL2 segments are species-specific , except for four shared ePVCVL loci and two shared ePVCVL2 loci , and coexist in each analyzed Oryza genome ( Fig 2C; details in S9 Fig , S4 and S5 Tables ) , thereby supporting the coexistence of the viruses of ePVCVL and ePVCVL2 during host divergence ., No major ePVCVL cluster related to a major ePVCVL2 cluster was present in the phylogenetic tree of ePVCVL and ePVCVL2 IGR sequences in the O . sativa genome ( Fig 3C ) ., On the contrary , three ePVCVL IGR sequences clustered with three ePVCVL2 IGR sequences in a strongly supported clade ( Fig 3C ) ., To confirm this finding , we examined single nucleotide polymorphisms ( SNPs ) among the six IGR sequences , which revealed six SNP sites shared by the IGRs of ePVCVL and ePVCVL2 ( Fig 3D ) ., We further carried out recombination analyses on these ePVCVL and ePVCVL2 sequences , which resulted in the identification of significant recombination events between the IGRs of the viruses of ePVCVL and ePVCVL2 ( P = 1 . 28 × 10−8 to 2 . 90 × 10−23; S7B Fig ) ., When we extended our phylogenetic analysis of IGR sequences to segments in other Oryza genomes , we also found that the IGR sequences of ePVCVL and ePVCVL2 clustered together ( S10 Fig ) ., The recombination of IGR sequences between the viruses of ePVCVL and ePVCVL2 , implied by the phylogenetic analysis , was likewise confirmed by recombination analyses of ePVCVL and ePVCVL2 sequences in these Oryza genomes ( P = 4 . 06 × 10−4 to 6 . 87 × 10−23; S7C Fig ) ., Taken together , these data thus provide strong evidence that two non-autonomous PRVs in a possible mutualistic partnership have recombined their IGR sequences to continue their coevolution during mixed infection ., By searching for homologous sequences of eRTBVL2 , ePVCVL , and ePVCVL2 and reexamining reported endogenous PRVs in non-Oryza grass genomes 18 , we found both ePVCVL and ePVCVL2 homologous sequences coexisting in the genomes of sorghum ( Sorghum bicolor ) and switchgrass ( Panicum virgatum ) ( Fig 2 and S3 Table ) ., These sequences formed a phylogenetic sister group ( non-Oryza group ) to either ePVCVL or ePVCVL2 segments of analyzed Oryza genomes ( Oryza group ) ( Fig 2A and 2B ) ., We constructed two ancestral virus circular genomes for these sequences ( Fig 4A; details in S3 Fig ) ., One was structurally equivalent to Oryza group ePVCVLs , that is , the RT/RH domain was absent ., The other genome resembled Oryza group ePVCVL2s , but lacked both MP and CP domains ( this genome contained a region slightly resembling the CP domain but without an essential zinc finger motif ) ( Fig 4A and 4B; details in S2 Table , S3 and S4 Figs ) ., IGR sequences of ePVCVL and ePVCVL2 non-Oryza groups shared extremely high nucleotide identities ( 97 . 1%; Fig 4B ) , whereas IGR sequence similarities between ePVCVL Oryza and non-Oryza groups and between ePVCVL2 Oryza and non-Oryza groups were low ( 43 . 6% and 44 . 6% nucleotide identities , respectively; S11 Fig ) ., In a phylogenetic tree based on sequences from the S . bicolor genome , IGR sequences of non-Oryza ePVCVL and ePVCVL2 groups were mixed together ( Fig 4C ) ., ( The number of IGR sequences in the P . virgatum genome was too limited for phylogenetic analysis ) ., We also performed recombination analyses on these sequences in the S . bicolor genome , which resulted in the detection of significant recombination events occurring between the IGRs of the viruses of ePVCVL and ePVCVL2 non-Oryza sequences ( P = 6 . 30 × 10−10 to 1 . 23 × 10−22; S7D Fig ) ., A close examination of the S . bicolor sequences revealed that virus-derived small insertion/deletion ( indel ) variations in IGRs were shared between partial non-Oryza ePVCVL and ePVCVL2 segments ( Fig 4D ) ., The presence of these indels is direct evidence that IGRs have frequently been recombined between the virus genomes of ePVCVL and ePVCVL2 ., Taking all of these results into consideration , we conclude that non-autonomous PRVs have adapted to a long-term partnership via IGR homogenization mediated by frequent recombination , leading to concerted evolution of NRSs ., The discovery and analysis of various EVEs in eukaryotic genomes has contributed to our understanding of viral origin and evolution as well as long-term interactions between viruses and hosts 3 , 31–33 ., Endogenous PRVs in plant genomes have been frequently reported 5 , 18 , 34 , and extreme cases of endogenous PRV reactivation under certain conditions , such as in endogenous banana streak virus , have been well documented 35–38 ., Using grass endogenous PRVs as ancient DNA records of viruses , we performed paleogenomic analyses of PRVs to explore their long-term virus–virus interactions ., In contrast to all previously known PRVs , which are autonomous , three non-autonomous PRV species were identified in this study , namely , the viruses of eRTBVL2 , ePVCVL , and ePVCVL2 ., Our examination of ePVCVL and ePVCVL2 sequences , which were first described by Geering et al . 18 , revealed the adaptation strategies of their corresponding non-autonomous viruses ., We have proposed two adaptation strategies used by non-autonomous PRVs: a possible commensal partnership with autonomous PRVs and a possible mutualistic partnership with other non-autonomous PRVs ( summarized in Fig 5A ) ., These proposed partnerships have been enabled by the existence of shared common NRSs in their IGRs ., We have also demonstrated the evolutionary dynamics of these partnerships: frequent recombination of IGRs ( >18 estimated major events; see below ) between two partners leading to NRS homogenization between different PRV species during host divergence ., This concerted evolution of NRSs is responsible for the maintenance of such partnerships and has driven the coevolution of interacting viruses ., The consensus NRSs of two partner viruses would be expected to recruit the same virus-encoded proteins and host factors to complete their life cycles in hosts ., In the possible commensal partnership suggested by this study ( Fig 5A ) , the non-autonomous virus of eRTBVL2 should benefit from sharing the RT/RH protein of the autonomous virus of eRTBVL ., With respect to the SFKTE domain of the virus of eRTBVL2 , neither the RT-like motif nor its degenerate residues could be distinguished in this domain by amino acid alignment with all known types of RT-like domains ( S4 Fig and S1 Dataset ) or by using HHpred , a sensitive detection method based on profile hidden Markov models ( S2 Table; see Materials and Methods ) 39 ., Although the possibility cannot be completely excluded and future biochemical verification is needed , the likelihood of RT activity in SFKTE proteins is very low ., In fact , plant PRV genomes usually possess various additional non-standard domains or ORFs that often play a role in vector transmission or immune suppression 26 , 27 ., SFKTE proteins may have functions similar to those of well-known additional PRV proteins , such as interaction with insect vector proteins or host antiviral factors 26 , 27 ., Although not necessary for its replication , the virus of eRTBVL may also benefit , to some extent , from such a function of SFKTE proteins encoded by the virus of eRTBVL2 during mixed infection ., Consequently , an alternative relationship may exist between the two viruses: a mutualistic partnership ., In the possible mutualistic partnership suggested for the viruses of ePVCVL and ePVCVL2 ( Fig 5A ) , the two non-autonomous viruses benefit from each other via functional complementary ., The RT/RH protein from the virus of ePVCVL2 reverse transcribes its own pregenomic RNA as well as that of the virus of ePVCVL , while the CP protein from the virus of ePVCVL assembles its own viral particles as well as those of the virus of ePVCVL2 ., Products from additional domains/ORFs of these two viruses ( the SFKTE domain of the virus of ePVCVL and ORF2 of the virus of ePVCVL2 ) may also contribute to the putative mutualistic partnership ., In the case of the non-Oryza group , the MP protein from the virus of ePVCVL is responsible not only for its own cell-to-cell movement , but also for that of the virus of ePVCVL2; at the same time , the region in the virus of ePVCVL2 slightly similar to the CP domain but lacking a zinc finger motif may encode defective CP proteins ( i . e . , those lacking viral DNA binding activity because of missing zinc finger motifs ) to bind host antiviral proteins to disable viral-CP-binding activities ., This system of two interplaying viruses is reminiscent of extant complex viruses possessing multiple polynucleotide sequences , which suggests that functional complementarity and co-regulation may have contributed to the origin of multipartite viruses ., The interspecies recombination event that generated the virus of eRTBVL2 ( Fig 1B and S2 Fig ) occurred between the viruses of eRTBVL ( Tungrovirus-related species 29 ) and ePVCVL ( Petuvirus-related species; Fig 2A ) , which belong to different genera and possess distinct genomic structures with very weak sequence similarities ., The presence of reshuffled domain combinations in the viral genome of eRTBVL2 relative to the virus of eRTBVL ( Fig 1A and 1B ) supports the theory of modular evolution that has been considered to be applicable to all known virus types 40 , 41 ., Putative interspecies recombination events have frequently been reported in viruses 42–46 ., We propose that interspecies recombination is one of the mechanisms driving viral modular evolution ., We particularly note that the frequent exchange of IGRs revealed in this study implies that modular evolution applies not only to coding domains , but also possibly to NRSs ., Other studies have observed that recombination between endogenous and exogenous retroviruses has occasionally occurred and produced recombinant viruses 47–52 ., This recombination may occur when exogenous and endogenous retroviral RNAs are coexpressed in host cells 47 ., Recombination between endogenous and exogenous PRVs has not been reported to date 12 ., Although in our study we also found no evidence to support the origin of any non-autonomous PRVs from such recombination , consideration of the evolutionary influence of this type of occasional albeit hypothetical recombination event is still of interest ., Concerted evolution has been widely observed to accompany the sequence homogenization process of some duplicated genes or elements in prokaryotic and eukaryotic genomes; one notable example is the sequence homogenization of ribosomal DNA repeats within a species 53 , 54 ., Concerted evolution has also been reported in nanoviruses , which are single-stranded DNA viruses 55 , 56 ., In our study , concerted evolution was observed during the homogenization of IGRs between a pair of partner viruses ., IGRs are noncoding and highly divergent across PRV genomes; for example , IGRs of RTBV and PVCV respectively share less than 44 . 4% and 35 . 1% nucleotide identities with those of other PRVs ( NCBI genome database ) ., Nevertheless , the overall set of IGRs ( and neighboring regions ) between the two partner viruses in this study displayed an extraordinarily high sequence similarity ( Figs 1B , 3B and 4B ) ., This finding suggests that recombination , rather than mutation selection , is the main contributor to IGR homogenization between partner viruses ., The results of our detailed phylogenetic and recombination analyses support the idea that persistent recombinations have driven this IGR concerted evolution ( Figs 1C , 3C and 4C; S7 and S10 Figs ) ., When we generated consensus sequences for eRTBVL2 , ePVCVL , and ePVCVL2 , we found a consensus pattern for each recombination breakpoint ( Figs 1B , 3B and 4B , S2 and S3 Figs ) ., This discovery suggests that these recombinations took place between homologous localized regions of two partner viruses; in other words , the recombinations were region-specific 23 ., We propose the following model to explain the process of concerted evolution of IGR sequences ( Fig 5B ) ., Once illegitimate recombination produced identical ( or highly similar ) IGR sequences between the viruses of eRTBVL and eRTBVL2 , mutations accumulated in these IGRs over time; however , region-specific recombination within homologous IGRs ( and neighboring regions ) of the two viruses exchanged these mutations between virus populations during mixed infection , with subsequent recombination within a viral population able to further spread the exchanged mutations ., The constant repetition of this mutation–recombination cycle caused the two viruses in the putative partnership to maintain highly similar IGRs ., As one of the two partner viruses diverged into a new lineage during evolution , the other coevolved via region-specific recombination between their homologous regions; this resulted in different viruses of eRTBVL2 possessing different IGRs that were highly similar to those of each of the viral lineages of eRTBVL groups ( Fig 1C ) ., Likewise , the constant repetition of this mutation–recombination cycle during the evolution of the viruses of ePVCVL and ePVCVL2 caused each partner of the virus pair infecting the same grass species to always maintain highly similar IGRs , even as the viruses of ePVCVL/ePVCVL2 diverged into distinct lineages infecting different host species in different habitats ( Figs 3C and 4C , and S10 Fig ) ., Consequently , divergent evolution occurred in each of the four studied virus species , whereas concerted evolution took place between the IGRs of each pair of partner viruses ( Fig 5B ) ., Although precise quantification of the recombination frequency in these viral partnerships appears to be difficult , we tried to estimate the number of major recombination events between IGRs of partner viruses based on phylogeny ., Phylogenetic clustering of eRTBVL2 IGRs with those of each of four eRTBVL groups ( Fig 1C ) suggested the occurrence of more than four major recombination events ., Similarly , a total of 10 major recombination events were suggested by phylogenetic analyses of ePVCVL and ePVCVL2 IGRs ( Figs 3C and 4C , and S10 Fig ) ., In regards to the remaining grass genomes , which were not phylogenetically analyzed because of the high truncation and limited number of sequences , the independent endogenization and IGR concerted evolution of ePVCVL and ePVCVL2 in each genome imply that more than one major recombination event has taken place in each genome ( a total of four ) ( Fig 2C and S3 Table ) ., We consequently detected more than 18 independent major recombination events , which supports the idea that partner viruses have frequently recombined IGRs with each other to maintain partnership and coevolution ., Although recombination has probably been much more frequent than we have estimated , these major events have had significant impacts on viral phylogeny during long-term evolution ., Similar to the recombination of retroviruses , PRVs such as cauliflower mosaic virus ( CaMV ) have been thought to recombine mostly through intermolecular template switching during reverse transcription in the host cytoplasm 23 , 57 , 58 ., In our study , however , locational patterns of viral strand discontinuities ( primer binding sites and polypurine tracts ) did not correspond well to patterns of sequence similarity between viral genomes ( Figs 1 , 3 and 4 , S2 and S3 Figs ) ., When present in the host nucleus , PRV DNA is organized into minichromosomes 27 , and indirect evidence exists that CaMV recombinations sometimes take place between viral minichromosomes 59 , 60 ., Consequently , the region-specific recombinations identified in this study may have occurred mainly through homologous recombination between local homologous regions of viral minichromosomes with the help of host recombination machinery ., One homologous recombination mechanism , gene conversion , has been suggested to be responsible for the concerted evolution of ribosomal DNA and other genes 53 , 61 , 62 ., Our study has provided paleogenomic evidence for non-autonomous PRVs as well as their adaptation ., Considering the abundance of diverse EVEs harbored in eukaryotic genomes and the rapid accumulation of genomic data 3 , many EVEs derived from previously unknown unusual virus types may still await discovery and analysis ., At the same time , plentiful remnants of ancient virus–virus interactions may have been recorded in host genomes; our study has revealed one such paleovirological case of interplay between viral NRSs ., One important future research focus should be evaluation of the prevalence and dynamics of NRS interactions between viral pathogens in mixed infections in plants and humans or within a viral population , as these may have significant impacts on viral evolution and pathology ., Whole-genome sequences of 20 grass species were downloaded mainly from the Gramene database 63 ( detailed data sources in S1 Table ) ., To identify endogenous PRVs , we first performed a BLASTn search ( with default settings ) using the BLAST+ 2 . 2 . 27 utility and previously reported sequences 64 ., The hit sites ( e-values < 1 × 10−10 and lengths >100 bp ) along with their 5 , 000-bp upstream and downstream sequences were retrieved and assembled into consensus sequences ( the nucleotide with the highest frequency at each position in the alignment was selected ) using the Vector NTI Advance 11 . 5 toolkit ( Invitrogen ) ., A second round of BLASTn searching and a BLASTp search were then performed using these consensus sequences and their translated amino acid sequences , respectively ., Only hit sequences longer than 100 bp were retained ., Each translated protein sequence was subjected to the HHpred server 39 , with all standard HHM databases ( as of 3 May 2014 ) chosen for homologous domain detection ( using default parameters ) ., To check unidentified domains/ORFs , their amino acid sequences were resubmitted to the HHpred server and also subjected to BLASTp and tBLASTn searches against NCBI databases ., Identified domains were confirmed by conserved motif alignment ., Coordinates of eRTBVL2 , ePVCVL , and ePVCVL2 sequences and their genes/regions in grass genomes are available in S2 Dataset ( BED format ) ., Dot plots were generated using the EMBOSS package ( word size = 10; threshold = 45 ) 65 ., Nucleotide sequences of each dataset were aligned in ClustalW 66 followed by manual editing ., After being translated from the aligned nucleotide sequences , amino acid sequences of each dataset were realigned using MUSCLE 67 followed by manual editing ., Highly truncated sequences ( generally shorter than 80% of the entire region ) and ambiguous regions were removed from the final alignments ., Best-fitting substitution models were determined for each aligned dataset according to the Akaike information criterion calculated using jModelTest version 2 . 1 . 4 68 or ProtTest version 3 . 2 69 ., For eRTBVL2 datasets comprising IGR ( nucleotide positions 6063–6704 of the consensus genome ) , MP ( 486–1853 ) , CP ( 1854–2845 ) , PR ( 2831–4090 ) , and ORFx ( 48–485 ) sequences , the best-fitting models were HKY+G , TrN+G , GTR+G , TrN+I+G , and TrN+G , respectively , with JTT+I+F chosen for the SFKTE sequences corresponding to amino acid positions 1220–1741 of the ORF2 protein sequence ., Models VT+F+G and LG+I+F+G were respectively selected for the ePVCVL CP dataset ( amino acid positions 709–996/722–1010 of the protein sequence of Oryza/non-Oryza groups ) and the ePVCVL2 RT/RH dataset ( amino acid positions 1017–1414/945–1342 of the ORF1 protein sequence of Oryza/non-Oryza groups ) ., Models HKY+G , GTR+G , and HKY+G were respectively chosen for the IGR datasets of ePVCVL and ePVCVL2 of O . sativa , genus Oryza , and S . bicolor genomes ( nucleotide positions 5878–6415/5786–6323 , 5878–6611/5786–6519

Introduction, Results, Discussion, Materials and methods

The interplay of different virus species in a host cell after infection can affect the adaptation of each virus ., Endogenous viral elements , such as endogenous pararetroviruses ( PRVs ) , have arisen from vertical inheritance of viral sequences integrated into host germline genomes ., As viral genomic fossils , these sequences can thus serve as valuable paleogenomic data to study the long-term evolutionary dynamics of virus–virus interactions , but they have rarely been applied for this purpose ., All extant PRVs have been considered autonomous species in their parasitic life cycle in host cells ., Here , we provide evidence for multiple non-autonomous PRV species with structural defects in viral activity that have frequently infected ancient grass hosts and adapted through interplay between viruses ., Our paleogenomic analyses using endogenous PRVs in grass genomes revealed that these non-autonomous PRV species have participated in interplay with autonomous PRVs in a possible commensal partnership , or , alternatively , with one another in a possible mutualistic partnership ., These partnerships , which have been established by the sharing of noncoding regulatory sequences ( NRSs ) in intergenic regions between two partner viruses , have been further maintained and altered by the sequence homogenization of NRSs between partners ., Strikingly , we found that frequent region-specific recombination , rather than mutation selection , is the main causative mechanism of NRS homogenization ., Our results , obtained from ancient DNA records of viruses , suggest that adaptation of PRVs has occurred by concerted evolution of NRSs between different virus species in the same host ., Our findings further imply that evaluation of within-host NRS interactions within and between populations of viral pathogens may be important .

This paper addresses the adaptive strategies of ancient defective viruses recorded in grass genomes ., We mined numerous virus segments from various grass genomes and assembled several defective pararetrovirus ( non-autonomous PRV ) species ., We attempted to understand how these non-autonomous PRVs can complete parasitic life cycles in host plants ., We determined that these non-autonomous PRV species have participated in interplay with autonomous PRVs or different non-autonomous PRV species ., This interplay between different virus genomes has involved the exchange of noncoding regulatory sequences , which consequently evolved to be extraordinarily highly similar in different viruses within the same host ., In non-autonomous PRVs , adaptive strategies to compensate for a lack of functionality have consequently involved concerted evolution of noncoding sequences establishing the partnerships .

taxonomy, oryza, computational biology, microbiology, phylogenetics, data management, phylogenetic analysis, genome analysis, paleontology, sequence motif analysis, paleogenetics, plants, microbial genomics, research and analysis methods, sequence analysis, viral genomics, computer and information sciences, grasses, sequence alignment, bioinformatics, biological databases, evolutionary systematics, virology, database and informatics methods, earth sciences, genetics, biology and life sciences, genomics, evolutionary biology, genomic databases, organisms

journal.pntd.0001523

A Novel G Protein-Coupled Receptor of Schistosoma mansoni (SmGPR-3) Is Activated by Dopamine and Is Widely Expressed in the Nervous System

The bloodfluke Schistosoma mansoni is one of three species of schistosomes that cause significant disease in humans ., Approximately 200 million people are infected and another 600 million are at risk of infection ., Over 90% of all human schistosomiasis is due to S . mansoni ., This species exists in Africa , the Middle East , South America and the Caribbean , in regions where the intermediate snail host , Biomphalaria glabrata , is also present ., There is no vaccine for schistosomiasis and the arsenal of drugs available for treatment is limited ., Praziquantel is the drug of choice but concerns over praziquantel resistance 1–3 have renewed interest in the search for alternative drug therapies ., The nervous system of helminth parasites is considered to be an excellent target for chemotherapeutic intervention ., Most of the anthelmintics currently in use , including the mainstay of nematode control , ivermectin , act by interacting with neurotransmitter receptors and cause disruption of neuronal signalling 4 ., Recent drug screens conducted on cultured S . mansoni suggest that biogenic amine ( BA ) neurotransmitters may be particularly suitable for development of anti-schistosomal drugs 5 , 6 ., Substances that normally disrupt BA neurotransmission , such as dopaminergic and serotonergic drugs were shown to halt larval development 5 and to produce aberrant motor phenotypes in culture 6 ., The BA systems of schistosomes have not been widely investigated at the molecular level and not much is known about the receptors or other proteins involved ., More information is needed to elucidate the mode of action of these neurotransmitters and to identify potential targets for drug discovery ., BAs constitute a group of structurally related amino acid derivatives that function broadly as neurotransmitters and modulators in a variety of organisms ., Included in this group are catecholamines ( dopamine , noradrenaline , adrenaline ) , serotonin ( 5-hydroxytryptamine: 5-HT ) , histamine and the invertebrate-specific amines , tyramine and octopamine ., In flatworms , including S . mansoni , BAs play important roles in the control of muscle contraction and movement , activities that are crucial for survival of the parasite within the host 7–9 ., The best characterized of these amines is serotonin , which is myoexcitatory in all the flatworm species studied to date ., Serotonin is synthesized by the parasite 10 , it is widely distributed in the nervous system and there is evidence for the existence of a serotonin transport system in S . mansoni 11 , 12 ., At least two putative serotonin receptors are encoded in the S . mansoni genome 13 , though neither has yet been characterized at the protein level ., Besides serotonin , flatworms have both dopamine and histamine within their nervous system 14–20 ., Dopamine , in particular , has important neuromuscular activities , which can be either excitatory or inhibitory depending on the flatworm species ., In S . mansoni , dopamine causes relaxation of the body wall muscles 21 , possibly by activating a receptor that is associated with neuromuscular structures 22 ., In addition to motor effects , BAs have been implicated in the regulation of metabolic activity in several flatworms 8 and recent evidence has shown that serotonin and dopamine are both involved in the transformation of S . mansoni miracidia to sporocyst stage 5 , suggesting a probable role in parasite development ., BAs exert their effects by interacting with cell-surface receptors , the majority of which belong to the superfamily of G protein-coupled receptors ( GPCR ) and are structurally related to rhodopsin ., GPCRs have a distinctive topology consisting of seven transmembrane ( TM ) domains separated by loops , the longest of which is the third intracellular loop ( il3 ) ., Rhodopsin-like ( or Class A ) GPCRs are further identified by having a relatively short extracelullar N-terminus , which is typically glycosylated , and an intracellular C-terminal tail of variable length 23 ., In mammals , BA receptors are classified according to their amine specificity , sequence homology , signalling mechanisms and pharmacological profiles ., Each BA interacts with multiple receptors ., Dopamine , in particular , interacts with five different receptors ( D1–D5 ) , which are classified according to two major structural types , D1- and D2-like 24 ., The current annotation of the S . mansoni genome has a total of 16 predicted BA receptors , all Class A GPCRs 13 ., A few of these sequences , for example the D2-like dopamine receptor of S . mansoni ( SmD2 ) 22 share sufficient homology with mammalian prototypes to be classified accordingly ., The majority , however , are novel sequences that share about the same level of homology with all different types of BA receptors and can only be defined as BA-like ., Among these sequences is a new clade of BA-like GPCRs ( named SmGPR ) that were previously described in our laboratory 15 and thus far have been detected only in schistosomes ., These receptors could prove to be particularly good candidates for selective drug targeting and deserve further investigation ., Two SmGPRs of S . mansoni were shown earlier to be functional histamine receptors 15 , 25–27 ., Here we describe a third structurally related receptor ( SmGPR-3 ) , which has different agonist specificity ., The results presented here show that SmGPR-3 is activated by dopamine and other catecholamines but does not resemble any one particular type of host dopamine receptor , either in terms of overall sequence homology , pharmacological profile or the predicted organization of the binding pocket ., Further analysis of the receptors tissue distribution revealed exceptionally abundant expression throughout the nervous system and suggests an important role for this novel dopamine receptor in neuronal and neuromuscular signalling ., A Puerto Rican strain of S . mansoni -infected Biomphalaria glabrata snails were kindly provided by Dr . Fred Lewis , Biomedical Research Institute , Rockville , Maryland , USA ., S . mansoni cercaria were collected from infected snails 35–45 days post-infection ., Schistosomula were produced from cercaria by mechanical transformation , as described 27 , 28 and were cultured at 37°C and 5% CO2 in OPTI-MEM I medium ( Invitrogen ) supplemented with 10% FBS , streptomycin 100 µg/ml , penicillin 100 U/ml and fungizone 0 . 25 µg/ml ., Adult parasites were obtained by infecting 28-day old female CD-1 mice with freshly collected cercaria ( 150 cercaria/animal ) by skin penetration ., Adult S . mansoni worms were recovered 7 weeks post-infection by perfusion of the liver 28 , washed extensively and either flash-frozen in liquid nitrogen for subsequent RNA extraction or fixed in 4% paraformaldehyde ( PFA ) for immunolocalization experiments ., Animal procedures were reviewed and approved by the Facility Animal Care Committee of McGill University ( Protocol No . 3346 ) and were conducted in accordance with the guidelines of the Canadian Council on Animal Care ., The full-length SmGPR-3 cDNA was cloned from adult S . mansoni based on a predicted coding sequence ( Smp_043290 ) obtained from the S . mansoni genome database ( S . mansoni GeneDB; http://www . genedb . org/Homepage/Smansoni ) ., Total RNA was purified from adult S . mansoni worms ( Qiagen RNeasy kit ) and was oligo-dT reverse-transcribed with MMLV reverse transcriptase ( Invitrogen ) , according to standard procedures ., SmGPR-3 was cloned with primers that targeted the beginning and end of the predicted coding sequence ., The primer sequences were as follows: 5′-ATGAATTTCATAAGAAACAAAACCAATTATTC-3′ ( sense ) and 5′-CTATCTACATCCTTTCAAAAGTACAATATG-3′ ( antisense ) ., A proofreading Platinum Pfx DNA polymerase ( Invitrogen ) was used to amplify the cDNA in a standard PCR reaction ( 35 cycles of 94°C/15 s , 53 . 1°C/30 s and 68°C/90 s ) ., The resulting amplicon ( 1 , 494 bp ) was ligated to pGEM-T Easy vector ( Promega ) and verified by DNA sequencing of two independent clones ., The SmGPR-3 coding sequence was sub-cloned between the NcoI/XbaI restriction sites of the yeast expression vector Cp4258 ( kindly provided by Dr J . Broach , Princeton University , NJ , USA ) ., The functional expression assay was adapted from the protocol of Wang and colleagues 29 as described 15 , 30 ., The receptor was expressed in Saccharomyces cerevisiae strain YEX108 ( MATα PFUS1-HIS3 PGPA1-Gαq ( 41 ) -GPA1-Gaq ( 5 ) can1 far1Δ 1442 his3 leu2 lys2 sst2Δ2 ste14::trp1::LYS2 ste18Δ6-3841 ste3Δ1156 tbt1-1 trp1 ura3; kindly provided by J . Broach , Princeton University , NJ , USA ) ., This strain expresses the HIS3 reporter gene under the control of the FUS1 promoter and contains an integrated copy of a chimeric Gα gene in which the first 31 and last five codons of native yeast Gα ( GPA1 ) were replaced with those of human Gαq 29 ., Other strains carrying chimeras of GPA1 and human Gαi2 , Gα12 , Gαo or Gαs were tested in preliminary experiments but were found to yield lower or no receptor activity compared with strain YEX108 ., YPD culture medium ( 1% yeast extract , 2% peptone and 2% dextrose ) was used to culture YEX108 strain , according to standard conditions ., The lithium acetate method was performed to transform yeast with either empty Cp4258 vector ( mock ) or Cp4258/SmGPR-3 expression plasmid , using 200 µl mid-log phase cells , 200 µg carrier single stranded DNA ( Invitrogen ) and 1 µg plasmid ., Positive transformants were selected on synthetic complete ( SC ) 2% glucose solid medium lacking leucine ( SC/leu− ) ., For agonist assays , single colonies of transformants were cultured overnight in SC/leu− liquid medium at 250 rpm/30°C ., The next day , cells were washed four times in SC 2% glucose liquid medium that lacked both leucine and histidine ( SC/leu−/his− ) ., Cells were finally resuspended in SC/leu−/his− medium supplemented with 50 mM 3- ( N-morpholino ) propanesulfonic acid ( MOPS ) , pH 6 . 8 and 1 . 5 mM 3-Amino-1 , 2 , 4-Triazole ( 3-AT ) ., The latter was used to reduce basal growth due to endogenous background signalling as it inhibits the gene product of HIS3 29 ., Aliquots containing approximately 3 , 000 cells were added to individual wells of a 96-well plate containing test agonist or vehicle plus additional medium for a total reaction volume of 100 µl ., The plates were incubated at 30°C for 22–26 h , after which 10 µl of Alamar blue ( Invitrogen ) was added to each well ., The plates were returned to the 30°C incubator until the Alamar blue color started to shift to pink ( approximately 1–4 h ) and fluorescence ( 560 nm excitation/590 nm emission ) was measured at 30°C every hour up to three hours , using a plate fluorometer ( FlexStation II , Molecular Devices , US ) ., Antagonist assays were done in a similar way , except that each well contained 10−4 M agonist ( dopamine ) and the antagonist at the specified concentration ., Data analyses and dose-response curve fits were performed using Prism v5 . 0 ( GraphPad software Inc . ) ., Polyclonal antibodies were produced in rabbits against two SmGPR-3 synthetic peptides ., The first peptide ( CYISYSKEYRIYSSV ) is located in the predicted 2nd extracellular loop ( ECL2 ) of SmGPR-3 ( positions 186–200 ) and the second peptide sequence ( CERKTERTIKTQRQF ) is in the third intracellular loop ( il3 ) ( positions 395–409 ) ., The two peptide sequences were tested against the S . mansoni GeneDB database and the general database at The National Center for Biotechnology Information ( NCBI ) to insure specificity ., The peptides were both conjugated to ovalbumin to increase immunogenicity ., The conjugated peptides and custom antibodies were purchased from 21st Century Biochemicals , Malboro , MA , USA ., Antibodies were raised in two rabbits , each of which was inoculated five times and the serum was collected prior to injection ( preimmune ) and up to 72 days following injection ( see http://21stcenturybio . com/ for further details of the antibody protocol ) ., ELISA was done to test the specificity of the antibodies to each of the two peptides ., SmGPR-3 –specific antibodies were subsequently affinity-purified , using the MicroLink Peptide Coupling kit ( Pierce ) , as described previously 22 ., Immunoprecipitation ( IP ) was performed with the Seize Primary Immunoprecipitation kit ( Pierce , USA ) , as described previously 22 ., Briefly , IP affinity columns were prepared by covalent coupling of purified anti-SmGPR-3 IgG to AminoLink Plus gel in the presence of sodium cyanoborohydride ., A solubilized membrane fraction was prepared from adult S . mansoni , using a commercial kit ( ProteoExtract Native Membrane Protein Extraction Kit , Calbiochem ) and aliquots of solubilized membrane proteins ( 14 µg protein ) were mixed with 50 µl IgG-linked gel and incubated overnight at 4°C with gentle rotation ., After incubation , the gel was washed extensively and the bound proteins were eluted under acidic conditions , using the elution buffer supplied by the kit ., The immunoprecipitated proteins were resolved on 4–12% Tris-Glycine precast gel ( Invitrogen ) and transferred to polyvinylidene fluoride ( PVDF ) membranes ( Millipore ) ., Western blot analysis was performed according to standard protocols , using purified anti-SmGPR-3 antibody ( dilution 1/10 , 000 ) and a goat anti-rabbit Horseradish Peroxidase ( HRP ) -conjugated antibody ( Calbiochem ) ( 1/20 , 000 ) ., To test for specificity , the western blots were repeated with primary antibody that was preadsorbed with 0 . 5 mg/ml of pooled peptide antigens ( 0 . 25 mg of each peptide ) or preimmune serum ., The procedure is based on the protocol of D . Halton and colleagues 31 , as described previously 22 , 27 ., S . mansoni cercaria and adult worms were fixed in 4% paraformaldehyde ( PFA ) for 4 h at 4°C , washed three times in blocking buffer ( 1× PBS , pH 7 . 4; 1% bovine serum albumin; 0 . 1% sodium azide and 0 . 5% triton X-100 ) and were incubated in 10 mM sodium citrate for 1 h at 70°C ., Animals were subsequently washed twice with PBS , incubated with affinity-purified anti-SmGPR-3 antibody ( diluted 1∶25 in blocking buffer ) for three days at 4°C , washed overnight with the same blocking buffer and finally incubated with goat anti-rabbit IgG conjugated to FITC ( Sigma , Canada ) for another three days at 4°C ( dilution 1∶300 ) ., If used as a counterstain , 200 µg/ml TRITC-labelled phalloidin was added during the last two days of incubation ., The samples were mounted using anti-quench mounting medium ( Sigma , Canada ) and examined with a BIO-RAD RADIANCE 2100 confocal laser scanning microscope ( CLSM ) equipped with a Nikon E800 fluorescence microscope for confocal image acquisition and the LASERSHARP 2000 software package ., As negative controls , we used preimmune serum , omitted the primary antibody and used primary antibody that was preadsorbed with 0 . 5 mg/ml of pooled peptide antigens ( 0 . 25 mg of each peptide ) ., 3-day old in vitro transformed schistosomula were placed in individual wells of a 24-well plate ( 30–40 animals/well ) in 300 µl of OPTI-MEM+10% dialyzed serum ., Following an adaptation period of 15 min at room temperature , test substances were added at a final concentration of 100 µM or as indicated ., Animals were monitored 5 min after drug addition by placing the 24-well plate on a compound microscope ( Nikon , SMZ1500 ) equipped with a digital video camera ( QICAM Fast 1394 , mono 12 bit , Qimaging ) and SimplePCI version 5 . 2 ( Compix Inc . ) for image acquisition ., Images were obtained at a rate of ≈3 frames/s for a period of 1 minute and the data were analyzed with ImageJ software ( version 1 . 41 , NIH , USA ) ., Cultured schistosomula display complex motor behaviours that are dominated by repeated changes in length , both shortening and elongation ., To quantify this type of movement , we used the “Fit-Ellipse” command of ImageJ to draw best-fit ellipses of individual animals in each recorded frame ., An estimate of body length was obtained by measuring the principal ( “major” ) axis of each ellipse , using calibrated units ( µm ) , and the frequency of length changes during the observation period was calculated ., Any change representing >10% of body length , whether an increase or decrease , was included in the calculation; changes ≤10% were disregarded ., Between 12–15 animals were monitored per well and the experiment was repeated a minimum of 3 times ., To monitor for possible drug induced toxicity , viability tests were performed routinely using the methylene blue dye exclusion assay described by Gold 32 ., Homology searches were performed by BLAST analyses ( tBLASTn or BLASTp ) of the S . mansoni Genome Database ( S . mansoni GeneDB; www . genedb . org/genedb/smansoni/ ) 13 , the S . japonicum Transcriptome and Proteome Database ( SjTPdb ) 33 , the most current genome annotations of the planarians , Schmidtea mediterranea ( SmedGD version 1 . 3 . 14 ) 34 and Macrostomum lignano ( www . macgenome . org/index . html ) and the general database available at the National Center for Biotechnology Information ( NCBI ) ., Sequences showing significant homology with SmGPR-3 were aligned with ClustalW and inspected manually for the presence of conserved Class A ( rhodopsin-like ) GPCR motifs 23 ., Phylogenetic trees were generated with MEGA4 35 , using two different methods , neighbour-joining and Unweighted Pair Group Method with Arithmetic mean ( UPGMA ) with similar results ., The trees were tested by bootstrap analysis with 1 , 000 replicates ., Predictions of transmembrane ( TM ) regions were made using the TMpred server ( http://www . ch . embnet . org ) and by comparison with the crystal structure of the human β2 adrenergic GPCR 36 ., To facilitate identification , S . mansoni sequences are described using both their S . mansoni GeneDB designation 13 and the corresponding GenBank accession numbers ., S . mediterranea sequences are identified by their SmedGD designation 34 ., All other sequences are identified by their GenBank accession numbers ., GPCR residues located within TM regions are described according to the system of Ballesteros and Weinstein 37 ., Each amino acid within a TM region is identified by the TM number ( 1–7 ) followed by the position in the TM helix relative to an invariant reference residue , which is arbitrarily assigned the number 50 ., Residue D3 . 32 , for example , is located in TM3 , 18 residues upstream of the invariant reference residue ., Amino acids of relevance to this study are as follows ( corresponding residue of SmGPR-3 ) : R2 . 64 ( Arg96 ) , D3 . 32 ( Asp117 ) , S5 . 42 ( Ser198 ) , S5 . 43 ( Ser199 ) , T7 . 39 ( Thr462 ) and Y7 . 43 ( Tyr466 ) ., A theoretical model of SmGPR-3 was built with Accelrys Discovery Studio ( DS ) ., Prior to generating the model , SmGPR-3 was aligned with the sequences of GPCR crystal structures available in the general protein database ( PDB ) ( Accession numbers: 2rh1 , 3eml , 1u19 , 2vt4 , 2z73 ) and the β-2 adrenergic receptor ( 2hr1 ) was selected as the best template based on similarity scores ., The sequence alignment was inspected to ensure that the positions of conserved residues in the structural template were properly aligned with those of SmGPR-3 , including the reference residues designated at position 50 of each helix 37 and conserved motifs , such as the DRY peptide at the end of TM3 and the NPxxY motif of TM7 ., Subsequent preparation of the template and construction of the model were performed with DS using default parameters ., The first stretch of 28 amino acid residues corresponding to the extracellular N-terminal end was not constructed due to lack of structural information ., In addition , we note that the β-2 adrenergic template is a chimeric receptor in which the region corresponding to the third intracellular loop ( il3 ) was replaced with the sequence of T4 lysozyme 36 ., Therefore the il3 of SmGPR-3 ( positions 224–417 ) could not be aligned and was omitted from the model ., Energy minimization was performed using the CHARMm forcefield in DS ., The conserved disulfide linkage that occurs between the beginning of TM3 and extracellular loop 2 ( Cys110 and Cys186 in SmGPR-3 ) was constrained during optimization of the model ., The energy-minimized model was subsequently verified by means of a Ramachandran plot analysis and the PROFILES-3D evaluation method available in DS ., The quality score obtained with PROFILES-3D was within acceptable range and the Ramachandran plot analysis showed 94 . 8% of the residues occurring in favourable regions of the plot , suggesting the model was reliable ., Superimposition of the model with the β-2 adrenergic receptor template showed a backbone ( Cα ) root-mean-square-deviation ( RMSD ) of 0 . 83 Å and the overall protein RMSD was 1 . 38 Å ., For ligand-docking , the structure of the ligand ( dopamine or epinine ) was generated with the molecular builder panel available in the software and was energy-minimized , as recommended ., Next , we used DS to search for potential binding cavities ., Six potential sites were identified , of which only one was located in the correct region based on the position of the binding pocket in the structural template ., The ligand was subsequently docked onto this site of the SmGPR-3 model with CDOCKER ., Each ligand was docked in multiple conformational states and orientations , which resulted in 240 different ligand poses for dopamine and 290 for epinine ., These were examined and evaluated using the CHARMm scoring method of CDOCKER to identify potential binding residues for each ligand ., Protein content was measured with a Lowry assay ( BioRad ) ., Indirect ELISA was performed in 96-well plates coated with individual or pooled SmGPR-3 peptides ( 50–500 ng/well ) and incubated with a serial dilution of rabbit anti-SmGPR-3 antiserum or preimmune serum ( 1∶30 , 000–1∶100 ) , followed by incubation with a horseradish peroxidase ( HRP ) -labeled secondary antibody ( goat anti-rabbit IgG , 1∶2 , 000 ) ., Quantitative PCR ( qPCR ) was performed as described previously 15 , 52 using the Platinum SYBR Green qPCR SuperMix-UDG kit ( Invitrogen ) and a Rotor-Gene RG3000 ( Corbett Research ) real-time PCR cycler ., Statistical comparisons were done with Student t-tests or a one-way ANOVA , followed by a Tukey pairwise comparison ., P≤0 . 05 was considered statistically significant ., Predicted S . mansoni BA-like receptors 13 were aligned with BA receptors from other species , including other flatworms for which genomic data are available ( S . japonicum , Dugesia japonica and S . mediterranea ) and both vertebrate and invertebrate representatives of dopaminergic , serotonergic , adrenergic , histaminergic , tyramine/octopamine and structurally related cholinergic muscarinic ( mACh ) receptors ., A phylogenetic tree of the alignment ( Fig . 1 ) shows a subset of schistosome GPCRs ( SmGPR ) that are derived from a common node and constitute a separate clade within the tree ., Included in this clade are seven S . mansoni sequences and two homologues from S . japonicum but no sequences from any of the other species examined , including the free-living planarians ., We have previously described two members of this new clade , SmGPR-1 ( formerly SmGPCR; AAF21638; Smp_043260; ) and SmGPR-2 ( GQ397114; Smp_043340 ) 15 , 25–27 ., In the present study , we cloned a third SmGPR ( SmGPR-3 ) cDNA from adult S . mansoni by RT-PCR ., The cDNA was verified by DNA sequencing ( Accession # GQ259333 ) and was found to be identical to the corresponding genomic prediction available at the S . mansoni GeneDB ( Smp_043290 ) ., SmGPR-3 has 497 amino acids and a predicted MW of 58 . 4 kDa ., NCBI BLASTp analyses confirmed the identity of SmGPR-3 as a member of the BA receptor family ., According to pairwise alignment analyses , the most closely related sequences are those of the SmGPR clade including ( % homology ) : SmGPR-1 ( Smp_043260 , 53 . 4% ) , Smp_043300 ( 47 . 4% ) , SmGPR-2 ( Smp_043340 , 46 . 9% ) , Smp_043270 ( 45 . 5% ) , Smp145520 ( 40 . 4% ) and the S . japonicum receptor FN328430 ( 46 . 1% ) ., SmGPR-3 is also related to other schistosome BA receptors ( non-SmGPRs ) , as well as BA receptors from other organisms but the level of homology is generally lower ( <40% ) ., Further analysis of the SmGPR-3 protein sequence detected all the hallmark features of Class A ( rhodopsin-like ) GPCRs ( Fig . 2 ) ., Aside from having the expected 7-TM topology , SmGPR-3 carries the signature DRY motif at the intracellular boundary of TM3 , the NPxxY motif of TM7 and all the conserved reference residues at position #50 of each TM helix 37 ., We also identified several residues that have been implicated in BA binding and receptor activation , notably the aromatic cluster FxxCWxPFF of TM6 and a highly conserved aspartate at position 3 . 32 of TM3 ( D3 . 32/Asp117 ) , which is considered to be one of the core binding sites in BA GPCRs 23 , 36 , 38 , 39 ., The presence of D3 . 32 marks an important difference between SmGPR-3 and other members of this clade ., The schistosome SmGPRs are unusual in that they carry an asparagine substitution at this position 15 ., SmGPR-3 is the only receptor in this group where the aspartate D3 . 32 is conserved ., Functional expression assays were performed in yeast ., Preliminary experiments failed to detect receptor expression in mammalian cells ( data not shown ) and therefore we used yeast as a heterologous expression system throughout the study ., The full-length SmGPR-3 cDNA was ligated to the Cp4258 vector and the recombinant plasmid was transformed into Saccharomyces cerevisae to test for receptor activity ., We used a genetically modified S . cerevisae strain that is designed for GPCR activity assays 29 ., The yeast is auxotrophic for histidine and expresses a HIS3 reporter gene under the control of the FUS1 promoter ., Activation of a recombinant GPCR in this system increases expression of the HIS3 reporter via the yeasts endogenous pheromone response , which in turn allows the cells to grow in selective histidine-deficient medium ., Thus receptor activity can be quantified based on measurements of yeast growth in the selective medium , using a fluorometric Alamar Blue assay ., Cells transformed with either empty vector ( mock control ) or SmGPR-3 were initially tested with all different biogenic amines , each at 2×10−4 M ( Fig . 3A ) ., The results obtained from five to six individual clones showed that SmGPR-3 was selectively activated by dopamine and its naturally occurring metabolite epinine ( deoxyepinephrine ) ., Other catecholamines , including noradrenaline , adrenaline and the adrenaline metabolite , metanephrine ( not shown ) also stimulated SmGPR-3 activity but not to the same extent as dopamine or epinine ., The receptor exhibited partial constitutive activity in the absence of agonist but there was further activation in the presence of catecholamines , whereas other biogenic amines had no significant effect relative to the no drug control ., Experiments were repeated with different concentrations of dopamine or epinine and their responses were shown to be dose-dependent ., The half maximal effective concentration ( EC50 ) for dopamine and epinine activation in the yeast expression system are 3 . 1×10−5 M and 2 . 85×10−5 M , respectively ( Fig . 3B ) ., Next we examined the effects of classical ( mammalian ) dopaminergic and other BA antagonists on the activity of SmGPR-3 ., Drugs were tested initially at a single concentration of 100 µM ( 10 µM in the case of flupenthixol ) in the presence of 100 µM DA ( Fig . 4A ) ., The drug effects revealed an unusual pharmacological profile , which did not resemble any of the dopaminergic or adrenergic receptors of mammals ., The most surprising observation was that spiperone , a mammalian D2 antagonist enhanced the activity of SmGPR-3 nearly 2-fold , thus behaving more as an agonist than a receptor blocker ., Propranolol , a β-adrenoceptor antagonist had no effect on this receptor , while the remaining drugs showed various degrees of inhibition ., Those drugs that produced significant inhibition ( >50% ) in the initial screen were subsequently tested at different concentrations to obtain dose response curves ( Fig . 4B–F ) ., The half-maximal inhibitory concentrations ( IC50 ) for these drugs were as follows: Haloperidol , 1 . 4×10−6 M; Flupenthixol , 3 . 9×10−6 M; Promethazine , 2 . 8×10−5 M; Mianserin , 4 . 5×10−5 M; Clozapine >10−4 M . Based on this analysis , the most effective antagonists of SmGPR-3 were haloperidol and flupenthixol , two classical DA antagonists , followed by promethazine , an antihistaminic drug not known to interact with dopaminergic receptors ., Mianserin , ( mixed adrenergic/5HT antagonist ) and cyproheptadine , ( mixed histamine/5-HT antagonist ) both produced significant inhibition of ≈70% at the highest concentration tested ., Finally the remaining drugs caused modest or no significant inhibition at 100 µM , including clozapine , a classical dopamine antagonist ., Because the assay is based on cell growth , we questioned whether the strong inhibition induced by promethazine , flupenthixol and haloperidol were due to drug-induced toxicity leading to cell death ., To test this possibility , we repeated the assay in medium supplemented with histidine ( 100 µM ) , which enables cell growth irrespective of receptor activation ., The results showed normal or nearly normal growth in the drug-treated cells in the presence of histidine , indicating that the inhibitory effect of the drug was receptor-mediated and not the product of generalized toxicity ( Fig . 4A ) ., To investigate the tissue localization of SmGPR-3 we obtained a specific antibody that targets two unique peptides of the receptor ., The antibody was affinity-purified and verified first by ELISA ., To test if the antibody could recognize the native receptor , we immunoprecipitated ( IP ) SmGPR-3 from solubilized S . mansoni membranes , using covalently-coupled anti-SmGPR-3 antibody beads and then probed the IP eluate by western blotting with affinity-purified anti-SmGPR-3 antibody ., The results ( Supplemental Fig . S1 ) detected a single major band of about 60 kDa , which is consistent with the expected size of SmGPR-3 ., The negative controls were similarly immunoprecipitated with the same antibody beads but were probed either with peptide-preadsorbed antibody or preimmune serum ., The results show much diminished or no immunoreactivity in the negative controls , suggesting the antibody recognizes SmGPR-3 specifically ., For the in situ immunolocalization studies , larval and adult stages of S . mansoni were probed with affinity-purified anti-SmGPR-3 , followed by a FITC-labelled secondary antibody ., Some animals were also treated with TRITC-conjugated phalloidin to label cytoskeletal elements and muscle 31 ., The results show strong SmGPR-3 green fluorescence in the nervous system of both cercaria and adult S . mansoni ., In cercaria we see immunoreactivity primarily in the CNS along the main longitudinal nerve cords and transverse commissures ( Fig . 5 ) ., The pattern of labelling is similar to that of dopamine itself , which localizes to the same regions of the CNS in cercaria of both S . mansoni and S . japonicum 14 ., Adult worms have high levels of SmGPR-3 immunoreactivity in the cerebral ganglia and major longitudinal nerve cords ., This was observed in adult males ( Fig . 6A ) as well as female worms ( not shown ) ., The peripheral nervous system ( PNS ) is also rich in SmGPR-3 ., Immunoreactivity can be seen in the innervation of the caecum ( Fig . 6B ) and the peripheral plexuses and fibers innervating the parasite musculature ( Fig . 6C ) , both circular and longitudinal muscles ( Fig . 6D , E ) ., There is no apparent co-localization of SmGPR-3 labelling ( green ) and the somatic muscles ( red ) that were counterstained with TRITC-conjugated phalloidin , suggesting the receptor is probably associated with the i

Introduction, Materials and Methods, Results, Discussion

Schistosomes have a well developed nervous system that coordinates virtually every activity of the parasite and therefore is considered to be a promising target for chemotherapeutic intervention ., Neurotransmitter receptors , in particular those involved in neuromuscular control , are proven drug targets in other helminths but very few of these receptors have been identified in schistosomes and little is known about their roles in the biology of the worm ., Here we describe a novel Schistosoma mansoni G protein-coupled receptor ( named SmGPR-3 ) that was cloned , expressed heterologously and shown to be activated by dopamine , a well established neurotransmitter of the schistosome nervous system ., SmGPR-3 belongs to a new clade of “orphan” amine-like receptors that exist in schistosomes but not the mammalian host ., Further analysis of the recombinant protein showed that SmGPR-3 can also be activated by other catecholamines , including the dopamine metabolite , epinine , and it has an unusual antagonist profile when compared to mammalian receptors ., Confocal immunofluorescence experiments using a specific peptide antibody showed that SmGPR-3 is abundantly expressed in the nervous system of schistosomes , particularly in the main nerve cords and the peripheral innervation of the body wall muscles ., In addition , we show that dopamine , epinine and other dopaminergic agents have strong effects on the motility of larval schistosomes in culture ., Together , the results suggest that SmGPR-3 is an important neuronal receptor and is probably involved in the control of motor activity in schistosomes ., We have conducted a first analysis of the structure of SmGPR-3 by means of homology modeling and virtual ligand-docking simulations ., This investigation has identified potentially important differences between SmGPR-3 and host dopamine receptors that could be exploited to develop new , parasite-selective anti-schistosomal drugs .

Bloodflukes of the genus Schistosoma are the causative agents of human schistosomiasis , a debilitating disease that afflicts over 200 million people worldwide ., There is no vaccine for schistosomiasis and treatment relies heavily on a single drug , praziquantel ., Recent reports of praziquantel resistance raise concerns about future control of the disease and show the importance of developing new anti-schistosomal drugs ., The focus of this research is on the nervous system of the model fluke , Schistosoma mansoni ., Many pesticides and antiparasitic drugs act by interacting with neuronal proteins and therefore the nervous system is a particularly attractive target for chemotherapeutic intervention ., Here we describe a novel receptor of S . mansoni that is activated by dopamine , an important neurotransmitter of the schistosome nervous system ., The study provides a first in-depth analysis of this receptor and suggests that it plays an important role in the control of muscle function and movement ., We also show that the schistosome receptor is substantially different from dopamine receptors of the mammalian host , both in terms of structure and functional properties ., We propose that this novel protein could be used to develop new , schistosome-specific drugs aimed at disrupting parasite motility within the host .

biology, molecular cell biology, zoology, neuroscience

journal.pcbi.1005529

Modelling of the cancer cell cycle as a tool for rational drug development: A systems pharmacology approach to cyclotherapy

Pharmacokinetic and pharmacodynamic ( PK/PD ) models of anticancer drug action have many potential applications 1–3 ., Among the most promising are the ability to match tumours with particular gene expression profiles to selective treatments 4 , the ability to search for potential synthetic lethalities 5 , and the ability to optimise combination protocols 6 ., Thousands of treatment protocols can be screened in silico , and the most promising selected for experimental or clinical evaluation 7 ., Modelling the cellular pharmacodynamics of anticancer drugs , whether they are cytotoxic agents or targeted agents requires , minimally , a description of three biological processes: the cell cycle , the associated signal transduction pathways , and the apoptotic cascade ., There are published models of all of these processes , and our model includes descriptions of the cell cycle , the EGF signalling pathway and apoptosis ., In a previous study we showed that the loss of the G1-S and/or SAC checkpoints were critical to the description of cancer 8 ., This was consistent with Duesberg’s theory 9 which suggested that cancer is , in essence , a disease of chromosomal instability ., According to this line of thought the phenotypic hallmarks of cancer that arise are the inevitable outcome of the selection process operating on the numerous chromosomal variants ., The evidence linking defective SAC function with cancer has been reviewed by Kops , Weaver and Cleveland 10 , 11 , and by Musacchio and Salmon 12 ., There are many deletions or mutations that can cause SAC over-ride , resulting in aneuploidy ., One of the commonest SAC abnormalities in human cancer appears to result from over-expression of aurora kinase A 13 , 14 ., Other mitotic proteins whose over-expression or mutation results in aneuploidy include Nek2 15 , 16 , Hec1 17 , and Mad2 18 ., Our model includes mathematical descriptions of the G1-S and SAC checkpoints where aurora kinase A expression can be manipulated ., There have been a number of attempts to enhance the selectivity of cancer chemotherapy by exploiting loss of checkpoint function in cancer cells , a concept that has been termed “cyclotherapy” 19–21 ., Cyclotherapy is an example of new biomarker-driven therapeutic strategies that will require more sophisticated pharmacodynamic modelling to realise their full potential ., Here we illustrate how a modelling approach that incorporates the cell cycle oscillator and descriptions of the G1-S and SAC checkpoints , together with EGF signalling and apoptosis pathways , can help in developing such strategies ., To study the effects of drugs in various cytokinetic configurations , the sites of action of different anticancer drugs can be incorporated ( Table A in S1 Text ) ., We consider here four different drugs: palbociclib , gemcitabine , actinomycin D and paclitaxel ., Sets of parameter values can also be used to describe different cell types ( Table B in S1 Text ) ., We investigated here with the malignant cell line MiaPaca-2 and normal cell line ARPE-19 ., Following a brief description of the implementation of this approach , we aim to demonstrate via a series of examples how the strategy can facilitate identifying drug selectivity by simulating drug effects on normal and transformed cells ., This approach allows the study of cohorts of normal and cancer cells and comparing the effects of drugs ., Fig 3 shows the simulated growth of cells in the absence of drug treatment ., For normal cells , the cell cycle dynamic can be modulated by the presence of ligands ., The model simulations results in normal cells having a shorter doubling time ( from 25 to 13 hours; ( Fig 3A ) while MiaPaca-2 cells , in which MAP kinase signalling downstream from ras was modelled as constitutively activated , were indifferent to EGF concentration ( Fig 3B ) ., As mentioned earlier , it has been argued that the selectivity of cancer chemotherapy could be enhanced by exploiting loss of checkpoint function in cancer cells , a concept termed cyclotherapy 19–21 ., Since arrest in G1 is non-cytotoxic to normal cells , one approach to cyclotherapy is to treat with a drug that will cause G1 arrest ., Cancer cells with impaired G1 checkpoint function , e . g . because of a p53 mutation , will progress into S phase , where they may be selectively killed by an S-phase-specific drug ., The cdk4/cdk6 inhibitor palbociclib ( PD332991 ) 22 , 23 prevents normal cells from progressing through the G1 checkpoint and entering S phase ., In cells with mutant ras such as MiaPaca-2 the G1 checkpoint is weakened by high levels of production of cyclin D , making them less prone to arrest following treatment with palbociclib ., Fig 4A shows that the simulated effect of exposure for 48 hours to 30 , 100 and 300 nM palbociclib was to reduce proliferation but was non-cytotoxic to both cell lines ( thus having minor utility as a single agent at these concentrations ) ., Nevertheless , progression from G1 to S was affected with these concentrations and resulted in a decrease of the population of cells in S phase which was more pronounced in normal cells ( Fig 4B ) ., Combining gemcitabine with palbociclib ., The anticancer drug gemcitabine is selectively cytotoxic to cells in S phase 24 ., Gemcitabine might possess inherent anticancer selectivity since cells lacking G1 checkpoint function should have a higher proportion of cells reaching S phase ., Fig 4C shows that consistent with this understanding , the model predicted that gemcitabine was more cytotoxic to MIA PaCa-2 cells compared to the normal cell line ( Fig 4C ) ., We then investigated combining gemcitabine with palbociclib since palbociclib was differentially active in allowing cancer cells to enter the S-phase compared to normal cells ., The simulated dose-responses showed that the combination affected more malignant cells than normal cells as per gemcitabine alone ( Fig 4D ) ., In particular , antagonistic effect was observed with normal cells when adding palbociclib ., We then wanted to investigate if delaying gemcitabine administration would enhance differential effect between normal and cancer cell lines ., Thus , we simulated cell growth following administration of palbociclib and gemcitabine where gemcitabine administration was delayed ( Fig 4E ) ., Concentrations of 30 nM for both drugs were used as these induce similar effects in normal and malignant cells ( Fig 4D ) ., The simulations showed that delaying gemcitabine addition by approximately 12 hours from the start of palbociclib resulted in the best differential effect ( Fig 4F ) , with a ratio of normal cell viability to malignant cells viability of 17 ., This illustrates from a mechanistic point of view the paradigm of cyclotherapy , in which cells with an intact G1 checkpoint can be selectively protected from cytotoxic agents acting later in the cell cycle ., A few experimental studies have demonstrated this effect , generally exploiting the fact that tumour cells with mutant p53 lack a functional G1 checkpoint 25 , 26 ., More advanced optimization approaches can also be employed to attempt the optimisation of both concentration and administration schedules in order to reach one or several pre-defined goals ., For instance normal cell viability can be set above a specific threshold or target malignant cell viability can be set below a specific threshold while exposure constraints can also be taken into account ., In addition to defects in the G1 checkpoint , most tumours may have impaired SAC function ., One cause of this is over-expression of AK-A 13 , 14 ., High levels of AK-A tend to be associated with low-level resistance to taxanes 13 ., It is not clear , intuitively , whether there is a mechanistic relationship between the AK-A over-expression and the taxane sensitivity ., 13 Paclitaxel causes M phase cell cycle arrest , and cells that remain arrested for several hours enter apoptosis ., Simulations were used to illustrate how during a 24 hour treatment with 10 nM paclitaxel , cells with a functional SAC accumulate in M phase ( Fig 5A ) ., If the drug is removed after 24 hours , a large cohort of cells moved synchronously into G1 ( Fig 5A ) ., Malignant cells , which are modelled with higher AK-A levels , also showed an initial increase in the M phase fraction ( 12 hours; Fig 5B ) ., However , after about 24 hours , the simulation captured the phenomenon of “checkpoint leakiness” , i . e . an increasing number of malignant cells entered cell division even though mitosis was not fully completed , thus appearing in G1 phase ( 24 hours; Fig 5B ) ., In contrast to normal cells , if the drug was removed after 24 hours , only an additional small cohort of cells move into G1 ( Fig 5A ) ., Actinomycin D is a transcription inhibitor that kills cells in all phases of the cell cycle except M phase ( because transcription is not active during mitosis ) ., Because simulations highlighted a preferential arrest of normal cells in M phase by paclitaxel , we therefore asked the question if combining this drug with actinomycin D could generate preferential kill of tumor versus normal cells ., Actinomycin D used as a single agent has similar activity in our modelled malignant and normal cell lines ( Fig 6A ) ., We then simulated the combination of actinomycin D with paclitaxel which also resulted in a similar combination dose-response , although with slightly more antagonistic effects for normal cells against malignant ones at the highest concentrations ( Fig 6B; antagonistic scores of -17% and -12% respectively ) ., Experiments confirmed differential combination effect , with slight antagonism for normal cells but mild synergy for malignant cells ( Fig A in S1 Text ) ., Earlier , we showed that normal cells tended to accumulate more in M phase in the presence of paclitaxel between 12 and 24 hours ( Fig 5 ) , thus suggesting that a combination treatment in which actinomycin D was added after the start of paclitaxel could enhance this differential effect ., Further simulations ( Fig 6C ) showed that antagonistic interactions were enhanced for both cells lines ( compared to concomitant treatment , Fig 6B ) , but indeed with greater protection achieved in normal versus malignant ( SAC-deficient ) cells ( Fig 6B ) ., Nevertheless , the resulting predicted efficacy of the paclitaxel + actinomycin D combination for malignant cells was not strong enough ( 40% of control ) and only marginally better than for normal cells ( 48% of control ) and therefore is not likely to be a therapeutic option ., Overall , these results highlight the potential to enhance the therapeutic window by considering inherent dynamical differences between malignant and normal cells ., Several hundred oncogenes and tumour suppressor genes have been identified ., What they have in common is that all are involved in control of the cell cycle or its associated signalling pathways ( including the apoptosis pathways ) ., 8 , 25–27Understanding the dynamics of tumour growth and the pharmacodynamics of anticancer drugs could be greatly assisted by quantitative descriptions of these processes ., It has been argued that malignant transformation involves , minimally , two kinds of somatic mutation ., Moreover , it is believed that cancer is a disease of genetic instability9 , 10 , 12 , 28and that all human tumours have some degree of aneuploidy ., Aneuploidy confers increased spontaneous cell loss , so that tumour cells can only survive and proliferate if they have a compensating growth advantage over competing normal cells ., These changes in cancer are usually the result of mutations or changes in expression levels leading to over-ride of cell cycle checkpoints 8 , 10–12 ., Modelling the cell cycle can therefore enable us to capture differences in dynamics between normal and cancer cells resulting from various mutations and associated phenotypes ., Although the essential features of the mammalian cell cycle have been the subject of detailed dynamic models 29–32 , the significance of cell-cycle checkpoints has not been well studied ., The present model builds upon these available models but also includes detailed kinetic descriptions of two of the major cell cycle checkpoints that are mutated or over-ridden in cancer: the G1 checkpoint and the spindle assembly checkpoint ., This approach can be used to explore the potential anti-tumour selectivity of drugs that act on essential components of these checkpoints and the signalling pathways leading to them ., This approach might facilitate exploring a therapeutic strategy termed”cyclotherapy”19 , 20 , 33 , which attempts to optimise drug selectivity against cells with defective checkpoint function ., The cell cycle , with its associated signalling pathways and apoptotic pathways , constitutes a complex interactive system ., Analysing the dynamics of such systems requires that we take into account multiple positive and negative feedbacks , cross-talks , and effects that span multiple spatial compartments and multiple levels of hierarchical organization ., Any model that attempts to describe the kinetics of the cell cycle must represent a compromise between this almost intractable complexity and over-simplifications so sweeping that the essential dynamics of the system are lost ., The simulations described in this report were chosen to illustrate capabilities and limitations of the suggested approach ., We also provide the tool developed for these studies ( CYCLOPS , https://sourceforge . net/projects/cyclops-simulations/ ) ., The CYCLOPS model does not incorporate all known biological processes ., Only major components of the cell cycle and simplified descriptions of apoptosis , EGF signalling , G1-S and SAC checkpoints are incorporated ., Additionally , the model is based on specific kinetics which are only reasonable average values ., It should be noted that great cell to cell variability and difficulties in quantifying temporal and spatial profiles of proteins and other cellular components currently excludes deriving models which are truly quantitative . 54, A more complete description of cell cycle dynamics could also consider the G2-M checkpoint , particularly as there is cross-talk between it and the spindle assembly checkpoint ., As illustrated here , models such as CYCLOPS can facilitate the understanding of underlying cellular dynamics and how to develop or optimize therapeutic strategies ., Mechanistic approaches as this one ( increasingly termed quantitative systems pharmacology ( QSP ) 34 ) bridges molecular and systems biology studies to traditional PK/PD studies ., They offer the potential for accelerating the drug discovery process and making it more cost-effective ., An obvious practical application is the design of rational drug combinations for which exhaustive experimental study can be impractical ., Potential combinations can be better understood and optimized if assisted by mathematical models ., In this context , modelling the cell cycle and its modulating components can facilitate the development of combinations , particularly within the scope of cyclotherapy as illustrated here ., Additional features that can be investigated could include multiple tumour cell populations and a description of mutations from drug sensitivity to resistance , and vice versa ., Double mutants with resistance to two drugs can also be modelled ., Indeed , another important rationale for combination chemotherapy is the use of combinations of non-cross-resistant drugs to prevent or delay treatment failure resulting from acquired drug resistance ., The approach presented here can be extended to facilitate the design and optimization of such combinations ., Although ambitious , it is possible to envision a future where pharmacodynamic models of the cell cycle can also be used to develop personalised chemotherapies ., Because each tumour is genetically unique and expressed against a unique genetic background , individualising therapy is essentially a multi-dimensional optimization problem ., Eventually , CYCLOPS type and other QSP approaches , together with more traditional PK modelling , might provide powerful tools for matching treatment regimens to each tumour’s particular expression profile ., We have developed and coded ( C language , code available on https://sourceforge . net/projects/cyclops-simulations/ ) a mathematical model to investigate cyclotherapy pharmacodynamics strategies ( CYCLOPS ) ., CYCLOPS allows simulating a cohort of cells in different phases of the cell cycle ., For each cell the following processes are modelled: the basic cell cycle , the G1-S checkpoint , the spindle assembly checkpoint , part of the MAP kinase signal transduction pathway and apoptosis ., We first describe each one of these cellular processes individually and how they have been incorporated ., Then we explain how this model has been used to simulate large cohorts of cells ., A comprehensive review of cell cycle modelling has been published by Csikásh-Nagy ., 29 Since our underlying premise is that malignant transformation requires , minimally , loss of function of two cell cycle checkpoints , it was necessary to model these checkpoints , and their effects on the pharmacodynamics of anticancer drugs ., The CYCLOPS model differs from previous models in incorporating a kinetic description of the spindle assembly checkpoint ( SAC ) and the G1-S checkpoint ( Fig 1A ) ., It is an update of a classical cytokinetic model35 , 36 to which has been added a version of the cell cycle oscillator based on that described by Novak and Tyson30 , 37 and elaborated by Gérard and Goldbeter 31 ., Portions of the cell cycle model have been published in Chassagnole et al . 32 ., The description of the mitotic spindle assembly checkpoint is based on that described by Mistry et al . 38 and by Kamei et al . 39 ., The normal G1-S checkpoint , as modelled by CYCLOPS , is summarised in Fig 1B ., Transcription of most of the enzymes required for DNA replication is driven by the transcription factor c-myc , which in turn is under the control of the transcription factor E2F ., E2F in G1 cells is bound to , and inactivated by the RB protein ., When the RB protein is phosphorylated , active E2F is released ., Phosphorylation of RB is catalysed by the cyclin-dependent kinases , cdk2 and cdk4 ., Cdk4 is activated by cyclin D , which is produced by a number of signalling pathways , including the MAP kinase pathway ., Cdk2 is activated by cyclin E 25 ., For progression of cells from G1 phase into S phase it is essential that their DNA is intact and that they have sufficient DNA precursors for DNA synthesis to commence ., In the presence of DNA damage , or if the nucleotide pools are depleted or unbalanced , the p53 protein is activated 26 ., This results in transcription of p21 and p27 , which inhibit cdk2 , and thus prevents release of the G1-S checkpoint ., When the DNA damage has been repaired , p27 transcription ceases , and the cell enters S phase ., If the DNA damage cannot be repaired within a certain time ( usually 8 to 24 hours , depending upon cell type ) the cell enters apoptosis ., About 50% of carcinomas have mutations or deletions of p53 while some tumours lack a functional retinoblastoma ( RB ) protein , resulting in a dysfunctional G1-S checkpoint ., In CYCLOPS , this is modelled via over-ride of the G1-S checkpoint ., Other tumours have mutations that also result in over-ride of the G1-S checkpoint: this can be caused by over-expression of cyclin D or cyclin E , or by mutations that result in constitutive activation of the EGF receptor , or of ras ., Thus the CYCLOPS model is able to model mechanisms of G1-S checkpoint defect or over-ride ., Nevertheless , this model represents a first approximation , and at present does not describe the effects of a number of physiological regulators , such as for instance CDKN2A ., The checkpoint model could be elaborated as additional kinetic data becomes available ., The SAC acts by sensing correct connections of kinetochores to the two opposite spindle poles ., All kinetochores are initially modelled as emitting a “wait” signal ., Once all kinetochores reach a state of tension this signal is stopped , thus allowing progression to anaphase ( Fig 1C ) ., The wait signal is mediated by the bistable , tension-sensitive Aurora Kinase B ( AK-B ) 40 ., Microtubules grow from the opposite spindle poles , and attach at random to the kinetochores ., This results in various configurations which are modelled here: Syntelic attachments are not in a state of tension , which results in the second attachment being removed through activity of the enzyme aurora kinase B . Amphitelic attachments are in a state of tension which results in aurora kinase B being inactive ., When all pairs of sister chromatids are correctly attached , the wait signal rapidly decays , and the cell progresses to anaphase ., Failure of the SAC results in premature exit from mitosis and aneuploidy ., Most tumours are aneuploid , but aneuploidy is never detected in normal replicating cells ., 41 We modelled in CYCLOPS the effects of three drug classes on the SAC ., Aurora kinase A ( AK-A ) inhibition , which slows the process of mitosis by increasing time to anaphase ( AK-A is essential for centrosome maturation ) ., Paclitaxel , which stabilises microtubules against depolymerisation and also increases time to anaphase ., Aurora kinase B ( AK-B ) inhibition , which slows down the removal of incorrect microtubule-kinetochore attachments ., The current model of the MAP kinase pathway used by CYCLOPS is based on the model of Brightman and Fell 42 ., Several other groups have modelled this pathway ( reviewed in Gilbert et al . 43 ) ., The CYCLOPS model captures much of the essential dynamics of EGF signalling ( Fig 1D ) and includes sites of action of five classes of drugs ., When EGF binds to its cell surface receptor , RAS is activated , and signals through RAF , MEK and ERK to up-regulate cyclin D and over-ride the G1-S checkpoint ( Fig 1D ) ., Caspases are produced as inactive procaspases ., One procaspase molecule , when activated ( by a cellular damage signal ) can then catalytically activate many other procaspase molecules ., The process is thus autocatalytic ., Like kinases , proteases can act as multi-stage amplifiers ., In apoptosis , procaspase 9 is activated to caspase 9 , which catalyzes the conversion of procaspase 3 to caspase 3 , which is the proximal cause of cell death ( Fig 1E ) ., Apoptosis has been modelled mathematically44–46 and the CYCLOPS model is adapted from these published models ., To model cancer cytokinetics requires that we can model asynchronous cell populations , which may contain millions of cells ., To model the cell cycle oscillator individually in each cell would be impractical ., Instead , cells are grouped into a succession of cohorts , assumed to be a few minutes apart ., CYCLOPS treats the cell as a sequence of 63 states , with transition rules based upon a combination of elapsed time and biochemical values ( Fig 2 ) ., Some of these quantities are modelled continually ( DNA , total protein ) , and others are calculated ., In these cohorts , the apparent cell cycle time is modulated by biochemical parameter values ., The 63 cytokinetic states are: 15 G1 states ( differing in total protein content and cyclin E level ) , 30 S phase states ( differing in DNA content ) , 10 G2 states ( differing in time elapsed from the start of G2 ) , 5 M states ( prophase , prometaphase , metaphase , anaphase , telophase ) , a single G0 phase , a single population of terminally differentiated and senescent cells , and a population of irreversibly damaged cells that are metabolically active but unable to replicate ., These 63 compartments can contain any number of cells ( Fig 2 ) ., In addition to progressing through the stages of the cell cycle , cells may leave the cycle irreversibly through cell death , differentiation or senescence ., Spontaneous cell loss after cell division is treated as a cytokinetic parameter characteristic of particular cell lines , as are rates of differentiation/senescence ( Table 1 ) ., Senescence , differentiation , and apoptosis may also be stimulated by drug treatment ., Cells may leave the cell cycle reversibly and enter a quiescent ( G0 ) compartment ( Fig 2 ) ., In the current study , a modelled MiaPaca-2 cancer cell line was used ., A goal of the model is to optimise drug selectivity and the selection of an appropriate normal control cell is essential ., Our approach is two-fold:, ( a ) for many anticancer drugs it is possible to identify a particular drug-sensitive normal cell type that represents the site of dose-limiting toxicity ., For most anticancer drugs this is bone marrow , intestinal mucosa , skin , or the immune system ., There is sufficient cytokinetic information for these tissues to be modelled in detail , and we can describe the effects of many drugs on these tissues explicitly ., We then assume that for the purposes of predicting efficacy and selectivity , drug effects on other cell types can be ignored ., ( b ) Our underlying premise is that cancer is primarily a defect of cell cycle checkpoints ., For modelling purposes we can then predict anticancer drug selectivity by assuming that normal cells differ from the cancer cell in having fully functional cell cycle checkpoints ., Specifically , the MiaPaca-2 cells are modelled as having mutant , constitutively active RAS , resulting in up-regulation of cyclin D , and causing override of the G1 checkpoint ., 47 ., Second , a 3-fold over-production , relative to the normal control of aurora kinase A , causing impaired function of the SAC was also incorporated ( Table 1 ) 13 , 48 , 49 ., Pharmacodynamic modelling using CYCLOPS ., Four sites of drug action modelled in CYCLOPS were investigated here: Drug effects were modelled using a standard Hill equation whose parameter values were obtained from the DrugCARD database 53 ., The rate of cytotoxicity was defined as a reduction of the cell count following treatment compared to the count at the start of treatment:, Cytotox=100 ( 1−#cellsatt=24h#cellsatt=0h ) CYCLOPS was coded in C and is available online ( https://sourceforge . net/projects/cyclops-simulations/ ) ., CYCLOPS generates graphical output using the open source program , gnuplot . 54, A flow chart of the model implementation is included in the supplementary material ( Fig B in S1 Text ) ., The list of components is given in Supplementary Table B in S1 Text ., In the present study the code was compiled using the free gcc compiler , release 4 . 6 . 2 .

Introduction, Results, Discussion, Materials and methods

The dynamic of cancer is intimately linked to a dysregulation of the cell cycle and signalling pathways ., It has been argued that selectivity of treatments could exploit loss of checkpoint function in cancer cells , a concept termed “cyclotherapy” ., Quantitative approaches that describe these dysregulations can provide guidance in the design of novel or existing cancer therapies ., We describe and illustrate this strategy via a mathematical model of the cell cycle that includes descriptions of the G1-S checkpoint and the spindle assembly checkpoint ( SAC ) , the EGF signalling pathway and apoptosis ., We incorporated sites of action of four drugs ( palbociclib , gemcitabine , paclitaxel and actinomycin D ) to illustrate potential applications of this approach ., We show how drug effects on multiple cell populations can be simulated , facilitating simultaneous prediction of effects on normal and transformed cells ., The consequences of aberrant signalling pathways or of altered expression of pro- or anti-apoptotic proteins can thus be compared ., We suggest that this approach , particularly if used in conjunction with pharmacokinetic modelling , could be used to predict effects of specific oncogene expression patterns on drug response ., The strategy could be used to search for synthetic lethality and optimise combination protocol designs .

Neoplastic transformation results from mutations , chromosomal abnormalities , or expression changes affecting components of the cell cycle , the signalling pathways leading into it , and the apoptosis pathways resulting from cell cycle arrest ., Cytotoxic agents , but also newer drugs that target the cell cycle and its signalling pathways , perturb this complex system ., Small differences in cell cycle control between normal and transformed cells could determine drug selectivity ., Using cell cycle and representative signalling and apoptotic pathway simulations , we examine the influence of cell cycle checkpoints ( frequently defective in cancer ) on drug selectivity ., We show that this approach can be used to derive insights in terms of drug combinations scheduling and selectivity .

cell death, medicine and health sciences, cell cycle and cell division, cancer treatment, cell processes, departures from diploidy, oncology, pharmacodynamics, pharmacology, synthesis phase, aneuploidy, signal transduction, apoptotic signaling cascade, cell biology, apoptosis, genetics, biology and life sciences, cell signaling, signaling cascades, cyclins

journal.pcbi.1005347

Multiscale mutation clustering algorithm identifies pan-cancer mutational clusters associated with pathway-level changes in gene expression

Somatic mutations are amongst the most frequent genomic aberrations associated with cancer ., Moreover , primary human cancer samples usually contain tens to hundreds of somatic mutations , depending on the tissue of origin 1 ., The identification of mutations that alter the function of protein coding genes , and a molecular understanding of the ensuing consequences of such mutations , remains a significant challenge ., To date , millions of distinct somatic mutations have been observed in human cancers through genome wide characterization projects such as The Cancer Genome Atlas ( TCGA ) and International Cancer Genome Consortium ( ICGC ) ., Computational methods are particularly well suited for the assessment of somatic mutations at this scale in order to identify those with cancer-associated functional consequences ., To this end , numerous mutation assessment methods have been developed based on a variety of underlying approaches and statistical models ., For example , MuSiC 2 , MutSig 1 , and SomInaClust 3 rank cancer-associated genes based on somatic mutations observed across a cohort of samples and normalized by factors including mutation type propensity , gene length , and replication timing ., In addition , methods including PolyPhen 4 , SIFT 5 , and CADD 6 utilize prior knowledge such as conservation and machine learning based on disease associated variants to predict functional mutation impact ., Yet other methods including OncodriveCLUST 7 and CLUMPS 8 , iPAC 9 , and graphPAC 10 take a parameterized , data-driven approach to predict cancer-associated mutations based on spatial clustering of linear sequence , three-dimensional protein structure , and graphical representations thereof ., Finally , enrichment of somatic mutations based on functional biological consequences such as protein-protein interaction interfaces 11 and deregulation of phosphorylation signaling 12 have been explored ., Importantly , functional mutations that occur within the coding sequence and are known to be associated with cancer do not occur at random positions ., On the contrary , hotspots or clusters are frequently observed as recurrent missense mutations across a significant fraction of cancer samples ., These sets of mutations are typically attributed to alterations in function at specific sites of the protein that give rise to a variety of cancer phenotypes ., Oftentimes , these mutation clusters can be readily interpreted in the context of their protein structure and function; for example , mutations in the GTP binding pocket of KRAS that modulates intrinsic GTPase activity , lead to constitutive activation of KRAS and persistent stimulation of downstream signaling pathways 13 , 14 ., Such mutation clusters need not be located within structural protein domains; for example , N-terminal mutations of beta-catenin ( CTNNB1 ) affect protein phosphorylation sites and thereby abrogate ubiquitin-mediated proteasomal degradation 15–17 ., These mutations then result in beta-catenin accumulating in the nucleus and continuously driving transcription of its target genes 18 , 19 ., While these examples highlight readily identifiable and interpretable focal mutation clusters that lead to cancer-associated effects on protein function , such mutation consequences need not be restricted to dense clusters at just a few amino acid positions in the protein sequence ., For example , tumor protein p53 ( TP53 ) contains a combination of mutations that are recurrently located at specific amino acids that bind directly to DNA ( e . g . , R248Q , R273C ) as well as more broadly distributed sets of mutations throughout the core DNA binding domain of TP53 that disrupt the folding and stability of the protein 20 , 21 ., Additionally , sets of mutations do not only affect individual regions or domains of proteins; rather , functional mutations are observed in distinct clusters within different regions or domains of individual proteins ( e . g . , PIK3CA ) , indicating the possibility for differential functional consequences of such mutation clusters 22 , 23 ., We have therefore developed a multiscale mutation clustering algorithm ( M2C ) that identifies variable length regions with high mutation density in cancer genes ., We have applied our algorithm on hundreds of frequently mutated genes using the combined mutation data in over twenty tumor types from TCGA and identified over a thousand multiscale mutation clusters ., We have statistically associated these multiscale clusters with gene expression from TCGA tumor samples and drug response data from cancer cell lines , illuminating the ( differential ) functional and associated therapeutic consequences of somatic mutations in cancer ., We ran M2C on the combined mutation calls from 23 cancers ( pan-cancer data set is described in T1 in S1 Tables ) across 549 genes ., Briefly , M2C works by smoothing the mutation density at many different amino acid length scales ., Then , a mixture model is fit to each scale ., Finally , these mixture models are merged together using a greedy algorithm that optimizes an information criterion ., We refer to a cluster as an interval along the linear amino acid chain , e . g . PIK3CA 339–350 ., After identifying these clusters , we assigned them as binary features to individual tumor types for each of the 23 cancers ., A cluster is assigned as positive ( 1 ) to a tumor sample if that sample contains at least one non-synonymous mutation within the cluster and negative ( 0 ) otherwise ., This assignment allowed us to relate cluster features with gene expression data from 2194 genes in the TCGA dataset ., We statistically combined these gene expression associations on the pathway level across 172 pathways linking mutation clusters to pathway-level gene expression changes ., We performed a similar analysis on all non-synonymous mutation features ( i . e . regardless of whether the mutation is or is not in a cluster ) ., Finally , we linked the multiscale mutation clusters to differential drug response using cancer cell line data ., Fig 1 illustrates our approach on PIK3CA in breast invasive carcinoma , a prototypical example of how our method identifies multiple mutation clusters with differential associations with gene expression data ., Additional details can be found in the Methods section and Supplemental Information ., M2C identified a total of 1255 multiscale clusters in 393 of the 549 genes analyzed ., These genes were selected by taking the highest ranked genes in MutSig for each cancer type ., The 156 genes without any clusters had all their mutations classified as uniform background noise by M2C and were omitted from further analysis ., The following results indicate that our method finds multiscale regions of proteins which are enriched for mutations and frequently overlap with annotated protein domains ., The multiscale clusters span a wide range of lengths: from 1 to 600 amino acids ., Additionally , clusters have a highly variable number of mutations: 15 to 338 mutations ., Finally , we note that each cluster is given a score which is the log of the ratio of its emission probability from its component of the mixture model to the emission probability under the null hypothesis that mutations are distributed uniformly across the gene ., Higher scores indicate increased robustness as shown by cross-validation analysis ( see Methods and S4 Fig ) ., T2 in S1 Tables details pan-cancer cluster definitions , cluster scores , and overlapping protein domains ., We assigned clusters to specific tumor samples if there was at least one non-synonymous mutation in a sample at an amino acid position within a cluster ., By combining tumor samples grouped by tumor tissue of origin , we were able to compare how clusters are assigned to different tumor types ., T3 in S1 Tables details cluster assignments to tumor types ., When clusters are assigned to specific tumor types , a high variability is seen in the way clusters are distributed between tumor types ., On the high end , lung squamous cell carcinoma and uterine carcinosarcoma have over 80% of their non-synonymous mutations in clusters ., On the low end , acute myeloid leukemia and thyroid carcinoma have 23% and 34% of their non-synonymous mutations in clusters , respectively ., Neither the total number of non-synonymous mutations nor the total number of synonymous mutations is a good indicator for what percent of mutations are located within clusters in a specific tumor type ., Interestingly , the ratio between the percentage of non-synonymous mutations in clusters and the percentage of protein sequence covered by clusters ( which contain non-synonymous mutations ) is much less variable; for the pan-cancer data set this ratio is 1 . 88 and when calculated between cancer types ranges from 0 . 88 ( acute myeloid leukemia ) to 2 . 23 ( thyroid carcinoma ) ., T4 in S1 Tables gives statistics on the distribution of clusters between different tumor types ., Finally , we searched for tumor specific clusters by using Fisher’s exact test to determine if specific tumor types are enriched for specific clusters ., We found 426 mutational clusters enriched for a specific tumor type at a false discovery rate of 1% and 996 at a false discovery rate of 10% ., Cluster tumor type enrichment results are detailed in T5 in S1 Tables ., We compared the multiscale clusters to a Density Based Spatial Clustering of Applications with Noise ( DBSCAN ) based method called OncodriveCLUST and to protein domains from Pfam 24 ., OncodriveCLUST’s approach also uses a kernel smoother to create a mutation density , albeit using only one predefined scale ., Despite finding fewer clusters ( OncodriveCLUST found 5185 clusters in 514 genes ) , we find that multiscale clusters tend to be larger and have more mutations ., Additionally , multiscale mutation clusters cover ( defined as >50% overlap ) 48% of the alternative method’s clusters ., On the other hand , OncodriveCLUST clusters cover only 18% of the multiscale clusters ., Finally , we note that M2C has 9% more coverage by protein domains from Pfam compared to OncodriveCLUST ( M2C has a total of 31% of multiscale clusters located within or overlapping with protein domains ) ., These statistics are summarized in panels A-C of Fig, 2 . We validated the M2C robustness by splitting our data set into two equally sized partitions and running the algorithm separately on each partition ., We then compared how clusters from the different partitions overlap ., We also make use the generative capabilities of the mixture models underlying our algorithm to use each partition’s model to predict the data from the other partition ., In brief , we found that M2C robustness is dependent on mutation density; clusters with many mutations , regardless of size , tend to be validated across the two different partitions ., Less populated small dense clusters are also well conserved ., However , larger sparse clusters are more poorly replicated between partitions ., Despite these differences , the mixture models generated by each partition do a very good job predicting the data set in the other partition ( Fig 2D ) ., For more details on our cross-validation tests , see the Methods section ., In order to begin to gain an understanding of the functional consequences of mutations in different clusters , we statistically associated gene expression data from TCGA by combining statistics from all gene expression data ( i . e . , global associations ) and from subsets of gene expression data representing molecular pathways ( i . e . , pathway associations ) ., The statistical associations were carried out on binary vectors that indicate whether a sample in a specific tumor type had a mutation in a specific cluster ( 1 ) or not ( 0 ) ., We refer to these binary vectors as cluster features ., This association pipeline works by combining P-values from Kruskal-Wallis tests between gene expression data and cluster features with the Empirical Brown’s Method 25 ., This methodology was chosen because , in general , gene expression from a particular pathway is not universally upregulated or downregulated due to cancer mutations ., A summary of the results from these associations at different false discovery rates ( FDR ) can be seen in Fig, 3 . Further information on the association analysis can be found in the methods section ., In order to assess the robustness of our association methodology , we performed a cross-validation analysis ., We measured two different robustness scores ., “Association robustness” compares the associations between two data partitions using the same underlying set of clusters ., “M2C plus association robustness” compares two data partitions each used to generate their own set of clusters which are subsequently analyzed for gene expression associations separately ., Our analysis finds association robustness at 80% and M2C plus association robustness at 60% ., This decrease is consistent with the observation that M2C robustness ( described previously ) is lower for lower scoring clusters , causing a decrease between partitions ., However , higher scoring clusters tend to have stronger associations , making M2C plus association robustness higher than might be naively expected simply by combining M2C association robustness and association robustness ., Further information on the cross-validation can be found in the Methods section ., We found that computing associations with gene expression comprises a complementary approach towards ranking cancer genes when compared with other methods such as MutSig 1 ., We compiled a list of the top 67 genes from the MutSig rankings from across all cancer types that represent many of the most important cancer genes ( see section B in S1 Text ) ., At a FDR of 10% , 36% ( a total of 24 genes ) of the top genes contained at least one mutation cluster that is associated with global or pathway changes in gene expression in a specific tumor type ., Our results from the gene expression analysis highlight that clusters of many different length scales are associated with changes in gene expression , see S1 Fig . This corroborates the fact that functional regions of genes can range in size from single amino acids to multiple protein domains ., As discussed later , these associations are often more specific than associations with any-non-synonymous mutation in the same gene ., These results indicate the importance of being able to dynamically determine multiscale regions of interest within genes in order to better understand the spectrum of mutations underlying cancer ., Furthermore , we have found a number of cases where the association P-value between a cluster feature and gene expression data ( pathway or global ) is lower than the association P-value between the feature ( variable ) that encodes the occurrence of any non-synonymous mutation ., These cases are of special interest because cluster features ( i . e . variables that encode the presence or absence of a mutation in a particular cluster ) are by definition subsets of the feature that encodes all non-synonymous mutations ., This means that cluster features have fewer samples and thus less statistical power to detect associations with expression data ., One would statistically expect them to have correspondingly higher P-values ., However , in many instances we find that the opposite is the case ., These decreased P-values provide additional evidence that mutation clusters can have specific functional consequences and provide a more nuanced view than considering an entire gene ., A number of specific examples are highlighted below and a full list of these cases can be found in T6A and T7A in S1 Tables ., The pathway association in T7A in S1 Tables also lists individual genes within a pathway with significantly differential expression between samples with a cluster feature and without ., We have also computed pathway and global associations for samples which contain mutations not in any of our clusters ., At a false discovery rate of 10% , about 90% of clusters are more strongly associated with a given pathway or global gene expression feature than mutations lying outside of all clusters ., In some cases , this is an indication that our clusters are strongly associated with changes in gene expression ., In other cases , the change in P-value can be attributed to the smaller sample size of mutations lying outside of all clusters ., Therefore the comparison to the any non-synonymous feature , which will always have a larger sample size than the corresponding cluster feature , is more interpretable ., A table of associations corresponding to the significant associations in T6A and T7A in S1 Tables for samples which do not contain mutations in any cluster can be found in T6D and T7D in S1 Tables ., Note that associations were not computed if fewer than 5 samples in a given gene and tumor type had non-synonymous mutations outside of all clusters ., Finally , we uncovered a few instances where different cluster features in the same gene are associated at substantially different levels with gene expression data at the pathway or global level ., These may be instances of multifunctional genes where mutations in different regions of the same gene have different functional consequences ., In the following sections , we highlight specific examples found in our analysis ., We note that the following discussion is not exhaustive and many more examples can be found by examining T6A S1 Tables ( features associated with global changes in gene expression ) and T7A in S1 Tables ( features associated with pathway changes in gene expression ) ., As a further comparison to our method , we also analyzed global and pathway gene expression associations for OncodriveCLUST clusters and PFAM domains ., Significant results for OncodriveCLUST can be found in T6B in S1 Tables ( global ) and T7B in S1 Tables ( pathway ) ., Significant results for PFAM domains can be found in and T6C in S1 Tables ( global ) and T7C in S1 Tables ( pathway ) ., Fig 3 compares the number of cluster features with significant gene expression associations and the total number of associations found by each method ., At a false discovery rate of 1% , M2C finds a slightly higher percent of clusters with lower P-values than the corresponding any non-synonymous association when compared to OncodriveCLUST and PFAM domains , at 29% , 28% , and 22% , respectively ., As the false discovery rate is increased , M2C continues to find a higher proportion of more strongly association clusters than OncodriveCLUST , despite typically finding fewer significant associations in total ., Interestingly , at higher false discovery rates PFAM domains are the most likely to have a lower association P-values than the corresponding any non-synonymous association ., The strong associations from PFAM domains are likely due to the overall large length and amino acid counts inside these domains which has the drawback that they contain much less positional information compared to the clustering methods ., T8 in S1 Tables contains a more detailed set of statistics similar to those shown in Fig, 3 . We identified highly cancer-related clusters in specific tumor types that are significantly associated with global changes in gene expression ., In the cases of PIK3CA and GATA3 , different clusters have widely different association levels with global changes in gene expression ., This may be a statistical indicator of different mutation clusters within a single gene affecting the cellular environment in different ways ., A full list of global gene expression associations can be found in T6A in S1 Tables ., Here , we highlight several well-known and novel clusters in the cancer literature recovered by M2C: Two of PIK3CA’s eleven mutation clusters are associated with global changes of gene expression in breast invasive carcinoma: amino acid positions 539–547 and 1043–1049 ., We note that the first clusters are significantly enriched for mutations in the following tumor types: breast invasive carcinoma , head and neck squamous cell carcinoma , uterine corpus endometrial carcinoma , cervical squamous cell carcinoma and endocervical adenocarcinoma ( FDR < 1% ) ., The second cluster is significantly enriched for mutations in breast invasive carcinoma and uterine corpus endometrial carcinoma ( FDR < 1% ) ., The 539–547 cluster contains three previously studied residues in an α-helical region which have been shown to increase the enzyme’s activity 22 ., A hotspot point mutation at 1047 inside the 1043–1049 mutation cluster has been speculated to affect the position and mobility of the activation loop 27 ., Interestingly , despite approximately 20% fewer mutations , the 539–547 cluster has a much lower global gene expression association P-value ( about 6 orders of magnitude smaller ) , signifying that perhaps mutations in this cluster have different functional consequences ., As seen in Fig 4A the 539–547 region of PIK3CA may be directly involved in binding to PIK3R1 , providing another interpretation for the large association with changes in gene expression 28 ., As one of the best studied cancer genes , it is unsurprising that many additional associations and cluster-tumor enrichments exist for PIK3CA which are detailed in our S1 Tables ., ROBO3 has a cluster from 195–509 which overlaps with 4 lg-like C2 domains 29 ., This cluster is associated with global changes in gene expression in uterine corpus endometrial carcinoma ., The cluster is also enriched for mutations in lung adenocarcinoma ( FDR < 5% ) , as well as in uterine corpus endometrial carcinoma ( FDR < 1% ) ., Previously , increased levels of ROBO3 expression have been associated with metastasis in pancreatic cancer 30 ., However , to our knowledge somatic mutations in this region have not been extensively studied ., Furthermore , we note that ROBO3 is ranked only 15540 in MutSig for uterine cancer 1 ., Thus this gene might be a candidate for further study of its role in uterine corpus endometrial carcinoma and possibly the other tumor types mentioned above ., GATA3 has a subset of three clusters in breast invasive carcinoma all containing predominantly nonsense ( i . e . frameshift , indel , or stop ) mutations: amino acids 325–334 with 15 mutations , amino acids 390–421 with 22 mutations and amino acids 429–443 with 15 mutations ., We note that the most densely populated of these clusters occurs after the zinc finger binding domain while the first cluster occurs within the binding domain 29 ., Interestingly , the 390–421 cluster is substantially more associated with global changes in gene expression than either of the other two clusters with a P-value nearly 2 orders of magnitude lower ., Different GATA3 mutations have been associated with Luminal A and B subtypes of breast cancer 31 and changes in survival prognoses 32 ., We note that all three of these clusters are enriched for mutations in breast invasive carcinoma ( FDR<1% ) ., We identified significant pathway associations with clusters in specific tumor types ., These pathway associations go beyond simply implicating certain mutation clusters in cancer by shedding light on possible phenotypic effects of each cluster ., At a false discovery rate of 10% , all of the genes associated with global changes in gene expression were also associated with at least one pathway ( and usually with many pathways ) ., This included 24 of 67 top ranked cancer genes from MutSig ., These results are not surprising because each set of genes in a pathway is a subset of the global gene expression data ., However , we note that by restricting our analysis to individual pathways , novel clusters associated with gene expression changes were detected indicating that this analysis is more nuanced ., For a complete list of cluster pathway associations see T7A in S1 Tables ., This table also included specific gene expression features in each pathway which are strongly associated with a mutation cluster ., Below we discuss several examples of specific clusters which are associated with pathways but are not associated with global changes of gene expression ., ZBTB20 has a cluster from 681–714 ., This cluster is associated with 9 pathways related to angiogenesis and regulation of the cell cycle in gastric adenocarcinoma ., Previously single nucleotide mutations in ZBTB20 have been associated with gastric cancer 33 ., We hypothesize that a larger region of the ZBTB20 gene as represented by this mutation cluster may be involved in gastric oncogenesis ., Specifically , the frameshift deletions between the two zinc finger domains likely disrupt DNA binding and the C-terminal function of the protein ., We note that this cluster is enriched in gastric cancer along with two other clusters in ZBTB20 , 190–248 and 345–504 ( FDR < 1% ) ., The 190–248 cluster is also enriched for mutations in low grade glioma ( FDR < 10% ) ., PPP2R1A has a cluster from 167–183 that overlaps a HEAT domain motif 34 ., This cluster is associated with pathways related to cell differentiation and MAPK signaling in uterine corpus endometrial carcinoma ., This gene has also been implicated previously in uterine and ovarian cancer 35 ., This cluster is enriched for mutations in uterine corpus endometrial carcinoma and uterine carcinosarcoma ( FDR < 1% ) ., Additionally , the 237–275 in PPP2R1A is enriched for mutations in lung squamous cell carcinoma and gastric cancer ( FDR<10% ) ., The 391–490 cluster is enriched for mutations in uterine corpus endometrial carcinoma ( FDR <1% ) ., CHD4 is a chromatin helicase remodeling protein ., It has a cluster from 945–1016 ., This cluster is associated with two pathways in uterine corpus endometrial carcinoma ., One of these pathways is signaling events mediated by HDCA Class II , which is thought to form a complex which includes CHD4 36 ., The above selection of examples is by no means exhaustive ., Additional examples can be found and investigated in more depth by examining T7A in S1 Tables ., We identified statistically significant clusters in specific tumor types with a lower combined P-value across all gene expression features than the corresponding any non-synonymous mutation feature in a specific tumor type ., These results are annotated in T6A in S1 Tables ., As examples , this analysis picks up two well-known mutation sites ., Our algorithm detected a cluster in BRAF from amino acids 600 to 601 which is more significantly associated with global changes in gene expression ( P<10−13 with 106 non-synonymous mutations ) in skin cutaneous melanoma despite having fewer mutations than the any non-synonymous feature ( P<10−10 with 126 total non-synonymous mutations across the entire gene ) ., Similarly , in thyroid carcinoma the 600–601 mutation cluster has 235 non-synonymous mutations and is more significantly associated to global changes in gene expression ( P<10−82 ) than any-non-synonymous mutation which has 237 total non-synonymous mutations ( P<10−80 ) ., These are common mutations previously implicated in cancer 37 ., Additionally , the 25–45 amino acid region of Beta-catenin ( CTNNB1 ) is found to be more significantly associated with global changes in gene expression ( P<10−24 with 67 non-synonymous mutations in the cluster ) than all non-synonymous mutations in the gene ( P<10−21 with 80 total non-synonymous mutations in the gene ) in uterine corpus endometrial carcinoma ., A similar result is seen in liver hepatocellular carcinoma where the cluster is more significantly associated with global change in gene expression ( P<10−14 with 37 non-synonymous mutations in the cluster ) than all non-synonymous mutations in the gene ( P<10−12 with 53 non-synonymous mutations ) ., This cluster is also enriched for mutations Adrenocortical carcinoma ( FDR < 5% ) , uterine corpus endometrial carcinoma ( FDR < 1% ) and in liver hepatocellular carcinoma ( FDR < 1% ) ., Beta-catenin is known to be implicated in numerous types of cancer 38 ., This region corresponds closely to a region of phosphorylated peptides along the CTNNB1 chain which are known to regulate the degradation of CTNNB1 39 , 40 ., Fig 4B shows the structure of the N-terminus region of Beta-catenin located within the 25–45 amino acid cluster bound to the WD40 domain of β-TrCP1 ( BTRC ) 41 ., Mutations in this cluster are likely to affect this binding and thereby the regulation of Beta-catenin ., The fact that this region is not an annotated protein domain illustrates the flexible nature of M2C in picking out different kinds of functional regions of interest ., We also identified cluster pathway associations in specific tumor types with lower pathway-cluster P-values than the corresponding any non-synonymous mutation feature in the same tumor type ., These results are annotated in T7A in S1 Tables ., One example is the 248–254 cluster in FGFR3 in bladder urothelial carcinoma ., Point mutations in this region have been previously implicated in low grade glioma tumors 42 ., We note that mutations in this region are more significantly associated with 10 pathways than the any non-synonymous feature ., These pathways are involved in a large number of molecular processes ranging from cell cycle control to Reelin signaling ., This suggests diversity in the role of FGFR3 as an oncogene in bladder cancer ., This cluster is also enriched for mutations in both bladder cancer ( FDR < 1% ) and lung squamous cell carcinoma ( FDR < 10% ) ., Another example is the 88–98 cluster in PGM5 in gastric adenocarcinoma which is more significantly associated with 12 pathways when compared to any non-synonymous mutation in the same gene ., We note that this cluster is enriched for mutations in gastric cancer ( FDR < 1% ) and the 456–522 cluster is enriched in lung adenocarcinoma ( FDR < 5% ) ., Although PGM5 has been ranked as a possible cancer gene according to MutSig , to the best of our knowledge this particular region has not been studied ., These cases demonstrate how decreasing sample size by considering a specific cluster as opposed to a specific gene can provide a more nuanced lens for finding statistical associations and aid in inferring the functional consequences of mutations ., This occurs because clusters can represent functional regions of a gene and thereby limit the analysis to mutations within that region ., This spatial specificity has the effect of excluding background mutations outside the cluster from the analysis ., PTEN has two clusters that are associated with global changes in gene expression in uterine corpus endometrial carcinoma ., The first of these clusters from 39–52 has 9 mutations , 8 of which are nonsense mutations ., The second cluster from 116–146 has 74 mutations which has 66 missense and 16 nonsense mutations ., This cluster encompasses the P-loop of the protein 43 ., Additionally the first cluster is associated with 16 pathways including 5 not associated with the second cluster ., Similarly , the second cluster is associated with 51 pathways including 40 not associated with the first cluster ., The differential associations between cluster features found in PTEN are visualized in Fig 5 ., FUBP1 has two clusters each with predominantly nonsense mutations with differential pathway association in lower grade glioma ., The 88–206 cluster overlaps well with a KH-1 domain which is involved in RNA and DNA binding 44 ., The second cluster does not overlap with any known domains 29 ., One possible explanation is that nonsense mutations earlier in the protein sequence result in total loss of function while nonsense mutations later in the sequence only effect the end of the protein structure ., This could result in differential effects from mutations in these two clusters ., See S2 Fig for an association heatmap ., E-Cadherin has 3 clusters with differential pathway associations in breast invasive carcinoma ., All these clusters have predominantly nonsense mutations ., Of particular interest are the second two clusters ., The cluster from 144–222 occurs at the beginning of the first cadherin

Introduction, Results and discussion, Conclusion, Methods

Cancer researchers have long recognized that somatic mutations are not uniformly distributed within genes ., However , most approaches for identifying cancer mutations focus on either the entire-gene or single amino-acid level ., We have bridged these two methodologies with a multiscale mutation clustering algorithm that identifies variable length mutation clusters in cancer genes ., We ran our algorithm on 539 genes using the combined mutation data in 23 cancer types from The Cancer Genome Atlas ( TCGA ) and identified 1295 mutation clusters ., The resulting mutation clusters cover a wide range of scales and often overlap with many kinds of protein features including structured domains , phosphorylation sites , and known single nucleotide variants ., We statistically associated these multiscale clusters with gene expression and drug response data to illuminate the functional and clinical consequences of mutations in our clusters ., Interestingly , we find multiple clusters within individual genes that have differential functional associations: these include PTEN , FUBP1 , and CDH1 ., This methodology has potential implications in identifying protein regions for drug targets , understanding the biological underpinnings of cancer , and personalizing cancer treatments ., Toward this end , we have made the mutation clusters and the clustering algorithm available to the public ., Clusters and pathway associations can be interactively browsed at m2c . systemsbiology . net ., The multiscale mutation clustering algorithm is available at https://github . com/IlyaLab/M2C .

Identifying driver mutations in cancer has been a major challenge in cancer research , with the ultimate goal of understanding the detailed molecular origins of cancer and providing genetically personalized treatments ., For decades , the cancer research community has known that mutations in certain genes—such as tumor suppressors like P53—can drive cancer ., In some cases it is also clear that mutations within cancer genes are localized in a single amino—such as the V600E mutation in BRAF ., With the existence of large multi-omic data sets including The Cancer Genome Atlas ( TCGA ) , it is now possible to apply big data approaches towards both identifying mutation features of interest and understanding their functional consequences ., We have bridged the gap between single amino acid mutations and the whole gene view by developing an algorithm that can identify variable length regions within cancer genes that which enriched for mutations ., Furthermore , we have been able to integrate our multiscale mutation clusters with additional molecular data to gain insight into possible functional consequences of the clusters .

medicine and health sciences, applied mathematics, carcinomas, endometrial carcinoma, cancers and neoplasms, simulation and modeling, oncology, algorithms, mutation, mathematics, clustering algorithms, nonsense mutation, research and analysis methods, proteins, gene expression, gynecological tumors, somatic mutation, biochemistry, point mutation, genetics, protein domains, biology and life sciences, physical sciences

journal.pcbi.1000045

The Dynamics of Human Body Weight Change

Obesity , anorexia nervosa , cachexia , and starvation are conditions that have a profound medical , social and economic impact on our lives ., For example , the incidence of obesity and its co-morbidities has increased at a rapid rate over the past two decades 1 , 2 ., These conditions are characterized by changes in body weight ( mass ) that arise from an imbalance between the energy derived from food and the energy expended to maintain life and perform work ., However , the underlying mechanisms of how changes in energy balance lead to changes in body mass and body composition are not well understood ., In particular , it is of interest to understand how body composition is apportioned between fat and lean components when the body mass changes and if this energy partitioning can be altered ., Such an understanding would be useful for optimizing weight loss treatments in obese subjects to maximize fat loss or weight gain treatments for anorexia nervosa and cachexia patients to maximize lean tissue gain ., To address these issues and improve our understanding of human body weight regulation , mathematical and computational modeling has been attempted many times over the past several decades 3–19 ., Here we show how models of body composition and mass change can be understood and analyzed within the realm of dynamical systems theory and can be classified according to their geometric structure in the two dimensional phase plane ., We begin by considering a general class of macronutrient flux balance equations and progressively introduce assumptions that constrain the model dynamics ., We show that two compartment models of fat and lean masses can be categorized into two generic classes ., In the first class , there is a unique body composition and mass ( i . e . a stable fixed point ) that is specified by the diet and energy expenditure ., In the second class , there is a continuous curve of fixed points ( i . e . an invariant manifold ) with an infinite number of possible body compositions and masses at steady state for the same diet and energy expenditure rate ., We show that almost all of the models in the literature are in the second class ., Surprisingly , the existing data are insufficient to determine which of the two classes pertains to humans ., For models with an invariant manifold , we show that an equivalent one dimensional equation for body composition change can be derived ., We give numerical examples and discuss possible experimental approaches that may distinguish between the classes ., The human body obeys the law of energy conservation 20 , which can be expressed as ( 1 ) where ΔU is the change in stored energy in the body , ΔQ is a change in energy input or intake , and ΔW is a change in energy output or expenditure ., The intake is provided by the energy content of the food consumed ., Combustion of dietary macronutrients yields chemical energy and Hesss law states that the energy released is the same regardless of whether the process takes place inside a bomb calorimeter or via the complex process of oxidative phosphorylation in the mitochondria ., Thus , the energy released from oxidation of food in the body can be precisely measured in the laboratory ., However , there is an important caveat ., Not all macronutrients in food are completely absorbed by the body ., Furthermore , the dietary protein that is absorbed does not undergo complete combustion in the body , but rather produces urea and ammonia ., In accounting for these effects , we refer to the metabolizable energy content of dietary carbohydrate , fat , and protein , which is slightly less than the values obtained by bomb calorimetry ., The energy expenditure rate includes the work to maintain basic metabolic function ( resting metabolic rate ) , to digest , absorb and transport the nutrients in food ( thermic effect of feeding ) , to synthesize or break down tissue , and to perform physical activity , together with the heat generated ., The energy is stored in the form of fat as well as in lean body tissue such as glycogen and protein ., The body need not be in equilibrium for Equation 1 to hold ., While we are primarily concerned with adult weight change , Equation 1 is also valid for childhood growth ., In order to express a change of stored energy ΔU in terms of body mass M we must determine the energy content per unit body mass change , i . e . the energy density ρM ., We can then set ΔU\u200a=\u200aΔ ( ρMM ) ., To model the dynamics of body mass change , we divide Equation 1 by some interval of time and take the limit of infinitesimal change to obtain a one dimensional energy flux balance equation: ( 2 ) where I\u200a=\u200adQ/dt is the rate of metabolizable energy intake and E\u200a=\u200adW/dt is the rate of energy expenditure ., It is important to note that ρM is the energy density of body mass change , which need not be a constant but could be a function of body composition and time ., Thus , in order to use Equation 2 , the dynamics of ρM must also be established ., When the body changes mass , that change will be composed of water , protein , carbohydrates ( in the form of glycogen ) , fat , bone , and trace amounts of micronutrients , all having their own energy densities ., Hence , a means of determining the dynamics of ρM is to track the dynamics of the components ., The extracellular water and bone mineral mass have no metabolizable energy content and change little when body mass changes in adults under normal conditions 21 ., The change in intracellular water can be specified by changes in the tissue protein and glycogen ., Thus the main components contributing to the dynamics of ρM are the macronutrients - protein , carbohydrates , and fat , where we distinguish body fat ( e . g . free fatty acids and triglycerides ) from adipose tissue , which includes water and protein in addition to triglycerides ., We then represent Equation 2 in terms of macronutrient flux balance equations for body fat F , glycogen G , and protein P: ( 3 ) ( 4 ) ( 5 ) where ρF\u200a=\u200a39 . 5 MJ/kg , ρG\u200a=\u200a17 . 6 MJ/kg , ρP\u200a=\u200a19 . 7 MJ/kg are the energy densities 3 , IF , IC , IP are the intake rates , and fF , fC , 1−fF−fC are the fractions of the energy expenditure rate obtained from the combustion of fat , carbohydrates ( glycogen ) and protein respectively ., The fractions and energy expenditure rate are functions of body composition and intake rates ., They can be estimated from indirect calorimetry , which measures the oxygen consumed and carbon dioxide produced by a subject 22 ., The intake rates are determined by the macronutrient composition of the consumed food , and the efficiency of the conversion of the food into a utilizable form ., Transfer between compartments such as de novo lipogenesis where carbohydrates are converted to fat or gluconeogenesis where amino acids are converted into carbohydrates can be accounted for in the forms of fF and fC ., The sum of Equations 3 , 4 , and 5 recovers the energy flux balance Equation 2 , where the body mass M is the sum of the macronutrients F , G , P , with the associated intracellular water , and the inert mass that does not change such as the extracellular water , bones , and minerals , and ρM\u200a= ( ρFF+ρGG+ρPP ) /M ., The intake and energy expenditure rates are explicit functions of time with fast fluctuations on a time scale of hours to days 23 ., However , we are interested in the long-term dynamics over weeks , months and years ., Hence , to simplify the equations , we can use the method of averaging to remove the fast motion and derive a system of equations for the slow time dynamics ., We do this explicitly in the Methods section and show that the form of the averaged equations to lowest order are identical to Equations 3–5 except that the three components are to be interpreted as the slowly varying part and the intake and energy expenditure rates are moving time averages over a time scale of a day ., The three-compartment flux balance model was used by Hall 3 to numerically simulate data from the classic Minnesota human starvation experiment 21 ., In Halls model , the forms of the energy expenditure and fractions were chosen for physiological considerations ., For clamped food intake , the body composition approached a unique steady state ., The model also showed that apart from transient changes lasting only a few days , carbohydrate balance is precisely maintained as a result of the limited storage capacity for glycogen ., We will exploit this property to reduce the three dimensional system to an approximately equivalent two dimensional system where dynamical systems techniques can be employed to analyze the dynamics ., The various flux balance models can be analyzed using the methods of dynamical systems theory , which aims to understand dynamics in terms of the geometric structure of possible trajectories ( time courses of the body components ) ., If the models are smooth and continuous then the global dynamics can be inferred from the local dynamics of the model near fixed points ( i . e . where the time derivatives of the variables are zero ) ., To simplify the analysis , we consider the intake rates to be clamped to constant values or set to predetermined functions of time ., We do not consider the control and variation of food intake rate that may arise due to feedback from the body composition or from exogenous influences ., We focus only on what happens to the food once it is ingested , which is a problem independent of the control of intake ., We also assume that the averaged energy expenditure rate does not depend on time explicitly ., Hence , we do not account for the effects of development , aging or gradual changes in lifestyle , which could lead to an explicit slow time dependence of energy expenditure rate ., Thus , our ensuing analysis is mainly applicable to understanding the slow dynamics of body mass and composition for clamped food intake and physical activity over a time course of months to a few years ., Dynamics in two dimensions are particularly simple to analyze and can be easily visualized geometrically 34 , 35 ., The one dimensional models are a subclass of two dimensional dynamics ., Three dimensional dynamical systems are generally more difficult to analyze but Hall 3 found in simulations that the glycogen levels varied over a small interval and averaged to an approximate constant for time periods longer than a few days , implying that the slow dynamics could be effectively captured by a two dimensional model ., Reduction to fewer dimensions is an oft-used strategy in dynamical systems theory ., Hence , we focus our analysis on two dimensional dynamics ., In two dimensions , changes of body composition and mass are represented by trajectories in the L–F phase plane ., For IF and IL constant , the flux balance model is a two dimensional autonomous system of ordinary differential equations and trajectories will flow to attractors ., The only possible attractors are infinity , stable fixed points or stable limit cycles 34 , 35 ., We note that fixed points within the context of the model correspond to states of flux balance ., The two compartment macronutrient partition model is completely general in that all possible autonomous dynamics in the two dimensional phase plane are realizable ., Any two or one dimensional autonomous model of body composition change can be expressed in terms of the two dimensional macronutrient partition model ., Physical viability constrains L and F to be positive and finite ., For differentiable f and E , the possible trajectories for fixed intake rates are completely specified by the dynamics near fixed points of the system ., Geometrically , the fixed points are given by the intersections of the nullclines in the L–F plane , which are given by the solutions of IF−fE\u200a=\u200a0 and IL\u200a= ( 1−f ) E, =\u200a0 . Example nullclines and phase plane portraits of the macronutrient model are shown in Figure, 1 . If the nullclines intersect once then there will be a single fixed point and if it is stable then the steady state body composition and mass are uniquely determined ., Multiple intersections can yield multiple stable fixed points implying that body composition is not unique 4 ., If the nullclines are collinear then there can be an attracting one dimensional invariant manifold ( continuous curve of fixed points ) in the L–F plane ., In this case , there are an infinite number of possible body compositions for a fixed diet ., As we will show , the energy partition model implicitly assumes an invariant manifold ., If a single fixed point exists but is unstable then a stable limit cycle may exist around it ., The fixed point conditions of Equations 8 and 9 can be expressed in terms of the solutions of ( 26 ) ( 27 ) where I\u200a=\u200aIF+IL , and we have suppressed the functional dependence on intake rates ., These fixed point conditions correspond to a state of flux balance of the lean and fat components ., Equation 26 indicates a state of energy balance while Equation 27 indicates that the fraction of fat utilized must equal the fraction of fat in the diet ., Stability of a fixed point is determined by the dynamics of small perturbations of body composition away from the fixed point ., If the perturbed body composition returns to the original fixed point then the fixed point is deemed stable ., We give the stability conditions in Methods ., The functional dependence of E and f on F and L determine the existence and stability of fixed points ., As shown in Methods , an isolated stable fixed point is guaranteed if f is a monotonic increasing function of F and a monotonic decreasing function of L . If one of the fixed point conditions automatically satisfies the other , then instead of a fixed point there will be a continuous curve of fixed points or an invariant manifold ., For example , if the energy balance condition 26 automatically satisfies the fat fraction condition 27 , then there is an invariant manifold defined by I\u200a=\u200aE ( F , L ) ., The energy partition model has this property and thus has an invariant manifold rather than an isolated fixed point ., This can be seen by observing that for f given by Equation 15 , Equation 26 automatically satisfies condition 27 ., An attracting invariant manifold implies that the body can exist at any of the infinite number of body compositions specified by the curve I\u200a=\u200aE ( F , L ) for clamped intake and energy expenditure rates ( see Figure 1C ) ., Each of these infinite possible body compositions will result in a different body mass M\u200a=\u200aF+L ( except for the unlikely case that E is a function of the sum F+L ) ., The body composition is marginally stable along the direction of the invariant manifold ., This means that in flux balance , the body composition will remain at rest at any point on the invariant manifold ., A transient perturbation along the invariant manifold will simply cause the body composition to move to a new position on the invariant manifold ., The one dimensional models have a stable fixed point if the invariant manifold is attracting ., We also show in Methods that for multiple stable fixed points or a limit cycle to exist , f must be nonmonotonic in L and be finely tuned ., The required fine-tuning makes these latter two possibilities much less plausible than a single fixed point or an invariant manifold ., Data suggest that E is a monotonically increasing function of F and L 36 ., The dependence of f on F and L is not well established and the form of f depends on multiple interrelated factors ., In general , the sensitivity of various tissues to the changing hormonal milieu will have an overall effect on both the supply of macronutrients as well as the substrate preferences of various metabolically active tissues ., On the supply side , we know that free fatty acids derived from adipose tissue lipolysis increase with increasing body fat mass which thereby increase the daily fat oxidation fraction , f , as F increases 37 ., Furthermore , reduction of F with weight loss has been demonstrated to decrease f 38 ., Similarly , whole-body proteolysis and protein oxidation increases with lean body mass 39 , 40 implying that f should be a decreasing function of L . In further support of this relationship , body builders with significantly increased L have a decreased daily fat oxidation fraction versus control subjects with similar F 41 ., Thus a stable isolated fixed point is consistent with this set of data ., We have shown that all two dimensional autonomous models of body composition change generically fall into two classes - those with fixed points and those with invariant manifolds ., In the case of a stable fixed point , any temporary perturbation of body weight or composition will be corrected over time ( i . e . , for all things equal , the body will return to its original state ) ., An invariant manifold allows the possibility that a transient perturbation could lead to a permanent change of body composition and mass ., At first glance , these differing properties would appear to point to a simple way of distinguishing between the two classes ., However , the traditional means of inducing weight change namely diet or altering energy expenditure through aerobic exercise , turn out to be incapable of revealing the distinction ., For an invariant manifold , any change of intake or expenditure rate will only elicit movement along one of the prescribed F vs . L trajectories obeying Equation 12 , an example being Forbess law ( 14 ) ., As shown in Figure 2 , a change of intake or energy expenditure rate will change the position of the invariant manifold ., The body composition that is initially at one point on the invariant manifold will then flow to a new point on the perturbed invariant manifold along the trajectory prescribed by ( 12 ) ., If the intake rate or energy expenditure is then restored to the original value then the body composition will return along the same trajectory to the original steady state just as it would in a fixed point model ( see Figure 2 solid curves ) ., Only a perturbation that moves the body composition off of the fixed trajectory could distinguish between the two classes ., In the fixed point case ( Figure 2A dashed-dot curve ) , the body composition would go to the same steady state following the perturbation to body composition but for the invariant manifold case ( Figure 2B dashed-dot curve ) , it would go to another steady state ., Perturbations that move the body composition off the fixed trajectory can be done by altering body composition directly or by altering the fat utilization fraction f ., For example , body composition could be altered directly through liposuction and f could be altered by administering compounds such as growth hormone ., Resistance exercise may cause an increase in lean muscle tissue at the expense of fat ., Exogenous hormones , compounds , or infectious agents that change the propensity for fat versus carbohydrate oxidation ( for example , by increasing adipocyte proliferation and acting as a sink for fat that is not available for oxidation 42–44 ) , would also perturb the body composition off of a fixed F vs . L curve by altering f ., If the body composition returned to its original state after such a perturbation then there is a unique fixed point ., If it does not then there could be an invariant manifold although multiple fixed points are also possible ., We found an example of one clinical study that bears on the question of whether humans have a fixed point or an invariant manifold ., Biller et al . investigated changes of body composition pre- and post-growth hormone therapy in forty male subjects with growth hormone deficiency 45 ., Despite significant changes of body composition induced by 18 months of growth hormone administration , the subjects returned very closely to their original body composition 18 months following the removal of therapy ., However , there was a slight ( 2% ) but significant increase in their lean body mass compared with the original value ., Perhaps not enough time had elapsed for the lean mass to return to the original level ., Alternatively , the increased lean mass may possibly have been the result of increased bone mineral mass and extracellular fluid expansion , both of which are known effects of growth hormone , but were assumed to be constant in the body composition models ., Therefore , this clinical study provides some evidence in support of a fixed point , but it has not been repeated and the result was not conclusive ., Using data from the Minnesota experiment 21 and the underlying physiology , Hall 3 proposed a form for f that predicts a fixed point ., On the other hand , Hall , Bain , and Chow 10 showed that an invariant manifold model is consistent with existing data of longitudinal weight change but these experiments only altered weight through changes in caloric intake so this cannot rule out the possibility of a fixed point ., Thus it appears that existing data is insufficient to decide the issue ., We now consider some numerical examples using the macronutrient partition model in the form given by Equations 18 and 19 , with a p-ratio consistent with Forbess law ( 13 ) ( i . e . p\u200a=\u200a2/ ( 2+F ) , where F is in units of kg ) ., Consider two cases of the model ., If ψ\u200a=\u200a0 then the model has an invariant manifold and body composition moves along a fixed trajectory in the L–F plane ., If ψ is nonzero , then there can be an isolated fixed point ., We will show an example where if the intake energy is perturbed , the approach of the body composition to the steady state will be identical for both cases but if body composition is perturbed , the body will arrive at different steady states ., For every model with an invariant manifold , a model with a fixed point can be found such that trajectories in the L–F plane resulting from energy intake perturbations will be identical ., All that is required is that ψ in the fixed point model is chosen such that the solution of ψ ( F , L ) =\u200a0 defines the fixed trajectory of the invariant manifold model ., Using Forbess law ( 14 ) , we choose ψ\u200a=\u200a0 . 05 ( F−0 . 4 exp ( L/10 . 4 ) ) /F ., We then take a plausible energy expenditure rate of E\u200a=\u200a0 . 14L+0 . 05F+1 . 55 , where energy rate has units of MJ/day and mass has units of kg ., This expression is based on combining cross-sectional data 36 for resting energy with a contribution of physical activity of a fairly sedentary person 3 ., Previous models propose similar forms for the energy expenditure 5 , 7 , 13 , 18 ., Figure 3 shows the time dependence of body mass and the F vs . L trajectories of the two model examples given a reduction in energy intake rate from 12 MJ/day to 10 MJ/day starting at the same initial condition ., The time courses are identical for body composition and mass ., The mass first decreases linearly in time but then saturates to a new stable fixed point ., The dashed line represents the same intake rate reduction but with 10 kg of fat removed at day 100 ., For the invariant manifold model , the fat perturbation permanently alters the final body composition and body mass , whereas in the fixed point model it only has a transient effect ., In the fixed point model , the body composition can ultimately exist only at one point given by the intersection of the nullclines ( i . e . , solution of I\u200a=\u200aE and ψ\u200a=\u200a0 ) ., For the invariant manifold , the body composition can exist at any point on the I\u200a=\u200aE curve ( dotted line in Figure 2D ) ., Since a ψ can always be found so that a fixed point model and an invariant manifold model have identical time courses for body composition and mass , a perturbation in energy intake can never discriminate between the two possibilities ., The time constant to reach the new fixed point in the numerical simulations is very long ., This slow approach to steady state ( on the order of several years for humans ) has been pointed out many times previously 3 , 5 , 7 , 13 , 18 ., A long time constant will make experiments to distinguish between a fixed point and an invariant manifold difficult to conduct ., Experimentally reproducing this example would be demanding but if the time variation of the intake rates and physical activity levels were small compared to the induced change then the same result should arise qualitatively ., Additionally , the time constant depends on the form of the energy expenditure ., There is evidence that the dependence of energy expenditure on F and L for an individual is steeper than for the population due to an effect called adaptive thermogenesis 46 , thus making the time constant shorter ., In this paper we have shown that all possible two dimensional autonomous models for lean and fat mass are variants of the macronutrient partition model ., The models can be divided into two general classes - models with isolated fixed points ( most likely a single stable fixed point ) and models with an invariant manifold ., There is the possibility of more exotic behavior such as multi-stability and limit cycles but these require fine-tuning and thus are less plausible ., Surprisingly , experimentally determining if the body exhibits a fixed point or an invariant manifold is nontrivial ., Only perturbations of the body composition itself apart from dietary or energy expenditure interventions or alterations of the fraction of energy utilized as fat can discriminate between the two possibilities ., The distinction between the classes is not merely an academic concern since this has direct clinical implications for potential permanence of transient changes of body composition via such procedures as liposuction or temporary administration of therapeutic compounds ., Our analysis considers the slow dynamics of the body mass and composition where the fast time dependent hourly or daily fluctuations are averaged out for a clamped average food intake rate ., We also do not consider a slow explicit time dependence of the energy expenditure ., Such time dependence could arise during development , aging or gradual changes in lifestyle where activity levels differ ., Thus our analysis is best suited to modeling changes over time scales of months to a few years in adults ., We do not consider any feedback of body composition on food intake , which is an extremely important topic but beyond the scope of this paper ., Previous efforts to model body weight change have predominantly used energy partition models that implicitly contain an invariant manifold and thus body composition and mass are not fully specified by the diet ., If the body does have an invariant manifold then this fact puts a very strong constraint on the fat utilization fraction f ., Hall 3 considered the effects of carbohydrate intake on lipolysis and other physiological factors to conjecture a form of f that does not lead to an invariant manifold ., However , our analysis and numerical examples show that the body composition could have an invariant manifold but behave indistinguishably from having a fixed point ., Also , the decay to the fixed point could take a very long time , possibly as long as a decade giving the appearance of an invariant manifold ., Only experiments that perturb the fat or lean compartments independently can tell ., The three compartment macronutrient flux balance Equations 3–5 are a system of nonautonomous differential equations since the energy intake and expenditure are explicitly time dependent ., Food is ingested over discrete time intervals and physical activity will vary greatly within a day ., However , this fast time dependence can be viewed as oscillations or fluctuations on top of a slowly varying background ., It is this slower time dependence that governs long-term body mass and composition changes that we are interested in ., For example , if an individual had the exact same schedule with the same energy intake and expenditure each day , then averaged over a day , the body composition would be constant ., If the daily averaged intake and expenditure were to gradually change on longer time scales of say weeks or months then there would be a corresponding change in the body composition and mass ., Given that we are only interested in these slower changes , we remove the short time scale fluctuations by using the method of averaging to produce an autonomous system of averaged equations valid on longer time scales ., We do so by introducing a second “fast” time variable τ\u200a=\u200at/ε , where ε is a small parameter that is associated with the slow changes in body composition and let all time dependent quantities be a function of both t and τ ., For example , if t is measured in units of days and τ is measured in units of hours then ε∼1/24 ., Inserting into Equations 3–5 and using the chain rule yields ( 28 ) ( 29 ) ( 30 ) We then consider the three body compartments to have expansions of the form ( 31 ) ( 32 ) ( 33 ) where 〈F1〉\u200a=\u200a〈P1〉\u200a=\u200a〈G1〉\u200a=\u200a0 for a time average defined by and T represents an averaging time scale of a day ., The fast time dependence can be either periodic or stochastic ., The important thing is that the time average over the fast quantities is of order ε or higher ., We then expand the energy expenditure rate and expenditure fractions to first order in ε: ( 34 ) ( 35 ) where E0 ( t , τ ) ≡E ( F0 , G0 , P0 , t , τ ) +O ( ε2 ) and i∈{F , G , P} ., We assume that the expenditure fractions depend on time only through the body compartments ., Substituting these expansions into Equations 28–30 and taking lowest order in ε gives ( 36 ) ( 37 ) ( 38 ) Taking the moving time average of Equations 36–38 and requiring that 〈∂F1/∂τ〉 , 〈∂G1/∂τ〉 , and 〈∂P1/∂τ〉 are of order ε or higher leads to the averaged equations: ( 39 ) ( 40 ) ( 41 ) In the main text we only consider the slow time scale dynamics so we drop the superscript and bracket notation for simplicity ., Hence , the system ( 3–5 ) can be thought of as representing the lowest order time averaged macronutrient flux balance equations ., We note that in addition to the daily fluctuations of meals and physical activity , there can also be fluctuations in food intake from day to day 23 ., Our averaging scheme can be used to average over these fluctuations as well by extending the averaging time T . A difference in the choice of T will only result in a different interpretation of the averaged quantities ., The dynamics near a fixed point ( F0 , L0 ) are determined by expanding fE and ( 1−f ) E to linear order in δF\u200a=\u200aF−F0 and δL\u200a=\u200aL−L0 34 , 35 ., Assuming solutions of the form exp ( λt ) yields an eigenvalue problem with two eigenvalues given by where ( 42 ) and ( 43 ) A fixed point is stable if and only if Tr J<0 and det J>0 ., In the case of an invariant manifold , detJ\u200a=\u200a0 , so the eigenvalues are Tr J and 0 ., The zero eigenvalue reflects the marginal stability along the invariant manifold , which is an attractor if Tr J<0 ., An attracting invariant manifold implies a stable fixed point in the corresponding one dimensional model ., Unstable fixed points are either unstable nodes , saddle points or unstable spirals ., In the case of unstable spirals , a possibility is a limit cycle surrounding the spiral arising from a Hopf bifurcation , where Tr J\u200a=\u200a0 and det J>0 ., In this case , body composition and mass would oscillate even if the intake rates were held constant ., The frequency and amplitude of the oscillations may be estimated near a supercritical Hopf bifurcation by transforming the equations to normal form ., Stability of a fixed point puts constraints on the form of f ., Physiological considerations and data imply that ∂E/∂L>∂E/∂F>0 3 , 36 ., Thus we can set ∂E/∂F\u200a=\u200aδ∂E/∂L where δ <1 ( the derivatives are evaluated at the fixed point ) ., Then detJ>0 implies that ( 44 ) and Tr J<0 implies ( 45 ) where K\u200a=\u200aδf+γ ( 1−f ) ( ∂E/∂L ) /E>0 and

Introduction, Results, Discussion, Methods

An imbalance between energy intake and energy expenditure will lead to a change in body weight ( mass ) and body composition ( fat and lean masses ) ., A quantitative understanding of the processes involved , which currently remains lacking , will be useful in determining the etiology and treatment of obesity and other conditions resulting from prolonged energy imbalance ., Here , we show that a mathematical model of the macronutrient flux balances can capture the long-term dynamics of human weight change; all previous models are special cases of this model ., We show that the generic dynamic behavior of body composition for a clamped diet can be divided into two classes ., In the first class , the body composition and mass are determined uniquely ., In the second class , the body composition can exist at an infinite number of possible states ., Surprisingly , perturbations of dietary energy intake or energy expenditure can give identical responses in both model classes , and existing data are insufficient to distinguish between these two possibilities ., Nevertheless , this distinction has important implications for the efficacy of clinical interventions that alter body composition and mass .

Understanding the dynamics of human body weight change has important consequences for conditions such as obesity , starvation , and wasting syndromes ., Changes of body weight are known to result from imbalances between the energy derived from food and the energy expended to maintain life and perform physical work ., However , quantifying this relationship has proved difficult , in part because the body is composed of multiple components and weight change results from alterations of body composition ( i . e . , fat versus lean mass ) ., Here , we show that mathematical modeling can provide a general description of how body weight will change over time by tracking the flux balances of the macronutrients fat , protein , and carbohydrates ., For a fixed food intake rate and physical activity level , the body weight and composition will approach steady state ., However , the steady state can correspond to a unique body weight or a continuum of body weights that are all consistent with the same food intake and energy expenditure rates ., Interestingly , existing experimental data on human body weight dynamics cannot distinguish between these two possibilities ., We propose experiments that could resolve this issue and use computer simulations to demonstrate how such experiments could be performed .

mathematics, biophysics/theory and simulation, diabetes and endocrinology/obesity, nutrition/obesity, computational biology/metabolic networks, physiology/integrative physiology, diabetes and endocrinology/type 2 diabetes

journal.pntd.0003952

Role of sph2 Gene Regulation in Hemolytic and Sphingomyelinase Activities Produced by Leptospira interrogans

Leptospirosis is a neglected zoonotic disease that afflicts humans and animals 1–3 ., Although the disease occurs worldwide , it is observed most frequently in tropical countries , where conditions for environmental survival and transmission are most favorable 4 ., The causative organisms are spirochetes belonging to the genus Leptospira , which comprise pathogenic and nonpathogenic species ., The bacteria are able to enter the mammalian host through skin abrasions and mucous membranes ., Following entry , the spirochetes disseminate via the bloodstream to many organs 2 ., Patients exhibit a wide range of signs and symptoms from undifferentiated fever to liver dysfunction , renal insufficiency , hemolytic anemia , bleeding , and respiratory failure 5 ., Proposed mechanisms of pathogenesis include vascular damage , which is frequently manifested as hemorrhage in the lungs and other organs on post-mortem examination 6 ., Additionally , reproductive failure is observed in livestock animals with leptospirosis 7 ., Leptospira bacteria are spread and maintained in the environment by rats and other reservoir hosts , whose renal tubules are persistently colonized by the spirochete ., Humans and animals can be infected by direct or indirect contact with contaminated urine or other tissue ., Many Leptospira strains secrete hemolysins and sphingomyelinases during in vitro growth 8–11 ., Strains of serovar Pomona secrete the highest levels of hemolytic activity 9 and cause hemolytic anemia in ruminants 12–14 ., The single peak of hemolytic activity detected by isoelectric focusing of the culture supernatant fluid of one Pomona strain co-purified with sphingomyelinase C activity , suggesting that hemolysis is due to sphingomyelinase 15 ., It is not known which gene or genes encode the secreted hemolytic and sphingomyelinase activities ., Hemolytic activity has been reported for purified recombinant forms of the L . interrogans protein HlyX , HlpA , TlyA , and the sphingomyelinase-like paralogs Sph1 , Sph2 , Sph3 , and SphH 16–19 ., Among the sphingomyelinase paralogs , sphingomyelinase C activity has been demonstrated only for Sph2 , which cleaves sphingomyelin to ceramide and phosphocholine 20 ., Sphingomyelinase activity has also been reported for Sph1 , Sph3 , and Sph4 21 , although these proteins lack some of the catalytic amino acid residues required for enzymatic activity 22 ., The genes encoding the sphingomyelinase-like proteins are missing from the nonpathogen Leptospira biflexa ., Similarly , sphingomyelinase activity has been detected only in pathogenic strains of Leptospira 10 ., These observations suggest that sphingomyelinase-like proteins function during infection 23 ., Hemolysins are assumed to assist microbial pathogens in acquiring iron by lysing host erythrocytes during infection 24 ., Heme can be used by L . interrogans as an iron source for growth 25 ., They express the hemin-binding protein HbpA , which may participate in transporting heme into the cell , and a heme oxygenase , which is required for heme utilization and for virulence in the hamster model of leptospirosis 26–28 ., Iron depletion resulted in increased levels of a sphingomyelinase-like protein , which is consistent with the notion that hemolysins are involved in iron acquisition by L . interrogans 29 ., Nevertheless , experimental evidence indicates that leptospiral hemolysins are capable of pathogenic processes that do not involve red blood cells ., For example , recombinant Sph2 protein is cytotoxic towards equine endothelial cells , mouse lymphocytes and macrophages , and a human liver cell line 18 , 30 ., Additionally , L . biflexa ectopically expressing sph2 captures plasma fibronectin in vitro 31 ., Sph2 has not been detected in appreciable amounts in cell lysates of L . interrogans grown in the standard culture medium EMJH except in strains of serovar Pomona 32 ., However , sph2 expression in the Fiocruz L1-130 strain is dramatically upregulated by environmental conditions that occur during infection ., In our previous whole-genome microarray study examining the changes in transcript levels to an increase in osmolarity from low osmolarity found in EMJH medium to physiologic levels found in the mammalian host , sph2 was the second most strongly upregulated gene in the Fiocruz L1-130 strain 33 ., Levels of cellular and extracellular Sph2 in the Fiocruz L1-130 strain were also upregulated upon addition of sodium chloride or sucrose to physiological levels of osmolarity 34 ., Anti-Sph2 antibody is detected in patients with leptospirosis , suggesting that sph2 is expressed during infection 32 ., In a recent RNA-seq experiment , sph2 transcript levels were higher in L . interrogans bacteria growing in dialysis membrane chambers in rats than in those growing in in vitro culture 35 ., These observations suggest that upregulation of sph2 is involved in L . interrogans infection ., The enzymatic domain of Sph2 is flanked by unusual sequences missing from bacterial sphingomyelinases whose crystal structures have been determined 20 ., Three or four ~20 residue imperfect tandem repeats are present to the amino terminus of Sph2 , and the carboxy terminus comprises a unique segment of ~186 amino acid residues 18 , 22 ., The enzymatic domain may not be sufficient for hemolytic activity of Sph2 ., A sphingomyelinase with a similar C-terminal extension is found in the sphingomyelinase protein of a fish isolate of Pseudomonas species 36 ., Removal of the C-terminal domain eliminates its hemolytic activity despite its enzymatic domain being left intact 36 ., L . interrogans releases Sph2 in smaller forms whose apparent molecular masses are ~13 and ~21 kDa less than that of the cell-associated form of Sph2 34 ., These observations raise the question of whether L . interrogans secretes Sph2 in an active form because the hemolytic activity of Sph2 was demonstrated with full-length recombinant protein instead of the shortened forms detected in culture supernatant fluids 16 , 18 , 34 ., One approach to establish that Sph2 contributes to the secreted hemolytic activity is to demonstrate inhibition of hemolysis when Sph2 antibody is added to the spent culture medium ., However , both anti-Sph2 antiserum and normal serum inhibited the hemolytic activity secreted by a Pomona strain of L . interrogans 32 ., In this study , we extend our earlier findings of osmotic induction of sph2 expression with the Fiocruz L1-130 strain to show that the sph2 genes of four additional strains of L . interrogans are also regulated by sodium chloride , including a Pomona strain that displays high levels of basal sph2 expression ., In addition , we demonstrate that the levels of secreted hemolytic and sphingomyelinase activities are regulated by osmolarity ., In our attempts to maximize sph2 expression , we also found that rat serum increased Sph2 production further at physiologic osmolarity , although the presence of serum inhibited hemolytic activity in liquid-phase assays ., Finally , we present genetic evidence that sph2 contributes to the hemolytic and sphingomyelinase activities secreted from L . interrogans ., The leptospiral strains Leptospira interrogans serovars Manilae ( strain L495 ) , Pomona subtype kennewicki ( strain LC82-25 ) , and Lai ( strains 56601 and L391 ) were grown at 30°C in EMJH medium assembled with Probumin BSA ( Vaccine Grade , lot 103 ) as described previously 37 or purchased as Probumin Vaccine Grade Solution ( lot 103 ) from EMD Millipore ( Temecula , California , USA ) ., The Pomona LC82-25 and Lai 56601 strains were obtained from Rich Zuerner ( National Animal Disease Center , Ames , Iowa , USA ) and Mathieu Picardeau ( Institut Pasteur , Paris , France ) , respectively ., Leptospira interrogans serovar Copenhageni strain Fiocruz L1-130 was grown at 30°C in EMJH medium supplemented with 1% heat-inactivated rabbit serum ( Rockland; Gilbertsville , Pennsylvania , USA ) ., The L391 strain and an L391 mutant with a kanr-marked Himar1 transposon inserted in the sph2 gene were provided by Gerald Murray 38 ., The sph2 mutant was maintained in EMJH containing 40 μg/mL kanamycin ., Culture densities were determined by directly counting the number of Leptospira using an AxioLab A1 microscope with a darkfield condenser ( Zeiss Microscopy Division; Pleasanton , California , USA ) ., To examine the influence of environmental conditions on sph2 expression , L . interrogans cells were grown to a cell density of 0 . 6–1 x 108 cells/mL and then supplemented with 120 mM NaCl , 10% rat serum ( Rockland Immunochemicals , Limerick , Pennsylvania , USA ) , or a mixture of nine parts of EMJH supplemented with 120 mM NaCl and one part of rat serum and allowed to incubate for 4 h ., Cultures were harvested by centrifugation at 9 , 200 x g for 20 min at 4°C in a Sorvall high-speed centrifuge ( Thermo Scientific; Marietta , Ohio , USA ) , and the spent medium was collected for storage ., Cells were washed once with cold PBS-5 mM MgCl2 ., Sodium chloride was added to the spent medium obtained from the EMJH cultures to equalize the osmolarity across all samples ., Cell pellets and spent growth medium were stored in -80°C ., For Western blots , the proteins were fractionated on 10% PAGEr Gold precast Tris-Glycine gels ( Lonza; USA ) ., Dual Color Precision Plus Protein Standards from Bio-Rad ( catalog # 161–0374 ) ( Hercules , California , USA ) were included in adjacent lanes ., Proteins were transferred from gels on to PVDF Immobilon-P transfer membrane ( EMD Millipore; USA ) using Trans-blot SD Semi-Dry transfer Cell system ( Bio-Rad ) ( 10V for 45 min ) ., The membranes were incubated in blocking solution ( 5% skim milk in PBS-0 . 05% Tween 20 PBS-T ) for 30 min and then incubated with rabbit anti-Sph2 and anti-LipL41 antisera 34 , 39 at dilutions of 1:1 , 000 and 1:10 , 000 , respectively , for 30 min and washed three times ( 5 min each ) with PBS-T ., The membrane was then incubated with donkey anti-rabbit antibody ( 1:5 , 000 ) ( Amersham Biosciences; Piscataway , New Jersey , USA ) or protein A-horseradish peroxidase conjugate ( 1:3 , 000 ) ( Amersham Biosciences ) in blocking solution for 30 min and again washed three times with PBS-T ., The membranes were developed with the ECL Western blot detection system ( Thermo Scientific ) , and the bands were visualized with Hyperfilm ( Amersham Biosciences ) ., Immunoprecipitation of Sph2 from the spent culture medium was performed as described 37 , except the culture supernatant fluid was preadsorbed with 25 μL of EZview Red Protein A Affinity gel ( Sigma-Aldrich; St . Louis , Missouri , USA ) for 60 min at 4°C prior to immunoprecipitation with anti-Sph2 antiserum 32 ., The resuspension volumes of the immunoprecipitates with Laemmli sample buffer were adjusted according to the cell densities of the cultures ., Restriction enzymes and T4 DNA ligase were obtained from New England Biolabs ( Beverly , Massachusetts , USA ) ., PCR amplifications were conducted with Phusion DNA polymerase ( Thermo Scientific ) ., L . interrogans sequences cloned into plasmid vectors were verified by Sanger sequencing ( Laragen; Culver City , California , USA ) ., The sph1 , sph3 , and sph4 sequences were amplified by PCR with the primer pairs lic12632 ( Nd ) -3F and lic12632 ( Xh ) -4R , lic13198 ( Nd ) -3F and lic13198 ( Xh ) -4R , and lic11040 ( Nd ) -3F and lic11040 ( Xh ) -4R , respectively ( Table 1 ) ., Forward and reverse primers included NdeI and XhoI restriction sites near the 5 ends ., Genomic DNA from L . interrogans Fiocruz L1-130 served as template ., Each amplicon was digested with NdeI and XhoI and ligated to pET20b+ ( EMD Biosciences; La Jolla , California USA ) that had been digested with the same enzymes ., The sph1 , sph3 , and sph4 expression plasmids were named pRAT547 , pRAT548 , and pRAT549 , respectively ., The sph2 expression plasmid pTOPO-Sph2 ( 27–623 ) was described previously 34 ., To construct the sph2 complementation plasmid , the oligonucleotides lic12631 ( Kp ) -17F and lic12631 ( Xh ) -18R ( Table 1 ) were used to PCR amplify sph2 along with its flanking regions using Fiocruz L1-130 genomic DNA as template ., The amplicon included sequences 327 bp upstream and 130 bp downstream of the start and stop codons , respectively ., Restriction sites for KpnI and XhoI were included near the 5 ends of the oligonucleotides ., Following digestion of the amplicon with KpnI and XhoI , the sph2-containing fragment was inserted into the similarly-digested plasmid pRAT575 40 to create pRAT613 for the current and future studies ., The KpnI-XhoI fragment containing sph2 was subsequently transferred to the mobilizable plasmid pAL614 , which contains KpnI and XhoI sites between the ends of a Himar1 element that also harbored a gene encoding resistance to spectinomycin 41 ., The resulting sph2 plasmid was designated pRAT708 ., Frozen competent E . coli BLR ( DE3 ) /pLysS ( EMD Biosciences ) was transformed with the plasmids pRAT547 ( sph1 ) , pRAT548 ( sph3 ) , pRAT549 ( sph4 ) , and pTOPO-Sph2 ( 27–623 ) ( sph2 ) , and transformants were selected on LB plates containing 100 μg/mL carbenicillin ., Colonies were inoculated into 10 mL LB containing carbenicillin for overnight growth at 37°C ., 5 mL of each overnight culture was then placed into 200 mL LB with carbenicillin and incubated at 37°C ., When the OD at 600 nm reached 0 . 6 , 1 M IPTG was added to a final concentration of 0 . 5 mM ., For expression of recombinant Sph1 , Sph3 , and Sph4 , the cultures were incubated for 2 hrs at 37°C ., The bacteria were collected by centrifugation , and the cell pellets were stored at -80°C ., For expression of recombinant Sph2 , the culture was incubated overnight at 16°C and harvested by centrifugation ., To lyse the bacteria , cell pellets were suspended in 5 mL ( for Sph1 , Sph3 , and Sph4 ) or 10 mL ( Sph2 ) of BugBuster ( EMD Biosciences ) containing 20 unit/mL DNase I ( Thermo Scientific ) and 0 . 25 mM phenylmethylsulfonyl fluoride ( Sigma-Aldrich ) , and the suspension was swirled for 20 min at room temperature ., The lysates were poured into Corex tubes and subjected to centrifugation at 15 , 000 x g for 20 min at 4°C in a Sorvall RC-5B Superspeed centrifuge ., To wash the Sph1 , Sph3 , and Sph4 inclusion bodies , the pellets were suspended in 10 mL of a 10-fold dilution of BugBuster ., Following centrifugation at 15 , 000 x g for 20 min at 4°C , the pellets were suspended in 5 mL of 100 mM NaH2PO4 , 10 mM Tris-HCl , and 8M urea , pH 8 . 0 ( Buffer B ) and swirled for 15 min at room temperature to solubilize the inclusion bodies ., The material was centrifuged at 10 , 000 x g at room temperature to pellet the cell debris ., 1 mL of 50% Ni2+-NTA slurry ( Qiagen ) was added to the solubilized inclusion bodies , and the suspension was mixed for 60 min at room temperature ., The material was then poured into a 5-mL polypropylene column ( Qiagen ) for purification of the recombinant protein ., The column was washed with 4 mL Buffer B that was adjusted to pH 6 . 3 and then with 2 mL Buffer B at pH 5 . 9 ., The recombinant protein was eluted with Buffer B adjusted to pH 4 . 5 , and 0 . 5 mL fractions were collected ., The fractions were neutralized by adding 55 . 5 μl of 1 M Tris-HCl , pH 8 . 0 , to each ., To purify soluble Sph2 , 250 μl of 0 . 5 M imidazole and 2 mL of the 50% Ni2+-NTA slurry ( Qiagen ) were added to 10 mL of lysate ., The mixture was mixed for 60 min at 4°C and then poured into a 5 mL polypropylene column ., After collecting the flow through , the column was washed at 4°C with a total of 30 mL of 20 mM imidazole in Wash Buffer ( 50 mM NaH2PO4 , 0 . 5 M NaCl , pH 8 . 0 ) ., The protein was eluted at 4°C with 0 . 5 M imidazole in Wash Buffer , and 1 mL fractions were collected ., Protein concentrations of all purified Sph proteins were determined using the Pierce BCA Protein Assay kit ( Thermo Scientific ) with BSA standards ., For preparation of RNA , 25 mL of culture was transferred into an Erlenmeyer flask , quickly chilled by swirling for 7 s in a dry ice-ethanol bath , and centrifuged at 9 , 200 x g for 20 min at 4°C ., RNA was isolated from leptospires as follows: 1 mL Trizol ( Life Technologies; Grand Island , New York , USA ) was added to the cell pellet , resuspended thoroughly by pipetting followed by incubation at room temperature for 5 min . 200 μL of chloroform ( Sigma-Aldrich ) was added , mixed vigorously for 15 s followed by incubation at room temperature for 3 min ., The tubes were centrifuged at 12 , 000 x g for 15 min at 4°C , and the upper aqueous layer was pipetted into fresh tubes ., RNA was precipitated by the addition of equal volumes of isopropanol , mixed and incubated for 10 min and subjected to centrifugation at 16 , 000 x g for 15 min at 4°C ., The pellet was washed with 1 mL of 75% ethanol , air dried and dissolved in 84 μL of RNase free water ., The dissolved RNA was protected from degradation by addition of 1μL of 20 U/μL SUPERase In RNase inhibitor ( Life Technologies ) ., DNA was digested by addition of 10 μL of 10X Turbo DNase buffer and 5 μL of Turbo DNase followed by incubation in 37°C water bath for 2 h ., The RNA samples were subjected to clean-up using the RNeasy Mini kit ( Qiagen; USA ) as described in the manufacturer’s instructions ., The concentration of the RNA was determined using the NanoVue spectrophotometer ( GE Healthcare; Piscataway , New Jersey , USA ) and the quality of RNA was assessed by examining the A260/A280 and A260/A230 ratios and by electrophoresis of 200 ng of RNA onto a 1 . 2% agarose gel ., cDNA was synthesized using the iScript cDNA Synthesis Kit ( Bio-Rad ) following the manufacturer’s instructions ., The reaction was set up in a microcentrifuge tube in a reaction volume of 20 μL containing 4 μL of 5X iScript Reverse Transcription Supermix , 1 μg RNA , and nuclease-free water ., cDNA synthesis was carried out in a Master Cycler gradient thermal cycler ( Eppendorf; Hauppauge , New York , USA ) with priming at 25°C for 5 min followed by reverse transcription at 42°C for 30 min and enzyme inactivation at 85°C for 5 min ., A separate reaction without reverse transcriptase was included as a negative control ., The synthesized cDNA was diluted with RNase free water ( Ambion ) to obtain a working concentration of 10 ng/μL ., The quantification of cDNA was done using real-time PCR using iQ5 SYBR Green Supermix ( Bio-Rad ) and the iQ5 Multicolor Real-time PCR Detection System ( Bio-Rad ) ., Each reaction contained cDNA derived from 50 ng of RNA , 10 pmol of each forward and reverse primers , and 12 . 5 μL of 2X iQ5 SYBR Green supermix in a total volume of 25 μL ., The sph1- , sph2- and lipL41-specific primers ( Table 1 ) were designed using Primer Premier ( Premier Biosoft; Palo Alto , California , USA ) ., The amplification protocol consisted of an initial denaturation for 15 min ( 95°C ) followed by 40 cycles of amplification ( 15 s at 95°C , 30 s at 58°C , 30 s at 72°C ) and a final extension of 2 min at 72°C ., Standard curves were constructed by 5-fold serial dilutions ( 50 ng to 0 . 08 ng ) of cDNA as template in triplicate ., Amplification efficiency was evaluated from the standard curves by determining the E value; results in the range of 90% to 110% were considered to be acceptable ., The CT values were normalized to that of lipL41 , and expression levels were calculated using 2ΔΔCT method 42 ., The relative fold change was calculated by comparison with the EMJH control ., The assay was performed using three biological replicates ., To assess hemolytic activity qualitatively , 5 μL of culture supernatant fluid was spotted onto BBL Trypticase Soy Agar with 5% sheep red blood cells ( TSA II ) ( Becton Dickinson; Sparks , Maryland , USA ) ., Because magnesium is necessary for sphingomyelinase activity , 100 mM MgCl2 was added to the culture supernatant to a final concentration of 10 mM prior to spotting the plates ., 0 . 05 units of Bacillus cereus sphingomyelinase ( Sigma-Aldrich ) was spotted as a positive control ., The plates were incubated for 20 hours at 37°C and then at 4°C for at least three days ., The liquid-phase hemolysis assay was set up in a 96 well round-bottomed microtiter plate as reported earlier 20 with some modifications ., Briefly , sheep erythrocytes were procured commercially from Quad Five ( Ryegate , Montana , USA ) as a 50% ( v/v ) suspension in Alseverss solution ., Erythrocytes were collected by centrifugation at 400 x g for 10 min at 8°C , washed three times with cold PBS ( pH 7 . 4 ) , and resuspended in cold PBS to a final concentration of 10% ., Each reaction mixture ( 200 μL ) contained 10 mM MgCl2 in PBS , with 40 μL 10% washed sheep erythrocytes and 100 μL of culture supernatant fluid ., For background measurements , EMJH with 120 mM sodium chloride replaced the spent medium ., Three biological replicates were examined ., The hemolysis reaction proceeded at 37°C for 90 min followed by incubation at 4°C for 30 min ., The plate was centrifuged at 800 x g in an Eppendorf 5430 centrifuge to pellet intact erythrocytes , and the supernatant fluid from each well was transferred to a flat-bottom 96-well ELISA plate ., The plate was read in an iMark Microplate Absorbance Reader ( Bio-Rad ) at 415 nm ., Percent hemolysis was calculated by multiplying the PBS background-subtracted absorbance of the sample by 100 and dividing by the absorbance of the osmotically-lysed erythrocytes ., Sphingomyelinase activity was measured by a coupled assay using the Amplex Red Sphingomyelinase assay kit ( Molecular Probes , Invitrogen , USA ) as described in the manufacturer’s instructions ., The reactions were set up in 96-well special optics flat clear bottom black polystyrene Microplate ( Corning , product # 3720 ) ., The reaction mixture ( 200 μL ) contained 100 μL test sample and 100 μL of 100 μM Amplex red reagent ( containing 2 U/mL horseradish peroxidase , 0 . 2 U/mL choline oxidase , 8 U/mL alkaline phosphatase , and 0 . 5 mM sphingomyelin ) ( Life Technologies ) ., The reaction proceeded for 90 min at 37°C ., The fluorescence was measured at excitation and emission wavelengths of 530 nm and 590 nm respectively using the Synergy2 Multi-Mode Microplate Reader ( BioTek; Winooski , Vermont , USA ) ., The background fluorescence was corrected by subtracting the negative control , which contained the reaction buffer without sphingomyelinase ., Standard curves were generated with B . cereus sphingomyelinase , and the measurements were fit by nonlinear regression to a hyperbola ( one-site binding ) model with GraphPad Prism , version 5 . 04 ( GraphPad Software; La Jolla , California , USA ) ., Experiments were performed with three biological replicates ., For complementation of the sph2 mutant , the mobilizable sph2 plasmid pRAT708 was transformed into the diaminopimelic acid auxotroph E . coli β2163 , which expresses the RP4 conjugation machinery 43 ., The plasmid was transferred into the L . interrogans sph2 mutant by conjugation as described 44 , 45 ., Transconjugants were selected on EMJH agar plates containing 40 μg/mL kanamycin and 40 μg/mL spectinomycin ., After two weeks of incubation at 30°C , colonies were inoculated into EMJH liquid medium ., The insertion site of the sph2-containing transposon was identified by nested PCR using primers TnK1 and Deg1 for the first PCR reaction and primers TnkN1 and Tag for the second 45 ., All values for hemolytic and sphingomyelinase activities were log transformed prior to statistical analysis to achieve similar variances across all groups ., One-way ANOVA was conducted with R version 3 . 0 . 3 46 ., The Tukey post test was used for group comparisons ., Our earlier work demonstrated that sph2 gene expression in L . interrogans strain Fiocruz L1-130 ( serovar Copenhageni ) increases substantially when incubated in EMJH supplemented with sodium chloride to attain physiological osmolarity 33 , 34 ., In the current study , we examined sph2 regulation in three additional strains of L . interrogans , L495 ( serovar Manilae ) , 56601 ( serovar Lai ) , and LC82-25 ( serovar Pomona subtype kennewicki ) ., The Pomona strain was included because members of the serovar produce larger amounts of hemolysin than other serovars 9 ., Immunoblots were performed to determine the effects of NaCl and serum on Sph2 protein levels ., As shown in Fig 1 ( lane 1 ) , Sph2 was not detected in the Fiocruz L1-130 34 , Manilae L495 , or Lai 56601 strain when grown in EMJH ., The addition of 120 mM sodium chloride to EMJH increased Sph2 production to detectable levels in these three strains ( lane 2 ) ., On the other hand , Sph2 was detected easily in the Pomona LC82-25 strain growing in EMJH , and Sph2 levels increased further when the medium was supplemented with 120 mM sodium chloride ( Fig 1C , lanes 1 and 2 ) ., The presence of rat serum to the culture medium had an effect on Sph2 expression independent of osmolarity; Sph2 levels were noticeably higher than that when only sodium chloride was present , despite the osmolarity of the culture medium being similar ( Fig 1 , lanes 2 and 4 ) ., Relative to LipL41 levels , Sph2 levels were substantially higher in the Pomona LC82-25 strain than in the Manilae L495 strain under all conditions ( Fig 1 ) ., The apparent molecular masses of Sph2 was higher than the calculated masses of 71 . 0 , 71 . 0 , 70 . 4 , and 73 . 5 kDa in the Copenhageni , Lai , Manilae , and Pomona strains , respectively ., The apparent molecular mass of Sph2 in the LC82-25 strain was ~13 kDa higher than those of the other strains ( Fig 1 ) ., As noted in our earlier study and by others , anti-Sph2 antibodies also recognized SphH in these samples ( Fig 1 ) 32 , 34 ., Sph2 breakdown products were visible by immunoblot after growth of the serovar Pomona strain in 120 mM NaCl with and without serum ( Fig 1C ) ., To exclude the possibility that some of the bands originated from the other sphingomyelinase-like proteins , we examined the cross-reactivity of our anti-Sph2 antiserum with purified recombinant forms of Sph1 , Sph3 , and Sph4 ., Equal masses of Sph1 , Sph2 , Sph3 , and Sph4 were subjected to SDS-polyacrylamide gel electrophoresis ( Fig 2A ) and immunoblot analysis with the anti-Sph2 antiserum ( Fig 2B ) ., As observed for the native form of Sph2 , the recombinant form ran more slowly than expected from its calculated molecular mass , whereas Sph1 , Sph3 , and Sph4 migrated as expected ( Fig 2A ) ., The immunoblot shows that the anti-Sph2 antibody does not cross-react with Sph3 or Sph4 and reacts poorly with Sph1 ( Fig 2B ) ., These results indicate that the bands observed below the Sph2 species in the Pomona LC82-25 lysate are Sph2 breakdown products , although we cannot rule out the possibility that the bands running below SphH arose from SphH degradation ., Immunoprecipitation of Sph2 was performed with the spent growth medium from cultures of the Manilae L495 and Pomona LC82-25 strains to assess the effects of salt and serum on the levels of extracellular Sph2 ., We did not detect Sph2 with the L495 strain grown in EMJH ., When EMJH was supplemented with sodium chloride , with or without rat serum , Sph2 released from the L495 strain was detected in two smaller forms ( Fig 3 , lanes 3 and 4 ) , as shown in our earlier study with Fiocruz L1-130 30 ., The Pomona LC82-25 strain exhibited similar regulation of extracellular Sph2 levels by sodium chloride and rat serum , except the amount of Sph2 detected was much greater than that in the L495 strain ( Fig 3 , lanes 2–4 vs . 5–7 ) ., The two Sph2 bands in the serovar Pomona immunoprecipitates were smaller than the species detected in the corresponding cell lysate ( Fig 3 , lanes 5–8 ) ., The apparent molecular masses of the extracellular forms of Sph2 were greater in the LC82-25 strain than those of the L495 strain , as was the case for the cellular form ., We compared sph2 transcript levels in the serovar Pomona strain LC82-85 with those of the serovar Manilae strain L495 in response to environmental conditions ., Quantitative RT-PCR measurements revealed that when grown in standard EMJH medium sph2 transcript levels were 21 fold higher in the Pomona strain than in the Manilae strain ( Fig 4 ) ., In both strains , the addition of 120 mM sodium chloride to the cultures increased sph2 transcript levels by over 100 fold ( Fig 4 ) ., At the higher osmolarity , levels of sph2 transcript remained nearly 20 fold higher in the Pomona strain than in the Manilae strain ., Addition of 10% rat serum increased sph2 transcript levels by four fold ., The ~20-fold difference in sph2 transcript levels between the two strains was maintained when both 10% rat serum and the additional 120 mM sodium chloride were present in the culture medium ., The levels of hemolytic and sphingomyelinase activities in the spent growth medium of the Pomona LC82-25 and Manilae L495 strains grown in EMJH and in EMJH supplemented with 120 mM sodium chloride were determined ., To assess hemolytic activity qualitatively , the culture supernatant fluid was spotted onto sheep erythrocyte agar plates ., The culture medium , which was adjusted to physiological osmolarity with sodium chloride , did not cause detectable lysis of the erythrocytes ( Fig 5A ) ., On the other hand , the spent growth medium from both strains , including those obtained from cultures containing rat serum , caused partial clearance of erythrocytes on the plate ( Fig 5A ) ., The amount of hemolytic activity in the spent growth medium was quantified with a liquid-phase assay ., In EMJH , the Pomona strain showed greater hemolytic activity for sheep erythrocytes than the Manilae strain ( Fig 5B ) ., Both strains released higher levels of hemolytic activity when EMJH was supplemented with 120 mM sodium chloride ., In the process of developing the hemolysis assay , we discovered that hemolytic activity in spent medium from the Pomona strain incubated in EMJH with 120 mM sodium chloride was at least 92% lower when 10% rat serum was also present , even though it was detected in sheep erythrocyte plates ( Fig 5A ) ., The hemolysis results with the strains grown in EMJH and EMJH with 120 mM sodium chloride were reflected in an assay for sphingomyelin hydrolase activity ( Fig 6 ) ., The results from the Western blots , hemolysis assay , and enzymatic assay reveal a correlation between sph2 expression and extracellular hemolysis and sphingomyelinase activities of L . interrogans ., To examine the contribution of sph2 to secreted hemolytic and sphingomyelinase activities by a genetic approach , we were provided with the highly-passaged L . interrogans strain L391 ( serovar Lai ) and an isogenic sph2 mutant , which was generated by transposon insertional mutagenesis 38 ., The transposon is located within sph2 near the end of the segment encoding the enzymatic domain of the protein ( Fig 7A ) ., The truncated form predicted to be generated by the mutant allele lacks the second catalytic histidine residue in the enzymatic domain ., Substitution of this histidine with alanine in Bacillus cereus sphingomyelinase completely abolishes its enzymatic activity 47 ., Therefore , the truncated form expressed from the mutant sph2 allele is unlikely to possess residual enzymatic function ., Immunoblot analysis with the sph2 mutant and its wild-type parent confirmed that the mutant was unable to produce the full-length form of Sph2 following incubation at physiological osmolarity for six hours ( Fig 8A , lane 4 ) ., The sph1 coding region lies 915 bp downstream of sph2 ( Fig 7A ) ., Quantitative RT-PCR analysis indicates that sph1 transcript levels were 34% lower in the sph2 mutant compared with that in the wild-type strain ., We introduced an intact copy of sph2 into the mutant for complementation studies ., We selected the sph2 gene from the Fiocruz L1-130 strain because of the possibility that the Sph2 protein synthesized by the highly-passaged L391 strain was not fully active ., The Sph2 protein sequences of the Fiocruz L1-130 and Lai 56601 strains are

Introduction, Materials and Methods, Results, Discussion

Pathogenic members of the genus Leptospira are the causative agents of leptospirosis , a neglected disease of public and veterinary health concern ., Leptospirosis is a systemic disease that in its severest forms leads to renal insufficiency , hepatic dysfunction , and pulmonary failure ., Many strains of Leptospira produce hemolytic and sphingomyelinase activities , and a number of candidate leptospiral hemolysins have been identified based on sequence similarity to well-characterized bacterial hemolysins ., Five of the putative hemolysins are sphingomyelinase paralogs ., Although recombinant forms of the sphingomyelinase Sph2 and other hemolysins lyse erythrocytes , none have been demonstrated to contribute to the hemolytic activity secreted by leptospiral cells ., In this study , we examined the regulation of sph2 and its relationship to hemolytic and sphingomyelinase activities produced by several L . interrogans strains cultivated under the osmotic conditions found in the mammalian host ., The sph2 gene was poorly expressed when the Fiocruz L1-130 ( serovar Copenhageni ) , 56601 ( sv . Lai ) , and L495 ( sv . Manilae ) strains were cultivated in the standard culture medium EMJH ., Raising EMJH osmolarity to physiological levels with sodium chloride enhanced Sph2 production in all three strains ., In addition , the Pomona subtype kennewicki strain LC82-25 produced substantially greater amounts of Sph2 during standard EMJH growth than the other strains , and sph2 expression increased further by addition of salt ., When 10% rat serum was present in EMJH along with the sodium chloride supplement , Sph2 production increased further in all strains ., Osmotic regulation and differences in basal Sph2 production in the Manilae L495 and Pomona strains correlated with the levels of secreted hemolysin and sphingomyelinase activities ., Finally , a transposon insertion in sph2 dramatically reduced hemolytic and sphingomyelinase activities during incubation of L . interrogans at physiologic osmolarity ., Complementation of the mutation with the sph2 gene partially restored production of hemolytic and sphingomyelinase activities ., These results indicate that the sph2 gene product contributes to the hemolytic and sphingomyelinase activities secreted by L . interrogans and most likely dominates those functions under the culture condition tested .

The spirochete Leptospira causes leptospirosis , a potentially deadly disease of humans and animals ., Candidate factors that promote infection include hemolysins encoded by several leptospiral genes ., Hemolysins rupture red blood cells in vitro ., Some hemolysins are sphingomyelinases , which target sphingomyelin in the host cell membrane ., Hemolysins have the potential to disrupt organ function during infection ., It is not known which hemolysins and sphingomyelinases are responsible for the hemolytic and sphingomyelinase activities secreted by L . interrogans ., We found that the production of hemolytic activity is regulated and is tied to expression of sph2 , which encodes a hemolysin with sphingomyelinase , cytotoxic , and fibronectin-binding activities ., Hemolytic and sphingomyelinase activities and sph2 expression were higher when the osmolarity of the culture medium was raised to the level found in the mammalian host ., Similarly , sph2 expression was substantially higher in an L . interrogans strain that secreted large amounts of hemolytic and sphingomyelinase activities than in a strain that generated negligible amounts ., Most importantly , disruption of the sph2 gene eliminated hemolysin production and yielded substantially less sphingomyelinase than the wild-type strain ., Our findings indicate that sph2 is a major contributor to the hemolytic and sphingomyelinase activities secreted by L . interrogans and that the hemolytic and sphingomyelinase activities measured in standard L . interrogans cultures may underestimate the levels produced during infection .

journal.pcbi.1003316

Conformational Changes in Talin on Binding to Anionic Phospholipid Membranes Facilitate Signaling by Integrin Transmembrane Helices

Integrins are cell surface receptors involved in many essential cellular processes , such as cell migration , and in pathological defects , such as thrombosis and cancer 1 ., Integrins are αβ heterodimers ., Each subunit has a large ectodomain , a single transmembrane ( TM ) helix and a short flexible cytoplasmic tail 2 ., Integrins are crucial for many signal transduction events 3–6 ., Unusually , they can transmit signals in both directions across the cell membrane 2 , 7 ., In the inside-out activation pathway , formation of a complex between talin , the integrin β cytoplasmic tail , and the membrane is thought to shift the integrin conformational equilibrium towards an active state 8–15 ., Activation is believed to proceed via changes in TM helix packing 2 , 3 coupled to substantive conformational changes in the integrin ectodomain ., Talin consists of a head domain ( ∼50 kDa ) and a large rod domain ( ∼220 kDa ) 16 , 17 ., A crystal structure of the talin head domain revealed a novel linear arrangement of four subdomains , F0 , F1 , F2 and F3 ( Fig . 1A ) ., Although the head domain has sequence homology with other FERM ( band four-point-one , ezrin , radixin , moesin ) domains 18 , the linear arrangement of the subdomains , an inserted loop in the F1 domain , and the extra F0 domain confer on talin significantly different features from canonical FERM domains 19 ., Experimental studies of the talin head have yielded valuable insights into the role of the different subdomains 8 , 12 , 17 , 20 , 21 ., A key step in integrin activation is binding of the F3 subdomain to the integrin β tail 9 , 10 , 12–14 ., The other subdomains have also been shown to contribute to integrin activation , although they do not bind directly to the integrin β subunit 8 ., The F2 subdomain has a positively charged patch which has been shown to enhance activation by interacting with negatively charged lipid headgroups in the membrane 11 , 21 ., The F0 and F1 subdomains have an ubiquitin-like fold 17 , 20 along with a flexible , positively charged loop of ∼40 residues inserted in the F1 domain ., This loop ( which was removed to facilitate structure determination of the head domain 17 ) has been proposed to form a transient helix stabilized by interactions with acidic phospholipids 20 ., Structural studies of the talin head domain and fragments have also suggested that the F2–F3 and the F0–F1 domain pairs are relatively rigid but the pairs are connected by a flexible linker 17 ., The inactive state of integrins is in part maintained by interactions between the α and β TM helices ., Important interactions are defined by regions known as the outer ( OMC ) and inner ( IMC ) membrane clasps ( Fig . 1B ) ., In the OMC , a GxxxG motif in the α integrin TM region allows close packing with the β integrin helix whereas in the IMC there are hydrophobic interactions involving a cluster of phenylalanines and a salt bridge between the α and β chains 22 ., Various models for TM helix rearrangements , including ‘scissor’ or ‘piston’ movements and increased helix separation have been proposed to explain activation and trans-membrane signaling 10 , 23–29 but there remains a need to distinguish and refine these models ., Molecular dynamics ( MD ) simulations allow us to explore the conformational dynamics and lipid interactions of membrane proteins 30 ., Multiscale approaches combine coarse-grained molecular dynamics ( CG-MD ) simulations 31 , 32 , which extend the time scales that can be studied , with subsequent all-atom simulations , which allow refinement of the system 33 ., We previously used this approach to develop a model that explained how the F2–F3 fragment of the talin head domain associates with the plasma membrane in a way that led to a scissoring movement of the two integrin TM helices 34 ., In the current study , we build upon these studies to elucidate the interactions between the complete talin head domain ( i . e . domains F0–F3 ) , a lipid bilayer , and the α/β integrin TM regions ., Our results provide novel information about the orientation of the intact talin head in complex with the lipid bilayer , and about changes induced in the TM helical regions ., Overall , the results reveal how binding of talin to the membrane and to integrins tails leads to integrin activation ., We have used a serial multiscale MD simulation 35 approach to explore the dynamics of the talin head ( F0–F3 ) domain , its interaction with lipid bilayers , and the resultant conformational changes of a talin/integrin TM complex embedded in a phospholipid bilayer ., This approach has previously been used to explore the interactions of a number of peripheral proteins with membrane surfaces and lipids 11 , 36–39 , of TM helices within a lipid bilayer 40 , 41 , and of more complex signaling and related assemblies within membranes 34 ., It enables one to combine coarse-grained ( CG ) simulations of membrane association and related events , with more detailed atomistic simulations to refine the resultant models ., It thus provides a complementary approach to extended atomistic simulations 42 ., The principal simulations underlying the current study are summarized in Table 1 and in Fig . 2 ., The talin head domain crystal structure ( PDB:3IVF 17 ) lacks a long loop region in the F1 domain and therefore atomistic simulations of the talin head domain ( tal-sol-AT ) in solution were first used to explore potential internal flexibility between the four component sub-domains ( F0–F3 ) along with possible conformations of the F1 domain insertion ., For these simulations the insertion in the F1 domain ( res: 134–172 ) was modeled in a random coil conformation , and was located away from the F0–F1 pair ( see Fig . 3B and Fig . S1A ) using Modeller 9v8 ( http://salilab . org/modeller/ ) 43 , 44 ., This configuration of the loop allows exploration of all possible conformations/orientations and selection of a preferred conformation/orientation relative to the talin head domain ., The conformation of the talin head domain suggested by the above simulations was used to model the association of the talin head domain with a phospholipid bilayer ., Since NMR studies suggested a helical propensity for the region involving residues 154–167 20 this region was modeled as an α-helix ( tal-h2F0-CG and Fig . S1 ) ., Subsequently , the same multiscale simulation approach was used to explore the dynamic behavior of a talin/TM integrin complex in a bilayer ( αβ-talh2-CG ) on a multi-microsecond timescale ., The talin head domain/αβ complex was constructed as described in Kalli et al . 34 using the αΙΙbβ3 TM region NMR structure 22 , the F2–F3/β1D complex crystal structure 12 and the talin head domain configuration obtained from the talin head domain simulations described in this study ., An ‘open’ model generated by these CG simulations was subsequently explored via a microsecond duration atomistic simulation ( αβ-talh2o-AT ) ., A number of control simulations were also performed to evaluate the robustness/sensitivity of the results and to explore the contributions of different regions and interactions ( e . g . flexibility within the domain , electrostatic interactions and other helical conformations in the F1 loop ) to the binding of the talin head to anionic lipid bilayers ., Detailed descriptions of these simulations are provided in the Supporting Information ( Tables S1 and S2 ) ., In total our study amounts to ca ., 60 µs of CG-MD and ca ., 2 µs of atomistic molecular dynamics simulations ( AT-MD ) simulation time ., To study the conformational dynamics of the talin head domain prior to the association with the bilayer atomistic ( AT-MD ) simulations of the talin head domain ( i . e . subdomains F0 to F3 ) in aqueous solution in the absence of a bilayer were performed ( tal-sol-AT in Table 1 ) ., During these simulations the flexible linker between the F2–F3 and F0–F1 pairs allowed transient displacement of the F0–F1 subdomain relative to the F2–F3 subdomain with the angle defined in Fig . 3A ., This angle , equal to 0° for a linear arrangement of F0-F1-F2-F3 as seen in the crystal structure , ranged from 0° to 90° in the simulations ., During these simulations the long loop in the F1 domain moved closer to the F0–F1 pair , and adopted an extended conformation on the same side of the protein as the positively charged patch on F2 ( Fig . 3B ) ., Calculation of the electrostatic field around the talin head conformation observed at the end of these simulations suggests that localization of this loop close to the F0–F1 pair creates an extensive positively charged surface on one side of protein; this could facilitate strong talin/bilayer interactions ( Fig . S2 ) ., Despite experimental evidence for an α-helical propensity in the F1 loop 17 , no helix formation was detected ( this might be due to insufficient simulation time for a coil-to-helix conformational transition to occur ) ., Although there was a relatively large change in the angle between the F0–F1 and F2–F3 domain pairs , no significant angle change was observed within either the F0–F1 or the F2–F3 domain pair , suggesting that each pair behaves approximately as a rigid body ., Having established in the tal-sol-AT simulations ( see above ) that the F1 loop interacts with F0–F1 to form a positively charged surface that extends the positive patch on F2–F3 , CG simulations with the loop in this location were performed to explore the nature of the interactions of the complete talin head domain with an anionic lipid bilayer ., Note that in this simulation system a small helical region ( h2 helix; see Fig . S1 ) was included within the F1 loop as indicated by NMR data 20 ., During this modeling of the loop the remainder of the structure , with the exception of the region modeled as helical ( res: 154–167 ) , was restrained to maintain the talin conformation derived from the above simulations ., These restraints were removed during the simulations ., In the tal-h2F0-CG simulation ( Table 1; Fig . 4 and Fig . 5 ) , talin was observed to associate with the bilayer in four out of five simulation and in all four of these simulations talin bound to the bilayer initially via a basic loop ( res: 318–330 ) in the F3 domain , and subsequently via the positively charged patch in the F2 domain ( res: 255–285 ) ( Fig . S3A ) ., Contacts were defined by using a distance cut-off of 7 Å between the protein residues and the lipids ., These two regions have been identified previously 11 to promote productive binding of the isolated F2–F3 fragment to an anionic lipid bilayer ., The additional surface created by the F1 loop also interacted with the bilayer ( Fig . 4C ) ., Interestingly , in all simulations which resulted in a talin/bilayer complex , the talin head domain with the F1 loop adopted a V-shaped conformation due to rotation of the F0–F1 pair relative to the F2–F3 pair in the bilayer plane ( Fig . 4A ) ., The reorientation of the F2–F3 and F0–F1 domain pairs prior to the binding to the bilayer was also observed in other simulations in which a different starting conformation of talin was used ( e . g . the talin crystal structure; data not shown ) ., In contrast to the more dynamic variations in the angle between the F0–F1 and F2–F3 observed when talin was in solution ( see above ) , the V-shaped conformer was stabilized by association with the bilayer ( Fig . 4B ) ., This conformation optimizes talin/lipid interactions and induces a more compact arrangement of domains , although this new arrangement is still different from the linear arrangement in the X-ray structure 17 and the canonical FERM domain packing of F0 to F3 18 ., During the AT-MD simulations ( tal-h2F0-AT; see Table 1 ) that started from the final snapshot of the tal-h2F0-CG simulation , the V-shaped conformation of talin was retained , with talin interacting preferentially with the headgroups of the anionic POPG lipids ( Fig . S6B ) ., No restrictions in the position/flexibility of the loop or the domains were imposed in the AT-MD simulations ., Simulations of the talin head domain with a neutral bilayer ( containing 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidyl-choline ( POPC ) lipids ) resulted in no association of talin with the bilayer ( Fig . S3B and Text S1 ) ., These results are in good agreement with the available experimental data 11 , 12 , 17 and augment our previous observation that electrostatic interactions are important in regulating the formation of a talin/membrane complex ., Control CG simulations starting with the talin head domain crystal structure ( i . e . without the F1 loop ) with the same POPC/POPG ( 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidyl-choline/1-palmitoyl-2-oleoyl-sn-glycero-3-phosphatidyl-glycerol ) bilayer showed that the positively charged region of F0–F1 augmented by the F1 loop promotes direct F0–F1/bilayer interactions ( data not shown ) ., CG models of talin with restricted flexibility between the F0–F1 and F2–F3 domain pairs did not bind to the bilayer in a way that would facilitate binding of F2–F3 to the β-tail in any of these simulations ( see Text S1 ) ., Disruption of the electrostatic interactions between talin and the membrane ( simulating with experimentally tested mutations 10 , 12 ) also resulted in the ‘non-productive’ orientation of the talin head domain relative to the membrane ( see Methods for description of mutations ) ., An orientation is judged here to be ‘non-productive’ when the talin/bilayer complex formed is incompatible with binding to the β integrin cytoplasmic region in a manner similar to that observed by Anthis et al . 12 ( see Text S1 ) ., Overall , our simulations suggest that optimal association of talin with the membrane is enhanced by conformational flexibility within the head domain , especially between the F0–F1 and F2–F3 pairs ., This flexibility facilitates optimal interactions of the V-shaped conformation of talin with the headgroups of anionic lipids ., Reduction in the anionic lipid content significantly decreased the talin/bilayer association and , in the presence of mutations that disrupted the talin/lipid electrostatic interactions , talin bound to the bilayer in a perturbed orientation ( Fig . S5 ) ., Having established the nature of the interactions of the intact talin head domain with a bilayer , we set out to explore the impact of these interactions on the structure of the integrin TM region ., To this end , the talin head domain with optimal bilayer interactions ( i . e . from simulation tal-h2F0-CG ) was modeled in a complex with the α/β integrin TM complex ( see Methods for details of modeling this complex ) ., This complex was inserted in a POPC/POPG bilayer ( Fig . 6A ) , and five extended CG-MD simulations ( αβ-talh2-CG; Table 1 ) were performed ., In these simulations all restraints between the integrin α subunit TM domain and the F2–F3/β TM complex were removed to allow the α TM domain to move relative to the rest of the complex ., Closer examination of the movement of the individual domains within the complex during the simulations revealed a reorientation of the talin head domain relative to the bilayer surface , comparable to the reorientation observed in our earlier studies of the F2–F3/αβ complex ( Fig . 6B ) ., This rotation , in turn , induced a ∼25° rotation of the β TM helix perpendicular to the bilayer normal , similar to that seen in our previous studies ( Fig . 6B ) 34 ., This resulted in the disruption of the interactions in the OMC and IMC regions of the α/β TM segment which were shown to maintain the integrin inactive state 22 , 27 , 29 , 45–52 ., In particular , the β TM helix rotation perturbed the close packing in the OMC region ( because of the 972GxxxG976 motif in the α TM helix ) and disrupted the hydrophobic interactions in the IMC region formed by F992 and F993 residues and the αIIb995β3723 salt bridge ., Calculation of the angle between the F2–F3 and the F0–F1 domain pairs during the simulations revealed a stable angle of ∼60° ( the definition of the angle is the same as described above ) , indicating that the V-shaped conformation of the talin head was retained in all the simulations ( Fig . S7A ) ., We note that simulations of the α/β dimer and the isolated β integrin tails in the same bilayer were performed in our previous study 34 ., To explore the talin/αβ complex in more detail , a structure which represented an “open” state of the integrin TM domain was selected from the αβ-talh2-CG simulation ( using similar criteria to those described in our previous simulations of the α/β dimer and the isolated β integrin tails in a bilayer 34 ) and converted to an AT representation ., In this “open” integrin model , the interactions in both the IMC and OMC regions are disrupted ., We have previously suggested that the scissoring motion of integrin TM helices may be “a trigger for inside-out activation” 34 ., To explore the consequences of activating this trigger , we performed an extended ( microsecond ) AT-MD simulation of the complex ( αβ-talh2o-AT; Table 1 ) ., At the end of this atomistic simulation the V-shaped conformation of the talin head domain was retained ., Calculation of the inter-helical distances in the OMC and IMC regions revealed an increase in the helix-helix distance in both the IMC and OMC regions ( Fig . 7A , B ) , corresponding to dissociation of the α and β TM helices ., Dissociation was preceded by a ‘scissoring’ movement similar to that seen in our previous studies of the F2–F3/αβ complex but with more extensive movement of the TM helices and a large increase in the tilt angle of the β helix relative to the bilayer normal , to a final value of 40° , ( Fig . S7B ) ., On a similar simulation timescale , the F2–F3 fragment alone induced a much smaller separation of the α/β subunits ( Fig . S8B ) with a comparable increase in the β TM helix tilt angle to that observed in our previous studies 34 ., This suggests that the F2–F3 domains are sufficient to modulate the β tail tilt angle but the entire head is more effective in producing TM helix separation ., The increase in tilt angle induced by the talin head increases the extent of membrane-embedding of the β tail TM region ., In the final orientation seen in the simulation with the talin/αβ complex , residues from K716 up to K725 are embedded in the membrane ( Fig . 8 ) ., A similar increase in the extent of the membrane-embedded region was observed in a recent experimental study 53 ., Despite a large increase in β-TM tilt angle , the T715 sidechain remains oriented toward the lipid phosphate atoms , but the K716 sidechain is no longer in contact with the lipid headgroups ., This is in good agreement with experimental data that identified interactions between the β3 K716 ε-amine group and the lipid phosphate in the integrin inactive state , suggesting that this interaction controls the tilt angle of the β3 integrin tail 54 ., Mutation of K716 in these experimental studies shifted the integrin conformational equilibrium towards an active state , possibly by perturbing the β subunit tilt angle and the α/β TM region crossing angle ., Thus , in the active state one would expect weaker K716/lipid headgroup interactions , as observed in our simulations of the integrin active state ., The tendency of the β TM helix to adopt a tilted orientation in the membrane is also suggested by other experimental 55 and computational 21 data ., Our studies show that conformational changes in the talin head on binding to anionic phospholipid membranes mediate transmembrane signaling by the integrin TM helix dimer ., Simulations have revealed how optimization of the interactions of the complete talin head domain ( F0–F3 ) with an anionic phospholipid bilayer promotes a rearrangement of the subdomains , which in turn initiates a conformational rearrangement of the integrin TM region ., In particular , on binding to the membrane the F0–F3 talin head domain undergoes a conformational change to a V-shape , rather than the linear arrangement of the F0-F1-F2-F3 subdomains observed in the crystal structure ., Formation of a complex between the rearranged talin head and the integrin TM domain subsequently triggers separation of the TM helices i . e . disassembly of the TM helix dimer ., The first stage of this disassembly is an initial scissoring motion of the helices , as seen in our previous studies 34 ., The current study reveals how the flexible linker between the F0–F1 and F2–F3 pairs allows talin to adopt a conformation which optimizes its contacts with anionic headgroups of lipids ( Fig . 4A ) ., This correlates well with the studies by Bouaouina et al . 8 which have suggested that the activation responses of β3 and β1 integrins have different dependencies on talin head fragments , indicating that formation of an optimal configuration of the talin/membrane complex could also have mechanistic importance ., Our simulations reveal how electrostatic interactions between the protein and anionic lipid headgroups orient the talin head domain and optimize its interactions with the membrane ., The cationic surface of the talin head , which binds to the anionic lipids , is formed by the F2 and F3 domains plus the F1 loop ., In silico mutations ( i . e . K324D and K256E , R277E , K272E , R274E ) of this surface perturb binding of talin to the membrane , in agreement with experimental studies 12 , 17 , 20 , 21 ., This binding surface is consistent with NMR 10 , crystallographic 12 and TIRF microscopy studies 21 ., Experimental studies indicate that inserted loop in the F1 domain is dynamic in nature 17 , 20 ., Our simulations suggest that it provides a binding surface close to the cationic surface of F2–F3 ., In all simulations that yielded a ‘productive’ orientation of the talin head domain on the membrane , the F2–F3 pair always associated prior to F0–F1 ., This agrees with recent data that the association constant of the complete talin head domain with lipids is similar to that of the F2–F3 fragment 56 ., We note that in kindlin , a homolog of talin that co-activates integrins , an even longer lysine-rich loop is inserted in the F1 domain ., This lysine rich loop in kindlin is highly conserved and is believed to support binding to anionic phospholipid head groups 57 ., Simulations that included the entire talin head/membrane/TM complex revealed that talin interactions with the integrin β tail and the membrane surface disrupt the interactions of both the OMC and IMC regions , resulting in eventual dissociation of the TM helices ., Calculation of the hydrogen bonds between the lipids and the membrane ( see Fig . S7C ) after the disruption of interactions in the TM region show an increase in the number of lipid/talin hydrogen bonds ., This suggests a stronger association between talin and the bilayer after formation of the talin/αβ complex ., This could explain why the intact talin head domain has more dramatic effect than F2–F3 alone ., The crystal structure of the integrin ectodomain and cysteine disulfide mapping of an intact integrin 58 , 59 suggest that the α and β domains are in close proximity to one another in the inactive state ., Thus a scissoring movement followed by dissociation of the two TM helices provides a plausible model for how the TM domain may trigger a conformational change in the ectodomain leading subsequent adoption of an extended active state ., From a more general perspective , this study reveals the interplay of membrane interactions and conformational changes involved in transmembrane signaling by receptors and associated proteins and/or domains ., In particular , it may be compared with recent simulation studies , e . g . of the EGF receptor 42 , which suggested that substantive repacking of the TM helix dimer and interactions between the intracellular kinase domain and anionic lipids play a key role in signaling across a membrane ., The mechanisms in these two classes of membrane receptors ( i . e . integrins and receptor tyrosine kinases respectively ) may be compared with movements of TM helices thought to mediate signaling in GPCRs ( as revealed by crystallographic , NMR and simulation studies 60 ) ., One possible consequence of the extensive movements of TM helices is that signaling mechanisms are likely to be modulated by changes in ( local ) lipid bilayer properties 61 ., This clearly merits further investigation by both computational and biophysical approaches ., The CG-MD simulations were performed using a local variant 31 , 62 of the MARTINI forcefield 32 ., A mapping of approximately 4∶1 heavy atoms to CG particles was used ., Harmonic restraints ( i . e . an elastic network model; ENM ) between backbone particles within a cut-off distance of 7 Å was applied with a harmonic restraint force constant of 10 kJ/mol/Å2 ., In the tal-l25-CG and tal-l50-CG simulations ( Table S1 ) the force constant for the ENM was set to 25 kJ/mol/Å2 and 50 kJ/mol/Å2 respectively and the cut-off distance was increased to 10 Å ., The bilayer was constructed by self-assembly CG-MD simulations ., In these simulations the lipids were placed randomly within a simulation box and solvated with CG water molecules and ions to neutralize the system ., Subsequently , a production simulation was performed for 200 ns ., After the first 10–15 ns of simulation the bilayer formed with an equal distribution of lipids in the two leaflets ., For the simulation systems discussed here , two different bilayers were constructed ., The first bilayer contained 832 zwitterionic POPC lipids and the second had 512 POPC and 320 POPG lipids ( ratio of 3∶2 ) ., In the CG simulations the center of mass of the protein was placed 120 Å from the center of mass of the preformed bilayer ( Fig . S3A ) ., This starting distance between protein and bilayer was chosen to be much larger than the cut-off distance used for the electrostatic and the van der Waals terms in the CG forcefield ., All systems were subsequently solvated with CG water molecules and neutralized with CG sodium particles , energy minimized for 250 steps and equilibrated for 5 ns with the protein Cα particles restrained ( force constant 10 kJ/mol/Å2 ) ., Finally , CG-MD simulations were performed ., The final snapshot of the CG-MD simulation was converted to an atomistic ( AT ) representation , using a fragment-based approach 33 , for further refinement ., For the simulations with the talin/αβ complex , the same POPC/POPG bilayer as above was used ., The TM region of the talin/αβ complex was inserted in the bilayer using GROMACS ., The lipids that overlapped with the integrin TM region were removed ., The same energy minimization and equilibration steps were performed as described above ., A modified CG model was used where all the ENM restraints between the integrin α TM region and the rest of the complex were removed ., All CG-MD simulations were performed using GROMACS 4 . 5 ( www . gromacs . org ) 63 , 64 ., A Berendsen thermostat 65 was used for temperature coupling with a coupling constant of 1 . 0 ps and a reference temperature of 310 K . The Lennard-Jones and Coulombic interactions were shifted to zero between 9 Å and 12 Å , and 0 to 12 Å respectively ., The time step was 20 fs ., A Berendsen barostat was used for pressure coupling ., The coupling constant was 1 . 0 ps , the compressibility was 5 . 0×10−6 bar−1 and the reference pressure was 1 bar ., The AT-MD simulations were performed using the GROMOS96 43a1 forcefield 66 ., The Parrinello-Rahman barostat 67 and the Berendsen thermostat 65 were used for pressure and temperature coupling , respectively ., The bond length was constrained using the LINCS algorithm 68 and the particle mesh Ewald ( PME ) algorithm 69 was used to model long-range electrostatic interactions ., A cut-off distance of 10 Å was used for the van der Waals interactions ., All the AT simulation systems were energy minimized using a steepest descent algorithm and equilibrated for 2 . 5 ns with the protein Cα atoms restrained ( force constant 10 kJ/mol/Å2 ) ., Subsequently , unrestrained AT-MD simulations were performed ., All the analyses were performed using GROMACS ( www . gromacs . org ) 63 , 64 , VMD 70 and locally written codes .

Introduction, Results/Discussion, Methods

Integrins are heterodimeric ( αβ ) cell surface receptors that are activated to a high affinity state by the formation of a complex involving the α/β integrin transmembrane helix dimer , the head domain of talin ( a cytoplasmic protein that links integrins to actin ) , and the membrane ., The talin head domain contains four sub-domains ( F0 , F1 , F2 and F3 ) with a long cationic loop inserted in the F1 domain ., Here , we model the binding and interactions of the complete talin head domain with a phospholipid bilayer , using multiscale molecular dynamics simulations ., The role of the inserted F1 loop , which is missing from the crystal structure of the talin head , PDB:3IVF , is explored ., The results show that the talin head domain binds to the membrane predominantly via cationic regions on the F2 and F3 subdomains and the F1 loop ., Upon binding , the intact talin head adopts a novel V-shaped conformation which optimizes its interactions with the membrane ., Simulations of the complex of talin with the integrin α/β TM helix dimer in a membrane , show how this complex promotes a rearrangement , and eventual dissociation of , the integrin α and β transmembrane helices ., A model for the talin-mediated integrin activation is proposed which describes how the mutual interplay of interactions between transmembrane helices , the cytoplasmic talin protein , and the lipid bilayer promotes integrin inside-out activation .

Transmission of signals across the cell membrane is an essential process for all living organisms ., Integrins are one example of cell surface receptors ( αβ ) which , uniquely , form a bidirectional signalling pathway across the membrane ., Integrins are crucial for many cellular processes and play key roles in pathological defects such as cardiovascular diseases and cancer ., They are activated to a high affinity state by the intracellular protein talin in a process known as ‘inside-out activation’ ., Despite their importance and the existence of functional and structural data , the mechanism by which talin activates integrin remains elusive ., In this study we use a multi-scale computational approach , which combines coarse-grained and atomistic molecular dynamics simulations , to suggest how the formation of the complex between the talin head domain , the cell membrane and the integrin moves the integrin equilibrium towards an active state ., Our results show that conformational changes within the talin head domains optimize its interactions with the cell membrane ., Upon binding to the integrin , talin facilitates rearrangement of the integrin TM region thus promoting integrin activation ., This study also provides a demonstration of the strengths of a computational multi-scale approach in studies of membrane interactions and receptor conformational changes and associated proteins that enable transmembrane signaling .

journal.pgen.1007049

Long-lasting masculinizing effects of postnatal androgens on myelin governed by the brain androgen receptor

The incidence and clinical course of many neurological disorders differ between sexes , and elucidating the underlying biological basis has become a high priority challenge 1 , 2 ., The potential impact of sex differences in brain structure has long been neglected ., They were indeed believed to be restricted to specific brain regions , in particular those involved in reproductive functions 3 ., This concept has changed with recent neuroimaging studies uncovering sex differences in neuronal connectivity across the entire brain 4 , 5 ., Moreover , structural sex differences in the human brain are shaped by fetal testosterone 6 ., Rodent models have provided valuable insights into mechanisms leading to sex differences in brain structure and function ., They can be reversible and only caused by the temporary actions of sex-specific hormones 7 ., Alternatively , sex dimorphism in brain may be persistent and result from developmental processes , including the masculinizing actions of testicular testosterone during sensitive perinatal and postnatal periods , and the shaping of neuronal circuits by sex chromosome-linked genes , epigenetic factors and the hormonal environment 8–11 ., In mice and rats , neural circuits are sensitive to the persistent differentiating ( organizational ) effects of gonadal steroids around birth and during a postnatal period which may extent to 4 weeks 3 , 12 ., During the perinatal period , the male brain is exposed to the masculinizing effects of a transient surge of testicular testosterone , driven by kisspeptin and gonadotropin released by hypothalamic neurons 13 ., In both rats and mice , the aromatization of testosterone to estradiol plays an important role in masculinization of the brain 14–16 ., However , disrupting androgen receptor ( AR ) signaling also interferes with the process of hormone-dependent sexual differentiation of the brain 17 , 18 ., The respective roles of estrogen receptor ( ER ) and AR signaling are not completely understood ., In mice , AR are sparse in the brain at the time of the neonatal testosterone surge , and their expression only increases by postnatal day 4 ( P4 ) 17 , 19 ., For this reason , it can be presumed that estrogens play a major role in the organizational effects of neonatal testosterone , when brain ER and aromatase are highly expressed , and that the role of AR signaling may become more important during postnatal brain development 17 ., However , aromatase knockout male mice , developmentally deprived of their brain estrogens , show normal coital behavior following adult hormone treatment 20 ., It is likely that respective organizational functions of androgens and estrogens are dependent on brain functions and differ between species ., Intriguingly , a sexual dimorphism affecting the density of oligodendrocytes , the myelin forming glial cells of the central nervous system ( CNS ) , and the structure of myelin has been reported in adult mice and rats 21 ., The density of oligodendrocytes was found to be 20–40% greater in adult males compared with females in the corpus callosum and other white matter tracts of the CNS ., Moreover , the expression of myelin basic protein ( MBP ) and proteolipid protein ( PLP ) , two myelin-specific proteins , was significantly greater in males ., Interestingly , the sexual dimorphism of oligodendrocytes and myelin was sensitive to long-term castration , over 3 months ., Indeed , the density of oligodendrocytes was decreased and became comparable to the one observed in females , pointing to a possible role of testicular secretions 21 ., However , a persistent organizational effect of neonatal androgens on myelin appeared highly unlikely , as oligodendrocyte progenitors arise in the rodent cerebral cortex and corpus callosum and begin to differentiate into myelinating oligodendrocytes between the first and second postnatal week 22 ., Herein , we report the unexpected observation that myelin is in fact sexually differentiated in mice by postnatal androgens and AR signaling ., We establish that sex differences in myelin are already present at postnatal day 10 ( P10 ) using a transgenic mouse line selectively expressing the enhanced green fluorescent protein ( EGFP ) in the oligodendroglial cell lineage 23 ., Furthermore , using gas chromatography coupled to tandem mass spectrometry ( GC-MS/MS ) 24 , 25 , we report that brain levels of testosterone and 5α-DHT , both endogenous agonist ligands of the AR , are significantly higher in males when compared with females between postnatal days P0 and P10 , We also show persistent effects of postnatal androgens on the density of oligodendrocytes and the structure of the myelin sheaths by postnatal pharmacological treatments ., Finally , we demonstrate a key role of brain AR in the structural phenotype of myelin by specifically deleting the receptor in neural cells of the CNS ., The role of AR in determining the structure of myelin was further strengthened in genetic male mice with the testicular feminization mutation ( Tfm ) , which lack functional AR ., These findings provide new insights into the sexual differentiation of the brain , moving persistent sex differences from neurons to myelin and uncovering new long-lasting effects of postnatal AR signaling ., To study the developmental origin and hormonal determinants of the sexual dimorphism of oligodendrocytes and myelin , we used a transgenic mouse line expressing the enhanced green fluorescent protein ( EGFP ) driven by the PLP promoter ( PLP-EGFP mouse ) ., In this mouse , EGFP is selectively expressed in cells of the oligodendroglial lineage throughout the brain ( Fig 1A ) 23 ., As expected , the density of fluorescent oligodendroglial cells was about 20% higher in the corpus callosum of adult male mice when compared with females ( Fig 1B ) ., As the sexual dimorphism of oligodendrocytes and myelin has been previously demonstrated only in adult rodents 21 , we investigated whether it originates early during postnatal brain development ., We observed a 17% higher density of cells expressing the transcription factor Olig2 ( oligodendroglial lineage marker ) in PLP-EGFP male mice when compared with females as early as postnatal day 5 ( P5 , Fig 1C and 1D ) ., However , the density of EGFP+ and EGFP+/Olig2+ oligodendroglial cells did not significantly differ between sexes ., At this early stage , Olig2 is still expressed in progenitors of both astrocytes and oligodendrocytes 26 , 27 ., Only 20% of the Olig2+ glial progenitors were also EGFP+ , which would be expressed in the maturing cells of thus belonging to the oligodendroglial lineage ., On the other hand , all EGFP+ cells expressed Olig2 , confirming the restricted expression of the fluorescent marker 28 ., Between P5 and P10 , there was an increase in the density of Olig2+ , EGFP+ and EGFP+/Olig2+ cells in the corpus callosum of both sexes ., At P10 , the density of all these markers was 20% higher in males when compared with females ( Fig 1E–1H ) ., Likewise , the number of cells labelled with the CC1 antibody directed against adenomatous polyposis coli , an accepted marker of differentiated oligodendrocytes 29 , and of Olig2+/CC1+ co-expressing oligodendrocytes was higher in males than in females at P10 ( Fig 1I–1K ) ., Thus , the density of oligodendroglial cells becomes sexually dimorphic between P5 and P10 in the mouse corpus callosum ., Moreover , the number of EGFP+ cells in the transgenic mice closely approximates the number of CC1+ cells in wild type mice ( Fig 1H–1K ) ., The higher density of oligodendroglial cells at P10 might suggest an early sexual dimorphism of myelin ., Sagittal brain sections from P10 male and female mouse brains were immunostained by using an antibody against MBP , a major component and established marker of CNS myelin 30 ., Although myelin was still sparse at P10 , there was nearly 35% more MBP-staining in corpus callosum of males when compared with females ( Fig 2A–2C ) ., Consistent with the immunohistochemistry results , qRT-PCR analysis showed about 30% higher levels of MBP transcripts in the male brain when compared with females ( Fig 2D ) ., Analysis by electron microscopy showed that only a small percentage of corpus callosum axons were myelinated at P10 ( < 5% ) , but that there were more than twice as many myelinated fibers in males than in females ., In contrast , the total number of axons did not differ between sexes ( Fig 2E–2H ) ., Thus , the density of oligodendrocytes , MBP expression and the number of myelinated axons already differ at P10 between sexes ., To assess whether the observed sex difference in the density of oligodendrocytes and the extent of myelination at P10 may be dependent on the postnatal hormonal environment , we first analyzed brain steroid levels between P0 and P10 by GC-MS/MS ., Levels of testosterone were higher in the male than in the female brain between P0 and P10 ( Fig 3A ) ., Their analysis by two-way ANOVA showed a significant effect of sex ( F ( 1 , 74 ) = 15 . 51 , p < 0 . 0001 ) and age ( F ( 2 , 74 ) = 4 . 7 , p = 0 . 012 ) ., The significant increase in testosterone at P10 in the male brain correlated to a marked drop of its immediate metabolite 5α-DHT ( Fig 3B ) ., At P0 and P5 , brain levels of the more potent androgen 5α-DHT were higher in males , as compared to females ( F ( 1 , 75 ) = 3 . 72 , p = 0 . 05 ) ., The combined levels of testosterone and 5α-DHT , both ligands of AR , were significantly higher in the male than in the female brain at P0 , P5 and P10 ( Fig 3C ) ., Analyzing their combined levels by two-way ANOVA showed a significant effect of sex ( F ( 1 , 77 ) = 10 . 6 , p = 0 . 002 ) ., A more extensive profiling of brain steroids at P10 revealed that only levels of testosterone ( p<0 . 001 ) and estradiol ( p<0 . 05 ) were higher in males than in females , whereas the levels of 13 other steroids , including progesterone , were similar in both sexes ( Table 1 ) ., The absence of sex differences in progesterone levels is interesting , as this neurosteroid also stimulates myelination during postnatal development 31 , 32 ., Higher brain levels of testosterone and 5α-DHT in males were concomitantly associated by significantly higher brain levels of AR mRNA , as determined by qRT-PCR ( Fig 3D ) ., To investigate a link between AR signaling , the density of oligodendrocytes and the extent of myelination in corpus callosum at P10 , male PLP-EGFP pups were subcutaneously injected every two days between P0 and P10 with 1 mg/kg of the selective AR antagonist flutamide ., Blocking AR caused a 30% decrease in the density of EGFP+ oligodendroglial cells in the male corpus callosum at P10 , which became similar to females ( Fig 4A ) ., Conversely , injecting female PLP-EGFP pups between P0 and P10 every two days with 1 mg/kg of testosterone or 5α-DHT , which is not aromatized into estrogens , increased the density of oligodendroglial cells in their corpus callosum to male-like levels ( Fig 4B ) ., To determine whether treatment with flutamide during the first 10 postnatal days also affected the organization of myelin , we measured at P10 the length of myelinated axon segments at the junction between the genu of the corpus callosum and the striatum , where they can be easily observed and quantified ., Myelin segments were significantly longer in males when compared with females , but treatment of male pups with flutamide completely abolished this sex difference ( Fig 4C and 4D ) ., Systemic treatment with flutamide or 5α-DHT affects AR signaling in the entire body ., To assess the role of cerebral AR in the development of postnatal sex differences in corpus callosum myelin , we deleted AR in neural cells using the Cre/Lox system ., A mouse line carrying a floxed exon 1 of the AR gene , located on chromosome X , was used ., In this line , Cre recombination results in the excision of the transcription start site and deletion of the N-terminal domain of AR 33 ., Female ARLox mice were crossed with male mice expressing the Cre recombinase under the control of the promoter and the CNS-specific enhancer of rat nestin 34 ., This NesCre mouse line is characterized by a very efficient and selective Cre-mediated recombination 35 , 36 ., In the resulting male ARNesCre mice , AR expression was deleted in CNS neurons , astrocytes and oligodendrocytes , but not in microglial cells ., ARLox males were used as controls ., The specific knockout of AR expression in the brain of ARNesCre males was verified by qPCR analysis ( Fig 5A ) ., AR mRNA expression was not affected in muscle and testis ., The genetic deletion of AR in CNS neurons and macroglial cells resulted in markedly reduced MBP expression in the brain at P10 ., Levels of MBP mRNA transcripts and protein isoforms were decreased by almost 40% and 70% , respectively ( Fig 5B–5D ) ., Immunofluorescence analysis of the corpus callosum at P10 showed a significant reduction in MBP staining density , as well as in the number of Olig2+ cells and mature oligodendrocytes ( CC1+ and CC1+/Olig2+ cells ) in ARNesCre males when compared with ARLox males ( Fig 5E–5I ) ., Both pharmacological and genetic inhibition of AR thus showed that postnatal androgens , via their receptor , are involved in the sexual dimorphism that affects the density of oligodendrocytes and the extent of myelination at P10 ., To assess whether exposure to androgens during the first ten postnatal days has a long-lasting impact on the sexual phenotype of corpus callosum myelin , we first used organotypic cultures of cerebellar slices prepared from P10 male and female PLP-EGFP pups 37 ., At this stage , the myelination of axons only starts in cerebellum 31 , 38 ., Importantly , the cerebellar tissue was exposed prior to culture to higher levels of endogenous testosterone and 5α-DHT in males when compared with females ., The cerebellar slices were then cultured for 2 weeks to allow axons to become fully myelinated ., Although the culture medium contained 25% horse serum , levels of androgens in the culture medium were below the limit of detection by GC-MS/MS ( 1 pg/ml for testosterone and 2 pg/ml for 5α-DHT , see Table 1 ) ., Therefore , myelination proceeded to completion in an androgen-deprived medium ., Consistent with a masculinizing effect of androgens on the process of myelination prior to P10 , the density of EGFP+ oligodendroglial cells in the cerebellar lobules was 36% higher in slices prepared from male pups than in those prepared from female pups ( Fig 6A and 6B ) ., Furthermore , MBP staining density was about 30% higher in the male slices ( Fig 6A and 6C ) ., These observations are consistent with a persistent effect of the postnatal androgen environment on myelin ., To determine whether postnatal androgen-dependent sex differences in myelin persist into adulthood , we treated again male PLP-EGFP pups with flutamide and female pups with 5α-DHT between P0 and P10 ( 1 mg/kg every two days ) ., We then counted oligodendrocyte cells in their corpus callosum at the age of 3 months ., As for P10 , postnatal treatment with flutamide decreased the density of oligodendroglial cells in the adult male corpus callosum by 30% ( Fig 6D ) ., Conversely , treating female PLP-EGFP pups between P0 and P10 every two days with 5α-DHT increased the density of oligodendroglial cells in the adult corpus callosum by about 20% ( Fig 6E ) ., Thus , in spite of the constantly very low levels of endogenous androgens in females , their exposure to exogenous 5α-DHT during the first 10 postnatal days was sufficient to induce a male-like density of oligodendrocytes in their corpus callosum ., Both in vitro and in vivo experiments thus documented persistent influences of postnatal androgens on myelin ., Conditional deletion of AR in the brain had a major impact on the density of oligodendroglial cells and the extent of myelination in the male corpus callosum at P10 ., Moreover , postnatal androgens exerted long-lasting masculinizing effects on myelin , extending into adulthood ., Adult ARNesCre male mice were thus expected to exhibit a female-like phenotype of myelin with a reduced density of oligodendroglial cells and decreased MBP immunostaining ., Indeed , immunohistochemical analysis of the corpus callosum by fluorescence microscopy revealed that at the age of 3 months , the densities of Olig2+ oligodendroglial cells , CC1+ mature oligodendrocytes and Olig2/CC1 co-expressing oligodendrocytes were reduced by 20 to 30% in ARNesCre male mice when compared with ARLox controls , thus becoming similar to females ( Fig 7A and 7C–7E ) ., On the other hand , MBP immunostaining was decreased by 20% in corpus callosum of ARNesCre males ( Fig 7A and 7B ) ., Analysis by electron microscopy showed that the percentage of myelinated axons in corpus callosum was decreased in ARNesCre males by 15% when compared to control ARLox males , but was similar to ARLox females ( Fig 7F ) ., However , the total number of callosal axons was not affected by AR deletion in the brain ., The mean g-ratio of myelinated callosal axons was significantly higher in ARNesCre males than in controls and similar in ARNesCre males and in ARLox females ( Fig 7G ) ., The thickness of the axons is approximately 0 . 89 +/- 0 . 07 μm in ARNesCre and 0 . 90 +/- 0 . 08 μm in ARLox whereas that of the whole fiber is approximately 1 , 09 +/- 0 , 06 μm in ARNesCre and 1 . 21 +/- 0 . 10 μm in ARLox males ., This suggests that myelin sheaths are thinner in males after AR deletion , becoming comparable to females ., Each myelinated axon segment between two nodes of Ranvier , named internode , is formed by a single oligodendrocyte process ., Importantly , oligodendrocytes can myelinate several internodes ., Thus , an increase in the density of oligodendroglial cells may result in a decreased number of internodes formed per oligodendrocyte , or alternatively in shorter internodes 39 ., The latter is what we observed by measuring internodal distances between two paranodal regions in cerebral cortex , where extended parts of myelinated axons can be observed in a same plane ., The length of internodes of myelinated axons was measured after triple immunolabeling of contactin-associated protein ( Caspr ) , a glycoprotein present in the paranodal region , of MBP and of neurofilaments ( NF200 ) ( Fig 7H ) ., Internodal distances in the cortex of ARLox males were about 40% shorter than in ARNesCre males , and they were comparable to normal females ( Fig 7I ) ., Thus , ARNesCre males exhibited a female-like phenotype of myelin , characterized by thinner myelin sheaths and longer internodes ., The conditional deletion of AR in the brain demonstrated its importance in the masculinization of myelin ., To corroborate this finding , myelin was also examined in adult male mice carrying the naturally occurring AR testicular feminization mutation ( ARTfm ) ., In these mice , a frame shift mutation in exon 1 of the AR gene results in a nonfunctional receptor in the entire body 40 , 41 ., The reduction in the densities of Olig2+ oligodendroglial cells , CC1+ oligodendrocytes and Olig2/CC1 co-expressing oligodendrocytes in the corpus callosum of 3 months ARTfm males was even more marked than for ARNesCre males , reaching 40 to 50% when compared with wild-type ( Fig 8A–8C ) ., Consistently , the amount of MBP analyzed by Western blot ( Fig 8D and 8E ) and MBP immunostaining in corpus callosum ( Fig 8F–8H ) were significantly lower in these ARTfm males when compared with wild-types ., Particularly marked was the reduction in myelinated axons within the corpus callosum of ARTfm males and the thinner myelin sheaths observed by electron microscopy ( Fig 8I–8O ) ., The thinner myelin in ARTfm males was reflected by a significant increase in the mean g-ratio ( Fig 8O ) ., However , the density of callosal axons did not differ between wild types and ARTfm males ( Fig 8M ) ., Taken together , the myelin phenotype of ARTfm mice resembled the one of ARNesCre mice , but differences with the controls appeared to be more marked ., Analyses by GC-MS/MS revealed that between P0 and P10 , at the time when the process of myelination begins and sex differences in myelin emerge , brain levels of testosterone and 5α-DHT are significantly higher in males than in females ., This is after the masculinizing surge of testosterone , which takes place around birth in males and only lasts for a few hours 13 , 44 , 45 ., Interestingly , an earlier biochemical work in rats already reported higher levels of ligand-occupied nuclear AR in male pups when compared with females during the postnatal period 46 ., This sex difference in AR ligand availability is consistent with a masculinizing action of postnatal androgens during developmental myelination ., A second important observation was that brain levels of 5α-DHT are markedly higher in males at P0 and P5 , but drop to low female-like levels at P10 ., This developmental period corresponds to the transient expression in the brain of the type 2 isoform of the 5α-reductase , which has a higher affinity for testosterone than the type 1 isoform and is normally expressed in peripheral androgen-target tissues such as the prostate 47 ., Although both testosterone and 5α-DHT act through a single receptor , 5α-DHT is a more potent agonist ligand of AR ., The conversion of testosterone to 5α-DHT thus amplifies the androgenic signal 48 ., Moreover , in contrast to testosterone , 5α-DHT cannot be converted to estrogens and its formation thus selects the AR signaling pathway ., The transient elevation in brain levels of 5α-DHT in males is likely to play a role in the masculinization of myelin ., Indeed , the density of EGFP+ oligodendroglial cells in corpus callosum and the length of myelinated processes below the genu were reduced in the P10 male brain by the AR antagonist flutamide , which also inhibits expression of the 5α-reductase type 2 47 , 49 ., Conversely , giving 5α-DHT to female pups between P0 and P10 resulted in a male-like density of oligodendroglial cells at P10 ., These two observations also provided a first line of evidence for a key role of AR signaling in the sexual differentiation of myelin ., Consistently , AR mRNA expression was higher in the postnatal male brain when compared with females ., We provide evidence for a key role of the brain AR in the masculinization of myelin by using ARNesCre mice with selective excision of AR in neural cells of the CNS ., Under the control of the nestin promoter , the Cre recombinase is expressed in neural precursor cells as early as embryonic day 10 34 ., Cre-dependent excision of the floxed exon 1 of the AR gene resulted in complete AR invalidation in the brain ., This genetic tool allowed us also to avoid the confounding effects of systemic hormonal changes caused by injections of flutamide or 5α-DHT ., At P10 , both mRNA and protein levels of MBP were markedly reduced in the brain of ARNesCre males when compared with control ARLox males ., Within the corpus callosum , MBP immunostaining , the densities of Olig2+ cells and of mature oligodendrocytes expressing CC1 or coexpressing CC1 and Olig2 , were also significantly reduced in ARNesCre males ., These observations demonstrate that sex differences in myelin observed at P10 are dependent on the presence of a functional AR in the male brain ., We then addressed the important question of a hormonal imprinting of developmental myelination by early postnatal androgens ., We first used organotypic cultures of cerebellar slices prepared from P10 male and female PLP-EGFP mice ., Remarkably , although cerebellar slices were cultured during 2 weeks in the absence of detectable levels of androgens , the myelin formed in vitro differed between sexes , with a higher density of EGFP+ oligodendroglial cells and of MBP+ staining density in males ., This observation was consistent with a persistent effect of the postnatal androgen environment on myelin prior to culture ., We then demonstrated that postnatal androgen-dependent sex differences in myelin persist into adulthood ., Treatment of male pups with flutamide between P0 and P10 reduced the density of EGFP+ cells in the adult corpus callosum to female-like levels ., Conversely , treatment of female pups with 5α-DHT between P0 and P10 significantly increased the density of callosal EGFP+ cells in adults ., It is important to emphasize that females treated during their first 10 postnatal days with 5α-DHT were no longer exposed to significant levels of androgens afterwards ., Thus , postnatal androgens have persistent effects on myelin , independent on the later hormone environment ., Because of the persistent and AR-dependent effects of postnatal androgens on myelin , a reduced density of oligodendrocytes and MBP expression could be expected in the corpus callosum of adult mice lacking functional AR ., This was indeed observed when comparing 3 months old ARNesCre males with ARLox males ., Moreover , analysis of corpus callosum axons at the electron microscopic level revealed a reduction in the percentage of myelinated axons and in the thickness of the myelin sheaths , reflected by an increased g-ratio in ARLox males ., Oligodendrocytes myelinate multiple internodes on different axons ., Thus , when their number is reduced , a single oligodendrocyte can be expected to myelinate more axonal segments or to form longer segments of myelin ( internodes ) ., In cerebral cortex , where internodes can be measured accurately , their mean length was significantly increased in ARNesCre males when compared with ARLox males ., Adjusting internode length has been identified as a means of regulating conduction velocity 50 ., However , the underlying determinants remain poorly understood ., Thus , variations in internode length have been proposed to result from neuronal signals during development or to reflect neuron-independent intrinsic properties of oligodendrocytes 51 ., A recent study has shown that in organotypic cultures of cerebral cortex slices , a reduced number of oligodendroglial cells resulted in longer internodes 52 ., Our results uncover a novel role of neural AR signaling in determining internode length ., Longer internodes in ARNesCre males and in females , when compared with control ARLox males , should result in faster conduction velocities ., However , ARLox males have thicker myelin sheaths , which reduce capacitance along internodes , thus allowing a faster propagation of action potentials ., We do not know whether the thicker myelin sheaths compensate for the shorter internodes in males , nor how the sex-specific characteristics of myelin affect information processing in the brain ., The analysis of conduction velocities and compound action potentials of fast conducting myelinated axons in mouse corpus callosum has so far not allowed identifying significant differences between males and females 53 ., However , these measures reflect mean responses of large numbers of electrically stimulated nerve fibers ., Thus , further studies are necessary to shed more light on this matter ., The myelin phenotype in the corpus callosum of ARTfm males , with a nonfunctional AR in all tissues , strongly resembled the one observed in ARNesCre males , including a lower density of oligodendroglial cells , a decreased MBP expression , a smaller percentage of myelinated axons and thinner myelin sheaths with higher g-ratio values ., Although in ARTfm mice , the absence of functional AR in all tissues is accompanied by endocrine abnormalities , this model has been widely used to probe the role of AR in shaping brain and behavior in rodents 54 ., Most important , related mutations of the AR gene in humans , also known as complete androgen insensitivity syndrome ( CAIS ) , suggest that functional AR are required to masculinize the human brain 55 ., Indeed , a recent study using diffusion tensor imaging has shown that individuals with CAIS show female-typical characteristic of white matter microstructure 56 ., Although influences of postnatal androgens on myelin are long lasting , they may not be entirely irreversible ., In the adult brain , myelin indeed shows structural plasticity and myelin remodeling has been proposed to participate in cognitive processes 57 , 58 ., Although the majority of oligodendrocytes are generated during the first postnatal weeks in mice , Oligodendrocyte Progenitors ( OP ) remain present in the adult brain , where they continue to differentiate into oligodendrocytes and form new myelin sheaths 59 , 60 ., The slow remodeling of myelin , which takes place throughout life and involves adult OP , could also be affected by the presence or absence of androgens ., Thus , long-term castration of adult male mice resulted after 3 months in a more female-like phenotype of myelin characterized by fewer oligodendrocytes 61 ., The role of androgens indeed goes beyond the sexual differentiation of myelin during development , as both testosterone and AR also play a key role in the regeneration of adult myelin ., We have recently shown that testosterone stimulates the formation of new myelin in a mouse model of severe and chronic demyelination ., In this study , we also identified the neural AR as a key target for the remyelinating actions of testosterone 36 ., Of note , after severe cuprizone-induced demyelination of the corpus callosum , testosterone treatment stimulated the formation of new myelin with a male-like phenotype in both sexes 36 ., Moreover , our recent study shows that after the acute demyelination of axons in the ventral white matter of the spinal cord , testosterone and a functional AR in the CNS are required for the spontaneous regeneration of myelin by oligodendrocytes 62 ., Despite a greater susceptibility to multiple sclerosis ( MS ) , women have a better prognosis with respect to disability progression than men 63 64 65 ., Sex-related differences in experimental autoimmune encephalomyelitis ( EAE ) , an accepted model of MS , are in line with these clinical observations 66 ., As an explanation to this disparity , other than distinct immune mechanisms in both sexes , hormone-dependent mechanisms of neuronal resilience have been proposed 67 ., The organization of the myelin sheaths may indeed impact their integrity and vulnerability to immune attacks 68 , 69 ., Thus , the long-term developmental effects of androgens on myelin assembly , observed in the present work , may contribute to sex differences in the maintenance and regeneration of the myelin sheaths and their vulnerability to immune attacks ., Therefore , the present observations in addition to our previous results , uncovering the efficacy of androgens as remyelinating agents 36 , 62 , provide a new conceptual framework for myelination and remyelination processes , with potential implications for demyelinating diseases such as multiple sclerosis ., All procedures were performed according to the European Communities Council Directive ( 86/806/EEC ) for the care and use of laboratory animals ., All mice except the ARTfm ( testicular feminization mutation , see below ) were bred in our animal facility under a 12 hours dark/light cycle with food and water ad libitum ., All mice were healthy with no obvious behavioral phenotypes , and none of the experimental mice was immune compromised ., Mouse lines used in this study are the following: newborn ( P0 ) , 5 days old ( P5 ) or 10 days old ( P10 ) C57Bl/6 wild type mice and mice expressing the enhanced green fluorescent protein under the control of the proteolipid protein gene promoter PLP-EGFP 23 at P5 , P10 and adulthood ., Mice of either sex were used and were randomly allocated to experimental groups ., ARTfm male mice , which carry a naturally inactivating mutation of the AR , were obtained from the French Atomic Energy Commission 41 ., We also generated mice lacking the androgen receptor in neural cells , using the Cre/Lox system ., A mouse line carrying a floxed exon 1 of the AR gene , located on chromosome X , was provided by CIE-CERBM of IGBMC ( Pr . Pierre Chambon ) 70 ., In this line , Cre recombination results in

Introduction, Results, Discussion, Matherials and methods

The oligodendrocyte density is greater and myelin sheaths are thicker in the adult male mouse brain when compared with females ., Here , we show that these sex differences emerge during the first 10 postnatal days , precisely at a stage when a late wave of oligodendrocyte progenitor cells arises and starts differentiating ., Androgen levels , analyzed by gas chromatography/tandem-mass spectrometry , were higher in males than in females during this period ., Treating male pups with flutamide , an androgen receptor ( AR ) antagonist , or female pups with 5α-dihydrotestosterone ( 5α-DHT ) , revealed the importance of postnatal androgens in masculinizing myelin and their persistent effect into adulthood ., A key role of the brain AR in establishing the sexual phenotype of myelin was demonstrated by its conditional deletion ., Our results uncover a new persistent effect of postnatal AR signaling , with implications for neurodevelopmental disorders and sex differences in multiple sclerosis .

Sex differences in brain structure are of great scientific and medical interest because the incidence and progress of many neurological and psychiatric disorders differ between males and females ., They affect neural networks and also the myelin sheaths that insulate and protect axons and thus allow the rapid conduction of electrical impulses ., In the central nervous system , myelin is formed by a particular type of cells named oligodendrocytes ., In the male mouse brain , the density of oligodendrocytes is greater and myelin sheaths are thicker when compared with females ., We show that these sex differences in myelin result from the long-lasting actions of androgens in males during their first 10 postnatal days ., Importantly , the postnatal masculinizing effects of androgens involve brain androgen receptors as shown by the use of pharmacological and genetic tools ., These findings are important for understanding sex-related differences in the susceptibility and progression of demyelinating diseases such as multiple sclerosis ., They also reveal a so far unknown role of androgen receptor signaling in sexual differentiation of the brain .

plant anatomy, stem anatomy, medicine and health sciences, internodes, nervous system, brain, neuroscience, macroglial cells, hormones, plant science, testosterone, nerve fibers, research and analysis methods, androgens, specimen preparation and treatment, staining, animal cells, axons, corpus callosum, glial cells, myelin sheath, biochemistry, cellular neuroscience, immunostaining, anatomy, cell biology, central nervous system, neurons, lipid hormones, biology and life sciences, cellular types

journal.ppat.1006179

Co-infecting Reptarenaviruses Can Be Vertically Transmitted in Boa Constrictor

Boid inclusion body disease ( BIBD ) is a transmissible , progressive and generally fatal disease of boid snakes ., First described in the 1970s , BIBD subsequently emerged as a major problem in boid snake collections worldwide 1 , 2 ., Several genera of boid species have been reported as susceptible to the disease , but its prevalence among snakes as well as its potential occurrence in wild populations is yet unknown 3 ., Clinically , BIBD is highly variable particularly in boas , where affected animals can be free of clinical signs , die from secondary infections , or develop neurological signs ., The latter are generally more pronounced in pythons ., The hallmark of BIBD are the characteristic intracytoplasmic electron dense inclusion bodies ( IB ) that are found in most cell types 1 , 2 , 4 , 5 ., The pathogenesis of BIBD is not yet characterized , and both subclinical as well as chronic disease has been described 2 , 6 ., A few years ago a novel group of arenaviruses were identified in and isolated from snakes with BIBD 4 , 5 , 7 ., Arenaviruses are negative-sense RNA viruses with two genome segments , L and S , which encode Z protein and RNA-dependent RNA polymerase , and glycoprotein precursor and nucleoprotein ( NP ) , respectively 8 ., Strong evidence of the causative relationship between reptarenavirus infection and BIBD is provided by the ability of reptarenavirus isolates to induce the pathognomonic IB in an in vitro model 4 , and by the fact that the IB contain or mainly consist of reptarenavirus NP 4 , 5 , 9 ., The identification of BIBD-associated arenaviruses led to the formation of a new genus , Reptarenavirus , in the family Arenaviridae , placing the previously known arenaviruses to another new genus , Mammarenavirus 8 ., More recently , we and others observed that snakes with BIBD often carry numerous distinct L and S segments , up to four S and 11 L segments were found in a single snake 10 , 11 ., The taxonomic classification of reptarenaviruses is currently under debate , and in this report we refer to the different L segments as representatives of different reptarenavirus species ( species share <76% identity in the L segment 8 ) ., The genomes of reptarenaviruses are highly variable 4 , 5 , 7 , 10–13 , as a consequence , the diagnosis of BIBD still relies mainly on the detection of IB in cells in tissues or in blood smears by light microscopy ., A recent study screened a large panel of blood samples from captive boid snakes , and found 19% of the snakes to be infected with reptarenavirus 14 ., Among Boa constrictors , 41 . 5% were infected , and 87% of the infected snakes were clinically healthy 14 ., The authors also compared various detection techniques and found immunohistochemistry ( IHC ) on blood cells , using a monoclonal anti-reptarenavirus NP antibody , to provide results comparable to hematoxylin-eosin ( HE ) stained peripheral white blood cells as the standard of diagnosis 14 ., However , a few BIBD positive ( 3/25 ) samples were negative in IHC 14 , which provides further evidence of the high variability among reptarenaviruses ., So far , the route of transmission and the incubation period of reptarenaviruses are unknown , and direct contact or vector mediated transmission by snake mites ( Ophionyssus natricis ) have been proposed 1 , 15 ., In line with the “transmission through a vector” hypothesis , we recently reported the growth of reptarenaviruses also in arthropod cell lines 15 ., Vertical transmission is defined as any transfer of an infectious agent from one generation to the next , including transmission through gametes ( i . e . oocyte or spermatocyte ) , transplacental transmission or perinatal infections 16 ., Mammarenaviruses can be vertically transmitted in their reservoir rodent hosts 17–20 ., Prenatal infection plays an important role in arenavirus maintenance , and , at least in the case of Lymphocytic choriomeningitis virus ( LCMV ) , Machupo virus ( MACV ) and Lassa virus ( LASV ) , leads to chronic infection 21 ., The vertical transmission of BIBD from dam to offspring in both egg-lying and live-bearing snakes has been considered by Chang and Jacobson 1 ., Reptiles are divided into oviparous ( egg layer ) or viviparous ( live bearers ) species ., They represent an important phylogenetic intermedium between anamniotes and amniote vertebrates , displaying all three embryonic membranes: the chorion , allantois and amnion 22 ., Viviparous snakes , including B . contrictor , have a simple placenta that is responsible for gas exchange , and water and nutrient supply 23 ., A thin eggshell exists between foetal and maternal placenta but it deteriorates in late gestation , allowing direct contact between the foetal ( chorioallantois ) and maternal ( uterine epithelium ) placenta 23 ., Normally foetal and maternal epithelia remain intact and the maternal and fetal blood does not mix ., Studies on the vertical transmission in reptiles are scarce and include only few viruses , such as equine encephalitis virus 24 , adenovirus 25; Herpesvirus M 26 , 27 and , very recently , Sunshinevirus 28 ., We set up this study to determine whether reptarenaviruses can be vertically transmitted ., For this purpose , five B . constrictor clutches , represented by parental animals diagnosed with BIBD by traditional methods , or RT-PCR positive for reptarenavirus , and their offspring , ranging from embryos in the first trimester to 20-month-old juveniles , were examined ., We applied next-generation sequencing ( NGS ) to identify the reptarenaviruses of each clutch , which we will refer to as the “reptarenavirome” throughout the manuscript ., We utilized virus-specific RT-PCRs to confirm the reptarenavirus L and S segments identified by NGS ., Primary cell cultures originating from the embryos served to evaluate the potential of the infecting viruses to induce IB formation and thereby also the disease ., The diagnosis of BIBD is confirmed when the characteristic eosinophilic cytoplasmic IB are seen within cells ., These IB contain abundant reptarenavirus NP which can be visualised by immunohistology ( IH ) 4 , 5 , 9; RT-PCR can serve to confirm reptarenavirus infection ., We verified the parental animals as BIBD positive and/or positive for reptarenavirus infection using histology and IH ., The detection of the IB in cells in cytological and/or histological specimens is currently the widely accepted gold standard for the diagnosis of BIBD , since the IB are pathognomonic; IH confirms the presence of reptarenavirus NP in the cells 4 ., For clutches 1 and 3–5 histology and IH were complemented by RT-PCR , which was set up at the time when only four reptarenaviruses were fully sequenced ( referred to as “initial RT-PCR” ) ; it targets the L segment of GGV , UHV , and Boa AV NL B3 ( Table 1 and Fig 1A and 1B ) ., Interestingly , the blood of both parental animals in clutch 4 was RT-PCR-positive , but no IB were detected in blood cells ., However , the subsequent post mortem analysis of the father revealed IB formation and expression of viral antigen in tissues , confirming BIBD ( Table 1 ) ., For clutch 1 , comprised of seven embryos in late first trimester ( age determined based on the body length of 15 to 17 cm ) , five embryos were processed for ( immuno ) histological examination ., These did not exhibit IB formation , but exhibited weak reptarenavirus antigen expression in occasional cells in brain , liver and kidneys ( Fig 1C ) ., The remaining two embryos ( E1 . 6 and E1 . 7 , Table, 1 ) were used to establish primary cell cultures ., These showed viral antigen expression , but no distinct IB formation ( Fig 1D; Table 1 ) ., The initial RT-PCR showed the presence of reptarenavirus RNA in the mother and embryos E1 . 1 , E1 . 6 , and E1 . 7 ., ( Table 1 ) ., For clutch 2 ( early first trimester embryos with a body length of 5–6 cm ) , similar results were obtained ., Two of the three embryos ( E2 . 1 and E2 . 2 ) were used to establish primary cell cultures , which also showed viral antigen expression but no IB ., The cell cultures remained persistently infected throughout the study period as confirmed by the expression of viral RNA and antigen ., The third embryo ( E2 . 3 ) was processed for histology and did not exhibit IB but showed occasional weak viral antigen expression in the brain ( Table 1 ) ., Clutch 3 comprised five animals , three of which had been perinatally aborted ( PNA3 . 1 to 3 . 3 ) ., Two of these ( PNA3 . 1 and 3 . 2 ) were tested reptarenavirus RNA positive , using the initial RT-PCR on the brain , and one ( PNA3 . 2 ) exhibited IB and reptarenavirus antigen in the tissues ( Fig 1E and 1F ) ., The remaining two animals ( J3 . 1 and 3 . 2 ) were euthanized as juveniles two months later ., Both were tested positive by the initial RT-PCR on the brain and one also exhibited IB and reptarenavirus antigen in tissues ( Table 1 ) ., The two perinatal abortions of clutch 4 were shown to be infected , using the initial RT-PCR , but did not exhibit IB formation or reptarenavirus antigen expression ., Clutch 5 comprised 21 animals ., Of the seven juveniles euthanized at the age of eight months , six were diagnosed with BIBD , based on the detection of IB and viral antigen in all examined tissues ( Fig 1G and 1H ) , and three of these ( 3/6 ) were found positive in the blood by the initial RT-PCR ., At the time of euthanasia the samples were collected purely for diagnostic purposes , and unfortunately no samples were stored for RNA isolation ., The remaining ( 1/7 ) animal ( J5 . 5 ) was negative in all these tests ., Another 11 siblings were euthanized at the age of 12 months ., In nine of these , BIBD was confirmed , with the presence of IB and reptarenavirus antigen in tissue and blood cells and a positive result in the initial RT-PCR ., Two ( 2/11 ) ( J5 . 8 , J5 . 11 ) were BIBD-negative , but RT-PCR positive in the brain ( Table 1 ) ., The last four ( 4/21 ) animals were kept by the breeder until they were euthanized at the age of 18 mo ( n = 2 ) and 20 mo ( n = 2 ) due to the breeder’s concern that they suffered from BIBD ., These all tested positive for BIBD by histology , IH and initial RT-PCR ( Table 1 ) ., The primers used for RT-PCR in the preliminary screening were designed for the detection of a subset of reptarenaviruses ( GGV , UHV , and Boa AV NL B3 ) at a time when only four reptarenaviruses were known ., Subsequently , we and others 10 , 11 observed that snakes with BIBD are often co-infected with multiple reptarenavirus species ., Therefore , we decided to utilise NGS for further analyses ., The NGS study included the first four clutches , but was limited to the animals of which frozen material was available ( Table 1 ) ., We removed the reads matching a known snake genome ( Python bivitattus ) from the NGS data and performed de novo genome assembly ., The generated contigs were checked using BLAST ( Basic Local Alignment Search Tool , https://blast . ncbi . nlm . nih . gov/Blast . cgi ) , and although occasional hits to bacterial sequences were identified in some samples , only reptarenavirus sequences were consistently recovered ., Similarly to our earlier observation 11 , several full-length or almost full-length ( at maximum some 200–300 nt missing ) reptarenavirus L and S segments were recovered from the parental samples ., The coverages ( Bowtie 2 , 29 ) of the reptarenavirus L and S segments derived from the parental animals were >10 ( lower coverage at the very last ~50 nts ) ( S2 Table ) ., In parental animals from breeder 1 ( clutches 1 and 3 ) , the following results were obtained: The mother of clutch 1 was positive for six L ( Aurora borealis virus-4 , ABV-4 , GenBank accession KX527594; Tavallinen suomalainen mies virus-1 , TSMV-1 , KX527595; Hans Kompis virus-1 , HKV-1 , KX527596; Keijut pohjoismaissa virus-1 , KePV-1 , KX527597; Bis spöter virus-1 , BSV-1 , KX527598; Suri Vanera virus , SVaV-2 , KX527599 ) and two S ( S6-like , KX527580; S5-like , KX527581 ) segments , and the mother of clutch 3 was positive for seven L ( SVaV-2 , KX527587; Kuka mitä häh virus-1 , KMHV-1 , KX527588; KePV-1 , KX527589; University of Helsinki virus-4 , UHV-4 , KX527590; TSMV-2 , KX527591; ABV-4 , KX527592; Grüetzi mitenand virus-1 , GMV-1 , KX527593 ) and two S ( S6-like , KX527578; S5-like , KX527579 ) segments ., Curiously , the brain of the father of clutch 4 was positive for only one pair of L ( TSMV-2 , KX527582 ) and S ( TSMV-2 , KX527575 ) segments , whereas no reptarenavirus genomes were recovered by NGS from the mother despite clear evidence of BIBD ( Table 1 ) ., The mother of clutch 2 owned by breeder 2 was positive for four L ( ABV-3 , KX527583; Kaltenbach virus-1 , KaBV-1 , KX527584; SVaV-1 , KX527585; UHV-3 , KX527586 ) and two S ( ABV-2 , KX527576; University of Giessen virus-1-like , UGV-1-like , KX527577 ) segments , whereas no reptarenavirus genomes were recovered from the serum of the father , whose blood cells were also found negative for IB in the cytological examination , providing further evidence that he was indeed not infected at all ., The NGS results for the different clutches are summarized in Table 2 and a phylogenetic tree of the de novo assembled L and S segments with database sequences is shown in Fig 2A and 2B ., The phylogeny indicates that the reptarenaviromes of the two snake collections ( which never exchanged animals; personal communication ) share some common species but also comprise unique viruses ., Initially de novo assembly was attempted for several embryos ( E1 . 1 , E1 . 2 , E1 . 7 , E2 . 1—E2 . 3 ) , however , this approach was not successful , likely due to inefficient removal of the genomic background during NGS library preparation and low amounts of viral RNA ., Instead , we used the reptarenavirus genomes obtained from the parental animal to “fish out” i . e . to map the matching reads from the embryos , an approach we then also took for clutches 3 and 4 ., However , only scattered reads matching the parental viruses could be recovered from the NGS data for most embryos ( S2 Table ) ., Thus we decided to confirm the NGS findings by conventional RT-PCR using virus species-specific ( VSS ) primers , primers of our previous study 11 and primers designed based on the de novo assembled arenavirus genomes ( primer sequences in S1 Table ) ., For most clutches we also included additional samples , from tissues or cell cultures generated from the embryos , into the RT-PCR analysis ( Tables 2 and 3 ) ., For the three embryos of clutch 1 , the mapping yielded reads matching five ( E1 . 1 ) , two ( E1 . 2 ) and three ( E1 . 7 ) of the six L segments and both S segments ( all embryos ) identified in the mother ., For the primary cell culture of E1 . 7 , the reads each covered the entire segments , which might be a consequence of the higher virus content in the supernatant compared to the tissues which were examined for E1 . 1 and E1 . 2 ., The VSS RT-PCRs confirmed the presence of several to all parental L and S segments in the embryonal tissues ( E1 . 1 and E1 . 2 ) and cultured brain cells ( E1 . 6 and E1 . 7 ) ( Tables 2 and 3 ) ., For clutch 2 , reads matching two L and both S segments were identified by mapping the NGS data of E2 . 1 ( kidney cell culture ) , E2 . 2 and E2 . 3 ( both tissue homogenates ) against parental viruses ( S1 Table ) ., The VSS RT-PCRs confirmed the NGS findings and identified the parental L and S segments also in homogenates of salpinx and placenta and in cultured cells from umbilicus , placenta and organs ( Tables 2 and 3 ) ., For clutch 3 , we identified reads matching three L and two S segments of the maternal viruses for two perinatal abortions ( PNA3 . 1 and 3 . 3 ) and reads matching each two L and S segments for the third ( PNA3 . 2 ) by the mapping approach ., VSS RT-PCRs on samples from several organs ( brain , kidney , liver ) confirmed the NGS findings ., They also identified maternal L and S segments in the liver and kidney of the juvenile snakes euthanized at the age of 2 months ( Tables 2 and 3 ) ., For clutch 4 , the mapping approach yielded a few reads matching both the L and S segment of the virus identified in the father in one perinatal abortion ( PNA4 . 1 ) , and for the second ( PNA4 . 2 ) , only a single read matching the L segment ., Since the subsequent VSS RT-PCR of the PNA samples yielded only a weak reaction for the TSMV-2 L segment , we then applied all L segment primers available from the different viruses to RNA extracted from paternal blood and lung , and from the maternal blood sample ., Curiously , while the brain of the father remained positive for only a single virus , the blood contained a further 7 reptarenavirus L segments , three of which were also found in the maternal blood ., VSS RT-PCRs then identified several paternal L and S segments in the tissues of both perinatal abortions ( Tables 2 and 3 ) ., Since the results obtained from clutches 1 , 3 and 4 suggested that we had characterized the “reptarenavirome” of breeder 1’s collection , we did not perform NGS for clutch 5 , but tested the father and several of his 12-month-old juvenile offspring , which were in the majority confirmed to suffer from BIBD based on the presence of viral IB and viral antigen in tissues , with all L and S segment VSS RT-PCRs of the present and an earlier study 11 ., The father was positive for four of these viruses , and the juveniles were all found to carry at least two of their father’s L segments ( Tables 2 and 3 ) ., The results for the L segment VSS RT-PCRs for each clutch are summarized in Fig 3 ., The raw data for VSS RT-PCRs are shown in S1–S5 Figs ( S1 Fig , S2 Fig , S3 Fig , S4 Fig and S5 Fig ) ., So far , studies on the transmission of reptarenaviruses are scarce , and transmission via direct contact , through droplets or aerosols , or via vectors has been discussed 1 , 2 ., In this study on naturally infected captive animals we combined classical and more modern techniques and could demonstrate that reptarenaviruses and BIBD can be vertically transmitted ., The study included five B . constrictor clutches with BIBD-positive parental animals , and by NGS combined with de novo genome assembly we could retrieve nearly complete reptarenavirus L and S segments in three of the four studied parental snakes ., Because the different L segments identified in the parental animals were <76% identical to each other , we interpreted their identification as evidence of reptarenavirus co-infection ., We could further show that co-infecting reptarenaviruses are often co-transmitted vertically from parents to offspring ., By combining NGS and virus species-specific ( VSS ) RT-PCRs we could confirm the vertical transmission ( s ) and show that the offspring retains co-infecting viruses over a long period of time , i . e . for at least 12 months after birth ., Currently the strongest evidence of reptarenaviruses being the causative agents of BIBD is the fact that the IBs pathognomonic to BIBD 2 consist mainly , if not solely , of reptarenavirus NP 4 , 5 , 9 , 14 ., Although this does not rule out the possibility of another , yet unidentified , microbe for example an ( endogenous ) retrovirus contributing to the development of the disease , it clearly demonstrates that reptarenavirus infection is a prerequisite for BIBD ., In the embryos , reptarenavirus infection was not associated with IB formation; however , viral antigen was found in occasional cells in brain , liver and kidneys ., Furthermore , primary cell cultures derived from embryos of BIBD positive mothers promoted ( part of ) the maternal reptarenavirome and also showed viral antigen expression ., IB formation was seen in older offspring , first in one of the PNA , consistently in all virus genome-positive juveniles from 2 months of age , confirming that reptarenavirus infection in vivo does indeed provoke all the characteristics of BIBD ., Vertical transmission occurs in the reservoir hosts of many arenaviruses ., For example , LCMV and MACV can be transmitted transovarially 17 , 18 and/or transplacentally 19 ., Additionally , infection through semen or maternal blood has been suggested for MACV and Latino virus 20 ., Prenatal infection plays an important role in virus maintenance , since for some arenaviruses ( LCMV , MACV , and LASV ) it may lead to chronic infections 21 ., For reptarenaviruses , the precise mode of vertical transmission is not yet known , but our study provides evidence that the viruses of both mother and father can be passed to the offspring , and that the transmission can occur already early in gestation ., We were able to isolate viruses also from cell cultures originating from placenta , salpinx , and umbilicus ., Since the B . constrictor embryo does not get into contact with the maternal blood , this indicates that transmission from the mother could also result from contact between maternal tissues and the chorioallantois ., However , more detailed studies on the reproductive tract of snakes with BIBD are needed to elucidate the exact mechanisms of transmission from both the maternal and paternal animal ., The convention among snake breeders that also both breeders in our study followed is that the neonates are removed from the mother’s cage within a few hours ., The clutch is then housed separately until the first shedding at 6–12 days of age , after which the animals are separated and housed in individual cages 30 ., This , together with the strict hygiene rules that are applied , does not exclude transmission of viruses between siblings during their first days of life , but renders horizontal infection unlikely thereafter ., It was overall surprising to see how many offspring exhibited reptarenavirus infection without evidence of IB formation or viral antigen expression ( 4/5 perinatal abortions , one 2-month-old juvenile ) or without IB formation and only occasional cells expressing viral antigen ( all tested embryos , two 12-month-old juveniles ) , i . e . BIBD ., Also , the fact that we found BIBD-negative animals to carry reptarenaviral RNA in the blood suggests that viremia may occur frequently , not only in association with the disease , but also in seemingly healthy animals ., However , light microscopy and IH are comparatively insensitive methods , and thus the above findings could also be due to low level viral replication ., Alternatively , our findings could indicate that reptarenavirus infection has a long incubation period , and that both endogenous and exogenous factors can influence the development of BIBD ., It has recently been suggested that transient reptarenavirus infections can occur 31 ., Although we cannot disprove this assumption , the fact that the vast majority of juvenile offspring from snakes with BIBD in our study eventually developed BIBD suggests that at least prenatal reptarenavirus infections generally persist ., We recently observed that snakes with BIBD rarely exhibit anti-reptarenavirus antibodies 32 ., This could indicate that prenatal infection results in tolerance to reptarenaviruses , allowing persistent infection ., Chang and co-workers recently reported that the vast majority of reptarenavirus infected and BIBD positive B . constrictors are clinically healthy 14 , which would be in line with the above hypothesis ., Further studies are required to show if the hypothesis is correct and what determines the subsequent IB formation ., Our observation on the vertical transmission of co-infecting viruses sheds light on the potential evolution of reptarenaviruses ., The research field of “reptarenavirology” is fairly young , and the taxonomical classification scheme of these viruses is yet to be determined ., After the most recent report from the arenavirus study group of the International Committee on Taxonomy of Viruses ( ICTV ) 8 our group and the group of Stenglein and co-workers reported a multitude of complete L and S segments identified by NGS in tissues of snakes with BIBD 10 , 11 ., Both groups also observed a seemingly unbalanced ratio of L and S segments in a single individual , similarly to what we report herein ., These findings are the challenge for the classification of reptarenaviruses ., For the present report we followed the ICTV 8 and considered the different reptarenavirus L segments to derive from different reptarenavirus species when their nucleotide sequence identity was below 76% ., We took this approach , because currently not all of the information required to fulfil all criteria of a virus species are available ., We also hypothesize that in the past , i . e . before multiple cross-species transfers ( between and from the unknown reservoir hosts ) , each L and S segment pair formed a definite , classifiable species ., For mammarenaviruses it has been reported that the persistent infection of cell cultures with one mammarenavirus excludes the replication of homologous and antigenetically related viruses , but enables the growth of non-related mammarenaviruses 33 ., Similarly , we have only identified L segments of different reptarenavirus species ( based on the criteria above ) in snakes with BIBD ., Assuming that there is ( or was ) a reservoir host for each reptarenavirus species , it can be hypothesised that , with more relaxed hygiene regimens , housing different snake species in the same facilities has enabled cross-species mixing of the viruses ., Co-infection might then have enabled the mixing of L and S segments , and reassortment , and vertical transmission of these persistently infecting viruses may have contributed to the plethora of reptarenaviruses that we now detect in captive boid snakes ., The apparent existence of more viral L than S segments might be related to the fact that the S segment harbours the viral glycoproteins ., As these are essential for host cell entry , the S segment that guarantees the most efficient gene transfer might be enriched during co-infections ., The selection pressure on the S segment may further be enforced by the functions of the NP in viral replication 34 ., If the L and S segments could pair more or less freely with each other , the selection pressure on the L segment ( harbouring the RNA dependent RNA polymerase and the viral matrix protein ) would be less strong ., It is also possible that more than a single pair of L and S segments are packed inside the virion , which would render the taxonomical classification of reptarenviruses even more complex ., Currently there is no data on the factors enabling or disabling the pairing of different L and S segments ., Since protein-protein interactions , among other factors , contribute to the formation of progeny viruses and cell-cell transmission , we speculate that L and S segment pairing would not occur in a completely random fashion ( otherwise one would expect to have roughly equal numbers of known L and S segments ) ., Further studies are needed to tackle these questions and to prove or disprove the above hypothesis , which only represents a simplified version of reality ., In order to avoid infection and/or spreading of the disease within a collection , it would be essential to know all the factors behind reptarenavirus transmission ., A six-month quarantine is generally recommended before a new animal is released into a collection , but whether this is sufficient to avoid reptarenavirus transmission is so far unknown 35 ., The results that we obtained from clutch 5 indicate that it can take several months before a prenatally infected snake exhibits definite signs of BIBD ., In any case , our results demonstrate that animals with BIBD/reptarenavirus infection should not breed , since the likelihood of offspring to become infected is high ., All animals included into the study were snakes that were submitted by their owners to the Department of Veterinary Pathology , Vetsuisse Faculty , University of Zurich , Switzerland ., They were euthanized according to ASPA , Animals ( Scientific Procedures ) Act 1986 , schedule 1 ( appropriate methods of humane killing , http://www . legislation . gov . uk/ukpga/1986/14/schedule/1 ) procedure and a full diagnostic post mortem examination was performed ., Tissue samples from the dead animals were subjected to the different tests with owners consent ., The owners consented both to the euthanasia and the use of collected samples in this study ., Because of suspected BIBD no ethical permissions were required for euthanasia nor the diagnostic-motivated necropsies ( both routine veterinary purposes ) ., The study was performed on five B . constrictor clutches from two breeders residing in Switzerland ., The two breeders confirm that they have never exchanged animals with each other ., All animals were examined for diagnostic purposes , i . e . BIBD diagnosis , upon the owners’ request , which was undertaken on a blood smear and/or by a full post mortem examination ., Parental animals that were not euthanized were bled from the tail vein or by cardiac puncture to prepare a blood smear ., For necropsy , animals were narcotized with CO2 followed by decapitation and immediate destruction of the brain by longitudinal sectioning ., Immediately after euthanasia , a full post mortem examination was performed ., The following B . constrictor snakes were examined ( Table 1 ) ; clutch 1: a BIBD-positive ( blood smear ) pregnant female ( euthanized due to emaciation and poor general health ) with seven embryos in the first third of gestation; clutch 2: a BIBD-positive pregnant female ( euthanized due to the owner’s suspicion of illness and BIBD ) with three embryos in the first third of gestation , the father was subsequently tested on blood smears; clutch 3: three perinatal abortions and two siblings euthanized at the age of two months for diagnostic purposes , blood tested from the mother for BIBD diagnosis; clutch 4: two perinatal abortions , blood tested from mother and father for BIBD diagnosis; clutch 5: 22 juveniles ( seven euthanized at the age of eight months , eleven at 12 months , two at 18 months , two at 20 months ) for BIBD diagnosis due to positivity of the father , euthanasia and post mortem examination of the father due to emaciation and chronic pyogranulomatous bacterial rhinitis ., The clutch had been separated from the mother within 8 h after birth and individual animals housed separately since after the first shedding at 6–12 days of age ., From all necropsied animals , tissue samples were collected from a range of organs ( brain , heart , lung , liver , pancreas , kidney , spleen and–in selected cases–spinal cord ) , fixed in 10% buffered formalin , and routinely paraffin wax embedded for histological and immunohistological examinations ., Selected embryos were fixed and paraffin wax embedded in toto , others were subjected to RNA extraction and/or establishment of cell cultures ( Table 1 ) ., For adult and juvenile snakes blood smears were prepared and air-dried for cytological examination , and the remaining blood was centrifuged at 1 , 000 x g for 5 min to separate serum and blood cells ., The samples for RNA extraction and/or virus isolation were collected and frozen freshly at -80°C without fixative or processed immediately ., Blood smears were stained with May-Grünwald-Giemsa and a cytological examination was performed to determine the presence of the pathognomonic cytoplasmic IB within blood cells , as previously described 4 ., From paraffin blocks , consecutive sections ( 3–5 μm ) were prepared , stained with hematoxylin-eosin ( HE ) for the identification of the cytoplasmic IB , and subjected to immunohistological staining , using a rabbit anti-UHV NP antibody 15 for the demonstration of reptarenavirus antigen , as described 4 ., Consecutive sections incubated with a non-reactive rabbit polyclonal antiserum served as negative controls ., From selected embryos ( Table 1 ) , samples of brain , heart , liver , kidney , umbilical cord and/or placenta were aseptically collected and subjected to tissue culture ( 30°C , 5% CO2 ) , as described 4 ., After passaging of the established cell cultures , aliquots of the cultures ( cell-rich

Introduction, Results, Discussion, Materials And Methods

Boid inclusion body disease ( BIBD ) is an often fatal disease affecting mainly constrictor snakes ., BIBD has been associated with infection , and more recently with coinfection , by various reptarenavirus species ( family Arenaviridae ) ., Thus far BIBD has only been reported in captive snakes , and neither the incubation period nor the route of transmission are known ., Herein we provide strong evidence that co-infecting reptarenavirus species can be vertically transmitted in Boa constrictor ., In total we examined five B . constrictor clutches with offspring ranging in age from embryos over perinatal abortions to juveniles ., The mother and/or father of each clutch were initially diagnosed with BIBD and/or reptarenavirus infection by detection of the pathognomonic inclusion bodies ( IB ) and/or reptarenaviral RNA ., By applying next-generation sequencing and de novo sequence assembly we determined the “reptarenavirome” of each clutch , yielding several nearly complete L and S segments of multiple reptarenaviruses ., We further confirmed vertical transmission of the co-infecting reptarenaviruses by species-specific RT-PCR from samples of parental animals and offspring ., Curiously , not all offspring obtained the full parental “reptarenavirome” ., We extended our findings by an in vitro approach; cell cultures derived from embryonal samples rapidly developed IB and promoted replication of some or all parental viruses ., In the tissues of embryos and perinatal abortions , viral antigen was sometimes detected , but IB were consistently seen only in the juvenile snakes from the age of 2 mo onwards ., In addition to demonstrating vertical transmission of multiple species , our results also indicate that reptarenavirus infection induces BIBD over time in the offspring .

Members of the genus Reptarenavirus are “newcomers” of the family Arenaviridae and have been associated with boid inclusion body disease ( BIBD ) , an economically important , fatal disease of captive boid snakes ., Recently , we and others observed that snakes with BIBD commonly harbour several S and L segments ( arenaviruses have a bisegmented genome ) , which we refer to as co-infection ., The above renders reptarenaviruses rather unique and a model for studying viral co-infection ., We herein report that reptarenaviruses , and remarkably a whole set of co-infecting reptarenavirus species ( based on the nucleotide difference in the L segment ) , can be transmitted vertically i . e . from parents to offspring ., While the parental animals had BIBD , we did not find evidence of the intracytoplasmic inclusions characteristic to BIBD in the infected embryos and perinatal abortions ., However , we could confirm the development of BIBD in offspring from an age of 2 months ., Our findings further suggest that vertical transmission can , and likely has , significantly influence ( d ) the evolution of reptarenaviruses , since co-infection will allow reassortment of the viral genomes .

reverse transcriptase-polymerase chain reaction, sequencing techniques, medicine and health sciences, body fluids, biological cultures, brain, vertebrates, animals, next-generation sequencing, developmental biology, reptiles, genome analysis, cell cultures, molecular biology techniques, embryos, kidneys, research and analysis methods, embryology, genomics, artificial gene amplification and extension, molecular biology, hematology, snakes, blood, anatomy, polymerase chain reaction, squamates, physiology, transcriptome analysis, genetics, biology and life sciences, renal system, computational biology, dna sequencing, amniotes, organisms

journal.pcbi.1000699

Estimating the Stochastic Bifurcation Structure of Cellular Networks

One of the primary goals of systems biology is to uncover the dynamics of cellular networks ., Sometimes , this has meant collecting time-series data and applying tools for time-series analysis such as Fourier methods to identify periodically expressed genes 1–3 or temporal clustering to identify different dynamic “modes” 4–6 ., In other cases , it has meant the construction of explicit state-based dynamical models , based either on qualitative expectations of system behavior 7–10 or based more directly on quantitative experimental data 11 , 12 ., Another common goal has been to characterize the steady-state behavior of the network , which is of particular interest if the system exhibits multistability 13–15 ., In these cases , the steady-states , along with their basins of attraction , have been likened to distinct cell types 16–18 , and thus define the repertoire of “behaviors” available to the cell ., Mathematically , the analysis of steady states falls into the domain of bifurcation theory , which addresses the existence , number and stability of fixed points or limit cycles/attractors of dynamical systems and how these change as a function of system parameters or inputs 19 ., Usually , this analysis is performed on deterministic mathematical models such as differential equations or difference equations ., Here , we are concerned with the experimental and computational quantification of bifurcation-like behavior in stochastic genetic switches ., There is considerable evidence that signalling networks in a population of genetically-identical cells exhibit large cell-to-cell variability in their output , despite operating in a homogeneous external environment ( see e . g . , 20 , 21 ) ., In some cases , inherent fluctuations in the internal state of the cells leads to distinguishable subpopulations , even when cells are genetically identical and experience a homogenous environment ., For example , a ubiquitous network motif is the bistable genetic switch , with output variability distributed about high and low states dependent upon the level of an external input signal 13 , 22–25 ., Accurate estimates of bifurcation structure from noisy experimental can provide important qualitative , and in some cases quantitative , information about system behavior , guide model development and parameter estimation efforts , or help to discriminate among competing hypotheses regarding network architectures ., For example , recent work has demonstated that the statistics of the fluctuations about the steady-states provides significant constraints on kinetic parameter estimation 26 ., Two ingredients are necessary for empirical analysis of the bifurcation behavior of a cellular network ., One is single-cell measurements of one or more cellular variables , such as gene expression ., Technologies such as microarrays , SAGE or quantitative mass spectrometry , which operate on collections of cells or whole tissues , obscure potential heterogeneity in the sample ., They do not discriminate , for example , between a 100% increase in expression of a gene , and a 200% increase in its expression in 50% of the cells ., With technologies such as fluorescent cell imaging and flow cytometry , however , the state of each cell can be ascertained ., As a result , one can determine whether the cell population is homogenous or if it comprises a set of subpopulations—each undergoing different dynamical behaviors corresponding to different growth strategies , differentiation endpoints , etc ., The other necessary ingredient is a method for experimental manipulation of some system parameter ( s ) or environmental condition ( s ) , in order to study how subpopulations change under varying conditions ., This may mean changing the concentration of ligands or nutrients in the cellular environment or artificially manipulating the activity of regulatory factors inside individual cells ., For example , Ozbudak et al . 13 recently used single-cell fluorescence microscopy to establish an empirical map of the two-dimensional bifurcation diagram for the lactose utilization network in Eschericia coli as a function of the systematic variation of two environmental parameters ., Moreover , targeted disruption of feedback loops within the galactose utilization network of Saccharomyces cerevisiae has provided key insights into the control of cell-cell variability in gene expression and mechanisms underlying the stochastic switching between distinct epigenetic expression states 25 , 27 ., Increased use of these techniques demands the establishment of methods for analyzing the generated data in a statistically robust and computationally efficient manner ., The organization of this paper is as follows ., First , we discuss traditional bifurcation analysis in greater detail , introducing in particular saddle-node bifurcations , a type of bifurcation widely associated with the dynamics of gene regulatory switches ., We then describe the necessity of generalizing the notion of bifurcation behavior to account for the inherent noise ( stochasticity ) in cellular networks ., Next , we present the data that motivated our study—single-cell flow cytometry data measuring activity in the yeast galactose utilization network over a range of extracellular galactose concentrations ., We then report on two broad approaches to analyzing this data and extracting estimates of bifurcation structure , namely , mixture density modeling and conditional mixture density modeling ., We evaluate the relative strengths of these approaches , and describe a number of novel qualitative and quantitative observations about switching in the galactose network ., Bifurcation analysis is a branch of dynamical systems theory concerned with steady-state or asymptotic behaviors of a dynamical system 19 ., Typically , bifurcation analysis is applied to a deterministic dynamical model , such as a system of difference equations or differential equations ., To give a concrete example inspired by the data presented and analyzed later in this paper , imagine a situation where a single gene is activated by an input signal , representing , for example , the activity of transcription factor protein ., Let denote the genes protein product ., Suppose that the gene is an auto-activator: the protein product acts as a transcription factor to upregulate its own expression ., Following standard modeling approaches ( e . g . 28 ) we describe the time-varying behaviour of the protein abundance by the differential equation ( 1 ) where the parameter corresponds to a basal level of protein production , is the maximal additional production attributable to regulation , and characterize the effects of the activators , and indicates the rate of protein degradation or dilution due to cell growth ., Figure 1A is a bifurcation diagram for this system , showing the steady state values of as a function of the input , which in this context is called the bifurcation parameter ., Intuitively , if levels of are low , then little is produced and the system reaches a steady state at a low level of ., Conversely , if is highly abundant , then a great deal of is produced , leading to a high steady state ., Most interestingly , when lies in and intermediate range , three steady states coexist ., Intermediate levels of and a large initial amount of will stimulate sufficient production to maintain at a high concentration ., However , if initially the level of is low , production is not maintained , and the system reaches a low steady state ., There is also a third , unstable steady state between the low and high steady states ., The values of at which the number of steady states changes , i . e . , the turns of the ‘S’-shaped curve in Figure 1 , are called bifurcation points and correspond in a deterministic system to the critical values of where a small change in this parameter may cause the system to transition between states of low and high levels of ., In contrast with deterministic models , real cellular networks can be significantly noisy , with system variables fluctuating over time for a variety of reasons , including , for example , fluctuations in biochemical reaction rates , random partitioning of cellular content at cell division , and variation in cell size and cell age ( see e . g . , 21 ) ., Thus , if one were to observe multiple instances of a bistable system—say , a culture of genetically identical cells experiencing a homogeneous medium—one would not expect the experimental measurements to agree with the predictions of a deterministic model , even after the culture has attained a steady behaviour 29 ., Noticeably , stochastic fluctuations will constantly push individual cells on excursions away from a stable expression state , causing a broadening of the population distribution around this state ., The mean of the population distribution will reflect the steady state expression only when these excursions are symmetric , and the mode of the distribution , which corresponds to the state where the system on average spends most time , may be the better surrogate of deterministic steady states in a stochastic dynamical system ( e . g . , 30 ) ., In a bistable system , fluctuations can induce stochastic transitions between the two expression states such that some cells are expressing at low level while others express at high levels ., The result is the emergence of a bimodal population distribution and subpopulations with distinct expression characteristics ., Figure 1B depicts what the steady state distribution for might look like as a function of , assuming the stochastic system would show a lognormal distribution for about the deterministic steady states ., ( For a graph of real data from that galactose network , see Figure 2 . ), In this case , the time-invariant steady state distribution of the system is reached when the probability that a cell will switch from the low to the high expression state is the same as that associated with a transition from the high to the low expression state ., The time it takes for the system relax to steady state , which is set by the kinetic rate parameters and the level of noise in the system , can range from the order of seconds to several tens of cell generations 31 ., It is also noted that very rapid transitions between expression states may result , at the population level , in a persistent subpopulation that is not associated with a steady state in the deterministic model , and that noise , under certain conditions , may shift the location of bifurcation points or induce new bifurcations ( see e . g . 32 ) ., How can we capture the bifurcation behavior of a stochastic dynamical system ?, Suppose that represents the bifurcation parameter ( e . g . , an externally controlled parameter or variable ) , and represents an observed variable of the system , such as the protein abundance ., Suppose that for any value of , and under a specified set of experimental conditions , we observe a population of cells with values of following some distribution ., We propose that the stochastic bifurcation structure of the system should specify four pieces of information as a function of the parameter : This is not a formal definition of stochastic bifurcation structure; these are principles , which might be formalized in a number of different ways ., For example , as mentioned above , the modes of the steady state distribution of a stochastic dynamical system have previously been proposed as analogs to the steady states of a deterministic model ., Thus , one might use the modes of the distribution to determine the number and location of subpopulations , satisfying the first two parts of the definition above ., In particular , one could use bimodality as a defining feature of bistability in a stochastic switching system and associate bifurcation points with parameter values where the population distributions change from unimodal to bimodal ., In many cases , this may work well , although below we will show some reason to question the use of modes as defining of the number of subpopulations ., If one can assign every cell to a subpopulation , then the variance of within each subpopulation and the relative sizes of the subpopulations provide natural answers to the third and fourth parts of the definition above ., As with the locations of the modes , these features of the stochastic bifurcation structure may be related to properties of a deterministic model ., For example , the degree of variation around a mode , or the fraction of time the system spends near the mode , are related to the degree of stability of the state in the deterministic model 32 ., Below , we use the formalism of mixture models to instantiate these four principles of stochastic bifurcation structure ., First , however , we present our experimental data on the galactose network ., Our thoughts on stochastic bifurcation structure and methods to estimate it were motivated , indeed necessitated , by data we collected on activity in the galactose utilization network in S . cerevisiae ., The network includes genes for the import and metabolism of galactose as well as various regulatory genes 33 , 34 , and is known to behave as a bistable switching network ., For a range of external galactose concentrations , cells stochastically switch between induced and non-induced states 25 ., To assay this behavior , a standard laboratory strain was augmented with a gene encoding a fluorescent protein under the control of the promoter region normally regulating the transcription of the endogenous Gal10 gene ( see Materials and Methods ) ., Gal10 is a general indicator of activation of the network , hence the fluorescent reporter should be expressed when and only when the native network is itself active ., Cells were cultured for 22 hours in 17 different constant concentrations of galactose from two different initial conditions—pregrowth in the absence of galactose to establish a non-induced initial state or pregrowth in the presence of galactose at high concentration to establish an initial state where all cells are induced ., The activity of the network in individual cells was quantified by flow cytometry to measure the intensity of the fluorescence emitted by the expressed reporter gene ., Four biological replicates were made of every experiment ., The collected data comprises counts of how many cells were detected in each of 1024 fluorescence channels , which are logarithmically related to real fluorescence intensity and have a dynamical range of four orders of magnitude ( i . e . , channel 1024 represents 10 , 000 times the intensity of channel 1 ) ., Figure 2 displays the data , which is broadly consistent with previous experiments 25 ., At low galactose levels , all cells show low network activity ., At higher galactose concentrations , a highly active subpopulation emerges , and at yet higher levels , the highly active subpopulation dominates and the low-activity subpopulation disappears ., While these overall trends in the data are visually clear , the challenges in analyzing the data quantitatively include robustly determining the locations and sizes of the subpopulations , especially when one is much smaller than the other , dealing with cells not clearly attributable to any one subpopulation , and separating cell-to-cell variability from replicate-to-replicate variability ., Ideally , these should be done in a statistically robust , computationally simple , and objective manner ., We have defined a notion of stochastic bifurcation structure suitable for studying the behavior of stochastic genetic switches , and we have generated an extensive map of the response of the canonical bistable yeast galactose utilization network to variation in external galactose concentrations ., While the data broadly conforms to our expectations for stochastic switching between low and high expression states within the network , several additional properties are noteworthy ., The establishment of a “high” expressing subpopulation occurs rather abruptly and fairly consistently at a concentration of approximately 0 . 003% galactose , although this state is initially overlapping the low expressing subpopulation ., By contrast , the low subpopulation fades away more gradually at higher concentrations , while maintaining clear separation from the high subpopulation ., Activity within the high subpopulation , in terms of fluorescent intensity , increases substantially as a function of galactose concentration—by approximately 300% over the range of concentrations tested ., Activity within the low subpopulation is fairly constant , and is , in most cases , indistinguishable from that of cells not expressing the reporter gene ( data not shown ) , though there may be a mild increase in expression as the galactose concentration increases ., Hence , the response of the network to varying conditions appears to combine a boolean-type “binary” switch between “on” and “off” expression states with a continuous “graded” modulation of activity within the “on” state ., From a methodological point of view , we proposed that mixture density estimation and conditional mixture density estimation are ideally suited to extracting stochastic bifurcation structure from real , noisy data ., Our tests of two different mixture fitting methods and one conditional mixture fitting method suggested that , in most respects , the methods are equally accurate in fitting the data ., It is possible that the conditional mixture model was less accurate ., Visually , it appears to overestimate the location of the high subpopulation at smaller galactose concentrations , and underestimate it at higher concentrations ( see Figures 3A or 4A , B ) ., However , this is due simply to the affine form assumed for the dependence of subpopulation location on concentration ., Alternative forms could readily be chosen to allow greater flexibility in fitting the data ., Regardless , the overall level of disagreement between methods appeared smaller than the variability between different biological replicates ., One potentially important distinction between the two mixture modeling approaches , standard EM and mode estimation followed by EM , is that the former is able to identify a “high” subpopulation at lower galactose concentrations than the second approach ., This is because , at the lowest galactose concentrations , the “high” subpopulation is very broad and partially merged with the typical low subpopulation—in some cases , to such a degree that the overall distribution is still unimodal ( see Figure 5 ) ., The standard EM method , because it requires multiple runs to avoid the problems of local minima and for cross-validation , is considerable slower than either of the other methods ., Still , all methods run orders of magnitude faster than the data collection takes , so this is a minor concern ., Conditional mixture models have several additional advantages compared to fitting the data at each galactose level separately: they use fewer total parameters , and are thus less likely to overfit the data , and they explicitly represent and make predictions for the bifurcation structure at all values of the bifurcation parameter—not only the values tested experimentally ., This approach worked well on our data ., The drawback of this approach is that it requires choosing functional forms to represent the dependence of mixture probabilities and mixture component parameters on the bifurcation parameter ., In this case , a proper means of representing mixture probabilities only became clear after doing the individual fits ., In early conditional mixture model fits , we assumed the mixture probabilities were independent of galactose concentration ., This had the unfortunate side affect that the high component would start to “capture” cells from the low subpopulation at low galactose levels , dragging down the whole mean curve for the high subpopulation until it intersected and overlapped with the low subpopulation ., The form we chose for the mixture probabilities avoids this problem by definitively assigning cells to the low component at all galactose levels below some threshold ., This illustrates that the strength of using few parameters and explicitly generalizing across bifurcation parameter values also implies a danger of poor performance if an inappropriate representation is chosen ., While this is a truism in the statistics and machine learning communities , it is all the more important to keep in mind in systems biology where there is a greater focus on interpreting models , as opposed to , say , being concerned only about prediction accuracy ., Despite our focus on mixture modeling , one can imagine other approaches for estimating stochastic bifurcation structure ., For example , clustering methods such as K-means or self-organizing maps could readily be applied in much the same way as we applied mixture density estimation ., Nonparametric density estimation techniques might also be applied , although it would take extra effort to extract subpopulations from a nonparametric density estimate ., Investigating such alternative approaches is an important topic for future research ., Part of our contribution is in specifying four types of information that should be included in a stochastic bifurcation analysis: the number of distinct subpopulations , the fraction of cells they contain , the level of expression and the variance within each subpopulation ., Our notion of stochastic bifurcation structure is considerably different from ideas employed in stochastic bifurcation theory , which addresses the behavior of explicitly stochastic dynamical models , such as stochastic differential equations 36 ., The primary concern of stochastic bifurcation theory is the number and stability of different steady state distributions of a model ., In gene regulatory networks , it is not unreasonable to assume that any given cell could eventually , through random fluctuations , reach the same state as any other cell 23 , 25 ., Such a system is said to be “communicating” , and under fairly general conditions , has only a single steady state distribution for each bifurcation parameter value 37 ., By the gross standard of stochastic bifurcation theory , such a system does not show any bifurcations at all ., We , by contrast , have attempted to paint a finer-grained picture of the dependence of a stochastic dynamical system on an experimentally manipulated parameter ., This picture is largely consistent with the expectation that fluctuations within the context of a deterministic network model constantly push individual cells on excursions away from a stable expression state , and induce stochastic transitions between the two expression states to generate bimodal population distributions 29 ., Indeed , our focus identifying subpopulations is closely related to the idea in Kepler and Elston 29 of defining bifurcations via the number of critical points in the steady state distribution ., However , our approach is much different; whereas they start with first-principles stochastic chemical descriptions of simple gene regulatory models , we start with empirical measurements of a complex gene regulatory system ., Stochastic bifurcation structure may provide useful information for the development of quantitative regulatory network models , however this remains to be investigated ., The exact relationship between stochastic observables and model features is not yet clearly established ., For example , models of gene regulatory networks are usually derived from molecular interactions within individual cells and rarely consider effects due to population dynamics ., The gradual fading of the low-expressing subpopulation observed in our experiments could be due the stochastic dynamics of the regulatory network itself , or it could be due to a reduced growth rate of the low-expressing cells ., Additionally , while we took steps to present the cells in each culture with homogenous extracellular conditions ( see Materials and Methods ) , it is likely that there was some variability in the conditions experienced by different cells or by the same cell over time ., Depending on the magnitude of this effect , it too might need to be estimated , if possible , and separated from intrinsic cell-to-cell variability if one wants accurate estimates of cellular network parameters ., Careful quantitative estimation of stochastic bifurcation structure facilitates comparison between different experimental conditions or genetic backgrounds ., For example , the yeast strain studied by Acar et al . ( W303 ) is much less sensitive to galactose and displays an almost 10-fold shift of the bimodal region ( to concentrations between approximately 0 . 02% and 0 . 3% ) compared to our strain ( an equivalent of BY4743; see also discussion in Bennett et al . 38 ) ., Thus , even subtle differences in DNA-encoded parameters may have significant impact on the stochastic bifurcation structure of a given gene regulatory network ., It should be possible to link DNA sequence information to quantitative properties of gene regulatory networks ., This may require the development of several methodological , in addition to experimental , approaches that can extract consistent information about stochastic bifurcation structures ., For example , it would be necessary to compare different , empirically-measured stochastic bifurcation structures associated with different genotypes to determine whether there is a statistically significant difference between them and , if so , identify the origin of the difference using a dynamical systems theory or other type of modelling framework ., In addition , such methods could be useful to investigate how gene regulatory networks have evolved , to infer regulatory relationships between genes , or refine our knowledge of them , based on stochastic bifurcation behavior in experiments involving systematic genetic perturbations , such as gene deletions , gene knockdown or overexpression experiments ., The experiments use a diploid Saccharomyces cerevisiae strain expressing a single copy of yeast-enhanced green fluorescent protein ( yEFPG ) from the native promoter of the GAL10 gene ( ) ., The diploid was obtained by mating two haploid strains , a Mat a strain ( yHP101 ) derived from BY4741 ( Mat a , ; ; ; , Open Biosystems ) by PCR-mediated replacement of the open reading frame of the Ade2 gene by a Leu2 expression cassette , and a Mat strain ( yHP201 ) derived from BY4742 ( Mat ; ; ; ; , Open Biosystems ) by PCR-mediated gene replacement of Ade2 by a DNA fragment carrying the reporter cassette and an expression cassette conferring histidine auxotrophy ., Following PCR validation of the appropriate gene replacements , the diploid strain , designated yHP301 ( Mat , ; ; ; ; ; ) was stored at in rich media ( YPD ) containing 20 g/L Yeast Bacto-Peptone ( Wisent ) , 10 g/L yeast extract ( Wisent ) 20 g/L glucose ( Sigma-Aldrich ) and 1% w/vol adenine ( Sigma-Aldrich ) supplemented with 15% w/vol glycerol ( Sigma-Aldrich ) ., Prior to quantification , yHP301 was streaked onto synthetic dropout medium ( Wisent , Inc . ) agar plates without leucine and histidine supplemented with 2% w/vol glucose and 1% w/vol adenine ., Individual colonies were used to inoculate 3 mL rich media ( YPR ) containing 20 g/L Yeast Bacto-Peptone , 10 g/L yeast extract , 1% w/vol adenine and 2% w/vol raffinose ( Wisent ) or YPR media supplemented with 2% w/vol galactose ( Becton , Dickenson ) ., Following growth for 24 hours at and continuous shaking ( 250rpm ) , twenty-one aliquots of each culture were transferred to a deep well block and washed twice with YPR media supplemented with varying amounts of galactose ( final concentrations 0 . 0 , 0 . 0015 , 0 . 0022 , 0 . 0033 , 0 . 0038 , 0 . 0043 , 0 . 0050 , 0 . 0057 , 0 . 0066 , 0 . 0076 , 0 . 0087 , 0 . 0100 , 0 . 0115 , 0 . 0132 , 0 . 0174 , 0 . 020 , 0 . 080 , 0 . 20 , 0 . 50 , 2 . 0%w/vol ) ., Following the wash , cells were resuspended in of the appropriate media and optical density ( OD ) quantified with a Perkin Elmer Victor3V plate reader using cultures ., A fraction of the remaining volume was subsequently used to inoculate fresh media containing the appropriate amount of galactose to an OD of , and grown in a 96 deep well block for 22 hours at and 250rpm prior to analysis ., Reporter gene expression was quantified in individual cells using a Beckman-Coulter FC500 flow cytometer ., A total of 60 , 000 events were collected for each condition and filtered using custom-written software script using a fixed elliptical forward/side-scatter autogate capturing approximately 50% of the events in each sample ., The fluorescence intensity ( 488nm excitation , 510–550nm emission ) associated with these events was used to generate representative expression distributions for each sample condition ., A total of four replicates were obtained , for each final galactose concentration and both pre-growth conditions ., Mixture density estimation using EM used 100 runs in an effort to avoid problems with stopping at solutions that were only locally optimal ., Each of the 100 runs began from different random initial parameters ., The means of each Gaussian component were chosen uniformly between the lowest and highest data point ., Standard deviations were initialized to 50—roughly the level observed at single-subpopulation galactose concentrations—and initial mixture probabilities for the Gaussians were set to where is the number of Gaussians ., ( Recall that a fixed 0 . 02-weighted uniform density is also part of the mixture ) ., The exception to this rule was the mode-estimation-plus-EM approach , for which means were initialized to the mode estimates , and we used a single run of EM ., The parameter updates during the M-step were as described , e . g . , in Bishop 35 ., If the variance of a Gaussian shrank below , the component was eliminated , because such a Gaussian is focussed on a single fluorescence channel , and does not represent a true subpopulation ., The EM fitting employed cross-validation to determine the proper number of Gaussian components to have in the mixture for each replicate and at each galactose level ., After fitting a model with Gaussian components , we tested whether an Gaussian model would be significantly better by performing 10-fold cross-validation ., In each fold , 90% of the data was used to fit an Gaussian model , which was scored by the mean ( across data points ) log likelihood of the remaining 10% of the data ., We calculated the mean and standard deviation ( across folds ) of the Gaussian model scores ., If the mean was standard deviations greater than the score of the Gaussian model , we accepted the increase to Gaussians , and performed the process again ., We chose , as we found this was sufficiently stringent to prevent splitting of what were clearly single subpopulations ( e . g . , at zero galactose concentration ) ., Mode estimation for the mode-estimation-plus-EM approach began by smoothing the data by taking a running average over a window of size 71 channels ., Call this ., First and second derivatives , and , were estimated by computing centered finite differences , with the same width of 71 channels ., A mode in the density was detected at channel point if the first derivative crossed from positive to negative ( i . e . , and ) and if ., Ordinarily , one might threshold only the second derivative ., However , small bumps in the data series are characterized by both smaller first and second derivatives in the vicinity of a mode , and combining them in this way lead to more robust and balanced detection of peaks of all sizes in preliminary tests ., The choices of a 71-width averaging window and the −0 . 0002 threshold were based on pilot testing on a separate , but related , set of flow cytometry data ., For fitting the conditional mixture density models , we used only a single run of EM , as further runs did not improve accuracy ., Updates are standard , as given in Bishop 35 ., Low and high subpopulation means were initialized to have means of 200 and 700 respectively ( independent of galactose l

Introduction, Results, Discussion, Materials and Methods

High throughput measurement of gene expression at single-cell resolution , combined with systematic perturbation of environmental or cellular variables , provides information that can be used to generate novel insight into the properties of gene regulatory networks by linking cellular responses to external parameters ., In dynamical systems theory , this information is the subject of bifurcation analysis , which establishes how system-level behaviour changes as a function of parameter values within a given deterministic mathematical model ., Since cellular networks are inherently noisy , we generalize the traditional bifurcation diagram of deterministic systems theory to stochastic dynamical systems ., We demonstrate how statistical methods for density estimation , in particular , mixture density and conditional mixture density estimators , can be employed to establish empirical bifurcation diagrams describing the bistable genetic switch network controlling galactose utilization in yeast Saccharomyces cerevisiae ., These approaches allow us to make novel qualitative and quantitative observations about the switching behavior of the galactose network , and provide a framework that might be useful to extract information needed for the development of quantitative network models .

Decades ago , Waddington , and later Kauffman , likened the dynamics of a differentiating cell to a marble rolling downhill on bumpy terrain—the epigenetic landscape ., In this metaphor , the valleys of the landscape represent the paths that cells can follow towards a stable cell type , and the fate of the cell is determined by the constant modulation of the epigenetic landscape by internal and external signals ., With new technologies for measuring single-cell gene expression , it is increasingly feasible to map out these valleys and how external variables influence cellular responses ., Moreover , it is possible to quantify population level effects , such as what fraction of a population of cells arrives at one valley or another , and variability at the cellular level , such as how individual cells bounce around within , and possibly between , valleys due to the stochasticity of cellular biochemistry ., In this paper , we discuss which characteristics of the epigenetic landscape can readily be extracted from single-cell gene expression data , and describe computational methods for doing so .

molecular biology/bioinformatics, computational biology/systems biology

journal.ppat.1006714

A mobile loop near the active site acts as a switch between the dual activities of a viral protease/deubiquitinase

Ubiquitin ( Ub ) is a 76-residue protein that is highly conserved throughout the eukaryotic kingdom ., Attachment of Ub to cellular proteins ( referred to as ubiquitylation ) is recognized as a key regulatory pathway , critical to a number of major cellular processes , including protein homeostasis , intracellular signalling , transcription , and immune responses 1 ., Ub is covalently linked to the target protein via an isopeptide bond between its C-terminal Gly residue , and an acceptor amino acid of the target protein substrate , in most cases a Lys residue ., Further conjugation steps to Ub itself can generate polyubiquitylated chains in a number of structurally different configurations 2 , 3 that may play different roles in the cell , illustrating the exquisite versatility of Ub conjugation 4 , 5 ., Importantly , ubiquitylation is a dynamic process that can be reversed by the action of enzymes known as Ub hydrolases , deubiquitinases or deubiquitylating enzymes ( DUBs ) 6 , 7 ., Most of these enzymes are Cys proteinases , which can either trim or remove poly-Ub chains from substrate proteins , thus contributing to the reversal of Ub-dependent processes in cells ., The breadth of DUBs in the regulation of cellular processes , and their role in promoting human disease , has become apparent over recent years 8 , 9 , leading to further insights into the mechanistic details of such enzymes and their regulation , combined with increased interest in their use as potential drug targets 10 , 11 ., It has become increasingly clear that the involvement of ubi- and deubiquitylation events also extends to the regulation of interactions between hosts and pathogens ., The ubiquitin proteasome system ( UPS ) is utilized not only by host cells in immune and biotic stress responses 12 , 13 , but can also be manipulated and subverted by pathogens—including viruses—for their own use 14–17 ., Viruses use various strategies to exploit Ub and Ub-like modifier pathways , with some recruiting host enzymes , whereas others encode their own ubiquitin ligases , or their own DUBs ., Such mechanisms may be beneficial to the virus either by creating a more favorable cellular environment , by fine-tuning viral regulatory processes , or by inhibiting host defense mechanisms ., Although important insights into the biochemical activities and molecular structures of viral-encoded DUB enzymes have been gained in recent years 18–23 , a major challenge in this field remains to define and understand enzyme specificity and regulation—both towards the various Ub-chain types or target protein substrates , as well as the physiological roles played by these modulators of the Ub pathway during the infection cycle ., This is particularly challenging for viruses encoding a DUB activity in a multifunctional enzyme , as the respective contributions to DUB and other functions are not easily disentangled ., In particular , several viral DUBs encoded by positive-strand ( + ) RNA viruses appear to have dual activities , as they were originally described as being papain-like cysteine proteases ( PRO ) , whose endopeptidase activity is involved in the processing of viral precursor proteins 24 , 25 ., Both PRO and DUB activities rely on the single catalytic site of the cysteine proteinase , but the determinants regulating these dual endo- and iso-peptidase activities remain largely unknown ., In this paper , using Turnip yellow mosaic virus ( TYMV ) , the type member of the genus Tymovirus , we address the question of how a viral PRO/DUB enzyme can switch from one activity to the other ., TYMV is a simple model of a ( + ) RNA virus that is well characterized at the molecular and cellular levels , and whose replication cycle appears to involve reversible ubiquitylation events ., We previously reported that TYMV-encoded proteins—including the viral RNA-dependent RNA polymerase—are targets of the UPS in vitro and in vivo 26–28 ., We also reported that the TYMV papain-like cysteine PRO domain involved in the proteolytic processing of the viral replication precursor protein 29–31 , also displays a DUB activity that was able to rescue the viral polymerase from degradation 32 ., The structure of the recombinant TYMV PRO/DUB domain we solved previously ( residues 728–879 of the TYMV replication precursor protein ) 21 highlighted its homology with members of the ovarian tumor domain-containing ( OTU ) superfamily of DUBs 33 ., However , TYMV PRO/DUB appeared to be a very peculiar OTU DUB , both functionally and structurally ., Indeed , in contrast to other OTU DUBs , TYMV PRO/DUB does not possess a general deubiquitylating activity , but instead displays a specificity towards particular ubiquitylated substrates , among them TYMV RNA-dependent RNA polymerase 32 ., In addition , the TYMV PRO/DUB structure displayed a surprisingly exposed and minimal catalytic site , with a Cys783-His869 catalytic dyad 21 instead of the conserved OTU DUB Cys-His-Asp/Asn catalytic triad 6 , 34 , 35 ., In TYMV polyprotein processing , one endopeptidase site lies at the C-terminus of PRO/DUB itself 31 ( S1 Fig ) , and , in our previously described crystal structure 21 , each molecule was found in complex with the neighbouring molecule , with its five C-terminal residues inserted into the catalytic cleft of the next molecule ., Thus , a product state of polyprotein processing activity was actually captured in that crystal ., This PRO:PRO complex revealed the structural basis of extensive recognition of protein substrates on the so called P side 36 , i . e . residues upstream of the scissile bond ., In contrast , the exposed nature of the catalytic dyad suggested that interactions may be rather limited on the P side , i . e . downstream of the cleavage site ., The crystal structure also allowed us to identify several structural elements , distant from the active site , that are involved in the interaction of PRO/DUB with itself , and possibly with its other substrate partners such as Ub ., Among them , a hydrophobic patch some 17 Å away from the active site was used by the cleaving PRO to recognize the cleaved PRO ., In silico modeling of PRO/DUB:Ub complexes 21 suggested that Ub chains could also be recognized by this distant hydrophobic patch in addition to the interactions in and around the catalytic site ., The involvement of such distant Ub recognition patches is now established as a prominent feature among viral and cellular DUBs 22 , 23 , 37 ., In addition , the PRO:PRO complex also engaged several elements close to the active site , in particular a loop in the cleaved PRO that represents an insertion with respect to the closest OTU DUB relatives of TYMV PRO/DUB ., This loop , conserved in Tymoviridae , bears an atypical 865-GPP-867 motif , that the structure revealed as having both prolines in cis conformation 21 ., Because this motif is directly upstream of the catalytic His869 , we could not rule out the possibility that such interactions may account , in part , for the unusually disordered state of the Cys783-His869 catalytic dyad ., To further understand the molecular , and possibly mechanistic , processes underlying the dual activities of the TYMV PRO/DUB enzyme , we extended our earlier structural analysis by obtaining the structure of a disengaged TYMV PRO/DUB domain , i . e . one not involved in a PRO:PRO complex ., For this purpose , two mutant versions of TYMV PRO/DUB designed to weaken PRO:PRO interactions were expressed and crystallized 38 ., The structures reported herein reveal that the loop harboring the 865-GPP-867 motif is a highly mobile flap on the otherwise rigid PRO/DUB framework a result that is confirmed by molecular dynamics simulations ., Closure of this flap brings the catalytic site region closer to the canonical OTU DUB active site as seen in complex with Ub , even though the catalytic dyad remains exposed and unusually flexible ., Functional analyses of structure-guided mutants indicate that the GPP flap acts as a switch for DUB activity , making it possible to dissociate the PRO and DUB activities of the enzyme , and to analyze their function in the viral life cycle independently ., Viruses carrying these mutations displayed a reduced replication efficiency , confirming the critical importance of the DUB activity of PRO/DUB in control of the TYMV replication cycle , independently of the polyprotein processing ., In addition , we observed that mutations affecting the loop mobility also markedly affected the severity of viral symptoms in planta ., The present study thus provides new structural insights into the mechanisms by which viral OTU-like enzymes with dual PRO/DUB activities can switch between these two activities , and also generates powerful tools with which to further study the importance of solely the DUB activity in virus/host interactions ., In order to get an atomic view of a conformation of PRO/DUB not constrained by the crystal-induced PRO:PRO interaction , we generated a recombinant mutant harbouring deletion of the five C-terminal residues ( hereafter ΔC5 ) , corresponding to residues 728–874 of the TYMV 206K polyprotein ., Crystals of ΔC5 diffracting to beyond 2 Å resolution were obtained 38 , and we have since collected data extending to beyond 1 . 7 Å resolution ., We therefore used these new data to solve the ΔC5 structure by molecular replacement from WT PRO ( Table 1 ) ., The ΔC5 crystal contains two molecules per asymmetric unit , hereafter referred to as molecules A and B ., Molecule B is slightly less ordered as revealed by a 10 Å2 higher average B-factor ( Table 1 , S2A Fig ) ., Clear electron density is present for residues 730–874 of molecule A , and 732–874 of molecule B ., Both molecules have their C-termini far from the catalytic sites of other molecules in the crystal packing ., We thus have two independent views of PRO/DUB not engaged as a substrate in a PRO:PRO complex ., Superimposition of molecules A and B of the ΔC5 mutant with the WT ( engaged ) PRO/DUB ( Fig 1A ) shows that PRO/DUB retains the same conformation in all three environments , with the exception of loop 864–868 ., This loop was strongly constrained in the previous structure due to the cleaved PRO molecules loop being bound by the cleaving PRO molecule ., It is now released in both molecules of the mutant protein ΔC5 , and displays high mobility in this disengaged form ., In molecule B , Pro866 at the tip of the loop ( Fig 1B ) moves by 9 Å to a position overhanging the catalytic cleft ., This shift is less pronounced in molecule A , and the loop therefore reaches an intermediate position ., Of note , temperature factors of the loop jump up in molecule A , indicating higher dynamic disorder in this intermediate conformation , and indeed are above those of molecule B in this region ( S2A Fig ) ., In fact , in molecule B , the mobility of this loop leads to the closure of a hydrophobic zipper between Pro866 and Pro867 on one side , and Leu820 and Leu781 across the catalytic cleft on the other side ( Fig 1B ) ., His869 and Cys783 also come significantly closer together ., Presence of unidentified atoms ( S3 Fig ) , possibly covalent adducts with DTT or sulfur compounds present in our cryosolution ( see Materials and Methods ) , complicated the unambiguous modeling of the Cys783 side chain ., However , it is clear that , in this closed conformation , His869 is now close enough to abstract a proton from Cys783 ., The large movement of the 865-GPP-867 motif observed in molecule B as compared to the WT engaged PRO is due to a distinct flexing of the tip of the beta sheet into which it is inserted ., This flexing extends to the neighboring 842–845 loop and , particularly , to the Asp843 at its tip that follows loop 864–868 ( Fig 1C ) ., In contrast , the conserved GPP motif itself , with its two cis-prolines , changes very little , although Gly865 shifts slightly , moving in the Ramachandran plot of molecule B to a region that is disallowed to non-glycine residues ., In summary , the crystal structure of ΔC5 indicates that , while TYMV PRO/DUB is a rigid molecule , it harbours a very mobile loop , 864-TGPPS-868 , that can shift the GPP flap at its tip with its atypical double cis-proline , from an open state where it can be recognized by another PRO molecule 21 , to two other states: a closed one where it forms half of a hydrophobic zipper , and an intermediate one , halfway in between ., The closed zipper constricts the P side of the catalytic cleft and also brings the catalytic His869 closer to the catalytic Cys783 ., Since we now had three structures with the GPP flap in three different conformations and a suspected connection to active site organization , we performed molecular dynamics simulations to scrutinize the dynamics of these two regions and an eventual link between them ., We used as starting model the previously published structure of wild type full-length TYMV PRO/DUB , with the GPP flap in open conformation 21 ., Relieved from the PRO:PRO interaction in that former crystal structure , the flap quickly closes to a conformation superimposable with the A molecules of the new structures , i . e . the intermediate state ., Monitoring the closure of the flap by the distance between Cαs of P867 and L820 ( Fig 1B ) clearly shows that the flap then oscillates around this central conformation , but intermittently either closes to the other side of the cleft or goes back to the more open conformation ( Fig 2A , top , the corresponding distances in the crystal structures are indicated as open , interm . and closed , respectively ) ., These results confirm that the new structures complete the range of conformations accessible to the GPP flap and further indicate that the A molecule best represents the PRO/DUB conformation when it neither is engaged as a substrate nor acts as a peptidase ( Fig 2A , middle ) ., Interestingly , concomitant with the initial closure of the flap , H869 comes sufficiently close to C783 that the latter realigns and the two side chains form a hydrogen bond ., This bond is formed in an orientation of side chains that is identical to that in other OTU DUBs ( see Discussion ) ., However the imidazole ring of H869 is still unengaged by a third catalytic residue and remains loose , frequently breaking the H-bond to C783 ( Fig 2A , bottom ) ., These events do not correlate with the mobility of the GPP flap around the intermediate , equilibrium position ., These results indicate that the organization of the catalytic site is also brought closer to the classical OTU DUBs by flap closure to the intermediate position , but is not further ordered in the closed flap conformation ., In order to assess the role of the 864-TGPPS-868 loop in the dual activities of the TYMV PRO/DUB domain , we next designed structure-guided mutants ., On the one hand , we prevented its ability to zip up against Leu820 and Leu781 by replacing the bulky , rigid double cis-proline by two glycines ( P866G/P867G mutant ) ., On the other hand , we kept the zipper intact but tried to interfere with loop mobility: First with a G865A mutant disfavouring complete loop closure , since its closed state involves a conformation of residue 865 disfavoured to non-glycine residues ( see above ) ; Second with a D843A mutant that should increase flexibility of the loop by disrupting the hydrogen bonds tethering it to the nearby strand ( Fig 1C ) ., Molecular dynamics simulations of these mutants , performed in the same conditions as for the wild type , show that the molecular effects of these mutations are globally as expected , but also offer new insights into the link between flap movement and active site reorganization ., Mutants P866G/P867G , G865A and D843A all significantly alter the dynamics of the GPP flap ., First off , it becomes more mobile than in the wild type ( S2B Fig ) ., However , it does so in very different ways: In mutant D843A ( Fig 2B ) the loop harboring A843 does not act anymore as a spring to the GPP flap ., As a result , the flap remains closed much longer , but also at times opens wider , than in the wild type ( Fig 2B , top ) ., At first sight G865A displays a similar behavior ( Fig 2C , top ) , somewhat surprisingly since the mutation was designed to disfavor closure of the flap ., However , examination of the trajectory shows that the constraint imposed by A865 leads to a twist of the flap ., Only P867 comes into contact with L820 even in the most closed conformation , leaving the hydrophobic zipper half open ( Fig 2C , middle ) , in contrast to the D843A closed conformation ( Fig 2B , middle ) that is identical to the crystallographic closed flap conformation ., The most disruptive mutant is P866G/P867G ( Fig 2D ) ., Although the loop initially relaxes into the intermediate position , as in the wild type and the two other mutants , it is more disordered and , strikingly , in the second half of the simulation shifts to a position that is on average in an open rather than intermediate conformation ( Fig 2D , top and middle ) ., Interestingly , this opening correlates with loss of the H-bond between H869 and C783 ( Fig 2D , bottom ) , leading to an inactive conformation of the catalytic site ., These results further confirm the connection between loop closure and active site organization ., In contrast , the catalytic dyads dynamics are much less affected in the two other mutants G865A and D843A ( Fig 2B and 2C , bottom ) ., To address the effect of these changes in flap dynamics and active site organization on the DUB activity in vitro , these flap-interfering mutations were first introduced into the recombinant His-tagged PRO/DUB domain of TYMV , and mutated proteins were expressed in E . coli and purified ., The enzymatic DUB activity of the mutants was then measured in vitro using the general DUB substrate ubiquitin-7-amino-4-methylcoumarin ( Ub-AMC ) and assessing release of the fluorescent AMC moiety , as previously described 21 , 32 ., As controls , we also assessed point mutants ( I847A and I847D ) of the distant ubiquitin-binding patch that we previously showed to be significantly impaired in DUB activity 21; the WT enzyme was assayed in parallel to each mutant to have a 100% activity control ., The results obtained ( Table 2 ) indicate that the three loop-targeting mutants display impaired DUB activity in vitro ., Preventing full loop closure ( G865A ) , or releasing it from its nearby strand ( D843A ) , resulted in significantly decreased activity , with about 30% residual activity , similar to that of the I847A mutant measured in the same conditions ., The most drastic effect was observed for the mutant P866G/P867G , whose loop can no longer zip up against Leu820 and Leu781 and tends to be more open than the wild type , while its catalytic dyad is even more disordered ., With 3 . 5% activity , this mutant falls in the range of I847D , the most severely impaired DUB distant patch mutant ., Altogether , these results indicate that affecting the GPP flap or its mobility impacts DUB activity in vitro ., Given the two similar classes ( mild or severe ) of DUB activity impairment , whether in loop mutants or distant patch mutants , we also examined the structure of the TYMV PRO/DUB I847A point mutant ( hereafter I847A ) , crystals of which were actually obtained first , and from which we seeded the ΔC5 crystals 38 ., The seeding procedure led to the same crystal form , and the I847A crystal is essentially identical to the ΔC5 crystal ., The only difference in packing between the ΔC5 and I847A crystals is a slight shift of molecule B with respect to molecule A ( S4 Fig ) ., Residues 730–877 are ordered in I847A molecule A and residues 732–876 in I847A molecule B ., The two I847A molecules appear superimposable to their counterparts in the ΔC5 crystal , with root-mean-square deviation of 0 . 09 Å for the 143 Cα carbons present and ordered in both A molecules , and 0 . 23 Å for 141 Cα carbons present and ordered in both B molecules ., Importantly , loops 864–868 thus occupy the same positions in the I847A and ΔC5 crystals , i . e . intermediate in A and zipped up in B ( Fig 3 ) ., The fact that the two mutants I847A and ΔC5 display identical conformations in the same crystal packing environments shows that the mutations themselves have altered neither the overall structure of the molecule , nor the conformation of loop 864–868 ., Thus , the mobility and zippering of loop 864–868 , and the associated changes in the active site conformations as compared to the WT PRO:PRO complex , are properties of the PRO/DUB framework and reflect available conformations of this mobile loop ., Additionally , the structure of the I847A mutant confirms that the mutation indeed reduces the hydrophobic surface of the distant patch ( black patches in Fig 3 ) ., To determine whether the mutations described above also impact the activity of the protein in vivo , and whether they affect DUB activity , PRO activity , or both , we next monitored the activities of TYMV 98K , i . e . the mature viral protein product encompassing the PRO/DUB domain 31 , using an Arabidopsis protoplast transient expression system ., Because the TYMV 66K protein encompassing the viral RNA-dependent RNA polymerase is the sole identified substrate of the 98K DUB activity to date 32 , we addressed the capability of 98K to remove poly-Ub chains from 66K protein ., To this end , the levels of 66K-Ub conjugates were assessed by co-expressing 66K in Arabidopsis cells in the presence of myc2-Ub a myc-tagged version of Ub 28 , together with WT 98K or one of the various mutant versions of 98K summarized in Table 2 ., The catalytically inactive 98K-C783S was used as a control ., As previously reported 28 , 32 , immunoprecipitation of 66K under denaturing conditions followed by immunoblot analysis with anti-myc antibodies readily allows the detection of 66K-Ub conjugates ( Fig 4 , lane 3 ) ., Consistent with previous reports , the amount of ubiquitylated 66K was drastically reduced when 66K was coexpressed in the presence of WT 98K ( Fig 4 , lane 4 ) , but was unaffected by expression of the catalytically inactive 98K-C783S protein used as a control ( Fig 4 , lane 5 ) ., Mutants with a mild phenotype in the DUB in vitro assay ( around 30% , see Table 2 ) all appeared to retain a significant amount of DUB activity in vivo , as shown by the partial disappearance of 66K-Ub conjugates ., This is true whether we consider loop-affecting mutants ( G865A and D843A , lanes 7–8 ) or distant patch mutant ( I847A , lane 9 ) ., In contrast , mutants in the hydrophobic zipper ( P866G/P867G ) , and the mutant introducing a charge in the distant patch ( I847D ) , which were the most severely defective in the in vitro DUB assay ( less than 5% , see Table 2 ) , both displayed a drastic decrease in their DUB activity in vivo , as shown by the high accumulation of 66K-Ub conjugates in transfected cells ( lanes 6 and 10 ) ., Altogether , these results reveal good agreement between in vitro and in vivo assessments of DUB activity , and demonstrate that , either mutating a distant patch presumed to interact with Ub , or interfering with GPP flap mobility and zippering , both severely impair the DUB activity of TYMV PRO/DUB in vivo ., We next sought to evaluate the impact of the above-described mutations on the PRO activity on the viral polyprotein transiently expressed in Arabidopsis cells ., TYMV PRO activity was previously reported to be involved in the processing of the 206K replication protein precursor at two distinct sites , located between its PRO , helicase ( HEL ) and polymerase ( POL ) functional domains 31 ( S1 Fig ) ., Cleavage at the HEL↓POL site ( position 1259/1260 of 206K precursor ) first releases the 66K protein encompassing the POL domain , and is absolutely required to promote viral replication ., Cleavage at the PRO↓HEL site ( residues 879/880 ) releases the mature proteins 98K and 42K , and appears to contribute to the fine regulation of viral RNA replication 31 ., As 98K can process both cleavage sites in trans 31 , we used the approach previously described to assess the PRO activity of WT or mutated 98K ., For this purpose , the expression plasmid pΩ-206K-C783S ( encoding the 206K precursor protein lacking protease activity to prevent any self-cleavage , but retaining the two cleavage sites to serve as a substrate ) and WT or mutant versions of pΩ-98K ( encoding the viral 98K protein to serve as a protease ) were transfected into Arabidopsis protoplasts ., Processing at the HEL↓POL and PRO↓HEL cleavage sites of the 206K substrate was assayed by immunoblotting of the corresponding protein samples using anti-66K and anti-98K antibodies , respectively ., As shown in upper panel of Fig 5 , lane 3 , the WT 98K protein processed the HEL↓POL cleavage site efficiently in trans , as evidenced by the disappearance of the 206K precursor and the immunodetection of the mature 66K product ., Note that the mature 98K product resulting from cleavage of 206K at the PRO↓HEL site ( lower panel ) is obscured by the 98K produced in trans from pΩ-98K , but that cleavage activity is evidenced by the disappearance of the 206K precursor ., As expected , cleavage of the substrate was inhibited upon mutation of the catalytic C783 residue ( lane 4 ) ., Interestingly , we observed that all of the mutants assayed—including P866G/P867G and I847D ( lanes 5 and 9 ) —retained the capability to process both the HEL↓POL and PRO↓HEL cleavage sites , although traces of intermediate cleavage products were occasionally detected ., These experiments therefore demonstrate that the PRO and DUB activities of 98K can be specifically decoupled ., This can be achieved through two distinct approaches: first , through mutants affecting the I847 distant patch , which were designed to interfere with recognition of the Ub surface 21; second , through mutants designed to affect GPP flap mobility , especially zippering , highlighting the importance of this flap in DUB activity ., Such mutants now provide very powerful tools to decouple the dual PRO and DUB activities of TYMV PRO/DUB , allowing specific assessment of the function of DUB activity in infected cells ., We previously reported that abolishing the DUB activity of TYMV PRO/DUB had a significant impact on the ability of TYMV to replicate in infected cells , causing a ~ 3-fold reduction in infectivity 32 ., However , because the only available DUB mutant at that time was the catalytic site C783S mutant that debilitates both PRO and DUB activities , and because polyprotein processing of the precursor 206K at the HEL↓POL junction is an absolute prerequisite for initiating viral replication 31 , those experiments required a sophisticated design , based on the trans-complementation of a polymerase deletion mutant with the mature cleaved product 32 , in order to functionally dissociate the requirement for polyprotein processing from the putative contribution of DUB activity to viral infectivity ., While demonstrating the importance of the DUB activity for viral replication , this approach suffered from possible flaws linked to overexpression of the polymerase in trans , and confined the analysis of viral infection to the level of single transfected cells ., Having now designed mutants of TYMV PRO/DUB with various levels of DUB activity impairment but retaining PRO activity , we sought to readdress this question by introducing these new mutations into our TYMV reverse genetics system ., For this purpose , mutations P866G/P867G , G865A , D843A , I847A and I847D were introduced into the plasmid E17 , which contains a full-length copy of the TYMV genome , and from which infectious viral transcripts can be obtained 27 ., Plasmid E17-C783S , mutated in the PRO/DUB catalytic cysteine , was used as a control ., Equal amounts of in vitro transcripts were transfected into Arabidopsis protoplasts , and viral infectivity was assessed by detecting viral genomic RNA progeny by RT-qPCR ( Fig 6A ) or capsid protein ( CP ) by Western blotting ( Fig 6B ) , as the latter is dependent on viral replication for its expression from a subgenomic RNA ., Mutation of the catalytic cysteine completely abolished accumulation of both viral RNA progeny and CP ( Fig 6A and 6B ) —a consequence of impaired HEL↓POL cleavage , consistent with previous reports 29 , 31 ., In contrast , all other mutants retained the capability to replicate , albeit at reduced levels ( Fig 6A and 6B ) ., Strikingly , viral mutants P866G/P867G and I847D , which displayed the lowest levels of DUB activity in vitro and in vivo ( Table 2 , and Fig 4 ) had the greatest impact on infectivity , being able to replicate at levels of 24% and 28% , respectively , of WT viral RNA ( Fig 6A ) ., Such results are thus in perfect agreement with our previous data 32 , and confirm that debilitation of the DUB activity leads to a 3- to 4- fold decrease in viral RNA replication in Arabidopsis cells ., Mutants I847A , G865A and D843A with in vitro DUB activities of ~ 30% of that of WT enzyme were found to replicate at levels of 60–80% that of WT viral RNA , indicating that even mild impairment of DUB activity has a detectable impact on viral replication ., Western blot analyses using anti-98K and anti-66K antibodies ( Fig 6C and 6D ) revealed that only fully mature viral proteins were detected in infected cells , thus confirming the fully functional PRO activity of the PRO/DUB mutants during infection , and ruling out a possible impact of partially impaired cleavage of 206K on viral infectivity ., To determine whether the viral mutants affected in DUB activity retained infectivity in planta , Arabidopsis thaliana plants were inoculated with equal quantities of the corresponding in vitro transcripts ., Viral infectivity and systemic movement were then assessed by observing the appearance of viral symptoms in the inoculated leaves and in leaves distant from the point of inoculation ( systemic leaves ) , as well as quantifying the viral genomic RNA progeny by RTqPCR ., Arabidopsis plants inoculated with WT transcripts E17 displayed typical symptoms of TYMV infection ( Fig 7A , panel b ) , i . e . chlorotic local lesions appearing on the inoculated leaves 10–12 days post inoculation ( dpi ) , as well as leaf distortion , chlorosis and mosaic symptoms appearing 2–3 days later on the systemic leaves ., Although all mutants appeared to be able to multiply based on the appearance of local lesions on the inoculated leaves , they strikingly differed in the type of symptoms they were causing ., While mutations in the distant patch involved in Ub recognition , I847A and I847D , led to the appearance of symptoms ( Fig 7A , panels f and g ) which were comparable to those caused by WT transcripts , the symptoms caused by the mutants affected in the GPP flap looked notably different ., In particular , E17-P866G/P867G induced the appearance of necrotic local lesions on the inoculated leaves ( Fig 7A , panel, c ) , which are reminiscent of those occurring during hypersensitive reactions ( HR ) , a process corresponding to an exacerbated immunity reaction of the host 39 , 40 ., Those lesions appeared 1–2 days earlier than those caused by a WT infection , were self-limiting , and were found to restrict systemic viral movement , as evidenced by the absence of symptoms in systemic leaves , as well as the absence of viral RNA progeny ( Fig 7B ) ., The E17-G865A mutant also displayed a peculiar phenotype ( Fig 7A , panel, d ) , as the small local lesions on the inoculated leaves were originally chlorotic and did not appear to restrict systemic viral movement , leading to severe chlorotic symptoms in systemic leaves ., However , around 15–17 dpi , the chlorotic areas then became necrotic , leading to tissue desiccation , and to a systemic necrosis phenotype , eventually leading to leaf loss ., On the other hand , the symptoms induced by E17-D843A ( Fig 7A , panel, e ) were milder than those caused by a WT infection , the local lesions being less chlorotic , as were the mosaic symptoms appearing on the systemic leaves ., From these observations , it thus appears t

Introduction, Results, Discussion, Materials and methods

The positive-strand RNA virus Turnip yellow mosaic virus ( TYMV ) encodes an ovarian tumor ( OTU ) -like protease/deubiquitinase ( PRO/DUB ) protein domain involved both in proteolytic processing of the viral polyprotein through its PRO activity , and in removal of ubiquitin chains from ubiquitylated substrates through its DUB activity ., Here , the crystal structures of TYMV PRO/DUB mutants and molecular dynamics simulations reveal that an idiosyncratic mobile loop participates in reversibly constricting its unusual catalytic site by adopting open , intermediate or closed conformations ., The two cis-prolines of the loop form a rigid flap that in the most closed conformation zips up against the other side of the catalytic cleft ., The intermediate and closed conformations also correlate with a reordering of the TYMV PRO/DUB catalytic dyad , that then assumes a classical , yet still unusually mobile , OTU DUB alignment ., Further structure-based mutants designed to interfere with the loops mobility were assessed for enzymatic activity in vitro and in vivo , and were shown to display reduced DUB activity while retaining PRO activity ., This indicates that control of the switching between the dual PRO/DUB activities resides prominently within this loop next to the active site ., Introduction of mutations into the viral genome revealed that the DUB activity contributes to the extent of viral RNA accumulation both in single cells and in whole plants ., In addition , the conformation of the mobile flap was also found to influence symptoms severity in planta ., Such mutants now provide powerful tools with which to study the specific roles of reversible ubiquitylation in viral infection .

Viruses have much smaller genomes than their hosts ., Consequently , they often encode proteins which are multifunctional ., For instance , some viral proteases have a dual function , being also deubiquitinases , i . e . enzymes capable of removing ubiquitin tags grafted onto proteins and that often target them for destruction ., The protease and deubiquitinase activities share a single active site that is used alternately for one function or the other , but how this switch between activities may be regulated is presently unknown ., To answer this question , we studied a simple plant virus that is a useful model system for these complex molecular biology phenomena , and that encodes a simplified protease/deubiquitinase ., Here , thanks to a combination of structural and functional analyses , we managed to decouple the two activities , killing the deubiquitinase activity while preserving the protease one ., This successful decoupling relies on our discovery that a loop inserted next to the active site is mobile , and can thus act as a switch between the two activities ., This result allowed us to demonstrate the importance of the specific deubiquinase activity in viral multiplication ., In addition , viral symptoms were also severely affected by mutations affecting the loop mobility ., Our data provide powerful tools for further studies , that may also be relevant for more complex or medically relevant viruses .

plant anatomy, microbial mutation, crystal structure, molecular dynamics, condensed matter physics, microbiology, brassica, crystals, plant science, model organisms, materials science, experimental organism systems, crystallography, plants, materials by structure, arabidopsis thaliana, research and analysis methods, solid state physics, chemistry, viral replication, leaves, physics, eukaryota, plant and algal models, molecular structure, virology, biology and life sciences, physical sciences, computational chemistry, chemical physics, organisms

journal.ppat.1002084

The Intrinsic Antiviral Defense to Incoming HSV-1 Genomes Includes Specific DNA Repair Proteins and Is Counteracted by the Viral Protein ICP0

Mammalian cells have evolved complex defenses to protect themselves from viral infections ., Innate and adaptive immune responses are well-characterized , but resistance mediated by pre-existing cellular factors has recently emerged as another important arm of antiviral defense ., In contrast to the canonical immune responses , which are slower acting and initiated by virus-induced signaling cascades , the pre-existing cellular factors are poised to protect the cell before the virus has even entered 1 ., This mechanism of resistance is called intrinsic antiviral defense , and is characterized by the fact that the antiviral proteins are intracellular and constitutively expressed , and that the restrictive factors can be overcome by viral countermeasures ., These intrinsic defense pathways provide a primary protective mechanism in the first cell infected in an immunologically naive host , making them an important front line of defense against viruses ., Herpes simplex virus type 1 ( HSV-1 ) is a common human pathogen that causes life-long recurrent disease ., Lytic HSV-1 infection is characterized by transcription in a temporal cascade of immediate-early ( IE ) , early ( E ) , and late ( L ) gene products ., The immediate early ( IE ) genes create a favorable intracellular environment for the virus , and regulate the expression of the E and L genes ., The IE protein ICP0 is one of the first viral proteins expressed during HSV-1 infection ( reviewed in 2 ) ., Although ICP0 is a not an essential viral protein , its deletion significantly impairs productive replication , especially at low multiplicity of infection ( MOI ) 3–6 ., ICP0 is a RING finger E3 ubiquitin ligase that induces degradation of several cellular proteins including the catalytic subunit of DNA-dependent protein kinase ( DNA-PKcs ) 7 , the cellular DNA damage ubiquitin ligases RNF8 and RNF168 8 , components of the nuclear domain structures known as ND10 ( or PML nuclear bodies ) 9 , 10 , and centromeric proteins 11–13 ., Prototypic intrinsic antiviral defense proteins , such as APOBEC3 proteins , are known to be active against a variety of viruses 1 ., However , to date , the only proteins demonstrated to mediate intrinsic defense against herpesviruses are all components of ND10 ., The first evidence that these proteins may mediate intrinsic immune defense against herpesviruses came from the observation that depletion of PML increased the plaque-forming efficiency of both human cytomegalovirus ( HCMV ) 14 and ICP0-null HSV-1 15 ., Similarly , it was found that the ND10 proteins hDaxx and ATRX induce a repressive viral chromatin structure on incoming HCMV genomes that is prevented by the viral tegument protein pp71 targeting hDaxx for degradation 16–21 ., Depletion of either hDaxx or ATRX also improves the plaque-forming efficiency of ICP0-null HSV-1 , providing further evidence that ND10 proteins have a general role in mediating intrinsic antiviral defense against herpesviruses 22 ., In the case of HSV-1 , the repressive ND10 proteins have been detected at sites juxtaposed to incoming viral genomes 22–24 ., During wild-type HSV-1 infection ICP0 rapidly disperses these inhibitory proteins , ensuring that replication can proceed ., In the absence of ICP0 , the recruitment of the ND10 proteins into novel structures associated with the viral genomes is readily observable as a very early cellular response , detectable within the first 30 minutes of infection 25 ., ICP0 has therefore emerged as one of the key viral counterattacks to the cellular attempt to limit the early stages of infection ., Cells have elaborate machinery in place to monitor damage to genomic DNA and ensure the fidelity of replication 26 ., Recent work has demonstrated that the cellular DNA repair machinery can also recognize viral genetic material 27 ., HSV-1 has a complex relationship with the DNA damage response , in that it appears to activate many components of the ATM-dependent arm of the signaling pathway , while inhibiting the DNA-PKcs- and ATR-dependent arms 7 , 28–30 ., During lytic infection , HSV-1 recruits several cellular DNA repair proteins into viral replication compartments where they enhance viral replication 28–31 ., Despite global activation of the ATM-dependent signaling pathway , we recently reported that RNF8 and RNF168 , which are key mediators in this pathway , are targeted for proteasome-mediated degradation by ICP0 8 ., During HSV-1 infection , the viral capsid docks at the nuclear pore and the linear viral genome is released into the nucleus 32 ., In this study , we asked whether the cellular DNA repair machinery recognizes this incoming viral DNA , and we explored the significance of ICP0-mediated degradation of RNF8 and RNF168 for the virus ., We report that cellular DNA repair proteins respond to incoming HSV-1 genomes and we identify RNF8 and RNF168 as novel components of the intrinsic antiviral defense against HSV-1 ., In order to investigate effects of incoming HSV-1 genomes on localization of DNA damage proteins , we utilized a previously described assay to visualize nuclei at the earliest stages of infection 23 , 24 ., In this assay , cells are infected at low multiplicity so that directional viral spread through developing plaques can be analyzed ., This directionality , combined with the fact that incoming viruses often congregate near the microtubule organizing center , means that nuclei of cells at the edge of plaques frequently display an asymmetric arc of incoming viral genomes 23 , 24 ., Human foreskin fibroblast ( HFF ) cells were infected at low MOI with wild-type or ICP0-null HSV-1 , fixed 24 hours post-infection ( hpi ) and processed for immunofluorescence ., Sites of incoming viral genomes were detected by staining with antiserum to the viral DNA binding protein , ICP4 , which has been previously shown to co-localize with viral genomes in this assay 23 , and the localization of certain cellular DNA repair proteins ( Figure S1A ) was assessed ., In mock infected cells there was minimal γH2AX staining , and the damage checkpoint mediators Mdc1 , 53BP1 , and BRCA1 were localized in a diffuse nuclear pattern ., In cells infected with ICP0-null virus , we detected that γH2AX , Mdc1 , 53BP1 , and BRCA1 accumulated in distinct asymmetric arcs in close proximity to incoming viral genomes ( Figure 1A ) ., In cells infected with wild-type virus , γH2AX and Mdc1 still re-localized to sites associated with viral DNA , but 53BP1 and BRCA1 remained diffusely nuclear ( Figure 1B , Figure S1C ) ., 53BP1 accumulated at sites associated with incoming ICP0-null viral genomes when high MOI infection was performed in the presence of α-amanitin , suggesting that viral transcription may not be essential ( Figure S1B ) ., These data indicate that redistribution of 53BP1 and BRCA1 in response to incoming viral genomes is an early response to HSV-1 infection that is inhibited by ICP0 ., We quantified the effect using 53BP1 and γH2AX as examples of DNA repair proteins that accumulated near incoming HSV-1 genomes ., We observed that γH2AX accumulated near incoming HSV-1 genomes in over 80% of cells in both the presence and absence of ICP0 ( Figure S1C ) ., In contrast , while 53BP1 accumulated near incoming HSV-1 genomes in approximately 90% of cells infected with ICP0-null virus , this was reduced to approximately 25% of cells in the presence of ICP0 ( Figure S1C ) ., It has previously been reported that components of ND10 , including hDaxx , PML , ATRX , and Sp100 accumulate at sites overlapping , but not precisely co-localizing with , incoming HSV-1 genomes 22–24 ., We wished to determine if the virus-induced accumulation of DNA repair proteins we observed co-localized with either viral genomes or ND10 proteins ., We found that while the γH2AX and 53BP1 staining co-localized , these DNA repair proteins did not co-localize with either ICP4 ( representing viral genomes ) or PML ( representing ND10 proteins ) ( Figure 2A; see Figure S2A for the corresponding cytofluorograms ) ., Despite this lack of co-localization , we observed a degree of overlap between the different structures ., To analyze this , a Manders overlap co-efficient 33 was determined for each image ( Figure S2B ) ., We observed that on average , approximately 50% of the PML signal overlapped with the ICP4 signal , whereas only 20% of the 53BP1 or γH2AX signal overlapped with the ICP4 signal ., These data suggest that incoming viral genomes are more closely associated with ND10 proteins than DNA repair proteins , and that all three structures have subtly distinct sub-nuclear localizations ., Next , we investigated whether the accumulation of DNA repair proteins at sites of incoming viral genomes was dependent on major ND10 proteins ., HepaRG cells depleted of PML or Sp100 34 were infected with wild-type or ICP0-null HSV-1 and processed for immunofluoresence at 24 hpi ., Infections in cells depleted for PML or Sp100 were indistinguishable from control cells with respect to γH2AX accumulation near incoming viral genomes in both the presence and absence of ICP0 ( data not shown ) , while 53BP1 accumulated only in the absence of ICP0 ( Figure 2B ) ., Therefore , the recruitment of 53BP1 to incoming HSV-1 genomes and the ability of ICP0 to block this process are not dependent on either PML or Sp100 ., Taken together , these observations suggest that accumulation of ND10 proteins and DNA repair proteins are independent events occurring at distinct physical locations ., We recently reported that ICP0 expression leads to proteasome-mediated degradation of the cellular DNA repair proteins and histone ubiquitin ligases RNF8 and RNF168 8 ., We therefore investigated whether these proteins were responsible for coordinating the recruitment of 53BP1 to sites associated with ICP0-null viral genomes ., We infected RNF8 depleted cells ( Figure S3 ) , or cells derived from a patient who has a biallelic mutation in RNF168 ( RIDDLE cells , 35 ) with wild-type or ICP0-null HSV-1 and assessed the recruitment of DNA repair proteins to incoming viral genomes ( Figure 3A and B and Figure S4 ) ., During infection with wild-type virus , ICP0 expression prevented 53BP1 recruitment in the presence or absence of RNF8 and RNF168 ., However , in cells infected with ICP0-null virus , 53BP1 was not recruited to sites associated with incoming viral genomes in the absence of RNF8 or RNF168 ( Figure 3A and B ) , despite the fact that γH2AX still accumulated ( Figure S4 ) ., To determine if RNF8 and RNF168 themselves were recruited to sites associated with incoming viral genomes , we generated a cell line that could be induced to express GFP-tagged RNF8 , or utilized RIDDLE cells complemented with a cDNA expressing HA-tagged RNF168 ., We observed that RNF168 clearly accumulated near incoming ICP0-null viral genomes ( Figure 3C ) ., Redistribution of RNF8 to the vicinity of HSV-1 genomes was also detectable , although this was weaker and more variable than recruitment of RNF168 ( Figure 3C ) ., Together , these data suggest that accumulation of RNF8 and RNF168 at sites associated with incoming viral genomes coordinates 53BP1 recruitment ., This implies that the reason ICP0 targets RNF8 and RNF168 for degradation is to prevent recruitment of specific DNA repair factors to viral genomes , suggesting that this recruitment is detrimental to incoming virus during early stages of lytic infection ., In uninfected mammalian cells , a tightly controlled hierarchy of events occurs following the induction of DNA double strand breaks 36 , 37 ., RNF8 and RNF168 coordinate the recruitment of 53BP1 to sites of cellular damage 38 and also to sites associated with incoming viral genomes ., We therefore predicted that the latter process would be disrupted by depletion of factors upstream of RNF8 and RNF168 in the DNA damage response pathway ., Phosphorylation of the histone variant H2AX is one of the first events to occur after induction of a double stranded DNA break 39 , 40 and it is required for sustained accumulation of factors such as 53BP1 at damage sites 41 , 42 ., Phosphorylated H2AX binds MDC1 , which in turn recruits RNF8 in a phosphorylation-dependent manner , and this interaction tethers 53BP1 and other downstream mediators at damage sites 38 ., H2AX is therefore upstream of RNF8 and RNF168 , and stable foci of 53BP1 do not form in H2AX-null cells ., We infected cells from mice deleted for H2AX or matched control cells 43 with wild-type and ICP0-null HSV-1 , and examined cells at the edge of developing plaques ., As predicted , 53BP1 was recruited to ICP0-null viral genomes in wild-type mouse embryonic fibroblasts ( MEFs ) , but did not accumulate during infection of cells lacking H2AX ( Figure 4A ) ., ATM and the Mre11 complex are also upstream regulators of the cellular response to DNA damage ., The Mre11 complex senses DNA double strand breaks and facilitates activation of ATM by recruiting it to the break sites 44–46 ., However , despite this upstream role , 53BP1 still accumulates at sites of cellular DNA damage in cells deficient in Mre11 complex members or ATM 47 ., We therefore assessed the requirement for ATM and Mre11 in coordinating the recruitment of 53BP1 to sites associated with ICP0-null viral genomes ., We infected cells from patients with ataxia telangiectasia ( A–T ) and ataxia telangiectasia-like disorder ( A-TLD ) that lack functional ATM and Mre11 respectively , and compared them to matched controls in which ATM or Mre11 had been reconstituted ., We observed that neither ATM ( Figure 4B ) or Mre11 ( Figure 4C ) were required for the accumulation of 53BP1 at sites associated with incoming ICP0-null viral genomes ., These data demonstrate that H2AX , RNF8 and RNF168 are required for accumulation of 53BP1 at sites associated with incoming viral genomes , but ATM and Mre11 are not required ., This hierarchy of signaling and recruitment events in response to viral genomes parallels the response to cellular DNA damage ., Our data therefore suggest that the host cell recognizes either the incoming viral genomes themselves , or the resultant changes in local chromatin structure induced by incoming viral genomes , as DNA damage ., RNF8 and RNF168 are ubiquitin ligases for the histone H2A 35 , 48–51 , and we have previously reported that ICP0 expression leads to loss of uH2A , concomitant with the degradation of these two ligases 8 ., We therefore examined ubiquitin conjugation at the sites associated with incoming ICP0-null viral genomes ., We infected RNF8-null MEFs , RIDDLE cells , and matched controls , with ICP0-null virus and examined conjugated ubiquitin staining ( FK2 ) at sites associated with incoming viral genomes at 24 hpi ( Figure 5A ) ., Asymmetric FK2 staining was detectable only in cells expressing RNF8 and RNF168 , suggesting that this represents uH2A , which we also detected associated with incoming ICP0-null viral genomes ( Figure S5A ) ., The FK2 signal co-localized with 53BP1 , but not PML , at sites associated with incoming viral genomes , suggesting that conjugated ubiquitin was a marker for sites of DNA repair protein accumulation rather than sites of ND10 protein accumulation ( Figure S5B ) ., SUMO modification has also recently emerged as an important regulator of cellular DNA damage signaling 52 , 53 and SUMO conjugates have been detected at sites associated with incoming ICP0-null genomes ( Cuchet-Lourenco , Boutell and Everett , unpublished observations ) ., In the case of cellular DNA double strand breaks , SUMO1 and SUMO2/3 recruitment is dependent on RNF8 and RNF168 52 ., We therefore determined whether SUMO recruitment to sites associated with incoming ICP0-null genomes was also dependent on RNF8 and RNF168 ., We infected cells depleted for RNF8 or lacking functional RNF168 , and their matched controls , with ICP0-null virus and analyzed cells at the edges of developing plaques for asymmetric accumulations of SUMO ., Both SUMO1 and SUMO2/3 were recruited to sites associated with incoming ICP0-null genomes even in the absence of RNF8 or RNF168 ( Figure 6A and B ) ., ND10 proteins are heavily SUMOylated , and SUMO modified forms of PML and Sp100 are known to be targets of ICP0 15 ., We therefore speculate that at least some of the SUMO conjugates we detected in the absence of RNF8 and RNF168 may represent sites of ND10 protein accumulation rather than DNA repair proteins , an idea supported by the observation that PML is still recruited to these sites in cells depleted for RNF8 and RNF168 ( Figure 6C and D ) ., Together , these data show that recruitment of ND10 components and DNA repair proteins are independent events , sharing the common themes of being disrupted by ICP0 and likely being coordinated by SUMO modification events ., Accumulation of cellular factors at sites associated with incoming HSV-1 genomes has been strongly linked to restricting the invading virus 22 , 34 ., We therefore wished to determine the biological significance of the accumulation of specific DNA repair proteins at sites associated with incoming viral genomes ., First , we assessed the ability of wild-type or ICP0-null virus to form plaques on cells deficient for H2AX or matched control cells expressing wild-type H2AX ., We observed that both wild-type and ICP0-null HSV-1 were approximately 10-fold more likely to form plaques in the presence of H2AX ( Figure 7A ) ., This is similar to our previous data demonstrating that certain DNA repair proteins , such as ATM and Mre11 , are beneficial for HSV-1 replication 28 , possibly via processing of intermediates generated during viral replication/recombination 54 ., Even though γH2AX is excluded from viral replication compartments 55 , this histone variant is one of the master regulators of DNA damage signaling , and it is likely that H2AX phosphorylation is required to activate or recruit specific downstream proteins required during viral replication ., Our FK2 data ( Figure 5 ) suggested that ubiquitination events at sites associated with incoming viral genomes are regulated by RNF8 and RNF168 ., Since uH2A has well-characterized roles in silencing 56–58 and ICP0 is a known transcriptional activator , we hypothesized that one reason for ICP0 to target RNF8 and RNF168 is to limit transcriptional repression of incoming viral genomes ., To test this hypothesis , we compared the transcriptional competence of viral genomes in the presence and absence of RNF8 ( Figure 7B ) ., Cells from RNF8 null mice transduced with empty retrovirus or retrovirus expressing human WT RNF8 8 were infected with wild-type or ICP0-null HSV-1 and harvested at 2 and 5 hpi ., RNA was isolated and reverse transcribed , and qPCR was performed to detect ICP27 transcripts as a marker of viral transcription ., We confirmed that input DNA was similar in all infections ( data not shown ) and analyzed the data by comparing transcription in the presence of RNF8 to transcription in the absence of RNF8 ( Figure 7B; see Figure S6A for transcript levels across all samples ) ., We observed that, a ) both viruses were transcriptionally repressed by RNF8 ,, b ) this repression was more significant in the absence of ICP0 and, c ) RNF8-mediated repression decreased over time during wild-type but not ICP0-null virus infection , presumably as a consequence of RNF8 degradation ( Figure 7B ) ., These data indicate that RNF8 is transcriptionally repressive to HSV-1 genomes and explains why HSV-1 forms plaques less efficiently in the presence of RNF8 8 and/or RNF168 ( Figure S6B and C ) ., In this study we discovered that RNF8 and RNF168 coordinate a repressive barrier to incoming HSV-1 genomes , and that ICP0 targets these cellular ubiquitin ligases to overcome this host antiviral effect ., We describe structures marked by DNA repair proteins and conjugated ubiquitin that form de novo in response to incoming viral genomes ., These DNA repair structures are associated with , but are independent of , similar ND10-like structures that also form near incoming viral genomes ., Our studies highlight the complexity of the interface between HSV-1 and the cellular DNA damage response ., Previous work demonstrated that certain recombination and repair proteins , such as Mre11 , ATM , ATR/ATRIP , and WRN are beneficial for HSV-1 replication 28 , 29 , 31 ., Here we show that H2AX is also required for optimal replication of HSV-1 , as previously suggested 59 ., In contrast , the NHEJ proteins , DNA-PKcs and Ku70 , have been reported to be detrimental to HSV-1 replication 7 , 31 ., We found that RNF8 and RNF168 also inhibit replication , likely by creating a repressive environment at the nuclear sites of incoming viral genomes ., Together , these observations suggest that HSV-1 temporally dissects the DNA repair pathway; this ensures that repressive proteins are degraded , while repair proteins required to coordinate signaling and facilitate replication or processing of viral genomes are retained ., Although accumulation of many cellular factors has been strongly linked to restricting the incoming viral genomes 15 , 22 , 34 , recruitment does not necessarily always correlate with repression ., For example , some PML isoforms accumulate at sites associated with incoming HSV-1 genomes but do not inhibit the plaque-forming ability of ICP0-null virus ( Cuchet-Lourenco , Boutell and Everett , unpublished observations ) ., Similarly , we observe γH2AX accumulation at sites associated with incoming viral genomes , but find that H2AX is required for optimal HSV-1 replication ., In contrast , proteins involved in intrinsic antiviral defense are not only recruited to incoming genomes , but limit viral progression , and are therefore inactivated by the virus during the earliest stages of infection ., Our data identify RNF8 and RNF168 as new members of the host cell antiviral arsenal against incoming HSV-1 ., When HSV-1 genomes enter the nucleus , they do so as naked DNA ., However , the cell responds by depositing repressive chromatin marks on the incoming nucleic acid 60–64 ., In turn , the virus recruits modification complexes containing histone demethylases and methyltransferases , and installs positive marks to facilitate IE transcription 65 ., These demethylases may act in concert with histone deacetylases , such as HDAC1 , which bind the transcriptionally repressive coREST/REST complex in the absence of ICP0 66 , 67 ., We observed γH2AX and uH2A in association with sites of incoming viral genomes at the earliest detectable stages of infection , suggesting that these post-translational histone modifications are a very early response to incoming viral DNA ., Our co-localization studies raise the possibility that these modified histones may be deposited on the displaced host chromatin around the incoming viral genomes ., Our data highlight the emerging parallels between cellular recognition of viral DNA and the cellular response to DNA damage ., In both cases , γH2AX is activated , Mdc1 accumulates , and downstream repair factors such as 53BP1 are recruited ., Furthermore , both processes are coordinated by the ubiquitin ligases RNF8 and RNF168 , and ICP0 is thus able to disrupt both by inducing the degradation of these cellular proteins ( Figure 7C ) ., However , in contrast to the situation at sites of cellular DNA damage , we observed that SUMO conjugates still accumulate near incoming viral genomes even in the absence of RNF8 or RNF168 ., This accumulation likely reflects SUMO modification of ND10 components , which we show are still recruited in the absence of RNF8 or RNF168 ., Recent work has demonstrated that the SIMs of PML , hDaxx and Sp100 are essential for their recruitment to virus-induced foci ( Cuchet-Lourenco , Boutell and Everett , unpublished observations ) raising the possibility that these ND10 components are recruited in response to upstream SUMO-dependent events at these sites ., RNF168 is known to contain SIMs and its recruitment to sites of cellular damage depends on the SUMO ligase PIAS4 52 ., It will therefore be interesting to determine whether the SIMs in RNF168 are required for its accumulation near incoming viral genomes , and whether disrupting the SUMO pathway can abrogate accumulation of both ND10 proteins and DNA repair proteins at these sites ., Conversely , it will be interesting to see if the accumulation of ND10 components at sites of cellular DNA damage 68 , 69 is SUMO-dependent and whether this still occurs in the absence of RNF8 and RNF168 ., It has recently been shown that sites of cellular DNA damage are characterized by transcriptional repression 70 ., The parallels we have uncovered between recruitment of DNA repair proteins to sites of cellular DNA damage and to incoming viral genomes raise the possibility that silencing is a defining characteristic of both sites ., We propose that the accumulation of SUMO conjugates , ND10 components and DNA repair proteins are hallmarks of a repressive cellular response to both damaged and foreign DNA ., Vero and U20S cells were purchased from the American Tissue Culture Collection ., MEFs from RNF8-/- knockout mice and matched wild-type controls were obtained from Razq Hakem 71 or Junjie Chen 72 and for some experiments RNF8-/- MEFs were complemented with human RNF8 8 were used ., Human foreskin fibroblasts ( HFFs ) , obtained from the University of California San Diego Medical Center , were kindly provided by Debbie Spector ., Cells were maintained in Dulbecco modified Eagles medium ( DMEM ) containing 100 U/ml of penicillin and 100 µg/ml of streptomycin , supplemented with 10% fetal bovine serum ( FBS ) and selection antibiotics as appropriate ., Cells were grown at 37°C in a humidified atmosphere containing 5% CO2 ., HepaRG hepatocyte cells 73 were grown in Williams medium E supplemented with 2 mM glutamine , 5 µg/ml insulin , and 0 . 5 µM hydrocortisone ., H2AX-/- MEFs were obtained from Andre Nussenzweig 43 ., A-T cells ( AT22IJE-T ) and matched ATM put-back cells were obtained from Yosef Shiloh 74 ., A-TLD-1 cells and matched cells with Mre11 reconstituted were described previously 75 ., The inducible RNF8-GFP cell line was constructed by cloning RNF8 into the previously described tet-inducible pLKO based expression system 76 ., Cells were induced with 0 . 1 µg/ml tetracycline for 8 hrs ., shRNA targeting RNF8 was 5′ ACATGAAGCCGTTATGAAT 3′ as previously described 49 ., This sequence was incorporated into a pLKO based ( for HepaRG cells ) or GFP-tagged HIV vector plasmids 77 ., Parental virus HSV-1 strain was 17 syn+ and the matched ICP0 deletion mutant was dl1403 5 ., Viruses were grown in Vero cells and titered in U2OS cells , in which ICP0 is not required for efficient plaque formation ., Infections were performed on monolayers of cells in DMEM with 0% FBS ., After 1 hr at 37°C , virus was removed and media containing 10% FBS was added ., For plaque edge experiments , this media was supplemented with 1% human serum to limit spread of the virus ., For plaque assays , 24 well dishes were infected with three-fold dilutions of wild-type or ICP0-null HSV-1 ., After adsorption , the cells were overlaid with medium containing 10% FBS and 1% human serum ., Plaques were stained with crystal violet 24–36 h post-infection ., Pseudotyped lentiviral stocks were generated by transfecting 293T cells with the appropriate vector plasmid and pVSV-G , pRev and pMDL plasmids as previously described 77 ., Primary antibodies were purchased from Bethyl ( PML ) , Abcam ( SUMO1 and SUMO2/3 ) , Rockland ( ATM S1981-P ) , Santa Cruz ( BRCA1 , 53BP1 ) , Millipore ( H2AX S139 , FK2 , H2A , uH2A ) , Calbiochem ( BRCA1 ) , Research Diagnostics Inc . ( GAPDH ) , Covance ( HA ) , Transduction Laboratories ( DNA-PKcs ) , and Sigma ( FLAG ) ., Rabbit antisera to Mdc1 was from J . Chen ., The 58S monoclonal antibody to ICP4 was generated from an ATCC hybridoma cell line 78 ., All secondary antibodies were from Jackson Laboratories or Invitrogen ., For immunoblotting , lysates prepared by standard methods ., For immunofluorescence , cells were fixed with 4% paraformaldehyde for 15 min and extracted with 0 . 5% Triton X-100 in PBS for 10 min ., For certain antibodies , cells were pre-treated with 0 . 5% Triton X-100 in PBS for 10 min prior to fixation ., Nuclei were visualized by staining with DAPI ., Images were acquired using a Leica TCS SP2 confocal microscope ., 2X106 cells were infected with WT or ICP0-null virus at an MOI of 0 . 01 and harvested at 2 and 5 hpi ., 75% of the cell pellet was used for RNA extraction and 25% for DNA purification ., 1 µg RNA was reverse transcribed using SuperScriptIII RT ( Invitrogen ) and oligo dT in a 20 µl reaction ., qPCR was run in triplicate with 3 µl cDNA or 100 ng genomic DNA using SYBR Green PCR master mix ( ABI ) on an ABI 7900HT system ., ICP27 transcript was detected using primers GCATCCTTCGTGTTTGTCATT ( F ) and GCATCTTCTCTCCGACCCCG ( R ) 65 and normalized to endogenous RPLPO transcript detected using primers CTGGAAGTCCAACTACTTCC ( F ) and TGCTGCATCTGCTTGGAGCC ( R ) .

Introduction, Results, Discussion, Materials and Methods

Cellular restriction factors responding to herpesvirus infection include the ND10 components PML , Sp100 and hDaxx ., During the initial stages of HSV-1 infection , novel sub-nuclear structures containing these ND10 proteins form in association with incoming viral genomes ., We report that several cellular DNA damage response proteins also relocate to sites associated with incoming viral genomes where they contribute to the cellular front line defense ., We show that recruitment of DNA repair proteins to these sites is independent of ND10 components , and instead is coordinated by the cellular ubiquitin ligases RNF8 and RNF168 ., The viral protein ICP0 targets RNF8 and RNF168 for degradation , thereby preventing the deposition of repressive ubiquitin marks and counteracting this repair protein recruitment ., This study highlights important parallels between recognition of cellular DNA damage and recognition of viral genomes , and adds RNF8 and RNF168 to the list of factors contributing to the intrinsic antiviral defense against herpesvirus infection .

The cellular DNA damage response pathway monitors damage to genomic DNA ., We investigated whether cellular DNA damage response proteins can also respond to incoming viral genetic material and how they impact virus growth ., Using Herpes Simplex Virus type 1 ( HSV-1 ) , we present evidence that DNA repair proteins are activated at the earliest times post-infection , and that they physically accumulate at sites associated with incoming viral genomes ., A subset of these DNA repair proteins deposit repressive ubiquitin marks , recruit other DNA repair proteins , and limit transcription from the viral genomes ., We demonstrate that the virus overcomes this anti-viral defense by targeting key DNA repair proteins for degradation ., Our study adds these DNA repair protein mediators to the list of intrinsic antiviral defense factors active against HSV-1 , and demonstrates that many aspects of the cellular recognition of foreign DNA parallel the recognition and response to cellular damage .

biology, microbiology, molecular cell biology

journal.pbio.0050077

The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific

The concept of microbial diversity is not well defined ., It can either refer to the genetic, ( taxonomic or phylogenetic ) diversity as commonly measured by molecular genetics methods , or, to the biochemical ( physiological ) diversity measured in the laboratory with pure or mixed, cultures ., However , we know surprisingly little about either the genetic or biochemical, diversity of the microbial world 1 ,, in part because so few microbes have been grown under laboratory conditions 2 , 3 , and also because it is likely that there are immense, numbers of low abundance ribotypes that have not been detected using molecular methods, 4 ., Our understanding of microbial, physiological and biochemical diversity has come from studying the less than 1% of organisms, that can be maintained in enrichments or cultivated , while our understanding of phylogenetic, diversity has come from the application of molecular techniques that are limited in terms of, identifying low-abundance members of the communities ., Historically , there was little distinction between genetic and biochemical diversity, because our understanding of genetic diversity was based on the study of cultivated, microbes ., Biochemical diversity , along with a few morphological features , was used to, establish genetic diversity via an approach called numerical taxonomy 5 , 6 ., In recent years the situation has dramatically changed ., The determination of genetic, diversity has relied almost entirely on the use of gene amplification via PCR to conduct, taxonomic environmental gene surveys ., This approach requires the presence of slowly, evolving , highly conserved genes that are found in otherwise very diverse organisms ., For, example , the gene encoding the small ribosomal subunit RNA , known as 16S , based on, sedimentation coefficient , is most often used for distinguishing bacterial and archaeal, species 7–10 ., The 16S rRNA sequences are highly conserved and can be, used as a phylogenetic marker to classify organisms and place them in evolutionary context ., Organisms whose 16S sequences are at least 97% identical are commonly considered to be the, same ribotype 11 , otherwise referred, to as species , operational taxonomic units , or phylotypes ., Although rRNA-based analysis has revolutionized our view of genetic diversity , and has, allowed the analysis of a large part of the uncultivated majority , it has been less useful, in predicting biochemical diversity ., Furthermore , the relationship between genetic and, biochemical diversity , even for cultivated microbes , is not always predictable or clear ., For, instance , organisms that have very similar ribotypes ( 97% or greater homology ) may have vast, differences in physiology , biochemistry , and genome content ., For example , the gene, complement of Escherichia, coli O157:H7 was found to be substantially different from the K12 strain, of the same species 12 ., In this paper , we report the results of the first phase of the Sorcerer II, Global Ocean Sampling ( GOS ) expedition , a metagenomic study designed to address questions, related to genetic and biochemical microbial diversity ., This survey was inspired by the, British Challenger expedition that took place from 1872–1876 , in which the diversity of, macroscopic marine life was documented from dredged bottom samples approximately every 200, miles on a circumnavigation 13–15 ., Through the substantial dataset, described here , we identified 60 highly abundant ribotypes associated with the open ocean, and aquatic samples ., Despite this relative lack of diversity in ribotype content , we confirm, and expand upon previous observations that there is tremendous within-ribotype diversity in, marine microbial populations 4 , 7 , 8 , 16 , 17 ., New techniques and, tools were developed to make use of the sampling and sequencing metadata ., These tools, include: ( 1 ) the fragment recruitment tool for performing and visualizing comparative, genomic analyses when a reference sequence is available; ( 2 ) new assembly techniques that, use metadata to produce assemblies for uncultivated abundant microbial taxa; and ( 3 ) a whole, metagenome comparison tool to compare entire samples at arbitrary degrees of genetic, divergence ., Although there is tremendous diversity within cultivated and uncultivated, microbes alike , this diversity is organized into phylogenetically distinct groups we refer, to as subtypes ., Subtypes can occupy similar environments yet remain genetically isolated from each other ,, suggesting that they are adapted for different environmental conditions or roles within the, community ., The variation between and within subtypes consists primarily of nucleotide, polymorphisms but includes numerous small insertions , deletions , and hypervariable segments ., Examination of the GOS data in these terms sheds light on patterns of evolution and also, suggests approaches towards improving the assembly of complex metagenomic datasets ., At least, some of this variation can be associated with functional characters that are a direct, response to the environment ., More than 6 . 1 million proteins , including thousands of new, protein families , have been annotated from this dataset ( described in the accompanying paper, 18 ) ., In combination , these papers, bring us closer to reconciling the genetic and biochemical disconnect and to understanding, the marine microbial community ., We describe a metagenomic dataset generated from the Sorcerer II, expedition ., The GOS dataset , which includes and extends our previously published Sargasso, Sea dataset 19 , now encompasses a, total of 41 aquatic , largely marine locations , constituting the largest metagenomic dataset, yet produced with a total of ~7 . 7 million sequencing reads ., In the pilot Sargasso Sea study ,, 200 l surface seawater was filtered to isolate microorganisms for metagenomic analysis ., DNA, was isolated from the collected organisms , and genome shotgun sequencing methods were used, to identify more than 1 . 2 million new genes , providing evidence for substantial microbial, taxonomic diversity 19 ., Several, hundred new and diverse examples of the proteorhodopsin family of light-harvesting genes, were identified , documenting their extensive abundance and pointing to a possible important, role in energy metabolism under low-nutrient conditions ., However , substantial sequence, diversity resulted in only limited genome assembly ., These results generated many additional, questions: would the same organisms exist everywhere in the ocean , leading to improved, assembly as sequence coverage increased; what was the global extent of gene and gene family, diversity , and can we begin to exhaust it with a large but achievable amount of sequencing;, how do regions of the ocean differ from one another; and how are different environmental, pressures reflected in organisms and communities ?, In this paper we attempt to address these, issues ., Microbial samples were collected as part of the Sorcerer II expedition, between August 8 , 2003 , and May 22 , 2004 , by the S/V Sorcerer II , a 32-m, sailing sloop modified for marine research ., Most specimens were collected from surface, water marine environments at approximately 320-km ( 200-mile ) intervals ., In all , 44 samples, were obtained from 41 sites ( Figure, 1 ) , covering a wide range of distinct surface marine environments as well as a few, nonmarine aquatic samples for contrast ( Table 1 ) ., Several size fractions were isolated for every site ( see Materials and Methods ) ., Total DNA was extracted from one or more fractions ,, mostly from the 0 . 1–0 . 8-μm size range ., This fraction is dominated by bacteria , whose, compact genomes are particularly suitable for shotgun sequencing ., Random-insert clone, libraries were constructed ., Depending on the uniqueness of each sampling site and initial, estimates of the genetic diversity , between 44 , 000 and 420 , 000 clones per sample were, end-sequenced to generate mated sequencing reads ., In all , the combined dataset includes, 6 . 25 Gbp of sequence data from 41 different locations ., Many of the clone libraries were, constructed with a small insert size ( <2 kbp ) to maximize cloning efficiency ., As, this often resulted in mated sequencing reads that overlapped one another , overlapping, mated reads were combined , yielding a total of ~6 . 4 M contiguous sequences , totaling ~5 . 9, Gbp of nonredundant sequence ., Taken together , this is the largest collection of, metagenomic sequences to date , providing more than a 5-fold increase over the dataset, produced from the Sargasso Sea pilot study 19 and more than a 90-fold increase over the other large marine metagenomic, dataset 20 ., Assembling genomic data into larger contigs and scaffolds , especially metagenomic data ,, can be extremely valuable , as it places individual sequencing reads into a greater genomic, context ., A largely contiguous sequence links genes into operons , but also permits the, investigation of larger biochemical and/or physiological pathways , and also connects, otherwise-anonymous sequences with highly studied “taxonomic markers” such as 16S or, recA , thus clearly identifying the taxonomic group with which they are, associated ., The primary assembly of the combined GOS dataset was performed using the, Celera Assembler 21 with, modifications as previously described 19 and as given in Materials and Methods ., The assembly was performed with quite, stringent criteria , beginning with an overlap cutoff of 98% identity to reduce the, potential for artifacts ( e . g . , chimeric assemblies or consensus sequences diverging, substantially from the genome of any given cell ) ., This assembly was the substrate for, annotation ( see the accompanying paper by Yooseph et al . 18 ) ., The degree of assembly of a metagenomic sample provides an indication of the diversity of, the sample ., A few substantial assemblies notwithstanding , the primary assembly was, strikingly fragmented ( Table 2 ) ., Only 9% of sequencing reads went into scaffolds longer than 10 kbp ., A majority ( 53% ) of, the sequencing reads remained unassembled singletons ., Scaffolds containing more than 50 kb, of consensus sequence totaled 20 . 7 Mbp; of these , >75% were produced from a single, Sargasso Sea sample and correspond to the Burkholderia or, Shewanella assemblies described previously 19 ., These results highlight the unusual abundance of, these two organisms in a single sample , which significantly affected our expectations, regarding the current dataset ., Given the large size of the combined dataset and the, substantial amount of sequencing performed on individual filters , the overall lack of, assembly provides evidence of a high degree of diversity in surface planktonic, communities ., To put this in context , suppose there were a clonal organism that made up 1%, of our data , or ~60 Mbp ., Even a genome of 10 Mbp—enormous by bacterial standards—would be, covered ~6-fold ., Such data might theoretically assemble with an average contig approaching, 50 kb 22 ., While real assemblies, generally fall short of theory for various reasons , Shewanella data make, up <1% of the total GOS dataset , and yet most of the relevant reads assemble into, scaffolds >50 kb ., Thus , with few scaffolds of significant length , we could conclude, that there are very few clonal organisms present at even 1% in the GOS dataset ., To investigate the nature of the implied diversity and to see whether greater assembly, could be achieved , we explored several alternative approaches ., Breaks in the primary, assembly resulted from two factors: incomplete sequence coverage and conflicts in the, data ., Conflicts can break assemblies when there is no consistent way to chain together all, overlapping sequencing reads ., As it was possible that there would be fewer conflicts, within a single sample ( i . e . , that diversity within a single sample would be lower ) ,, assemblies were attempted with individual samples ., However , the results did not show any, systematic improvements even in those samples with greater coverage ( unpublished data ) ., Upon manual inspection , most assembly-breaking conflicts were found to be local in nature ., These observations suggested that reducing the degree of sequence identity required for, assembly could ameliorate both factors limiting assembly: effective coverage would, increase and many minor conflicts would be resolved ., Accordingly , we produced a series of assemblies based on 98% , 94% , 90% , 85% , and 80%, identity overlaps for two subsets of the GOS dataset , again using the Celera Assembler ., Assembly lengths increased as the overlap cutoff decreased from 98% to 94% to 90% , and, then leveled off or even dropped as stringency was reduced below 90% ( Table 3 ) ., Although larger assemblies, could be generated using lower identity overlaps , significant numbers of overlaps, satisfying the chosen percent identity cutoff still went unused in each assembly ., This is, consistent with a high rate of conflicting overlaps and in turn diagnostic of significant, polymorphism ., In mammalian sequencing projects the use of larger insert libraries is critical to, producing larger assemblies because of their ability to span repeats or local polymorphic, regions 23 ., The shotgun sequencing, libraries from the GOS filters were typically constructed from inserts shorter than 2 kb ., Longer plasmid libraries were attempted but were much less stable ., We obtained paired-end, sequences from 21 , 419 fosmid clones ( average insert size , 36 kb; 24 , 25 ) from the 0 . 1-micron fraction of GS-33 ., The effect of these long mate pairs, on the GS-33 assembly was quite dramatic , particularly at high stringency ( e . g . , improving, the largest scaffold from 70 kb to 1 , 247 kb and the largest contig from 70 kb to 427 kb ) ., At least for GS-33 this suggests that many of the polymorphisms affect small , localized, regions of the genome that can be spanned using larger inserts ., This degree of improvement, may be greater than what could be expected in general , as the diversity of GS-33 is by far, the lowest of any of the currently sequenced GOS samples , yet it clearly indicates the, utility of including larger insert libraries for assembly ., In the absence of substantial assembly , direct comparison of the GOS sequencing data to, the genomes of sequenced microbes is an alternative way of providing context , and also, allows for exploration of genetic variation and diversity ., A large and growing set of, microbial genomes are available from the National Center for Biotechnology Information, ( NCBI; http://www . ncbi . nlm . nih . gov ) ., At the time of this study ,, we used 334 finished and 250 draft microbial genomes as references for comparison with the, GOS sequencing reads ., Comparisons were carried out in nucleotide-space using the sequence, alignment tool BLAST 26 ., BLAST, parameters were designed to be extremely lenient so as to detect even distant similarities, ( as low as 55% identity ) ., A large proportion of the GOS reads , 70% in all , aligned to one, or more genomes under these conditions ., However , many of the alignments were of low, identity and used only a portion of the entire read ., Such low-quality hits may reflect, distant evolutionary relationships , and therefore less information is gained based on the, context of the alignment ., More stringent criteria could be imposed requiring that the, reads be aligned over nearly their entire length without any large gaps ., Using this, stringent criterion only about 30% of the reads aligned to any of the 584 reference, genomes ., We refer to these fully aligned reads as “recruited reads . ”, Recruited reads are, far more likely to be from microbes closely related to the reference sequence ( same, species ) than are partial alignments ., Despite the large number of microbial genomes, currently available , including a large number of marine microbes , these results indicate, that a substantial majority of GOS reads cannot be specifically related to available, microbial genomes ., The amount and distribution of reads recruited to any given genome provides an indication, of the abundance of closely related organisms ., Only genomes from the five bacterial genera, Prochlorococcus , Synechococcus , Pelagibacter , Shewanella , and, Burkholderia yielded substantial and uniform recruitment of GOS, fragments over most of a reference genome ( Table 4 ) ., These genera include multiple reference genomes , and we observed, significant differences in recruitment patterns even between organisms belonging to the, same species ( Figure 2A–2I ) ., Three genera ,, Pelagibacter ( Figure, 2A ) , Prochlorococcus ( Figure 2B–2F ) , and Synechococcus ( Figure 2G–2I ) , were found abundantly in a wide range of samples and together accounted for, roughly 50% of all the recruited reads ( though only ~15% of all GOS sequencing reads ) ., By, contrast , although every genome tested recruited some GOS reads , most recruited only a, small number , and these reads clustered at lower identity to locations corresponding to, large highly conserved genes ( for typical examples see Figure 2E–2F ) ., We refer to this pattern as nonspecific recruitment as it reflects, taxonomically nonspecific signals , with the reads in question often recruiting to, distantly related sets of genomes ., Most microbial genomes , including many of the marine, microbes ( e . g . , the ubiquitous genus Vibrio ) , demonstrated this, nonspecific pattern of recruitment ., The relationship between the similarity of an individual sequencing read to a given, genome and the sample from which the read was isolated can provide insight into the, structure , evolution , and geographic distribution of microbial populations ., These, relationships were assessed by constructing a “percent identity plot” 27 in which the alignment of a read to a, reference sequence is shown as a bar whose horizontal position indicates location on the, reference and whose vertical position indicates the percent identity of the alignment ., We, colored the plotted reads according to the samples to which they belonged , thus indirectly, representing various forms of metadata ( geographic , environmental , and laboratory, variables ) ., We refer to these plots that incorporate metadata as fragment recruitment, plots ., Fragment recruitment plots of GOS sequences recruited to the entire genomes of, Pelagibacter, ubique HTCC1062 , Prochlorococcus marinus MIT9312 , and, Synechococcus WH8102 are presented in Poster S1 ., Characteristic patterns of recruitment emerged from each of these abundant marine, microbes consisting of horizontal bands made up of large numbers of GOS reads ., These bands, seem constrained to a relatively narrow range of identities that tile continuously ( or at, least uniformly , in the case when abundance/coverage is lower ) along ~90% of the reference, sequence ., The uninterrupted tiling indicates that environmental genomes are largely, syntenic with the reference genomes ., Multiple bands , distinguished by degree of similarity, to the reference and by sample makeup , may arise on a single reference ( Poster S1D and S1F ) ., Each of these, bands appears to represent a distinct , closely related population we refer to as a, subtype ., In some cases , an abundant subtype is highly similar to the reference genome , as, is the case for P ., marinus MIT9312 ( Poster S1 ) and Synechococcus RS9917, ( unpublished data ) ., P ., ubique HTCC1062 and other Synechococcus strains like, WH8102 show more complicated banding patterns ( Poster S1D and S1F ) because of the presence of multiple subtypes that, produce complex often overlapping bands in the plots ., Though the recruitment patterns can, be quite complex they are also remarkably consistent over much of the reference genome ., In, these more complicated recruitment plots , such as the one for P . ubique HTCC1062 ,, individual bands can show sudden shifts in identity or disappear altogether , producing a, gap in recruitment that appears to be specific to that band ( see P . ubique recruitment, plots on Poster, S1B and S1E , and specifically between 130–140 kb ) ., Finally , phylogenetic analysis, indicates that separate bands are indeed evolutionarily distinct at randomly selected, locations along the genome ., The amount of sequence variation within a given band cannot be reliably determined from, the fragment recruitment plots themselves ., To examine this variation , we produced multiple, sequence alignments and phylogenies of reads that recruited to several randomly chosen, intervals along given reference genomes to show that there can be considerable, within-subtype variation ( Figure, 3A–3B ) ., For example , within, the primary band found in recruitment plots to P . marinus MIT9312 , individual pairs of overlapping, reads typically differ on average between 3%–5% at the nucleotide level ( depending on, exact location in the genome ) ., Very few reads that recruited to MIT9312 have perfect, ( mismatch-free ) overlaps with any other read or to MIT9312 , despite ~100-fold coverage ., While many of these differences are silent ( i . e . , do not change amino acid sequences ) ,, there is still considerable variation at the protein level ( unpublished data ) ., The amount, of variation within subtypes is so great that it is likely that no two sequenced cells, contained identical genomes ., Variation in genome structure in the form of rearrangements , duplications , insertions , or, deletions of stretches of DNA can also be explored via fragment recruitment ., The use of, mated sequencing reads ( pairs of reads from opposite ends of a clone insert ) provides a, powerful tool for assessing structural differences between the reference and the, environmental sequences ., The cloning and sequencing process determines the orientation and, approximate distance between two mated sequencing reads ., Genomic structural variation can, be inferred when these are at odds with the way in which the reads are recruited to a, reference sequence ., Relative location and orientation of mated sequences provide a form of, metadata that can be used to color-code a fragment recruitment plot ( Figure 4 ) ., This makes it possible to visually identify, and classify structural differences and similarities between the reference and the, environmental sequences ( Figure 5 ) ., For the abundant marine microbes , a high proportion of mated reads in the “good” category, ( i . e . , in the proper orientation and at the correct distance ) show that synteny is, conserved for a large portion of the microbial population ., The strongest signals of, structural differences typically reflect a variant specific to the reference genome and, not found in the environmental data ., In conjunction with the requirement that reads be, recruited over their entire length without interruption , recruitment plots result in, pronounced recruitment gaps at locations where there is a break in synteny ., Other, rearrangements can be partially present or penetrant in the environmental data and thus, may not generate obvious recruitment gaps ., However , given sufficient coverage , breaks in, synteny should be clearly identifiable using the recruitment metadata based on the, presence of “missing” mates ( i . e . , the mated sequencing read that was recruited but whose, mate failed to recruit; Figure 4 ) ., The, ratio of missing mates to “good” mates determines how penetrant the rearrangement is in, the environmental population ., In theory , all genome structure variations that are large enough to prevent recruitment, can be detected , and all such rearrangements will be associated with missing mates ., Depending on the type of rearrangement present other recruitment metadata categories will, be present near the rearrangements endpoints ., This makes it possible to distinguish among, insertions , deletions , translocations , inversions , and inverted translocations directly, from the recruitment plots ., Examples of the patterns associated with different, rearrangements are presented in Figure, 5 ., This provides a rapid and easy visual method for exploring structural, variation between natural populations and sequenced representatives ( Poster S1A and S1B ) ., Variation in genome structure potentially results in functional differences ., Of, particular interest are those differences between sequenced ( reference ) microbes and, environmental populations ., These differences can indicate how representative a cultivated, microbe might be and shed light on the evolutionary forces driving change in microbial, populations ., Fragment recruitment in conjunction with the mate metadata helped us to, identify both the consistent and the rare structural differences between the genomes of, microbial populations in the GOS data and their closest sequenced relatives ., Our analysis, has thus far been confined to the three microbial genera that were widespread in the GOS, dataset as represented by the finished genomes of P . marinus MIT9312 ,, P ., ubique HTCC1062 , and to a lesser extent Synechococcus, WH8102 ., Each of these genomes is characterized by large and small segments where little or, no fragment recruitment took place ., We refer to these segments as “gaps . ”, These gaps, represent reference-specific differences that are not found in the environmental, populations rather than a cloning bias that identifies genes or gene segments that are, toxic or unclonable in E . coli ., The presence of missing mates flanking, these gaps indicates that the associated clones do exist , and therefore that cloning, issues are not a viable explanation for the absence of recruited reads ., Although the, reference-specific differences are quite apparent due to the recruitment gaps they, generate , there are also sporadic rearrangements associated with single clones , mostly, resulting from small insertions or deletions ., Careful examination of the unrecruited mates of the reads flanking the gaps allowed us to, identify , characterize , and quantify specific differences between the reference genome and, their environmental relatives ., The results of this analysis for P . ubique and, P ., marinus have been summarized in Table 5 ., With few exceptions , small gaps resulted from, the insertion or deletion of only a few genes ., Many of the genes associated with these, small insertions and deletions have no annotated function ., In some cases the insertions, display a degree of variability such that different sets of genes are found at these, locations within a portion of the population ., In contrast , many of the larger gaps are, extremely variable to the extent that every clone contains a completely unrelated or, highly divergent sequence when compared to the reference or to other clones associated, with that gap ., These segments are hypervariable and change much more rapidly than would be, expected given the variation in the rest of the genome ., Sites containing a hypervariable, segment nearly always contained some insert ., We identified two exceptions both associated, with P . ubique ., The first is approximately located at the 166-kb position, in the P ., ubique HTCC1062 genome ., Though no large gap is present , the mated reads, indicate that under many circumstances a highly variable insert is often present ., The, second is a gap on HTCC1062 that appears between 50 and 90 kb ., This gap appears to be less, variable than other hypervariable segments and is occasionally absent based on the large, numbers of flanking long mated reads ( Poster S1A ) ., Interestingly , the long mated reads around, this gap seem to be disproportionately from the Sargasso Sea samples , suggesting that this, segment may be linked to geographic and/or environmental factors ., Thus , hypervariable, segments are highly variable even within the same sample , can on occasion be unoccupied ,, and the variation , or lack thereof , can be sample dependent ., Hypervariable segments have been seen previously in a wide range of microbes , including, P ., marinus 28 , but, their precise source and functional role , especially in an environmental context , remains, a matter of ongoing research ., For clues to these issues we examined the genes associated, with the missing mates flanking these segments and the nucleotide composition of the, gapped sequences in the reference genomes ., In some rare cases the genes identified on, reads that should have recruited within a hypervariable gap were highly similar to known, viral genes ., For example , a viral integrase was associated with the P . ubique HTCC1062, hypervariable gap between 516 and 561 kb ., However , in the majority of cases the genes, associated with these gaps were uncharacterized , either bearing no similarity to known, genes or resembling genes of unknown function ., If these genes were indeed acquired through, horizontal transfer then we might expect that they would have obvious compositional, biases ., Oligonucleotide frequencies along the P . ubique HTCC1062 and, Synechococcus WH8102 genomes are quite different in the large, recruitment gaps in comparison to the well-represented portions of the genome ( Poster S1 ) ., Surprisingly , this was less true for P . marinus MIT9312 , where the gaps have been linked, to phage activity 28 ., These results, suggest that these hypervariable segments of the genome are widespread among marine, microbial populations , and that they are the product of horizontal transfer events perhaps, mediated by phage or transposable elements ., These results are consistent with and expand, upon the hypothesis put forward by Coleman et al . 28 suggesting that these segments are phage mediated ,, and conflicts with initial claims that the HTCC1062 genome was devoid of genes acquired by, horizontal transfer 29 ., Though insertions and deletions accounted for many of the obvious regions of structural, variation , we also looked for rearrangements ., The high levels of local synteny associated, with P ., ubique and P . marinus suggested that large-scale rearrangements, were rare in these populations ., To investigate this hypothesis we used the recruitment, data to examine how frequently rearrangements besides insertions and deletions could be, identified ., We looked for rearrangements consisting of large ( greater than 50 kb ), inversions and translocations associated with P . marinus; however , we did, not identify any such rearrangements that consistently distinguished environmental, populations from sequenced cultivars ., Rare inversions and translocations were identified, in the dominant subtype associated with MIT9312 ( Table 6 ) ., Based on the amount of sequence that, contributed to the analysis , we estimate that one inversion or translocation will be, observed for every 2 . 6 Mbp of sequence examined ( less than once per P . marinus genome ) ., A further observation concerns the uniformity along a genome of the evolutionary history, among and within subtypes ., For instance , the similarity between GOS reads and, P ., marinus MIT9312 is typically 85%–95% , while the similarity between, MIT9312 and P ., marinus MED4 is generally ~10% lower ., However , there are several, instances where the divergence of MIT9312 and MED4 abruptly decreases to no more than that, between the GOS sequences and MIT9312 ( Poster S1G ) .

Introduction, Results, Discussion, Materials and Methods, Supporting Information

The worlds oceans contain a complex mixture of micro-organisms that are for the most, part , uncharacterized both genetically and biochemically ., We report here a metagenomic, study of the marine planktonic microbiota in which surface ( mostly marine ) water samples, were analyzed as part of the Sorcerer II Global Ocean Sampling, expedition ., These samples , collected across a several-thousand km transect from the North, Atlantic through the Panama Canal and ending in the South Pacific yielded an extensive, dataset consisting of 7 . 7 million sequencing reads ( 6 . 3 billion bp ) ., Though a few major, microbial clades dominate the planktonic marine niche , the dataset contains great, diversity with 85% of the assembled sequence and 57% of the unassembled data being unique, at a 98% sequence identity cutoff ., Using the metadata associated with each sample and, sequencing library , we developed new comparative genomic and assembly methods ., One, comparative genomic method , termed “fragment recruitment , ” addressed questions of genome, structure , evolution , and taxonomic or phylogenetic diversity , as well as the biochemical, diversity of genes and gene families ., A second method , termed “extreme assembly , ” made, possible the assembly and reconstruction of large segments of abundant but clearly, nonclonal organisms ., Within all abundant populations analyzed , we found extensive, intra-ribotype diversity in several forms: ( 1 ) extensive sequence variation within, orthologous regions throughout a given genome; despite coverage of individual ribotypes, approaching 500-fold , most individual sequencing reads are unique; ( 2 ) numerous changes in, gene content some with direct adaptive implications; and ( 3 ) hypervariable genomic islands, that are too variable to assemble ., The intra-ribotype diversity is organized into, genetically isolated populations that have overlapping but independent distributions ,, implying distinct environmental preference ., We present novel methods for measuring the, genomic similarity between metagenomic samples and show how they may be grouped into, several community types ., Specific functional adaptations can be identified both within, individual ribotypes and across the entire community , including proteorhodopsin spectral, tuning and the presence or absence of the phosphate-binding gene, PstS .

Marine microbes remain elusive and mysterious , even though they are the most abundant, life form in the ocean , form the base of the marine food web , and drive energy and, nutrient cycling ., We know so little about the vast majority of microbes because only a, small percentage can be cultivated and studied in the lab ., Here we report on the Global, Ocean Sampling expedition , an environmental metagenomics project that aims to shed light, on the role of marine microbes by sequencing their DNA without first needing to isolate, individual organisms ., A total of 41 different samples were taken from a wide variety of, aquatic habitats collected over 8 , 000 km ., The resulting 7 . 7 million sequencing reads, provide an unprecedented look at the incredible diversity and heterogeneity in naturally, occurring microbial populations ., We have developed new bioinformatic methods to, reconstitute large portions of both cultured and uncultured microbial genomes ., Organism, diversity is analyzed in relation to sampling locations and environmental pressures ., Taken together , these data and analyses serve as a foundation for greatly expanding our, understanding of individual microbial lineages and their evolution , the nature of marine, microbial communities , and how they are impacted by and impact our world .

viruses, archaea, ecology, virology, microbiology, computational biology, evolutionary biology, genetics and genomics, eubacteria

TheSorcerer II GOS expedition, data sampling, and analysis is described. The immense diversity in the sequence data required novel comparative genomic assembly methods, which uncovered genomic differences that marker-based methods could not.

journal.pntd.0005238

Molecular Mimicry between Chikungunya Virus and Host Components: A Possible Mechanism for the Arthritic Manifestations

Chikungunya fever is caused by a arbovirus belonging to Family Togaviridae and Genus Alphavirus ., CHIKV is positive sense RNA virus with about 11 . 8 kb long genome ., The prevalence of CHIKV has increased globally ., It caused massive outbreaks when it re- emerged in 2005 in French Reunion islands where it affected about 33% of the total population ., CHIKV outbreaks also occurred in India during the same period , southern states in India recorded a total of 1 . 3 million cases 1 , 2 ., Chikungunya fever is characterized by fever , headache , myalgia and arthralgia ., Though Chikungunya is a self limiting illness 3 , a small proportion of 10–20% of affected individuals develop persistent arthralgia ., The risk factors associated with the development of persistent arthralgia include older age of patients ( >40 years ) and pre existing rheumatic problems ., However , the precise molecular mechanisms of pathogenesis that lead to the development of these complications are poorly understood ., Experimental evidence of CHIKV persistence in macrophages of Macaca species has been demonstrated 4 and it has been suggested as one of the factors contributing to residual arthralgia ., Although , molecular mimicry as the cause of prolonged joint manifestations had not been proved conclusively in Chikungunya infection , there are reports which suggest that such a phenomenon might be operational ., Therefore , in this study we investigated the possible occurrence of molecular mimicry between CHIKV E1 and host components using a three pronged strategy:, ( i ) identification of homologous regions between CHIKV proteins and host tissue components using bioinformatics tools ,, ( ii ) establishing cross reactivity between serum samples obtained from CHIKV infected patients and peptides exhibiting molecular mimicry and, ( iii ) validating the ability of the cross reactive peptides in inducing joint and muscle pathology in a mouse model ., We demonstrate the occurrence of molecular mimicry between CHIKV envelope glycoprotein ( E1 ) and the host components ., A clinical isolate of CHIKV ( Chikungunya virus strain DRDE-06; GenBank accession number: EF210157 . 2 ) was used for all the in vivo experiments in this study ., The bioinformatics related work was carried out using the CHIKV E1 protein sequence from the prototype strain CHIKV S27 available in the SWISS PROT ( ID:Q8JUX5 ) ., Further , a multiple sequence alignment of the E1 glycoprotein of DRDE-06 sequences and CHIKV S27 revealed a 98% homology between the two strains ., CHIKV peptides were custom synthesised from commercial sources ( Hysel Pvt Ltd . , India ) and obtained as a lyophilised powder ., The non-specific peptide was a gift from XCyton diagnostics private Ltd , Bangalore , India ., Rabbit anti-human polyclonal-HRP conjugate was procured from Dako , Denmark , while Goat anti-mouse IgG-HRP was obtained from Genei , Bangalore ., All work related to animals was conducted with good animal practice defined by committee for the purpose of control and supervision of experiments of animals ., The use of animals was approved by the institutional animal ethics committee ( IAEC ) of NIMHANS ( Approval reference no: AEC/41/222 ( B ) /NV dated 05 . 10 . 2010 ) ., The animals were housed in cages maintained in hygienic conditions with good ventilation , in a room maintaining the usual day and night cycle ., The animals used for the experiments were euthanized by cervical dislocation and animal ethics were strictly adhered to at all times , while bleeding and sacrificing the animals ., The use of human samples for the study was approved by was approved by institute ethics committee at NIMHANS ( Approval reference no: NIMHANS/68th IEC/2010 ) which adheres to the ethical guidelines for biomedical research on human participants developed by the Indian Council for Medical research ( ICMR ) ., Written informed consent was obtained from all the subjects themselves in the study ., C57BL/6J strain of mice were obtained from NIMHANS Central animal research facility and used in the study ., Eight day old mouse pups were procured from the animal facility along with the mother and the mouse pups were used for the experiments ., The human samples used in this study were received at the Department of Neurovirology , National Institute of Mental Health and Neurosciences ( NIMHANS ) , which is one of the twelve designated national apex laboratories for the diagnosis of Chikungunya in India ., All the subjects enrolled in the study presented to the hospital/clinics with fever , joint pain , rash , myalgia , conjunctival redness , and headache ., Additionally , the prevalence and local outbreaks in the region aided in making a clinical diagnosis of Chikungunya fever ., Blood samples ( 3–5 ml clotted blood ) were collected from thirty six subjects , serum separated and stored in aliquots at -70°C until all the tests were performed ., The CHIKV infection was confirmed by detection of CHIKV specific IgM antibodies using an ELISA ( National Institute Virology , Pune ) and/or CHIKV RNA by TaqMan real time PCR targeting the NSP4 region 5 ., Serum samples collected from 31 healthy individuals served as controls ., CHIKV was grown in C6/36 cell line and infectious fluid was harvested ., CHIKV infected C6/36 fluid was centrifuged at 10 , 000 rpm for 20minutes to remove debris and NaCl was added to the supernatant to obtain a final concentration of 0 . 5 molar ., Subsequently , polyethylene glycol was added to the mixture to obtain a final concentration of 10% ( w/v ) and the suspension stirred on ice bath for 20 minutes ., The mixture was incubated overnight at 4°C , and centrifuged at 3000 rpm for 30 minutes to obtain the virus rich precipitate ., The precipitate was dissolved in 1/100th of original infected cell culture fluid volume using GTNE buffer ., CHIKV was purified using a discontinuous sucrose gradient method ., Briefly , 5ml of 20% sucrose ( w/v ) in GTNE buffer was carefully overlaid onto 2 . 5ml of 50% ( w/v ) sucrose ., Subsequently , 2 . 5ml of CHIKV obtained after PEG concentration was overlaid onto the discontinuous sucrose gradient and centrifuged at 28 , 000 rpm for 2 hours at 4°C using a ultracentrifuge ( Beckman Coulter , USA ) ., The band at the inter-phase was collected and re-suspended in 10–12 volumes of PBS ( pH7 . 2 ) and centrifuged at 28 , 000 rpm for 2 hours to obtain a purified virus pellet ., The pellet was dissolved in 1ml of fresh PBS and frozen in small aliquots at -70°C ., The complete genome sequence of a prototype CHIKV S27 belonging to African genotype was obtained from the Gen Bank ., The sequence of CHIKV E1 glycoprotein was obtained from SWISSPROT ( Q8JUX5 ) ., This sequence was uploaded into Immune Epitope Database and Analysis Resource ( IEDB ) server available at http://www . immuneepitope . org/ ., The server predicts the antigenic determinants using five different algorithms—Chou and Fasman beta turn prediction , Emini surface accessibility prediction , Karplus and Schulz flexibility prediction , Kolaskar and Tongoankar antigenicity prediction , Parker hydrophilicity prediction ., The antigenic peptides from E1 region were deduced after considering hydrophilicity , surface probability , chain flexibility and secondary structure antigenic index both as text and graphs ., The results obtained from the server were combined to construct a graph using MS- EXCEL , which in turn yielded putative epitopic regions of CHIKV E1 glycoprotein ., The peaks with antigenic propensity , surface accessibility , flexibility , hydrophilicity and beta turns were considered ., The results obtained through IEDB were further confirmed using additional server- European Molecular Biology Open Software Suite ( EMBOSS ) available at http://liv . bmc . uu . se/cgi-bin/emboss/antigenic ., The results obtained using the Chou and Fasman criteria for beta turn prediction was also verified using COUDES server ., The existence of sequence similarity between CHIKV E1 glycoprotein and Human HLA-B27 was investigated using BLAST ., The existence of structural similarity between the CHIKV E1 glycoprotein and human host components were determined by using BioXGEM server and number of hits obtained in the non-redundant protein database ( nrPDB ) were limited to first 100 in the output ., Multiple sequence alignment of E1 glycoprotein sequences of Alphaviruses- CHIKV , ONNV , RRV , SFV , and EEEV was done using CLUSTALW available at http://www . ebi . ac . uk/Tools/clustalw2/index . html ., The optimal concentration of the peptide to be coated onto the ELISA plate was predetermined in an initial experiment and was found to be 25μg/well ., The peptides were coated onto the ELISA microwells using carbonate buffer and incubated overnight at 4°C ., The plate was washed three times with phosphate buffered saline with tween ( PBST ) and quenched using 1% skimmed milk powder in PBST for one hour at 37°C ., The plates were washed with PBST and reacted with 100μl of serum samples ( 1:100 dilution in PBS containing 0 . 25% triton-X 100 ) obtained from patients infected with CHIKV ( n = 36 ) as well as serum samples obtained from control subjects ( n = 31 ) ., The samples were incubated for 90 minutes at 37°C , followed by five washes with 1X PBST ., Subsequently , rabbit polyclonal anti-human antibodies tagged with HRP ( Dako , Denmark ) was diluted 1:1000 and 100μl was added to each well and the plate was incubated at room temperature for 90 minutes ., The plate was washed five times with PBST and 100μl of the TMB solution was added and incubated in the dark for 10 minutes ., The reaction was stopped by the addition 4N sulphuric acid and the OD values were read at 492nm using ELISA reader ( Thermo scientific , USA ) ., Eight day old C57BL/6J pups were procured along with the mother and the pups were used for the in vivo experiments ., They were assigned to nine different groups with each group comprising of 6 pups as shown in Table 1 ., Prior to determining the role of peptides in the possible enhancement of pathology related to CHIKV infection , the pathological changes induced by the CHIKV in C57BL/6J mice were studied ( Group 1 ) ., For this purpose , CHIKV ( 105 PFU/50μl ) was inoculated into the foot pad of 8 day old mice ., The control group of mice ( Group 2 ) received equal volume ( 50 μl ) of Eagles Minimum Essential Medium ( EMEM ) ., The mice were kept under observation for 12 days only post infection ., At the end of the observation period , blood was collected from the mice through retro orbital plexus bleeding , and the mice euthanized to obtain the following organs- brain , thymus , heart , lungs , spleen , liver , kidneys , upper limbs and lower limbs ., For histopathological studies the tissues were fixed in 4% paraformaldehyde , while for PCR the tissues were collected in EMEM ., CHIKV infection was confirmed by two methods- presence of CHIKV specific IgG antibodies in the serum and detection of CHIKV nucleic acid in the serum and harvested tissues using TaqMan real- time PCR 5 ., Eight day old pups in Group 3 , 4 and 5 were injected with two doses ( 50μg /dose ) of CHIKV Peptide A , CHIKV Peptide B and Non-specific peptide respectively on day 0 and day 5 ., The peptides were reconstituted in sterile 1X PBS ( pH 7 . 2 ) ., Equal volumes of peptide solution and Freund’s incomplete adjuvant were mixed and emulsified to obtain water in oil emulsion ., The emulsion was stored at -70°C until use ., In all these three groups blood was collected 12th day post inoculation by retro orbital bleeding and fresh tissues were harvested and processed for PCR and paraformaldehyde fixed tissues for histopathology ., Mice in Group 6 were injected with EMEM alone followed by PBS emulsified in Freund’s incomplete adjuvant 5 days post infection with CHIKV ., C57BL/6J mice in Group 7 , 8 and 9 were injected with CHIKV ( 105 PFU/50 μl ) followed by 50μg of the CHIKV Peptide A , CHIKV Peptide B and Non-specific peptide respectively , on 5th day post CHIKV inoculation through the same route at the same site ., In these groups of mice the blood was collected and tissues harvested on 12th day post infection , and processed for PCR and histopathological examination ., Serum was separated from the blood and stored at -70°C ., Polystyrene ELISA microwells ( Nunc , Denmark ) were coated with purified CHIKV in carbonate buffer at a concentration of 1μg/well diluted in carbonate buffer ( Appendix I ) ., The plate was incubated overnight at 4°C , washed thrice with PBST and quenched using 1X PBST containing 1% skimmed milk powder ., Serum samples were diluted 1:100 in PBST and 100μl added to the wells and incubated at 37°C for 1 hour followed by washing for five times with PBST ., Subsequently , 100 μl of a 1:5000 dilution of Goat anti-mouse IgG conjugated with HRP ( Genei , Bangalore ) was added to the wells , incubated at 37°C for 1 hour followed by washing for 5 times with PBST ., Finally , 100μl of the substrate solution ( TMB ) was added to all the wells and incubated in the dark for 10 minutes ., The reaction was stopped by the addition 4N sulphuric acid ., The OD values were read at 450nm using an ELISA reader ( Thermo scientific , China ) ., The tissues collected in EMEM were homogenised using a motorised hand held homogeniser ., The homogenates were spun at 8 , 000 rpm for 10 minutes at 4°C ., The supernatants were collected , 200 μl of the supernatant was used for RNA extraction using QIAmp viral RNA extraction kit ( Qiagen , Germany ) ., The eluted RNA was converted to cDNA using high capacity reverse transcription kit ( ABI , USA ) ., The cDNA was stored at -20°C until tested ., Similarly whole blood RNA extraction kit was used to extract RNA from blood and converted to cDNA which was used in the TaqMan real time PCR as described earlier 5 ., All tissues were fixed in paraformaldehyde and embedded in paraffin for processing and 4 μm thick sections obtained were mounted on selin coated glass slides ., Subsequently , the tissue sections were de-paraffinised with two changes of xylene and rehydrated in absolute alcohol followed by washing briefly in tap water ., Staining of the sections was carried out using Harris haemotoxylin for 5–8 minutes , followed by washing under running tap water for 5 minutes and differentiated in 1% acid alcohol for 30 seconds ., The sections were subsequently washed under running tap water for 1 minute , treated with bluing saturated lithium carbonate solution for 30–60 seconds and washed under tap water for 1 minute ., Counter staining of sections was carried out using eosin-phloxine solution for 1 minute followed by dehydration in 95% alcohol and absolute alcohol for 5 minutes each ., The sections were finally immersed in xylene twice for 2 minutes each for clearing and then mounted with DPX ., As described in the materials and methods section , the sequence of the African prototype of CHIKV S27 EI glycoprotein ( Q8JUX5 ) was obtained from the SWISSPROT database and subjected to immune epitope analysis using five algorithms in IEDB and EMBOSS programs ., The results are depicted in Fig 1 . The scores obtained for the five algorithms were uploaded into an MS Excel sheet to generate a combined graph which yielded the putative epitopic regions of E1 glycoprotein of CHIKV ., Analysis of the peaks in the graph with respect to antigenic propensity , surface accessibility , flexibility , hydrophilicity and B turns enabled prediction of the following epitopic regions: These regions of CHIKV E1 glycoprotein satisfy all the criteria necessary for a given peptide to be considered immunogenic and capable of eliciting an immune response in the host system ., Subsequently multiple sequence alignment of E1 glycoprotein of Alphaviruses- CHIKV , Onyong Onyong Virus ( ONNV ) , Ross River Virus ( RRV ) , Semiliki Forest Virus ( SFV ) , and Eastern Equine Encephalitis Virus ( EEEV ) was done through CLUSTALW ., The results of the alignment are depicted in Fig 2 . As evident from the figure , the alignment revealed two motifs SKD and KCA present only in arthritogenic Alphaviruses ( CHIKV , ONNV , RRV ) and not in encephalitogenic Alphaviruses ( SFV , EEEV ) ., Furthermore , the amino acid sequences SKD and KCA were present in the immunodominant peptides 1 and 2 deduced from E1 glycoprotein respectively ., All further experiments using human serum samples and mouse models were therefore restricted only to these two peptides ., The sequence similarity between the CHIKV E1 glycoprotein and HLA- B27 was determined using BLAST ., The alpha chain of HLA-B27 molecule shared a partial homology from the stretch ranging from 216–220 of CHIKV E1 glycoprotein as well as the immunodominant region of Peptide A ( Fig 3 ) ., The output obtained through the BioXGEM 3D BLAST performed on the CHIKV E1 was limited to first 100 hits ., The output of the BLAST was further analyzed and limited only to human proteins ( Table 2 ) ., Further analysis of these human proteins was limited to those that are known to contribute to the inflammation and arthritic pathology ., Amongst these , six human proteins- Human complement component 3 , complement component 5 6 , fibronectin 7 , kelch like protein 8 , mast/stem cell growth receptor 9 and beta arrestin 1 10 which exhibited maximum similarity to CHIKV EI protein were only considered for further analysis ., Prominent among these six were complement proteins C3 and C5 ., When the structural similarity between the E1 glycoprotein and complement component C3 was analyzed , the sequence of amino acids in the region of CHIKV E1 glycoprotein which shared homology with complement component C3 were also present in Peptide A and Peptide B ( Fig 4 ) ., All the patients in the study were from the state of Karnataka , South India ., Among the 36 patients with confirmed CHIKV infection , 17 were females and 19 were males ., The mean age of the patients was 40 . 64 years ., All the patients whose samples were used in the study presented with fever , while 28 ( 78% ) had arthralgia , 30 ( 83% ) had myalgia , 20 ( 56% ) had complained of headache ., Rash and gastrointestinal symptoms were seen in 14 ( 39% ) and 10 ( 28% ) of the patients each , and conjunctival redness was seen in 1 ( 3% ) of the patients ., Amongst 3/36 patients ( 8% ) persistent arthralgia was reported at 12 weeks after onset of initial symptoms ., Hence follow up samples blood samples could be collected from these three patients at 12 weeks ., In all other patients no follow up samples could be collected ., A sample was considered to be positive in the peptide ELISA if it had an OD value equal to or greater than that of the cut-off value ., The cut off value for each of the peptides was calculated by using OD values obtained with sera of healthy control subjects ( n = 31 ) using the formula Mean OD of control samples + 2SD ., The cut off OD value for Peptide A was 0 . 373 and for Peptide B it was 0 . 408 ., Amongst the 36 samples obtained from confirmed CHIKV patients , 24 ( 66 . 66% ) showed reactivity to the peptide A and 27 ( 75% ) to peptide B ( Fig 5 ) ., The OD values of CHIKV positive samples towards these peptides ranged from 1 . 501 to 0 . 11 for Peptide A , while it varied from 1 . 378 to 0 . 203 for Peptide B . These experiments were carried out to determine the possible synergistic role of peptides in the enhancement of pathology in CHIKV infection: All the mice were confirmed to have CHIKV infection by the detection of anti CHIKV antibodies in the serum by ELISA ., The cut-off in the ELISA was determined using the mean + 2SD OD values of serum samples obtained from uninfected control mice and it was 0 . 253 ., The OD values of serum samples from all the infected animals were found to be > 0 . 253 and hence considered positive for CHIKV-specific antibodies ., In addition , the presence of CHIKV nucleic acids was demonstrable in the muscle tissue by TaqMan real time PCR while , it was not detected in the blood and other tissues of the infected group of mice ., All the control group of animals were negative for CHIKV nucleic acids ., In order to have an objective assessment of the pathological features noted in all groups of animals , a semi quantitative scale for scoring the degree of inflammation centred mainly around the muscles was evolved and the slides were evaluated by a pathologist blinded to the groups and the same is depicted in Fig 6 . The inflammation was graded as minimal ( 1+ ) , mild ( 2+ ) , moderate ( 3+ ) and severe ( 4+ ) ., The salient histopathological features noted in CHIKV infected mice were as follows:, ( i ) the soft tissue around the elbow and knee joints were relatively normal with no evidence of synovitis or arthritis , while tissues close to the shoulder and the hip joint revealed moderate degree of lymphohistiocytic infiltration virus ,, ( ii ) the major muscles of the hip and the shoulder had multifocal lymphocytic infiltrate in the endomysium with myonecrosis ,, ( iii ) random and occasion muscle fibres close to inflammation revealed cytoplasmic basophilia and central nucleation with prominent nucleolus indicative of regenerative activity , similar to polymyositis noted in human subjects, ( iv ) the synovium and periarticular soft tissue had sparse inflammation while the articular cartilage and the articular cavity were free of inflammation ,, ( v ) the epimysium around the muscle and tendinous portion close to the insertion had variable lympho-histiocytic inflammation indicating tenosynovitis ,, ( vi ) the striking pathology was mineralization of the necrosed muscle belly especially the lateral group of muscles close to the hip and shoulder joints similar to some cases of chronic polymyositis in human subjects ., Other than these features noted in the limbs and joints all the other organs did not reveal any significant pathological changes ., Some of the salient features noted in CHIKV infected mice ( Group, 1 ) are depicted in Fig 7 . The degree of pathological damage centred on the muscles in the various groups of mice was graded and a comparative chart was prepared ( Table 3 ) ., As evident from the table , the group of mice that were mock infected ( Group, 2 ) and subsequently did not receive any peptides revealed sparse ( 1+ ) inflammation in the muscles probably related to the procedure ., Similar findings were also noted in the mock infected animals that received a single dose of Freund’s incomplete adjuvant ( Group 6 ) ., In mice that received two doses of non-specific peptide but no virus ( Group 5 ) hyperplasia of the bone marrow was noted with sparse inflammation ( 1+ ) ., On the other hand , mice that received two doses of CHIKV specific peptides but no virus ( Groups 3 & 4 ) exhibited myositis , muscle necrosis , vasculitis and hyperplasia of the marrow ( immune mediated inflammatory muscle and marrow reactive changes ) and an overall inflammation score of 3+ ( Fig 8 ) ., In the three groups of mice that received an initial inoculum of virus followed by a single dose of either CHIKV specific peptides ( Groups 7&8 ) or non-specific peptide ( Group, 9 ) the pathological features were more florid ( Fig 9 ) ., Amongst these three groups of animals , the mice that received virus followed by non-specific peptide exhibited features similar to those observed in mice that received virus alone ( Group 1 ) ., The animals in Groups 7 and 8 had the highest overall inflammatory score ( 4+ ) and showed multiple features including myositis , muscle necrosis , focal regeneration , mineralization and calcification of the necrotic muscles , hyperplasia of marrow in the long bones ., Molecular mimicry represents shared immunologic epitopes between a microbe and the host ., In a viral system , viruses have been shown to have cross reactive epitopes with host self proteins 11 ., Molecular mimicry can be either in the form of sequence homology wherein the host and the infectious agent share the sequence of similar or identical amino acids or it might be due to the conformational similarity between the host and the infectious agent in question 11 ., Molecular mimicry is one of the major mechanisms for the induction of autoimmune diseases through the activation of autoreactive T cells in the host immune system ., Several elegant examples of molecular mimicry leading to autoimmune manifestations have been described following bacterial and viral infections 11 ., Chikungunya fever is a self limiting illness , however in 20–30% of the patients arthralgia persists for a period of two years and above 12 ., More than half of all CHIKV infected patients in La Reunion Island during the 2005–2006 epidemics had complaints of persistent joint pain / recurring stiffness 13 ., The arthritis attributed to CHIKV infection indeed mimics rheumatoid arthritis , as discussed by Bouquillard et al 14 wherein 21 patients infected with CHIKV in Reunion islands developed RA ., Further , Malvy et al 15 suggested that molecular mimicry may be responsible for chronic manifestations as symptoms continue to persist despite CHIKV not being detectable in the synovial tissue ., We investigated molecular mimicry in this study by using a combined approach of identifying homologous regions between CHIKV glycoprotein E1 protein and host tissue components using bioinformatics tools , the ability of these designed peptides to cross react with serum samples from CHIKV infected patients and inducing immune mediated joint and muscle pathology in a mouse model ., In order to determine if there are any “arthritogenic” motifs within the E 1 protein , a multiple sequence alignment of amino acid sequences of E1 glycoprotein of CHIKV was carried out with other alphaviruses such as ONNV , RRV , SFV and EEEV using CLUSTALW ( Fig 2 ) ., The alignment revealed that two common motif ( s ) SKD and KCA were present only in arthritogenic alphaviruses such as CHIKV , RRV and ONNV but not in the ‘encephalitogenic’ alphaviruses such as SFV and VEEV ( Fig 2 ) ., The presence of these two motifs seen only in arthritogenic alphaviruses lead us to postulate that these two motifs may have a role in the development of arthralgia which is a hallmark in the disease produced by these group of viruses ., Simultaneously , the immunodominant epitopes in the CHIKV E1 glycoprotein were deduced using IEDB and EMBOSS programs which use criteria like presence of beta turn , surface accessibility , flexibility , antigenicity and hydrophilicity of the regions to be studied ., On combining the common results obtained with these two programs , four immunodominant regions were obtained in CHIKV E1 glycoprotein ( Peptide A , B , C and D ) ., Interestingly , it was observed that the “arthritogenic” motifs SKD and KCA were also present in the immunodominant Peptides A and B respectively ., Sequence homology comparisons between CHIKV E1 glycoprotein and various human proteins using BLAST revealed that a homology of four consecutive amino acids TQLV/TELV exist between the CHIKV E1 glycoprotein and HLA-B27 molecule ( Fig 3 ) ., It is a well established that HLA B27 has been implicated in the pathogenesis of autoimmune arthritis and ankylosing spondylitis 16 ., Interestingly this consecutive sequence of amino acids was also present in one of the immunodominant peptides ( Peptide A ) of CHIKV E1 glycoprotein ( Fig 3 ) ., Having ascertained that there is amino acid homology between CHIKV E1 glycoprotein and HLA B27 molecule , the occurrence of structural homology was also explored using the BioXGEM program ., This program searches for the longest common substructures existing between the query structure and every structure in the database ., The output of the query while executing the program was however limited to the first 100 hits ., Amongst these 100 proteins , further analysis was restricted only to human proteins present in the output ., Amongst the 20 human proteins in the list ( Table 2 ) , six proteins were shortlisted as they are known to contribute to either inflammation or arthritic pathology ., The most prominent amongst them was complement components C3and C5 ., Indeed , C3 complement component has been implicated in inflammation and tissue injury in other related alphaviral infections 17 and therefore further analysis was confined to the homology between the CHIKV E1 glycoprotein and the C3 complement component ., The analysis showed that the homology occurred between Von Willebrand Factor ( VWF ) domain of C3 and CHIKV E1 glycoprotein ( Panel A , Fig 5 ) ., Interestingly , the amino acid sequences in the region of CHIKV E1 glycoprotein exhibiting homology were also present in the immunodominant Peptides A and B ( Panel B , Fig 5 ) ., In summary , combining the data from the various bioinformatics approaches and employing a logical algorithm relevant to the pathogenesis of arthritis the choice narrowed down to two immunodominant peptides of CHIKV E protein ( Peptides A & B ) ., Therefore all further experiments were carried out using only these two peptides . The Peptides A and B were used in an ELISA to assess the immunoreactivity of serum samples obtained from CHIKV confirmed patients as well as healthy control subjects ., The serum samples obtained from CHIKV confirmed patients ( n = 36 ) showed reactivity to both the peptides to varying degrees ., Antibodies to Peptide A was noted in 24/36 ( 66 . 66% ) of samples while 27/36 ( 75% ) of serum samples showed reactivity to Peptide B ( Fig 5 ) ., These results indicate that the two peptides are indeed being recognized by the host immune system during CHIKV infection ., As evident form Fig 5 , it was interesting to note that the sera from three patients who had persistent arthralgia 12 weeks after the onset of initial symptoms indeed exhibited much higher OD values ( 0 . 9 to 1 . 3 for peptide A and 1 . 2 for peptide B ) to the two peptides A & B as compared to other patients ( OD values were close to cut off and between 0 . 4 and 0 . 6 ) ., Although , a quantitative ELISA would have delineated these differences better our ELISA was designed only as qualitative assay ., Further , experiments were conducted to investigate if peptide A and peptide B were capable of inducing pathology in an experimental mouse model ., The results indicated that these two peptides on their own were able to induce significant inflammation in the muscles of C57BL/6J mice ( Fig 8 and Table 3 ) similar to that observed in animals ( 3+ ) that were injected with CHIKV ., Further , animals that were primed initially with CHIKV followed by a subsequent injection of the two CHIKV peptides exhibited enhanced inflammatory pathology ( 4+ ) as compared to animals that were injected with peptides or virus alone ( Fig 9 and Table 3 ) ., On the contrary , animals that received an unrelated peptide ( i . e . not containing the arthritogenic motifs or exhibiting homology to host proteins ) either before or after priming with CHIKV exhibited minimal muscle inflammation ( 1+ ) ., Collectively these observations validate the hypothesis that molecular mimicry between CHIKV E1 protein and host proteins does contribute to pathology in CHIKV infection ., Such observations have not been reported hitherto in CHIKV infection although molecular mimicry as a mechanism leading to autoimmune phenomena has been demonstrated in several microbial infections including viruses and bacteria 18 ., Among the viruses , molecular mimicry has been noted in Theiler murine encephalitis virus ( TMEV ) , Hepatitis B virus ( HBV ) , and SFV and Coxsackie viral infections 11 ., In SFV infection of C57BL/6J mice a similar approach using bioinformatic tools derived peptides and validation of these peptides invivo for their ability to induce autoimmune demyelination was undertaken 19 ., An algorithmic approach was used to demonstrate amino acid homology between immunogenic epitopes of SFV and various myelin proteins ., The criterion used for the occurrence of molecular mimicry was the presence of three similar consecutive amino acids ., It was observed that myelin oligodendrocyte protein ( MOG ) shared homology with SFV E2 glycoprotein ., The injection of a peptide contai

Introduction, Methods, Results, Discussion

Chikungunya virus ( CHIKV ) , a reemerging pathogen causes a self limited illness characterized by fever , headache , myalgia and arthralgia ., However , 10–20% affected individuals develop persistent arthralgia which contributes to considerable morbidity ., The exact molecular mechanisms underlying these manifestations are not well understood ., The present study investigated the possible occurrence of molecular mimicry between CHIKV E1 glycoprotein and host human components ., Bioinformatic tools were used to identify peptides of CHIKV E1 exhibiting similarity to host components ., Two peptides ( A&B ) were identified using several bioinformatic tools , synthesised and used to validate the results obtained in silico ., An ELISA was designed to assess the immunoreactivity of serum samples from CHIKV patients to these peptides ., Further , experiments were conducted in a C57BL/6J experimental mouse model to investigate if peptide A and peptide B were indeed capable of inducing pathology ., The serum samples showed reactivity of varying degrees , indicating that these peptides are indeed being recognized by the host immune system during CHIKV infection ., Further , these peptides when injected into C57BL/6J mice were able to induce significant inflammation in the muscles of C57BL/6J mice , similar to that observed in animals that were injected with CHIKV alone ., Additionally , animals that were primed initially with CHIKV followed by a subsequent injection of the CHIKV peptides exhibited enhanced inflammatory pathology in the skeletal muscles as compared to animals that were injected with peptides or virus alone ., Collectively these observations validate the hypothesis that molecular mimicry between CHIKV E1 protein and host proteins does contribute to pathology in CHIKV infection .

The outcome of Chikungunya virus infection is usually benign but persistent arthritis has been reported in 10–20% of patients after Chikungunya fever ., However , some reports have suggested that similarity between host proteins and viral proteins ( molecular mimicry ) leads to immune mediated damage ., However , this has not been proved conclusively ., Therefore , this study was undertaken to identify if molecular mimicry exists between CHIKV and host components ., Using various bioinformatics tools we identified common sequences and structural homology between glycoprotein of the virus and two human host tissue proteins- HLA-B27 molecule and a domain of complement C3 ., Two peptides having homology to these human tissue components were synthesized ., These peptides were recognized by antibodies present in serum of CHIKV patients ., Experiments were conducted to investigate if the peptides were capable of inducing pathology in an experimental C57BL/6J mouse model ., Both the peptides on their own were able to induce significant inflammation in the muscles of C57BL/6J mice similar to that observed in animals that were injected with CHIKV alone ., Additionally , animals that were injected initially with CHIKV followed by a subsequent injection of the two CHIKV peptides exhibited increased pathology as compared to animals that were injected with peptides or virus alone .

medicine and health sciences, immune physiology, pathology and laboratory medicine, togaviruses, chikungunya infection, pathogens, immunology, tropical diseases, microbiology, alphaviruses, viruses, muscle regeneration, developmental biology, chikungunya virus, rna viruses, signs and symptoms, organism development, neglected tropical diseases, glycoproteins, antibodies, molecular mimicry, morphogenesis, research and analysis methods, sequence analysis, immune system proteins, infectious diseases, inflammation, sequence alignment, bioinformatics, proteins, medical microbiology, microbial pathogens, molecular biology, immune response, biochemistry, diagnostic medicine, regeneration, viral pathogens, database and informatics methods, physiology, biology and life sciences, viral diseases, glycobiology, organisms

journal.pbio.2005970

CellProfiler 3.0: Next-generation image processing for biology

Image analysis software is now used throughout biomedical research in order to reduce subjective bias and quantify subtle phenotypes when working with microscopy images ., Automated microscopes are further transforming modern research ., Experiments testing chemical compounds or genetic perturbations can reach a scale of many thousands of perturbations , and multidimensional imaging ( time-lapse and three-dimensional 3D ) also produces enormous data sets that require automated analysis ., In light of this data scale , computer algorithms must deliver accurate identification of cells , subcompartments , or organisms and extract necessary descriptive features ( metrics ) for each identified object ., Racing to keep up with the advancement of automated microscopy are several classes of biologist-focused image analysis software , such as companion packages bundled with imaging instruments ( e . g . , MetaMorph—Molecular Devices , Elements—Nikon ) , stand-alone commercial image processing tools ( e . g . , Imaris—Bitplane ) , and free open-source packages ( e . g . , ImageJ/Fiji , CellProfiler , Icy , KNIME ) ., Commercial software is often convenient to use , especially when bundled with a microscope ., Although cost and lack of flexibility may limit adoption , there is a focus on usability , particularly for applications of interest to the pharmaceutical industry ., Still , the proprietary nature of the code in commercial software limits researchers from knowing how their data is being analyzed or modifying the strategy of a given algorithm , if desired ., The open-source biological image analysis software ecosystem is thriving 1 ., ImageJ 2 was the first and is still the most widely used package for bioimage analysis; several other packages are based on its codebase ( most notably , Fiji ) ., ImageJ excels at the analysis of individual images , with a user interface analogous to Adobe Photoshop ., Its major strength is its community of users and developers who contribute plugins , although an associated drawback is the sheer number of plugins , with varying degrees of functional overlap , usability , and documentation ., Multitasking toolboxes like KNIME 3 offer a more modular approach , which is better suited to automated workflows ., KNIME equips users with a wide breadth of powerful utility , from performing image analysis to data analytics ., CellProfiler , our open-source software for measuring and analyzing cell images , has been cited more than 6 , 000 times , currently at a rate of more than 1 , 000 per year ., The first version of CellProfiler was introduced in 2005 and published in 2006 4 ., It is widely adopted worldwide , enabling biologists without training in computer vision or programming to quantitatively measure phenotypes robustly from thousands of images ., A second major version of CellProfiler , rewritten in Python from its original MATLAB implementation , was published in 2011 5 and included methods for tracking cells in movies and measuring neurons , worms , and tissue samples ., In 2015 , a laboratory unaffiliated with our team rigorously compared 15 free software tools for biological image analysis: CellProfiler was ranked first for both usability and functionality 6 ., CellProfiler provides advanced algorithms for image analysis , organized as individual modules that can be placed in sequential order to form a pipeline ., This pipeline is then used to identify and measure cells or other biological objects and their morphological features ., CellProfiler’s modular design and carefully curated library of image processing and analysis modules benefits biologists in several ways: Reproducibility at scale: CellProfiler is designed to produce high-content information for each cell or other object of interest in each image and to apply the same objective analysis in high-throughput , e . g . , across thousands or millions of images ., Flexible feature extraction: Individual modules measure standard morphological features such as size , shape , intensity , and texture ., Customized combinations of modules can extract even more complex information ., As such , CellProfiler is commonly used for morphological profiling experiments such as Cell Painting 7 , 8 , which is being adopted in pharmaceutical companies to speed several steps in drug discovery 9 ., Easy to learn: Each of the 70+ modules includes carefully crafted documentation , curated by both imaging and biology experts , to make image processing more approachable and understandable for the average scientist ., Further , each individual setting is explained in practical terms to aid researchers in configuring it ., The number of modules and settings is carefully limited to avoid overwhelming users , while a plugin system allows the flexibility of a larger array of contributed modules ., Community: CellProfiler has an active community of more than 3 , 000 people on its online question and answer forum ., With more than 15 , 000 posts , users provide feedback that fuels improvements to CellProfiler , find pipelines related to their area of research , interact with developers , get input on challenging problems , and improve image analysis skills and knowledge by helping other users design solutions ., This new version of CellProfiler has support for analysis of 3D images in many of its modules ( S1 Fig ) ., Although open-source software tuned to 3D problems exists ( e . g . , Vaa3D , BioImageXD , Slicer ) 10 , it often emphasizes visualization and rendering; these new 3D capabilities of CellProfiler meet the community’s demand for modular high-throughput 3D analysis ., CellProfiler 3 . 0 can apply image processing , segmentation , and feature extraction algorithms to entire image volumes ( volumetric analysis ) , in addition to the more typical iterative and separate analysis of two-dimensional slices from a 3D volume ( “plane-wise” analysis ) ., Whole-volume algorithms consider 3D neighborhoods and incorporate information from surrounding planes , yielding more accurate results , but require more available memory , particularly for large files ., CellProfiler’s volumetric algorithms can be configured to account for anisotropic data ( in which the distance between Z planes does not match the distance between pixels in the X and Y dimensions ) ., While we focused on adding 3D capability to most of our image processing and feature extraction modules , we will continue increasing the number of CellProfiler modules that support image volumes for situations in which it is not computationally prohibitive ., We developed 3D pipelines to identify cells and subcompartments of cells for a number of experimental situations and sample types across a number of laboratories ., We identified nuclei based on a DNA stain ( Fig 1A ) in 3D image stacks of human induced pluripotent stem cells ( hiPSCs ) ., After processing by several CellProfiler modules ( Fig 1C ) , the final results agree well with manually annotated nuclei ( Fig 1D ) ., Results for a variety of images with a range of complexity are shown in Fig 2 , with more detailed views in S2–S5 Figs ., We characterized CellProfiler’s segmentation accuracy in two ways: in the first , we used real microscopy images ( Fig 1A , Fig 2A , Fig 2B ) whose ground truth was manually annotated by an expert image analyst; such images are realistic , but the manual annotation introduces some subjectivity ., We therefore also used synthetic images ( Fig 2C , Fig 2D ) 11 , 12 , which , depending on the model used to create them , may not perfectly represent real microscopy images but whose ground truth can be unambiguously known ., To determine how well the segmented objects agreed with ground truth , CellProfiler’s “MeasureImageOverlap” module was used to calculate the plane-wise Rand index 13 , a performance metric of accuracy ( Fig 1B , Fig 2E ) ., Rand index values showed good agreement ( 0 . 919–0 . 976 ) between each tested image and its ground truth ., The results produced by CellProfiler 3 . 0 were comparable to results produced by the commonly used Fiji plugin MorphoLibJ ( 0 . 930–0 . 977 ) ( Fig 1B , Fig 2E and S2–S5 Figs; the MorphoLibJ macro codes are provided in S1 Table ) ., We demonstrate several kinds of analysis , including analyses of cell count in a time series that was synthetically generated 11 , 14 ( S5 Fig ) ; identification and quantification of children objects inside parent objects , such as speckles of transcripts within cells ( Fig 3 ) ; and measurement of various features of hiPSCs located at the center and the edge of the cell colony ( Fig 4 ) ., All pipelines , annotated with notes to understand the function of each module , are provided at https://github . com/carpenterlab/2018_mcquin_PLOSBio ., All raw images , together with ground truth annotations used to test CellProfiler 3 . 0 performance , are publicly available for further community algorithm development in the Broad Bioimage Benchmark Collection 15 , as indicated in the legends for Fig 1 and S2–S5 Figs ., Convolutional neural networks ( CNNs ) are a type of deep learning model that transforms input images into outputs specified by the problem type 16 ., For instance , image classification models transform images into categorical labels 17 , while image segmentation models transform images into segmentation masks 18 ., CNNs are now widely used to solve many computer vision tasks , given their ability to produce accurate outputs after learning from examples ., CellProfiler now can be configured to make use of cutting-edge CNNs to analyze biomedical images ., While CellProfiler does not yet incorporate user-friendly functionalities to train neural networks , various models that have been already trained by researchers can be run inside CellProfiler ., Running neural network models requires the installation of certain deep learning frameworks that are distributed separately , such as TensorFlow or Caffe ., TensorFlow 19 is an open-source software library for machine learning that interfaces with Python and is compatible with CellProfiler when installed from source on Linux , Mac , and more recently , Windows ., Caffe 20 is a deep learning framework designed for high-performance neural networks and is primarily available for Linux systems ., Some network models may need special graphics processing units ( GPUs ) installed and configured in the system to run the computations efficiently , but this is not always required ., Fortunately , both TensorFlow and Caffe can easily switch between running on GPUs and traditional central processing units ( CPUs ) just by changing the corresponding configuration ., We created the CellProfiler 3 . 0 module ClassifyPixels-Unet to segment nuclei in images stained with DNA labels ( https://github . com/CellProfiler/CellProfiler-plugins ) ., This plugin implements a U-Net18 model using TensorFlow and can be run on CPUs ., We have also provided the network architecture with training routines in case users have their own annotated images to learn a segmentation model for different images and objects of interest ( https://github . com/carpenterlab/unet4nuclei ) ., The ClassifyPixels-Unet module classifies pixels into one of three classes: background , nucleus interior , or nuclear boundary ( S7 Fig ) ., A pretrained network for nuclei segmentation is available for download and is automatically loaded by the plugin; a pipeline and image to run this are available as S4 File ., We also created a CellProfiler 3 . 0 module , MeasureImageFocus , in collaboration with Google Accelerated Science , who trained a model to detect focus in images 21 ., The module displays a table with the predicted focus score and certainty for the whole image , as well as a figure with the focus scores and corresponding certainties of individual 84 × 84 patches represented by color and opaqueness ., It uses TensorFlow as its underlying deep learning framework ., Independently , Sadanandan and colleagues created a CellProfiler 2 . 2 . 0 module—CellProfiler-Caffe bridge—that enables running a pretrained model for cell segmentation within a CellProfiler pipeline 22 ., We created Distributed-CellProfiler ( https://github . com/CellProfiler/Distributed-CellProfiler ) , a script-based interface that allows running thousands of batches of images through CellProfiler in parallel on Amazon Web Services ( AWS; S8 Fig ) ., While Distributed-CellProfiler does require basic knowledge of AWS and interaction with the command line , it is well documented and has been successfully run by biologists without formal computational training ., The script handles infrastructure creation and removal as well as creation and storage of logs , allowing users without access to a local cluster computing environment to analyze large data sets with only minimal time devoted to having to set up those resources ., Sample pipelines and configuration files are available as S5 File ., Plug-ins: CellProfiler-plugins is a new repository for the community to share and distribute new CellProfiler modules ( https://github . com/CellProfiler/CellProfiler-plugins ) ., Documentation: All of CellProfiler’s documentation was updated for content and readability; detailed help is available for 100% of module configuration options ( excluding plugins ) ., New image processing features: CellProfiler 3 . 0 introduces an extended suite of modules for feature detection , feature extraction , filtering and noise reduction , image processing , image segmentation , and mathematical morphology operations ., Infrastructure improvements: The project team reengineered major core components of CellProfiler ., CellProfiler’s codebase was trimmed down , in part because of better integration with Python’s scientific community ., We have adopted and contributed to the standard libraries of the scientific Python community , including NumPy , SciPy , and scikit-image ., CellProfiler’s code is now 100% Python , which improves interoperability with the robust Python scientific ecosystem and simplifies third-party contributions ., As well , we upgraded support to 64-bit on Linux , MacOS , and Windows , and a continuous integration process ensures the software is well tested on a variety of platforms ., We made substantial progress simplifying CellProfiler’s installation ., In addition to our previously existing Mac and Windows builds , a Python wheel is now available from the Python Package Index , and a Docker image is now available from Docker Hub ., In an effort to expand CellProfiler’s flexibility , we made CellProfiler much simpler to compile on a variety of familiar and unusual platforms by requiring fewer dependencies and only using ubiquitous build systems ., Educational resources: CellProfiler’s many examples and tutorials are now publicly available on GitHub ( https://github . com/CellProfiler/examples and https://github . com/CellProfiler/tutorials ) and have been updated for compatibility with CellProfiler 3 . 0 ., Speed: CellProfiler 3 . 0’s processing speed is faster than version 2 . 2 on the most common types of pipelines; the degree of difference depends on the exact modules involved: CellProfiler 3 . 0 ran at a comparable or faster speed than CellProfiler 2 . 2 for 11 of 16 example pipelines tested ( S9 Fig ) ., While the total amount of time needed to run the five pipelines shown in S9 Fig was comparable between CellProfiler and MorphoLibJ ( 482 versus 542 seconds ) , the relative speed was highly specific to the individual pipeline ( S6 File ) , ranging from 2× faster in CellProfiler to 6× faster in MorphoLibJ ( S2 Table ) ., In addition , CellProfiler can run multiple images in parallel , depending on the individual’s number of threads , computing power , and access to cloud computing resources , making it suited to large-scale experiments ., As well , CellProfiler’s modules enable more readily configurable complex analyses than MorphoLibJ , such as associating cytoplasm regions ( as in Fig 3 ) , transcripts ( as in Fig 3 ) , and other entities to nuclei and measuring a wide variety of morphological properties of each , including intensities , shapes , textures , colocalization metrics , and neighborhood relationships ( as in Fig 4 ) ., CellProfiler is mature software serving a large community and making an impact through its thousands of users’ biological discoveries ., It has been involved in the discovery of potential life-saving drugs for infectious diseases , leukemia , and cerebral cavernous malformation 23–27 and in clinical trials for hematological malignancies 28 and will continue to fuel basic and applied research around the world ., CellProfiler can readily generate a large amount of morphological information for each biological entity that is measured ., We see advancements in data mining , downstream and apart from CellProfiler , as blossoming in the coming years ., Already , 20 laboratories in the field of morphological profiling have gathered for two annual meetings/hackathons ( now called CytoData ) 29 , collaborated to outline best practices 30 , and begun a community library ( Cytominer , https://github . com/cytomining/cytominer ) ., In addition to our user-friendly tool for classical machine learning based on measured features , CellProfiler Analyst 31 , we have begun creating Deepometry ( http://github . com/broadinstitute/deepometry ) , a tool that enables scientists without training in machine learning to perform single-cell phenotype classification using deep learning and other advanced downstream data analytics ., Interoperability of CellProfiler with popular notebook tools like Jupyter would allow seamless workflows involving other complementary software tools ., Finally , deep learning has revolutionized computer vision and other fields in the past few years 16 , 32 , and bioimaging will be no exception ., As noted , already some models trained for specific tasks can be used via CellProfiler , and we expect that over time , more generalizable models will be created that can accomplish useful tasks such as detecting common cellular structures across diverse types of images and experimental setups , as in , for example , the 2018 Data Science Bowl challenge ., Community-driven collections of images and ground truth , as well as “model zoos , ” will be instrumental for this ., We have also begun creating libraries ( Keras-ResNet https://github . com/broadinstitute/keras-resnet and Keras-RCNN https://github . com/broadinstitute/keras-rcnn ) that will provide the foundation for interfaces that allow biologists to annotate , train , and use deep learning models ., We expect that over time , these models will reduce the amount of time biologists spend tuning classical image processing algorithms to identify biological entities of interest in images ., Images were kindly provided by Javier Frias Aldeguer and Nicolas Rivron of Hubrecht Institute for Developmental Biology and Stem Cell Research and Li Linfeng of MERLN Institute for Technology-Inspired Regenerative Medicine ., As per Rivron and colleagues 33 , mouse embryos ( 3 . 5 dpc ) were fixed right after isolation from the mother’s uterus ., Fixation was performed using 4% PFA in RNAse-free PBS containing 1% acetic acid ., ViewRNA ISH Cell Assay kit ( cat# QVC0001 ) was used for performing smFISH on the embryos ., The protocol includes steps of permeabilization and protease treatment as well as probes , preamplifier , amplifier , and label hybridizations ., Embryos were then mounted in Slowfade reagent ( Thermofisher cat# S36937 ) and directly imaged in a PerkinElmer Ultraview VoX spinning disk microscope in confocal mode by using a 63×/1 . 40 NA oil immersion lens ., Images were acquired by collaborators from the Allen Institute for Cell Science , Seattle , as per Roberts and colleagues 34 ., Briefly , wild-type C ( WTC ) hiPSCs were cultured in a feeder-free system on tissue culture dishes or plates coated with GFR Matrigel ( Corning ) diluted 1:30 in cold DMEM/F12 ( Gibco ) ., Undifferentiated cells were maintained with phenol red containing mTeSR1 media ( 85850 , STEMCELL Technologies ) supplemented with 1% ( v/v ) penicillin-streptomycin ( P/S; Gibco ) ., Cells were not allowed to reach confluency greater than 85% and are passaged every 3–4 days by dissociation into single-cell suspension using StemPro Accutase ( Gibco ) ., When in single-cell suspension , cells were counted using a Vi-CELL Series Cell Viability Analyzer ( Beckman Coulter ) ., After passaging , cells were replated in mTeSR1 supplemented with 1% P/S and 10 μM ROCK inhibitor ( Stemolecule Y-27632 , Stemgent ) for 24 hours ., Media is replenished with fresh mTeSR1 media supplemented with 1% P/S daily ., Cells were maintained at 37°C and 5% CO2 ., Cells were maintained with phenol red–free mTeSR1 media ( 05876 , STEMCELL Technologies ) 1 day prior to live cell imaging ., Three to four days after cells are plated and mature and healthy colonies are observed on 96- and 24-well imaging plates , the cells are stained with NucBlue Live ready probe reagent ( R37605 , ThermoFisher ) and CellMask Deep Red plasma membrane stain ( C10046 , ThermoFisher ) to visualize DNA and plasma membrane , respectively ., The protocol is available online: http://www . allencell . org/uploads/8/1/9/9/81996008/sop_for_cellmask-and-nucblue_v1 . 0_1 . pdf ., Phenol red–free mTeSR1 is preequilibrated to 37°C and 5% CO2 ., 1X NucBlue solution made in preequilibrated phenol red–free mTeSR1 is spun for 60 minutes at 20 , 000 g ., The 2X and 10X working stocks of CellMask Deep Red lot #1730970 and #1813792 , respectively , are made in 1X NucBlue solution ., All solutions are kept at 37°C and 5% CO2 until used ., The 100 μL and 400 μL of NucBlue solution are added per well of 96-well imaging plates and 24-well imaging plates , respectively , and incubated at 37°C and 5% CO2 for 20 minutes ., An equal amount of CellMask Deep Red working stock is added to the wells containing NucBlue solution ., Final dye concentrations in the wells are 1X NucBlue and 1X and 5X CellMask Deep Red lots #1730970 and #1813792 , respectively ., Cells are incubated at 37°C and 5% CO2 for 10 minutes and gently washed with preequilibrated phenol red–free mTeSR1 ., Fields of view as shown in Fig 4 that are acquired near the edge ( and the center as a control ) of hiPSC colonies receive an additional photoprotective cocktail treatment which serves to minimize singlet oxygen and free radical formation ., The photoprotective cocktail is used at a working concentration of 0 . 3 U/ml ( 1:100 ) OxyFluor as defined by the OxyFluor product insert , with the addition of 10 mM sodium lactate and 1 mM ascorbic acid ( OxyFluor OF-0005 , Oxyrase ) ., As per Roberts and colleagues 34 , cells were imaged on a Carl Zeiss spinning disk microscope with a Carl Zeiss 20×/0 . 8 NA plan APOCHROMAT or 100×/1 . 25 W C-APOCHROMAT Korr UV Vis IR objective , a CSU-X1 Yokogawa spinning disk head , and Hamamatsu Orca Flash 4 . 0 camera ., Microscopes were outfitted with a humidified environmental chamber to maintain cells at 37°C with 5% CO2 during imaging ., Cells are imaged immediately following the wash step and for up to 2 . 5 hours after dye addition on a Zeiss spinning disk microscope at 100× with the following general settings: 405 nm at 0 . 28 mW , 200 ms exposure; 638 nm at 2 . 4 mW , 200 ms exposure; acquiring each channel at each z-step ., Experienced bioimage analysts drew outlines around nuclear boundaries on each slice of the 3D images and labeled background regions in a different color with GIMP ( https://www . gimp . org ) , an open-source drawing and annotation software ., These annotated layers were then exported from GIMP as an image ., This outline image is converted to 3D objects via a CellProfiler pipeline ( https://github . com/CellProfiler/tutorials/tree/master/Annotation ) , and an object label matrix image is exported , in which each object’s voxels are assigned a unique integer value ., These label images are referenced as ground truth .

Introduction, Results, Future directions, Materials and methods

CellProfiler has enabled the scientific research community to create flexible , modular image analysis pipelines since its release in 2005 ., Here , we describe CellProfiler 3 . 0 , a new version of the software supporting both whole-volume and plane-wise analysis of three-dimensional ( 3D ) image stacks , increasingly common in biomedical research ., CellProfiler’s infrastructure is greatly improved , and we provide a protocol for cloud-based , large-scale image processing ., New plugins enable running pretrained deep learning models on images ., Designed by and for biologists , CellProfiler equips researchers with powerful computational tools via a well-documented user interface , empowering biologists in all fields to create quantitative , reproducible image analysis workflows .

The “big-data revolution” has struck biology: it is now common for robots to prepare cell samples and take thousands of microscopy images ., Looking at the resulting images by eye would be extremely tedious , not to mention subjective ., Thus , many biologists find they need software to analyze images easily and accurately ., The third major release of our free open-source software CellProfiler is designed to help biologists working with images , whether a few or thousands ., Researchers can download an online example workflow ( that is , a “pipeline” ) or create their own from scratch ., Pipelines are easy to save , reuse , and share , helping improve scientific reproducibility ., In this release , we’ve added the capability to find and measure objects in three-dimensional ( 3D ) images ., We’ve also made changes to CellProfiler’s underlying code to make it faster to run and easier to install , and we’ve added the ability to process images in the cloud and using neural networks ( deep learning ) ., We’ve also added more explanations to CellProfiler’s settings to help new users get started ., We hope these changes will make CellProfiler an even better tool for current users and will provide new users better ways to get started doing quantitative image analysis .

open science, blastocysts, methods and resources, engineering and technology, signal processing, applied mathematics, biologists, simulation and modeling, algorithms, developmental biology, scientists, mathematics, science and technology workforce, research and analysis methods, specimen preparation and treatment, embryology, staining, computer and information sciences, imaging techniques, people and places, professions, cell staining, science policy, image processing, careers in research, computer software, software engineering, biology and life sciences, population groupings, open source software, physical sciences, software tools, image analysis

journal.pcbi.1000635

Network-Free Inference of Knockout Effects in Yeast

High-throughput technologies are routinely used to map molecular interactions within the cell ., These include chromatin immuno-precipitation experiments for measuring protein-DNA interactions ( PDIs ) 1 , and yeast two-hybrid assays 2 and co-immunoprecipitation screens 3 for measuring protein-protein interactions ( PPIs ) ., The resulting maps provide a scaffold from which one can extract regulatory-signaling mechanisms that underlie cellular processes and responses ., Physical interactions however may not be sufficient to deduce causal roles played by genes in regulation and signaling ., For such deduction , perturbation studies are necessary and are traditionally employed 4 ., Here , we focus on perturbation studies in which a gene is knocked out and as a result multiple genes change their expression levels ., These measurements can be used to derive a functional map of genes , providing a complementary view to the physical one ., While in the physical map an edge between two proteins ( PPI ) or between a protein and a genes promoter sequence ( PDI ) indicates a direct association , in the functional map an edge connects two genes if knocking out one of them affects the expression level of the other ., The problem of explaining knockout experiments using a physical network was first introduced by 5 ., The authors looked at a specific setting of the problem where the objective is to annotate each physical edge with the direction in which information flows through that interaction , and a sign , representing the regulatory effect of the interaction ( activation or suppression ) ., A followup work by Ourfali et al . 6 introduced the SPINE algorithm , aimed at annotating the physical network while maximizing the expected number of knockout effects that can be explained by the physical model ., In both cases , the annotated physical network was used for predicting new knockout effects ( up- or down-regulation ) ., Another line of work , related to the analysis of single knockout experiments , is the analysis of genetic interactions ., Qi et al . 7 used a functional network of genetic interactions for inferring physical and genetic associations in yeast ., They identified relations of complex/pathway co-membership with paths of even length in the functional network , whereas novel genetic relations were identified with odd-length paths ., Segre et al . 8 studied a partition of the yeast metabolic system into groups based on patterns of aggravating and alleviating effects in response to double gene perturbations ., The groups were constructed hierarchically so as to interact with each other monochromatically , i . e . , with purely aggravating or purely alleviating effects across groups , enabling the authors to predict new genetic interactions ., Here we present a novel approach for analyzing a functional network to infer knockout effects ., In contrast to previous work , our method does not depend on knowledge of a physical network , but in fact decouples the task of predicting knockout effects from the task of annotating the edges of the physical network ., The method is based on partitioning the genes into functional groups whose members are indistinguishable with respect to the rest of the ( functional ) network ., We start by considering a partition of the genes into two “chromatic” groups with links of up-regulation between the groups and links of down-regulation within each group ., To motivate this model , we show that if the latent physical network that underlies the functional data has no cycles with an aggregate negative sign ( i . e . , the product of the signs along the cycles edges is negative ) , then such a partition is indeed possible ., We devise several tests for the two-group assumption and find that it is sufficient to explain a large fraction of the analyzed data ., Nevertheless , we find that negative feedback mechanisms within signaling pathways lead to deviations of the experimental data from this model ., To tackle such deviations , we extend our algorithm to more than two groups , based on ideas from the work of 8 ( described above ) ., We validate our methods using a collection of over two hundred knockout experiments in yeast 9 ., We conduct cross validation experiments by hiding a subset of the resulting knockout pairs ( of a deleted gene and an affected gene ) , and using the remaining pairs to predict the effects of the hidden pairs ( up- or down-regulation ) ., We attain high accuracy ( 88% ) and coverage ( 73 . 8% ) levels in the prediction task ( when applying the extended algorithm ) ., Moreover , the high efficiency of our algorithms allows us to analyze the entire data set in seconds ., These results provide a substantial improvement over the state of the art SPINE algorithm 6 , and over a previous benchmark by Yeang et al . 5 ., In contrast to our approach , these methods are not “network-free”; instead they depend on a brute-force enumeration of all possible physical pathways between every knockout pair ., Often times , such an enumeration is not feasible , which limits the applicability of these methods to gene pairs that are at most 3 edges apart in the physical network ., In yeast , this limits the algorithms to a miniscule fraction of 4% of the knockout pairs available ., Consequently , SPINE attains a coverage level of 2 . 6% , a 25-fold reduction in comparison to our method; at the same time , it also yields a lower accuracy ( 72% ) ., Finally , we tackle the task of annotating the physical edges with signs of activation or suppression ., We provide an efficient algorithm for annotating a given physical network so as to explain a maximal number of functional relations ., We validate the algorithm by using manual annotation of the filamentous growth pathway 10 , and the high osmolarity glycerol ( HOG ) pathway 11 ., Altogether , we obtain accuracy levels that are comparable to those of SPINE 6 while significantly improving on its coverage ., We say that a functional network is sign-linear if there exists a Boolean assignment for every gene such that the sign of each edge in the network is ( a condition which can be cast in the form of a linear equation , hence the name of the model; see Methods ) ., In this case we also say that explains the input functional relations ., Assuming that a given functional network is sign-linear essentially means that we can retain all the information from the knockout experiments by partitioning the genes into two groups ., Gene pairs linked by a down-regulation edge in the functional network will be on the same group and pairs linked by an up-regulation edge will be on different groups ., To motivate this assumption , it is imperative to consider its implication on the physical network that underlies the observed knockout effects ., We say that a physical network is sign-consistent if it does not contain an undirected cycle ( i . e . , any loop in the network when disregarding edge directions ) with a negative aggregate sign ( Methods ) ., Notably , the sign-consistency assumption is reminiscent of the acyclicity assumption that is the basis for Bayesian modeling of biological networks 12 , 13 ., As we show in Text S1 , a sign-consistent physical network implies a sign-linear functional network , and for every functional network , one can construct a sign-consistent physical network that explains it ., If a network is sign-linear then one can efficiently compute a Boolean assignment that explains the input functional relations , and the task of predicting a knockout effect translates to computing the product of the signs of the participating nodes ., In the general case , such a perfect Boolean assignment might not exist ., Instead , we aim to find an assignment that will satisfy as many of the observed functional relations as possible ( see Methods and Figure 1 ) ., To tackle this computationally hard problem , we use an efficient randomized heuristic that is guaranteed to converge to a local maximum ., Given a locally-optimal Boolean assignment , the sign of the effect of gene on gene is predicted to be ., We run the randomized procedure multiple times , potentially obtaining different assignments , and compute a consensus assignment ( Methods ) ., It should be noted that the algorithm is restricted to genes that are implicated in at least one experiment ( either as a knocked out gene or as an affected gene; see Methods ) ., We tested the validity of the sign-linearity assumption using the yeast knockout data ., Applying a single iteration of the sign-linear algorithm to the entire data set , we obtained a Boolean assignment that satisfies over 83% of the knockout pairs ( , Text S1 ) ., This result indicates that the respective functional network is highly structured and can be readily utilized for predicting knockout effects under the sign-linear model ., We use the yeast mating network , studied in 5 , 6 , as a first test case ., The mating network contains 46 genes involved in pheromone response and 58 physical interactions ( 25 PPIs and 33 PDIs ) ., The 46 genes span 149 ( of 24 , 457 ) functional relations ., Due to scalability problems , the application of both previous methods was limited to 103 of the functional interactions , considering only pairs of genes that are at most 5 edges apart in the physical network ., Two variants of SPINE 6 were employed for predicting the results of knockouts in the mating network , one that assigns signs to edges , and one that assigns signs to nodes ( forcing all the edges that emanate from a node to carry its sign ) ., We compare the performance of the sign-linear algorithm on the restricted set of 103 knockout pairs to the results of 5 and both variants of 6 ., All algorithms were applied in a leave-one-out cross validation setting , each time hiding a single knockout pair and using the remaining ones to predict its outcome ., The ensuing performance is evaluated using two quality measures:, ( i ) Accuracy: the percentage of correct predictions out of all predictions made; and, ( ii ) coverage: the percentage of knockout pairs that were predicted correctly out of the entire set of knockout pairs ., Table 1 summarizes the performance of the different approaches ., While the best performance is achieved by 5 and the edge variant of 6 , the accuracy and coverage of the sign-linear algorithm are only slightly lower ., Importantly , our model employs a substantially simpler model with the number of variables being equal to the number of nodes , rather than to the number of edges ( as in the other two models ) , making it less prone to over-fitting ., Comparing the sign-linear model to the node variant of SPINE , which has an equivalent number of variables ( one binary variable per gene ) , the sign-linear algorithm is found superior in both accuracy and coverage ., We further tested our method using varying sizes of the training set ( leaving out 10% , 20% and 50% of the knockout pairs ) ., The accuracy level remained stable at 90% even when leaving out 50% of the pairs ., The coverage level was at 90% when leaving out 10% or 20% of the pairs , but dropped to 38% when leaving out 50% of the pairs ., The simplicity of the model and the independence of physical data allows the sign-linear algorithm to be applied on large data sets on which the methods of 5 and 6 could not be applied ., Considering the complete data set of 210 knockout experiments , the applications of 5 and 6 were confined to less than 4% ( 974 ) of the knockout pairs , for reasons of scalability ., The limited set contained only pairs of genes that are at most 3 edges apart in the physical network ., For the same reason , a cross-validation scheme similar to the one used for the mating subnetwork could not be applied with those algorithms , even with the limited data set ., In contrast , the sign-linear algorithm could be tested in cross validation ( each time leaving out 200 knockout pairs ) , and generated predictions for over 95% ( 23 , 312 ) of the pairs ., We compare the results of the sign-linear algorithm to results from 6 , who applied the node variant of SPINE on the reduced data set without using cross validation ( Text S1 ) ., The results in Table 1 show that the sign-linear algorithm outperforms SPINE both in accuracy ( 80 . 2% vs . 72 . 5% ) and , more strikingly , in coverage ( 76 . 4% vs . 2 . 6% ) ., Thus far , we predicted a functional edge to be ( for instance ) up-regulation if the majority ( more than 50% ) of the obtained assignments implied so ., Further probing the results of the sign-linear algorithm , we calculated the levels of accuracy and coverage obtained for more stringent decision cutoffs ( i . e . , predict an effect only if a certain percentage ( larger than 50% ) of the assignments agree ) ., Figure 2 plots the resulting accuracy-coverage curve ., Evidently , the curve decreases monotonically , where for a coverage level of 10% we achieve over 98% accuracy ., We also investigated the stability of the predictions across the different runs , observing that over half of the knockout pairs are predicted consistently by at least 90% of the runs ( Figure S2 ) ., Finally , we tested the robustness of the sign-linear algorithm to noise in the input data ., Following 6 , we flipped 5% , 10% and 15% of the input signs and applied the sign-linear algorithm to the perturbed data ., The algorithm was highly consistent in its predictions , maintaining consistency levels of 93 . 3% , 90 . 1% and 86% under the different noise levels ., While the sign-linear algorithm gave promising results , its underlying assumption is quite restrictive and about 20% of the data do not follow it ., To characterize the deviations from the linearity assumption in a finer manner , we devised several local linearity tests for the following properties:, ( i ) Local linearity 1 ( LL-1 ) occurs when the effects of two knocked out genes on a common target is consistent with their effect on each other ( Figure 3a ) ., ( ii ) LL-2 entails that two different knocked-out genes should have the exact same influence on all of their common targets or the exact opposite influence ( Figure 3b ) ., ( iii ) LL-3 requires symmetry , i . e . , if two genes affect each other then the effects have to be equal ( Figure 3c ) ., Notably , the three tests represent all the ways in which a contradiction to the sign-linearity property can be reached with at most two knockout genes and two affected genes ( Text S1 ) ., We evaluated the prevalence of these three properties in the yeast knockout data set and compared the results to those obtained on randomized data sets ( Text S1 ) ., The results in Figure 3d show that the regularities represented by LL-2 and LL-3 are indeed more prevalent than the random expectation ., On the other hand , it is apparent that LL-1 is significantly less prevalent than in random ., A possible explanation for the deviation from LL-1 may be the prevalence of signaling pathways in our data ., It is reasonable to hypothesize that knocking out different components of the same pathway will result in deprivation of similar substrates and consequently generate a similar cellular response ., Furthermore , the cellular response might utilize negative feedback mechanisms for activating the malfunctioning pathway by increasing the expression levels of the respective genes ( rather than reducing it , as expected by LL-1; see Figure 3e ) ., To provide support for these hypotheses we examined knockout profiles of components in manually curated pathways from the KEGG database 14 ., For each pair of knocked out genes that are members of the same pathway we checked how many of their common targets are affected in the same manner ., We found that genes in the same pathway indeed tend to affect the same genes ( ) , have similar effects on their common targets ( ) , but increase each others expression when knocked out ( ) ., Similar results were obtained for genes that co-reside in the same MIPS 15 complex ( data not shown ) ., One particular example is the biosynthesis of steroids pathway ( KEGG:sce00100 ) ., Out of the 23 genes in the pathway we consider a subset of nine genes that were knocked out in 9 ., Overall there are 26 knockout pairs involving these genes where all of the respective effects are up-regulation ., The performance of the sign-linear algorithm in predicting these effects is understandably low , due to the violation of the LL-1 property , with 20 of the 26 effects wrongly assigned as down-regulation ( notably , due its limited applicability , SPINE could not generate predictions for any of the knockout pairs within this set ) ., The algorithm we present next uses a more flexible ( albeit more complex ) model designed to account for the under representation of the LL-1 property and to correctly model the relations exhibited within signaling pathways ., A natural extension of the sign-linear model is to partition the genes into multiple ( greater than two ) groups , and use this as a baseline for predicting knockout effects ., Taking an approach similar to 8 , we assign the genes into groups by clustering together genes that are functionally similar ., For a given pair of genes , our measure of functional similarity reflects both the similarity in their response to knockouts as well as the similarity of their effects on other genes when knocked out themselves ( Methods ) ., The sign-clustering algorithm ( Methods , Figure 1D ) constructs the groups using a ( randomized ) hierarchical clustering procedure ., Denote by the group to which is assigned ., To predict the effect of ( knocking out ) gene on gene , the effects of genes from on genes from are considered ., The prediction is made according to the majority of the considered effects ( Methods ) ; if no such effects were observed , the prediction is left undecided ., Similar to the sign-linear algorithm , we run the clustering procedure multiple times , potentially obtaining different partitions , and compute a consensus prediction ( Methods ) ., Notably , the algorithm does not explicitly determine the number of groups ., Instead , it uses a top-down procedure of iteratively partitioning the genes , until a certain stopping criterion is met ., The partitioning is stopped when the concordance between the genes of the current candidate group is higher than the chance expectation ( Methods ) ., While the obtained groups do not necessarily correlate with densely connected regions of the physical network , almost half of them ( 49% ) are functionally coherent with respect to the gene ontology ( GO ) annotation ( see Text S1 for functional coherency computation ) ., This is expected as these groups contain genes with similar functional relations according to the knockout data ., The sign clustering algorithm was applicable to over 83% ( 20 , 445 ) of the knockout pairs ., The sizes of the resulting clusters varied from 1 to 35 with an average size of 4 . 5 ( Figure S1 ) ., The algorithm attained an accuracy level of 88 . 3% and a coverage level of 73 . 8% ( Table 1 ) ., Considering more stringent decision cutoffs as before , the resulting accuracy-coverage curve ( Figure 2 ) points to a clear advantage in comparison to the sign-linear algorithm ., The stability of the predictions over the different runs was similar to that of the sign-linear algorithm ( Figure S2 ) ., The robustness to noise was slightly lower ( consistencies of 88 . 2% , 86 . 7% and 84 . 3% when flipping 5% , 10% and 15% of the input signs , respectively ) ., Zooming in on the biosynthesis of steroids pathway , we see that the sign-clustering algorithm correctly captures the respective functional relations ., It predicts correctly 24 out of 26 effects where in 17 of the cases the correct prediction was made unanimously by all the computed partitions ., The partition into functional groups introduced above can also facilitate the annotation of edges in a physical network with signs of activation or suppression ., Given a physical network , hypothesized to provide the underlying “wiring” for the knockout effects , the problem of assigning signs ( “+” for activation and “−” for suppression ) on its edges so as to explain a maximum number of knockout pairs is computationally hard ( Text S1 ) ., We present a novel algorithm for this problem that determines the sign of a physical edge between two proteins according to the functional relations between the groups of their respective genes , associating “negative” functional relations ( up-regulation ) with “negative” physical interactions ( suppression ) and vice versa ( Text S1 ) ., In the following we concentrate on partitions into two groups , where the algorithm predicts a physical edge from node to to be ., As before , we use multiple Boolean assignments and compute a consensus prediction ., We constructed a network of physical interactions in yeast , containing 5 , 850 nodes , and 45 , 512 interactions ( 39 , 946 PPIs and 5 , 566 PDIs ) , using information from public data bases 16 , 17 and from large scale assays 1 , 3 , 18 , 19 ., We annotated the network using the knockout data ., Altogether , the algorithm annotated 74% of the edges as activating or suppressing ., We validate these predictions using manual annotations of the filamentous growth pathway 10 and the high osmolarity glycerol ( HOG ) pathway 11 ., Figure 4 depicts the annotation of the two pathways by our method and by SPINE ., Comparing to the literature benchmark , our algorithm obtained an accuracy of 75% and coverage of 69% in predicting signs in the filamentous growth pathway; and an accuracy of 72% and coverage of 65% with respect to the HOG pathway ., These results compare favorably with those of SPINE 6 , which attained accuracy levels of 44% and 100% and coverage levels of 15% and 10% for the filamentous growth pathway and the HOG pathway , respectively ., One interesting finding of our algorithm concerns the annotation of the interactions between the suppressor of sensor kinase 2 ( Ssk2 ) and Actin 1 ( Act1 ) in the HOG pathway ., While the manual annotation of this edge 11 is undecided , the algorithm predicted it to be stimulatory ( activating ) ., This finding is in line with evidence that Ssk2 is required for the actin reassembly and for the recovery from osmotic stress ., While the mechanism behind this dependency is not clear , it was previously suggested that actin is a potential substrate of the Ssk2 kinase 20 ., We devised two clustering methodologies for predicting knockout effects based solely on a given network of functional interactions ., The first algorithm employs a restrictive assumption on the structure of the functional network; nevertheless , its underlying model is sufficient for describing the majority of the knockout effects in the large scale yeast data set that we analyzed ., In cross validation tests it was shown to provide very efficient means for predicting held-out knockout effects , dramatically improving upon the state-of-the-art benchmark ., The second , refined algorithm extends the two-group logic that is at the heart of the first algorithm , aiming to partition the genes into several groups that behave similarly with respect to the knockout data ., We show that this refined model allows capturing functional relations within signaling pathways , which could not be explained by the previous model , leading to superior accuracy ., Notably , since the input data contains only single-gene perturbations , both algorithms cannot decipher combinatorial regulation functions involving multiple inputs ( as in 4 ) ., Instead , the algorithms treat the functional relations independently and try to find the best way to consolidate them ( i . e . , maximizing the number of relations that can be explained by the model ) ., Being “network-free” ( i . e . , independent of physical interaction data ) is a unique feature of our algorithms , which allows their application to organisms on which no comprehensive interaction data is available ., To complement the analysis when a physical network is available , we show how to use the information embedded in a functional network to annotate the physical edges with signs of activation or suppression ., In comparison with a previous method , our algorithm is again shown to provide a substantial improvement in terms of coverage while attaining comparable levels of accuracy ., In a recent paper , Maayan et al . 21 studied the prevalence of sign-consistent versus sign-inconsistent loop motifs in the yeast physical regulatory network ., Their findings suggest that sign-consistent loops are more prevalent and that , overall , the network is close to being sign-consistent ., Our work provides further support to this observation through the results of the local linearity tests and the overall good performance of the sign-linear model on the yeast data ., It will be interesting to test how well do gene perturbation maps in higher organisms conform to the simplistic sign-linear model ., As data from perturbation experiments in human gradually accumulates 22 , this is an appealing direction for future research ., Let be a connected , directed network of physical interactions ., We denote by the network annotated with signs on its edges ., The undirected form of is an undirected graph of the same topology as whose edges are annotated according to ., In case there are contradicting signs: , , then the undirected form of is not defined ., We say that an annotated network is sign-consistent if its undirected form is defined and does not contain cycles with a negative aggregate sign ., Let be a functional network defined on a subset of the nodes in the physical network ., An edge in is explained by the annotated network if and only if there exists a path in from to such that its aggregate sign is equal to the sign of the knockout relation ., Similarly , we say that can generate the relation ., We say that can be explained by if there exists a Boolean assignment such that can explain all the knockout effects in ., Similarly , we say that can generate if it explains all the edges in ., The following two lemmas motivate our sign-linear algorithm; their proofs appear in Text S1 ., The sign-linear algorithm is based on finding a Boolean assignment for every gene in the functional network that maximizes the number of knockout pairs such that ., This maximization problem is also known as MAX-E2-LIN2 , and can be reformulated in a set of linear equation in the space ., An approximation algorithm to MAX-E2-LIN2 was previously presented 23 , however , for reasons of simplicity and scalability we chose to use a greedy approach ., The latter starts from a random Boolean assignment and proceeds by choosing a gene at random and changing its assignment if it improves the result ( i . e . , if it increases the number of explained pairs ) ., The algorithm terminates when it reaches a local maximum , and no more modifications can be made ., We predict the sign of a hidden knockout effect as ., We repeat this randomized procedure 100 times and report the percentage of runs that predicted up- or down-regulation ., Notably , the algorithm is only applicable to pairs of genes that lie in the same connected component of the ( undirected ) functional network ., To obtain general partitions into more than two groups we use a hierarchical clustering procedure ., For a given pair , let be the set of genes whose knockout affected both and , and let denote the set of genes that are affected by the knockout of and by the knockout of ( this set is not empty only if the data set includes a knockout of and a knockout of ) ., Let be the set of genes whose knockout affected and in a similar manner ., Similarly , let comprise of genes who responded similarly to the knockouts of and ., The pairwise similarity score that we use for the clustering procedure is calculated using a binomial cumulative distribution function where is the number of trials , and is the number of “failures” ( namely , the number of times and behaved differently ) ., The resulting score is the probability of observing up to failures in independent trials ., The probability of a failure in any given trial is set to , where is the frequency of “+” relations in the functional network ., We use a standard complete-linkage hierarchical clustering procedure ., We define the groups by finding inner nodes in the hierarchy whose score is lower than the a-priori probability for functional similarity ( ) and the score of their ancestors in the hierarchy is larger than ., We predict the sign of a hidden knockout effect according to the groups and to which and were mapped ., If in the majority of the cases knocking out members of decreases members of , then is predicted as down-regulation and vice versa ., Due to its greedy nature , the order in which the genes are processed by the clustering procedure can affect the resulting clusters ., Therefore , we repeat the procedure using 100 random orderings , and report for each pair the percentage of runs in which its relation was predicted to be up- or down-regulation .

Introduction, Results/Discussion, Materials and Methods

Perturbation experiments , in which a certain gene is knocked out and the expression levels of other genes are observed , constitute a fundamental step in uncovering the intricate wiring diagrams in the living cell and elucidating the causal roles of genes in signaling and regulation ., Here we present a novel framework for analyzing large cohorts of gene knockout experiments and their genome-wide effects on expression levels ., We devise clustering-like algorithms that identify groups of genes that behave similarly with respect to the knockout data , and utilize them to predict knockout effects and to annotate physical interactions between proteins as inhibiting or activating ., Differing from previous approaches , our prediction approach does not depend on physical network information; the latter is used only for the annotation task ., Consequently , it is both more efficient and of wider applicability than previous methods ., We evaluate our approach using a large scale collection of gene knockout experiments in yeast , comparing it to the state-of-the-art SPINE algorithm ., In cross validation tests , our algorithm exhibits superior prediction accuracy , while at the same time increasing the coverage by over 25-fold ., Significant coverage gains are obtained also in the annotation of the physical network .

Observing a complex biological system in steady state is often insufficient for a thorough understanding of its working ., For such inference , perturbation experiments are necessary and are traditionally employed ., In this work we focus on perturbations in which a gene is knocked out and as a result multiple genes change their expression levels ., We aim to use a given set of perturbation experiments to predict the results of new experiments ., Using a large cohort of gene knockout experiments in yeast , we show that the emerging map of causal relations has a very simple structure that can be utilized for the prediction task ., The resulting prediction scheme , and its extension to more complex functional maps , greatly improve on extant approaches , increasing the coverage of known relations by 25-fold , while maintaining the same level of prediction accuracy ., Unique to our approach is its independence of physical network data , leading to its high efficiency and coverage as well as to its wide applicability to organisms whose interactions have not been mapped to date ., We further extend our method to annotate the interactions of a physical network as activating or suppressing , obtaining significant coverage gains compared to current approaches .

computational biology/systems biology, computational biology/transcriptional regulation

journal.pntd.0004259

Assessment of Inhibitors of Pathogenic Crimean-Congo Hemorrhagic Fever Virus Strains Using Virus-Like Particles

Crimean-Congo hemorrhagic fever ( CCHF ) is a rapidly progressing inflammatory illness with high case fatality rates and a vast endemic area 1–6 ., The etiological agent , CCHF virus ( CCHFV ) , is a tri-segmented virus belonging to the Nairovirus genus of the Bunyaviridae family; it is primarily maintained in and transmitted by Hyalomma species ticks 1 , 5 , 6 ., Human infection is usually associated with tick bites or by unprotected contact with bodily fluids of infected animals or humans ., Subclinical and mild cases of CCHFV infection usually consist of non-specific “flu-like” symptoms ( fever , vomiting , and diarrhea ) , and are self-resolving ., Severe CCHFV infection progresses to CCHF , which is characterized by petechiae , ecchymosis , epistaxis , gingival hemorrhage , and , frequently , gastrointestinal and cerebral hemorrhage 1 , 7 , 8 ., Case fatality rates of CCHF vary among outbreaks and potentially among strains of CCHFV , but are approximated to 30% of clinical cases 9 , 10 ., The broad endemic region and high fatality rate of CCHF necessitate further research into the biology of CCHFV and development of effective prophylactic and therapeutic options to treat CCHFV infections for mitigating the negative public health impact of this pathogen ., Basic research on CCHFV and the development of CCHF therapies and prophylaxes have been severely hampered by a number of factors ., Safe handling of CCHFV requires high-containment facilities ( biosafety level 3 ( BSL-3 ) and BSL-4 facilities in endemic and non-endemic areas , respectively 9 ) ., In addition , while CCHFV strains are highly variable in nature , laboratory strain availability is limited; the majority of basic research uses strain IbAr10200 , which has unknown pathogenicity in humans ., Furthermore , due to technical difficulties in engineering recombinant CCHFV and pseudo-typing CCHFV glycoproteins onto other viruses , few and very limited reporter systems of CCHFV are available 11–14 ., The major viral components of CCHFV particles consist of the viral genome and proteins ., CCHFV , like all Bunyaviridae members , has a tri-segmented , negative sense RNA genome ., The 3 segments , named small ( S ) , medium ( M ) , and large ( L ) , encode the viral nucleocapsid protein ( NP ) , the glycoprotein precursor ( GPC ) , and the viral polymerase ( L ) , respectively ., Each of the CCHFV genomic segments consist of a coding region flanked by 5′ and 3′ non-coding regions ( NCRs ) ., The NCRs are sufficient to initiate viral transcription , replication , encapsidation of RNA by NP and L , and packaging the RNA into viral particles 12 , 15 ., An infectious CCHFV particle consists of at least all 3 viral RNA segments bound to NP and L ( ribonucleoprotein complexes , RNPs ) that are encapsulated by a lipid membrane containing the mature glycoproteins Gn and Gc , which are generated by post-translational processing of the GPC ., Unlike many other negative-strand RNA viruses , bunyaviruses do not encode a separate matrix protein responsible for driving virus formation and incorporating RNPs into nascent viral particles ., Rather , these processes are thought to be mediated by interaction between the RNPs and Gn and/or Gc 16–19 ., Furthermore , as Gn and Gc mediate CCHFV entry into cells , these proteins are thought to be the major targets of host neutralizing antibody responses ., Presumably due to this immunologic pressure , rapid mutation and frequent reassortment of the M segment have been reported in phylogenetic studies of CCHFV 20 , 21 ., CCHFV reassortment occurs when the same cell is co-infected with at least 2 CCHFV strains ., In order for reassortment to occur , the genomic NCRs and viral proteins must be compatible ( i . e . , the strains must possess both RNA-protein and protein-protein compatibility ) ., How interaction of NCRs with RNPs and structural glycoproteins influences the reassortment observed in CCHFV is unknown 20 ., To accelerate studies of CCHFV , we optimized a virus-like particle ( VLP ) system for use in a BSL-2 setting ., The VLPs are transcription- and entry-competent VLPs ( tecVLP ) , do not produce infectious CCHFV , and are morphologically similar to CCHFV ., We show that the tecVLP system is suitable for addressing the following points: ( 1 ) screening antivirals; ( 2 ) testing potency of monoclonal antibodies against divergent CCHFV strains; and ( 3 ) identifying potential molecular determinants of CCHFV reassortment , such as compatibility between NCRs and glycoproteins from various CCHFV strains ., Unlike previous CCHFV reporter systems 12–15 , transfection or other pre-treatment of target cell lines is not required for tecVLP activity , so diverse cell lines and human primary cells may be used in tecVLP assays without special modification ., This molecular tool may therefore be used in elucidating important aspects of CCHFV biology , in high-throughput screening , and in developing effective clinical countermeasures against CCHFV ., Trained personnel performed all procedures involving potentially infectious agents ( work with infectious CCHFV and initial safety experiments with tecVLPs ) in a BSL-4 facility according to standard operating procedures approved by the institutional biosafety committee ., Other procedures were carried out under BSL-2 conditions ., The use of human blood products was approved by Emory University Institutional Review Board ( IRB reference IRB00045947 ) ., Under this protocol , no donor personal information was provided , and informed consent was given ., All adult blood donors provided informed consent , and a parent or guardian of any child participant provided informed consent on the child’s behalf ., HuH7 cells ( obtained from Apath LLC , Brooklyn , NY , USA ) were propagated in Dulbecco’s modified Eagle’s medium ( DMEM ) supplemented with 10% fetal bovine serum ( FBS ) , 1% non-essential amino acids , and 1% penicillin/streptomycin ( all from Life Technologies , Grand Island , NY , USA ) ., BSR-T7 cells ( a kind gift from K . K . Conzelmann , Ludwig-Maximilians-Universität , Munich , Germany ) , which constitutively express T7 polymerase , were propagated in DMEM supplemented with 5% FBS , 1% sodium pyruvate , 400 ng/mL G418 , and 1% penicillin/streptomycin ., A549 cells ( ATCC , Manassas , VA , USA ) and SW-13 cells ( a kind gift from P . Leyssen , Rega Instituut KU , Leuven , Belgium ) were both propagated in DMEM supplemented with 10% FBS , 1% sodium pyruvate , and 1% penicillin/streptomycin ., All cells were grown in a humidified 37°C , 5% CO2 incubator ., Peripheral blood mononuclear cell ( PBMC ) pheresis products were obtained from a single healthy human donor at Emory University hospital ( Atlanta , GA , USA ) ., PBMC were purified from whole blood pheresis products using Ficoll-Paque ( GE Healthcare , Atlanta , GA , USA ) according to manufacturers instructions ., Monocytes were isolated from the purified PBMC using the human Monocyte Isolation Kit II ( Miltenyi Biotec Inc . , San Diego , CA , USA ) according to manufacturer’s instructions , and frozen in liquid nitrogen until use ., Monocytes were plated in 96-well cell culture plates at a density of 2 . 5 × 105 cells/cm2 in RPMI media supplemented with 10% FBS and 1% penicillin/streptomycin , and cultured for 10 days to allow differentiation into monocyte-derived macrophages ., RPMI media was replaced every 2–3 days ., GM05659 cells ( apparently healthy , non-fetal human fibroblasts from chest skin , obtained from Coriell Institute , Camden , NJ , USA ) were cultured in DMEM supplemented with 10% FBS , 1% sodium pyruvate , and 1% penicillin/streptomycin , and grown in a humidified 37°C , 5% CO2 incubator ., Sequences of the M genomic segments of the novel CCHFV isolates were generated as previously described 21 ., Reverse transcription PCR ( RT-PCR ) amplification products of the M segments were analyzed by Sanger sequencing and uploaded to GenBank ., CCHFV helper plasmid genes were synthesized from GenBank sequences by GenScript USA Inc . ( Piscataway , NJ , USA ) ., The CCHFV reference strain IbAr10200 L polymerase gene was codon-optimized and cloned into the mammalian expression plasmid pCAGGS-LCK to produce helper plasmid pC-L , as previously described 11 ., GPC genes from CCHFV strains IbAr10200 ( reference CCHFV strain ) , Afg2990 ( human lethal case ) , and from CCHFV isolated from human patient samples collected in Oman and Turkey were codon-optimized , synthesized by GenScript , and cloned into the mammalian expression plasmid pCAGGS ( pC ) ., These constructs were named pC-GPC-IbAr , pC-GPC-Afg , pC-GPC-Oman , and pC-GPC-Turk ., Both pCAGGS and pCAGGS-LCK ( both abbreviated to pC ) have the same mammalian promoter for expression , but the pCAGGS-LCK is a low copy plasmid in bacteria to facilitate efficient bacterial production of the unstable pC-L construct ., The previously described pC-NP plasmid was used to express NP from CCHFV strain IbAr10200 11 , 12 ., The previously described T7 polymerase plasmid pC-T7 was also used 11 ., Minigenomes encoding the NanoLuc luciferase gene ( Promega , Madison , WI , USA ) sequence flanked by S , M , or L NCRs from CCHFV strain IbAr10200 , or with L NCRs from the CCHFV samples from Oman or Afg2990 , were gene-synthesized ( IDT , Coralville , IA , USA ) ., The minigenomes were cloned into pSMART-LCK plasmid ( Lucigen , Middleton , WI , USA ) ., The resulting plasmids ( pS-Luc , pM-Luc , pL-Luc , pOmanL-Luc , and pAfgL-Luc ) expressed viral-sense ( i . e . , negative or non-coding sense ) RNA fragments containing CCHFV recognition signals under the control of a T7 promoter , and containing one extra G at the 5′ end of the transcripts to enhance transcription by the T7 polymerase ( Fig 1A ) ., BSR-T7 cells were seeded in multi-well plates overnight and transfected with combinations of minigenome and helper plasmids ., Plasmids were transfected using TransIT-LT1 Transfection Reagent according to manufacturer’s recommendations ( Mirus Bio LLC , Madison , WI , USA ) ., Helper plasmids were transfected in a weight ratio of pC-NP:pC-GPC:pC-L:pC-T7:minigenome of 4:10:2:4:1 unless otherwise noted , and if a helper plasmid was omitted , it was replaced with an equal weight of empty pC ., The ratio of minigenome to helper plasmid was kept constant ., The total amount of transfected DNA varied according to the size of the culture plate well: a 6-well plate was transfected with 5 μg of total DNA per well , while a 24-well plate was transfected with 1 μg of total DNA per well ., To minimize carry-over of plasmids to the subsequent passage , transfection media was removed ~16–18 h post transfection and replaced with fresh media ., Cell lysates , or supernatants for tecVLP passaging , were collected 3 days post transfection ., NanoLuc signal was assayed in BSR-T7 cells transfected with CCHFV minigenome plasmids and in A549 , BSR-T7 , GM05659 , HuH7 , monocyte-derived macrophages , and SW-13 incubated with tecVLPs ., Briefly , culture media was removed from the cells , and the cells were then washed once with PBS , and either frozen at -20°C or lysed by incubating for 30–45 min in passive lysis buffer ( Promega ) at room temperature ., 20 μL of cell lysate was removed and assayed with Nano-Glo Luciferase Assay System ( Promega ) to detect NanoLuc signal ., All luminescence readings were carried out in opaque , white 96-well plates using the Synergy 4 instrument ( BioTek Instruments Inc . , Winooski , VT , USA ) ., BSR-T7 cells were transfected with plasmids for tecVLP rescue as described in “Plasmid transfections for VLP production” section , or with pC as a mock transfection control ., In parallel , supernatants from tecVLP-producing or mock-transfected BSR-T7 cells , and CCHFV strain IbAr10200 viral stock ( SW-13 cell supernatants ) , were clarified by low speed centrifugation ( 1500 × g for 10 min ) ., SW-13 cells were seeded at ≥ 90% confluence and allowed to adhere overnight ., The media was removed from the SW-13 cells , and the cells were incubated with supernatants from mock- , tecVLP- , or CCHFV-treated cells for 2 h at 37°C ., After incubation , the supernatants were removed and replaced with fresh media ., Two days post incubation , cells were fixed with 10% formalin-buffered solution and permeabilized with Triton-X100 ., Presence of CCHFV antigens was detected by incubation with CCHFV hyperimmune mouse ascetic fluid ( HMAF , made in-house at CDC , Atlanta , GA , USA ) or monoclonal antibody ( mAb ) 13G8 ( BEI Resources , Manassas , VA , USA ) followed by incubation with goat anti-mouse Alexa 488-conjugated antibody ( Life Technologies ) ., Images were captured with a TCS SP5 confocal microscope ( Leica Microsystems , Buffalo Grove , IL , USA ) ., BSR-T7 , HuH7 , SW-13 , A549 , and GM05659 cells were seeded at ≥ 70% confluence and allowed to adhere overnight ., Plates of primary human macrophages were seeded at 2 . 5 × 105 cells/cm2 , and allowed to differentiate for 10 days ., For all cells , media were removed and cells incubated with tecVLP-containing supernatants from transfected BSR-T7 cells for at least 2–3 h at 37°C ., After incubation , the tecVLP-containing supernatants were removed , and the cells were washed 3 times with sterile PBS or RPMI media prior to adding fresh media ., The cells were incubated overnight at 37°C to allow biosynthesis of NanoLuc in the cells ., Following the incubation period , the cell medium was removed and the cells were washed once more with PBS; the cells were then incubated with passive lysis buffer for NanoLuc assays , or frozen at -20°C ., tecVLP titration was performed on 10-fold serial dilutions ( prepared in DMEM ) of transfected cell supernatants , using a TCID50 assay on SW-13 cells in 96-well plates ., Plates were incubated with the supernatants overnight at 37°C , and were then treated with passive lysis buffer for NanoLuc assays , or frozen at -20°C ., Wells that displayed NanoLuc signal at least 3 standard deviations above background levels were considered positive for tecVLP signal ., tecVLP concentrations were calculated using the Reed and Muench formula 22 , and expressed as TCID50 per mL of stock ., CCHFV IbAr10200 PreGn- and Gc-specific mAbs ( 13G8 for PreGn; 11E7 and 12A9 for Gc ) , and CCHFV IbAr10200 NP-specific mAb ( 9D5 ) were obtained from BEI Resources ., mAbs were diluted in DMEM supplemented with 5% FBS to equal starting concentrations , and further diluted in a 2-fold dilution series ( concentration range 1 × 101 to 8 × 10−2 μg/mL , or ~1:100 to 1:12800-fold dilution ) ., The mAb dilutions were mixed with an equal volume of tecVLP-containing supernatant and incubated for 1–2 h at 37°C ., The mixture was then applied to confluent monolayers of SW-13 cells , and incubated as outlined in tecVLP passaging ., Ribavirin and chloroquine sulfate were obtained from U . S . Pharmacopeia ( Rockville , MD , USA ) ., The inhibitors were dissolved in DMSO ( Sigma-Aldrich , St . Louis , MO , USA ) and diluted in OptiMem ( Life Technologies ) to starting concentrations , and further diluted in a 2-fold dilution series ( final concentration range of 400 μM to 3 μM for ribavirin , and 500 μM to 3 . 9 μM for chloroquine ) ., The diluted drugs were added to monolayers of SW-13 cells , and cells were incubated for 15–20 min at 37°C ., After incubation , equal volume of tecVLP-containing supernatants were added to the drug mixtures , and cells were incubated for 1–2 h at 37°C ., The inocula were removed and the cells washed 3 times with sterile PBS prior to addition of fresh media with the same compound concentration; cells were then incubated overnight as described in tecVLP passaging ., Cell viability was determined concurrently with tecVLP signal inhibition experiments , but on only compound-treated cells ., Viability was determined using the CellTiter-Glo Luminescent Cell Viability Assay ( Promega ) according to manufacturer’s instructions ., BSR-T7 cells in 6-well plates were transfected with plasmids necessary for the production of tecVLP as described in the “Plasmid transfections for tecVLP production” section above ., After 3 days , tecVLP-containing cell supernatants were removed , clarified by centrifugation ( 5–10 min at 1500 × g ) , filtered through 0 . 22 μm pore size filters ( EMD-Millipore , Billerica , MA , USA ) , and concentrated in 100 kDa cutoff Centricon Plus-70 centrifugal filter units ( EMD-Millipore ) to 5–100-fold concentration ., Concentrated tecVLP-containing supernatants were fixed by incubation with an equal volume of 5% paraformaldehyde and stored at 4°C until use ., The concentrated , fixed tecVLP samples were processed as follows ., First , 2 μL of each sample was pipetted onto a 300-mesh formvar/carbon-coated nickel grid ( EMS , Hatfield , PA , USA ) , and the sample was incubated overnight at 4°C ., The samples were then blotted , rinsed with bacitracin ( 50 μg/mL ) 23 , blotted , negatively stained with 5% ammonium molybdate ( pH 6 . 9 ) and 0 . 1% ( w/v ) trehalose , and blotted a final time 24 ., The grid was examined using a Tecnai BioTwin transmission electron microscope ( FEI Company , Hillsboro , OR , USA ) operating at 120 kV , and images were captured with a 2K × 2K camera ( AMT Corp . , Woburn , MA , USA ) ., Concentrated tecVLP-containing supernatants or pC-GPC-transfected BSR-T7 cell lysates were incubated with NuPAGE LDS Sample Buffer for 10 min at 70°C prior to loading onto NuPAGE Novex 3–8% tris-acetate protein gels ( all from Life Technologies ) ., The gels were run at a constant 200 V for 45 min , and transferred onto nitrocellulose membranes using the iBlot instrument ( Life Technologies ) according to manufacturer’s instructions ., The membranes were incubated overnight at 4°C with anti-N mAb 9D5 ( BEI Resources; diluted 1:1000 ) , anti-Gn polyclonal rabbit sera 25 ( a kind gift from A . Mirazimi , Karolinska Institutet , Sweden; diluted 1:500 ) , or anti-Gc mAb 7E11 ( BEI Resources; diluted 1:1000 ) ., Signals were detected with Fast Western Blot Kits mouse or rabbit SuperSignal West Dura ( Thermo Fisher Scientific Inc . , Waltham , MA , USA ) according to manufacturer’s recommendations ., Full-length CCHFV M gene sequences were translated and aligned using the ClustalW algorithm , and phylogenetic trees were constructed using Mega ( Biodesign Institute , Tempe , AZ , USA ) 26 via the Jukes-Cantor neighbor-joining method with bootstrapping to 10000 iterations ., Analyses were done using one-way or two-way analyses of variance ( ANOVA ) with Tukey’s multiple comparisons test ., The analyses were performed using GraphPad Prism version 6 . 00 for Mac OS X ( GraphPad Software Inc . , La Jolla , CA , USA ) ., For inhibitor dilutions , GraphPad Prism was used to fit a 4-parameter equation to semilog plots of the concentration-response data ., The plot was used to interpolate the concentration of compound that inhibited 50% of the NanoLuc signal in target cells ( EC50 ) ., The 50% cytotoxic concentration ( CC50 ) was derived using luciferase signal levels from inhibitor-treated cells ., The selectivity index ( SI ) was calculated by dividing the CC50 by the EC50 ., Helper plasmid genes were based on published and novel CCHFV , and T7 polymerase sequences ., Replication machinery plasmids relied on published reference strain IbAr10200 gene sequences ( NP gene accession no . NC_005302; L gene accession no . AY389508 ) ., GPC helper plasmid gene sequences were based on published sequences of CCHFV strains IbAr10200 ( accession no . NC_005300 ) and Afg2990 ( accession no . HM452306 . 1 ) , or on sequences of novel CCHFV isolates ( Oman-811466 , accession no . KR864901; Turkey-810473 , accession no . KR864902 ) ., T7 polymerase helper plasmid was based on a published gene sequence ( accession no . M38308 ) ., Minigenomes sequences were based on a published NanoLuc luciferase gene sequence ( accession no . JQ437370; ) flanked by CCHFV strain IbAr10200 S , M , or L NCRs ( S accession no . NC_005302; M accession no . NC_005300; L accession no . AY389508 ) , or by L NCRs from strains Oman ( accession no . DQ211619 ) or Afg2990 ( accession no . HM452307 ) ., While the tecVLP system reported here is based on similar premises as previous systems 12 , 13 , it incorporates 2 modifications: the reporter is NanoLuc , a smaller protein that generates a brighter signal than many other luciferases; and the CCHFV L 11 and GPC helper plasmid sequences are codon-optimized ., Because previous reports have indicated differences in the efficiency of transcription , replication and/or packaging of the 3 genomic CCHFV segments 12 , 13 we first tested which segment NCR was transcribed most efficiently in our system ., In order to assess the reporter signal produced , we transfected cells with IbAr10200 helper plasmids and minigenomes containing S , M , and L genome segment NCRs ( Fig 1A ) ., The resulting NanoLuc signal was similar regardless of segment used , suggesting no differences in transcription or replication of the 3 genomic segment NCRs ( Fig 1B ) ., To determine whether using minigenomes of all 3 segments together would boost the overall signal , equal amounts of S-Luc , M-Luc , and L-Luc were transfected into BSR-T7 together ., We then compared the resulting signal to that in cells transfected with triple the amount of individual minigenomes ( 3 × S-Luc , 3 × M-Luc , or 3 × L-Luc ) ., The highest signal levels were seen when using 3 × L-Luc or S-Luc + M-Luc + L-Luc , ( Fig 1B ) ., While there was a difference in signal levels in cells transfected with different minigenomes , the titers of tecVLPs ( ~2–6 ×104 TCID50/mL; Fig 1C ) were not apparently different ., Likewise , when the tecVLPs resulting from minigenome transfections were passaged to SW-13 cells , the NanoLuc signal levels generated by tecVLPs were similar regardless of minigenome used ( Fig 1D ) ., To be consistent in subsequent experiments , we used the L-Luc minigenomes at the original concentration ., Due to safety concerns , we also verified that cells treated with tecVLPs do not release infectious virus ., SW-13 cells were treated with infectious CCHFV , tecVLP ( in supernatants from transfected BSR-T7 cells ) , or supernatants of untransfected BSR-T7 cells ( negative control ) ., Following 2 days of incubation , the SW-13 cells were stained by standard immunofluorescent assay ., The cells inoculated with CCHFV had readily detectable viral antigens ( S1A Fig ) , while cells incubated with tecVLPs or control supernatants did not ( S1B and S1C Fig ) ., In addition , SW-13 cells incubated with tecVLPs did not produce new tecVLPs , as supernatants of SW-13 cells treated with tecVLPs did not result in production of NanoLuc signal when passaged onto naïve cells ( S1D Fig ) ., Therefore , we concluded that the tecVLPs are incapable of spreading ., To compare tecVLP morphology and cell entry to those of authentic CCHFV , we used electron microscopy and a neutralization assay , respectively ., Electron microscopy showed that tecVLPs were morphologically consistent with CCHFV and other bunyaviruses , and were relatively uniform in size ( 94 ± 3 nm , n = 13 , from 5 fields; average ± standard error of the mean , Fig 2A ) ., Furthermore , tecVLPs were neutralized by previously reported neutralizing monoclonal antibodies 27 targeting IbAr10200 strain glycoprotein Gc ( 11E7 and 12A9 ) , but not by mAbs targeting strain IbAr10200 NP ( 9D5 ) or the glycoprotein PreGn ( 13G8 ) ( Fig 2B ) ., These data suggest that CCHFV IbAr10200 tecVLPs are , from a structural perspective , consistent with bona fide CCHFV particles ., Due to the sequence variability between the NCRs of different CCHFV strains , we expected differences in the efficiency with which viral sense RNAs are recognized and transcribed or packaged into particles by IbAr10200 proteins ., To study the role of NCR sequences in tecVLP production , L NCR minigenomes from strains isolated from severe human cases of CCHFV ( Oman and Afg09 ) were expressed in the tecVLP system ., tecVLPs were generated using minigenome plasmids containing Oman or Afg09 L NCRs flanking NanoLuc , and the same IbAr10200 helper plasmids ( NP + GPC + L ) as used previously ( Fig 1A ) ., The results show that NCRs from all 3 strains were transcribed by strain IbAr10200 replication machinery ( NP and L ) at approximately the same efficiency in transfected cells , as signal levels ( Fig 3A ) and titers of tecVLPs produced in BSR-T7 cells ( Fig 3B ) were similar regardless of the NCR in the minigenome ., The differences in signal produced by SW-13 cells incubated with tecVLPs with different NCRs were minor and not statistically significant ( p > 0 . 05 , Fig 3C ) ., As M segments , and therefore GPCs , are commonly exchanged between CCHFV strains 20 , 21 , 28–30 , we assessed the compatibility between IbAr10200 replication machinery and mature glycoproteins from several pathogenic strains of CCHFV ., To study the production of tecVLPs by the IbAr10200 replication machinery , GPCs from CCHFV strains Turkey , Oman , and Afg09 were expressed in the tecVLP system in place of IbAr10200 GPC ., The full-length sequences of strain Turkey and Oman GPCs were elucidated prior to use in the tecVLP system ., The GPC of the commonly used CCHFV strain IbAr10200 phylogenetically clusters with African CCHFV strains , while Turkey , Oman , and Afg09 GPCs cluster in different nodes of the phylogenetic tree ( S2 Fig ) ., Based on amino acid sequences of the entire GPC , these strains were 14–20% different from IbAr10200 GPC ( 86% , 83% , and 80% amino acid identity between IbAr10200 and Afg09 , Oman , and Turkey GPCs , respectively ) ., Using GPCs from these strains in transfected cells did not considerably affect NanoLuc signal levels in transfected BSR-T7 cells ( Fig 4A ) , but did affect the amounts of tecVLPs produced ., Turkey and Oman strain GPCs led to the highest tecVLP titers , followed by IbAr10200 and Afg09 GPCs ( Fig 4B ) ., Only the mature forms of Gn and Gc were detected by western blotting in the cell supernatants containing the tecVLP ( ~35 kDa for Gn and ~70kDa for Gc , Fig 4C ) ., Levels of Gn were highest when using Turkey and Oman GPCs , lower when using IbAr10200 GPC , and lowest when using Afg09 GPC ., Mature Gn is synthesized from a larger precursor that requires post-translational processing ., We tested if the ratios of Gn precursor to mature Gn were equivalent in BSR-T7 cell transfected with GPCs of different CCHFV strains ., Western blotting of the cell lysates showed a lower proportion of Afg09 mature Gn over Gn precursor in comparison to other GPCs ( Fig 4D ) ., Due to a previous report that mature CCHFV glycoproteins interact with the NCR regions of the CCHFV genome 17 , we used the tecVLP system to address whether this interaction affects the efficiency of tecVLP release in a strain-specific manner ., The results demonstrated that while altering the GPC resulted in a substantial difference in tecVLP titer and signal strength , changing minigenome NCRs had a minimal effect on tecVLP signal strength ( Fig 5 ) ., In addition , using the minigenome NCR and GPC from the same strain together did not affect tecVLP production or NanoLuc signals synergistically; Afg09 GPC did not preferentially increase the signal or titer of Afg09 minigenome tecVLPs ( Fig 5A ) , nor did the Oman GPC preferentially increase the signal of Oman minigenome tecVLPs ( Fig 5B ) compared to the minigenome containing NCRs from other strains ., In order to study the effects of GPC from different CCHFV strains on cell entry in the absence of confounding factors like differences in NP or L , we compared NanoLuc signal levels produced in immortalized and primary cells incubated with tecVLPs containing GPC from several CCHFV strains ., tecVLPs containing IbAr10200 , Turkey , Oman , or Afg09 GPCs were capable of entering several immortalized cell lines ( Fig 6A ) ., Predominantly , the strength of the resulting signal corresponded to tecVLP titers ( Fig 4B ) , with Turkey and Oman GPC tecVLPs yielding the highest titers and signal levels , IbAr1200 GPC producing intermediate levels , and Afg09 GPC tecVLPs yielding the lowest titers and signal levels ( compare Figs 4B with 6A ) ., However , NanoLuc signal generated by IbAr10200 GPC-containing tecVLP in A549 cells was on par with Afg09 GPC-containing tecVLPs ., Furthermore , when primary human cells were used instead of immortalized cell lines , tecVLPs containing IbAr10200 GPC generated less signal than tecVLPs containing Afg09 GPC; this was especially clear in monocyte-derived macrophages , which seemed refractory to entry by tecVLPs with IbAr10200 GPC ( Fig 6B ) ., Efforts to screen CCHFV inhibitors have been hindered in part by the lack of high-throughput quantitative molecular systems that can be used in a BSL-2 setting ., In order to overcome this limitation , we evaluated the potential of the tecVLP system to be used to screen compounds and antibodies in a high-throughput , 96-well format ., We screened reported inhibitors of CCHFV cell entry ( mAbs and chloroquine ) and of viral transcription ( ribavirin ) using SW-13 cells treated with tecVLPs expressing glycoproteins from several CCHFV strains ., The non-neutralizing mAb 13G8 ( targeting IbAr10200 PreGn; Fig 2B ) and the neutralizing mAb 12A9 ( targeting IbAr10200 Gc; Fig 2B ) did not neutralize tecVLPs containing Turkey , Oman , or Afg09 GPCs ., However , the neutralizing mAb 11E7 ( targeting the IbAr10200 Gc ) effectively neutralized all tecVLPs ( Fig 7A ) ., This demonstrates that the tecVLP system can be used to differentiate between strain-specific and broadly acting neutralizing mAbs and to rapidly assess mAb effectiveness ., While mAbs are expected to bind different CCHFV glycoproteins with varying efficacy , small molecule CCHFV inhibitors should have similar inhibitory effects regardless of the GPC used ., Indeed , using chloroquine or ribavirin on tecVLP-treated SW-13 cells decreased the signal produced by tecVLPs with equal efficiency regardless of the GPC constructs used to produce tecVLPs ( Fig 7B ) ., The average EC50 was 23 . 23 ± 0 . 47 μM for chloroquine and 47 . 48 ± 1 . 43 μM for ribavirin , which is consistent with previously reported values 31–33 ., The CC50 of chloroquine and ribavirin were 94 . 58 μM and 307 . 4 μM , respectively , with a corresponding average SI of 4 . 1 and 6 . 5 , respectively ( Fig 7B ) ., These data suggest that the tecVLP system may be effective in screening inhibitors in a medium- or high-throughput set-up ., CCHF is a geographically widespread , life-threatening illness characterized by severe flu-like and hemorrhagic symptoms and relatively high case fatality rates ., Research on CCHFV , the causative agent of CCHF , has been hampered by a number of factors ., Requirement for high-containment laboratories , limited strain availability , and lack of robust molecular tools for studying CCHFV all stalled basic CCHFV research ., The development of VLP systems , especially those that can be simply scaled as needed , and that allow the study of CCHFV in a safe and effective manner , is a major step towards accelerating basic and applied research on this important public health risk ., While our work builds on a previously reported minigenome system 12 and is not the first VLP system for CCHFV to be documented 13 , our system possesses several important advantages ., ( 1 ) We demonstrated that the generated tecVLPs are morphologically consistent with CCHFV , and cell entry was neutralized by same monoclonal antibodies as authentic virus ( Fig, 2 ) 27 ., ( 2 ) A high yield of tecVLPs may be generated in 3 days without passaging in transfected cells ( Fig 1C ) ., ( 3 ) Recipient cells may be used without resource-consuming pre-treatment steps , such as transfection of helper plasmids ( Fig 4 ) ; ( 4 ) therefore , the system reflects primary transcription/replication in a more natural setting than plasmid overexpression , and ( 5 ) a wider range of cell lines may be used ( Fig 5 ) ., ( 6 ) GPCs and minigenomes from divergent CCHFV strains may be used ( Figs 3 , 4 and 6 ) , expanding the utility of this s

Introduction, Materials and Methods, Results, Discussion

Crimean-Congo hemorrhagic fever ( CCHF ) is an often lethal , acute inflammatory illness that affects a large geographic area ., The disease is caused by infection with CCHF virus ( CCHFV ) , a nairovirus from the Bunyaviridae family ., Basic research on CCHFV has been severely hampered by biosafety requirements and lack of available strains and molecular tools ., We report the development of a CCHF transcription- and entry-competent virus-like particle ( tecVLP ) system that can be used to study cell entry and viral transcription/replication over a broad dynamic range ( ~4 orders of magnitude ) ., The tecVLPs are morphologically similar to authentic CCHFV ., Incubation of immortalized and primary human cells with tecVLPs results in a strong reporter signal that is sensitive to treatment with neutralizing monoclonal antibodies and by small molecule inhibitors of CCHFV ., We used glycoproteins and minigenomes from divergent CCHFV strains to generate tecVLPs , and in doing so , we identified a monoclonal antibody that can prevent cell entry of tecVLPs containing glycoproteins from 3 pathogenic CCHFV strains ., In addition , our data suggest that different glycoprotein moieties confer different cellular entry efficiencies , and that glycoproteins from the commonly used strain IbAr10200 have up to 100-fold lower ability to enter primary human cells compared to glycoproteins from pathogenic CCHFV strains .

The tick-borne Crimean-Congo hemorrhagic fever virus ( CCHFV ) is the causative agent of a frequently life-threatening disease ., CCHFV is present in a wide geographic area with potential for expansion ., Moreover , CCHFV segmented genome reassortment leads to new strains with potentially different virulence ., Studying CCHFV is highly necessary , but requires dedicated , resource-intensive , high biosafety and security laboratories ., In part due to the need for high containment , CCHFV studies have been limited , and developing tools to study CCHFV has been difficult ., We report the development of a system that mimics the CCHFV life cycle and produces virus-like particles ( VLPs ) that are similar to CCHFV in cell culture , but do not form infectious CCHFV and therefore do not require the use of special laboratories ., We generated VLPs representing several pathogenic CCHFV strains with robust reporter signal activity ., This allows VLPs to be used in testing cell entry inhibitors against a wide array of CCHFV strains ., In addition , VLPs can be used in a variety of cell lines and in cells directly isolated from humans ., Our results also suggest that the CCHFV strain IbAr10200 , which is commonly used in the laboratory , may not accurately reflect the activity of circulating pathogenic CCHFV strains , as the surface glycoproteins of IbAr10200 confer reduced entry efficiency of VLP into cells derived directly from humans ., In addition , we show that drugs with proven anti-CCHFV properties inhibit VLP activity , and identify a monoclonal antibody that prevents cell entry of VLP made using glycoprotein genes from different , pathogenic CCHFV strains .

journal.ppat.1004499

Restriction of Francisella novicida Genetic Diversity during Infection of the Vector Midgut

Genetic diversity within a single microbial species can lead to infection of hosts with mixtures of pathogen genotypes ., Remarkably , studies across numerous systems have demonstrated that mixed-genotype infections are more common than infections with a single clonal variant 1–5 ., The degree of genotypic diversity , defined here as the number of unique genotypes within a population , has been associated with pathogen transmission rates and virulence 6–9 ., For example , greater numbers of circulating Plasmodium faliciparum genotypes were positively correlated with increased virulence or a greater probability of transmission 6 , 8 ., Competition experiments among Dengue virus serotypes resulted in the more virulent serotype being selected at the expense of less virulent serotypes during both human and mosquito infection 7 ., Additionally , during the early years of West Nile virus circulation in New York , transmission intensity was associated with increases in viral genetic diversity 9 ., The capacity of hosts to sustain multiple pathogen genotypes , and the within-host interactions among co-infecting genotypes , can impact pathogen transmission , virulence , and immune evasion ., However , for pathogens that cycle among multiple host species , especially vector-borne pathogens that cycle between disparate species ( mammals and arthropods ) , the impact of genotypic diversity and genotypic interactions on individual genotype transmission and infection success is largely unknown ., Vector-borne pathogens , which cause diseases of importance for human and animal health , therefore provide a platform to study how genotypic diversity and interactions among genotypes affect colonization of the vector and resulting pathogen transmission ., Genetic diversity is a hallmark of vector-borne pathogens ., Numerous studies have described the circulation and infection of individual hosts or vectors with multiple genotypes of bacterial ( e . g . , Anaplasma sp . , Borrelia sp . ) , viral ( e . g . , West Nile virus , Dengue virus ) or protozoal ( e . g . , Trypanosoma sp . , Plasmodium sp . ) vector-borne pathogens 2 , 5 , 10–17 ., Competition among vector-borne pathogen genotypes within the mammalian host is common , with competitive success frequently achieved by the more virulent genotype 1 , 4 , 18–22 ., For example , in experiments with P . falciparum and B . burgdorferi , the more virulent genotype replicated to greater levels compared to the competitor , resulting in numerical dominance and preferential transmission ., Whether similar genotypic diversity-limiting competition occurs within the arthropod vector is unknown ., Further , most studies examine the interactions of only two genotypes at a time; therefore , whether the degree of pathogen genotypic diversity influences the number of genotypes able to infect individual hosts and particularly individual vectors is similarly unknown ., Similar to other tick-borne bacterial pathogens , natural genetic variation within Francisella tularensis , including subspecies , is well described 23–28 ., For example , using multiple loci variable-number tandem repeat analysis on only two loci , 10 unique F . tularensis genotypes were recovered from ticks; with the most genotypic diversity found in areas with the greatest prevalence of F . tularensis in ticks 23 ., The large degree of circulating genotypic diversity observed in that study was indicative of long-standing enzootic transmission of multiple genotypes 23 ., Additionally , unlike the majority of tick-borne bacterial pathogens which are refractory to genetic manipulation , F . tularensis subsp ., novicida ( herein referred to as F . novicida ) can be genetically manipulated with relative ease , and thus can serve as a powerful model to address broader questions concerning tick-borne bacterial pathogens ., Here , we used a set of differentiable Francisella novicida transposon mutants and Dermacentor andersoni ticks , which are a natural vector of Francisella sp ., 29 , to investigate how genotypic diversity affects the success of individual genotypes in colonizing the tick vector as compared to the mammalian host ., Specifically , we determined, ( i ) if similar numbers of genotypes were able to co-infect mice and ticks ,, ( ii ) whether exposure of hosts and vectors to differing numbers of genotypes affected the proportion of genotypes able to be recovered from the host or vector , and, ( iii ) if competition limits the ability of certain genotypes to colonize the vector ., To address these questions , pools of F . novicida genotypes of varying diversity were inoculated into mice ., The genotypes able to infect mice , be acquired by feeding D . andersoni nymphs , and persist in the tick midgut through the molt to the adult stage at population and individual host and vector levels were identified ., As the tick midgut is the primary site of colonization for most tick-borne pathogens , it serves as a relevant location to examine the effects that varying genotypic diversity has on individual genotype transmission success between host and vector 30 , 31 ., Finally , we designed a population model to demonstrate how variations in pathogen genotypic diversity , vector and host abundance , and vector-to-host ratios could influence the retention of genotypic diversity in a pathogen population over time ., We first determined whether the breadth of pathogen genotypic diversity is similarly sustained among mice and ticks at a population level ., In all experiments ‘genotypic diversity’ refers to the number of different genotypes , the ‘vector’ refers to the tick and the ‘host’ refers to the mouse ., The genotypes ‘available’ to colonize mice and ticks will refer to those genotypes that were inoculated into mice and those genotypes that were detected in terminal mouse blood during peak bacteremia , respectively ., Our experiments were initiated by infection of mice , instead of ticks , because of the difficulty and more importantly the variability of artificially infecting ticks ., To simulate diverse genotype populations we used differentiable F . novicida transposon-containing genotypes in two large pools ( Pool A\u200a=\u200a93 genotypes , Pool B\u200a=\u200a94 genotypes ) each comprised of a different set of F . novicida transposon-containing genotypes ( Table S1 ) ., Genotypes were identified in mouse blood at peak bacteremia ( concurrent with completion of nymph feeding ) and in adult tick midguts ., Ticks fed as nymphs on infected mice over the entire duration of mouse bacteremia and genotypes were identified from the midgut of ticks after the infected nymphs molted to adults ., This time point was specifically chosen to avoid detection of genotypes present in the undigested blood meal and confirm that any detected genotype ( s ) were able to infect and be transstadially maintained in the tick midgut ., One limitation of this approach is that we were unable to determine if genotypic diversity was lost prior to or during early infection of the midgut or during transstadial transmission ., Our readout of genotype success is colonization of the adult tick midgut , a time point which reflects the cumulative loss of genotypic diversity at any prior point during tick infection ., Of the genotypes present in the large-pools , 84% and 81% of Pool A and Pool B genotypes were recovered from their respective mouse cohorts ( Table 1 ) ., As these large pools encompassed genotypes with variable fitness , it was expected that some genotypes would not be recovered ., Of the genotypes that successfully colonized mice , 76% and 54% of genotypes from Pool A and Pool B , respectively , were also acquired by the feeding nymph cohort and transstadially maintained in tick midguts ( Table 1 ) ., The percentage of genotypes recovered from large-pools was significantly lower for ticks compared to mice ( χ2\u200a=\u200a13 . 5 , P\u200a=\u200a0 . 0002 ) ., These results demonstrate that at a population level , despite simultaneous exposure to a large number of genotypes , not all available genotypes colonize mice and ticks ., The inability of some in vitro generated genotypes to colonize mice was expected given the presence of the introduced transposon; however , the results also suggested additional loss of genotype diversity upon infection of the tick cohort ., To determine whether reducing genotypic diversity affected the recovery of genotypes from ticks during mixed-genotype infections , genotypes from pools A and B that had successfully infected mice but were not recovered from ticks were divided into three smaller pools ( Pool C\u200a=\u200a16 genotypes , Pool D\u200a=\u200a17 genotypes , Pool E\u200a=\u200a16 genotypes ) and the experiment was repeated ( Table S1 ) ., As expected , all of the genotypes in the small pools ( Pools C–E ) were recovered from their respective mouse cohorts ( Table 1 ) ., Interestingly , 81 , 88 , and 94% of genotypes the from small-genotype pools C , D , and E , respectively , were recovered from their respective tick cohorts despite not being recovered from ticks during the large-genotype pool experiments ( Table 1 ) ., Similar to the large-pools , the percentage of genotypes recovered from ticks was significantly lower compared to mice ( χ2\u200a=\u200a6 . 39 , P\u200a=\u200a0 . 012 ) ., In summary , at a population level , a smaller proportion of available genotypes were recovered from ticks as compared to the mammalian host irrespective of the size of the genotype pool ., Further , a greater proportion of available genotypes were recovered from ticks when genotypic diversity was reduced ( χ2\u200a=\u200a9 . 30 , P\u200a=\u200a0 . 0023 ) ., These results support that at a population level , F . novicida genotype diversity is not equally sustained by mammalian hosts and tick vectors , and suggests that the latter serve as greater ecological filters for F . novicida diversity ., To determine if the observation that the greater reduction in genotypic diversity in the vector population compared to the mammalian host population was also reflected at the level of an individual , we identified the F . novicida genotype ( s ) that colonized individual mice and ticks ., For example , if 59 genotypes were recovered from the population of ticks that fed upon mice inoculated with 93 genotypes in Pool A , we determined whether an individual tick was colonized by all or subsets of those 59 genotypes ., In the large-genotype pool experiments , individual mice were colonized by a significantly greater percentage of the available genotypes ( 78 and 53% of the available genotypes in pools A and B , respectively , colonized individual mice ) compared with individual ticks ( 12 and 10% of the available genotypes in pools A and B , respectively , colonized individual ticks ) ( χ2\u200a=\u200a707 . 4 , P<0 . 001 ) ( Figure 1 ) ., With regard to ticks in the large-genotype pool experiments , ticks were exposed to a mean of 62 genotypes while feeding on infected mice , and individual ticks were colonized with a mean of 8 . 5 genotypes ( range\u200a=\u200a1 to 25 , median\u200a=\u200a6 . 5 ) ( Figure S1 ) ., These results indicate that the observed genotype diversity sustained by ticks at a population level was the cumulative product of individual ticks infected with subsets of the available genotypes ., To determine if reducing genotypic diversity affected the overall number or proportion of genotypes recovered we identified the genotypes that colonized individual mice and ticks from the small-genotype pool experiments ., Similar to the large-genotype pool experiments , a significantly smaller proportion of the available genotypes colonized individual ticks ( 23 , 29 , and 21% from Pools C–E , respectively ) compared to individual mice ( 100 , 82 , and 100% from Pools C–E , respectively ) ( χ2\u200a=\u200a227 . 5 , P<0 . 0001 ) in the small-genotype pool experiments ( Figure 1 ) ., In the small-genotype pool experiments overall , ticks were exposed to a mean of 14 . 3 genotypes and individual ticks were colonized by a mean of 4 genotypes ( range\u200a=\u200a1 to 11 , median\u200a=\u200a3 . 5 ) ( Figure S1 ) ., Examining genotype recovery from individual mice and ticks supported the population level genotype recovery results , and demonstrate that genotype diversity is most severely constrained in the tick ., Further , the degree of genotypic diversity influenced both the mean number and proportion of genotypes that colonized ticks ., Ticks exposed to more diverse F . novicida populations were colonized by a greater total number of genotypes ( Z\u200a=\u200a2 . 14 , P\u200a=\u200a0 . 033 ) , but a smaller proportion of the available genotypes ( χ2\u200a=\u200a44 . 8 , P<0 . 0001 ) as compared to ticks exposed to less diverse genotype populations ( Figure 1 , S1 ) ., To determine if the low number of genotypes colonizing ticks compared to mice was the result of a few dominating genotypes , the number of times each genotype was recovered from each tick and mouse was quantified ., In general , individual genotypes were recovered from a greater proportion of mice than ticks ( Figure S2 , S3 ) ., On average an individual genotype was recovered from significantly fewer ticks in large pools ( 11% ) compared to small pools ( 24% ) ( χ2\u200a=\u200a871 . 1 , P<0 . 001 ) ., Thus the reduction in genotype diversity during tick infection was not the result of a small subset of genotypes infecting ticks at a greater frequency ., Importantly , identification of different genotype combinations from individual ticks that fed upon similarly infected mice indicated that ticks were exposed to a wider array of genotypes then those that were recovered from an individual tick ., Further , since ticks fed on mice during their entire duration of bacteremia ( approximately 3 days ) , ticks were likely exposed to all or most or the genotypes identified in the terminal mouse blood ., Therefore , the decreased genotype diversity observed in ticks is unlikely to be due to limited sampling opportunities or exposure to a limited number of genotypes ., The reduction in F . novicida genotypic diversity upon infection of ticks at both the population and individual level may reflect competition among genotypes ., Alternatively , this reduction in diversity may be due to the inability of specific genotypes to infect the tick ., To test these hypotheses , the only six genotypes ( Genotype 1–6 , Table S4 ) that were consistently recovered from mice but absent from ticks in pooled genotype experiments were further explored ., First , we determined if each of these six genotypes , when inoculated individually into mice , were able to colonize feeding ticks ., All six genotypes colonized both mice and ticks at infection levels ( CFU/ml mouse blood or tick midgut ) similar to wild-type with the exception of Genotype 3 that failed to colonize infect ticks ( Figure 2A , B ) ( F5 , 40\u200a=\u200a0 . 88 , P\u200a=\u200a0 . 50 ) ., Moreover , with the exception of Genotypes 3 , the other genotypes were recovered from a similar proportion of ticks as wild-type ( P>0 . 30 for all comparisons ) ( Figure 2C ) ., As all of these genotypes , except Genotype 3 , were competent to infect ticks , each was examined in 1∶1 competition experiments with wild-type to determine if a single additional genotype wild-type produced sufficient competition to result in competitive exclusion or suppression of the genotype of interest ., In addition to wild-type , the competing genotype in all competition experiments was recovered from the terminal mouse blood ( 1 . 1×106 , 2 . 1×107 , 2 . 3×103 , 1 . 9×105 , 4 . 0×106 , and 1 . 6×105 CFU/ml blood for Genotypes 1–6 , respectively ) , thus confirming that ticks were exposed to the genotype of interest during feeding ., The mean wild-type bacterial level recovered from terminal blood during competition with individual genotypes was 1 . 6×107 cfu/ml blood ., During competition with wild-type , Genotypes 3 , 4 and 6 , which had the lowest bacteremia in mice , failed to colonize ticks ( Figure 3A ) ., The absence of Genotype 3 in ticks during competition was expected as , when alone , it resulted in a low bacteremia in mice and was not recovered from ticks ( Figure 2 ) ., The absence of Genotypes 4 and 6 during competition with wild-type is indicative of competitive exclusion as these genotypes , when alone , had similar infection levels in mice and ticks compared to wild-type ., When examined individually , both Genotype 4 and 6 were similar to wild-type in terms of both percent infected ticks ( χ2\u200a=\u200a1 . 05 , P\u200a=\u200a0 . 30 for both comparisons ) and midgut infection level ( F2 , 26\u200a=\u200a0 . 18 , P\u200a=\u200a0 . 83 ) ( Figure 2B , 2C ) ., Genotypes 1 , 2 , and 5 were able to colonize ticks during competition with wild-type ( Figure 3 ) ; however , a smaller percentage of ticks were colonized by these genotypes compared with wild-type ( χ2\u200a=\u200a3 . 60 , P\u200a=\u200a0 . 058 , χ2\u200a=\u200a3 . 81 , P\u200a=\u200a0 . 051 , χ2\u200a=\u200a3 . 53 , P\u200a=\u200a0 . 060 for genotypes 1 , 2 , and 5 , respectively ) ., In ticks colonized by Genotypes 1 , 2 , or 5 , colonization by wild-type was also observed ., Although wild-type could exclude Genotypes 1 , 2 , or 5 in individual ticks , none of these three genotypes excluded wild-type ., Moreover , Genotypes 1 and 2 established significantly lower infection levels in the tick midgut compared to wild-type indicating that these two genotypes were competitively suppressed by wild-type ( Figure 3B ) for Genotype 1 and wild-type , t13\u200a=\u200a2 . 59 , P\u200a=\u200a0 . 023; for Genotypes 2 and wild-type , ( t12\u200a=\u200a3 . 87 , P\u200a=\u200a0 . 0022 ) ., Interestingly and despite a lower colonization prevalence compared to wild-type , Genotype 5 achieved infection levels in the tick midgut similar to wild-type ( Figure 3 ) ( t15\u200a=\u200a0 . 42 , P\u200a=\u200a0 . 68 ) ., As a control to demonstrate that wild-type specifically out-competed Genotypes 1–6 , we performed a 1∶1 competition assay with wild-type and Genotype 7 , which has a transposon in a non-coding region ( isftu-2 ) , and behaves similarly to wild-type in both mice and ticks 29 ., In the terminal mouse blood the bacterial levels for wild-type and Genotype 7 were 9 . 3×106 and 7 . 0×106 CFU/ml blood , respectively , confirming ticks were exposed to both genotypes ., Equal proportions of ticks were colonized by Genotype 7 and wild-type together , Genotype 7 alone , and wild-type alone ., In ticks that were co-infected , both Genotype 7 and wild-type achieved similar infection levels in the tick midgut ( Figure 3 ) ( t6\u200a=\u200a0 . 25 , P\u200a=\u200a0 . 81 ) ., The equal success of Genotype 7 and wild-type in colonizing ticks during competition with one another demonstrated that Genotypes 1–6 were diminished or excluded due to competition rather than random effects ., In summary , these results indicate that Genotypes 1–6 have a fitness disadvantage in the vector as compared to wild-type as co-infection of any of these genotypes with wild-type results in their competitive exclusion ( e . g . , Genotype 3 , 4 , and 6 ) or competitive suppression ( e . g . , Genotypes 1 , 2 , and 5 ) ., This demonstrates that co-infection with a single , more fit genotype is sufficient to alter the success of the competing genotype even if the less fit competitor is competent upon single-infection ., Further , both competitive suppression and competitive exclusion offer explanations for the loss of genotypic diversity observed during pathogen infection of ticks ., Our experiments suggest that pathogen genotypic diversity is restricted within the tick vector at both population and individual levels ., This restriction in diversity is most pronounced within individual ticks , suggesting that the abundance of ticks will strongly affect pathogen genotypic diversity within an environment ., To further explore how variations in vector and host populations influence pathogen genotypic diversity , we developed a simple population model that incorporated data from our experiments ., The model contained separate functions for vectors ( ticks ) , hosts ( mice ) , and pathogens ( Francisella genotypes ) ( Figure S4 ) ., We used this model to investigate how vector-to-host ratios , vector and host abundance , and the initial number of pathogen genotypes within a population influenced the overall maintenance of genotype diversity in the population ., With all model conditions , individual mice harbored greater pathogen genotypic diversity than ticks ( Figure 4 , S5 , S6 ) ., Thus , rare pathogen genotypes were more likely to be lost from the vector population than from the mammalian host population ., At the population level , vector-to-host ratios strongly influenced the retention of pathogen genotypic diversity ( Figure 4 ) ., When vector densities declined and vector-to-host ratios approached 1 , pathogen genotypic diversity rapidly declined as individual genotypes were lost from the system ., In contrast , high vector-to-host ratios increased the retention of genotypic diversity because the filtering effects of individual ticks were reduced due to large population sizes ( Figure 4 ) ., Variation in vector or host abundance did not influence pathogen genotypic diversity as strongly as vector-to-host ratios; however , in general , larger vector and host populations led to greater maintenance of pathogen genotypic diversity ( Figure S5 ) ., Initial pathogen genotypic diversity also influenced the number of pathogen genotypes maintained in the vector and host populations ( Figure S6 ) ., Not surprisingly , both vectors and hosts individually harbored more pathogen genotypes when the number of initial genotypes was greater ., However , the proportion of genotypes in the population infecting individual vectors and hosts declined with greater initial pathogen genotypic diversity ( Figure S6 ) as observed in our experiments with large- and small-genotype pools ( Figure 2 ) ., Thus , the model showed a trade-off between the raw number of pathogen genotypes that infected individual vectors and hosts and the proportion of the pathogen genotype population they represented ., Our results demonstrate that pathogen genotypic diversity is restricted to a greater degree in the tick vector as compared to the mammalian host ., Moreover , the extent to which hosts and vectors contribute to the maintenance of pathogen genotypic diversity is influenced by the initial degree of genotypic diversity in the pathogen population and competition among genotypes during infection of the vector ., Within a host or vector , competitive interactions among genotypes can result in the reduction or elimination of one or more genotypes ., Studies on co-infecting Plasmodium genotypes illustrate how a more virulent genotype can competitively suppress or prevent a less virulent genotype from being transmitted between the mammalian host and mosquito vector 32–35 ., Additionally , studies on arboviruses such as West Nile virus and Dengue virus have demonstrated that genotypic diversity can be correlated with transmissibility or virulence 7 , 9 ., Similar to our results , intrahost examination of West Nile virus revealed that viral genetic diversity was restricted in mosquito midguts compared to the input pool 36 , 37 ., Interestingly , however , despite a reduction of viral diversity in the mosquito midgut , corresponding salivary samples were similar in diversity to the input pool , perhaps contributed to accumulation of mutations as a result of relaxed purifying selection during infection of the mosquito 36 ., In this study , F . novicida genotype diversity was not equally sustained by mice and ticks , and the greatest restriction in genotypic diversity occurred in individual ticks ., This reduction in diversity was mediated by a combination of both stochastic and selective forces , and was unlikely to be an artifact of tick feeding ., Based on our results , despite exposure to a large array of mutants , individual ticks were not able to support the same number of mutants as mice ., One possible reason for genotypic restriction is that resources for bacterial colonization , such as nutrient availability or receptors for cell entry , are more limited in ticks than in mice , which could lead to competition among genotypes for limited resources ., Several lines of evidence suggest that strong competition among genotypes occurred in ticks ., First , individual ticks that fed upon the same mouse infected with up to 94 genotypes were colonized by different combinations of genotypes ., Second , five genotypes not recovered from ticks during pooled-genotype experiments were competent to colonize ticks , in most cases to wild-type levels , in the absence of a second genotype ., Third , in competition assays with wild-type and a wild-type-like genotype ( Genotype 7 ) , both were equally able to compete and colonize ticks , which further indicated that the absence of Genotypes 1–6 from the pooled-genotype experiments was not random ., Our experimental design allowed us to examine the genotypic diversity that was sustained by ticks from the genotypes present during the nymphal blood meals to recovery of genotypes from adult tick midguts ., This period of time encompassed several points where genotypic diversity could have been lost in the tick midgut including during initial entry into the nymph midgut , early replication and colonization events in the nymph midgut , transstadial transmission from nymph to adult , or continued colonization in the adult midgut ., Although our results clearly demonstrate that competition is occurring among F . novicida genotypes during infection of the tick vector , it is interesting to note that a previous study speculated that facilitative interactions among genotypes in mixed B . burgdorferi genotype infections conferred an advantage for the bacteria to establish and maintain infection in ticks 15 , 38 ., It is possible that such interactions may occur in this system ., Additional variables that could further influence competition among genotypes and contribute to the observed reduction in genotype diversity in ticks include the infection level for an individual genotype , transmission priority ( the order in which genotypes are transmitted ) , and genotype fitness ., With regard to the latter two variables , our results indicated that reduction in fitness can in some instances overwhelm the stochastic forces that dictate tick infection by pathogen genotypes 29 , 39 , 40 ., Overall , F . novicida bacterial levels did not vary based on genotype diversity and were similar to previously reported single-genotype infection levels 29 ., This suggests that ticks have an infection threshold limit for F . novicida , such that as the number of genotypes a tick is exposed to increases , the maximum infection level of any individual genotype is proportionately reduced 29 ., Therefore , genotypes that are able to replicate first will have a greater opportunity to colonize the tick while reducing the amount of available resources for incoming genotypes ( founder effect ) 12 ., Additionally , greater numerical success in one host or vector will confer a greater probability of subsequent transmission ., The transmission priority of genotypes between mice and ticks was stochastic , such that ticks had an opportunity to acquire the genotypes that colonized mice relative to the genotype-specific infection level in mice ., Transmission priority is potentially important if resources are more limited within the tick and monopolized by genotypes on a “first come , first serve” basis ., In most pooled-genotype experiments , genotype recovery was random and ticks were colonized by small subsets of the available genotypes in different combinations ., Although we strived to initiate our pooled-genotype experiments with equal ratios of genotypes , four genotypes in Pool B were recovered from a greater percentage of mice and ticks implying that they had a numerical advantage in the initial inoculum , maintained that advantage while colonizing mice and were available at a greater frequency for feeding ticks to acquire ( Figure S2 ) ., These four genotypes , which were recovered from a greater percentage of ticks than the other genotypes comprising Pool B , provide evidence for genotypes with an initial advantage having greater transmission and colonization success ., Importantly , although these four genotypes were identified in a greater percentage of ticks , they were not the sole genotypes observed and were commonly identified in individual ticks with less frequently occurring genotypes ., These results are similar to those of a Trypanosoma brucei study and recently a B . burgdorferi study where vector acquisition of genotypes from mice , infected with multiple , similarly fit pathogen genotypes , was noted as random and the first genotype able to infect an individual vector had an advantage during dissemination to other tissues and in subsequent transmission 12 , 41 ., Stochastic forces also play a prominent role in shaping arboviral transmission , and has been demonstrated for West Nile Virus and Venezuelan equine encephalitis virus 42 , 43 ., Genotype fitness can influence competitive ability as well as virulence as demonstrated by co-infection studies using genotypes with known fitness differences 33–35 ., A range of fitness among the F . novicida genotypes examined was expected , depending on the location of the transposon ., The overall genetic similarly of genotype populations suggests that the majority likely shared similar abilities to infect mice and ticks ( Table 1 ) ., We surmised that the six genotypes absent from ticks in the pooled-genotype experiments were out-competed ., This postulation was supported by the results of the 1∶1 competition assays between wild-type and Genotypes 1–6 , where wild-type succeeded disproportionately in terms of infection prevalence and infection load compared to the competing genotype ., The competition assay between wild-type and Genotype 7 confirmed that if Genotypes 1–6 had been similarly fit as wild-type , they would have succeeded to a similar extent as Genotype 7 did during competition with wild-type ., The finding that Genotypes 1–6 were able to colonize ticks during single-genotype experiments but not in during competition with more fit genotypes supported the notion that the location of the transposon in these genotypes exacts some fitness cost , although the exact mechanism by which this is occurring remains unknown ., In the field , genotypic diversity is likely to be dynamic and heavily influenced by environmental variables ., Genotypic diversity , when measured , generally occurs as insertions , deletions , and polymorphisms in individual and small numbers of nucleotides 44–46 ., Additionally , gene duplications and deletions do occur 47 ., While insertions are over-represented in our population , the use of naturally occurring genotypes is not possible , as a collection of greater than 150 different genotypes that can easily be distinguished one from another do not exist for any tick borne bacterial pathogen ., Importantly , the alterations in phenotype in our population are likely highly variable and represent a broad spectrum , from complete knock-out of gene function to no alteration in gene function ., Thus , while the type of genetic mutation represented in our population is limited as compared to a natural population , a broad spectrum of alterations in phenotype is likely to be represented ., Further , in our experiments more mutational robustness was observed in the vertebrate , however , within a host infected with naturally occurring genotypes those genotypes could possess very different fitness abilities , thus altering the outcome of within-host interactions and ongoing transmission ., To extrapolate our results to a broader range of field scenarios we created a model to explore how variations in vector-to-host ratio , vector and host abundance , and initial pathogen genotypic diversity affected the retention of

Introduction, Results, Discussion, Materials and Methods

The genetic diversity of pathogens , and interactions between genotypes , can strongly influence pathogen phenotypes such as transmissibility and virulence ., For vector-borne pathogens , both mammalian hosts and arthropod vectors may limit pathogen genotypic diversity ( number of unique genotypes circulating in an area ) by preventing infection or transmission of particular genotypes ., Mammalian hosts often act as “ecological filters” for pathogen diversity , where novel variants are frequently eliminated because of stochastic events or fitness costs ., However , whether vectors can serve a similar role in limiting pathogen diversity is less clear ., Here we show using Francisella novicida and a natural tick vector of Francisella spp ., ( Dermacentor andersoni ) , that the tick vector acted as a stronger ecological filter for pathogen diversity compared to the mammalian host ., When both mice and ticks were exposed to mixtures of F . novicida genotypes , significantly fewer genotypes co-colonized ticks compared to mice ., In both ticks and mice , increased genotypic diversity negatively affected the recovery of available genotypes ., Competition among genotypes contributed to the reduction of diversity during infection of the tick midgut , as genotypes not recovered from tick midguts during mixed genotype infections were recovered from tick midguts during individual genotype infection ., Mediated by stochastic and selective forces , pathogen genotype diversity was markedly reduced in the tick ., We incorporated our experimental results into a model to demonstrate how vector population dynamics , especially vector-to-host ratio , strongly affected pathogen genotypic diversity in a population over time ., Understanding pathogen genotypic population dynamics will aid in identification of the variables that most strongly affect pathogen transmission and disease ecology .

Co-infection , the presence of multiple genotypes of the same pathogen species within an infected individual , is common ., Genotype diversity , defined as the number of unique genotypes , and the interaction between genotypes , can strongly influence virulence and pathogen transmission ., Understanding how genotypic diversity affects transmission of pathogens that naturally cycle among disparate hosts , such as vector-borne pathogens , is especially important as the capacity of the host and vector to sustain genotypic diversity may differ ., To address this , we exposed Dermacentor andersoni ticks , via infected mice , to variably diverse populations of Francisella novicida genotypes ., Interestingly , we found that ticks served as greater ecological filters for genotypic diversity compared to mice ., This loss in genotypic diversity was due to both stochastic and selective forces ., Based on these data and a model , we determined that high numbers of ticks in an environment support high genotypic diversity , while genotypic diversity will be lost rapidly in environments with low tick numbers ., Together , these results provide evidence that vector population dynamics , vector-to-host ratios , and competition among pathogen genotypes play critical roles in the maintenance of pathogen genotypic diversity .

medicine and health sciences, vector-borne diseases, microbiology, bacterial diseases, population modeling, bacterial pathogens, veterinary science, animal models of infection, infectious diseases, veterinary diseases, zoonoses, veterinary microbiology, medical microbiology, microbial pathogens, infectious disease modeling, francisella tularensis, rabbit fever, biology and life sciences, computational biology, tularemia

journal.pgen.1000721

Evolutionary Convergence and Nitrogen Metabolism in Blattabacterium strain Bge, Primary Endosymbiont of the Cockroach Blattella germanica

In 1887 , Blochmann first described symbiotic bacteria in the fatty tissue of blattids 1 ., Later , Buchner 2 suggested that symbionts are involved in the decomposition of metabolic end-products from the insect host ., A classic example is the cockroach ., Several pioneering studies correlated the presence of cockroach endosymbionts with the metabolism of sulfate and amino acids 3 , 4 ., These endosymbionts were classified as a genus Blattabacterium 4 , belonging to the class Flavobacteria in the phylum Bacteroidetes 5 and they live in specialized cells in the host’s abdominal fat body ., Apart from cockroaches , they were only found in the primitive termite Mastotermes darwiniensis 6 ., Phylogenetic analyses for the Blattabacterium-cockroach symbiosis supported the hypothesis of co-evolution between symbionts and hosts dating back to an ancient feature of more than 140 million years ago 7 , 8 ., Recently , genome sizes of the Blattabacterium symbionts of three cockroach species , B . germanica , Periplaneta americana , and Blatta orientalis were determined by pulsed field gel electrophoresis as approximately 650±15 kb 9 ., Similarly , the authors demonstrated the sole presence of Blattabacterium strains in the fat body of those cockroach species by rRNA-targeting techniques ., Phylogenetic analyses based on 16S rDNA also confirmed the affiliation of these endosymbionts to the class Flavobacteria 9 ., Therefore , they are phylogenetically quite distinct from the majority of intensively studied insect endosymbionts that belong to the phylum Proteobacteria , mainly class Gamma-Proteobacteria ., Recently , the highly reduced genome of “Candidatus Sulcia muelleri” ( from now S . muelleri ) , an insect endosymbiont belonging to the class Flavobacteria has been also completely sequenced 10 ., Primary endosymbionts such as Buchnera aphidicola or Wigglesworthia glossinidia complement the metabolic capacity of aphids or tsetse flies , respectively that feed on different nutrient-deficient diets 11 ., There are also examples of metabolic complementation between two co-primary endosymbionts and their hosts ., This is the case of S . muelleri , living in the sharpshooter Homalodisca vitripennis , which coexists with another Gamma-Proteobacteria endosymbiont , “Candidatus Baumannia cicadellinicola” ( hereafter B . cicadellinicola ) ., Both have developed a metabolic complementation to supply the host with the nutrients lacking in the limited xylem diet 12 ., Another example is the case of B . aphidicola and “Candidatus Serratia symbiotica” , co-primary endosymbionts of the cedar aphid Cinara cedri that complement each other in the provision of essential nutrients 13 , 14 ., Omnivorous insects also harbor endosymbionts ., It is the case , for example , of ants of the genus Camponotus and their primary endosymbionts , the Gamma-Proteobacteria “Candidatus Blochmannia floridanus” 15 and “Candidatus Blochmannia pennsylvanicus” 16 ( from now B . floridanus and B . pennsylvanicus , respectively ) ., In this association endosymbionts play an important role in nitrogen recycling 17 ., Evolutionary convergences are generally considered as evidence of evolutionary adaptation ., The study of endosymbiont evolution could provide examples of evolutionary convergences if we were able to show that very distant phylogenetic groups present similar functional repertoires and metabolic capabilities when they have evolved endosymbiosis in organisms having similar feeding behaviors ., This may be the case of Blochmannia ( a gamma-proteobacterium ) and Blattabacterium ( a flavobacterium ) that have independently evolved in carpenter ants and cockroaches , two omnivorous insects ., In this study , we determine the genome sequence of an endosymbiotic flavobacterium , Blattabacterium strain Bge , primary endosymbiont of the German cockroach B . germanica ., We have also inferred the metabolism to try to understand why cockroaches excrete ammonia , instead of being uricotelic like other terrestrial invertebrates , thus breaking the so-called “Needhams rule” 18 , a question that has puzzled physiologists for a long time ., Finally , we compare the inferred metabolism with the corresponding one of B . floridanus , the primary endosymbiont involved in nitrogen recycling in the carpenter ant Camponotus floridanus , an insect that has also a complex diet ., The general features of the genome of Blattabacterium strain Bge ( CP001487 ) and their comparison with those of other selected bacteria are shown in Table 1 ., The size of the circular chromosome is 637 kb , and the G+C content is 27 . 1% ., Only 23 . 4 kb are not-coding and they are distributed in 480 intergenic regions with an average length of 49 bp ., The overall coding density ( 96 . 3% ) is the highest among insect endosymbionts known to date , indicating a highly compact genome ., It is surprisingly higher than the most reduced insect endosymbiont “Candidatus Carsonella ruddii” ( 93 . 4% ) 19 ., In addition , 1 . 5 kb correspond to 139 overlapping regions with an average length of 11 bp ., Of these overlaps , 94 ( 67 . 6% ) are between genes on the same strand and 1 to 70 bp long ., The other 45 cases ( 32 . 4% ) involve two genes on opposite strands and are between 2 and 50 bp long ., Of these , only in one case the two genes overlap with their start regions , whereas in the rest the overlap is in the terminal region of the genes ., On the other hand , in “Ca Carsonella ruddii” 92% of the 126 overlaps are in tandem orientation , and thus on the same strand , and only five cases are between opposite strands , involving the termini and starts of the overlapping genes ., Assembly of the pyrosequencing data gave highly reliable contigs that combined with the data from Sanger sequencing resulting in a single contig , representing the entire genome ., Probably due to the formation of a secondary structure , only a 33 bp stretch in an intergenic region upstream of the GroEL gene was not covered by pyrosequencing data but only by Sanger reads ., Furthermore , annotation of the ORFs allowed a clear assignation of protein functions even in cases with only weak similarities with existing database entries ., Not a single case of a possible host gene incorporated in the symbiont genome was found ., Neither had we found coding sequences affiliated with Blattabacterium strain Bge outside the genome that could have been assigned to the host genome ., A total of 627 putative genes have been assigned ( Figure S1 ) , 586 of which are protein coding genes ( CDS ) , 40 are RNA-specifying genes ( 34 tRNAs , 3 rRNAs located in a single operon , one tmRNA , and the RNA components of RNase P and the Signal recognition particle ) ., The only pseudogene found corresponds to the protein component of RNase P . This gene coding for 118 amino acids is disrupted by an in-frame stop codon at amino acid position 53 ., The RNase P proteins of the free-living F . psychrophilum 20 , Flavobacterium johnsoniae ( http://genome . jgi-psf . org/flajo/flajo . info . html ) and Gramella forsetii 21 contain a lysine residue at that position ., Therefore , it is possible that the stop codon has been generated by an A–T point mutation in position 157 of the nucleotide sequence ., Despite this mutation , the RNase P could be functional as it has been described that in vitro the RNA component can act enzymatically without a functional protein component 22 ., Regarding the coding genes , it is interesting that , despite the compactness of the genome , there are eight gene duplicates: miaB , rodA , serC , lpdA , ppiC , argD , hemD , and uvrD ., No specific sequence of the origin of replication ( oriC ) , such as dnaA boxes , was found in the genome 23 ., Likewise dnaA , which codes for the protein that initiates replication by binding to such sequences , was also absent ., Thus , the putative origin of replication was determined by GC skew analysis ., The transitional region where the GC skew changes from negative to positive one ( Figure S2 ) showed the position of replication origin to be in the gene dapB ., It is worth mentioning that neither dnaA nor any of the genes normally adjacent to the replication site in bacteria ( dnaN , hemE , gidA , hemE , and parA ) have been found in this genome ., However , Blattabacterium strain Bge , has retained recA , which could trigger replication by an alternative mechanism 15 , 23 ., We have inferred the metabolism of Blattabacterium strain Bge from its complete genome ( Figure 1 ) ., Blattabacterium strain Bge possesses a limited capacity for nutrient uptake with only one ABC-type transport system , which may be specialized in fructose transport because this bacterium , contrary to the other sequenced endosymbionts , seems unable to use glucose as a nutrient ., On the other hand , Blattabacterium strain Bge also codes for a glycerol uptake facilitator that enables transport of solutes , such as O2 , CO2 , NH3 , glycerol , urea , and water ., Therefore , it is possible that Blattabacterium strain Bge obtains carbon from glycerol as a supplementary source ., A sodium/drug antiporter , NorM , is also encoded by this genome ., This system of efflux drug transport is common among enterobacteria but not among flavobacteria ., In this group it is only known for the free-living bacteria F . psychrophilum and G . forsetii ., This system can act as a multidrug transport as well as transporting oligosaccharidyl lipids and polysaccharide compounds ., There is an array of metal ion homeostasis transporters ., In Blattabacterium strain Bge , there is a Trk transport system , a uniporter of the monovalent potassium cation , which requires a proton motive force and ATP in order to function ., Only W . glossinidia has a similar transport system , although the encoded subunits differ: trkA and trkB in Blattabacterium; trkA and trkH in W . glossinidia ., Other solutes are also transported by symport systems ., Blattabacterium strain Bge is able to uptake glutamate and aspartate via a proton symporter ., Both metabolites play an important role in the metabolism of this bacterium ( see below ) ., A phosphate/sodium symporter is also present ., Regarding electron transport , the encoded NADH-dehydrogenase ( ndh ) oxidizes NADH without proton translocation ., There is also a succinate dehydrogenase ( sdhABD ) ., Electrons are transferred to a membrane-bound menaquinone ( MQ ) and a molybdenum-oxidoreductase , which accepts electrons from the MQ ., With these elements , a proton motive force can be generated ., Blattabacterium strain Bge seems to be able to reduce intracellular sulfate to sulfite ., A number of genes required for sulfur assimilation present in the genome , include those encoding for the two subunits of the sulfate adenylyltransferase , cysN and cysD , the adenosine phosphosulfate ( APS ) reductase cysH and the sulfite reductase proteins cysI , J ., There is a missing step for the conversion of adenosine-5′-phosphosulfate ( APS ) into 3′-phospho adenosine-5′-phosphosulfate ( PAPS ) ., The generated sulfite is reduced to sulfide further on and assimilated into the sulfur-containing amino acids L-cysteine and L-methionine ., Blattabacterium strain Bge is able to synthesize its own cell wall and plasma membrane ., However , it has lost the entire pathway required for lipopolysacharide ( LPS ) biosynthesis , like all sequenced Buchnera strains and B . cicadenillicola ., This property explains why Blattabacterium strain Bge , similarly to these bacteria , are surrounded by a host vacuolar membrane , as shown in the electron-microscopy images ( Figure S3 ) ., Regarding amino acid biosynthesis , Blattabacterium strain Bge has the genes encoding biosynthetic enzymes needed to synthesize 10 essential ( His , Trp , Phe , Leu , Ile , Val , Lys , Thr , Arg , and Met ) and 7 nonessential ( Gly , Tyr , Cys , Ser , Glu , Asp , and Ala ) amino acids ., Thus , the endosymbiont metabolism relies on Pro , Gln and Asn supplied by the host ., Also present is the complete machinery to synthesize nucleotides , fatty acids , and the cofactors folic acid , lipoic acid , FAD , NAD , pyridoxine , and riboflavin ., Finally , genes encoding enzymes for the synthesis of siroheme and menaquinone were also identified ., With respect to the metabolism of carbohydrates , genome analysis of Blattabacterium strain Bge indicates the presence of a truncated glycolysis pathway , since the genes that encode for phosphofructokinase ( pfkA ) and pyruvate kinase ( pyk ) are missing , as well as any sugar phosphorylating system except for fructose ., Therefore , the pathway begins with fructose-1 phosphate and continues with the canonical enzymatic steps until the synthesis of phosphoenolpyruvate ( PEP ) ., Given the lack of pyruvate kinase genes , Blattabacterium strain Bge must produce pyruvate via the malic enzyme ( NADP+-dependent malate dehydrogenase ) ., Additionally , a complete non-oxidative pentose phosphate pathway is encoded in Blattabacterium strain Bge ., As it is the case with Wigglesworthia , the glycolytic enzymes seem to be involved in gluconeogenesis rather than glycolysis complementing the non-oxidative pentose phosphate pathway 24 ., In summary , although Blattabacterium strain Bge genome shows a strong reduction in gene number in all the functional categories , compared to their free-living relatives ( see below ) , the core of essential functions and pathways is particularly well preserved ., The protein genes of Blattabacterium strain Bge were classified according to COG categories ( Figure 2 , Table 2 ) ., This distribution was compared with those of twelve selected bacteria: four Flavobacteria , which included three free-living species ( F . psychrophilum , F . johnsoniae and G . forsetii ) and the endosymbiont S . muelleri , and eight Proteobacteria endosymbionts , seven Gamma-Proteobacteria ( B . floridanus , B . pennsylvanicus , B . cicadellinicola , B . aphidicola Aps , B . aphidicola Cce , S . glossinidius , and W . glossinidia ) and one Alfa-Proteobacterium ( Wolbachia sp . from Drosophila simulans ) ., Taking the observed distribution of COG categories for Blattabacterium strain Bge as the expected distribution followed by each of the other bacteria examined , the hypothesis of equal distribution was rejected in all but the carpenter ant endosymbionts , Gamma-Proteobacteria B . floridanus and B . pennsylvanicus ( Table 2 ) ., These results suggest that it is the hosts’ diet ( cockroaches and carpenter ants are both omnivores ) rather than phylogenetic closeness which is more strongly linked with the type of genes retained ., This appears to be a clear case of functional evolutionary convergence in a broad sense ., The proximity between the endosymbionts from omnivorous hosts was also confirmed when a dendrogram was created using the matrix of Kulczynski phenetic distances ( Figure 3A ) ., To locate the phylogenetic position of Blattabacterium strain Bge and compare it with the COG-based functional analysis , we used a phylogenetic tree based on 16S rDNA gene sequences ( Figure 3B ) ., As expected , the 16S rDNA gene analysis clearly separate Bacteroidetes from Proteobacteria phyla ., Blattabacterium strain Bge clusters monophyletically within the Bacteroidetes phylum ., The functional clustering differs clearly from the phylogenetic one ., A striking trait of this genome is the presence of a complete urea cycle ( Figure 4 ) ., This feature has been described in few bacteria , and in only one member of the Bacteroidetes phylum , the cellulolytic soil bacterium Cytophaga hutchinsonii 25 ., Moreover , to date , there are no reports of a complete urea cycle in an endosymbiont ., The Blattabacterium strain Bge genome also retains the genes for the catalytic core of urease and we have detected urease activity in endosymbiont-enriched extracts of cockroach fat body ( see below ) ., The genome of Blattabacterium strain Bge has two urease genes , ureAB and ureC , coding for the catalytic subunits , but lacks all genes for the accessory proteins supposedly required to produce an active enzyme in most bacteria ., The ureAB fusion is not a novel situation since fused urease genes have also been described in other bacterial genomes , as it is the case of the free-living Flavobacterium C . hutchinsonii 25 ., Regarding the lack of accessory genes , a similar situation is found in Bacillus subtilis cells expressing urease activity , which are able to grow with urea as sole nitrogen source 26 ., To corroborate the presence of an active urease in Blattabacterium strain Bge , we performed an enzymatic assay on crude extracts of the endosymbiont-enriched fraction of the B . germanica fat body ., Figure S4 shows a representative result for the urease assay ., Although the detected specific activity under our experimental conditions was low ( 2 mU mg−1 protein; 1 U of urease corresponds to the formation of 1 µmol of ammonia per min ) , it was reproducible ., Urease activity was also reproducibly detected in endosymbiont extracts from P . americana fat body ( data not shown ) ., To further study the inferred metabolism in relation to nitrogen economy , we carried out a stoichiometric analysis of the reactions involved in the Krebs and urea cycles as well as other directly related reactions , such as urease , the malic enzyme , and their links to amino acid utilization ( Figure 1 and Figure 4 ) ., Our results strongly suggest a key involvement of the endosymbionts in nitrogen metabolism and excretion in the German cockroach , in addition to their role in providing essential amino acids and coenzymes to the host ., It is also worth mentioning that the endosymbiont metabolism relies on a supply of Gln from the host to cater for all its biosynthetic needs , including the urea cycle ., Stoichiometric analysis shows that eleven out of fourteen elementary modes produce ammonia ( Table S1 ) ., It follows that the metabolic network of Blattabacterium strain Bge could potentially use amino acids efficiently as energy and reducing-power sources , generating nitrogen waste in the form of ammonia ( Figure 4 ) ., Urease genes are also present in the Blochmannia endosymbiont genome 15 and the biochemical function of the urease in the carpenter ant endosymbionts is completely different from Blattabacterium ., Studies of gene expression 27 and feeding experiments with 15N-labelled urea 17 in carpenter ants corroborate the role of urease in the transfer of nitrogen from dietary urea into the hemolymph amino acid pool ., This requires an endosymbiont glutamine synthase to act as an essential step in nitrogen conservation during amino acid anabolism ., Thus , although carpenter ants are omnivorous , their bacterial endosymbionts may upgrade their diet via an efficient nitrogen economy 17 ., German cockroaches are also omnivorous; however , their endosymbionts lack genes encoding a glutamine synthase-like activity , a clear indication that the metabolic function of urease is not the same in the German cockroach and carpenter ant endosymbionts because generated ammonia cannot be re-assimilated ., Therefore , although we have revealed a functional convergence between the cockroach and carpenter ant endosymbionts , which is probably due to their hosts’ omnivorous diets , they differ greatly from a metabolic viewpoint in detail , particularly in terms of nitrogen metabolism ., Traditionally , Blattabacterium endosymbionts have been postulated to be involved in the metabolism of uric acid in cockroaches ., For instance , uric acid accumulation has been observed in aposymbiotic cockroaches 28 , 29 ., Metabolic use of nitrogen derived from fat body urates has been observed in B . germanica under certain conditions ( e . g . , in females on low-protein diet 30 and consumption of empty spermatophores by starved females 31 ) ., Interestingly , fat body endosymbionts have been involved in uric acid degradation to CO2 in experiments with the wood cockroach Parcoblatta fulvescens injected with 14C-hypoxanthine 32 ., Although involvement of gut microbiota cannot be completely ruled out , endosymbiont metabolism seemed more likely 33 ., However , our results show that the endosymbiont genome does not code for any activity related to either the synthesis or the catabolism of urates ., Therefore , and contrary to early reports based on putative cultured endosymbiotic bacteria 29 , Blattabacterium strain Bge cannot participate in the metabolism of this nitrogen compound directly ., Since uricase activity has been detected in the fat body of the cockroach 28 , 34 , 35 , the host could contribute with uric-derived metabolites to the nitrogen economy of the endosymbiont which , in turn , would produce ammonia and carbon dioxide as final catabolic products ., The genome sequencing , metabolic inference , detection of a urease in the endosymbiont and the stoichiometric analysis of the central pathways of Blattabacterium strain Bge shed light on a whole series of hitherto unexplained classical physiological studies on ammonotelism in cockroaches 33 , 36 , 37 ., Contrary to the speculation that some terrestrial invertebrates , like gastropods , annelids 36 and isopods 38 , exploit ammonia excretion as “a return to the cheapest way” 38 to eliminate nitrogen , the case of the German cockroach and its bacterial endosymbionts indicates that this might not be the case ., The evolution of terrestrial-living metazoa has favored the emergence of uricotely ( e . g . the majority of insects ) and ureotely ( e . g . mammals ) as water-saving strategies ., Meanwhile , ammonotely , the ancestral character present in aquatic animals , has classically been considered maladaptive for terrestrial animals 18 ., Symbiosis seems to play a role in this “return” of cockroaches to ammonotely by providing new enzymes required for this new nitrogen metabolism ., Thus the metabolic capabilities acquired by symbiogenesis 39 afford to explore new ecological niches and dietary regimes ., B . germanica ( Blattaria: Blattellidae ) was reared in the Entomology laboratory ( Cavanilles Institute for Biodiversity and Evolutionary Biology , University of Valencia ) ., The cockroaches were kept in the laboratory at 25°C and fed with a mixture of dog food ( 2/3 ) and sucrose ( 1/3 ) ., The bacterial endosymbionts were extracted from the fat body of B . germanica females ., To do so , cockroaches were killed by a 15 to 20 min treatment with ethyl acetate and the bacterial cells were separated from the fat body as in 15 ., An enriched fraction of bacteriocytes is then obtained that is used to extract total DNA following a CTAB ( Cetyltrimethylammonium bromide ) method ., The complete genome sequence of Blattabacterium strain Bge was obtained by a hybrid sequencing approach based on ABI 3730 sequencers and the pyrosequencing system ( 454; Life Science ) ., To construct shotgun libraries , DNA fragments were generated by random mechanical shearing with a sonicator and posterior separation in a pulsed field gel electrophoresis ., Insert sizes of 1–2 kb and 3–5 kb were purified and cloned into vector from XL-TOPO PCR cloning kit ., Plasmid DNA was extracted using 96-well plates ( Millipore ) with the PerkinElmer MULTIPROBE II robot according to the manufacturers ., DNA sequencing was performed on an ABI PRISM 3730 Genetic Analyzer ( Applied Biosystems ) ., In the initial random sequencing phase 9 , 227 sequences were obtained with 1 . 5-fold sequence coverage ., Given the lack of joining between sequences , which may have been due to a large number of sequences from the host , a strict sequence analysis was performed with a specific bioinformatic tool called a Categorizer ., It carries out a sequence classification method based on n-mers composition to correctly distinguish between Blattabacterium strain Bge and contaminating host sequences ., This classifier was trained with sets of sequences identified from Blattabacterium strain Bge and the host ., With these sets , we constructed a feature vector or model representing the 4- to 7-mers usage pattern of each organism ., Then the n-mers composition of each read was compared with these generated models with a k-nearest neighbor clustering algorithm ( KNN ) ., Although the number of retrieved host sequence reads was higher than the one of Blattabacterium strain Bge sequences for both sequencing approaches , the pyrosequencing approach generated enough sequences to close the gaps identified with the first method ., The tool Gap4 from Staden Package 40 was used for the total assembly ., Fat body of B . germanica was isolated and prefixed in a 2 . 5% paraglutaraldehyde fixative mixture buffered with 0 . 1 M phosphate at pH 7 . 2 ( PB ) ., Prefixation was performed at 4°C for 24 h and then rinsed several times in PB ., To avoid the loss of this dispersed tissue , the fat body was placed in agar ( 2% ) forming small blocks ., After prefixation , these blocks were fixed in 2% osmium tetroxide for one hour , dehydrated in graded alcohol and propylene oxide , stained in a saturated uranyl acetate solution 2% and embedded in araldite to form the definitive blocks ., Thin sections ( 0 . 05 µm ) were made using the Reichert-Jung ULTRACUT E ( Leica ) ultramicrotome , and then were stained with uranyl acetate and lead citrate ., A JEOL-JEM 1010 electron microscope was used for the analysis ., The putative coding regions ( CDSs ) in the Blattabacterium strain Bge genome were identified with the GLIMMER3 program 41 ., This program was first trained with closely related organism sequences from the Flavobacteria group ., The coding sequence model obtained was then used by GLIMMER3 to scan the genome to predict potential coding regions by considering the putative existence of initiation codons and ORF length ., Start and stop codons of each putative CDS were curated manually through visual inspection of the Blattabacterium strain Bge Genome Browser , a database specially designed for this symbiont ., The putative coding proteins were initially analyzed by reciprocal best hits to determine orthology between genes of the Blattabacterium and those from bacteria belonging to the Flavobacteria group ., According to these criteria , two genes are orthologs when a gene in one genome matches as the best hit with a gene in the other genome ., Sequences that could not be assigned to any function in comparison with flavobacterial genomes were identified by searching a non-redundant protein database using BLASTX 42 ., Final annotation was performed using BLASTP comparison with proteins in the NCBI and Pfam domains identified using the Sanger Centre Pfam search website ., Non-coding RNAs were identified by different approaches ., The tRNAscan program was used to predict tRNAs , as well as other small RNAs , like tmRNA , the RNA component of the RNase P . Signal Recognition Particle RNA were identified by programs like ARAGORN , BRUCE and SRPscan , as well as consulting the Rfam database 43–45 ., In the absence of a diagnostic cluster of DnaA boxes , the origin of replication was identified by GC-skew calculated as ( C−G ) / ( C+G ) using the program OriginX 46 ., The origin is located in the transitional region where the GC-skew changes from negative to positive values ., The ORFs orthologous to known genes in other species were catalogued based on non-redundant classification schemes , such as COG ( Clusters of Orthologous Groups of Proteins ) ., A metabolic network was reconstructed using the automatic annotator server from KAAS-KEEG 47 ., According to our genome annotation , each pathway was examined checking the BRENDA 48 and EcoCyc databases 49 ., Comparison between the COGs distribution of each species with that of the Blattabacterium strain Bge was carried using chi-square tests ., To avoid the problem of multiple testing , we applied the Bonferroni correction so that for each individual test the significance level was 0 . 05/12\u200a=\u200a0 . 0042 ., That is , if the p-value is lower than 0 . 0042 then the hypothesis is rejected ., The first p-value corresponds to the standard chi-square test ( Chi2 p-value , df\u200a=\u200a19 ) ., Due to the asymptotic nature of this test , expected frequencies should be higher than 5 ., However , we might expect some frequencies with low values ., To correct this situation we also performed a Monte-Carlo version of this test ( MC p-value ) ., We performed 19 , 999 simulations under the null hypothesis , which together with the observed Chi2 statistics constituted a set of 20 , 000 values ., The MC p-value cannot be lower than 1/20 , 000\u200a=\u200a5 . 00E-5 ., The Kulczynski distance between species 1 and 2 is given by 1−0 . 5 ( Σjmin ( y1j , y2j ) /Σjy1j + Σjmin ( y1j , y2j ) /Σjy2j ) where j ( from 1 to 20 ) refers to the corresponding normalized COG categories ( from 0 to 1 ) ., The dendrogram was derived from the corresponding distance matrix by applying a complete clustering method in which the distance between clusters A and B is given by the highest distance between any two species belonging to A and B , respectively ., The statistical significance of the clusters of the dendrogram was evaluated by bootstrap analysis based on 100 , 000 replicates ., The sequences of 16S rDNA were aligned with MAFFT ( v6 . 240 ) 50 program ., The positions for the phylogenetic analysis were derived by Gblocks v0 . 91b 51 ., In total , 1530 nucleotides were selected ., The phylogenetic reconstruction was carried out by maximum likelihood using the PHYML program 52 ., The best evolutionary model chosen by MODELTEST 53 was a GTR + Gamma ( G ) + I ( Proportion invariant ) ., Bootstrap values were based on 1000 replicates ., Abdominal fat bodies from dissected B . germanica adult females were homogenized with a Douce homogenizer adding a 50 mM HEPES buffer containing 1 mM EDTA , pH 7 . 5 ., The crude extract was centrifuged for 25 min at 6000 rpm at 4°C , and the pellet was resuspended with the homogenization buffer ., The supernatant and a crude extract of cockroach heads ( host tissue without endosymbionts ) were used in control experiments ., The resuspended pellet or bacteria-enriched fraction was treated with lysozyme ( 3 . 5 U mL−1 ) for 30 min at 4°C and sonicated for 5 sec ., Urease activity was determined incubating the extract at 37°C with 110 mM urea ., At different time intervals the reaction was stopped by adding 1 vol ., 10% trichloroacetic acid and the produced ammonia was measured by the colorimetric Berthelot method 54 as described in 55 ., The protein content was measured with a Nanodrop ND1000 equipment ., Stoichiometric analysis ( using METATOOL ) 56 was performed on the central pathways directly involved in amino acid catabolism , including the Krebs and urea cycles ., Information about the reversibility of reactions was checked in the BRENDA database 48 ., The input file for METATOOL is available upon request to the corresponding author ., The genome was sent to GenBank and has been assigned accession number CP001487 .

Introduction, Results/Discussion, Materials and Methods

Bacterial endosymbionts of insects play a central role in upgrading the diet of their hosts ., In certain cases , such as aphids and tsetse flies , endosymbionts complement the metabolic capacity of hosts living on nutrient-deficient diets , while the bacteria harbored by omnivorous carpenter ants are involved in nitrogen recycling ., In this study , we describe the genome sequence and inferred metabolism of Blattabacterium strain Bge , the primary Flavobacteria endosymbiont of the omnivorous German cockroach Blattella germanica ., Through comparative genomics with other insect endosymbionts and free-living Flavobacteria we reveal that Blattabacterium strain Bge shares the same distribution of functional gene categories only with Blochmannia strains , the primary Gamma-Proteobacteria endosymbiont of carpenter ants ., This is a remarkable example of evolutionary convergence during the symbiotic process , involving very distant phylogenetic bacterial taxa within hosts feeding on similar diets ., Despite this similarity , different nitrogen economy strategies have emerged in each case ., Both bacterial endosymbionts code for urease but display different metabolic functions: Blochmannia strains produce ammonia from dietary urea and then use it as a source of nitrogen , whereas Blattabacterium strain Bge codes for the complete urea cycle that , in combination with urease , produces ammonia as an end product ., Not only does the cockroach endosymbiont play an essential role in nutrient supply to the host , but also in the catabolic use of amino acids and nitrogen excretion , as strongly suggested by the stoichiometric analysis of the inferred metabolic network ., Here , we explain the metabolic reasons underlying the enigmatic return of cockroaches to the ancestral ammonotelic state .

Bacterial endosymbionts from insects are subjected to a process of genome reduction from the moment they interact with their host , especially when the symbiosis is strict ( the partners live together permanently ) and the endosymbiont is maternally inherited ., The type of genes that are retained correlates with specific metabolic host requirements ., Here , we report the genome sequence of Blattabacterium strain Bge , the primary endosymbiont of the German cockroach B . germanica ., Cockroaches are omnivorous insects and Blattabacterium cooperates with their metabolism , not only with essential nutrient metabolism but also through an efficient use of amino acids and the nitrogen excretion by the combination of a urea cycle and urease activity ., The repertoires of functions that are maintained in Blattabacterium are similar to those already observed in Blochmannia spp ., , the primary endosymbiont of carpenter ants , also an omnivorous insect ., This constitutes a nice example of evolutionary convergence of two endosymbionts belonging to very different bacterial phyla that have evolved a similar repertoire of functions according to the host ., However , the current set of genes and , more importantly , those that were lost in the process of genome reduction in both endosymbiont lineages have also contributed to a different involvement of Blattabacterium and Blochmannia in nitrogen metabolism .

genetics and genomics/genome projects, genetics and genomics/microbial evolution and genomics, evolutionary biology/microbial evolution and genomics, evolutionary biology/evolutionary and comparative genetics

journal.ppat.1007445

Whole genome screen reveals a novel relationship between Wolbachia levels and Drosophila host translation

Insects are common vectors for devastating human viruses such as Zika , Yellow Fever , and Dengue ., A novel preventative strategy has emerged to combat vector-borne diseases that exploits the consequences of vector-insect infection with the bacteria Wolbachia pipientis 1–4 ., Wolbachia is a vertically transmitted , gram-negative intracellular bacterium known to infect 40–70% of all insects 5 , 6 ., Wolbachia provides hosts with resistance to pathogens such as viruses 7–10 ., Remarkably , Wolbachia infections can reduce host viral load enough to render insect hosts incapable of transmitting disease-causing viruses effectively 1 , 2 , 11–24 ., The relationship between Wolbachia and a host is complex and dynamic ., Understanding how bacterial levels can change is vital because it dictates how Wolbachia manipulates the host insect ., For example , the antiviral protection provided by Wolbachia is strongest when Wolbachia levels within a host are high 10 , 25–27 ., On the other hand , Wolbachia can become deleterious to the host when Wolbachia population levels are too high leading to cellular damage and reduced lifespan28–30 ., To apply Wolbachia as an effective tool to combat vector-borne viruses we need a better understanding of host influences on Wolbachia levels ., Wolbachia infects a large host and tissue range suggesting interaction with various host systems and pathways for successful intracellular maintenance within a host 5 , 31 ., To date , reports suggest that Wolbachia levels may be influenced in various contexts by interaction with host cytoskeletal components 32–35 , the host ubiquitin/proteasome 36 , host autophagy 37 , and by host miRNAs 16 , 38 ., A comprehensive analysis of host systems that influence Wolbachia levels has not been carried out and will further our knowledge of this symbiotic relationship and reveal molecular mechanisms that occur between Wolbachia and the host to maintain it ., Wolbachia-host interactions can be studied in the genetically tractable Drosophila melanogaster system which allows for the systematic dissection of host signaling pathways that interact with the bacteria using the wide array of genetic and genomic tools available ., The Drosophila system enables rapid unbiased screening of host factors that impact Wolbachia at the cellular and organismal level ., While some influences on the relationship , such as systemic effects , require studies in the whole organism , many aspects of molecular and cellular signaling can be studied in a Drosophila cell culture-based system ., Drosophila cells are particularly amenable to genome-scale screens because of the ease and efficacy of RNAi in this system 39-40 ., Previous cell culture-based RNAi screening has been a successful approach to study a wide range of intracellular bacteria-host interactions in Drosophila cell lines 41–44 ., Thus , we reasoned that this was a feasible approach for studying Wolbachia-host interactions ., Here we performed a whole genome RNAi screen in a Wolbachia-infected Drosophila cell line , JW18 , which was originally derived from Wolbachia-infected Drosophila embryos and has previously proven suitable for high-throughput assays 36 , 45 ., The goal was to determine in an unbiased and comprehensive manner which host systems affect intracellular Wolbachia levels ., The primary screen identified 1117 host genes that robustly altered Wolbachia levels ., Knock down of 329 of these genes resulted in increased Wolbachia levels whereas 788 genes led to decreased Wolbachia levels ., To characterize these genes , we generated manually curated categories , performed Gene Ontology enrichment analysis , and identified enriched host networks using bioinformatic analysis tools ., The effects on Wolbachia levels were validated in follow-up RNAi assays that confirmed Wolbachia changes visually by RNA FISH as well as quantitatively using a highly sensitive DNA qPCR assay ., We uncovered an unexpected role of host translation components such as the ribosome and translation initiation factors in suppressing Wolbachia levels both in tissue culture using RNAi and in the fly using mutants and a chemical-based translation inhibition assay ., Furthermore , we show a decrease in overall translation in Wolbachia-infected JW18 cells compared to Wolbachia-free JW18DOX cells and that an inverse trend exists between Wolbachia levels and host translation levels in JW18 cells ., This work provides strong evidence for a relationship between Wolbachia and host translation and strengthens our general understanding of the Wolbachia-host intracellular relationship ., Wolbachia is an intracellular bacterium that resides within a wide range of insect hosts ., To identify host factors that enhance or suppress intracellular Wolbachia levels , we performed a genome-wide RNAi screen in Wolbachia-infected JW18 Drosophila cells that were originally derived from Wolbachia-infected embryos 45 ., In order to visually detect Wolbachia levels we established a specific and sensitive RNA Fluorescence In Situ Hybridization ( FISH ) method consisting of a set of 48 fluorescently labeled DNA oligos that collectively bind in series to the target Wolbachia 23s rRNA ( Fig 1A ) ., This enabled detection of infection levels ranging from as low as a single bacterium in a cell to a highly infected cell and could clearly distinguish Wolbachia-infected cells from Wolbachia-free cells ( Fig 1B ) ., Thus , we were able to assess Wolbachia infection levels in the JW18 cell population and found that under our culturing conditions we could stably maintain JW18 cells with a Wolbachia infection level of 14% of the JW18 cells ( Fig 1C ) ., Of the infected cells , 73% of the cells had a low Wolbachia infection ( 1–10 bacteria ) , 13 . 5% had a medium infection ( 11–30 bacteria ) , and 13 . 5% were highly infected ( >30 bacteria ) ., Though Wolbachia levels may change in different culturing conditions , the JW18 cell line maintained Wolbachia levels stably for the duration of the screen ., These experiments confirmed the feasibility and sensitivity of RNA FISH to detect different levels of Wolbachia infection in Drosophila cells in a highly sensitive manner ., Prior to screening we characterized the JW18 cell line and its associated Wolbachia strain by generating a JW18 DNA library and sequencing it using DNAseq technology ( S1 Fig ) ., This allowed for phylogenetic analysis of the Wolbachia strain and revealed that it clustered most closely with the avirulent wMel strain which is well characterized for its antiviral effect on RNA-based viruses in Drosophila as well as in mosquitos ( S1A Fig ) 1 , 2 , 7 , 8 ., Further analysis included gene copy number variation of the Wolbachia genome and identified one deleted and one highly duplicated region ( 3–4 fold increased ) ( S1B Fig ) ., The deleted region contained eight genes known as the “Octomom” region postulated to influence virulence 27 , 46 ., The loss of “Octomom” has also been reported in wMelPop-infected mosquito cell lines after extended passaging over 44 months 47 ., This suggests that loss of this region happened independently in two cases and may be related to passage in cell culture ., A highly duplicated region spans approximately from positions 91 , 800–127 , 100 and contains 38 full or partial genes , including those with unknown function as well as genes predicted to be involved in metabolite synthesis and transport , molecular chaperones , DNA polymerase III subunit , DNA gyrase subunit , and 50S ribosomal proteins ., For analysis of gene copy number variation in the JW18 cell line , the DNA library was aligned to the Release 6 reference genome of D . melanogaster ., This revealed that the cell line is of male origin with an X:A chromosomal ratio of 1:2 and tetraploid in copy number ( S1C Fig ) ., Bioinformatic analysis on genes of high or low copy number did not reveal an enrichment for any particular molecular or cellular functional class and the majority ( 72% ) of genes in the JW18 cell line were at copy numbers expected for a tetraploid male genotype ( 4 copies on autosomes , 2 copies on X ) ., This made the JW18 cell line suitable for RNAi screening ., As a first step to uncovering Wolbachia-host interactions , we asked whether gene expression changes occur in the host during stable Wolbachia infection ., To do this , we used a control Wolbachia-free version of the JW18 cell line which was previously generated through doxycycline treatment ( JW18DOX ) ( S2A Fig ) ., A comparison of host gene expression changes in the presence and absence of Wolbachia through RNAseq analysis revealed 308 and 559 host genes that were up- or down-regulated respectively by two-fold or more ( padj<0 . 05 ) ( S2B Fig ) ., Of these genes , 21 displayed major expression changes of log2 fold >4 ( DptB , Wnt2 , SP1173 , bi , FASN3 , CG5758 , CP7Fb , beta-Man ) or log2-fold <-4 ( CG12693 , CG13741 , Tsp74F , esn , cac , CG4676 , CG42827 , CG18088 , CG17839 , 5-HT2A , CG43740 , CG3036 , aru ) ( also see S2C Fig ) ., The presence of Wolbachia led to elevated gene expression of several components of the host immune response including the Gram-negative antimicrobial peptide Diptericin B ( DptB ) , which was the most highly upregulated gene in the presence of Wolbachia ( S2C Fig ) ., Gene ontology ( GO ) analysis further confirmed a host immune response with enriched terms such as ‘response to other organism’ and ‘peptidoglycan binding’ that included genes for antimicrobial peptides ( attA , AttB , AttC , DptB , LysB ) and peptidoglycan receptors ( PGRP-SA , -SD , -LB , -LF ) as well as antioxidants such as Jafrac2 , Prx2540-1 , Prx2540-2 , Pxn , GstS1 with ‘peroxiredoxin’ and ‘peroxidase activity’ ., Other expression changes included extracellular matrix components such as upregulation of collagen type IV ( Col4a1 and vkg ) and downregulation of genes for integral components of the plasma membrane including cell adhesion components ( kek5 , mew , Integrin , and tetraspanin 42Ed and 39D ) ., Gene ontology analysis further identified a significant enrichment of ion transporters and channels that were downregulated as well as genes encoding several proteins such as myosin II , projectin and others associated with the muscle Z-disc that were downregulated ., Finally , we observed an overall upregulation of host proteasome components at the RNA level in the presence of Wolbachia ( S3 Fig ) , which is in line with proteomics data of proteasome upregulation in the presence of Wolbachia 48 , 49 ., In summary , these host factors may play an important role in the Wolbachia-host relationship however their specific roles in this interaction remain to be determined ., The screening approach combined the visual RNA FISH Wolbachia detection assay ( Fig, 1 ) with in vitro RNAi knockdown of host genes to ask which host genes influence Wolbachia levels ( Fig 2A ) ., Prior to screening , we tested whether RNAi was a feasible approach in JW18 cells ., First , we confirmed that RNAi had no adverse effects such as cytotoxicity on the cells using a negative control dsRNA targeting LacZ which was not present in our system ( Fig 2B and 2C ) ., Second , we tested RNAi knockdown efficiency in the JW18 cell line ., To do this a Jupiter-GFP transgene present in the cell line was targeted for knockdown using dsRNA to GFP ., High knockdown efficiency was achieved using this RNAi protocol as seen by the efficient knockdown of the Jupiter-GFP transgene both visually by RNA FISH ( 90 . 2% reduction ) ( Fig 2D and 2E ) and by protein levels as shown by Western blot ( 97 . 9% reduction ) ( Fig 2F ) compared with either the ‘no dsRNA’ knockdown ( Fig 2B ) or ‘LacZ’ knockdown ( Fig 2C ) conditions ., This confirmed the suitability of the JW18 cell line for an RNAi-based screening approach ., For controls that alter Wolbachia levels , we identified a host ribosomal gene , RpL40 , from a pilot screen that consistently led to increased Wolbachia levels when depleted by RNAi ( Fig 2G ) compared to cells that were not treated by RNAi ( Fig 2B ) or treated with lacZ dsRNA treatment ( Fig 2C ) ., We achieved 96 . 3% RNAi knockdown efficiency as confirmed by qPCR for RpL40 levels relative to a no knockdown control ( Fig 2H ) ., At the time of the screen we did not know of any host protein whose knockdown would decrease Wolbachia levels ., Therefore , as a Wolbachia-decreasing control , cells were incubated with 5μM doxycycline for 5 days which successfully reduced the Wolbachia levels in the cells by 91 . 9% as measured by RNA FISH ( Fig 2I and 2J ) ., To quantify the effect of the controls on Wolbachia levels we isolated genomic DNA from each treated sample and used quantitative PCR DNA amplification to detect the number of Wolbachia genomes per cell by measuring Wolbachia wspB copy number relative to the Drosophila gene RpL11 ( Fig 2K ) ., Relative to control cells , the RNAi treatment with RpL40 resulted in a 3 . 4-fold increase in Wolbachia , doxycycline decreased Wolbachia levels 6 . 3-fold , whereas LacZ and GFP RNAi had no significant effect confirming that our controls allowed us to manipulate Wolbachia levels in the JW18 cell line and that this cell line with its relative low infection rate ( Fig 1C ) provided a sensitive tool for detecting dynamic changes in Wolbachia levels through an RNAi screening approach ., The layout of the whole genome screen is illustrated in Fig 2A ., Briefly , Wolbachia-infected JW18 cells were incubated with the DRSC Drosophila Whole Genome RNAi Library version 2 . 0 which was pre-arrayed in 384 well tissue culture plates such that each well contained a specific dsRNA amplicon to target one host gene ., The 5-day incubation period allowed for efficient host gene knockdown ., Thereafter the cells were processed for RNA FISH detection of Wolbachia 23s rRNA ., Total fluorescence signal was detected using automated microscopy and served as a readout for Wolbachia levels within each plate well ., Host cells within each well were detected by DAPI staining ., Finally , the Wolbachia fluorescence signal was divided by the total number of DAPI-stained host cells detected to provide an average Wolbachia per cell readout which was normalized to the plate average ( represented as a robust Z score ) ., The library was screened in triplicate ., The raw screening data were subjected to several quality control steps ( S4 Fig ) ., Briefly , we realigned the DRSC Version 2 . 0 Whole Genome RNAi library dsRNA amplicons with Release 6 of the D . melanogaster genome using the bioinformatic tool UP-TORR 50 ., This provided an accurate updated description of the gene target for each dsRNA amplicon ., Initially the library included 24 036 unique dsRNA amplicons targeting 15 589 genes , however owing to updates in gene organization and annotation models of the reference genome since the initial release of the library we removed 1499 outdated amplicons from our subsequent analysis as they were no longer predicted to have gene targets ( S1 Table ) ., We also excluded 1481 amplicons that were annotated in UP-TORR to target multiple genes ( S2 and S3 Tables ) ., We further excluded 66 amplicons for a positional effect on the dsRNA library tissue culture plates at the A1 position ( S5 Fig , S4 Table ) ., Thus , we effectively screened 20 990 unique dsRNA amplicons targeting 14 024 genes ( 80% of D . melanogaster Release 6 genome ) ., A further quality control step to reduce false positive hits was to cross-reference potential hits with RNAseq gene expression data for the JW18 cell line to exclude genes with undetectable expression in the cell line ( S5 Table ) ., To identify and select for hits from the primary data , we first analyzed the screen-wide controls ., A plot of all controls included in the whole genome screen revealed that RpL40 knockdown increased Wolbachia levels ( median robust Z score of 2 . 2 ) , conversely doxycycline treatment decreased Wolbachia levels throughout the screen ( median robust Z of -3 . 5 ) , whereas a standard control included in the whole genome library , Rho1 , and GFP RNAi knockdown did not significantly affect Wolbachia levels ( Fig 3A ) ., We used this range as a guide to set robust Z limits for primary hits at ≥ 1 . 5 or ≤ -1 . 5 ., Every dsRNA amplicon was screened in triplicate ., To be considered as a ‘hit’ amplicon at least 2 of the 3 replicates needed to satisfy the robust Z score limits ( S4 Fig ) ., To categorize the primary screen hits , each gene was assigned to a ‘High’ , ‘Medium’ , and “Low’ bin based on the confidence level ( S4 Fig ) . This was determined based on the total number of different dsRNA amplicons representing a hit gene in the library and how many of these dsRNA amplicons had a significant effect on Wolbachia levels ( S4 Fig ) . In this manner , we were able to stratify the primary screen hits to assist in follow up analysis . The screen identified 1117 genes that when knocked down had a significant effect on the Wolbachia levels in JW18 cells ( S6 Table ) . Knock down of 329 of the 1117 genes resulted in increased Wolbachia levels , suggesting that these genes normally restrict Wolbachia levels within the host cell ( Fig 3B ) . Knockdown of 788 genes resulted in decreased Wolbachia levels , suggesting Wolbachia may be dependent on these host genes for survival within the host cell ( Fig 3B ) . For each of the two hit categories , genes were classified by confidence level ( described in S4 Fig , and Fig 3C ) . We found a higher proportion of low confidence hits ( 21% ) in the category of genes that decreased Wolbachia levels compared to genes that led to Wolbachia level increases which only contained 12 . 5% low confidence hits . To analyze the expression of the 1117 genes , the hits were distributed into 9 bins based on their gene expression level from JW18 RNAseq data ( S6A Fig ) . Hits displayed a wide range of expression and an enrichment of low expression for hits that decreased Wolbachia levels ( S6B Fig ) . We did not observe any biases for variation in gene DNA copy number based on DNAseq data for the JW18 cell line ( S6C Fig ) . Next , we asked whether changes in Wolbachia levels could be explained by effects on host cell proliferation or were independent of effects on host cell proliferation . We measured cell proliferation using the raw screen data by normalizing the number of cells scanned per well ( DAPI ) to the number of fields of view required to capture the cells . This allowed us to generate a robust Z score measure of cell proliferation effects for the 1117 genes identified as hits . For genes that increased Wolbachia levels , 12% ( 41 genes ) increased cell proliferation ( robust Z>1 ) , 45% ( 147 genes ) decreased cell proliferation ( robust Z<-1 ) , and 43% ( 141 genes ) had no effect on cell proliferation ( Fig 3D , S7 Table ) . These data suggest that a significant number of gene knockdowns ( 45% ) may indirectly lead to an increase in Wolbachia levels through slowed cell proliferation . Importantly , 43% of hits identified had no effect on cell proliferation whilst increasing Wolbachia levels . These results suggest that changes in Wolbachia levels are not strictly linked to host cell proliferation . For genes that decreased Wolbachia levels , the majority ( 82% , 644 genes ) did not affect cell proliferation and 2% ( 19 genes ) increased and 16% ( 125 genes ) decreased cell proliferation ( Fig 3D , S7 Table ) . To summarize , the screen identified 1117 host genes that act to support or suppress Wolbachia levels within the host Drosophila cell . To classify the 1117 gene hits identified in the whole genome screen , we first manually curated the hits using gene annotation available on FlyBase ( http://www . flybase . org ) relating to each gene such as gene family , domains , molecular function , gene ontology ( GO ) information , gene summaries , interactions and pathways , orthologs , and related recent research papers . We identified distinct categories of genes that when knocked down by RNAi increased ( Fig 4A ) or decreased ( Fig 4B ) Wolbachia levels . The largest gene category that led to decreased Wolbachia levels by RNAi knockdown contained genes for host metabolism and transporters suggesting that Wolbachia strongly relies on this aspect of the host ( Fig 4B ) . On the other hand , gene knockdowns that increased Wolbachia contained many components of the core ribosome network , translation factors , and the proteasome core and regulatory proteins network ( Fig 4A ) . Six of the broad gene categories could be further sub-classified for processes that either enhanced or suppressed Wolbachia levels . First , RNAi knockdown of members in the category containing cytoskeleton , cell adhesion and extracellular matrix components decreased Wolbachia , these included cadherins , formins , spectrin and genes involved in microtubule organization , whereas knockdowns that resulted in increased Wolbachia were actin and tubulin-related . Second , Wolbachia levels may be sensitive to disturbances in membrane dynamics and trafficking . Specifically , knockdown of SNARE components , endosomal , lysosomal and ESCRT components decreased Wolbachia , whereas knockdown of components of COPI , endosome recycling , and several SNAP receptors increased Wolbachia levels . Third , disruptions in several cell cycle-related components decreased Wolbachia levels , while Wolbachia levels increased upon disruption of cytokinesis , the separase complex and the Anaphase Promoting Complex . Fourth , the knockdown of components related to RNA helicases and the exon junction complex decreased Wolbachia , while disruption of many spliceosome components increased Wolbachia . Fifth , epigenetic changes influenced Wolbachia levels: knockdown of members involved in heterochromatin silencing , Sin3 complex and coREST decreased Wolbachia levels , whereas knocking down members of the BRAHMA complex resulted in increased Wolbachia levels . Finally , Wolbachia levels were sensitive to changes in host transcription . We observed that disruption of components in the mediator complex and regulators of transcription from Polymerase II promoters decreased Wolbachia , whereas knockdown of the BRD4pTEFb complex involved in transcriptional pausing and other transcriptional elongation factors resulted in increased Wolbachia levels . Together , this manual curation revealed that this whole genome screen yielded host genes that suppress or enhance Wolbachia levels and that these primary hits could be classified into distinct gene categories . Further GO term enrichment analysis using the online tool Panther ( http://www . pantherdb . org/ ) suggested that the 329 genes resulting in Wolbachia increases formed a robust dataset as many of the enriched terms overlapped with our manual curation ( S7 Fig ) . In contrast , there was a lack of enrichment for the 788 Wolbachia-decreasing genes even though manual curation had sorted many of these genes into categories . For this reason , further analysis focused on the 329 host genes that increased Wolbachia when knocked down by RNAi . To assess whether specific host networks were enriched within the 329 host genes identified as potential suppressors of Wolbachia we used two bioinformatic tools namely the Kyoto Encyclopedia of Genes and Genomes ( KEGG ) , and the protein complex enrichment analysis tool ( COMPLEAT ) with criteria for a network restricted to complexes with 3 or more components ( p<0 . 05 ) 51 . This analysis revealed enrichment of several host networks among the 329 genes whose knockdown resulted in Wolbachia increases including a striking 67 . 5% of the core cytoplasmic ribosome ( 56/83 expressed ribosomal proteins ) and 31 . 3% of all translation initiation components ( 10/32 expressed proteins ) as well as 70 . 1% the core proteasome ( 24/34 expressed proteins ) ( Fig 5A , and S8 Fig ) . These findings strongly suggested that perturbations in host translation components could alter Wolbachia levels . For both networks , the majority of components did not significantly affect cell proliferation within the duration of the RNAi screen assay ( circles ) , though some did have a negative impact ( robust Z<-1 ) ( square ) ( Fig 5A , see Fig 3D ) . Importantly , these data show that Wolbachia level fluctuations are independent of host cell proliferation changes because Wolbachia levels increased in RNAi knockdowns of network components regardless of the presence or absence of cell proliferation changes ( Fig 5A , S9 Fig ) . We chose to validate and characterize the novel Wolbachia-host translation interaction identified in the whole genome RNAi screen . We validated the influence of the ribosome , translation initiation complex , and proteasome on Wolbachia levels by knocking down representative members of each network using RNAi knockdown in JW18 cells ( Fig 5B ) . Each gene was validated using two different dsRNA amplicons that were designed to target different parts of the gene . Effects on Wolbachia levels were assessed quantitatively by DNA qPCR measuring the number of Wolbachia genomes ( wspB DNA copies ) relative to the number of host cell nuclei ( RpL11 DNA copies ) . Network validation is represented relative to untreated JW18 control cells ( No RNAi ) and the positive control RpL40 RNAi knockdown is included for reference . For the translation initiation network , we selected eIF-4a , eIF-2 subunit beta , eIF-3c , eIF-3i , and eIF-3ga . All ribosome components’ RNAi knockdown significantly increased Wolbachia levels by 5-fold or more ( Fig 5B ) . For the ribosomal network , we selected RpL10 , RpL36 , RpLP1 , RpS4 , RpLP2 , RpS3 and RpS26 for validation and each RNAi knockdown resulted in a significant increase of nearly 10-fold or higher Wolbachia levels relative to untreated JW18 control cells ( Fig 5B ) . We also validated the proteasome network using RNAi knockdown of three selected genes ( Rpn11 , Rpt2 , Rpn2 ) which resulted in significant Wolbachia increases ( S10 Fig ) . To summarize , we were able to validate that RNAi knockdown of ribosomal , translation initiation , and proteasomal networks leads to striking increases in Wolbachia levels in JW18 cells . To characterize the changes in Wolbachia levels in the JW18 cell line when ribosome or proteasome ( S10 Fig ) components are perturbed by RNAi , we visually classified the level of Wolbachia infection in cells using the Wolbachia-detecting 23s rRNA FISH probe combined with DAPI staining and the GFP-Jupiter transgene labelling microtubules to identify the cells . Each cell was classified according to its Wolbachia infection into the following categories: uninfected ( no Wolbachia ) , low ( 1–10 Wolbachia ) , medium ( 11–30 Wolbachia ) , and high ( >30 Wolbachia ) infection . Similar to Fig 1C , in a control LacZ knockdown JW18 control population 14% of the total number of cells were infected . In contrast , RNAi knockdown of the ribosome component RpS3 resulted in an overall dramatic increase in the total number of infected cells ( 73% ) ( Fig 5C ) . Of the infected cells in the control population , 73% had a low level of infection whereas 13 . 5% had a medium level infection and 13 . 5% had a high level of infection ( Fig 5D ) . A comparison of the extent of infection revealed a 1 . 6-fold increase in medium and highly infected cells after knockdown of network components compared to the control ( Fig 5D ) . Similar results were obtained in proteasome RNAi knockdown cells showing an increase in Wolbachia-infected cells to 87% ( S10 Fig ) . Together , our results show an increase in the total number of infected cells after ribosomal network knockdown and within this population a relative increase of medium to high infected cells , however the majority ( 57% ) of cells maintained a low level of infection . Next , we tested whether these networks could influence Wolbachia in the fly ( Fig 6 and S10 Fig ) . In Drosophila , Wolbachia are found abundantly in the ovary . To test the effect of perturbing the ribosome , females from a Wolbachia-infected stock were crossed to available ribosomal mutant alleles for RpL27A and RpS3 at 25°C . Then , the Wolbachia infection level in the ovaries of 5 day-old Wolbachia-infected heterozygous mutant and wild-type siblings were compared by RNA FISH for Wolbachia 23s rRNA . We observed dramatic increases in Wolbachia levels in the ribosomal mutants compared to the control sibling ovaries at early stages of oogenesis in the germarium as well as in maturing egg chambers ( Fig 6A and 6C ) . Quantification of the integrated density of the 23s rRNA Wolbachia FISH probe in Z-stack projections of germaria for ribosomal mutants confirmed a 2 . 94-fold ( RpL27A ) and 3-fold ( RpS3 ) increase in the mutant compared to control siblings ( Fig 6B ) ( Non-parametric Mann Whitney , RpL27A and RpS3 p<0 . 0001 ) . Further , quantification of stage 10 egg chambers revealed a 1 . 6-fold ( RpL27A ) and 1 . 27-fold ( RpS3 ) increase compared to their respective control siblings ( Fig 6D ) ( Non-parametric Mann Whitney , RpL27A p = 0 . 0002 , RpS3 p = 0 . 0089 ) . The fecundity of both ribosomal mutant lines was assessed by counting eggs laid per female as well as assessing the embryo viability . We found no significant difference between ribosomal mutant and control Wolbachia-infected siblings nor between Wolbachia-infected and uninfected flies , suggesting that the rate of oogenesis and viability of offspring are not affected by reducing the levels of ribosomal proteins nor by the level of Wolbachia infection ( S11 Fig ) . In conclusion , these results demonstrate that Wolbachia levels are sensitive to changes in the host ribosomal network in both early and late stages of Drosophila oogenesis and that under the conditions tested Wolbachia-infection does not impact fecundity of the animals . Similar results were obtained in proteasomal subunit Prosβ6 ( DTS5 ) mutant flies ( S10 Fig ) . Apart from the Drosophila ovary , we tested the effect of ribosomal and proteasome mutations on Wolbachia levels in other tissues including larval imaginal discs , adult male testes , and in the whole fly . To do this we processed RpS3 mutant and control larval imaginal discs for Wolbachia RNA FISH visualization ( S12 Fig ) . We found significantly increased levels of Wolbachia in haltere- , wing- , leg- discs ( 2 . 23-fold ( p<0 . 0001 ) , 1 . 15-fold ( p = 0 . 0229 ) , and 1 . 74-fold ( p<0 . 0001 ) Non-parametric Mann Whitney ) respectively . Similar results were obtained in proteasomal mutant ( DTS5 ) flies ( S12 Fig ) showing increased Wolbachia in haltere- , wing- , and leg- discs ( 2 . 55-fold ( p<0 . 0001 ) , 1 . 91-fold ( p = 0 . 0005 ) , 2 . 0-fold ( p<0 . 0001 ) Non-parametric Mann Whitney ) as well as in the larval brain ( 2 . 26-fold ( p = 0 . 0003 ) . These data suggest that increases in Wolbachia levels in RpS3 and Prosβ6 ( DTS5 ) mutants occur early in development and in a variety of tissue types ( S12 Fig ) . In addition , we found significantly increased Wolbachia levels ( 2 . 43-fold ( p = 0 . 0360 ) in the hub of adult RpL27A mutant testes compared to control siblings as well as a significant 2 . 8-fold increase in Wolbachia in proteasomal DTS5 mutant testes compared to sibling controls ( p = 0 . 0093 ) ( Non-parametric Mann Whitney ) ( S13 Fig ) . Finally , we assessed the Wolbachia level increase in whole flies using DNA qPCR and found increased Wolbachia levels in RpS3 mutants for males ( 1 . 22-fold ) and females ( 1 . 56-fold ) and RpL27A mutant females ( 2 . 74-fold ) compared to control siblings ( S14 Fig ) . Together these data suggest that Wolbachia level increases in ribosomal and proteasomal mutants occur in a wide range of tissue types and is not sex specific . Next , we asked whether a direct relationship exists between Wolbachia and host translation . To do this we asked whether chemical inhibition of host translation by cycloheximide would alter Wolbachia levels in host Drosophila . Wolbachia-infected D . melanogaster were fed cycloheximide-containing food or control food for 7 days prior to genomic DNA extraction of whole flies . We tested the Wolbachia-levels in individual whole flies using DNA qPCR and found increased Wolbachia levels in flies fed on cycloheximide compared to control flies ( Fig 6E ) . This suggested that Wolbachia levels are sensitive to host translatio

Introduction, Results, Discussion, Materials and methods

Wolbachia is an intracellular bacterium that infects a remarkable range of insect hosts ., Insects such as mosquitos act as vectors for many devastating human viruses such as Dengue , West Nile , and Zika ., Remarkably , Wolbachia infection provides insect hosts with resistance to many arboviruses thereby rendering the insects ineffective as vectors ., To utilize Wolbachia effectively as a tool against vector-borne viruses a better understanding of the host-Wolbachia relationship is needed ., To investigate Wolbachia-insect interactions we used the Wolbachia/Drosophila model that provides a genetically tractable system for studying host-pathogen interactions ., We coupled genome-wide RNAi screening with a novel high-throughput fluorescence in situ hybridization ( FISH ) assay to detect changes in Wolbachia levels in a Wolbachia-infected Drosophila cell line JW18 ., 1117 genes altered Wolbachia levels when knocked down by RNAi of which 329 genes increased and 788 genes decreased the level of Wolbachia ., Validation of hits included in depth secondary screening using in vitro RNAi , Drosophila mutants , and Wolbachia-detection by DNA qPCR ., A diverse set of host gene networks was identified to regulate Wolbachia levels and unexpectedly revealed that perturbations of host translation components such as the ribosome and translation initiation factors results in increased Wolbachia levels both in vitro using RNAi and in vivo using mutants and a chemical-based translation inhibition assay ., This work provides evidence for Wolbachia-host translation interaction and strengthens our general understanding of the Wolbachia-host intracellular relationship .

Insects such as mosquitos act as vectors to spread devastating human diseases such as Dengue , West Nile , and Zika ., It is critical to develop control strategies to prevent the transmission of these diseases to human populations ., A novel strategy takes advantage of an endosymbiotic bacterium Wolbachia pipientis ., The presence of this bacterium in insect vectors prevents successful transmission of RNA viruses ., The degree to which viruses are blocked by Wolbachia is dependent on the levels of the bacteria present in the host such that higher Wolbachia levels induce a stronger antiviral effect ., In order to use Wolbachia as a tool against vector-borne virus transmission a better understanding of host influences on Wolbachia levels is needed ., Here we performed a genome-wide RNAi screen in a model host system Drosophila melanogaster infected with Wolbachia to identify host systems that affect Wolbachia levels ., We found that host translation can influence Wolbachia levels in the host .

invertebrates, medicine and health sciences, rna interference, pathology and laboratory medicine, cell processes, genomic library screening, animals, wolbachia, invertebrate genomics, animal models, drosophila melanogaster, model organisms, experimental organism systems, epigenetics, molecular biology techniques, bacteria, drosophila, research and analysis methods, cell proliferation, genetic interference, animal studies, gene expression, pathogenesis, molecular biology, insects, animal genomics, molecular biology assays and analysis techniques, arthropoda, biochemistry, rna, eukaryota, cell biology, nucleic acids, host-pathogen interactions, genetic screens, library screening, gene identification and analysis, genetics, biology and life sciences, genomics, organisms

journal.pcbi.1002582

Activity Dependent Degeneration Explains Hub Vulnerability in Alzheimers Disease

Like many other complex networks , the human brain contains parts that are better connected to the rest than others: ‘hub’ regions ., Evidence is increasing that a collection of brain hub regions forms a ‘structural core’ or ‘connectivity backbone’ that facilitates cognitive processing 1 , 2 , 3 ., Brain hub regions are mainly located in heteromodal association cortices ( which integrate information coming from primary cortices ) , and show a striking overlap with the Default Mode Network 4 , 5 ., Furthermore , their function has been related to fundamental cognitive features such as consciousness , memory , and IQ 6–10 ., The central role and large responsibility of hub network regions has an obvious downside: hub damage can have a dramatic impact on network integrity 11 , 12 ., One of the most intriguing recent insights in this regard has emerged from network-related studies in the field of Alzheimers disease ( AD ) : cortical hub areas turn out to be exceptionally vulnerable to amyloid deposition , hypometabolism and , eventually , atrophy 13–15 ., This fascinating link between connectivity and susceptibility to AD pathology deserves further study: what could be causing the hub vulnerability ?, The prevailing amyloid-cascade hypothesis of AD states that interstitial amyloid-beta proteins exert a toxic effect on surrounding neurons and synapses , thereby disturbing their function and eventually causing dementia 16 ., However , this theory does not provide an explanation for the selective vulnerability of highly connected hub areas ., Could an activity-driven mechanism , i . e . hub areas suffering most damage due to their higher connectivity and activity level have any legitimacy ?, Chronic , excessive metabolic demand can lead to tissue damage in many organs , and the human brain has extraordinary energy demands ., Furthermore , major AD risk factors such as age , ApoE genotype , vascular damage and female gender have all been linked to an increased burden on neuronal metabolism , activity and plasticity 17–19 ., Recently , direct evidence was presented that excessive neuronal and/or synaptic activity leads to amyloid deposition 20 , 21 , 22 ., However , whether this relation between neuronal activity and AD pathology exists in humans , and whether hub regions are indeed the most active areas of the brain has not yet been explored ., We speculated that an ‘activity dependent degeneration’ scenario , in which hub regions are preferentially affected due to high neuronal activity levels , could be a plausible disease mechanism ., To test this hypothesis , a model is required that incorporates both large-scale connectivity as well as ( micro-scale ) neuronal activity ., The macroscopic level is needed to provide a realistic structural human brain topology , including hub regions ., Topological maps are well within reach nowadays , since an increasing amount of imaging data describing the human connectome is becoming available 1 , 23 , 24 ., Imposed on this structural framework , a realistic representation of network dynamics is required ., For this purpose , so-called neural mass models ( NMMs ) can be employed 25–27 ., Here , each neural mass reflects activity in a brain region by representing a large population of interconnected excitatory and inhibitory neurons , characterized by an average membrane potential and spiking density ., Multiple neural masses can be coupled according to any desired structural topology ( e . g . human anatomical data ) to form a dynamic brain model , which can then be employed to investigate the relationship between connectivity and neuronal activity 28–30 ., Structural ( anatomical ) connectivity and functional ( dynamical ) connectivity are strongly related , but not always in a straightforward way 5 , 31–33 ., It has been shown that macroscopic models of mammalian brain networks combined with graph theoretical analysis may explain the topology of functional networks at various time scales 34–36 ., To simulate disease , macroscopic models and graph theory have been used to predict the structural and functional consequences of various types of lesions on brain networks 11 , 12 , 30 ., Similarly , the gradually progressive neuronal damage of neurodegenerative processes such as AD can be modeled using this approach , and analyzed with graph theoretical tools 14 , 37–39 ., The novel aspect of the present study is that the degenerative damage is based on neuronal activity itself ., In short , by simulating neuronal dynamics on a network that is modeled on a realistic human cortical connectivity structure we explore the relation between large-scale connectivity and neuronal activity in normal and abnormal conditions ., In the present study we use this approach to, a ) establish that cortical hub regions , because of their high connectivity , possess the highest intrinsic neuronal activity levels , and, b ) demonstrate that ‘Activity Dependent Degeneration’ ( ADD ) , in which brain connectivity is damaged based on local neuronal activity levels , may serve as a computational model of AD that offers a potential explanation for hub vulnerability ., To assess whether the most highly connected cortical regions also showed the highest levels of neuronal activity , we plotted spike density and total power for all regions against the structural degree of nodes ( figure 1A ) ., The group of 13 regions with the highest ( ‘very high’ category in the figure ) structural degree were defined as hubs; the remaining 65 regions were labeled as non-hubs ., In non-hubs , spike density actually showed a weak negative relation with structural degree , but in hubs clearly higher levels were found compared to non-hubs ( p<0 . 01 ) ., Furthermore , the total power of hubs was significantly higher than that of non-hubs ( p<0 . 0001 ) ., Figure 1B shows the same relations , but now plotted for all regions , and for three different initial coupling strengths ., When S\u200a=\u200a1 . 5 , the correlations between structural degree and spike density ( r\u200a=\u200a0 . 35 ) and structural degree and total power ( r\u200a=\u200a0 . 94 ) indicate that especially the link between structural degree and total power is strong ., For higher coupling strengths between the NMMs ( S\u200a=\u200a2 . 0 ) , a strong positive correlation between structural degree and spike density was observed as well ( r\u200a=\u200a0 . 86 ) ., Thus , although coupling strength has an influence on these results , overall the positive relation between structural connectivity and neuronal activity is apparent ., Since activity level might also be influenced by a nodes functional role rather than its structural connectivity status , we performed comparisons between structural and functional degree ( sum of all weighted functional connections of a node ) of all nodes for the common frequency bands ( delta 0–4 Hz , theta 4–8 Hz , lower alpha 8–10 Hz , higher alpha 10–13 Hz , beta 13–30 Hz , gamma 30–45 Hz ) ., Results of this analysis and of direct comparisons between functional degree and neuronal activity are reported in Text S1 section 1 ., In most bands , clear positive correlations were found , demonstrating that functional hub regions generally have high neuronal activity levels as well ., Table 1 shows all 78 regions ranked by structural degree , with their functional degree , total power and spike density levels ., Our first aim was to find out whether the level of activity in a region is related to its degree of structural connectivity ., An expected positive correlation was indeed found in repeated experiments across all degrees of connectivity ( see figures 1 , 3 , and 4 ) : structural hub regions possess the highest average power and spike densities ., As can be judged from figure 1 , an exception is the relation between structural connectivity and spike density for low values of NMM coupling ( S ) ., This result indicates that the relation between connectivity and activity might be more complex than we expected ., Nevertheless , similar analysis performed using functional connectivity results ( see figure S1 ) led to clear positive correlations in the large majority of cases ., It should further be noted that there is no unique definition of hub status , and in this experiment ( and the rest of the study ) we adhered to the pragmatic choice of taking a selection of nodes ( n\u200a=\u200a13 ) with the highest structural degree ., However , since connectivity and activity are clearly positively related in regions with higher structural degrees , we do not believe that a different hub definition would have led to a different interpretation ., Still , although high neuronal activity in hub regions was a solid finding that might have been expected intuitively , it should ultimately be verified in experimental data ., As can be judged from table 1 , many Default Mode Network ( DMN ) -related regions possess a high degree of connectivity and activity ., The well-documented high resting-state activity level of the DMN is therefore in line with our findings 5; however , instead of being attributed to ongoing cognitive processing or mental phenomena like introspection , high resting-state activity in the DMN might actually be ( partially ) explained by the underlying degree of structural and functional connectivity Based on the findings in our first experiment , we expected that ADD would probably preferentially target hub regions , since they possessed the highest level of activity ., Analyses of both structural and functional connectivity changes due to ADD seem to be in agreement with this expectation ( see figures 2–5 ) ., Furthermore , total ( or absolute ) power decreases rapidly , largely accounted for by weakening of hub regions , and the initial correlation between degree and power is lost ( figure 3 ) ., Thus , large-scale brain connectivity loses its efficient ‘hub’ topology in ADD , like in AD ., Surprisingly , the steady loss of power is accompanied by an initial rise of spike density on average ( see figure 4 ) , before a final oscillatory slowing sets in ., This effect is stronger in hubs; spike density rises more quickly , reaches its peak rate sooner , and seems to slow down more rapidly ., One explanation for the increase in spike density observed in our results is neuronal disinhibition ., In fundamental neuroscience disinhibition is a well-known phenomenon and it is widely accepted that inhibitory interneurons have a large influence on oscillatory behavior 40 ., Besides damaging excitatory connections , ADD impairs connectivity to and from inhibitory neurons within the neural masses , and the resulting loss of inhibition seems to be a dominant influence on spike density in the first stage ., This then leads to a vicious spiral of increasing activity , more activity-dependent damage , etc . until the weakening network can no longer support an increase in spike density ( the inter-mass excitatory coupling weakens substantially , which leads to breakdown of the system , see also figure 6 ) ., The eventual spike density decrease due to ADD resembles the oscillatory slowing known from AD neurophysiologic literature 41 , 42 ., Several authors have argued for a prominent role of neuronal disinhibition in AD pathophysiology: for example , Gleichmann et al . propose a process they call ‘homeostatic disinhibition’ , which is based on a different underlying mechanism but might explain the higher prevalence of epilepsy that is seen in AD , reduced gamma band activity , and , interestingly , the increase in neuronal activity as measured by fMRI 43 ., Schmitt argues that AD is accompanied by a loss of inhibition that leads to alterations in calcium homeostasis and excitotoxicity , respectively 44 ., Olney et al . hypothesize that a disinhibition syndrome caused by hypoactive NMDA receptors triggers excitotoxic activity and widespread neurodegeneration 45 ., Palop & Mucke suggest that amyloid itself causes dysfunction of inhibitory interneurons causing an increase in neuronal activity 46 , 47 , possibly also accounting for the higher prevalence of epileptic activity in AD 48 ., Kapogiannis & Mattson review reports that in aging excitatory imbalance is due to a decrease in GABA-ergic signaling , and that this mechanism is exacerbated in AD 19 ., An early but transient rise was also found in functional connectivity results ( see figure 5 ) , and interestingly , this is in line with experimental data of Mild Cognitive Impairment ( MCI ) patients , where increased functional connectivity levels are often interpreted as a compensatory mechanism 49–52 ., However , this increase of functional connectivity has not been directly related to cognitive improvement , and according to our model , it might well be a part of the degeneration process itself ., Finally , the ADD induced changes in functional network topology , such as the weakening of small-world structure and modularity ( see figure 5 ) , are in line with recent findings in resting-state EEG and MEG studies in AD 14 , 39 , 53–55 ., In recent years , brain disconnectivity and disturbed network topology has been observed in an increasing number of disorders ( for example schizophrenia , multiple sclerosis , brain tumor , autism , epilepsy ) 56–59 ., It is conceivable that different disease mechanisms and types of network damage ( for example extensive non-hub network damage ) could lead to a similar situation of hub overload and decay ., Computational models like the one described here could be employed to investigate various underlying pathologies and to examine the differences between them ., Several recent studies support the notion that node properties such as degree and centrality may play a crucial role in the pathophysiology of degenerative brain disease 60–62 ., The results of this study suggest that hub regions are vulnerable due to their intrinsically high activity level ., The assumption of activity dependent degeneration leads to hub vulnerability along with many neurophysiologic features of AD ( i . e . as found in quantitative EEG and MEG literature ) ., A recently conducted large fMRI study demonstrated that highly connected cortical regions like the precuneus are even stronger hubs in females than in males: could this perhaps explain the higher levels of early amyloid deposition ánd the higher prevalence of AD in women 63 , 64 ?, The computational model used in this study offers a possible mechanism by which excessive neuronal activity in hubs might lead to the observed macro-scale disruption of brain connectivity and dynamics in AD ., In addition to the presumed role of disinhibition mentioned in the previous paragraph , a prominent role of excessive neuronal activity in AD pathogenesis has been suggested before: several studies have demonstrated a direct link between neuronal activity and the development of amyloid plaques in transgenic mice 20 , 21 , 22 ., Regions that are most active during resting-state show the most outspoken AD-related pathology 4 , 5 , 13 ., Excessive hippocampal activity is related to cortical thinning in non-demented elderly persons , is present in MCI patients , and is related to neurodegeneration in AD 49 , 65 , 66 ., Finally , known risk factors for AD such as genetic profile , age , vascular damage , or common comorbidities like sleep disorders and epilepsy , all predispose to excessive activity and a subsequent burden on metabolism and plasticity 17 , 18 , 66–68 ., On the other hand , protective factors like high level of education and sustained cognitive activity might relieve the burden on hub regions due to frequent activation of task-related circuits , and accompanying DMN deactivation ., Summarizing , vulnerability of cortical hub regions due to their high activity levels may be aggravated or alleviated by the presence of one or more predisposing or protective factors , respectively ( see figure 7 ) ., This line of reasoning implies that changes in brain activity and connectivity are already involved in the very early stages of AD pathology ., In this regard , it is interesting to note that an increasing number of studies show that changes in activity and functional connectivity can be detected before cognitive complaints arise or pathological levels of amyloid are detected with PET and CSF analysis 18 , 69–73 ., Although activity dependent degeneration is quite different from amyloid-induced damage , they need not be mutually exclusive: chronic , excessive activity might lead to amyloid deposition , which in turn causes aberrant activity and neuronal damage: a pathological cycle with different stages ( see also figure 6 ) ., Relatively small increases of extracellular amyloid-beta can increase neuronal activity , especially in neurons with low activity , whereas higher levels cause synaptic depression 74 , 75 ., Palop and Mucke emphasize the role of inhibitory interneuron dysfunction , leading to hypersynchronization 47 ., In conclusion , although these studies provide compelling evidence for a prominent role of neuronal activity , our predictions that hub regions might form the weakest links in AD pathogenesis should be tested in further studies ., Several recent studies use similar computational modeling approaches to examine AD related neurophysiological phenomena: Bhattacharya et al . focus on thalamo-cortico-thalamic circuitry and its relation with alpha band power in AD 38 ., By varying the synaptic strengths in the thalamic module of the model they find that especially the connectivity of synaptic inhibitory neurons in the thalamus has a large influence on alpha power and frequency ., Pons et al . use a neural mass model and human EEG data to investigate the influence of structural pathways on functional connectivity in the aging brain and pre-clinical stages of AD 37 ., Findings in line with our present results are the higher functional connectivity values in MCI and the relation between structural and functional connectivity ., An increase in functional connectivity and network randomness during a memory task was found by Buldú et al . in a MEG study of MCI patients 76 ., Interestingly , the authors also provide a network degeneration model which might explain these observations ., The combination of neural mass modeling and graph theory was used in a recent study from our group 36 ., This study explores the manifestation of modularity in developing networks and investigates the effect of more acute lesions on network dynamics ., The gradual recovery of functional network characteristics that was observed after lesions raises the question whether and to what extent similar mechanisms play a role in neurodegenerative damage; this should be subject of further study ., To describe functional network modularity , the same algorithm and heuristic was used as in the present study ., The computational models used in these studies provide a framework to address different questions and hypotheses concerning brain disease , e . g . different functional lesions ., A novel aspect of the approach in the present study is that a single hypothesis ( ADD ) is proposed as main pathophysiological mechanism of AD ., Comparison to a ‘random degeneration’ ( RD ) model provides further support for the ADD hypothesis , but does not rule out the possibility that other plausible degenerative models exist ., Various methodological choices might have affected our results , and should be taken into account when interpreting them ., First , although the DTI-derived connectivity matrix that served as the basis of our model is in our opinion a solid overall large-scale representation of human cortical connectivity , it was based on data of healthy young adults 24 ., Since AD mainly affects the aging population , and since it has been shown that structural connectivity is altered during aging 77 , results might have been different if structural connectivity data of older subjects had been implemented ., However , the major hub regions seem largely independent of age , justifying our approach that mainly focuses on hub versus non-hub differences ., Furthermore , we now know that AD affects many people below the age of 65 , and that AD pathology is presumably already present for decades before initial symptoms appear ., In a similar way we expect that individual variability in structural connectivity will not have had a major influence on our present approach , since major hub regions appear to be consistent across studies 3 , 64 ., Although the computational model used here could be refined in many ways , e . g . by implementing a larger number of regions , assigning different weights to structural connections , using DSI-derived data , correcting for spatial scale and/or DTI biases , or by using more elaborate and detailed graph analysis , we believe that this would not have affected our main outcome dramatically , since comparing characteristics of hub and non-hub cortical regions does not necessarily require a high level of detail ., By keeping the model and hypotheses as simple as possible , it might be easier to discover or test underlying basic principles and mechanisms of degeneration ., The main motivation to use an NMM network of this size was the observation that topographical maps and atlases of the human cerebral cortex of this order of magnitude are quite common in macroscopic structural and functional connectivity studies ( for an overview , please refer to 39 , 56–58 ., Also , since EEG and MEG studies have comparable network sizes ( 21–300 sensors ) , this is a fairly realistic spatial resolution for NMM-generated dynamics ., Two relevant references are recent computational modeling studies by Deco et al . 27 and Pons et al . 37 ., Varying the structural coupling strength S in our neural mass model can lead to different results , and therefore we have reported its influence on our outcomes ., Similarly , the arbitrary ‘loss’-rate parameter of the ADD function will affect the speed of the degeneration process ., However , since we were mainly concerned with a topological ‘hub versus non-hub’ comparison , the absolute rate of degeneration was of minor importance for this study ., Moreover , loss-rates were equally applied to all connections; network distribution was not selectively influenced by these parameters ., Observations from this study that could be explored further include ADD-induced changes in structural network topology , the relation between spike density and anatomical region , and the lower alpha peak frequency in hub regions ( see Text S1 section 4 ) ., Predictions from our model , especially the close link between local neuronal activity and large-scale connectivity should be verified in longitudinal clinical studies , preferably of normal aging as well as patients with subjective memory complaints ( SMC ) , Mild cognitive impairment ( MCI ) and AD ., To assess structural and functional connectivity as well as large-scale neuronal activity , a combination of DTI and MEG might be the most appropriate method ., Source space analysis of MEG data may help to develop topologically accurate neural mass models ., On a fundamental level , the relation between neuronal connectivity , activity and pathology should be further explored in animal models ., Interestingly , the relation between regional activity and large-scale functional connectivity has recently also addressed with respect to schizophrenia 78 , 79 ., In both studies it is argued that more knowledge of this relation is essential for understanding mechanisms of altered functional connectivity , and this is very much in line with the main message of this study ., Different disease conditions may have specific causes or patterns in which this relationship is harmed , but at the same time universal principles may apply that can help us gain more insight in a range of neuropsychiatric disorders ., In this study we used a neural mass model with DTI-based human topology to demonstrate that brain hub regions possess the highest levels of neuronal activity ., Moreover , ‘Activity dependent degeneration’ ( ADD ) , a damage model motivated by this observation , generates many AD-related neurophysiologic features such as oscillatory slowing , disruption of functional network topology and hub vulnerability ., Early-stage , transient rises of firing rate and functional connectivity in ADD matches observations in pre-clinical AD patients , suggesting that this chain of events is not compensatory , but pathological ., Overall , the results of this study favor a central role of neuronal activity and connectivity in the development of Alzheimers disease ., We used a model of interconnected neural masses , where each neural mass represents a large population of connected excitatory and inhibitory neurons generating an EEG or MEG like signal ., The model was recently employed in two other graph theoretical studies 30 , 36 ., The basic unit of the model is a neural mass model ( NMM ) of the alpha rhythm 26 , 80 , 81 ., This model considers the average activity in relatively large groups of interacting excitatory and inhibitory neurons ., Spatial effects ( i . e . distance ) are ignored in this model; brain topology is introduced later by coupling several NMMs together ., The average membrane potential and spike density of the excitatory neurons of each of the NMMs separately were the multichannel output related to neuronal activity that was subject to further analysis ., All neural mass model parameters and functions are summarized and explained in Text S1 , section 3 ( see also figure S4 and table S1 ) ., A diffusion tensor imaging ( DTI ) based study by Gong et al . published in 2009 that focused on large-scale structural connectivity of the human cortex resulted in a connectivity matrix of 78 cortical regions 24 , 82 ., The connectivity matrix was implemented in our model software , and used as topological framework for the 78 coupled NMMs ., Coupling between two NMMs , if present , was always reciprocal , and excitatory ., Note that at the start of the simulation , the coupling strength between all NMM pairs ( S ) was identical , and the only difference between the cortical regions ( or NMMs ) was their degree of connectivity to other neural masses ( cortical regions ) ., Since the coupling strength S was an arbitrarily chosen parameter , repeated analyses were performed with different values of this variable ( see for example figure 3 ) ., For the present study the model was extended to be able to deal with activity dependent evolution of connection strength between multiple coupled NMMs ., Activity dependent degeneration ( ADD ) was realized by lowering the ‘synaptic’ coupling strength as a function of the spike density of the main excitatory neurons ., For each neural mass the spike density of the main excitatory population is stored in a memory buffer that contains the firing rates of the last 20 steps in the model ., Step size depends on the sample frequency ., At each new iteration , the highest spike density value of the last 20 sample steps is determined and designated as maxAct ., From maxAct a loss is determined according to the following relation: ( 1 ) Since maxAct is non-negative , loss will be a number between 0 and 1 ., Next , the coupling values C1 ( connections between main excitatory population and inhibitory population ) , C2 ( connections between inhibitory population and main excitatory population ) , Pt ( thalamic input to main excitatory population ) and S ( structural coupling strength between neural masses ) are all multiplied by loss to obtain their new lower values ., To assess the specificity of ADD , results were compared with a random degeneration ( RD ) model in which the maxAct variable was discarded , so damage was equally applied to all regions , regardless of their level of activity ., The effects of ADD and RD were measured by changes in total power ( local average membrane potential ) and spike density , and these two measures were used as representations of neuronal activity in further analyses ., Note that the time scale of the data generated by the model is equal to normal brain activity and EEG/MEG data , but that the ADD and RD procedures have a more abstract time scale ., The exact duration of the degenerative procedures was not considered relevant to our present focus on the relation between connectivity and activity , but could be considered to reflect a length that is representative of a neurodegenerative process , spanning years to decades ( see figures 3–5 ) ., The computational model was programmed in Java and implemented in the in-house developed program BrainWave ( v0 . 9 . 04 ) , written by C . J . Stam ( latest version available for download at http://home . kpn . nl/stam7883/brainwave . html ) ., Since spectral analysis is a common neurophysiological procedure that provides clinically relevant information in neurodegenerative dementia , we included this in our experiments ., Fast Fourier transformation of the EEG-like oscillatory output signal was used to calculate for all separate regions the total power ( absolute broadband power , 0–70 Hz ) as well as the absolute power in the commonly used frequency bands delta ( 0 . 5–4 Hz ) , theta ( 4–8 Hz ) , lower alpha ( 8–10 Hz ) , higher alpha ( 10–13 Hz ) , beta ( 13–30 Hz ) and gamma ( 30–45 Hz ) ., To quantify large-scale synchronization as a measure of interaction between different cortical areas , we used the Synchronization likelihood ( SL ) , which is sensitive to both linear and non-linear coupling 83 , 84 ., SL was calculated for all frequency bands , and the matrix containing all pairwise SL values served as the basis for all further graph theoretical analyses of functional network characteristics ., Graph theoretical properties of the structural DTI network that were relevant for our hub study such as node degree , betweenness centrality , and local path length were published in the original article by Gong et al 24 ., One new measure we introduced was the ‘normalized node strength’ , which is the ratio of the structural degree of a node after activity dependent damage over its original degree ., This measure was used to track structural connectivity loss and to compare the loss of degree in hubs and non-hubs ., For functional network analysis , connectivity matrices were subjected to topographical analysis ., The functional degree of a node is defined as the sum of all its link weights 85 ., Averaging the functional degree over all nodes gives the overall functional degree of a network ., To match the functional network to the given structural network ( minimizing effects of graph size and density ) , we constructed a binary , unweighted matrix that was obtained after using a threshold that resulted in a network with an average degree of 8 , close to that of the structural network ( which was 8 . 1 ) ., All graph theoretical measures used in this study are summarized in table 2 , for more exact definitions please refer to 14 , 85 ., For functional modularity analysis , we used Newmans modularity metric combined with a simulated annealing process ( previously described in 55 , 86 ) ., For the baseline , pre-ADD analysis in experiment 1 and 2 , the data-generating procedure using the model was repeated twenty times to obtain a representative amount of data ., On each run the subsequent spectral , functional connectivity and graph theoretical analysis was performed , and then all results of these twenty runs were averaged prior to further statistical analysis ., Regional results were visualized using 6 bins ascending in structural degree , each containing 13 regions ., All 13 regions in the bin with the highest mean degree were classified as hubs ., Standard deviations of bins are displayed as error bars ., For bivariate correlations Pearsons test was used .

Introduction, Results, Discussion, Materials and Methods

Brain connectivity studies have revealed that highly connected ‘hub’ regions are particularly vulnerable to Alzheimer pathology: they show marked amyloid-β deposition at an early stage ., Recently , excessive local neuronal activity has been shown to increase amyloid deposition ., In this study we use a computational model to test the hypothesis that hub regions possess the highest level of activity and that hub vulnerability in Alzheimers disease is due to this feature ., Cortical brain regions were modeled as neural masses , each describing the average activity ( spike density and spectral power ) of a large number of interconnected excitatory and inhibitory neurons ., The large-scale network consisted of 78 neural masses , connected according to a human DTI-based cortical topology ., Spike density and spectral power were positively correlated with structural and functional node degrees , confirming the high activity of hub regions , also offering a possible explanation for high resting state Default Mode Network activity ., ‘Activity dependent degeneration’ ( ADD ) was simulated by lowering synaptic strength as a function of the spike density of the main excitatory neurons , and compared to random degeneration ., Resulting structural and functional network changes were assessed with graph theoretical analysis ., Effects of ADD included oscillatory slowing , loss of spectral power and long-range synchronization , hub vulnerability , and disrupted functional network topology ., Observed transient increases in spike density and functional connectivity match reports in Mild Cognitive Impairment ( MCI ) patients , and may not be compensatory but pathological ., In conclusion , the assumption of excessive neuronal activity leading to degeneration provides a possible explanation for hub vulnerability in Alzheimers disease , supported by the observed relation between connectivity and activity and the reproduction of several neurophysiologic hallmarks ., The insight that neuronal activity might play a causal role in Alzheimers disease can have implications for early detection and interventional strategies .

An intriguing recent observation is that deposition of the amyloid-β protein , one of the hallmarks of Alzheimers disease , mainly occurs in brain regions that are highly connected to other regions ., To test the hypothesis that these ‘hub’ regions are more vulnerable due to a higher neuronal activity level , we examined the relation between brain connectivity and activity in a computational model of the human brain ., Furthermore , we simulated progressive damage to brain regions based on their level of activity , and investigated its effect on the structure and dynamics of the remaining brain network ., We show that brain hub regions are indeed the most active ones , and that by damaging networks according to regional activity levels , we can reproduce not only hub vulnerability but a range of phenomena encountered in actual neurophysiological data of Alzheimer patients as well: loss and slowing of brain activity in Alzheimer , loss of synchronization between areas , and similar changes in functional network organization ., The results of this study suggest that excessive , connectivity dependent neuronal activity plays a role in the development of Alzheimer , and that the further investigation of factors regulating regional brain activity might help detect , elucidate and counter the disease mechanism .

medicine, neurobiology of disease and regeneration, neural networks, neuroscience, mathematics, computational neuroscience, alzheimer disease, biology, dementia, nonlinear dynamics, central nervous system, neurological disorders, neurology, neurophysiology

journal.pntd.0001650

Genetic Characterization of Trypanosoma cruzi DTUs in Wild Triatoma infestans from Bolivia: Predominance of TcI

Trypanosoma cruzi , the agent of Chagas disease , is a serious threat to health in the Americas , accounting for the highest disease burden in Latin American , with eight to nine million people infected and 25–90 million at risk 1–3 ., This parasite , which belongs to the order Kinetoplastida , is mainly transmitted by blood-sucking bug vectors ( Hemiptera , Reduviidae , Triatominae ) but also by blood transfusion and oral transmission ., Moreover , newborns can be infected through vertical transmission ., There are currently 141 recognized species of triatomines , but only five of them , belonging to three genera ( Triatoma , Rhodnius , and Panstrongylus ) can be considered important vectors of Chagas disease 4 ., With the exception of one species ( T . rubrofasciata ) , all Triatominae have populations living in natural habitats in contact with wild mammals , birds , or reptiles 5–8 ., T . cruzi is found in three overlapping ecosystems ., One is related to the wild environment and involves wild populations of triatomines and mammals ( sylvatic cycle ) ; the second one depends on artificial structures surrounding human dwellings where vector populations associated to domestic and synanthropic animals live ( peridomestic cycle ) ; the third one occurs in dwellings and involves triatomines living indoors , humans , and domestic animals ( domestic cycle ) ., Population genetics analyses have shown that T . cruzi has a predominantly clonal mode of evolution and exhibits considerable phenotypic and genetic diversity 9 ., This population genetics model refers to genetic clonality , i . e . , limited or absent genetic recombination with persistence of durable multilocus associations , whatever the cytological mechanism of reproduction 10 ., Six distinct genetic lineages or discrete typing units ( DTUs ) 11 have been described 12 , 13 ., They have recently been validated by a committee of experts and labeled TcI to TcVI 14 ., TcI is ubiquitous and prevalent in different sylvatic cycles ., However , it is responsible for the large majority of human infections in the Amazon basin and more northern countries as well as part of the infections in South Cone countries of South America ., It exhibits considerable genetic diversity 9 , 15 , 16 with possible subclustering 17 , 18 ., TcII , V , and VI are mainly associated with domestic cycles and prevalent in human infections in the Southern Cone countries; TcV and TcVI are hybrid genotypes , whose putative ancestors are TcII and TcIII 19 , 20 ., Finally , TcIII and IV are more rarely sampled throughout the endemic area and seem to be specific to sylvatic cycles , with few reports of human infection ., In Bolivia , Triatoma infestans ( Hemiptera: Reduviidae ) remains the main domestic vector of T . cruzi ., It is the target of the National Control Program based on house-spraying with residual insecticides ., Wild populations of T . infestans are now seriously considered a problem to keep the villages free of triatomines 21–23 ., Sylvatic populations of the vector have been described in different Andean valleys in Bolivia 21 , 22 , 24 , 25 ., Moreover , the detection of wild foci of T . infestans in the Bolivian Chaco has extended the distribution of wild populations to the lowlands of Bolivia 26 ., Two main genotypes belonging to TcI and TcV were previously identified in the domestic cycle in regions where T . infestans was the vector 27–33 ., Moreover , these genotypes had been identified in strict sympatry in the same host 27 , 34 ., In contrast , few data are available on DTUs circulating in sylvatic T . infestans ., Dujardin et al . 35 found that wild T . infestans were infected with the same T . cruzi genotypes as domestic T . infestans ( TcI and TcV ) , with the same frequencies ., They took this as additional evidence of a lack of speciation between wild and domestic T . infestans ., Another study identified TcI as the only DTU in a wild focus located in the valley of Cochabamba , Bolivia 25 ., Among the genetic markers that can identify the different T . cruzi groups the non-transcribed spacer region of the mini-exon gene was previously proposed to discriminate T . cruzi I ( now TcI ) , T . cruzi II ( now TcII ) , T . cruzi zymodeme 3 ( now TcIV ) , using the mini-exon multiplex PCR ( MMPCR ) 36 ., Recently , the MMPCR analysis was applied on a large sample of stocks ( previously characterized by multilocus typing ) belonging to the six DTUs , showed 3 DTU tags: a 200 bp PCR product for TcI , a 250 bp for TcII , TcV and TcVI and a 150 bp for TcIII and TcIV 37 ., This method was also successfully applied for rapid DTU identification in triatomine digestive tracts 5 , 38 ., Moreover , among housekeeping genes , the glucose-6-phosphate isomerase ( Gpi ) , a single-copy nuclear gene , presented a sequence polymorphism that is valuable for characterization of DTUs 19 , 39 ., In this study , we applied the MMPCR and Gpi sequencing for the characterization of T . cruzi DTUs directly in the digestive tract of wild T . infestans collected in Bolivia ., The triatomines were sampled in sylvatic environments from April to November 2009 ( Figure 1 ) ., Collections were carried out using mice-baited adhesive traps 40 in different ecotopes such as under bush and bromeliads , rocks , burrows , hollow trees , and stone walls ., The bugs were transported alive to the laboratory for species confirmation using morphological taxonomic keys 41 ., Table 1 summarizes the geographical and ecotope origin of the collected T . infestans according to the ecoregions defined in 42 ., Briefly , the majority of the bugs were collected in Andean valleys where sylvatic foci have been previously described 21 , 24 and the others were collected from new foci in the Bolivian Chaco where the “dark morph” type of T . infestans was discovered 22 , 26 ., Before dissection , feces from each bug were examined for the presence of trypanosomatid by direct microscopic observation at ×400 magnification ( mo ) ., Bugs were then dissected under a safety hood , and the digestive tracts stored at −20°C ., DNA was extracted from triatomine digestive tracts with the QIAamp DNA mini kit ( Quiagen , Courtaboeuf , France ) , according to the blood sample protocol ., The multiplex primer set was as previously described: three oligonucleotides derived from the hypervariable region of T . cruzi mini-exon repeats , and a common downstream oligonucleotide , corresponding to sequences present in the best conserved region of the mini-exon gene used as opposing primer in the multiplex reaction ., PCR conditions were according to Fernandes et al . 36 , with slight modifications ., DNA was amplified in a 25 µl reaction volume containing 1× reaction buffer , 1 . 5 mM MgCl2 , 50 µM of each nucleotide , 0 . 2 µM of each primer , 0 . 5 UI of Taq polymerase ( Roche Applied Science , Penzberg , Germany ) ., The amplifications were performed in a thermocycler ( Eppendorf , Hamburg , Germany ) , in previously described conditions 36 ., PCR products were separated on 3% agarose gel using the molecular weight marker Smart Ladder ( Eurogentec , Angers , France ) and visualized under UV with Ez-vision ( Amresco , Solon , OH , USA ) ., The discrimination between DTUs was according to Aliaga et al . 37 ., A 652 bp fragment was amplified with a set of primers , forward ( Gpi-L ) starting at position 591 of the gene ( 5′-CGCCATGTTGTGAATATTGG-3′ ) and reverse ( Gpi-R ) starting at position 1246 ( 5′–TTCCATTGCTTTCCATGTCA-3′ ) , from a subsample of 15 DNAs which had given an intense MMPCR band ., DNA was amplified in a 25 µl reaction volume containing 0 . 75 mM MgCl2 , 0 . 2 mM of each nucleotide , 0 . 4 µM of each primer , 2 . 5 UI of Taq DNA polymerase ( Roche Applied Science , Penzberg , Germany ) , and 20 ng of DNA template ., The amplification took place in a thermocycler ( Eppendorf , Hamburg , Germany ) , with the following cycle conditions: 94°C for 3 min; 94°C for 1 min , 58°C for 1 min , and 72°C for 1 min ( 35 cycles ) ; 72°C for 5 min ., Purification and direct sequencing of both strands of DNA amplicons were performed by the company MACROGEN ( Seoul , South Korea ) ., Sequences were aligned and corrected using BioEdit software v . 7 . 0 . 9 43 , and a 458 bp partial sequence was resolved for each sequence ( from nucleotide site 691–1148 ) ., A total of 333 DNA samples from digestive tracts of wild T . infestans were processed in MMPCR for DTU identification ., Among them 20 . 1% were adults of both sexes , 64 . 1% 4th and 5th instar nymphs , and 15 . 8% 2nd and 3rd instar nymphs ., Before dissection , the bug feces were examined ( 85 . 0% of the total sample ) using microscopy ., The parasite infection rate was 71 . 7% in Andean specimens while no positive insect was found among the 17 specimens from Chaco ( GC ecoregion ) ., The accordance between microscopic observation and MMPCR was 82% , with 93 . 1% of positive MMPCR when mo was positive and 17 , 5% when mo was negative ., The identification of the three DTUs was assessed by determining the molecular weight of the MMPCR products for each sample ( Table 1 , Figure 2 ) ., The results showed that the large majority ( 98 . 3% ) of the 234 wild T . infestans specimens were infected by TcI ( PCR products of 200 bp ) ., Only one sample from an adult T . infestans ( “dark morph” type ) collected at site 32 ( GC ecoregion ) gave a 250 bp MMPCR product corresponding to TcII , TcV , or TcVI ., Three other samples ( sites 23 , 25 , and 29 ) gave a 150 bp MMPCR products corresponding to either TcIII or TcIV ., The MMPCR product of the specimen of the latter group captured at site 29 was sequenced and the DNA fragment ( 64 bp ) matched the TcIII reference stock named M5631 ( accession No AF050521 . 1 and AY367126 . 1 , 98% identity ) ., A partial sequence of the Gpi obtained from 15 samples ( 13 from the set corresponding to TcI , one from the set corresponding to TcII , TcV , or TcVI , and one from the set corresponding to either TcIII or TcIV ) were sequenced in order to explore the variability within TcI and to discriminate the DTUs within the other sets ., The 458 bp partial sequences ( starting at site 691 and ending at site 1148 of the entire CL Brener stock gene , accession no . XM815802 . 1 ) were aligned with the sequences corresponding to T . cruzi reference stocks belonging to the six DTUs previously deposited in GenBank ( Table 2 ) ., With no ambiguity , each sequence under study had been attributed to a DTU ., Within TcI , 3 sequences were observed: the most frequent ( 11 stocks ) presented 100% identity with the two identical sequences from TcI reference stocks ( OPS21 and P/209 ) deposited in GenBank; the two other sequences exhibited a single mutation and the Vis01 stock identified in a triatomine bug captured at site 27 , presented a heterozygous pattern at nucleotide position 940 ., The sequence of the Char09 of the second set ( corresponding to TcII , TcV , or TcVI ) , detected in a “dark morph” ( site 32 ) , presented 100% identity with two identical TcII reference stocks ( Tu18cl2 and CBBcl3 ) ., For the sample of the last set corresponding to either TcIII or TcIV ( Tor05 from site 25 ) , the sequence presented 100% identity with two identical TcIII reference stocks ( M6241cl6 and X110/8 ) ., Recently , an active search for new foci of wild T . infestans in Bolivia enabled us to show that their distribution was broader than initially described 21 , 44 ., Also , few data on the genetic characterization of T . cruzi stocks infecting these vector populations were available , apart from the work by Dujardin et al ( 35 , conducted using multilocus enzyme electrophoresis , and the detection of the only TcI at Cotapachi 15 km west of Cochabamba city ( Andean area ) 25 ., In the present context , where wild T . infestans highly infected can enter houses and recolonize them after domestic populations have been eliminated by insecticide spraying , it is important to know which T . cruzi DTUs are carried by the vectors ., In this study , 234 T . cruzi stocks isolated from wild T . infestans were characterized by MMPCR ., The vectors came from several areas mainly situated in two ecoregions in Bolivia , the Inter-Andean Dry Forest and the Gran Chaco where the “dark morph” was found ., Regarding the detection of parasites in bugs , the correlation between detection of infection by microscopy ( mo ) and by the method of MMPCR was high ( 82% ) ., However , some infected bugs ( mo positive ) were MMPCR negative probably due to the presence of inhibitors factors of the polymerase in the DNA extracts ., At the contrary , several samples mo negatives were MMPCR positive , which allowed us to detect and identify few strains in dark morph specimens ., In the overall sample , the TcI DTU is widely dominant , but in the Andean and intermediate areas TcIII stocks were detected ., In the lowlands , only TcI and TcII were characterized in the “dark morph” specimens ., Interestingly , the DTU distribution in wild T . infestans is very different from that reported in domestic T . infestans collected before the vector control campaigns undertaken on a large scale in Bolivia since 2003; the frequencies of TcI only , TcV only , and mixed infections ( TcI and TcV ) were 38 . 6% , 16 . 8% and 32 . 7% respectively 31 ., At the same time , TcV was mostly detected in patients during the chronic phase of the infection while both TcI and TcV were detected in younger patients with early infection 28 , 45 ., As for the vectors , it was suggested that the domiciliation of T . infestans had taken place in high Andean valleys and that the dispersal of domestic T . infestans to other areas had occurred by human transport 46 , 47 ., The current observations do not fit these hypotheses , since the only TcI ( and to a lesser extent TcIII ) would then have been introduced into domestic cycles but not TcV , unless it is assumed that TcV disappeared from the wild T . infestans cycle in the Andes valleys after its domiciliation ., Among the six T . cruzi DTUs , TcV and TcVI are composed of stocks that appear to be recent hybrids between TcII and TcIII 19 ., Consequently , it is tempting to speculate that they might have arisen in an area where the putative parental DTUs coexist ., Moreover , this hybridization event is still considered to have occurred much earlier than human colonization in South America 48 ., Consequently , parental and hybrid DTUs are likely to coexist in the sylvatic cycle in a putative geographical area in South America ., Lately , the Andean origin of T . infestans was challenged by the hypothesis of Chaquean origin 26 , 44 , 49 , 50 ., If parental and hybrid DTUs are not found in the sylvatic cycle in the Andes , an alternative might be the Gran Chaco region ., These is no information regarding the genetic characterization of T . cruzi in the sylvatic cycle at the Bolivian lowlands , except for a report of a TcVI stock isolated from a Didelphis marsupialis specimen captured on the Amazon slope 51 ., In the Paraguayan Chaco , TcII , TcIII and TcV have been identified in different wild mammal species 52 and in the Argentinean Chaco TcI was identified in Didelphis albiventris and TcVI in one Conepatus chinga 53 ., In spite of fairly scarce data , the hypothesis that hybrid DTUs may have originated in Chaco should be considered , especially considering the detection of all DTUs except for TcIV in the domestic cycle in the Bolivian Gran Chaco ( unpublished data ) ., The search for DTUs circulating in sylvatic cycles will provide more valid information on the evolution of T . cruzi than studies conducted in domestic cycles where the geographical distribution of the DTUs is skewed by passive transport of parasites ( human migration , triatomine transports ) and by the selection of specific DTUs by hosts , considering that host diversity is lower in the domestic cycle than in sylvatic cycles .

Introduction, Materials and Methods, Results, Discussion

The current persistence of Triatoma infestans ( one of the main vectors of Chagas disease ) in some domestic areas could be related to re-colonization by wild populations which are increasingly reported ., However , the infection rate and the genetic characterization of the Trypanosoma cruzi strains infecting these populations are very limited ., Of 333 wild Triatoma infestans specimens collected from north to south of a Chagas disease endemic area in Bolivia , we characterized 234 stocks of Trypanosoma cruzi using mini-exon multiplex PCR ( MMPCR ) and sequencing the glucose phosphate isomerase ( Gpi ) gene ., Of the six genetic lineages ( “discrete typing units”; DTU ) ( TcI-VI ) presently recognized in T . cruzi , TcI ( 99 . 1% ) was overdominant on TcIII ( 0 . 9% ) in wild Andean T . infestans , which presented a 71 . 7% infection rate as evaluated by microscopy ., In the lowlands ( Bolivian Chaco ) , 17 “dark morph” T . infestans were analyzed ., None of them were positive for parasites after microscopic examination , although one TcI stock and one TcII stock were identified using MMPCR and sequencing ., By exploring large-scale DTUs that infect the wild populations of T . infestans , this study opens the discussion on the origin of TcI and TcV DTUs that are predominant in domestic Bolivian cycles .

Chagas disease is a neglected parasitic disease transmitted by bugs ( vectors ) and represents a serious health problem in the Americas ., Although the transmission generally occurs in the houses where the bugs are living , wild populations of vectors are now considered a problem because these populations might enter the houses and recolonize them after eliminating of house populations by insecticide spraying ., This is the case of the Southern countries where Triatoma infestans , the principal vector , transmits Trypanosoma cruzi the agent of the disease ., This parasite presents a large genetic variability and it is important to know which T . cruzi genotypes are carried by the vectors ., The authors found that in the wild T . infestans from the Bolivian Andean region , a principal group of genotype was circulating ., In the lowlands ( Bolivian Chaco ) , another additional genotype group was detected ., Together with exploring at large scale which genotypes are infecting T . infestans wild populations , this study opens the discussion on the origin T . cruzi genotype groups ., Also this study completes our basic knowledge on T . cruzi subspecific genetic variability , and therefore brings new tools for molecular epidemiology of Chagas disease .

medicine, infectious diseases, biology, population biology, evolutionary biology

journal.pgen.1007582

Structure and function of archaeal histones

In eukaryotes , octameric histone cores compact DNA by wrapping an approximately 150-bp unit twice around its surface , forming a nucleosome 12 , 13 ., Nucleosomes interact with each other , yielding an additional level of DNA organization in the form of a fibre ., Besides a role in compaction , histones also play roles in genome organization , replication , repair , and expression , which highlights the nucleosome as a very important complex affecting a vast array of cellular processes ., Characteristic of core histone proteins of all different origins is a common “histone fold”: two short and one long α-helix , separated by loops 14–18 ., In eukaryotes , the histone core consists of two H2A-H2B dimers and a H3-H4 tetramer , around which approximately 146 bp of DNA is wrapped twice ( Fig 1A ) ., It has been suggested that smaller histone assemblies , such as tetrasomes ( H3-H4 tetramers ) , hexasomes ( H3-H4 tetramers plus one H2A/H2B dimer ) , and hemisomes ( a H3-H4 dimer plus one H2A/H2B dimer ) , have functional roles as intermediate structures during , for example , transcription elongation 19–22 ., The linker histone H1 ( which lacks the characteristic histone fold ) binds at the entry and exit points of the DNA wrapped around the octameric histone core 23 , 24 ., The association of histone H1 constrains an additional 20 bp of DNA and allows for the formation of the 30-nm fibre , which results in tighter compaction 25 , 26 ., Also , flexible N-terminal tails that protrude from eukaryotic histones contribute to tighter DNA packaging ., These tails may interact with either the DNA or the histone surface on another nucleosome , which stabilizes the close association of nucleosomes 27–29 ., Furthermore , post-translational modifications of amino acid residues in the N-terminal tails , such as acetylation , methylation , phosphorylation , ubiquitination , and biotinylation , are a key instrument for the cell to regulate gene expression , the DNA damage response , and many other processes 30–32 ., For instance , while heterochromatin ( tightly packed DNA ) is typically devoid of acetylated lysines , euchromatic ( lightly packed ) regions typically contain histones with acetylated lysines ., In general , euchromatin contains actively transcribed genes ., Histone acetylation is believed to cause a locally less condensed chromatin structure in vivo , which is permissive to transcription ., In particular the lysine-rich histone H4 tail seems to be crucial in the modulation of chromatin structure 27 ., In vitro , H4 tails are required for higher order chromatin folding 33–35 , which can be disrupted by acetylation of K16 27 ., Nucleosome function and level of genome compaction can be altered in a multitude of ways , providing flexible and versatile mechanisms for tuning the cell’s dynamic chromatin structure and transcription regulation ., Archaeal genomes also encode proteins that are involved in shaping DNA architecture ., Genes coding for histones are found in many species throughout the domain ( Table 1 ) ., In some species a homologue of the bacterial DNA bender HU was identified 36 , 37 ., Nucleoid-associated proteins ( NAPs ) from the Alba family ( also known as the Sulfolobus solfataricus 10b ( Sso10b ) protein family ) are abundant and widely conserved in Archaea ., Notably , Alba family proteins have also been identified in eukaryotes 38 ., Characteristic of these proteins is the formation of protein–DNA filaments and bridges between DNA duplexes 39–42 ., Two Alba family proteins with different functionalities have been studied in Archaea ., Alba1 cooperatively forms filaments in a sequence-independent and concentration-dependent manner in Crenarchaeota , whereas Alba2 only occurs as heterodimer with Alba1 and does not form filaments 38 , 42 ., Alba proteins have been shown to repress transcription in vitro 43 ., In Euryarchaeota , some species express sequence-specific Alba proteins 44 , which , like Alba1 homodimers at low-protein concentrations and Alba1-Alba2 heterodimers , may form loops by bridging two DNA duplexes 45 ., Other proteins affecting DNA conformation are Sso10a family proteins , which are able to bend and bridge DNA as well as form filaments on DNA 46 , 47 and the monomeric DNA benders Cren7 and Sul7 48 , 49 ., Cren7 and Sul7 have exclusively been identified in members of the Crenarchaeota phylum , whereas Sso10a has been found in some Crenarchaeota and Euryarchaeota ., Other less widespread NAPs include transcription regulator of the maltose system-like 2 ( TrmBL2 ) , methanogen chromosomal protein 1 ( MC1 ) , Methanopyrus kandleri 7 kDa protein ( 7kMk ) , Sulfolobus solfataricus protein 7c ( Sso7c ) , and crenarchaeal chromatin protein 1 ( CC1 ) 50–55 ., The histones found in Archaea are widespread throughout the domain but are absent in most Crenarchaeota ., They have the same histone fold as eukaryotic histones , but N-terminal histone tails have not been identified ( Fig 1B ) ., Linker histones , homologous to eukaryotic H1 , have not been found ., Archaeal histones exist as dimers in solution , which have been shown to bend DNA 56 , 57 ., These histone dimers can be homodimeric or heterodimeric 58 , as many archaeal species express , or at least encode , more than one histone variant ., In Methanothermus fervidus ( class Methanobacteria ) , the two histone variants are expressed at different levels and ratios at different growth phases , suggesting a distinct function for both proteins 59 ., In addition to binding as dimers , archaeal histones have been reported in vivo and in vitro to bind DNA as tetramers 60–62 , wrapping the DNA once ., However , micrococcal nuclease ( MNase ) digestion patterns of Thermococcus kodakarensis ( class Thermococci ) chromatin suggest that histone–DNA complexes consist of discrete multiples of a dimeric histone subunit ( i . e . , not limited to dimers and tetramers ) in vivo without obvious dependence on the DNA sequence 63 ., Based on the latter observations , it was proposed that histone dimers multimerize and wrap DNA into a filament of variable length 17 , 63 ., The crystallography study of Luger and coworkers on histone HMfB from M . fervidus indicates that these histones assemble into an endless left-handed rod in vitro , which we propose to call a “hypernucleosome” ( Fig 2 ) ., Note that these complexes were assembled on SELEX-optimized DNA previously shown to favor tetrameric nucleosome assembly 64 ., The number of wraps in the hypernucleosome , which is the DNA bending 360° around the histone multimer , scales linearly with the number of histone subunits , resulting in a tight packaging of DNA ., The authors also provide evidence that mutation-directed perturbation of hypernucleosome function in vivo alters response to nutrient change in T . kodakarensis , suggesting a role in transcription ., Both eukaryotes and Archaea encode histone proteins , which seem to be involved in response to environmental cues by their involvement in transcription regulation ., It has been suggested that eukaryotic histones evolved from archaeal histones 65 ., This hypothesis is supported by the high similarity at the amino acid sequence level and in secondary structure 66 , 67 ., Suggestive of an archaeal origin of eukaryotic histones is also the dimeric nature of archaeal histones; archaeal histone complexes are built from dimers , but members of the archaeal class Halobacteria express a “tandem histone . ”, In these tandem histones , the histone folds are linked end-to-end 68–70 ., This implies that the histone folds always occupy the same position and role in the naturally linked dimer ., This leads to the relaxation of evolutionary constraints in parts of the histone , an example of subfunctionalization 71 , 72 ., According to this hypothesis , the histone folds further evolved in a divergent way , leading to an asymmetric dimer ., This may have been an ancestor of H3-H4 , which later separated to become two individual proteins and corresponding genes 66 ., The eukaryotic H3-H4 tetramer resembles the tetramer found in Archaea , and it has been suggested that H2A and H2B have arisen from H3 and H4 later on in histone evolution 66 ., Indeed , H3 and H4 are more similar to archaeal histones than H2A and H2B , supporting this hypothesis ., From this point , eukaryotic histones have further evolved into histone variants , highly homologous substitutes of canonical eukaryotic histones , which often play a specialist role in a wide variety of cellular processes 73 ., Unlike canonical histones , which are mainly expressed during DNA replication , histone variants are expressed in a replication-independent manner 74 , 75 ., Histone variants of H2A and H3 are widely known and studied , whereas only a few examples have been found of diversified H2B and H4 76 ., The evolutionary pressure for the evolution of dimer-based histones to octameric histones and their subsequent variants was long believed to be DNA compaction 66 ., The fact that eukaryotic cells undergo mitosis , in which chromosomes are highly compacted , together with the abundance of gene-poor regions may have favored a histone conformation that wraps DNA twice ( eukaryotic octamer ) instead of once ( archaeal tetramer ) and that via its N-terminal tails has the ability to compact DNA at a higher order ., Open questions that remain are how histone evolution was driven and what the roles of archaeal histones and their variants are in genome packaging and regulation ., Here , we discuss the amino acid residues that are responsible for the formation of the hypernucleosome based on a sequence analysis of a subset of archaeal histones that includes histones from all phyla that contain genes coding for histones ( Fig 3 ) ., Also , we analyze the ability of histones to form a hypernucleosome and the effects of N- or C-termini longer or shorter than the consensus on histone multimerization and transcription regulation ., We emphasize the histones in species from recently discovered phyla , which are believed to be an evolutionary link to eukaryotes 11 , 77 ., Based on elements that archaeal histones have in common and elements that differ from that consensus , we discuss some of the open questions regarding gene regulation by archaeal histones ., A striking finding based on the amino acid sequence comparison reveals that two histones from Candidatus Heimdallarchaeota archaeon LC_3 ( only Histone A HA shown in Fig 3 ) , one from Candidatus Huberarchaea archaeon CG_4_9_14_3_um_filter_31_125 and one from Candidatus Bathyarchaeota archaeon B23 , contain an N-terminal tail , which was previously thought to exist only in eukaryotic histones and only recently reported for Heimdallarchaeota 64 ., In eukaryotes , these tails stabilize a higher order of compaction by interacting with either the DNA or another nucleosome ., The tails of the two histones from Heimdallarchaeota and Huberarchaea are of roughly the same length and sequence composition as eukaryotic H4 tails ( see Fig 3 ) ., Prompted by the importance of the eukaryotic histone tails in modulating chromatin structure and function 27 , 32 , we constructed a molecular model of a hypernucleosome formed by Histone A ( HA ) from Heimdallarchaeota LC_3 to investigate its potential function ( see Methods section ) ., The model illustrates how three subsequent arginines ( R17–R19 ) could facilitate passing of the tails through the DNA gyres ( Fig 4 ) ., The tails exit the hypernucleosome through DNA minor grooves , similar to eukaryotic histone tails , and might position their lysine side chains to bind to the hypernucleosomal DNA or to other DNA close by , facilitating ( long-range ) genomic interactions in trans ., Like the H4 tail that is subject to acetylation of lysines K5 , K8 , K12 , and K16 91 , lysines in the Heimdallarchaeal histone tail may well be subject to acetylation ., Archaeal genomes are known to have several candidate lysine acetyltransferase and deacetylase enzymes , including proteins belonging to the ELP3 superfamily , to which transcription elongation factor and histone acetyltransferase ELP3 belongs 92–94 ., Searches using the ProSite database ( http://prosite . expasy . org , 95 ) and Protein Information Resource ( http://pir . georgetown . edu , 96 ) further reveal that the Heimdallarchaeota LC_3 genome contains multiple gene products containing the Gcn5-related N-acetyltransferase domain , which is present in many histone acetyltransferases 97 ., Interestingly , a potential “reader” protein that binds modified lysines can also be identified ., This protein , HeimC3_47440 , contains a YEATS-domain , which has recently been shown to bind histone tails that carry acetylated or crotonylated lysines 98–101 ., Comparison with the closest homolog of known 3D structure , YEATS2 ( 35% identity , PDB-id 5IQL , 102 ) , shows that the binding site for the modified lysine side chain is strictly conserved in the archaeal protein ., Notably , only Candidatus Bathyarchaeota , which also features tailed histones , contains a detectable homolog of HeimC3_47440 ., The presence of lysine-containing N-terminal tails in combination with histone modification writers and readers suggests that Archaea use post-translational modifications in a similar way to Eukaryotes as modulators of genome compaction and gene activity ., The tail of the Huberarchaea histone also contains lysine residues that are found at the same position as some of the lysines of the H4 tail ., However , no proteins involved in post-translational modification of histone tails have been identified in this phylum ., Other histones , for example from Candidatus Lokiarchaeota CR_4 , Candidatus Odinarchaeota LBC_4 , Nanoarchaeum equitans , and Thermofilum pendens , contain a short N-terminal tail of 5–10 residues ., Also , histones with a C-terminal tail have been found ., The histone from the euryarchaeal species Methanocaldococcus jannaschii ( class Methanococci ) has a 28-residue tail , which seems to be unique among archaeal histones ., Other C-terminal tails are up to 11 residues long ( as compared to Methanothermus fervidus HMfB ) and appear in Caldiarchaeum subterraneum , Candidatus Bathyarchaeota SMTZ-80 , Candidatus Heimdallarchaeota LC_3 , Candidatus Lokiarchaeota CR_4 , and all histones found in Crenarchaeota ., These short C-terminal tails are similar in length to the H4 C-terminal tail , that is reported to play a role in the promotion of histone octamer formation in eukaryotes 103 ., The genomes of some archaeal species contain genes for histone truncates ., The histone from Haloredivivus sp ., G17 , member of the candidate phylum Nanohaloarchaeota , and the histone from Candidatus Bathyarchaeota archaeon B24 both lack part of the N-terminal α-helix ( α1 ) , and one histone from Candidatus Lokiarchaeota GC14-75 is reduced in length at the C-terminus ., The remainder of the C-terminal amino acids likely does not form a C-terminal helix ( α3 ) in this histone from Candidatus Lokiarchaeota ., Although histones of reduced length or containing tails lack part of the histone fold , they likely still possess DNA-binding properties ., Therefore , they possibly have functional roles in the regulation of genes ., Both eukaryotic histones and HMfB form dimers , a process that is driven by a hydrophobic core ( involving residues A24 , L28 , L32 , I39 , and A43 in HMfB ) as well as a crucial salt bridge for a stable histone fold ( R52-D59 in HMfB ) 14 ., These hydrophobic residues and the salt bridge are conserved among Archaea ., This indicates that archaeal histones have very similar tertiary structures 14 , 104 ., Also , residues that play an important role in DNA binding are present in all examined histones , including the arginines that anchor archaeal histone dimers to the DNA minor grooves ( R10 and R19 in HMfB ) 14 ., Both eukaryotic H3-H4-dimers and HMfB dimers can form tetramers by hydrogen bonding of H49 and D59 ( HMfB ) and additional hydrophobic interactions in the interface ( L46 and L62 in HMfB ) 105 , pairs of residues that , too , are generally conserved among archaeal histones ( Fig 3 ) ., The HMfB–DNA cocrystal structure reveals left-handed wrapping of DNA around a histone-multimer core 64 ( Fig 2 ) ., This structure supports the model in which HMfB dimers multimerize along DNA into an “infinite” hypernucleosome , thereby linearly compacting the DNA approximately ten-fold ., It is likely that hypernucleosomes grow or shrink by association or dissociation of dimers at both ends ., The resolution of the crystal structure allowed us to identify several interacting residues between layers of dimers that may be important for stabilizing the complex ( Fig 5 ) ., Based on this structural information , the propensity of different archaeal histones to multimerize can be predicted ., In Table 2 , we set out three criteria for hypernucleosome formation by archaeal histones ., Firstly , conservation of residues in the dimer–dimer interface ( L46 , H49 , D59 , and L62 in HMfB ) is required , as forming a tetramer is the first step in multimerization ., Secondly , residue G16 , which is positioned at the stacking interface of the hypernucleosome ( Fig 5 ) , is crucial in permitting formation of the hypernucleosome 64 ., Bulkier residues at this position interfere with multimerization 64 ., Lastly , favorable interactions between histone dimers i and i+2 and i+3 , here termed stacking interactions , will contribute to stability of the compacted hypernucleosome ., The HMfB hypernucleosome crystal structure shows three stacking interactions , hydrogen bonds from K30 to E61 , E34 to R65 , and R48 to D14 ( Figs 3 and 5 ) ., Scrutiny of histone sequences reveals that most archaeal histones meet these criteria and are thus likely to form hypernucleosomes ( Table 2 , marked + ) ., We identified two to seven potential stacking interactions for this group of histones , which may affect hypernucleosome stability and compactness ., Fewer interactions may allow for more “breathing” of the hypernucleosome structure , yielding hypernucleosomes that are more flexible or “floppy . ”, We predict such structures to be formed also by a number of archaeal histones that do not fully meet our criteria ( Table 2 , marked ± ) ., For example , Candidatus Heimdallarchaeota LC_3 HA and Candidatus Lokiarchaeota GC14_75 HLkE have H49N and D59S substitutions , respectively , which likely weakens the crucial hydrogen-bonding interaction at the dimer–dimer interface 105 ., Similarly , substitution of the hydrophobic residues 46 and 62 for more hydrophilic or bulkier ones would lead to a less stable dimer–dimer interface , as for Candidatus Heimdallarchaeota LC_3 HC and Candidatus Bathyarchaeota B23 ., In the presence of the canonical dimer–dimer interface , bulky substitutions at position 16 likely also result in a more open hypernucleosome structure , as for Candidatus Odinarchaeota LCB_4 ., Three archaeal histone species fail multiple criteria in our analysis , indicating that these cannot form hypernucleosomes ., These histone species are Haloredivivus sp G17 , Nanosalina J07AB43 HB , and Euryarchaeal Methanococcoides methylutens ( class Methanomicrobia ) that all combine defects in the dimer interface with a bulky substitution at position 16 and few potential stacking interactions ( Table 2 , marked– ) ., In particular , Nanosalina J07AB43 Histone B ( HB ) shows a H49D substitution and a glutamic acid at position 62 , making the dimer surface highly negatively charged and thus very unlikely to interact with another dimer ., It is remarkable that most of the histones having N- or C-terminal tails or N- or C-terminal truncations additionally have substitutions in the dimer–dimer and/or stacking interface that will affect hypernucleosome formation ., Histones with reduced ability to form compact hypernucleosomes are expected to exhibit different roles in shaping the genome , like simple DNA bending or site-specific interference with histone multimerization ., Interestingly , the genomes of several organisms encode histones that we predict are able to multimerize as well as histones that probably do not multimerize ., This suggests that they may , in addition to directly binding to promoters , also be able to affect gene regulation by multimerization ., MNase-seq experiments have shown that histones position upstream and downstream of a promoter region 106 ., This , in combination with knock-out studies showing both up- and down-regulation of transcription levels , leads to the hypothesis that histones are important for transcription regulation in the relatively well-studied phylum Euryarchaeota 45 , 69 , 107 , 108 and may play a similar role in other histone-coding phyla ., The exact mechanisms by which histones act in regulation are at this moment largely unknown ., What is the mechanistic role of histones in the regulation of gene expression ?, Is the hypernucleosome , with a mechanism analogous to that in bacterial gene repression , able to block promoter regions and other regulatory elements , thereby making them inaccessible to the transcription machinery 109–112 ?, In Bacteria , such a mechanism exists for H-NS and partition protein B ( ParB ) proteins , in which filaments laterally spread from a nucleation site , often a high-affinity DNA sequence 113–116 ., Specific high-affinity sites have been identified both in vivo and in vitro in Archaea 61 , 106 , 117 , 118 ., The role of such high-affinity sites may be to position the hypernucleosome on the genome and could be a key feature in archaeal genome regulation ., In Archaea , cooperative lateral spreading of filaments has been reported for Alba proteins 40 , 42 , 119 , 120 ., Also , promoter occlusion mechanisms and competitive binding of archaeal NAPs and transcription factors have been reported 45 , 121 , 122 ., In addition , how dynamic are hypernucleosomes , and how does the cell control the size of the hypernucleosome in order for it to be functional ?, Is up- and down-regulation of histone expression important in fine tuning this process ?, Another option for control of hypernucleosome size is heteromerization of histone variants with different stacking propensity ., Heteromerization of such histone variants , for instance HA and HB from Nanosalina J07AB43 ( Table 2 ) , could restrict hypernucleosome size to fewer subunits ., Distinct expression patterns of histone variants at different growth phases or as a result of environmental cues such as osmolarity 59 , 107 , may alter the composition and size of the hypernucleosome ., However , so far , histone variants have been poorly studied in Archaea ., The results of our predictions on hypernucleosome formation clearly point out the need for in vitro and in vivo studies explicitly addressing all of these questions ., Histones from Archaea and eukaryotes are similar in tertiary but not in quaternary structure when bound to DNA ., While eukaryotic histones form octamers on the DNA , archaeal histones form filaments of variable size: hypernucleosomes ., Important residues responsible for DNA binding , dimer–dimer interactions , and stacking interactions are mostly conserved among Archaea , including Asgard Archaea , Bathyarchaeota , and other newly discovered Archaea ., In these recently discovered Archaeal phyla , histone tails and truncated histone variants were also found ., In terms of evolution , it appears that , based on fragmentary data derived from extant lineages , the hypernucleosome has progressively become more flexible as histones with N-terminal and C-terminal tails and additional terminal helices ( like in H2A and H2B in the nucleosome ) developed ., Furthermore , the appearance of additional DNA-binding residues and positively charged N-terminal tails may have increased the affinity of histones for DNA 123 ., These changes in dimer structure and DNA affinity may have stabilized octameric nucleosomes and disfavored multimerization ., Specifically , the emergence of the eukaryotic H2A-H2B heterodimer blocked hypernucleosome formation since H2A lacks the dimer–dimer interface , and H2B contains an additional helix at its C-terminus that blocks the stacking interface ., The histone tails from Candidatus Heimdallarchaeota are likely to function in similar ways as those of eukaryotic histones ., They are lysine rich and potentially subject to post-translational modification , thereby possibly affecting the histone’s interactions with other actors ., Alternatively , they may provide stabilization of the hypernucleosome via interactions with DNA in cis or in trans ., Since it is believed that eukaryotes share their latest common ancestor with Candidatus Heimdallarchaeota , eukaryotic histones may have evolved from the predecessors of the tail-containing Heimdallarchaeal histones ., As some histone proteins that have an N-terminal tail ( Candidatus Heimdallarchaeota LC_3 HA and Bathyarchaeota archaeon B23 ) seem to form less stable hypernucleosomes , these histones may represent an evolutionary transition towards a different mechanism of gene regulation , switching from regulation by multimerization and compaction toward regulation by histone tail modifications ., Although the hypernucleosome structure is suggestive of stacking interactions between dimers in adjacent turns , experimental evidence for such interactions is lacking ., Also , the functional role of tails , as well as truncates , has yet to be proven experimentally ., In vitro hypernucleosome reconstitution experiments and in vivo foot-printing assays of species expressing nonstandard histones combined with mutation of the residues proposed to be involved in stacking interactions could answer these questions ., Lastly , the existence of post-translational modifications of residues in archaeal histone tails , as well as their effect on transcription regulation , remains to be discovered and would give an important insight into the evolution of transcription regulation and genome folding from Archaea to eukaryotes ., We have included histones from every histone-encoding ( candidate ) phylum within the archaeal domain in our analysis ., We show different histones from the same organism if the predicted stacking properties are very dissimilar ., Sequences were aligned with Clustal Omega 124 using default parameters , removing gaps ., Structural analysis of the selected archaeal histones and assessment of potential hypernucleosome formation was done by inspecting the conservation of residues that are important for multimerization in the published HMfB hypernucleosome structure 64 ., Comparative multichain modeling was performed in MODELLER 125 using default parameters to construct dimer models of the archaeal histones ., These models were superimposed onto HMfB dimers in the hypernucleosome crystal structure to assess whether alternative or additional interactions were possible in the different archaeal histone complexes ., The molecular model of the histone HA dimer from the Heimdallarchaeota LC_3 genome was constructed by multitemplate modeling in MODELLER 125 using otherwise default parameters ., The HMfB dimer in the hypernucleosome 64 was used as a structural template for the histone fold and eukaryotic histone H3 and H4 as structural templates for the N-terminal tails ., An initial model for the Heimdall HA hypernucleosome was obtained by superimposing the HA dimer model onto HMfB in the hypernucleosome crystal structure , with either an H3-like or an H4-like tail conformation ., To optimize the path of the tails through the DNA gyres and remove major steric clashes , the HA dimer model and surrounding DNA was excised from the initial model and water refined separately using High-Ambiguity Driven Docking ( HADDOCK ) 126 , imposing ambiguous interaction restraints between HA residues 14–19 and the surrounding 3-bp section of DNA , using otherwise default parameters .

Introduction, Histones are found in some newly discovered Archaea, Conclusion, Methods

The genomes of all organisms throughout the tree of life are compacted and organized in chromatin by association of chromatin proteins ., Eukaryotic genomes encode histones , which are assembled on the genome into octamers , yielding nucleosomes ., Post-translational modifications of the histones , which occur mostly on their N-terminal tails , define the functional state of chromatin ., Like eukaryotes , most archaeal genomes encode histones , which are believed to be involved in the compaction and organization of their genomes ., Instead of discrete multimers , in vivo data suggest assembly of “nucleosomes” of variable size , consisting of multiples of dimers , which are able to induce repression of transcription ., Based on these data and a model derived from X-ray crystallography , it was recently proposed that archaeal histones assemble on DNA into “endless” hypernucleosomes ., In this review , we discuss the amino acid determinants of hypernucleosome formation and highlight differences with the canonical eukaryotic octamer ., We identify archaeal histones differing from the consensus , which are expected to be unable to assemble into hypernucleosomes ., Finally , we identify atypical archaeal histones with short N- or C-terminal extensions and C-terminal tails similar to the tails of eukaryotic histones , which are subject to post-translational modification ., Based on the expected characteristics of these archaeal histones , we discuss possibilities of involvement of histones in archaeal transcription regulation .

Both Archaea and eukaryotes express histones , but whereas the tertiary structure of histones is conserved , the quaternary structure of histone–DNA complexes is very different ., In a recent study , the crystal structure of the archaeal hypernucleosome was revealed to be an “endless” core of interacting histones that wraps the DNA around it in a left-handed manner ., The ability to form a hypernucleosome is likely determined by dimer–dimer interactions as well as stacking interactions between individual layers of the hypernucleosome ., We analyzed a wide variety of archaeal histones and found that most but not all histones possess residues able to facilitate hypernucleosome formation ., Among these are histones with truncated termini or extended histone tails ., Based on our analysis , we propose several possibilities of archaeal histone involvement in transcription regulation .

chemical compounds, dna-binding proteins, microbiology, organic compounds, review, archaea, basic amino acids, amino acids, epigenetics, chromatin, chromosome biology, proteins, gene expression, chemistry, histones, nucleosomes, archaean biology, biochemistry, eukaryota, cell biology, organic chemistry, genetics, biology and life sciences, lysine, physical sciences, organisms

journal.ppat.1003249

Dimeric RNA Recognition Regulates HIV-1 Genome Packaging

Retroviruses are RNA viruses that replicate through a DNA phase , in which viral DNA is integrated into the host genome to form a provirus 1 ., Retroviral genomes in virions are dimers , consisting of two copies of full-length , unspliced RNA , each of which encodes all of the genetic information needed for virus replication 2 , 3 , 4 , 5 , 6 ., Packaging of the retroviral genome is mediated by interactions between the viral structural protein Gag and the cis-acting element ( s ) , collectively called the packaging signal , in the viral RNA 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ., In some retroviruses , such as HIV-1 , RNA partner selection and the initiation of the dimerization process occur in the cytoplasm 15; therefore , RNA dimerizes before encapsidation 16 , 17 ., A 6-nt sequence located at the 5′ untranslated region of the HIV-1 genome , termed the dimerization initiation signal ( DIS ) , forms intermolecular base-pairs between two HIV-1 RNAs to initiate the dimerization process 13 , 18 , 19 , 20 , 21 ., There are multiple DIS sequences in the circulating HIV-1 strains; two of the most common sequences are GCGCGC and GUGCAC 22 , 23 ., As base-pairing is involved in RNA partner selection for the initial dimerization process , the identity of the DIS sequence affects the ability of two HIV-1 RNAs derived from different proviruses to be copackaged together to form heterozygous particles 16 ., Many aspects of retroviral RNA genome encapsidation , such as the cis- and trans-acting elements that mediate the specific packaging of the viral genome into particles , are well-studied ., In contrast , other aspects of RNA packaging , such as how retroviruses regulate the number of genomes packaged into virions , are poorly understood ., For example , it is not known whether the viral particle would package more than two copies of RNA from a viral genome that is much smaller than wild type ., The possibility that multiple smaller RNA genomes are incorporated into one virion has been suggested previously 24 ., However , the sensitivity of RNA detection method has made it difficult to confirm this suggestion ., Similarly , it is also unclear whether retroviruses can efficiently package two copies of RNA that are much longer than their respective wild-type genomes ., The lengths of the RNA genomes for most of the known avian and mammalian orthoretroviruses are close to 8–10 kb 25 ., Additionally , the inability to efficiently encapsidate RNAs that are >2 kb larger than the wild-type genome led to the proposal of “packaging limit” of viral RNA 26 , 27 , 28; this hypothesis implies that there may be physical barrier ( s ) to the encapsidation of larger RNA genomes ., Other studies showed that vector RNAs much larger than viral genomes can be packaged into particles 29 , 30; however , it is not known whether one copy or two copies of vector RNAs were encapsidated into a particle ., By determining the viral RNA content of individual particles , our recent study demonstrated that most HIV-1 particles contain viral RNA; furthermore , two copies of RNA are packaged in each virion 16 ., These observations indicate that the amount of HIV-1 RNA packaged into a particle is tightly regulated ., We propose that HIV-1 RNA packaging is regulated by one of two mutually exclusive mechanisms: encapsidation may be regulated either by the total mass of viral RNA packaged or by the copy number of the packaged RNAs ., These two hypotheses predict that viral RNA length will have different effects on packaging ., The “RNA mass” hypothesis predicts that when the viral RNAs are smaller , more copies of RNA will be packaged; additionally , HIV-1 particle cannot accommodate two copies of viral RNAs much larger than those of “wild-type” virus ., In contrast , the “copy number” hypothesis predicts that two copies of packagable viral RNAs will be encapsidated , independent of their size ., To test these two hypotheses , we examined packaging of HIV-1 genomes that are much larger or smaller than the wild-type 9-kb RNA by directly visualizing the viral RNA contents of individual particles ., In this previously described single-virion analysis assay 16 , HIV-1 genomes are engineered to contain stem-loop sequences recognized by either the coat protein of bacteriophage MS2 or the BglG protein from Escherichia coli , which are tagged with yellow fluorescent protein ( YFP ) and a red fluorescent protein , mCherry , respectively ., Viral particles are visualized by fluorescence microscopy as a portion of the Gag proteins are tagged with cerulean fluorescent protein ( CeFP ) , and the packaged viral RNAs are distinguished by their YFP or mCherry signals ., In the current study , we modified this system to express RNA genomes that are ≈3 kb or nearly 17 kb so that we could examine the effects of viral RNA length on RNA packaging ., Our results indicate that HIV-1 packages two copies of viral genome regardless of whether the RNA is ≈3 kb or ≈17 kb ., We also found that HIV-1 can package one copy of the genome when the RNA contains two dimerization/packaging signals allowing for the formation of an intramolecular dimer ( self-dimer ) ., This observation indicates that the recognition of the dimeric RNA structure is a key element in the regulation of RNA encapsidation by HIV-1 ., We generated a series of constructs expressing larger RNA genomes based on the previously described HIV-1 constructs GagCeFP-MSSL and GagCeFP-BglSL 16 , which are referred to as Base-MSL and Base-BSL , respectively ( Figure 1A ) ., Base-MSL and Base-BSL are derived from NL4-3 and contain all of the cis-acting elements required to express , export , and package HIV-1 RNA; they express functional Gag-CeFP , Tat , and Rev proteins , whereas the pol , env , vif , vpr , and vpu genes were inactivated by deletions ., Additionally , Base-MSL and Base-BSL contain stem-loop sequences in the pol gene positions that are recognized by the coat protein of MS2 bacteriophage and the BglG protein of E . coli , respectively ., We inserted viral and nonviral sequences into Base-MSL and Base-BSL to generate the HIV-1 constructs Long-MSL and Long-BSL , respectively; the full-length viral RNAs expressed from these two constructs are 13–14 kb ( Figure 1A ) ., We then inserted additional nonviral sequences into Long-MSL and Long-BSL to generate XLong-MSL and XLong-BSL , respectively , which express 16–17-kb full-length viral RNAs ., Although not shown , each Gag-CeFP-expressing construct has a corresponding construct that expresses untagged Gag; in each experiment , Gag-CeFP- and Gag-expressing constructs were cotransfected at equimolar ratios to preserve normal particle morphology 31 ., The size of HIV-1 genomic RNA from the NL4-3 molecular clone is 9 . 1 kb ., Previously we showed that genomic RNAs from our Base constructs , ranging from 6 to 8 kb in size , are efficiently packaged; RNA signals were detected in >90% of the viral particles 16 ., To determine the efficiency with which HIV-1 packages RNAs larger than wild-type genomes , we cotransfected HIV-1 constructs expressing untagged and CeFP-tagged Gag along with MS2-YFP and Bgl-mCherry into 293T cells , collected viral particles , and analyzed them by fluorescence microscopy ., In these experiments , the Gag signal is detected in the CeFP channel , whereas MS2 and BglG stem-loop-containing RNA signals are detected in the YFP and mCherry channels , respectively ., The RNA-packaging efficiency is calculated as the percentage of CeFP+ particles that are positive for RNA signals ( mCherry+ or YFP+ ) ., Our results showed that viral RNAs were packaged efficiently ( >93% ) in particles derived from Base , Long , and XLong constructs ( ≥4 independent experiments; >9 , 000 viral particles from each construct analyzed ) ., Similar results were obtained from BglG and MS2 stem-loop-containing constructs ( Table S1 ) ; for simplicity , these results are combined and shown in Figure 1B ., These findings indicate that RNA genomes close to 17 kb can be packaged efficiently into HIV-1 particles ., To determine whether viral particles generated by Long constructs encapsidate one or more genomic RNAs , we coexpressed Long-BSL and Long-MSL and performed single-virion analyses on the particles ., If HIV-1 RNAs from two viruses are expressed at equal levels in the cells and assorted randomly before encapsidation , 50% of the viral population should be heterozygous particles that contain one RNA from each parent virus ( Hardy-Weinberg equilibrium ) ., In our previous experiments using the Base constructs , we found that ≈45% of the CeFP+ particles were YFP+mCherry+ , which was very close to the predicted 50% heterozygous particles in the viral population 16 ., Our analyses of the particles that were generated by coexpressing the Long-MSL and Long-BSL constructs showed that ≈40% of the CeFP+ particles were YFP+mCherry+; similarly , ≈38% of the CeFP+ particles that were generated by coexpressing XLong-MSL and XLong-BSL were YFP+mCherry+ ( ≥3 independent experiments; >20 , 000 particles from each pair of constructs analyzed; Figure 1C , Table S2 ) ., Compared with particles produced by the Base constructs ( ≈44%; Figure 1C , Table S2 ) , virions derived from Long and XLong constructs generated heterozygous particles at efficiencies of 91% ( 40%/44% ) and 86% ( 38%/44% ) ., These results revealed that most of the particles derived from Long and XLong constructs contained two copies of HIV-1 RNAs ., In Base , Long , and XLong RNAs , the RNA-binding protein recognition sites are located in pol; therefore , only unspliced RNA should contain these sequences and be labeled with MS2-YFP or Bgl-mCherry ., To confirm that the full-length RNAs were encapsidated , we first performed denaturing Northern analyses using virion RNA generated from NL4-3 as a control ., As shown in a representative Northern blot ( Figure 2A ) , full-length RNAs are evident in all four samples at the expected sizes; however , their abundance decreases with the size of the genome ., This result is most likely caused by nicking of the RNA either in the particle or during preparation of the RNA samples ., If the breakage of RNA is the same in a given length of RNA ( for example , one break every 5 kb ) , then more of the smaller RNA ( such as 7 kb ) than the larger RNA ( ≈17 kb ) can be expected to remain intact ., It is worth noting that we did not observe distinct bands of short transcripts corresponding to spliced variants of the Long and XLong RNAs in these analyses ., To assess the size of the RNA dimer in the viral particles , we analyzed virion RNAs by velocity sedimentation through a sucrose gradient ., We isolated RNAs from the viral particles derived from NL4-3 or XLong-BSL and loaded the RNA samples without heat treatment to preserve viral dimers on a 15%–30% sucrose gradient along with cellular RNAs isolated from uninfected 293T cells ., After centrifugation , 37 fractions were collected , and the amounts of HIV-1 RNA and human 28S rRNAs were analyzed by quantitative real-time RT-PCR using primers and probes annealing to gag and 28S rRNAs , respectively ., In these experiments , RNAs with higher molecular weights should sediment faster and migrate to fractions closer to the bottom of the tube ., Results from a set of representative velocity sedimentation experiments are shown in Figure 2B ., The NL4-3 RNA dimer ( ≈18 kb ) migrates as a distinct population with the peak located at fraction 24 , whereas the XLong RNA dimer migrates to fractions closer to the bottom of the tube with a peak at fraction 19 ., The 28S rRNAs ( 5 kb ) from the uninfected 293T cells migrated slower than RNAs from NL4-3 or XLong and were detected in fractions closer to the top of the tubes with a peak at fraction 33 ., These results confirm that the dimeric RNA genomes packaged into viral particles from XLong constructs are larger than those from NL4-3 ., Results from our single-virion analyses revealed that most of the viral particles derived from Long and XLong constructs contained two copies of viral RNA genomes that were larger than 9 kb ., To examine whether encapsidating larger RNA distorts virion morphology , we performed EM studies on various viral particles ., The pro genes of Base , Long , and XLong constructs were deleted; therefore , we expected that these constructs would generate immature particles ., As a control , we included viral particles produced from an NL4-3-derived construct ( H0-PR* ) that contains intact gag-pol with an inactivating mutation ( D25N ) in pro ., Particles derived from all of these constructs exhibit similar immature morphology ( Figure S1A ) ; furthermore , these particles have similar sizes regardless of whether they were derived from Base , Long , or XLong constructs ( >250 viral particles measured in each category; Figure S1B ) ., These results indicate that packaging genomic RNAs almost twice as large as the wild-type , 9-kb RNA genome does not significantly alter the virion morphology; therefore , HIV-1 viral particles have the capacity to encapsidate two copies of genomes that are significantly larger than 9 kb ., The “RNA mass” hypothesis proposes that a certain mass of viral RNA is packaged into each particle ., Therefore , this hypothesis predicts that when the HIV-1 full-length RNA is much shorter than the wild-type HIV-1 genome , more than two copies of RNA should be encapsidated into one virion ., To test this hypothesis , we generated Mini HIV-1 constructs that express ≈3-kb viral RNAs containing elements essential for the expression and packaging of the RNA , including an intact 5′ untranslated region , the first 200 nt of gag , the Rev-response element , and sequences recognized by the MS2 or BglG RNA-binding proteins ( Figure 3A ) ., To test whether Mini RNAs are packaged efficiently by HIV-1 Gag , we coexpressed Mini-MSL or Mini-BSL with helper constructs that express Gag , Gag-CeFP , Tat , Rev , and MS2-YFP and Bgl-mCherry ., We found that ≈70%–80% of the CeFP+ particles were mCherry+ or YFP+ ( 5 independent experiments , >14 , 000 particles analyzed for each construct; Table S3 ) , indicating that Mini RNAs were packaged into viral particles , albeit at an efficiency lower than that of Base RNA ., The ability of two HIV-1 RNAs to be copackaged into the same virus is strongly influenced by their DIS sequences ., We have shown that RNAs from two Base vectors can be copackaged at a near-random level when both RNAs have GCGCGC in their DIS , whereas copackaging is decreased 2 . 3-fold when the two RNAs contain discordant DIS palindromes , one with GCGCGC and the other with GUGCAC 16 , 17 ., GCGCGC and GUGCAC are the DIS sequences in most subtype B and subtype C HIV-1 , respectively 22 ., We used this feature of HIV-1 biology to examine the number of Mini RNAs encapsidated into HIV-1 particles by single-virion analysis ., We reasoned that if one Mini RNA dimer is packaged into a viral particle , then the inefficient heterodimer formation will be directly reflected in the ratio of particles with two RNA colors ( YFP+mCherry+ ) ( Figure 3B ) ., Therefore , compared with two Mini RNAs that both contain GCGCGC in their DIS , Mini RNAs containing discordant palindromes ( one with GCGCGC and another with GUGCAC ) should have a reduced ratio of viral particles with two RNA colors; furthermore , the level of reduction should be similar to that of the Base vectors ., In contrast , if multiple dimers are packaged into a viral particle , then the effects of reducing RNA heterodimers on the generation of particles with two RNA colors will be lessened , because many particles will contain one dimer in which both RNAs have GCGCGC and one dimer in which both RNAs have GUGCAC , thereby increasing the proportion of particles with two RNA colors ( Figure 3B ) ., To test the effects of DIS sequences on the formation of particles with two RNA colors , we coexpressed Mini-MSL and Mini-BSL , both of which have GCGCGC in their DIS , along with helper constructs; ≈25 . 7% of the CeFP+ particles exhibited both mCherry and YFP signals ., When we coexpressed Mini-BSL with Cdis-Mini-MSL that contains GUGCAC in its DIS , we found that ≈9 . 6% of the CeFP+ particles exhibited both RNA signals ( YFP+mCherry+ ) , which corresponds to a 2 . 7-fold reduction ( 25 . 7%/9 . 6% ) from that produced by two Mini RNAs with GCGCGC in their DIS ., We also examined the effects of changing the DIS sequences of Mini constructs into two complementary nonpalindromic sequences and generated 6G-Mini-MSL and 6C-Mini-BSL that contain GGGGGG and CCCCCC in their DISs , respectively ., When coexpressed with helper constructs , 6G-Mini-MSL and 6C-Mini-BSL produced ≈40 . 4% of heterozygous particles , which corresponds to a 1 . 6-fold increase from that generated by Mini-MSL and Mini-BSL ( 5 experiments , >15 , 000 particles analyzed for each set of constructs; Table S4 ) ., The effects of altering DIS sequences in Base vectors and in Mini vectors on the production of YFP+mCherry+ particles among CeFP+ particles are shown in Figure 3C ., In these comparisons , the ratio of the heterozygous particles produced from two RNAs with GCGCGC in their DIS was set to 1 and the effects of changing the DIS sequence to discordant palindromes or complementary nonpalindromes are shown relative to that of vectors with GCGCGC ., These comparisons revealed that changing DIS sequences influenced the ratios of heterozygous particles at the same levels in both Base and Mini constructs ., Therefore , these results indicate that , even when HIV-1 genomic RNAs were much shorter than wild type ( ≈3 kb versus ≈9 kb ) , they were still encapsidated as one dimer and not as multiple dimers ., Taken together , these results show that HIV-1 particles have the capacity to accommodate HIV-1 RNA significantly larger than the 9-kb wild-type genome ., However , regardless of whether the RNA was ≈17 kb or ≈3 kb , one dimer was packaged into a viral particle ., These findings do not support the “RNA mass” packaging hypothesis; rather , they are consistent with the hypothesis that HIV-1 regulates its genome encapsidation by copy number ., Our results from constructs with large RNA or Mini RNAs are consistent with the hypothesis that HIV-1 regulates genome packaging by RNA copy number ., It is possible that HIV-1 regulates genome packaging by encapsidating one RNA dimer; alternatively , HIV-1 may exert the regulation by packaging two copies of viral RNA ., We sought to determine the unit ( i . e . , one dimeric RNA or two monomeric RNAs ) that HIV-1 uses to regulate RNA packaging ., Sakuragi and colleagues previously demonstrated that by inserting into the env gene a second copy of the 5′ leader sequence including the DIS , the resulting RNAs can form both intermolecular dimers and intramolecular dimers ( self-dimers ) 32 ., The intermolecular dimer and self-dimer are distinguished by using nondenaturing Northern analyses; the intermolecular dimer is a complex of two RNAs , whereas the self-dimer contains one RNA that migrates to a position expected for a monomeric RNA ., In the experiments by Sakuragi et al . , ≈60% and ≈40% of the virion RNAs were intermolecular and self-dimers , respectively 32 ., We used this strategy and generated HIV-1 vectors containing two packaging signals ( Figure 4A ) by inserting into the pol gene of the Base vectors a segment of the NL4-3 genome from U5 to the first 34 nt of gag , with a mutation in the splice donor site to abolish its function 32 ., We generated viruses derived from these vectors containing two identical dimerization signals and analyzed the virion RNAs by nondenaturing Northern analyses ., A representative nondenaturing Northern analysis is shown in Figure 4C ., Before heat treatment , most of the virion RNAs from the parental Base vectors were dimers ( 94%; mean from 6 experiments ) ; in contrast , virion RNAs from vectors with two identical dimerization signals contained both an intermolecular dimer and a faster-migrating population consistent with monomeric RNAs ., The mean of the intermolecular dimers in 6 experiments was 66% , similar to that reported by Sakuragi and colleagues 32 ., However , these Northern analyses results cannot address whether one or two copies of self-dimers are packaged in one particle; thus , we performed single-virion analyses to address this issue ., If HIV-1 regulates genome encapsidation by packaging two copies of RNAs , then two copies of the self-dimer should be packaged , and the ratio of YFP+mCherry+ particles should remain the same as that generated by the Base vectors ., In contrast , if HIV-1 regulates the virion genome by packaging one dimeric RNA , then one copy of the self-dimer should be packaged , and the ratio of YFP+mCherry+ particles should be lower than that generated by the Base vectors ( Figure 4B ) ., We examined particles produced from the coexpression of constructs in which the DIS sequence in both packaging signals are from subtype B ( GCGCGC ) , Bdis-BSL-Bdis and Bdis-MSL-Bdis , and found that the YFP+mCherry+ particle ratio in the viral population was reduced from ≈44% to ≈32% ( Table S2 and S5 ) ., Our Northern analyses showed that approximately 66% of the virion RNAs were intermolecular dimers , assuming equal expression and random copackaging , half of these viruses should be heterozygous particles ( ≈33% ) ., If one copy of the self-dimer was packaged , these particles will only confer one RNA color and will not increase the ratio of heterozygous particles; hence , we would expect that ≈33% of the virions would be YFP+ mCherry+ particles ., Therefore , the level of reduction in heterozygous particles was consistent with the theoretical prediction based on one copy of the self-dimer being packaged ( Figure 4D ) ., To exclude the possibility that the duplicated HIV-1 sequences containing the second DIS affected RNA packaging , we generated and examined additional constructs containing discordant DIS palindromes ., These constructs , Cdis-MSL-Bdis and Cdis-BSL-Bdis , contain GUGCAC in their DIS at the 5′ end of the viral RNA and GCGCGC in the second DIS within the sequences inserted in the pol gene ( Figure 4A ) ., As the two DIS sequences within the RNA are discordant palindromes , we expected that self-dimer formation would be discouraged and RNAs derived from these constructs would preferentially form intermolecular dimers ., If so , these constructs would generate heterozygous particles efficiently ., Nondenaturing Northern analyses demonstrated that most of the RNAs ( 98% ) migrated to the position of the intermolecular dimer ( Figure 4C ) ., Single-virion analyses showed that coexpression of Cdis-MSL-Bdis and Cdis-BSL-Bdis produced heterozygous particles at a level similar to that of Base-MSL and Base-BSL viruses ( Figure 4D and Table S5 ) ., These results indicate that when HIV-1 RNAs form self-dimers , only one copy of RNA is packaged ., Therefore , HIV-1 can bypass the requirement for packaging two copies of RNA by recognizing a single copy of RNA containing a dimeric structure ., Taken together , these findings suggest that HIV-1 regulates RNA packaging by recognizing a dimeric RNA , which leads to the packaging of two copies of the wild-type , full-length viral genome ., One of the essential steps in generating an infectious retroviral virion is the packaging of the RNA genome ., The mechanism that regulates RNA packaging has many implications for retroviral evolution ., For example , if a virion cannot accommodate two copies of full-length genomes exceeding a certain size , then this limitation would be a major factor in shaping the economical organization of the viral genome ., Additionally , the mechanism that regulates RNA packaging and heterozygosity can affect genome diversity by altering the potential for recombination ., Recombination occurs when reverse transcriptase copies genetic information from different portions of the two copackaged viral RNAs ., If a certain mass of RNA is packaged , then multiple copies of smaller RNAs , or one large viral RNA , would be packaged into one virion; therefore , the size of the viral genome would directly affect the recombination potential of the virus ., In this report , we address the long-standing question of how a retrovirus regulates the number of viral genomes packaged into each virion and show that HIV-1 regulates its RNA encapsidation not by the mass of the viral genome , but by packaging two copies of RNAs based on the recognition of an RNA dimer structure ., A major mechanism for retroviral oncogene transduction is the packaging of read-through transcripts followed by recombination 33 , 34 , 35 ., The read-through transcripts are often significantly larger than the full-length genome , demonstrating that retroviruses can package RNAs larger than their genomes ., However , the encapsidation efficiency of these read-through RNAs and whether one or two copies of the RNAs are packaged remains unknown ., In this study , we found that most of the particles derived from XLong constructs with RNA close to 17 kb contain two copies of viral genomes; furthermore , EM analyses showed that the size and morphology of these particles are indistinguishable from those containing RNA genomes that are less than 9 kb ., Therefore , immature HIV-1 particles can accommodate two copies of genomes larger than 9 kb ., Despite its ability to package larger RNAs , HIV-1 uses overlapping open reading frames and splicing , rather than expanding its genome size , to encode all the genes that are required for efficient replication ., We speculate that a larger genome has other fitness costs , such as inefficient expression , lower RNA stability , and reduced efficiency in completing reverse transcription , nuclear import , or integration , some of which were observed in another retrovirus 29; the collective fitness burden associated with these factors may limit the ability of HIV-1 to expand its genome ., We have shown that HIV-1 regulates RNA packaging by recognizing a dimeric RNA structure ., Specifically , particles packaging ≈3-kb Mini RNAs still contained only one dimer , not multiple dimers ., Furthermore , RNAs containing two packaging signals that form self-dimers were encapsidated into HIV-1 particles as a single copy of the genome ., How does HIV-1 package one dimer and not multiple dimers ?, We hypothesize that Gag-dimeric RNA interaction is the nucleation point of HIV-1 virus assembly , and that this interaction promotes the association of other Gag proteins leading to the formation of the virion ., Because only one nucleation point is required to promote Gag recruitment , the dynamics of the virus assembly leads to the packaging of one dimer ., The molecular mechanism of preferential packaging of dimeric RNAs is well-established for murine leukemia virus ., It was shown that RNA dimerization causes conformational changes and exposes high affinity nucleocapsid ( NC ) binding sites that are buried in the monomeric RNA 36; furthermore , mutations of these binding sites led to significant decrease in the levels of RNA genome packaging 37 ., A recent study shows that in HIV-1 dimeric RNA , interaction occurs between U5 and a region near the Gag translation start codon that leads to increases in NC binding 38 ., Hence , using the differences in Gags affinities to bind to dimeric versus monomeric viral RNA may be a general mechanism used by many retroviruses to ensure the packaging of dimeric RNA ., As shown in this and other studies , the DIS plays an important role in the initiation of RNA dimerization and the selection of the copackaged RNA partner ., However , the DIS is not the only RNA element in the viral genome that directs RNA dimerization as the virion RNAs from a DIS-deletion mutant are still dimeric 5 , 6 , 39 ., Therefore , other currently unknown RNA elements must play a role in RNA dimerization as well ., Retroviruses are known to package an assortment of cellular mRNA into their particles , especially in the absence of the viral genome 40 ., It is unclear whether the packaging of cellular mRNA is also regulated; if so , how the regulation is achieved ., Additionally , we also do not know whether a nucleation point exists in the assembly of particles that lack viral RNA but contain cellular mRNA ., It is possible that multiple lower-affinity Gag-nonviral RNA interaction events replace the nucleation point for virus assembly ., Future studies are required to examine these questions ., Regulation of genome encapsidation has implications for multiple aspects of viral replication and evolution ., Our results reveal that dimeric RNA recognition is the key element that regulates viral genome packaging into HIV-1 virions ., These findings have direct implications for the dynamics of virus assembly , the potential for recombination to generate viral diversity , and the adaptive strategies employed by retroviruses for their replication ., For simplicity , previously described GagCeFP-BglSL and GagCeFP-MSSL 16 are referred to as Base-BSL and Base-MSL , respectively ., Although only constructs expressing Gag tagged with Cerulean fluorescent protein ( CeFP ) are shown , each has a corresponding construct that expresses untagged Gag ., Long-BSL was generated by first replacing a portion of pol that was deleted in Base-BSL , then inserting into the nef gene two DNA fragments , one from pB0-Spe6C 41 containing the mouse surface marker B7 gene and a mutated green fluorescent protein gene and another containing lacZ ., The ouabain-resistance gene from pSVα3 . 6 42 was cloned into Long-BSL to generate XLong-BSL ., The AscI-to-SphI DNA fragments of Long-BSL and XLong-BSL plasmids were replaced with that from Base-MSL to generate Long-MSL and XLong-MSL plasmids , respectively ., Mini-MSL and Mini-BSL were derived from pKD-HIV ( GFP-I-Hy ) 43 by inserting sequences recognized by the MS2 coat protein or the BglG protein , respectively , in place of the sequences from the cytomegalovirus promoter to the end of hygromycin B phosphotransferase gene ., Derivatives of Mini-MSL or Mini-BSL were generated by altering the DIS sequences ., Bdis-BSL-Bdis and Bdis-MSL-Bdis were generated by inserting a PCR fragment containing nucleotides 555–823 ( NL4-3 Genebank numbering ) with a splice donor mutation from GGTG to GATC 32 into the pol gene of Base-BSL and Base-MSL , respectively ., The 5′ DIS sequences of the two aforementioned plasmids were changed to GTGCAC to generate Cdis-BSL-Bdis and Cdis-MSL-Bdis ., The structure of all plasmids was verified by restriction digests; PCR-amplified regions were confirmed by sequencing ., Plasmids expressing MS2-YFP and Bgl-mCherry have been reported previously 16 , 44 ., Helper constructs that were used to generate particles containing Mini RNAs include pSYNGP 45 , which expresses codon-optimized HIV-1 Gag/GagPol; pSynGag-CeFP , which was generated by replacing mCherry in pSynGag-mCherry 46 with cefp; pTat-1 47; and pCMV-rev 48 ., H0-PR* was derived from pON-H0 49 with a D25N mutation in pro ., Human 293T cells were maintained as previously described 16 ., Transfections were performed using poly ( ethylenimine ) ( PEI ) reagent 15 , FuGeneHD ( Roche ) , or TransIT-LT1 ( Muris ) ., Supernatants were harvested 19–24 h post-transfection , clarified through a 0 . 45-µm-pore-size filter to remove cellular debris , and either stored at −80°C or used immediately ., Fluorescence microscopy used in single-virion analyses 16 and electron microscopy ( EM ) analyses 50 were performed as previously described ., Viral particles were treated with RNase-free DNase to remove DNA prior to virion RNA isolation ., Virion RNA isolation 51 and nondenaturing Northern blots were performed as previously described using riboprobes generated from a gag fragment 17 ., Signal intensity was quantified using a phosphorimager ., For denaturing Northern analyses , virion RNA isolation and hybridization were performed as previously described using a random-primed , 32P-labeled probe generated from an 8-kb AvaI digestion fragment that covered most of the NL4-3 seq

Introduction, Results, Discussion, Methods

How retroviruses regulate the amount of RNA genome packaged into each virion has remained a long-standing question ., Our previous study showed that most HIV-1 particles contain two copies of viral RNA , indicating that the number of genomes packaged is tightly regulated ., In this report , we examine the mechanism that controls the number of RNA genomes encapsidated into HIV-1 particles ., We hypothesize that HIV-1 regulates genome packaging by either the mass or copy number of the viral RNA ., These two distinct mechanisms predict different outcomes when the genome size deviates significantly from that of wild type ., Regulation by RNA mass would result in multiple copies of a small genome or one copy of a large genome being packaged , whereas regulation by copy number would result in two copies of a genome being packaged independent of size ., To distinguish between these two hypotheses , we examined the packaging of viral RNA that was larger ( ≈17 kb ) or smaller ( ≈3 kb ) than that of wild-type HIV-1 ( ≈9 kb ) and found that most particles packaged two copies of the viral genome regardless of whether they were 17 kb or 3 kb ., Therefore , HIV-1 regulates RNA genome encapsidation not by the mass of RNA but by packaging two copies of RNA ., To further explore the mechanism that governs this regulation , we examined the packaging of viral RNAs containing two packaging signals that can form intermolecular dimers or intramolecular dimers ( self-dimers ) and found that one self-dimer is packaged ., Therefore , HIV-1 recognizes one dimeric RNA instead of two copies of RNA ., Our findings reveal that dimeric RNA recognition is the key mechanism that regulates HIV-1 genome encapsidation and provide insights into a critical step in the generation of infectious viruses .

Viruses must package their genomes in particles to pass their genetic information to the next generation ., Although many aspects of RNA packaging are well-studied , how retroviruses regulate the number of genomes in the particle is currently unknown ., Based on the dimeric nature of retroviral genomes in particles , it was often assumed that two copies of RNA were packaged into one particle ., This assumption was validated recently when we demonstrated that most HIV-1 particles contain two copies of viral RNA , which revealed that the number of genomes packaged is tightly controlled ., In this report , we examined the mechanism that regulates the amount of RNAs encapsidated into HIV-1 particles ., Our results showed that RNA packaging is not regulated by the mass of the viral RNA as two copies of small or large genomes are packaged ., However , packaging of two copies of RNA can be perturbed; HIV-1 can encapsidate one copy of its genome when the RNA contains two packaging/dimerization signals that allow for intramolecular dimer ( self-dimer ) formation ., These studies revealed that HIV-1 regulates genome packaging by recognizing the dimeric RNA structure , and suggest that the interaction of viral protein Gag and dimeric RNA serves as the nucleation point of virus assembly .

medicine, biology

journal.pgen.1006739

Predicting the impact of Lynch syndrome-causing missense mutations from structural calculations

Due to mutations , stress , or failures during synthesis , cells produce proteins that misfold ., Accumulation of misfolded proteins represents a considerable threat to cells , which have therefore evolved efficient protein quality control ( PQC ) mechanisms 1–3 ., These rely on molecular chaperones that either refold the misfolded proteins or target them for degradation via the ubiquitin-proteasome system ( UPS ) ., Early studies showed that certain missense protein variants are more rapidly degraded than wild type proteins 4 ., Since then a number of proteins involved in targeting the misfolded proteins for degradation have been identified , particularly in yeast cells , where mutants in UPS components were identified as extragenic suppressors of point mutants in essential genes 5 , 6 ., These observations suggest that PQC is highly diligent and important , but the issue of what determines whether a mutant protein is degraded or not remains unanswered ., To further our understanding on the intricate relationship between protein stability , degradation and biological function , we performed in silico and cellular studies of the mismatch repair protein MSH2 , which has previously been shown to be a target of a PQC pathway in yeast cells 7 ., Point mutations in MSH2 are linked to hereditary nonpolyposis colorectal cancer ( HNPCC ) or Lynch syndrome , an inherited disorder that increases the risk of many types of cancer , in particular colon cancer 8 ., Identification of pathogenic MSH2 mutations would be of direct clinical relevance , because an early diagnosis can strongly increase survival 9 , but many mutations are of unknown pathogenic significance ., We found that the predicted structural stability of MSH2 correlates with the cellular protein stability , but even slight structural perturbations may result in MSH2 degradation ., Treating cells with the proteasome inhibitor bortezomib or stabilizing MSH2 mutants by lowering the temperature strongly reduced MSH2 degradation , showing that the proteasomal degradation of MSH2 variants is a direct consequence of a structural destabilization ., Thus , in conclusion our data show for the first time that biophysical modelling can predict the stability of proteins in cells and suggest that biophysical modelling can provide both mechanistic insight and a novel diagnostic approach to Lynch syndrome and other genetic diseases ., Missense mutations in MSH2 and other mismatch repair proteins have been linked to the hereditary cancer predisposition disorder , known as Lynch syndrome ., Obviously , missense mutations may ablate protein function e . g . by mutation in an active site , but also because in general missense proteins are less structurally stable than the wild type protein 10 ., To study such stability effects for MSH2 , we employed structure-based energy calculations to predict the effects of mutations in MSH2 on the structural ( thermodynamic ) stability ., As a starting point , we used the published crystal structure of the human MSH2-MSH6 heterodimer 11 to perform in silico saturation mutagenesis , introducing all possible single site amino acid substitutions into the wild type human MSH2 sequence ., We then used two of the most established and tested energy functions for large-scale biophysical modeling , FoldX 12 and Rosetta 13 , 14 , to predict the change in thermodynamic folding stability with respect to the wild type protein ( ΔΔG ) ( Supporting information S1 File , S2 File and S3 File ) ., Both energy functions provide a quantitative description of the inter- and intramolecular interactions that stabilize proteins , and have been extensively benchmarked for ΔΔG prediction over a set of test proteins , with accuracies of about 0 . 8 kcal/mol 12 and 0 . 7 kcal/mol 13 , respectively ., The results presented here are mostly based on our FoldX calculations , but as described further below , calculations using Rosetta gave very similar results ., The calculated values report on the change in structural stability of the MSH2-MSH6 complexes , such that negative values indicate variants that are more stable than the wild type , while positive values indicate that the variants are less stable than the wild type MSH2 protein ., Thus , mutant variants with ΔΔG>0 have , compared to the wild type sequence , a higher population of ( partially ) unfolded structures that are prone to misfold or aggregate ., Our dataset comprises 19 ( amino acids , not including the wild type residue ) * 855 ( resolved residues in the crystal structure ) = 16 , 245 MSH2 variants ., Heat map representations of all the FoldX calculations are included in the supporting information ( Supporting information S2 File ) and , for the first 95 residues , shown here ( Fig 1A ) ., From these data , it is clear that mutations at some positions are tolerated ( blue/turquoise vertical columns , e . g . S13 ) , while for other positions most mutations are predicted to destabilize the structure ( red vertical columns , e . g . A50 ) ., In addition , the structural constraints typically induced by substituting with proline are also evident ( red horizontal line for P ) ., The resulting distribution of ΔΔG values is similar to those described previously for other proteins 10 , 15 and reveal that most MSH2 mutations only moderately affect MSH2’s thermodynamic stability , i . e . that many ΔΔG values are relatively close to 0 kcal/mol ( Fig 1B ) ., Few mutations appear to stabilize MSH2 ( e . g . 5% have ΔΔG < -1 kcal/mol ) , while more are predicted to structurally destabilize MSH2 ( e . g . 9% have ΔΔG > 5 kcal/mol ) ., The known disease-causing mutations generally appear to display higher ΔΔG values ( mean ΔΔG is 9 kcal/mol for the cancer predisposing variants compared to an average of 2 kcal/mol over all mutants ) and are therefore likely to structurally destabilize the MSH2 protein ( Fig 1B ) ., Intriguingly , however , some Lynch syndrome-linked MSH2 mutations are predicted to have only a minor effect on protein stability ( Table 1; Supporting information S3 File ) , suggesting a more complex relationship between mutations and disease ., Previous studies have shown that the steady-state level of certain disease-linked MSH2 variants is reduced 16 , 17 ., To test this in a more general manner , we selected 24 different missense MSH2 mutants with predicted ΔΔGs spanning from -0 . 3 to 39 . 7 kcal/mol for further studies ( Table 1 ) ., When selecting these , we ensured that the mutations were scattered evenly throughout the MSH2 structure ( Fig 1C ) ., To ensure that our observations did not depend on the potential special nature of pathogenic mutations , we included mutations that have been linked to Lynch syndrome and others that to our knowledge have not ., Data and known clinical relevance for each of the selected variants are summarized in Table 1 ., The selected point mutants were introduced in U2OS cells and expressed with an N-terminal 6His-tag ., Several of the variants had strongly reduced steady-state levels ( Fig 2A ) ., When we treated the cells with the proteasome inhibitor bortezomib ( BZ ) , we observed substantially higher steady-state levels , suggesting that the reduced levels are caused by proteasomal degradation of the MSH2 variants ( Fig 2A ) ., This effect was not a result of introducing the 6His-tag , since CFP-tagged MSH2 variants were also unstable ( Supporting information S1A Fig ) ., In addition , the observed destabilization was also valid for other disease-causing MSH2 variants listed in the OMIM database , such as R524P , P622L , A636P , H639Y and G669V ., In contrast , the G322D variant that is also present in OMIM , appeared stable ( S1B Fig ) ., Accordingly , previous studies have shown this variant to be benign 18 ., As it is also found at a high frequency in the Exome Aggregation Consortium ( ExAC ) database 19 , we suggest that this variant is indeed non-pathogenic , which further strengthens our finding that in general the disease-linked MSH2 variants are structurally more destabilized than non-pathogenic sequence variation ( see also below ) ., Recently , it was proposed that when wild type MSH2 is produced in excess of its binding partners , MSH3 and MSH6 , it is ubiquitylated by the histone deacetylase HDAC6 and degraded by the proteasome 20 ., Indeed , the MSH2 proteins , produced here , are overexpressed ( Fig 2B ) ., We did , however , not observe any change in wild type MSH2 levels upon treatment with proteasome inhibitors , suggesting that the overexpressed wild type MSH2 is not rapidly turned over ., Moreover , since knock-down of HDAC6 did not affect the steady-state level of wild type MSH2 or any of the selected MSH2 mutants ( Supporting information S1B Fig ) , we conclude that the reported HDAC6-dependent turnover of orphan MSH2 is not relevant for the MSH2 quality control mechanism that we describe here ., When examining the steady-state level of the MSH2 variants relative to wild type MSH2 , we observed that those variants with high ΔΔG values displayed a reduced steady-state level ( Fig 2C ) ., However , some MSH2 variants predicted to be structurally rather stable ( low ΔΔG values ) exhibit low steady-state levels ( Fig 2C ) , showing that the PQC is highly sensitive to abnormal proteins ., The thermodynamic stability ( ΔG value ) of each mutant can be calculated as the sum of the stability of the wild type protein ( ΔGWT ) and the difference in stability between the wild type and mutant ( ΔΔG ) ., While FoldX can predict the latter , ΔGWT remains unknown ., We can , however , estimate an effective value of ΔGWT from the data under the rough assumption that the steady-state level in the cell is proportional to the fraction of folded protein ( Fold/ ( Unf+Fold ) ) , which we in turn can determine from the relationship ΔGMut = ΔGWT+ΔΔG = -RT ln ( Unf/Fold ) ., Fitting the data to this relationship results in ΔGWT ~ -3 . 1 ± 0 . 4 kcal mol-1 ( Fig 2C ) ., We note that this value is an estimate of the effective stability of MSH2 inside the cellular environment and in the presence of the PQC system , and might potentially differ from the absolute stability of MSH2/MSH6 in vitro ., The value obtained is also in line with a visual analysis of the data , which suggests a general drop in protein levels for mutants that have ΔΔG > 3 kcal/mol , and an independent estimate obtained using functional studies of MSH2 mutants ( see below ) ., We note , however , also the substantial scatter of the data around the fitted line ., These deviations may for example be due to inaccuracies in the ΔΔG predictions , differences between in silico and cellular stabilities and the specific mechanisms by which the PQC recognizes misfolded proteins ., We conclude that a destabilization of roughly 3 kcal/mol is sufficient to cause degradation ., Next , we analyzed if the reduced steady-state levels of the mutant MSH2 variants were indeed caused by rapid degradation ., In cultures treated with the translation inhibitor , cycloheximide ( CHX ) , we followed the amounts of MSH2 by Western blotting ., We found that wild type MSH2 was relatively stable with a half-life of 19 ± 3 hours ( Fig 3A ) while those mutant proteins that displayed a reduced steady-state level were rapidly degraded ( Fig 3A ) ( Table 1 ) ., Since certain DNA repair components are degraded as part of their normal function 21 , we also followed the degradation of wild type MSH2 in cultures treated with the alkylating agent , methylnitronitrosoguanidine ( MNNG ) ., However , since MNNG treatment did not affect MSH2 stability ( Supporting information S2A Fig ) , we conclude that the MSH2 variants are turned over as part of a cellular quality control mechanism and not as consequence of their function in mismatch repair ., In addition , the turnover of the MSH2 variants was not a result of the overexpression , since wild type MSH2 was degraded with similar kinetics to that observed for endogenous MSH2 ( Supporting information S2B Fig ) , and cells stably transfected to produce selected MSH2 variants at near endogenous levels ( Supporting information S2C Fig ) degraded the proteins with kinetics identical to those observed for the overexpressed variants ( Supporting information S2D Fig ) ., In general , the rapidly degraded MSH2 variants carried mutations in residues buried within the MSH2 protein and , interestingly , appear to cluster towards the C-terminal ATPase domain ( Table 1 ) ., Since mutations in residues that are buried and form many contacts often lead to a greater structural destabilization than mutations in surface residues 10 , 22 , we observed that in general those proteins that are predicted as structurally highly destabilized were also rapidly degraded ( Table 1 ) ., Accordingly , when plotting the half-lives of the MSH2 variants , we observed that those variants with high ΔΔG values displayed a more rapid degradation ( Fig 3B ) ., The correlation between ΔΔG and turnover rate was statistically significant ( Fig 3C ) ., However , some variants that were rapidly degraded did not display strongly increased ΔΔG values , which suggests that the over-zealous PQC system targets some MSH2 variants that are structurally stable and perhaps retain function ., From the experiments above , we conclude that turnover , at least in part , correlates with the predicted thermodynamic stability of the protein ., To test this further , we generated another four MSH2 point mutants , exchanging thermodynamically unfavorable residues ( high ΔΔG ) into more favorable residues ( low ΔΔG ) at the same position in the protein , and analyzed their degradation as before ., Indeed , we found that this dramatically stabilized the MSH2 proteins ( Fig 4A ) ., For instance , while the C333Y variant ( ΔΔG = 21 . 7 kcal/mol ) is rapidly degraded ( t½ = 5 ± 0 . 4 hours ) , the C333T variant ( ΔΔG = -0 . 3 kcal/mol ) is degraded slowly at a rate comparable to wild type ( t½ = 16 ± 3 hours ) ( Fig 4A ) ., Thus , it is not just the location of the mutation in the sequence or the structure that is important , but the exact nature of the change in the amino acid side chain chemistry ., In addition to changes in the amino acid sequence , a number of chemical and physical parameters are known to affect the structural stability of proteins ., For instance , several misfolded proteins are stabilized at lower temperatures , but some are also destabilized at lower temperatures 23 ., To further corroborate the relation between structural protein stability and protein turnover , we repeated the protein degradation assays on the full set of MSH2 variants , but now lowering the temperature from 37°C to 29°C ., For some MSH2 variants ( R39E , A54Y , L75K ) this radically slowed the degradation ( Figs 3A and 4B ) ( Table 1 ) , while one variant ( P622T ) appeared less stable at 29°C than at 37°C ( Table 1 ) ., The reduced turnover of the MSH2 R39E , A54Y and L75K variants at 29°C compared to 37°C is not simply a consequence of a reduced UPS activity at lower temperatures , since the turnover of most others variants was entirely unaffected by this change in temperature ( Figs 3A and 4B ) ., Also , the cellular amounts of ubiquitin-protein conjugates and proteasomes were unchanged in this temperature interval ( Supporting information S3 Fig ) ., Surprisingly , when we mapped the temperature sensitive mutations onto the MSH2 structure , we found that all clustered towards the MSH2 N-terminal DNA binding region ( Fig 4C ) ., Thus , local unfolding of this domain might be particularly temperature dependent rendering these mutations more temperature sensitive ., Notably , the corresponding region in the bacterial MSH2 homologue has been shown to be highly dynamic in solution 24 ., Although our data suggest that the thermodynamic stabilities of MSH2 variants is the primary factor that decides their turnover , some of the variants , included in our selection , are rapidly degraded despite having structural stabilities only slightly lower than wild type ., For instance , the disease-causing D603N variant is rapidly degraded ( t½ = 6 ± 2 hours ) while the mutation is not predicted to strongly affect MSH2 structure ( ΔΔG = 1 . 0 kcal/mol ) ., We therefore speculated whether this and other MSH2 variants are still functional , and in this way , similar to other genetic disease such as cystic fibrosis 25 , Lynch syndrome could be explained by the over-zealous degradation machinery ., To test this hypothesis , we first analyzed the subcellular localization of the selected MSH2 variants ., All variants localized to the nucleus similar to the wild type protein 26 , ( Supporting information S4A and S4B Fig and Table 1 ) , although the signal intensity varied ( Supporting information S4A Fig ) as expected , based on the reduced steady-state level ., To better discriminate between functional and dysfunctional MSH2 variants we therefore turned to mapping the interaction partners of wild type MSH2 and of L187P that displays a high ΔΔG value ( 8 . 8 kcal/mol ) , is rapidly degraded and therefore likely to be highly misfolded and not functional ., To quantify any differences in terms of protein binding between wild type MSH2 and the L187P variant , a quantitative proteomics experiment was undertaken ., All mass spectrometry data are included in the supporting information ( Supporting information S4 File ) ., Affinity purification was used to purify proteins from cells treated with bortezomib ( to ensure that L187P was not degraded ) expressing vector ( control ) , 6His-MSH2 ( wild type ) or 6His-MSH2-L187P ( Supporting information S5A Fig ) in quadruplicates ., The purified proteins were digested with trypsin , and LC-MS/MS in combination with MaxQuant data analysis was used for identification and quantification ., Label-free intensities were converted to ratios by comparison of the 6His-MSH2 purification protein intensities with those derived from the control purifications ., MSH2 , MSH3 and MSH6 were the three most enriched proteins in 6His-wild-type MSH2 preparations ( Supporting information S5B Fig ) ., In the presence of proteasome inhibitors L187P was expressed at roughly half the level of the wild type ( Supporting information S5A , S5B and S5C Fig ) ., MSH6 was almost 8 fold reduced in abundance , and MSH3 almost 30 fold reduced in the L187P samples compared to wild type ( Supporting information S5B and S5C Fig ) ., The reduced binding of L187P to MSH3 and MSH6 was confirmed independently by Western blotting ( Supporting information S5D Fig ) ., A wider screen showed that wild type MSH2 and several MSH2 variants co-precipitated with endogenous MSH6 ( Fig 5A ) ., However , some MSH2 variants displayed only weak interactions with MSH6 ( e . g . P696F ) and yet others appeared completely inept at MSH6 binding ( e . g . G683R ) ., Variants that interact with MSH6 had an average loss of stability of 5 ± 1 kcal/mol ( mean ± SEM ) while those that interacted poorly with MSH6 were more structurally unstable ( 15 ± 6 kcal/mol; p < 0 . 001 ) ( Fig 5B ) ., We conclude that the structural stability calculations allow prediction of MSH2-MSH6 dimerization potential ., We next performed experiments to see whether certain unstable MSH2 variants would at least retain some function ., A characteristic feature of MSH2 loss-of-function and Lynch syndrome cancers is increased resistance to DNA damage 27 ., Accordingly , we found that siRNA-mediated knockdown of endogenous MSH2 rendered U2OS cells resistant to an otherwise lethal dosage of the alkylating agent , MNNG ( Fig 5C ) ., Stable introduction of siRNA resistant , wild type MSH2 did not lead to appreciable overexpression ( Fig 5D ) , but re-established the MNNG sensitivity ( Fig 5C ) ., The tested MSH2 variants appeared partially sensitive ( Fig 5E ) ., Indeed , we found a strong correlation ( r2 = 0 . 81; p = 0 . 02 ) between the predicted loss of stability ( ΔΔG ) and the ability to grow in the presence of MNNG ( Fig 5E ) , suggesting that the assay is able to probe the amount of functional MSH2 ., Even at the highest MNNG concentration ( 100 nM ) the L187P variant was not statistically different from the vector control ., This is in agreement with the interaction data , and indicates that L187P is misfolded to an extent where it has lost all activity ., The correlation between the change in stability and resistance to MNNG offers us an opportunity to provide an independent estimate of the stability of MSH2 ( ΔGWT ) ., Assuming that the wild type protein is fully folded and L187P is mostly unfolded , we can fit the observed activities ( percent survival at 100 nM MNNG ) to estimate ΔGWT ( Fig 5F ) ., The value obtained ( -2 . 7 kcal/mol ) is in good agreement with the independent estimate obtained from the intracellular protein levels ( -3 . 1 kcal/mol , Fig 2C ) , and again corresponds visually also to the magnitude of destabilization that is needed to see a substantial difference from the wild type protein ., Although both estimates are associated with uncertainty , their agreement lends additional credibility to the values and suggests a relatively low effective stability of MSH2 in the cell that , as demonstrated above , also includes effects from interactions with partner proteins ., We note also that the general agreement between the effect on steady-state levels and residual activity also suggests that loss of stability is a major factor leading to loss-of-function for these variants ., An advantage of the structural stability calculations on missense variants that we present here is that it may potentially bypass laborious laboratory testing and immediately provide a clinical geneticist with an estimate of whether a particular MSH2 missense variant is pathogenic ., The currently employed clinical tools ( e . g . CADD , SIFT , PolyPhen2 , PROVEAN ) provide sequence-based predictions of whether a mutation is likely to be pathogenic 28–31 ., While sequence-based predictors have the clear advantage of being technically applicable to virtually all proteins , the structural calculations utilize atomic details and thus allows not only more accurate predictions , but may also enable mechanistic insights ( e . g . our observation on ts mutations in the DNA binding domain above ) 32 , 33 ., ΔΔG values for variants with half-life > 16 h are significantly lower than those for variants with a half-life < 8 h ( Fig 6A ) ., Importantly , the biophysical calculations are able to separate the group of moderately stable proteins ( half-life 8–16 h , Fig 6A ) , while the sequence-based predictors we tested considered those variants as equally pathogenic as the rapidly degraded variants ( Supporting information S6 Fig ) ., To compare more directly FoldX and the four sequence-based methods for their ability to distinguish known pathogenic and non-pathogenic variants 34 , we repeated these calculations for CADD , SIFT , PolyPhen2 , PROVEAN also ( Supporting information S6 Fig ) ., Interestingly , we found that while all methods perform reasonably well , FoldX is substantially better at distinguishing pathogenic from non-pathogenic variants ( Fig 6B and Supporting information S6 Fig ) , demonstrating the potential power of structure-based methods ., To corroborate our studies on MNNG sensitivity suggesting that the structural stability of MSH2 variants correlate with MSH2 function , we analyzed the recent data of Houlleberghs and co-workers 34 ., ΔΔG values for the majority of the variants reported to be pathogenic by Houlleberghs et al . are > 3 kcal/mol and would thus also have been predicted to be pathogenic from the biophysical calculation ( Fig 6A ) ., As a separate method for predicting the biological consequences of mutations , we also turned to more detailed analyses of a multiple sequence alignment of MSH2 homologues ., In particular , we created a statistical model of such an alignment that both takes residue conservation into account , but also the non-trivial couplings that occur as a consequence of amino acid co-variation ( see Materials and Methods section ) ., Such calculations are known to provide accurate predictions of changes in stability 35 and have recently been used to assess pathogenicity 36 , 37 ., In contrast to the structure-based calculations , in which we examine whether loss of stability is correlated with disease , these calculations do not assume or provide direct insight into the molecular mechanisms that underlie the disease-causing variants ., To quantify the ability of FoldX , Rosetta , co-variation , and the more established sequence-based methods ( CADD , SIFT , PolyPhen2 and PROVEAN ) to distinguish known pathogenic and non-pathogenic variants 34 , we used all these methods to assess the impact of known neutral and Lynch-syndrome-causing mutations ( Supporting information S6 Fig ) ., In particular , we performed a “receiver-operating characteristic” analysis in which we compare the different methods’ ability to separate the two classes of mutations ., Interestingly , we found that while all methods perform reasonably well , the biophysical calculations ( FoldX , Rosetta ) and co-variation are substantially better at distinguishing pathogenic from non-pathogenic variants ( Fig 6B and Supporting information S6 Fig ) , demonstrating the potential power of structure-based methods ., In line with recent findings 37 we also find that the co-variation analysis increases the predictive power over a simpler conservation analysis ( Supporting information S6 Fig ) ., Finally , we analyzed the predicted protein stabilities of MSH2 missense mutations found the Exome Aggregation Consortium ( ExAC ) database 38 ., Indeed this revealed that those MSH2 variants that are found at a high frequency in the population , and therefore likely to be benign , all display low ΔΔG values , indicating that these MSH2 proteins are stable and functional ( Fig 6C ) ., We note also that while a few more destabilizing mutations are found with much lower frequencies , we cannot assess whether these are due to prediction noise , whether these individuals have an increased risk of Lynch syndrome , or whether these individuals have other compensatory mutations in their genomes ., Nevertheless , the finding that all common variants are predicted to have little effect on stability supports the observation that computational ΔΔGs can help identify damaging mutations and should eventually be considered for use in clinical practice in addressing the challenging issue of which rare mutations are pathogenic and which are neutral 39 ., In conclusion , our results demonstrate that biophysical calculations performed in silico can predict the structural stability , function and turnover of proteins in cells , and allow insights into the molecular mechanisms underlying disease ., Such methods may therefore after further testing perhaps be applied diagnostically to sort disease-causing missense mutations from harmless genetic variations ., Lynch syndrome is a common autosomal syndrome characterized by early onset neoplastic lesions in a variety of tissues and microsatellite instability caused by heterozygous loss-of-function germline mutations in genes encoding components of the DNA mismatch repair ( MMR ) system 8 ., In eukaryotes , MMR is accomplished by the MutS heterodimers MSH2 and MSH6 or MSH2 and MSH3 , which first recognize and bind mismatched base pairs and then recruit downstream repair components 40 ., Loss-of-function mutations in these components result in a mutator phenotype , which consequently leads to cancer predisposition ., In addition , mismatch repair-defective tumors are often associated with resistance to conventional chemotherapies , including temozolomide , 5-fluoruracil and cisplatin 8 ., PQC systems root out abnormal or misfolded proteins 1 , 2 , such as those encoded by missense mutations 5 , 6 ., In general , these systems rely on molecular chaperones to either refold the misfolded proteins or target them for degradation via autophagy or through the UPS 41–47 ., Degradation of proteins that are structurally perturbed , but still functional , has been linked to disease , as in cystic fibrosis 25 , 48 and , as we show here for Lynch syndrome , which should therefore be considered a protein folding disease ., At present , our understanding of what determines whether a misfolded protein is refolded or degraded is limited , though presumably the structural stability of the substrate protein is one crucial determinant ., To formally test this requires , however , that biological and thermodynamic stabilities of closely related proteins are determined in parallel ., To accomplish this , we chose the MSH2 protein as a model substrate for the following reasons: First , structural data for MSH2 are available 11 , thus allowing us to perform accurate thermodynamic stability predictions ., Second , the wild type MSH2 protein is stable and is not turned over as part of its normal cellular function ., Hence , any degradation that we may observe for MSH2 mutants can be attributed solely to a reduced structural stability ., Third , MSH2 is a rather large protein , and is therefore likely to be highly dependent on temperature for correct folding 49 , allowing us to use this simple physical parameter to regulate the degree of misfolding ., Previous studies on yeast MSH2 mutants suggest that a high proportion of missense mutations affect the steady-state protein levels 7 , 50 ., Our studies on human MSH2 variants confirm these findings ., Out of the 24 MSH2 variants studied here , we found that 18 displayed a lower steady-state level and were more rapidly degraded than the wild type protein ., For all those variants , the protein levels could be increased by treating cells with proteasome inhibitor , demonstrating that the turnover occurs via the UPS ., As hypothesized , those mutations that were predicted to be highly structurally destabilizing were also scored as being rapidly degraded , whereas other structurally less destabilizing mutations at the same positions slowed protein turnover ., These observations , combined with our finding of some variants that display a temperature sensitive degradation ( i . e . degraded at 37°C , but stable at 29°C ) , support that the structural stability of the mutants is a primary determining factor for the degradation ., This is further reinforced by our finding that several of the variants that we had scored as highly structurally unstable displayed a strongly reduced MSH6 binding ., However , in all cases the structural destabilization inferred by the mutations appeared rather subtle , since all the tested MSH2 variants localized , like wild type MSH2 , to the nucleus , and none formed protein aggregates ., Some MSH2 variants were rapidly degraded although the structure-based energy calculations only predicted them to be moderately destabilized ., Thus , although we find a clear overall relationship between the predicted change in thermodynamic stability and the cellular protein degradation rates , the details of that relationship are likely more complex ., For example , our calculations focus upon the effect of the mutations on the global stability of the protein , but different regions of a protein can differ in local stabilities 24 ., Thus , mutations with the same overall destabilization , but located in different regions of the protein structure , might differentially affect local stabilities ., A more quantitative analysis would thus require knowledge about any possible local unfolding as well as the mechanisms by which these are recognized by the PQC systems ., Our results are also reminiscent of the results of a study on the relationship between destabilization and function in ubiquitin 51 ., That study found that core mutations that were only mildly destabilizing were fully functional , whereas mutations with intermediate levels of destabilization had more varied functional effects ., Our finding that MSH2 variants are targets of the cellular PQC system prompts the question as to the upstream components such as chaperones and E3 ubiquitin-protein ligases that target the variants for degrad

Introduction, Results, Discussion, Materials and methods

Accurate methods to assess the pathogenicity of mutations are needed to fully leverage the possibilities of genome sequencing in diagnosis ., Current data-driven and bioinformatics approaches are , however , limited by the large number of new variations found in each newly sequenced genome , and often do not provide direct mechanistic insight ., Here we demonstrate , for the first time , that saturation mutagenesis , biophysical modeling and co-variation analysis , performed in silico , can predict the abundance , metabolic stability , and function of proteins inside living cells ., As a model system , we selected the human mismatch repair protein , MSH2 , where missense variants are known to cause the hereditary cancer predisposition disease , known as Lynch syndrome ., We show that the majority of disease-causing MSH2 mutations give rise to folding defects and proteasome-dependent degradation rather than inherent loss of function , and accordingly our in silico modeling data accurately identifies disease-causing mutations and outperforms the traditionally used genetic disease predictors ., Thus , in conclusion , in silico biophysical modeling should be considered for making genotype-phenotype predictions and for diagnosis of Lynch syndrome , and perhaps other hereditary diseases .

The protein quality control system targets misfolded proteins for degradation ., So far it has not been possible from sequence or structural data to predict the biological stability of a misfolded protein , or the effect of mutations on intracellular protein levels ., Here we demonstrate that in silico saturation mutagenesis and biophysical calculations of the structural stability of the human mismatch repair protein MSH2 correlate with cellular protein levels , turnover and function ., Of 24 different MSH2 variants , some of which are linked to Lynch syndrome , a destabilization of as little as 3 kcal/mol is sufficient to cause rapid degradation via the ubiquitin-proteasome pathway ., Thus , biophysical modeling can , to a large extent , predict the metabolic stability of proteins ., We also show that the same biophysical calculations can be used to distinguish with high accuracy neutral sequence variation from pathogenic variants , and that the calculations outperform several traditionally used disease predictors ., We therefore suggest the method to be of potential value for patient stratification in Lynch syndrome , and perhaps other hereditary diseases .

medicine and health sciences, pathology and laboratory medicine, gene regulation, genetic diseases, mutation, hereditary nonpolyposis colorectal cancer, chaperone proteins, autosomal dominant diseases, thermodynamics, missense mutation, small interfering rnas, proteins, gene expression, pathogenesis, biophysics, clinical genetics, physics, mutagenesis, biochemistry, rna, nucleic acids, genetics, biology and life sciences, physical sciences, non-coding rna

journal.pcbi.1003913

Inference of Epidemiological Dynamics Based on Simulated Phylogenies Using Birth-Death and Coalescent Models

In many applications determining the past dynamics of populations is of interest ., In an epidemiological context in particular , the interest lies in knowing two quantities: the basic reproductive ratio and the growth rate of the epidemic ., is a key parameter that determines the probability and the extent of spread of the disease in the population ., It is defined as the number of secondary infections a single pathogen is expected to cause when introduced into a population of susceptible individuals 1 , 2 ., The growth rate determines the speed of spread of the pathogen ., Accurate estimation of these two parameters ( and ) is required in order to take appropriate measures of intervention , e . g . vaccinations or isolation of infected individuals ., Until recently , estimation of these parameters was exclusively based on prevalence and incidence epidemiological data ., However , recent progress in phylodynamics has enabled the inference of these parameters from pathogen sequence data by integrating methods of phylogenetics with those of mathematical epidemiology ( for review see 3 ) ., SIR-type models have been widely used to describe epidemiological dynamics 1 , 4 ., In essence , these models are based on separating the population into different classes of individuals , namely susceptibles ( ) , infecteds ( ) and recovereds ( ) ., Individuals can change their status , i . e . switch from one class to another ., The epidemiological dynamics depend on two rates: a birth rate and death rate , where is the number of susceptibles and the total population size ., The birth rate , or transmission rate , is the rate with which one infected individual will infect another uninfected individual ., In the transmission tree , an infection event will be displayed as a bifurcation or split of one lineage into two lineages ., The death rate , or removal rate , is the rate with which an infected individual becomes non-infectious , e . g . recovers from the disease , dies , or changes behavior ., In the transmission tree , becoming non-infectious is a lineage that stops growing , i . e . becomes a tip in the tree ., Various sampling schemes select a proportion of the infected individuals from the complete transmission chain to be included into the observed phylogeny ., The observed phylogeny is the subtree induced by the complete transmission chain that connects the sampled individuals ., This sub-selection of the individuals reflects the fact that in empirical datasets the pathogens of only a small fraction of the infected hosts have been sequenced and included into an epidemiological study ., For an epidemic following SIR dynamics , the growth of the population size at the initial stage of the spread follows an exponential trend , although it slows down at later stages due to a depletion of susceptibles ., We focus on a special scenario , where only the early epidemic outbreak , i . e . exponential growth of the infected population , is being considered ., We can simplify the model to a constant rate birth-death process , where we assume no significant decrease in the number of susceptibles over time , formalized as , implying that birth rate is constant ., Recent genetic sequencing efforts have produced many pathogen sequences from different hosts ., To reconstruct their phylogenetic relationships , numerous methods have been developed ( refer to books 5 , 6 and references therein ) ., The resulting phylogenetic trees are used as a proxy for the ( incomplete ) transmission tree , and thus provide us with insights into the dynamics of the epidemic ., They enable us to estimate parameters such as transmission rate ( ) , removal rate ( ) , growth rate ( ) , or basic reproductive ratio ( ) ., Methods based on Bayesian inference coupled with a Markov chain Monte Carlo ( MCMC ) procedure 7 infer the posterior distribution of trees ( ) together with the epidemiological parameters ( ) and sequence evolution parameters ( ) from genetic sequencing data based on the following relation: ( 1 ) Here , is the posterior distribution of the parameters and trees; is the likelihood of the parameters ( and ) that is usually computed by the Felsenstein algorithm 8; is the probability density of the phylogeny given the epidemiological parameters ( e . g . assuming an SIR-type model ) ; and are priors for evolutionary and epidemiological parameters , respectively; and is the normalizing constant representing the integral of the numerator over all phylogenies and parameters ., As data are fixed , is a constant and thus irrelevant for the estimation of the posterior probability density of the parameters in the MCMC procedure ., Here , we focus on the impact of the underlying epidemiological model when calculating ., Two models are mostly used in epidemiological contexts for this purpose: the coalescent 7 , 9–14 ( uses and review of the model are described in 15 ) and the birth-death process 16–25 ( reviewed in 26 ) ., Both models have been used to estimate and/or the growth rate parameter of HCV 27 , 28 and HIV epidemics 24 , 25 , 29 ., Since we only focus on phylogenies resulting from early epidemic outbreaks , we apply a special case of the birth-death model , namely the constant rate birth-death model with incomplete sampling , and a special case of the coalescent model , namely the coalescent with deterministic exponential infected population growth ., Both models are implemented in the software package BEAST v2 . 0 30 and have been used interchangeably for parameter inferences ., The constant rate birth-death model implemented for parameter inference in BEAST v2 . 0 is precisely the epidemic outbreak model introduced above ., The specific sampling scheme used in this study is the implementation of a constant sampling probability upon “death” ( happening with rate ) for each individual ( in BDSKY add-on of BEAST v2 . 0 ) , and is known as the incomplete sampling version of the birth-death model 24 ., The coalescent with deterministic exponential infected population growth has been introduced in population genetics , and is now also used as an approximation for epidemiological dynamics ., Classically , the coalescent has been used in phylodynamic studies ., The coalescent reconstructs the ancestry of sampled individuals towards the most recent common ancestor ( MRCA ) ., In fact , it reconstructs the probabilistic structure of the tree by merging lineages progressively going back in time as a function of the population size until there is only a single lineage left 31 ., The coalescent thus provides a prior distribution of trees given a population size , where the population size may change through time ., In the epidemiological context the population size of interest is that of the infected individuals ., This probability density function allows for the calculation of the probability of the tree for given population size parameters 11 , 12 , 29 ., The coalescent seems to be a good approximation to many processes arising in biology ( e . g . 11 , 28 , 32 ) ., However , violations of the model assumptions can lead to consequences whose nature and extent are still not fully explored 24 , 29 ., The coalescent can be interpreted as a continuous time approximation of the discrete time Wright-Fisher model 33 , 34 ., Based on this approximation , as stated by Rodrigo and Felsenstein in 29 , the requirements for the studied population to be well approximated by the coalescent are: In most of the coalescent models , a further assumption is made: The coalescent can also be interpreted as a continuous time limit of the discrete time Moran process 35 ., The assumptions above with exception of, 1 ) are also required for the continuous time approximations of the discrete time Moran model , rather than Wright-Fisher population model ., Continuous time versions of Wright-Fisher and Moran population models can also be formulated directly rather than by approximation of discrete time models ., This is done by assuming a rate of coalescence in continuous time instead of approximating it by a conversion from discrete to continuous time space , as we point out in the Supplementary Material S1 ., Such continuous time Wright-Fisher and Moran population models can be formulated as a coalescent process without the assumptions, 1 ) and 3 ) ., Furthermore , extensions to avoid deterministic population size changes , i . e . assumption 5 ) , have been developed 36 ., In an epidemiological context , the sampling proportion in a recent epidemic can be quite high ., For instance , the sampling proportion of the HIV epidemic in Switzerland has been estimated to be 0 . 75 37 ., This high sampling proportion is a misspecification for the discrete time Wright-Fisher and Moran process-based coalescent models ., Although some studies suggest that the violation of this assumption should not be of significant importance 38 , its consequences on the model performance when applied to empirical data are so far unknown ., In addition , impacts on the parameter inference under the coalescent in the context of deviations from the deterministic population size assumption are not well explored either ., The birth-death model also requires assumptions, 2 ) and 4 ) to be fulfilled ., Additionally , the generation times are assumed to be exponentially distributed , instead of discrete generations as in the assumption 1 ) ., We see the major difference between the birth-death model and the coalescent in three factors: In this paper , we want to shed light on the practical importance of the theoretical points raised above for parameter estimation ., We investigate the comparative performance of the birth-death and the deterministic coalescent model in phylodynamic parameter estimation by doing a simulation study ., We first simulated both constant rate birth-death model trees with incomplete sampling ( from now on simply referred to as the birth-death model , unless specified otherwise ) , and coalescent model trees with deterministic exponential infected population growth ( from now on simply referred to as the coalescent model , unless specified otherwise ) ., We then applied phylodynamic methods based on the birth-death model and the coalescent model within the BEAST v2 . 0 software package to the simulated phylogenies ., In this fashion , we estimate the phylodynamic parameters and compare coverage ( measured as the fraction of simulated trees where the HPD captures the true parameter ) , accuracy ( measured as the root mean square error ( RMSE ) ) and precision ( measured as the width of HPD intervals ) of the parameter estimates ., The ability of this specific birth-death model and the coalescent model to capture the true growth rate in the 95% highest posterior density ( HPD ) interval at fixed is summarized in Table 1 , and shown in Figure 1 , Figure S1 and Figure S2 ., Note that none of these , nor any of the results below , change substantially if we use quantiles instead of HPD intervals ( data not shown ) ., For , the birth-death model successfully recovers the growth rate parameter for trees simulated under the birth-death model , whereas the coalescent model successfully recovers the growth rate parameter for those trees that were simulated under the coalescent ., This observation is not surprising but confirms the basic expectations that the model used for simulation should be good when it is also applied for inference ., In the critical case where , the birth-death model recovers the true growth rate only in 78% of the birth-death trees ., This is because the birth-death likelihood is conditioned on the time of origin of the process ( length of epidemic ) ., Our simulated trees are all of different lengths , however , as we stop once reaching 100 tips ., That means that for low growth rates , we select a very biased set of relatively big trees , as most realizations would die out before producing 100 tips ., By looking at Figure S1 we observed that especially for low values , i . e . and , the selective inclusion of the relatively big trees into the final set results in the median estimates of the growth rate parameter to be biased towards higher values than the truth ., To show that the birth-death inference method has no bias if applied to trees with a large fixed time of origin , we simulated trees under , , until reaching fixed time , or ( Figure S3 ) ., The 95% HPD intervals of the birth-death model growth rate estimate capture the true value of the growth rate in 95% and 96% of the cases , respectively ., The distribution of the medians of the 100 HPD intervals is spread out evenly around the true value of , meaning the effect of growth rate over-estimation decreases for increasing ., When applying the birth-death method to coalescent trees , the growth rate coverage is higher than when applying the coalescent method to birth-death trees ., The higher coverage of the birth-death model comes partially at the cost of a larger 95% HPD interval size ( see Table 1 ) ., The normalized 95% HPD interval sizes of produced by the birth-death process and by the coalescent are almost identical for very large ( ) , and they increase for both coalescent and birth-death model parameter estimates with decreasing ., However , the coalescent intervals become smaller than birth-death intervals , and at their widths differ by a factor ., This discrepancy between the HPD sizes does not translate in decreased accuracy of the birth-death model compared to the coalescent model ., Accuracy can be measured by the root mean square error ( RMSE ) of the median of each posterior interval ., The accuracy of the birth-death model is higher ( RMSE is lower ) than the accuracy of the coalescent when applied to the birth-death trees ., The accuracy of the birth-death model is lower than the accuracy of the coalescent model on the coalescent trees for our analyses with ., As expected , the above observations do not change when the branch lengths , i . e the time units , are scaled ., This corresponds to multiplying the birth and the death rates , and thus the growth rate parameter , by a constant factor while keeping unchanged ( Table S1 ) ., The increase in HPD interval size when lowering may be due to increasing stochasticity in population size variation over time ., This increased stochasticity is caused by decreased population growth resulting from more death events per birth event ., We will now discuss the reason for biases when applying the birth-death method to coalescent trees and vice versa ., When applying the birth-death method to coalescent trees , for , the true growth rate is recovered very reliably ., The birth-death process has a small bias when applied to the coalescent trees for and ., This can be explained again by the simulation scheme and vanishes if simulating for a fixed time rather than until a number of samples is reached ( Figure S3 ) ., When applying the coalescent method to birth-death trees , the coalescent misses the true growth rate value ( the HPD does not contain the true growth rate ) on the birth-death model trees more often than the birth-death model on the coalescent trees ., This is accentuated with decreasing ., The coalescent has a tendency to overestimate the growth rate ( see Figure 1 ( ) and Figure S1 ) ., The growth rate overestimation can be explained by the push-of-the-past effect described by Nee et al 39 ., The exponential growth coalescent model assumes a constant population growth rate ., The push-of-the-past effect causes the expected population size under the birth-death model to initially increase faster than with rate , and then slow down to grow with the constant rate ., As a consequence , when a final population size is fixed the coalescent trees are predicted to be longer than the birth-death trees ., Put in other words , given a fixed time after the start of the process , the expected birth-death population size is bigger than the expected coalescent population size ., This push-of-the-past effect becomes more severe for smaller values of ., For inference , this means that the coalescent applied to birth-death trees infers inflated growth rates as coalescent trees with the true growth rate would be expected to be longer ., We observe this push-of-the-past effect and the resulting differences in coalescent and birth-death trees in our simulations ., We investigated the difference between birth-death and coalescent trees for the parameter combinations where most stochasticity in population size variation over time and most bias of the inferred parameters was observed: , i . e . ., Both the mean and the median measure of the tree lengths without the root-origin distance showed that the coalescent had a strong preference to produce overall longer simulated trees ., Median tree length of the coalescent trees was 35 . 2 while that of birth-death trees was only 26 . 1 ., Similarly , the mean tree length was 36 . 1 and 27 . 7 for coalescent and birth-death trees , respectively ., Upon visual inspection of these trees , we noticed that the coalescent mostly produced trees with longer inner branches , especially close to the root , as compared to trees simulated under the birth-death model ., As the population size growth curves in semi-logarithmic plots are parallel lines with the slope for large , the relative difference between the coalescent and birth-death tree lengths should become smaller for increasing ., This means that the ratio of the length of the birth-death tree and of the length of the coalescent tree tends to 1 when letting the trees ( populations ) grow for longer times ., In fact , by increasing the final population size through sampling more tips ( , 500 or 1000 ) and thereby suppressing the importance of the push-of-the-past , the median of the coalescent estimates of the growth rate gets closer to the true value ., Nevertheless , the coverage of the coalescent model does not improve due to overconfident estimates , i . e . shrinking HPD intervals ( Figure S4 ) ., When applying the birth-death model to the long coalescent trees we occasionally observed an overestimation of the growth rate ., In most cases the growth rate is estimated correctly by the birth-death model though ., The coalescent trees typically have long branches close to the root ., In the following we will demonstrate that these early long branches do not strongly impact the overall likelihood calculation in the birth-death model ., To investigate the impact of long early branches on parameter estimation under each model further , we changed the simulated trees systematically ., We looked at trees simulated under and ., In these trees , we extended each branch existing after 10% , 20% , 30% , 40% , 50% , 60% , 70% , 80% , 90% of the time between the root an the present ., We extended branches in all these cases by 48 for and by 0 . 18 for , which was approximately the full length of the birth-death tree ( Figure 2 and Figure S5 ) ., Re-analyzing the trees with using the birth-death model and the coalescent model revealed high sensitivity of the coalescent estimates to early but not so much to the late perturbations ., The birth-death model estimates of the growth rate proved to be more robust to early perturbations than to late perturbations , see Figure 2 ., We note that the early perturbations actually only affect very few branches , and thus only introduce minor changes to the data ., We hypothesize that the ability of the birth-death model to account for stochasticity in population size determines its robustness to above-introduced changes in branching times ., When perturbing the tree close to present , the birth-death model growth rate estimates decrease as all branches occurring prior to the perturbation are not allowed to produce sampled descendants later on ., For that particular reason , the birth-death method infers a much too low median growth rate when we only extend the single branch leading to the most recent bifurcation ( data not shown ) ., Thus , the birth-death method is very sensitive to even minor perturbations close to the present ., Re-analyzing the trees with revealed that both the birth-death and the coalescent method are very sensitive to perturbations ( Figure S5 ) ., This is most likely because the process is very deterministic at high ( Figure S2 ) ., The perturbation cannot be considered to be the result of stochastic population size changes as at low , and therefore they significantly influence the overall likelihood values of the inference methods ., Another way to simulate trees under a different model than the birth-death or the coalescent model is to simulate SIS/SIR trees ., In both these alternative models , there is an exponential growth of the infected population early on , followed by a decrease in transmissions ( births ) due to a depletion of susceptibles ., In trees produced by an SIS model with small total population size ( ) , the curve of infected individuals over time follows a logistic trend ., In the case of the SIR model assuming a small total population size the growth slows down after the exponential phase , then stops and finally becomes negative , i . e . the population size of infected individuals decreases ., When applying the exponential growth birth-death and coalescent models to the SIS/SIR trees , the birth-death process underestimates the growth rate more severely than the coalescent model in trees which reach the post-exponential growth phase ., This fits very well to the results presented above for the constant rate birth-death trees ., The birth-death model uses all available information in the tree , and thus obtains an average growth rate estimate from the SIS/SIR trees which is lower than the initial growth rate due to the post-exponential slowdown in transmission ., The coalescent mainly uses information from the early epidemic ., It thus puts less emphasis on the post-exponential phase and consequently does not severely underestimate the growth rate ., The birth-death model does not produce consistently larger HPD intervals than the coalescent ( see Table 2 ) , in contrast to the exponential growth trees ., In summary , the coalescent model estimates of the growth rate seem to be influenced most strongly by the early branching patterns in the tree ., These early patterns most strongly reflect stochasticity in population size ., In contrast , the birth-death method averages the information throughout the tree ., Since the sampling probability is fixed to quite a high value ( ) in all the trees simulated above , the trees are not only relatively short , but also a lower translates to more death events per birth event , and consequently means higher sampling from the population ( as we sample from the individuals that become non-infectious with sampling probability ) ., We further investigated if this relatively high sampling causes the coalescent methods to fail for low ., We simulated trees at low , medium and high , , but this time also at different sampling probabilities ( see Table 3 ) ., For some parameter settings with very low sampling probability , the tree simulations did not finish within 7 days of simulation , and the results are thus not displayed in the summary table ., We observed that the size of the 95% HPD interval become smaller with lower sampling probability for both birth-death and coalescent estimates of the growth rate ., This means that both methods become increasingly confident in the growth rate estimates with increasing tree length due to decreased sampling ., Additionally , the smaller the sampling probability in the birth-death tree simulation , the more often the true growth rate parameter is recovered by the coalescent ., The same is observed for the growth rate parameter estimate produced by the birth-death model on the coalescent trees ., In fact there are two ways to grow the tree longer , either by decreasing the sampling probability or by sampling more tips ., As discussed in the previous section , growing the tree longer decreases the push-of-the-past effect ., In contrast to decreasing the sampling probability , the coverage of the coalescent method does not improve when sampling more tips ., This is presumably because stochastic effects are not diluted when sampling densely ., This is best seen when comparing the coverage of the coalescent on birth-death trees grown for longer by increasing the final sample size ( as in Figure S4 ) to that on the trees grown longer by decreasing the sampling probability ( Table 3 ) ., An especially informative comparison at can be made between trees where the final sample is equal to 10000 , the average tree length of which ( root-origin distance not included ) is 105 . 9 , and the coverage is 8/100 ( figure not shown ) and those trees grown with , reaching average length of 99 . 7 and coverage of 55/100 ., Overall , the coalescent struggles most with correct growth rate estimation for datasets with low and high sampling probability ., At low , compared to high , we have a strong push-of-the-past effect and remain for longer in the phase of strong stochastic changes in population size over time ., A high sampling probability means that most samples are taken in the early phases of the epidemic , the phase with the push-of-the-past effect and reflecting stochasticity in population size the most ., It could be argued that a high sampling probability which leads to a high sampling proportion is a violation of one of the main assumptions of the coalescent model and the main reason for the biases of the coalescent when applied to birth-death trees ., Indeed , for the discrete time Wright-Fisher and Moran model , we have to assume a small sampling proportion when deriving the continuous time coalescent approximation ., However , as we show in the Supplementary Material S1 , the coalescent can also be interpreted as a continuous time Wright-Fisher and Moran model , and these models do not require a small sampling proportion ., In fact , one can even assume complete sampling , i . e . ., Therefore , we suspect that the high sampling proportion just unmasks the real reason for the frequent inability of the coalescent method to include the true value of the growth rate parameter of the birth-death trees in the 95% HPD interval ., The real reason being the stochastic population size variation over time ., Finally , we investigated the sensitivity of the models towards variation in sampling schemes ., We simulated trees where periods of no , ( or low , ) , sampling at the beginning were followed by a period of complete sampling , ., Furthermore , we simulated trees where a period of initial complete sampling was followed by a period of no ( or low ) sampling , and later again followed by a period of complete sampling ( Figure 3 and Figure S6 ) ., The coalescent model is very robust to these changes in sampling schemes ., The birth-death model is robust to slight sampling variations , but overestimates growth rate severely for extreme changes to the sampling scheme in particular for high ., Use of the birth-death skyline model , assuming a time-varying sampling probability rather than a constant sampling probability , reduces this bias ( Figure 3 and Figure S6 , Table S2 ) ., So far we only investigated the inference of epidemic growth rate using the birth-death and the coalescent models ., Both models also estimate other epidemiological parameters ., The birth-death model is parameterized by the transmission rate , becoming-non-infectious rate and the sampling probability ., The coalescent model is parameterized by , and ., These parameters as well as the compound parameter can be inferred given we fix one of the three model-specific parameters ., For the birth-death process , so far we fixed to the true value during the analyses ., Now we investigate to what extent we can estimate the individual parameters , including , using the birth-death method ., We re-analyzed all birth-death and coalescent trees simulated above applying the birth-death model estimating , and setting to , , ( and/or , if this was used for tree simulation ) , and not fixed sampling probability but assume a uniform prior over interval ., For example , trees produced under were analyzed under ( true ) , , and ., The likelihood of a tree only depends on and , rather than on three parameters , , 25 ., We could confirm that no matter what is used for the analysis , true growth rate is equally well estimated by the birth-death process for both stochastic birth-death trees or coalescent trees ( Figure S7 shows results for trees simulated under ) ., The same holds for estimation of ( Figure S8 displays the results for trees simulated under ) ., During this analysis , we also noticed when we set to its true value ( i . e . the value used during the tree simulation ) , we are able to recover the true and parameters , and consequently also the true from both the trees generated under the birth-death model and those generated under the coalescent model ( see Figures S9 , S10 , S11 and S12 ) ., In the Supplementary Material S1 , Section “Parameter correlations under the birth-death process” , we show analytically that for fixed and , increases , and and decrease with increasing , and vice versa ., In Figure 4 , we plot the impact of changing on the value ., We confirmed this theoretically predicted bias in parameter estimation in our simulation study ., If we fixed during the birth-death analysis to a bigger value than the true used during the birth-death simulations , then we overestimated and underestimated and , and vice versa ., Similarly , when analyzing the coalescent trees with the birth-death model , we observed an upward shift for and downward shift for and when assuming a value bigger than used in the simulation of the sampling times , and vice versa ., Using a uniform prior for over the interval had different effects on estimation of , and , depending on the sampling probability used for simulation ., First , in cases where the true , use of uniform prior for during the analysis resulted in wider 95% HPD intervals that either fully , or mostly , contained the 95% HPD interval produced when was fixed to the true value ( Figures S7 , S8 , S9 , S10 , S11 and S12 ) ., This is because the value is the median of the prior ., Second , for simulated trees with a true , the 95% HPD intervals produced using a uniform prior on were shifted away from the 95% HPD intervals that resulted from analysis where was fixed to the true value ( data not shown ) ., As predicted by derivations in the Supplementary Material S1 , for a true below , the estimated interval for and was shifted downward , compared to the interval estimated when the was fixed to the true value , and the estimated interval for was shifted upward ., When the true used for simulations was higher than , the posterior intervals for and shifted upwards , whereas the posterior interval for shifted downwards ., Overall , the birth-death method recovers two out of the three individual epidemiological parameters reliably if one of these parameters is fixed ( here ) ., The epidemic growth rate can be recovered well even if is misspecified ., If , or any of the other two parameters , or , is set to the true value , we can recover ( Figures 5 , S13 and S14 , for fixed ) ., If any of the parameters is fixed to a wrong value , e . g . if one assumes incorrect , then the original ( true ) cannot be recovered ., Equivalently , when using the coalescent for inference , and knowing one of the parameters , or present day infected population size , we can also recover the parameter , given we estimated the growth rate correctly ( Figure 5 and Figure S14 display the scenario where is known ) ., We use the transformation 40 to obtain estimates from the posterior estimates of the growth rates ., The birth-death inference method is partially informed by the sampling times , as the sampling times are outcomes of the birth-death process with constant rate sampling ., The coalescent is only informed by the branching times in the tree , as the coalescent conditions on sampling .,

Introduction, Results, Discussion, Materials and Methods

Quantifying epidemiological dynamics is crucial for understanding and forecasting the spread of an epidemic ., The coalescent and the birth-death model are used interchangeably to infer epidemiological parameters from the genealogical relationships of the pathogen population under study , which in turn are inferred from the pathogen genetic sequencing data ., To compare the performance of these widely applied models , we performed a simulation study ., We simulated phylogenetic trees under the constant rate birth-death model and the coalescent model with a deterministic exponentially growing infected population ., For each tree , we re-estimated the epidemiological parameters using both a birth-death and a coalescent based method , implemented as an MCMC procedure in BEAST v2 . 0 ., In our analyses that estimate the growth rate of an epidemic based on simulated birth-death trees , the point estimates such as the maximum a posteriori/maximum likelihood estimates are not very different ., However , the estimates of uncertainty are very different ., The birth-death model had a higher coverage than the coalescent model , i . e . contained the true value in the highest posterior density ( HPD ) interval more often ( 2–13% vs . 31–75% error ) ., The coverage of the coalescent decreases with decreasing basic reproductive ratio and increasing sampling probability of infecteds ., We hypothesize that the biases in the coalescent are due to the assumption of deterministic rather than stochastic population size changes ., Both methods performed reasonably well when analyzing trees simulated under the coalescent ., The methods can also identify other key epidemiological parameters as long as one of the parameters is fixed to its true value ., In summary , when using genetic data to estimate epidemic dynamics , our results suggest that the birth-death method will be less sensitive to population fluctuations of early outbreaks than the coalescent method that assumes a deterministic exponentially growing infected population .

The control or prediction of an epidemic outbreak requires the quantification of the parameters of transmission and recovery ., These parameters can be inferred from phylogenetic relationships among the pathogen strains isolated from infected individuals ., The coalescent and the birth-death process are two mathematical models commonly used in such inferences ., No benchmark on the performance of these models currently exists ., We aimed to objectively compare two specific models , namely the constant rate birth-death model and the coalescent with a deterministic exponentially growing infected population ., We compare coverage , accuracy , and precision with which they can capture the true epidemic growth rate parameter using simulated datasets ., We find that the constant rate birth-death process can account for early stochasticity and is thus capable of recovering the epidemic growth rates more successfully ., Provided one of the parameters is known , e . g . the sampling proportion of infected individuals , then the basic reproductive ratio can also be estimated reliably ., We conclude that a birth-death-based method is generally a more reliable method than a deterministic coalescent-based method for epidemiological parameter inference from phylogenies representing epidemic outbreaks ., Care should be taken if sampling is not constant through time or across individuals , such scenarios require so-called birth-death skyline models or multi-type birth-death models .

phylogenetics, plant science, medicine and health sciences, infectious disease epidemiology, epidemiology, plant pathology, biology and life sciences, population biology, evolutionary biology, evolutionary systematics

journal.pntd.0002253

Schistosoma japonicum Soluble Egg Antigens Attenuate Invasion in a First Trimester Human Placental Trophoblast Model

Schistosomes are parasitic worms endemic to many parts of Africa , South America and Southeast Asia ., They represent a significant disease burden in endemic regions , and have been estimated to be responsible for as many as 13–15 million disability-adjusted life years ( DALYs ) lost per year , with the true number potentially much higher 1 ., Of the estimated 200 million people worldwide infected at any one time , approximately 40 million are women of reproductive age 2 , 3 ., A 2002 World Health Organization policy statement recommended the use of praziquantel in pregnant and lactating women 4 , however many women still experience multiple cycles of pregnancy and lactation with schistosomiasis ., This is due to the fact that in many regions of the world , pregnant and lactating women are still routinely excluded from treatment initiatives due to the Federal Drug Administration Class B designation that praziquantel still carries , as well as barriers to praziquantel acquisition and distribution ., Data from our laboratory and others have demonstrated poor reproductive outcomes in rodents and humans in the context of schistosomiasis ., In rodent models , schistosome infection has profound impacts on birthweight and litter size 5–7 ., We have previously shown that schistosome infection in a population of pregnant women residing in The Philippines is positively associated with increased risk for chorioamnionitis and increased pro-inflammatory cytokines in both maternal and cord blood 8 ., Although very few adult worms and/or eggs are thought to directly traffic to the tissues of the maternal-fetal interface 9 , 10 , the residency of adult worms in the mesenteric vasculature and lodging of eggs in the liver allow for continuous secretion of antigens directly into the blood stream of the host ., These antigens are known to traffic to , and cross , the human placenta , and have been found in fetal circulation 11–17 ., We hypothesized that these antigens may have a direct effect on the cells at the maternal-fetal interface , and in the studies described herein , have chosen to focus specifically on processes specific to extravillous trophoblast cells ., Many events occur early in gestation that can have profound effects on the subsequent health of the fetus from gestation into adulthood ., A lack of data pertaining to schistosomiasis during this critical window of development prompted us to utilize the first trimester cell line , HTR8/SVneo , to investigate the influence of schistosome infection on early events of pregnancy ., Initial investigation was performed with co-culture of HTR8 cells and plasma collected from pregnant women infected with schistosomiasis or matched controls ., To isolate the schistosome soluble egg antigen ( SEA ) specific effect on trophoblasts from any contribution of the host response , we next evaluated the direct impact of SEA on HTR8 cells ., Events critical to placentation , including cytokine production , cellular migration and invasion were all assessed in an in vitro setting ., For the HTR8 human plasma co-culture experiment , written informed consent was obtained from each participant , and the study was approved by the institutional review boards at Rhode Island Hospital and the Philippines Research Institute of Tropical Medicine ., We used the immortalized first trimester cell line HTR8/SVneo , originally obtained from a human pregnancy terminated in the first trimester , and displaying properties of invasive extravillous cytotrophoblast cells 18 ., Cells were maintained at 37°C with 5% CO2 in 1∶1 DMEM/F-12 media ( Invitrogen , Grand Island , NY ) supplemented with 1% L-glutamine ( Invitrogen ) , 1% penicillin/streptomycin ( Invitrogen ) and 5% fetal bovine serum ( Atlanta Biologicals , Lawrenceville , GA ) ., All experiments were performed with the addition of SEA ( 25 µg/ml ) in complete media for 24 h or media only control , unless otherwise noted ., This dose was chosen based on dose response curves performed previously in our laboratory using purified primary trophoblast cells 19 ., Schistosoma japonicum SEA was generously donated by Dr . Chuan-Xin Yu ( Jiangsu Provincial Institute of Schistosomiasis Control , Wuxi , Jiangsu , China ) after having been prepared according to standard procedure 20 ., The SEA was prepared under endotoxin free conditions , with all reagents and equipment used to isolate the SEA from collected livers being LPS free prior to use ., Preparations were evaluated for contaminating endotoxin using an FDA-cleared LAL-assay ( Lonza Group , Basel , Switzerland ) ., Endotoxin levels for all SEA preparations used were <6 EU/mg protein , which , in our culture conditions , is at least 1000-fold lower than levels that have been previously shown to influence human trophoblast cells 21 ., Following the treatment period , media from HTR8/SVneo cells was collected and levels of multiple cytokines , chemokines , and fibrotic markers were assessed on a bead-based platform ( BioPlex , Bio-Rad , Hercules , CA ) using a sandwich antibody-based assay as previously described 22 ., Cytokines evaluated included interleukin ( IL ) -1β , IL-6 , interferon ( IFN ) -γ , tumor necrosis factor ( TNF ) -α , IL-4 , IL-5 , IL-10 , IL-13 , IL-12 , IL-8 and IL-2 ., These specific cytokines were measured , as they were all components of a multiplex analysis developed and validated in our laboratory ., The majority of these cytokines have been reported to be expressed by the placenta , although expression is highly variable depending on culture conditions , gestational age and disease status 23–26 ., In addition , we measured levels of tissue inhibitor of metalloproteinases ( TIMP ) -2 , TIMP-4 , insulin-like growth factor binding protein ( IGFBP ) -5 , matrix metalloproteinase ( MMP ) -9 , tenascin C , syndecan 1 , Fas ligand , osteopontin , TIMP-1 , connective tissue growth factor ( CTGF ) , macrophage inflammatory protein ( MIP ) -1α , MMP-1 , IGF-1 , MMP-8 , monocyte chemotactic protein ( MCP ) -1 , and TIMP-3 ., These analytes were developed into multiplexed assays due to their importance in schistosomiasis-associated and idiopathic pulmonary fibrosis 27–29 ., However , they are also widely implicated in invasion and remodeling at the maternal-fetal interface , underscoring the similarities between these two processes ., For the human plasma assays , we utilized plasma collected from pregnant women at 32 weeks gestation residing in Leyte , the Philippines , an area endemic for S . japonicum ., The study population and sample collection has been described elsewhere 8 , with socioeconomic status ( SES ) , gravida , parity , body mass index ( BMI ) , smoking status and age determined via questionnaire 30 ., Schistosomiasis and co-infections ( Ascaris lumbricoides , Trichuris trichuria , and hookworm ) were determined from stool samples using the Kato Katz method ., Infection intensities for each were determined using the WHO guidelines 31 ., From this larger cohort of 150 women , we selected 29 women infected with schistosomiasis ( 20 lightly infected 1–99 eggs per gram , 9 moderate infection 100–399 epg ) , and 29 uninfected women matched to the infected women for SES , co-infections , gravida , parity , gestational age , BMI , smoking status and maternal age ( Table 1 ) ., Schistosomiasis was evaluated as a nominal ( yes/no ) variable due to the low numbers of women with moderate-high infection intensities ., Serum was collected at the only pre-natal study visit ( 32 wks gestation ) ., HTR8/SVneo cells were cultured to 80% confluency in complete media before being cultured for 48 hours in serum free media with the addition of 10% plasma from the aforementioned pregnancies ., Following 48 h incubation , trophoblast culture supernatants were collected and analyzed for cytokine production as described above ., HTR8/SVneo cells were cultured to 100% confluence in complete media ., Once completely confluent , a scratch was made across the well using a sterile pipette tip ., The underside of each of the wells was cross-hatched for reference ., Cells were briefly washed with PBS in order to remove all detached cells after scratching , and cultured in serum-free media with the addition of SEA ( 25 µg/ml ) for 48 h ., Phase contrast images of the denuded region were taken at 0 h , 24 h and 48 h after scratch formation using an Olympus IX70 inverted tissue culture microscope ( Olympus Corp . , Tokyo , Japan ) ., The same region of each well was imaged at each time point , using the cross-hatching as reference ., The area free of cells was quantified using ImageJ software ( NIH , Betheseda , MD ) ., The denuded area at 24 h and 48 h for a specific well was expressed as a percentage of the denuded area that had been present in that well at 0 h , thus controlling for well-to-well variation in original scratch sizes ., MTT assays were done on HTR8/SVneo cells in parallel to the migration assays ., MTT ( Sigma Aldrich , St . Louis , MO ) was added to each well and the cells were incubated for 4 h at 37°C in a humidified environment ., Media and MTT were aspirated from each well , MTT solvent ( 4 mM HCl , 0 . 1% Nonidet P-40 , in isopropanol ) added , and the plate incubated at 25°C in the dark with rotation for 15 minutes ., Absorbance for each well was read at 560 nm and 630 nm ., HTR8/SVneo cells at 80% confluency were treated with SEA ( 25 µg/ml ) for 24 h in complete media before being gently trypsinized , washed with complete media and resuspended in serum-free media ., 25 , 000 cells/well were plated on matrigel-coated transwell inserts with an 8 µm pore size ( Corning , Tewksbury , MA ) ., The bottom chamber contained complete HTR8 media ., Following 48 h incubation , cells and matrigel were gently removed from the top of the transwell , and those cells that had invaded through the matrigel , traversed the pores , and reached the bottom of the transwell were stained with hematoxylin ( Sigma Chemical ) ., Stained cells were visualized and counted using an Olympus BH-2 microscope ( Olympus Corp . ) ., Data analysis was performed using JMP v . 10 ( SAS Institute , Cary , NC ) ., All data were evaluated for normality using the Shapiro-Wilk test ., Those experiments for which all data were normally distributed were further evaluated with ANOVA and t-tests , with means ± SEM reported ., For data that was not normally distributed , Wilcoxon Signed Rank analyses were performed , with data reported as median ± IQR ., Specifically , cytokine production by HTR8 cells was compared between cells exposed to uninfected plasma and those exposed to infected plasma ( Figure 1 ) as well as cells cultured with media alone and media with SEA ( Figures 2 and 3 ) ., Similarly , HTR8 migration and invasion were compared between cells cultured with media alone and those with SEA addition to the media ( Figures 4 and 5 ) ., Statistical significance was considered as P<0 . 05 ., For these experiments , we selected a sub-set of plasma samples collected from women at 32 weeks gestation as part of a previous study 8 , matching samples from 29 infected women with 29 samples from uninfected women on key potential confounding covariates ( Table 1 ) ., HTR8/SVneo cells were cultured in serum-free media supplemented with 10% plasma collected at 32 weeks gestation , and allowed to remain in culture for 48 h ., Media from HTR8/SVneo cells cultured with plasma from schistosome-infected women had significantly higher levels of the pro-inflammatory cytokines IL-6 and IL-8 ( 29% , P<0 . 02 and 42% , P<0 . 01 , respectively ) , compared with cells cultured in the presence of plasma from uninfected pregnant women ( Fig . 1 ) ., Levels of both IL-6 and IL-8 in the plasma alone were in most cases undetectable , with the highest level of either cytokine across all plasma samples being 32 pg/ml ( data not shown ) ., These data indicate that schistosomiasis results in the production of some factor ( s ) , present in maternal circulation , that stimulate a pro-inflammatory cytokine response by first trimester trophoblasts ., Given that SEA are found in the circulation of infected individuals and are known to cross the placental barrier , we treated HTR8/SVneo cells with purified SEA ( 25 µg/ml ) in culture ., Within 24 h of culture , HTR8 cells treated with SEA secreted higher levels of IL-6 ( 3 . 2-fold , P\u200a=\u200a0 . 01 ) , IL-8 ( 1 . 5-fold , P\u200a=\u200a0 . 02 ) and the pro-inflammatory chemokine , MCP-1 ( also known as CCL2; 1 . 7-fold , P 0 . 04 ) into the culture media , as compared to HTR8/SVneo treated with media alone ( Fig . 2 ) ., Not only do first trimester cell models respond to SEA with a pro-inflammatory signature , they also secrete higher levels of a chemo-attractant protein that may help recruit specific immune cells to the placenta , exacerbating the inflammatory reaction initiated by SEA at the maternal-fetal interface ., In addition to cytokine analysis , media from HTR8/SVneo cells treated with SEA for 24 h in culture were assessed for altered levels of a number of fibrotic markers ., Of these , TIMP-1 production was increased in media from HTR8 treated with SEA , compared to media from cells with no SEA exposure ( Fig . 3 ) ., In contrast , production of CTGF , CCL18 , TIMP-3 and fibronectin all showed no difference in levels secreted from HTR8/SVneo cells exposed to SEA compared to those with media alone ( data not shown ) ., Given the important role of TIMP-1 in the inhibition of MMPs , SEA may influence the ability of these first trimester cells to remodel and migrate into the maternal uterine wall ., We performed in vitro wound assays to assess migration of the first trimester cell line , HTR8/SVneo , following exposure to SEA ., There was little difference in cell migration at 24 h ( Fig . 4a ) ., By 48 h however , HTR8 cells in culture with SEA had filled only 57±8% of the denuded area compared to 92±6% for untreated cells ( P<0 . 01 , Fig . 4 ) ., Cell proliferation was measured using MTT assay in all wells , however no differences were observed between those wells with SEA added compared to those with media alone indicating the wound closure was not due to a proliferative effect of SEA treatment ( data not shown ) ., Together , these data suggest that migration of first trimester trophoblast cells is decreased in the presence of SEA ., We next assessed the ability of the HTR8/SVneo cell line to invade through matrigel , a model of extracellular matrix , and a transwell insert after being treated with SEA , using a standard invasion assay 32 ., Cells were pretreated with SEA for 24 h to minimize any direct effect of SEA on the matrigel ., Enumeration of the cells that had traversed the matrigel and the pores of the transwell after an additional 48 h incubation showed 2 . 7-fold lower absolute cell numbers in those wells that had been pre-treated with SEA compared to the HTR8 cells that received media alone for the initial 24 h ( P\u200a=\u200a0 . 04 , Fig . 5 ) ., These data indicate that SEA inhibits the migratory and invasive properties of the first trimester cell line , HTR8/SVneo ., Despite a 2002 WHO recommendation that pregnant and lactating women be considered for inclusion in treatment programs 33 , pregnant women with schistosomiasis are still excluded in many regions pending further evaluation of praziquantels safety during pregnancy ., Data regarding the impact of schistosome infection on human pregnancy is rather scant , although we have reported an increase in pro-inflammatory markers in maternal , placental , and newborn compartments of pregnancies complicated by schistosomiasis , as well as increased rates of acute subchorionitis in these women 8 ., Several human studies have evaluated the role of schistosomiasis during pregnancy 11 , 34 , 35 ., Two observational studies reported lower birth weights among infants from infected mothers ., However , methodological issues , including lack of control for important potential confounders such as socioeconomic status and maternal nutritional status 35 , and potential selection bias 11 make interpretation difficult ., A recently completed RCT evaluating treatment of schistosomiasis during pregnancy in Uganda reported no change in birth weight among women treated for schistosomiasis during the second trimester and untreated controls 34 ., Any effect ( s ) of schistosomiasis on early placentation however would not be detected because treatment occurred late in gestation ., Thus , questions regarding the influence of schistosome infection during the first trimester of pregnancy in humans remain largely unanswered ., Although direct trafficking of the adult worm or schistosome eggs to the maternal-fetal interface is thought to be a rare event 9 , schistosomiasis is known to produce a distinct antigenic signature in the circulation of infected individuals , including the presence of high levels of soluble egg antigens ( SEA ) which can cross the placental barrier 12 ., Previously , we demonstrated that SEA can cause pro-inflammatory cytokine production in primary human trophoblast cells taken at term and allowed to syncytialize in vitro 19 ., However , placentation is a dynamic process requiring trophoblast populations distinct in time and differentiation lineage to behave in specific and unique ways ., In this manner , a syncytialized term trophoblast is responsible for very different functions ( i . e . nutrient transfer , cytokine and hormone production ) than a first trimester invasive trophoblast cell ., In this report , we have focused on the effect of SEA on first trimester trophoblast cells , using the cell line HTR8/SVneo , as these cells represent one of the best model systems available for studying the behavior and characteristics of invasive , extravillous trophoblast cells 36 ., As we have previously reported in term syncytialized trophoblasts , HTR8 cells exposed to SEA for 24 h ( 25 µg/ml ) exhibited a pro-inflammatory cytokine signature ., These findings echoed the pro-inflammatory signature we observed in HTR8 cells exposed to plasma from pregnant women infected with schistosomiasis , compared to plasma from pregnant , uninfected controls ., A potential limitation of these experiments is that HTR8 cells were cultured with maternal plasma collected during the third trimester because the original study from which these samples originated did not enroll pregnant women until 32 weeks gestation 8 ., We do not expect this to influence either the validity or generalizability of our results as schistosomiasis infection status and its consequent host response should not be altered during gestation ., Although little is known regarding the impact of localized pro-inflammatory cytokines during the first trimester , they have been suggested to contribute to reduced migration and invasion of trophoblast cells , increased migration of innate immune cells to the maternal fetal interface , and , at very high levels , are postulated to play a role in preterm delivery and/or miscarriage 21 , 37 , 38 ., Another major role of extravillous trophoblast cells , particularly in the first trimester , is to remodel and invade deep into the maternal endometrium ., This process is tightly regulated , and failure to invade to the appropriate degree has been associated with the development of a number of gestational diseases , most importantly preeclampsia , preterm birth and low birth weight 39 , 40 ., Failure to identify any difference in the cellular metabolic activity ( MTT assay as a surrogate for cell proliferation ) between the untreated and SEA-exposed cells supports the idea that SEA is not simply cytotoxic to HTR8 cells ., Rather , HTR8 cells display increased production of TIMP-1 , inhibition of cellular migration and decreased levels of invasion through matrigel when exposed to SEA ., Of the fibrosis-associated molecules measured , TIMP-1 is arguably the most relevant to trophoblast migration/invasion 41 , 42 ., These data suggest that the process of placentation could be compromised in pregnancies complicated by schistosomiasis during the first trimester ., To our knowledge , there have been no studies examining the effect , if any , of schistosomiasis on gestational diseases such as preeclampsia ., Our data are consistent with previous work from our laboratory regarding schistosome induced pro-inflammatory cytokine production across different models of trophoblast cells 19 ., Outside of pregnancy , we and others have related these responses to nutritional , hepatic and hematologic morbidities in infected individuals 43–46 ., Surprisingly , maternal schistosomiasis during pregnancy elicits a pro-inflammatory response detectable in the neonate 8 , and neonates exposed to maternal schistosomiasis during pregnancy display a more robust response to antigenic challenge and have elevated levels of antibodies against schistosome antigens at birth than their unexposed counterparts 13 , 47 ., Together , these results suggest that maternal schistosomiasis may influence the outcome of initial pediatric schistosome infections acquired during early childhood ., The finding that SEA may modify invasion of extravillous trophoblasts and alter the cytokine milieu at the maternal fetal interface lends support to an aggressive treatment approach for women of reproductive age , such that they enter pregnancies infection free ., It should also be noted that studies which have , and will , evaluate the efficacy of praziquantel given after the first trimester ( ClinicalTrials . gov , registered study number NCT00486863 ) , may not capture the full benefit of treatment as it relates to early placentation processes ., Studies regarding the incidence of gestational diseases such as preeclampsia in the context of high schistosome prevalence are warranted and may shed additional light on the impact of schistosomiasis on the early development of the human placenta .

Introduction, Materials and Methods, Results, Discussion

Schistosomiasis affects nearly 40 million women of reproductive age , and is known to elicit a pro-inflammatory signature in the placenta ., We have previously shown that antigens from schistosome eggs can elicit pro-inflammatory cytokine production from trophoblast cells specifically; however , the influence of these antigens on other characteristics of trophoblast function , particularly as it pertains to placentation in early gestation , is unknown ., We therefore sought to determine the impact of schistosome antigens on key characteristics of first trimester trophoblast cells , including migration and invasion ., First trimester HTR8/SVneo trophoblast cells were co-cultured with plasma from pregnant women with and without schistosomiasis or schistosome soluble egg antigens ( SEA ) and measured cytokine , cellular migration , and invasion responses ., Exposure of HTR8 cells to SEA resulted in a pro-inflammatory , anti-invasive signature , characterized by increased pro-inflammatory cytokines ( IL-6 , IL-8 , MCP-1 ) and TIMP-1 ., Additionally , these cells displayed 62% decreased migration and 2 . 7-fold decreased invasion in vitro after treatment with SEA ., These results are supported by increased IL-6 and IL-8 in the culture media of HTR8 cells exposed to plasma from Schistosoma japonica infected pregnant women ., Soluble egg antigens found in circulation during schistosome infection increase pro-inflammatory cytokine production and inhibit the mobility and invasive characteristics of the first trimester HTR8/SVneo trophoblast cell line ., This is the first study to assess the impact of schistosome soluble egg antigens on the behavior of an extravillous trophoblast model and suggests that schistosomiasis in the pre-pregnancy period may adversely impact placentation and the subsequent health of the mother and newborn .

Approximately 40 million women of childbearing age suffer from schistosome infection globally at any given time ., Multiple studies in rodent models , as well as a few reports in humans , suggest that schistosome infection results in poor pregnancy outcomes ., We have previously shown that antigens released from schistosome eggs result in a pronounced pro-inflammatory response in syncytialized third trimester trophoblasts ., Herein , we examine the effect of schistosome egg antigens on a first trimester trophoblast cell line , an accepted model for early placental development ., Not only is the pro-inflammatory response recapitulated in this model system , but we also observed a decrease in migration and invasion of trophoblast cells after exposure to these antigens ., Both migration and invasion are key aspects in early placental development , and inadequate invasion has been implicated in pregnancy-related diseases such as growth restriction and preeclampsia ., This study is the first to examine the impact of schistosome antigens on early placental development , and may have implications for the subsequent health of both the pregnancy and the child .

medicine, infectious diseases, schistosomiasis, obstetrics and gynecology, pregnancy, global health, neglected tropical diseases

journal.pgen.1001374

Genome-Wide Association Analysis of Soluble ICAM-1 Concentration Reveals Novel Associations at the NFKBIK, PNPLA3, RELA, and SH2B3 Loci

A member of the immunoglobulin superfamily of adhesion receptors , ICAM-1 is expressed on endothelial cells where it serves as a receptor for the leukocyte integrins LFA-1 and Mac-1 1 ., A soluble form of ICAM-1 ( sICAM-1 ) is present in plasma and is thought to arise from proteolytic cleavage of the extra-cellular domains of ICAM-1 ., Although the physiologic function of soluble ICAM-1 remains to be fully defined , plasma concentration of sICAM-1 have a predictive value for the risk of myocardial infarction , ischemic stroke , peripheral arterial disease and noninsulin-dependent diabetes mellitus in epidemiological studies 2–4 ., We recently described a genome-wide association study of sICAM-1 in 6 , 578 apparently healthy women from the Womens Genome Health Study ( WGHS ) , which confirmed a known association at the ICAM1 locus and identified a novel association at the ABO locus 5 ., These results were subsequently replicated in large-scale genomics studies from Barbalic 6 et al . and Qi 7 et al . Nevertheless , the total variance explained by these associations remained low ( 8 . 4% ) as compared to the relatively high heritability estimates ( from 0 . 34 to 0 . 59 ) 8 , 9 for sICAM-1 ., We therefore hypothesized that other , weaker , common genetic determinants of sICAM-1 remained to be discovered ., To explore this issue , we performed a larger genome-wide association study ( GWAS ) , evaluating 334 , 295 SNPs in 22 , 435 apparently healthy women of European ancestry from the WGHS ., We found that 67 SNPs passed our pre-specified threshold of genome-wide significance of P<5×10−8 for association with sICAM-1 ( Table S1 and Figure 1A ) ., These SNPs clustered within 5 loci in the vicinity of ABO ( 9q34 . 2 ) , RELA ( 11q13 . 1 ) , SH2B3 ( 12q24 . 12 ) , ICAM1 ( 19p13 . 2 ) and PNPLA3 ( 22q13 . 31 ) ., The ICAM1 10 , 11 and ABO 5 loci have previously been identified as contributing to sICAM-1 levels , but the SH2B3 , RELA and PNPLA3 loci were not previously shown to be associated with sICAM-1 ., The genomic context of these three latter loci is illustrated in Figure 2A , 2B and 2C ., In order to determine whether more than one non-redundant association signal could be detected at each of these five loci , we applied a model selection algorithm ., The SNP with the lowest P-value for association was the only one retained at every locus with the exception of the ICAM1 locus , where 5 SNPs were selected by the model ( Table 1 ) ., Interestingly , model selected SNPs at the ICAM1 locus showed lower P-value when they were all included in a single multivariate model than when considered separately ., Three of the model selected SNPs at the ICAM1 locus ( rs281437 , rs1801714 and rs11575074 ) were not significant at a genome-wide level of significance in a univariate analysis ., We performed two analyses to determine if the multiple SNPs selected at the ICAM1 locus were the result of an underlying association with a known but untyped variant ., First , we tested all imputed SNPs ( using MACH ) within 1 . 5 Mb of rs1799969 ( the lead SNP at that locus ) for association with adjusted sICAM-1 levels ., No imputed SNP was more significant than the directly genotyped rs1799969 ., Second , we tested the same set of imputed SNPs after additional adjustment of sICAM-1 levels for the effect of model selected SNPs ., No additional SNP was associated at genome-wide significance ., The 5 SNPs at the ICAM1 locus selected by our algorithm were also used in haplotype analysis using WHAP 12 , as implemented in PLINK 13 ( Table 2 ) ., The estimate of the proportion of variance attributable to haplotypes , as well as their regression coefficients , is consistent with the linear model of these same SNPs , reinforcing the adequacy of an additive model to explain the association ., Next we tested whether any additional SNPs are associated with sICAM-1 levels after adjustment for the model selected SNPs ( see Figure 1B ) ., A single SNP was associated with sICAM-1 at genome-wide significance ( P\u200a=\u200a5 . 4×10−9; −4 . 1 ng/mL per minor allele ) in the vicinity of the NFKBIB locus at 19q13 . 2 ( Figure 2D ) ., This SNP , rs3136642 , is intronic to NFKBIB and had a minor allele frequency of 0 . 38 ., The model selection algorithm retained no other SNP at the NFKBIB locus ., Further adjustment of sICAM-1 values for rs3136642 did not identify any additional SNP with genome-wide significant association with sICAM-1 ., We also performed GWAS analysis using imputed genotypes ( using MACH ) ., Because no new locus reached genome-wide significance after adjustment for model selected SNPs , only results of directly genotyped SNPs are presented ., These results were essentially unchanged when the first 10 components of a principal component analysis were included as covariates to account for sub-Caucasian stratification ., All 4 novel loci identified in WGHS were replicated ( one-sided P<0 . 05 ) in 9 , 813 individuals from the CHARGE consortium 14 ( Table 3 ) ., Collectively , the 5 SNPs at the ICAM1 gene locus explained 6 . 5% of sICAM1 total variance , whereas the other loci explained from 0 . 1 to 1 . 4% of the variance ., In comparison , clinical covariates explained 19 . 5% of the variance ( Table 4 ) ., For 4 of the loci , there was no strong evidence for non-additive effects of the minor allele as judged by lack of significance for a likelihood ratio test comparing the additive regression model to an alternative genotype model with an additional degree of freedom ., However , the non-additive component was significant for rs507666 ( P\u200a=\u200a9 . 3×10−6 ) at the ABO locus with a tendency toward a dominant effect ( mean sICAM-1 of 362 . 1 , 342 . 4 and 335 . 4 ng/mL for 0 , 1 and 2 minor alleles , respectively ) ., The PNPLA3 SNP rs738409 also showed evidence of non-additive association ( P\u200a=\u200a4 . 6×10−5 ) with a tendency toward a recessive model ( mean sICAM-1 of 352 . 8 , 356 . 0 and 367 . 7 ng/mL for 0 , 1 and 2 minor alleles , respectively ) ., In spite of these non-additive trends , no additional locus reached genome-wide significance when a genotypic test , which does not assume an additive model of association , was conducted ., Model selected SNPs were tested for association with other available inflammation markers ( C-reactive protein and fibrinogen ) ., No significant association was noted ( P>0 . 01 ) after adjusting for multiple hypothesis testing ., Model selected SNPs were also tested for association with incident cardiovascular events ( myocardial infarction , coronary revascularization , stroke and total cardiovascular event ) over a mean follow-up period of 14 years ., A Cox proportional hazard model was used adjusting for age at study entry ., Only the SH2B3 SNP rs3184504 was associated with incident myocardial infarction ( 315 events ) , with each minor allele increasing the risk ( P\u200a=\u200a0 . 011; OR 1 . 23 95% CI 1 . 05–1 . 43 ) ., The association remained significant after further adjustment for sICAM-1 levels ( P\u200a=\u200a0 . 028; OR 1 . 20 95% CI 1 . 02–1 . 41 ) ., Given the known association of sICAM-1 with cardiovascular risk and the association of selected SNPs with sICAM-1 , we estimated the power to detect an association between the SH2B3 SNP rs3184504 and myocardial infarction to be 6% , for alpha\u200a=\u200a0 . 05 ., In comparison , power varied from 5% ( rs281437 ) to 11% ( rs5498 ) for other SNPs ., The PNPLA3 SNP rs738409 was tested for association with triglyceride , LDL cholesterol , HDL cholesterol and BMI as this gene is known to be involved in lipid metabolism and association with BMI has been previously suggested 15 ., No significant association was observed ., Since smoking accounts for a large fraction of the variation in sICAM-1 levels , we tested associated SNPs for interaction with smoking ., A significant interaction was observed for the ICAM1 SNP rs1799969 ( interaction P\u200a=\u200a1 . 6×10−9 ) whereby current smokers had a stronger genetic association , as we previously reported 16 ., A novel interaction was also observed with the ABO SNP rs507666 , again with a stronger genetic association in current smokers ( P\u200a=\u200a0 . 0003 ) ., When restricting the GWAS analysis to current smokers , an additional association was observed with rs8034191 ( P\u200a=\u200a3 . 5×10−8 ) ., This latter SNP is located on chromosome 15 near the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5 ., This locus is known to be associated with smoking behavior 17 , 18 and rs8034191 has recently been associated with smoking quantity 19 ., No novel association was observed when restricting the GWAS analysis to non-smokers after adjustment for the previously described loci ., We also tested whether multiple variants of individually weak effect could contribute to sICAM-1 levels ., In cross-validation procedures , no increase in variance explained was observed when using P-value cut-offs less significant than 10−8 for inclusion of SNPs in gene scores ( see Figure 3 ) ., In other words , selection of SNPs on the basis of P-value alone was not able to identify more of the genetic variance than could be explained by the SNPs with association P-value <10−8 ., Six loci – ABO , ICAM1 , NFKBIK , PNPLA3 , RELA and SH2B3 – have been identified in this report for association with sICAM-1 ., While the ABO 5 and ICAM1 10 , 11 loci had been previously reported , we extended the number of non-redundantly associated variants at the ICAM1 locus by demonstrating association of rs11575074 and rs1801714 in multivariate analysis along with the known rs1799969 , rs5498 and rs281437 SNPs 5 ., Neither rs1801714 nor rs11575074 are predicted eQTL ( http://eqtl . uchicago . edu/Home . html ) , but rs1801714 is a missense variant ( P352L ) and rs11575074 is located in a predicted binding site for several transcription factors including PPARG 20 ., The NFKBIK , PNPLA3 , RELA and SH2B3 associations are novel ., No strong contribution of weakly associated variants was observed in the polygene analysis whereby SNPs of varying statistical significance were included in gene scores ., Nuclear factor kB ( NF-kB ) proteins are a family of transcription factors involved in a number of physiological processes that include cell survival , proliferation , and activation ., The NF-kB proteins ( NFKB1 or NFKB2 ) are bound to REL , RELA , or RELB to form the NF-kB complex ., These complexes are typically localized in the cytoplasm , where they are trapped by binding to IkB inhibitory proteins NFKBIA or NFKBIB ., Upon inflammatory simulation , IkB kinase A and B phosphorylate IkB inhibitory proteins and mark them for degradation via the ubiquitination pathway , thereby allowing activation of the NF-kappa-B complex ., Activated NF-kB complexes translocate into the nucleus and bind to NF-kB DNA binding motifs ., NF-kB triggers transcription of various genes critical to inflammation , such as cytokines , chemokines and cell adhesion molecules including ICAM1 21 , 22 ., Remarkably , two of the novel associations involve genes physically interacting with NF-kB ., No genetic interaction , however , was noted between these two SNPs ( data not shown ) ., Taken together , these results emphasize the importance of the NFKB pathway in the regulation of sICAM-1 levels ., PNPLA3 encodes a protein of unknown function that belongs to the patatin-like phospholipase family ., Members of that family are believed to complement hormone sensitive lipase for adipocyte triacylglycerol lipase activity ., The methionine allele of the missense PNPLA3 SNP rs738409 ( Ile148Met ) has recently been associated with increased hepatic fat levels , hepatic inflammation and plasma levels of liver enzymes ( traits linked to insulin resistance and obesity ) 23 , 24 ., Nevertheless , rs738409 has been shown not to be associated with insulin resistance 25 although a previous study demonstrated an association with insulin secretion in response to oral glucose tolerance test 15 ., Levels of the inflammatory marker sICAM-1 are known to be correlated with insulin resistance and obesity 4 ., Consistent with rs738409 modulating the response to insulin resistance and associated phenotypes , the risk allele for fatty liver disease was associated with increased sICAM-1 levels ., SH2B3 encodes Lnk , an adaptor protein that mediates the interaction between extra-cellular receptors , such as the T-cell receptor and the thrombopoietin receptor MPL , and intracellular signaling pathways ., Cells from Lnk-deficient mice show an increased sensitivity to several cytokines and altered activation of the RAS/MAPK pathway in response to IL3 and stem cell factor 26 ., The same SH2B3 SNP rs3184504 identified in our study has previously been associated with multiple other traits , including blood pressure 27 , 28 , blood eosinophil number 29 , myocardial infarction 29 , celiac disease 30 , type I diabetes 31 , LDL-cholesterol 32 , asthma 29 , blood platelet number 33 , hemoglobin concentration 34 and hematocrit 34 ., Furthermore , rs3184504 is a non-synonymous SNP ( Arg262Trp ) whose derived allele ( Trp ) is part of a haplotype that has been suggested to have been introduced 3 , 400 years ago and selectively swept in European populations 33 ., The derived allele is the risk allele for coronary artery disease and was the allele associated with higher sICAM-1 concentration ., Association of rs3184504 with sICAM-1 further demonstrates the remarkable pleiotropy of that genetic variant by extending its effect to endothelial cell adhesion molecules ., An interesting hypothesis is whether changes in sICAM-1 are mediated through increased sub-clinical atherosclerosis , but further studies will be needed to address this question ., In this report , we demonstrate genetic association of sICAM-1 with the ABO , ICAM1 , NFKBIK , PNPLA3 , RELA and SH2B3 loci ., These findings broaden our current knowledge of the genetic architecture of sICAM-1 with identification of four novel loci ., The novel association at PNPLA3 reinforces the importance of insulin resistance-related processes in the regulation of sICAM-1 levels ., The observed associations also provide evidence of functional genetic variation at two genes – NFKBIK and RELA – well known for their implication in the NF-kB pathway , therefore providing a basis for the study of these polymorphisms in other conditions where this same pathway is involved ., The results also extend the effect of the SH2B3 SNP rs3184504 to endothelial function ., All analyses were performed with approval of the institutional review board of the Brigham and Womens Hospital ., All members of the WGHS cohort were participants in the WHS who provided an adequate baseline blood sample for plasma and DNA analysis and who gave consent for blood-based analyses and long-term follow-up ., All participants in this study were part of the Womens Genome Health Study ( WGHS ) 35 ., Briefly , participants in the WGHS include North American women from the Womens Health Study ( WHS ) with no prior history of cardiovascular disease , diabetes , cancer , or other major chronic illness who also provided a baseline blood sample at the time of study enrollment ., For all WGHS participants , EDTA anticoagulated plasma samples were collected at baseline and stored in vapor phase liquid nitrogen ( −170°C ) ., Circulating plasma sICAM-1 concentrations were determined using a commercial ELISA assay ( R&D Systems , Minneapolis , Minn . ) ; the assay used is known not to recognize the K56M ( rs5491 ) variant of ICAM-1 36 and the 82 Caucasian carriers of this mutation were therefore excluded from further analysis ., The intra-assay coefficient of variation was 6 . 7% and the reported intra-individual coefficient of variation 7 . 6% 37 ., This study has been approved by the institutional review board of the Brigham and Womens Hospital ., Additional clinical characteristics of this sample are provided in Table S2 ., Samples were genotyped with the Infinium II technology from Illumina ., Either the HumanHap300 Duo-Plus chip or the combination of the HumanHap300 Duo and I-Select chips was used ., In either case , the custom content was identical and consisted of candidate SNPs chosen without regard to allele frequency to increase coverage of genetic variation with impact on biological function including metabolism , inflammation or cardiovascular diseases ., Genotyping at 318 , 237 HumanHap300 Duo SNPs and 45 , 571 custom content SNPs was attempted , for a total of 363 , 808 SNPs ., Genetic context for all annotations are derived from human genome build 36 . 1 and dbSNP build 126 ., SNPs with call rates <90% were excluded from further analysis ., Likewise , all samples with percentage of missing genotypes higher than 2% were removed ., Among retained samples , SNPs were further evaluated for deviation from Hardy-Weinberg equilibrium using an exact method 38 and were excluded when the P-value was lower than 10−6 ., Samples were further validated by comparison of genotypes at 44 SNPs that had been previously ascertained using alternative technologies ., SNPs with minor allele frequency >1% in Caucasians were used for analysis ., After quality control , 334 , 295 SNPs were left for analysis ., Because population stratification can result in inflated type I error in a GWAS , a principal component analysis using 1443 ancestry informative SNPs was performed using PLINK 13 to confirm self-reported ancestry ., Briefly , these SNPs were chosen based on Fst >0 . 4 in HapMap populations ( YRB , CEU , CHB+JPT ) and inter-SNP distance at least 500 kb in order to minimize linkage disequilibrium ., Different ethnic groups were clearly distinguished with the two first components ., 31 self-identified Caucasian women were removed from analysis because they did not cluster with other Caucasians , leaving 22 , 435 non-diabetic participants with non-missing sICAM-1 information for analysis ., To rule out the possibility that residual stratification within Caucasians was responsible for the associations observed , a principal component analysis 39 was performed in Caucasians ( only ) using 64 , 205 SNPs chosen to have pair-wise linkage disequilibrium lower than r2\u200a=\u200a0 . 2 ., The first ten components were then used as covariates in the association analysis ., As adjustment by these covariates did not change the conclusions , we present analysis among Caucasian participants without further correction for sub-Caucasian ancestry unless stated otherwise ., Plasma concentrations of sICAM-1 were adjusted for age , smoking , menopause and body mass index using a linear regression model in R to reduce the impact of clinical covariates on sICAM-1 variance ., The adjusted sICAM-1 values were then tested for association with SNP genotypes by linear regression in PLINK 13 , assuming an additive contribution of each minor allele ., A conservative P-value cut-off of 5×10−8 was used to correct for the roughly 1 , 000 , 000 independent statistical tests thought to correspond to all the common genetic variation of the human genome 40 , 41 ., To investigate whether more than one SNP in each locus is independently associated with sICAM-1 , a forward selection multiple linear regression model was used ., For each locus with at least one genome-wide significant SNP ( i . e . P<5×10−8 ) , all genotyped SNPs within 1 . 5 Mb of the most significantly associated SNP and passing quality control requirements were selected for potential inclusion in our model ., The forward selection algorithm then proceeded in two steps ., In the first step , all SNPs not yet included in the multiple regression model were tested for association with sICAM-1 ., In step two , the SNP with the smallest P-value was included in the model if its multiple regression P-value was less than 5×10−8 ., We then repeated steps one and two , such that a single SNP was added to the multiple regression model at each iteration ., The algorithm was stopped when no more SNP passed the P<5×10−8 requirement ., To test whether multiple genetic variants of individually weak effect could explain a substantial fraction of sICAM-1 variance , we performed a “polygene” experiment as previously described 42 ., Briefly , we randomly divided our dataset in 5 equal parts ., We then tested SNPs for association with sICAM-1 using 4 out the 5 parts and performed linkage disequilibrium pruning as implemented in PLINK ( r2>0 . 05 and distance <1 Mb ) ., We then derived a gene score with non-redundant associated SNPs using varying P-value thresholds and weighting each SNP for its beta coefficient ., Finally , we tested the gene score for association with sICAM-1 in the remaining one fifth of the total sample and calculated the adjusted R2 ., This experiment was repeated 5 times using each one of the five parts as the gene score validation group alternatively ., We sought to replicate the 4 novel loci identified in 9 , 813 individuals from the Cohorts for Heart and Aging Research in Genome Epidemiology ( CHARGE ) consortium 14 for whom plasma sICAM-1 concentration and genotypes were available ., The CHARGE sample consists of 4 meta-analyzed cohorts: the Framingham Heart Study , the Cardiovascular Health Study , the Atherosclerosis Risk in Communities study , and the Rotterdam Study ., Complete information on each study is available as Text S1 ., Association analyses were performed on imputed genotypes using an additive genetic model on age and sex adjusted log-transformed sICAM-1 values .

Introduction, Results, Discussion, Methods

Soluble ICAM-1 ( sICAM-1 ) is an endothelium-derived inflammatory marker that has been associated with diverse conditions such as myocardial infarction , diabetes , stroke , and malaria ., Despite evidence for a heritable component to sICAM-1 levels , few genetic loci have been identified so far ., To comprehensively address this issue , we performed a genome-wide association analysis of sICAM-1 concentration in 22 , 435 apparently healthy women from the Womens Genome Health Study ., While our results confirm the previously reported associations at the ABO and ICAM1 loci , four novel associations were identified in the vicinity of NFKBIK ( rs3136642 , P\u200a=\u200a5 . 4×10−9 ) , PNPLA3 ( rs738409 , P\u200a=\u200a5 . 8×10−9 ) , RELA ( rs1049728 , P\u200a=\u200a2 . 7×10−16 ) , and SH2B3 ( rs3184504 , P\u200a=\u200a2 . 9×10−17 ) ., Two loci , NFKBIB and RELA , are involved in NFKB signaling pathway; PNPLA3 is known for its association with fatty liver disease; and SH3B2 has been associated with a multitude of traits and disease including myocardial infarction ., These associations provide insights into the genetic regulation of sICAM-1 levels and implicate these loci in the regulation of endothelial function .

Soluble Intercellular Adhesion Molecule 1 ( sICAM-1 ) is an inflammatory marker that has been associated with several common diseases such as diabetes , heart disease , stroke , and malaria ., While it is known that blood concentrations of sICAM-1 are at least partially genetically determined , our current knowledge of which genes mediate this effect is limited ., Taking advantage of technologies allowing us to interrogate genetic variation on a whole-genome basis , we found that variation in the NFKBIK , PNPLA3 , RELA , and SH2B3 genes are important determinant of sICAM-1 blood concentrations ., The NFKBIB and RELA genes are involved in regulation of inflammation ., These observations are significant because this is the first report of genetic association within these extensively studied inflammation genes ., The PNPLA3 gene has previously been associated with liver disease , and the SH2B3 gene has been associated with a multitude of traits including cardiovascular disease ., Extension of these associations to sICAM-1 adds to the intriguing diversity of effects of these genes .

cardiovascular disorders/coronary artery disease, immunology/genetics of the immune system, genetics and genomics/complex traits

journal.pcbi.1005884

Cardinal features of involuntary force variability can arise from the closed-loop control of viscoelastic afferented muscles

Involuntary fluctuations in muscle force are inherent to human motor control ., Evidence suggests that this apparent ‘noise’ is functionally significant for movement execution and learning 1–5 ., Furthermore , amplification of force variability or distortion of its frequency content is an almost universal phenomenon whenever neuromuscular control is altered , for example by aging 1 , 6 , fatigue 7 , 8 , and neurological diseases 9–13 ., However , whether such phenomenon is caused by common or distinct factors is not known because the sources of involuntary force variability and their potential interactions are not well understood ., By some descriptions , involuntary force variability is a manifestation of broad-band neural noise 3–5 ., However , neural drive to muscles is known to have a highly structured frequency spectrum 14 ., Accordingly , different neural sources of involuntary force variability , such as descending drive 15–18 and proprioceptive feedback 19–22 , are often described specifically in terms of their frequency content ., Frequency-specific force variability can also stem from mechanical sources ( e . g . mechanical resonance ) , even if the neural drive itself contains no distinct oscillatory components 23 , 24 ., Attempts to understand the relative contribution that each ‘source’ of involuntary force variability makes to the total have been difficult , given that they all act concurrently during muscle activation , and are difficult to experimentally isolate and manipulate ., While different sources of involuntary force variability may be distinct , they are not likely to be independent ., For example , there is recent evidence suggesting an inverse relationship between low ( 1-5 Hz ) - and high-frequency ( 5-12 Hz ) neural drive to muscles 25–27 ., The high-frequency drive may originate from stretch-reflex circuitry 19 , 22 ., The low-frequency drive ( the so-called ‘common drive’ ) does not have a known origin , but appears to be negatively influenced by Ia afferent feedback , since it is strongest in muscles which have low spindle densities 25 ., Further , experimental conditions which increase high-frequency neural drive and H-reflex amplitudes also decrease low frequency neural drive 26 , 27 ., Together , the clear implication is that Ia afferent feedback oppositely affects high and low frequency neural drive ( and thus force variability ) , but the mechanistic details are not yet understood ., In this study , we establish how neural and mechanical sources of force variability interact to produce the structured force spectrum observed experimentally using a physiologically-grounded model of afferented muscle ., Our simulation of an afferented musculotendon set inside of a closed-loop control scheme allowed us probe the mechanistic interactions that exist among an error correction mechanism for muscle force , proprioceptive feedback , and mechanical properties of muscle ., Further , we describe these interactions in terms of their effects on involuntary force variability and on the behavior of a simulated pool of motor units ., Our hypotheses were, 1 ) neuromechanical interactions inherent to the closed-loop control of viscoelastic musculotendon would suffice to produce low-frequency force variability ,, 2 ) tuning of proprioceptive feedback ( i . e . , known modulation of fusimotor drive or presynaptic gains ) would impact the entire frequency spectrum of force variability , and, 3 ) those changes in force variability would be reflected in motor unit synchronization ., Our findings not only support these predictions , but, ( i ) emphasize the importance of neuromechanical interactions to levels not previously recognized , and, ( ii ) they describe how isolated changes in each proprioceptive pathway gain influences the full spectrum of involuntary force variability ., This novel demonstration fills a critical gap in our understanding of how error correction mechanisms , proprioceptive feedback , noise , and musculotendon mechanics are interrelated , and our results emphasize the critical importance of investigating involuntary force variability within the context of closed-loop control ., Our results are an important step towards a unifying theory that relates spinal circuitry to various manifestations of altered involuntary force variability in functional performance 1 , aging 1 , 6 , fatigue 7 , 8 and neurological disease 9–13 ., First , we investigated the interactions between mechanical properties of the musculotendon and broad-band neural noise using an open-loop input without any feedback ( Simulation 1 . 1 ) ., For this simulation , our control input was simply the target trajectory ( i . e . , 1-sec zero input , 2-sec ramp-up and 32-sec hold at 20% MVC ) , with added signal-dependent noise ., The coefficient of variation of force was 8 . 73% ., This open-loop control resulted in force variability which fell almost entirely below 5 Hz , within the ‘common drive’ range ( red line in Fig 4A ) ., It is worth noting that there was no distinct peak within this frequency range ( i . e . , 1-5 Hz ) ., Accordingly , the neural drive produced in this simulation also caused a small degree of common drive , as measured by the ‘common drive index’ ( red boxplot in Fig 4B ) ., A similar result was observed using motor unit coherence analysis ., It is also important to note that high-frequency force variability ( 5-12 Hz ) did not arise from the interaction between mechanical properties of musculotendon and broad-band noise ., We then ran the simulation in closed-loop condition using only the error correction mechanism ( i . e . , tracking controller ) ( Simulation 1 . 2 ) ., The amplitude of overall force variability was 8 . 39% , which was not significantly different from the open-loop condition ( independent sample t-test using Yuen’s method , p = 0 . 19 ) ., This addition of an operational tracking controller resulted in the generation of a peak at ∼ 1 . 8 Hz in the power spectrum of muscle force ( green line in Fig 4A ) ., Also , the degree of motor unit synchronization in this range increased accordingly ( green boxplot in Fig 4B ) ., These results altogether suggest that low-frequency force variability and common drive are primarily an emergent property of a close-loop control of muscle force ., Also , these results show that high-frequency force variability does not emerge in the absence of proprioceptive feedback ., Gain control of Ia afferent feedback at the spinal cord , often experimentally quantified by H-reflex amplitude , plays an important role in human motor control and learning to achieve a variety of movements 42 , 43 ., Here , we examined how changes in the gain of Ia afferent feedback , modeled as presynaptic control input , influence force variability ., We systematically altered the level of this presynaptic control input from the value of -0 . 5 to 0 while keeping the other gain parameters constant ( 70 pps for dynamic and static fusimotor drives and -0 . 3 for presynaptic control level of Ib afferent feedback ) ., This range was set such that the mean input contribution of Ia afferent feedback to the neural drive spanned a range from 0 ( i . e . , no contribution from Ia afferent feedback ) to 30% of the maximum neural drive ., The amplitude of force variability decreases as the presynaptic input level is increased and becomes minimal at the value of -0 . 15 ( Fig 5A ) ., Further increases negatively affect the amplitude of force variability ( the presynaptic control level of -0 . 05 and 0 in Fig 5A ) ., Analyses of force variability in the frequency domain show the change in force variability amplitude occurred across the frequency range ( p < 0 . 01 at all the frequencies between 1 and 12 Hz ) , but prominent peaks exist in the two distinct frequency ranges , namely the common drive range ( 1-5 Hz ) and physiological tremor range ( 5-12 Hz ) ( shown as blue and red bands for common drive range and physiological tremor range , respectively , in Fig 5B ) ., These observations demonstrate that modulation of the strength of Ia afferent feedback is an important factor that influences overall force variability during ‘isometric’ force production ., Further analyses on frequency-specific effects of Ia afferent feedback show increasing the gain of Ia afferent feedback reduces force variability within the common drive range ( Fig 6A ) while it increases the amplitude of physiological tremor ( Fig 6B ) ., Excessive Ia gain led to excessive physiological tremor as suggested in previous studies 20 , 44 ., As Ia afferent feedback increases , common drive decreases more than physiological tremor increases , after which physiological tremor dominates the spectrum and a monotonic increase in total force variability is observed ., These observations suggest that the U shaped response comes from the relative contribution of common drive and physiological tremor to total force variability ., Importantly , these concurrent changes in the common drive and physiological tremor are consistent with previous speculations 25–27 ., These observations suggest that relatively faster excitation cycles of Ia afferent feedback can function as a negative feedback ( i . e . , withdrawal of Ia afferent input during muscle shortening and its excitation during muscle stretch ) , thereby interrupting the development of low-frequency force fluctuations , characteristic of a close-loop control of muscle force ., Changes in motor unit synchronization in the common drive and physiological tremor ranges are shown in ( Fig 6C–6E ) ., Stronger Ia afferent feedback reduces the degree of common drive ( Fig 6C and 6D ) ., In contrast , it induces a higher degree of synchronization in the physiological tremor range ( Fig 6E ) ., These results further confirm a previously suggested relationship between the strength of Ia afferent feedback and motor unit synchronization in the common drive and physiological tremor ranges 25–27 ., Understanding the operation of the fusimotor system is hindered by the lack of techniques which can directly measure γ-motoneuron activities 45 ., However , experimental evidence based on human group Ia and II afferent activities has suggested that humans have control over the fusimotor system which is independent of α-motoneuron drive , and which can be modulated by attention and task requirements 46–48 ., Here , we postulate that fusimotor-induced changes in the dynamic sensitivity and static bias of Ia afferent activity will have profound effects on force variability as well ., Therefore , we tested three scenarios;, 1 ) co-modulation of γ dynamic and static fusimotor drives ,, 2 ) modulation of γ dynamic or, 3 ) γ static fusimotor drive independently while the other is held constant , as done previously 33 ., In this study , we varied them from 10 to 250 pps by increment of 20 pps ., When γ dynamic or static fusimotor drive was varied independently , the other was kept at 70 pps ., The presynaptic control levels of Ia and Ib afferent feedback were set at -0 . 15 and -0 . 3 ., Results show that the amplitude of overall force variability depends on the levels of fusimotor drives ( Fig 8A–8C top figures ) ., When both γ dynamic and γ static fusimotor drives are varied , the amplitude of overall force variability shows a similar response to the presynaptic manipulation of Ia afferent feedback ( Fig 8A top figure ) ., Also , the changes again occur predominantly in the common drive and physiological tremor ranges ( p < 0 . 01 at all the frequencies between 1 and 12 Hz ) as indicated by prominent peaks in those ranges ( Fig 8A bottom figure ) ., Independent modulation of the only γ dynamic fusimotor drive has comparably smaller effects on the amplitude of overall force variability ( Fig 8B top figure ) ., On the contrary , modulation of γ static fusimotor drive produces effects similar to co-modulation of both fusimotor drives ( Fig 8C top figure ) ., Again , their effects occur in the common drive and physiological tremor ranges ( Fig 8B and 8C bottom figures ) ., These results show that the fusimotor system , especially γ static fusimotor drive , has profound effects on force variability in a frequency specific manner similar to presynaptic modulation of Ia afferent gain ., This differential sensitivity to γ dynamic and static fusimotor drives might speak to differences in their functional significance during isometric force production ., Also , it is important to note that too high levels of γ static fusimotor drives can lead to greater overall force variability accompanied by excessive physiological tremor , which might be similar to effects of fatigue 7 , 49 ( see Discussion ) ., Further analyses in the two frequency ranges show greater fusimotor drives are associated with smaller force variability in the common drive range and larger physiological tremor ( Fig 9A and 9B ) ., The effects of γ static fusimotor drive are substantially larger than those of dynamic fusimotor drive in both frequency ranges and the combination of those effects is illustrated in the case of co-modulation of γ dynamic and static fusimotor drives ., These results are consistent with those from presynaptic Ia afferent feedback gain such that increased bias level ( mean input contribution ) of Ia afferent feedback , rather than the dynamic sensitivity of Ia afferent feedback , plays a more important role in shaping the power spectrum of force variability and generating physiological tremor 50 ., Changes in motor unit synchronization correspond well to changes in force variability , as shown in Fig 9C–9E ., Greater fusimotor drives result in lower CDI values and low-frequency coherence ( Fig 9C and 9D ) , as well as higher coherence in the physiological tremor range ., These results suggest that modulation of γ dynamic and static fusimotor drives can also alter the degree of motor unit synchronization across the force-relevant frequencies ., Given that Ib afferent feedback in general provides inhibition of α-motoneurons as a function of force level , one can easily expect that it helps stabilize force fluctuations 45 , 51 ., However , exactly how such a feedback system influences either overall amplitude or frequency-specific components of involuntary force variability is unknown ., Here , the presynaptic control value of Ib afferent feedback was varied from -0 . 5 to 0 , while the presynaptic control value of Ia afferent feedback was kept at -0 . 3 and dynamic and static fusimotor drives at 70 pps ., This range corresponds to a Ib contribution of 0 to 45% of the maximum neural drive , respectively ., The upper range of these values would be non-physiological as the Ib input contribution of 45% of the maximal neural drive , for example , means 45% total input is continuously inhibited and it requires other compensatory mechanisms through Ia afferent feedback and a tracking controller to maintain the target force level ., Here , we merely try to fully characterize effects of Ib afferent feedback on force variability and thereby highlight differences between Ia and Ib afferent feedback ., As expected , greater inhibition of α-motoneurons through Ib afferent feedback reduces the amplitude of overall force variability ( Fig 10A ) ., However , excessive Ib gain can also lead to increased force variability at ∼4 Hz ( Fig 10A ) although it requires non-physiologically large Ib input contributions ., In the frequency domain , changes in force variability occur across the frequencies ( p < 0 . 01 at all the frequencies between 1 and 12 Hz ) , but mainly in the common drive as indicated by peaks appearing only in that range ( Fig 10B ) ., The slightly lower frequencies at which the second peak occurs compared to those of Ia afferent feedback might result from the longer loop delay of Ib afferent feedback ( Fig 10B ) ., These results highlight that Ib afferent feedback can regulate force variability much like presynaptic/fusimotor modulation of Ia afferent feedback , but its effects are mostly confined in the common drive range ., Increasing the strength of Ib inhibition results in smaller force variability in the common drive range ( Fig 11A ) , but excessive Ib inhibition can lead to excessive force fluctuations in this range as shown in ( Fig 10A ) ., Its effects on physiological tremor are considerably smaller than presynaptic/fusimotor modulation of Ia afferent feedback ( Fig 11B ) ., These results highlight the differences in cross-frequency interactions between Ia and Ib afferent feedback pathways , which has not been reported previously ., As before , the frequency-specific effects of presynaptic Ib modulation on force variability are also reflected in motor unit synchronization ( Fig 11C–11E ) ., Higher Ib feedback gain is associated with lower synchronization in the common drive range and higher synchronization in the physiological tremor range ., Interestingly , CDI and coherence in the common drive range respond differently to excessive force fluctuations at ∼4 Hz seen with excessive Ib inhibition ( Fig 11C and 11D ) , suggesting that these two measures have differing sensitivity to synchronization at different frequencies within 1-5 Hz ., A series of closed-loop simulations of an afferented muscle show that many cardinal features of involuntary force variability emerge from closed-loop neuromechanical interactions ., Our results reveal that closed-loop control of a viscoelastic musculotendon unit , combined with the tuning of proprioceptive feedback gains , naturally generate both low-frequency ( 1-5 Hz ) force variability and high-frequency oscillations analogous to physiological tremor ( 5-12 Hz ) ., Moreover , we show that these low- and high-frequency phenomena are in fact mechanistically related to each other—which suggests novel and fruitful directions for future research ., This study is , to our knowledge , the first to directly confirm mechanistic links between low- and high-frequency force variability , as was proposed earlier 25–27 ., Finally , we also used the emergent time histories of closed-loop net neural drive ( ‘ND’ in Fig, 1 ) to drive the model of a motor unit pool ., We find that these inputs suffice to produce motor-unit synchronization compatible with experimental findings 25–27 ., Involuntary force variability at low frequencies ( 1-5 Hz ) can arise from various sources , including low-frequency variability in the neural drive to muscle ( the so-called ‘common drive’ ) 36 ., As such , the amplitude of this common drive is a contributor to error during voluntary control of precision forces 14 , 15 , 36 , 52 ., Although common drive has been studied for over 30 years , its origins remain debatable 10 ., Our results are significant because they suggest that common drive can emerge due to a combination of factors inherent to any neuromuscular control loop ., Foremost among them is the viscoelasticity of the musculotendon , which acts as a mechanical low-pass filter that naturally allows the preferential conversion of low frequencies in the neural drive into muscle force as previously shown in 53–55 ., It is this low frequency component ( 1-5 Hz ) of muscle force that would be selectively reinforced by any imperfect physiological error correction mechanism ., Thus , our results demonstrate that low-frequency force variability emerges naturally when controlling viscoelastic muscles—and do not require the presence of proprioceptive feedback ., This is a novel alternative to other peripheral explanations ., For example , Watanabe and Kohn suggested that high-frequency neural drive can be demodulated into lower frequencies 18 , which still remains to be tested ., In fact , our results are congruent with previous evidence for peripheral mechanisms , such as the fact that common drive persists even after disruption of the cortico-spinal tract , as in capsular stroke 41 ., Another component of force variability is oscillations in the 5-12 Hz range , often called ‘physiological tremor . ’ Physiological tremor may arise from multiple factors 56 ., One of the earliest and most well-supported mechanisms is cycles of excitation around the stretch reflex loop 19 , 20 , 22 ., The first important implication of our results is that , in contrast to common drive , physiological tremor does require the proprioceptive feedback in order to arise as shown experimentally 19 , 22 and in computational simulations 20 ., Thus mechanical resonance of musculotendons as proposed by 23 , 24 , did not suffice ., In fact , we could not elicit physiological tremor via interactions between broad-band noise and the mechanical properties of musculotendon using an open-loop input which consisted of the target trajectory and signal-dependent noise ., This result is consistent with previous experimental evidence and simulation 19 , 20 , 22 ., Moreover , our simulations allowed us to characterize how physiological tremor amplitude is modulated by proprioceptive pathway gains ., Those include both presynaptic control levels of inhibition/disinhibition ( ‘PCIa’ & ‘PCIb’ in Fig, 1 ) and ‘descending’ γ fusimotor drive to muscle spindles ( ‘fusimotor drive’ in Fig 1 ) ., This detailed characterization was not possible in the previous simulation study by Stein and Oguztoreli 20 and added a new insight that physiological tremor amplitude is mostly determined by the bias level ( i . e . , mean input contribution ) of Ia afferent feedback , not dynamic sensitivity of muscle spindle ., Importantly , excessive Ia afferent gains could produce excessive oscillations primarily in the physiological tremor range in Figs 5 and 8 , similar to what has been shown previously in animal models 44 ., Interestingly , excessive Ib afferent gains could lead to excessive oscillations in the lower frequency range ( 3-5 Hz ) possibly due to the longer delay along this pathway ( Fig 10 ) ., These findings are particularly important to design hypotheses about how peripheral mechanisms interact with descending neural drive to produce physiological and other kinds of tremor in healthy and pathological conditions 16 , 17 , 57 , 58 ., Although we find that proprioceptive feedback is not strictly necessary to generate common drive , we do find that it can influence its strength ., This is compatible with experimental findings 25–27 ., Specifically , De Luca and colleagues report a negative correlation between the degree of common drive and muscle spindle density 25 ., Further , Laine and colleagues showed that heightening the perception of task-related errors during a force tracking task led to increases in physiological tremor and H-reflex—while common drive decreased 26 , 27 ., Their interpretation was that the changes in common drive and physiological tremor both stemmed from the tuning of proprioceptive gains due to alterations in psycho-sensory state 46–48 , 59–61 ., These lines of experimental evidence , however , could not test a mechanistic link between common drive and physiological tremor ., Here , we show that increasing the strength of proprioceptive feedback ( via ‘PCIa’ and ‘γ static fusimotor drive’ ) increases physiological tremor but concurrently decreases common drive ( Fig 6 ) ., Thus , our results demonstrate that peripheral mechanisms suffice to reproduce those experimental findings ., This close link between the amplitude of involuntary force variability and proprioceptive pathway gains ( in Figs 5 , 8 and 10 ) may explain many experimental findings ., For example , removing proprioceptive feedback leads to greater overall involuntary force variability ( i . e . , smaller values of ‘PCIa’ , ‘PCIb’ and ‘γ static fusimotor drive’ in Figs 5 , 8 and 10 ) ., This is similar to what has been seen in patients with deafferentation 12 ., Moreover , we show that excessive proprioceptive pathway gains result in greater overall force variability and excessive physiological tremor ( i . e . , larger values of ‘PCIa’ , ‘PCIb’ and ‘γ static fusimotor drive’ in Figs 5 , 8 and 10 ) ., Interestingly , fatigue can produce similar effects on force variability and physiological tremor 7 , 8; however , a precise mechanism for this phenomenon has not been established ., The enhancement of physiological tremor in fatigue can be attenuated by blocking Ia afferent feedback 7 ., Further , the sensitivity of stretch/tonic vibration reflex responses is enhanced during fatigue 49 ., An emerging picture is that Ia afferent feedback gains are increased during fatigue , but it is not clear how this occurs ( i . e . , via presynaptic inhibition or fusimotor modulation ) , and it is not clear why fatigue influences overall force variability rather than just physiological tremor ., Biro and colleagues suggested that augmented Ia afferent feedback during fatigue reflects a fusimotor-dependent compensation for reduced descending drive 49 ., This suggestion was based on previous findings in cat where, 1 ) the activity of fusimotor system is enhance by activation of group III and IV afferents 62 , 63 , which respond to an accumulation of metabolites during fatigue 64 , 65 ,, 2 ) Ia afferent firing rates increase accordingly during fatigue contractions 62 , 66 , and, 3 ) group III and IV afferents , on the contrary , enhance presynaptic inhibition of Ia afferent feedback 67 ., Since presynaptic inhibition would reduce Ia afferent feedback gain , only the increased fusimotor activation seems a plausible compensatory mechanism ., Thus it is important to mention that , when we tested the effects of increased fusimotor drive in our simulation , the results of γ static fusimotor drive ( ‘γ static fusimotor drive’ in Fig 8 ) accurately predicted changes in force variability , as might occur during fatigue ., Our findings therefore may provide a mechanistic link between several complementary lines of investigation related to fatigue ., As demonstrated in the cases of deafferentation and fatigue , the close link between our results and experimental findings may represent an important step in developing a unifying theory of human sensorimotor control that further relates spinal circuitry to manifestations of altered involuntary force variability under various neuromuscular conditions such as aging 1 , 68 , 69 , stroke 10 , cerebral palsy 9 , Parkinson’s disease 13 , and essential tremor 70 ., For example , we show that increased Ia afferent feedback gains result in increased force variability below 0 . 5 Hz ( i . e . , larger values of ‘PCIa’ and co-modulation and ‘γ static fusimotor drive’ in Figs 5 and 8 ) ., This might provide a link between increased force variability below 0 . 5 Hz seen in patients post stroke 10 , 15 and their heightened Ia afferent feedback gains 71 or lower reflex threshold 72 ., Thus , a unifying principle emerges ., Namely , that the task-specific tuning of proprioceptive pathway gains in spinal circuitry—or its disruption—produces characteristic changes in the spectra of neural drive ., Importantly , these can be quantified by measuring force variability ., Our results highlight the significance of considering closed-loop control of afferented muscle in the generation and modulation of involuntary force variability in motor control research ., Historically , the force fluctuations have been considered as manifestation of ‘neural noise’ that is intrinsic to neural drive 73 ., Despite the fact that such noise ( e . g . , signal dependent noise ) is usually not frequency-specific , involuntary force fluctuations tend to be highly structured 14 ., Our results now show that neuromechanical interactions impose structure onto noisy neural drive , and thus involuntary force variability and ‘noise’ are not independent , as is often assumed 15 ., This idea may be significant in formulation of theoretical frameworks in motor control ., For example , the ability of the proprioceptive feedback system to regulate the amplitude of overall involuntary force variability provides a neural mechanism to minimize it , as suggested by some 3 , 74 ., It is important to discuss how the limitations of our model do not affect our conclusions ., Our afferented muscle model was not intended to represent the full complexity of the spinal cord circuitry ., We used a simplified version of a previously described model of a spinal-like regulator 75 , 76 that can replicate experimental behavior ., Specifically , we did not include Renshaw inhibitory interneurons , which are known to provide recurrent inhibition of α-motoneurons and inhibition of Ia inhibitory internuerons 77 ., However , in our simulation of a single muscle , the role of Renshaw inhibitory interneurons would be restricted to recurrent inhibition and therefore have effects similar to that of Ib inhibitory feedback , which we did include ., Secondly , our model did not attempt to replicate the exact biophysical structure of α-motoneurons and sensory afferents ., Rather , we used a single-input/single-output structure to describe the population behavior of each system ., We believe this simplification is reasonable because, 1 ) the population response of an α-motoneuron pool is linear with respect to its common/shared synaptic input , since noise and non-linear properties of individual neurons get canceled out in the overall population behavior 14 , 78 , and, 2 ) the common input to an α-motoneuron pool is the ‘effective’ neural drive , that is , the input that is actually translated into muscle force 14 ., Therefore , it was appropriate for the contractile element in the afferented muscle to be modeled as a single input-output element ., Another outcome of using a lumped parameter model of muscle is that force is not generated by the summation of twitches from progressively recruited motor units ., However , neither physiological tremor nor ‘common drive’ is thought to relate directly to this aspect of physiological force generation 79 ., It is also worth noting that since we simulated constant-force contractions , the number of units recruited/derecruited during each trial would have been very small and therefore would have only minor influence on the overall amplitude of force variability ., Similarly , the population behavior of muscle spindles can be appropriately modeled as a single element , as muscle spindles are in general believed to distribute their synaptic inputs widely across a motor unit pool 80 ., While potential non-uniformity of Ia projections has been suggested 81 , this remains to be validated , and confirmed across different muscles ., Thirdly , we did not include modulation of α-motoneuron excitability through various neuromodulatory inputs arising from the brainstem , which can influence reflex sensitivity 82 ., Such neuromodulatory effects would be widespread and more difficult to interpret , while also greatly increasing the complexity of our analyses ., Finally , our simulation was limited to that of a single muscle during isometric contraction , which is a valuable and informative experimental paradigm 1 , 9 , 26–28 ., As in those experimental studies , it is difficult to extrapolate our findings to complex actions involving movement and coordination among multiple muscles ., Still , we believe that our results help establish a strong basis for future study of peripheral and neuromechanical factors influencing the control of muscle force ., Lastly , we demonstrate that the modulation of involuntary force variability via proprioceptive pathway gains gives the nervous system a certain degree of control over involuntary force variability ., Properly regulating those gains is important if disruptive tremor is to be avoided 44 ., Our ability to understand and modify these relationships will be instrumental to providing insights into the neural mechanisms and circuits associated with functional performance 1 , aging 1 , 6 , fatigue 7 , 8 and neurological disease 9–13 ., Finally , our approach of combining experimental observations with a computational simulation should provide a springboard for future investigation of neuromechanical interactions and task-dependent tuning of sensorimotor integra

Introduction, Results, Discussion, Materials and methods

Involuntary force variability below 15 Hz arises from , and is influenced by , many factors including descending neural drive , proprioceptive feedback , and mechanical properties of muscles and tendons ., However , their potential interactions that give rise to the well-structured spectrum of involuntary force variability are not well understood due to a lack of experimental techniques ., Here , we investigated the generation , modulation , and interactions among different sources of force variability using a physiologically-grounded closed-loop simulation of an afferented muscle model ., The closed-loop simulation included a musculotendon model , muscle spindle , Golgi tendon organ ( GTO ) , and a tracking controller which enabled target-guided force tracking ., We demonstrate that closed-loop control of an afferented musculotendon suffices to replicate and explain surprisingly many cardinal features of involuntary force variability ., Specifically , we present, 1 ) a potential origin of low-frequency force variability associated with co-modulation of motor unit firing rates ( i . e . , ‘common drive’ ) ,, 2 ) an in-depth characterization of how proprioceptive feedback pathways suffice to generate 5-12 Hz physiological tremor , and, 3 ) evidence that modulation of those feedback pathways ( i . e . , presynaptic inhibition of Ia and Ib afferents , and spindle sensitivity via fusimotor drive ) influence the full spectrum of force variability ., These results highlight the previously underestimated importance of closed-loop neuromechanical interactions in explaining involuntary force variability during voluntary ‘isometric’ force control ., Furthermore , these results provide the basis for a unifying theory that relates spinal circuitry to various manifestations of altered involuntary force variability in fatigue , aging and neurological disease .

Involuntary fluctuations in muscle force are an unavoidable consequence of human motor control and underlie movement execution errors ., Amplification and distortion of involuntary force variability are common phenomena found in various neurological conditions and in fatigue ., However , the underlying mechanisms for this are often unclear ., We investigated the generation and modulation of involuntary force variability arising from different sources , as well as their interactions ., We used a closed-loop simulation which included a physiologically-grounded model of an afferented musculotendon and an error-controller ., We show that interactions among neural noise , musculotendon mechanics , proprioceptive feedback , and error correction are critical components of force control , and by taking these into account , our model was able to both replicate and explain many cardinal features of involuntary force variability previously reported experimentally ., Also , our results suggest previously unrecognized pathways through which force variability may be altered in fatigue and in certain neurological diseases ., Finally , we emphasize the potential for important clinical and scientific information to be extracted from relatively simple , non-invasive measurements of force .

medicine and health sciences, classical mechanics, myoclonus, pathology and laboratory medicine, fatigue, engineering and technology, signal processing, biomechanics, noise reduction, simulation and modeling, signs and symptoms, research and analysis methods, musculoskeletal mechanics, muscle physiology, sensory physiology, connective tissue, biological tissue, statics, physics, diagnostic medicine, anatomy, tendons, physiology, biology and life sciences, physical sciences

journal.pntd.0003878

Machine Learning Models and Pathway Genome Data Base for Trypanosoma cruzi Drug Discovery

In the 1980’s the pharmaceutical industry took advantage of advances in molecular biology/genetic engineering and began replacing phenotypic , whole-cell HTS with target-based screening assays 1 ., Target-based screens using simple recombinant protein enzymatic assays offer advantages in terms of cost and scalability ., Nonetheless , in the last decade , there has been a shift back towards using phenotypic screens as a starting point for drug discovery , especially for infectious diseases where drug targets are poorly understood or target-based approaches have been unsuccessful in the past 1 ., In fact , analysis of the origin of first-in-class small molecules found that phenotypic screens identified more novel inhibitors than any other approach between 1999 and 2008 2 , 3 ., One such disease area , where target-based drug discovery has largely failed , is in the field of neglected tropical diseases ( NTDs ) ., NTDs are a collection of infectious diseases that disproportionately affect marginalized or poor populations in the developing world 4 ., Many of these pathogens are eukaryotic parasites with complex life cycles and diverse approaches for evading the host immune system ., Furthermore , many of these parasites are not genetically tractable in the laboratory and receive only a small amount of research investment from scientists and pharmaceutical companies in the developed world 5 ., The trend towards using phenotypic screens over target-based screens is particularly strong for NTDs as well as bacterial and fungal pathogens ., For these infectious diseases , it is generally considered more difficult to convert a strong targeted hit into a cell permeable , non-toxic drug than it is to identify the target of a non-toxic compound with phenotypic , whole-cell activity 6 , especially in the case of intracellular parasites in which the compound has to cross an extra membrane of the host cell to hit its final target ., Chagas disease is an NTD caused by the eukaryotic parasite Trypanosoma cruzi 7 ., The disease is endemic to Latin America but is increasingly found in North America and Europe , primarily through immigration 8–11 and the spread of this disease is bringing new attention to the need for novel , safe , and effective therapeutics to treat T . cruzi infection ., The current clinical and preclinical pipeline for T . cruzi is extremely sparse and lacks drug target diversity ( currently focused on 3 targets , CYP51 , cruzain and genes associated with DNA damage ) 12–14 ., Pre-clinical development of oxaboroles is being led by a partnership between DNDi and Anacor 15 ., The most advanced product is the re-evaluation of a toxic general DNA damage agent benznidazole , approved for use in Chagas disease outside the U . S but not by the US FDA ., It requires dosing of sixty days or more and has significant toxicity 16 , 17 ., The remaining products in clinical development ( Phase I and II ) target a single enzyme , CYP51 , which has been the focus of Chagas disease drug development to date 18–23 ., Recent results from Phase II trials demonstrated that repurposed drugs targeting fungal CYP51 did not eliminate recrudescent parasites at 6 months post therapy as determined by PCR 24 ., Attention has therefore shifted to drug development targeting the parasite CYP51 itself 20 , 22 such as fexinidazole 25 , 26 ., The only additional novel drug target with a single compound in preclinical development is cruzain , a T . cruzi cysteine protease and there is considerable literature surrounding this class of inhibitors 27 , 28 as well as overlap with CYP51 29 ., There have been some target-based high throughput screens for inhibitors of CYP51 23 and cruzain 28 as well as virtual screening of inhibitors for cruzain 27 ., Several whole-cell , phenotypic high throughput screens have been completed for T . cruzi , including most recently a screen of 1 . 8 million compounds at GlaxoSmithKline in Spain 30 , another of over 300 , 000 molecules at the Broad Institute 31–34 and a proprietary screen by the Genomics Institute of the Novartis Research Foundation ( GNF ) 35 ., Therefore more HTS is leading to new hits 31–39 from academia 40 , industry , and the non-profit sector , primarily with the support of NIAID and the Drugs for Neglected Diseases Initiative ( DNDi ) ., However , there is a disconnect between the currently identified targets and outcomes obtained in clinical trials 41 ., The latest HTS hits are also early in the pipeline ., Methods for identifying and prioritizing novel targets of phenotypic screening hits will become increasingly important as well as approaches to screen vast libraries of molecules using computational approaches prior to in vitro testing ., In the past we have used a used a combined bioinformatics-cheminformatics approach to compile , analyze , and prioritize novel metabolic enzyme targets from Mycobacterium tuberculosis ( Mtb ) , then suggest compounds that might interact with these targets 42 ., One study identified 12 enzymes that are in vivo essential enzymes in Mtb , absent in humans , have known reactions in TBCyc ( http://tbcyc . tbdb . org/index . shtml; an Mtb-specific metabolic pathway database ) , and are not targets of known TB drugs ., These targets and their metabolites were used with a 3D pharmacophore approach to screen vendor libraries 43–45 before filtering with additional computational models 43 , 46 , 47 ., Ultimately novel inhibitors were identified showing moderate minimal inhibitor concentration values against M . tuberculosis in vitro 42 ., These are currently undergoing further validation ., In contrast to tuberculosis , there are significantly fewer public , curated , and compiled data on metabolic pathways and computational drug screening efforts in T . cruzi 48–50 ., In the current study we have compiled and curated relevant biological and chemical compound screening data including, ( i ) compounds and biological activity data from the literature ,, ( ii ) high throughput screening datasets , and, ( iii ) predicted metabolites of T . cruzi metabolic pathways ., To this end , we identified and extracted associated biological data for 584 compounds with activity data against T . cruzi in the published literature and made this available as a public dataset in CDD Public ., In addition we have created a BioCyc database for T . cruzi , which complements other sources of related metabolic pathway data ( including KEGG T . cruzi pathways 51 , BioCyc databases for the closely related pathogens Leishmania major 52 and Trypanosoma brucei 28 , and the PathCase Metabolic Workbench dataset for T . cruzi 53 ) and can be used in future drug discovery efforts ., We have also compiled public screening data for the over 300 , 000 additional compounds screened against T . cruzi and the related pathogen Trypanosoma brucei 54 , 55 ., Subsets of these data have been used to build machine learning models for compound selection as we have previously done with Mtb datasets 43 , 46 , 47 , 56–61 ., All of these efforts and curated information on T . cruzi may be used for target inference 62 , 63 which combines cheminformatics and bioinformatics capabilities ., Ultimately we highlight how our approach lead to in vivo testing of compounds and the discovery of a promising lead candidate ., An analysis of the Chagas disease literature was performed resulting in the curation of over 500 molecules with associated target information ( when available ) ., The Broad Chagas screening data 31–34 were also collected and both datasets were uploaded into the CDD database ( Collaborative Drug Discovery Inc . Burlingame , CA ) 64 from sdf files and mapped to custom protocols 65 ., All public datasets used in model building are available for free public read-only access and mining upon registration in the CDD database 66 ., The Broad dataset ( TRYPANOSOME: Broad Primary HTS to identify inhibitors of T . Cruzi Replication ) used in this study is also available in PubChem ( AID 2044 ) ., In addition we curated Chagas compounds from the literature and made these public ( TRYPANOSOME: Chagas Disease Literature Compounds ) ., By using a combination of genetic validation from the literature , bioinformatic analyses , and available assays , we prioritized T . cruzi targets for experimental validation as the binding targets of screening hits ., Furthermore , SRI has developed “choke point” analyses to assess the likelihood that a particular metabolic pathway step is essential for an organism 67 , 68 ., In order to use such approaches we constructed a Pathway Genome Data Base ( PGDB ) for T . cruzi ( which we coined as “TCruCyc” ) using the complete genome sequence of the Dm28c strain ., The Dm28c strain was chosen over the more common CL-Brener strain since it is a model organism for studying Chagas disease and its recently assembled genome sequence 69 is more complete than CL-Brener ( whose repeat sequences have hindered complete assembly ) ., This was completed by using the “Pathologic” workflow within the Pathway Tools suite 70 , 71 ., The existing workflow imports the complete genome sequence and then assigns proteins from annotated sequences ., A patch to Pathologic to enable proteins to be searched by Uniprot/TrEMBL identifiers was used ., This process will not assign proteins unless they are annotated in the genome sequence , which will miss some obvious sequence-based homologies ( e . g . the tubulin gene is not annotated in the Dm28c sequence ) ., We also explored workflows that would enable the automatic import of protein annotations from a closely related organism ( e . g . CL-Brener ) , but ended up manually annotating a number of orphan proteins for our current dataset ., The underlying genome sequence consisted of 5 , 287 contigs assembled into 1 , 378 scaffolds of 30 , 716 , 540 base pairs ., Pathologic found 11 , 349 distinct gene products , at least 880 of which were found to be enzymes and at least 16 of which are transporters ., Pathologic was able to infer 1030 enzymatic reactions and 122 pathways from these assignments as well as the existence of 806 metabolic compounds ., This set was filtered to 358 molecules after removal of compounds with R- groups and small nuisance molecules ., This dataset was then used to infer potential targets by comparing the Tanimoto similarity with a phenotypic screening hit 42 ., The T . cruzi PGDB can be accessed at http://node2 . csl . sri . com:1555/ ., In our previous publications we have described the generation and validation of the Laplacian-corrected Bayesian classifier models developed with bioactivity and cytotoxicity data to create dual-event models 72–74 using Discovery Studio versions 3 . 5 and 4 . 1 ( Biovia , San Diego , CA ) 75–79 ., We have now applied this approach to the Broad Chagas dose response data ( AID 2044 ) 31–33 using the EC50 data , where values less than 1 μM are classed as actives and were used for the single event models ., We further refined the actives using the cytotoxicity data when a greater than 10 fold difference with cytotoxicity was observed and these compounds were considered active ., The models were all generated using the following molecular descriptors: molecular function class fingerprints of maximum diameter 6 ( FCFP_6 ) 80 , AlogP , molecular weight , number of rotatable bonds , number of rings , number of aromatic rings , number of hydrogen bond acceptors , number of hydrogen bond donors , and molecular fractional polar surface area which were all calculated from input sdf files ., The resulting single- and dual-event datasets were validated using leave-one-out cross-validation , 5 fold validation and by leaving out 50% of the data and rebuilding the model 100 times using a custom protocol to generate the receiver operator curve area under the curve ( ROC AUC ) , concordance , specificity and selectivity as described previously 72–74 ., These models were used to score the following drug libraries; Selleck Chemicals ( Houston , TX ) natural product library ( 139 molecules ) , GSK kinase library ( 367 molecules ) 81 , Malaria box ( 400 molecules ) 82 , Microsource ( Gaylordsville , CT ) Spectrum ( 2320 molecules ) , CDD FDA drugs ( 2690 molecules ) , Prestwick Chemical ( Illkirch , France ) library ( 1280 molecules ) and Traditional Chinese Medicine components ( 373 molecules , kindly provided by Dr . Ni Ai , Zhejiang University , China ) ., The top scoring molecules with the dual event model were selected and purchased from eMolecules ( La Jolla , CA ) and then 97 underwent primary in vitro screening ., Mouse myoblast cell line C2C12 ( ATCC #CRL-1772 ) was cultivated in Dulbecco’s Modified Eagle’s Medium containing 4 . 5 g/l glucose ( DMEM ) , supplemented with 5% fetal bovine serum ( FBS ) , 25 mM HEPES , 2 mM L-glutamine , 100 U/ml penicillin and 100 μg/ml streptomycin ., T . cruzi CA-I/72 trypomastigotes were obtained from C2C12 infected-culture supernatants after 4–7 days of infection ., Cultures were maintained at 37°C with 5% CO2 ., For the infection assay to assess anti-parasitic activity of the compounds , 500 C2C12 cells were seeded in 384-well plate in 40 μl of DMEM media per well ., Compounds were added at 10 mM in 50 nl per well using a Biomek FX ( Beckman Coulter ) for a final 10 μM concentration in 50 μl total volume , and 2 , 500 parasites were added in 10 μl per well ., The plate was incubated for 72 hours at 37°C with 5% CO2 ., After the incubation , the plate was fixed with the addition of 50 μl of 8% paraformaldehyde solution , followed by two successive washing steps using PBS ., Finally , a staining solution containing 0 . 5 μg/ml of 4 , 6-diamidino-2-phenylindole ( DAPI ) was added to each well of the plate and incubated for at least 4 hours prior to reading ., Images were acquired by an IN Cell Analyzer 2000 ( GE Healthcare ) and analyzed by IN Cell Analyzer Developer 1 . 6 software ., The size parameters used to segment host and parasite organelles were 125 μm2 for host nucleus , and 1–2 μm2 for parasite nucleus/kinetoplast ., Numbers of host cells and intracellular amastigotes were determined based on host cell and parasite nucleus quantification , providing a measure of growth inhibition during the first 72 h of post-infection treatment compared to untreated controls ., The anti-parasitic results were expressed in terms of relative activity normalized based on the average infection ratio ( number of infected cells/total number of cells ) of negative controls ( 0 . 1% DMSO , 0% activity ) and positive controls ( 50 μM of benznidazole , EC100 , 100% activity ) ., The host cell viability was assessed based on the total number of cells divided by the average number of cells from untreated controls ( 0 . 1% DMSO ) , being <0 . 5 considered a cytotoxic compound ., This assay was performed in duplicate ., The hit selection criteria: >50% activity at 10 μM and >0 . 5 host cell viability in the primary screening ., To determine the potency of the hit compounds , we performed a dose-response assay ., EC50 values of compounds were determined applying the same assay used in the primary screening ., For this , an intermediate plate ( 384-well plate ) was prepared by serial diluting each hit compound ( 10 mM , 5 mM , 2 . 5mM , 1 . 125 mM , 0 . 625 mM , 0 . 312 mM , 0 . 156 mM , 78 μM , 36 μM , 18 μM ) in 100% DMSO ., Then , 50 nl of each sample were diluted in 50 μl media ( DMEM H-21 ) and added to the experimental plate followed by incubation at 37°C with 5% CO2 for 72 h ., To assess in vivo efficacy of test compounds , a 4-day mouse model of infection by transgenic T . cruzi Brazil luc strain expressing firefly luciferase was used as previously described 83 ., Six-week-old female Balb/c mice ( average weight 20g ) were obtained from Simonsen Labs ( Gilroy , CA ) ., All animal protocols were approved and carried out in accordance with the guidelines established by the Institutional Animal Care and Use Committee from UCSD ( Protocol S14187 ) ., Mice were housed at a maximum of 5 per cage and kept in a specific-pathogen-free ( SPF ) room at 20 to 24°C under a 12-h light/12-h dark cycle and provided with sterilized water and chow ad libitum ., To infect the mice , trypomastigotes of T . cruzi Brazil luc strain were used ., The parasites were harvested from culture supernatant 7 days after the infection of C2C12 myocytes in T . 75 culture flasks using DMEM media supplemented with 5% FBS ., The harvested parasites were counted and the density was adjusted for 106 parasites per milliliter of DMEM media without FBS ., For the mouse infection , 100 ul of the parasite solution was injected intraperitoneally ( 105 trypomastigotes ) per mouse ., Starting on day 3 the infected mice were treated with test compounds at 50 mg/kg administered in 20% Kolliphor , IP , b . i . d . , for four consecutive days ., Two control groups included untreated mice , which received a vehicle ( 20% Kolliphor HS 15 ,, a . k . a . Solutol ) , and the positive control groups , which received 50 mg/kg benznidazole , IP , twice a day ( b . i . d ) ., At day 7 post-infection , the luminescent signal from infected mice was read upon injection of D-luciferin ., The absolute numbers of measured photons/s/cm2 were averaged between all five mice in each group ., The average photons/s/cm2 from the group treated with benznidazole was normalized as 100% efficacy and the average photons/s/cm2 from the group treated with vehicle only was normalized as 0% efficacy ., Using a linear correlation , the average photons/s/cm2 of each compound was normalized in the same efficacy scale as the controls ., Two tailed paired Student t test was used to verify the hypothesis that the luminescence values from vehicle-treated and compound-treated groups at day 7 post-infection were significantly different ( p≤ 0 . 05 ) ., A PGDB was constructed for T . cruzi using the complete genome sequence of the Dm28c strain ( Fig 1 ) ., The underlying genome sequence consisted of 5 , 287 contigs assembled into 1 , 378 scaffolds of 30 , 716 , 540 base pairs ., Pathologic found 11 , 349 distinct gene products , at least 880 of which were found to be enzymes and at least 16 of which are transporters ., Pathologic was able to infer 1030 enzymatic reactions and 122 pathways from these assignments as well as the existence of 806 metabolic compounds ., This set was filtered to 358 molecules after removal of compounds with R- groups and small nuisance molecules ., This dataset was then used to infer potential targets by comparing the Tanimoto similarity with a phenotypic screening hit 42 ., Using either dose response data alone ( S1 Dataset ) or the combination of dose response and cytotoxicity ( dual activity , S2 Dataset ) resulted in statistically comparable models ., Both had leave one out Receiver Operator Curve ( ROC ) values greater than 0 . 8 ( Table 1 ) ., The use of FCFP_6 fingerprints enabled the features important for activity ( termed good features ) to be visualized in the dose response data alone model ( S1 Fig ) which included tertiary amines , piperidines and aromatic fragments containing basic nitrogen functionality while those features that were negatively related to activity included cyclic hydrazines prone to tautomerization as well as a number of electron-poor chlorinated aromatic systems ( S2 Fig ) ., Similarly for the dual activity the good features were tertiary amines , piperidines and aromatic fragments containing basic nitrogen functionality ( S3 Fig ) and the bad features were again a number of cyclic hydrazines prone to tautomerization and a number of electron-poor chlorinated aromatic systems ( S4 Fig ) Upon 5 fold cross validation the ROC was greater than 78% for both models and sensitivity , specificity and concordance values were comparable and greater than 77% ( Table 1 ) ., The more exhaustive leave out 50% x 100 fold for the dual activity model resulted in an external ROC of 0 . 79 and while concordance and specificity was greater than 73% , sensitivity declined to 66% ( S1 Table ) ., Approximately 7200 molecules were screened using the Bayesian model ., Molecules with the highest Bayesian score in the dual event model were selected by an experienced medicinal chemist and purchased ., Ninety seven molecules were tested and 11 were found to have EC50 values less than 10μM ( S2 Table ) ., Five of these molecules ( verapamil , pyronaridine , furazolidone , tetrandrine and nitrofural ) had in vitro EC50 values less than 1μM ( Table 2 ) ., To assess in vivo efficacy of test compounds , a 4-day treatment mouse model of infection by transgenic T . cruzi Brazil luc strain35 expressing firefly luciferase was used 83 which enabled the activity in the mouse to be visually measured ( S5 Fig ) ., All compounds were dosed at 50mg/kg bid ., Benznidazole was used as a positive control and showed 100% efficacy alongside furazolidone ( Fig 2 and Table 2 ) ., Hydroxymethylnitrofurazone is a prodrug of nitrofural ( which had in vitro activity ) and is an additional known active compound against Chagas Disease , with an efficacy of 78 . 5% ., We chose the prodrug form to reduce the toxicity of nitrofural in the mouse model 84 ., Pyronaridine showed 85 . 2% efficacy while verapamil showed 55 . 1% and tetrandrine 43 . 6% , respectively ., Apart from tetrandrine , these are statistically significant ( Fig 2 and Table 2 ) ., Using several available datasets and resources we investigated the potential target/s of pyronaridine ., First we performed a similarity search in the Chagas Disease dataset composed of literature data and targets which was curated in this study ., The molecules with the highest Tanimoto similarity in CDD were T . cruzi GAPDH inhibitors ( S6 Fig ) ., We also searched the metabolites created from the T . cruzi pathway model created in this study ., The most similar molecule being S-adenosyl 3- ( methylthio ) propylamine with a Tanimoto similarity of 0 . 67 using the MDL Keys in Discovery Studio ( Biovia , San Diego , CA ) ., This would point to polyamine biosynthesis 85 ., A further approach was to query the ChEMBL database from within the MMDS mobile app ( S7 Fig ) ., This retrieved several analogs similar to the antimalarial quinacrine , suggesting trypanothione disulfide reductase 86 , 87 as a possible target ., Quinacrine has also been shown to be a Topoisomerase VI inhibitor elsewhere 88 ., These targets will be evaluated in future studies to identify whether they have a role in the mechanism of action of pyronidine in T . cruzi ., Our prior computational drug discovery work in Mycobacterium tuberculosis 42 was made possible by the existence of datasets with genetic validation of essential genes in vivo ., The work profited from the existence of the tier one TBCyc metabolic pathway database , the natural divergence of prokaryotic M . tuberculosis genome from the genome of the eukaryotic human host , and the availability of a well-annotated M . tuberculosis genome 24 , 34 ., In contrast , T . cruzi , the eukaryotic parasite that causes Chagas disease , and several other eukaryotic human pathogens including the parasites that cause malaria , human African trypanosomiasis , and leishmaniasis , have larger genomes , higher similarity to human enzymes and biological pathways , and have less well annotated genomes ., Investment in high throughput screening efforts has resulted in the release of screening data and hit lists for several of these eukaryotic pathogens 35–36 ., However , identification of targets of hit compounds has seen relatively slow progress ., Therefore , we hypothesized that for pathogens , such as T . cruzi , with fewer sources of available data to support bioinformatics approaches to target identification , we can take a reverse approach as compared to our work in Mycobacterium tuberculosis ., More specifically , we can start with interesting phenotypic screening hits and apply cheminformatic and bioinformatic approaches to map those hits onto potential targets ., As a preliminary step in this direction we have used public data to build computational models ., The CDD Public database now includes structural and biological activity data for over 300 , 000 molecules from the Broad Institute compounds that have been screened against T . cruzi ., In addition we have curated over 500 compounds and their known targets and over 740 compounds from DNDi based around the fungicide fenarimol , as separate datasets ., In this study , we have utilized a subset of the Broad HTS screening data to build Bayesian machine learning models to classify compounds as likely actives against T . cruzi in vitro ., We then used these models to virtually screen several libraries of compounds including drugs and drug-like compounds , to identify compounds with potential activity that may have not been tested yet ., Some of these compounds were purchased and tested in vitro and then several more tested in vivo ., Historically , for a diversity-based library undergoing HTS , it is expected a range of 1 to 2% of hits based on observed activity ( usually >50% antiparasitic activity at 10 μM and no signs of cytotoxicity at this concentration ) will be observed 34 ., Applying the current method , 11/97 ( 11% ) hits were identified and confirmed with EC50 < 10 μM ., Out of these hits derived from searching 8 relatively small libraries of compounds , several of the compounds were found to be known actives against T . cruzi ., Verapamil was previously shown as active in the Broad dataset with an EC50 < 0 . 1μM , and has a well-known effect in reducing acute mortality in mice 89 , 90 and cardiomyopathy if treated early in infection 91 ., It should be noted that others have retested some of the active HTS hits from the Broad T . cruzi screen and found higher IC50 values ., For example the IC50 for verapamil in one study was >50 μM 38 ., Pyronaridine is in clinical use as an antimalarial 92 , 93 , is a P-glycoprotein inhibitor 94 and was given a positive opinion by the European Medicines Agency using this molecule in a combination therapy 95 ., It was shown to have an EC50 < 0 . 587μM in the Broad dose response dataset , which is comparable to this study ( EC50 0 . 225 μM ) ., Apparently both of these compounds were retrieved as various salt forms from the vendor databases and were initially not considered to be in the training sets ., Pyronaridine as far as we can tell , was overlooked following the published initial screening 34 and so we pursued these compounds further in vivo ., Furazolidone is used as a H . pylori treatment 96 and has known in vivo activity against T . cruzi 97 and was not in the dose response training set ( but is in the larger Broad screening dataset of over 300 , 000 compounds ) , so can be considered a true ‘prediction’ ., Tetrandrine is a P-glycoprotein inhibitor 98 that has been tested in malaria in combination with chloroquine 99 ., This molecule was not in the training dataset but was in the larger Broad HTS screening dataset to identify inhibitors of replication as an ‘inactive’ , so our ability to identify a previous false negative as an active prediction is an interesting observation , although this compound does not appear to have statistically significant efficacy in vivo ., The known T . cruzi active compound Nitrofural ( nitrofurazone ) 97 was also not in the model training set or the Broad dataset , but was predicted as ‘active’ in vitro ( experimentally confirmed EC50 0 . 77μM and CC50 > 10μM ) , and its prodrug form hydroxymethylnitrofurazone was used as an internal control ( while benznidazole was a positive control ) in the in vivo experiments ., These results illustrate that the dose response and cytotoxicity machine learning model based on T . cruzi replication HTS data 34 used in this case , could retrieve known active compounds useful for Chagas Disease ., While the Broad screen and the assay used in this study are similar in that they are both cell-based , they each use different cell lines for T . cruzi culture and different readouts ., The Broad screen used the Tulahuen genetically modified to express Beta-galactosidase 34 , 54 which is biased towards finding CYP51 inhibitors 35 , while we used the CA-I/72 strain with an image-based readout ., We are not aware of publications describing pyronaridine being tested in the mouse model for Chagas disease and our observation of 85 . 2% efficacy ( higher than nitrofural ) suggests this molecule is therefore worthy of further study ( Fig 2 and S5 Fig ) ., In particular , the identification of the likely target or targets for this molecule would be very important ., Using various informatics resources we have attempted to predict these in this study ., Our prior work on Mtb resulted in many datasets relating to small molecules and their targets in the bacteria , which in turn lead to the development of the TB Mobile app which contains Bayesian models that can be used for target prediction 56 , 62 , 63 ., While we do not have as much published data for T . cruzi a similar approach could be undertaken in future for target prediction in NTDs more broadly ., This study made wide use of public datasets in CDD as well as the collaborative sharing of data in the CDD Vault ., We have also highlighted how the in vivo transgenic T . cruzi Brazil luc strain expressing firefly luciferase data can be stored in the software ( Fig 3 ) ., These data will ultimately be made publically accessible in this format alongside the datasets we have already made public ., In the process of this study we have curated T . cruzi data , constructed a Pathway Genome Data Base for T . cruzi ( Fig 1 ) , developed multiple Bayesian machine learning models , tested molecules in vitro and in vivo as well as proposed potential targets for one of the in vivo active compounds ., In the process we have identified pyronaridine as having promising in vivo activity in the mouse model of Chagas disease ., Future studies will evaluate efficacy in longer term models and identify the target or targets of this molecule ., The approaches taken are broadly applicable to other NTDs and extend our prior work with Mtb 42 , 43 , 46 , 47 , 56–63 ., Leveraging published data to create additional resources and models for either re-mining known or new datasets to suggest compounds that can be rapidly progressed all the way through to in vivo animal models , may lead to new clinical studies in a shorter time scale ., There are many steps we could take to update our computational models such as incorporating the current data and using other machine learning algorithms ., If we can in future narrow down the list of possible targets computationally as well and accelerate experimental target validation that will also be of importance ., The combination of computational and experimental approaches represents a multistep workflow ( S8 Fig ) that was undertaken in this study that could be applicable in any NTD drug discovery project ., Efforts to automate , streamline and learn from the resulting data would further increase the efficiency of the approach we have described .

Introduction, Methods, Results, Discussion

Chagas disease is a neglected tropical disease ( NTD ) caused by the eukaryotic parasite Trypanosoma cruzi ., The current clinical and preclinical pipeline for T . cruzi is extremely sparse and lacks drug target diversity ., In the present study we developed a computational approach that utilized data from several public whole-cell , phenotypic high throughput screens that have been completed for T . cruzi by the Broad Institute , including a single screen of over 300 , 000 molecules in the search for chemical probes as part of the NIH Molecular Libraries program ., We have also compiled and curated relevant biological and chemical compound screening data including, ( i ) compounds and biological activity data from the literature ,, ( ii ) high throughput screening datasets , and, ( iii ) predicted metabolites of T . cruzi metabolic pathways ., This information was used to help us identify compounds and their potential targets ., We have constructed a Pathway Genome Data Base for T . cruzi ., In addition , we have developed Bayesian machine learning models that were used to virtually screen libraries of compounds ., Ninety-seven compounds were selected for in vitro testing , and 11 of these were found to have EC50 < 10μM ., We progressed five compounds to an in vivo mouse efficacy model of Chagas disease and validated that the machine learning model could identify in vitro active compounds not in the training set , as well as known positive controls ., The antimalarial pyronaridine possessed 85 . 2% efficacy in the acute Chagas mouse model ., We have also proposed potential targets ( for future verification ) for this compound based on structural similarity to known compounds with targets in T . cruzi ., We have demonstrated how combining chemoinformatics and bioinformatics for T . cruzi drug discovery can bring interesting in vivo active molecules to light that may have been overlooked ., The approach we have taken is broadly applicable to other NTDs .

Chagas disease is a neglected tropical disease ( NTD ) caused by the eukaryotic parasite Trypanosoma cruzi ., The disease is endemic to Latin America but is increasingly found in North America and Europe , primarily through immigration , and the spread of this disease is bringing new attention to the need for novel , safe , and effective therapeutics to treat T . cruzi infection ., We have used data from a phenotypic screen to build Bayesian models to predict anti-parasitic activity against T . cruzi in vitro ., These models were used to score various small libraries of molecules ., We selected less than 100 compounds for testing and found in vitro actives , some of which were tested in an in vivo efficacy model ., We identified the antimalarial pyronaridine as having in vivo efficacy and provides us with a new starting point for further investigation and optimization .

journal.pntd.0002545

The Burden of Cholera in Uganda

Cholera was first reported in Uganda in 1971 , when 757 cases were reported to the World Health Organization ( WHO ) ., During the subsequent years up to 1993 , Uganda reported cholera cases every 2–4 years to the WHO ., From 1994 to 1998 , cholera was reported annually in Uganda 1 ., In 1998 , Uganda reported almost 50 , 000 cases with incidence throughout the country 2 ., The reported incidence has fluctuated between 250 and 5 , 000 cases every year since 2000 ( Figure 1 ) ., The reported case fatality ratio has decreased from 4–7% in the late 1990s to about 2–3% during 2004–2010 ., Cholera in Uganda appears to be largely an epidemic disease ., However , endemic cholera occurs in high-risk areas along the southwestern border with DRC and in Kampala city slums ., Endemic cholera is commonly noted before and during the rainy season , from December through March ., Epidemic cholera can occur any time , but is often associated with extreme rain events or water supply disruptions ., The frequency of reported cholera cases varies among districts in Uganda ., The highest risk areas include the border areas with the Democratic Republic of Congo ( DRC ) , Sudan , and Kenya as well as urban slums in Kampala ., Displaced populations and their neighboring communities are at elevated risk ., The ongoing migration of people into and within Uganda can lead to rapid spread of the disease ., The African Great Lakes , including Lake Albert and Lake Victoria on the border of Uganda , may provide a reservoir for cholera bacteria ., Further , increases in incidence among the nations bordering these lakes have been shown to be correlated with El Niño warm weather events 3 ., The WHO 4 has recently revised its guidelines and states in a position paper that cholera vaccines should be used in combination with other prevention and control strategies in areas where the disease is endemic ., “Endemic” is defined as areas with occurrence of culture-confirmed cholera in at least three of the previous five years ., The WHO also recommends that cholera vaccines should be considered for preemptive use in areas at risk for epidemic cholera as long as vaccination does not interfere with efforts to treat cholera cases , improve water and sanitation , and mobilize communities ., The vaccine may also be considered for reactive use if local infrastructure is sufficient to conduct mass campaigns depending on the current and historical epidemiology ., There is a dearth of information about the burden of cholera in low-income countries such as Uganda ., A more accurate picture of this burden is particularly important because it can be used to inform cholera prevention and control intervention questions: whether or not to introduce vaccination as a complement to other cholera prevention and control interventions , where and when it would be most effective to do so , and what demographic population should be targeted ., This article presents available disease burden data for Uganda that may help inform such questions ., This is a retrospective study in which we collected data from Ugandas health information management system and Diarrheal Disease Control program ., District-specific data were used to classify districts as endemic or non-endemic based on the WHO criterion and to identify high-incidence districts ., A convenience sample of more detailed data from individual cholera outbreaks were summarized to estimate the age distribution of reported cholera cases and to develop epidemic curves ., Because the Ugandan surveillance system is designed primarily to identify and respond to cholera outbreaks , a sensitivity analysis was performed to explore the potential limitations of the existing surveillance system to identify cases outside of recognized outbreaks ., These data were used to compile national statistics and for reporting to the World Health Organizations ( WHO ) Weekly Epidemiological Record 5 ., The cholera case definition was based on WHO criteria , depending on whether or not cholera is endemic in the area: The identification of such cases should have triggered laboratory investigation ., A cholera outbreak was confirmed when Vibrio cholerae O1 or O139 was isolated from at least one stool sample ., Only cases meeting the standard case definition above were investigated and included in the official cholera data ., Summary laboratory data were obtained from the Head of the Central Public Health Laboratory from the Ministry of Health ., Prior to analysis , stool samples from suspected cholera patients were transported from the field in Cary Blair transport media ., Culture plates were set at 37°C overnight ( for 18–24 hours ) using three culture media: TCB , XLD and MacConkey ., Biochemical identification of cholera organisms were based on Oxidase or Indole tests ., Polyvalent antisera were used to differentiate between the Inaba and Ogawa serotypes , and specific monovalent tests further confirmed which of the Inaba , Ogawa or O139 ( Bengal ) strains caused disease ., Isolates were refrigerated at −80°C and sent to the WHOs collaborating laboratory ( Unité de La Rage , Institut Pasteur , Paris , France ) for quality control ., District-specific data were abstracted from the Uganda Ministry of Health , Health Management Information System disease surveillance database for the period 2005–2010 ., The 2005–2010 period was chosen based on the WHO criteria 4 for identifying endemic cholera ( i . e . areas in which cholera has been reported in three of the previous five years . ) The districts shown are based on the 2002 district boundaries , which were in existence during the most recent census ., Cases in new districts created after 2005 were apportioned back to the 2002 districts ., Age-specific morbidity and mortality data are stored at the district level ., These ‘line list’ records include: patient age , outcome of treatment ( i . e . discharge or death ) , and date of admission or death ( for suspected cholera patients who die prior to seeking treatment ) ., We were able to obtain these data from 15 outbreaks , which occurred in 12 districts spanning the time period from 2002–2010 ., In total , the line list data included records of 6 , 125 cholera cases with at least 154 deaths ., The actual number of deaths could not be ascertained because some of the records lack data on patient outcomes ., These records included seven instances in which death occurred in the community , ( i . e . prior to receiving treatment ) ., In addition , there were 923 records with data on inpatient and outpatient treatment and the duration of inpatient treatment ., For this retrospective analysis , the study team compiled data from samples that were previously collected and analyzed as part of routine surveillance activities ., The incidence of hospitalized cholera was estimated by district based on the annual average number of cases reported over the six-year period from 2005–2010 ., The district-specific reporting does not include data by age group ., Thus , the age distribution of cases was estimated based on the 15 line lists ., It was assumed that these 15 outbreaks were representative of the age distribution of cholera incidence in Uganda ., The numbers of cases by age group were calculated from the product of total cases and the national average percentage distribution of cases by age from the line list data ., Age-specific incidence rates were then calculated by dividing the age-specific cases by the age-specific populations ( 2010 UN population data ) ., All analyses and graphs were produced with Microsoft Excel ( Microsoft , Redmond , WA ) and maps were created with ArcGIS ( ESRI , Redlands , CA ) ., Cholera case fatality rates were estimated from the number of reported cases and deaths by age group from the line list data ., The Fishers exact test was used to compare case fatality rates across age groups ., Statistical analyses were performed using STATA software ( Version 8 , College Station , TX , USA ) ., In addition to hospitalized cases , we also estimated the number of non-hospitalized or non-reported cholera cases in Uganda ., In a recent analysis , Kirigia et al . 7 estimated that 10% of cholera cases could be classified as severe and require hospitalization ., In addition , Poulos et al . 8 reported that 22–38% of cholera patients were hospitalized during multi-site surveillance studies conducted in Jakarta , Indonesia and Kolkata , India ., In Uganda , patients with mild diarrhea often do not seek formal seek care , but do receive oral rehydration therapy at home ., In this analysis , we assumed that the official statistics include 25% of cholera patients with severe cholera who would seek treatment and be reported in official statistics and that 75% took oral rehydration therapy at home ., This assumption is greater than that assumed by Kirigia et al . , but falls at the lower end of the actual data presented by Poulos et al . Thus , we estimated that there were three non-hospitalized cholera cases requiring treatment at home per one officially reported case ., Estimation of asymptomatic cholera infections were omitted from this analysis ., Since the reported numbers of deaths were based on individually-identified cholera patients , these reports should be a lower bound ., While deaths that occurred outside treatment facilities were included in official reports when identified , it remains likely that some cholera deaths were missed and not reported in official statistics ., In a recent study in neighboring Kenya , an active case finding exercise identified a 200% increase in the number of cholera deaths that occurred during a 2008 cholera outbreak 9 ., This is a worst-case-scenario , since the outbreak occurred during a chaotic period of post-election violence ., However , in addition to deaths that were missed during outbreaks , isolated cholera deaths that occur outside of recognized outbreaks may also contribute to underreporting in official statistics ., As an upper bound estimate of the annual number of cholera deaths in Uganda , we applied the 200% correction factor from the Kenya study to the number of cholera deaths identified in Uganda ., In addition , for an upper bound estimate of the number of hospitalized cases , we assumed that a number of severe acute watery diarrheal cases that occurred outside of recognized outbreaks were the result of infection by Vibrio cholerae ., These endemic cholera cases have frequently been omitted from totals in other cholera endemic countries 10 , 11 ., Thus , we assumed the number of reported hospitalized cases may have only been about 50% of the actual cases although this rate is difficult to estimate in the absence of sentinel surveillance data ., The estimated annual number of cholera cases by age and by district risk group is shown in Table 1 ., Inclusive of unreported cases treated at home , our estimated annual average number of cases was around 11 , 000 , with around 81% of the cases occurring in the high risk districts ., On average , about 61 cholera deaths were reported per year during 2005–2010 ., Using the 200% correction factor reported from the Kenyan study 9 , the potential range of annual cholera mortality is 61–182 deaths per year ., The epidemic curves for fifteen cholera outbreaks that occurred in Uganda between 2002 and 2010 are shown in Figure 6 ., The average duration of the 15 outbreaks was about 15 weeks from the identification of the first case through the identification of the last case ., The range of outbreak duration was between 4 weeks and 44 weeks ( Table 2 ) ., Almost half of the observed cases ( 43% ) occurred within six weeks of the first case ., It should be noted that the cases reported for Kasese were more likely to be representative of endemic disease , as this district is one of the few that report cases on an ongoing basis ., Arua district reported four outbreaks between 2005 and 2008 , but weekly cases declined from the peak observed in early 2008 ., This retrospective analysis showed that there is a clear subdivision between high-risk districts and low-risk districts in Uganda with about 24% of the population residing in high-risk districts accounting for 81% of the average reported cholera burden ., These high-risk districts may be considered for preventive cholera vaccination campaigns in combination with other cholera control activities ., Cholera affects all age groups in Uganda ., The age distribution of cases matched the population distribution ., This may be due to low levels of background immunity , so that the entire population is equally susceptible ., This age distribution deviates from age distributions in other cholera-endemic areas , where young children tended to be at greater risk when systematic surveillance was conducted 14 ., Systematic sampling of diarrheal cases from endemic areas has never been attempted in Uganda and may reveal that outbreak-based surveillance findings are not representative of the true cholera burden ., A comparison of age-specific cholera incidence rates from Bangladesh demonstrated that the average age of cholera infection was much higher during outbreaks 15 than for endemic cholera 16 ., Outbreak data from the sub-district level suggests that there may be considerable heterogeneity of cholera incidence ., Thus , surveillance efforts and reporting should be improved to facilitate better epidemiological characterization of cholera incidence and improved targeting of interventions to reach those at greatest risk ., The estimate of 61 deaths per year involves accreditation of all cholera deaths to specific individuals , either at treatment centers or in the community ., This is a relatively small fraction of the estimated 30 , 000 diarrheal deaths per year in Uganda ( exclusive of deaths attributed to cholera and bloody diarrhea ) 17 ., It is certainly possible that a significant proportion of these 30 , 000 deaths was caused by unrecognized cholera than would be estimated from individually identified deaths ., Although the outbreak response focus of cholera surveillance in Uganda may be insufficient to accurately estimate the numbers of cases and deaths caused by cholera , these data are very useful for identifying areas to target for surveillance in consideration of future vaccine introduction ., In order to better quantify the burden of cholera in Uganda , sentinel site surveillance should be undertaken in at least two regions with districts at high risk for cholera for a period of at least two years ., It would be better to continue surveillance for at least five years , since cholera incidence is highly variable from year to year ., The Ministry of Health is participating in the AFRICHOL cholera surveillance in Africa project ( www . africhol . org ) , led by Agence de Médecine Préventive ( AMP ) and the African Field Epidemiological Network ( AFENET ) , which is an African-based non-government organization working to improve field epidemiology and public health laboratories in sub-Saharan Africa ., As part of this project , enhanced cholera surveillance is being conducted in five districts in Eastern Uganda ( Mbale , Tororo , Manafwa , Butaleja and Busia ) and , whenever outbreaks occur , throughout the country ., Such data may be combined with the available national reporting statistics to better model cholera burden within Uganda , which in turn may be used to conduct economic analyses ( e . g . , cost effectiveness or cost benefit studies ) of the potential use of cholera vaccines in Uganda ., Given the health challenges facing Uganda , the decision to pursue cholera vaccination must be weighed against the introduction of other health interventions that may have a greater impact on mortality ( e . g . , pneumococcal conjugate vaccines , rotavirus vaccines , future malaria vaccines or other interventions ) ., In addition to targeting high-risk endemic populations , Uganda may consider using cholera vaccines from a recently established international stockpile to mitigate epidemic cholera ., A review of 15 epidemic curves showed that about 57% of the cases occurred after six weeks across all outbreaks ., This 57% figure may represent an upper bound on the number of cases that could be averted via reactive use of cholera vaccines from a global stockpile , assuming it would take at least three weeks to diagnose an outbreak and prepare for a mass vaccination campaign plus three weeks to generate immunity from the two-dose vaccine ., This stockpile may also be used to prevent the spread of cholera to neighboring districts , such as when it was used in an Adjumani district refugee camp in 1997 18 ., While cholera incidence in Uganda has been manageable over the past decade , elimination of the disease is likely to take time especially given the slow progress on provision of safe water and sanitation among other risk factors ., Most of the areas with the highest incidence rates either border countries with political instability and endemic cholera ( e . g . , DRC and Sudan ) or contain semi-nomadic populations ., For these districts , it would be difficult to prevent cholera-infected persons from crossing borders , achieve high vaccination coverage rates , or to construct reliable water and sanitation infrastructure for semi-nomadic populations 19 ., Some global trends in cholera disease burden may lead to an increase in the number of cases and should be considered in cholera control planning ., At present , cholera is more prevalent in rural areas than in urban areas within Uganda ., This may change if present urbanization trends continue and the maintenance and expansion of water and sanitation infrastructure cannot keep pace with the rapidly growing urban population ., The urban population in Uganda is projected to increase more than seven-fold from 4 . 5 million in 2010 to 31 million by 2050 20 ., Some studies have found multidrug resistant V . cholerae in Uganda , including strains resistant to trimethoprim , sulfonamides , ampicillin , tetracycline , chloramphenical and streptomycin 21 ., In addition , there is evidence that the severity of clinical cases of cholera in Asia and Africa is increasing , especially during outbreaks ., Some scientists attribute the increase in severity of cholera cases seeking treatment to the emergence of a new altered strain of V . cholerae O1 El Tor that secretes the classical cholera toxin , making it more virulent 22 ., It is not presently known if this strain is present in Uganda ., However , it has been isolated from recent African outbreaks in Mozambique and Zimbabwe 23 , 24 ., Due to global warming , the average temperature in Uganda is estimated to increase by up to 1 . 5 degrees over the next 20 years 25 ., Recent research suggests a strong correlation between increased rainfall and elevated temperatures with higher cholera incidence 26 , 27 , 28 , 29 , 30 ., This may pose an elevated risk for districts bordering Lake Albert and Lake Victoria , which may provide an endemic reservoir of V . cholerae 31 , 32 ., There are also trends suggesting a reduced need for cholera vaccination in Uganda ., The multidisciplinary cholera outbreak response activities have been effective in mitigating the severity of outbreaks , both in terms of morbidity and mortality ., The cholera case fatality rate has steadily declined since the large , nationwide outbreak in 1998 ( refer to Figure 1 ) ., While improving treatment does not reduce the incidence of cholera cases , it does reduce the social and economic burden of the disease ., Improvements in access to improved water and sanitation would also lead to a concomitant decrease in cholera incidence ., These cholera incidence data may also be used to target priority districts for improvements in water , sanitation , and hygiene efforts ., Cholera incidence is likely to be associated with high prevalence of other enteric diseases , for which cholera vaccination would have no effect ., Considering that an estimated 30 , 000 persons die from diarrheal disease every year in Uganda , improved water , sanitation , and hygiene are urgently needed even if cholera vaccine is deployed ., In conclusion , the existing surveillance system is geared toward mitigating the impacts of cholera outbreaks , not quantifying the burden of endemic cholera ., Cholera control activities have been effective in slowing the spread of cholera and reducing cholera fatalities ., However , cholera cases continue to be reported on an annual basis ., The combination of sentinel surveillance with national cholera incidence data could be used to develop an economic analysis to inform cholera vaccination policy .

Introduction, Methods, Results, Discussion

In 2010 , the World Health Organization released a new cholera vaccine position paper , which recommended the use of cholera vaccines in high-risk endemic areas ., However , there is a paucity of data on the burden of cholera in endemic countries ., This article reviewed available cholera surveillance data from Uganda and assessed the sufficiency of these data to inform country-specific strategies for cholera vaccination ., The Uganda Ministry of Health conducts cholera surveillance to guide cholera outbreak control activities ., This includes reporting the number of cases based on a standardized clinical definition plus systematic laboratory testing of stool samples from suspected cases at the outset and conclusion of outbreaks ., This retrospective study analyzes available data by district and by age to estimate incidence rates ., Since surveillance activities focus on more severe hospitalized cases and deaths , a sensitivity analysis was conducted to estimate the number of non-severe cases and unrecognized deaths that may not have been captured ., Cholera affected all ages , but the geographic distribution of the disease was very heterogeneous in Uganda ., We estimated that an average of about 11 , 000 cholera cases occurred in Uganda each year , which led to approximately 61–182 deaths ., The majority of these cases ( 81% ) occurred in a relatively small number of districts comprising just 24% of Ugandas total population ., These districts included rural areas bordering the Democratic Republic of Congo , South Sudan , and Kenya as well as the slums of Kampala city ., When outbreaks occurred , the average duration was about 15 weeks with a range of 4–44 weeks ., There is a clear subdivision between high-risk and low-risk districts in Uganda ., Vaccination efforts should be focused on the high-risk population ., However , enhanced or sentinel surveillance activities should be undertaken to better quantify the endemic disease burden and high-risk populations prior to introducing the vaccine .

Uganda has reported cholera cases to the World Health Organization every year since 1997 ., Thus , the country may consider the introduction of a WHO-prequalified oral cholera vaccine ., This article reviews cholera surveillance data from 1997–2010 with a focus on the 2005–2010 time period to identify high risk populations that may be targeted for preventive vaccination campaigns ., We estimated that an average of about 61–182 deaths occur each year ., Most cases ( 81% ) occurred in a relatively small number of districts comprising just 24% of Ugandas total population ., While there is a clear distinction between low and high-risk districts , sentinel surveillance would help to better quantify the burden in endemic districts ., An economic analysis should also be undertaken prior to making a decision to introduce a cholera vaccine .

journal.pcbi.1000490

Pushing Structural Information into the Yeast Interactome by High-Throughput Protein Docking Experiments

In the last decade , many genome-sequencing projects started delivering nearly complete lists of the macromolecules present in several model organisms ., However , taken individually , knowing the components reveal relatively little about how complex systems , such as a eukaryotic cell , assemble and coordinate the many discrete functions needed for its correct functioning ., Most cellular processes are carried out by large macromolecular complexes and regulated through a complex network of transient protein-protein interactions , defining the Interactome of a given organism ., Accordingly , the last years have seen the emergence of many high-throughput proteomics initiatives devoted to the identification of new protein interactions and macromolecular complexes in model organisms 1–6 , including human 7 , 8 ., These efforts have developed mostly around two different techniques: the yeast two-hybrid system , more suitable for identifying binary interactions , and affinity purifications coupled to mass spectrometry analyses , for discovering multi-protein assemblies ., Taken together , they have unveiled thousands of new unsuspected interactions , which are now properly stored and classified in public databases 9 , and have changed the way biologists approach complex cellular functions , setting the ground for systems biology 5 ., However , these techniques can only identify whether two proteins interact or the composition of molecular complexes and , in the best cases , which are the individual domains mediating the interaction ., A full comprehension of how proteins bind and form complexes can only come from high-resolution three-dimensional ( 3D ) structures , since they provide the atomic details necessary to understand how the interactions occur and the high degree of specificity observed can be achieved 10 ., Unfortunately , despite the efforts of ongoing structural genomics ( SG ) projects to extend the structural coverage of the sequence space for the proteome of several organisms , it seems that structural biology is somehow lagging behind the new trends in high-throughput biology ., In fact , since the first genome-wide interaction discovery experiments were published , there has been an increasing gap between the number of identified interactions and those for which their 3D structure is known 11 ., It is thus crucial to come up with effective strategies to incorporate structural information into interactome networks ., Indeed , we belong to a pan-European venture , the 3D-Repertoire project , which aims at solving the structures of all amenable protein complexes in yeast at the best possible resolution ( http://www . 3drepertoire . org ) ., The 3D-Repertoire consortium will attempt to experimentally solve the structure of some 100 yeast complexes by means of X-ray crystallography , nuclear magnetic resonance ( NMR ) , electron microscopy ( EM ) or a combination of these techniques ., However , the vast majority of complexes and interactions will be tackled with computational methods in combination with low resolution structural data ( e . g . low-resolution EM or small-angle x-ray scattering ( SAXS ) ) ., The first step in the structural bioinformatics pipeline that we have established within the consortium is to model by homology as many yeast interactions as possible , in the same way that we can model individual proteins ., This is certainly possible , since it has been shown that most interologues ( i . e . homologous interacting pairs ) do indeed interact in the same way 12 ., These models will then be complemented with low-resolution structural information , whenever it is available , to build the most complete possible models 13 ., However , unfortunately , interaction templates are only available for a very limited number of interactions and thus , to get a more complete picture of the yeast interactome , it is necessary to apply methodologies that are template-independent ., Computational docking aims to predict the structure of a complex formed by two interacting proteins starting from the structures of the individual components ., Many different docking methods have been reported , with increasing success rates ( see 14–16 for a review ) ., However , given the number and variety of available docking methods , the community found it desirable to validate and compare them in a blind contest ., The recent CAPRI experiments ( http://www . ebi . ac . uk/msd-srv/capri/ ) provide an objective assessment of current docking methods and their successes and limitations 17–19 ., The majority of the most popular docking methods are based on a rigid-body approach ( i . e . they do not allow backbone flexibility ) , and can be roughly classified in two types:, i ) those that focus on exhaustive sampling in search for geometric surface correlation ( mainly through FFT -Fast Fourier Transform- , or geometric hashing algorithms ) , and, ii ) those that place more emphasis on energy-based sampling ( usually by minimization , molecular dynamics or Monte-Carlo ) and/or scoring ., Two representative FFT-based methods of the first type are FTDock 20 and ZDOCK 21 , 22 , in its several versions of increasing complexity and prediction accuracy 23 ., Other successful geometric-based docking methods are Hex 24 or MolFit 25 ., On the other hand , energy-based sampling and scoring schemes have also been evaluated in the CAPRI experiment ., For instance , methods like ICM-DISCO 26 , which used a Monte Carlo rigid-body search on grid-based potentials with an essential evaluation step based on electrostatics and desolvation 27 , 28 were very successful in the first two CAPRI editions ., This evaluation scheme was recently implemented in pyDock 29 to permit the rescoring of docking sets generated by other independent methods , which yielded top results as scorer tool in the most recent CAPRI meeting 30 ., Other methods do also successfully apply energy evaluation during or after the docking generation phase , like Haddock 31 , ClusPro/SmoothDock 32 , 33 , RosettaDock 34 , or ATTRACT 35 ., However , despite the improvement in docking methods , it is still difficult to know in advance whether the predicted binding modes will be close to the real interaction topology or not ., The CAPRI initiative has identified the large conformational changes upon association as the best measure for assessing the difficulty of docking experiments 36 , 37 , but these changes cannot be foreseen before the experimental structure of the complex is available and thus have very limited predictive value ., In this work , we test the performance of two of the best docking programs in the market ( FTDock and ZDOCK ) , together with one of the most successful docking scoring schemes ( pyDock ) , against the most recent and comprehensive benchmark set available 38 ., We then use the results of the benchmark to explore the possibility of setting a general confidence threshold for docking scores to increase the reliability of the predictions ., In addition , we also assess how the use of homology models of varying quality in docking experiments , instead of experimental structures , would affect the global performance of the methods ., Finally , we apply all the gained knowledge to run the first ever high-throughput docking experiment , which provides putative models for over 3 , 000 protein-protein interactions in the yeast interactome ., We generated a collection of docking solutions based only on geometry complementarity by running FTDock 20 under the standard conditions recently reported 29 ( i . e . no electrostatics , 1 . 2 Å grid size , 12° angle resolution ) ., In addition , we also tested two different versions of ZDOCK that include additional functions in the FFT-based correlation , with expectedly better success ., ZDOCK 2 . 3 21 combines pairwise shape complementarity 39 with desolvation calculations based on atomic contact energies 40 and Coulombic electrostatics 22 ., ZDOCK 3 . 0 23 is a novel and improved version that replaces the simplified averaged atomic contact energies with atomic pairwise statistical potentials using an optimized atom type alphabet 41 ., We used default parameters on all the versions of ZDOCK tested ., Additionally , we applied pyDock 29 to re-score the sets of rigid-body solutions provides by each docking program ., The pyDock scoring function is composed of Coulombic electrostatics with distance-dependent dielectric constant , ASA-based desolvation with atomic solvation parameters previously optimized for rigid-body docking , and van der Waals energy ( with 0 . 1 weighing factor , and truncated to +1 . 0 kcal/mol to allow certain overlap of the structures ) ., This scoring function was shown to be the best for several targets of the CAPRI experiment 30 ., In this work , we tested the use of pyDock with and without the van der Waals energy term ., Before applying the different docking procedures , the coordinates of each x-ray structure were automatically checked with the pyDock module “setup” , re-building incomplete sidechains with SCWRL 3 . 0 42 and removing missing backbone atoms ( usually incomplete N-terminal or C-terminal residues ) ., We also excluded cofactors , ions and other heteroatoms from docking and scoring calculations ., To assess the accuracy of the docking methods used in the study , we used the most recent , and well-accepted , benchmark set of protein-protein interactions 38 ( Benchmark3 . 0 ) ., We also used the same benchmark set to identify a confidence threshold on the score assigned by the docking programs ., This set consists of 124 docking non-redundant cases , for which high-resolution crystal structures are available for both the bound complex and for the single unbound components ., Docking experiments are run on the unbound structures and the results evaluated by comparing them to the solved structure of the bound complex ., The dataset is non redundant in the sense that it does not contain interactions that share the same family-family class in Pre-SCOP ( http://www . mrc-lmb . cam . ac . uk/agm/pre-scop/ ) ., Test cases presenting more than two missing residues in the interface or presenting different cofactors at the binding site between the bound and unbound structures are excluded from the dataset ., The Benchmark3 . 0 also classifies the 124 cases based on their level of docking complexity into Rigid body ( 88 ) , Medium ( 19 ) and Difficult ( 17 ) ., The three levels span a large variety of interaction types including enzyme-inhibitor , antigen-antibody and other types of transient interactions ., We assessed the quality of the solutions provided by the different docking programs using the same criteria that are used in the CAPRI experiment 30 ., Of the two docked structures one is conventionally called the receptor ( usually the biggest ) and the other is called the ligand ( usually the smallest ) ., Docking solutions are then classified as Incorrect , Acceptable , Medium and High based on the RMSD between the bound and unbound ligands after superposition of the receptor , the RMSD of the interface and the number of conserved/non-conserved native interactions ., Details on the method used to calculate the classification can be found in Méndez et al . 18 ., In this case , we did not apply the CAPRI filter to remove solutions presenting an excessive number of clashes ., It is worth noting that , in contrast to the ligand RMSD evaluation strategy used in the CAPRI experiment , we did not apply any filter to remove from the calculation those parts of the structures that do not move as rigid bodies ( turns and small loops ) ., First of all , we would like to stress that our goal is not to predict interactions between yeast proteins , but to provide putative models of those interactions that have already been experimentally determined ., Thus , the first step towards predicting the structure of yeast complexes was to identify and compile all the available structures for the individual protein components ., We started by downloading all the sequences for the systematically named ORFs in the Saccaromyces Genome Database ( SGD , ftp://ftp . yeastgenome . org/yeast/ , 43 ) as of September 2008 ., We excluded dubious ORFs and pseudogenes and eliminated duplicated ORFs from the dataset ., We then used the yeast ORF sequences to search the space of known high-resolution three-dimensional ( 3D ) structures in the Protein Data Bank 44 ( PDB , www . pdb . org ) using BLAST 45 ., To infer the 3D structure of a given ORF we required a BLAST E-value ≤1e-4 , a sequence identity ≥98% and a coverage ≥90% ., NMR structures and PDB files including multiple models of the same structure were discarded ., For all the sequences for which it was not possible to find a complete structure , we searched ModBase 46 ( http://salilab . org/modbase ) for homology models ., We retained all the models with more than 30% sequence identity , spanning more than 90% of the ORF length and having a score higher than 0 . 7 ., For every ORF with multiple models , we selected the one with the highest sequence identity as a representative ., For all the sequences without experimental structures and complete homology models , we retained partial models ( having less than 90% coverage ) , provided that they were spanning at least 90% of one PFAM 47 domain as identified on the sequence of the original ORF ., For the domains , we always kept the longest model spanning that domain ( i . e . the one with the best coverage ) ., For some of the ORF we collected multiple partial models ., Table 1 summarizes the results of the collection of yeast protein structures ., Once identified all those yeast proteins for which we know the 3D structure , or can model it , of at least one domain , we need to compile all those protein-protein interactions and complexes that have been experimentally identified in yeast ( Table 2 ) ., We took directly inferred binary interactions for those coming from two hybrid experiments , and used a SPOKE expansion ( i . e . the bait against every prey ) whenever the interacting partners were discovered through affinity purification techniques ., We used a MATRIX expansion ( i . e . all against all ) for protein complexes ., We also merged interactions from Intact 48 and MINT 49 and selected only those ones that were confirmed by either more than one source , more than one method or by x-ray crystallography ., For pairs of interacting ORFs having multiple partial models we run docking experiments on all the possible pairs ., We also identified all those interactions that either had a known 3D structure already deposited in the PDB ( termed experimental structures of the interacting protein pair ) or that could be modelled by homology ( see Text S1 the Supplementary materials for details on how interactions between chains were identified ) ., To find the structural templates for homology modelling we searched the PDB and looked for protein chains homologous to the yeast ORFs involved in our interaction set , excluding those for which an experimental structure is available ., We considered only those with a BLAST E-value ≤1e-4 , a coverage ≥90% and a sequence identity ≥30% ., We then matched the hits found with our set of interacting pairs ., The interactions were modeled by superposing the structure or model of the interacting partners to the corresponding structure of the homologous protein in the template ., Alignments and superpositions were performed using RAPIDO 50 with default parameters and selecting the rigid superposition ., We also applied an additional filter to exclude models of poor quality ( presenting strong incompatibilities , like large clashing areas or poor structural alignments between the original structures and the template ) ., See Text S1 in the Supplementary materials for details on the filtering procedure ., To assess the validity of running docking methods on homology models , rather than on experimentally determined structures , we collected models for each protein in Benchmark 3 . 0 from ModBase 46 ( http://salilab . org/modbase ) ., We selected the models based on two criteria: the template used had more than 30% and less than 98% sequence identity to the target , and the score of the model was higher than 0 . 7 ., Using these criteria , we finally picked 283 models for 75 of the 248 single proteins in Benchmark 3 . 0 ( receptor and ligand for all the 124 cases ) ., For many of the proteins several models were available based on different templates ., It is known that the structural similarity of a model to the real target is affected by the sequence identity of the template to the target protein 51 ., For this reason we randomly generated different sets of models ( in every set one model was selected for each protein ) in such a way that the distribution of the sequence identity of the templates in every one of the sets corresponded to the distribution of the sequence identity observed in the set of models selected for the large scale docking experiment ( Figure S1 in the Supplementary materials ) ., For it to be possible every set had to be composed by no more than 56 models ., For every one of those subsets we calculated the average RMSD between the models , the bound and the unbound structures ., A plot of the distribution of the three average RMSDs ( model/bound , model/unbound , bound/unbound ) is shown in Figure S2 , in the Supplementary materials ., Finally we selected a set of complete cases ( for which there were models both for the receptor and the ligand ) with a distribution of the sequence identity corresponding to the one observed in the models from the large scale experiment ., This was possible for 13 out of 124 cases ., For those cases we ran ZDOCK 3 . 0 on the model to predict the structure of the binary complex and we evaluated the resulting predictions by comparing them with the crystallographic structure of the complex ., We considered as binary interactions those that involve only two proteins and have been identified by one of the following techniques: array technology , cross-linking study , cytoplasmic complementation assay , nuclear magnetic resonance , two hybrid or x-ray crystallography ., Alternatively , we considered an interaction as multi-component if both the interacting proteins belong to the same aggregate in a list of multi-component aggregates generated by merging data extracted from MPACT 52 , MINT 49 and Intact 48 ., We collected all the known complexes in yeast from MPACT and added them to the list together with all the interactions involving more than two proteins and reported in one of the following publications about large scale experiments using tandem affinity purification techniques: Gavin et al . 2002 53 , Ho et al . 2002 54 , Krogan et al . 2004 55 , Gavin et al . 2006 5 , Krogan et al . 2006 6 ., The first step in this study is to thoroughly benchmark some state-of-the-art docking strategies and decide which one is the optimal to approach our high-throughput docking experiment in yeast ., To carry out this first task , we selected the most recent and well-accepted benchmark set for protein docking developed in the Zlab laboratory 38 ( http://www . zlab . bu . edu ) ., As explained in the Methods section , this dataset consists of 124 interacting pairs for which a high-resolution structure of the complex and the individual components exist ., We generated ranked docking poses for the 124 interactions in the benchmark set with FTDock 20 , ZDOCK 2 . 3 21 and ZDOCK 3 . 0 23 ., We then rescored the docking solutions generated by these three programs with pyDock 29 ., We selected these docking programs because they are among the ones having the best performance in the last rounds of the CAPRI experiment 17 and also for their availability as standalone programs , which makes them suitable for a large scale docking experiment ., It is important to note that the programs used in the test only produce rigid body solutions , meaning that no conformational change is introduced in the interacting molecules ., In more standard applications of docking programs to individual cases , there is usually the possibility of integrating biological knowledge ( i . e . site directed mutagenesis studies ) on the interacting interface , model conformational changes and flexibility and to perform several iterations of refinement to remove impossible solutions and improve the quality of the remaining ., However , unfortunately , this is not feasible in our study due to the large number of docking experiments and the computational cost of the refinement step , which forces us to assess the accuracy of the docking solutions as they come out of the programs , without applying any further biological filtering ., Figure 1 shows the results of the benchmark ., ZDOCK 3 . 0 and ZDOCK 3 . 0+pyDock are the two methods having the best performance ., By using one of them it is possible to obtain an acceptable solution among the top 3 for roughly 20% of the cases ( see also Table S1 in the Supplementary materials ) ., If we consider only the top solution for each interacting pair , we find an acceptable solution for 14 out of the 124 cases tested ., This figure goes up to 25 if we contemplate an acceptable solution in the top 3 and to 42 when considering the top 10 solutions generated by ZDOCK 3 . 0 ., The results of FTDock and ZDOCK 2 . 3 both , individually and with the rescoring provided by pyDock , are clearly outperformed by ZDOCK 3 . 0 and ZDOCK 3 . 0+pyDock , which have similar success rates on the top 3 solutions ( even if in different cases ) , with ZDOCK 3 . 0 showing a few more successful cases in the top 5 and 10 solutions ., We also explored the possibility of merging and re-scoring the results provided by the different programs which , unfortunately , did not improve the results ( Figure 1 ) ., Thus , in light of the obtained results , we proceeded to the next steps using only ZDOCK 3 . 0 and pyDock , excluding FTDock and ZDOCK 2 . 3 ., It is important to highlight that many of the correct predictions are classified as “rigid body” docking cases in the Benchmark 3 . 0 dataset ., These are the interaction pairs that do not undergo important conformational changes upon association and thus it is easier to find a good docking solution starting from the unbound states ., Table S5 ( in the Supplementary Materials ) shows the distribution of good cases ( i . e . cases having at least one acceptable solution among the top n ) between the different categories of difficulty as reported in Benchmark 3 . 0 ., There is evidence that the raw scores provided by docking methods often show a poor correlation with the probabilities of a given solution to be correct and , perhaps more importantly , these scores are not comparable between experiments involving different molecules , since they are very much dependent on the size and shape of the molecules tested 56 ., However , given that we are benchmarking state-of-the-art methods on a large set of protein interactions , and that we need to drastically reduce the number of solutions to be included in the 3D-Repertoire modelling pipeline , we decided to explore the possibility of increasing the accuracy of the results by identifying a general score threshold , at expenses of reducing the coverage ., This is to reject those results that are more likely to be incorrect and to keep the ones that have a higher probability of being acceptable predictions of the real interaction ., Our aim , in fact , is to select a small subset of cases on which we can have higher confidence about the correctness of the generated predictions ., After several trials , we found that by imposing a threshold on the average score of the top 3 solutions we could improve the success rate of a 10% , going from 20% to almost 30% of successful cases ., In particular , we analysed the ratio between the number of good cases and the number of total cases satisfying the threshold ., It is important to note that while the score produced by pyDock is minimized the one produced by ZDOCK is maximized ., Thus in the case of pyDock a case is selected if the average score of the top n solutions is lower than the threshold while for ZDOCK the average score must be higher then the threshold ., The analysis was repeated for the top 1 , 3 , 5 and 10 solutions for both the programs and for different values of the threshold ( see Figure S2 in the Supplementary materials ) ., The analysis shows that there is no defined trend in the success rate for the selected cases ., The plots are not strictly monotonic but they show a moderate degree of variation for increasing values of the threshold ., Nevertheless in all the cases the threshold shows a certain degree of success in filtering out cases showing no acceptable solution , increasing in this way the accuracy for the selected ones ., We picked as the best result the one produced by ZDOCK 3 . 0 , without the rescoring provided by pyDock , on the top 3 solutions ( Figure 2 ) , corresponding to a score threshold of 1386 , which results in an increase of the accuracy from 20% ( 25 good cases on 124 without the threshold ) to 29 . 7% ( 11 good cases over the 37 selected ) ., We would like to stress that the success rates obtained in our high-throughput strategy are in good agreement with the average success rate of the best predictors in the CAPRI experiment ( see Text S1 , Figure S4 and Table S6 in the Supplementary materials ) , confirming that our fully automated results do indeed match the state-of-the-art of the docking field ., We have also explored the added value of expert manual intervention in specific docking predictions , and discovered that it represents an improvement in the capacity of docking to produce accurate models of about 8% on average ., The choice of the top 3 cases was taken as a compromise between the increased accuracy and the number of predictions to be analysed for every case ., In fact , even if it would be desirable to have just one correct prediction for every case , taking only the top first solution yields to a probability of success significantly lower ( 18% in the best case ) ., On the other hand , considering the top 10 solutions would raise the accuracy to the highest success ratio ( 38 . 5% ) but it would lead to an explosion in combinatorial complexity of the model building procedure within the 3D-Repertoire pipeline ., In the attempt to build higher order structures ( i . e . larger subcomplexes ) from binary interactions , we assemble all possible combinations of binary structures and assess their fit by , for instance , computing the number of clashes and the binding energies by means of empirical force fields and testing the fit of available experimental information ., This is a very time consuming process that grows exponentially with the number of complex components ( 14 on average for our set of complexes ) ., It is thus unfeasible to test many possibilities for each binary interaction , and that is why we have reduced the number of docking solutions kept for further exploration to three which , we think , it is a good accuracy/coverage compromise ., Although the overall results , in terms of accuracy , are very similar between ZDOCK 3 . 0 and ZDOCK 3 . 0+pyDock , the interaction pairs that each method correctly identifies are not the same ( see Table S2 in the Supplementary materials ) ., A deeper investigation of the differences between the successful cases of the two programs could help the further development of the docking methods themselves ., As there is no general agreement between the two on the good cases , we could not use this information to improve the accuracy in the selection of successful docking cases ., Unfortunately , and despite the success achieved by the many ongoing structural genomics efforts , the availability of high-resolution structures for most proteins is still very limited ., Thus , as for individual structures , a good strategy to increase the coverage of the structural space is to build models by homology 57 and , as described in the methods section , many of the individual structures that we used in the high-throughput docking experiment in yeast are , in fact , homology models ., Consequently , on the one hand we need to test whether the level of accuracy in docking experiments is similar to the one achieved when experimental structures are used and , on the other , if the score thresholds derived from experimental structures are still valid for docking homology models ., The ideal test would be to generate homology models for the 124 protein interacting pairs in the benchmark set using structural templates in the range of sequence identities similar to those used for modelling the yeast proteins and , obviously , discarding the real structures ., Unfortunately , we could build homology models , in the same fashion as that used in the high-throughput experiment , for both individual structures of only 13 of the interacting pairs ., Of these , 5 passed the score threshold and in only one case we found an acceptable solution among the top 3 , which would represent an accuracy of 20% , somewhat below the almost 30% achieved when using experimental structures ., However , it is clear that we have too few cases to extract any relevant conclusion , which prompted us to look for alternative ways of assessing the use of homology models and score threshold in docking experiments ., The success of docking experiments largely depends on the structural conformational changes that the two protein components suffer upon association ., In other words , when the unbound and bound forms of the interacting proteins are similar , it is very likely to obtain docking solutions of high-quality and , when the two proteins undergo severe conformational changes , it is almost impossible to get any acceptable solution ., Consequently , we could compare the structural differences between the unbound/bound protein forms and models/bound forms in the benchmark set to estimate the validity of using homology models in docking experiments ( see the Methods section for details as to how we selected the models ) ., For the interacting protein pairs in the benchmark set , we observed that the difference between the models and the bound structures is , in general , a bit higher than the difference between the unbound and bound structures ( Figure S3 in the Supplementary materials ) suggesting that the docking procedure would yield a lower success rate when using models instead of the experimental structures ., Nevertheless for more than 50% of the cases the difference between the RMSDs of models/bound and unbound/bound is very little ( less than 1 Å ) ., For 101 cases the conformational changes are more pronounced between the models and the bound forms than for the unbound/bound pairs ., However , interestingly , there are 23 models that are more similar to the bound structures than the corresponding unbound experimental forms , suggesting that for those cases the docking experiment might have higher probability of success by using the models ., Overall , our analyses show that it is indeed reasonable to run docking experiments using homology models of the individual proteins , but it is likely that it can decrease the success rate of the experiments although it is difficult to quantify its real impact ., The starting point of our experiment was a high-confidence set of 13614 protein-protein interactions in yeast obtained by merging the biological data contained in the different available databases ( Table 1 ) ., We then collected structural data , in the form of experimental structures or homology models , for the proteins involved in the interactions ( see Methods ) ., We found experimental structures for 217 of the proteins in the high-confidence interactome , while for another 249 proteins we could collect a complete model built by homology ., For the remaining 774 proteins we only obtained partial models , corresponding mainly to individual domains ., This is not a problem , since it has been shown that in the vast majority

Introduction, Methods, Results/Discussion

The last several years have seen the consolidation of high-throughput proteomics initiatives to identify and characterize protein interactions and macromolecular complexes in model organisms ., In particular , more that 10 , 000 high-confidence protein-protein interactions have been described between the roughly 6 , 000 proteins encoded in the budding yeast genome ( Saccharomyces cerevisiae ) ., However , unfortunately , high-resolution three-dimensional structures are only available for less than one hundred of these interacting pairs ., Here , we expand this structural information on yeast protein interactions by running the first-ever high-throughput docking experiment with some of the best state-of-the-art methodologies , according to our benchmarks ., To increase the coverage of the interaction space , we also explore the possibility of using homology models of varying quality in the docking experiments , instead of experimental structures , and assess how it would affect the global performance of the methods ., In total , we have applied the docking procedure to 217 experimental structures and 1 , 023 homology models , providing putative structural models for over 3 , 000 protein-protein interactions in the yeast interactome ., Finally , we analyze in detail the structural models obtained for the interaction between SAM1-anthranilate synthase complex and the MET30-RNA polymerase III to illustrate how our predictions can be straightforwardly used by the scientific community ., The results of our experiment will be integrated into the general 3D-Repertoire pipeline , a European initiative to solve the structures of as many as possible protein complexes in yeast at the best possible resolution ., All docking results are available at http://gatealoy . pcb . ub . es/HT_docking/ .

Proteins are the main perpetrators of most biological processes ., However , they seldom act alone , and most cellular functions are , in fact , carried out by large macromolecular complexes and regulated through intricate protein-protein interaction networks ., Consequently , large efforts have been devoted to unveil protein interrelationships in a high-throughput manner , and the last several years have seen the consecution of the first interactome drafts for several model organisms ., Unfortunately , these studies only reveal whether two proteins interact , but not the molecular bases of these interactions ., A full comprehension of how proteins bind and form complexes can only come from high-resolution , three-dimensional ( 3D ) structures , since they provide the key quasi-atomic details necessary to understand how the individual components in a complex or pathway are assembled and coordinated to function as a molecular unit ., Here , we use protein docking experiments , in a high-throughput manner , to predict the 3D structure of over 3 , 000 interactions in yeast , which will be used to complement the complex structures obtained within the 3D-Repertoire pan-European initiative ( http://www . 3drepertoire . org ) .

biochemistry/structural genomics, computational biology/protein structure prediction, computational biology/macromolecular structure analysis

journal.pcbi.1004295

Inferring Regulatory Networks from Experimental Morphological Phenotypes: A Computational Method Reverse-Engineers Planarian Regeneration

Advances in developmental biology and regenerative medicine require a mechanistic understanding of the generation and repair processes that construct and repair complex anatomical structures 1 ., For example , a salamander can regenerate complete limbs , eyes , tails , and jaws 2; a tail grafted to its flank will , within a few months , becomes re-patterned into a structure more appropriate to its new location—a limb 3 , 4 ., During metamorphosis , tadpole faces with very abnormal organ positions become transformed into normal frog faces , as each organ undergoes evolutionarily-novel movements to ensure that it ends up in the right position relative to the others 5 ., Planarian flatworms regenerate their complex body from almost any surgical amputation , and cease new growth and remodeling when their correct body pattern has been restored 6 ., Learning to understand and harness these high-order pattern control programs is of high importance not only to basic developmental and evolutionary biology , but also underlies the roadmap to transformative advances in regenerative medicine , birth defects , and synthetic bioengineering ., High-resolution genetic analyses are revealing an increasing number of regulatory genes , while developmental and regenerative research is producing a rich dataset of in vivo experimental manipulations and their resultant morphological phenotypes 7 ., Unfortunately , our ability to understand and manipulate 3-dimensional patterning outcomes has not kept pace ., A fundamental gap exists between the gene products experimentally identified as necessary for producing a morphological phenotype , and a mechanistic regulatory network that would be sufficient to explain exactly how and why a complex morphology is generated in the precisely correct size , shape , and orientation 8–10 ., There exist individual examples of models that incorporate geometry 11–19 and attempt to understand the dynamics of patterning 20–29 , but the most prevalent arrow diagrams derived from genetic experiments largely do not specify , constrain , or explain the remarkable geometry and regenerative regulation of biological systems ., Finding the mechanisms responsible for a given set of anatomical phenotypic data remains a significant challenge due to the non-linearity of many biological processes 30 ., The increasing deluge of genetic data does not generally result in constructivist models that truly explain dynamic morphogenesis of living structures because it is simply too hard for human scientists to invent a model with all of the appropriate higher-order patterning properties ., Indeed each additional dataset on patterning outcomes from some perturbation makes it more difficult , not easier , to come up with a model that matches all of the results ., Thus , there is a clear need for automated tools to assist in the discovery of mechanistic models that explain the ever-increasing set of functional phenotypic results in the scientific literature on developmental and regenerative biology 1 ., Tremendous progress has been made in developing bioinformatics tools for the reverse-engineering of dynamical models of regulatory networks from microarrays and quantitative PCR gene expression profiling data 31–43 as well as of metabolic networks from time-series concentration data 44–46 ., However , these approaches produce models lacking spatial information and are not applicable to patterning and morphological experimental data ., Indeed , inferring characterized regulatory networks from experimental resultant spatial patterns is exceedingly challenging due to the difficulties in robustly quantifying phenotypic data 47 , evaluating spatial-temporal models with these data 48 , and automatically characterizing known and unknown products and their underlying complex , non-linear interactions resulting in the desired patterning behavior 49 , 50 ., The gene circuit method 51–53 and subsequent automated approaches 54–63 have successfully reverse engineered a complex dynamical regulatory network from spatial data: the gap gene network controlling Drosophila blastoderm patterning ., However , these methods are still limited to quantitative 1-dimensional gene expression data and are not amenable for morphological phenotypes resulting from surgical manipulations and genetic and pharmacological treatments that are common in developmental and regenerative biology ., No tools yet exist for mining the published datasets of experimental morphological data in regeneration and developmental biology ., The complexity of anatomical and morphological data , the elaborate surgical , genetic , and pharmacological perturbation experiments , and the lack of methods to formalize in a mathematical language these data prevent us from reverse-engineering the key regulatory networks in development and regeneration ., In consequence , the discovery of mechanistic regulatory networks has not kept pace with the increasing generation of phenotypic data from perturbation experiments ., For example , despite over 100 years of focused attention , no quantitative model has been found that reproduces more than a few of the main features of the rich functional dataset on planarian regeneration 64 ., We have learned much about the molecular pathways regulating stem cell decision-making 65 , 66 , but the understanding of axial polarity , morphogenesis , and persistent changes to the bodyplan 67 still lacks constructivist models ., In order to make use of the ever-increasing data on patterning outcomes of genetic , pharmacological , and surgical experiments , bioinformatics must be extended to anatomy and pattern formation ., We present here an automated method for the discovery of regulatory networks explaining the morphological patterning results from surgical , genetic , and pharmacological perturbation experiments ( Fig 1 ) ., Our system integrates a formalization of the published results in planarian regeneration , a versatile in silico simulator in which the patterning properties of any regulatory network can be quantitatively tested in a regeneration assay , and a machine learning module that evolves networks whose patterning behavior optimally matches the dataset of planarian results ., We demonstrate that regulatory networks comprising specific biological products can be automatically inferred from phenotypic morphological data resulting from functional experiments by an evolutionary computation process ., The formalized experimental descriptions of surgical manipulations , genetic and pharmacologic treatments , and resultant phenotypes are used to infer the necessary and sufficient molecular products , their interactions , and the spatial and temporal dynamics of a regulatory network explaining the given set of phenotypic experiments ., In inferring regulatory networks from phenotypic experimental data , unambiguous mathematical formalisms must be used to describe the relevant characteristics of the experimental dataset to explain ( Fig 1 ) ., To this purpose , we used a functional mathematical ontology with an adequate level of abstraction for the formalization of developmental and regenerative experiments 47 ., In contrast to ontologies based on natural language , our functional ontology uses mathematical language for unambiguously describing the experimental procedures as a hierarchy of elemental actions and their morphological outcomes as a set of interconnected body regions ( head , trunk , and tail ) ., Thus , the formalized experimental procedures can be reliably performed in a simulator in silico , and the phenotypes of the formalized experiments are amenable to automated comparison with the predictions of models ., Using an evolutionary algorithm search module , our system discovered the first quantitative , constructive model that predicts the main features of planarian regeneration ., We developed a generalized method to infer regulatory networks from a set of formalized , morphology-based experiments ( Fig 2 ) ., Focusing on the planarian regeneration data 68 for the first proof-of-principle , our goal was to identify a regulatory network that could be executed on every cell in a virtual worm such that the patterning outcomes of simulated experiments would match the published data ., Based on evolutionary computation principles 69 , the algorithm maintains an evolving population of candidate regulatory networks for searching the space of possible networks ., The algorithm searches simultaneously for the necessary products , topology , specific regulatory interactions , and parameters of the regulatory networks , which are implemented as a non-linear system of partial differential equations ., Nodes in the regulatory network can represent either signaling products or special products with a phenotypic meaning specific to the dataset ( head , trunk , and tail regions in the worm ) ., The initial population of candidate regulatory networks is made of simple networks with random regulations and parameters ., New regulatory networks are created in each generation , by combining two existent ( parent ) regulatory networks from the current population and probabilistically adding and removing products and regulations , and altering their parameters ( see methods section ) ., The population is cyclically updated replacing old regulatory networks with the new regulatory networks that better fit the experimental dataset ., The algorithm stops when a regulatory network is found that perfectly reproduces the same resultant phenotypes in all the experiments as formalized in the input dataset ., Our system uses in silico experiments equivalent to the in vivo experiments formalized in the dataset to evaluate the predictive ability of candidate regulatory networks ., For this , we implemented a simulator capable of performing the same kind of experiments formalized in the dataset , including surgical manipulations and genetic and pharmacological perturbations ., An experiment stored in the dataset is simulated using a specific regulatory network in two stages: the wild-type morphology stage where the regulatory network can reach a stable state and the experimental stage where the resultant phenotypes are obtained ., During the first stage , the product concentrations are initialized and the system of partial differential equations with this initial condition is numerically solved for a constant time interval ., Phenotypic products are initialized to match the morphological regions pattern ( head-trunk-tail ) of the formalized wild-type morphology , while the signaling products are set to a continuous parameter value automatically found by the inferring method for each product ., The second stage proceeds by applying the surgical manipulations and pharmacological treatments ., Surgical manipulations change the system boundaries , while genetic and pharmacological treatments alter specific parameters of the differential equations corresponding to the perturbed products ., Next , the new system of partial differential equations with the new initial condition and boundary is numerically solved for an additional constant time interval ., The final state represents the resultant phenotype corresponding to the simulated experiment ., Thus , each candidate network model is tested in a virtual worm , under simulated experiments , to determine its patterning properties in each case ., Then , to determine the quality of a candidate regulatory network , the algorithm compares the resultant phenotypes from the simulation of each experiment with real published data in our planarian database 47 , 70 ., To quantitatively ascertain the predictive quality of each model ( how well it matches the available data ) , we calculate a composite error score representing how well each experiment’s final pattern matches the known result of such an experiment in real planaria ., For this purpose , we implemented a phenotypic distance metric that measures how different any two morphological phenotypes are 71 ., The metric calculates the average differences between the phenotypic product concentrations of the two phenotypes ., The predictive error of a regulatory network is then calculated as the average phenotypic distance between the resultant phenotypes from the simulated experiments and those corresponding to the formalized experimental dataset ., Using this algorithmic approach , we inferred novel regulatory networks ( Fig 3 and S1–S6 Movies ) explaining the experimental data presented in a selection of key papers 72–79 studying the head-versus-tail regeneration decision making in the planarian flatworms S . mediterranea and D . japonica ., First , we formalized datasets containing the surgical manipulations , pharmacological and genetic treatments , and their resultant experimental phenotypes for each of the selected papers ., We next applied the method individually to each dataset to infer the subjacent regulatory networks explaining the experimental data presented on each of the papers ., For each dataset , the algorithm found a complete system of differential equations ( S1 File ) that represent a regulatory network explaining the dynamical regeneration of the correct position , 2D shape , and proportions of the head , trunk , and tail regions of all the experimental phenotypes in each dataset ., Remarkably , without any prior knowledge of genetic expression patterns or regulatory interactions among genes , but using only the pharmacological , genetic , and surgical experimental perturbations and the position , shape , and proportions of their morphological outcomes ( encoded as head , trunk , and tail regions ) , the algorithm discovered the correct known regulatory pathways of several signaling mechanisms ( Fig 3 ) ., For example , the algorithm discovered the Wnt/β-catenin canonical regulation ( Fig 3C and 3D ) , the inhibition of head structures and promotion of tail structures by β-catenin ( Fig 3A , 3C and 3D ) , the inhibition of β-catenin by both APC ( Fig 3A ) and notum ( Fig 3C ) , and the cryptic lack of posterior tissue re-specification ( remaining as trunk ) due to the knock-down of wnt1 and notum ( Fig 3C ) , hh ( Fig 3D ) , or wnt1 and hh ( Fig 3D ) ., In addition , several novel regulatory interactions and unidentified products were detected as necessary for the correct prediction of the experiments in the datasets ., Fig 4 shows two experiments performed in silico using the regulatory network discovered from the search of the model in Fig 3A ., The concentration dynamics during both experiments are shown for a selection of locations in the virtual worm ., In the control experiment ( Fig 4B ) , no genetic or pharmacological perturbation was applied to the worm , resulting in the regeneration of the correct head-trunk-tail pattern ., However , when β-catenin is blocked in the second experiment ( Fig 4C ) , the same regulatory network predicts the regeneration of a double-head worm , which is the exact phenotype resulting from the experiments in vivo ., The discovered regulatory network also predicted the known role of APC inhibiting β-catenin , which explains the resultant double-tail phenotype after APC ( RNAi ) ( Fig 3A ) ., Multiple knock-downs in the wnt1/wnt11-5 regulatory pathway are necessary to perturb the resultant phenotype from a trunk fragment 75 ( wnt1 and wnt11-5 were known as wntP-1 and wntP-2 , respectively 80 ) ., When we applied the automated method to this dataset , the resultant model found consisted in a redundant modular network ( Fig 3B ) ., Fig 5 illustrates the experiments in this dataset performed in silico with the network automatically discovered ., The regulatory network presents both wnt1 and wnt11-5 activating the regeneration of tail and inhibiting the regeneration of head , and both of them activated by an unknown common product ., Due to this redundancy in the network design , the knock-down of either wnt1 or wnt11-5 results in the same phenotype than the control: the correct head-trunk-tail pattern ., However , when both wnt1 and wnt11-5 are simultaneously knocked down , the regenerated phenotype is then a double-head worm , similarly to the phenotypes obtained in vivo ., The inferring method iteratively produces regulatory networks that better predict the experiments in the dataset ., Fig 6 shows a selection of candidate regulatory networks generated during the search of the model in Fig 3C ( see S1 File for the system of equations for each regulatory network ) ., The initial random regulatory networks ( generation 0 ) usually cannot reproduce any of the resultant phenotypes in the dataset , neither maintain the wild type morphology pattern ., New candidate regulatory networks are generated by randomly combining previous networks and performing random changes , additions , and deletions , including nodes representing knocked-down genes in the experiments or unknown nodes found de novo ., Incrementally , the new candidate networks can explain a higher number of experiments , and the final regulatory network can correctly explain all the experiments in the dataset ., The time to converge to a satisfactory regulatory network depends on the complexity and quantity of the experiments included in the input dataset ( Fig 7 ) ., The inferring algorithm is intrinsically parallel , since the simulation and evaluation of a population of candidate regulatory networks can be done independently ., Using a parallel implementation of the algorithm in a computer cluster , the time to find a regulatory network from knock-down experiments ranged from an average of one hour for four-experiment datasets to seven hours for eight-experiment datasets ., Datasets with experiments blocking the diffusion of a product averaged four hours ., The dataset with three classical cut experiments averaged a time to find of 21 hours , suggesting a higher difficulty in inferring de novo all the unknown components in the regulatory network ., We can visualize the evolution of regulatory networks during a search process by tracking the error of the best network ( lowest error ) in the population and its complexity ( the sum of the number of products and number of regulatory interactions in the network ) over time ( S1 Fig ) ., The graphs show how the initial population contains networks with low complexity and high error ., Gradually over time , the error of the networks improves , while their complexity increases , until a network with zero error is found by the algorithm ., During the search process , regulatory networks can evolve products and regulations that do not participate directly or indirectly in the regulation of phenotypic products , and hence do not affect the dynamics of the phenotypic products ( S2 Fig ) ., These auxiliary products are not included during the simulation of a regulatory network , but they can evolve independently through neutral mutations , and be reused at later generations by the search algorithm ., We next applied the algorithm to a combined experimental dataset comprising all the selected head-versus-tail planarian regeneration papers to determine whether our approach could identify a comprehensive regulatory network of planarian regeneration ( Fig 8 and S7 Movie ) ., Remarkably , after 42 hours , the algorithm returned the discovered system of equations ( S1 File ) representing a regulatory network that correctly predicts all 16 experiments included in the dataset ., The network comprises seven known regulatory molecules inferred from knock-down experiments , one unknown gap junction-permeable diffusible product inferred from a gap junction blockage experiment , and two unknown general regulatory products ., This automatically inferred regulatory network represents the most comprehensive model of planarian regeneration found to date , the only known model that mechanistically explains anterior-posterior polarity determination in planaria under many different functional experiments , and the first patterning model discovered from morphological outcomes by an automated method—a new successful application towards the augmenting of scientific discovery with artificial intelligence 81–83 ., In order to characterize the two unknown regulatory products identified by the algorithm , we searched for products with similar interactions in public molecular interaction databases ., Using the MiMI database 84 , 85 , we extracted all the known products ( in Homo sapiens ) interacting with the products that were predicted to regulate node ‘b’ ( β-catenin and hh; see Fig, 8 ) and found hnf4 as the only common product interacting with both of them ., This is thus an excellent candidate for node ‘b’ , and a homolog for this gene has already been found in planaria 86 ., For node ‘a’ , we used the STRING database 87 , and identified the Frizzled family of receptors as commonly interacting ( with the highest confidence score of 0 . 9 ) with β-catenin , wnt1 , and wnt11 ., Indeed , several Frizzled protein homologs have been already identified in planaria 72; since phenotypes for each individual Frizzled gene product have not yet been uncovered by loss-of-function analyses in the literature ( suggesting redundancy ) , our network’s node ‘a’ likely represents the regulatory actions of several of these family members as a group ., We next tested whether some regulatory pathways were robustly found by the search method—consistently discovered by independent evolutionary searches ., To this end , we performed multiple runs of the method with the same comprehensive set of experiments ., These searches resulted in three different regulatory networks that can correctly reproduce the complete set of experiments ( Fig 9A–9C; A being the most parsimonious network that was presented in Fig 8 ) ., All the regulations shared by these three networks are seen together in a common subnetwork ( Fig 9D ) ., Remarkably , 14 regulatory interactions were consistently found by the search method , suggesting that these relationships are the most important interactions explaining the comprehensive dataset of experiments ., Finally , we tested the robustness and predictive ability of the regulatory networks found by our automated method in an experiment designed to test the predictive value of the discovered models for data they had never seen ( not included in the search process ) ., We omitted three key experiments from the comprehensive dataset , and used the automated method to find a regulatory network that could correctly reproduce this reduced ( partial ) dataset ( Fig 10A and 10B ) ., Crucially , the found regulatory network correctly predicted the outcomes of these three novel experiments—the model correctly explained the outcomes that were not known to it during the search ( Fig 10C ) ., These results validate the ability of the automated search method to find regulatory networks capable of not only explaining the resultant phenotypes from the experiments performed in vivo included in the learning dataset , but also of predicting the resultant phenotypes from novel experiments ., We conclude that the networks uncovered by this system have predictive value for novel results , in addition to helping to understand existing data from which they were extracted ., Our system addresses the gap between the wet-lab discovery of genetic regulatory interactions and an understanding of the dynamic patterning behavior of regenerative systems ., Comprising ( 1 ) a formalized database of functional patterning outcomes in the planarian literature , ( 2 ) a simulator in which any ( human- or computer-derived ) regulatory model can be evaluated for fit to known anatomical data , and ( 3 ) a network discovery machine learning method , this system is a first step towards a new bioinformatics of shape ., These three modules are integrated into a workflow designed to help human scientists discover mechanistic , constructivist models that optimally match the ever-growing dataset of regeneration data ., Our results demonstrate the discovery of regulatory networks directly from formalized experimental morphological data with the use of an automated computational algorithm—the first automated linkage of morphological output and molecular-genetic underpinnings ., With no prior information beyond the input dataset of functional outcomes of surgical , genetic , and pharmacological experiments , the method is capable of identifying the necessary biochemical products and their regulations and parameters that form a system of partial differential equations explaining the resultant phenotypes from the dataset ., The networks discovered by our system represent immediately testable hypotheses for the control algorithms underlying regeneration ., Our method improves the current state of the art for reverse-engineering dynamic regulatory networks in several areas ., Foremost , our method is the first to be applicable to data containing morphological outcomes and surgical perturbations , which is essential for the regeneration field ., Current methods are limited to inferring networks from dimensionless gene expression profiling data or 1-dimensional expression data resulting from genetic perturbations ., In contrast , our method is flexible enough to extract regulatory networks directly from resultant 2-dimensional morphological patterning outcomes and to process a wide array of experimental perturbations , including surgical manipulations , pharmacological treatments , and genetic knock-downs ., To this end , we implemented a whole-body developmental simulator that differs from current approaches 88–91 in that the input is a formalization of both the experimental surgical and genetic perturbations to perform and the dynamical regulatory model to test ., This allows our method to be applicable to the reverse engineering of regulatory networks from the morphological outcomes of developmental systems , previously out of the range of automated inference methods , including the large experimental dataset of regenerative model organisms lacking mechanistic dynamical explanations ., Importantly , our method can infer regulatory networks containing not only the specific products and genes perturbed in the input experimental dataset , but also discover completely de novo unknown products detected as necessary to explain the resultant phenotypes: predict their existence , functional roles in the network , and properties of interaction with known molecular components ., This makes our approach applicable to even datasets with perturbations affecting unknown mechanisms , as well as datasets lacking all the experimental perturbations necessary to explain all the experimental data ., In this way , our method can infer regulatory pathways not apparent from the input dataset and novel interactions not reported in the literature , whose yet-to-be-characterized products can be identified from the multiple interactome databases available in the literature—these inferences then serve as predictions of the model which can be empirically tested ., We are currently implementing an automated method to characterize such unknown products ., The inferred regulatory networks by our method contain more versatile regulatory interactions than previous approaches ., Due to their capability to model a diverse set of biological regulations , we employed Hill functions to model the regulation between two products ., Using two parameters per interaction ( the Hill coefficient and the disassociation constant ) , the model can accommodate a richer variety of non-linear interactions compared to linear and one-parameter non-linear functions ., Furthermore , the regulatory networks inferred by our method improve current approaches by permitting different types of aggregated interactions between multiple regulations for a single product , such as necessary regulations ( both regulators are required ) , sufficient regulations ( any regulator is enough ) , or any combination of them ., The high flexibility of the inferred regulatory networks makes our method a very versatile approach ., The discovered regulatory networks reveal several interesting properties of the inference method ., Networks matching many functional experiments that quantitatively and qualitatively explain regeneration of anatomical polarity—which had eluded human scientists—could be discovered in acceptable time by an automatic search performed by a computer ., Surprisingly , the fully parameterized regulatory networks that were identified by this process are not highly complex tangles , but are similar in complexity to qualitative models proposed by human scientists in the literature and thus readily understandable ., Moreover , the discovered networks contain only a few to-be-identified gene products , which facilitate their identification from known interactome data ., Indeed , we could manually identify the two gene unknown products found by our method using publicly available molecular interaction databases ., Currently , we are developing an automated method to facilitate the characterization of such products ., Precise predictions can readily be made from the simulation of novel experiments with the discovered networks , guiding in the design of the best next set of experimental manipulations ., The comprehensive model of planarian regeneration reverse-engineered by our method represents the first quantitative model able to recapitulate regeneration under genetic knock-downs , pharmacological treatments , and surgical manipulations ., Unlike conventional arrow diagrams derived from molecular genetic experiments , this system identifies models that not only include necessary components ( without which regeneration cannot occur normally ) , but are fully-specified as a constructive model showing which dynamics are sufficient to give rise to the remarkable pattern homeostasis of planaria ., Most models of regeneration are based on generalized mechanisms and do not consider the specific dynamic regulatory mechanisms or network topology necessary to precisely recapitulate the observed patterning phenotypes 92–96 ., Meinhardt’s pioneering work on the mechanisms of pattern formation represents the only dynamic models of planarian regeneration proposed to date , based on reaction-diffusion mechanisms and able to recapitulate the head-versus-tail polarity regeneration and midline formation 23 , 64 , 97 , 98 ., However , this approach was purely numerical as a proof of the general dynamic mathematical principles , without characterizing any of the regulatory products , and hence accounting only for surgical amputations ., Our model was inferred directly from experimental data and includes particular genetic regulatory components able to precisely predict genetic and pharmacological interventions in addition to surgical manipulations ., Hence , the models inferred with our method can be used to predict the morphological outcomes in specific genetic knock-downs ., The method can identify those interactions most strongly implied by the dataset , by performing multiple searches and extracting the common pathways found in the resultant set of regulatory networks ., Interestingly , the consensus model found in this way includes most of the genetic regulations of head vs . tail planarian regeneration published in the field to date , as well as novel genetic regulations only discovered recently in other model organisms , such as the inhibition of wnt by notum 99 ., Furthermore , the method can be used as a generable protocol for automatically finding the less-universal regulatory interactions inferred from the data , and for automatically suggesting additional perturbations for in vivo experimental testing ., Importantly , the robustness of the method to infer predictive regulatory networks was validated with a subtraction control test , which successfully produced a regulatory network that not only predicted all the experiments in the dataset used during the search , but also predicted the exact resultant phenotypes from a set of new in vivo experiments that were not part of the search process ., In summary , these results validate the capacity of our method to reverse engineer robust regulatory networks with a high predictive power .

Introduction, Results, Discussion, Methods

Transformative applications in biomedicine require the discovery of complex regulatory networks that explain the development and regeneration of anatomical structures , and reveal what external signals will trigger desired changes of large-scale pattern ., Despite recent advances in bioinformatics , extracting mechanistic pathway models from experimental morphological data is a key open challenge that has resisted automation ., The fundamental difficulty of manually predicting emergent behavior of even simple networks has limited the models invented by human scientists to pathway diagrams that show necessary subunit interactions but do not reveal the dynamics that are sufficient for complex , self-regulating pattern to emerge ., To finally bridge the gap between high-resolution genetic data and the ability to understand and control patterning , it is critical to develop computational tools to efficiently extract regulatory pathways from the resultant experimental shape phenotypes ., For example , planarian regeneration has been studied for over a century , but despite increasing insight into the pathways that control its stem cells , no constructive , mechanistic model has yet been found by human scientists that explains more than one or two key features of its remarkable ability to regenerate its correct anatomical pattern after drastic perturbations ., We present a method to infer the molecular products , topology , and spatial and temporal non-linear dynamics of regulatory networks recapitulating in silico the rich dataset of morphological phenotypes resulting from genetic , surgical , and pharmacological experiments ., We demonstrated our approach by inferring complete regulatory networks explaining the outcomes of the main functional regeneration experiments in the planarian literature; By analyzing all the datasets together , our system inferred the first systems-biology comprehensive dynamical model explaining patterning in planarian regeneration ., This method provides an automated , highly generalizable framework for identifying the underlying control mechanisms responsible for the dynamic regulation of growth and form .

Developmental and regenerative biology experiments are producing a huge number of morphological phenotypes from functional perturbation experiments ., However , existing pathway models do not generally explain the dynamic regulation of anatomical shape due to the difficulty of inferring and testing non-linear regulatory networks responsible for appropriate form , shape , and pattern ., We present a method that automates the discovery and testing of regulatory networks explaining morphological outcomes directly from the resultant phenotypes , producing network models as testable hypotheses explaining regeneration data ., Our system integrates a formalization of the published results in planarian regeneration , an in silico simulator in which the patterning properties of regulatory networks can be quantitatively tested in a regeneration assay , and a machine learning module that evolves networks whose behavior in this assay optimally matches the database of planarian results ., We applied our method to explain the key experiments in planarian regeneration , and discovered the first comprehensive model of anterior-posterior patterning in planaria under surgical , pharmacological , and genetic manipulations ., Beyond the planarian data , our approach is readily generalizable to facilitate the discovery of testable regulatory networks in developmental biology and biomedicine , and represents the first developmental model discovered de novo from morphological outcomes by an automated system .

journal.pcbi.1000301

Understanding Pitch Perception as a Hierarchical Process with Top-Down Modulation

Modelling the neural processing of pitch is essential for understanding the perceptual phenomenology of music and speech ., Pitch , one of the most important features of auditory perception , is usually associated with periodicities in sounds 1 ., Hence , a number of models of pitch perception are based upon a temporal analysis of the neural activity evoked by the stimulus 2–5 ., Most of these models compute a form of short-term autocorrelation of the simulated auditory nerve activity using an exponentially weighted integration time window 6–13 ., Autocorrelation models have been able to predict the reported pitches of a wide range of complex stimuli ., However , choosing an appropriate integration time window has been problematic , and none of the previous models has been able to explain the wide range of time scales encountered in perceptual data in a unified fashion ., These data show that , in certain conditions , the auditory system is capable of integrating pitch-related information over time scales of several hundred milliseconds 14–22 , while at the same time being able to follow changes in pitch or pitch strength with a resolution of only a few milliseconds 14 , 15 , 21–24 ., Limits on the temporal resolution of pitch perception have also been explored by determining pitch detection and discrimination performance as a function of frequency modulation rate 25–27 , the main conclusion being that the auditory system has a limited ability to process rapid variations in pitch ., The trade-off between temporal integration and resolution is not exclusive to pitch perception , but is a general characteristic of auditory temporal processing ., For instance , a long integration time of several hundred milliseconds is required to explain the way in which the detectability and perceived loudness of sounds increases with increasing sound duration 28 , 29 ., In contrast , much shorter integration times are necessary to explain the fact that the auditory system can resolve sound events separated by only a few milliseconds 28–30 ., Therefore , it appears that the integration time of auditory processing varies with the stimulus and task ., Previously it was proposed that integration and resolution reflect processing in separate , parallel streams with different stimulus-independent integration times 28 ., More recently , in order to reconcile perceptual data pertaining to temporal integration and resolution tasks , it was suggested that the auditory system makes its decisions based on “multiple looks” at the stimulus 31 , using relatively short time windows ., However , to our knowledge no model has yet quantitatively explained the stimulus- and task-dependency of integration time constants ., Another major challenge for pitch modelling is to relate perceptual phenomena to neurophysiological data ., Functional brain-imaging studies strongly suggest that pitch is processed in a hierarchical manner 32 , starting in sub-cortical structures 33 and continuing up through Heschls Gyrus on to the planum polare and planum temporale 34–36 ., Within this processing hierarchy , there is an increasing dispersion in response latency , with lower pitches eliciting longer response latencies than higher pitches 37 ., This suggests that the time window over which the auditory system integrates pitch-related information depends on the pitch itself ., However , no attempt has yet been made to explain this latency dispersion ., In this study , we present a unified account of the multiple time scales involved in pitch processing ., We suggest that top-down modulation within a hierarchical processing structure is important for explaining the stimulus-dependency of the effective integration time for extracting pitch information ., A highly idealized model , formulated in terms of interacting neural ensembles , is presented ., The model represents a natural extension of previous autocorrelation models of pitch in a form resembling a hierarchical generative process 38 , 39 , in which higher ( e . g . , cortical ) levels modulate the responses in lower ( e . g . , sub-cortical ) levels via feedback connections ., Without modification , the model can account not only for a wide range of perceptual data , but also for novel neurophysiological data on pitch processing ., The role of the feed-forward process ( solid lines in Figure, 1 ) is to predict the pitch of the incoming stimulus ., The perceived pitch of periodic sounds corresponds approximately to the reciprocal of the repetition period of the sound waveform ., This is why temporal models of pitch perception , such as autocorrelation models , usually analyze the periodicities of the signal within the auditory-nerve channels , and then use these periodicities to derive a pitch estimate by computing the reciprocal of the periodicity that is most prevalent across frequency channels 2 ., The cochlea in the inner ear acts as a frequency analyzer , in that different sound frequencies activate different places along the cochlea , which are in turn innervated by different auditory nerve fibres 1 ., Thus , the cochlea can be modelled as a bank of band-pass filters ., In the current model , each cochlear filter was implemented as a dual resonant nonlinear gammatone filter , which accounts for the sound level-dependent non-linear properties of cochlear processing 40 ., The filter output was then passed through a hair cell transduction model 41 to simulate the conversion of the mechanical cochlear response into auditory-nerve spiking activity ., The model was implemented using DSAM ( Development System for Auditory Modelling http://www . pdn . cam . ac . uk/groups/dsam/ ) ., It contained a total of 30 frequency channels with centre frequencies ranging from 100 to 10000 Hz on a logarithmic scale ., The hair cell transduction model generates auditory-nerve spike probabilities , p ( t , k ) , as a function of time , t , in each frequency channel , k ., The first processing stage ( open boxes in Figure, 1 ) computes the joint probability that a given auditory nerve fibre produces two spikes , one at time t and another at t-l , where l is a time delay or lag 10 ., These joint probabilities are generated by computing the cross-product of the auditory-nerve firing probability , p ( t , k ) , with time-delayed versions of itself for a range of time delays ., The cross-products are then summed across all frequency channels , k , to generate the output of the first stage of the model A1 ( t , l ) : ( 1 ) The activity at the second processing stage , A2 ( t , l ) ( green circles in Figure 1 ) , is computed as a leaky integration , ( i . e . , a low-pass filter using an exponentially decaying function 42 ) of the input activity , A1 ( t , l ) , using relatively short time constants , τ2 ., It may therefore be assumed to represent sub-thalamic neural populations 43–46 ., The time constants at the second stage are lag-dependent ( τ2\u200a=\u200aτ2 ( l ) ) , as suggested by recent psychoacoustic studies 23 , 37 ., However , for clarity , the lag dependency will not be explicitly stated in the following equations ., In the third stage , A3 ( t , l ) ( red circles in Figure 1 ) , the output of the second stage is integrated over a longer time scale , τ3 , as suggested by neuroimaging studies of pitch in the cortex 37 , 47 ., This stage is assumed to be located more centrally ., Both integration stages can be simply described as time-varying exponential averages , ( 2 ) In equation ( 2 ) , Δt is the time step of the integration and En ( t ) is the instantaneous exponential decay rate of the response at each integration stage ( En ( t ) ≤τn ) , which will henceforth be referred to as the effective integration window ., Establishing an appropriate time constant is as has been mentioned one of the major difficulties in formulating a general model of pitch perception ., Hence , the value of En ( t ) in the model proposed here is not constant but is controlled by changes in the properties of the stimulus ., The control of En ( t ) will be explained below ., The factors gn ( t ) normalize the input to each stage by the corresponding integration window ( g2≡1; g3 ( t ) =\u200aE2 ( t ) /τ2 ) ., At each time step An ( t , l ) will have a maximum at some value of l which we will write as Ln ., The inverse of this lag for the output of stage 2 , 1/L2 ( t ) , represents the intermediate pitch prediction of the model ( see Figure 1 ) ., Similarly , the inverse of the lag corresponding to the maximum response in stage 3 , 1/L3 ( t ) is the final pitch prediction ., For convenience , we refer to the final pitch prediction from the preceding time step 1/L3 ( t-Δt ) as the pitch expectation , 1/LE ., In all simulations presented in the current study , we used 200 lags , with reciprocals logarithmically distributed , representing pitches between 50 to 2000 Hz 48 ., As an example , Figure 2 shows the model response to a sequence of pure tones ( Figure 2A ) with random frequencies and durations ., Figure 2B shows the first stage of the model A1 ( t , l ) and Figure 2C the effective integration windows ., Figure 2D shows the final model output; the red colour highlights the lag-channels with strong responses ., The lag of the channel with the maximum response at a given time corresponds to the reciprocal of the pitch predicted by the model ., Note that the response A3 ( t , l ) in Figure 2D was normalized to a maximum of unity after each time step and mapped exponentially onto the colour scale to make the plot clearer ., However , this transformation is monotonic and thus does not affect the model predictions ., The necessity for stimulus-driven modulation of the effective integration time , En ( t ) , becomes clear from a consideration of existing autocorrelation models ., If E2 ( t ) were constant over time , i . e . , E2 ( t ) ≡ τ2 , then A2 ( t , l ) would correspond to the summary autocorrelation function ( SACF ) proposed by Meddis and colleagues 6 , 7 ., If , in addition , E3 ( t ) ≡ τ3 then A3 ( t , l ) would represent an additional leaky integrator with a longer time constant ., This is equivalent to the cascade autocorrelation model proposed by Balaguer-Ballester et al . 13 ., The right panel in Figure 3A illustrates the success of the purely feed-forward model in response to a click train stimulus with alternating inter-click intervals 49 , 50 ., The arrow indicates the average pitch reported by listeners ., The pitch of such alternating click train stimuli has been difficult to predict with autocorrelation models consisting of only one integration stage with a short time constant ( see right panel in Figure 3B ) ., However , the longer time scale used in the second stage of the cascade autocorrelation model prevents the detection of rapid pitch changes such as in the sequence of pure tones shown in Figure 2 ., The left panel in Figure 3A clearly shows that the cascade autocorrelation model fails to distinguish the pitches of individual tones in the tone sequence used in Figure 2 , while the left panel in Figure 3B shows that the SACF model does so fairly well ., Therefore , stimulus-dependent changes in the effective integration windows are required ., Autocorrelation is usually considered to be a simplified phenomenological model of pitch perception , which is not straightforward to implement in a biologically plausible way 8 , 43 ., This is also the case for the proposed model ., Nevertheless , an alternative , more formal way to express the second and third model stages ( equation, 2 ) is shown in equation ( 3 ) , below ., This is equivalent to an expression for the response of a neural population which integrates activity from the previous stage 42: ( 3 ) The dot indicates a partial temporal derivative and τn is defined as the processing time constant of an idealized homogeneous population of neurons at stage n ., The “activation” functions , Ψn , in equation ( 3 ) , which typically use a fixed sigmoid function in standard models of neural assembles 51 , are in the model proposed here time-dependent multiplicative gains: ( 4 ) where ω1/λ1≡0; and ωn , λn are defined in the next section ., Substituting equation ( 4 ) into equation ( 3 ) and integrating , allows us to obtain the effective integration windows , En ( t ) , used in equation ( 2 ) : ( 5 ) In contrast with the feed-forward model , the goal of the feedback processing ( dotted lines in Figure, 1 ) is to detect unexpected changes in the input stimulus , such as the offset of a tone in a sequence , and to modulate the integration times involved in the feed-forward processing when such changes occur ., In the case where the stimulus is constant the pitch predictions at successive time steps will not differ ., However , if the stimulus changes then the height of the peak corresponding to the current pitch prediction 1/Ln ( t ) will change from one time step to the next ., A mismatch between the pitch predictions at each level and the pitch expectation therefore indicates a change in the input stimulus ., A stimulus change typically requires a fast system response , so that information occurring around the time of the change can be updated quickly; this corresponds to using small En ( t ) values ., Thus , during periods when there is a significant discrepancy between the current and expected pitch estimates , the effective integration time windows at both integration stages should become very short , so that the “memory” component of the model response is reduced to near zero and essentially reset ., Similar rapid changes of activity in response to variations in the input have been previously reported in neural ensemble models 51 , 52 ., Figure 2C illustrates the dynamics of E2 ( t ) ( solid green line ) and E3 ( t ) ( dotted red line ) in response to a random tone sequence , the spectrogram of which is shown in Figure 2A ., After the end of each tone , both time constants , E2 and E3 , decrease for a brief period of time and then recover back to their maximum values ( En ( t ) ≈τn ) when the next tone begins ., As E2 is lag-dependent , the values plotted in Figure 2C represent the integration time constant at the lag , L2 ( t ) , corresponding to the current maximum of A2 ( t , l ) ., The small overshoots after the initial dips in E2 reflect transient variations in L2 before a new stable prediction is achieved ., The effective integration windows , En ( t ) , can vary over a large range of values , far exceeding the range of plausible neural time constants ., However , it should be noted that the neural processing time constants used in the model , τn ( see equation 3 ) , only take on biologically plausible values ( shown in Table 1 ) ., The effective integration windows , derived from the activation functions ( equation 5 ) , do not represent neural processing time constants ., This aspect will be further addressed in the Discussion section ., During the steady-state portions of each tone , the model essentially behaves like the cascade autocorrelation model 13 ., The feedback mechanism simply allows the model to adapt quickly to changes in the stimulus ., A natural measure of the mismatch between pitch expectations and pitch predictions is the relative error gradient of the maximum response in An ( t , Ln ) , ( 6 ) where the expected lag , LE , is fixed in the temporal derivative; and Ln ( t ) is the lag corresponding to the maximum response at each time step as defined earlier ., The gradient at stage three in the model , is an “error” measure: if there is mismatch between the expected pitch estimate and the current prediction , i . e . , LE ≠ L3 ( t ) , then ρ3<0 ., Similarly , at the second stage , ρ2<0 represents a mismatch , or error , between the expected pitch and the current intermediate prediction at stage two , 1/L2 ( t ) ., The goal of the feedback modulation triggered by changes in the stimulus is to adjust the effective time constants En ( t ) ., The error gradients ρn give us a measure of stimulus change therefore , when ρn is negative enough ( compared to a threshold value θn ) there is a discrepancy between the pitch prediction and the pitch expectation which requires that the time constants be adjusted ., This is achieved by temporarily activating the recurrent term in equation 4 , i . e . , by defining ( 7 ) where Θ ( x ) is the Heaviside function ( equal to unity if x>0 and zero otherwise ) and θn are small positive thresholds for the error terms , ρn ., For example , during the gaps between tones in a sequence of tones , ρn<−θn and the gains ωn ( t ) /λn ( t ) temporarily become nonzero , thereby modulating the effective temporal integration windows , En ( t ) ., This approach leads to a problem with the model as described so far in that the response to stimuli where there is a continuous discrepancy between expectations and predictions , very short effective time windows ( En ( t ) ≪τn ) produce oscillatory responses which do not correspond to the stable pitch perceived by listeners ( see , for example , Figure 3B , right panel ) ., The dynamics of the ‘adaptation’ variable , λn ( t ) , defined in equation 8 below , serve to modulate uncontrolled corrections to the effective integration windows ., Initially the value of λn ( t ) is small ( λn ( 0 ) ≪τn ) so that when change is first detected En ( t ) also becomes small ( equation 5 ) ., However , in situations where there is a continuous mismatch between the predicted and the expected pitch , λn ( t ) grows and En ( t ) recovers to a value closer to τn ., Then , when there is no longer any discrepancy between expectation and prediction , λn ( t ) recovers to a small value again but without affecting En ( t ) because , in the absence of a mismatch , ωn\u200a=\u200a0 ., Therefore , the dynamics of λ are described in general by: ( 8 ) Where η and μ are the constants that control the rate of increase in λ during periods of mismatch and the rate of decay in λ during periods where no mismatch occurs ., Figures 2C and 8B illustrate two opposite instances of the effect of this top-down processing ., In response to a sequence of tones , the effective integration windows shorten precisely at the tone offsets before returning to their maximum values , τn , during the tones ( Figure 2C ) ., In response to a click train with alternating inter-click intervals ( Figure 8B ) , the window length settles to a maximum value after a longer period of transient fluctuations ., Figure 4 illustrates the discrete processing steps of the model in the form of a flowchart ., Table 1 gives the set of parameter values used in the simulations ., Further neurobiological justifications for the model are presented in the Discussion ., A Matlab-based software implementation of the model is freely available from the first author ., Hall and Peters experiment highlighted an unsolved problem concerning the balance between synthetic and analytic listening in response to a sequence of pure tones 14 , 15 ., The stimuli of the pioneering Hall and Peters study 14 consisted of three tones played sequentially either in quiet ( Figure 5A , left panel ) or against a background of white noise ( Figure 5A , right panel ) ., Each tone lasted 40 ms and was separated from the following tone by a gap of 10 ms . Tone frequencies were 650 , 850 and 1050 Hz ( similar results were obtained with a harmonic sequence ) ., The overall level of the noise was about 15 dB above the level of the tones ., The individual tones in the sequence were perceived in both conditions ., In the experiment , listeners were instructed to match the lowest pitch that they perceived , and in the quiet condition , this was the first of the tones ( 650 Hz ) ., However , in the noise condition , the non-simultaneous tones combine to create a lower global pitch of about 213 Hz , which is not perceived in the quiet condition ., Recently , it was shown that the cascade autocorrelation model , which used two fixed integration stages , could account for the perception of the global pitch in the noise condition when the time constant of the second stage was long enough 13 ., However , the same , long , integration stage could not be used to simultaneously predict the perception of the individual tones in quiet ., Figure 5B shows the responses A3 ( t , l ) over time ., As in Figure 2 , the responses after each time step have been normalized for visualization purposes ( however , it should be noted that their real magnitudes , which are close to zero during the silent gaps , are not evident in the figure ) ., The maximum of A3 ( t , l ) correctly predicts the pitches perceived in quiet , which correspond approximately to the frequencies of the individual tones at each moment in time ( left plot ) ., Thus , the peak in the profile of the final response at the end of the stimulus correctly reflects the period of the last tone of the sequence at 0 . 95 ms , and the lowest reported pitch corresponds to the first tone in the sequence ( horizontal arrow in Figure 5B ) ., However , when background noise is present ( Figure 5B , right plot ) , a global pitch gradually emerges ( horizontal arrow in the right plot ) , and the peak in the final response occurs at the reciprocal of the perceived pitch of 213 Hz ( 4 . 7 ms , right panel of Figure 5C ) ., The above results match precisely the listeners responses in this study 14 ., Many other studies have explored more explicitly the characteristics of temporal integration in pitch perception ., Earlier findings showed that the accuracy of pitch discrimination increases with stimulus duration 1 , 19 , depends on the resolvability of the harmonics 20 , and on the sudden onsets and offsets of overlapping tones 21 , 22 ., In Figure 6 , another example of the models ability to simulate the integration of pitch information across noise-filled gaps is presented 17 , 18 ., Figure 6A shows a sequence of two unresolved complex tones of 20-ms duration , containing 100 harmonics of a 250-Hz base frequency , high-pass filtered from 5500 to 7500 Hz ., After the first of the tones , there was either a short silent gap ( silent-gap condition ) or a noise-filled gap , having a similar mean level to the harmonic complex ( noise-burst condition ) ., Background noise was added to mask distortion products ., In their study , Plack and White reported that subjects perceived pitch continuity through the gap in the noise-burst condition , but not in the silent-gap condition 17 ., The normalized model output A3 ( t , l ) ( Figure 6C ) is qualitatively consistent with a continuous pitch sensation in the noise-burst condition ( right panel ) , which does not occur in the silent-gap condition ( left panel ) ., Conditions under which pitch encoding is affected by the presence of other sounds have been also studied using non-simultaneous stimuli such as temporal “fringes” ( consisting of complex tones played immediately before and after a “target” tone ) 16 , 53 , 54; and by mistuning delayed harmonics of the complex 12 , 55–57 ., The model described here also accounts for the “reset” of pitch information occurring for large frequency differences between fringe and target tones 53 ( data not shown ) ., The previous section shows the models ability to generate stimulus-dependent changes in the effective time scale of temporal integration for extracting pitch information ., This raises the question of whether the ability of the model to adjust the effective integration windows could also account for the temporal resolution of the auditory system ., While there is substantial evidence for temporal integration in pitch perception , temporal resolution in pitch perception is perhaps still poorly understood ., Therefore , we conducted a psychoacoustic experiment specifically to investigate the temporal resolution of pitch information ., It should be stressed that this experiment was conducted independently of the model development and was subsequently used to test the models predictions ., Figure 2B showed that the model uses very short integration times for pitch information when a change in pitch occurs ., However , it is possible to construct a class of stimuli , in which the periodicities change continually over very short time scales but which nevertheless elicit a single pitch 49 , 50 , suggesting that pitch information is integrated across these rapid changes in periodicity ., The stimuli in question are high-pass-filtered click trains where the interval between successive clicks varies ., Previously we showed that the cascade autocorrelation model with fixed integration times 13 predicted the pitch percept elicited by a range of click train stimuli , which had proved problematic for conventional autocorrelation models 49 , 50 , 60–63 ., Here , we test whether the current model ( which generalizes the model reported in 13 by including variable integration times ) retains this ability ., This is an important question , because a rapid reset of pitch information is apparently in contradiction with the long-term integration used in 13 , as was illustrated in the Methods section ( Figure 3 ) ., As an example , Figure 8 shows the response of the model to one of these stimuli ., In this case , the inter-click intervals alternate between 4 and 6 ms , but listeners usually report a single pitch somewhere in between these extremes and closer to the longer interval ., Carlyon et al . 49 , 50 presented the click trains with a duration of 400 ms . Stimuli were band-pass-filtered with cut-off frequencies of 3900 and 5300 Hz in order to avoid the harmonic spectral components being resolved by the cochlear filters ., They also added a pink noise to avoid audible distortion products ., Carlyon et al . 50 demonstrated that the combined auditory nerve responses , measured as compound action potentials ( CAPs ) , were stronger for the largest inter-click interval ( 6 ms ) than for the shorter interval ( 4 ms ) ., Therefore , they suggested that a population of more central neurons , which respond only when their inputs exceed a fixed threshold value , would respond preferentially to the longer intervals , thereby explaining listeners preference for matching a pitch close to 6 ms . Figure 8C shows that the predicted pitch of the model ( red highlight ) varies almost randomly for approximately 80 ms and then progressively stabilizes at a lag in the region of 5 . 5–6 ms ( see horizontal arrow in Figure 8C ) ., Thus , the model prediction is in good agreement with the geometric average of the reported pitch values ( shown by vertical dashed line in Figure 8D ) ., While the final snapshot of A3 ( tfinal , l ) ( Figure 8D ) peaks close to the geometric mean of the reported pitches ( vertical dashed line ) , there are other prominent peaks in A3 ( tfinal , l ) close to this maximum; this is consistent with the large variability in reported pitches for these alternating click trains ., A prediction of the model yet to be tested is that no reliable pitch estimate would be possible for stimuli shorter than 100 ms . To conclude , it is worth remarking that this model can similarly account for the pitches of the other click train stimuli considered in 13 ., The model proposed here is not a formal model of neural populations; nevertheless , it is neurophysiologically based ( see Methods and Discussion sections ) ., This raises the question as to whether the model can explain aspects of the responses of neural ensembles in a pitch perception task ., Krumbholz et al . 37 identified a transient neuromagnetic response in Heschls Gyrus , which they termed the “pitch onset response” ( POR ) ., In their experiment , they used iterated rippled noise ( IRN ) stimuli with delays of 4 , 8 , 12 and 16 ms . IRN differs from the RN stimulus described previously in that the delay-and-add process is iterated N times ., Increasing the number of iterations , N , increases the degree of serial correlation and therefore the pitch strength ., Figure 9A shows the spectrogram of an IRN stimulus with a 12 ms delay and 16 iterations ., Neuromagnetic responses were recorded to the onset of an IRN , which was directly preceded by an uncorrelated noise with the same energy and spectral composition ., Recordings showed that the transition from noise to IRN produced a reliable POR with a mean latency of approximately four times the delay , d , plus a constant offset of about 120 ms ( left panel in Figure 9D , solid blue line ) ., The authors concluded that the POR reflects pitch-related processing within Heschls Gyrus in the human auditory cortex ., This has been supported by other more recent studies 36 ., Figure 9B shows the output of the model , ( A3 ( t , l ) without any normalization , in contrast to previous plots ) , for the example shown in Figure 9A ., After some time the maximum of A3 ( t , l ) ( red colour ) stabilises and becomes prominent ., The predicted pitch is the reciprocal of L3\u200a=\u200a12 ms , which corresponds to the delay of the IRN stimulus ., However , the maximum value of A3 ( t , L3 ) in Figure 9B emerges gradually ., Therefore , there seems to be no obvious correlate of the latency at around 150 ms of the measured cortical response in the model ., A number of previous studies have suggested that the temporal derivative of the neural population responses at lower levels of processing might correlate with the measured activity in higher ( i . e . , cortical ) levels 44 , 45 , 64 ., Therefore we investigated whether the latency of the pitch onset response might correspond to the latency of the peak in the derivative of A3 ( t , L3 ) ., Here , we calculated a smoothed version of the temporal derivative of A3 ( t , L3 ) by convolving A3 ( t , L3 ) with the first differential of a Gaussian function ( representing connection efficacies to higher areas 44 , 45 ) ., We then used the first maximum of this smoothed derivative to predict the latencies of the POR for different pitch values ., Figure 9C illustrates the smoothed derivative of A3 ( t , L3 ) for the example shown in Figure 9A ( red dotted line ) ., The derivative has a maximum at approximately 168 ms , which is consistent with the POR latency for this condition ., The green solid line shows the variance of A3 ( t , l ) ( calculated at each fixed t ) for the same stimulus ., It appears that the variance of A3 ( t , l ) , which might be taken to represent the uncertainty of the pitch estimate , reaches a minimum at a similar time as the derivative of A3 ( t , L3 ) reaches a maximum ( in general , however , the smoothed derivative is a more accurate predictor of the experimental latencies ) ., The red dotted line in Figure 9D ( left plot ) shows the time at which the smoothed derivative of A3 ( t , L3 ) reaches its first maximum as a function of pitch value , which appears to correlate remarkably well with the POR latencies ( solid line ) ., Krumbholz et al . 37 also found that the POR latency mainly depended on the delay of the IRN stimulus and was influenced little by the number of iterations ., The right panel in Figure 9D shows the latencies when the delay is fixed at 16 ms and the number of iterations varies ( solid line ) ., Consistent with experimental results , the number of iterations of the stimulus do not significantly affect the smoothed derivative of A3 ( t , L3 ( t ) ) ( dotted line ) ., To conclude , it is worth mentioning that the model also accounts for the minimum duration of IRN stimuli for reliable perceptual discrimination of the pitch , also reported in 37 ., The solid line in Figure 10 indicates the average perceptual results ., The dashed line shows the duration of the transient period in A3 ( t , L3 ) , i . e . , the time window during which the pitch prediction is not stable ( e . g . around 100 ms in the stimulus shown in Figure 8C ) ., Clearly , the model simulations match the data extremely well ( dashed line in Figure 10 ) ., Therefore , the initial period in which the model output varies rapidly seems to correlate with unstable pitch perception ., This model prediction might be

Introduction, Methods, Results, Discussion

Pitch is one of the most important features of natural sounds , underlying the perception of melody in music and prosody in speech ., However , the temporal dynamics of pitch processing are still poorly understood ., Previous studies suggest that the auditory system uses a wide range of time scales to integrate pitch-related information and that the effective integration time is both task- and stimulus-dependent ., None of the existing models of pitch processing can account for such task- and stimulus-dependent variations in processing time scales ., This study presents an idealized neurocomputational model , which provides a unified account of the multiple time scales observed in pitch perception ., The model is evaluated using a range of perceptual studies , which have not previously been accounted for by a single model , and new results from a neurophysiological experiment ., In contrast to other approaches , the current model contains a hierarchy of integration stages and uses feedback to adapt the effective time scales of processing at each stage in response to changes in the input stimulus ., The model has features in common with a hierarchical generative process and suggests a key role for efferent connections from central to sub-cortical areas in controlling the temporal dynamics of pitch processing .

Pitch is one of the most important features of natural sounds ., The pitch sensation depends strongly on its temporal context , as happens , for example , in the perception of melody in music and prosody in speech ., However , the temporal dynamics of pitch processing are poorly understood ., Perceptual studies have shown that there is apparently a wide range of time scales over which pitch-related information is integrated ., This multiplicity in perceptual time scales requires a trade-off between temporal resolution and temporal integration , which is not exclusive to pitch perception but applies to auditory perception in general ., As far as we are aware , no existing model can account simultaneously for the wide range and stimulus-dependent nature of the perceptual phenomenology ., This article presents a neurocomputational model , which explains the temporal resolution–integration trade-off observed in pitch perception in a unified fashion ., The main contribution of this work is to propose that top-down , efferent mechanisms are crucial for pitch processing ., The model replicates perceptual responses in a wide range of perceptual experiments not simultaneously accounted for by previous approaches ., Moreover , it accounts quantitatively for the stimulus-dependent latency of the pitch onset response measured in the auditory cortex .

otolaryngology/audiology, computational biology/computational neuroscience

journal.pntd.0002171

Transcriptional Profiling of the Circulating Immune Response to Lassa Virus in an Aerosol Model of Exposure

Lassa virus ( LASV ) is a segmented negative-strand RNA virus and a member of the Arenavirus genus ., LASV is a human pathogen that is endemic to several countries in West Africa ., It is estimated to infect more than 300 , 000 people each year , killing over 3 , 000 with fatality rate for Lassa fever ( LF ) being approximately 15% in hospitalized patients 1 ., In several outbreaks 50% case fatality have been reported 2 ., LF was initially described as Lassa hepatitis and liver pathology is a significant histological finding in LF patients 3 and in animal models of LF infection 4 ., The natural host of LASV is a highly commensal rodent , Mastomys natalensis 5 , 6 , and infection with LASV is thought to occur by direct contact with the host or via aerosol ., In part because of its ability to be transmitted through aerosol means 7 , as well as potential for high lethality , LASV has been characterized as a Category A bioweapon agent 8 ., There are currently no FDA-approved vaccines or antiviral drugs to treat LASV infection ., Ribavirin treatment has been suggested to reduce morbidity in infected patients 9 when initiated within few days of disease onset 10 ., Ribavirin is often poorly tolerated and has been associated with a number of severe adverse events ., Given the number of side effects associated with this drug , the potential for severe adverse events , and the limited efficacy , there is a strong need for more effective and safer drugs as well as vaccines ., In order to develop and test candidate countermeasures , it is critical that there be well-characterized animal models that accurately reflect the human disease ., Currently there are several animal models for LASV infection ., These include humanized mice expressing human HLA-A2 . 1 instead of murine MHC Class I gene 11 , IFN receptor knock-out 12 mice that can be infected with LASV , and Strain 13 guinea pigs ., None of these models completely reproduce the human disease 13 ., As a result , more emphasis has been placed on several NHP species that are susceptible to LF 14–21 ., Both African green monkeys and rhesus macaques show a lethal response to low challenge doses but only a partial response to high challenge doses 14 , 22 ., Cynomolgus macaques have been shown to be uniformly susceptible to lethal LASV infection at low and high challenge doses 4 , 16 , 17 , 23 ., The development and characterization of these animal models has facilitated the examination of host responses to LASV exposure ., In the work presented here , we have used a whole genome microarray-based approach to determine the temporal host response to exposure in PBMCs from cynomolgus macaques following aerosol LASV exposure ., Sequential sampling throughout the disease course provided us the opportunity to characterize the circulating immune response to LASV during different stages of infection ., Furthermore , we were able to refine this characterization through analysis of immune cell subsets to define the responses of specific effector cells ., These analyses showed that there are both rapid and delayed transcription events following LASV exposure , including the upregulation of Toll-like receptor signaling pathways and innate antiviral transcription factors ., Our data show that the immune response to LASV involves the expression of a large number of immunosuppressive events in exposed NHPs leading to an inefficient adaptive immune response observed in LASV infections ., Peripheral blood mononuclear cells ( PBMCs ) were isolated from blood pre-diluted with saline using ACCUSPIN System-Histopaque-1077 tubes as per manufacturers recommendations , and subsequently lysed in TRI Reagent LS ( Sigma-Aldrich ) at USAMRIID ., PBMCs were processed for microarray analysis as described earlier 24 ., Briefly , total RNA was extracted from the TRI Reagent LS samples , then amplified using the Low-Input Quick Amp Labeling kit ( Agilent ) and hybridized to Whole Human Genome Oligo Microarrays ( Agilent ) in a 2-color comparative format along with a reference pool of messenger RNA ( mRNA ) ., Images were scanned using the Agilent High-Resolution Microarray Scanner and raw microarray images were processed using Agilents Feature Extraction software ., The quality of the microarray hybridization for pre-exposure samples obtained from three out of fourteen NHPs in the study was lower than needed for background corrections and normalization ., Thus , DNA Microarray data from these three NHPs was not used for data analysis ., In summary , out of the 46 samples ( from fourteen NHPs ) hybridized to microarrays , a subset of 30 ( from eleven NHPs ) were used in the subsequent analyses as shown in Figure 1A ., The resulting microarray dataset has been submitted to the Gene Expression Omnibus ( GEO ) database , under series record GSE41752 ., Subsets of immune cells ( CD4+ , CD8+ , CD14+ , and CD20+ ) were separated from PBMCs by sequential positive selection using nonhuman primate microbeads ( Miltenyi Biotec , Auburn , CA ) as per the manufacturers recommendations ., Although cell separation procedures can cause activation that may affect transcriptional changes , in our study , a number of steps were taken to mitigate these effects ., Cells were kept cold , and pre-chilled buffers were used to reduce nonspecific antibody binding , cell surface capping and activation ., Additionally , all the pre-exposure ( day −8 ) as well as post-exposure samples were separated using same separation procedure , and all changes in transcription of the pre-exposure samples were subtracted from the post-exposure samples ., Therefore , we believe there was little contribution of the separation procedure on the observed transcription ., Following incubation with FcR blocking reagent , CD20+ cells were isolated using MS columns and the flow-through fraction was then utilized for the CD14+ isolation ., The CD14 flow-through fraction was used for the CD4+ isolation , and the CD4 flow-through fraction for the CD8+ isolation ., To increase the purity of the CD20+ and CD14+ magnetically labeled fractions , these were passed over two prepared columns ., Aliquots from positive fractions were retained for determination of cell numbers as well as assessment of purity using flow cytometry; the remainder was lysed in TRI Reagent LS ., Data were first background-corrected to remove noise from background intensity levels , and afterwards were normalized within the arrays using the Limma package in R 25 ., After normalization , the reference and experimental samples were compared to generate log2 fold-change values that represent a change in mRNA expression ( either positive or negative ) ., At this step , the internal array control probes were removed ., Each array was then further normalized using the pre-exposure control array for that animal to remove monkey-specific expression changes from baseline ., A comparison of gene expression was done for day −8 and day 0 samples and there seems to be no difference in the gene expression ( data not shown ) ., The resulting dataset was filtered for differential expression and annotated with gene names ., The dataset was hierarchically clustered using the Cluster 3 . 0 26 and visualized using Java Treeview 27 ., Functional annotations of gene clusters were assigned using the Database for Annotation , Visualization and Integrated Discovery ( DAVID ) ( http://david . abcc . ncifcrf . gov/ ) 28 ., The p-values reported are the value reported by DAVID and are based on the EASE score ., The EASE score is an alternative name of Fisher Exact Statistics used in the DAVID system , referring to a one-tail Fisher Exact Probability Value for gene-enrichment analysis ., Cytokines were assayed in the plasma of LASV exposed NHPs using a NHP magnetic 23-plex multiplex assay ( Millipore EMD ) in accordance with manufacturers instructions ., Briefly , samples from both pre- and post-exposure time points were assayed in triplicate and washed using a Bio-Rad Bio-Plex Pro II Wash Station equipped with a magnetic manifold ., Data were acquired using a Bio-Rad Bio-Plex 3D system and analyzed using Bio-Plex Manager 6 . 0 software and a 5-parameter logarithmic fit ., Cytokine levels in assayed samples were derived from the standards run for each assay plate and presented as plasma cytokines in pg/mL ., Cytokines/chemokines assayed included granulocyte-macrophage colony-stimulating factor ( GM-CSF ) , interferon gamma ( IFNγ ) , Interleukin ( IL ) -1 beta ( IL1β ) , IL1 receptor antagonist ( IL1RA ) , IL2 , IL4 , IL5 , IL6 , IL8 , IL10 , IL12/23 ( p40 ) , IL13 , IL15 , IL17 , IL18 , monocyte chemoattractant protein-1 ( MCP1 ) , macrophage inflammatory protein ( MIP ) -1 alpha ( MIP1α ) , MIP-1 beta ( MIP1β ) , transforming growth factor-alpha ( TGFα ) , vascular endothelial growth factor ( VEGF ) , sCD40L , and tumor necrosis factor-alpha ( TNFα ) ., RNA was isolated from serum of LASV exposed NHPs prepared with TRI Reagent LS ( Sigma-Aldrich ) ., The aqueous phase was extracted using Phase Lock heavy gel tubes ( 5 Prime ) and 1-Bromo-3-chloropropane ( BCP , Sigma-Aldrich ) and mixed with 70% ethanol ., Following 5 min incubation at room temperature , it was added to an RNeasy column and extracted according to the manufacturers recommendations ( QIAGEN ) ., RNA was eluted through two consecutive additions of 50 µL of nuclease-free water and stored at −80°C until analysis ., One-step quantitative real-time RT-PCR reactions were performed on a LightCycler 480 ( Roche , Indianapolis , IN , USA ) in 20 uL volumes with 5 uL of purified RNA and the Superscript II One-Step RT-PCR System ( Life Technologies ) ., Primers and probe were specific for the LASV GP gene Forward , 900 nM: TgCTAgTACAgACAgTgCAATgAg; Reverse , 900 nM: TAgTgACATTCTTCCAggAAgTgC ( Oligos Etc . , Wilsonville , OR ) ; Probe , 200 nM: TgTTCATCACCTCTTC-MGBNFQ ( Applied Biosystems ) ., Cycling conditions were reverse transcription at 50°C for 15 minutes , and denaturation at 95°C for 5 minutes; then 45 cycles of 95°C for 1 second , 60°C for 20 seconds , followed by a single acquisition; and a final cooling step of 40°C for 30 seconds ., Absolute quantification was compared to a viral RNA standard using LC480 software ( version 1 . 5 . 0 . 39 ) and a standard calibrator on each plate ., We present viremia data as PFU equivalents/mL using a 10∶1 PCR genome equivalent:PFU ratio that has been previously determined 29 and validated on human LASV samples ., RT-PCR assays were carried out to quality check and validate our findings on the DNA microarrays ., RT2 Profiler PCR Array from QIAGEN was used to run the RT-PCR ., 125 genes of interest were plated on the custom array along with control genes ., RNA extracted from the PBMCs ( as described under RNA Processing and DNA Microarrays ) was used for RT-PCR Array ., RNA samples collected at three different time points from each animal were run on one plate ., In all , six custom plates ( each with a copy of the same probes for 125 genes ) with samples from six different animals were run in this experiment ., The RT-PCR experiment was performed as directed by RT2 RNA QC PCR Array Handbook 2012 ( QIAGEN ) ., Plates were then run on an ABI 7900 HT qPCR system ( 10 minutes at 95°C , 15 seconds at 95°C followed by 1 minute at 60°C×60 cycles ) ., Following the PCR run for all 6 plates , the threshold was made uniform to be consistent among all the plates ., Ct values for each sample were obtained ., Results were interpreted using SDS software version 2 . 4 and data analysis software from SA Biosciences ., Finally , data from different animals on a given day were pooled together and averaged ., Data were then presented as fold change over day −8 expressions ( Figure S4 ) ., Prior studies in NHPs have described the course of LASV infection ., However , most of these models have focused on disease caused by intramuscular ( IM ) injection ., Only one study used aerosol model of LASV exposure 7 ., Because human Lassa infection is likely caused by aerosol contact , we were interested in studying the immune response to infection in an aerosol model of exposure ., To determine whether the disease progression following exposure to Lassa is similar to studies that carried out exposure to Lassa via IM injection , a pilot confirmation of virulence study was performed in which four NHPs were exposed to LASV via aerosol ., At the target dose of 1 , 000 PFU , the actual dose each animal received ranged from 200 to 300 PFU ( Figure S1A ) ., Exposed animals all showed signs of disease and succumbed by day 16 post-exposure , with a mean time-to-death of 14 . 5 days ( Figure S1B ) ., All four NHPs showed increased levels ( 2–9 fold ) of aspartate transaminase ( AST ) and showed signs of anorexia onset and recumbency at late times ( day 11 onwards ) ., These observations and their onset are consistent with low-dose intramuscular LASV challenges in cynomolgus macaques 4 ., Animals experienced neurological signs to include seizures ( three of four NHP ) ( Figure S1C ) ., Following this confirmation that LASV infection via aerosol exposure led to similar expected clinical signs and disease course development when compared to previous studies , we were interested in determining how circulating immune cells responded to LASV infection ., Thus , we participated in a larger study analyzing multiple parameters of infection by sequential sampling of LASV exposed animals throughout the course of disease ., A subset of animals was euthanized at different times post-exposure to understand the temporal progression of disease following LASV exposure ., From this study , we obtained samples from eleven monkeys ( represented by letters A–K in Figure 1A ) that had been exposed to LASV ( Josiah strain ) via the aerosol route ( target 1000 PFU ) ., Prior to challenge , samples were taken for use as pre-exposure baseline controls ( eleven samples at day −8 ) ., The samples we obtained were circulating immune cells ., At increasing times post-exposure , blood samples were collected and PBMCs were prepared from whole blood ., Figure 1A illustrates the distribution of samples in the study ., In the table , samples are sorted by days post-exposure ., Corresponding clinical observations , viremia , and chemistry data that was also collected is briefly summarized in Figure 1B ., The clinical data highlight that there were few signs of disease until 6–8 dpe , when viremia began and increased AST levels began to be observable ., Clinical indications as well as severity increased throughout the disease course , with signs of anorexia and initial neurological signs appearing around 10 dpe ., Based on this data , we conceptually divided the disease course into three separate stages , early ( pre-symptomatic , 2 to <6 dpe ) , middle ( early symptomatic , 6 to <10 dpe ) , and late ( terminal disease , 10 to 12 dpe ) ., These stages and their duration are pictorially illustrated in Figure 1C to highlight their correlation with asymptomatic disease ( early ) , early symptomatic disease ( middle ) , and increasing signs of disease ( late ) ., Overall , 30 PBMC samples were processed and hybridized onto DNA microarrays ., This resulted in the generation of more than 1 . 3 million data points for analysis ., Results from all arrays were computationally analyzed using the Limma software package in Bioconductor , a suite of packages in R . Our initial analysis focused on determining the major changes in mRNA expression over the course of LASV disease ., This analysis showed that when experimental arrays were compared to the pre-exposure controls , more than 2 , 000 genes showed at least a 1 . 5 log2-fold change in their expression pattern in at least three arrays ( Figure S2 ) ., These genes fall into categories such as immune response ( 153 genes ) , defense response ( 142 genes ) , response to wounding ( 113 genes ) , and inflammatory response ( 83 genes ) , each with a p-value <0 . 00001 ., Out of the 2 , 000 differentially expressed genes , 26 . 7% were downregulated and 73 . 3% were upregulated ., These genes clustered into several different patterns that were particularly evident when arrays were grouped into categories of early , middle and late disease , similar to the arrangement of Figure 1A ., The patterns observed did not appear to be due to the contribution of any one animal , as the removal of multiple arrays followed by re-clustering did not change the patterns observed ( data not shown ) ., From this set of 2 , 000 genes that were significantly regulated following LASV exposure , we were particularly interested in the most strongly regulated genes ., Figure 2A shows a clustered heatmap of probes that showed a fold-change of greater than 2 . 5 log2 ( >5-fold change ) following exposure ., Within these highly regulated probes , three major types of regulation are readily visible in the clustered image ., Probes contained in the upper green-boxed region ( expanded in Figure 2D ) showed little change expression early in infection but increased expression late in infection ., Probes in the middle box ( expanded in Figure 2C ) showed upregulation at middle and late times post-exposure ., Probes in the lower box ( also Figure 2B ) showed strong expression early post-exposure that was maintained throughout the course of the study ., Early induced genes were associated with the innate immune response by gene ontology analysis ., Genes induced in the middle of the disease course were mRNAs associated with the inflammatory response ., Genes strongly induced late post-exposure ( Figure 2D ) were associated with cell cycle regulation , cell division , and DNA packaging ., To assess the reproducibility of these findings a number of up and down-regulated genes were assayed by rtPCR ( Figure S4 ) ., The selected genes validated well , illustrating that the answers derived from our array analysis are transferrable to a PCR-based detection format ., We noted that probes associated with genes involved in the innate immune response markers such as TRAIL ( TNFSF10 ) , MX1 , and CCRL2 demonstrated a rapid innate response that was evident before there are clinical signs of disease ( Figure 2B ) ., Early induced genes also included IRF3-responsive genes such as phospholipase A2 gamma , OAS1/2 , and GBP1 30 ., Additionally , we observed that GCH1 showed rapid upregulation ., GCH1 is the rate-limiting enzyme regulating the synthesis of pain hormone BH4 and increased GCH1 is associated with increased BH4 levels ., Patients with LF often complain of increased pain 31 ., Probes that recognized the IL6 gene dominated the list of probes that were upregulated in the middle of the disease course ( Figure 2C ) ., IL6 showed little to no induction at early times post exposure but was strongly upregulated by 6 dpe ., Several pattern recognition receptors like MARCO , TLR4 , TLR7 , NOD2 , and Fc receptor FCGR2A showed a similar time course of transcriptional activation , as do inhibitory receptors for macrophages function like CMKLR1 , CD200R1 , and CD300LF ., Basophil activation marker CD63 was also upregulated beginning around 6 dpe , suggesting the activation of these cells ., There was also transcriptional evidence for significant immune cell movement during this stage of LASV disease ., At 6 dpe , chemotactic markers such as CCR5 , CCL23 , CXCL12 , and TNFAIP6 were upregulated ., This coincident upregulation of innate immune sensors and responses along with repressors of adaptive responses in the form of inhibitor receptors suggests a skewing of the immune response towards the innate ., Genes that were upregulated at late times in the LASV disease course ( Figure 2D ) included a large number of granulocyte markers such as TCN1 , CEACAM molecules , and SIGLEC5 ., Transcripts of exocytic granule components such as BPI , ELANE and the neutrophil chemotaxis promoting receptor FPR1 , are also upregulated late in response to LASV exposure ., Analysis of the cell composition of the blood in the LASV-exposed NHPs at different times post-exposure also suggests the predominance of neutrophils at late times post-exposure ( Figure S3 ) ., In addition to upregulation of neutrophil-specific transcripts , we also detected upregulation of immunomodulatory proteins at late times post-exposure: ORM1 , tyrosine kinase genes like BMX and NTRK1 , myeloid specific siglec3 ( CD33 ) , and enzymes such as CHI3L1 , carbonic anhydrase ( CA4 ) , and the apoptosis regulator , NLRC4 ., When the strongly upregulated genes from Figure 2A were analyzed using Ingenuity Pathway Analysis ( IPA ) , 65 were observed to be connected through transcriptional or protein-protein interactions ( Figure 3 ) ., As was expected from the identification of many innate immune stimulated genes in the early induced population of transcripts , our analysis identified two transcriptional nodes that are upregulated in response to LASV exposure: ( 1 ) IRF3/IRF7 induced genes such as OASs , IFITs , MX1 , and TRIM5; and ( 2 ) STAT1 induced genes such as ISG15 , TRIM25 , GBP2 , TRAIL , and PKR ( EIF2AK2 ) ., The identification of both types of genes suggests that the circulating immune cells are generating a strong innate immune response very early post-LASV exposure ., Interestingly , there was not a strong transcriptional upregulated interferon α , β or γ in PBMCs , despite the expectation that these genes would be strongly upregulated due to the potential signaling through STAT1 ., The identification of IRF3/IRF7 responsive genes in LASV exposure suggested that there was some signaling through either Toll-like receptor ( TLR ) or RIGI-like receptor ( RLR ) ., Consistent with this , we saw significant ( more than 1 . 5 log2 fold ) increases levels of several TLR receptors ( TLR1 through TLR7 ) induced at early times post-exposure ( Figure S5 ) ., Of the upregulated TLRs , transcripts of TLR3 , TLR4 , TLR5 , TLR6 , and TLR7 were the most strongly expressed ., Along with TLR upregulation , we also saw significant increase in the transcripts of RLR genes such as DDX58 ( RIGI ) and DHX58 ( LGP2 ) at early times post-exposure ., The coordinated upregulation of these genes strongly indicates that during challenge with LASV the infected host increases its ability to respond through these pathways ., IPA analysis also identified a collection of genes that are associated with neutrophil granules ., These include three proteases that are upregulated during the course of infection: the matrix metalloprotease MMP9 , the serine proteases neutrophil elastase ( ELANE ) , and cathepsin G ( CTSG ) , as well as the protease inhibitor SERPINB1 ., These proteases have important antimicrobial properties , as do other neutrophil granule proteins increased during LASV infections such as lactotransferrin ( LTF ) and lipocalin 2 ( LCN2 ) ., This correlates with the increase in the neutrophils seen at late times post-exposure ( percentage and counts , Figure S3 ) in the peripheral blood of LASV-exposed NHPs ., This coordinated upregulation of multiple neutrophil granule proteins in the PBMC samples may reflect accelerated granulopoiesis and the mobilization of neutrophil precursors into the blood , consistent with a neutrophillic response to severe LASV infection ., Interestingly , protease upregulation has been noted in other animal models of filovirus infection 32 ., Moving beyond the analysis of highly differentially expressed genes , we were interested in how the gene expression changes in our arrays compared with changes already noted in animal and human models of disease; therefore , we analyzed the expression of IL1β , IL8 , IL10 , IL6 , IL12 , IFNγ , and TNFα ., Earlier reports have followed the expression of these cytokines in LASV-infected animals and humans 4 , 21 , 33 , 34 and have shown that plasma levels of IL8 , IL10 , IL6 , IL10 , TNFα , and IFNγ increase with LASV infection ., IL1β was induced very late ( day 13 onwards ) ., Our data show that IL1β transcripts are down-modulated following LASV exposure ( Figure 4A and 4C ) ., This suppression was clear at early times post-exposure ( animals showing a 1–2 log2 fold decrease ) and persisted throughout the disease course with some variability between animals ., Transcripts of IL6 were upregulated during the middle stage of disease , from 6 dpe ( Figure 4A and 4C ) ., Transcripts of IL8 were not differentially expressed in PBMCs at any stage ( early , middle or late ) of LASV disease ., Similarly , IL10 transcripts were not differentially expressed in the PBMCs of LASV-exposed NHPs , nor were IL12 transcripts ., These results are in line with earlier reported studies on fatal cases of LASV patients 33 ., Transcripts of IFNγ were down , and transcripts TNFα were not differentially expressed in our model of LASV infection; however , during the middle stage of LASV disease , there was an upregulation of the TNFα-induced genes , TNFAIP2 and TNFAIP6 ( Figure 4B and 4C ) ., This finding correlates well with an earlier study on human patients of LF 35 ., In addition to this data showing concordance of our data with previous studies , we also saw differential expression of cytokines that have not been previously reported in the context of LASV infection ., Transcripts of IL21 were slightly upregulated ( 1–1 . 5 log2 fold ) in PBMCs at 10 dpe ( Figure 4D and 4E ) ., Transcripts of IL21 were strongly upregulated ( more than 3 log2 fold ) in the separated CD4 positive cells ( Figure 4G and 4H ) ., Upregulation of IL21 has been reported in LCMV model of HF and has been linked to the clearance of infection but had not previously been noted in LASV infection 36 ., Transcripts of IL23α and IL24 were down-modulated ( 2–1 . 5 log2 fold ) at 3 dpe and 6 dpe respectively , during LASV exposure ( Figure 4D ) ., Interestingly , we detected upregulation ( 1 . 5 log2 fold ) at 6 dpe of transcripts of an immunosuppressive cytokine , IL27 ( Figure 4D and 4E ) ., This upregulation of IL27 was more pronounced ( 4 log2 fold ) at 6 dpe in CD4 positive cells than in the PBMC population ( Figure 4G and 4H ) ., IT is possible that IL27 protein may play an important function in the response to LASV infection ., In addition to these changes in cytokine expression , chemokines such as CCRL2 and CCL23 are also upregulated in the LASV-exposed NHPs ., CCRL2 , a marker of monocytic in-filtration in inflammatory diseases 37 , is significantly upregulated ( 1 . 5–2 log2 fold ) early ( 3 dpe ) following LASV exposure , and CCL23 also known as macrophage inflammatory protein 3 ( MIP3 ) , a potent chemoattractant for T lymphocytes and monocytesis , is upregulated ( more than 3 log2 fold ) at middle times post-exposure ( 8 dpe ) ( Figure 4F ) ., Together , the upregulation of CCRL2 and CCL23 suggests that LASV exposure causes an increase in the call for the recruitment of inflammatory cells ., To understand the relation between the timing of transcript upregulation in PBMCs and the onset of viremia in LASV disease , viral load was evaluated throughout the course of infection by RT-PCR ., Transcripts a selected set of upregulated genes were compared to the viral load in the plasma at different time points in Figure 5 ., Comparisons in Figure 5A highlights that while viremia was not observed until 8 dpe , transcripts of cytokine IL27 showed increased expression earlier ( approximately 6 dpe ) ., More dramatically , innate antiviral response molecules such as IRF7 , STAT1 , and IFIT2 , and chemokines such as CXCL12 are upregulated by 3 dpe ( Figure 5B ) ., This demonstrates that the expression of host transcripts ( immune response ) in response to LASV exposure significantly precedes the onset of circulating viremia in infected NHPs ., We also compared the cytokine gene expression to the cytokine protein expression seen in the plasma of LASV exposed NHPs ( Figure 6 ) ., Cytokine levels in blood samples were determined prior to LASV exposure and throughout the course of disease ., This comparison of gene expression with the actual protein levels of these cytokines ( Figure 6 ) revealed that the increased gene expression of a subset of cytokines correlated with protein expression ( e . g . IL8 , IL6 and IL18 ) ., However , this correlation was not always observed ., For IL8 , an initial correlation of mRNA and protein expression at early times broke down by 12 dpe , where we observed a loss of mRNA expression but protein expression was still readily detectable ., This could be due to either loss of the IL8 producing cell population from the PBMCs due to migration or cell death , or it is also possible that another cell population accounts for the observed protein in the plasma at late times of disease ., We also observed that array analysis correctly characterized proteins that were expressed in response to viral exposure , such as IL1β , IL12 , and IL10 ( data not shown ) ., As LASV disease in humans is associated with an inefficient host immune response to infection 38 , we determined the expression level of several immune suppressive genes ( negative regulators of immune responses ) with the hypothesis that PBMCs would upregulate immunosuppressive genes would be upregulated in PBMCs in response to LASV exposure ., Table 1 describes the upregulation of genes involved in regulating T cell function , such as PDL1 ( CD274 ) 39 and TNFRSF21 40 , that are upregulated within the first 6 dpe ., We also see upregulation of the regulatory players that are involved in intracellular signaling and have been linked to immunosuppressive function ., For example , transcripts of the following were upregulated during LASV disease: ITIM domain containing receptor LILRB2 , an adapter protein DOK3 41 , 42 , an acute phase protein ORM1 43 , an ADP ribosylation factor PARP14 44 , a phosphogluconate dehydrogenase PGD 45 , a translational repressor SAMD4A , a neutrophil elastase inhibitor SERPINB1 46 , and TGFβ induced protein TGFBI ., TGFBI can cause decrease the p53 binding to DNA leading to down-modulation of p53 mediated genes ( DR5 , JUNB , TET2 , and GADD45 , as seen in our gene expression data ) ., TGFβ has been reported in the plasma of LCMV-infected rhesus macaques 34 ., Along with upregulation of these immunosuppressive genes , there was a significant early down-modulation of positive immune response regulating genes such as JUN , FOS , CD69 , CD83 , STAT4 , GADD45A , and SIGLEC10 in PBMC from LASV exposed NHPs ( as shown in Table 1 ) , suggesting the lack of key players in the generation of immune response to infection ., Expression of these genes is shown as a heatmap and line graphs in Figure S6 ., Together , the behavior of these genes is consistent with an upregulation of immunosuppressive signals , compounded by the lack of factors that lead to the development of adaptive immune response such as production of IL2 and IL4 following virus infection ., At the outset of this study , it was unclear whether the PBMC population would provide an accurate report of the systemic immune response; however , our analysis has proven that these cells appear to respond to LASV exposure in ways that closely mirror what has already been described for the response to infection in humans and NHP 4 , 21 , 33 , 47 ., These changes include the lack of IL1β , IL8 , TNFα , IL2 , IFNγ , IL4 , IL12 , and IL10 , and the upregulation of IL6 ., In our system , we see an induction of IL6 , consistent with earlier descriptions of LASV associating this cytokine with fatal cases of human patients 48 as well as animal models 4 ., Our data show that there is both an early induction as well as sustained activation of IRF3/IRF7 responsive genes following aerosol exposure to LASV ( Figure 2 ) ., This robust response was evident very early post-exposure prior to onset of clinical signs or detection of viremia and was maintained throughout the disease course ., This potent innate response does not appear to be coupled to a strong adaptive immune response , as IL4 , IFNγ , and IL2 were not upregulated in the PBMC at any point in response to LASV exposure ., One suggestion for why there is a poor adaptive resp

Introduction, Materials and Methods, Results, Discussion

Lassa virus ( LASV ) is a significant human pathogen that is endemic to several countries in West Africa ., Infection with LASV leads to the development of hemorrhagic fever in a significant number of cases , and it is estimated that thousands die each year from the disease ., Little is known about the complex immune mechanisms governing the response to LASV or the genetic determinants of susceptibility and resistance to infection ., In the study presented here , we have used a whole-genome , microarray-based approach to determine the temporal host response in the peripheral blood mononuclear cells ( PBMCs ) of non-human primates ( NHP ) following aerosol exposure to LASV ., Sequential sampling over the entire disease course showed that there are strong transcriptional changes of the immune response to LASV exposure , including the early induction of interferon-responsive genes and Toll-like receptor signaling pathways ., However , this increase in early innate responses was coupled with a lack of pro-inflammatory cytokine response in LASV exposed NHPs ., There was a distinct lack of cytokines such as IL1β and IL23α , while immunosuppressive cytokines such as IL27 and IL6 were upregulated ., Comparison of IRF/STAT1-stimulated gene expression with the viral load in LASV exposed NHPs suggests that mRNA expression significantly precedes viremia , and thus might be used for early diagnostics of the disease ., Our results provide a transcriptomic survey of the circulating immune response to hemorrhagic LASV exposure and provide a foundation for biomarker identification to allow clinical diagnosis of LASV infection through analysis of the host response .

Lassa virus ( LASV ) , a member of the Arenaviridae family , is a viral hemorrhagic fever causing virus endemic to several countries in West Africa with a history of sporadic importation into the United States ., It has been characterized as a Category A agent , and despite the significant public health issues posed by LASV and the potential biodefense risks , little is known about the immune response to the virus ., In the study presented here , we have taken an unbiased genomics approach to map the temporal host response in the peripheral blood mononuclear cells ( PBMCs ) of non-human primates ( NHP ) exposed to LASV ., Gene expression patterns over the entire disease course showed that there are strong transcriptional changes of the immune response to LASV exposure , including the upregulation of Toll-like receptor signaling pathways and innate antiviral transcription factors ., However , there was a lack of pro-inflammatory cytokine response in LASV exposed NHPs similar to what is seen in human disease ., Our data suggests that LASV induces negative regulation of immunological events , leading to an inefficient adaptive immune response as observed in LASV-infected human patients ., Our results provide a picture of the hosts circulating immune response to hemorrhagic LASV exposure and demonstrate that gene expression patterns correlate with specific stages of disease progression .

immune activation, immunology, host-pathogen interaction, microbiology, animal models, adaptive immunity, model organisms, immune defense, immunoregulation, animal models of infection, biology, immune response, macaque, immunity, virology, innate immunity

journal.pntd.0005822

Predicting spatial spread of rabies in skunk populations using surveillance data reported by the public

A central focus for disease ecologists and epidemiologists is to quantify processes that determine geographic spread of disease 1 ., Surveillance systems , which rely on reporting by the public 2–5 , provide data that can improve understanding disease dynamics and planning interventions ., However , passive surveillance data are challenging to interpret because the underlying sampling design is opportunistic ., Raw patterns may depend on observation processes that produce a biased representation of disease occurrence ., Interpretation of passive surveillance data from wildlife populations can be especially challenging because the underlying ecological processes , such as host population density , distribution , and demographic dynamics , are often unknown e . g . , 6 ., In these cases a phenomenological method that does not rely on explicit representation of often unavailable host ecological data , may be valuable for quantifying disease spread—especially when surveillance data are too sparse , or disease prevalence too low , to enable estimation of multiple unknown parameters representing non-linear processes from both the host and disease dynamics ., Rabies virus ( RABV ) is a globally-distributed zoonotic pathogen that circulates naturally in a variety of carnivore and bat host species and has among the highest case fatality rate of known infectious diseases 7 ., The principal burden of human and animal cases is associated with domestic dog populations 8 , but emergence in wild carnivores has been observed , especially in areas where domestic dog rabies has been managed or controlled 9 , 10 , or in areas with a long history of disease absence 11 ., Risk of rabies transmission from wildlife is traditionally monitored through public health surveillance systems , which involve voluntary reports by the public of domestic animal or human exposures to potentially sick wildlife ( appearance of atypical behavior , and especially signs of neurologic illness ) , followed by diagnostic testing 7 ., Animal movement can have significant consequences on geographic spread of RABV 12 , but the transmission distance during individual infections , an important component of geographic spreading , is poorly documented ., Analysis of spatial public-health surveillance data have the potential to improve our understanding of the geographic spreading processes of RABVs , which will in turn help with planning prevention and response strategies , and prioritizing resources in space and time ., In the United States of America ( USA ) , rabies infections of humans and animals became nationally reportable during 1938 13 ., There are several distinct enzootic lineages of RABVs circulating in bats and wild carnivores in the USA 14 , though most control and management efforts are focused on raccoon RABV 15 ., The South Central Skunk ( SCSK ) variant of RABV was likely first detected in Texas as early as 1953 16 , although typing methods which could detect and identify this particular variant were not reported until 1986 17 ., Recent studies have documented that this variant of RABV has been expanding in geographic distribution 18 and recently invaded novel areas in the USA , causing epizootics in the northern part of Colorado for the first time 19 ., This well-documented invasion presents an opportunity to quantify spatial emergence dynamics of skunk RABVs , and assess the validity of estimating spatial epidemiological parameters from surveillance data reported by the public ., Ecologists have developed occupancy models for quantifying species invasion processes 20 , which are essentially the same phenomena as the emergence and geographic spread of novel pathogens ., Occupancy is the process of a target species or pathogen being present or absent across space ., Dynamic occupancy processes additionally consider a time component ., Occupancy frameworks can incorporate an ecological process ( es ) of geographic spread and observation error separately , allowing for clearer interpretation of the influence of factors driving each process ., Recently , occupancy models have begun to be applied to disease systems for estimating pathogen prevalence 21–23 but are under-utilized for quantifying parameters that describe the spatial spread of disease ., Using surveillance data from the SCSK epizootic in Colorado , we developed a dynamic patch-occupancy model to jointly estimate parameters describing the spatial spread of rabies while accounting for variable sampling effort ., Considering surveillance data from dead skunks , we used the framework to:, 1 ) quantify the rate of geographic spread and transmission distance per infection;, 2 ) quantify effects of local infection density , seasonality and direction on the probability of geographic invasion; and, 3 ) predict the occupancy probability and prevalence of RABV on the landscape through space and time ., We also conducted a phylogeographic analysis of viral genetic data from a subset of the reported rabies positive skunks to validate the estimated rates of spatial spread and transmission distance that we estimated using the occupancy model ., The occupancy framework we present is simple to implement and can be used for inferring rates and direction of geographic spread in a variety of emerging disease systems , providing basic insight for understanding disease emergence and practical insight for risk assessment and response planning ., The decision and implementation of euthanasia of animals was conducted by county authorities , before samples were received for the current study ( i . e . , we had no role in this ) ., For the current study , the State of Colorado Department of Natural Resources issued annual Scientific Collection Licenses ( 14SALV2060 , 15SALV2060 ) in order for us to receive a subset of dead carcasses for testing ., The study area included 8448 km2 ( 88 x 96 km; Fig 1 ) of Larimer , Boulder and Weld counties , Colorado , USA ., Skunk samples were obtained by reports made by public to local health departments , which decided whether to submit a skunk for rabies testing to one of two diagnostic laboratories in the state ., The decision and implementation of euthanasia of animals was conducted by county authorities , before samples were received for the current study ., For the current study , the State of Colorado Department of Natural Resources issued annual Scientific Collection Licenses ( 14SALV2060 , 15SALV2060 ) in order for us to receive a subset of dead carcasses for testing ., There was no vaccination campaign ( trap-vaccinate-release or oral rabies vaccination ) targeting wildlife during the epizootic , but there were alerts through local public media and leash laws in effect ., Strange-acting or dead mammals found on the landscape with no reported human or domestic animal contact were not typically tested by the local health departments—especially later in the study period when the perceived risk of rabies infection was much lower and funding for testing had decreased ., Because of the ongoing epizootic , these samples were referred for testing by United States Department of Agriculture , Animal and Plant Health Inspection Service , Wildlife Services , National Wildlife Research Center ( NWRC ) ; hereafter referred to as ‘enhanced surveillance’ ., Enhanced surveillance accounted for 0% , 1% and 47% of surveillance data in 2012 , 2013 and 2014 respectively ( Table SM1 . 1 in S1 Text ) ., Enhanced surveillance samples were identified through the public health surveillance system by the local health departments , but with carcass referral to NWRC for rabies testing ., Carcasses were referred to NWRC when the perceived risk to public health was low ( i . e . , due to a lack of contact with humans or pets ) ., Because the rabies epizootic was waning in 2014 , the high proportion of enhanced surveillance in 2014 relative to 2012 and 2013 did not contribute much extra information ( except for improving uncertainty levels ) using the occupancy model ., Thus , typical surveillance systems could likely make the same type of inferences , but would show greater uncertainty as sampling decreased ., An address matching-geocoding technique , using ArcGIS 10 . 3 ( Environmental Systems Research Institute , Redlands , CA , USA ) , was used to convert street addresses of the case reports to UTM ( Universal Transverse Mercator ) coordinates ., Before 2012 , carnivore rabies had not been detected in the study area for several decades ., From 2012–2014 there were 246 skunk reports and 379 non-skunk terrestrial animal reports tested for rabies ( Fig 1 , Table SM1 . 2 in S1 Text ) , with a total of 139 rabies-positive skunks ( raw prevalence ~ 57% , Table SM1 . 2 in S1 Text ) , and 21 rabies-positive cases from the 379 non-skunk terrestrial species tested ( 5 . 5% positive; Table SM1 . 2 in S1 Text ) including: raccoons ( 6 ) , bison ( 2 ) , foxes ( 6 ) cats ( 2 ) , horses ( 2 ) , cows ( 2 ) , coyotes ( 1 ) ., All non-skunk terrestrial animals were excluded from the analysis because they comprised such a small proportion of the samples and because surveillance efforts may have differed in some of these hosts—especially raccoons which are higher density and more peridomestic , and were experiencing an ongoing epizootic of canine distemper virus ., Human population data from 2013 24 were organized at the grid-cell scale ( 8 x 8km ) to be included in the occupancy model as a potential factor contributing to reporting rate ., We used a discrete-time , dynamic , patch-occupancy model to quantify the occurrence probability of skunk rabies in space and time ., In a typical occupancy framework , there is the partially observable occupancy process and an observation process that is conditional on the occupancy process ., For this dynamic model the occupancy process describes the probability of rabies being present by site ( grid cells ) and time ( months ) ., The ecological process of rabies occurrence changed over grid cells and months due to the local component processes of colonization , local extinction , and persistence ., Within the latent ecological process , we estimated important parameters describing the initial colonization dynamics ( i . e . , spatial invasion ) of skunk rabies , specifically: direction of invasion , distance to nearest infected grid cell , and density of infection in the local neighborhood ., Conditional on the latent ecological process of occupancy , we modeled the observation process as the probability that an animal sampled has rabies , given that rabies is present ( prevalence given rabies occupancy ) ., Within the observation process , we considered the effects of human population size on the proportion of skunks that were rabies positive ., Thus , our approach allowed us to quantify parameters underlying the spatio-temporal dynamics of geographic spread while accounting for imperfect detection ., For the occupancy model , we assumed the landscape was homogenous because our genetic model suggested that spread rates were homogenous across the landscape and we did not have enough degrees of freedom to incorporate more parameters in our occupancy model ., We used a monthly time step to scale with the incubation period for rabies virus in skunks 25 , 26 ., We divided the study area ( much of Larimer , Weld and Boulder Counties in Northern Colorado , USA ) into 8 x 8 km grid cells ( “patches” in occupancy modeling terms; Fig 1 ) ., This spatial scale was much larger than a skunk home range size 27 , 28 suggesting that local transmission ( i . e . , due to skunk movements alone ) should be primarily within grid cells or nearest-neighbor grid cells ., Also , at this spatial scale , there were multiple samples collected within grids ( Fig 1 ) at a given time step ( mean number of samples in grids with samples: 3 . 6 sampled skunks , range 1–19 sampled skunks ) , which is necessary for estimating grid-cell level prevalence and informing the observation process , but at finer spatial scales multiple samples per grid cell and time step were rare ., Thus , because of the sparse sampling intensity in our study area , the 8 x 8 km grid cell size was the smallest size we could choose for approximating the skunk home range ( ~ 2 x 2 km; 25 , 26 ) and hence skunk-to-skunk transmission distances ., Data on the presence of rabies in dead skunks ( sampling unit = 1 skunk ) were collected repeatedly in grid cells i = 1 , … , M during each month t = 1 , … , T ( spanning January 2012 through December 2014 ) ., We modeled the true occupancy status zi , t conditioned on the previous time step zi , t-1 , as a latent Bernoulli variable defined by parameter Ψi , t ( occupancy probability ) , where zi , t = 0 indicates that grid cell i is not occupied ( no rabies in skunks ) in time step t , and zi , t = 1 indicates that grid cell i is occupied ( at least one skunk with rabies ) in time step t ., We assumed the initial occupancy state for each grid cell ( zi , 1 ) was also a Bernoulli random variable described by occupancy probability Ψi , 1 , which had a Uniform prior distribution ( Ψi , 1~Unif ( 0 , 1 ) ) ., We allowed occupancy probability in grid cell i at time t ( Ψi , t ) to be determined by three types of local dynamic processes: initial colonization ( γi , t ) , recolonization ( ζi , t ) and persistence ( ϕi , t ) , which were conditional on the latent occupancy status in the previous time step ( zi , t-1 ) ., We used parameter Ai , t to distinguish an initial colonization event from subsequent colonization events ., Although it is more typical to consider only occupancy and extinction dynamics in occupancy models , we further distinguished initial colonization from recolonization ( as in 29 ) because we were most interested in factors ( described below ) driving spatial invasion into new sites ., We quantified the effects of multiple different factors ( direction , distance to nearest infected neighbor , neighborhood infection density ( Eqs 3–8 ) on initial colonization probability ( γi , t-1 ) , and treated the other processes as inherent contributors to occupancy probability ( Ψi , t ) without quantifying their potential drivers ( i . e . we used a global parameter for persistence- ϕit ~ Beta ( αϕ , βϕ ) and recolonization—ζit ~ Beta ( αζ , βζ ) ., For γi , t-1 , an intercept term ( βo ) absorbed non-spatial effects ( Eq 3 ) ., Direction ( north-south ( N ) or east-west ( E ) ) was modeled as increasing integers ( 1 representing the southernmost grids and 12 representing the northernmost grids for N; and 1 representing the westernmost grids and 11 representing the easternmost grids for E; Eq 4 , βk , where k = 1 , … , K for the number of initial colonization effects considered ) ., East-west direction was represented similarly but using columns in the grid ., We modeled an interaction between direction and a trend in time ( T ) by multiplying the direction covariate data by the time step ( Ni · T or Ei · T ) ., We calculated distance to the nearest infected grid cell by taking the minimum distance for all pairwise distances between the centroid of target grid cell i* in time t and all other grid cell centroids, ( i ) in time t-1 ( dii* ) ., We estimated the relationship of initial colonization probability and distance using an exponential decay function which included a parameter describing the decay rate of initial colonization probability ( α ) with distance and a scaling parameter relative to maximum initial colonization probability ( βk , Eq 5 ) ., We estimated the local neighborhood infection density for grid cell i* in time t using the occupancy prevalence in all immediate grid-cell neighbors ( queen’s neighbors; j = 1 , … , J; note j’s are a special subset of i—restricted to the closest neighbors of i* ) in time step t-1 and estimated its effect with parameter βk ( Eq 6 ) ., We incorporated a seasonal effect as a factor where one level represented spring/summer ( Feb . –Aug . ) and the second level represented fall/winter ( Sept . –Jan . ) ( Eq 7 ) ., We chose these time frames because the literature and surveillance data suggest peaks of rabies in spring and summer following arousal and mating activities , and subsequent peaks in fall and winter associated with dispersal and contact involving susceptible young of the year 16 , 25 ., We modelled the infection density ( Eq 6 ) and distance effects ( Eq 5 ) separately because we were interested in the separate effects of distance versus infection intensity ., Together these effects describe the spatial kernel for local transmission ., Additionally , we modelled a weighted spatial kernel ( Eq 8 ) which accounted for the density of neighbors with infections but discounted the impact an infected cells at further distances from the grid of interest ( dii* represents the distance between grid cell ‘i and the grid cell of interest ‘i*’ ) ., logit ( γi , t-1 ) =, β0 ( non\xa0spatial ), ( 3 ), β1N+β2T+β3N*T ( direction\xa0by\xa0time ), ( 4 ), β4e−αmin ( dii* ) ( transmission\xa0distance ), ( 5 ), β5 ( 1/J ) ∑Jj=1zj , t−1 ( local\xa0neighborhood\xa0effect ), ( 6 ), β6S ( season\xa0–\xa0Feb . –\xa0Aug . vs\xa0Sept . –\xa0Jan . ), ( 7 ), β7∑zi , t−1=11/dii* ( weighted\xa0spatial\xa0kernel ), ( 8 ), We considered these effects ( Eqs 4–8 ) separately and additively ( up to three effects including the Eq 3 ) in our model selection procedure ( described below ) ., We did not present the fullest model because parameter estimates became inconsistent when more than three additional effects were in the model ., Persistence and recolonization were important processes in the occupancy dynamics , thus we modeled them explicitly using Beta prior distributions with shape and scale parameters ( αϕ = 1 , βϕ = 1 , αζ = 1 , βζ = 1 ) ., We did not include covariates that could potentially drive persistence and recolonization because we were only interested in the process of spatial spread ( i . e . , initial colonization ) ., Prior distributions for all βk parameters were: βk ~ Norm ( 0 , 1 ) , except for the scaling parameter for the exponential decay model ( Eq 5 ) which was modeled as a Gamma ( 5 , 1 ) ., For the observation layer of our model , we represented the number of skunks that were positive for rabies , y , in grid cell i at time t when rabies was present as the observed data using a binomial distribution where p was the estimated prevalence of rabies in dead skunks ( given occupancy ) and the number of trials ( Ri , t ) were the observed number of samples collected in grid cell i at time t ., To account for variation in rabies detection due to variation in human population size , we examined prevalence as a linear function of human population size ( N ) ., We only investigated this effect in the intercept-only model ( Eq 3 ) and the two-effect model we were most interested in ( Table SM2 . 1 in S1 Text for list of all models that were fit ) ., The general model specification is given in section SM2 . 1 in S1 Text ., To calculate the posterior distribution for the parameters of interest , we fit models using a Markov chain Monte Carlo ( MCMC ) algorithm with a Gibbs sampler including Metropolis-Hastings steps 30 custom written in program R 31 ., Posterior estimates for the data are based on 50 , 000 iterations of the MCMC algorithm with the first 5 , 000 iterations discarded as burn-in ., Convergence and mixing were assessed graphically ., Example MCMC chains and posterior distributions are given in SM2 . 1 in S1 Text ., Convergence of the best predictive model was additionally assessed using the Gelman-Rubin statistic 30 ., The joint and conditional distributions are specified in SM2 . 3 in S1 Text ., We evaluated the ability of our models to recover parameters by simulating data from known parameter values and estimating the parameter values using our fitting framework ( Model Validation Method in SM5 in S1 Text ) ., We used a combination of Watanabe Akaike Information Criterion ( WAIC ) 32 , 33 , Area Under the receiver operator Curve ( AUC ) 34 , and leave-one-out cross validation ( looCV ) 31 to compare the importance of different effects on γ and p and assess goodness-of-fit ( presented in Table 1 , schematic of work flow shown in Fig . SM3 . 1 ) ., WAIC is a model selection criterion based on the posterior predictive distribution , and was used to compare fits of models ., WAIC was not used to compare models with human population modelled on prevalence ( p ) because the data were different and thus the WAIC values would not be comparable ., AUC is a measure of how well variation is explained by the model—in our case , the ability to distinguish a presence from an absence—and was used to assess how well a particular model explained the data ( i . e . a measure of goodness of fit as in 35 ) ., LooCV is a measure of the model’s ability to predict out of sample , and thus was used to compare predictive ability among models ., When comparing predictive ability between models we used both AUC and looCV ., We calculated WAIC and AUC ( Section SM4 in S1 Text ) for fits to the full data as well as the looCV predictions ., WAIC is preferable to DIC ( Deviance Information Criterion—another Bayesian method for model selection ) for model selection using hierarchical models 32 because it considers the posterior predictive distribution explicitly and penalizes for complexity of model structure , not just the number of parameters ., Similar to DIC , lower values of WAIC indicate a better model of the data ., However , in contrast to DIC 36 , there is no standard quantitative difference between WAIC values from alternative models that indicates a significant difference between them ( i . e . , the only criterion is that lower is better ) ., We presented two AUC scores: 1 ) AUC1 measured predictive ability of p using predicted y’s from posterior values of z and observed y’s , 2 ) AUC2 measured predictive ability of Ψ using the posterior values of z and the observed y’s transformed to binary data ., For the out-of-sample predictions , we conducted looCV for each point in the data ( using all other points as the training data ) and presented means of AUC1 and AUC2 as measures of predictive ability ., To further evaluate the predictive ability of our approach we predicted occupancy status over space and time using only parameters estimated from the “best predictive model” ( i . e . , considering out-of-sample statistics for AUC1 , AUC2 and looCV ) and the sample size data ., First , we fit the best model to the full data and then predicted all yi , t from the posterior predictive distribution 30 ., Each predicted yi , t depended on the predicted yi , t-1 , rather than the data , but the actual data for sample size ( Ri , t ) were used in the prediction in order to scale yi , t predictions appropriately ., Using the model with the best out-of-sample looCV score , we predicted grid cell occupancy over time ( zi , t; rabies presence -1 , or absence -0 ) , and estimated the rate of southerly spread of the predictions using regression ., First , we obtained the zi , t values from each MCMC iteration ( minus the burn in ) for grids across time ., Each grid cell corresponded to a distance in kilometers from the southernmost border of the study area ( e . g . , 1 , … , 88 km; “North value” ) ., For zi , t = 1 values , we ran a simple linear regression where the independent values were the months in time and the dependent values were the north values ., The slope of this regression represents the monthly rate of southerly spread ., We then converted this to an annual rate of southerly spread by multiplying the monthly rate by 12 ., We calculated the annual variance using the delta method 37 ., To verify spatial patterns indicated by our occupancy model , we sequenced the whole or partial glycoprotein ( G , 1575 base pairs bp ) and the non-coding region between the glycoprotein and polymerase genes ( GL , 560 bp ) for 53 viruses collected in Colorado between August 2012 and December 2014 ( Table SM1 . 3 in S1 Text ) ., We added sequence data from 20 viruses collected through May 2015 ( 27 . 4% of the total genetic dataset ) to increase the precision of parameters of the molecular evolutionary models ., We confirmed that these additional sequences comprised an extension of the same epizootic though preliminary phylogenetic analysis and the absence of changes in the inferred rate of spread in 2015 which could have biased our overall spread rate ., Rabies virus RNA was extracted from brainstem tissue samples of rabies positive skunks using Trizol reagent following the manufacturer’s protocol ., The conversion of RNA to cDNA ( RT ) and primary PCR amplification was accomplished using a Superscript III One-step RT-PCR system with Platinum Taq DNA Polymerase ( Invitrogen ) , targeting a 2 , 135bp region including the full glycoprotein gene with previously published primers 18 , 38 ., PCR products were visualized by UV-light on a 2% agarose gel with ethidium bromide , and cleaned using ExoSap-IT ( Affymetrix ) following the manufacturer’s protocol ., Sequencing reactions were performed using Big Dye Terminator v . 3 . 1 ( Applied Biosystems ) , with flanking and internal primers as previously described 18 , 38 ., Sequencing products were cleaned using Sephadex G-50 columns ( GE Healthcare ) and run on a 3130 or 3500 analyzer ( Applied Biosystems ) ., Forward and reverse sequences were aligned in Sequencher v . 5 . 2 . 4 ( Gene Codes Corporation ) , and ambiguities were resolved visually ., Alignment of full or partial glycoprotein sequences was performed using BioEdit v . 7 . 2 . 0 ., Continuous phylogeographic analysis was conducted in BEAST v . 1 . 8 . 4 39 ., Briefly , this method estimates the phylogenetic history connecting samples and conducts ancestral state reconstruction of the latitudes and longitudes of inferred nodes using time and location-annotated sequence data ., Phylogenetic uncertainty is accommodated by summarizing estimates across the posterior distribution of trees from a Bayesian search ., Analyses in TempEst ( software that analyzes correlations between the temporal and genetic distances between sequences ) showed evidence of a molecular clock signal in the coding ( G ) and non-coding ( GL ) regions of the RABV genome , but faster evolution in the G-L region compared to G 40 ., Our BEAST analysis therefore modeled a single tree topology ( given that the samples represent the same underlying epidemic history ) using different molecular clock and substitution models to allow for differences in evolutionary rates in different parts of the RABV genome ., Preliminary BEAST runs using the lognormal relaxed molecular clock indicated little variation in rates among branches , indicating the use of the strict molecular clock for both partitions , which was supported by equivocal differences in Bayes Factors ( BF ) between models assuming strict or relaxed molecular clocks ( BF = 1 . 5 in favor of strict clocks ) 41 ., Final runs used customized substitution models for the following data partitions: G codon positions 1+2 = TPM1uf; G codon position 3 = TIM1+G , GL = TPM1uf+I , as suggested by AIC in jModeltest2 42 ., Among the 3 random walk phylogeographic models tested ( homogenous , Cauchy and gamma ) , marginal likelihoods estimated by the stepping stone method were highest in the homogenous model ( -3574 . 86 ) followed by the gamma ( -3577 . 27 ) and Cauchy ( -3594 . 05 ) models , suggesting relatively little variation in spread rates among branches or little power to detect such variation ., To account for the possibility of among branch variation , we present the results of the gamma model , but note that parameter estimates were nearly identical in the similarly supported homogeneous rate model ., All analyses used the Bayesian skyline model of demographic growth ., MCMC chains were run for 100 million generations which generated effective samples sizes >200 after removal of burn-in ., We used the Seraphim package of R to calculate viral diffusion rates from 500 randomly selected ( post burn-in ) trees from the posterior distribution of the BEAST analysis 43 ., We estimated viral spread rates as, ( i ) the “mean branch velocity” calculated as the average velocity across the branches of each tree , averaged across all trees and, ( ii ) the weighted “diffusion rate” , obtained by summarizing distances and times spanning each tree and taking the average of that value across all trees ( i . e . , sum distances/sum time lengths over entire tree ) ., To calculate the transmission distance per viral generation ( i . e . , per infected animal ) , we divided the distance traversed along each branch by the expected number of infections along that branch , assuming a generation time of 30 days , which corresponds to the incubation period of rabies virus in skunks 25 , 26 ., However , the substitution rates that we estimated ( G: 4 . 1x10-4 substitutions per site per year ( 95% highest posterior density ( HPD ) = 2 . 3–6 . 05x10-4 ) ; GL: 8 . 7x10-4 HPD95 = 4 . 1–13 . 4 x10-4 ) , imply that some transmission events would be expected to occur without detectable evolution in the partial genomes that we sequenced ., This led to branch lengths that were less than the assumed generation time of RABV , which caused an upward bias in inferred transmission distances per infection ., We corrected for this bias by forcing all branches to have a minimum generation time of 1 infection ., The probability of initial colonization ( γ ) was best explained by the minimum distance to infections , infection density in the local neighborhood , season , and spatial kernel ( see AUC2 of Models 1–7; Table 1 ) ., Similarly , the best two-effect models included pairs of these effects ( distance + neighborhood , distance + season , kernel + season; Models 9 , 11 , 15; Table 1 ) ., Accounting for direction ( i . e . , N∙T or E∙T ) provided more minor improvements to quantifying initial colonization probability ( AUC2 Table 1 ) ., The inclusion of human population size on p ( rabies prevalence in occupied grid cells ) significantly improved predictive power of the model ( see AUC1 and looCV in Table 1 , Models 1b , 9b , 11b , 16b ) because it explained some of the prevalence variation due to differences in the number of samples reported by humans ., We considered Model 11b to be the best predictive model because it had the lowest looCV , and we used this model for prediction ., However , in order to study the effects of the most significant single predictors ( neighborhood , season and distance ) , we fit Model 16 to quantify the relative importance of these factors ( results presented in Fig 2 ) ., We modeled the effect of distance to nearest infection using an exponential decay function ., Using the estimated decay rate parameter , we calculated the distance at which initial colonization probability decayed to half its maximum ( referred to as “transmission distance” ) using the asymptotic limit as the minimum ( see section SM54 in S1 Text for calculation ) ., We interpreted the transmission distance as a measure of skunk-to-skunk transmission distance ., Transmission distance was 3 . 9 km ( 95% credible intervals ( CI95 ) : 1 . 4 , 11 . 3; Fig 2A ) ., Similarly , using the genetic data independently , the mean distance per viral generation ( another proxy for skunk-to-skunk transmission distance ) was estimated to be 2 . 3 km ( 95% Highest Posterior Density ( HPD95 ) = 0 . 04–5 . 7; Fig 3C ) ., Initial colonization probability increased exponentially with local neighborhood infection density , especially after 0 . 2 ( i . e . , > 1grid cell occupied; Fig 2B , Model 16 , Table 1 ) ., Initial colonization probability was substantially higher during the spring/summer season relative to the fall/winter season ( Fig 2C , Model 16 ) ., Of the 76% of variation in occupancy probability that was explained by the three-factor model ( AUC2 , Model 16 , Table 1 ) , 57% of initial colonization probability was explained by local effects ( distance or neighborhood ) , 41% was explained by season and 2% was explained by other factors ( which could include translocation or other unknown f

Introduction, Methods, Results, Discussion

Prevention and control of wildlife disease invasions relies on the ability to predict spatio-temporal dynamics and understand the role of factors driving spread rates , such as seasonality and transmission distance ., Passive disease surveillance ( i . e . , case reports by public ) is a common method of monitoring emergence of wildlife diseases , but can be challenging to interpret due to spatial biases and limitations in data quantity and quality ., We obtained passive rabies surveillance data from dead striped skunks ( Mephitis mephitis ) in an epizootic in northern Colorado , USA ., We developed a dynamic patch-occupancy model which predicts spatio-temporal spreading while accounting for heterogeneous sampling ., We estimated the distance travelled per transmission event , direction of invasion , rate of spatial spread , and effects of infection density and season ., We also estimated mean transmission distance and rates of spatial spread using a phylogeographic approach on a subsample of viral sequences from the same epizootic ., Both the occupancy and phylogeographic approaches predicted similar rates of spatio-temporal spread ., Estimated mean transmission distances were 2 . 3 km ( 95% Highest Posterior Density ( HPD95 ) : 0 . 02 , 11 . 9; phylogeographic ) and 3 . 9 km ( 95% credible intervals ( CI95 ) : 1 . 4 , 11 . 3; occupancy ) ., Estimated rates of spatial spread in km/year were: 29 . 8 ( HPD95: 20 . 8 , 39 . 8; phylogeographic , branch velocity , homogenous model ) , 22 . 6 ( HPD95: 15 . 3 , 29 . 7; phylogeographic , diffusion rate , homogenous model ) and 21 . 1 ( CI95: 16 . 7 , 25 . 5; occupancy ) ., Initial colonization probability was twice as high in spring relative to fall ., Skunk-to-skunk transmission was primarily local ( < 4 km ) suggesting that if interventions were needed , they could be applied at the wave front ., Slower viral invasions of skunk rabies in western USA compared to a similar epizootic in raccoons in the eastern USA implies host species or landscape factors underlie the dynamics of rabies invasions ., Our framework provides a straightforward method for estimating rates of spatial spread of wildlife diseases .

Rabies is a deadly zoonotic infection with a global distribution ., In 2012 , an epizootic of skunk rabies established in northern Colorado , USA and spread rapidly through three counties ., The epizootic was documented through reports of dead skunks by the public ., We examined the reports to determine how rapidly rabies was moving and which factors could explain the patterns of spread ., We compared these estimates of spatial movement of rabies to those obtained from analyzing rabies genetic sequences that we obtained from some of the dead skunks reported by the public ., By both methods , we found the virus was moving south at a little over 20 km/year and that most transmission between skunks occurred at short distances ( < 4 km ) ., Rabies was most likely to spread to new areas during the first half of the year , when skunk populations were producing new offspring ., Our genetic model suggested that roads and rivers in the study landscape did not affect movement speed of rabies ., We developed a framework that used the spatial data in the public reports to predict where and when skunk rabies would occur next ., This framework could be used on public health surveillance data for other diseases or countries .

epizootics, biogeography, animal types, medicine and health sciences, ecology and environmental sciences, animal diseases, pathology and laboratory medicine, pathogens, population genetics, tropical diseases, microbiology, spatial epidemiology, animals, viruses, rabies, mathematics, rna viruses, forecasting, statistics (mathematics), neglected tropical diseases, population biology, infectious disease control, zoology, research and analysis methods, rabies virus, infectious diseases, geography, zoonoses, medical microbiology, epidemiology, mathematical and statistical techniques, microbial pathogens, phylogeography, infectious disease surveillance, lyssavirus, wildlife, earth sciences, viral pathogens, genetics, disease surveillance, biology and life sciences, viral diseases, physical sciences, evolutionary biology, statistical methods, organisms

journal.pgen.1005614

Genus-Wide Comparative Genomics of Malassezia Delineates Its Phylogeny, Physiology, and Niche Adaptation on Human Skin

Over 100 years ago Malassezia was recognized as an inhabitant of human skin and implicated in a common skin disorder i . e . seborrheic dermatitis 1 ., Since then , Malassezia has been found on the skin of all tested warm blooded animals 2 , 3 , including dogs , horses , pigs , goats , cats and lambs 4–8 , and associated with other common skin disorders including dandruff 9 , atopic eczema/dermatitis , pityriasis versicolor , seborrheic dermatitis , and in systemic disease 10 ., Recent investigations of the skin microbiome using culture-free approaches have highlighted the overwhelming dominance of Malassezia among eukaryotes on all human surface body sites , with only the exception of three foot sites 11 , 12 ., Other studies have suggested that they are abundant in body sites beyond skin , including the human oral microbiome 13 , but a systematic characterization of Malassezia species and their functional repertoires represented in metagenomic datasets has been hampered by the lack of reference genomes ( only 2 out of 14 known species have reference genomes i . e . M . globosa 2 and M . sympodialis 14 ) ., In addition , several reports have suggested that Malassezia-like organisms are found in a wide range of environmental habitats , from deep sea sediments , hydrothermal vents and arctic soils , to marine sponges , stony corals , eels , lobster larvae , and nematodes 15 ., These studies have relied on high-identity DNA sequence matches to short amplified barcode regions , but concerns about amplification bias or laboratory contamination raise doubts about the results and the lack of a comprehensive genus-wide genomic resource for known species has made it challenging to investigate this question further ., Malassezia belong to the class Malasseziomycetes in the subphylum of Ustilaginomycotina , ( phylum of Basidiomycota , Kingdom of Fungi ) 16 , which are otherwise comprised exclusively of more than 1 , 500 species of plant pathogens 17 ., Other known fungal residents on human skin , such as Candida albicans and the dermatophytes are in distant branches of the fungal tree of life and are likely to have evolved independently to adapt to life on animal skin 18 , 19 ., The genetic basis of the unique lipophilic nature of Malassezia and its adaptation to animal skin ( putatively starting from an ancestral state as a plant or soil resident ) is thus an intriguing and open question ., Answers to this question could also serve as the basis for developing new anti-fungals and therapeutics for associated skin disorders ., Analysis of the two existing Malassezia genomes 2 , 14 highlights that their small genomes ( among the smallest for free living organisms in the fungal kingdom ) likely contain only the minimal complement of information necessary for existence in their specific ecological niche ., In this context , the expansion of several gene families as noted before ( e . g . lipases , phospholipases , and aspartyl proteases ) may point to their functional importance 2 , 14 ., However , it has not been clear if these observations are indeed genus-wide features ., In addition , the limited availability of reference genomes has precluded the systematic characterization of genomic features unique to Malassezia ( such as gene gain or loss , horizontal gene transfers , linkage between mating type loci , and regions undergoing positive or negative selection ) that could serve as the basis of understanding its unique physiology and niche adaptation ., To address this limitation , we sequenced and assembled high-quality , annotated genomes of all known Malassezia species and multiple strains of the species most common on humans ( including a re-annotation of existing references ) , representing a 7-fold increase in available reference genomes ( from 2 to 14 ) , and providing a comprehensive genomic resource for the investigation of Malassezia biology and its ecological distribution ( 24 Malassezia strains in total ) ., Showcasing this , we established the abundance and surprising diversity of Malassezia species on various human skin sites and their scarcity in other environments ., We then used comparative genomic analysis to systematically compare Malassezia genomes with a broad panel of fungal genomes to reveal genomic features unique to Malassezia , including hundreds of gene gain and loss events , gene family expansions , and positive selection events ., Our analysis revealed several hallmarks of Malassezia genomes , including key horizontally transferred genes ( a few of bacterial origin ) that we characterized functionally and which may be prime candidates to explain the emergence of host and niche-adaptation in Malassezia ., A larger set of genes ( >700 ) were found to be lost in all Malassezia compared to other Basidiomycota , with an enrichment for glycosyl hydrolases and genes involved in carbohydrate metabolism , concordant with adaptation to a carbohydrate-deficient environment ., Combining genomic analysis with an experimental re-evaluation of culture characteristics , we revert previous assumptions and established the likely lipid dependency of all Malassezia species ., Finally , our analysis of lineage-specific gene family expansions revealed extensive turnover in the gene repertoire of Malassezia and underlined the importance of secretory lipases , phospholipases , aspartyl proteases and other peptidases in the experimentally observed lipid specificity of this genus ., Genome sequences for all 14 known Malassezia species , including multiple strains of the more widely studies species ( 24 in total ) were obtained by high-throughput sequencing and de novo assembly ( Table 1; see Methods ) ., The high coverage data ( median coverage of 322X ) was systematically assembled with an assembly pipeline incorporating parameter optimization , contig construction , scaffolding and gap closure steps to produce assemblies with a median N50 of 54 kbp and a maximum N50 of 1 . 4 Mbp ( Table 1 ) ., In particular , we noted that the N50s of the four new M . globosa assemblies were comparable to that of a gold-standard reference M . globosa genome 2 obtained previously using Sanger sequencing with significant directed finishing ( Table 1 ) ., Assembly sizes typically varied from 7 . 2 Mbp ( for M . restricta ) to 9 . 0 Mbp ( for M . globosa ) as expected but we noted that 4 out of the 6 M . furfur assemblies were twice this size , suggesting that they might have undergone whole genome duplication or hybridization events ( see S1 Text and S1 Fig for further details ) ., To assess the completeness of our assemblies we evaluated them using matches to a well-established set of core eukaryotic genes ( CEGs ) 20 ., As can be seen in Fig 1A , our de novo assemblies are comparable to the reference genomes of M . globosa and M . sympodialis 2 , 14 in terms of the number of complete and partial CEGs identified ., In addition , comparison to the gold standard Saccharomyces cerevisiae genome suggests that our assemblies are more than 95% complete ( Fig 1A ) ., We assessed the correctness of our assemblies by comparing them to the published reference genomes of the same strains ( M . globosa 7966 and M . sympodialis 42132 ) ., Overall , we found that our assemblies agreed very well with that of the reference genomes ( Fig 1B , S1 Table ) , showing a high degree of colinearity ( <5 breakpoints per 100 kbp in our assemblies ) and identity ( >99 . 92% ) as expected from the comparison of two high-quality assemblies of the same strain ( Fig 1B , S1 Table ) ., Assuming that the reference genome is correct , we noted that the observed differences in our assembly affected <0 . 5% of all genes in M . globosa , indicating that our assembly is of particularly high quality in genic regions ., To systematically annotate the protein-coding complement of the genomes , we used an iterative and automated pipeline that combines transcriptome data ( where available ) , ab initio predictions , and protein evidence from related species ( see Methods ) ., We evaluated results from this pipeline by comparison to the manually curated annotations for the M . globosa 7966 reference genome and using the S . cerevisiae annotations as gold standard ., As shown in Fig 1C , as a whole , annotations from our pipeline match the S . cerevisiae proteome better ( >1 , 100 S . cerevisiae proteins are better aligned to the new annotation versus ~600 proteins for the reference annotation ) indicating that we have a comparable or better annotation ., In addition , the new annotation has more matches to known domain families than the original annotation ( unique PFam domains and total PFam domains , pfam . xfam . org/ Table 2 ) as well as improved identification of intron-exon boundaries , highlighting the value of the iterative approach employed here ( the utility of transcriptome data is highlighted in S2 Table and the lack of alternative isoforms is noted in Methods ) ., As observed before , we found that Malassezia species code for a compact proteome of ~4 , 000 genes with the exception of M . slooffiae and M . furfur ( after excluding those with doubled genome sizes ) which appear to have a somewhat larger set of genes ( Table 1 ) ., It is of note that the lower N50 of the M . furfur and M . slooffiae assemblies may cause a spurious increase in gene count due to coding regions being split ., To showcase the utility of this genomic resource , we studied the distribution and diversity of Malassezia species in the environment and in human microbiomes by extensive reanalysis of publicly available metagenomic datasets ., We first used in silico benchmarks to confirm our analysis pipeline is highly sensitive and specific in identifying Malassezia species from short , shotgun metagenomic reads ( S2 Fig ) ., We then applied this approach to a wide spectrum of environmental metagenomic datasets including ocean ( www . microb3 . eu/osd ) , marine sediments 21 , soil 22 , and rhizosphere samples 23 ., Despite recent reports of Malassezia-like organisms being widely distributed in the environment 15 , we were unable to detect evidence for this , suggesting they are present in abundances below our detection limit or are sufficiently diverged from known Malassezia species to elude our detection based on known genomes ., Similarly , we re-analyzed oral microbiome samples from six different oral sites ( five samples from each site ) and found no evidence for the presence of Malassezia , in contrast to a recent report 13 ., These results are consistent with either contamination artefacts or Malassezia being presented at lower abundance in the oral mycobiome and thus being detectable only using more sensitive 18S rRNA sequencing approaches which utilize an amplification step 13 ., In contrast , analysis of metagenomic datasets from different sites on healthy human skin 12 readily revealed the abundance and diversity of Malassezia ( detected in 247 out of 280 samples analyzed , from 18 sites on 15 adults and two children; Fig 2A ) ., In general , our analysis reconfirmed that M . globosa and M . restricta are the two most abundant species on human skin , found in 199 samples on 16 individuals and 247 samples on 17 individuals , respectively ., M . sympodialis is a distant third , detectable in 69 samples on 12 individuals , though it is the most abundant species in several samples ( Fig 2A ) ., In addition , nine other species were also found either less frequently or in lower abundance ., For example , M . slooffiae , which has previously not been detected on human skin via ribosomal RNA sequencing 11 , was found in high abundance in several samples , mostly from one individual ( Fig 2A ) ., It is also accompanied in three samples by M . obtusa ( in one individual ) and apparently excluding M . sympodialis ( in two individuals ) ( Fig 2A ) ., We further probed the relative abundance of various genes from the Malassezia pan-genome in skin metagenomic samples 12 to identify those that are highly variable , likely reflecting strain-level variations in the commensal population 24 ., As M . restricta is the most abundant species on human skin , we were readily able to find samples with sufficient read coverage of the genome ( >5X ) for robust analysis ( see Methods ) ., Genome-wide we found significant copy number variations in >100 genes across 6 skin samples and 4 body sites ( Fig 2B , S3 Table ) , though analysis of more samples is likely to reveal even more variable genes ., Our proof-of-concept analysis revealed several highly variable genes including genes of unknown function , a glutathione S-transferase ( known to be involved in detoxification of xenobiotic substrates ) , a peptidase and a sugar transporter ( Fig 2B ) ., As changes in carbohydrate and lipid metabolism are key features of Malassezia genomes ( see Results below ) , this analysis suggests our reference genomes will serve as an important resource for characterizing strain variations contributing to different phenotypes on human skin ., Leveraging the comprehensiveness of our genomic resource for Malassezia , we set out to compare it against a broad panel of 16 fungal genomes ( including all sequenced species in Ustilaginomycotina , a few other Basidiomycetes and several Ascomycetes as outgroups ) ., Using a genome-wide multi-gene approach we first established a robust phylogenetic view of Malassezia’s relationship with other fungi and each other ( Fig 3A and 3B; see Methods ) ., In contrast to earlier reports placing Malassezia among the Exobasidiomycetes 17 or Ustilaginomycetes 25 , our analysis suggests that it may be an isolated group ( namely the class Malasseziomycetes ) in the subphylum of Ustilaginomycotina , in agreement with Wang et al 16 ., However , Wang et al placed Ustilaginomycetes close to Malassezia , and placed Exobasidiomycetes as the basal group 16; our tree placed Malassezia as the basal group , indicating early divergence from its plant-pathogenic relatives ., Within Malassezia , our phylogeny supports three main clusters ( Fig 3B ) : Cluster A consists of fungemia-causing species M . furfur 26 and three other species ( M . japonica , M . obtusa , and M . yamatoensis ) , rarely found on healthy human skin ( Fig 2A ) ; Cluster B includes a sub-cluster of the most common human skin residents M . globosa and M . restricta 11 , the slightly less common M . sympodialis 14 ) as well as related species in another sub-cluster; Cluster C consists of two outliers , M . cuniculi and M . slooffiae ( Fig 3B ) , both of which are rare on human skin ( Fig 2A ) ., Notably , while broadly in agreement , our phylogeny disagrees with the placement of several species compared to a four-gene tree 27 and an earlier AFLP based tree 28 , though its concordance with the mitochondrial phylogeny as well as alternative approaches to reconstruct phylogeny ( S3 Fig ) suggest that it is likely to be more reliable ., Note that , as expected , our phylogeny also confirms the definition and molecular distinctness of various Malassezia species as well as the entire genus ., We then used comparative genomics to reveal genomic elements unique to Malassezia , identifying a small set of 13 functional domains ( PFam families 29 ) to be Malassezia-specific , compared to a much larger set of 741 domains likely lost in the common ancestor to all Malassezia ( Fig 3B , S3 Table; see Methods ) , in addition to gene family expansions and signatures of selection ( S3 Table ) ., The set of Malassezia-specific genes contains mainly genes of unknown function and is not enriched for a specific functional category ( S3 Table ) ., On the other hand , the set of genes lost in all Malassezia varies widely in function , from genes encoding enzymes to transcriptional regulators to known accessory genes ( S3 Table ) ., However , we did detect significant enrichment for two lost functional categories , specifically , enzymes involved in carbohydrate metabolic process ( q-value < 4 . 5×10−4 ) and in hydrolysis activity ( hydrolyzing O-glycosyl compounds; q-value < 4 . 5×10−4 ) , as expected for a genus of skin-adapted fungi that use lipids as their main carbon source ., In addition , we also noted that the gene encoding the fatty acid synthase ( FAS ) was missing in all Malassezia , indicating that the genus is lipid-dependent and not just lipophilic as suggested earlier 30 ., The idea that a subset of Malassezia is not lipid-dependent is based on the observation that some M . pachydermatis isolates can grow in media ( Sabouraud-dextrose agar ) without added lipids , though it does require fatty acids to grow in simple defined media 31 ., We experimentally re-investigated the contents of Sabouraud-dextrose agar media and noted that the added peptone contains 0 . 6% lipid , with 6 μg of palmitic acid per gram of peptone and lesser amounts of other fatty acids ., Furthermore , in 2 X YNB defined media , M . pachydermatis strains ( 1879 and 7550 ) were able to grow only in the presence of added lipids confirming the unique lipid-dependent nature of all Malassezia species ( S2 Text ) ., At the structural level we confirmed linkage between the two mating loci ( MAT ) in three Malassezia species ( belonging to clusters A and B , S4 Fig; i . e . likely a pseudo-bipolar configuration ) , a feature that is hypothesized to contribute to pathogenesis 32 , but is unique to Malassezia among Basidiomycetes ( S3 Text , S4 Fig and S4 Table ) ., We also noted a loss of the RNAi pathway and a concomitant reduction in transposon element density in all Malassezia genomes ( S4 Text ) ., Finally our selection analysis revealed a diverse set of noteworthy genes undergoing positive selection ( S5 Text and S3 Table ) , with the strongest signal being observed in a protein ( with match to the PFam domain PF12481 ) known to be induced by aluminum , a common component of deodorant , shaving cream and gel 33 ., The Malassezia-specific gene families identified using known domain families ( PFam ) contain many interesting candidates for horizontally transferred genes ( HTGs ) ., We also used a clustering based approach to expand this analysis to gene families with or without PFam domains , obtaining an additional set of 44 Malassezia-specific gene clusters , most of which have unknown function ( S3 Table ) ., Finally , we used two additional approaches based on similarity searches and phylogenetic analysis to catalog genes with more subtle evidence of horizontal transfer from bacteria into Malassezia 34 , 35 to identify 6 additional genes , many of which appear to be associated to oxidative stress response ( including two oxidoreductases and one catalase; S3 Table and S6 Text ) ., We further investigated the role and function of three specific gene families that likely represent key gain-of-function events in Malassezia ., The first of these is unique as it is the only one found to be conserved in all Malassezia ., This gene family is defined by matches to the PFam domain PF06742 ( a domain of unknown function ) and is present in a single gene copy in all Malassezia , except for the M . furfur hybrids and M . slooffiae which have two gene copies ., Its universal presence in all Malassezia and absence in all other Basidiomycetes suggests that a lateral gene transfer event in the ancestor of all Malassezia is the most parsimonious explanation ., In addition , while the likely source of this gene could not be determined due to its ancient origin , we noted that it is seen in diverse and often pathogenic bacteria ( e . g . Mycobacterium tuberculosis , Listeria monocytogenes and Salmonella enterica ) and fungi ( e . g . Aspergillus flavus ) and is surprisingly well conserved ( http://pfam . xfam . org/ ) ., Furthermore , we noted that the gene in M . globosa is significantly up-regulated in nutrient deficient conditions ( Fig 4A ) while its ortholog in Chlamydomonas reinhardtii is dramatically up-regulated under sulfur depletion conditions ( http://tinyurl . com/nyjd3md ) , suggesting that they might serve an essential biological role ., Proteomics evidence from M . sympodialis 14 indicates that this gene is likely translated ( Fig 4C ) and secreted ( based on a signal peptide match ) ., Homology modeling predicted its likely function to be a glycosyl hydrolase ( EC 3 . 2 . 1 . x , www . genome . jp/kegg/ ) ( Fig 4D , S7 Text ) ., The exact substrate remains to be determined but based on structural considerations there is slightly higher similarity to beta-galactosidases or mannosidases near the predicted substrate binding site ( S7 Text ) ., In addition , hydrolyzing activity on fungal cell wall glucans , which have been determined to be mainly ( 1->6 ) beta-D-glucans in M . sympodialis 36 , cannot be excluded based on profile sequence searches ( S7 Text ) ., Adding to the functional context , co-expression analysis in M . globosa revealed that this putative hydrolase gene’s expression is highly correlated with that of an aspartyl protease ( mgl_641 , Pearson Correlation = 0 . 955 , FDR = 2 . 16×10−20 ) ., Interestingly , in another fungus , Candida glabrata , an aspartyl protease is required for pH-change-induced reduction in total beta-glucan levels in the cell wall 37 which could be achieved by coordinating with a beta-glucan hydrolase ., Further experimental work should help clarify this hypothesis and the gene’s impact on Malassezia biology ., The second gene family , with a match to the PFam domain PF00199 , likely represents a case of inter-kingdom gene transfer ( from bacteria ) of a catalase gene whose product carries out the key function of removing the reactive oxygen species H2O2 38 ., Intriguingly our phylogenetic analysis suggests that while , in general , all Malassezia have one catalase that is more closely related to bacterial catalases ( from Blastomonas and Sphingomonas ) , M . slooffiae has an additional , presumably ancestral , catalase that is more closely related to fungal catalases ( S5 Fig ) ., The acquisition of a bacterial catalase in Malassezia could have provided a selective advantage in adapting to life on a new host , especially considering the numerous secreted proteins ( for example , GMC oxidoreductases ) that could generate hydrogen peroxide 2 ., Within the genus , catalase genes are missing in two species , M . restricta and M . pachydermatis , and this was confirmed by BLAST search 39 to both genomes and proteomes ., For M . restricta , absence of catalase enzyme activity has been confirmed by enzyme test 40; given the fact that Malassezia live in an aerobic environment on skin 1 alternative metabolic pathways might exist to detoxify oxygen in M . restricta ., For M . pachydermatis , catalase activity has been observed 40 and alternative catalases might exist which are sufficiently diverged from catalases in other Malassezia species ., The third gene family , defined by matches to the PFam domain PF13367 ( a family of putative PrsW proteases ) , was found to be present in all genomes of Malassezia cluster B ( containing species commonly found on human skin ) while being absent in other Malassezia and Basidiomycetes ( S3 Table ) , suggesting that it may have been horizontally acquired in the lineage leading to cluster B . Genes belonging to this gene family were readily found in skin resident bacteria ( e . g . Propionibacterium , Streptococcus and Staphylococcus ) as well as a few parasitic protists ( e . g . Toxoplasma gondii , Neospora caninum , Cryptosporidium and Plasmodium ) ( http://pfam . xfam . org/ ) ., In Bacillus subtilis , PrsWs sense antimicrobial peptides and then cleave the anti-ϬW factor to activate the ϬW factor 41 ., However , in the absence of the anti-ϬW factor or the ϬW factor in Malassezia , these genes are likely to have a different role ., We confirmed that this gene is expressed and translated ( Fig 4B and 4C ) and significantly down-regulated in nutrient deficient conditions ( Fig 4B ) ., PrsW-like proteases belong to the endopeptidase family M82 that is related to the family M79 ( that includes Rce1 peptidases ) ( Fig 4E ) 41 , 42 with a recently resolved crystal structure 43 ., Homology modeling confirmed the known catalytically important residues 41 , 43 to be conserved between these two families ( S6 Fig , S7 Text ) and located in the center of the transmembrane bundle forming the active site ( Fig 4E ) ., The Rce1 peptidases typically cleave C-terminal tripeptides from isoprenylated proteins ( e . g . fungal mating factor a ) 43 ., However , this is not likely the function of Malassezia PrsW-like family due to the presence of direct Rce1 homologs in Malassezia ( e . g . MGL_3383 , Fig 4E ) ., Malassezia are known to have varying host tropism and highly specific preferences for environmental niches and food sources 3 , 44 ., For example , some highly sebaceous sites such as scalp ( including occiput ) and back are typically dominated by M . globosa 11 ., To further understand niche-specificity in Malassezia , we evaluated their preference for growth in various lipid media using “Lipid Assimilation Assays” ( see Methods ) ., These experiments highlight a strong specificity in Malassezia’s preference for lipids ( S4 Table ) that is not well correlated with their phylogenetic relatedness ., For example , M . furfur and M . sympodialis are functionally similar as the most robust of the lipid-dependent species in culture , sharing the broadest range of lipids that support growth ( Fig 5A , S5 Table ) ., However , they are not closely related and are placed in different sub-clusters of the Malassezia phylogeny ( Fig 3B ) ., Also , the closely related species M . globosa and M . restricta have different lipid assimilation profiles ( Fig 5A , S5 Table ) ., To understand the genetic basis of these phenotypes , we reexamined the list of gene family expansions and selection in Malassezia ., Strikingly , the most expanded gene family in Malassezia was found to be a phospholipase family , and a secretory lipase family was also among the list of 13 families with a 2-fold increase in median copy number in Malassezia compared to other fungi ( S3 Table ) ., Lipase and phospholipase activities have been detected in multiple Malassezia species 45 , 46 ., Their genes are highly expressed in vivo on human scalp 2 , 45 , 47 , and are thought to play an essential role in supporting their growth ., Therefore , we hypothesized that expansion of these gene families might explain Malassezia niche-specificity ., Other than lipases , many peptidases in multiple families are found in the most expanded gene families in Malassezia , underlining their importance in Malassezia biology ( S3 Table , S8 Text ) ., To understand the evolution of these families , we inferred parsimonious reconstructions of gain and loss events ( see Methods ) ., In particular , the most expanded gene family , a family of phospholipases ( phosphoesterases , PF04185 ) showed a striking pattern of extensive turnover , with a dramatic expansion of the family in the ancestor of cluster B species followed by lineage specific losses ( Fig 5B ) ., Recent duplications were also observed in M . japonica , M . slooffiae and M . cuniculi while cluster A species seem to have experienced a significant contraction in this gene family that is thought to be relevant to fungal pathogenesis 48 ., Extensive turnover in the lipase gene repertoire of Malassezia was also seen in the secretory lipases ( PF03583 ) ( S7 Fig ) ., Species-specific duplication events were found in seven species and , in particular , there has been rapid recent expansion of the family in M . slooffiae and M . pachydermatis ( S7 Fig ) ., We observed frequent lineage-specific duplication and loss of genes in other lipase families as well ( e . g . PF01764 ) and together these could explain the complex patterns of lipid-specificity observed in Malassezia ., Further experiments should help establish the exact roles specific lipase genes play in the process of human colonization and pathogenesis ., Malassezia , while found on all humans and associated with many common human skin diseases , are poorly understood in large part due to a lack of genomic tools ., Here , we report generation and analysis of the genomes of all 14 accepted Malassezia species , including multiple strains of those most commonly found on human skin ( for a total of 24 strains ) ., Malassezia are unique in several ways , including their adaptation to life on animal skin , their dominance as eukaryotic residents on human skin ( in contrast to the diversity seen among prokaryotic commensals ) , and their lipid-dependent lifestyle ., Even within Malassezia , we noted there is substantial variability in preference for food sources and thus environmental niches ., As a first step , the analysis in this study serves to systematically catalog and characterize genomic features unique to Malassezia and its lineages , which could then be associated with the observed phenotypes ., This was aided by the characterization of all known species in the genus as well as multiple strains for key species , allowing robust conclusions to be drawn despite potential analysis pitfalls ., Correspondingly , several of the genes identified in this study are prime candidates for further experimental study ., It is tempting to speculate , for example , that the gene containing the PFam family PF06742 serves an essential function in Malassezia such that loss of the gene could be lethal ., As this gene was likely horizontally acquired by the ancestor of all Malassezia , its function could also be tied to the origin of the genus , particularly if it relates to utilizing energy sources from the host ., Similarly , the role of PF13367 could be linked to the ability of cluster B Malassezia to thrive on human skin ., In general , Malassezia are not facile experimental systems as they are challenging to cultivate and typically recalcitrant to genetic manipulation ., In this context , recent success in performing gene deletion in M . furfur is encouraging ( Giuseppe Ianiri and Alexander Idnurm , personal communication ) and could enable in vivo functional characterization ., Among other gene families of interest , particularly due to their association with niche-specificity , are several lipase families ., Interestingly , there are a total of 25 lipases found in the two major lipase families ( PF03583 and PF01764 ) in M . slooffiae , a species found on both animals and humans 9 , 49 with little known about its involvement in diseases ., This is the most in any haploid Malassezia strain ( S3 Table ) , with the closely related M . cuniculi having only 16 lipases and M . globosa 14 ( S3 Table ) ., Many lipases in M . slooffiae are derived from unique species-specific duplication events ( S7 Fig ) ., However , it remains an open question if M . slooffiae is indeed able to leverage this large arsenal of lipases to utilize a wider range of lipids and hence live in more diverse ecosystems ., Intriguingly , we observed in our skin metagenomic datasets that the only three samples with relatively high abundance of M . obtusa are in co-occurrence with M . slooffiae ( Fig 2A ) , suggesting the possibility that M . slooffiae breaks down lipids for utilization by M . obtusa , a rare and hard-to-culture species ., Further studies are needed to establish this relationship but it is clear that the availability of genomes for all Malassezia species will be critical to understanding their distribution and role in human diseases ., The question of whether Malassezia or Malassezia-like species are abundant in habitats other than on the skin of warm-blooded animals is an intriguing one ., Our analysis of samples from varying habitats suggests that they are either not common or not similar enough to known Malassezia species ., With the availability of a catalog of Malassezia-specific genes , sensitive models can be built to detect remote homologies to these sequences 29 as a way to search for distant Malassezia-like species in the environment ., This in turn should help clarify the emergence and role of Malassezia as a skin-adapted fungus ., Fungal mating is hypothesized to play an important role in pathogenesis by increasing g

Introduction, Results, Discussion, Methods

Malassezia is a unique lipophilic genus in class Malasseziomycetes in Ustilaginomycotina , ( Basidiomycota , fungi ) that otherwise consists almost exclusively of plant pathogens ., Malassezia are typically isolated from warm-blooded animals , are dominant members of the human skin mycobiome and are associated with common skin disorders ., To characterize the genetic basis of the unique phenotypes of Malassezia spp ., , we sequenced the genomes of all 14 accepted species and used comparative genomics against a broad panel of fungal genomes to comprehensively identify distinct features that define the Malassezia gene repertoire: gene gain and loss; selection signatures; and lineage-specific gene family expansions ., Our analysis revealed key gene gain events ( 64 ) with a single gene conserved across all Malassezia but absent in all other sequenced Basidiomycota ., These likely horizontally transferred genes provide intriguing gain-of-function events and prime candidates to explain the emergence of Malassezia ., A larger set of genes ( 741 ) were lost , with enrichment for glycosyl hydrolases and carbohydrate metabolism , concordant with adaptation to skin’s carbohydrate-deficient environment ., Gene family analysis revealed extensive turnover and underlined the importance of secretory lipases , phospholipases , aspartyl proteases , and other peptidases ., Combining genomic analysis with a re-evaluation of culture characteristics , we establish the likely lipid-dependence of all Malassezia ., Our phylogenetic analysis sheds new light on the relationship between Malassezia and other members of Ustilaginomycotina , as well as phylogenetic lineages within the genus ., Overall , our study provides a unique genomic resource for understanding Malassezia niche-specificity and potential virulence , as well as their abundance and distribution in the environment and on human skin .

Malassezia are the dominant eukaryotic residents of human skin and are associated with the most common skin disorders , including dandruff , atopic dermatitis , eczema , and others ., Despite significant effort , the role of Malassezia in skin disease and homeostasis remains unclear ., Malassezia are also unique among fungi by requiring lipids for growth , but the breadth and genetic basis of their lipophilic lifestyle has not been comprehensively studied ., Here we report the complete genomes of all 14 Malassezia species ( including multiple strains of the most common species found on humans ) and systematically identify features that define the genus and its sub-lineages , including horizontally transferred genes likely to represent key gain-of-function events and which may have enabled evolution of the genus from plant to animal inhabitants ., Genus wide expansion of lipid hydrolases and loss of carbohydrate metabolism genes underscore the entire genus’ gradual evolution to lipid-dependency , which was confirmed even in the previously thought to be lipophilic M . pachydermatis , via genomics with experimental confirmation ., Finally , these reference genomes will serve as a valuable resource for future metagenomic investigations into the role of Malassezia species in normal healthy skin and diseases .

journal.pgen.1006298

Nuclear Localised MORE SULPHUR ACCUMULATION1 Epigenetically Regulates Sulphur Homeostasis in Arabidopsis thaliana

Sulphur ( S ) is one of the essential macronutrients required for plant growth and plays a pivotal role in plant development and metabolism ., Plants take up S in the form of inorganic sulphate from the rhizosphere mainly by two high-affinity sulphate transporters , SULTR1;1 and SULTR1;2 1–3 ., Before reduction , sulphate is first activated by ATP sulfurylase ( ATPS ) to adenosine 5′-phosphosulfate ( APS ) 4 , 5 ., APS is either reduced by APS reductase ( APR ) to sulphite , or phosphorylated by APS kinase ( APK ) to form 3′-phosphoadenosine 5′-phosphosulfate ( PAPS ) which provides activated sulphate for many sulphation reactions ., In the primary sulphate assimilation branch , sulphite is further reduced to sulphide by sulphite reductase ( SiR ) ., Subsequently , O-acetylserine ( thiol ) lyase ( OAS-TL ) catalyzes the condensation of sulphide and O-acetylserine ( OAS ) to form cysteine ( Cys ) , the first organic-reduced sulphur compound ., Cys then serves as a precursor for the biosynthesis of methionine ( Met ) , glutathione ( GSH ) , vitamins and other sulphur derivatives ., Met can be further used for the biosynthesis of S-adenosylmethionine ( SAM ) which is a universal methyl group donor for many methylation reactions 6 , suggesting a potential yet unexplored connection between S metabolism and methylation reactions , including DNA methylation ., Compared to the well-characterized sulphate uptake and S assimilation pathway 4 , 5 , our knowledge of the regulation of S homeostasis in plants is limited ., The transcription factor SLIM1 ( SULFUR LIMITATION 1 ) acts as a central transcriptional regulator which controls sulphate uptake and the balance of global sulphur utilization under S deficiency by regulating the expression of SULTR1;1 and SULTR1;2 and genes involved in the degradation of glucosinolates 7 ., Another regulator involved in S starvation response is miR395 ., miR395 targets to ATPS1 , ATPS4 and the low-affinity sulphate transporter gene SULTR2;1 and regulates their expression 8 , 9 ., miR395 is strongly induced by S deficiency and regulates the translocation of sulphate from old to young leaves as well as from roots to shoots under sulphate limited conditions 10 , 11 ., The induction of miR395 by S deficiency is controlled by SLIM1 and thus SLIM1 and miR395 are two important components of the regulatory circuit controlling plant sulphate assimilation in S deficient conditions 8 , 11 ., The expression of SULTR1;1 and SULTR1;2 is controlled by SLIM1 7 , while APR1 and APR2 are controlled by the transcriptional factor LONG HYPOCOTYL5 ( HY5 ) 12 ., Unlike SULTR1;1 and SULTR1;2 , which are induced by S deficiency in both shoots and roots 2 , SULTR2;1 shows the opposite response to S deficiency in shoots and roots , with decreased expression in shoots but strong induction in roots 1 ., The repression of SULTR2;1 in shoots is consistent with the upregulation of miR395 under S deficiency , which targets to SULTR2;1 mRNA and suppresses its expression 8 ., However , both SULTR2;1 and miR395 are upregulated in roots under S deficiency ., This is due to their cell-type-specific expressions in roots , in which miR395 only expresses in the phloem companion cells and is unable to target the SULTR2;1 mRNA in xylem parenchyma and pericycle cells 8 ., Several cis-acting elements responsive to S deficiency have been identified , such as sulphur-responsive element ( SURE ) in the promoter of SULTR1;1 13 , and SURE21A and SURE21B in the 3’-untranslated region of SULTR2;1 14 ., However , the transcription factors targeting these cis-acting elements have not been identified ., Regulation of gene expression at the transcriptional and posttranscriptional level is known to play critical roles in the way plants respond to environmental stresses ., Recent studies have suggested that epigenetic regulation of gene expression also plays an important role in these responses 15 , 16 ., DNA methylation is one of the most studied epigenetic modifications , in which the methyl group from SAM is transferred to the 5’ position of a cytosine to form 5-methylcytosine ., In plants , DNA methylation occurs in the three different sequence contexts CG , CHG and CHH ( where H is A , C or T ) , through different pathways 17 ., DNA methylation is involved in genomic imprinting , silencing of the expression of genes and transposons , and regulating gene expression under environmental stresses , including in response to nutrient status 15 , 16 ., Dynamic DNA methylation changes have been recently shown to modulate the expression of genes in response to phosphorus starvation in A . thaliana 18 and in rice 19 ., However , the involvement of altered DNA methylation in response to other nutrient deficiencies is not clear ., In this study , we describe the identification and characterization of the more sulphur accumulation1-1 ( msa1-1 ) mutant in A . thaliana which has high leaf S . We propose that MSA1 functions in the nucleus to maintain DNA methylation including that required for epigenetic regulation of sulphur-homeostasis through an involvement in the maintenance of SAM levels ., In our previous search for A . thaliana mutants with altered leaf elemental composition ( ionome ) , we identified 51 fast neutron–mutagenized mutants , and several of them have now been well characterized 20–25 ., To further identify mutants with an altered leaf ionome , we conducted a screen of ethyl methanesulfonate ( EMS ) –mutagenized plants ., Here , we describe the msa1-1 mutant identified as containing elevated leaf S . The msa1-1 mutant accumulated 54% higher total leaf S compared to the wild type ( WT ) Col-0 when grown in soil , and 63% higher when grown on agar-solidified media , without obvious visible morphological changes ( Fig 1A–1C ) ., The high S phenotype was observed only in shoots and not in roots when grown on agar-solidified media with different concentrations of sulphate ( S1A and S1B Fig ) ., Further analysis showed that both sulphate and sulphite concentrations are elevated in the shoots of msa1-1 ( Fig 1D and 1E ) ., Of the 20 elements measured , selenium ( Se ) was also found to be higher in the leaves of msa1-1 compared to WT ( Fig 1F and 1G ) , which is likely due to the uptake of selenate by sulphate transporters in plants 26 ., Leaf S and Se accumulation in F1 plants derived from the msa1-1 × Ler-0 cross , as well as the segregation of the S and Se phenotype in the F2 population , revealed that msa1-1 is a recessive mutation ( S2A–S2D Fig ) ., The causal locus was mapped to a 10 Mb interval on chromosome 1 using bulk segregant analysis ( BSA; Fig 2A ) ., Two genes with nonsynonymous mutations in the BSA mapping interval were identified by whole genome sequencing , AT1G23935 annotated as apoptosis inhibitory 5 and AT1G36370 previously annotated as serine hydroxymethyltransferase 7 ( SHM7 ) based on sequence homology but without functional data 27 ( Fig 2B ) ., The C to T transition in AT1G23935 and G to A transition in AT1G36370 lead to P447S and S186F mutations , respectively ( Fig 2B ) ., Notably , the S186 amino acid residue mutated in the protein encoded by AT1G36370 is conserved among authentic plant SHM proteins ( S3A Fig ) ., Serine hydroxymethyltransferase is a ubiquitous and conserved enzyme in living organisms from bacteria to higher plants and mammals , playing important roles in glycine-into-serine interconversion and cellular one-carbon ( C1 ) folate metabolism 28–30 ., As a pyridoxal-5’-phosphate ( PLP ) dependent enzyme , SHM catalyses the reversible conversion of serine ( Ser ) and tetrahydrofolate ( THF ) to glycine ( Gly ) and 5 , -10-methylene-THF 31 ., Homology modelling of the protein encoded by AT1G36370 using a known SHM indicated that Y185 and E187 , neighbouring amino acid residues to S186 , form part of the binding site of SHM for the co-factor PLP and folate 32 ( S3B–S3D Fig ) ., Mutation of S186 to Phe is predicted to destroy the H-bonds between S186 and its neighbouring residues and generate steric hindrances ( S3C and S3D Fig ) , which may affect the function of the protein encoded by AT1G36370 ., To establish which gene is driving high S and Se in msa1-1 , we obtained one T-DNA insertion allele for AT1G23935 ( SALK_069606 ) and two for AT1G36370 ( SALK_044268 and SALK_118251 ) ( Figs 2B and S4A–S4C ) ., The S and Se concentration in leaves of both the T-DNA alleles of AT1G36370 were significantly higher than WT and similar to msa1-1 , while no changes were observed for the AT1G23935 T-DNA allele , indicating AT1G36370 is likely the causal gene ( S4D and S4E Fig ) ., To further establish that AT1G36370 is the causal gene , we crossed SALK_044268 ( designated msa1-2 ) and SALK_118251 ( designated msa1-3 ) with Col-0 WT and with msa1-1 ., All F1 plants from the msa1-1 × Col-0 WT , msa1-2 × Col-0 WT and msa1-3 × Col-0 WT crosses showed similar leaf S and Se concentrations to Col-0 WT , as expected for a recessive mutation ., However , the F1 progeny from the msa1-2 × msa1-1 and msa1-3 × msa1-1 crosses all contained higher leaf S and Se than Col-0 WT , indicating these mutants are allelic ( Fig 2C and 2E ) ., We further transformed the WT genomic DNA fragments of MSA1 into msa1-1 ., Six independent T2 complementation lines all showed leaf S and Se levels similar to WT ( Fig 2D and 2F ) ., Both genetic and transgenic complementation demonstrated that AT1G36370 is the causal gene underlying the high S and Se phenotype of msa1-1 , and we name AT1G36370 as MSA1 ., To determine the tissue expression pattern of MSA1 , WT was transformed with a MSA1 promoter-GUS construct ., In T2 transgenic seedlings a strong GUS signal was observed in roots and leaves , along with a weak signal in hypocotyls ( Fig 3A–3C ) ., In plants grown under S-sufficiency , GUS staining was mainly observed in the root maturation zone ( Fig 3A ) ., However , GUS staining was detected throughout the roots of the plants grown under S-deficiency ( Fig 3B ) , indicating the MSA1 promoter was activated by S-deficiency ., This is confirmed by qRT-PCR ( Fig 3H ) ., GUS staining was also observed in the inflorescence ( Fig 3D ) , especially in the stigma and anther ( Fig 3E ) , and in the young siliques ( Fig 3F ) but not in the mature seeds ( Fig 3G ) ., To investigate the subcellular localization of MSA1 , a GFP-MSA1 fusion construct under the control of the cauliflower mosaic virus ( CaMV ) 35S promoter was stably expressed in WT ., GFP fluorescence was detected exclusively in the nucleus , as stained by the nuclear specific dye DAPI , suggesting MSA1 localizes to the nucleus ( Fig 3I ) ., Furthermore , the mutated MSA1 from msa1-1 had the same nuclear localization as the WT protein ( Fig 3I ) ., The nuclear localization of MSA1 was not affected by S-deficiency and MSA1 localized to the nucleus in both leaves and roots ( S5A and S5B Fig ) ., To explore whether nuclear localization is required for MSA1 function , we directed MSA1 into the cytosol by attaching a nuclear export signal ( NES ) at the C terminus of GFP-MSA1 and transforming the DNA construct into msa1-1 with expression driven by the MSA1 native promoter ( Fig 4A ) ., The NES is derived from the mammalian PKI protein and has been used to confer cytosolic localization of phytochrome B in A . thaliana 33 , 34 ., Similar to the GFP-MSA1 control lines , the GFP signal was still found in the nucleus of GFP-MSA1-NES lines , suggesting that fusion of the NES to the C terminus of MSA1 is not sufficient to completely export MSA1 from the nucleus ( Fig 4B ) ., Not surprisingly , the total S in these lines was restored to WT levels ( Fig 4C ) ., MSA1 was predicted to have a putative bipartite nuclear localization signal ( NLS ) containing two clusters of lysine/arginine residues in the N terminus ( S3A Fig ) ., We mutated the native NLS of MSA1 by replacing both lysine and arginine with glutamines ( Fig 4A ) ., The basic amino acids lysine and arginine in an NLS are essential for the transport of nuclear localized proteins into the nucleus 35 ., The MSA1 construct with a mutated native NLS and fusion with NES at the C terminal was expressed in msa1-1 from the MSA1 native promoter ., Nuclear localization of MSA1 was abolished in these transgenic lines , with the GFP signal being observed in the cytosol ( Fig 4B ) ., Total leaf S levels in these transgenic plants were the same as in msa1-1 , demonstrating that MSA1 localized in the cytosol could not complement msa1-1 ( Fig 4C ) ., These results indicated that nuclear localization is essential for MSA1 function in the regulation of S homeostasis ., Given that MSA1 had previously been annotated as SHM7 based on sequence homology without functional data 27 , we tested whether MSA1 has SHM activity in vitro by expressing MSA1 in E . coli and measuring SHM activity of purified protein with monoglutamylated THF as the substrate ., We were able to detect the SHM activity of two well characterized SHM isoforms SHM1 and SHM2 , with activities in the range previously reported 36 ., However , no SHM activity was detected for MSA1 ( S6A Fig ) ., SHM enzymes exhibit different activity with monoglutamyl folate and polyglutamylated forms 37 ., We have previously shown that SHM1 , SHM2 , SHM3 and SHM4 all exhibit SHM activity with either mono or polyglutamylated THF 38 , 39 ., However , we were unable to detect SHM activity of MSA1 using hexaglutamylated THF ., Further , expression of MSA1 in an E . coli loss-of-SHM function mutant 40 failed to rescue the glycine auxotrophy of this mutant ( S6B Fig ) ., Together these results strongly suggest that MSA1 is not a conventional SHM ., To probe the function of MSA1 in S homeostasis , we determined the concentrations of S related metabolites in msa1-1 grown under S sufficient and deficient conditions ( Fig 5A ) ., Under S-sufficiency , we observed no significant changes in the concentration of Ser and Gly in either shoots or roots of msa1-1 ( Fig 5B and 5C ) ., The fact that Gly was not accumulated to high levels in shoots of msa1-1 suggests that unlike the shm1 knock-out mutant 41 , msa1-1 is not a photorespiratory mutant ., OAS concentration was also not affected in shoots of msa1-1 compared to WT but significantly decreased in roots under S-deficiency ( Fig 5D ) ., The msa1-1 mutant accumulated higher levels of total S , as well as sulphate and sulphite , in shoots ( Fig 1B–1D ) ., We further showed that the S-containing amino acids Cys and Met and the Cys-containing tripeptide glutathione ( GSH ) were also elevated in both shoots and roots of msa1-1 compared to WT ( Fig 5E–5G ) ., Under S-deficiency , the msa1-1 mutant accumulated higher Cys in both shoots and roots but only high levels of GSH in shoots , and no significant difference of Met in either shoots or roots compared to WT was observed ( Fig 5E–5G ) ., These results indicated that not only the accumulation of sulphate but also S assimilation is enhanced in msa1-1 ., To summarize , msa1-1 accumulates higher levels of Cys , Met and GSH ., However , the msa1-1 mutant generally maintained the same level of Gly , Ser and OAS under S-sufficiency as the WT but had lower levels under S-deficiency ( Fig 5A ) ., These results suggest that MSA1 is likely not a SHM involved in the conversion of Gly to Ser for the biosynthesis of Cys ., However , under S-deficiency more Gly , Ser and OAS were used for the biosynthesis of Cys in msa1-1 , consistent with the enhancement of S assimilation in msa1-1 ., As Met is the precursor of the methyl group donor SAM , we further determined the concentration of SAM ., We observed that the level of SAM , as well as 5-methylthioadenosine ( MTA ) , an intermediate of the endogenous Yang-cycle that recycles SAM after transfer of the methyl-group for synthesis of nicotinamide , polyamines or ethylene 42 , were significantly lower in roots of msa1-1 compared to WT ( Fig 5H and 5I ) ., To investigate whether a shortage of SAM might drive the high S phenotype of msa1-1 , we performed a supplementation experiment ., External supplementation with SAM in the growth medium completely suppressed the high S phenotype of msa1-1 ( Fig 5J ) ., These results suggested that loss of function of MSA1 results in a shortage of SAM leading to the high S phenotype of msa1-1 ., Reduction of SAM levels by inhibition of folate biosynthesis has been shown to reduce global DNA methylation in A . thaliana 43 ., Given that the concentration of SAM is reduced in roots of msa1-1 ( Fig 5J ) , we performed whole-genome bisulfite sequencing of WT and msa1-1 to determine whether mutation of MSA1 affects global DNA methylation ., We achieved an average sequencing depth of 28 times , with more than 92% of cytosines in the nuclear genome covered , indicating the high-quality of our sequencing data ( S1 Table ) ., The overall cytosine methylation was lower in roots of msa1-1 compared to the WT ( Table 1 ) ., However , no reduction in DNA methylation was observed in msa1-1 shoots ( Table 1 ) , and this may be due to the fact that SAM concentration is only reduced in roots but not in shoots of msa1-1 ( Fig 5J ) ., Reduced DNA methylation in roots of msa1-1 was found to be due to reduced cytosine methylation in both genes and transposon elements ( TE ) ( S2 Table ) ., Normalization of methylation level in 100 kb windows revealed that the overall difference in methylation between WT and msa1-1 was mainly at cytosines in the CG sequence context and not in the CHG or CHH contexts ( Figs 6A and S7 ) ., Using a sliding-windows approach , we identified 3 , 646 and 3 , 421 differentially methylated regions ( DMRs ) in the shoot and root between WT and msa1-1 , respectively ( S3 and S4 Tables ) ., We defined genes with significant differential methylation ( adjusted p-value < 0 . 05 ) in the gene body , and 2 kb upstream and 2 kb downstream as differentially methylated genes ( DMGs ) ., In total , 4 , 977 and 4 , 444 DMGs were identified in the shoot and root of msa1-1 , respectively ( S5 Table ) ., Most of DMGs ( > 92 . 7% ) were differentially methylated on either the gene body or 2 kb upstream or downstream ( S8 Fig ) ., Among DMGs in the shoot , 2 , 773 are hyper-DMRs and 2 , 204 are hypo-DMRs showing significantly increased or decreased methylation , respectively ., Among root DMGs , 1 , 662 are hyper-DMRs and 2 , 782 are hypo-DMRs ., Comparison of the overlapping DMGs showed that only 14 . 7% hyper-DMGs in roots are hyper-methylated in shoots and 10 . 1% hypo-DMGs in roots are hypo-methylated in shoots ., However , 51 . 8% hypo-DMGs in roots are hyper-methylated in shoots and 57 . 3% hyper-DMGs in roots are hypo-methylated in shoots ( Fig 6B ) ., Gene ontology enrichment analysis of DMGs revealed the enrichment of genes involved in various biological processes , especially in nucleotide binding ( S6 Table ) ., To determine whether genes involved in S homeostasis are differently methylated in msa1-1 , we searched DMRs for genes that have previously been shown to be responsive to S starvation , and also included glucosinolate and anthocyanin metabolisms genes which are involved in S homeostasis 44 ., We identified four genes involved in glucosinolate and anthocyanin metabolisms and 15 S responsive genes that were differentially methylated in msa1-1 , including two high-affinity sulphate transporter genes SULTR1;1 and SULTR1;2 , and genes encoding 5-adenylylsulfate reductase ( APR3 ) and ATP sulphurylase ( APS4 ) ( S7 Table and Fig 6C–6E ) ., We found that the flanking sequence of the S responsive element ( SURE ) in the promoter of SULTR1;1 , which is essential for the S deficiency response 13 , was hypo-methylated in msa1-1 roots ( Fig 6C and S8 Table ) ., Using chop-PCR , we confirmed that the SURE flanking sequence of SULTR1;1 is hypo-methylated in msa1-1 roots ( Fig 6F ) ., Significantly , using chop-PCR we also show that in WT this SURE flanking sequence appears to be hyper-methylated in S sufficient conditions and hypo-methylated in S deficient conditions ( Fig 6F ) ., To better understand the connection between MSA1 function , DNA methylation and the elevated accumulation of total S in leaves of msa1-1 , we investigated the expression level of genes involved in S homeostasis ., Quantitative RT-PCR revealed that expression of SULTR1;1 , SULTR1;2 , and SULTR4;2 genes encoding sulphate transporters involved in root uptake and translocation of sulphate was higher in roots of msa1-1 compared to WT ( S9 Fig ) ., Further , transcription of genes encoding the APS reductases APR1 , APR2 , and APR3 , which are required for sulphate reductive assimilation , were also increased in roots of msa1-1 compared to WT ( S9 Fig ) ., The increased expression of SULTR1;1 and SULTR1;2 in the roots of msa1-1 was confirmed in plants grown on agar-solidified media ( Fig 7A and 7B ) ., These observations support the conclusion that the enhanced sulphur uptake and assimilation of msa1-1 is driven by constitutive induction of S-deficiency response genes ., To directly test if the high leaf S phenotype of msa1-1 is dependent on the high-affinity sulphate transporters SULTR1;1 and SULTR1;2 , we generated an msa1-1 sultr1;1 sultr1;2 triple mutant by crossing msa1-1 with the sultr1;1 sultr1;2 double knockout mutant in the Wassilewskija ( Ws ) background 45 ( Figs 7C and S10 ) ., To exclude the possibility that the different genetic backgrounds of Col-0 and Ws may affect the S phenotype , we selected one msa1-1 SULTR1;1 SULTR1;2 line which has the msa1-1 mutant allele and SULTR1;1 and SULTR1;2 WT alleles , and one MSA1 sultr1;1 sultr1;2 line with the MSA1 WT allele in a sultr1;1 sultr1;2 double knockout background ( Figs 7C and S10 ) ., The leaf S concentration of the sultr1;1 sultr1;2 double mutant was significantly lower than WT ( Fig 7D ) , consistent with its low sulphate uptake rate 45 , 46 ., There was no significant difference in leaf S concentration between msa1-1 and the msa1-1 SULTR1;1 SULTR1;2 line , or between sultr1;1 sultr1;2 double knockout and the MSA1 sultr1;1 sultr1;2 line , suggesting that the differences in genetic backgrounds between Col-0 and Ws had no effect on the leaf S phenotype ( Fig 7D ) ., The leaf S concentration of the msa1-1 sultr1;1 sultr1;2 triple mutant was similar to the sultr1;1 sultr1;2 double mutant and the MSA1 sultr1;1 sultr1;2 line indicating that the MSA1 mutation acts through elevated expression of SULTR1;1 and SULTR1;2 to enhance S accumulation ( Fig 7D ) ., The uptake , assimilation and metabolism of S in plants has been well explored ., However , our understanding of the regulation of S homeostasis remains much more limited ., In this study , we present evidence supporting the function of the nuclear-localized MSA1 in controlling S homeostasis in A . thaliana ., Loss of function of MSA1 reduces SAM levels , alters genome-wide DNA methylation levels , and leads to a constitutive S deficiency response ( Fig 5A ) ., MSA1 shows a high level of sequence homology to well characterised SHM enzymes , and recent studies have shown that yeast SHM2 , as part of a larger complex with other proteins , is involved in the biosynthesis of SAM in the nucleus for histone methylation 47 ., We have established that MSA1 localizes to the nucleus , and that this nuclear localization is essential for MSA1 function ( Figs 3I and 4A–4C ) ., We therefore propose that MSA1 functions in the production of a nuclear pool of SAM , though the existence of such a pool of SAM remains to be directly tested ., However , our hypothesis is supported by the known nuclear localization of various enzymes involved in SAM biosynthesis in A . thaliana , including the SAM synthetases AtSAMS1 , AtSAMS2 and AtSAMS3 48–50 , and enzymes involved in the recycling of the by-products of SAM-dependent transmethylation , including the SAH hydrolases SAHH1 and SAHH2 , and adenosine kinase ADK1 51 ., Blockage of SAM biosynthesis by inhibition of folate biosynthesis using sulfamethazine has previously been shown to reduce global DNA methylation 43 ., Here , we show that the overall level of DNA methylation is reduced in msa1-1 roots ( Table 1 ) , which is consistent with the reduced SAM concentration in this tissue ( Fig 5J ) ., Sulphate uptake and assimilation are repressed in normal S supply and de-repressed during S deficiency 4 , 5 ., In msa1-1 we identified several differentially methylated genes which are known to be responsive to S deficiency ( S7 Table ) , including two high-affinity sulphate transporter genes SULTR1;1 and SULTR1;2 , and the S assimilation gene APR3 ( Fig 6C–6E ) which are hypo-methylated in msa1-1 compared to WT ., Furthermore , msa1-1 shows a strong constitutive S-deficiency response , including increased expression of SULTR1;1 , SULTR1;2 and APR3 ( Figs 7A , 7B and S9 ) ., These results suggest that de-repression of S responsive genes at ample S supply in msa1-1 is likely caused by their differential methylation , and it is this de-repression that leads to the strong constitutive S-deficiency response in msa1-1 ., This is exemplified by the sulphur responsive element SURE upstream of SULTR1;1 ., In WT the flanking sequence of this element is hyper-methylated in S sufficient condition but hypo-methylated in S deficient condition ( Fig 6F ) ., Whereas , we observed that the flanking sequence of this SURE element is hypo-methylated in msa1-1 even under S sufficient conditions ( Fig 6F ) ., This suggests that the constitutive elevation of expression of SULTR1;1 in msa1-1 is due to the hypo-methylation of the SURE element in its promoter ., Similarly , hypo- and hyper-methylation in the vicinity of cis-acting elements known to regulate expression of phosphate–responsive genes have been shown to correlate with increased or decreased expression of low-phosphate responsive genes 52 ., Furthermore , SULTR1;3 is hypo-methylated and its expression is upregulated under phosphate starvation 18 , indicating another example of regulation of SULTR gene expression by DNA methylation ., The concurrence of hypo-methylation of SULTR1;1 as well as SULTR1;3 and their upregulated expressions suggests the existence of a potentially common mechanism in the regulation of SULTR transporter gene expression through altered DNA methylation under nutrient deficiency ., Previous studies have shown that expression of MSA1 is regulated by SLIM1 7 ., MSA1 expression is significantly elevated by S-deficiency ( Fig 3H ) ., Meanwhile , the expression of SULTR1;1 and SULTR1;2 is also induced by S-deficiency ( Fig 7A and 7B ) ., This suggests that the induction of SULTR1;1 and SULTR1;2 under S-deficiency by demethylation is not controlled by the upregulation of MSA1 which would be expected to enhance DNA methylation by increasing SAM supply ., One possible function of MSA1 under S-deficiency could be in prioritising SAM biosynthesis in the nucleus to maintain overall DNA methylation , and this is supported by our observation of an overall decrease in DNA methylation in roots of msa1-1 ., A second possibility is that the upregulation of MSA1 under S-deficiency is to specifically suppress , by methylation , genes down-regulated during the S-deficiency response ., This possibility is supported by the fact that the biosynthesis of glucosinolates , a group of S-rich secondary metabolites , is inhibited during S-deficiency , and many genes involved in their biosynthesis are strongly down-regulated , such as genes encoding a branched-chain amino acid aminotransferase , methylthioalkylmalate synthases , and cytochrome P450s 7 , 53 ., Interestingly , the branched-chain amino acid aminotransferase genes BCAT3 and BCAT4 , and the cytochrome P450 gene CAP79B2 are differentially methylated between WT and msa1-1 ( S7 Table ) , supporting this hypothesis ., However , further studies are required to test the idea that methylation suppresses expression of genes involved in S consumption ., It is also possible that MSA1 and SULTR1;1 as well as SULTR1;2 are induced in different cell types under S-deficiency ., Such cell-type-specific induction by S-deficiency has been observed for miR395 and SULTR2;1 ., The induction of miR395 by S-deficiency is restricted to the phloem companion cells in roots , which fails to digest the miRNA target SULTR2;1 expressed in xylem parenchyma and pericycle cells leaving the SULTR2;1 mRNA intact 8 ., SAM is also the methyl donor for RNA methylation ., It is possible that the decreased SAM pool in msa1-1 might reduce RNA methylation of sulphur deficiency responsive genes and thus affect their expression ., Methylation at the N6 of adenosine ( m6A ) on messenger RNA ( mRNA ) has been shown to be correlated with mRNA abundance in A . thaliana 54 , 55 ., It is also possible that loss of function of MSA1 affects histone methylation , and that the increased expression of S responsive genes in msa1-1 may be also due to differential methylation of histones ., Mutation of the folylpolyglutamate synthetase FPGS1 that disrupts folate and SAM metabolism has been shown to reduce global DNA and H3K9 dimethylation in A . thaliana 56 , and deletion of SHM2 in yeast was observed to reduce H3K4 methylation 47 ., Consistent with this , several S deficiency response genes in A . thaliana have been identified as targets of the trimethylated histone 3 H3K27me3 57 ., Therefore , we hypothesise that MSA1 is involved in maintaining an adequate pool of SAM in the nucleus , though the mechanism remains unclear ., This pool of SAM is required for DNA methylation , including that underpinning the epigenetic regulation of S homeostasis ., The T-DNA insertion mutants for At1g36370 ( MSA1 , SALK_044268 and SALK_118251 ) and for At1g23935 ( SALK_069606 ) were obtained from the Arabidopsis Biological Resource Center ( ABRC , http://www . arabidopsis . org/abrc/ ) ., The sultr1;1 sultr1;2 double mutant was kindly provided by Dr . Hideki Takahashi ., The msa1-1 sultr1;1 sultr1;2 triple mutant was generated by crossing sultr1;1 sultr1;2 double mutant with msa1-1 and the homozygous triple mutant was selected from the F2 population using the primers listed in S9 Table ., A . thaliana plants for ICP–MS analysis were grown as described previously 20 ., Briefly , seeds were germinated on moist soil ( Pro-Mix ( Premier Horticulture ) or Bulrush multipurpose compost ) in a 20-row tray ., After stratification at 4°C for 3 days , plants were grown in a climate-controlled room at 19–22°C with photoperiod of 10 h light ( 100 ± 10 μmol m-2 s-1 ) /14 h dark and humidity 60% ., Plants were bottom-watered at regular intervals with modified 0 . 25× Hoagland solution 20 ., For plants grown in axenic conditions , surface sterilized seeds were vertically grown on MGRL agar media 58 with 1% UltraPure sucrose ( Sigma ) at 22°C with photoperiod of 16 h light ( 100 μmol m-2 s-1 ) /8 h dark ., For preparation of agar medium , agar ( Sigma , type A ) was washed three times with 5 liters of deionized water and vacuum filtrated to dry ., Sulphur deficiency agar medium ( S0 ) was prepared by replacement of MgSO4 with MgCl2 ., The determination of tissue elemental concentration was performed as described previously 20 ., For plants grown in soil , 1 to 2 leaves of five-week-old plants were harvested for analysis ., For plants grown on agar plates , shoots or roots of 4 to 5 two-week-old plants were combined as one sample separately for analysis ., Elemental analysis for Li , B , Na , Mg , P , S , K , Ca , Mn , Fe , Co , Ni , Cu , Zn , As , Se , Rb , Sr , Mo and Cd was performed with an inductively couple plasma mass spectrometer ( Elan DRC II , PerkinElmer; or NexION 300D , PerkinElmer ) ., For the plants grown in soil , data for elements are available in the iHUB ( www . ionomicshub . org ) ., SNP-tilling array-based bulk segregant analysis was performed as previously described 59 ., Briefly , 40 F2 plants with high leaf S or normal S compared to Col-0 WT , from a cross between msa1-1 and Ler-0 , were pooled separately ., Genomic DNA was extracted from the two pools using a DNeasy Plant Maxi Kit ( Qiagen ) and labelled using a BioPrime DNA labelling system ( Invitrogen ) ., The labelled pooled DNA was separa

Introduction, Results, Discussion, Materials and Methods

Sulphur ( S ) is an essential element for all living organisms ., The uptake , assimilation and metabolism of S in plants are well studied ., However , the regulation of S homeostasis remains largely unknown ., Here , we report on the identification and characterisation of the more sulphur accumulation1 ( msa1-1 ) mutant ., The MSA1 protein is localized to the nucleus and is required for both S-adenosylmethionine ( SAM ) production and DNA methylation ., Loss of function of the nuclear localised MSA1 leads to a reduction in SAM in roots and a strong S-deficiency response even at ample S supply , causing an over-accumulation of sulphate , sulphite , cysteine and glutathione ., Supplementation with SAM suppresses this high S phenotype ., Furthermore , mutation of MSA1 affects genome-wide DNA methylation , including the methylation of S-deficiency responsive genes ., Elevated S accumulation in msa1-1 requires the increased expression of the sulphate transporter genes SULTR1;1 and SULTR1;2 which are also differentially methylated in msa1-1 ., Our results suggest a novel function for MSA1 in the nucleus in regulating SAM biosynthesis and maintaining S homeostasis epigenetically via DNA methylation .

Sulphur is an essential element for all living organisms including plants ., Plants take up sulphur from the soil mainly in the form of inorganic sulphate ., The uptake of sulphate and assimilation of sulphur have been well studied ., However , the regulation of sulphur accumulation in plants remains largely unknown ., In this study , we characterize the high leaf sulphur mutant more sulphur accumulation1 ( msa1-1 ) and demonstrate the function of MSA1 in controlling sulphur accumulation in Arabidopsis thaliana ., The MSA1 protein is localized to the nucleus and is required for the biosynthesis of S-adenosylmethionine ( SAM ) which is a universal methyl donor for many methylation reactions , including DNA methylation ., Loss of function of MSA1 reduces the SAM level in roots and affects genome-wide DNA methylation , including the methylation of sulphate transporter genes ., We show that the high sulphur phenotype of msa1-1 requires elevated expression of the sulphate transporter genes which are differentially methylated in msa1-1 ., Our results suggest a connection between sulphur homeostasis and DNA methylation that is mediated by MSA1 .

biotechnology, plant anatomy, chemical compounds, salts, brassica, sulfur, organisms, plant science, model organisms, genetically modified plants, epigenetics, dna, molecular biology techniques, plants, dna methylation, chromatin, genetic engineering, arabidopsis thaliana, research and analysis methods, genetically modified organisms, artificial gene amplification and extension, chromosome biology, gene expression, sulfates, chemistry, chromatin modification, dna modification, leaves, molecular biology, agriculture, biochemistry, plant and algal models, cell biology, nucleic acids, polymerase chain reaction, genetics, biology and life sciences, physical sciences, agricultural biotechnology, plant biotechnology, chemical elements

journal.pcbi.1003324

Quantifying Chaperone-Mediated Transitions in the Proteostasis Network of E. coli

Protein homeostasis ( proteostasis ) is essential for the viability of an organism ., The disruption of protein homeostasis involving the misfolding and subsequent aggregation of proteins is implicated in many diseases , including Downs syndrome , type-II diabetes , Alzheimers , Parkinsons and Huntingtons disease 1–4 ., In addition , inherited mutations that lead to excessive degradation of proteins can lead to loss-of-function diseases , such as cystic fibrosis and Gaucher disease 2 , 5 ., Thus , in every living cell , a system of chaperones – called the proteostasis network – has evolved to help proteins fold , correct or clear misfolded protein , and prevent ( or even reverse ) the formation of protein aggregates ., Proteostasis networks can be broken down into chaperone subsystems ( such as the Hsp60 , Hsp70 and Hsp90 systems in eukaryotes ) 4 , and these systems can be studied individually ., Much work has focused on cataloguing the proteins that are clients of these different chaperone systems , and examining their structural features 6–9 ., The molecular mechanisms of interaction of chaperones with client proteins in each system has been studied 10–15 ., Data from experiment has been synthesized into theoretical models , which describe the passage of client proteins through a given chaperone system 16–18 ., However , different chaperone systems do not operate in isolation in vivo ., Most chaperone activity relies on the consumption of ATP , which is derived from a shared source ., Also , chaperones can have more than one function; DnaK in prokaryotes can bind both to unfolded or misfolded protein in order to prevent aggregation , or it can help prepare aggregates for binding to another chaperone , ClpB , for disaggregation ., Complicating matters is that proteins are not selective in the chaperone system to which they bind: it has been shown that there is significant overlap in the sets of client proteins whose solubilities increase under the action of different chaperone groups 19 ., Furthermore , experimental studies have shown the synergistic action of multiple chaperone groups 9 , 19 , 20 ., Thus , chaperones form complex networks of interaction in the cell ., This motivates a holistic , systems-based approach , in which experimental data from a variety of contexts is synthesized to study the proteostasis network in its entirety ., This is precisely the goal of FoldEco 21: a recently presented tool that describes the proteostasis network in Escherichia coli ., FoldEco synthesizes previously established models of various chaperone systems into a single network of reactions , whose rates are parameterized using experimental data ., Dynamics on the FoldEco network describe the synthesis , folding , unfolding , misfolding , aggregation and degradation of a client protein , as well as the passage of the client protein through three chaperone systems , which work to correct misfolded structures , prevent aggregation and maintain a population of functional native protein ., The FoldEco program uses a set of initial conditions and reaction rates to propagate the concentrations of the different species in the network forward in time ., However , lost in this approach is the ability to track the trajectories of single molecules , which would allow us to answer fundamental questions such as: how often is a given chaperone system used to get from one point in the network to another ?, We have developed a network analysis technique that quantitatively determines mediation probabilities in complex networks ( i . e . , how often a state A is found on transition paths from B to C ) ., This analysis was previously used to detect hub-like activity in protein configuration space networks , and was referred to as “hub scores” 22 , 23 ., Here , we show how mediation probabilities can be calculated from the output of the FoldEco program by constructing transition matrices for states of the client protein ., We show that these probabilities can provide insight into proteostasis networks by revealing how often different competing pathways , involving different chaperone systems , are used to connect different regions of the network , such as the misfolded and unfolded states of the client protein ., For client proteins , we choose four characteristic biophysical protein profiles based on the Monte Carlo results of Powers et al 21 , which demonstrate a range of characteristic behaviors ., We calculate the relative probabilities of taking transition paths through each chaperone system for the four different protein types , and demonstrate how these probabilities change as a function of system parameters , such as the protein synthesis rate , and the total chaperone concentration ., In the FoldEco model , there are a large number of parameters that can be adjusted in order to more accurately describe the activity of a particular protein ., As exploring this entire parameter space is infeasible , in Powers et al . 21 a subset of six variables were chosen , and the resulting six-dimensional space was explored using a large number of points chosen by Monte Carlo sampling ., The six variables comprise folding rate constants ( and ) , misfolding rate constants ( and ) , and well as two parameters that control the aggregation propensity of the protein ( and ) ., We instead study a small number ( four ) of proteins in depth , which are chosen to demonstrate a range of preferences for the different chaperone systems ., Using the results of the Monte Carlo study , Powers et al . determined biophysical profiles of optimal substrate proteins for the GroELS system while the KJE system is present , and vice versa ., They were chosen based on the percent increase of native concentration upon the addition of either the GroELS or the KJE chaperone system ( in the presence of the other ) ., We choose parameters from the distributions of the optimal GroELS and KJE substrates to define two of our client proteins ., As the optimal GroELS substrates characteristically showed slow folding rates , we refer to the GroELS protein as “Slow Folder” ., Similarly , the optimal KJE substrates characteristically show high misfolding rates , and we refer to the KJE protein as “Bad Folder” ., We also define a protein using the average biophysical profile of a set of proteins that were found to aggregate at a low synthesis rate ( ) 21; we refer to this protein as “Aggregator” ., Finally , a fourth protein is defined using values of the six parameters that are intermediate between the three proteins defined so far ., Since these values are close to the default values given by the FoldEco program , we refer to this protein as “Default” ., The values of these 6 parameters are given for each of the four proteins in Table, 1 . Using mediation probabilities , we determine how often certain transition pathways are used between two given states in the FoldEco network ., An overview of the network is given in Figure 1 , and more information on its construction and the FoldEco model is given in Methods ., In this section , we study how the network corrects misfolded states ( transitions in Figure 1 ) ., With all three chaperone systems active , there are five distinct transition paths possible from the misfolded state to the unfolded state ., These transitions are: direct , GroELS-mediated , KJE-mediated , B+KJE-mediated , and degradation followed by re-synthesis ., We study how often these pathways are visited as a function of synthesis rate ., As the synthesis rate increases , correcting misfolded states becomes increasingly important if aggregation is to be prevented ., The synthesis rate is controlled through the ribosome activation rate ( rate constants and in Powers et al . 21 ) , which takes on the values of and , resulting in synthesis rates of and , respectively ., We initialize the simulation with no client protein , and a fixed concentration of chaperone species ( given in Table 2 ) ., The simulation is stopped at and the concentrations at that time are used to construct the rate matrix used for analysis as described in Methods ., We found that after , the native protein concentrations have reached equilibrium and the protein dynamics are approximately steady state in systems that do not feature runaway aggregation ., The relative pathway probabilities for the four characteristic proteins are shown in Figure, 2 . The Default , Bad Folder , and Aggregator proteins show similar behavior: at low synthesis rate , the clearance of misfolded protein is mostly governed by the KJE system , and this responsibility is shifted gradually to the B+KJE system as the synthesis rate increases ., This is expected , since the KJE and B+KJE systems produce unfolded protein , via similar mechanisms , from misfolded and aggregated protein , respectively ., More aggregated protein is present at higher synthesis rates , which causes the fraction of misfolded protein cleared by B+KJE to increase ( Figure S1 shows native , unfolded , misfolded and aggregated protein concentrations ) ., We test this hypothesis by comparing the ratio of activity of the KJE and B+KJE systems with the ratio of the concentrations in the misfolded and aggregated states ( Figure 3 ) ., The ratio can be fit to the function , where is the ratio of the concentration of misfolded and aggregated protein ., This is consistent with the hypothesis that the relative probability of the two pathways is governed by the relative concentrations of their starting points ., The proportionality constant , , indicates that if the concentration ratio of misfolded to aggregate protein is , then the KJE pathway ( from M to U ) is preferred over the B+KJE pathway by a factor of about ., The crossover from the KJE pathway to the B+KJE pathway is shifted to lower and lower synthesis rates as we move from Default to Bad Folder to Aggregator ., This can be explained by the values of the three proteins: and , respectively ., For a given synthesis rate , the relative population of aggregates grows with larger , which would lead to increased usage of the B+KJE system , as shown in Figure, 3 . The Slow Folder protein shows markedly different behavior ., The highest probability pathway is the GroELS system , and the probabilities are roughly constant as a function of synthesis rate ., As the Slow Folder protein was chosen as the ideal client for the GroELS system , this is not surprising , but the question remains as to specifically why the GroELS pathway is favored ., The main entries from the misfolded state into the KJE and GroELS systems , respectively , are the ( misfolded protein associated with ATP-bound DnaK ) and ( misfolded protein associated with ATP-bound∶GroEL ) states ., As such , we compare the entrance rates into these states from the misfolded state at the lowest synthesis rate ( Figure 4a ) ., Although the entrance rate into the GroELS cycle is about higher for Slow Folder as compared to the others , this is not sufficient to explain the drastic difference in path preference shown in Figure, 2 . Figure 4b shows committor probabilities starting from the and states ., These are the probabilities of reaching the unfolded state before the misfolded state given a starting point in either or , and are computed from the matrices described in the Methods section “Getting mediation probabilities” , where states and in this context are the misfolded and unfolded states ., For Slow Folder , transition paths from have a much higher likelihood of reaching the unfolded state ( ) as compared with the other three proteins ( ) ., This is due to the fast transitions in the GroEL cavity ( the rates of which are set to be the same in solution ) , equal to and for the Default , Bad Folder and Aggregator proteins respectively , and for the Slow Folder protein ., Figure 4b also reveals why the KJE pathway is favored for the other proteins ., Although the entrance rate into the GroELS cycle is higher than the KJE cycle , the committor probability of reaching the unfolded state is to times higher for KJE ., This underscores the importance of folding kinetics to the efficiency of the GroELS cycle ., These results give different information than a more conventional “knockout” analysis wherein a particular chaperone system is disabled and the effects on a particular observable is measured – usually either representative of the concentration of native species , or the concentration of aggregated species ., To demonstrate this we choose a particular protein and synthesis rate for analysis that is particularly interesting: the Default protein at a ribosome activation rate of ., As shown in Figure 2 , at this synthesis rate the Default protein uses the chaperone systems GroELS , KJE and B+KJE with approximately equal probability ., We then study this protein with the GroELS system knocked out , achieved by setting the initial concentration of GroEL and GroES to zero ., The native , unfolded , misfolded and total aggregate concentrations at are shown in Figure 5a ., We see that the native concentration is approximately unchanged , and the amount of aggregated species is still negligible ( the percent of insoluble protein is and with and without GroELS , respectively ) , indicating that knocking out the GroELS system would not result in a measurable change in the observables corresponding to the native species or aggregated species concentrations ., Figure 5b shows the contributions to the flux by the GroELS , KJE and B+KJE chaperone systems both with and without the GroELS system present ., We see that the absence of the GroELS system is more than compensated for by an enhanced contribution of the B+KJE system ., Even though the GroELS system is used to clear over of the misfolded protein under these conditions , its removal had no effect on the native state concentration ., This example highlights the advantages of using a transition-path based analysis over knockout experiments when multiple chaperone systems are present ., It is also interesting to see that the contribution along the KJE pathway also decreased as GroELS is removed , even though the concentration of its starting state ( the misfolded state ) increased ., This is because the B+KJE pathway uses the chaperones DnaK , DnaJ and GrpE , leaving a lower concentration available for the KJE pathway ., In order to see if binding affinities can govern chaperone preferences , we vary the binding rates to the chaperones DnaK and GroEL and observe the impact on the relative probabilities of each chaperone pathway ., The coefficients are varied using a multiplicative factor ., When , we recover the binding rates used above ( which are for X to , for X to GrL∶X , and for X to , where X is either U or M ) ., The GroEL binding rates are multiplied by , and the DnaK binding rates are divided by , which allows to act as a tuning variable that encourages usage of either the KJE or the GroELS chaperone systems ., In Figure 6a , we use the Default protein at the lowest ribosome activation rate ( ) , which has a natural preference for the KJE pathway ., As increases , the pathway preference smoothly switches from KJE to GroELS , crossing over between and ., In Figure 6b , we use the Slow Folder protein at the lowest synthesis rate , which has a preference for the GroELS pathway ., As decreases ( from right to left ) , the KJE probability initially increases , and then decreases for ., This behavior is counter-intuitive: why would increasing the DnaK binding rate discourage usage of the KJE pathway ?, Figure 6c demonstrates that even though the binding rate increases with decreasing , the committor probability from to the unfolded state decreases ., This is due to the decreasing concentration of free DnaJ , which is spent by the formation of and complexes which can result from unproductive binding of unfolded protein to DnaK ( Figure S2 ) ., Nevertheless , substantial switching of path preferences can again be achieved by adjusting the binding affinities by a factor between and ., This suggests , for the M to U transition , that relative usage of the two chaperone systems can be controlled through modest adjustments of the relative binding affinities to the two chaperones ., We now study mediation of the ( or folding ) transition ., This is less complicated than the transition in that there are only two possible pathways: direct and GroELS-mediated ., We construct an analytical model of GroEL-mediated folding in supplemental file Text S1 ., The performance of the GroELS chaperone system depends on both the capacity of the system ( quantified by the concentration of total GroEL and total GroES ) , and the demands on the system ( quantified by the concentrations at the entry points to the GroEL system – the unfolded and misfolded states ) ., We study the percentage of GroELS-mediated folding trajectories as a function of the system capacity over the set of four proteins , each of which have different system demands ., Figures 7a–d show the pathway flux through the GroELS system ( solid bars ) as well as the direct flux ( transparent bars ) from the unfolded state to the folded state ., The total concentrations of GroEL and GroES are varied together by a multiplicative factor ranging from to ( plotted on the horizontal axes ) , where results in the concentrations used in the previous section ., For all four of the proteins , both the absolute and percentage usage of the GroEL-mediated folding pathway goes down with decreasing total GroEL concentration ., For three of the proteins ( Default , Slow Folder , and Bad Folder , shown in panels a , b and c , respectively ) , this decrease is mostly compensated for by an increase in usage of the direct pathway ., Of these , Slow Folder has the largest decrease in total folding flux , resulting in a decrease in native yield of , while Default and Bad Folder have decreases in native yield of and , respectively ., The compensation for lack of GroEL folding flux occurs by accumulation of unfolded protein that would otherwise enter the GroELS system ( as the direct folding flux is given by ) ( Figure 7e ) ., Therefore , GroEL is not needed for folding , but these proteins will take the GroEL pathway ( almost exclusively ) if it is available ., As the compensation for the lack of GroELS occurs by building up population in the unfolded state , we note that without the GroELS system these proteins will be more vulnerable to degradation , and we expect to see a stronger dependence of native protein yield on GroELS at higher concentrations of the protease Lon ., In contrast , the folding flux for the Aggregator protein is highly dependent on the available concentration of GroEL ., This is because the GroEL system acts as a “holder” to keep proteins from aggregating , and the extra unfolded protein resulting from its removal does not accumulate , but is transferred to the misfolded state , and subsequently aggregates ., The Aggregator protein can thus be seen as a “class-III substrate” in that the total folding flux ( and thus the concentration of the native state ) is dependent on the availability of GroEL 7 ., The other three proteins ( Slow Folder , Default and Bad Folder ) can be seen as “class-I” or “class-II substrates” of the GroEL system ( see Text S1 ) , in that they do not strictly require the GroELS system to fold ., It is striking that the Slow Folder protein is not a “class-III substrate” , even though it was parameterized to be an optimal substrate of the GroELS system ., We note that this is done using parameters at a lower synthesis rate ., At this higher synthesis rate , the GroELS system primarily serves to rescue proteins from aggregation , as opposed to degradation , and as such the optimal clients of the GroELS system misfold and aggregate easily 21 ., The biophysical profiles of top GroELS substrates in the presence of KJE at this synthesis rate are similar to that of the Aggregator protein ( see Figure S4B of Powers et al . 21 ) ., It is important to note that the simulations conducted here only take into account one client protein at a time , whereas in vivo , there are about different proteins that act as GroELS substrates , which compete to bind to a shared pool of GroELS chaperones 7 ., It is then easy to see how competition can arise between class-I/II and class-III substrates , as strong-binding class-I/II substrates would lower the effective concentration of total GroEL , reducing the yield of class-III substrates without increasing their yield of native protein ., There should thus be an evolutionary drive to increase the binding affinity of class-III substrates in comparison to class-I/II substrates ., In Text S1 we examine whether increasing the GroEL binding affinity for the Aggregator protein can compensate for lower concentrations of GroEL chaperone , and we find that it cannot ., This underscores the importance of increasing binding affinity from the perspective of inter-protein competition ., We have used transition path analysis in combination with the FoldEco program to study the proteostasis network in E . Coli ., The analysis reveals features of the network dynamics that are undetectable by observing concentrations of network components alone ., For the misfolded to unfolded transition , we find that the usage of the KJE vs B+KJE systems depends mostly on the relative concentrations of the misfolded and aggregated states ., We also observe that the efficiency , and hence the pathway probability , of the GroELS cycle depends mostly on folding kinetics of a client protein within the GroELS cycle ., If the folding kinetics within the GroEL-GroES chaperone complex are the same as in bulk , in order for the GroELS system to increase native yields , either the degradation or aggregation processes need to be competitive with folding ., We have also shown that modest adjustments in the binding affinities to the two chaperones DnaK and GroEL can control which chaperone system is used to correct misfolded states at a given synthesis rate ., This study serves as a proof of principle that a transition path analysis can be applied to proteostasis-type networks with little complication ., We expect that this analysis will become more valuable as networks become larger and more interconnected , since the behavior of the transition paths will become less intuitive ., The computational cost of the analysis is dominated by multiplications of matrices that are approximately size by , where is the number of states in the network ., Although matrix multiplication scales as , GPU architectures allow fast multiplications of large matrices ( a two-GPU cluster can multiply matrices at a speed of 24 , hence matrices of size by can be multiplied on a two-GPU cluster in about minutes ) ., This would make the analysis presented here feasible on networks up to about states with current hardware ., For larger networks , rather than multiply matrices it would be easier to generate a large number of “psuedo-trajectories” using the state-to-state transition probabilities , and calculate mediation probabilities directly from the trajectories , as done in our previous work 23 ., Mediation probabilities would be extremely challenging to measure experimentally , since they would rely on the tracking of single molecules in vivo ., For instance , to determine the relative fraction of GroELS- , KJE- and B+KJE-mediated misfolded to unfolded transitions , one would need to distinguish between misfolded , unfolded , GroELS-bound , DnaK-bound and aggregated states in real time ., However , even without verifying the mediation probabilities directly , the overall proteostasis network model can be verified by comparing the concentrations of species in the model with those from experiment over a range of system parameters ( as is done for firefly luciferase in Powers et al . 21 ) ., This prescribes a complex synergy between theory and experiment , where experiment is first used to parametrize the reactions , theory is used to construct a network , experiment then used again to validate the network , and theory used again for the network analysis described here ., There are four main chaperone systems acting to maintain proteostasis in E . coli ., The first is the Hsp70-like system , consisting of chaperones DnaK , DnaJ and GrpE ( the KJE system ) ., DnaK has a hydrophobic pocket that preferentially binds to unfolded peptides with exposed stretches of hydrophobic residues 9 , 12 ., Bound peptides can be locked in through the motion of a helical lid domain that is closed by the hydrolysis of bound ATP , which is regulated by the binding of co-chaperone DnaJ ., After DnaJ unbinds , the binding of GrpE catalyzes ADP release , and subsequent ATP rebinding results in lid opening and release of the peptide ., Because the protein is kept unfolded throughout the cycle , the KJE system can allow misfolded , aggregation-prone proteins to return to an unfolded state ., A large part of the E . coli proteome ( at least proteins ) binds to DnaK 9 , making the KJE system extremely important in preventing aggregation 4 , 25 ., The second is the Hsp60-like GroEL/GroES chaperonin system ( GroELS ) , which is the only chaperone system that is absolutely necessary for the viability of an E . coli cell 26 ., GroEL exists as two stacked seven-membered rings which form a cylindrical complex that is capable of encompassing a single protein , acting as an infinite-dilution cage ., GroES forms a single seven-membered ring that acts as a cap to the cylinder , enclosing the protein ., It has been shown that enclosure within the GroEL∶GroES complex can increase folding rates 27 , although the chaperonin system works to prevent aggregation even when folding kinetics are unchanged ., The unbinding of the GroES cap is mediated by allosteric ATP binding , and occurs after seconds 4 , which gives the peptide time to fold in a sterically-confined environment that is isolated from other misfolded copies of the peptide that encourage aggregation ., Discharged protein that is not folded can be rapidly rebound , and consequently many proteins are known to undergo many GroELS cycles before folding 27–29 ., GroEL binds to a wide variety of proteins , comprising at least to of cytosolic proteins under normal growth conditions 6 ., An in vitro study by Kerner et al . shows that of about proteins that interact with GroEL , about are absolutely dependent on the chaperonin system to fold 7 ., However , a more recent study has shown that only of these are strictly dependent ( or “obligate” ) on GroELS in vivo 8 ., The KJE system can also cooperate with the Hsp104-like chaperone ClpB to pull monomers from amorphous aggregates ( the B+KJE system ) 11 , 13–15 , 25 ., ClpB is an oligomeric , ring-like machine that uses the energy from ATP hydrolysis to exert mechanical force on protein aggregates ., Both DnaK and DnaJ are used to prepare aggregates for ClpB which then can extract monomers from the aggregate 30 ., Two mechanisms have been proposed for the disaggregation mechanism of ClpB: one in which ClpB acts as a “crowbar” to break apart an aggregate 13 , the other in which ClpB threads a single monomer through a central pore 14 ., The last chaperone is trigger factor , which can bind to translating polypeptides and protect them from aggregation 7 , 19 ., As trigger factor only acts as a holder chaperone , trigger-factor–bound states cannot act as intermediates on transition paths between the major client protein states ( e . g . native , unfolded , misfolded ) ., We thus exclude trigger factor from our transition path analysis , and focus on the first three chaperone systems mentioned above ., The FoldEco program was recently introduced to study the proteostasis of a client protein ., It describes , in a holistic fashion , the synthesis , folding , misfolding , aggregation , degradation and recovery of misfolded and aggregated proteins through the KJE , GroELS and B+KJE chaperone systems ., It uses coupled kinetic equations that evolve a particular set of initial conditions ( which are the concentrations of each species in the system ) forward in time , using reactions that are parameterized from in vitro experimental data ., In Figure 1 we show the network of client protein states used here ., The nodes in the network are particular configurations of a single protein molecule , and mostly describe the formation and destruction of complexes with different chaperones in the proteostasis network ., This can be compared with Figure 1 of Powers et al 21 , where there is more information about the nature of the transitions , but does not explicitly include all of the connections between client states ., We note that FoldEco also describes reactions that do not involve the client protein , such as the binding and unbinding of ATP from DnaK ., We omit these from Figure 1 since they are not part of the network of client states ., To simplify our analysis , we also connect the processes of degradation and re-synthesis through a “null” state ., This does not affect our results , and allows us to examine the steady-state dynamics of a single protein traversing the network ., The rate constants for the transitions between the states in this network are determined from a large body of experimental literature ., In theory these rate constants can be tailored in a protein-specific fashion to more accurately connect with experiment , although for simplicity we fix all but six rate constants , and the values for these fixed constants are given in Table S4 of Powers et al 21 ., The same table also describes the initial concentrations of the chaperone species used here , which are reproduced in Table 2 ., Although FoldEco is a powerful tool for synthesizing experimental data , there are some simplifications used by the model that affect our analysis ., Firstly , it does not account for the effect of bacterial growth , which would lead to the dilution of proteins as they are being synthesized ., One effect of this is that steady-states reached by FoldEco tend to have much larger concentrations of protein than are observed in experiment ., Thus , in our analysis we do not analyze the networks at steady-state , we instead choose a common analysis time for each system ( ) ., FoldEco also does not take into account the presence of the background proteome , and does not describe competition for binding to chaperones ., Above , we study this competition indirectly by lowering the concentration of GroEL and GroES that is accessible to the client protein ., We note that both of these limitations are planned to be addressed in future versions of FoldEco 21 ., The kinetic equations in FoldEco are formulated as a set of equations that describe the time evolution of the concentrations of different client and non-client species in the system ., For our analysis , we wish to convert this into a master equation of the form , where is a vector of the concentrations of different states in the model , and is a time-independent rate matrix , the elements of which describe the rate of transition from state to state ., This allows for the transitions of a single tagged protein molecule to be tracked from state to state , and for the analysis of its dynamical properties ., The first complication to arise is that some of the states in the network involve multiple copies of the client protein ., For instance , a GroEL-GroES complex can accommodate two client proteins , one in the cis ring and one in the trans ring ., It is important to maintain a distinction between proteins in the same complex if they are in different states ( e . g . , one is folded and the other is unfolded ) ., We thus artificially separate the multi-client complex states into two states , depending on which protein is the tagged protein ., This allows us to track the tagged protein in a continuous fashion once the complex has dissolved ., Aggregated structures are also multi-client states , but to rigorously keep track of a specific monomer in a large aggregate would be unfeasible: describing aggregates up to monomers in length w

Introduction, Results, Discussion, Methods

For cells to function , the concentrations of all proteins in the cell must be maintained at the proper levels ( proteostasis ) ., This task – complicated by cellular stresses , protein misfolding , aggregation , and degradation – is performed by a collection of chaperones that alter the configurational landscape of a given client protein through the formation of protein-chaperone complexes ., The set of all such complexes and the transitions between them form the proteostasis network ., Recently , a computational model was introduced ( FoldEco ) that synthesizes experimental data into a system-wide description of the proteostasis network of E . coli ., This model describes the concentrations over time of all the species in the system , which include different conformations of the client protein , as well as protein-chaperone complexes ., We apply to this model a recently developed analysis tool to calculate mediation probabilities in complex networks ., This allows us to determine the probability that a given chaperone system is used to mediate transitions between client protein conformations , such as folding , or the correction of misfolded conformations ., We determine how these probabilities change both across different proteins , as well as with system parameters , such as the synthesis rate , and in each case reveal in detail which factors control the usage of one chaperone system over another ., We find that the different chaperone systems do not operate orthogonally and can compensate for each other when one system is disabled or overworked , and that this can complicate the analysis of “knockout” experiments , where the concentration of native protein is compared both with and without the presence of a given chaperone system ., This study also gives a general recipe for conducting a transition-path–based analysis on a network of coupled chemical reactions , which can be useful in other types of networks as well .

To maintain proper amounts of folded , functional proteins , cells use systems of chaperones to correct misfolded proteins , disassemble aggregates , and provide sheltered environments in which proteins fold to their native structure ., Typically , an individual system is studied in isolation , and its effects on a given protein are studied using “knockouts” , where the amount of native protein is compared with and without the active chaperone system ., However , when multiple chaperone systems are operating simultaneously , knockouts can fail to reveal chaperone activity , as different chaperone systems can compensate for one another ., We use a previously introduced computational model of chaperone systems in Escherichia coli , in combination with our transition-path analysis methods for networks , to analyze paths of individual proteins through the set of possible chaperone-bound and -unbound states ., Our analysis allows us to answer questions that are inaccessible to knockout experiments , such as: How often will a given chaperone system be used to rescue a protein from a misfolded state ?, This approach provides a clear view of how the different systems of chaperones cooperate and compete under varying conditions .

journal.pntd.0001047

Individual Predisposition, Household Clustering and Risk Factors for Human Infection with Ascaris lumbricoides: New Epidemiological Insights

Much of our understanding of the epidemiology of Ascaris lumbricoides infections of humans has been acquired from the analysis of worm counts collected from infected individuals ., The only practical way of obtaining such data is by chemo-expulsion ., This procedure is best performed using anthelmintic drugs which paralyse gut-dwelling worms 1 so that they are expelled intact in the feces ., The number of A . lumbricoides per host ( worm burden ) is the most important epidemiological variable with respect to the parasites transmission potential and population dynamical behavior 2 , as well as to the degree of individual and community morbidity 3 ., Worm counts have been used to explore a number of aspects of the epidemiology of A . lumbricoides infection at both individual and household levels ., At the individual level , “predisposition” describes the observed association between an individuals worm burden recovered after treatment , with the worm burden recovered after a period of re-infection and subsequent treatment ., This phenomenon has been demonstrated frequently between two consecutive estimates of worm burden 4 , 5 , 6 , 7 , 8 and also over multiple rounds of treatment 9 , 10 , 11 ., Predisposition is also evident at the household level: worm burdens tend to be associated among members of the same household 12 , 13 , 14 and average household worm burdens tend to be similar between rounds of treatment and re-infection 7 , 15 ., ( For a review of predisposition to soil-transmitted helminthiases see Keymer and Pagel 16 and to A . lumbricoides in particular see Holland 17 . ), The causes of predisposition at both the individual and household level are incompletely understood ., Heterogeneities in exposure , innate ( genetic ) and immunologically-mediated susceptibility are likely to contribute 16 , 17 , 18 ., While advances have been made in immunoepidemiology 19 , 20 and genetically mediated susceptibility21 , 22 , 23 , little progress has been made in understanding the role of exposure to infective stages ., This is largely due to the practical difficulties in measuring exposure 24; estimation has been restricted to the measurement of concentrations of fecal silica as a proxy for soil contamination of food and geophagic activity 25 , 26 , 27 ., Patterns of exposure may be inferred indirectly by exploring risk factors for worm burden ., Numerous studies have identified factors associated with high A . lumbricoides egg output ( those published since 2004 are described by Scott 28 ) , but only three have used worm counts as the dependent variable 14 , 29 , 30 ., These studies have identified household- , agricultural- , host sex- and poverty-related factors associated with A . lumbricoides worm burdens ., The majority of chemo-expulsion studies were carried out between the early 1970s and the late 1980s ( Table S1 ) ., Since this time , many statistical approaches have become increasingly accessible to parasitologists and easier to implement as research tools with personal computers ., Such approaches include generalized linear models ( GLMs ) for non-normally distributed errors 31 , 32 , and longitudinal or hierarchical ( random effects ) models for repeated measures or clustered data 33 , 34 ., Bayesian methods provide a unifying framework with which to handle these and increasingly complex models 34 , 35 , affording a powerful tool to the analyze epidemiological and parasitological data 36 , 37 ., Many of these statistical methods have not before been applied to data on worm counts ., For example , household clustering has largely been explored by dichotomising individual worm burdens as either “heavy” or “light” using an arbitrary threshold and estimating whether the number of worms per household observed , and the number expected by chance , are statistically significantly different 7 ., This is a useful hypothesis-testing approach but does not quantify the clustering effect of interest 38 ., Dichotomisation of continuous data also incurs a loss of statistical power ., Hierarchical modelling is a more powerful and suitable approach which is becoming increasingly used for quantifying , and accounting for , the effects of household clustering in other helminth infections of humans 39 , 40 , 41 , 42 ., In this study , we explore evidence for individual predisposition , household clustering , and household risk factors for worm burdens of A . lumbricoides by analysing data from the largest of the chemo-expulsion studies conducted to date ( Table S1 ) ., We define a statistical model capable of quantifying the effects of multiple , and potentially interacting , epidemiological phenomena by exploiting the longitudinal ( multiple measurements made on a cohort ) and hierarchical ( individuals within households ) structure of the data ., Specifically , we examine the following:, a ) the interplay between individual predisposition and household clustering;, b ) the extent to which clustering of infections within households is explained by socioeconomic , physical and cultural differences among households , and, c ) the relative risk of worm burdens associated with these household variables ., Data were collected in Mirpur , an urban suburb of Dhaka , Bangladesh between 1988 and 1989 by Hall and colleagues 11 ., Briefly , households were visited by these authors and all their occupants invited to take part in the study with the aim of recruiting as many individuals as possible ., Each participating household was administered a basic questionnaire to describe socio-economic status and household characteristics ., These variables are listed in Table S2 ., A dose of pyrantel pamoate was given to each consenting subject and their stools were collected for a period of 48 hours post-treatment ., The worms recovered ( A . lumbricoides ) from the feces of each individual were sexed and counted ., Treatments and worm counts were repeated on two further occasions at six-monthly intervals ., Pyrantel pamoate paralyzes A . lumbricoides in the gut so they are expelled intact from the gut by peristalsis 1 with a “cure” rate of approximately 88% 43 ., Hence , these data provide a reliable and accurate measure of the number of worms ( male and female ) per host ., The population of worms recovered after the first round of chemotherapy is termed the “baseline” population , after the second round of chemotherapy , the “first re-infection” population , and after the third and final round , the “second re-infection” population ., This paper is concerned only with analyzing data previously collected by Hall and colleagues 11 ., All analyses were conducted using anonymized data ., In the original data collection study , informed consent was obtained in the following manner ., A written statement was read to either the mother or father ( usually the mother ) of all children in the same household that were taking part in the study ., The statement explained the aim of the study , what was to happen , telling them that they could refuse to take part or drop out at any time , and asking if they were willing to take part ., The form was left with the household if they wanted to take advice from either religious or community leaders or if the father was absent and the mother wanted to defer to him to decide ., One person , usually the father , signed or applied their thumb print ( if the subject could not write ) for all people in the household ., This , however , did not mean that everyone in the household was able to participate ., For example , there were relatively fewer adolescent and adult male participants because they tended to be out at work during the day and so could not collect their stools , which was a voluntary process ., Approval was given by the Ethical Review Committee of the International Centre for Diarrhoeal Disease Research , Dhaka , Bangladesh ., A total of 2 , 929 subjects from 502 households originally enrolled to participate in the study ., Participants were excluded from the study for any one of the following reasons: if stools were not collected for at least 48 h after treatment or if subjects reported not collecting all their stools; if a subject returned no worms although A . lumbricoides eggs had been seen in the fecal sample examined before treatment; or if a subject returned only male worms but eggs had been seen in the fecal sample collected before treatment ., The subjects not excluded according to these criteria were classified as having been de-wormed satisfactorily 44 ., Participants who were not satisfactorily de-wormed at a given round of treatment were not subsequently followed up ., On this basis , 1 , 765 participants from 459 households were satisfactorily de-wormed after the first round of treatment , 1 , 257 after the first six-month period of re-infection and 1 , 017 after the second re-infection period ( Table 1 ) ., Overall , a maximum of three worm burdens were measured from each subject ( one at baseline and a further two after consecutive six month periods of re-infection ) ., Figure 1 illustrates the hierarchical and longitudinal structure of the data ., All data analyzed were anonymized at the individual level retaining characteristics such as age , sex , ethnicity and household , but not allowing personal identification ., Here we give a brief description of the key features of the statistical model which was fitted to the data ., A formal definition can be found in Text S1 ., The model is structured into three nested hierarchies; multiple measurements per individual and measurements made on multiple members of the same household ( Figure 1 ) ., The correlations between potentially dependent measures are accounted for by two random effects , the variances of which are denoted and ., The subscripts ID and HH and the parameters to which they pertain quantify the magnitude of individual predisposition and household-level clustering respectively ., Informed by a previous analysis of these data 44 , worm burdens were assumed to be negatively binomially distributed ., Covariates were included at each hierarchical level ., At the measurement-level ( measurements made on a single participant ) , the population from which the worm burden was measured was the sole covariate ( i . e . baseline , first or second re-infection population ) ., The individual-level covariates included host age , defined as an 11-level categorical variable using the groupings defined in Hall et al . 11 , 44 , and sex ., In addition , age-population and age-sex interactions were included ., Adjustments for host age were necessary because , in this population , baseline worm burden varies with age in a “convex” manner 11 , 44 typical of A . lumbricoides infection 2 ., The sex-age interaction was incorporated because analyses by Hall et al . 44 had suggested that adult women tend to harbor higher worm burdens than adult men , with no apparent difference between the sexes in children ., The host age-population interaction allowed the rate of re-infection to vary with age , where the former is defined as the proportion of the baseline worm burden attained after 6 months ., Two previous studies have demonstrated age dependency in the rate of re-infection , showing that children become re-infected at a faster rate than adults 4 , 5 ., At the household-level , additive covariates were included pertaining to the socioeconomic status of the household , the quality of construction of the house and the hygiene facilities available to household members ., An interaction between the ethnicity of the household and whether rent was paid was also included ., This was done because of the pronounced differences in circumstances between Biharis and Bangladeshis ., Biharis live effectively as refugees and tend to be confined to an extremely crowded and poverty-stricken refugee camp ., Those not confined to the camp were more likely to pay rent for their home ., By contrast , the difference in living conditions between households paying or not paying rent in the Bangladeshi community was much less conspicuous ., The average worm burden in each population stratified by each household covariate can be found in Table S2 ., Preliminary analyses were carried out in order to reduce the number of household covariates ( Table S2 ) , eliminating those that did not contribute enough to the likelihood of the fitted model to warrant inclusion in the subsequent analysis ., This was achieved using an Akaikes information criterion ( AIC ) -based 45 forward and backward stepwise selection procedure implemented using the stepAIC function in R 46 , 47 ., For this procedure , the simplest model was defined as that described in the above section Summary of Statistical Model , minus the household-level covariates and the random effects ., The most complex model included the household covariates listed in Table S2 , but again omitted the random effects ., By removing random effects in this way , the potential correlations among infection intensities at the individual- and household-levels are ignored ., This is a conservative approach to the preliminary elimination of explanatory variables because variables will contribute relatively more to the likelihood of the fitted model when the variability arising from the clustering of data at each hierarchical level ( random effects ) is ignored ., This technique has been used previously for the reduction of covariates in a hierarchical statistical model of Ascaris suum infections in swine 48 ., The number of worms per host was assumed to be negatively binomially distributed with an unknown overdispersion parameter ( estimated from the model ) ., Models were fitted by maximum likelihood using the glm . nb function in R 46 ., The most parsimonious model arrived at by the selection procedure included household ethnicity , number of sleepers , number of children , number of rooms , rent , floor type , source of water for washing dishes and latrine facility ( Table 2; see Table S3 for coefficient estimates ) ., The “full” model described in Summary of Statistical Model and nested models were fitted to the data using hierarchical Bayesian techniques in the Windows program for Bayesian inference using Gibbs Sampling ( WinBUGS ) 49 ., Parameters were assigned non-informative priors 35 , e . g . , a normal distribution with mean\u200a=\u200a0 and a variance\u200a=\u200a1000 or , for the precision ( 1/variance ) of random effects , a gamma distribution with shape and scale parameters\u200a=\u200a0 . 001 ., Following techniques suggested by Gelman and Rubin 50 three starting values for the Gibbs sampling algorithm were assigned in order to asses convergence on the parameter posterior distributions and to check that our conclusions were not sensitive to the choice of starting values ., In general , the first 20 , 000 samples of each chain were discarded as “burn in” and a further 40 , 000 samples were used to compute the posterior distributions ., The goodness-of-fit/parsimony of each model was assessed using the Deviance Information criterion ( DIC ) 51 ., This is a Bayesian generalization of AIC , based on a trade-off between the fit of the model to the data and its complexity ., Like AIC , the smaller a models DIC , the more parsimonious the fit ., A total of 8 models were fitted to the data , each incorporating a different combination of epidemiological features nested within the full model ( Model 1 in Table 3 ) ., The full model included individual predisposition , household clustering and household covariates ., The simplest or null model ( Model 8 in Table 3 ) omitted these components ., All models included the other features described in the Methods section entitled Summary of Statistical Model ., The “best-fit” model according to the DIC is the full model ( Table 3 ) ., Estimates of and from each of the fitted models are also given in Table 3 ., Two aspects of these parameter estimates are noteworthy ., First , the magnitude of household clustering is much larger when estimated from models that do not incorporate household covariates compared to estimates from models which do account for household covariates ( comparing in Model 2 with in Model 1 or in Model 7 with in Model 4 , Table 3 ) ., Household clustering is reduced by approximately 58% having adjusted for household covariates ., The second notable point is that the magnitude of individual predisposition is extremely small , except in models in which household clustering is unaccounted for ( compare Model 3 or Model 5 vs . Model 1 or Model 2 in Table 3 ) ., The fitted relationship between the mean worm burden at baseline , host age and sex estimated from Model 1 is depicted in Figure 2 ., This highlights the convex age-burden profile at baseline and the tendency of adult women to harbor heavier worm burdens than adult men ., Also apparent are the wide 95% Bayesian credible intervals ( BCI ) which are , in part , the result of the additional uncertainty introduced by household clustering and , to a much lesser extent , individual predisposition ., Figure 3 depicts the fitted relationship between the proportion of the baseline mean worm burden and host age in the first and second re-infection populations ., The figure shows that children tend to re-acquire their pre-treatment worm burdens more rapidly than adults ., Indeed children aged 1–4 years at baseline tended to re-acquire slightly heavier worm burdens in the first re-infection population than they had at baseline ., Moreover , children aged 1–2 years at baseline had re-acquired twice their baseline worm burden in the second re-infection population ., In contrast , teenagers and adults harbored approximately 50% of their baseline worm burden in both re-infection populations ., Table 4 gives the posterior means and 95% BCIs for the relative risks of household covariates on the worm burden of A . lumbricoides estimated from Model 1 ., The BCIs for the following variables do not include 1 , indicating statistically significantly more intense infections in: Bihari households , households using a common tap to wash dishes , households with an earth floor , and those with no latrine ., The modeling approach taken in this paper to analyze data on A . lumbricoides worm counts has enabled the effects of multiple epidemiological phenomena and their interplay with one another to be considered into a single coherent inference framework for the first time in the study of human ascariasis ., There are two key findings ., First , the degree of individual predisposition to worm burden is extremely small once the clustering of infections within households has been accounted for ., Second , approximately 58% of residual intra-household variability ( clustering ) is accounted for by household covariates , the effects of which have been quantified in the form of relative risks ., The limited impact of individual predisposition on the fitted models suggests that heterogeneity in susceptibility or exposure among members of the same household is of little epidemiological importance ., This result complements that of Chan et al . 52 who failed to find any difference among the associations between the worm burdens of parents and their ( genetically related ) children and between unrelated parents ., These authors surmised that any genetic basis to individual predisposition must be overwhelmed by household-related behavioral or environmental factors ., The results of this study lend support to this supposition demonstrating that individual predisposition is weak 11 , 16 and swamped by putative effects within the household 52 ., The genetic component of susceptibility to A . lumbricoides infection 21 , 22 , 23 is not challenged by our results ., Children within a household are presumably closely genetically related to each other and to their parents ., Consanguineous relationships among parents were also fairly common in this community ( 12% of wives reported being directly related to their husbands ) ., In this way , household members are often closely genetically related and so clustering may be partly due to shared genetics ., However , the lower household clustering with the inclusion of household covariates suggests that household-related exposures also play an important role in transmission ., Conway et al . 53 reached similar conclusions regarding the cause of clustering within households of the soil-transmitted helminth ( STH ) Strongyloides stercoralis by analyzing prevalence data ( presence or absence of eggs in feces ) also collected from the study described in this paper ., These authors found that household clustering of S . stercoralis was only partially explained by household risk factors and surmised , “Household aggregation of S . stercordis may be partly due either to close contact person to person transmission within households , or to familial genetic predisposition to infection . ”, The importance of the household in the transmission of the three most prevalent STH infections ( A . lumbricoides , Trichuris trichiura and the hookworm species Ancylostoma duodenale and Necator americanus 54 ) was first considered by Otto et al . in the 1930s 55 ., In 1996 , Cairncross et al 56 suggested that the household and public environments are fundamental “arenas for disease transmission” in formulating their “domain theory” of transmission ., The view is that A . lumbricoides is primarily transmitted within the “domestic ( household ) domain” , a notion based on a variety of epidemiological observations , including clustering of infections within households 24 ., Direct evidence for this assertion has , however , only recently been presented ., Criscione et al 57 found that A . lumbricoides collected from a Nepalese community were genetically clustered within households and that nearby households shared genetically similar worms ., These results are in accordance with the peri-domiciliary environment as the focus of transmission ., This novel work also demonstrated the power of using the genetic information from individual worms to gain insight into the mechanisms behind observed epidemiological patterns at the host and household levels ., Many studies have used statistical models to explore putative risk factors for A . lumbricoides infection and other STHs ., Surprisingly few , however , have employed estimates of worm burden as the dependent variable , often using weak , dichotomous data on presence or absence of worms ( for recent examples see 40 , 58 ) ., In areas of moderate to high transmission where the average worm burden per host is high , prevalence is not a suitable response measure because individuals with a high exposure to infectious larvae will be indiscernible from those less exposed ., This arises because of the non-linear relationship between infection prevalence and worm burden 2 , 59 ., Studies that have used either direct ( worm counts ) or indirect ( egg counts ) measures of worm burden have identified an array of behavioral , cultural , occupational , socio-economic and host sex-related risk factors ( e . g . 14 , 29 , 30 , 60 ) ., The risk factors identified in the present study relate broadly to socio-economic status: individuals with large worm loads tend to live in households with an earth floor , without a latrine and rely on a common tap for their washing water ., The average worm burden of Bihari and mixed ethnicity households not paying rent was approaching twice that of Bangladeshi households not paying rent ( relative risk\u200a=\u200a1 . 85 ( 1 . 49 , 2 . 28 ) , Table 4 ) ., In Bangladesh , Biharis are an impoverished minority group living as refugees in overcrowded insanitary camps , a legacy of the secession of East Pakistan in the creation of Bangladesh after the Bangladeshi War of Independence 61 ., The null component of all of the fitted models has two important features hitherto relatively unexplored in the literature on A . lumbricoides ., These are:, a ) the interaction between age at baseline and the rate of re-infection , and, b ) the effect of host sex on worm burden ., Figure 3 indicates that the rate of return to baseline worm burden is slower with increasing age ., Indeed children aged 1–2 years at baseline reacquired , on average , a heavier worm burden after six months of re-infection in both the first and second re-infection populations compared to their worm burden at baseline ., Anderson and May 2 identified that if the rate at which individuals acquire parasites remains constant then the rate of return to baseline/endemic worm burdens depends on the life-expectancy of the parasite ., Assuming A . lumbricoides live for 1–2 years 2 one would expect hosts to re-acquire , on average , 40-60% of their baseline worm burden after six months of re-infection ., This is what is seen for teenagers and adults ( Figure 3 ) ., The higher proportion of the baseline worm burden attained by children suggests that the rate at which they are acquiring worms is increasing as the cohort ages over the one year study period ., Similar differences between the relative rates of re-infection in children compared with adults have been reported in other longitudinal chemo-expulsion studies 4 , 5 , 9 ., These authors cited age-specific rates of exposure as the likely cause ., The results presented here are in accordance with this explanation ., In adults , aging by a maximum of one year is unlikely to affect patterns of exposure ., In children , exposure which is behaviorally mediated may change rapidly with age especially over the first three years of life as they learn to walk and explore their environment , which will increase their exposure to A . lumbricoides eggs ., The inclusion of host age and sex as interacting covariates indicated that adult women tend to have higher worm burdens than adult men , with no discernable difference between children by sex ( Figure 2 ) ., The most forthcoming explanation is that exposure has a sex-specific component ., This has also been demonstrated to be the case in other nematode infections of humans such as Onchocerca volvulus 62 ., In the study community , teenage and adult males tend to spend their days at work away from the household ., By contrast females seldom leave the peri-domiciliary environment ., Similar sex differences have been reported between children in a Madagascan community where boys spend their days away from the village and girls remain at home looking after the younger children 30 ., The consequence is that it was possible to de-worm satisfactorily more women than men ., At baseline , 38% of the de-wormed participants over 16 years were male; this figure was 34% after each period of re-infection ., By contrast , 49% of those under 16 were male ., The sampling bias in favour of adult females was induced by the absence of men from the household during the day which made them less able to collect their feces ., This is likely to have had two effects on our results ., First , there would have been a slight loss of power in discerning between the worm burdens of adult men and women ., Second , and more importantly , it is conceivable that those men not de-wormed harbored fewer worms that those who were de-wormed because they were away from the household ( the focus of transmission ) more often ., If this was the case then over results would underestimate the true difference between the worm burdens of men and women ., That is , men may have been infected with , on average , even fewer worms than the results suggest ., In the late 1980s , when the study described in this paper was being carried out , the first public-private partnership between Merck and the Onchocerciasis Control Programme was being forged to deliver donated ivermectin to treat onchocerciasis 63 ., Numerous public-private initiatives have since ensued , fuelling a rise in mass drug administration- ( MDA ) based helminth control programs and heeding the calls of the World Health Organizations ( WHO ) campaign against neglected tropical diseases 64 , 65 ., The recommended protocol for MDA against the STHs is annual or biannual treatment with benzimidazole drugs ( albendazole or mebendazole ) targeted at school-age children , as children tend to harbor the highest burden of STHs and suffer the most from the insidious effects of chronic infection 66 ., The schools infrastructure facilitates high coverage permitting cost-effectiveness treatment 67 , 68 , 69 and regular de-worming has beneficial effects on the nutrition , growth , physical fitness and cognitive performance of school-age children 54 ., Despite the unequivocal benefits of school-based de-worming , it is inevitable that such an approach will miss potentially heavily infected groups outside of the target population ., For instance , in the study population , the Bihari refugees were approximately twice as heavily infected as Bangladeshis ( Table 4 ) ., In general , the epidemiological relevance of missing potentially heavily infected groups will be highly location-specific and will critically depend on the number of individuals comprising the overlooked groups and on the portion of the worm burden harbored by them ., Identifying and targeting such groups ( in addition to school-age children ) prior to treatment would amount to a selective treatment strategy 70 albeit possibly at a household- rather than at an individual-level ., Such an approach requires potentially costly prior epidemiological assessment and may not be as cost-effective 71 , 72 , although cost-effectiveness will be improved in areas of high population density such as the Bihari refugee camp described in this study ., In locations where control efforts are successful in suppressing worm burdens , the relevance of consistently missing heavily infected groups will be increased and more community-specific strategies may be necessary to complement the school-based approach . In this analysis , we have exploited the flexibility of a Bayesian statistical modeling approach to simultaneously consider a number of epidemiological phenomena associated with A\xa0lumbricoides infections of humans ., This approach has enabled for the first time exploration within the same framework of the interplay between individual predisposition , household clustering and household risk factors ., We have found that the magnitude of individual predisposition to high or low worm burdens became extremely small once the effect of the household has been accounted for ., That is , the predominant unit of predisposition is the household rather than the individual ., Furthermore , a number of household risk factors associated with worm numbers have been identified which together account for approximately 58% of the variation in worm counts among households ., These risk factors , like others identified before , are invariably associated with socio-economic status and relative affluence even in what is overall an extremely poor community ., Thus , while highlighting the importance of heterogeneous exposures to transmission , such risk factors , above all , confirm that A . lumbricoides is associated with acute poverty , and that its control is inextricably linked to help achieving the Millennium Development Goals 73 , 74 .

Introduction, Methods, Results, Discussion

Much of our current understanding of the epidemiology of Ascaris lumbricoides infections in humans has been acquired by analyzing worm count data ., These data are collected by treating infected individuals with anthelmintics so that worms are expelled intact from the gastrointestinal tract ., Analysis of such data established that individuals are predisposed to infection with few or many worms and members of the same household tend to harbor similar numbers of worms ., These effects , known respectively as individual predisposition and household clustering , are considered characteristic of the epidemiology of ascariasis ., The mechanisms behind these phenomena , however , remain unclear ., In particular , the impact of heterogeneous individual exposures to infectious stages has not been thoroughly explored ., Bayesian methods were used to fit a three-level hierarchical statistical model to A . lumbricoides worm counts derived from a three-round chemo-expulsion study carried out in Dhaka , Bangladesh ., The effects of individual predisposition , household clustering and household covariates of the numbers of worms per host ( worm burden ) were considered simultaneously ., Individual predisposition was found to be of limited epidemiological significance once household clustering had been accounted for ., The degree of intra-household variability among worm burdens was found to be reduced by approximately 58% when household covariates were included in the model ., Covariates relating to decreased affluence and quality of housing construction were associated with a statistically significant increase in worm burden ., Heterogeneities in the exposure of individuals to infectious eggs have an important role in the epidemiology of A . lumbricoides infection ., The household covariates identified as being associated with worm burden provide valuable insights into the source of these heterogeneities although above all emphasize and reiterate that infection with A . lumbricoides is inextricably associated with acute poverty .

Numerous analyses have found that people infected with roundworm ( Ascaris lumbricoides ) are predisposed to harbor either many or few worms ., Members of the same household also tend to harbor similar numbers of worms ., These phenomena are called individual predisposition and household clustering respectively ., In this article , we use Bayesian methods to fit a statistical model to worm count data collected from a cohort of participants at baseline and after two rounds of re-infection following curative treatment ., We show that individual predisposition is extremely weak once the clustering effect of the household has been accounted for ., This suggests that predisposition is of limited importance to the epidemiology of roundworm infection ., Further , we show that over half of the variability in average worm counts among households is explained by household risk factors ., This implies that exposures to infectious roundworm eggs shared by household members are important determinants of household clustering ., We argue that these results support the hypothesis proposed in the literature that the household is a key focus of roundworm transmission .

infectious diseases/helminth infections, infectious diseases/epidemiology and control of infectious diseases, mathematics/statistics

journal.pbio.2005317

Amino acid starvation sensing dampens IL-1β production by activating riboclustering and autophagy

Dietary amino acid restriction , without malnutrition , offers enormous health benefits , including longevity of lifespan 1 , acute stress resistance , increased insulin sensitivity , and modulation of inflammation 2 , 3 ., However , the underlying mechanisms through which amino acid restriction extends its beneficial effects remain poorly defined ., General control nonderepressible 2 kinase ( GCN2 ) is a well-known metabolic sensor , which senses amino acid starvation conditions and programs protein synthesis through activation of the homeostatic integrated stress response ( ISR ) 4 ., The ISR is an evolutionarily conserved homeostatic process that enables mammalian cells to sense , adapt , and appropriately respond to a wide variety of extracellular and intracellular stress signals ., Four distinct eukaryotic initiation factor 2 ( eIF2 ) kinases—including GCN2 , RNA-dependent protein kinase-like endoplasmic reticulum kinase ( PERK ) , protein kinase R ( PKR ) , and heme-regulated eIF2α kinase ( HRI ) —mediate the ISR 5 ., GCN2 senses amino acid insufficiency , PERK is activated by endoplasmic reticulum stress , PKR senses viral double-stranded RNA ( dsRNA ) , and HRI senses heme deprivation , respectively 6 ., Depletion of intracellular amino acids results in the accumulation of uncharged transfer RNAs ( tRNAs ) that bind to GCN2 6 , leading to a conformational change and kinase activation ., Phosphorylated GCN2 , in turn , triggers inhibitory phosphorylation of eIF2α , a crucial eukaryotic translation initiation factor , resulting in impaired assembly of eIF2-guanosine triphosphate ( GTP ) -tRNAMet and polysome formation 5 ., This represses translational initiation and protein synthesis in cells to economize energy and adapt to the conditions of amino acid starvation ., GCN2-mediated translational blockade results in the accumulation of translationally silenced mRNAs , which further undergo various post-transcriptional reprogramming ( PTR ) via recruitment of RNA-binding proteins ( RBPs ) , leading to the formation of RBP–RNA complexes known as “riboclusters” such as stress granules ( SGs ) ., In SGs , the RBP–RNA composition determines the fate of mRNAs translatability , decay , or its storage 7 ., In addition to GCN2 , mammalian target of rapamycin ( mTOR ) , a serine/threonine kinase , directly senses amino acid availability in the cytosol via an unknown mechanism ., mTOR orchestrates anabolic processes such as fatty acid synthesis and cell growth by integrating the supply of energy , nutrients , and growth factors 8 ., Pharmacological inhibition of mTOR signaling also increases lifespan , suggesting cross-talk between GCN2 and mTOR signaling pathways 8 ., Halofuginone ( HF ) , a mimetic of the amino acid starvation response ( AAR ) , is a small molecule derived from the plant alkaloid febrifugine , extracted from the herb Dichroa febrifuga , and has gained significant attention for its therapeutic value 9 ., In mammals , HF has displayed therapeutic promise for conditions such as muscular dystrophy 10 , hepatic fibrosis 11 , and cancer , by inhibiting tumor metastasis 12 , 13 ., HF has also been shown to protect mice from ischemia-reperfusion–induced inflammation 2 ., Recent studies have demonstrated that HF acts by inhibiting the prolyl-tRNA synthetase activity of glutamyl-prolyl-tRNA synthetase ( EPRS ) 14 ., HF actively competes with proline for the active site of prolyl-tRNA synthetase , resulting in the accumulation of uncharged tRNApro ., This mimicks conditions of cellular proline deficiency , which in turn triggers simultaneous activation of the GCN2–AAR pathway 14 ., Recently , system-wide analysis of immunological responses to yellow fever vaccine ( YF-17D ) identified gene signatures encompassing GCN2 15 , which plays a critical role in programming YF-17D–induced T-cell responses 16 ., Furthermore , recent studies suggest that HF-mediated activation of the GCN2–AAR pathway inhibits T helper 17 cell ( Th17 ) responses and protects mice from Th17-associated pathologies in a mouse model of experimental autoimmune encephalomyelitis ( EAE ) 17 ., Also , GCN2 has recently been shown to play a central role in controlling intestinal inflammation through the inhibition of interleukin 1β ( IL-1β ) , a key proinflammatory mediator 4 ., While the connections between the GCN2 and AAR pathways controlling immune responses is evident , the mechanism underlying its association with regulation of IL-1β and other inflammatory mediators remains poorly understood ., The production of IL-1β includes the processing of inactive IL-1β precursor protein ( 31 kDa ) into the bioactive or mature IL-1β ( 17 kDa ) and release through K+ efflux-dependent mechanisms , which can be triggered by ATP , a known inducer of potassium efflux 18 ., Processing of active IL-1β is largely dependent on caspase-1 activation triggered by inflammasome activation following toll-like receptor ( TLR ) stimulation ., Here , we show that HF dampens endotoxin-induced IL-1β production in macrophages by a novel mechanism of PTR and translation reprogramming ., HF-elicited PTR events resulted in the repositioning of IL-1β mRNA transcripts from polysomes to riboclusters , such as SGs , in a GCN2-dependent manner ., Also , IL-1β mRNAs targeted to SGs are further cleared through the induction of autophagy ., HF impaired the processing and secretion of mature IL-1β in lipopolysaccharide ( LPS ) -primed macrophages by controlling the reactive oxygen species ( ROS ) production and inflammasome activation ., Furthermore , HF protected mice from dextran sulfate sodium ( DSS ) –induced intestinal inflammation ., Collectively , these findings highlight a crucial role for the GCN2–AAR pathway in the regulation of IL-1β production and unveil a novel mechanism through which AAR modulates inflammatory responses ., Recent studies have shown that HF exerts its therapeutic benefits through inhibition of Th17 17; however , its role in controlling innate inflammatory responses is not yet clear ., To investigate the immunomodulatory function of HF , LPS-primed bone marrow–derived macrophages ( BMDMs ) were treated with nontoxic concentrations of HF for different time points ( S1 Fig ) ., We observed a substantial reduction of IL-1β in a dose- and time-dependent manner by HF ( Fig 1A and S2A and S2B Fig ) , while tumor necrosis factor α ( TNF-α ) showed a decrease , and there was no suppressive effect on IL-6 during HF treatment ( S2B and S2C Fig ) ., HF treatment inhibited IL-1β production in peritoneal as well as transformed macrophages ( S2D and S2E Fig ) ., By contrast , treatment of LPS-primed macrophages with MAZ1310 , an inactive derivative of HF , did not show any effect on IL-1β production , indicating that the observed effects are specific to HF ( Fig 1B ) ., Next , we assessed the effect of HF on damage associated molecular patterns ( DAMPs ) -induced IL-1β production ., HF substantially impaired IL-1β production in macrophages treated with inflammasome activators such as aluminum hydroxide ( ALU ) or monosodium urate ( MSU ) ( Fig 1C ) ., Apart from its effects on model TLR ligand ( which represent less complex stimuli than bacteria or viruses ) –induced IL-1β , HF also inhibited IL-1β production by BMDMs infected with Salmonella typhimurium ( Fig 1C ) ., Next , we examined the effect of HF on IL-1β processing and secretion ., Results in ( Fig 1D ) show that HF substantially reduces mature IL-1β induced by LPS plus ATP , as well as cleaved caspase-1 , in the supernatant of treated cells ., These results indicate that HF affects IL-1β production by interfering with inflammasome activation ., Inflammasomes are multiprotein complexes activated during microbial infection or stress , and they elicit caspase-1 activation and the processing of IL-1β 19 ., Although the mechanisms underlying inflammasome activation are still unclear , one of the proposed models suggests that cellular ROS play a pivotal role in the inflammasome activation 20 ., Although the source of ROS is still unclear , studies suggest an association with NADPH oxidase activation 21 ., We therefore examined the effect of HF on ROS production by stimulating BMDMs with LPS for 3h , followed by HF treatment ., We found a significant reduction of ROS levels in LPS plus HF–stimulated macrophages compared to LPS alone ( Fig 1E ) , suggesting that HF may limit IL-1β secretion through the suppression of the ROS generation as well ., TLR family members TLR2 and TLR4 recognize bacterial components and play a crucial role in the antibacterial response 22 , and our results suggest that inhibition of IL-1β by HF is pronounced when triggered by a TLR4 ligand compared to TLR2 ligands ( S2F Fig ) ., Concomitantly , HF did not show much effect on IL-1β in the macrophages primed with viral nucleotide-sensing TLR ligands TLR3 , TLR9 , and TLR7 ., HF dramatically decreased pro–IL-1β levels in the cell lysates , whereas the pro–caspase-1 levels were not affected by HF in LPS- and LPS plus HF–stimulated macrophages ( Fig 1D , S2G Fig ) , indicating that HF affects IL-1β not only at the level of processing but also at the level of expression of pro–IL-1β ., To examine the mechanism by which pro–IL-1β is reduced by HF , we examined the effect of HF on mRNA for IL-1β and other cytokines by quantitative reverse transcription PCR ( qRT-PCR ) ., The results revealed a significant decrease in the levels of mature IL-1β and IL-18 mRNA compared to other cytokines ( Fig 1F , S3A Fig ) ; however , pre–IL-1β mRNA was not affected ( Fig 1G ) ., These data suggest that the effect of HF on IL-1β mRNA might not be at the transcriptional level ., We next speculated that HF might target IL-1β partly through modulation of transcriptional/PTR events ., To investigate whether HF-induced inhibition of IL-1β mRNA is a result of PTR , BMDMs were treated with LPS for 2 h , followed by treatment with the transcriptional inhibitor actinomycin-D ( Act-D ) for 2h before the addition of HF ., Surprisingly , we observed a remarkable reduction in IL-1β mRNA transcripts in LPS plus HF–stimulated macrophages compared to LPS alone in Act-D–primed cells ( Fig 1H ) ., HF also inhibited IL-1β mRNA when IL-1β was constitutively expressed in human embryonic kidney cells 293T ( HEK293T ) by transfection of an IL-1β–expressing plasmid driven by the cytomegalovirus ( CMV ) promoter independent of TLR signaling ( S3B Fig ) ., Translational control of IL-1β by HF was further confirmed by inhibiting translation in BMDMs with cycloheximide , a translational inhibitor , prior to LPS and HF treatment ( S3C Fig ) ., Altogether , these results suggest that HF controls IL-1β production by affecting inflammasome activation and triggering of PTR events without significant effect on transcriptional reprogramming ., Post-transcriptional control of cytokines and other immune effector mRNAs ensures rapid temporal or spatial changes in protein expression in response to changing environmental cues 23 , mediated by sensors such as GCN2 , PERK , PKR , and HRI , which sense particular stress conditions 5 ., GCN2 senses amino acid deprivation and triggers activation of the ISR pathway , which in turn programs translatability and/or decay of mRNAs as per cellular requirements through riboclustering 7 ., Recent studies have shown that HF-induced biological activities are attributed to the activation of the AAR pathway 14 ., Therefore , we next studied the role of the GCN2–AAR pathways in the ability of HF to regulate IL-1β ., We observed elevated phosphorylation of GCN2 and eIF2-α in macrophages with increasing concentrations of HF ( Fig 2A ) , which is in agreement with earlier studies performed on T cells 17 , fibroblasts , and epithelial cells 24 ., Concomitantly , the formation of punctate SGs increased with HF treatment ( Fig 2B and 2C and S4 Fig ) ., In addition , GCN2−/−mouse embryonic fibroblast cells ( MEFs ) showed impaired ability to form SGs during HF treatment compared to wild-type ( WT ) cells ( Fig 2D and 2E ) , suggesting that HF-induced SG formation is GCN2 dependent ., To elucidate the role of GCN2 in HF-induced negative regulation of IL-1β production , BMDMs isolated from WT or GCN2−/− mice were primed with LPS followed by HF treatment ., While LPS plus ATP treatment enhanced IL-1β production in GCN2−/− BMDMs when compared to WT cells , HF only suppressed IL-1β production in WT but not in GCN2−/−macrophages ( Fig 2F ) ., On the otherhand , HF modestly decreased LPS-induced TNF-α in WT cells , and HF did not affect LPS-induced TNF-α levels in GCN2−/− cells ( Fig 2G ) ., Similar results were observed in levels of pro and active IL-1β protein as well as mature IL-1β and TNF-α mRNA in macrophages with transiently silenced GCN2 ( Fig 2H , S5A , S5B and S5C Fig ) ., The observed effects of HF were specific to GCN2 because PKR-specific small interfering RNA ( siRNA ) -mediated knockdown of PKR—another eIF2 kinase—did not affect HF’s ability to reduce IL-1β production , indicating a PKR-independent effect ( S6 Fig ) ., HF induces serine 51 ( Ser51 ) phosphorylation of eIF2-α via GCN2 , and so we examined the effect of eIF2-α on IL-1β by silencing eIF2-α in macrophages ., We found that HF significantly suppressed IL-1β production only in control cells , while no significant inhibition was observed in eIF2-silenced macrophages ( S5D Fig ) ., Altogether , these results suggest that HF limits IL-1β production by activating the GCN2–eIF2-α axis ., Having observed that HF induces SG formation , we next studied the mechanisms involved by monitoring the expression pattern of the SG proteins TIA-1/TIAR ., TIA-1/TIAR are RBPs involved in the regulation of mRNA transcripts ., HF alone induces expression of TIA-1/TIAR in a time- and concentration-dependent manner ( Fig 3A ) ., A similar increase was observed in LPS plus HF–or HF only–stimulated macrophages , while LPS alone shows no effect on TIA-1/TIAR levels ( S5E Fig ) ., To elucidate whether HF-induced TIA-1/TIAR plays a role in the suppression of IL-1β expression , we silenced the expression of TIA-1 or TIAR in macrophages using smart pool TIA-1 or TIAR siRNAs ., These cells were then primed with LPS followed by treatment with HF ., We observed an enhancement of IL-1β production both at the protein and mRNA levels in TIA-1/TIAR–silenced macrophages in response to LPS stimulation , and HF-induced suppression of IL-1β production is reduced in these cells ( Fig 3B and S5F and S5G Fig ) ., Conversely , we overexpressed TIA-1/TIAR along with the IL-1β in HEK293T cells , driven by heterologous promoters , and examined IL-1β protein expression by immunoblotting ., We detected reduced IL-1β expression in the lysates of HEK293T cells overexpressing TIA-1/TIAR compared to vector controls ( Fig 3C ) ., These data strongly suggest that TIA-1/TIAR play a significant role in the regulation of IL-1β expression ., Furthermore , we studied whether eIF2-α signaling plays any significant role in controlling the expression levels of TIA-1/TIAR ., Our results suggest eIF2-α–dependent expression of TIA-1/TIAR ( S5H Fig ) ., IL-1β mRNA bear ARE sequences in the 3’UTR 7 , which tempted us to speculate that IL-1β mRNA might recruit RBPs—including TIA-1/TIAR—through these ARE sequences and lead to SG formation during GCN2-eIF2 activation ., To test this hypothesis , we stimulated macrophages with LPS or LPS plus HF and prepared cell lysates for RNA immunoprecipitation ( RIP ) using TIA-1/TIAR antibodies or immunoglobulin G ( IgG ) control ., Using both by qRT-PCR and RT-PCR analysis , we found that TIA-1/TIAR pull-downs were associated with IL-1β and TNF-α—but not caspase-1 and IL-6 transcripts—in the LPS plus HF–treated groups but not the LPS-alone groups ( Fig 3D and S7 Fig ) ., Control IgG failed to pull down IL-1β transcripts in both LPS- and LPS plus HF–stimulated macrophages ., Furthermore , nontarget β-actin was amplified to the same extent in both groups ( Fig 3D ) , indicating the presence of equal amounts of nonspecific contaminating mRNAs in both the immunoprecipitation ( IP ) material as observed in the earlier studies 25 ., Because we noticed that HF induced a significant increase in IL-1β transcripts in the RIP material , we examined its impact on pro–IL-1β expression in the cell lysates ., Our results show a substantial decrease in pro–IL-1β expression in LPS plus HF–treated compared to LPS alone–treated cells , whereas pro–caspase-1 levels were comparable in both LPS- and LPS plus HF–treated groups ( Fig 3E ) ., Furthermore , when RIP was performed using TIA-1/TIAR antibodies in LPS-primed macrophages treated with different concentrations of HF , we observed a proportionate increase in IL-1β transcripts coprecipitating in a HF-dose–dependent manner ( Fig 3F ) ., Because TIA-1/TIAR bind to ARE sequences found at the 3’UTRs of mRNAs and regulate their expression , we reasoned that IL-1β mRNA carrying a mutation in 3’UTR ARE elements might not be affected by TIA-1/TIAR ., To test this speculation , we overexpressed IL-1β mRNAs carrying WT ( no mutation in AREs of 3’UTR ) or ARE mutation ( Δ3’UTR ) in HEK293T cells using a heterologous promoter ( CMV ) , along with TIA-1/TIAR , and assessed the expression of IL-1β mRNA ., We found that TIA-1/TIAR overexpression reduced IL-1β mRNA ( as detected by qRT-PCR ) only in cells expressing WT plasmid but not in the cells expressing the ARE-deleted plasmid ( Fig 3G ) ., These results clearly demonstrate that HF triggers riboclustering through recruitment of TIA-1/TIAR proteins to the ARE sequences in the IL-1β mRNA and facilitates its movement to cytoplasmic caches of SGs for degradation ., Recent studies have shown that mice deficient in autophagy proteins , autophagy-related 16-like 1 ( Atg16L1 ) or autophagy related protein 7 ( Atg7 ) are prone to enhanced IL-1β production 26 ., It has also been demonstrated that SGs are cleared by autophagy 27 ., Therefore , we examined the status of autophagy during HF stimulation by monitoring autophagy marker , microtubule-associated protein 1A/1B light chain 3 ( LC3 ) ., We found that autophagy vesicles ( revealed by LC3 punctate staining ) were enhanced at 6 and 12 h of HF stimulation ( Fig 4A and 4B ) ., Furthermore , analysis of conversion of LC3-I to a membrane-bound form , LC3-II 28 , in the cell lysates by immunoblotting revealed an increase in the conversion and accumulation of LC3-II in response to HF ( Fig 4C , and S8A Fig ) ., Furthermore , LC3-II conversion was enhanced in LPS plus HF–treated cells compared to LPS alone–treated cells ( Fig 4D ) ., Next , we assessed the autophagy flux by monitoring the levels of LC3-II and sequestosome 1 ( p62/SQSTM1; a well-known autophagy substrate ) in the presence or absence of autophagy inhibitors chloroquine ( CQ ) or Bafilomycin A1 ., LPS is known to induce expression of p62 29 , and we found that there was a dramatic reduction of p62 in macrophages treated with LPS plus HF plus ATP compared to cells treated with LPS plus ATP alone ( Fig 4E ) ., This effect of HF on p62 levels was inhibited by Bafilomycin A1 in LPS plus HF–treated cells ( Fig 4E ) ., Accumulating evidence suggests that p62 and LC3-II are degraded by autophagy during long-term starvation , i . e . , from 2 h 30 , 31 ., CQ or Bafilomycin A1 strongly inhibited basal autophagy , marked by the accumulation of LC3 and p62 , and HF treatment caused a decrease in p62 and LC3-II levels in the presence of CQ ( possibly due to activation of autophago-lysosomal degradation ) , but not in Bafilomycin A1–treated cells ( Fig 4C and S8A and S8B Fig ) , suggesting that Bafilomycin A1 exhibits more lysosomal inhibitory effect than CQ , as previously described 31 ., However , levels of LC3-II are still higher during HF treatment in the presence of CQ compared to cells treated with HF in the absence of CQ ( S8A Fig ) ., Together , these data clearly indicate that HF triggers autophagy flux ., Furthermore , similar to amino acid starvation–induced p62 mRNA expression 32 , HF caused up-regulation of p62 expression , which is dependent on GCN2 ( S8C and S8D Fig ) ., Collectively , these results suggest that HF triggers induction of autophagic processes ., We next examined whether autophagy has any effect on expression of SG proteins TIA-1/TIAR in macrophages treated with LPS plus HF ., Our results show that LPS plus HF in presence of Bafilomycin A1 induces the accumulation of TIA-1/TIAR ( Fig 4F ) ., Furthermore , TIA-1/TIAR colocalizes with autophagy marker LC3 as revealed by immunofluorescence microscopy ( S9A Fig ) ., Together , these results indicate that autophagy mediates the clearance of SGs ., Next , we investigated whether manipulation of autophagy had any effect on HF-induced suppression of IL-1β expression in LPS-primed macrophages ., Pharmacological inhibition of autophagy by using phosphotidyl inositol 3 kinase inhibitors 3-methyl adenine ( 3-MA ) or Wortmannin 33 , 34 , lysosomal inhibitor Bafilomycin A1 , or knockdown of autophagy gene ATG16L1 in macrophages resulted in elevated levels of IL-1β in LPS plus HF–stimulated cells compared to their controls ( Fig 4G–4I and S9B Fig ) ., These results suggest that HF-induced autophagy plays a significant role in the suppression of IL-1β ., To explore the mechanism , IL-1β was overexpressed using a heterologous promoter in HEK293T cells alone , or along with plasmids expressing TIA-1 and TIAR ., Following transfection , cells were treated with autophagy inhibitor ( Bafilomycin A1 ) or activator ( rapamycin ) and levels of IL-1β mRNA were monitored ., We observed a significant reduction in IL-1β expression in TIA-1/TIAR–overexpressing cells—which was further decreased by rapamycin treatment—whereas Bafilomycin A1 reversed the inhibition ( Fig 4J and 4K ) ., RIP also revealed an increase in IL-1β mRNA association with TIA-1/TIAR under the conditions of inhibition of autophagy ( Fig 4L ) ., Recent studies have shown that activation of autophagy decreases mitochondria-derived oxidative stress ( a potent inflammasome activator ) by clearing damaged mitochondria , thereby affecting IL-1β levels 29 ., We therefore measured the levels of mitochondrial ROS using MitoSOX in LPS-primed macrophages during HF or control MAZ1310 treatment ., HF significantly inhibited MitoSOX production ( S9C and S9D Fig ) ., Together , these data suggest that HF-induced autophagy triggers IL-1β suppression via degradation of IL-1β mRNA transcripts bound to SG components , TIA-1/TIAR , and by reducing inflammasome activation signals ., Recent studies have demonstrated a link between AAR and autophagy in yeast and mammals 35 ., The above results support the previous findings 17 , showing that AAR mimetics such as HF act through the activation of GCN2 ., However , the link between HF-induced autophagy and GCN2 remains unclear ., Therefore , we examined the conversion of LC3 in GCN2−/− MEFs treated with varying concentrations of HF as well as in lysates of GCN2-silenced macrophages during HF treatment ., We observed a dose-dependent increase in LC3-I to LC3-II conversion in the WT MEFs treated with HF , but not in GCN2−/−MEFs ( Fig 5A ) ., Furthermore , analysis of autophagosomal puncta ( LC3II accumulation ) in the cytosol via confocal microscopy revealed that the appearance of LC3+ dots was enhanced in WT but not in GCN2−/− MEFs exposed to HF ( Fig 5B and 5C ) ., Similar results in LC3 conversion were observed in GCN2 siRNA–transfected cells compared to its control siRNA–transfected macrophages ( Fig 5D ) ., Conversely , when GCN2 was overexpressed in HEK293T cells and the transfected cells starved for different time periods , LC3 conversion was significantly increased compared to cells cultured in normal media ( S8E Fig ) ., These results indicate that HF-triggered induction of autophagy is dependent on GCN2 ., Anomalous expression of proinflammatory cytokines , particularly IL-1β , has been shown to be associated with colitis 36 , 37 ., Furthermore , transcriptome meta-analysis performed on publicly available microarray datasets ( Gene Expression Omnibus GEO , Array Express ) from samples of human inflammatory bowel disease ( IBD ) patients reveals that GCN2 was significantly down-regulated in the peripheral blood mononuclear cells ( PBMCs ) of both ulcerative colitis ( UC ) and Crohn’s disease ( CD ) , whereas it was up-regulated in colon tissues ( S10A–S10C Fig ) ., Given our findings that HF inhibits IL-1β , we therefore investigated the therapeutic potential of HF in a murine model of DSS-induced colitis ., We induced colitis in C57BL/6J mice by oral administration of 5% DSS in drinking water and found that daily intraperitoneal injections of HF ( 0 . 2 mg/kg ) prevented the loss of body weight observed in the absence of HF ( Fig 6A ) ., Levels of IL-1β in the serum and the colonic tissues of DSS plus HF–treated mice were markedly reduced compared to the DSS-treated mice ( Fig 6B , 6G and 6H ) ., Furthermore , we observed that HF treatment ameliorated DSS-induced rectal bleeding ( Fig 6C ) ., Shortening of colon length , an important characteristic of colitis , was also reduced in DSS plus HF–administered mice compared to only DSS-treated mice ( Fig 6D and 6E ) ., Microscopic examination of colon tissue sections from DSS plus HF–treated mice showed that HF prevented the DSS-induced inflammation , crypt loss , and epithelial damage ( Fig 6F ) ., These results suggest the therapeutic potential of HF-mediated GCN2–AAR pathways in controlling intestinal inflammation ., Amino acid restriction is associated with enormous health benefits , including longevity of lifespan 38 , acute stress resistance 39 , increased insulin sensitivity , and modulation of inflammation 2 , 3 in yeast to nonhuman primates ., However , the clinical benefits of amino acid restriction to humans has yet to be achieved ., Our study has established that pharmacological activation of AAR with a plant-derived small biomolecule HF modulates innate inflammatory responses by activating the homeostatic ISR pathway ., Our findings demonstrate that HF-induced cytoprotective AAR is dependent on GCN2 because HF failed to trigger AAR in GCN2-ablated cells ., The HF-induced AAR pathway triggers a significant reduction in LPS-induced IL-1β , with little inhibitory effect on another inflammatory cytokine TNF-α and no effect on IL-6 ., Although HF has been previously shown to activate GCN2 kinase , the molecular basis by which it controls IL-1β and other inflammatory mediators has been poorly understood ., Our study has revealed the mechanisms by which HF suppresses IL-1β expression in response to LPS ., IL-1β is a potent inflammatory cytokine with diverse cellular and physiological functions 40 and as such , requires stringent regulatory mechanisms to control its expression and secretion ., IL-1β is translated as an inactive 31 kD precursor protein ( pro–IL-1β ) in response to TLR4 stimulation , which is further cleaved into mature bioactive IL-1β ( p17 ) by caspase-1 , triggered by inflammasome activation 41 ., Our study shows that HF impairs processing of mature IL-1β by reducing inflammasome activation , which could be partly attributed to reduced ROS production ., We further show that HF reduces mature IL-1β transcripts , but not its transcription ., These findings suggest that HF inhibits IL-1β production by programming PTR/translational events ., It is notable that dexamethasone was recently reported to inhibit IL-1β production by programming PTR events , without much effect on transcriptional machinery function , similar to what we find for HF 42 ., Various studies have reported that IL-1β plays a crucial role in the commitment of Th17 cells 4 , and it was recently demonstrated that HF inhibits the Th17 response and protects mice from EAE-associated inflammation through the activation of the GCN2–AAR pathway in T cells 17 ., However , how HF controls innate regulation of Th17 cell responses remained elusive ., Our findings suggest that HF likely determines the effector commitment of Th17 cells in part by also inhibiting IL-1β production through programming PTR/translational events ., In line with the above studies , it was recently shown that the AAR sensor GCN2 plays a pivotal role in controlling intestinal inflammation 4 ., HF preconditioning protects the mice from surgical stress–induced inflammation in a mouse model of ischemia-reperfusion injury 2 ., Our study mechanistically supports previously published work highlighting the beneficial aspects of HF preconditioning or AA restriction in the context of inflammation ., It is well known that HF activates GCN2 by acting competitively with proline , thereby inhibiting prolyl-tRNA synthetase activity of EPRS , known to participate in the interferon γ ( IFNγ ) -activated inhibitor of translation complex ( GAIT ) 14 ., Activation of the GAIT complex by IFNγ results in the suppression of proinflammatory gene expression through the binding of the GAIT complex to the 3’UTRs of inflammatory cytokines 43 ., However , LPS-induced IL-1β production is not impaired in PBMCs incubated with IFNγ 44 , indicating stress-specific regulation of IL-1β ., Our findings indicate that HF-induced eIF2-α phosphorylation triggers riboclustering or SG formation , an important PTR event in the regulation of inflammatory cytokines such as IL-1β 7 ., Previous studies suggest that cytokine mRNAs , including IL-1β , bear ARE sequences at 3’UTR 45 , which confer tight post-transcriptional regulation mainly mediated by stress-driven activation of ISR pathways , resulting in translational blockade 7 ., Translationally silenced mRNAs trigger ribocluster or SG formation via recruitment of RBPs , which in turn dictate the mRNA’s stability/decay 23 ., The existence of such regulatory mechanisms assist the cells in turning “on” or “off” its protein synthesis machinery as per cellular requirements to overcome stressful conditions like amino acid starvation 7 ., Our findings reveal that HF-induced GCN2 activation causes translational silencing of IL-1β mRNA through enhanced expression of translation silencer RBPs , TIA-1 and TIAR , which in turn assists cytoplasmic repositioning of the IL-1β mRNA transcript from polysomes to SGs ( Fig 7 ) ., Previous studies have shown that the eukaryotic SGs are cleared by autophagy 27 ., In line with the earlier observations , our findings also reveal that HF induces autophagy and facilitates the post-transcriptional degradation of SG-bound IL-1β mRNAs ., Furthermore , autophagy could also influence mature IL-1β production post-translationally through ROS-dependent inhibition of inflammasome activation 46 ., Our results demonstrate that HF-induced autophagy suppresses ROS production , thereby inhibiting active IL-1β production post-translationally ., These findings highlight the centrality of autophagy in controlling post-transcriptional as well as post-translational regulatory events ., Exacerbated expression of IL-1β is associated with the development of IBDs such as colitis 36 , and pharmacological neutralization of these cytokines reduces the severity of colitis 47 ., Meta-analysis of publicly available UC and CD gene expression data revealed a substantial reduction of GCN2 expression in the PBMCs ., Our data indicate that HF significantly reduces serum IL-1β levels and the inflammatory pathology in a murine model of DSS-induced colitis and highlight the therapeutic potential of HF-induced GCN2–AAR pathways in controlling intestinal inflammation ., Altogether , this study reveals a novel mechanism of IL-1β regulation and further suggests that pharmacological activation of the evolutionarily conserved cytoprotective AAR pathway might offer an effective therapeutic intervention against inflammatory diseases ., The institutional animal care and use committee of the University of Hyderabad approved all the animal experiments ., The study approval number is IAEC/UH/151/2017/01/NK/P21/Mice C57BL/6 or BALB/c/M-54 ., DR-Wildtype ( ATCC CRL2977 ) and GCN2-KO-DR ( ATCC CRL2978 ) MEFs , purchased from American Type Culture Collection ( ATCC ) , we

Introduction, Results, Discussion, Materials and methods

Activation of the amino acid starvation response ( AAR ) increases lifespan and acute stress resistance as well as regulates inflammation ., However , the underlying mechanisms remain unclear ., Here , we show that activation of AAR pharmacologically by Halofuginone ( HF ) significantly inhibits production of the proinflammatory cytokine interleukin 1β ( IL-1β ) and provides protection from intestinal inflammation in mice ., HF inhibits IL-1β through general control nonderepressible 2 kinase ( GCN2 ) –dependent activation of the cytoprotective integrated stress response ( ISR ) pathway , resulting in rerouting of IL-1β mRNA from translationally active polysomes to inactive ribocluster complexes—such as stress granules ( SGs ) —via recruitment of RNA-binding proteins ( RBPs ) T cell–restricted intracellular antigen-1 ( TIA-1 ) /TIA-1–related ( TIAR ) , which are further cleared through induction of autophagy ., GCN2 ablation resulted in reduced autophagy and SG formation , which is inversely correlated with IL-1β production ., Furthermore , HF diminishes inflammasome activation through suppression of reactive oxygen species ( ROS ) production ., Our study unveils a novel mechanism by which IL-1β is regulated by AAR and further suggests that administration of HF might offer an effective therapeutic intervention against inflammatory diseases .

Reduced intake of food ( also known as dietary restriction ) without malnutrition has been shown to benefit health in humans and animals , including an increase in life expectancy , metabolic fitness , and resistance to acute stress ., Recent studies have attributed the benefits associated with dietary restriction to the reduced intake of amino acids ., However , the underlying mechanisms through which amino acid restriction regulates various homeostatic processes are poorly defined ., Here , we show that activation of amino acid starvation response ( AAR ) by the small molecule Halofuginone ( HF ) results in a significant inhibition of production of interleukin 1β ( IL-1β ) , a proinflammatory mediator ., We find that AAR provides protection from intestinal inflammation–associated pathology in a mouse model of colitis through a novel mechanism involving the formation of riboclusters ( groups of RNA-binding proteins ( RBPs ) and stalled mRNA complexes ) and autophagy ., We further show that HF-mediated inhibition in IL-1β production is dependent on general control nonderepressible 2 kinase ( GCN2 ) , an amino acid deprivation sensor ., This study provides the mechanisms regulating AAR-induced benefits in the context of inflammation and further suggests that the administration of HF might offer an effective therapeutic intervention against inflammatory diseases in mammals .

blood cells, cell death, innate immune system, medicine and health sciences, autophagic cell death, immune cells, immune physiology, cytokines, pathology and laboratory medicine, gene regulation, immunology, cell processes, messenger rna, animal models, developmental biology, model organisms, signs and symptoms, experimental organism systems, molecular development, research and analysis methods, small interfering rnas, white blood cells, inflammation, animal cells, gene expression, mouse models, immune response, immune system, biochemistry, rna, diagnostic medicine, cell biology, nucleic acids, physiology, genetics, biology and life sciences, cellular types, macrophages, non-coding rna

journal.pcbi.1007090

Connecting signaling and metabolic pathways in EGF receptor-mediated oncogenesis of glioblastoma

Diseases like cancer involve a large range of components that interact via complex and highly dynamic networks 1–3 , and are interconnected with biochemical pathways 4–7 ., These multipath interconnections may allow cancer and other diseases to take alternate routes and bypass the effects of therapeutic interventions ., Traditional approaches to biological studies which focus on single molecules or pathways may not be able to capture and understand these complex networks of molecular interactions ., To predict alternative or escape routes around blockades and to develop effective therapies 8 , sophisticated mathematical and computational models are required 9–10 ., Transforming traditional drug discovery approaches toward smarter therapeutic strategies , the field of systems biology is emerging 1 , 9 , 11–20 ., Systems approach generally involve large-scale data collections , most often from high-throughput transcriptome or proteome analyses , incorporation of the data into mathematical models to deduce systems properties , model building and finally computational and/or experimental validation of model-derived hypotheses ., Systems biology approaches may predict combination therapies for cancers driven by different oncogenic signaling and metabolic pathways ., Signaling and metabolic networks were studied using separate model systems 15 , 21–26 ., Mathematical models for signaling pathways had been based on logic models 27–30 , kinetic models 31–33 , decision tree 34 , and other differential equation-based models 35 ., Computational models of molecular signaling 36–41 have the potential to improve drug discovery and development 32 , 42–44 ., Analyses of knockdown experiments 45 using mass spectrometry 46 and transcriptomics 47–49 are progressively refined and tuned towards specific physiological situations ., While these studies have helped considerably to extend our understanding of tumor biology , they are still restricted to signaling pathways and do not integrate the metabolic pathways , which in some initial studies have been subjected to separate systems biology analysis ., Predicting the effects of multiple targeted drugs 8 , 50 with modeling the information flow from new molecular interactions within pathways is challenging 51–55 ., Here we report the development , test and validation of an integrated model for signaling and metabolic pathways in cancer using glioblastoma multiforme ( GBM ) as an example 47 , 56–59 ., GBM is the most prevalent and most aggressive brain tumor ., In the majority of cases , tumor development is dependent on signaling via the epidermal growth factor receptor ( EGFR ) and requires EGF in lower-grade forms or is EGF-independent in the more aggressive forms ., In most cases , the expression of EGFR is up-regulated , often related to the amplification of the EGFR gene ., More than fifty per cent of EGFR-amplified GBM cases have in-frame deletions of exons 2–7 that code for the extracellular ligand-binding domain ( EGFRvIII mutation ) resulting in EGF-independent constitutive signaling and more aggressive tumor growth , higher invasiveness , increased resistance to treatment , and poor prognosis 60–62 ., In this study , we choose the cell line U87MG expressing the EGF-dependent EGFRwt and its derivative U87MGvIII expressing the EGF-independent EGFRvIII-mutant , as models of low and high-grade GBM , respectively ., Except for the EGFR mutation , the two cell lines have the same genotype but differ in growth behavior suggesting different metabolic requirements , and are experimental examples for the regulation of the interconnection of signaling and metabolic pathways , which is considered among the basic characteristics of cancer 63–64 ., We implemented a probabilistic approach based on the Hidden Markov Model ( HMM ) utilizing the information of experimentally established protein-protein interactions ( PPIs ) 65–66 to extract novel paths and interconnections between signaling pathway proteins ( S ) and metabolic pathway proteins ( M ) ., To cope with the limitations of PPI identification , for example high error rates of the detection methods 67 , incomplete data sets , ignorance of the physiological conditions in the cell or tissue compartments 68 , technical problems and study biases 69 , we collected information from curated sources ( http://string-db . org ) with high experimental score cut-off , which reduces false positive rates , and used transcriptome data from clinical samples to build more reliable and GBM context-specific PPI networks ., To bridge the gap between transcriptome data and cell-biological processes , we incorporated proteome data from the GBM cell lines complemented with transcriptome data to solve the missing data problem caused by the failure of proteomics to capture all proteins in the cells due to sensitivity and reproducibility issues ., Including experimental data , our dynamic model can make use of multiple weighted network properties to add biologically relevance and can extract novel paths of information propagation in networks ., The results of the model were tested in rigorous in-silico perturbation experiments and experimentally validated in cell culture systems ., Fig 1 depicts the overall strategy implemented with this study ., As a starting model , we constructed an integrated network where signaling ( S ) and metabolic ( M ) pathway proteins were connected through protein-protein interactors ( PPIs ) ., The signaling pathways were the apoptosis , Akt , EGFR , hedgehog ( Hh ) , JAK-STAT , JNK , MAPK , mTOR , NF-kappa B ( NF-κB ) , Notch , p53 , Ras , TGF-β , and Wnt pathways ., On the metabolic site , there were 81 pathways grouped into the six categories carbohydrate , lipid , amino acid , nucleotide , energy and xenobiotic metabolism ( Fig 2 ) ., To build an initial human protein-protein interactome network ( HPPIN ) , the total of all human protein-protein interactions was extracted from the protein-interaction database STRING 70 for experimentally validated interactions including physical and functional associations ., Then to structure the signaling-metabolic interaction network ( SMIN ) , cross-connections between the fourteen signal transduction and six groups of metabolic pathway proteins selected from pathway databases ( see Materials and methods ) were constructed based on protein-protein interacting proteins ( node ) and cross-connecting links/paths ., All possible connections between any given signaling pathway protein ( S ) and metabolic pathway protein ( M ) via protein-protein interconnectors ( PPIs ) were included ( Fig 2A ) ., To derive a simplified but informative network , the interactions were restricted to the second level of protein interactors , i . e . up to interactors of interactors ., This led to four different types of cross-connected paths where signaling pathway proteins were either directly connected with metabolic pathway proteins or through one , two or three PPIs , respectively ( Fig 2A ) ., These paths connecting all above-mentioned signaling ( S ) and metabolic ( M ) pathway proteins were then converted into networks based on the protein-protein interaction status between the involved proteins ( Fig 2B ) ., The resulting signaling-metabolic interaction network ( SMIN ) is shown in the left panel of Fig 3A with nodes in orange and edges ( connection between two nodes ) in blue , within the total interactome containing HPPIN network with grey nodes and edges ., As a result of afore-said restriction criteria , Fig 3A shows the reduction of the network size ( number of nodes/proteins and their interactions/edges ) of SMIN ( 11 , 059 interactions formed by 2 , 785 proteins ) from HPPIN ( 16 , 828 interactions formed by 5 , 703 proteins ) ., As an example of network links , the detailed connections and interactions between one signaling pathway protein , CSNK2A1 , and one metabolic pathway protein , NDUFA13 , through different PPIs in the SMIN are shown on the right panel of Fig 3A ., These two selected examples are indicated by asterisks ., The SMIN was found to contain 158 direct ( S-M ) linking paths , 4 , 036 with one interactor ( S-P-M ) , 91 , 847 with two interactors ( S-P-P-M ) and 2 , 110 , 205 with three interactors ( S-P-P-P-M ) ., These paths were formed between 158 , 2 , 967 , 22 , 307 , 69 , 032 S-M pathway protein pairs , respectively ( Table 1 ) ., Comparisons with respective random networks proved that our selected HPPIN and SMIN are non-random scale-free networks ( S1 Fig and S1 Table ) ., To render the above condition-independent SMIN GBM-specific , quantitative comparative proteome analysis of the low- and high-grade GBM cell lines U87MG and U87MGvIII were performed and the data incorporated in the model to derive an enriched GBM-specific network ., The use of cell lines helped to reduce the noise associated with the usual small size and heterogeneous cellular compositions of clinical samples ., Proteomes was chosen as primary expression data to focus the models on the protein level , which is more closely related to the biological processes to be modeled than transcriptome data ., Transcriptome date from clinical samples ( see below ) were subsequently added to reduce the missing data problem of proteomics and link the model to the clinical level ., To decrease the complexity of the proteins in the cell extracts and minimize signal suppression by overabundant peptides , the proteins were first separated by SDS-PAGE ., Then the gels were cut into one mm slices followed by separate in-gel digestion of the proteins with trypsin ., The resulting fragments were extracted from the gel slices as individual samples , separated by reverse-phase nano-HPLC and analyzed on-line by ESI-Q-TOF mass spectrometry ( S2 Fig ) ., Quantification was done label-free by calculating the exponentially modified protein abundance index ( emPAI ) to avoid the drawback of mere signal intensity-based measurements ., Only proteins with at least two tryptic fragments were identified by MS/MS with high confidence were considered , which further reduced the noise of protein identification although resulting in lower numbers of hits ., The analyses were done with three independent replicates per cell line and the resulting data processed in two different ways ., First , each of the three datasets for U87MG was compared to each of the three datasets for U87MGvIII resulting in nine pair-wise comparisons ., Second , the average of three datasets of one U87MG was compared with the average of the three datasets for U87MGvIII ., A total of 907 unique proteins were identified , 771 from U87MG and 664 from U87MGvIII ., Five hundred twenty-eight proteins were expressed in both cell lines , 243 only in U87MG ( EGFRwt ) and 136 only in U87MGvIII ( EGFRvIII ) ., These distinctively expressed proteins were considered as overexpressed in the respective cell lines in comparison to the other ., Compared quantitatively , 458 of the 528 common proteins were expressed at similar levels , 70 proteins were either up ( 45 ) or down ( 25 ) regulated in U87MGvIII compared to U87MG ( Fig 4A ) ., Together , nearly half of the identified proteins ( 449 ) were differentially expressed , 268 down-regulated and 181 up-regulated in U87MGvIII versus U87MG ( Fig 4B ) ., Over-representation analysis ( ORA ) based enrichment for cellular pathways , biological processes and molecular functions were performed using cell line specific and commonly overexpressed proteins ., Fig 4C and 4D show the top 20 most enriched pathways for proteins exclusively overexpressed in U87MGvIII ( EGFRvIII ) and U87MG ( EGFRwt ) , respectively ., Similarly , Fig 4E and 4F provide the top 20 enriched pathways for commonly over and under expressed proteins , respectively ., The most significantly enriched pathways were found to be proteasome ( p-value = 0 . 97763E-07 ) in U87MGvIII ( EGFRvIII ) and TCA cycle ( p-value = 1 . 48369E-06 ) in U87MG ( EGFRwt ) ., However , Fructose and mannose metabolism ( p-value = 6 . 7086E-04 ) and pentose phosphate pathways ( p-value = 3 . 1503E-05 ) were found to be most significantly enriched for proteins commonly overexpressed and underexpressed , respectively ., Gene ontology ( GO ) based biological process and molecular function over-representation analysis was also performed using cell line specific overexpressed proteins ., Most significantly enriched biological processes and molecular function were found to be Tricarboxylic acid metabolic process ( p-value = 2 . 7304E-04 ) and Threonine-type peptidase activity ( p-value = 0 . 001053394 ) , respectively in U87MGvIII ( EGFRvIII ) ., In U87MG ( EGFRwt ) , TCA metabolic process ( p-value = 7 . 76842E-08 ) and pre-mRNA binding ( p-value = 0 . 007857451 ) were found to be the most significantly enriched biological process and molecular functions , respectively ( S3 Fig ) ., To uncover the signature of the reprogramming of global cellular processes by the EGF-independent constitutively active EGFRvIII in GBM , we mapped the data from the comparative proteomics of U87MGvIII ( EGFRvIII ) versus U87MG ( EGFRwt ) onto the above-described SMIN ., All S-M interconnecting paths with at least one differentially expressed protein were extracted from the SMIN to generate a GBM-specific network ( Fig 3B , left panel: highlighted with yellow nodes and green edges within the orange nodes and blue edges of the SMIN ) with the assumption that those paths will have higher probability to be differentially active in mutant GBM condition ., As an illustration of proteome data mediated extraction of a GBM-specific network , the interconnections between the aforementioned signaling pathway protein , CSNK2A1 and the metabolic pathway protein , NDUFA13 are highlighted after extraction from SMIN ( Fig 3B right panel in comparison to Fig 3A right panel ) ., Table 1 provides the details of the paths/pairs present at different stages of network development ., To make this network further enriched with potentially disease-relevant paths/pairs , weights specifying disease-related biological properties and expression states of the proteins were assigned to each node ( protein/gene ) and edge ( interaction ) of the GBM-specific network ., The following three categories of proteins ( nodes ) were given additional weights ., First , proteins cross-talking between different signaling pathways ( signaling cross-talk , SC ) , second , rate-limiting enzymes ( RLE ) for their roles in regulating metabolic rates and pathways , third , EGFR mutation-specific differentially expressed proteins ( dEXP ) for their GBM-specific impact ., The GBM-specific network included 446 dEXP , 349 SC , and 267 RLE proteins ., Of these , 11 SC and 17 RLE proteins were up or down regulated suggesting their involvement in signaling-metabolic cross-connection in EGFR-mutated condition ( S4A Fig ) ., For systems-level interpretation and understanding the network property , local signaling entropy ( Si ) was introduced ., Previous studies 71–73 showed that Si can be used as a measure of uncertainty in signaling information flow over a network and to identify important signaling pathways and genes/proteins in cancer ., Effect-on-node ( effs ) of every protein ( node ) in the network provided significance of a protein based on SC , RLE and dEXP in its local network ., To identify probable paths of information flow from a signaling to metabolic pathways , network entropy ( Si ) and effect-on-node ( effs ) properties were incorporated as node weights into the logic of the Hidden Markov Model ( HMM ) ., The edge weight of every two interacting nodes ( gene/protein ) were defined based on the principle of mass action ( assuming that the probability of interaction of two genes in a given sample is proportional to the product of their expression values in the study samples ) as probability of interaction ( pij , where i and j are the two nodes ) in GBM condition ., To assign the expression value of each node present in the GBM specific network , the average expression value of each gene was calculated from the normalized transcriptome data from 239 GBM patients ., Incorporating these transcriptome data as edge weights linked the network with biological information from GBM patients ., It helped to assign an extra weight other than previously mentioned node weight for all connections made by two nodes based on their expression in clinical GBM patients ., Furthermore , it helped to add another level of constraint on over-prediction of information flow for nodes , which got an extra weight based on SC , RLE , and dEXP but are not expressed at higher levels in GBM patients ., This helped to incorporate the contribution of those nodes to the disease , which were identified by neither of the three before-mentioned node weights nor by proteomics ., Moreover , the much broader coverage of gene expression by genome-wide transcriptomics compared to proteomics helped to overcome some of the missing-data-problem of proteomic datasets ., An HMM-based simple mathematical formalism was used to understand context-specific information propagation from signaling to metabolic pathways in the human biological network ., Node weights and edge weights were used to define the two major parameters of the Markov model , emission ( Ef ) and transition ( Tj ) probabilities , respectively ., Two model systems were implemented to apply HMM logic , Model 1 for SM pair identification ( Fig 5A ) and Model 2 for S-M linking path identification ( Fig 5B ) ., Model 1 emphasized source ( Signaling Pathway Protein , S ) and destination ( Metabolic Pathway Protein , M ) pairs i . e . SM pairs having higher chances of information flow for each type of connections ( Figs 2A and 5A ) ., Model 2 was applied to find S-M linking paths between those selected pairs from Model, 1 . Selection of SM pairs ( Model 1 ) and S-M linking paths ( Model 2 ) was based on Pathscore ( see Methods for more details ) a mathematical function of emission probability ( Ef ) and transition probability ( Tj ) ., For Model 1 positional emission probability was calculated considering the similar number of proteins because Model 1 was applied after grouping interconnecting links having the similar number of proteins forming the connection ( Fig 5Ai–5Aiv ) ., The calculated path scores of linking paths from the individual models were converted to statistical Z scores to identify the paths deviating from the mean ., Based on the Z score under the individual models , the signaling-metabolic linking paths were classified as highly significant with Z score ≥3 ( more stringent ) or less significant with Z score ≥1 ( less stringent ) in EGFR-mutated GBM ., The signaling and metabolic pathway proteins from the two ends of linking paths containing significant Z sores of each of the models were defined as the significant SM pairs ., Multiple identifications of the same S-M pair from different models i . e . the formation of different types of significant linking paths involving different PPIs or different numbers of PPIs were nullified by considering them as a single ., With that we identified 1 , 2 , 114 and 758 SM pairs meeting the more stringent cut-off ( Z score ≥ 3 ) and 1 , 8 , 334 and 1 , 961 pairs with the less stringent cut-off ( Z score ≥ 1 ) for the S-M , S-P-M , S-P-P-M and S-P-P-P-M linking types , respectively ( Table 1 , Model-1 ) ., In total 801 Z ≥ 3 and 2 , 055 Z ≥ 1 signaling-metabolic cross-connected SM pairs were identified between the 14 signaling and 6 groups of metabolic pathways as potentially important in EGFR-mutated GBM ., These SM pairs were categorized according to the proteomic expression states of the source ( S ) and destination ( M ) proteins as UP-DOWN , UP-UP , DOWN-UP , and DOWN-DOWN ., Including unidentified proteins in proteomics analysis ( NA ) of the cell lines , the couplet categories UP-NA , DOWN-NA , NA-UP and NA-UP , and unchanged expression states ( NC ) in the cell lines with SM categories UP-NC , DOWN-NC , NC-UP and NC-DOWN , and unidentified and unchanged SM pairs NA-NA and NC-NC were added ( S4B Fig ) ., A number of cross-connections between signaling and metabolic pathways were identified with significant cutoff levels where either one or both pathway proteins ( S and/or M ) were not identified ( NA ) and/or unchanged ( NC ) by mass spectrometry indicating that the integrated network model can identify connections also where intermediate interactors are more important than SC , RLE or dEXP ., The identification of pathway cross-connections is thus not dependent on proteomic identification of all constituent members but can be based on signaling crosstalk proteins and their expression status in GBM patients ., It is important to include unchanged ( NC ) proteins in the model building as they might represent nodes in the paths that include other proteins that are differentially expressed ., They might also play a role in the crosstalk between signaling paths and pathways or they might become important when known primary paths are blocked , e . g . by therapeutic intervention ., The model could thus help to identify potential therapeutic targets for alternative therapies in cases of treatment failures or to design combination therapies that target primary together with potential escape pathways ., Mapping the pathway information of proteins in SM pairs showed which signaling pathways made a higher number of connections with which type of metabolic pathways in EGFR-mutated GBM ., Six hundred one significant pairs with Z ≥ 1 were cross-connecting the MAP kinase pathway with all six groups of metabolic pathways ( S2 Table ) ., Similarly , the Ras , EGFR , AKT and p53 pathways were significantly connected to metabolic pathways through 570 , 543 , 549 and 179 SM pairs , respectively ( S2 Table ) ., As crosstalk between availabe signaling pathways is common whereas it is less common in between metabolic pathways ( S5A Fig ) , some identified SM pairs and the respective proteins/genes may be shared ., Analyzing the shared components in the five most connected signaling pathways revealed that MAPK pathway had the highest number of unique significant SM pairs ( 256 ) and genes/proteins ( 63 ) involved , followed by 194 , 129 , 116 and 95 unique significant SM pairs ( S5B Fig ) and 20 , 30 , 36 , 19 genes/proteins ( S5C Fig left ) for the EGFR , AKT , p53 and Ras pathways respectively ., Twelve cross-connected pathway protein pairs were common to all five pathways and 141 pairs shared by the Ras , EGFR , AKT and MAPK pathways indicating high connectivity between them , and their cross-connection with metabolic pathways suggesting important roles of the respective proteins in EGFR-mutated GBM ( S5B Fig for pairs , C for genes left ) ., In turn , the amino acid , carbohydrate , and nucleotide metabolic pathway groups were connected to all fourteen signaling pathways through 327 , 289 and 326 cross-connected SM pairs ( S2 Table , S5B Fig right ) and 296 , 260 and 268 genes in significant S-M paths , respectively ( S5C Fig right ) ., This indicates that altered cellular signaling related to the EGFR mutation and its constitutive activity affects most strongly these three metabolic pathway groups ., As metabolic pathway enzymes interact via their substrates and products , there are few possibilities for interconnection between metabolic pathways except for the end steps , which is confirmed by the analysis of shared proteins ., Twenty-three shared pairs were identified between the amino acid and the carbohydrate metabolic pathway , which relates to the low number of amino acids metabolites feeding into the tri-carboxylic acid cycle ( S5B Fig right ) ., Multiple linking paths of different types ( S-M , S-P-M , S-P-P-M , and S-P-P-P-M ) or of the same type but through different PPIs were possible between SM pairs ., Not all of these linking paths could be equally significant in EGFR-mutated GBM ., To find the significant linking paths between the above-identified significant SM pairs , all possible paths between a single SM pair were considered under a single model ( Model 2 , Fig 5B ) ., Since in Model 2 proteins forming interconnections between SM pairs vary , positional emission probabilities were calculated for these proteins ., As an example , in Fig 5B the second position contained two proteins and third position one protein ., Paths with path scores ≥80% of the highest path score for each SM pair were selected as significant from Model, 2 . Accordingly , all the significant paths were identified from high ( more stringent Z ≥ 3 ) and less ( less stringent Z ≥ 1 ) significantly specified SM pairs to identify the total of significant linking paths in the network ., These analyses showed that 2 , 21 , 228 , 625 and 876 significant paths were present with more stringent cut-off ( Z ≥ 3 ) and 5 , 84 , 600 , 1 , 564 and 2 , 253 paths with the less stringent cut-off ( Z ≥ 1 ) for the four pathway types ( Table 1 ) ., By these pathway-based analyses under less stringent condition ( Z ≥ 1 ) , 652 significant linking-paths were identified between 570 cross-connected SM pairs of the Ras pathway with all six groups of metabolic pathways ( S2 Table ) ., In addition , 668 , 629 and 569 significant linking-paths were identified between 601 , 549 and 543 cross-connected S-M pairs between the MAPK , AKT and EGFR pathways , respectively , and all 6 groups of metabolic pathways ., Together , in EGFR-mutated GBM , these four signaling pathways were involved in the highest number of SM cross-connections with metabolic pathways: 368 , 344 and 298 cross-connecting paths were found between the fourteen signaling pathways and 327 , 326 and 289 cross-connected SM pairs involving the amino acid , nucleotide and carbohydrate metabolism , respectively ( S2 Table ) ., Based on the identified significant ( less stringent Z ≥ 1 ) SM pairs and the significant linking paths ( path score ≥80% of the highest path score of each SM pair ) , we converted ( as in Fig 2B ) the significant paths into a network ( Fig 3C left panel: highlighted with blue nodes and red edges within yellow nodes and green edges of the GBM network ) ., This filtered network is more specific for EGFR-mutated GBM conditions ., The filtration further eliminated non-significant or unimportant SM pairs and PPIs , which is shown , as an example , for the interconnections between signaling pathway protein CSNK2A1 and metabolic pathway protein NDUFA13 ( Fig 3C right panel compared to Fig 3B right panel ) ., The GBM-specific network based on significant SM pairs and linking paths was restructured to implement the biological consequences as network properties i . e . color-coded proteomic expression states ( up , down , no change and not identified ) , size of node symbols proportional to the numbers of connections passing through it , colors of the edges as connection formed between more ( Z ≥ 3 ) or less ( Z ≥ 1 ) stringently defined SM pairs , width of the edge as the probability of interaction or product of the average expression values of two interacting genes in GBM patients from the transcriptome data ( Fig 6A ) ., The resulting network showed the important signaling pathways and their interconnections with metabolic pathways in EGFR-mutated GBM with the significance of every protein ( size of the node ) and their interactions with interacting partners ( width of the edge ) ., Around the network , representative paths between 14 signaling to metabolic pathways are shown as examples ( Fig 6A , S6 Fig as more details of RAS pathway ) ., To explore the importance of signal-crosstalk proteins in signaling to metabolic pathway interconnections in EGFR-mutated GBM , the sub-network dependent on the top fifteen crosstalk protein-based interconnecting paths were extracted from the GBM-specific significant network ( Fig 6B ) ., These sub-networks showed which signaling pathways are mostly cross-talking and how they are connected with metabolic pathways ., This information was used to extract candidate genes/proteins/paths of EGFR-mutated GBM for further analysis ., In silico perturbation analysis was performed for identification of paths that significantly change upon removal ( e . g . by mutation or down-regulation ) of a node ( protein ) ., To test the importance of the nodes/proteins in the final weighted network , each of the 654 nodes present in the Z ≥ 1 network ( Table 1 ) was removed individually from the human interactome ( HPPIN ) and the node and edge weights were recalculated for the resulting networks and paths by recalculating Model 1 and Model 2 ( Fig 2 ) ., Accordingly , new significant SM pairs ( Z ≥ 3 or 1 ) were identified on the basis of Model 1 and significant paths ( path score ≥80% of the highest path score ) between them on the basis of Model, 2 . We mapped the pathway details of the SM pairs and calculated the average path scores before and after perturbation for the 14 signaling pathways to all 6 groups of metabolic pathways and vice-versa ., The difference of values ( before vs . after perturbation ) for the 654 proteins for all pathways were converted to Z-scores and plotted for each perturbed node for each signaling pathway ( Fig 7A ) ., The nodes for which the Z-scores deviated from the mean as -2 ≥ Z ≥ 2 were selected as effective for the respective signaling pathway to all metabolic pathway interconnections in EGFR-mutated GBM ( Fig 7B ) ., S3 Table lists the numbers of significant and effective proteins identified for the individual signaling pathways connected to all metabolic pathways and from all signaling pathways to the individual metabolic pathways ., As a measure of its effect on signaling-metabolic interconnection , each perturbed node was ranked according to the difference between baseline and perturbed condition ., This means that highly ranked proteins have an important role in the connections of the respective signaling pathway to all metabolic pathways or vice-versa ., The NOTCH pathway is shown as an example for the level of reduction in the network size when the GBM network is transformed to the significant GBM network to identify significant interconnecting paths and proteins ( Fig 8 ) ., Fig 8A shows the interconnections of NOTCH pathway proteins with all metabolic pathway proteins present in the signaling-metabolic interaction network ( SMIN ) and Fig 8B shows only those NOTCH pathway proteins with interconnections to metabolic pathway proteins passing through effective nodes , i . e . nodes identified by the perturbation experiments ( Fig 7B ) ., Fig 8C presents the interconnections of the NOTCH pathway to all metabolic pathways in the GBM-specific network filtered on the basis of the weightage parameters for the nodes and edges , and Fig 8D shows the interconnections between the significant proteins only ., Fig 8E shows the interconnections of the NOTCH pathway to all metabolic pathways in the GBM-specific significant network and Fig 8F the same only for significant nodes ., The comparison of Fig 8B , 8D and 8F on basis of the effective node identified by the perturbation study ( red colored ) indicate the levels of filtration from the starting SMIN to the final GBM-specific significant network ., We found 457 , 111 paths involving 1941 genes/proteins and 8047 interactions out of a total of 2 , 206 , 246 paths in the SMIN connecting the NOTCH signaling pathway to all metabolic pathways , of which 10% were found to have a significant ( Z-score cut-off -2 ≥ Z ≥ 2 ) perturbation impact ( PI ) ( Fig 8B ) ., Comparable reductions of approximate 75% ( Fig 8C ) and 50% ( Fig 8D ) for both nodes and interaction were found when going to the GBM-specific condition ., After Pathscore based filtration ( Z-score ≥ 1 ) , 146 paths involving 125 nodes and 166 interactions were identified in the NOTCH pathway ( Fig 8E ) of which 51 paths involving 59 nodes and 65 interactions formed by nodes with significant PIs ., The result of perturbation study proved the importance of the respective nodes in the final network for information flow from signaling to metabolic pathways ., To validate these findings of interconnections between signaling pathway alterations and metabolic rearrangement , some of SM connectio

Introduction, Results, Discussion, Materials and methods

As malignant transformation requires synchronization of growth-driving signaling ( S ) and metabolic ( M ) pathways , defining cancer-specific S-M interconnected networks ( SMINs ) could lead to better understanding of oncogenic processes ., In a systems-biology approach , we developed a mathematical model for SMINs in mutated EGF receptor ( EGFRvIII ) compared to wild-type EGF receptor ( EGFRwt ) expressing glioblastoma multiforme ( GBM ) ., Starting with experimentally validated human protein-protein interactome data for S-M pathways , and incorporating proteomic data for EGFRvIII and EGFRwt GBM cells and patient transcriptomic data , we designed a dynamic model for EGFR-driven GBM-specific information flow ., Key nodes and paths identified by in silico perturbation were validated experimentally when inhibition of signaling pathway proteins altered expression of metabolic proteins as predicted by the model ., This demonstrated capacity of the model to identify unknown connections between signaling and metabolic pathways , explain the robustness of oncogenic SMINs , predict drug escape , and assist identification of drug targets and the development of combination therapies .

Complex and highly dynamic interconnected networks allow cancer to take different routes and circumvent chemotherapy ., Therefore , understanding these context-specific networks and their dynamics of molecular interactions driven by different oncogenic signaling and metabolic pathways is very much needed to predict drug targets and the effect of therapeutics ., We incorporated high-throughput transcriptome and proteome data into mathematical models to deduce properties of cancer cells through systems biology approach ., Here we report the development , testing and validation of an integrated systems biology model of information flow between signaling and metabolic pathways to understand the regulation of the interconnection between them in cancer ., Our model efficiently identified unique connections and key nodes important in signaling-metabolic information flow ., We predicted some potential novel targets before performing actual drug tests ., We have successfully applied this model to identify the interconnections altered in the constitutive signaling of the mutated EGFR by comparing EGF-dependent and wild-type EGFR signaling in glioblastoma multiforme .

protein interaction networks, signaling networks, protein expression, network analysis, molecular biology techniques, research and analysis methods, computer and information sciences, oncogenic signaling, proteins, metabolic pathways, ras signaling, proteomics, metabolism, molecular biology, molecular biology assays and analysis techniques, gene expression and vector techniques, biochemistry, signal transduction, cell biology, proteomes, biology and life sciences, cell signaling

journal.pgen.1002914

A Sexual Ornament in Chickens Is Affected by Pleiotropic Alleles at HAO1 and BMP2, Selected during Domestication

Domestication is the strongest form of short term , highly directional selection known to man ., Darwin himself cited domestication as a model for evolution 1 ., The domestication process itself is associated with a whole raft of changes in phenotype , despite intentional selection on only a few traits ., For example , simultaneous changes in colour , skull shape , and behaviour 2–4 are often observed in domestic populations , and can emerge even when selection is limited to tameness 5 , 6 ., The genetic mechanisms affecting such correlated changes are largely unknown ., Pleiotropy , where one allele affects multiple traits , would be one potential mechanism 5 , 6 ., Alternatively , traits may be linked at a genetic level , but with separate genetic architectures 7 , 8 ., Of the relatively few domestic animals , the chicken is one of the most viable as a research animal ., A combination of small size , rapid generation times , small genome size ( ∼1 . 09 Gb ) 9 and high recombination ( 350 kb/cM on average ) 10 , along with extreme changes in phenotype ( focused on production traits , namely egg production and increased growth and overall size ) due to domestication make it particularly amenable as a model organism ., As well as an increase in production traits , modern domestic chickens also show a large increase in relative comb size , a sexually selected ornament , and bone production 8 ., The comb of the chicken is used to base mating decisions on by both males and females in wild-derived populations 11 , 12 ., In males it is an indicator of social rank , with females actively soliciting matings from males with larger combs 13 , 14 , as well as also correlating with bone mass 8 ., In females it is indicative of greater reproductive potential , through an increase in egg production 8 , 15 ., In turn , egg production is highly dependent on bone morphology in the chicken , with one of the principal limitations to egg production being calcium deposition ., Calcium is stripped from the hard outer cortical bone and transferred into the soft , inner medullary and trabecular bone and from there mobilised to the egg shell 16 ., Similarly , calcium is also mobilised away from the ends of the bones ( the metaphyses ) and into the central part ( the diaphysis ) during egg-laying periods , where it is then more easily mobilised into medullary bone ( see Figure 1 ) ., The genetic architectures for comb mass , egg production and bone allocation ( important for egg production ) have all been shown to overlap in the chicken 8 ., Therefore fine-scale quantitative trait loci ( QTL ) mapping and expression quantitative trait locus ( eQTL ) mapping of these separate traits ( comb mass , bone allocation and egg production ) should allow the assessment of the importance of pleiotropy in both domestication and a sexual ornament ., The bi-sexual expression of the comb in both males and females makes the comb somewhat unusual as a sexual ornament , with male-only effects of ornaments more often considered 17 , and also makes the genetic analysis of this ornament in particular of greater relevance ., Here we present the identification of a two-gene block controlling multiple phenotypic and fitness traits ., This block was identified due to its extreme effect on comb mass , with its effects on bone allocation and fecundity then ascertained ., A total of three different intercrosses , based on two different Layer populations and two different Red Junglefowl populations , were used to QTL map these traits ., The two Layer populations consisted of two White Leghorn ( WL ) layer breeds ( termed Obese strain ( OS ) and Line 13 ( L13 ) hereafter ) , whilst these were crossed with two different populations of Red Junglefowl ( RJF ) chickens ( the wild ancestor to the domestic chicken ) ., Both Layer populations show a marked increase in comb mass and bone allocation , as compared to the RJF populations ., The L13 cross was in addition expanded from an F2 population to an F8 advanced intercross ., Therefore a F2L13 , a F8L13 and a F2OS cross were all utilised ., Fine-scale resolution and replication was achieved by over-laying the multiple QTL signals from the different crosses in combination with the small ( 40 kb ) signatures of selected loci that have been previously identified in the domestic chicken using extensive resequencing 18 ., Finally , eQTL analysis was performed using specific tissue types from the F8L13 cross to further test the causal association of genes affecting the sexual ornament of comb mass and the fecundity and fitness trait of medullary bone allocation ., The genetic architecture for comb mass from all three cross populations was shown to be principally restricted to six distinct regions in the genome ( see Table S1 ) , with a strongly significant degree of overlap between the different crosses ( clustering test with 10000 permutations , P\u200a=\u200a0 . 0016 , see Tables S1 and S2 and Figure S1 ) ., In five of the six clusters , all QTL in the cluster had the same direction of effect ( i . e . the allele conferring the larger effect was always based in the same population ) , adding strength that these were independent replicates of the same QTL ., All of these alleles that were associated with larger combs derived from the WL strain , with the exception of a locus on chromosome one that showed transgressive segregation ., Even here , the same pattern of transgressive segregation was mirrored in all crosses , once again reinforcing the idea of independent replication ., The largest effect QTL in the F2L13 and F8L13 crosses was on chromosome three , with an additive effect of 4 . 3 grams and 10 . 9 grams , respectively , and LOD scores of 10 and 38 ( R2\u200a=\u200a11% in F2L13 males , 30% in F8L13 males and 10% in F8L13 females ) ., The confidence interval ( C . I . , calculated with a 1 . 8 LOD drop ) for the F2L13 cross was from 6 . 6 Mb to 15 . 6 Mb , whereas the C . I . for the F8L13 was from 15 . 6 Mb to 16 . 0 Mb ., The two intervals therefore only overlap at 15 . 6 Mb ( see Figure 2 ) , with the two markers defining these regions in the F2L13 and F8L13 crosses within 100 bp of one another ., Of the other significant QTL identified in the F8L13 , the one with the second smallest C . I . was located on chromosome 8 , from 19 . 6–21 . 6 Mb with a LOD score of 10 ., In both the case of the chromosome three and eight QTL regions , although the loci were found in the F2OS cross , the size of the overall C . I . was not reduced ., Strong selection has been hypothesised to leave signatures of selection ( selective sweeps ) identifiable through linkage disequilibrium ( LD ) blocks present in the genome ., The identification of such sweeps is becoming more common , though as yet all the successes of gene identification that have occurred through this approach have been ones of major effect , more akin to a straight Mendelian than a quantitative trait ( but see 19 ) ., Indeed , there is a great deal of debate as to whether such sweeps are even relevant for quantitative traits , or whether polygenic adaptation ( small changes in gene frequency ) is a more viable mechanism 20 ., Despite this , resequencing of domestic and wild strains has yielded a number of putative selective sweeps in the chicken 18 , each ∼40 Kb in size ., These regions were overlaid with the six overlapping C . I . from the comb QTL studies , with five of the six overlap regions containing one or more sweeps , a significant enrichment ( clustering test , P\u200a=\u200a2×10−4 ) ., All of these were either layer-specific ( i . e . regions fixed in all layers ) or all-domestic ( i . e . regions fixed in both layers and broilers ) , see Table S2 ., The largest QTL on chromosome 3 contained a selective sweep , located at 15 . 6 Mb , at precisely the region where the overlap between the F2L13 and F8L13 crosses occurred ., The chromosome 8 QTL also contained a selective sweep present at the QTL peak , at 19 . 7 Mb ., The chromosome 3 QTL C . I . contains 2 reference genes and a single selective sweep ., This sweep contains the gene hydroxyacid oxidase ( HAO1 ) , whilst the gene bone morphogenetic protein 2 ( BMP2 ) is adjacent to it ( see Figure 2 ) ., Gene expression analysis was performed for the two putative causative genes on chromosome 3 using material from the comb base of 41 male F8L13 birds and from the diaphyseal medullary bone of 20 F8L13 female birds ., Medullary bone tissue was used as analysis of medullary area showed a suggestive QTL co-localising at the identical location as to the comb mass QTL , with its peak also at 15 . 6 Mb ., Both genes were found to be differentially expressed between RJF and WL alleles in comb base tissue ( HAO1 P\u200a=\u200a2×10−6 , BMP2 P\u200a=\u200a0 . 02 ) , and the expression levels were strongly positively correlated with comb mass in both cases ( HAO1 P\u200a=\u200a0 . 0001 , BMP2 P\u200a=\u200a0 . 002 , see Table 1 ., A test to distinguish linkage disequilibrium ( LD ) from true causality was conducted using expression as a covariate in the genotype/phenotype model 21 ., Theoretically , if the QTL effect on the phenotype drops in significance when the gene expression covariate is included this should indicate the gene in question is causal to the QTL 21 , 22 , as the quantitative trait transcript ( QTT ) will explain more of the variance previously accounted by the genotype factor ., In the case of HAO1 this occurs , with the variance explained by genotype ( R2 ) falling from 44% to ∼18% ( P\u200a=\u200a2×10−6 to P\u200a=\u200a0 . 003 ) with this covariate inclusion , with the AIC for the model falling from 289 to 288 . 2 , see Table 1 ., BMP2 is significantly correlated with comb mass when placed as a covariate in a model already containing genotype ( P\u200a=\u200a0 . 04 ) , and genotype significance also decreases with this covariate inclusion ( P\u200a=\u200a1 . 8×10−6 to P\u200a=\u200a3 . 4×10−5 , see Table 1 , with the R2 falling from 44% to 29% ) , whilst the AIC for the model decreases from 289 to 285 . 9 ., Although the principal effect on comb mass therefore appears to be coming from HAO1 expression , BMP2 also appears to have an effect ., A model including both HAO1 and BMP2 gene expression levels explains more of the variation present in comb mass , the R2 rising from 40% to 51% and the AIC decreasing from 300 . 6 to 293 . 2 , compared to the model with only HAO1 present , with both HAO1 and BMP2 having significant effects on comb mass in the model ( HAO1 P\u200a=\u200a0 . 0003 , BMP2 P\u200a=\u200a0 . 005 ) ., The model with HAO1 , BMP2 and the QTL effect had the lowest AIC , 284 . 2 , and the highest R2 , 62% , see Table 1 ., This demonstrates that both genes are important for determining comb mass , between them explaining the majority of the variation in comb size in the sample used for comb base gene expression ., Associations between a QTL locus , the expression of a gene and a complex trait can be used to try model the path of the QTL effect 23 ., Using this idea , both HAO1 and BMP2 expression levels can be modelled on all the QTL for comb mass detected in the F8L13 cross ., This analysis can indicate if the other detected QTL are acting through the same HAO1/BMP2 pathway as the chromosome 3 QTL , though with the caveat that such tests are still in essence correlative and not a substitute for functional assays ., Three QTL in a combined model explain more of HAO1 expression than the chromosome 3 locus in isolation ( chr1@35 . 5 Mb P\u200a=\u200a0 . 03 , chr1@172 Mb P\u200a=\u200a0 . 02 , chr3@16 Mb P\u200a=\u200a2×10−6 , R2 rose from 63% to 71% with all three QTL in the model with the AIC decreasing from −401 . 1 to −408 . 8 ) ., Similarly , BMP2 expression is correlated with a total of three QTL in a combined model , two of which are interacting ( chr1@35 . 5 Mb P\u200a=\u200a0 . 02 , chr1@35 . 5 Mb×chr5@32 . 7 Mb P\u200a=\u200a0 . 03 , chr3@16 Mb P\u200a=\u200a0 . 03 , R2 rose from 32% to 50% with all three QTL in the model , with the AIC decreasing from −126 . 8 to −134 ) ., It therefore appears that both HAO1 and BMP2 are determining comb mass , functioning on two separate pathways ., The first one involves HAO1 ( and therefore the chr3 QTL ) , with the QTL on chromosome 1 at 35 . 5 Mb and 172 Mb are also part of this pathway ., The second , involving BMP2 , involves the chromosome 3 QTL again , with the QTL on chromosome 1@35 . 5 Mb and chromosome 5@32 . 7 Mb also involved in this pathway ., While comb mass is known to be an indicator of reproductive capacity 8 , 15 , the links between bone allocation and comb mass are less known 8 , though given the close relationship between egg production and bone allocation , some correlation may be expected ., Therefore we measured metaphyseal and diaphyseal ( see Figure 1 , Figure 3a and 3b ) bone morphometrics in both the F2OS and F8L13 mapping populations , using a Computerised Tomography ( pQCT ) machine ( see Materials and methods ) , and then modelled these bone characteristics on comb mass ., Results of the combined General Linear Models are shown in Table S3 and Figure, 3 . Females with larger combs deposit more calcium in the diaphysis where it is more easily mobilised into the eggshell ., This is shown by diaphyseal cortical bone measures being positively correlated with comb mass in both the F2OS and F8L13 , whereas metaphyseal total bone density is negatively correlated in the F2OS , and metaphyseal medullary density is negatively correlated in the F8L13 ., Females with larger combs are also producing more of the diaphyseal medullary bone required for egg production ., This is indicated by diaphyseal medullary area being positively correlated with comb mass in the F2OS ( see Figure 3 ) and diaphyseal medullary density being positively correlated and metaphyseal medullary density negatively correlated with comb mass in the F8L13 ., In males , metaphyseal medullary area and density and diaphyseal cortical content are also positive predictors of comb size , once again showing that males with larger combs are more likely to have greater overall bone density and strength ( see Figure 3 ) ., A QTL for medullary bone was also identified at the same locus that effected comb mass on chromosome, 3 . HAO1 and BMP2 were therefore also tested as potentially causative loci for effecting medullary bone ., The results for medullary bone similarly indicate a greater effect of HAO1 rather than BMP2 on medullary bone area , though the causal tests themselves are less conclusive ., When HAO1 is included in the QTL model as a covariate , the variance explained by QTL genotype increases from R2\u200a=\u200a14% to 27% ( P\u200a=\u200a0 . 02 to 0 . 002 ) , with HAO1 significant in this model ( P\u200a=\u200a0 . 03 , R2\u200a=\u200a10% ) , whilst the AIC decreases from 135 . 5 to 131 . 4 , see Table, 2 . Comb mass was negatively correlated with medullary area ( this mirrors the correlation seen between comb and medullary bone in the F8L13 cross , where medullary density was more strongly and positively correlated with comb mass ) ., Once again , BMP2 expression has less effect on QTL genotype , with the variance explained by genotype unchanging ( R2\u200a=\u200a14% to 17% ) , and the AIC increasing from 135 . 5 to 136 . 3 , and BMP2 expression itself is also not significant ( P\u200a=\u200a0 . 35 ) ., Expression levels of BMP2 and HAO1 in bone tissue were found to be correlated with several related relevant bone strength and fecundity phenotypes , see Table, 3 . HAO1 was positively correlated with an increase in egg production ( t\u200a=\u200a2 . 8 , P\u200a=\u200a0 . 02 ) , a lack of broodiness ( egg incubation behaviour , see Materials and methods , t\u200a=\u200a−2 . 4 , P\u200a=\u200a0 . 03 ) , and an increase in the number of eggs produced ( t\u200a=\u200a2 . 6 , P\u200a=\u200a0 . 02 ) ., BMP2 expression was correlated with an increase in total density of the diaphyseal endosteal cavity ( t\u200a=\u200a2 . 4 , P\u200a=\u200a0 . 03 ) and a decrease in diaphyseal endosteal cavity area ( t\u200a=\u200a−2 . 2 , P\u200a=\u200a0 . 05 ) ., It therefore appears that the two linked genes have a pleiotropic effect between them , affecting comb mass , egg production , medullary bone area , and total area and density of the endosteal cavity ., In wild chicken populations , sexual ornaments are closely related with fitness traits 15 ., We demonstrate that a block of two genes is controlling multiple aspects of the comb and bone allocation in the chicken , though the relative effects of both must be verified through functional assays ., BMP genes have been shown to have numerous effects on bone physiology and increasing bone deposition 24 , stimulating osteoblast proliferation and differentiation and stimulating bone formation ., Cartilage is the precursor to all bone formation ( the skeleton is first laid down cartilaginously , with this then becoming ossified 25 ) ., This explains the link between the cartilage production required for comb growth and bone production physiology ., HAO1 on the other hand is a novel candidate for altering bone and cartilage deposition , to date principally shown to be expressed in the liver ., Previous work has also highlighted its role in the liver metabolic pathways as a peroxisomal enzyme 26 , and it is also implicated in lymphoblastic leukemia 27 ., Peroxisomal enzymes feature heavily in the catabolism of long chain fatty acids 28 and are important for energy metabolism ., Peroxisomal oxylate enzymes have also been linked to effects on calcium binding , through links with hereditary calcium oxalate kidney stone diseases ( Primary Hyperoxaluria ) 29 , demonstrating potential interactions with calcium binding which may also affect the cartilage/bone allocation system ., Despite the pleiotropic effects seen between comb mass and bone allocation/egg production the overlap between comb mass QTL and selective sweeps may be due to two potential effects ., On the one hand , if all comb mass loci exhibit pleiotropic effects on bone allocation or egg production , the strong selection for egg production will naturally cause this overlap to occur ., However , an alternative explanation for this overlap is direct selection for comb mass itself is potentially occurring in these Layer breeds ., This may be due to breeders intentionally selecting for large combs , realising they are a good indicator of egg production or indeed are a beneficial trait in of themselves ., The comb is involved in heat regulation in the chicken 30 , and therefore may also assist in survival in crowded domestic conditions ., The results also show that with one or both genes of this two-gene block , allelic variants can be seen to have effects on bone allocation , fecundity , brooding and comb growth ., This highlights the importance of both pleiotropy and linkage in such systems ., In this instance the extremely close linkage between the two genes in this block results in essentially a pleiotropic effect occurring from the alleles at these loci ., This has important ramifications for understanding multiple complex trait interactions in domestication ., Pleiotropy and close-linkage can work together to produce a pleiotropic effect on multiple divergent traits ., Even here , the close linkage can be disrupted by recombination , enhancing the flexibility of the system ., These results show that the pleiotropic core of domestication ‘modules’ ( regions of the genome controlling multiple aspects of the domestication phenotype 7 , 31–33 ) contain both alleles with pleiotropic effect and extremely close linkage between QTL ., Such a pattern of linkage and domestication modules are also seen in domesticated plants 31 , 32 , 34 , 35 , indicating such a system of loose and tight linkage is responsible for the domestication phenotype in a diverse range of taxa ., This study also potentially sheds light on the genetics of sexual selection ., Although in the case of the domestic population it is no longer subject to sexual selection , the current artificial selection will be occurring on the pre-existing genetic architecture of the ancestral Red Junglefowl ., In this instance , the natural selection constraints acting in the wild Junglefowl ( which will limit the expensive investment of bone into fecundity in females to conserve longevity and fitness ) can be ‘decoupled’ from the sexual ornament ., The domestic population can potentially therefore show a counterpoint to the wild population in this regard ., Interestingly , the observed pleiotropy ties in well with much of the previous work on sexual selection theory , as it has long been considered important in an accurate ornamental indicator signal 36 and in sexual selection theory in general 37 ., Pleiotropy may also underlie the genetic covariation between traits that may in turn constrain the individual evolution of each trait independently 38 , 39 ., Similarly , condition dependence of a sexual ornament may also be due to pleiotropic effects 40 ., Examples of the importance of pleiotropy in sexual ornaments can also be found by the overlap of multiple , functionally related , sexually selected traits ., In Drosophila cuticular hydrocarbons 41 and wing song components 42 , 43 have all been found to exhibit this overlapping architecture ., Pleiotropy can also have large consequences for long term phenotypic evolution and on complex traits in general 44 ., Using global expression data , large-scale expression variation in between-sex 45 and breed 46–48 comparisons is at odds with the relatively modest QTL genetic architecture which is also detected in these comparisons ( although power of detection is always a potential issue with QTL analysis ) ., In these instances modules of genes ( either pleiotropic or closely linked ) once again appear to underlie the large-scale transcriptional changes ., For example in a laboratory×wild cross of S . cerevisiae strains , genetic variation at a single gene was shown to have a major impact on global transcription 49 , whilst recombinant inbred line studies using mice 50 and Drosophila 51 have also found modular effects of gene expression ., In the case of Drosophila , wild-derived inbred lines also show a high degree of modularisation in transcription 52 ., It is perhaps pertinent that the modular patterns observed in domesticated animals is also seen once again in the wider variety of organisms previously listed ., In the case of the chicken model presented here , the effect of domestication has been to decouple a sexual ornament from the limitations of natural selection ., In wild populations , natural selection will act to limit the extreme allocation of calcium into medullary bone ( which will increase short term reproductive gains at the expense of longer term survival ) , thereby preventing these alleles from becoming fixed in the population ., During domestication this barrier has been removed ( in fact this increased short-term fecundity is being actively selected for ) ., However , by revealing the pleiotropic effects acting between the sexual ornament and the fitness trait , this does reveal how the indicator mechanism is maintained in the wild population , albeit not to the extreme levels seen in domestic populations ., Two separate crosses were conducted for this analysis , the first consisting of an F2 intercross between Obese Strain White Leghorn ( WL-OS ) chickens and a Red Junglefowl ( RJF ) population derived from a Swedish zoo population and maintained in Götala , Sweden ( RJF-Götala ) ., The second intercross was an eighth generation intercross between a line of selected White Leghorn ( WL-L13 ) maintained from the 1960s and a population of RJF originally obtained from Thailand ( RJF-T ) ., This F8 advanced intercross line ( AIL ) is based on an F2 intercross that has been measured for comb mass and a variety of bone morphologies , see 8 , 33 , 53 ., The WL-OS and WL-L13 cross have been separated since approximately 1955 , so although they come from the same base population of White Leghorn chickens they have now had over 50 years of separation ., Hereafter the abbreviation F2-L13 will be used for the F2 RJF-T/WL-L13 cross , F8-L13 for the F8 RJF-T/WL-L13 AIL , and F2-OS for the WL-OS/RJF-Götala F2 cross ., In the case of the F2-OS cross , these were raised in a total of 4 batches at Götala research station of the Swedish University of Agricultural Sciences , Skara , Sweden ., Chickens were maintained on standardised conditions and fed ad libitum , under a 12∶12 hour light/dark regime ., Pens measuring 3 m×3 m were used for housing , with perches also provided ., Individuals were culled at 200 days of age , with their combs surgically removed post-mortem and weighed and both femoral bones extracted ., The F8-L13 cross used in this study was generated in 5 batches and reared at the research station of Linköping University , Sweden ., Pens for these animals were 2 m×2 m and comprised of three separate levels , perches and food were once again supplied ad libitum ., Animals were culled at 212 days of age , with the comb surgically removed post-mortem and weighed , and both femoral bones extracted ., A total of 640 F2 individuals were used for the comb mass analysis , consisting of 308 males and 332 females ., Of these , 543 of the individuals were also measured for a variety of bone morphology traits ( 245 males and 298 females ) ., The WL-OS strain was originally isolated as a model of Hashimotos thyroiditis , as they suffer from hypothyroidism and therefore require thyroxin to be provided in a food supplement ( 500 µg/kg food ) ( Levaxin Tablets , Nycomed AB , Sweden , were administered to all but one batch until 180 days of age ) ., Although this strain tends to be slightly smaller than usual WL strains , they are still substantially larger , and with far greater sized combs , as compared to RJF ., In addition , the degree of infiltration that had occurred in the F2 animals thyroid was also measured , and correlated with both comb mass and body weight ., No significant correlations were found , and additionally the majority of the QTL discovered in this cross ( 7 of 11 ) all go in the expected direction , i . e . alleles of greater effect come from the WL , not RJF , line ( with one of the three transgressive QTL also corroborated as transgressive in the other crosses as well ) ., If any comb loci were caused by thyroiditis this would be expected to be the reverse , so we have good cause to consider these QTL are due to generic WL-strain based differences ., Animals were culled at 200 days of age ., The animal experiments were approved by the ethical committee for animal experiments in Göteborg , Sweden , no . 55-2005 and 233-2006 , and by the ethical committee for animal experiments in Uppsala no . 2008-12-19 , C321/8 ., A total of 447 F8 individuals were assayed for comb measurements ( 216 males and 231 females ) ., These individuals were generated from a total of 107 families using 122 F7 individuals ( 63 females and 59 males ) ., Average family size 4 . 7+/−3 . 1 ( mean , standard deviation ) ., These were the continuation of an inter-cross initially based on 3 WL females and one RJF male , which were then expanded into 811 F2 progeny and then maintained with at a population size of approximately 100 birds per generation until the F7 generation ., Of the F8 individuals , a total of 41 males were used as the basis of a qPCR experiment using comb base tissue to check for candidate genes at the major chromosome 3 QTL locus at 15 . 7 Mb ( 12 RJF homozygotes , 15 WL homozygotes , 14 heterozygotes ) ., These individuals came from two of the five batches used to produce the F8 ., Additionally , 20 females were used as the basis for a qPCR experiment using diaphyseal medullary bone ( 10 of each homozygote class , though one had no metaphyseal CT bone measurements , reducing the correlation with bone density to 19 individuals ) ., The study was approved by the local Ethical Committee of the Swedish National Board for Laboratory Animals ., A total of 352 males and 122 females were used in this analysis , with these QTL already detailed in 8 , ., The right femoral bone of each F2OS and F8L13 individual was measured using a Peripheral Quantitative Computerised Tomography ( pQCT ) machine ( Stratec XCT – Research SA machine , Stratec Medizintechnik , Germany ) , with two sections taken at 6% ( metaphyseal ) and two at 50% ( diaphyseal ) of the total femoral length ., Cortical bone was measured using the CORTMODE1 setting , with a density threshold of >1000 mg/cm3 ., Cortical measures were cortical area , cortical bone content , cortical thickness and cortical density ., Medullary measures were recorded using the PEELMODE2 function , using an inner threshold of 1000 mg/cm3 to separate cortical from medullary bone and gives total density , total bone content and total area of the endosteal cavity measures ., An additional inner threshold of 150 mg/cm3 in a combination of two PEELMODE2 gives the medullary area , medullary density and medullary bone content ., Two separate fecundity trials were performed ., Initially birds were housed individually and eggs were collected daily over a two-week period ., The second trial was performed immediately after the first and was identical except birds were given two dummy eggs to incubate and were allowed to keep all eggs laid over a two-week period ., At the end of each trial , number of eggs produced , total weight of eggs produced and mean egg weight were recorded ., Chickens which are actively brooding ( incubating ) their eggs will stop producing eggs when a clutch size of around 6–8 eggs has been produced , whilst domestic layers will continually produce eggs and never go into such brooding behaviour ., Therefore one method for ascertaining if an individual is brooding will be to deduct the total number of eggs produced in the first trial from the number of eggs produced in the second trial ., Negative values therefore indicate that an individual is decreasing egg production when allowed to build up a clutch ., To analyse phenotypic correlations between comb mass and bone characteristics in the F2OS and F8L13 crosses , a GLM was fitted for each individual bone trait measured and included batch and sex fixed effects and weight at slaughter as a covariate ., All significantly correlated bone measures were then combined into a global model ., The significance values of each measure was then ascertained in this global model ., The clustering test was performed using a permutation test based on the total length of the chicken genome ( 1 . 09 Gb ) , which then had a number of regions equal to the number of QTL detected in the F2OS and F8L13 cross randomly distributed along it ., The size of these regions was equal to the average C . I . of QTL from the F2-OS cross ( 15 Mb ) and the F8L13 cross ( 5 Mb ) , and tested against the observed number of overlaps between the F2OS and F8L13 ( 6 ) ., The F2L13 cross was not used in this analysis , as 4 of the 5 QTL detected in this cross were strongly replicated in the F8L13 and their inclusion could artificially inflate the degree of replication observed between the two different cross populations ., This was repeated 1000 times , with the number of overlaps recorded each time used to generate a significance value ., A similar procedure was used to predict the probability of selective sweeps occurring within the overlap regions ., In this instance the total number of sweeps detected ( 133 ) and the six overlap regions were used , based against the observation that 13 sweeps were observed within the six 6 Mb overlap intervals ., When calculating the overlap regions , an extra 1 Mb was added to the region size upstream and downstream , in case using only the overlap between the OS and L13 crosses gave an artificially small region ., DNA preparation for both crosses was performed by Agowa GmbH ( Berlin , Germany ) , using standard salt extraction ., A total number of 347 SNPs and 20 microsatellite markers were used for the OS cross , w

Introduction, Results, Discussion, Materials and Methods

Domestication is one of the strongest forms of short-term , directional selection ., Although selection is typically only exerted on one or a few target traits , domestication can lead to numerous changes in many seemingly unrelated phenotypes ., It is unknown whether such correlated responses are due to pleiotropy or linkage between separate genetic architectures ., Using three separate intercrosses between wild and domestic chickens , a locus affecting comb mass ( a sexual ornament in the chicken ) and several fitness traits ( primarily medullary bone allocation and fecundity ) was identified ., This locus contains two tightly-linked genes , BMP2 and HAO1 , which together produce the range of pleiotropic effects seen ., This study demonstrates the importance of pleiotropy ( or extremely close linkage ) in domestication ., The nature of this pleiotropy also provides insights into how this sexual ornament could be maintained in wild populations .

The genetic analysis of phenotypes and the identification of the causative underlying genes remain central to molecular and evolutionary biology ., By utilizing the domestication process , it is possible to exploit the large differences between domesticated animals and their wild counterparts to study both this and the mechanism of domestication itself ., Domestication has been central to the advent of modern civilization; and yet , despite domesticated animals displaying similar adaptations in morphology , physiology , and behaviour , the genetic basis of these changes are unknown ., In addition , though sexual selection theory has been the subject of a vast amount of study , very little is known about which genes are underpinning such traits ., We have generated multiple intercrosses and advanced intercrosses based on wild-derived and domestic chickens to fine-map genomic regions affecting a sexual ornament ., These regions have been over-laid with putative selective sweeps identified in domestic chickens and found to be significantly associated with them ., By using expression QTL analysis , we show that two genes in one region , HAO1 and BMP2 , are controlling multiple aspects of the domestication phenotype , from a sexual ornament to multiple life history traits ., This demonstrates the importance of pleiotropy ( or extremely close linkage ) in controlling these genetic changes .

animal genetics, genomics, functional genomics, gene expression, genetics, biology, evolutionary biology, evolutionary genetics, genetics and genomics

journal.pgen.1001064

Ancient Protostome Origin of Chemosensory Ionotropic Glutamate Receptors and the Evolution of Insect Taste and Olfaction

Ionotropic glutamate receptors ( iGluRs ) are a conserved family of ligand-gated ion channels present in both eukaryotes and prokaryotes ., By regulating cation flow across the plasma membrane in response to binding of extracellular glutamate and related ligands , iGluRs represent an important signalling mechanism by which cells modify their internal physiology in response to external chemical signals ., iGluRs have originated by combination of protein domains originally encoded by distinct genes ( Figure 1A ) 1–2 ., An extracellular amino-terminal domain ( ATD ) is involved in assembly of iGluR subunits into heteromeric complexes 3 ., This precedes the ligand-binding domain ( LBD ) , whose two half-domains ( S1 and S2 ) form a “Venus flytrap” structure that closes around glutamate and related agonists 4 ., Separating S1 and S2 in the primary structure is the ion channel pore , formed by two transmembrane segments and a re-entrant pore loop 5 ., S2 is followed by a third transmembrane domain of unknown function and a cytosolic carboxy-terminal tail ., Animal iGluRs have been best characterised for their essential roles in synaptic transmission as receptors for the excitatory neurotransmitter glutamate 1 , 6 ., Three pharmacologically and molecularly distinct subfamilies exist , named after their main agonist: α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid ( AMPA ) , kainate and N-methyl-D-aspartate ( NMDA ) ., AMPA receptors mediate the vast majority of fast excitatory synaptic transmission in the vertebrate brain , while Kainate receptors have a subtler modulatory role in this process ., NMDA receptors require two agonists for activation , glutamate and glycine , and function in synaptic and neuronal plasticity ., Representatives of these iGluR subfamilies have been identified across vertebrates 7 , as well as invertebrates , such as the fruit fly Drosophila melanogaster , the nematode worm Caenorhabditis elegans and the sea slug Aplysia californica 8–10 ., While most iGluRs have exquisitely tuned synaptic functions , identification of iGluR-related genes in prokaryotic and plant genomes provided initial indication of more diverse roles for this class of ion channel ., A bacterial glutamate receptor , GluR0 , was first characterised in the cyanobacterium , Synechocystis PCC6803 11 ., GluR0 conducts ions in response to binding of glutamate and other amino acids in vitro , suggesting a potential function in extracellular amino acid sensing in vivo ., The flowering plant Arabidopsis thaliana has 20 iGluR-related genes , named GLRs 12–13 ., Genetic analysis of one receptor , GLR3 . 3 , has implicated it in mediating external amino acid-stimulated calcium increases in roots 14 ., We recently described a family of iGluR-related proteins in D . melanogaster , named the Ionotropic Receptors ( IRs ) 15 ., Several lines of evidence demonstrated that the IRs define a new family of olfactory receptors ., First , the IR LBDs are highly divergent and lack one or more residues that directly contact the glutamate ligand in iGluRs ., Second , several IRs are expressed in sensory neurons in the principal D . melanogaster olfactory organ , the antenna , that do not express members of the other D . melanogaster chemosensory receptor families , the Odorant Receptors ( ORs ) and Gustatory Receptors ( GRs ) 16 ., Third , IR proteins localise to the ciliated endings of these sensory neurons and not to synapses 15 ., Finally , mis-expression of an IR in an ectopic neuron is sufficient to confer novel odour-evoked neuronal responses , providing direct genetic evidence for a role in odour sensing 15 ., The identification of the IRs as a novel family of olfactory receptors in D . melanogaster provides a potential link between the well-characterised signalling activity of iGluRs in glutamate neurotransmitter-evoked neuronal depolarisation and a potentially more ancient function of this family in environmental chemosensation ., In this work , we have combined comparative genomics , molecular evolutionary analysis and expression studies to examine the evolution of the IRs ., Four principal issues are addressed: first , when did olfactory IRs first appear ?, Are they a recent acquisition as environmental chemosensors in D . melanogaster , or do they have earlier origins in insect or deeper animal lineages ?, Second , what is the most recent common ancestor of IR genes ?, Do they derive from AMPA , Kainate or NMDA receptors , or do they represent a distinct subfamily that evolved from the ancestral animal iGluR ?, Third , what mechanisms underlie the expansion and diversification of this multigene family ?, Finally , do IRs function only as olfactory receptors or are they also involved in other sensory modalities ?, Through answers to these questions , we sought insights into IR evolution in the context of the origins of iGluRs , the appearance and evolution of other chemosensory receptor repertoires and the changing selective pressures during animal diversification and exploitation of new ecological niches ., iGluRs and IRs are characterised by the presence of a conserved ligand-gated ion channel domain ( the combined Pfam domains PF10613 and PF00060 17 ) ( Figure 1A ) ., All iGluRs additionally contain an ATD ( Pfam domain PF01094 ) , which is discernible , but more divergent , in only two D . melanogaster IRs , IR8a and IR25a ., Most IRs have only relatively short N-terminal regions preceding the LBD S1 domain ( Figure 1A ) ., To identify novel iGluR/IR-related genes , we therefore constructed a Hidden Markov Model ( HMM ) from an alignment of the conserved iGluR/IR C-terminal region , which is specific to this protein family ., In combination with exhaustive BLAST searches , we used this HMM to screen raw genomic sequences and available annotated protein databases of 32 diverse eukaryotic species and 971 prokaryotic genomes ( see Materials and Methods and Table S2 in Supporting Information ) ., These screens identified all previously described eukaryotic iGluRs and all D . melanogaster IRs , as well as 23 prokaryotic iGluRs ., Novel sequences were manually reannotated and classified by sequence similarity , phylogenetic analysis and domain structure as either non-NMDA ( i . e . AMPA and Kainate ) or NMDA subfamily iGluRs , or IRs ( Figure 1B , Table S3 , and Datasets S1 and S2 ) ., Like D . melanogaster IRs , newly annotated IRs have divergent LBDs that lack some or all known glutamate-interacting residues , supporting their distinct classification from iGluRs ., iGluRs are widespread in eukaryotes , present in all analysed Metazoa ( except the sponge , Amphimedon queenslandica 18 ) and Plantae , but absent in unicellular eukaryotes ( Figure 1B , Table S3 , and Datasets S1 and S2 ) ., Analysis of iGluR subfamilies on the eukaryotic phylogeny suggests that NMDA receptors may have appeared after non-NMDA receptors , as we identified them in Eumetazoa but not in the placozoan Trichoplax adhaerens ., Further support for this conclusion will require additional genome sequences ., One member of the Eumetazoa , the sea urchin Strongylocentrotus purpuratus , may have secondarily lost NMDA receptors ., Different species contain distinct numbers of each iGluR subfamily: vertebrates , for example , have more NMDA receptor subunits than invertebrates ., Notably , IRs were identified throughout Protostomia , encompassing both Ecdysozoa ( e . g . nematodes and arthropods ) and Lophotrochozoa ( e . g . molluscs and annelids ) ( Figure 1B , Table S3 , and Datasets S1 and S2 ) ., There is substantial variation in the size of the IR repertoire , from three in C . elegans to eighty-five in the crustacean Daphnia pulex ., Amongst insects , Diptera ( i . e . flies and mosquitoes ) generally had a larger number of IRs than other species ., We did not identify IRs in Deuterostomia , Cnidaria or Placozoa ., To explore the evolutionary origin of the IRs , we examined phylogenetic relationships of the identified protostome IRs ., Reciprocal best-hit analysis using D . melanogaster sequences as queries revealed that a subset of this species IRs was conserved in several distant lineages , allowing us to define putative orthologous groups ., These include one group containing representatives of all protostome species ( IR25a ) , one represented by all arthropods ( IR93a ) , nine by most or all insects , and three by dipteran insects ( Figure 2A and 2B ) ., For most orthologous groups , a single gene for each species was identified ., In a few cases , for example the IR75 group , certain species were represented by several closely related in-paralogues , some of which appeared to be pseudogenes ( Figure 2A and 2B , Table S3 , and Datasets S1 and S2 ) ., Consistent with its conservation in Protostomia , IR25a is the IR with the most similar primary sequence to iGluRs , suggesting that it is the IR gene most similar to the ancestral IR ., Analysis of the phylogenetic relationship of IR25a and eukaryotic iGluRs locates it clearly together with the animal iGluR family , in the non-NMDA receptor clade ( Figure 2C ) ., To substantiate this conclusion , we asked whether the IR25a gene structure resembles more closely that of NMDA or non-NMDA receptors ., Intron positions and numbers are extremely variable across IR25a orthologues , with multiple cases of intron loss , gain and putative intron sliding events by a few nucleotides ( Figure 2D ) ., Nevertheless , we identified eight intron positions that are conserved between at least subsets of IR25a orthologues and D . melanogaster non-NMDA receptor genes , some of which may represent intron positions present in a common ancestral gene ., By contrast , only a single intron that was conserved in position ( but not in phase ) was identified between DmelIR25a ( but not other IR25a orthologues ) and DmelNMDAR1 ( Figure 2D ) ., A phylogram of intron positions in IR25a , non-NMDA and NMDA sequences reveals greater similarity of IR25a intron positions to those of non-NMDA receptors than NMDA receptors ( Figure 2D ) ., Together , these observations support a model in which IR25a evolved from a bilaterian non-NMDA receptor gene ., The conserved D . melanogaster IRs encompass the entire subset of its IR repertoire that is expressed in the antenna 15 ., Moreover , evidence for antennal expression of the three additional genes , DmelIR41a , DmelIR60a and DmelIR68a , has been obtained by reverse transcription ( RT ) -PCR analysis , although we have not yet been able to corroborate this by RNA in situ hybridisation ( data not shown ) ., These combined phylogenetic and expression properties led us to designate this subfamily of receptors the “antennal IRs” ., We examined whether antennal expression of this subfamily of IRs is conserved outside D . melanogaster by performing a series of RT-PCR experiments on the honey bee , Apis mellifera , for all six putative antennal IR orthologues: IR8a , IR25a , IR68a , IR75u , IR76b and IR93a ( see Materials and Methods for the nomenclature of newly-identified IRs ) ., As in D . melanogaster , we could reproducibly amplify all of these bee genes from antennal RNA preparations but not in control brain RNA , except for AmelIR68a and AmelIR75u , which are also detected in the brain ( Figure 2E ) ., Thus , antennal expression of this subgroup of IRs is conserved across the 350 million years separating dipteran and hymenopteran insect orders 19 , and therefore potentially in all insects ., To investigate whether IRs are likely to have an olfactory function beyond insects , we examined expression of the IR repertoire from a representative of a distantly related protostome lineage , Aplysia molluscs , whose last common ancestor with D . melanogaster probably existed 550–850 million years ago 20 ., We first used RT-PCR to analyse the expression of the ten Aplysia IR genes in a variety of sensory , nervous and reproductive tissues ( Figure 3A ) ., Notably , the Aplysia IR25a orthologue is predominantly expressed in the olfactory organs , the rhinophore and oral tentacle 21 ., Two other Aplysia-specific IR genes , IR214 and IR217 , are expressed in the rhinophore and oral tentacle , respectively , and not detected in other tissues , except for the large hermaphroditic duct ( IR214 ) and skin ( IR217 ) ., Five additional IRs are also expressed in the oral tentacle , but displayed broader tissue expression in skin and the central nervous system; both of these tissues are likely to contain other types of chemosensory cells 22–23 ., Expression of two IR genes , IR209 and IR213 , was not detected in this analysis ( data not shown ) ., To further characterise Aplysia IR25a , we analysed its spatial expression in the mature A . dactylomela rhinophore by RNA in situ hybridisation ., An antisense probe for AdacIR25a labels a small number of cells in rhinophore cryosections ., Their size and morphology is typical of neurons , although we lack an unambiguous neuronal marker to confirm this identification ( Figure 3B–3D ) ., These cells are found either singly or in small clusters adjacent or close to the sensory epithelial surface in the rhinophore groove , in a similar position to cells expressing other types of chemosensory receptors 21 ., A control sense riboprobe showed no specific staining ( Figure 3E ) ., Together , these results are consistent with at least some of these molluscan IRs having a chemosensory function ., The expression of putative IR25a orthologues has previously been reported in two other Protostomia ., An IR25a-related gene from the American lobster , Homarus americanus , named OET-07 , is specifically expressed in mature olfactory sensory neurons 24–25 ., In C . elegans , a promoter reporter of the IR25a orthologue , GLR-7 , revealed expression in a number of pharyngeal neurons 9 , which might have a role in food sensing 26 ., While both crustacean and nematode genes were classified in these studies as iGluRs , there is no evidence that they act as canonical glutamate receptors , and we suggest that they fulfil instead a chemosensory function ., The antennal IR subfamily accounts for only a small fraction of the IR repertoire in most analysed insects and only 1–2 genes in other Protostomia ., The remaining majority of IR sequences are - amongst the genomes currently available - largely species-specific , with low amino acid sequence identity ( as little as 8 . 5% ) with other IR genes in either the same or different species ., We refer to this group of genes here as the “divergent IRs” ., Dipteran insects have particularly large expansions of divergent IRs ( Figure 1B ) ., Phylogenetic analysis revealed no obvious orthologous relationships of these genes either between D . melanogaster and mosquitoes or amongst the three mosquito species ( Aedes aegypti , Culex quinquefasciatus and Anopheles gambiae ) ( Figure 4 ) ., Instead , this subfamily of IRs displays a number of species-specific clades , perhaps reflective of the distinct ecological niches of these insects ., By contrast to antennal IRs , divergent IR expression has not been detected in D . melanogaster olfactory organs 15 , leading us to test whether these genes are expressed in other types of chemosensory tissue ., As endogenous transcripts of non-olfactory chemosensory genes , such as GRs , are difficult to detect 27–28 , we employed a sensitive transgenic approach to investigate divergent IR expression ., We transformed flies with constructs containing putative promoter regions for these genes upstream of the yeast transcription factor GAL4 and used these “driver” transgenes to induce expression of a GAL4-responsive UAS-mCD8:GFP fluorescent reporter 29 ., We sampled divergent IRs from several distinct clades , including IR7a , IR11a , IR52b , IR56a and IR100a ( Figure 4 ) ., All IR promoter-GAL4 constructs were inserted in the same genomic location using the phiC31 integrase system 30 , eliminating transgene-specific position effects on expression resulting from their site of integration ., Expression of three of these divergent IR reporters was observed in highly selective populations of neurons in distinct gustatory organs ( Figure 5A ) ., In the adult , IR7a is expressed in at least eleven neurons in the labellum , a sense organ involved in peripheral taste detection ( Figure 5B ) 31 ., Two reporters labelled neurons in internal sense organs in the pharynx: IR11a is expressed in one neuron in the ventral cibarial sense organ and IR100a is expressed in two neurons in the dorsal cibarial sense organ ( Figure 5C and 5D ) ., These internal pharyngeal neurons are thought to play a role in assessment of ingested food prior to entry into the main digestive system 16 ., Expression was not detected in any other neurons or other cell types in the adult head ( data not shown ) , although we cannot exclude expression in other regions of the body ., IR52b and IR56a reporters were not detected in these experiments ., We also examined expression of these reporters at an earlier stage in the D . melanogaster life cycle , third instar larvae , which display robust gustatory responses 16 ., The same three IR reporters were exclusively detected in unique bilaterally-symmetric larval gustatory organs: IR7a was expressed in two neurons in the terminal organ at the periphery , IR11a in a single neuron in the ventral pharyngeal sense organ and IR100a in two neurons in the posterior pharyngeal sense organ ( Figure 5E–5H ) ., Notably , all of these neurons in both adult and larval tissues ( except for a single IR7a-expressing cell in the terminal organ ) co-express IR25a , as revealed by a specific antibody against this receptor ( Figure 5 ) 15 ., IR25a is also expressed in several other cells in each of the gustatory organs , which may express other divergent IRs not examined here ., Together these results support a role for divergent IRs as taste receptors in distinct taste organs and stages of the D . melanogaster life cycle ., To obtain more detailed insights into the processes underlying the expansion and diversification of IR repertoires , we investigated their evolution over a shorter timescale by comparative analysis of D . melanogaster with 11 additional sequenced drosophilid species 32–33 ., The last common ancestor of these drosophilids is estimated to have existed 40 million years ago 34 , by contrast to the ∼250 million years since the last common ancestor of D . melanogaster and the mosquito A . gambiae 35 ., Certain species may have diverged much more recently , such as D . simulans and D . sechellia , whose last common ancestor may have existed only 250 , 000 years ago 36 ., We used D . melanogaster sequences as queries in exhaustive BLAST searches of the drosophilid genomes ., Retrieved sequences were manually reannotated to unify gene structure predictions across species and , in some cases , genes were partially resequenced to close sequence gaps or verify them as pseudogenes ( see Materials and Methods , Table S3 , and Datasets S1 and S2 ) ., Although predicted full-length gene sequences could be annotated for most genes , 28 sequences remain incomplete - but assumed in further analysis to be functional - because of a lack of sequence data or difficulty in precise annotation of exons in divergent regions of these genes ., Of the 926 drosophilid sequences identified ( including those of D . melanogaster ) , 49 genes were classified as pseudogenes because they consisted of only short gene fragments or contained frameshift mutations and/or premature stop codons ., We clustered all genes into orthologous groups by examining their sequence similarity , phylogenetic relationships and , in the case of IR47a , IR47b , IR47c , IR56e and IR60f , their micro-syntenic relationships ( Table S1 and Figure 6 ) ., For drosophilid species that are most distant from D . melanogaster , definition of precise orthologous relationships was not always possible , particularly for groups of closely related IR genes ( e . g . IR52a–f , IR60b–f ) ( Table S1 ) ., Orthologous groups were named after their D . melanogaster representatives or a logical variant in groups where no D . melanogaster gene was identified ( see Materials and Methods ) ., This analysis identified 14 iGluR and 58–69 IR genes in each of the twelve drosophilid species ( Figure 6A and Table S1 ) ., iGluRs are highly conserved , with a mean amino acid sequence identity of 89±1% s . e . m . , and a single representative for each species in every orthologous group ., Antennal IRs are also well conserved ( mean sequence identity\u200a=\u200a76±2% ) and amongst these genes we identified only a single pseudogenisation event , in D . sechellia IR75a , and a single gene duplication event , of D . mojavensis IR75d ., By contrast , divergent IRs , though also largely classifiable into monophyletic groups , display a more dynamic pattern of evolution ( mean sequence identity\u200a=\u200a61±2% ) , with multiple cases of gene loss , pseudogenisation or duplication ( Figure 6 and Table S1 ) ., We reconciled the gene phylogeny with the drosophilid species phylogeny to estimate the number of IR gene gain and loss events ., While this analysis is necessarily constrained by our ability to accurately define gene orthology , we estimated across the entire phylogeny there to be sixteen gene gain events ( gene birth rate , B\u200a=\u200a0 . 0006/gene/million years ) and 76 gene loss events ( gene death rate , D\u200a=\u200a0 . 0030/gene/million years ) ( Figure 7A , see Materials and Methods ) ., Most ( 46/76 ) gene losses are pseudogenisation events , which indicates that many of these events must have occurred relatively recently , as drosophilid species appear to eliminate pseudogenes rapidly from their genomes 37–38 ., Notably , 13 gene loss events – 12 of which reflect the presence of just one or a small number of premature stop codons or frameshift mutations – occur on the branch leading to the specialist D . sechellia ., Consequently , the gene loss rate on this branch is remarkably high compared with its generalist sister species D . simulans ( Figure 7A and 7B ) ., We studied the selective forces acting on drosophilid iGluRs and IRs by calculating the ratio of nonsynonymous to synonymous nucleotide substitution rates ( dN/dS , ω1 ) in these genes from all 12 species ., All tested iGluR , antennal IR and divergent IR genes are evolving under strong purifying selection ( ω1<<1 ) ( Figure 7C , left and Table S4 ) , suggesting that they all encode functional receptors ., iGluRs have the lowest estimated dN/dS ratio ( median ω1\u200a=\u200a0 . 060 ) , consistent with a conserved role in synaptic communication ., Antennal IRs have an intermediate dN/dS ratio ( median ω1\u200a=\u200a0 . 107 ) and divergent IRs the highest ( median ω1\u200a=\u200a0 . 149 ) , suggesting that divergent IRs have evolved under weaker purifying selection and/or contain more sites that have been shaped by positive selection ., Amongst the IRs , IR25a has the lowest dN/dS ratio ( ω1\u200a=\u200a0 . 028 ) , consistent with its high sequence conservation in and beyond drosophilids ( Figure 2 ) ., To compare these properties with those of other insect chemosensory receptor families ( ORs and GRs ) 39 , we also calculated dN/dS ratios for IR genes from only the five sequenced species of the melanogaster subgroup ( D . melanogaster , D . sechellia , D . simulans , D . erecta and D . yakuba ) ., For this subset of sequences , the relative differences between median dN/dS ratios ( ω2 ) for the iGluR and IR gene subfamilies observed with all twelve species was reproduced ( Figure 7C , right ) ., The GR gene family has previously been noted to evolve under weaker purifying selection than ORs 39 ., Notably , we found that the median dN/dS ratios for antennal IRs ( ω2\u200a=\u200a0 . 120 ) is statistically indistinguishable from that of ORs ( ω2\u200a=\u200a0 . 137 ) ( p>0 . 4 , Wilcoxon rank-sum test ) , and that the median dN/dS ratio of divergent IRs ( ω2\u200a=\u200a0 . 176 ) is statistically indistinguishable from that of GRs ( ω2\u200a=\u200a0 . 217 ) ( p>0 . 5 , Wilcoxon rank-sum test ) ., Thus , the selective forces acting on the IR receptor gene subfamilies parallel those on the ORs and GRs and appear to correlate with their putative distinct chemosensory functions in olfaction and gustation ( Figure 7C , right ) ., The reason for this difference is unknown , but might reflect reduced evolutionary constraints on co-expressed and partially redundant taste receptor genes or selection for higher diversity in taste receptor sequences to recognise more variable non-volatile chemosensory ligands in the environment ., Most residues of IR proteins can be expected to have evolved under purifying selection to maintain conserved structural and signalling properties , which may mask detection of positive selection ( ω>1 ) at a small number of sites that contribute to their functional diversity ., To obtain evidence for site-specific selection we applied site class models M7 and M8 in PAML to analyse 49 sets of orthologous IR genes of the six species of the melanogaster group ., This test did not identify any sites significantly under positive selection after Bonferroni correction ( Table S4 ) , a result consistent with orthologous IR genes having the same function across drosophilids ., Site-specific positive selection may be more easily detectable in relatively recent IR gene duplicates potentially undergoing functional divergence ., We therefore analysed the sole duplication of an antennal IR , IR75d . 1 and IR75d . 2 in D . mojavensis ., Assuming an estimated divergence time of 35 My between D . virilis and D . mojavensis 40 , and based on analysis of dS of IR75d genes in these species ( see Materials and Methods ) , we estimated this duplication to have occurred relatively recently , approximately 2 . 6–5 . 1 My ago ., Using a branch-site test we identified two sites ( p<0 . 05 ) that have evolved under positive selective pressure , where DmojIR75d . 1 and DmojIR75d . 2 appear to contain the ancestral and derived residues , respectively: DmojIR75d . 2-S670 maps to the third transmembrane domain and DmojIR75d . 2-Q365 maps to the putative ligand binding domain ., Functional characterisation of these variant receptors will be required to determine their significance ., From potentially one ancestral IR , what genetic processes underlay the generation of large repertoires of IR genes ?, We initially sought evidence for these mechanisms through analysis of the D . melanogaster IR family ., Several monophyletic groups of IR genes exist in clusters in the genome suggesting an important role of gene duplication by non-allelic homologous recombination ., For example , eight divergent IRs of the IR94 orthologous groups are located in three close , but separate , tandem arrays on chromosome arm 3R ( Figure 8A ) ., Other genes in the same clade are also found scattered on other chromosome arms ( X , 2R , 3L ) ( Figure 6 and Figure 8A ) , indicating that interchromosomal translocation has also occurred frequently , most likely both during and after formation of the tandem arrays ., Similar patterns are observed in the orthologous/paralogous sequences of these IRs in other drosophilid species ( Figure 8A ) , as well as for other IR clades ( data not shown ) ., These features are also observed in IR repertoires in other insects , although incomplete genome assembly prevented a more precise analysis ., For example , in Aedes aegypti the 23 IR7 clade members are found in arrays of 1 , 1 , 2 , 5 , 7 and 7 genes on 6 different supercontigs ( data not shown ) ., We also noticed an unusual pattern in D . melanogaster IR gene structures , in which antennal IRs ( as well as iGluRs ) contain many ( 4–15 ) introns , while the vast majority of divergent IRs are single exon genes ( Figure 8B ) ., Drastic intron loss in multigene families is a hallmark of retroposition , where reverse-transcription of spliced mRNAs from parental , intron-containing genes and reinsertion of the resulting cDNA at a new genomic location may give rise to a functional , intronless retrogene 41 ., The few introns that are present in these IRs in D . melanogaster have a highly biased distribution towards the 5′ end of the gene ( 19/25 introns in the first 50% of IR gene sequences ) ( Figure 8C ) , which is characteristic of recombination of partially reverse-transcribed cDNAs ( a process which initiates at the 3′ end ) with parental genes 42 ., Sequence divergence of IRs prevented us from identifying parental gene-retrogene relationships ., Nevertheless , these observations together suggest that divergent IRs arose by at least one , and possibly several , retroposition events of ancestral antennal IRs ., Once “born” , single exon IRs could presumably readily further duplicate by non-allelic homologous recombination ., Our comprehensive survey and phylogenetic analysis of iGluR/IR-like genes permits development of a model for their evolution ( Figure 9 ) ., The shared , unusual “S1-ion channel-S2” domain organisation of prokaryotic GluR0 and eukaryotic iGluRs is suggestive of a common ancestor of this family by fusion of genes encoding the separate domains that were present in very early life forms ( Figure 9 ) 11 ., However , we have found prokaryotic glutamate receptors in only a very small number of bacterial species ., Thus , if an iGluR evolved in the common ancestor of prokaryotes and eukaryotes , it must have subsequently been lost in a large number of prokaryotic lineages ., It is possible , therefore , that iGluRs only originated in eukaryotes and were acquired by certain prokaryotic species by horizontal gene transfer 43 ., If the latter hypothesis is true , the presence of closely related iGluRs in both plants and animals implies their early evolution within eukaryotes , potentially in the last common eukaryotic ancestor 44 ., However , the absence of iGluRs in sponges and all examined unicellular eukaryotes raises the alternative possibility that animal and plant receptors evolved independently , or were acquired by horizontal transmission , perhaps from prokaryotic sources ., Whatever the precise origin of iGluRs in animals , their subsequent divergence into AMPA , Kainate and NMDA subfamilies also occurred early , although variation in the size of these subfamilies suggests continuous adaptation of the synaptic communication mechanisms they serve to nervous systems of vastly different complexities ., Several outstanding issues regarding IR evolution can now be addressed ., First , we have shown that the IRs were very likely to have been present in the last common ancestor of Protostomia , an estimated 550–850 million years ago 20 ., IR25a represents the probable oldest member of this repertoire and conservation of chemosensory organ expression of IR25a orthologues in molluscs , nematodes , crustaceans and insects strongly suggests that this receptor may have fulfilled a chemosensing function in the protostome ancestor ., Second , the apparent absence of IRs in Deuterostomia suggests the parsimonious model that IRs evolved from an animal iGluR ancestor rather than representing a family of chemosensing receptors that was present in a common ancestor of Animalia and lost in non-protostomes ., Our phylogenetic and gene structure analysis suggests that IR25a may have derived from a non-NMDA receptor gene ., The transition from an iGluR to an IR may not have involved drastic functional modifications: both receptor types localise to specialised distal membrane domains of neuronal dendrites ( post-synaptic membranes and cilia , respectively ) and , in response to binding of extracellular ligands , depolarise these domains by permitting transmembrane ion conduction which in turn induces action potentials 45 ., Thus , it is conceivable that IRs arose simply by a change in expression of an iGluR from an interneuron ( where it detected amino acid signals from a pre-synaptic partner ) to a sensory neuron ( where it could now detect chemical signals from the external environment ) ., Third , our analyses of IR repertoires across both divergent and relatively

Introduction, Results, Discussion, Materials and Methods

Ionotropic glutamate receptors ( iGluRs ) are a highly conserved family of ligand-gated ion channels present in animals , plants , and bacteria , which are best characterized for their roles in synaptic communication in vertebrate nervous systems ., A variant subfamily of iGluRs , the Ionotropic Receptors ( IRs ) , was recently identified as a new class of olfactory receptors in the fruit fly , Drosophila melanogaster , hinting at a broader function of this ion channel family in detection of environmental , as well as intercellular , chemical signals ., Here , we investigate the origin and evolution of IRs by comprehensive evolutionary genomics and in situ expression analysis ., In marked contrast to the insect-specific Odorant Receptor family , we show that IRs are expressed in olfactory organs across Protostomia—a major branch of the animal kingdom that encompasses arthropods , nematodes , and molluscs—indicating that they represent an ancestral protostome chemosensory receptor family ., Two subfamilies of IRs are distinguished: conserved “antennal IRs , ” which likely define the first olfactory receptor family of insects , and species-specific “divergent IRs , ” which are expressed in peripheral and internal gustatory neurons , implicating this family in taste and food assessment ., Comparative analysis of drosophilid IRs reveals the selective forces that have shaped the repertoires in flies with distinct chemosensory preferences ., Examination of IR gene structure and genomic distribution suggests both non-allelic homologous recombination and retroposition contributed to the expansion of this multigene family ., Together , these findings lay a foundation for functional analysis of these receptors in both neurobiological and evolutionary studies ., Furthermore , this work identifies novel targets for manipulating chemosensory-driven behaviours of agricultural pests and disease vectors .

Ionotropic glutamate receptors ( iGluRs ) are a family of cell surface proteins best known for their role in allowing neurons to communicate with each other in the brain ., We recently discovered a variant class of iGluRs in the fruit fly ( Drosophila melanogaster ) , named Ionotropic Receptors ( IRs ) , which function as olfactory receptors in its “nose , ” prompting us to ask whether iGluR/IRs might have a more general function in detection of environmental chemicals ., Here , we have identified families of IRs in olfactory and taste sensory organs throughout protostomes , one of the principal branches of animal life that includes snails , worms , crustaceans , and insects ., Our findings suggest that this receptor family has an evolutionary ancient function in detecting odors and tastants in the external world ., By comparing the repertoires of these chemosensory IRs among both closely- and distantly-related species , we have observed dynamic patterns of expansion and divergence of these receptor families in organisms occupying very different ecological niches ., Notably , many of the receptors we have identified are in insects that are of significant harm to human health , such as the malaria mosquito ., These proteins represent attractive targets for novel types of insect repellents to control the host-seeking behaviors of such pest species .

evolutionary biology, genetics and genomics/comparative genomics, neuroscience/sensory systems

journal.pgen.1000080

BRCA1 and BRCA2 Missense Variants of High and Low Clinical Significance Influence Lymphoblastoid Cell Line Post-Irradiation Gene Expression

Approximately 7% of breast cancer cases occur in women with a strong family history of the disease 1 ., Mutations in BRCA1 and BRCA2 account for a considerable proportion of these familial breast cancer cases , with the average cumulative risk in BRCA1 and BRCA2 mutation carriers by age 70 years estimated at 65% and 45% , respectively 2 ., The Breast Cancer Information Core ( BIC ) database ( http://research . nhgri . nih . gov/bic/ ) currently has more than 1400 and 1800 unique sequence variants listed in the BRCA1 and BRCA2 genes , respectively ., These include frameshift , nonsense , missense , splice site alterations and polymorphisms ., Greater than a third of the BRCA1 and greater than half of the BRCA2 unique variants are “unclassified variants” without compelling evidence of pathogenicity or functional significance ., The majority of unclassified variants recorded in the BIC database are predicted missense changes ( more than 400 BRCA1 and 800 BRCA2 ) ., However other variants which may be categorised as unclassified variants are in-frame deletions or duplications , variants that may disrupt splicing , or variants in the 3′UTR that may affect RNA stability ( www . kconfab . org ) ., BRCA1/2 unclassified variants represent a problem in the clinical setting as it is not known which variants are associated with the high risk of disease reported for classical truncating mutations ., Several functional assays may be used to determine the significance of unclassified variants , including transcription activation and complementation assays 3–9 , but a disadvantage of biochemical assays is that they rely on the functions of specific domains of the protein , require specialized laboratory skills , and are time–consuming to perform ., Other methods for classifying variants include the analysis of clinical and histopathological data 10 , loss of heterozygosity analysis 11 and bioinformatic analysis to predict the effect of the amino acid change on structure and multiple sequence alignment strategies 12; 13–15 ., Integrated evaluation of unclassified variants which combines several approaches , such as the analysis of co-segregation of the mutation with disease , co-occurrence of the variant with a deleterious mutation , sequence conservation of the amino acid change , severity of amino acid change , tumor loss of heterozygosity , and tumor histopathology classification , provides a quantitative tool for the classification of variants 16–22 ., This multifactorial method was developed to classify such rare unclassified variants into two categories , variants with features of classical high-risk mutations ( termed pathogenic ) , and variants that do not have the features of a high-risk mutation ( termed neutral or low clinical significance ( LCS ) ) ., While the availability of appropriate biospecimens ( e . g . number of families and tumors ) for inclusion in likelihood prediction is a major factor determining the classification of any single variant , another major caveat of the multifactorial approach is that it is not appropriate for the evaluation of possible moderate or low risk variants , since it uses high-risk mutations as reference for the underlying assumptions 16 , 19 , 20 ., Therefore , the current multifactorial method cannot exclude the possibility that rare variants classified to be of low clinical significance may be associated with a moderate or low risk of cancer ., Gene expression profiling has increased our understanding of the molecular events in breast tumor development , has been used to predict prognosis , and has characterised breast tumors into subtypes 23–27 ., The value of expression profiling for identifying underlying high-risk gene mutation status is indicated by a number of studies ., A distinct gene expression profile has been reported for breast tumors of BRCA1 mutation carriers 23 , 28 , 29 , expected to be homozygous for loss of BRCA1 function at the somatic level ., In addition , the existence of distinct gene expression profiles for heterozygous loss of BRCA1 and BRCA2 function is supported by accurate separation of short-term cultures of fibroblasts carrying a germline mutation in the BRCA1 or BRCA2 genes , compared to healthy women undergoing reduction mammoplastic surgery with no family or personal history of any cancer or sporadic breast-cancer-affected controls 30 , 31 ., Lymphoblastoid cell lines ( LCLs ) have also been shown to have distinct mRNA expression phenotypes for heterozygous carriers of ATM mutations , some of which are known to be associated with an increased risk in breast cancer 32 , 33 ., These findings suggest that germline gene expression signatures , including those from fibroblasts or LCLs , may be used to define BRCA1 or BRCA2 mutation status and to assist in assessing the clinical significance of BRCA1 and BRCA2 unclassified variants ., In this study we compared LCL gene expression signatures of breast cancer cases carrying pathogenic mutations in BRCA1 or BRCA2 , to familial breast cancer cases with no known BRCA1/2 mutations ( BRCAX ) ., We also considered the possibility that BRCAX individuals with a BRCA1 or BRCA2 sequence variant classified to be neutral/low clinical significance ( LCS ) using multifactorial likelihood analysis may differ in gene expression profile from BRCAX individuals without such sequence variants ., In addition , since truncating alterations comprise the majority of known pathogenic mutations but most BRCA1 and BRCA2 unclassified variants are predicted missense alterations , we compared profiles from individuals with known missense or truncating mutations to determine if mutation effect will affect the mutation-associated expression profile for each gene ., We derived gene lists to predict mutation status defined by gene and mutation effect , and then tested the efficacy of these gene lists to predict the gene mutation status of LCLs ., We provide evidence that gene lists differ according to gene and mutation effect , and according to the presence of sequence variants of low clinical significance ., We also demonstrate that the use of appropriately-derived gene lists improves the prediction of pathogenicity of known mutations ., The ultimate aim of this experiment was to establish if gene expression profiles could distinguish between BRCA1 or BRCA2 pathogenic mutation carriers and familial breast cancer cases whose disease was not attributable to BRCA1 or BRCA2 mutations ( BRCAX cases ) ., BRCAX breast cancer families are likely to result from mutations in several other genes , and thus represent a heterogeneous group ., Moreover , included in the BRCAX group were a subset of 10 BRCAX individuals who carried a BRCA1/2 variant previously classified to be of low clinical significance using multifactorial likelihood approaches 8 , 19 , 21 , 22 ., Unsupervised hierarchical clustering showed that BRCAX LCLs containing a BRCA1 or BRCA2 variant of low clinical significance clustered away from the majority of remaining BRCAX samples ( Figure 1 ) ., A t-test with Benjamini and Hochberg multiple testing correction 34 was performed to determine if there were gene expression differences between the BRCAX individuals with an LCS variant and those without an LCS variant ., Expression of 631 genes differed between the two BRCAX subgroups ( 5% of the 631 genes identified would be expected to pass this restriction by chance ) ., For this reason , BRCAX samples were stratified according to the presence of an LCS variant for further analyses ., Gene expression is similar for carriers of BRCA1 and BRCA2 truncating mutations and rare sequence variants of low clinical significance , but differs from BRCA1 and BRCA2 missense mutations and BRCAX non-BRCA1/2 familial cases ., Unsupervised hierarchical clustering ( Figure 2 ) of LCL expression data from all samples revealed that BRCA1 and BRCA2 samples were more similar to each other than BRCAX samples without an LCS variant ., This result suggests that germline effects of heterozygous mutations in BRCA1 and BRCA2 cannot easily be separated using the experimental conditions used in this study ., Although BRCAX samples tended to cluster distinctly from BRCA1/2 samples , nine of ten BRCAX individuals who carried a BRCA1/2 variant previously classified to be of low clinical significance fell within the major BRCA1 or BRCA2 mutation cluster ., In contrast , six of the nine pathogenic missense mutations of BRCA1 or BRCA2 fell into a BRCA1/2 outlier group , which clustered closer to the BRCAX samples ., To determine the accuracy of using gene expression data from LCLs to predict BRCA1/2 pathogenic carriers and BRCAX individuals , we used a Gaussian Process Classifier ( GPC ) ., GPC analysis was used previously in an analysis of microarray profiles from irradiated short-term fibroblasts of BRCA1/2 mutation carriers 31 , and allows for multiway comparison of groups ., For GPC analysis 2031 genes which were significantly over/under-expressed at the 5% significance level were selected ., The GPC was used in a three way comparison to compare BRCA1 truncating mutation carriers to BRCA2 truncating mutation carriers , and to BRCAX samples without an LCS variant ., Samples with BRCA1 or BRCA2 pathogenic missense mutations or classified as BRCAX with an LCS variant were then included to determine their affect on the prediction accuracy ., A summary of the prediction accuracy is shown in Table 1 ., The highest prediction accuracy ( 62 . 26% ) was achieved when the analysis excluded samples classified as BRCAX with an LCS , and samples with BRCA1 or BRCA2 missense mutations ., This prediction accuracy is above the expected performance , as a random prediction with three classes comprised of a similar sample number would be 33% accuracy ., When BRCA1 and BRCA2 samples were compared to only BRCAX samples with an LCS variant , the prediction dropped to 43 . 46% , and the addition of the BRCAX samples without an LCS variant improved the accuracy ., In all comparisons the inclusion of the pathogenic non-truncating mutations of BRCA1 and BRCA2 lowered the prediction accuracy ., In the clinical setting , unclassified sequence variants of BRCA1 or BRCA2 are generally identified after full sequencing of both genes ., Therefore the most common clinical question is whether a variant in BRCA1 or BRCA2 is pathogenic or not ., We thus performed pair wise analyses to determine if BRCAX samples could be distinguished from those with pathogenic mutations in BRCA1 or BRCA2 ., Based on observations from hierarchical clustering analyses and the GPC analysis , we also considered the possibility that the effect of pathogenic BRCA1/2 mutations ( truncating or missense ) affected LCL gene expression ., T-tests were performed using the 20 , 874 detected probes to elucidate gene differences between, i ) BRCA1 or BRCA2 truncating mutations vs BRCAX without an LCS variant;, ii ) BRCA1 or BRCA2 missense mutations vs BRCAX without an LCS variant ., The number of genes which passed these restrictions and the overlap between them is outlined in Figure 3A and 3C ., The comparisons were then repeated with BRCAX with an LCS variant ( Figure 3B and 3D ) ., As expected when BRCA1 and BRCA2 were compared to BRCAX samples without an LCS variant , a greater number of genes were deemed significant compared to BRCA1 or BRCA2 vs BRCAX samples with an LCS variant ., SVM is a widely accepted classification approach for assessing differences in mRNA expression , and was used to compare BRCA1 or BRCA2 individually to BRCAX samples ., Since our detailed analysis of gene lists showed that mutation effect ( truncating or missense substitution ) appears to affect the genes that are differentially expressed in the carriers after IR ( Figure 3 ) , we assessed if these gene differences will affect the predictions ., We used SVM with the top 200 genes from the comparison of BRCA1 or BRCA2 truncating mutations to BRCAX , and the top 200 genes from the comparison of BRCA1 and BRCA2 missense mutations to BRCAX ( Figure 3A and 3C ) ., The genes which differed between BRCA1 or BRCA2 and BRCAX with an LCS variant were not used in this comparison as too few genes passed the restriction ( Figure 3B and 3D ) ., The top 200 genes are listed in Tables S2 , S3 , S4 , and S5 and the overlap of the top 200 genes used for prediction from BRCA1 ( missense ) vs BRCAX ( noLCS ) and BRCA1 ( truncating ) vs BRCAX ( noLCS ) was 16 transcripts , with no overlap between the top 200 genes from BRCA2 ( missense ) vs BRCAX ( noLCS ) and BRCA2 ( truncating ) vs BRCAX ( noLCS ) ., A total of 715 different genes were represented in the four lists of top 200 gene-lists from comparison of BRCAX ( no LCS ) to the different BRCA1/2 groups above ., The results are summarised in Tables 2 and, 3 . The BRCA2 truncating pathogenic carriers were consistently predicted with higher accuracy compared to BRCA1 truncating pathogenic carriers ., The accuracy of prediction was improved when the gene list used for prediction was appropriate to the mutation effect ( truncating or missense ) being tested ., When the missense-associated gene list was used , pathogenic truncating mutations were predicted with 35% and 68% accuracy for BRCA1 and BRCA2 , respectively ., Predictions increased to 71% and 84% for BRCA1 and BRCA2 , respectively , using the truncating-associated genes ., Similarly , the pathogenic missense mutation carriers were predicted with 83% and 100% accuracy when the missense-associated gene list is used , but this accuracy was lower or remained the same when the truncating-specific gene list was used ( 83% and 0% ) ., Prediction of BRCAX samples that did not carry an LCS variant was high in all comparisons ( 82–94% ) ., In contrast , prediction of BRCAX samples that did carry an LCS variant was poor ( 40–50% ) ., When using SVM , the significance of the predictions can also be represented by the distance the prediction is from the plane , where predictions called with greater confidence are further from the plane that separates the BRCA1 ( or BRCA2 ) and BRCAX samples ., The significance of the predictions for the BRCA1 pathogenic missense mutations is summarised in Figure, 4 . Although both missense and truncating gene lists correctly predicted 5 of 6 missense mutations , the results show that there is much greater confidence in the 5 correctly predicted missense mutations when using the missense-derived list ., Ingenuity Pathway Analysis of genes which differed between the LCLs carrying pathogenic truncating or missense mutations of BRCA1 or BRCA2 compared to BRCAX samples without an LCS variant was performed to determine the potential functional relevance of the differentially expressed genes ., All BRCA1 and BRCA2 pathogenic mutations resulted in gene expression changes relating to cell cycle , cancer and cellular growth and development , while BRCA1 and BRCA2 missense mutations shared some additional similarities ( cell death and cell development pathways ) ., There were also alterations in several pathways that were unique to BRCA1 truncating mutations , BRCA2 truncating mutations , BRCA1 missense mutations , or BRCA2 missense mutations ( Figure S1 ) ., It is difficult to counsel patients with a strong family history of breast cancer who are found to carry an unclassified variant in BRCA1 or BRCA2 ., While management at the level of the family should remain unchanged from that of a BRCAX family with no knowledge of a BRCA1/2 mutation , some individuals from high-risk families may nevertheless interpret information about an unclassified variant to alter their choices regarding prophylactic surgery for example , and so require careful counselling ., Gene expression profiling can be used to classify samples based on phenotype , and its frequent use in laboratories world-wide holds great promise for clinical application , to the extent that profiling tools are being developed for diagnostic use e . g . Agendia Inc . ( http://www . agendia . com/ ) ., Expression profiles of short-term fibroblasts have previously been reported to separate carriers of a heterozygous mutation in the BRCA1 or BRCA2 genes from sporadic breast-cancer-affected controls 30 , 31 ., We wished to determine if expression profiling of LCLs could similarly be used to predict BRCA1 or BRCA2 mutation status , with the ultimate aim of predicting the significance of unclassified variants of BRCA1 or BRCA2 ., We chose LCLs as a minimally invasive source of germline material that can be maintained as long term cultures , and because previous studies have shown that LCL array profiling is robust to sourcing of LCLs established in different laboratories 33 ., We compared expression profiles of irradiated LCLs from BRCA1 and BRCA2 carriers to those of non-BRCA1/2 BRCAX familial breast cancer patients , an appropriate reference group for the proposed evaluation of unclassified variants identified in familial breast cancer patients ., A relatively early time-point of 30 minutes post-irradiation was chosen to capture gene expression initiation , and minimize possible downstream compensation effects ., It has previously been shown that 10Gy IR treatment of normal LCLs has an effect on the transcriptional response , with greatest change in mRNA levels for most genes within one hour post-treatment 35 , and studies of mouse brain gene expression after whole-body low-dose irradiation have shown that a large number of early IR response genes can be measured at the 30 minute time point 36 ., A number of BRCAX cases carried BRCA1 or BRCA2 sequence variants that had been previously classified using multifactorial likelihood modelling methods to be neutral or of low clinical significance-that is , these rare variants are extremely unlikely to be a high-risk mutation in either of these genes , but the modelling methods used cannot assess whether they are truly neutral or associated with a much lower risk of disease ., We found that the BRCAX samples with such LCS variants were separated from the majority of BRCAX samples without such LCS variants using unsupervised hierarchical clustering ., This result indicates that LCS samples differ in expression profile as a result of their BRCA1 or BRCA2 sequence variant , and was substantiated by the class prediction methods: GPC prediction of the BRCAX samples decreased in accuracy when BRCAX samples with an LCS were included ., In addition , SVM to detect BRCA1 or BRCA2 mutation-related gene lists yielded differences in the significant genes for comparisons to BRCAX samples without an LCS variant , compared to BRCAX samples with an LCS variant ., Accordingly , prediction of BRCAX subgroup status based on the more robust gene list derived from comparisons to BRCAX individuals without an LCS variant was generally poorer for BRCAX samples with an LCS ( 40–50% ) compared to those without an LCS ( 82%–94% ) ., These rather provocative results indicate that the possible effect of all rare variants should be considered in development of assays to assess which variants have features of high-risk mutations ., Moreover , the similarity in expression profile of these variants to other BRCA1/2 pathogenic mutations suggests that at least some of these LCS variants may confer small-moderate risks of breast cancer , presumably acting in concert with alterations in other genes in the BRCA1/2 pathway to lead to breast cancer ., Given the rarity of these variants , alternative statistical approaches will be required to assess the risk of cancer associated with them ., The assay conditions used in this study could not distinguish between samples with pathogenic BRCA1 mutations and those with pathogenic BRCA2 mutations ., Ionising radiation has previously been show to separate fibroblast cells which carry BRCA1 or BRCA2 mutations from sporadic cases with 100% accuracy 31 , but our experiment differs in several respects ., We compared BRCA1 and BRCA2 cases to familial BRCAX cases as an appropriate reference group for familial breast cases likely to be identified as carriers of BRCA1/2 mutations or unclassified variants , we used LCLs instead of fibroblasts , we selected a lower IR exposure ( 10Gy vs 15Gy ) , and we chose a relatively early time point of 30 mins after exposure to IR in order to gain a better understanding of the functional differences in response to IR between the BRCA1 , BRCA2 and BRCAX cell lines ., Some or all of these factors may explain the difference in the ability of this study to distinguish BRCA1 from BRCA2 , both of which are involved in DNA damage repair ., However , differences in post-irradiation response between BRCAX individuals and BRCA1/2 mutation carriers are supported by alternative analysis we have conducted of the subset of genes reported to be involved in post-irradiation response , comparing mutation-negative normal female controls to BRCAX individuals without an LCS variant , or to BRCA1 or BRCA2 truncating mutation carriers ., Our results indicate substantial differences in radiation response between normal controls and the patient groups , and also considerable differences between the BRCAX group and BRCA1 and BRCA2 carriers 37 ., Alternative IR exposures and/or post-IR timepoints , and possibly different DNA damaging agents , should be considered for future experiments ., The ultimate aim of this experiment was to identify array profiles that would be useful for the classification of unclassified sequence variants of BRCA1 or BRCA2 ., In the clinical setting , individuals generally present with full sequencing of both genes , and presence of a variant in one gene or the other ., We thus assessed the ability to distinguish BRCA1 or BRCA2 , separately , from BRCAX individuals ., Importantly , since most unclassified variants are predicted to cause amino acid substitutions , we also assessed the relevance of mutation effect for expression profiles ., We found that the genes which significantly differed between BRCA1 or BRCA2 and BRCAX LCLs were dependent on mutation effect ., Accordingly , the SVM prediction for each mutation effect was best if the appropriate gene list was used , in terms of both accuracy of prediction ( BRCA1 or BRCA2 vs BRCAX ) and confidence in the classification as determined by the distance of the prediction from the SVM plane ., Thus we strongly urge that mutation effect is taken into account if this type of assay is to be developed for use in predicting the clinical significance of BRCA1/2 variants ., The current challenge is that few missense variants have been classified with respect to their clinical significance , with the only 23 individual missense variants termed clinically important by BIC , 17 in BRCA1 and six in BRCA2 ., Moreover , these are restricted in terms of the domains/regions in which they occur , residing in the BRCA1 start site ( n\u200a=\u200a2 ) , ring finger ( n\u200a=\u200a4 ) or transactivation domains ( n\u200a=\u200a11 ) , and the BRCA2 CDK2 phosphorylation site ( n\u200a=\u200a3 ) or at one codon ( 2336 , n\u200a=\u200a3 ) in a region of unknown function ., It will thus be difficult to accrue a panel of known pathogenic missense variants for use in such predictive assays , and will require a concerted collaborative effort ., Assuming sufficient pathogenic variants are identified , the successful execution of such a study may eventually distinguish missense-associated gene expression patterns that are generic to missense mutations , and/or those that are specific to the domain location of missense mutations ., In addition , a possibly greater challenge will be identifying assay conditions ( cell type , perturbation , time-point etc ) that can also identify gene expression differences between patients with rare variants of low clinical significance in BRCA1 and/or BRCA2 and those with truly high-risk pathogenic mutations ( truncating or missense ) in these genes ., Our study , using conditions that were not optimal for separating BRCA1 and BRCA2 mutations nevertheless identified gene expression differences between BRCA1/2 pathogenic mutations and LCS variants , suggesting that larger sample sizes and further experimentation may identify a more robust gene list to separate pathogenic mutations , variants of low clinical significance , and individuals with no sequence alterations in BRCA1/2 ., Pathway analysis confirming altered expression of cancer , cell proliferation and cell cycle pathways in BRCA1 and BRCA2 mutation carrier groups is consistent with the known functions of BRCA1 and BRCA2 38 , 39 ., The pathway differences by mutation type such as cell death and development may reflect that the majority of truncating mutations result in activation of the nonsense mediated decay pathway 40 and complete loss of protein , whereas most missense mutations are likely to result in more subtle effects through ablation of individual functional domains ., Some pathways identified were unexpected and are only present in a single mutation type , and it is thus likely that at least some of these pathways were generated by chance alone ., In conclusion , we have provided evidence that carriers of BRCA1 and BRCA2 variants considered to be of low clinical significance have array profiles distinct from other non-BRCA1/2 familial cases , but resembling profiles of pathogenic BRCA1/2 cases , indicating that further work will be required to evaluate their possible association with a low-moderate risk of cancer ., We have also shown that it will be important to consider mutation effect when developing array-based assays for predicting the clinical significance of BRCA1 or BRCA2 unclassified variants ., Lastly , our findings demonstrate the ability of array profiling of immortalized lines derived from lymphoblastoid cells to detect germline mutations in genes that result in breast and ovarian cancer , and thus have relevance to the investigation of other genetic diseases irrespective of the organs or tissues they affect ., LCLs were derived from breast cancer-affected women recruited into the Kathleen Cuningham Foundation for Research into Breast Cancer ( kConFab ) , a consortium which ascertains multiple-case breast cancer families 41 ., These include families in which one or more carriers of a BRCA1 or BRCA2 mutation have been identified , and families in which no predisposing mutation has been identified ( BRCAX ) ., The recruitment criteria for BRCAX families are:, 1 ) at least one member of the family at high-risk according to the National Breast Cancer Centre Category III guidelines ( http://www . nbcc . org . au ) , and four or more cases of breast or ovarian cancer ( on one side of the family ) , and two or more living affecteds with breast or ovarian cancer , and four or more living first or second degree unaffected female relatives of affected cases , over the age of 18 ; 2 ) two or three cases of breast or ovarian cancer ( on one side of the family ) in same or adjacent generations , if at least one of these cases is ‘high risk’ ( i . e . male breast cancer , bilateral breast cancer , breast plus ovarian cancer in the same individual , or breast cancer with onset less than 40 years ) , and two or more living affected cases with breast or ovarian cancer , and four or more living first or second degree unaffected female relatives of affected cases , over the age of 18 ., Classifications for BRCA1 and BRCA2 pathogenic mutations and variants of low clinical significance ( LCS ) are described on http://www . kconfab . org/Progress/Classification . shtml ., Briefly , LCS variants include BRCA1 or BRCA2 variants described in trans with a deleterious mutation in the same gene in an individual and occur at a frequency of less than 1% in unaffected controls , or considered neutral/low clinical significance as measured using multifactorial likelihood approaches 16 , 19 , 21 , 22 ., A cohort of 72 LCLs were used in this study ., The full listing of mutation details for LCLs is shown in Table S1 ., In brief , the study included: LCLs were grown in RPMI 1640 media with 15% fetal bovine serum , 1% penicillin-streptomycin and 1% L-glutamine ., The cell number was normalised and fresh medium was added to cells 24hr prior to irradiation with 10Gy , using a calibrated Cs137 c-source delivering 1 Gy/1 . 5 min ., Total RNA was harvested 30min later using an RNeasy kit ( Qiagen , Doncaster , VIC ) ., The Illumina Totalprep RNA amplification kit ( Ambion , Austin , TX ) was used to amplify and biotinylate 450ng of total RNA ., Biotinylated RNA was hybridised overnight at 55°C to Illumina Human-6 version 1 BeadChips containing >46 , 000 probes ( Illumina Inc . , San Diego , CA ) ., The microarrays were washed , stained with streptavidin-Cy3 , and then scanned with an Illumina BeadArray Scanner ., Duplicate arrays were performed for eight cell lines across the different groups for quality control purposes , with duplicates performed on different days ., All duplicate arrays showed highest correlation with each other ( correlation >0 . 98 ) ., Duplicate samples were not included in analysis ., Comparative real-time PCR was performed for ten genes on 6–8 samples , using GAPDH to normalise all data , and the comparative cycle threshold method for analysis ., Paired student t tests were performed to determine the significance of gene expression changes ., Expression differences were validated for 8/10 genes tested ., Raw data was imported into Illumina Beadstudio and then exported into Genespring v7 . 3 ( Agilent Technologies , Forest Hill , VIC ) for further analysis ., Data was normalised ( per chip normalized to 50th percentile and per gene normalized to median ) and filtered using an Illumina detection score of >0 . 99 in at least one sample , which yielded 20 , 874 probes that were used in all further analyses ., The majority of these probes used in the analysis were designed by Illumina to assay the curated portion of the NIH Ref sequence database-16 , 923 were present in the Ref sequence database , comprising 65% of all Ref sequence-listed probes on the array ., Transcripts which had a >2-fold change versus the mean were visualised using unsupervised Hierarchical Clustering ( Figures 1 and 2 ) ., The clustering method used was a Pearson correlation similarity measure with an average linkage clustering algorithm ., Two different methods were used to classify LCLs based on mutation status: ( 1 ) A multi comparison Gaussian Process Classifier ( GPC ) 42 with Leave-One-Out cross-validation to determine the prediction errors , as previously used to predict BRCA1/BRCA2 mutation status of irradiated fibroblasts 31; ( 2 ) A linear classification method commonly used for classification of microarray data , Support Vector Machines ( SVM ) 43 with Leave-One-Out cross validation ., The GPC analysis used 2031 genes which were derived from a t-test to select the genes that were significantly over/under-expressed at the 5% significance , while the SVM used genes from the 20 , 874 detected probes which differed between groups of LCLs using a t-test p of 0 . 05 ., All resulting gene lists are available as supplementary data and all data is available via GEO: Accession number GSE10905 ., Ingenuity Pathway Analysis ( Ingenuity Systems , www . ingenuity . com ) was used for biological interpretation of gene lists ., Analysis of the transcripts found to be up- and down-regulated in irradiated LCLs as identified for the different mutation categories identified those biochemical networks most likely to be affected by a BRCA1 and BRCA2 truncating and missense mutation , relative to BRCAX ., Those pathways with multiple hits or a significance score ≥20 were then compared .

Introduction, Results, Discussion, Materials and Methods

The functional consequences of missense variants in disease genes are difficult to predict ., We assessed if gene expression profiles could distinguish between BRCA1 or BRCA2 pathogenic truncating and missense mutation carriers and familial breast cancer cases whose disease was not attributable to BRCA1 or BRCA2 mutations ( BRCAX cases ) ., 72 cell lines from affected women in high-risk breast ovarian families were assayed after exposure to ionising irradiation , including 23 BRCA1 carriers , 22 BRCA2 carriers , and 27 BRCAX individuals ., A subset of 10 BRCAX individuals carried rare BRCA1/2 sequence variants considered to be of low clinical significance ( LCS ) ., BRCA1 and BRCA2 mutation carriers had similar expression profiles , with some subclustering of missense mutation carriers ., The majority of BRCAX individuals formed a distinct cluster , but BRCAX individuals with LCS variants had expression profiles similar to BRCA1/2 mutation carriers ., Gaussian Process Classifier predicted BRCA1 , BRCA2 and BRCAX status , with a maximum of 62% accuracy , and prediction accuracy decreased with inclusion of BRCAX samples carrying an LCS variant , and inclusion of pathogenic missense carriers ., Similarly , prediction of mutation status with gene lists derived using Support Vector Machines was good for BRCAX samples without an LCS variant ( 82–94% ) , poor for BRCAX with an LCS ( 40–50% ) , and improved for pathogenic BRCA1/2 mutation carriers when the gene list used for prediction was appropriate to mutation effect being tested ( 71–100% ) ., This study indicates that mutation effect , and presence of rare variants possibly associated with a low risk of cancer , must be considered in the development of array-based assays of variant pathogenicity .

Inherited mutations in the genes BRCA1 and BRCA2 increase risk of breast cancer and contribute to a proportion of breast cancer families ., However , more than half of the reported sequence alterations in BRCA1 and BRCA2 are currently of unknown clinical significance ., We analysed gene expression in lymphoblastoid cell lines derived from blood of patients with sequence alterations in BRCA1 and BRCA2 and compared these to lymphoblastoid cells from familial breast cancer patients without such alterations ., We then classified these lymphoblastoid cells based on their gene profiles ., We found that BRCA1 and BRCA2 samples were more similar to each other than to familial breast cancer patients without BRCA1/2 mutations , and that the type of sequence change in BRCA1 and BRCA2 ( missense or truncating ) influenced gene expression ., We included in the study ten familial breast cancer samples , which carried sequence changes in BRCA1 or BRCA2 , that are believed to be of little clinical significance ., Interestingly these samples were distinct from other familial breast cancer cases without any sequence alteration in BRCA1 or BRCA2 , indicating that further work needs to be performed to determine the possible association of these “low clinical significance” sequence changes with a low to moderate risk of cancer .

oncology/breast cancer, genetics and genomics/gene function, genetics and genomics/cancer genetics, genetics and genomics/gene expression

journal.pgen.1005122

SERPINB11 Frameshift Variant Associated with Novel Hoof Specific Phenotype in Connemara Ponies

Many of the signaling molecules involved in patterning ectodermal derivatives , such as teeth and hair , are also involved in organizing mammalian distal limb appendages , including nails , claws and hooves 1 ., Perissodactyla ( odd-toed ungulates ) are a diverse group of mammals that include the horse , zebra and donkey ., These animals bear their weight almost entirely on the third toe ., In the modern horse , the non-weight bearing digits have atrophied while the third digit has enlarged ., The nail has also evolved to become fully interdigitated with the underlying soft tissue and to form a full weight bearing structure , the hoof ., As horses are prey animals , the development of hooves illustrates a major evolutionary innovation based on the need for rapid acceleration and sustained speed ., Since only the hooves touch the ground , the remaining portions of the foot have become parts of the limb , substantially increasing the length of stride ., Additionally , raising the heel and digits off the ground increased the number of joints that move the limbs forward and thereby increased the rate of stride ., Although these modifications substantially increase the potential speed and acceleration of these animals , extensive structural integrity of the hoof is required to support all of the body weight ., On average , adult horses and ponies weigh 1000 and 880 lbs , respectively ., At an evenly balanced stance when motionless , this places an average of 220–250 lbs of force on each limb ., When in motion , ground reaction forces increase at various phases of the gait , resulting in additional force applied to each hoof 2 ., Ectodermal dysplasias ( EDs ) are a heterogenous group of congenital disorders characterized by alterations in two or more ectodermal structures ( hair , teeth , nails or sweat glands ) 3 ., Genetic conditions that exclusively involve the nails are rarer and have been classified as nonsyndromic congenital nail disorders ( NDNC ) ., Currently , there are ten characterized NDNCs in people , of which four have associated genetic alterations ., Leukonychia ( NDNC3 ) , characterized by white discoloration of the nails , is caused by an alteration in PLCD1 4; Anonychia/hyponychia ( NDNC4 , absence of hypoplasia of nails ) due to an alteration in RSPO4 5; toenail dystrophy ( NDNC8 ) , caused by an alteration in COL7A1 6 and onychodystrophy ( NDNC10 ) , associated with an alteration in FZD6 7 ., There are currently six NDNCs for which a genetic alteration has not been identified , including isolated congenital onychodysplasia ( NDNC7 ) , a disease characterized by longitudinal streaks , thinning and splitting at the distal nail edge of all finger and toenails 8 ., This study describes an inherited disorder , termed Hoof Wall Separation Disease ( HWSD ) , characterized by separation and breaking of the dorsal hoof wall in the Connemara pony ., Without the integrity of the hoof wall , ponies cannot support their weight effectively ., The associated chronic inflammation often leads to laminitis , a debilitating condition characterized by separation of the third phalanx from the epidermal laminae that connect the bone to the dorsal hoof wall ., Chronic laminitic episodes in horses are very painful and often warrant euthanasia ., There are no known alterations that affect the hoof wall in horses and a comparative approach was not feasible due to the unique nature of the hoof ., Our goal was to identify the molecular etiology of the disease in order to reduce its prevalence through genetic testing and to provide insight into this unique ectodermal structure ., Genome-wide association analysis , coupled with whole genome next-generation sequencing , identified a frameshift variant in SERPINB11 associated with this novel , hoof specific phenotype in Connemara ponies ., SERPINB11 remains an uncharacterized protein in humans 9 , 10 and further investigation of the potential role of SERPINB11 in NDNCs may be warranted ., Two clinically-affected Connemara females were examined at 5-months and 1-year of age ., The onset of hoof pathology in these two index cases had become evident at 3 and 5-months of age , respectively ., With the exception of the hooves , physical examinations revealed no other abnormalities; haircoat , underlying skin , mucous membranes and mucocutaneous junctions appearing normal ., Abnormal sweating was not reported in either case ., In both cases , all four hooves displayed a dorsal hoof wall separation at the sole with a normal coronary band appearance ( Fig 1A ) ., Proliferative horn was evident on the solar aspect of all four hooves ( Fig 1B ) ., The 5-month old pony , which had markedly proliferative solar horn , was lame on both front feet at the walk while the yearling , which had undergone a recent hoof trimming , appeared sound at the walk ., Distal extremity radiographs of both front feet and the dental arcade revealed no abnormalities ., Hooves from three additional HWSD-affected female ponies ( aged 1 , 4 and 5 years ) underwent complete gross examination ., Age of onset in these three cases was less than 6-months of age ., In the three cases , all four hooves showed variable degrees of splitting within the dorsal hoof wall , most prominent along the distal margin and variably spreading more proximally ., The horn of the hoof wall was brittle and easily broken while the horn of the sole appeared stronger ., The coronary band appeared normal ., All four feet were bisected sagittally ., Coffin bone rotation , an indication of laminitis where the toe of the distal phalanx ( i . e . coffin bone ) has dropped due to loss of lamellae support , was evident in 2/3 ponies ( aged 4 and 5 years ) ., The white line was intact and there was no hyperaemia or scarring in the corium or lamina ., In the one HWSD pony ( 1-year of age ) with no evidence of coffin bone rotation , the distance of the white line to the horn capsule measured 1 cm and was consistent proximal to distal ., This horn to white line distance is within the normal radiographic reference range reported for adult ponies 11 ., The dorsal hoof wall separation was outside of the white line ( Fig 1C ) ., Histologic examination of coronary band ( Fig 1D ) , periople and proximal lamina , skeletal muscle and liver performed in one of three ponies ( 1-year of age ) revealed no pathologic changes ., A case-control allelic GWA was performed on the 51 , 453 SNPs that passed quality control ., Initial genomic inflation ( λ = 1 . 48 ) was reduced ( λ = 1 . 09 ) by eliminating seven unaffected outliers identified by multi-dimensional scaling ( Fig 2A and 2B ) ., Within this new sample set , there was a positive association between HWSD and a 1 . 7 Mb region on equine chromosome 8 ( Fig 2C and 2D ( S3 Table ) ) ; top SNVs at chr8: 80 , 772 , 490 and chr8:80 , 648 , 576 praw = 1 . 37x10 = 10 , pcorrected = 1 . 92x10-5 ) ., Ponies affected with HWSD had a distinct homozygous haplotype within the identified region; the same haplotype was not observed in control animals in the homozygous state ., SNP genotypes of the associated region are provided in S4 Table ., The homozygous region identified in affected ponies ( 79 , 936 , 024–81 , 676 , 900 bp ) , contains a family of SERPINB genes ., Based on the Equus caballus 2 . 0 genome assembly 12 , horses have three additional copies of SERPINB3/B4 within this interval as compared to humans ( Fig 3A ) ., The rest of the genes , orientations and order are conserved with respect to human ., Due to limited information regarding comparative diseases related to the SERPIN gene family , whole-genome next generation sequencing was performed instead of selectively sequencing predicted candidate genes ., Whole-genome sequencing was performed on two Connemara HWSD cases and two unaffected Connemara ponies that were homozygous reference for the associated haplotype ., A published Quarter horse whole-genome sequence was used as an additional control 13 ., Sequencing revealed a total of 9 , 758 single nucleotide variants ( SNVs ) within the interval , of which 363 segregated with HWSD in the two cases and three controls ( S5 Table ) ., Of these 363 SNVs , 16 were located within annotated genes or were fewer than 700 base pairs from the ATG ., Although the promoters for the genes are not identified in horses , we selected 700 base pairs in order to cover key regulatory regions close to the start of translation ., One additional SNV was chosen based on its location between SerpinB2 and SerpinB10 ., These gene-associated SNVs were selected for follow-up genotyping within a larger sample set ., In addition , sequencing identified 1 , 230 small insertions and deletions ( indels ) , of which 28 segregated with disease ( S6 Table ) ; 11 were within or close ( <700 bp ) to genes ., Of these 11 indels , 6 were intronic , 1 was coding , and 4 were up/downstream ., The coding and up/downstream indels were tagged for genotyping in a larger sample set ., Of the 22 variants ( 17 SNVs and 5 indels ) genotyped on the custom Sequenom panel , 21 passed quality control ., Three variants were unique to the 23 affected Connemara ponies and heterozygous in 27 obligate carriers ( Table 1 ) ., Although one indel failed Sequenom genotyping , we retained it in our analysis ( Table 1 ) ., None of these variants were identified in the 169 non-Connemara equines also genotyped on the Sequenom array , however 82 unaffected Connemara ponies were heterozygous for the three variants and 244 were wild-type ., On chromosome 8 , base pair 80 , 259 , 666 is located downstream of SERPINB2 and upstream of SERPINB10; 80 , 319 , 671 and the position of the four-base-pair deletion that failed quality control ( 80 , 319 , 673 ) are both within the rolling circle ( RC ) repeat element Helitron3Na_Mam located upstream of SERPINB8; 80 , 111 , 598 is in the fifth exon of SERPINB11 ( Fig 3 ) ., The insertion within SERPINB11 introduces a frameshift that first alters the two amino acids following residue 168 , and then introduces a premature STOP codon ., 55% of the protein is predicted to be truncated , including a large portion of the serpin scaffold and the entire reactive site loop 9 ., As potential regulatory variants were not identified in our analysis and in order to prioritize the four remaining candidate variants , expression analysis of SERPINB2 , SERPINB8 , SERPINB10 and SERPINB11 was performed ., These were the four genes closest to segregating SNVs and indels ( Fig 3 ) ., Evaluation of mRNA levels in coronary band samples from four HWSD-affected ponies and four unaffected controls revealed that coronary band SERPINB11 expression was significantly reduced in affected ponies ., Relative Expression Software Tool ( REST ) analysis indicated down-regulation ( 0 . 064; S . E . range 0 . 010–0 . 274 ) in the affected group by a mean factor of 16 and a probability that the difference between sample and control groups was due only to chance P ( H1 ) of <0 . 001 ( Fig 4A ) ., By contrast , REST showed no difference in expression of SERPINB2 , SERPINB8 , or SERPINB10 between the affected and unaffected sample groups ( Fig 4B ) ., SERPINB11 was also found to be a very abundant transcript in the coronary band of an unaffected horse , relative to its levels in other tissues ., RT-PCR showed gene expression in lung , stomach , skin , coronary band , and brain ., Levels were subjectively highest in the stomach and coronary band ., By contrast , SERPINB2 appeared most highly expressed in stomach and skin; SERPINB8 was widely expressed and most abundant in stomach and skin; SERPINB10 was minimally expressed in coronary band and most prominent in stomach and skin ( Fig 4C ) ., The SERPINB11 frameshift variant was found to be homozygous in a total of 31 affected ponies indicating complete penetrance ., The severity of phenotype ranged from mild ( cleft between dorsal hoof wall and white line apparent on solar aspect of hoof but the pony was able to be maintained with frequent hoof trimming and shoeing ) to severe ( Fig 1 ) ., Within the entire 423-Connemara-pony data set , allele frequency was 18 . 7% and a total of 96 ponies were heterozygous for the SERPINB11 insertion ., The heterozygous animals are all phenotypically unaffected by HWSD ., Within a 324-pony subset of individuals unrelated to the affected animals , carrier frequency for the variant is 14 . 8% ., Based on our clinical and histologic assessment , we have defined the phenotype of HWSD in the Connemara pony to include an early age of onset ( within the first 6-months of life ) and characteristic splitting and separation of the dorsal hoof wall ( Fig 1A ) ., Lesions are specific to the dorsal hoof wall and do not appear to involve any other ectodermal structures ., Laminitis may be a sequelae to HWSD ., Splitting of the dorsal hoof wall may be observed in inflammatory or infection conditions of the equine hoof , including white line disease or as a complication of laminitis or solar abscessation ., For this study , we characterized HWSD-affected ponies based upon the following criteria: ( 1 ) Connemara pony breed ( 2 ) age of onset within the first six months of life and ( 3 ) characteristic clinical signs , supported by digital photographs of all four feet ., Other diseases , including white line disease and abscessation , are associated with infection; the sole or white line is visibly discolored and unhealthy on examination ., With HWSD , the sole and white line appear healthy , with the exception of proliferative sole in the animal’s effort to support the limb on a structure other than the dorsal hoof wall ., The Connemara pony originated in the County Galway of western Ireland and the breed standard is characterized by hard strong hooves 14 ., These ponies are often unshod when used for performance activities ( jumping , dressage , driving ) , evidence of a strong and healthy hoof within the breed ., Based on the breed standard , this makes the phenotype of HWSD all the more striking in affected individuals ., The age for the control ponies within this study was set at >2 years ., We have not observed any reports of an older age of onset of HWSD; most ponies are affected within the first 6 months of life ., In the 23 ponies positive for the identified SERPINB11 variant , all demonstrated an abnormal dorsal hoof wall ., However , variable expressivity was apparent in HWSD-affected cases ., Mild HWSD cases demonstrated evidence of the dorsal hoof wall separation on the solar aspect of the hoof without severe splitting evident on a lateral view ., In more severely affected cases ( Fig 1 ) , the dorsal hoof wall separation was readily apparent ., We did not identify any potential genetic modifiers that could account for the variability in phenotype ., Owners of HWSD-affected ponies often notice that the splitting of the distal hoof wall worsens when the environmental conditions change from dry to wet or vice versa ., Management typically consists of frequent trimming of hooves and , in some cases , glue-on shoes as the dorsal hoof wall of most HWSD-affected ponies will split if nails are used ., The dorsal hoof wall is composed of keratins , which provide strength , hardness and insolubility due to disulfide bonds between and within the long chain fibrous molecules 15 ., There are dozens of different keratin molecules , with molecular weights in the range of 40–70 kDa and varying degrees of hardness and sulfur concentration 16 ., Terminally differentiated keratinocytes , originating from the coronary band , are arranged in specialized tubular and intertubular configurations in four distinct zones 17 ., The gradient in tubule density mirrors the gradient in water content across the hoof wall , with the innermost layers of the hoof having the highest relative water content , which confers high crack resistance 18 ., In healthy horses , by the time the shock of the impact with the ground reaches the first phalanx , about 90% of the energy has been dissipated , mainly at the innermost hoof wall layer ( i . e . the lamellar interface ) 19 ., Distal hoof wall splitting does not , in it of itself , result in lameness ., Rather , repeated stress on the innermost layer of the lamellar interface results in separation of the interdigitating lamellae from the distal phalanx ( i . e . laminitis ) ., As laminitis progresses , radiographs of the distal phalanx may reveal the separation of the dorsal hoof wall from the distal phalanx ., Although radiographs were unremarkable in the examined 5-month old filly , the lameness was most likely due to lamellar inflammation that had not yet progressed to full separation ., The original population of ponies used in this study was stratified , which was markedly improved by removing seven control ponies visualized as outliers on the multidimensional scaling plot ( Fig 2A ) ., Alternatively , a mixed model approach could have been utilized to correct for the level of stratification; however , removal of the seven control ponies did not affect power to detect a significant association on ECA8 ., The associated region on ECA8 encompassed ~1 . 7 Mb , which contained 13 annotated SERPBINB genes ., None of these genes have been previously associated with any of the human EDs nor was there any supporting evidence documenting expression of these genes in any ectodermal structures ., Targeted re-sequencing of the ~1 . 7 Mb candidate region was considered; however , it has become more cost effective to perform whole-genome sequencing , when an autosomal recessive mode of inheritance is suspected ., With Mendelian disorders , putative functional variants may more readily be uncovered on smaller sample sets using whole genome sequencing whereas if a more complex mode of inheritance was likely , capture re-sequencing could allow for many individuals to be sequenced for the particular region of interest at a relatively comparable cost 20 ., Sequencing revealed 363 SNVs and 28 indels that segregated with HWSD , with 17 and 11 , respectively , located within or adjacent to annotated genes ., After genotyping a larger population of HWSD-affected and unaffected equids , four segregating variants remained ( Table 1 ) , including a 4-bp deletion that had failed Sequenom genotyping ., Of these four variants , only one was coding ., The insertion within SERPINB11 introduced a frameshift , leading to a premature stop codon ., Based upon the severity of the HWSD phenotype , priority was placed on coding variants as we presumed the variant would alter the amino acid structure of the protein involved ., In addition , q-RT-PCR data demonstrated decreased expression of SERPINB11 in coronary band tissue , where keratinocytes of dorsal hoof wall originate ., The decision to focus on the one coding variant for HWSD was validated in this study; however , we acknowledge that non-coding variants have been increasingly associated with disease 21 ., The three other HWSD-segregating variants may be a part of the haplotype on which the frameshift variant originated ., Across species , serpins represent that largest and most functionally diverse family of serine protease inhibitors ., Some serpins exhibit alternative functions , such as hormone transport and blood pressure regulation 22 ., Serpins have been classified into clades according to their sequence similarity ., Clades are classified as A-P , with clades A-I representing human serpins 22 ., Serpins have a highly conserved secondary structure , with three β-sheets ( A , B and C ) , nine α-helices and a reactive center loop ( RCL ) , which serve as bait for the target proteases ., The tertiary structure allows for a conformational change critical to protease inhibitor activity 22 ., Serpins exist as monomeric proteins in their native state , which is defined by an exposed RCL that allows it to interact with the protease ., Serpins can transition back and forth between latent and active forms 10 ., The SERPINB clade is considered the ovalbumin or ov-serpin clade , based on their high sequence similarity to chicken ovalbumin , and exist intracellularly 23 ., In humans , clusters of genes on HSA6 and 18 have evolved from a common ancestor by one or two interchromosomal duplications with several intrachromosomal duplications 24 ., A similar clustering is evident in the horse , with the clusters of SERPINB genes located on ECA8 and 20 12 ., Of the clade B serpins , only human SERPINB11 and mouse Serpinb11 have yet to be characterized 9 , 10 ., Current evidence suggests that , in mice , Serpinb11 can function as a trypsin inhibitor yet SERPINB11 has lost inhibitory activity in humans and may have evolved a non-inhibitory function 9 ., The inhibitor function of SERPINB11 has not been assessed in the horse ., In the chicken , there is no orthologue for human SERPINB11 , suggesting that these genes were either lost in the chicken or arose after the merging of avian and mammalian lineages 25 ., Based on these limited studies of SERPINB11 , it may be that there exist different roles , including varying abilities to function as protease inhibitors , of SERPINB11 between species ., Most research efforts into the characterization of major structural proteins of the equine hoof wall are targeted at the lamellae , as this is the site of biomechanical failure in equine laminitis ., One study focused on characterizing proteins of the equine hoof wall that included the laminar epithelium ( i . e . stratum internum ) , outer highly cornified hoof wall ( stratrum medium ) and coronary band epithelium 26 ., From this investigation , it was evident that the keratin types within the coronary band epithelium were highly similar to those found in the stratum medium ., Additionally , the high-sulfur and high-tyrosine protein components were rich in cysteine only in the stratum medium ., Experimental studies from the same laboratory have determined that 35S-cysteine is preferentially taken up into the terminally differentiating hoof wall layers 26 ., These cysteine-rich proteins are thought to contribute to stabilization of the interfibrillar matrix of the stratum medium through disulfide bonding ., If SERPBINB11 retains cysteine peptidase inhibitory activity in the horse , as it does in the mouse 9 , it may function to inhibit proteolytic cleavage of cysteine residues in the terminally differentiating hoof wall layer ., A loss of SERPINB11 function could therefore result in a loss of peptidase inhibition and structural failure of the cysteine-rich hoof wall upon impact ., Alternatively , SERPINB11 may play a specific role in relative keratinocyte proportions in the equine hoof wall , thereby weakening the overall structure and allowing it to fragment at the most distal end ., Although keratinocytes may appear histologically intact at the level of the coronary band , the most distal aspect ( i . e . most mature portion of the cell that is undergoing the majority of concussive impact ) may be structurally abnormal ., The distal dorsal hoof wall is difficult to evaluate histologically unless tissue is embedded in acrylic , which was not performed in this study ., Further histologic investigation of the entire population of mature keratinocytes within the dorsal hoof wall of HWSD-affected ponies is warranted ., A bacterial artificial chromosome transgene expressing Cre under the control of Serpinb7 regulatory elements was recently developed 27 ., Although the original aim of the study was to evaluation the expression in kidney mesangial cells , the authors discovered that the Serpinb7-Cre transgene mediated loxP-recombination in all epidermal layers of the skin , hair follicle cells and the epithelium of the mouse forestomach and esophagus ., The transgene colocalized with Keratin10 and Keratin14 in the suprabasal and basal layer of the epidermis , respectively 27 ., Similar to these expression patterns , the expression of SERPINB11 in our study was highest in the coronary band and stomach , with expression also detected in skin ., In humans , SERBPINB11 is located on chromosome 18q21 . 33 ., To date , there have been no reports of naturally occurring alterations of clade B serpins leading to a disease phenotype in humans ., The only disease association of SERPINB11 is with endometroid ovary carcinoma 28 ., Of the six characterized NDNCs that do not have an associated genetic alteration to date , none map to this region , including isolated congenital onychodysplasia ( NDNC7 ) , which phenotypically resembles HWSD with thinning and splitting at the distal nail edge of all finger and toenails 8 ., Of interest , SERPINB11 was identified as a potential candidate gene for adaptive evolution in Yoruba 29 ., Another mechanistic alternative is that SERPINB11 functions as member of a protein chaperoning complex , similar to the role of SERPINF1 and SERPINH1 in association with procollagen ., A loss-of-function alteration in SERPINF1 has been demonstrated to cause osteogenesis imperfecta type VI in humans 30 while variants in SERPINH1 have been associated with ostogenesis imperfecta in both humans 31 and dogs 32 ., In a similar manner , SERPINB11 may have a role as a chaperone protein for those proteins involved in hoof wall structure ., In the horse , we identified one full-length transcript variant from hoof capsule ., In humans , there have been seven major SERPINB11 transcripts identified; three correspond to a protein product with various splice variants and one contains an insertion , leading to a frameshift and premature stop codon at position 90 that results in a nonfunctional variant ( NP_001278207 ) 9 ., There are no deleterious effects of this truncated transcript reported in humans ., However , based on the results from this study , further investigation into the potential role of the truncated transcript in nail health may be warranted ., Tissue expression of SERPINB11 in humans demonstrates expression in the tonsil , lung , placenta and prostate while Serpinb11 demonstrates expanded expression in mice with transcripts identified in eye , lung , lymphocytes , thymus , stomach , uterus , heart , brain , liver , skeletal muscle and whole embryonic tissue at day 7 , 15 and 17 9 ., Tissue expression of SERPINB11 in the horse was similar to mouse and included lung , stomach and brain ( Fig 4A ) ., However , expression of SERPINB11 in the horse was strongest in the coronary band , or most proximal part of the hoof capsule ., The coronary band is the tissue from which the dorsal hoof wall arises and is analogous to a human cuticle ., To the authors’ knowledge , expression of SERPINB11 in skin and nail tissue has not been examined in humans and mice ., The results of this study demonstrate a strong association of the SERPINB11 c . 504_505insC variant with the HWSD phenotype ., Additionally , HWSD is the first disease to be described that results in a hoof-specific phenotype , with no other ectodermal structures affected ., Further studies are necessary to determine the mechanism by which SERPINB11 maintains structural integrity of the hoof wall of healthy ungulates and if SERPINB11 plays a similar role in nail and claw health of non-ungulate species ., Blood samples from index cases were collected at the University of California , Davis School of Veterinary Medicine William R . Pritchard Veterinary Medical Teaching Hospital ., Additional samples were drawn by private veterinarians and mailed by individual Connemara owners ., All animal samples were obtained following protocol number 17491 approved by the University of California Davis Institutional Animal care and Use Committee ., Two affected Connemara ponies were evaluated at the University of California , Davis ( UCD ) Veterinary Medical Teaching Hospital in 2011 by a board-certified equine internist ( CF ) ., Following humane euthanasia , hooves from three additional affected ponies were evaluated by a board-certified pathologist ( VKA ) ., Distal extremity radiographs were available from the five index cases ., From these index cases and the histologic assessment of affected hooves , the phenotype of HWSD was established ., For additional cases , inclusion criteria as an HWSD case consisted of ( 1 ) Connemara pony breed ( 2 ) age of onset within the first six months of life and ( 3 ) clinical signs consistent with a receding dorsal hoof wall and secondary solar proliferation , supported by digital photographs of all four feet ., Inclusion criteria for unaffected animals in the genome wide association study consisted of ( 1 ) Connemara pony breed ( 2 ) >2 years of age ( 3 ) no apparent hoof pathology , supported by digital photographs of all four feet when available ., Distal extremity radiographs of affected horses were evaluated , when available ., Control animals used for genotyping of putative functional variants were reported by their owners to be >2 years of age and had no apparent hoof pathology ., DNA was collected and purified from all horses ( Gentra Puregene blood kit , Quiagen , Valencia , CA ) ., Hooves from four affected Connemara ponies 3 female ( aged 2 . 5 , 3 , and 4 . 5 years ) and 1 male ( 6 months ) , including three of the index cases , were available for RNA purification ., Four unaffected 2 female ( 1 . 5 years ) , 2 male ( 1 and 5 years ) horses were euthanized for reasons unrelated to this study and hooves collected as controls ., All tissue samples were flash frozen in liquid nitrogen and stored at -80°C until RNA isolation ., RNA was isolated ( RNeasy Fibrous Tissue Mini Kit , Quiagen , Valencia , CA ) and cDNA synthesized ( QuantiTect Reverse Transcription Kit , QIAGEN , Valencia , CA ) ., Negative reverse transcriptase controls were made simultaneously and the final products were assessed with the housekeeping gene , ACTB , as previously described 33 ., 15 affected ( 4 male , 11 female ) and 24 unaffected ( 7 male , 17 female ) Connemara ponies were genotyped on the Illumina SNP70 Genotyping Beadchip ( Illumina , San Diego , CA ) ., Quality control was implemented using PLINK ( Purcell et al 2007 ) and Single Nucleotide Polymorphisms ( SNPs; based on EquCab2 . 0 ) were excluded with a minor allele frequency ( MAF ) <5% or genotyping call rate <90% ., A case-control standard allelic genome-wide association ( GWA ) was performed using PLINK ., Population stratification was determined using genomic inflation values in PLINK and visualized using multi-dimensional scaling ( MDS ) and quantile-quantile ( Q-Q ) plots ., After removing seven control animals based upon the MDS plot , a repeat case-control standard allelic GWA analysis was performed with the remaining 15 affected and 17 unaffected ( 4 male , 13 female ) animals ., To correct for multiple testing , 52 , 000 permutations were performed ., Manhattan plots and QQ plots were generated using the ggplot2 package 34 implemented in R v . 3 . 1 . 1 35 ., Sequence data was generated using an Illumina HiSeq ., Library preparation and sequencing was performed by the UC Davis Genome Center ., Average library insert size was 300 bases ., Four samples ( 2 affected 1 female , 1 male and 2 unaffected 1 female , 1 male ) were bar-coded and pooled and 2 lanes of sequence of 100bp paired-end reads were obtained ., An average of 79 . 2M reads were obtained per sample ., Average read length after trimming was 98 . 25bp , resulting in 5 . 4-6X coverage for each of the four horses ., Sequence data was processed on a 2U custom built rack server with 256GB DDR3 memory , 2 x Xeon ( R ) E5-2690 Eight-Core Processor ( 32 virtual cores ) , 22TB HD and Ubuntu 12 . 04 . 2 operating system ., Read quality was assessed using qrqc ( version 1 . 9 . 1 ) 36 , while Scythe ( version 0 . 990 ) 37 and Sickle ( version 1 . 20 ) 38 were used for Illumina adapter & quality trimming ., The Burrows-Wheeler Aligner ( BWA version 0 . 6 . 2-r126 ) 39 was used to align reads to the horse genome ( UCSC assembly ID: equCab2 ) ., Sequenced reads from an American Quarter Horse 13 were downloaded and used as an additional control ., To call variants , the Genome Analysis Toolkit ( GATK

Introduction, Results, Discussion, Materials and Methods

Horses belong to the order Perissodactyla and bear the majority of their weight on their third toe; therefore , tremendous force is applied to each hoof ., An inherited disease characterized by a phenotype restricted to the dorsal hoof wall was identified in the Connemara pony ., Hoof wall separation disease ( HWSD ) manifests clinically as separation of the dorsal hoof wall along the weight-bearing surface of the hoof during the first year of life ., Parents of affected ponies appeared clinically normal , suggesting an autosomal recessive mode of inheritance ., A case-control allelic genome wide association analysis was performed ( ncases = 15 , ncontrols = 24 ) ., Population stratification ( λ = 1 . 48 ) was successfully improved by removing outliers ( ncontrols = 7 ) identified on a multidimensional scaling plot ., A genome-wide significant association was detected on chromosome 8 ( praw = 1 . 37x10-10 , pgenome = 1 . 92x10-5 ) ., A homozygous region identified in affected ponies spanned from 79 , 936 , 024-81 , 676 , 900 bp and contained a family of 13 annotated SERPINB genes ., Whole genome next-generation sequencing at 6x coverage of two cases and two controls revealed 9 , 758 SNVs and 1 , 230 indels within the ~1 . 7-Mb haplotype , of which 17 and 5 , respectively , segregated with the disease and were located within or adjacent to genes ., Additional genotyping of these 22 putative functional variants in 369 Connemara ponies ( ncases = 23 , ncontrols = 346 ) and 169 horses of other breeds revealed segregation of three putative variants adjacent or within four SERPIN genes ., Two of the variants were non-coding and one was an insertion within SERPINB11 that introduced a frameshift resulting in a premature stop codon ., Evaluation of mRNA levels at the proximal hoof capsule ( ncases = 4 , ncontrols = 4 ) revealed that SERPINB11 expression was significantly reduced in affected ponies ( p<0 . 001 ) ., Carrier frequency was estimated at 14 . 8% ., This study describes the first genetic variant associated with a hoof wall specific phenotype and suggests a role of SERPINB11 in maintaining hoof wall structure .

Inherited diseases affecting only the nails in humans are rare; however , humans do not support themselves entirely on one appendage ., Horses bear their entire weight on their third toe , resulting in a large amount of force on each hoof ., An inherited disease characterized by a phenotype restricted to separation and breaking of the dorsal hoof wall was identified in a specific breed of pony , the Connemara ., This disease has been termed hoof wall separation disease ( HWSD ) ., Parents of affected ponies appeared clinically normal , suggesting an autosomal recessive mode of inheritance ., A genome-wide association analysis identified a region associated with HWSD which was further assessed through whole genome next-generation sequencing and genotyping of potential variants ., Here , we present the discovery of a frameshift variant , leading to a premature stop codon in SERPINB11 of HWSD-affected ponies ., Significantly decreased expression of the SERPINB11 transcript was identified in the hoof capsule of HWSD-affected ponies ., This study describes the first genetic variant associated with a hoof wall specific phenotype and suggests a role of SERPINB11 in maintaining hoof wall structure .

journal.pgen.1005137

HOMER2, a Stereociliary Scaffolding Protein, Is Essential for Normal Hearing in Humans and Mice

Targeted genomic enrichment and massively parallel sequencing ( TGE+MPS ) have revolutionized the field of hereditary hearing loss ( HHL ) by making comprehensive genetic testing for deafness a clinical reality and by accelerating the discovery of novel deafness-causing genes 1 , 2 ., To date over 80 genes have been causally implicated in non-syndromic hearing loss ( NSHL; Hereditary Hearing Loss Homepage ) ., The proteins encoded by these genes are involved in a broad array of molecular and cellular mechanisms essential for the development and maintenance of normal auditory function 3 ., At the centerpiece of this intricate system are the outer and inner hair cells ( OHCs , IHCs ) —key structures in the mechanotransduction process by which mechanical stimuli are translated into electrical impulses 4 ., The precise and efficient tuning of OHCs and IHCs is closely linked to their anatomical integrity and the coordinated movement of their apical stereocilia ., The Homer proteins are scaffolding proteins crucial to many intracellular signaling cascades; their function underpins a variety of neuronal processes ranging from calcium homeostasis and cytoskeletal organization to synaptic plasticity associated with learning and memory 5 , 6 ., Homer proteins are encoded by three genes; HOMER1 , 2 , and 3 ( MIM 604798 , MIM 604799 and MIM 604800 , respectively ) that are translated into multiple isoforms as a result of alternative splicing 7 ., All isoforms share an N-terminal conserved EVH1 ( Ena VASP Homology 1 ) domain , which binds proline-rich regions of target proteins ., The long isoforms ( HOMER1b and c , HOMER2 , HOMER3 ) additionally have a coiled-coil ( CC ) region and leucine zipper motifs in their divergent C-termini 8 ., The CC region is required for homo/hetero-multimerization to form tetrameric hubs ( in which the CC domains align in a parallel fashion ) and for interaction with Rho family GTPase proteins like CDC42 ( MIM 116952 ) 9–11 ., Although the short isoforms lack CC domains and therefore do not form multimers , like their longer counterparts they bind target proteins through their EVH1 domain ., This interaction regulates the activity of proteins involved in Ca2+ signaling complexes including metabotropic glutamate receptors ( mGluR ) 12 , inositol tri-phosphate receptors ( IP3R ) 8 and transient receptor potential canonical channels ( TRPC ) 13 ., Homer proteins also regulate basal cytosolic calcium via an interaction with the plasma membrane calcium reuptake pump , PMCA 14 , 15 ., Aberrant Homer signaling has been associated with several developmentally related neurological syndromes including Fragile X syndrome , epilepsy , addiction , schizophrenia , neuropathic pain and Alzheimer’s disease 16–19 ., To this list we now add autosomal dominant NSHL ( ADNSHL ) ., The family segregating the deafness-causing mutation in HOMER2 ( MIM 604799; RefSeq NM_004839 ) is a multi-generational kindred of European descent ( Fig 1A ) ., Pure tone audiometric evaluation of affected members showed bilateral post-lingual progressive hearing loss that segregated as an autosomal dominant trait; bone conduction thresholds excluded conductive hearing impairment ., Clinical examination was negative for any findings consistent with syndromic hearing loss and also ruled out autoimmune phenotypes ., Hearing impairment had a typical onset in the first decade of life in the high frequencies , with significant subsequent progression of hearing loss over all frequencies ., To evaluate progression at each frequency , we performed linear regression analyses of threshold on age 20 ., The resulting annual threshold deterioration ( ATD ) was 1 . 2 to 1 . 6 dB per year ( Fig 1B , S1 Fig ) ., The age-related typical audiogram ( ARTA ) derived from these data confirmed the down-sloping audiometric configuration and demonstrated fairly similar progression across all frequencies ., Our initial strategy was to screen one family member ( III . 10 ) for pathogenic variants in known deafness-causing genes using a deafness-specific TGE+MPS panel ( OtoSCOPE ) 21 ., Plausible pathogenic variants were excluded ( S1 Table ) ., Whole exome sequencing ( WES ) was therefore completed on three affected family members ( II . 2 , IV . 5 , and IV . 10 ) and variants were filtered according to guidelines detailed in Materials and Methods section 22 ., The resultant final candidate variant lists included 163 , 150 and 158 nucleotide changes for these three individuals , respectively ( S1 Table ) ., Of these variants , only four were shared amongst the three sequenced exomes ( S2 Table ) and only one segregated with the ADNSHL phenotype in the extended family ( Fig 1A and 1C ) ., The segregating variant , c . 554G>C; p . Arg185Pro in HOMER2 on chromosome 15q25 . 2 , is a novel non-synonymous change that substitutes a highly conserved arginine for a proline , a substitution that is predicted to be pathogenic and disease-causing by Polyphen2 , LRT , SIFT and MutationTaster ( S3 Table ) ., HOMER2 belongs to the homer family of post-synaptic density scaffolding proteins and is expressed as two isoforms , HOMER2 isoform 1 ( NM_004839 , 343 aa ) and isoform 2 ( NM_199330 . 2 , 354 aa ) , which differ by 11 amino acids ., The p . Arg185Pro variant lies in the CC domain ( Fig 1D ) ., A lysine is found at the orthologous position in HOMER1 and HOMER3 , which like arginine is a basic polar amino acid that confers similar chemico-physical properties to the protein ., Proline , in contrast , is non-polar and is predicted to alter the conformational structure of the CC domain or affect its ability to multimerize and/or interact with partner proteins ., Homer2 is extensively expressed in the CNS throughout development 23 ., It is also expressed in skeletal muscle , heart , liver , spleen , lung and kidney ., To investigate and define its expression pattern in inner ear , we immuno-labeled whole mount P2 mouse cochlea with HOMER2 antibody ., Organ of Corti expression was particularly enriched in the tips of stereocilia of both IHCs and OHCs ( Fig 2 ) , consistent with data by Hertzano and colleagues using cell sorting and RNASeq to identify Homer2 enrichment in the sensory cells of P0–P1 mice 24 ., This expression pattern suggests a role for HOMER2 in hair bundle function , formation , development or maintenance ., To evaluate whether the p . Arg185Pro mutation affects subcellular localization , we transfected HEK293T and COS7 cells with cMYC-tagged HOMER2WT proteins or FLAG-tagged HOMER2p . Arg185Pro ., Both wild-type ( WT ) and mutant proteins distributed in a diffuse manner in the cytoplasm with no obvious differences in localization patterns ( S2 Fig ) ., A similar pattern of distribution was observed when both WT and mutant constructs were cotransfected ., These data indicate that the p . Arg185Pro mutation does not alter subcellular localization of HOMER2 ., To assess the impact of the p . Arg185Pro mutation in HOMER2 in vivo we used the zebrafish model ., Zebrafish hair cells share similarities with their mammalian counterparts in morphology and function ., They reside inside the otic vesicles and in the neuromasts of the lateral line system , a sensory system on the surface of fish important for sensing propulsion through water , capturing prey , or avoiding predators and obstacles ., The zebrafish homer2 ( NP_001018470 . 1 ) is 67% identical to human HOMER2 and is expressed mostly in the developing musculature although there is faint expression in the otic capsule at 24 hours post-fertilization ( hpf ) 25 ., We used morpholino antisense oligonucleotides ( S4 Table ) to induce altered splicing and protein truncation ., Although knockdown of homer2 altered neither ear size nor morphology ( p>0 . 5 ) ( S3 Fig ) , injection of in vitro synthesized mRNA encoding HOMER2 P185-mutant RNA ( P185RNA ) resulted in significantly smaller ear size in larvae as compared to injections with WT HOMER2 ( wtRNA ) ( p<0 . 001 ) ( Fig 3A i-iii and 3B; S4 Fig ) ., In addition , the number of kinocilia detected per neuromast was decreased ( p = 0 . 03 ) ( Fig 3C i-iii and 3D ) ., We also noted that P185RNA-injected embryos exhibited shorter kinocilia , an observation that needs to be confirmed with more detailed methods ., These results show that HOMER2 plays an essential role in the normal development and/or maintenance of hair cells in the zebrafish inner ear and that the p . Arg185Pro mutation has a dominant-negative effect on this process ., Dominant-negative activity has been previously validated in vivo for another homer protein , HOMER1 ., Its long isoforms , HOMER1b and 1c , are constitutively expressed while the short isoform , HOMER1a is an immediate early gene product that is rapidly and transiently induced by high synaptic activity 26 ., Because HOMER1a lacks the CC domain , it cannot form multimers but it can still competitively bind the target proteins of HOMER1b and 1c , suggesting that relative expression of each of these homer proteins is critical 27–29 ., The importance of this dynamic balance has been validated by the observation that overexpression of Homer1a in a mouse impedes normal development and through a dominant-negative effect leads to significant defects in motor coordination and learning , with increased levels of fear-associated behavior and anxiety 30 ., The role of Homer2 has been thoroughly studied in the murine brain and pancreas where it functions to decrease the intensity of Ca2+ signaling by reducing signaling by G protein-coupled receptors ( GPCRs ) 31 ., Mice homozygous for the targeted deletion of homer2 ( Homer2-/- mice generated by deletion of exon 3 32 ) show extensive behavioral and neurochemical similarities to cocaine or alcohol-sensitized animals , and demonstrate Homer2 involvement in appetitive pathways underlying responses to those drugs and their induced behavioral/cellular neuroplasticity within the nucleus accumbens 33 , 34 ., Studies in Homer2-/- Homer3-/- mice show upregulation of cytokine expression and an increase in effector-memory T cells leading to an autoimmune-like pathology , indicating that Homer2 negatively regulates T cell activation 35 ., To evaluate cochlear function of Homer2-/- mice , we measured auditory brainstem responses ( ABR ) , an electrophysiological hearing test that reflects the activity of afferent auditory neurons downstream of IHCs ., We examined hearing in 2- , 4- and 8-week-old Homer2-/- , Homer2+/- and WT Homer2+/+ mice and observed no differences between age-matched WT and Homer2+/- animals ( P>0 . 2 ) , however Homer2-/- mice showed progressive hearing loss ( Fig 4 ) ., At P14 Homer2-/- mice had slightly elevated broad band click ABR thresholds as compared to Homer2+/- mice Homer2-/- ( n = 11 ) ; 67 . 3±2 . 56 verse Homer2+/- ( n = 17 ) ; 61 . 20±2 . 50 db SPL ., This difference progressively increased ( P28 Homer2-/- ( n = 10 ) ; 82 . 0±5 . 40 db SPL vs Homer2+/- ( n = 6 ) ; 59 . 20±2 . 71 db SPL; P56 Homer2-/- ( n = 5 ) ; 100 . 0±0 . 0 db SPL vs Homer2+/- ( n = 9 ) ; 55 . 6±1 . 76 db SPL ) ( Fig 4A ) ., Tone bursts showed that P14 Homer2-/- mice had profound deafness at 32 kHz and that the rate of hearing deterioration at 8 kHz was dramatic ( P14 , 55 . 5±2 . 28 dbSPL; P28 , 73 . 0±6 . 84; P56 , 87 . 0±1 . 22 dbSPL ) ( Fig 4B-4D ) ., We assessed OHC function using distortion product otoacoustic emissions ( DPOAEs ) and observed no significant differences in DPOAE thresholds between Homer2+/- and WT mice ( P>0 . 05 ) ., In Homer2-/- mice , significant decreases in mid- to high-frequency DPOAE levels were seen at P14 and P28 that culminated in profound deafness at all frequencies by P56 ( Fig 4E-4G ) ., To determine whether the auditory deficit in Homer2-/- mice was secondary to hair cell loss we analyzed whole mount preparations of the organ of Corti at P56 ., No differences in IHCs and OHCs were observed in any animals regardless of genotype indicating the absence of Homer2 does not impair hair cell formation and development , and that the hearing loss in Homer2-/- mice is not due to hair cell death ( Fig 4H ) ., Whether the hearing deficit in these mice is due to abnormal hair bundle morphology remains to be elucidated ., We additionally investigated spiral ganglion morphology and found no obvious indication of spiral ganglion degeneration in Homer2-/- mice as compared to their WT littermates ( S5 Fig ) ., In aggregate , our data show that HOMER2 is required for normal hearing ., Its complete absence in mice leads to early onset progressive hearing loss starting at the high frequencies and rapidly involving all frequencies ., The recessive phenotype exhibited by null alleles of Homer2 makes it a strong candidate for autosomal recessive hearing loss due to loss of function in humans as well ., The absence of a disease phenotype in Homer2+/- mice suggests that haploinsufficiency does not cause hearing loss ., Together with results obtained from zebrafish experiments , these data strongly suggest that the p . Arg185Pro mutation in HOMER2 exerts its effect through a dominant-negative mechanism on wild-type protein by either inhibiting multimerization or competing for other partner proteins ., In defining the effect of the p . Arg185Pro mutation in HOMER2 at a molecular level , we believe two hypotheses warrant consideration ., One hypothesis posits that HOMER2 exerts its function by regulating actin dynamics in stereocilia through its interaction with CDC42 , a highly conserved small GTPase of the RHO family that fine-tunes actin-turnover ( S6 Fig ) ., HOMER2 is known to couple with and regulate CDC42 through its CDC42-binding domain ( CBD ) within the CC domain ( Fig 1D ) 10 ., In HeLa cells , CDC42 induces the formation of filopodia-like protrusions , while overexpression of HOMER2 suppresses this phenotype 36 ., In the cochlea , CDC42 localizes to stereocilia membranes and its targeted deletion in murine HCs leads to their degeneration and results in progressive hearing loss particularly at the high frequencies , a phenotype similar to the human HOMER2 mutant phenotype and the Homer2-/- murine phenotype 36 ., A second hypothesis focuses on the role of HOMER2 in cytoplasmic Ca2+ control ., Several studies have shown that HOMER2 regulates a number of Ca2+ handling proteins including TRPC and PMCA channels ., Two TRPCs—TRPC3 ( MIM 602345 ) and TRPC6 ( MIM 603652 ) —are expressed in both sensory neurons and cochlear hair cells and are required for normal function ., Their targeted deletion in mice causes significant dysregulation of Ca2+ re-entry that leads to hearing impairment and vestibular deficits 37 ., While both proteins are potentially interacting partners for HOMER2 , to date only an interaction with TRPC1 ( MIM 602343 ) has been established 13 ., However , a recent study has demonstrated a critical role for Homer2 in modulating PMCA activity by regulating the duration of the Ca2+ signaling in parotid acinar cells 38 ., This finding suggests a possible role for Homer2 in cytosolic Ca2+ clearance to balance TRPC-mediated Ca2+ influx and PMCA-mediated Ca2+ extrusion ., A suitable interacting partner of HOMER2 may be the PMCA2 pump ( MIM 108733 ) , which represents the only system for clearance of Ca2+ from hair cell stereocilia 39 ., While both of these hypotheses are attractive , further functional studies are needed to identify the partner proteins of HOMER2 in inner ear and investigate the effect of the p . Arg185Pro mutation on these interactions ., In summary , we have identified HOMER2 as essential to normal auditory function and have shown that the p . Arg185Pro HOMER2 mutation causes ADNSHL through a dominant-negative mechanism of action , thus expanding the phenotypic spectrum associated with Homer protein dysfunction ., A five-generation family of European descent segregating bilateral post-lingual progressive ADNSHL was ascertained for this study ( Fig 1A ) ., After obtaining written informed consent from all participants with approval by the Institutional Review Board of the University of Iowa , clinical examination of the subjects was completed to exclude any additional and/or syndromic findings ., Blood samples were obtained from 19 family members ., Pure tone audiometry was performed according to current standards to determine air conduction thresholds at 0 . 25 , 0 . 5 , 1 , 2 , 3 , 4 , 6 and 8 kHz ., Bone conduction thresholds were determined at 0 . 5 , 1 , 2 and 4 kHz in some patient to exclude conductive hearing impairment ., After validating binaural symmetry , the binaural mean air conduction threshold ( dB Hearing Level , HL ) at each frequency was used for further analyses ., An arbitrary value of 130 dB HL was used to indicate out-of-scale measurements ., Linear regression analyses of threshold on age were used to evaluate progression of hearing impairment at individual frequencies ., These analyses comprised both individual longitudinal data derived from serial audiograms and overall cross-sectional last-visit data ., Progression was considered significant if the 95% confidence interval for slope did not include zero for two or more frequencies ., Progression was expressed in dB-per-year and designated Annual Threshold Deterioration ( ATD ) ., Cross-sectional regression data conformed to individual longitudinal regression data ., Regression data from the last-visit thresholds were used to derive Age-Related Typical Audiograms ( ARTA ) , which show expected thresholds by decade steps in age 20 ., OtoSCOPE v1 was used to evaluate all known genetic causes of NSHL ( including the non-syndromic mimics like Usher Syndrome ) in one affected individual ( III . 10 ) , as previously described 21 , 40 ., Whole exome capture was performed with the Agilent SureSelectXT Human All Exon V4 ( Agilent Technologies , Santa Clara , CA ) according to the manufacturer’s protocol ., All enriched libraries were sequenced on the Illumina HiSeq 2000 ( Illumina , Inc . , San Diego , CA ) using 100bp paired-end reads ., Data analysis was performed on a local installation of Galaxy using the Burrows-Wheeler Alignment ( BWA ) for read mapping to the reference genome ( hg19 , NCBI Build 37 ) , Picard for removal of duplicate reads , GATK for local re-alignment and variant calling , and ANNOVAR and a custom workflow for variant annotation ., Variant filtering was based on: quality ( >10X ) ; minor allele frequency ( MAF<0 . 0005 ) as reported in the 1000 Genomes Project database and the National Heart , Lung , and Blood Institute ( NHLBI ) Exome Sequencing Project Exome Variant Server ( EVS ) ., Variants were annotated for conservation ( GERP and PhyloP ) and predicted pathogenicity ( PolyPhen2 , SIFT , MutationTaster and LRT ) ., Variants were then filtered based on coding effect ( non-synonymous , indels and splice-site variants ) ; heterozygosity and allele sharing amongst the three sequenced affected individuals ( II . 2 , IV . 5 , and IV . 10 ) ., Sanger sequencing was completed in all family members to confirm segregation of c . 554G>C; p . Arg185Pro in HOMER2 gene ( MIM 604799; RefSeq NM_004839 ) using primers HOMER2-6F: 5’-ATGGGAGAGGCAGCAAGTCT-3’ and HOMER2-6R: 5’-AGACCCACCTGCCAGCTTAC-3’ ., Cochleae from Balb/c mice were harvested at P2 , locally perfused , fixed in 4% paraformaldehyde for 30min , and rinsed in PBS ., Tissues were microdissected into cochlear and saccule subsets and stored at 4°C in preparation for immunohistochemistry ., Following infiltration using 0 . 3% Triton X-100 and blocking with 5% normal goat serum , we incubated the tissues in rabbit HOMER2 polyclonal primary antibody ( NB100-98712 , Novus Biologicals , Littleton , CO ) diluted 1:1000 in PBS overnight at 4°C ., Specificity of HOMER2 antibody was confirmed by staining whole mount cochlea from Homer2-/- mice ( S7 Fig ) ., Subsequently , a secondary antibody Alexa-Fluor 568 Goat anti-rabbit ( Life Technologies , Carlsbad , CA , USA; 1:1000 ) was applied for 1h ., Alexa-Fluor 488 phalloidin ( Life Technologies , Carlsbad , CA , USA; 1:500 ) was added for 15min to selectively visualize F-actin ., We used anti-neurofilament 200 monoclonal primary antibody ( N0142 , Sigma-Aldrich , Saint Louis , MO ) and Alexa-Fluor 488 Goat anti-mouse as a secondary antibody to visualize spiral ganglions neurons ., Whole-mount tissues were mounted in ProLong Gold Antifade Reagent ( Life Technologies , Carlsbad , CA , USA ) ., Confocal images were collected using Leica TCS SP5 confocal microscope ( Leica Microsystems Inc . , Bannockburn , IL , USA ) and analyzed in LSM 5 Image Browser and Adobe Photoshop ., Transfected HEK293T and COS7 cells were fixed in 4% paraformaldehyde in 0 . 1 M PBS ( pH 7 . 4 ) ; cells were permeabilized with 0 . 1% Trition-X100 ., Fixed cells were incubated with primary antibody at room temperature in PBS for 1 . 5hrs ., The following primary antibodies were used: monoclonal Anti-FLAG ( Sigma-Aldrich , St . Louis , MO , USA; 1:400 ) and Anti-cMYC ( Sigma-Aldrich , St . Louis , MO , USA; 1:400 ) ., Secondary antibody incubation was for 1hr at room temperature ., Secondary antibodies used: Alexa-Fluor-488 goat anti-mouse ( Invitrogen , Grand Island , NY , USA; 1:500 ) to stain FLAG-tagged HOMER2 p . Arg185Pro and Alexa-Fluor-568 goat anti-rabbit ( Invitrogen , Grand Island , NY , USA; 1:500 ) to stain cMYC-tagged HOMER2WT ., F-actin was immuno-stained with Alexa-Fluor-647-phalloidin ( Invitrogen , Grand Island , NY , USA;1:500 ) ., Cells were mounted in SlowFade Gold Antifade Reagent with DAPI ( Life Technologies , Grand Island , NY , USA ) ., Images were taken using a Zeiss LSM 510 with ZEN 2009 confocal microscope ( Zeiss , Pleasanton , CA , USA ) ., The Gateway PLUS shuttle clone for HOMER2 ( AF081530 . 1 ) was ordered from GeneCopoeia ( GeneCopoeia Inc , Rockville , MD , USA ) ., QuickChange Site-Directed Mutagenesis Kit ( Stratagene , Cambridge , UK ) was used for site-specific mutagenesis to introduce the P185 mutation ( S4 Table ) ., The mutant expression plasmid was sequence verified ., Both full length open reading frames for WT protein HOMER2WT and mutant HOMER2p . Arg185Pro were cloned into the expression vectors pCMV-Tag3 ( cMYC-tagged ) and pCMV-Tag2 ( FLAG-tagged ) , respectively ( Agilent technologies , Santa Clara , CA , USA ) ., All constructs were verified by sequence analysis ., HEK293T cells and COS7 cells ( ATCC , Manassas , VA , USA ) were grown in Dulbeccos Modified Eagles Medium ( DMEM ) supplemented with 10% FBS ( Life Technologies , Carlsbad , CA , USA ) ., Cells were incubated in a 5% CO2-humidified incubator at 37°C ., Cells were grown on Poly-L-Lysine Coated coverslips ( Corning , Tewksbury , MA , USA ) ., Clonal cells were obtained by transfection with Transit-LT Transfection Reagent ( Mirus Bio , Madison , WI USA ) using cMYC-tagged HOMER2WT and FLAG-tagged HOMER2p . Arg185Pro plasmid constructs according to the manufacturer’s instructions ., Zebrafish embryos were raised at 28 . 5°C as described 41 ., All animal procedures were approved by the University of Iowa Office of Animal Resources ( OAR ) principle for the care and use of laboratory animals and the Institutional Animal Care and Use Committee ( IACUC ) ., Antisense morpholino oligonucleotides ( MOs ) were designed to block the exon/intron splice junctions between exon 1 and intron 1 ( MO i1e1 5′- GGTACACATGTATCTGTCTGACCTT-3′ ) or intron 3 and exon 4 ( 5′-CGCAATGAAAACTGTAAACACTCTT-3′ ) of homer2 ( ENSDART00000124088 ) and bought from Gene Tools ( Philomath , OR , USA ) ., They were injected at 2 . 2 mg/ml along with 1 mg/ml p53 MO ( 5’-GCGCCATTGCTTTGCAAGAATTG-3’ ) ., A standard control MO ( 5’-CCTCTTACCTCAGTTACAATTTATA-3’ ) was used for injection of negative controls along with p53 MO ., The efficacy of homer2 knockdown by each morpholino was assessed by RT-PCR analysis with the following primer sets: forward ( 5’- GGTTCCCGCCAGTAAACAG-3’ ) and reverse ( 5’-GTTTGAGCTCCGTCTTCAGG-3’ ) , which amplified the region between exons 1 and 12; B-actin primers were forward ( 5′-GAGATGATGCCCCTCGTG-3 ) and reverse 5-GCTCAATGGGGTATTTGAGG-3 ) ., MO i1e1 morpholino was used for all subsequent experiments ., For in vivo mRNA synthesis , HOMER2WT and HOMER2p . Arg185Pro plasmids were transferred into the expression vector pCS2+ with Gateway LR Clonase according to the manufacturer’s instructions ( Life Technologies , Carlsbad , CA , USA ) ., This cDNA was used as a template for HOMER2 capped mRNA synthesis using an Ambion mMESSAGE mMACHINE SP6 kit ( Applied Biosystems , Foster City , CA , USA ) , and the product was tested for quality and yield by electrophoresis and spectroscopy ( NanoDrop Thermo Scientific , Waltham , MA , USA ) before injection ., Microinjection was performed at the one- to two-cell stage using a microinjection system consisting of a SZX9 stereomicroscope ( Olympus , Tokyo , Japan ) and an IM300 Microinjector ( Narishige , Tokyo , Japan ) ., Overexpression of injected mRNA was assessed by quantitative PCR with the following primer sets: forward ( 5’-GACCCCAACACCAAGAAGAA-3’ ) and reverse ( 5’CACTGTGTTGGCTCTGCTGT-3’ ) ., Primers for B-actin were forward ( 5’-CGCGCAGGAGATGGGAACC-3’ ) and reverse ( 5’-CAACGGAAACGCTCATTGC-3’ ) ., At 72 hours post fertilization ( hpf ) , live larvae were submerged in 3 μM FM1-43 FX ( Invitrogen , Grand Island , NY , USA ) for 30 sec ., They were then rinsed X3 in embryo media ( ddH2O with 5 . 03 mM NaCl , 0 . 17 mM KCl , 0 . 33 mM CaCl2 , 0 . 33 mM MgSO4 , 0 . 1% w/v methylene blue ) and fixed in 4% paraformaldehyde ., Before viewing , fish were rinsed X3 in PBS and viewed in 75% glycerol , 25% PBS with a Zeiss 700 confocal microscope ., We focused on neuromasts that reside around the surface of the otic vesicle: o1 , ml1 , ml2 , o2 and io4 ., Z-stacks were prepared using the max intensity z-projection function in ImageJ ( NIH , Stapleton , New York City , USA ) ., The morphous and structure of otic vesicles was observed in live larvae at 72hpf with a Leica MZFIII3 light microscope after anesthetizing with Tricaine ., Images were analyzed with ImageJ ( NIH ) ., Mouse studies were carried out in accordance with University of Iowa Office of Animal Resources ( OAR ) principle for the care and use of laboratory animals and the Institutional Animal Care and Use Committee ( IACUC ) ., Mice were culled using methods approved by the American Veterinary Medical Association ( AVMA ) Guidelines for the Euthanasia of Animals ., The knockout Homer2-/- mice were donated by Paul F . Worley at John Hopkins University ( Baltimore , Maryland , USA ) ., These mice have a neomycin cassette inserted into exon 3 of Homer2 abolishing gene expression 31 ., The Homer2 -/- colony was maintained on a C57BL/6J background ., Mice were genotyped by PCR , as previously described ( S8 Fig and S4 Table ) 31 ., Hearing thresholds were measured by click and tone burst ( 8 , 16 , and 32 kHz ) ABR and DPOAE in Homer2-/- , Homer2+/- and WT mice at two ( P14 ) , four ( P28 ) and eight ( P56 ) weeks ., At least 23 animals were tested at each time point ., Mice were anesthetized using intraperitoneal Ketamine/Xylazine at 0 . 1ml/10g body weight ., Reference , ground and earth electrodes were placed subcutaneously just posterior to the tested ear ( left ear ) , anterior to the contralateral ear and at the vertex of the head , respectively ., ABRs were performed using an experimental setup and testing protocol , as described 42 ., Briefly , clicks and tone-bursts were delivered to the testing ear through a plastic acoustic tube ., ABRs were measured using an Etymotic Research ER10B+ probe microphone ( Etymotic Research , Elk Grove , IL , USA ) coupled to two Tucker-Davis Technologies MF1 multi-field magnetic speakers ( Tucker-Davis Technologies , Alachua , FL , USA ) ., Click and tone-burst stimuli were presented and recorded using custom software running on a PC connected to a 24-bit external sound card ( Motu UltraLite mk3 , Cambridge MA , USA ) ., A custom-built differential amplifier with a gain of 1 , 000 dB amplified acoustic ABR responses ., Output was passed through 6-pole Butterworth high-pass ( 100 Hz ) and low-pass ( 3 kHz ) filters and then to a 16-bit analog-to-digital converter ( 100 , 000 sample/s ) ., Responses were recorded using standard signal-averaging techniques for 500 or 1000 sweeps ., Hearing thresholds ( db SPL ) were determined by decreasing the sound intensity by 5 and/or 10 db decrements and recording the lowest sound intensity level resulting in a recognizable and reproducible ABR response wave pattern ., Maximum ABR thresholds were capped at 100 db SPL ., DPOAEs were measured unilaterally ( left ear ) using an experimental setup and testing protocol , as described 42 ., In brief , DPOAE levels were elicited using two primary tone stimuli , f1 and f2 , with sound pressure levels of 65 and 55 db SPL , respectively , with f2/f1 = 1 . 22 ., A custom plastic ear probe was inserted into the ear canal and DPOAE amplitudes were measured at f2 frequencies at 4000 , 5657 , 8000 , 11314 , 16000 , 22627 and 32000 Hz and plotted after subtraction of noise floor amplitude ., IPA ( Ingenuity Systems , Mountain View , CA , USA ) was used to map interactions between genes involved in NSHHL ( 86 genes ) and HOMER2 and CDC42 ., Networks were created from user-specified seed molecules by searching the knowledge base for molecules that are known to biologically interact with the seeds and connecting them ., Networks are displayed graphically as nodes ( genes/gene products ) and edges ( the biological relationships between the nodes ) ., IPA computes a score for each network according to the fit of all significant genes ., A detailed description is given in the online repository ( http://www . ingenuity . com ) ., All results are displayed as mean ± standard error of the mean ( mean ± SEM ) ., Statistical analyses were performed using one-way ANOVA ( Zebrafish data ) or one-way ANOVA with post hoc T-test analysis using GraphPad Prism 6 ( La Jolla , CA , USA ) for ABR and DPOAE data ., P-values < 0 . 05 were assigned as significant ., The URLs for data presented herein are as follows: 1000 Genomes; http://www . 1000genomes . org Hereditary Hearing Loss Homepage , http://hereditaryhearingloss . org MutationTaster , http://www . mutationtaster . org NHLBI Exome Sequencing Project Exome Variant Server; http://evs . gs . washington . edu/EVS/ Online Mendelian Inheritance in Man ( OMIM ) , http://www . omim . org/ PolyPhen-2 , http://genetics . bwh . harvard . edu/pph2/ RefSeq , http://www . ncbi . nlm . nih . gov/RefSeq SIFT , http://sift . jcvi . org/

Introduction, Results and Discussion, Materials and Methods

Hereditary hearing loss is a clinically and genetically heterogeneous disorder ., More than 80 genes have been implicated to date , and with the advent of targeted genomic enrichment and massively parallel sequencing ( TGE+MPS ) the rate of novel deafness-gene identification has accelerated ., Here we report a family segregating post-lingual progressive autosomal dominant non-syndromic hearing loss ( ADNSHL ) ., After first excluding plausible variants in known deafness-causing genes using TGE+MPS , we completed whole exome sequencing in three hearing-impaired family members ., Only a single variant , p . Arg185Pro in HOMER2 , segregated with the hearing-loss phenotype in the extended family ., This amino acid change alters a highly conserved residue in the coiled-coil domain of HOMER2 that is essential for protein multimerization and the HOMER2-CDC42 interaction ., As a scaffolding protein , HOMER2 is involved in intracellular calcium homeostasis and cytoskeletal organization ., Consistent with this function , we found robust expression in stereocilia of hair cells in the murine inner ear and observed that over-expression of mutant p . Pro185 HOMER2 mRNA causes anatomical changes of the inner ear and neuromasts in zebrafish embryos ., Furthermore , mouse mutants homozygous for the targeted deletion of Homer2 present with early-onset rapidly progressive hearing loss ., These data provide compelling evidence that HOMER2 is required for normal hearing and that its sequence alteration in humans leads to ADNSHL through a dominant-negative mode of action .

The most frequent sensory disorder worldwide is hearing impairment ., It impacts over 5% of the world population ( 360 million persons ) , and is characterized by extreme genetic heterogeneity ., Over 80 genes have been implicated in isolated ( also referred to as ‘non-syndromic’ ) hearing loss , and abundant evidence supports the existence of many more ‘deafness-causing’ genes ., In this study , we used a sequential screening strategy to first exclude causal mutations in known deafness-causing genes in a family segregating autosomal dominant non-syndromic hearing loss ., We next turned to whole exome sequencing and identified a single variant—p . Arg185Pro in HOMER2—that segregated with the phenotype in the extended family ., To validate the pathological significance of this mutation , we studied two animal models ., In zebrafish , we overexpressed mutant HOMER2 and observed inner ear defects; and in mice we documented robust expression in stereocilia of cochlear hair cells and demonstrated that its absence causes early-onset progressive deafness ., Our data offer novel insights into gene pathways essential for normal auditory function and the maintenance of cochlear hair cells .

journal.pcbi.1000818

Fast- or Slow-inactivated State Preference of Na+ Channel Inhibitors: A Simulation and Experimental Study

Sodium channels are the key proteins in action potential firing for most excitable cells ., They exhibit a complex , membrane potential-dependent gating behavior 1 ., Even minor disturbances in the gating behavior can lead to hyperexcitability , which can be one of the causes of various disorders such as epilepsy , migraine , neuropathic and inflammatory pain , muscle spasms , and chronic neurodegenerative diseases ., For several decades , sodium channel inhibitors ( SCIs ) have been successfully used to lower excitability as , for example , local anesthetics , anticonvulsants , antiarrhythmics , analgesics , antispastics and neuroprotective agents ., Interestingly , the majority of antidepressants were also found to be potent SCIs ., In a recent study 2 the highest incidence of SCI activity was found amongst this therapeutic class ., We intend to test if the mechanism of action on sodium channels is similar to that of classic SCIs ., Thus far only a single drug binding site is established unequivocally on sodium channels , the “local anesthetic receptor” , located within the inner vestibule , its key residue being the phenylalanine located right below the selectivity filter , on domain 4 segment 6 3 ., However , the contribution of individual residues within the inner vestibule changes from drug to drug 4–6 ., For certain drugs an alternative binding site have been proposed , which is supposed to be located within the outer pore 7 , 8 , but the exact position of the binding site ( s ) for specific SCIs ( other than local anesthetics ) is currently unsettled ., For our case the exact location of the binding site is not relevant , we only need to suppose that the major mechanism of inhibition is preferential affinity to- , and stabilization of a specific inactivated state ., The major mechanism of SCIs is stabilization of an inactivated channel conformational state as a result of a preferential affinity for that state ., The question of which inactivated state is preferred is under debate for many SCI drugs ( e . g . 9–12 , or 13–17 ) ., Sodium channels are capable of fast inactivation ( complete within a few milliseconds ) , and different forms of slow inactivation ( time constants ranging from ∼100 ms to several minutes ) 18 ., Slow-inactivated state preference has been proposed as a therapeutic advantage 19–21 ., Mutations of sodium channel genes which affect slow inactivation are associated with several diseases 22 ., Slow inactivation determines sodium channel availability , and thereby contributes to overall membrane excitability , determining the propensity to generate repetitive firing , and the extent of action potential backpropagation ., Slow inactivated state preference has been proposed as a potential therapeutic advantage in specific types of epilepsy , neuropathic pain and certain arrhythmias 19–22 ., Furthermore , this mechanism of sodium channel inhibition has been proposed to modulate neuronal plasticity 22 ., In recent years a number of novel slow-inactivated state-preferring drug candidates have been described , including the recently approved antiepileptic drug lacosamide ( Vimpat ) 19 , 23 ., This drug has been found to be effective in a model of treatment-resistant seizures , and of diabetic neuropathic pain , in which tests conventional anticonvulsants were found ineffective 19 ., Special voltage protocols are used to evoke and study the slow-inactivated state ., Availability of channels is studied after a prolonged depolarization ( to induce slow inactivation ) , followed by a hyperpolarizing gap ( to allow recovery from fast , but not slow inactivation ) ., Because availability in such protocols is solely determined by the extent of slow inactivation , a drug that decreases availability is considered to be slow-inactivated state-preferring ., However , gating rates ( the rate of inactivation and rate of recovery from inactivation ) are altered by drug binding ., A fast-inactivated state-preferring drug stabilizes this state by delaying recovery ., A delayed recovery does not necessarily indicate actual modification of the gating rate ., For example if the bound drug prevents recovery from inactivation , then recovery will appear to be slowed because the drug needs first to dissociate 24 , 25 ., In our current study , however , we chose to use a model according to the modulated receptor hypothesis 26 , 27 , i . e . , the change in affinity equals the actual modification of the gating rates ., For this reason in our model increased affinity is synonymous with state stabilization ., Altered gating rates have been experimentally demonstrated using gating charge measurements 28 , 29 ., Because of the altered gating , the rate of recovery from fast inactivation in the presence of the drug can easily overlap with the rate of recovery from slow-inactivated state ., The rate of state-dependent association and dissociation of the drug should also be taken into account ., As a result , interpretation of data obtained with these protocols is not straightforward ( e . g . 9 , 30 ) ., With the help of simulations , we intended to understand the interactions between binding and gating rates and wanted to test the major prototypical inhibitor mechanisms in commonly used protocols ., We wanted to explore what could be deduced from these data , and wanted to find the right protocols that could help to determine the inhibition mechanisms ., Our data suggest that conclusions based on conventional protocols are not reliable ., For example , the fact that one drug preferentially shifts the “steady-state slow inactivation curve” as compared to another drug does not necessarily mean that the drug prefers the slow-inactivated state ., Figure 1 illustrates two ( simulated ) drugs investigated in “steady-state inactivation” protocols ( protocols are discussed below ) ., Both drugs shifted the “fast inactivation curve” ( Figure 2 , “FInact_V” ) to the same degree , but Drug 1 caused a larger shift in the “slow inactivation curve” ( Figure 2 , “SInact_V” ) ., In this special case , however , Drug 1 was defined to have a higher affinity to fast inactivated state , while Drug 2 had a higher affinity to slow inactivated state ., We observed , on the other hand , that fast- and slow-inactivated state-preferring drugs tended to preferentially affect the onset of inactivation and recovery , respectively ., Therefore we combined the information from these two protocols by plotting effectiveness in one protocol as a function of effectiveness in the other ., We observed that data points for fast- and slow-inactivated state-preferring drugs were confined to definite areas of the effectiveness ( inactivation ) – effectiveness ( recovery ) plane ., The two areas were found to overlap; therefore , explicit determination of the mechanism was not possible in all cases ., Using patch-clamp experiments , we tested three classic SCIs ( lidocaine , phenytoin and carbamazepine ) and two antidepressants ( fluoxetine and desipramine ) ., Properties of inhibition by classic SCIs were consistent with fast-inactivated state preference with fast binding kinetics ., Inhibition by antidepressants was distinctly different ., Whether the difference was caused by slow binding kinetics or slow-inactivated state preference could not be determined ., For simulations two different kinds of models were used: a phenomenological Hodgkin-Huxley type model and a state model similar to the one published by Kuo and Bean 31 ., In both models , however , we introduced slow-inactivated states and drug-bound states with altered gating transition rates ., For a detailed description of the models see Methods and Text S1 ., The Hodgkin-Huxley type model , which will be referred to as the “tetracube” model because of its topology ( see Methods ) , was used for most simulations ., The Kuo-Bean type model , referred to as the “multi-step-activation” ( MSA ) model , was only used for testing the robustness of our observations ., In the models , both the degree of alteration of the transition rates and the state preference ( the difference between affinities for different states ) were given by a single factor CF ( for fast-inactivated state-preferring drugs ) or CS ( for slow-inactivated state-preferring drugs ) ., The kinetics of association and dissociation to the resting state are defined by the rate constants ka and kd , respectively ., Association and dissociation rate constants to other states were calculated as described in Methods ., To compare simulated data with experimental results , we used similar voltage protocols in both the simulations and experiments ( Figure 2 ) ., Throughout this study we used four protocols: “FInact_V” is a standard “steady-state fast inactivation” protocol in which availability is assessed as a function of pre-pulse membrane potential ., The pre-pulse duration was 0 . 1 s when we compared the effects of “FInact_V” and “SInact_V” ., In electrophysiology experiments , because drugs with differing mechanisms of action and association kinetics had to be compared , a 2 s pre-pulse duration was used ., Note , that although we use the widespread term “steady-state fast inactivation” protocol , the term is incorrect for two reasons ., First , it is not necessarily “steady-state” in the sense that the pre-pulse duration may not be long enough for reaching equilibrium of either drug binding or channel gating ( 2 s is enough for the development of some degree of slow inactivation ) ., Second , “availability” would be a better term than “inactivation” , because the protocol does not necessarily reflect only inactivation in the presence of a drug , since we cannot separate blocked open and inactivated channels; however , “fast availability” and “slow availability” protocols are improper terms ., “SInact_V” is a “steady-state slow inactivation” protocol in which occupancy of the slow-inactivated state is intended to be measured as a function of the membrane potential ., It differs from the previous protocol in two respects: pre-pulse duration is longer ( 10 s ) , allowing more complete development of slow inactivation; and this protocol contains a 10 ms hyperpolarizing ( −150 mV ) gap between the pre-pulse and the test pulse ., The hyperpolarizing gap serves to separate occupancy of the slow-inactivated state from that of fast-inactivated states: >95% of channels recover from the fast-inactivated state within this period ., Despite the name , however , neither “FInact_V” nor “SInact_V” is able to measure drug effects on a pure population of fast or slow-inactivated channels ., Fast inactivation practically reaches equilibrium at most membrane potentials within ∼10 ms . With longer durations of pre-pulses in the “FInact_V” protocols the ratio of slow-inactivated channels increases from ∼5% ( 0 . 1 s pre-pulse ) to ∼40% ( 2 s pre-pulse ) ., This is accompanied by a minor shift of the curve ( ΔV1/2<4 mV ) ., Drug effects can further change this distribution depending on binding kinetics and state preference ., In “SInact_V” protocols most unavailable channels are in a slow-inactivated state in the absence of drugs ., However , the presence of a drug may alter the distribution of channel states ., The unavailable fraction does not consist of slow-inactivated channels only but also is “contaminated” with drug-bound fast-inactivated channels ., The conventional name “steady-state” therefore is absolutely untrue for this protocol , as the extent and V1/2 of slow inactivation is strongly dependent on pre-pulse duration ., We nevertheless need to use this terminology as we have discussed above ., “SInact_t” ( “Slow inactivation onset as a function of time” ) monitors the effect of prolonged depolarizations on sodium channel availability ., In the absence of drugs , the onset of slow inactivation is monitored as a function of time ( duration of depolarizing pulses ) ., In the presence of a drug , it is not clear whether it reflects pure slow inactivation or a mixture of fast and slow inactivation ( see below for a detailed explanation ) ., “Rec_t” ( “Recovery from inactivation as a function of time” ) monitors recovery after a 5 s depolarization to −20 mV as a function of hyperpolarizing gap duration ( the gap is between the 5 s pre-pulse and the test pulse ) ., In the absence of drugs , a 5 s depolarization causes both fast and slow inactivation ( approximately 45–55% , respectively ) , and the protocol monitors recovery from both states ., The time constants for recovery were 2 . 21 and 58 . 25 ms 32 ., In the presence of drugs , measured recovery reflects the combination of dissociation and recovery from both inactivated states ., Concentration-response curves were simulated using single depolarizations to 0 mV from holding potentials of −150 , −90 and −60 mV ., We plotted the nSOD values of the “Rec_t” protocol as a function of the nSOD values of the “SInact_t” protocol ., ( Figure 5 ) ., We investigated the effect of changing the following parameters:, i ) binding kinetics of drugs ,, ii ) state preference factors ( CF and CS ) ,, iii ) drug concentration ,, iv ) sodium channel model parameters , and, v ) hyperpolarizing gap duration in the “SInact_t” protocol ., Binding kinetics: We simulated 10 different pairs of rate constants spanning five orders of magnitude from 5*10−4 to 15 µM−1s−1 ( ka ) and from 0 . 1 to 3000 s−1 ( kd ) ., The ratio of ka and kd was kept constant ka/kd\u200a=\u200a5*10−3 , ensuring that the affinity of the drug toward the resting channel remained constant ., State preference factors: CF and CS were given the following values: 2 , 5 , 10 , 20 or 50 ., Using the five CF and the five CS values , each with the ten pairs of ka and kd values , we simulated altogether 100 “drugs” in both “SInact_t” and “Rec_t” protocols ., To correct for different potencies , the concentration of each drug was scaled: we used the concentration that caused 50% inhibition of single depolarizations at −90 mV holding potential ( Table S5 ) ., Figure 5A shows the distribution of “Rec_t” nSOD vs ., “SInact_t” nSOD values ., As the binding kinetics were accelerated , data points for specific CF/CS values proceeded clockwise along a closed loop ., The explanation is that binding kinetics have a range of optimal effectiveness; kinetics that are too slow do not allow for sufficient association during depolarizations , while kinetics that are too fast cause drug molecules to dissociate more during hyperpolarizations ., Around the optimum conditions , effectiveness in the “SInact_t” protocol increases with an acceleration in the kinetics in parallel with a decrease of effectiveness in the “Rec_t” protocol ., When CF and CS values were changed without concentration correction , the absolute value of the change was proportional to the value of CF and CS , but the characteristic clockwise loop pattern was unchanged ( Figure 5B ) ., Drug concentration: The effect of changing concentrations while keeping CF or CS constant ( CF\u200a=\u200a10 or CS\u200a=\u200a10 ) is shown in Figure 5C ., The concentration was decreased and increased tenfold ., The effect increased with increasing concentration , while acceleration of the binding kinetics caused the points to move along the clockwise loop as described above ., When all simulation results were plotted on the nSOD ( Rec_t ) – nSOD ( SInact_t ) plane , we observed that fast- and slow-inactivated state-stabilizing drugs were confined to limited but overlapping areas of the plane ( Figure 5D ) ., Because of the clockwise progression of the points upon acceleration of the binding kinetics , the overlapping area contains mostly “FI_sb” and “SI_fb” type drugs ., Sodium channel model parameters: To test the influence of channel parameters , we plotted the results from Monte Carlo simulations of the four prototypical drugs on the nSOD ( Rec_t ) – nSOD ( SInact_t ) plane , and compared those with the areas based on Figure 5D ., “FI” drugs were almost exclusively located within the “fast area , ” while “SI” drugs were located within the “slow area , ” practically irrespective of model parameters ., The overlapping area was populated mostly by “FI_sb” and SI_fb” drugs , confirming the reliability of “fast” and “slow” areas ( Figure 5E ) ., Hyperpolarizing gap duration of the “SInact_t” protocol: In simulations and experiments , we used a 10 ms gap duration , which is enough for a >90% recovery from the fast-inactivated state under control conditions ., In the presence of a fast-inactivated state-stabilizing drug , recovery is slowed down ., For this reason , in experiments where slow-inactivated state-stabilizing drugs are to be identified , gap duration is often chosen to be of a longer duration ( up to 1 s ) to ensure that the recovery from fast inactivation is complete ., Our simulations indicated that “FI_sb” and “SI” type drugs nevertheless overlap in behavior no matter what hyperpolarizing gap duration is chosen ( see Figure 3E ) ., We tested the effect of setting the gap duration to 1 s ( Figure 5F ) ., “FI” and “SI” type drugs were no better separated with a 1 s than with the 10 ms gap duration ., In summary , localization on the nSOD ( Rec_t ) – nSOD ( SInact_t ) plane can reveal the state preference of a drug if it falls on one of the non-overlapping areas ., However , many “SI_fb” and “FI_sb” type drugs are expected to fall in the overlapping section and , therefore , their state preference cannot be determined ., The following SCI drugs were used: the local anesthetic and antiarrhythmic lidocaine ( 300 µM ) , the anticonvulsants phenytoin ( 300 µM ) and carbamazepine ( 300 µM ) , and the antidepressants fluoxetine ( 30 µM ) and desipramine ( 30 µM ) ., The concentrations were chosen to be similarly effective in causing a hyperpolarizing shift ( −10 to −18 mV ) of the “steady-state inactivation” curve ( “FInact_V” – 2 s pre-pulse ) ( Figure 6A ) ., In the “SInact_t” protocol ( Figure 6B ) , carbamazepine and phenytoin caused only a small acceleration in the process of inactivation ., Fluoxetine and desipramine caused an obvious shift , similar to the one caused by the prototypical drugs “FI_sb , ” “SI_fb” and “SI_sb . ”, Lidocaine strongly shifted the curve ( especially in the early phase ) , which is typical of “FI_fb” type drugs ., The reason for the small effect of carbamazepine was its fast dissociation kinetics ., When the hyperpolarizing gap duration was changed from 10 ms at −150 mV to 5 ms at −120 mV ( similar to the protocol used by Kuo et al . 12 ) , carbamazepine became as effective as lidocaine ( Figure 6B inset ) ., In the “Rec_t” protocol ( Figure 6C ) , fluoxetine and desipramine shifted the curve of recovery , similar to the prototypical drugs “FI_sb , ” “SI_sb” and “SI_fb . ”, Carbamazepine , phenytoin and lidocaine only altered the early phase of the recovery curve , similar to the drug “FI_fb . ”, We created the nSOD ( Rec_t ) – nSOD ( SInact_t ) plots for all five drugs ( Figure 6D ) ., The data points for fluoxetine and desipramine were in the overlapping area ., The data points for carbamazepine , phenytoin and lidocaine fell into the non-overlapping area of fast inactivation stabilizing drugs ., Slow-inactivated state preference has been proposed to be a therapeutic advantage 19–21 , and therefore different drugs have been tested for this property ., The question of fast- or slow-inactivated state preference is a complex problem because of the interdependence of binding and gating equilibria ., Multiple interconnected equilibria can be relatively easily handled by modeling; therefore , we used this approach to test hypotheses regarding state preference ., Our current simulation data suggest that conclusions based on conventional protocols 19–21 , 33–35 are not reliable ., A shift of the “steady-state slow inactivation curve” ( “SInact_V” protocol ) , a shift of the “slow inactivation onset” curve ( “SInact_t” protocol ) and a shift of the recovery curve ( “Rec_t” protocol ) could all be caused by both fast- or slow-inactivated state stabilization ., This conclusion was confirmed both by testing whether our observations were true for the entire parameter space and by applying a different type of model ., We found that , with all combinations of parameters ( within the reasonable range ) , our observations held true ., Furthermore , both the phenomenological tetracube model and the MSA state model gave qualitatively similar results ., Nevertheless , the four prototypical mechanisms behaved appreciably differently ., For this reason , we investigated the extent to which the two major mechanisms ( “FI” and “SI” ) could be distinguished using the combined information from different voltage protocols ., Based on the nSOD ( Rec_t ) – nSOD ( SInact_t ) plots , we concluded that “FI” type drugs can be recognized , provided that their binding kinetics are fast enough ., However , “FI” drugs with slower binding kinetics will overlap with “SI” drugs ., Determination of the state preference would only be possible if we could measure the binding kinetics of individual drugs ., However , distinguishing slow association from association to a slow-inactivated state is not trivial ., In order to separate gating kinetics from binding kinetics , a rapid pulse application of the drug is necessary 32 , 36 ., Even in this case , association and dissociation rates cannot be correctly determined because the drug binding site on sodium channels is not extracellularly localized ., Therefore , the onset rate of a drug effect may be determined by multiple processes: aqueous phase – membrane partitioning , outer to inner leaflet translocation , intramembrane diffusion and association , itself ., Any one of these may be the rate limiting step , which obscures the microscopic association rate ., We investigated three well-known SCI drugs ( lidocaine , phenytoin and carbamazepine ) and two antidepressants ( fluoxetine and desipramine ) ., The uniquely high incidence of SCI activity among antidepressants 2 , as well as their high affinity to sodium channels as compared to classic SCIs , suggests that the inhibition of sodium channels may contribute to their therapeutic effect ., The therapeutic profile of antidepressants is different from that of classic SCIs ( anticonvulsants , local anesthetics , antiarrhythmics ) , and we also intended to study whether the mechanism of inhibition was similar to that of classic SCIs ., The experimental behavior of the five drugs was remarkably similar to the behavior of prototypical drugs in simulations ., We suggest that lidocaine , phenytoin and carbamazepine stabilize the fast-inactivated state , and that they have fast binding kinetics ., Their nSOD ( Rec_t ) – nSOD ( SInact_t ) plot clearly fell into the “fast area . ”, Furthermore , their effect on the “Rec_t” curve was similar to the effect of “FI_fb . ”, Lidocaine behaved similarly to “FI_fb” in the “SInact_t” protocol as well ., We hypothesized that the moderate effect of phenytoin and carbamazepine was due to their extra fast dissociation kinetics ., This hypothesis was verified in the case of carbamazepine , which produced the characteristic “FI_fb” type effect on “SInact_t” curves upon minor modifications to the protocol ., The nSOD ( Rec_t ) – nSOD ( SInact_t ) plots of fluoxetine and desipramine fell into the overlapping area ., Thus , their state preference could not be unambiguously determined ., However , their properties of inhibition definitely differed from those of classic SCIs ., Patch clamp electrophysiology was done on native sodium channels in cultured hippocampal neurons ., Cell culture preparation and electrophysiology were performed as published previously 32 ., Cultured hippocampal neurons ( prepared on the 17th day after gestation ) were found to express mostly the Nav1 . 2 and Nav1 . 6 isoform , but Nav1 . 1 , Nav1 . 3 and Nav1 . 7 isoforms were also detected in a some cells 37 ., In spite of the differences in expression pattern biophysical properties of sodium currents were remarkably similar 32 , 37 , and potency of individual drugs showed no higher variance than in experiments using Nav1 . 2 expressing HEK 239 cells ( data not shown ) ., Error bars on the figures represent SEM , and the number of cells tested ( n ) was between 4 to 10 ., All experimental procedures were approved by the Animal Care and Experimentation Committee of the Institute of Experimental Medicine , and as stated by the decision of the Animal Health and Food Control Department of the Ministry of Agriculture and Regional Development , were in accordance with 86/609/EEC/2 Directives of European Community ., The simulation was based on a set of differential equations with the occupancy of each state ( i . e . , the fraction of the ion channel population in that specific state ) given by the following equation: ( 1 ) where Si ( t ) is the occupancy of a specific state at time t and Sj ( t ) is the occupancy of a neighboring state ., Neighboring states are states where direct transitions are possible ., n is the number of neighboring states , and kij and kji are the rate constants of transitions between neighboring states ., Differential equations were solved during simulations using a fourth-order Runge-Kutta method ., We used either Berkeley Madonna v8 . 0 . 1 ( http://www . berkeleymadonna . com/ ) or a program written in C++ .

Introduction, Results, Discussion, Methods

Sodium channels are one of the most intensively studied drug targets ., Sodium channel inhibitors ( e . g . , local anesthetics , anticonvulsants , antiarrhythmics and analgesics ) exert their effect by stabilizing an inactivated conformation of the channels ., Besides the fast-inactivated conformation , sodium channels have several distinct slow-inactivated conformational states ., Stabilization of a slow-inactivated state has been proposed to be advantageous for certain therapeutic applications ., Special voltage protocols are used to evoke slow inactivation of sodium channels ., It is assumed that efficacy of a drug in these protocols indicates slow-inactivated state preference ., We tested this assumption in simulations using four prototypical drug inhibitory mechanisms ( fast or slow-inactivated state preference , with either fast or slow binding kinetics ) and a kinetic model for sodium channels ., Unexpectedly , we found that efficacy in these protocols ( e . g . , a shift of the “steady-state slow inactivation curve” ) , was not a reliable indicator of slow-inactivated state preference ., Slowly associating fast-inactivated state-preferring drugs were indistinguishable from slow-inactivated state-preferring drugs ., On the other hand , fast- and slow-inactivated state-preferring drugs tended to preferentially affect onset and recovery , respectively ., The robustness of these observations was verified:, i ) by performing a Monte Carlo study on the effects of randomly modifying model parameters ,, ii ) by testing the same drugs in a fundamentally different model and, iii ) by an analysis of the effect of systematically changing drug-specific parameters ., In patch clamp electrophysiology experiments we tested five sodium channel inhibitor drugs on native sodium channels of cultured hippocampal neurons ., For lidocaine , phenytoin and carbamazepine our data indicate a preference for the fast-inactivated state , while the results for fluoxetine and desipramine are inconclusive ., We suggest that conclusions based on voltage protocols that are used to detect slow-inactivated state preference are unreliable and should be re-evaluated .

Sodium channels are the key proteins for action potential firing in most excitable cells ., Inhibitor drugs prevent excitation ( local anesthetics ) , regulate excitability ( antiarrhythmics ) , or prevent overexcitation ( antiepileptic , antispastic and neuroprotective drugs ) by binding to the channel and keeping it in one of the inactivated channel conformations ., Sodium channels have one fast- and several slow-inactivated conformations ( states ) ., The specific stabilization of slow-inactivated states have been proposed to be advantageous in certain therapeutic applications ., The question of whether individual drugs stabilize the fast or the slow-inactivated state is studied using specific voltage protocols ., We tested the reliability of conclusions based on these protocols in simulation experiments using a model of sodium channels , and we found that fast- and slow-inactivated state-stabilizing drugs could not be differentiated ., We suggested a method by which the state preference of at least a subset of individual drugs could be determined and tried the method in electrophysiology experiments with five individual drugs ., Three of the drugs ( lidocaine , phenytoin and carbamazepine ) were classified as fast-inactivated state-stabilizers , while the state preference of fluoxetine and desipramine was found to be undeterminable by this method .

physiology/neuronal signaling mechanisms, biophysics/biomacromolecule-ligand interactions, neuroscience/neurobiology of disease and regeneration, pharmacology

journal.pcbi.1003224

Swimming in Light: A Large-Scale Computational Analysis of the Metabolism of Dinoroseobacter shibae

The Roseobacter clade is a versatile group of Gram-negative α-proteobacteria , which can be found in all oceans worldwide ., Especially during phytoplankton blooms they account for a large fraction of the marine bacterial community 1 , 2 ., Here , we focus on the aerobic anoxygenic phototroph Dinoroseobacter shibae DFL12T 3 ., Although the bacterium has been isolated from the surface of the dinoflagellate Prorocentrum lima , it can also be motile by the means of a single polar flagellum ., It harbors five plasmids and needs additional vitamins ( biotin , nicotinate , and 4-aminobenzoate ) to grow in minimal seawater medium ., To obtain energy , D . shibae can use oxygen , nitrate or dimethyl sulfoxide ( DMSO ) as terminal electron acceptor ., Additionally , energy generation is possible via light dependent aerobic anoxygenic photosynthesis 3 , 4 ., Despite of their common taxonomic classification , many members of the Roseobacter clade have adopted a unique life style and accordingly have developed a tailored metabolism 1 , 5 ., For instance , D . shibae is believed to live in symbiosis with its host and to provide the algae with vitamin B12 in exchange for carbon sources originating from photosynthesis 6 , 4 ., Some members of the Roseobacter clade produce storage compounds belonging to the group of polyhydroxyalkanoates , which are biopolymers with a potential industrial use 7 , 8 ., Under optimal conditions , Dinoroseobacter sp ., JL1447 , a close relative of D . shibae , has been found to produce large quantities of polyhydroxyalkanoates 9 ., Moreover , D . shibae and other Roseobacters produce dimethyl sulfide ( DMS ) during DMSO respiration and if dimethylsulfoniopropionate ( DMSP ) is used as carbon source ., This molecule contributes to the characteristic odor of the ocean and affects climate by seeding cloud formation 10 ., All these properties demonstrate that D . shibae notably differs from well-studied organisms ., For the aforementioned reasons , the Roseobacter clade and its members were subjects of intensive research in the past few years 1 , 2 ., Since the initial description of D . shibae in 2005 , further studies on the genome sequence and transcriptome analyses under changing illumination conditions have been published 3 , 4 , 11 ., Furthermore , important parts of the metabolism of D . shibae were elucidated by 13C labeling experiments , a study on DMSP catabolism , and a study targeting energy conservation 12 , 13 , 14 ., Remarkably , no phosphofructokinase activity has been observed in D . shibae during growth on glucose 12 ., Recently , a basic metabolic model of Rhodobacter sphaeroides , another member of the Roseobacter clade , has been created 15 ., However , no systematic and detailed computational analysis of the metabolism of any member of this ubiquitous group of marine bacteria has been carried out to date ., In this work , a large-scale computational analysis of the metabolism of the marine bacterium Dinoroseobacter shibae DFL12T is presented ., Prior to the analysis , we created an elaborate genome-scale metabolic model 16 , 17 of D . shibae , denoted iDsh827 ., It has been validated against experimental data and covers 827 open reading frames ., Moreover , iDsh827 is the first genome-scale metabolic model which explicitly takes the energy demand of bacterial motility into account ., Additionally , our model is the first one which uses aerobic anoxygenic photosynthesis ., In total , 391 , 560 distinct simulations featuring a large variety of different growth conditions , e . g . varying carbon and nitrogen sources , aerobic and anaerobic conditions , and the availability of light have been carried out ., A large fraction of the simulations is dedicated to plasmid and single gene knock-out mutants ., In detail , the aim of this work is to, A fundamental assumption made in most constraint-based simulations is that the organism tries to maximize its growth rate as much as possible under the given environmental conditions ., Therefore , the model iDsh827 contains a biomass reaction , which consumes appropriate quantities of 113 different metabolites needed for the reproduction of D . shibae ., The flux through this reaction corresponds to the growth rate and is the objective function of all simulations presented here ., Hence , to obtain precise adjustments , the contribution of each metabolite to the biomass composition was either quantified experimentally for D . shibae or estimated based on literature values of related organisms ( Table 1 ) ., For a detailed description of the experimental procedures used to determine the different fractions see the materials and methods section ., Literature values originating from Roseobacter denitrificans were used to estimate the amount of lipopolysaccharides produced by D . shibae ., Furthermore , the fraction of peptidoglycan was presumed to be approximately equal to the values determined for Escherichia coli 18 ., Moreover , compounds whose proportion of the biomass is still unknown were estimated to make up 0 . 1% of the total dry weight all together ., Finally , the soluble pool was assumed to account for the remaining biomass fraction ., Since some biomass compounds could not be separated by the analytical procedures used during the laboratory experiments , we corrected the measured values for these fractions ., As the original lipid fraction also contained the lipopolysaccharide and the bacteriochlorophyll α fractions , we subtracted these values to obtain the actual lipid content ., Moreover , we corrected the original protein fraction by subtracting the peptidoglycan value ., The relative portions of the nucleotides and amino acids were estimated from the genome sequence as suggested by the reconstruction protocol 19 ., The predominant respiratory lipoquinone ( ubiquinone-10 ) and the predominant cellular fatty acid ( 18:1ω7c ) were chosen to represent their corresponding compound group 3 ., Furthermore , the ratio of Kdo2-lipid A to the O-antigen in the lipopolysaccharide was calculated from values measured for Roseobacter denitrificans 20 ., Under anaerobic conditions , the oxygen atoms in the singlet oxygen quencher spheroidenone neither originate from water nor from CO2 but probably from other cell components 21 ., Hence , we included its precursor methoxyneurosporene in the biomass reaction ., A detailed measurement of the content of the soluble pool is only available for E . coli 22 ., Nevertheless , we used these values to constitute the soluble pool content in iDsh827 ., Therefore , we removed compounds which were either specific for the growth conditions used in the study or which were not part of our model ., As the genome annotation did not give any indications for spermidine production in D . shibae , we did not include this compound into the biomass reaction ., Due to the fact that aerobic anoxygenic phototrophs produce bacteriochlorophyll α exclusively in the dark under aerobic conditions 23 , we included two biomass reactions in the model: one with and one without bacteriochlorophyll α ., The former reaction contains a term corresponding to 4 nmol/mgprotein as determined for D . shibae 3 ., Before running a simulation , the appropriate biomass reaction is enabled automatically based on the preset conditions ., Both biomass reactions are normalized to one gram dry weight ., To elucidate the usability of carbon sources by D . shibae , we conducted Phenotype MicroArray experiments ., Respiration occurred on the carbonic acids succinate , fumarate , 2-oxoglutarate , L-lactate , acetate , propionate , ( R ) -3-hydroxybutanoate , glycolate , glyoxylate , pyruvate , the carbohydrates α-D-glucose , D-fructose , L-rhamnose , maltose , D-xylose and on the sugar alcohols D-ribitol , myo-inositol , D-arabitol , and D-xylitol ., Out of 190 different carbon sources tested , D . shibae was able to utilize only 19 , which confirms the poor variety of potential nutrients used by the organism 3 ., Most of the mentioned carbon sources were applied for growth experiments in batch cultures and gave a positive biomass yield ., The maximal growth rate on the reference substrate succinate was 0 . 25 h−1 , on glyoxylate 0 . 15 h−1 ., As not all cells in a bacterial culture display the same degree of motility 24 , we tracked the movement of D . shibae cells grown on glucose and succinate experimentally to determine the distribution of swimming velocities ., The resulting frequency distributions , shown in Figure 1 , slightly differ from each other ., The average velocity of cells is 5% higher ( 1 . 68 µm/s ) on glucose than on succinate ( 1 . 59 µm/s ) ., However , the shapes of the distributions differ significantly as a two-sample Kolmogorov-Smirnov test yielded a p-value of 1 . 06×10−5 ., The reconstructed metabolic network of D . shibae gives insight into the metabolism of this representative of the cosmopolitan Roseobacter clade ., The metabolic model iDsh827 consists of 1488 reactions covering 827 open reading frames ( Table 2 ) and is based upon an aggregated genome annotation provided by the EnzymeDetector database 25 ., To fill gaps in important pathways , we included a few enzymes with low evidence scores into the model ., A graphical representation of the distribution of evidence scores can be seen in Supplementary Figure S1 and the complete list of all genes covered in iDsh827 is given in Supplementary Dataset S1 ., Moreover , 199 genes retrieved from the annotation database were not taken into consideration either because their gene product was a non-metabolic enzyme or because the annotation was ambiguous ., To reproduce growth on minimal medium as accurately as possible , multiple refinements guided by experimental results found in the literature were made ., Important refinements are highlighted in the next sections ., The final metabolic model in the SBML format 26 can be found in Supplementary Dataset S2 and in the BioModels database ( accession ID: MODEL1308180000 ) ., In total , we carried out 391 , 560 simulations to study the metabolic network of D . shibae under various environmental conditions ( Table 4 ) and the effect of genetic perturbations in terms of single gene and plasmid knock-outs ., Figure 2A gives a rough summary of all simulations ., The leftmost bar corresponds to physiological states with no or only very little growth ., Furthermore , the modes visible in the figure are mainly caused by the different carbon uptake rates and other environmental conditions ., As visualized in Figure 2B , some simulations of distinct states yielded exactly the same flux distribution ., This happens if the changed parameter has no effect on the metabolism ., For instance , an additional nutrient may remain unused or the function of a knocked-out gene can be carried out by another one ., Hence , we observed only 55 , 390 distinct flux distributions in our simulations ( Figure 2B ) ., Prominently , the most common distribution is the one where no fluxes are active at all and hence no growth occurs ., This is often due to insufficient carbon uptake or a lethal knock-out ., In contrast , more than 23 , 000 flux distributions are unique for only one physiological state ., In the following sections , we will take a closer look at the fluxes in some selected simulations ., Although the precise connection between swimming velocity and the corresponding energy demand depends on many unknown variables ( e . g . viscosity of the medium and efficiency of the motor protein ) , it is very likely that the energy demand increases with velocity ., As this demand has a great influence on the energy metabolism and hence on growth , this parameter is worth to be considered in genome-scale metabolic models ., We suggest to evaluate values within physiological reasonable ranges in multiple simulations to account for the different swimming velocities occurring within a culture and under different environmental conditions ., Our simulations showed that the phenotype of D . shibae growing on glycerol or glyoxylate can only be reproduced if these compounds are allowed to pass the membrane without active transport ., Otherwise , the total energy requirement of the cell , including the energy needed for the active transport , cannot be covered by the degradation of these compounds ., Hence , we suppose that other transporters requiring less energy exist in D . shibae ., Indeed , the existence of a glycerol-conducting channel in E . coli is known 43 ., Glycerate is already in a highly oxidized state so that the oxidation to CO2 does not yield enough energy to sustain growth if the import is ATP-dependent ., In most physiological states shown in Figure 3 , the TCA cycle operates in forward direction and thus is used to obtain energy ., However , in the simulations with glycolate as sole carbon source the isocitrate lyase converts glyoxylate to D-threo-isocitrate ., At this point the flux splits: 80% follow the TCA cycle in forward direction and 20% are routed to citrate ., Hence , the citrate synthase runs backwards in the glycolate simulations ., According to our results , the activity of the TCA cycle and the oxidative phosphorylation in general is significantly reduced in light ., This is supported by our simulations , the experimentally observed decrease of the specific carbon dioxide production rates , and metabolome analyses during light shift experiments in continuous cultivations of D . shibae ., Hence , we conclude that the aerobic anoxygenic photosynthesis satisfies the energy demand of the organism in large parts ., Moreover , under illuminated conditions the production of oxaloacetate is increased to supply anabolic reactions ., Another effect of the reduced TCA cycle activity is a decreased production of NADPH by the isocitrate dehydrogenase ., Hence , the organism must increase the flux through other reactions for compensation under illuminated conditions ., For instance , our simulations showed an increased usage of the pentose phosphate pathway in comparison to dark conditions if glucose is used as carbon source ., In general , a flexible energy metabolism is probably very important for D . shibae because the amount of energy generated by the aerobic anoxygenic photosynthesis can vary greatly ., This can be due to changing illumination conditions but also due to the degradation of the light harvesting complex in the course of time 44 ., Recent experimental results suggest , that the activity of the aerobic anoxygenic photosynthesis is reduced or even stopped under anaerobic conditions 45 ., If this holds for D . shibae and all environmental conditions , the illuminated anaerobic simulations would coincident with the dark anaerobic simulations ., As we pointed out in the results section , the anaerobic glycolate simulation in light is the only one displaying variance ., This is probably due to an energy overflow , which occurs if more energy ( external protons ) is generated by the aerobic anoxygenic photosynthesis than needed for growth ., Thus , energy-wasting futile reactions can take place in any part of the metabolic network ., Futile reactions waste energy and produce heat and thus are usually disadvantageous for the organism ., Hence , they are probably tightly regulated ., However , the uncertainty stems from the fact that the simulations cannot predict which set of futile reactions take place ., A possibility we did not test in our simulations is a leakage of protons through the membrane ., Another uncertainty in energy balance is the precise number of periplasmic protons used for motility ., This number may vary greatly depending on environmental conditions and the average velocity of the motile bacteria 38 ., Furthermore , our simulations confirmed that formation of phosphoenolpyruvate from pyruvate via pyruvate orthophosphate dikinase neither needs to be active during the glucose simulations 12 nor in any other physiological state studied here ., However , we found that the reverse reaction is active in all physiological states ., According to our results , light and the presence of oxygen stimulate the usage of the DMSP demethylation pathway ., Interestingly , the effect depends on the carbon uptake rate ., While oxygen always enhances the activity of the demethylation pathway , the effect of light is much more pronounced in physiological states with a medium uptake rate ., On average , the cleavage pathway is used to a greater extend but the fraction of demethylation decreases as the uptake rate increases ., This is consistent with measurements made using fifteen different Roseobacter strains 46 ., Although DMSP may have become an abundant compound only recently 10 , D . shibae and other Roseobacters seem to be well-suited to use it efficiently under various conditions ., Whether the regulation of these pathways is really as sophisticated as predicted by our model remains an open question ., Our results support the hypothesis proposed in a recent review stating that under certain conditions , nitrate respiration is not only being used for energy generation but has some other function , which may be redox balancing 47 ., As described above , the energy demand of the organism is more than covered by the aerobic anoxygenic photosynthesis under low nutrient conditions ., Nevertheless , our simulations showed that a terminal electron acceptor is still required for optimal growth to be retained in the light ., This is due to an excess of reducing equivalents produced by different metabolic processes ., These reducing equivalents need to be reoxidized to retain redox homeostasis ., Again , flux balance analysis is not able to exactly determine which terminal electron acceptor is preferably used to achieve this goal ., However , we used flux variability analysis to identify alternate optimal solutions ., Indeed , some solutions involve denitrification under aerobic illuminated conditions ., Intriguingly , this behavior has been reported for Roseobacter denitrificans 48 ., We speculate that aerobic denitrification might be the preferred way to dispose electrons in vivo because it does not waste carbon and produces multiple oxidized redox equivalents ., D . shibae is very likely adapted to an oligotrophic marine environment and a regular day/night cycle 49 ., Hence , the organism may benefit only moderately from phosphofructokinase activity ., This is especially true during the day when the energy demand is covered by the aerobic anoxygenic photosynthesis in large parts ., On the one hand , this suggests a permanent shutdown of the phosphofructokinase ., On the other hand , another possible explanation is that light induces a down regulation of the phosphofructokinase in D . shibae because the energy generated in lower glycolysis is not needed ., This would also explain the inactivity of the enzyme observed by Fürch et al . as their cultures were grown in constant light 12 ., In general , it can be informative to simulate a great variety of physiological states for each knock-out mutant ., Since some effects do not occur in all physiological states , more subtle differences between the mutants can be revealed this way ., This would not be possible with the commonly used method , which relies on one or two minimal media states 50 , 33 ., The simulated loss of plasmids brought up two interesting aspects regarding the 153 kb and the 86 kb plasmid ., The 153 kb plasmid harbors the only copy of a catalase ( Dshi_3801 ) , which decomposes hydrogen peroxide to oxygen and water ., The reason for the decreased growth is that the hydrogen peroxide created during the production of pyridoxal 5′-phosphate and the operation of the glycine oxidase now have to be degraded by a cytochrome-c peroxidase ., The reaction catalyzed by this enzyme depends on reducing equivalents and hence is metabolically more expensive ., Albeit hydrogen peroxide is created by other metabolic reactions in our simulations , singlet oxygen is also created during aerobic anoxygenic photosynthesis , which imposes an elevated oxidative stress level on the organism 51 ., Hence , the catalase gene might be a beneficial acquisition for D . shibae ., Furthermore , this hypothesis is supported by the fact that the catalase gene is upregulated in response to light 11 ., The 86 kb plasmid contains the complete synthesis pathway leading to dTDP-α-L-rhamnose , which is an important compound of the outer membrane ., Hence , our simulations predicted no growth in case of a loss of this plasmid for an unmodified carbolipid content ., However , pseudogenes and transposases located on the plasmid may indicate it is no longer needed by the organism 4 ., An explanation might be that D . shibae is able to change its surface structure or biofilm formation capabilities ., For the first time , we demonstrated that in theory a single gene knock-out is sufficient to significantly enhance the production of the cloud-seeding molecule DMS ., In case of the mutants with a blocked demethylation pathway , the DMSP must be degraded inevitable by the cleavage pathway to retain growth ., Hence , the more than 60% increase of the DMS production can be explained by the fact that 39 . 0% of the DMSP degraded by the demethylation pathway ( Figure 6 ) is now redirected to the DMS-producing cleavage pathway ., A similar effect occurred when the gene coding for the L-serine ammonia-lyase was removed ., Although the demethylation pathway was kept intact , its activity was greatly reduced ., The reason is that the carbon atom bound to tetrahydrofolate during the demethylation of DMSP , can no longer be routed to the central carbon metabolism ., This is due to the fact that the glycine hydroxymethyltransferase binds this carbon atom to glycine producing L-serine whose degradation to pyruvate and ammonia is now inoperative ., Alternatively , a salvage via the methionine synthase would be possible but we found no methionine degradation pathway in D . shibae ., As the growth on the other carbon sources is not affected , the mutants could be grown on succinate for example and transferred to DMSP for DMS production afterwards ., The D . shibae DFL12T wild-type strain 3 was inoculated in complex medium ( 40 g l−1 Marine Bouillon medium , Carl Roth , Karlsruhe , Germany ) and incubated in darkness at 30°C and 150 rpm for 48 h before being diluted 1∶50 in freshly prepared defined artificial sea water medium ( SWM ) 12 containing the following components per liter of medium: 4 . 0 g NaSO4 , 0 . 2 g KH2PO4 , 0 . 25 g NH4Cl , 20 . 0 g NaCl , 3 . 0 g MgCl2°6 H2O , 0 . 5 g KCl and 0 . 15 g CaCl2·2 H2O , 0 . 19 g NaHCO3 , 1 ml trace element solution ( 2 . 1 g Fe ( SO4 ) ⋅7 H2O , 13 ml 25% ( v/v ) HCl , 5 . 2 g Na2EDTA⋅2 H2O , 30 mg H3BO3 , 0 . 1 g MnCl2⋅4 H2O , 0 . 19 g CoCl2⋅6 H2O , 2 mg CuCl2⋅2 H2O , 0 . 144 g ZnSO4⋅7 H2O and 36 mg Na2MoO4⋅2 H2O per liter ) and 10 ml vitamin solution ( 0 . 2 g biotin , 2 . 0 g nicotinic acid and 0 . 8 g 4-aminobenzoic acid per liter ) ., Succinate ( 2 to 4 g l−1 ) and glucose ( 3 . 6 g l−1 ) were used as sole carbon sources ., Main cultures were inoculated from these overnight cultures in fresh artificial sea water medium ( SWM ) with an OD600 of 0 . 05 ., Main cultures of D . shibae DFL12T were cultivated in 300 ml baffled shaking flasks filled with 50 ml of SWM ( 2 gl−1 succinate as sole carbon source ) ., Cells were harvested at an OD600 of 1 ( mid-exponential phase ) ., Quantifications of macromolecules ( lipids , proteins , DNA , RNA ) were performed at least in three independent experiments sampling triplicates ., Average amounts of total macromolecules were calculated in relation to cell dry weight of D . shibae DFL12T ., The experiments were carried out following the manufacturers instructions ( Biolog Inc . , USA ) with modifications ., The inoculation and incubation solutions IF-0a GN/GP were adapted to the artificial sea water medium by adding a 10-fold concentrated SWM solution including vitamins and trace elements without carbon source ( final concentration of components equal to SWM ) ., Cultivation of D . shibae DFL12T was performed as described above using 2 g l−1 succinate as sole carbon source ., Sampling took place every hour until cultures ( three in parallel ) entered the early stationary growth phase ( approximately 16 h ) ., Sampling was performed by transferring 1 ml cell culture into 2-ml tubes ., Cells were separated from the medium by centrifugation ( 10 , 000 g , 4°C , 5 min ) ., Supernatants were transferred into fresh tubes and stored at −20°C until further processing with GC/MS ., For the comparison of bacterial motility D . shibae DFL12T was cultivated as described above using 2 g l−1 succinate as sole carbon source and 1 . 3 g l−1 glucose , respectively ., During exponential growth phase subsamples were taken and transferred to a stage micrometer ., To prevent false positive movements due to evaporation of medium the cover slip was sealed with nitrocellulose ., Cells were monitored through a microscope ( Zeiss Axiostar plus ) equipped with a 40× objective ., The bacterial movement was digitally recorded for 60 seconds with a Canon PowerShot A640 camera ( 640×480 pixels resolution , 16× zoom ) which was connected to the microscope ., Five still images per second were extracted from the videos and the hqdn3d filter of FFmpeg version 0 . 8 . 5-6 was used to filter noise ., Subsequently , all images were converted to grayscale and bright spots were mapped to black pixels with ImageMagick; version 6 . 7 . 7-10 ., Spot detection and tracking was performed using Icy version 1 . 3 . 1 . 0 58 ., Only tracks with a length of at least two were kept for further analysis ., The starting point of our reconstruction process was the genome annotation of Dinoroseobacter shibae DFL12T provided by the EnzymeDetector database 25 ., This database aggregates annotations from different sources and assigns a relevance score to each entry indicating the level of confidence ., To exclude poorly annotated enzymes , we selected only entries with a minimum relevance score of 9 ., Occasionally , multiple enzymes are annotated for one open reading frame ., In such cases the two best entries were compared with each other ., Both were kept if their score was equal or above 13 ., Otherwise only the entry with the best score was kept ., Furthermore , manual additions were made to the annotation during the reconstruction to fill gaps in the metabolic network ., The final annotation used for the creation of the model can be found in the Supplementary Dataset S1 ., To create the metabolic model , the enzymes from the annotation were mapped to the corresponding chemical reactions via their EC number ., This mapping was based on the MetaCyc database version 16 . 0 60 ., Next , spontaneous reactions were added under the condition that all educts of the reaction were already part of the model ., This step has been repeated until no new reactions were found ., Transport and boundary reactions were added manually to allow certain nutrients , additional vitamins and waste products to enter or leave the system ., This preliminary model was iteratively refined by adding additional enzymes and reactions to fully reproduce growth under different conditions ., Stoichiometric balancing was performed computationally for all reactions ., The non-growth-associated maintenance requirement ( nGAM ) and the growth-associated maintenance requirement ( GAM ) were assumed to be the same as in E . coli ( 3 . 15 mmol ATP/ ( gDW h ) and 53 . 95 mmol ATP/ ( gDW h ) 33 ., While the first value models the energy demand of processes not related to growth like DNA repair and preservation of turgor pressure , the second value accounts for the energy needed for reproduction ., Most of this energy is needed for the synthesis of proteins , DNA , and RNA ., The number of protons in mmol/ ( gDW h ) needed to drive the flagellar motor Pmotility has been estimated based on the number of protons needed for one rotation of the motor N\u200a=\u200a1200 61 , the average number of rotations per second v\u200a=\u200a10 s−1 62 , and the average dry weight of one Roseobacter cell m\u200a=\u200a300fg 63:However , only about 10% of the organisms in a culture of marine bacteria are motile during the early exponential growth phase modeled here 24 ., Hence , we constrained the motility proton flux to 24 mmol/ ( gDW h ) ., We simulated the physiological states using flux balance analysis 64 , 65 ., Furthermore , each flux was tested for variability under the additional constraint of optimal biomass production by ( fast ) flux variability analysis 66 , 67 ., All computational analyses were carried out on a computer equipped with a 2 . 67 GHz Intel Core i7 CPU and 4 GB of RAM ., The software in use was the metano toolbox ( Riemer et al , in preparation , http://metano . tu-bs . de ) ., Altogether , the simulations took about four days to finish ., For further evaluation , the resulting fluxes were stored in a relational database ., Single gene knock-outs were simulated by constraining the flux through all reactions associated with that particular gene to zero .

Introduction, Results, Discussion, Materials and Methods

The Roseobacter clade is a ubiquitous group of marine α-proteobacteria ., To gain insight into the versatile metabolism of this clade , we took a constraint-based approach and created a genome-scale metabolic model ( iDsh827 ) of Dinoroseobacter shibae DFL12T ., Our model is the first accounting for the energy demand of motility , the light-driven ATP generation and experimentally determined specific biomass composition ., To cover a large variety of environmental conditions , as well as plasmid and single gene knock-out mutants , we simulated 391 , 560 different physiological states using flux balance analysis ., We analyzed our results with regard to energy metabolism , validated them experimentally , and revealed a pronounced metabolic response to the availability of light ., Furthermore , we introduced the energy demand of motility as an important parameter in genome-scale metabolic models ., The results of our simulations also gave insight into the changing usage of the two degradation routes for dimethylsulfoniopropionate , an abundant compound in the ocean ., A side product of dimethylsulfoniopropionate degradation is dimethyl sulfide , which seeds cloud formation and thus enhances the reflection of sunlight ., By our exhaustive simulations , we were able to identify single-gene knock-out mutants , which show an increased production of dimethyl sulfide ., In addition to the single-gene knock-out simulations we studied the effect of plasmid loss on the metabolism ., Moreover , we explored the possible use of a functioning phosphofructokinase for D . shibae .

The oceans are home to a large variety of microorganisms , which interact in several ways with world-wide metabolic cycles ., A representative of an important group of marine bacteria called the Roseobacter clade is Dinoroseobacter shibae ., This organism is known to use a variant of photosynthesis to obtain energy from light ., Another feature of D . shibae and many other Roseobacters is the ability to degrade an abundant compound in the ocean called dimethylsulfoniopropionate ., Importantly , one degradation pathway of dimethylsulfoniopropionate releases a side product , which affects climate by seeding cloud formation ., In this work , we constructed a genome-scale metabolic model of D . shibae and carried out a detailed computational analysis of its metabolism ., Our model simulates the light-harvesting capabilities of D . shibae and also accounts for the energy needed to swim ., Thanks to our exhaustive simulations we were able to elucidate the effect of light on the growth of D . shibae , to study the consequences of genetic perturbations , and to identify mutants which produce more cloud-seeding compounds ., Foremost , our computational results help to understand an important part of the complex processes in the ocean in greater detail ., Besides , they can be a valuable guide for future wet-lab experiments .

journal.pntd.0003497

Ecotype Evolution in Glossina palpalis Subspecies, Major Vectors of Sleeping Sickness

A capital step for species diversification is the initiation of some kind of disruptive selection , driving the newly diverged group of entities to some level of genetic adaptive divergence 1 , 2 ., There has been a continuous debate on the respective role of geography and ecology in speciation , especially the speed at which these factors drive organisms to divergence 3 ., These debates are important as they focus on key processes involved in evolution ., For parasites and their vectors , the role of ecology and geography in driving divergence has important implications for management , as rapid evolution can occur in response to control practices or introductions to new environments 4 ., This can have consequences on dispersal capacity 4 , behaviour 5 and vectorial capacities 6–8 ., Tsetse flies ( Diptera: Glossinidae ) are the sole cyclical vectors of human ( HAT or sleeping sickness ) and animal ( AAT or nagana ) African trypanosomoses , two major plagues that are seriously impeding African development 9 ., Among these , Glossina palpalis palpalis and Glossina palpalis gambiensis , which are major vectors of both HAT and AAT , have recently been the subject of several population genetics studies ( see 10 for a review ) ., These studies , mainly based on spatio-temporal variation at microsatellite loci , have recurrently revealed some degree of genetic divergence , in some cases above the reasonable amount expected from geographically based population structure 11–14 ., Because control programs against trypanosomoses often rely on tsetse eradication or suppression , it is important to specify the amount of such divergences and , if possible , if it could be linked to some ecological factors ., Indeed , adaptive divergence may be correlated to variation in behaviour , host preference ( or attractiveness to trapping devices ) and vectoring ability ., In this paper , we combined and synthesized published and unpublished microsatellite data sets of these two taxa from populations sampled in West Africa and central Africa ., We analysed the whole data set in order to evaluate the genetic divergence between the two taxa as assessed with microsatellite markers and then we analysed separately G . p ., gambiensis and G . p ., palpalis in order to assess the respective role of geographic distance , date of capture ( time distance ) , landscape type and river basin in determining the level of genetic divergence of tsetse flies ., The observed levels of divergence provide support for changes in the taxonomic status of these subspecies ., Furthermore , based on both genetic and ecological criteria , we propose that several additional taxonomic groups should be recognized ., The importance of these findings in developing novel control strategies and facilitating future research endeavours is discussed ., Subspecies G . p ., gambiensis and G . p ., palpalis may have split no more than 13000 years ago 15 , 16 ., The ecotypes evidenced in the present study necessarily are much younger and illustrate on how swift ecological divergence can be ., Study sites are located as represented in Fig . 1 ., The species , country , landscape type , river basin , date of capture , GPS coordinates and sample sizes are presented in Table 1 ., Raw data are available in S1 Table ., Most of the samples studied in this paper were already used and genotyped for publications relating to other , though related purposes ., These papers are cited in Table 1 and sites samples can be seen in the Fig . 1 ., Folonzo sample was never published and was sampled during April 2007 following the same method as in 17 ., Guekedou sample was never published and was sampled during May 2007 following the same procedure as in 18; Senegal 1 and Senegal 3 samples were never published and were kindly provided by the Insect Pest Control Laboratory , Joint FAO/IAEA Program of Nuclear Techniques in Food and Agriculture and sampling followed the same procedures as described in 9 ., Azaguié sample was never published and was sampled and genotyped for another project of our team by S . Ravel in collaboration with Dr D . Kaba ( Pierre Richet / Institut National de Santé Publique , Abidjan , Ivory Coast ) and Dr G . Acapovi-Yao ( Laboratoire de Zoologie , Université d’Abidjan-Cocody , Abidjan , Ivory Coast ) ( Acapovi-Yao et al . , manuscript in preparation ) ., Published papers are available at http://gemi . mpl . ird . fr/SiteSGASS/SiteTDM/ArtiPDF . html ., These 28 samples summed to 614 genotyped individuals , with 9 unpublished samples ., Genotyping of unpublished data followed the same protocol as described in 17 and 19 ., For Mali 12 , Mali 8 , Senegal 1 and Senegal 3 , the genotypes of the flies were kindly provided by the Insect Pest Control Laboratory , Joint FAO/IAEA Program of Nuclear Techniques in Food and Agriculture and protocols were the same ., Some of analyses undertaken do not tolerate missing data ., For the sake of consistency between all analyses , only complete genotypes at seven loci were kept ., These loci were: Gpg55 . 3 ( X linked ) 20; B104 ( X linked ) , B110 ( X linked ) and C102 that were kindly supplied by A . Robinson , Insect Pest Control Laboratory ( formerly Entomology Unit ) , Food and Agricultural Organization of the United Nations/International Atomic Energy Agency FAO/IAEA , Agriculture and Biotechnology Laboratories , Seibersdorf , Austria; pGp13 ( X linked ) and pGp24 21; and GPCAG 22 ., Protocols followed what was described in references cited above ( e . g . 18 ) ., All genotyping were handled or supervised by the same person ( SR ) who ensured perfect calibration of allele sizes across sub-samples ., A total of 614 tsetse flies from 28 sites displayed a full genotype at the seven microsatellite loci ., All genotypic data were coded as they appeared , hence males were coded as homozygous at X-linked loci ., Sex information was missing in samples from Gambia and assessed through genotypes found on X-linked loci ., All data sets were built in appropriate text files and converted with Create V 1 . 1 23 into the appropriate format as needed except for bootstrap analysis with Phylip for which we used Convert V 1 . 31 24 ., Genetic distances were computed with MSA 4 . 05 25 ., We used a Cavalli-Sforza and Edwards chord distance matrix 26 for dendrogram construction with a Neighbour-joining tree ( NJTree ) 27 and for regression analyses , as recommended 28 , 29 ., The NJTree dendrogram showing relationships between all tsetse subsamples was built with Mega V 5 30 ., Robustness of nodes was assessed through 1000 bootstraps over loci with Phylip v 3 . 68 31 ., For that purpose , nodes in G . p ., gambiensis were studied after rooting the tree with Malanga subsample ( DRC ) , while for G . p ., palpalis nodes , tree was rooted with Banjul North subsample ( Gambia ) ., Sample sizes are represented in Table 1 ., Relationships between genetic distances and the other parameters were tested with partial Mantel tests ( for robustness ) and also explored with linear regressions ( for illustrations and strength of signals measures ) ., Explanatory variables and factors were the subspecies distance ( whether the two compared samples contain the same subspecies or not ) , geographic distance ( in km ) , time distance ( in days ) , landscape and river basin ( same or not ) ., The different landscapes and river basins are presented in Table 1 ., For the Mantel tests , these factors were coded as 0 when the two sites compared shared the same value ( e . g . both G . p . gambiensis ) or 1 when different ., For the linear regression , factors were coded as Same when similar in both sites and a combination of two modalities when different ( e . g . Savannah-Coast ) ., Mantel test for global data was undertaken to test for the effect of sub-speciation between G . p ., palpalis and G . p ., gambiensis ., Because there are probably interactions with this effect , other factors were then analyzed more precisely within each subspecies separately ., Partial Mantel tests were undertaken under Fstat V 2 . 9 . 3 32 ( updated from 33 ) with 10000 Monte-Carlo randomizations of genetic distance matrix items ., We also undertook Principal Component Analyses ( PCA ) on each sub-species data set ., For this we used PCAGen 1 . 2 . 1 34 that works on allele frequencies in subsamples and reorganize the data into a multidimensional space the metric of which is equivalent to Wrights FST 35 , i . e . the part of inbreeding that is explained by population subdivision ., The significance of the first axes was tested with the broken stick criterion 36 and also with 10000 permutations of individuals across subsamples ., We then submitted subsample coordinates of each significant axis to multiple regressions ., The general model to start with was always of the form Axisi ∼ Lat + Long + Day + Landscape + RiverBasin + Lat:Long where i identifies the axis number being investigated , Lat and Long mean the latitudinal and longitudinal GPS coordinates in degrees , Day means the number of days after the oldest sub-sample , Lanscape is as described above , RiverBasin is the name of the river basin as described above and : stands for interaction between two explanatory variables ., Here the variables were weighted for subsample sizes ., All multiple regressions were undertaken under R 37 using sample sizes as weights ., For all linear regressions , the best ( minimum ) model was selected after a stepwise procedure , using the Akaike Information Criterion 38 , significance tested with a F test and multiple comparisons ( when useful ) were done with the Student-Newman-Keuls ( SNK ) test ., Order of entry of explanatory variables matters both in Fstat ( Mantel ) and R analyses ., We thus chose to enter these variables following an order of decreasing importance we thought they would have: geographic distance , time , landscape , basin and interactions ( if any ) ., Ecological factors were entered last to make sure the response was controlled for the other parameters ., Null alleles and X-linked loci produce an artificial excess of inbreeding in subsamples that should not affect the tests in any predictable direction but a decrease in power ., These issues are thus relevant only in those cases where tests do not appear significant ., A coming work involving one of the authors ( TDM ) will be devoted to the robustness of different genetic distances to such issues ( manuscript in preparation ) ., NJTrees were also built without X-linked loci , on females only and on males only ., This did not change the general aspect of the tree even if a few populations happened to branch in slightly different places ., These NJTrees can be seen in S1 File ., The complete data set is available in S1 Table ., Geographic locations , landscape types and genetic relationships between all subsamples are presented in Table 1 , Fig . 1 and Fig ., 2 . It can be seen that the two subspecies are clearly separated ., In G . p ., gambiensis , distinction between Savannah , Niayes and Coastal populations , in some instances , overcome geographic differentiation ., This is particularly clear for samples from Gambia and Senegal ( Fig . 1 ) ., For instance , as can be seen from Figs ., 1 and 2 , subsamples Senegal 1 and 3 are genetically closer to Burkina-Faso and Mali sites ( Savannah ) than from the geographically closer Dakar , Pout ( Niayes ) , Missira , Banjul North and South ( Coast ) ., In G . p ., palpalis , Central and Western African flies are clearly separated and geography seems to be the predominant factor within each of the two zones ., Again , genetic distances are quite pronounced and bootstrap values relatively high ., Results of partial Mantel test for the whole data set provided a highly significant contribution of subspecies ( partial R2 = 0 . 65 , P-value<0 . 0001 ) ., For G . p ., gambiensis , partial Mantel test highlighted two major factors that best explained genetic distance between subsamples ( Table 2 ) ., The first is geographic distance , which explains 41% of the variance , followed by landscape distances that explain 10% of the variance of genetic distances and are highly significant ., Other parameters ( time and river basin ) contribute little to the coefficient of determination R2 , though significantly so ., Regarding the linear model , the stepwise procedure could not simplify the model ., Nevertheless , river basin distances did not display consistent results since the response mainly was due to higher genetic distances between sites from the same basin as compared to other comparisons ., This incoherence , which probably comes from interaction with geographic distance , led us to remove this factor from the analysis ., Results are presented in Fig ., 3 . The total R2 = 0 . 66 ., Here the main factor is geographic distance , followed by landscape distance ., Both explained not less than 62% of the total genetic variance ( which is quite big given the variation expected for genetic distances ) ., Time explained very little of the variance though significant: the more time between subsamples , the more genetic divergence between them ., For landscape distances , paired comparisons led to the conclusion that genetic distances between similar landscapes were smaller than any other comparison , and that Niayes subsamples were always genetically more distant from the other sites than any other comparison ., In G . p ., palpalis subsamples , the Mantel test , partial R2 and corresponding P-value are presented in Table, 3 . Only geographic distance displayed a significant effect here , with 34% of the variance explained ., For the multiple regression approach , only geography seemed to play a significant role ( partial R2 = 0 . 34 , P-value = 0 . 0002 ) ( Fig . 3D ) ., In particular , very high bootstrap values are observed between Central and West Africa and between Cameroon and RDC subsamples ., For PCA analysis of G . p ., gambiensis sub-samples , the first two axes appeared significant both with the broken stick criterion and with permutation testing ( P-value≤0 . 0001 for Axis 1 and P-value = 0 . 0345 for Axis 2 , permutation test ) ., Axes 1 and 2 represent 41% and 16% of total inertia respectively ., After stepwise procedures , Axis 1 is explained by all initial variables but Day ( Table 4 ) ., By far the two most important variables are the latitude and the landscape that explain respectively 66% and 29% of the total variance in axis 1 ( both P-values<0 . 0001 ) ., For the second axis , the minimum model was Axis2 ∼ Lat + Long + Landscape + RiverBasin ( Table 5 ) ., Here , the most important variables are Lanscape and Latitude that respectively explain 39% and 30% of the total variance in axis 2 ( P-values<0 . 00001 ) ., For PCA analyses of G . p ., palpalis sub-samples , the first three axes appeared significant both with the broken stick and permutation tests , with permutation P-value≤0 . 0001 for the two first axes and P-value = 0 . 011 for the third ., They respectively represent 34 , 27 and 17% of total inertia respectively ., Here , variable Landscape was not introduced as it does not vary in the sampled zones for G . p ., palpalis ., For axis 1 , no simplification of the model was possible ( Table 6 ) ., The only significant effect comes from the latitude which explains 95% of the total variance on axis 1 ( P-value = 0 . 0038 ) ., On axis 2 , only two variables stayed in the model after the stepwise procedure ( Table 7 ) ., However only longitude really mattered and explained no less than 94% of axis 2 ( P-value≤0 . 0001 ) ., Finally , for axis 3 , the minimum model was Axis3 ∼ Lat + Long + RiverBasin + Lat:Long and the most important explanatory variables were the river basin , explaining 81% of axis 3 variance ( P-value = 0 . 0053 ) , and the interaction between latitudinal and longitudinal coordinates that explained 14% of axis 3 variance ( P-value = 0 . 0291 ) ( Table 8 ) ., The importance of geographic distance for determining genetic relationships between tsetse populations has been recurrently reported 9 , 19 , 39 ., Its predominant effect above the effect of river basin was an expected result , at least for G . p ., gambiensis 9 and is newly demonstrated here for G . p ., palpalis ., The genetic distance that separates the two subspecies and the high bootstrap level obtained with microsatellite markers ( known for their homoplasic nature ) are advocating for a revision of the nomenclature of those taxa as different species ., This is also in line with the biological definition of species , although the usefulness of such a concept is debatable 40 , since the heterozygous males of the F1 crossing between these taxa are completely sterile 41 , 42 which leads to a very sharp allopatry between them in Ivory Coast 43 , 44 ., Moreover , these taxa can be discriminated on a morphological basis using the size of the palette of the inferior claspers ( larger in G . p . gambiensis ) and the length of hairs on the inferior claspers ( longer in G . p . gambiensis ) 45 ., This is even more justified as we also find evidence in the present paper of the existence of subunits within these two taxa , some of which are of an ecological nature ., The stronger impact of river basins on G . p ., gambiensis than on G . p ., palpalis is not surprising , taking into account that the savannah environment of the former makes it much more difficult to cross the interfluve than the dense forest environment of the latter ., Time did not play a very pronounced effect on G . p ., gambiensis and apparently had no effect on G . p ., palpalis ., For the latter , smaller sample sizes are probably the cause of this absence of detectable effect ., For G . p ., gambiensis , the significance of the effect is in line with genetic drift due to small effective population sizes that could be estimated in several studies in these taxa 9 , 10 , 12 , 17–19 , 39 , 46 but also in other tsetse taxa ( see 47 for review ) ., It highlights the need to take into account this factor in population genetics studies and the necessity to avoid pooling individuals that do not belong to the same cohort , in particular to estimate population differentiation , isolation by distance and migration ., In G . p ., gambiensis , an important and significant effect of landscape where tsetse flies are found was evidenced ., Interestingly , in several instances , genetic distances between subsamples from different landscapes are far above those between subsamples from the same landscape , even between the most remote ones ., This strong impact of landscape was confirmed by the regression analyses where this variable explained as much , and sometimes more , the genetic composition of G . p ., gambiensis sub-samples ., Our study also confirms the genetic isolation of G . p ., gambiensis from the Niayes 12 , 48 which has led to an eradication program in Senegal ( http://www . fao . org/news/story/en/item/211898/icode/ ) ., It is clear from the different analyses that tsetse from the Niayes ( Senegal ) represent an objective subspecies , adapted to a specific environment 48 , 49 ., This subspecies is able to reproduce in the complete absence of perennial hydrographic network ., Moreover , tsetse from savannah and those from coastal landscapes also represent original diverged entities that can deserve the denomination of ecotypes , if not subspecies ., There is however no pre- or post-mating barriers between these taxa , as evidenced by successful mating observed between tsetse flies from the Niayes and savannah tsetse flies from Mali and Burkina-Faso 50 ., They can thus be considered as subspecies ., It is the first time that such subspecies are evidenced ., It has to be underlined that the discovery of these ecotypes may have important consequences ., In particular , data from several studies made in the coastal part of Guinea have shown that the G . p ., gambiensis ecotype caught in the sleeping sickness foci of this country do not display any infection with the pathogenic trypanosomes usually identified ( including human and animal trypanosomes ) in this species ., Nevertheless , G . p ., gambiensis is the only vector of sleeping sickness there 51 , 52 ., To what extent the fact that they constitute a distinct ecotype can be linked to a different vector capacity remains to be documented , but may be of paramount importance for control programmes against both human and animal trypanosomoses ., It was also demonstrated that Trypanosoma brucei gambiense from Guinea were genetically very different than those from Ivory Coast , and that this was probably due to the fact that they were not transmitted by the same tsetse taxa , i . e . G . p ., gambiensis of the coastal landscape for T . b ., gambiense from Guinea , and G . p ., palpalis for the T . b ., gambiense from Ivory Coast 53 ., For G . p ., palpalis , the very high bootstrap values observed between Central and West Africa and between Cameroon and RDC subsamples suggest subspeciation , if not more , in the ecological sense of it ( adaptively divergent but not necessarily sexually isolated entities , see 40 , 54 ) ., The existence of three subspecies ( or even species ) separating flies from West Africa ( Ivory Coast ) , South of Cameroon , Equatorial Guinea and DRC has already been suggested , based on mtDNA ( COI ) 13 and there are probably more than that 55 ., Here , our seven microsatellite loci provide a strong confirmation that G . p ., palpalis is a strongly heterogeneous taxon ., Moreover , 56 found significant differences in the morphology of the head between G . p ., palpalis from West Africa and DRC ., Regression analyses on PCA axes also highlighted the relevance of river basins ., Nevertheless , many sites in the range of this species are missing ( Gabon , Nigeria , Benin , Togo and Ghana ) and other environmental measures are missing as well ., Future studies , implying GIS approaches should bring more information and more precision on the mechanisms of ecotype and population delimitations in tsetse flies ., These observations are not only of academic interest as they have important repercussion as regard to vector control ., Such ecological entities might represent different cases as regard to control success and reinvasion probabilities ., It is thus key that such newly defined entities be ecologically characterized in order to compare their respective ecology ( hygrometric and temperature preferences , host preferences , symbiotic flora and vector competences ) ., Mating preferences or differential competitiveness may also alter the success of sterile insect technique ( SIT ) if inappropriate ecotypes are released in the wrong environment ., This thus opens the gate to many and very productive new research topics on trypanosomes and their vectors ., It also highlights how useful genetic markers can be in exploring the ecology of difficult organisms ., Finally , our results call for an urgent taxonomic review of the status of G . palpalis subspecies ., The split between G . p ., gambiensis and G . p ., palpalis was dated as old as 3 . 2 million years according to COI mtDNA assuming molecular clock 13 ., Nevertheless , this result is based on a single mtDNA marker known to behave very oddly at the beginning of a split ( for less than 1 million year the divergence can vary from 4 to 20% ) 57 ., Moreover , because of their lack of neutrality 58–60 , mtDNA markers might not be ideal to estimate divergence time ., We thus prefer relying on experts of the life history of tsetse flies who dated the split between the two sub-species around 13000 years ago ( around 91000 tsetse fly generations ) when the initial forest was separated into two isolated masses by drought 15 , 16 ., It is probably the most parsimonious interpretation of tsetse flies history ., The mean genetic distance between the two taxa is 0 . 65 ( which is very high for a distance bonded to 1 ) ., It is 0 . 48 between savannah and coast subsamples , 0 . 53 between savannah and the Niayes and 0 . 5 between coast and the Niayes ., Assuming constant microsatellite divergence with time , we can extrapolate that the ecological split in G . palpalis gambiensis occurred around 10000 years ago ( around 70000 generations ) , hence at the end of last glaciation ., These estimates probably correspond to considerable overestimates as divergence speed probably strongly decreased as the two sub-species increased in population size ( which tends to freeze genetic drift ) when meteorological constraints were progressively relaxed at the end of the Würm ice age ., These results provide another powerful illustration on how swift ecological divergences can occur , in particular in host-parasite-vector systems 4 , 54 .

Introduction, Material and Methods, Results, Discussion

The role of environmental factors in driving adaptive trajectories of living organisms is still being debated ., This is even more important to understand when dealing with important neglected diseases and their vectors ., In this paper , we analysed genetic divergence , computed from seven microsatellite loci , of 614 tsetse flies ( Glossina palpalis gambiensis and Glossina palpalis palpalis , major vectors of animal and human trypanosomes ) from 28 sites of West and Central Africa ., We found that the two subspecies are so divergent that they deserve the species status ., Controlling for geographic and time distances that separate these samples , which have a significant effect , we found that G . p ., gambiensis from different landscapes ( Niayes of Senegal , savannah and coastal environments ) were significantly genetically different and thus represent different ecotypes or subspecies ., We also confirm that G . p ., palpalis from Ivory Coast , Cameroon and DRC are strongly divergent ., These results provide an opportunity to examine whether new tsetse fly ecotypes might display different behaviour , dispersal patterns , host preferences and vectorial capacities ., This work also urges a revision of taxonomic status of Glossina palpalis subspecies and highlights again how fast ecological divergence can be , especially in host-parasite-vector systems .

The role of environmental factors in driving adaptive trajectories of living organisms is still being debated ., This is even more important to understand when dealing with important and /or neglected diseases and their vectors ., In this paper , we analysed genetic divergence , computed from several genetic markers , of 614 tsetse flies ( Glossina palpalis gambiensis and Glossina palpalis palpalis , major vectors of animal and human trypanosomes ) from 28 sites of West and Central Africa ., We found that the two subspecies are so divergent that they deserve the species status ., We found that G . p ., gambiensis from different landscapes ( Niayes of Senegal , savannah and coastal environments ) were significantly genetically different , and thus represent different adaptive entities or even subspecies ., We also confirm that G . p ., palpalis from Ivory Coast , Cameroon and DRC are strongly divergent ., These results provide an opportunity to examine whether these different types of tsetse fly might display different behaviour , dispersal patterns , host preferences and vectorial capacities ., This work also urges a revision of taxonomic status of Glossina palpalis subspecies and highlights again how fast ecological divergence can be , especially in host-parasite-vector systems .

journal.pcbi.1004170

Detailed Contact Data and the Dissemination of Staphylococcus aureus in Hospitals

Chains of transmission in communicable diseases are often identified by ad hoc strategies , combining retrospective information on locations attended and pathogen genetics to identify time-consistent transmission paths . 1, In contrast with such undertakings , “digital epidemiology” propose to use new technologies to prospectively measure contacts and understand transmission2 , 3 ., Close-proximity interactions ( CPIs ) between persons recorded by wireless sensors4 in real-life settings like schools or hospitals5 , 6 have been used as indicators of contact in this respect ., However , there is no evidence yet that such CPIs actually capture contacts explaining transmission ., To test this hypothesis , we designed a study where both Staphylococcus aureus carriage and CPIs were measured in a 200-bed long-term care facility with 5 wards ., This setting has several advantages for our purpose: S . aureus is commonly found in healthcare facilities , colonizing patients and healthcare workers ( HCWs ) ; S . aureus carriage in the nares is usually prolonged as the nares are the most consistent area from which it can be isolated , 7 allowing its detection by repeated routine screenings; identical genetic and antibiotic-resistance profiles show that S . aureus strains spread among patients and HCWs8 , 9; long-term care facilities harbor a stable population , with patients staying for extended periods under the care of dedicated staff ., The control of S . aureus transmission is also relevant for hospital hygiene , because its carriage increases the risk of healthcare-associated infections . 10, In our study , S . aureus carriage was identified in patients and , importantly , in HCWs every week by repeated nasal swabbing ., During the same period , all participants wore small wireless sensors that recorded their CPIs with each other in real time ( every 30 s ) ., To make the best use of this new data and account for the difference in temporal granularity , we first assessed the ability of several statistics to test the correlation of CPI records and S . aureus carriage ., These characteristics are first presented based on the analysis of simulations where a pathogen spread according to the CPI network edges , then applied to the original data ., Authorizations were obtained in accordance with French regulations regarding medical research and information processing ., All French IRB-equivalent agencies accorded the i-Bird program official approval ( CPP 08061; Afssaps 2008-A01284-51; CCTIRS 08 . 533; CNIL AT/YPA/SV/SN/GDP/AR091118 N°909036 ) ., Signed consent by patients and staff was not required according to the French Ethics Committee to which the project was submitted ., The I-Bird ( individual-based investigation of resistance dissemination ) study was conducted in a 200-bed long-term and rehabilitation hospital in northern France ., The hospital is organized in 5 wards corresponding to medical specialties ( geriatrics , neurology , nutrition , orthopedics , post-operative care ) ., During the study period , 329 distinct patients stayed in the facility ., Hospital staff ( HCWs and other administrative personnel ) numbered 261 ., In the text , all hospital staff is referred to as healthcare workers ( HCWs ) ., More details about the I-Bird investigation are provided in S1 Text ., When relevant , patients , nurses , nurses’ aides and physicians were analyzed according to the ward in which they stayed or worked; night-shift staff , reeducation therapists and administrative personnel were excluded from these analyses as they were not assigned to a particular ward ., During the study period , all individuals ( patients and HCWs ) wore a small wireless sensor that recorded , every 30 s , the identity of other sensors that were in close proximity ( typically < 1 . 5m , front-facing ) ., The deployment of such sensors did not rely on any stationary infrastructure to record CPIs , as each sensor directly stored timestamped CPIs on its on-board flash memory ., Further details on CPI collection and network reconstruction , along with descriptive characteristics of contact patterns , are available in S2 Text ., In the following , analyses are conducted on a dynamic CPI network aggregated at a daily scale by pair of individuals ( ie network edges ) ., We defined an individual’s k-hop neighborhood as all other individuals in the network who were found within k steps from him ., For example , the 1-hop neighborhood of an individual contained all his direct neighbors , while his 2-hop neighborhood contained both his direct neighbors and these neighbors’ neighbors ., All participants underwent weekly nasal swabs to monitor S . aureus carriage ., Upon detection of S . aureus colonization , the isolated strains were spa-typed11 , 12 and their resistance profiles to 20 antibiotics were determined ( see also S1 Text for detailed protocol ) ., The screening procedure had an expected sensitivity of 61 . 5% and specificity of 98 . 8% , 13 although higher sensitivity value has been reported ( ∼80% ) . 14, The anterior nares were preferred to other body areas because they harbor the most stable S . aureus colonization and also reflect on overall body carriage . 7 , 15, Furthermore , eradication of nasal carriage is also associated with eradication of skin carriage . 16 , 17, S . aureus strains were considered identical when they had the same spa type and antibiotic-resistance profile , in accordance with studies comparing spa typing to other molecular techniques . 11 , 12 , 18 , 19, Transmission events were identified by the isolation of a new S . aureus strain from a patient’s swabs , defining “incident colonization episodes” ., Because HCWs may be transiently colonized , 8 , 20 , 21 which would mostly be missed with weekly swabs , we only considered incidence in patients ., To account for imperfect S . aureus detection in case of multiple carriage , we also required that the new S . aureus strain had not been detected in the patient’s previous 2 swabs had he been colonized with another strain in the preceding week ., Each incident colonization episode was investigated to identify time-consistent CPI paths linking the incident case to a previous carrier of the same strain ., Recent CPI paths were favored over others by applying the following algorithm:, - All individuals carrying the same strain in the three preceding weeks were defined as “candidate transmitters” , regardless of their CPI connections ., - CPI paths to all candidate transmitters were looked for ., In case of existence , the candidate transmitter became a “CPI-supported candidate transmitter” ., In this case , the CPI path length ( in hops ) was computed ., - We sorted all CPI-supported candidate transmitters according to distance in time , then distance in hops ., The first CPI-supported candidate transmitter in this list was the “CPI supported transmitter” ., In other words , it was the least remote in number of hops among all candidate transmitters arising the least remote in time ., In case of ex aequo , one of the candidate transmitters was chosen at random if required ., We investigated 3 weeks before incidence as it allowed finding a CPI-supported transmitter for all incident episodes but 4 , which were not CPI-supported by exploring further back in time ., Testing for CPI supported transmission: test statistics & simulations ., We identified three observable quantities that would provide evidence for the correlation between CPIs and observed transmission: S1—The proportion of incident colonization episodes with least one CPI-supported transmitter; S2—The proportion of CPI-supported transmitters in direct CPI with an incident case; and S3—the length of the shortest CPI-supported transmission path ( a good proxy to the actual transmission path22 ) ., Each of these characteristics can be used to build a test , where , classically , observations would be compared to the expected values under the null hypothesis of independence between CPIs and transmission ., These expected values can be computed by a Monte Carlo approach: carriage information was first randomly permuted between participants ., To keep autocorrelation between successive swabs in the same individuals , we permuted carriage information over the 3 preceding weeks simultaneously ., As S . aureus prevalence was similar between patients and HCWs ( Table 1 ) , we did not take occupation into account for permutations ., For each incident colonization episode , 100 replicates of permuted carriage statuses were generated to simulate the distribution of statistics of interest for investigated strategies ., For S1 and S2 , we compared the observed percentages of CPI supported paths to that expected after random permutations of carriage ( e . g . in S1 , the observed percentage of CPI-supported episodes was compared to the average proportion of CPI-supported episodes among all permutations ) ., For S3 , the shortest CPI path length was averaged across all replicates for each incident colonization episodes , thus providing the expected distribution under the null ., The observed CPI path length distribution was then compared to that expected under the null using the Wilcoxon signed rank paired test ., To first study the characteristics of the three approaches and choose the most powerful , we used a simulation study based on a Susceptible—Colonized—Susceptible transmission model on the CPI network ., Stochastic simulations were performed to mimic observations of incident colonization episodes in our study ., First , the dynamic CPI network between all participants in a random 3-week long period was selected ., All individuals in this network were assumed initially noncolonized ( i . e . susceptible ) , except one randomly chosen to be the initial carrier in the first week ., For each day d in the following 3 weeks , a noncolonized individual ( say i ) could become colonized with probability, Pi ( d ) =1− ( 1−PPA ) nPA ( i , d−1 ) * ( 1−PHCW ) nHCW ( i , d−1 ) , where nPA ( i , d-1 ) is the number of carrier-patient neighbors on day d-1 and nHCW ( i , d-1 ) the number of carrier-HCW neighbors of i , PPA the probability of transmission per contact with a carrier patient and PHCW with a HCW ., Colonized individuals cleared colonization at a constant rate qPA = 0 . 1 days−1 for patients and a gHCW = 0 . 45 days−1 for HCWs in agreement with other studies . 23 , 24, The observation model closely imitated those of our investigation: from the wholly simulated transmission chain , we only selected data determining carriage once a week in each participant ., As in practice , the status of participants in the same ward was determined the same day ., Finally , an incident colonization episode was selected from new carriers in week 3 of the simulation , as in the original data ., Simulations were run to produce observations of a variable number of incident cases ., The power of each statistical approach , S1 to S3 , was determined as a function of the number of incident episodes ., We assessed the power of all proposed strategies with increasing numbers of incident colonization episodes ( 10 , 20 , 30 , 50 , 100 and 153 ) ., For each of these amounts , 500 replicates of 100 permutations were performed ., Each strategy was performed on every replicate ., Table 2 shows the power of the tests to reject the null hypothesis of independence between CPIs and transmission ., Strategy S1 , based on the existence of CPI supported transmitters , yielded very poor results ., Indeed , in almost all situations , a CPI-supported transmitter existed in the original data as well as in the permuted data , so that no difference from random was seen in this characteristic ., The percentage of incident cases in direct CPI with CPI-supported transmitters and the shortest CPI-supported path length yielded more useful procedures ., The length of the shortest CPI path from transmitter to incident case ( S3 ) was slightly more powerful than the percentage of transmitters in direct CPI with the incident case ( S2 ) , although both approaches had large power for rejecting the null for samples of size 153 ., The time to first S . aureus colonization was analyzed for 201 patients who were not colonized at admission: 73 experienced incident colonization ., The cumulative incidence of S . aureus colonization was 33% ( 95% confidence interval ( C . I . ) 25–41% ) 1 month after admission , with almost equal incidence of methicillin-resistant S . aureus ( MRSA ) ( 23 . 2% ( 95% C . I . 15 . 1–30 . 6% ) ) and methicillin-sensitive S . aureus ( MSSA ) ( 16 . 5% ( 95% C . I . 9 . 3–23 . 2% ) ) ., The risk of colonization did not change with the number of distinct direct neighbors during the preceding week ( ie weekly degree; hazard ratio ( HR ) = 1 . 05 ( 95% C . I . 0 . 95–1 . 21 ) for a 5-neighbor increase , P = 0 . 4 ) , using either the raw number of CPIs ( ie the sum of daily degrees; HR = 1 ( 95% C . I . 0 . 95–1 . 10 ) for a 5-CPI increase , P = 0 . 4 ) or the cumulative duration of CPIs ( ie the weight of network edges; HR = 1 ( 95% C . I . 0 . 99–1 . 01 ) for a 1-h increase , P = 0 . 6 ) ., The same conclusions were drawn for MSSA or MRSA colonizations ., Overall , 237 incident-colonization episodes were documented in 111 patients ( 144 MRSA , 93 MSSA ) ., For each incident episode , we identified “candidate transmitters” , i . e . people who had carried the same strain at any time in the preceding three weeks ., Among the 237 incident-colonization episodes , 173 ( 73% ) had 307 candidate transmitters , with no difference between MRSA and MSSA episodes ( 76% ( 110/144 ) vs . 68% ( 63/93 ) , P = 0 . 16 ) ., Episodes without a candidate transmitter did not occur earlier post-admission than others ( 8 . 1 vs . 8 . 3 weeks , P = 0 . 8 ) , or preferentially in some wards ( P = 0 . 13 ) ., Twenty of the 173 episodes with candidate transmitters were discarded as the incident case had a missing CPI record due to sensor failure in the preceding weeks ., We investigated all 153 incident colonization episodes detected from longitudinal swab data ., As expected from the simulations , a CPI path existed between the candidate transmitter and the incident case in almost all instances ( 97% , Table 3 ) ., The characteristics of the shortest CPI paths lengths from candidate transmitter to incident case were in favor of transmission along CPIs , with shorter paths in the original data than after random permutations ( Fig . 3 , Strategy S3: P < 0 . 001 ) ., This was therefore the sign that S . aureus transmissions detected from the I-Bird swabs were driven by CPIs ., A direct CPI contact existed between the candidate transmitter and incident case ( i . e . a CPI path of length 1 ) in 48% of the cases vs . only 30% expected by chance ( Strategy S2: P < 0 . 0001 ) ., A CPI path of length 2 was observed in 38% ( vs . 53% ) of the episodes and of length larger than 2 in the rest of the cases ., In most cases ( 64% ) , a CPI supported transmitter was found in the preceding week ., The remaining were found in the preceding two ( 23% ) or three weeks ( 13% ) ., We next investigated transmission differences according to occupation , focusing on dissemination events for which the path from candidate transmitter to incident case had exactly one ( noncolonized ) intermediary ., For these 2-hop CPI paths , S . aureus spread was more frequently observed when the intermediary was a HCW than a patient ( 2 . 1% vs . 1 . 8% , P = 0 . 0004 ) ., The relative risk ( RR ) of transmission by a HCW was therefore 1 . 2 ( 95% C . I . 1 . 1–1 . 3 ) ., This increased risk was more pronounced when the initial carrier was a patient ( 1 . 9% vs . 1 . 5% , P < 0 . 0001; RR = 1 . 3 ( 95% C . I . 1 . 1–1 . 4 ) ) rather than an HCW ( 3 . 2% vs . 3 . 0% , P = 0 . 48; RR = 1 . 1 ( 95% C . I . 0 . 9–1 . 2 ) ) ., All analyses were repeated using a thinned dynamic CPI network obtained by excluding short individual interactions lasting < 5 min prior to daily aggregation ., The thinned CPI network was still dense , including 8 . 1% of all potential interactions ., As expected , this decreased density increased the number of intermediaries between 2 persons ( mean = 11 ± SD = 6 ) compared to the full network and decreased the number ( mean = 4 ± SD = 1 ) and duration of daily CPIs ., The distribution of within/outside-ward CPIs was almost the same as before , with 75% of CPIs occurring within the ward ., Again , no risk factor that could increase the risk of colonization was identified ., The CPI support of transmission was even larger than before , with shorter path lengths in the original network than expected by chance ( P < 0 . 0001 ) ., In the thinned network , 64% of candidate transmitters were in the incident case’s 2-hop neighborhood ( i . e . direct or with one intermediary ) , compared to 46% with the permuted carriage data ., Keeping all candidate transmitters with a path to the incident case , rather than only the closest ones did not change the conclusions ., Excluding repeated incident episodes of the same strain in a given patient , leading to consider 129 transmission episodes only , did not affect the conclusions drawn regarding CPI support ., Finally , keeping only episodes that were CPI-supported in the first week before incidence led to the same conclusion ( 96 CPI-supported episodes , CPI path length significantly shorter ( P = 0 . 01 ) ) ., To account for imperfect sensitivity and possible false negatives in swabs , we discarded all incident episodes where the carriage had been positive/negative/positive with the same strain at both ends , as those may be false negatives ., This led to retain 129 incident colonization episodes with candidate transmitters and CPI records , among which 126 were CPI supported ( ie 98% ) ., The shortest CPI path length was significantly shorter than chance predicted ( P < 0 . 0001 ) ., The contribution of the contact network between patients and HCWs in explaining hospital-associated infections is widely accepted25 , 26 , although it has never been tested empirically ., Here , using electronic wireless devices to record close proximity interactions among persons in a hospital , we find evidence that these interactions are indeed informative for S . aureus transmission ., To date , such high-resolution contact networks were used to structure contacts in computerized models and study how characteristics of individuals27 , 28 and network topology2 , 29 could influence the course of outbreaks ., However , these findings relied on the underlying hypothesis that CPI paths actually captured dissemination paths ., Our results provide evidence that CPIs recorded by electronic sensors are indeed relevant to explain transmission ., This validates using such networks in future epidemiological studies , and should provide a powerful tool to better characterize risk and plan control measures for pathogens transmission in specific settings ., Recording interactions among individuals is increasingly easy using remote sensors30 ., In the study facility , wearing sensors was well accepted by patients and HCWs ., Because the recording was limited ( typically < 1 . 5 m ) , these signals may be a good proxy for real-life contacts between people ., Although contact surveys were shown to document contacts hardly overlapping with those recorded by electronic sensors , 31 direct observations by dedicated investigators provided more congruent data . 32, Individual surveys tend to omit contacts of short duration , 31 and therefore electronic sensors might capture more complete data regarding contact patterns ., In our study , although most CPIs occurred within the wards ( leading to clustered communities , as seen in Fig . 1 ) , the CPI network was rather dense , covering up to 20% of all possible interactions among participants ., The shortest path between any two individuals had few intermediaries , a typical “small-world” feature33 ., HCWs spent approximately 20% of their work shifts in direct contact with patients ( 1h50 out of 8h ) , in the same range as that reported in an emergency unit ( ∼30% ) 34 and the cumulated CPIs duration in a HCW compared with that reported for another ICU ( ∼2 . 2 h ) 32 ., However , in sharp contrast with an earlier study conducted in a pediatric setting30 , where almost no contacts existed between patients , CPIs between patients herein were frequent and prolonged , as expected in a long-term care and rehabilitation facility where patients can initiate social interaction with others more easily than in acute-care hospital ., Finally , ward organization was important in structuring contacts between patients and HCWs ., This contact-clustering suggests that some interaction patterns between patients and HCWs could be rather constant from one hospital to the next , e . g . , the numbers and durations of interactions , but that the full contact network may depend on type of care and ward organization ., Other features of interest were the quick encounter of most of direct contacts during the first week after admission and that an incoming patient’s 3-hop neighborhood quickly encompassed almost all individuals in the hospital ., This small-world feature may profoundly affect the potential spread of pathogens , increasing the size and speed of outbreaks33 , 35 ., S . aureus carriage was common among patients and HCWs , with a significant percentage of patients already colonized at admission ( 33 . 7% for S . aureus; 17% for MSSA; 18 . 9% for MRSA ) ., Approximately one-third of noncolonized , newly admitted patients became colonized with S . aureus within the first month post-admission , as frequently observed in long-term care facilities36 , 37 ., The cumulative MRSA and MSSA incidence rate were similar after 1 month , as were their mean carriage prevalence , suggesting little , if any , difference in transmission between resistant and non-resistant strains ., Patients admitted to long-term care facilities come from other hospitals and prevalence of carriage at admission is large ., We analyzed incident carriage episodes only to focus on transmission occurring within the long-term care facility ., The numbers and durations of contacts , although pointing towards increased risk for participants with more and longer contacts , were not by themselves strongly associated with S . aureus colonization: additional information on whether the contacts were carriers is likely to be required in this respect ., Finally , because only weekly nasal swabs were conducted , neither hand carriage nor transient colonization episodes ( <1 week ) were considered , although hand carriage by HCWs has been described8 ., In our analysis , this may lead to imperfect observation of the carriage status , as participants may have cleared colonization between successive swabs ., In our study , S . aureus carriage was determined by nasal swabs ., As previously stated , skin colonization was not detected ., Yet , previous studies have shown that while S . aureus can be isolated from many anatomic location , nasal swabs were consistent with carriage isolated from other area of the body in 82% of the case38 ., In HCWs , where transient colonization is more likely to occur , this might lead to underestimate prevalence and therefore overestimate incidence , as an ( unobserved ) skin carriage could lead to a longer , more stable , colonization of the nares ., For this reason , we chose not to include incident colonization episodes occurring in HCWs ., Although the main route of transmission for S . aureus remains physical contact with carrier individuals , transmission through the environment is also possible , for example in the form of fomites . 26, Our procedure cannot distinguish between routes of transmission leading to short CPI paths , for example , if contact with fomites occurred only when people were at CPI range of a known carrier ., Yet , our results suggest that CPIs , as defined in our study setring , correlated with S . aureus transmission routes and are therefore a good proxy35 for interactions leading to S . aureus dissemination ., The choice of a test statistic to test for transmission along CPIs required giving some consideration to the setup of this study ., First , the density of contacts between participants was large , as in occupational networks measured in health care structures . 24 , 30, This leads to percolation , 39 with typical short distance between participants ., Therefore , a CPI path between a S . aureus carrier and an incident case was not specific enough of transmission: it was the rule rather than the exception , explaining why strategy S1 could not evidence association ., Second , while CPIs could be recorded continuously , it is obvious that S . aureus carriage cannot be determined as frequently ., Some participants could therefore clear carriage between two successive swabs , hiding their role in transmission ., In this case , direct CPI would not be seen in the recorded CPIs ., However , a short CPI path may be found to a more distant carrier through non-colonized intermediaries and would still be supportive of transmission ., The shortest CPI path between CPI-supported transmitters and incident cases allowed to account for carriage gaps in observed transmission chains and was more informative than mere existence of a connecting path ., The power comparison between strategies S1 , S2 and S3 showed that this was indeed the case ., It also evidenced that the proposed tests actually discriminated between transmission along the CPI paths and random transmission with no relationship to the CPI network ., In contrast to weekly swabbing , wireless sensors recorded interactions permanently and made it unlikely that the network of interactions was imperfectly observed ., S . aureus transmission is thought to occur mainly through physical contacts and these should be present in the CPI recordings ., However , the CPI network may capture additional interactions that are unlikely to lead to transmission ., To focus on interactions that were the most likely to lead to transmission , for example nursing care , we discarded all CPIs lasting less than 5 min ., In this thinned network , we found again evidence that paths defined by CPIs supported transmission ., Finally , the increased likelihood of S . aureus spread through a—seemingly noncolonized—HCW intermediary was in good agreement with their importance in transmission and the occurrence of transient colonization among them ., The hypothesis that CPIs are a good proxy for contacts leading to transmission of S . aureus is highly plausible a priori ., Indeed , the main route of transmission for S . aureus is physical contact; those led to CPI records as sensors recorded all physical proximity ( <1 . 5m ) ., The mechanism of transmission was therefore captured by CPIs ., With the evidence we provide on the correlation between CPI paths and transmission events , this strengthens the interest of this proxy measure as a cheap , feasible and informative method for studying S . aureus transmission ., The joint analysis of S . aureus carriage and CPI data collected during 4 months provided evidence that CPIs capture contacts associated with transmission ., This supports using CPI information to improve the realism of transmission models ., This also suggests that a more systematic in-depth study of CPI networks could provide new directions for controlling S . aureus transmission in hospitals .

Introduction, Materials and Methods, Results, Discussion, Conclusion

Close proximity interactions ( CPIs ) measured by wireless electronic devices are increasingly used in epidemiological models ., However , no evidence supports that electronically collected CPIs inform on the contacts leading to transmission ., Here , we analyzed Staphylococcus aureus carriage and CPIs recorded simultaneously in a long-term care facility for 4 months in 329 patients and 261 healthcare workers to test this hypothesis ., In the broad diversity of isolated S . aureus strains , 173 transmission events were observed between participants ., The joint analysis of carriage and CPIs showed that CPI paths linking incident cases to other individuals carrying the same strain ( i . e . possible infectors ) had fewer intermediaries than predicted by chance ( P < 0 . 001 ) , a feature that simulations showed to be the signature of transmission along CPIs ., Additional analyses revealed a higher dissemination risk between patients via healthcare workers than via other patients ., In conclusion , S . aureus transmission was consistent with contacts defined by electronically collected CPIs , illustrating their potential as a tool to control hospital-acquired infections and help direct surveillance .

Recent advances in communication technologies allow monitoring high-resolution contact networks ., Close proximity interactions ( CPIs ) measured by wireless sensors are increasingly used to inform contact networks for the dissemination of pathogens in computational models , although empirical justification is lacking ., Here , we conducted a longitudinal prospective study for four months in a hospital , including both patients and healthcare workers ( HCWs ) ., High-resolution CPIs were recorded continuously , and participants undertook weekly nasal swabs to detect S . aureus carriage ., We set out to test whether the contact network measured by CPIs supported observed transmission episodes ., A simulation study was first conducted to choose a test statistic for the association of CPI paths with transmission , showing that CPI path length from transmitter to incident case was the most powerful ., Then , we selected patients presenting incident S . aureus colonization in the data ., We showed that CPI paths existed to carriers of the same strain , with path lengths significantly shorter than between random pairs of participants , in agreement with the transmission hypothesis ., In-hospital contact networks measured by CPIs inform on opportunities for pathogen transmission ., These could be used in surveillance systems to help prevent the spread of nosocomial pathogens .

journal.pcbi.1000072

Transient Cognitive Dynamics, Metastability, and Decision Making

The dynamical approach for studying brain activity has a long history and is currently one of strong interest 1–7 ., Cognitive functions are manifested through the generation and transformation of cooperative modes of activity ., Different brain regions participate in these processes in distinct ways depending on the specific cognitive function and can prevail in different cognitive modes ., Nevertheless , the mechanisms underlying different cognitive processes may rely on the same dynamical principles , e . g . , see 8 ., The execution of cognitive functions is based on fundamental asymmetries of time – often metaphorically described as the arrow of time ., This is inseparably connected to the temporal ordering of cause-effect pairs ., The correspondence between causal relations and temporal directions requires specific features in the organization of cognitive system interactions , and on the microscopic level , specific network interconnections ., A key requirement for this organization is the presence of nonsymmetrical interactions because , even in brain resting states , the interaction between different subsystems of cognitive modes also produces nonstationary activity that has to be reproducible ., One plausible mechanism of mode interaction that supports temporal order is nonreciprocal competition ., Competition in the brain is a widespread phenomenon ( see 9 for a remarkable example in human memory systems ) ., At all levels of network complexity , the physiological mechanisms of competition are mainly implemented through inhibitory connections ., Symmetric reciprocal inhibition leads to multistability and this is not an appropriate dynamical regime for the description of reproducible transients ., As we have shown in 5 , 10 , nonsymmetric inhibition is an origin of reproducible transients in neural networks ., Recently functional magnetic-resonance imaging ( fMRI ) and EEG have opened new possibilities for understanding and modeling cognition 11–15 ., Experimental recordings have revealed detailed ( spatial and temporal ) pictures of brain dynamics corresponding to the temporal performance of a wide array of mental and behavioral tasks , which usually are transient and sequential 16–18 ., Several groups have formulated large-scale dynamical models of cognition ., Based on experimental data these models demonstrate features of cognitive dynamics such as metastability and fast transients between different cognitive modes 15 , 16 , 19–24 ., There is experimental evidence to support that metastability and transient dynamics are key phenomena that can contribute to the modeling of cortex processes and thus yield a better understanding of a dynamical brain 18 , 25–30 ., Common features of many cognitive processes are:, ( i ) incoming sensory information is coded both in space and time coordinates ,, ( ii ) cognitive modes sensitively depend on the stimulus and the executed function ,, ( iii ) in the same environment cognitive behavior is deterministic and highly reproducible , and, ( iv ) cognitive modes are robust against noise ., These observations suggest, ( a ) that a dynamical model which possesses these characteristics should be strongly dissipative so that its orbits rapidly “forget” the initial state of the cognitive network when the stimulus is present , and, ( b ) that the dynamical system executes cognitive functions through transient trajectories , rather than attractors following the arrow of time ., In this paper we suggest a mathematical theory of transient cognitive activity that considers metastable states as the basic elements ., This paper is organized as follows ., In the Results section we first provide a framework for the formal description of metastable states and their transients ., We introduce a mathematical image of robust and reproducible transient cognition , and present a basic dynamical model for the analysis of such transient behavior ., Then , we generalize this model taking into account uncertainty and use it for the analysis of decision making ., In the Discussion , we focus on some open questions and possible applications of our theory to different cognitive problems ., In the Methods section , a rigorous mathematical approach is used to formulate the conditions for robustness and reproducibility ., A dynamical model of cognitive processes can use as variables the activation level Ai ( t ) ≥0 of cognitive states ( i\u200a=\u200a1…N ) of specific cognitive functions 31 ., The phase space of such model is then the set of Ai ( t ) with a well-defined metric where the trajectories are sets of cognitive states ordered in time ., To build this model , we introduce here several theoretical ideas that associate metastable states and robust and reproducible transients with new concepts of nonlinear dynamics , i . e . , stable heteroclinic sequences and heteroclinic channels 4 , 5 , 10 , 32–34 ., The main ideas are the following: Based on these ideas we model the temporal evolution of alternating cognitive states by equations of competitive metastable modes ., The structure of these modes can be reflected in functional neuroimage experiments ., Experimental evidence suggests that for the execution of specific cognitive functions the mind recruits the activity from different brain regions 35–37 ., The dynamics of such networks is represented by sequences of switchings between cognitive modes , i . e . , as we hypothesize , a specific SHC for the cognitive function of interest ., We suggest here that the mathematical image of reproducible cognitive activity is a stable heteroclinic channel including metastable states that are represented in the phase space of the corresponding dynamical model by saddle sets connected via unstable separatrices ( see Figure 1 ) ., Note that the topology of Figure 1 reminds a ‘chaotic itinerancy’ 38 ., However , based only on Milnor attractors we cannot demonstrate the reproducibility phenomena which is the main feature of the SHC ., To make our modeling more transparent let us use as an example the popular dynamical image of rhythmic neuronal activity , i . e . , a limit cycle ., At each level of complexity of a neural system , its description and analysis can be done in the framework of some basic model like a phase equation ., The questions that can be answered in this framework are very diverse: synchronization in small neuronal ensembles like CPGs , generation of brain rhythms 39 , etc ., Our approach here is similar ., We formulate a new paradigm for the mathematical description of reproducible transients that can be applied at different levels of the network complexity pyramid ., This paradigm is the Stable Heteroclinic Channel ., As a limit cycle , the SHC can be described by the same basic equation on different levels of the system complexity ., The sense of the variables Ai ( t ) ≥0 , of course , is different at each level ., Before we introduce the basic model for the analysis of reproducible transient cognitive dynamics , it is important to discuss two general features of the SHC that do not depend on the model ., These are:, ( i ) the origin of the structural stability of the SHC , and, ( ii ) the long passage time in the vicinity of saddles in the presence of moderate noise ., To understand the conditions of the stability of SHC we have to take into account that an elementary phase volume in the neighborhood of a saddle is compressed along the stable separatrices and it is stretched along an unstable separatrix ., Let us to order the eigenvalues of a saddle asThe number is called the saddle value ., If vi>1 ( the compressing is larger than the stretching ) , the saddle is named as a dissipative saddle ., Intuitively it is clear that the trajectories do not leave the heteroclinic channel if all saddles in the heteroclinc chain are dissipative ., A rigorous analysis of the structural stability of the heteroclinic channel supports our intuition ( see Methods ) ., The problem of the temporal characteristics of the transients is related to the “exit problem” for small random perturbations of dynamical systems with saddle sets ., This problem was first solved by Kifer 40 and then discussed in several papers , in particular , in 41 ., A local stability analysis in the vicinity of a saddle fixed point allows us to estimate the time that the system spends in the vicinity of the saddle: ( 1 ) where τ ( p ) is the mean passage time , |η| is the level of noise , and λ is an eigenvalue corresponding to the unstable separatrix of the saddle ., A biologically reasonable model that is able to generate stable and reproducible behavior represented in the phase space by the SHC has to, ( i ) be convenient for the interpretation of the results and for its comparison with experimental data ,, ( ii ) be computationally feasible ,, ( iii ) have enough control parameters to address a changing environment and the interaction between different cognitive functions ( e . g . , learning and memory ) ., We have argued that the dynamical system that we are looking for has to be strongly dissipative and nonlinear ., For simplicity , we chose as dynamical variables the activation level of neuronal clusters that consist of correlated/synchronized neurons ., The key dynamical feature of such models is the competition between different metastable states ., Thus , in the phase space of this basic model there must be several ( in general many ) saddle states connected by unstable separatrices ., Such chain represents the process of sequential switching of activity from one cognitive mode to the next one ., This process can be finite , i . e . , ending on a simple attractor or repetitive ., If we choose the variables Aj ( t ) as the amount of activation of the different modes , we can suppose that the saddle points are disposed on the axes of an N-dimensional phase space , and the separatrices connecting them are disposed on a ( N−n ) -dimensional manifold ( n<N−1 ) , which are the boundaries of the phase space ., We will use two types of models that satisfy the above conditions:, ( i ) the Wilson-Cowan model for excitatory and inhibitory neural clusters 42 , and, ( ii ) generalized Lotka-Volterra equations – a basic model for the description of competition phenomena with many participants 32 , 43 ., Both models can be represented in a general form as: ( 2 ) Here Aj ( t ) ≥0 is the activation level of the j-th cluster , Θz is a nonlinear function , i . e . , a sigmoid function in the case of the Wilson-Cowan model and a polynomial one for the generalized Lotka-Volterra model ., The connectivity matrix ρji can depend on the stimulus or change as a result of learning ., σ ( I ) is a parameter characterizing the dependence of the cognitive states on the incoming information I ., The parameter β represents other types of external inputs or noise ., In the general case , Aj ( t ) is a vector function whose number of components depends on the complexity of the intrinsic dynamics of the individual brain blocks ., The cognitive mode dynamics can be interpreted as a nonlinear interaction of such blocks that cooperate and compete with each other ., To illustrate the existence of a stable heteroclinic channel in the phase space of Equation 2 , let us consider a simple network that consists of three competitive neural clusters ., This network can be described by the Wilson-Cowan type model as ( 3 ) where ρjj<0 , ρj≠i≥0 , β>0 , N\u200a=\u200a3 . The network can also be described by a Lotka-Volterra model of the form: ( 4 ) where ρji≥0 ., In all our examples below we will suppose that the connection matrix is non symmetric , i . e . , ρji≠ρij , which is a necessary condition for the existence of the SHC ., Figure 2 illustrates how the dynamics of these two models with N\u200a=\u200a3 can produce a robust sequential activity: both models have SHC in their phase-spaces ., The main difference between the dynamics of the Wilson-Cowan and Lotka-Volterra models is the type of attractors ., System 3 contains a stable limit cycle in a SHC and a stable fixed point ( the origin of the coordinates for β\u200a=\u200a0 ) ., In contrast , there is one attractor , i . e . , a SHC , in the phase space of System 4 . Both models demonstrate robust transient ( sequential ) activity even for many interacting modes ., An example of this dynamics is presented in Figure 3 . This figure shows the dynamics of a two-component Wilson-Cowan network of 100 excitatory and 100 inhibitory modes ., The parameters used in these simulations are the same as those reported in 44 where the connectivity was drawn from a Bernoulli random process but with the probability of connections slightly shifted with respect to the balanced excitatory-inhibitory network ., The system is organized such that a subgroup of modes fall into a frozen component and the rest produce the sequential activity ., The model itself is sufficiently general to be translated to other concepts and ideas as the one proposed here in the form of cognitive modes ., Figure 4 illustrates the reproducibility of transient sequential dynamics of Model 4 with N\u200a=\u200a20 modes ., This simulation corresponds to the following conditions:, ( i ) ρji≠ρij and, ( ii ) vi>1 ( see 10 for details ) ., In this figure each mode is depicted by a different color and the level of activity is represented by the saturation of the color ., The system of equations was simulated 10 times , each trial starting from a different random initial condition within the hypercube ., Note the high reproducibility of the sequential activation among the modes , which includes the time interval between the switchings ., Because of the complexity of System 4 with large N , the above conditions cannot guarantee the absence of other invariant sets in this system ., However we did not find them in our computer simulations ., For a rigorous demonstration of the structural stability of the SHC see Methods section ., It is important to emphasize that the SHC may consist of saddles with more than one unstable manifold ., These sequences can also be feasible because , according to 40 and 45 , if a dynamical system is subjected to the influence of small noise , then for any trajectory going through an initial point in a neighborhood of such saddle , the probability to escape this neighborhood following a strongly unstable direction is almost one ., The strongly unstable direction corresponds to the maximal eigenvalue of the linearization at the saddle point ., In other words , everything occurs in the same way as for the SHC; one must only replace the unstable separatrices in the SHC by strongly unstable manifolds of saddle points ., As we mentioned above , the variables Ai ( t ) ≥0 in the basic Equations 2 or 4 can be interpreted in several different ways ., One of them which is related to experimental work is the following ., Using functional Principal Component ( PC ) analysis of fMRI data ( see , for example 46 ) it is possible to build a cognitive “phase space” based on the main orthogonal PCs ., A point in such phase space characterizes the functional cognitive state at instant t ., The set of states in subsequent instants of time is a cognitive trajectory that represents the transient cognitive dynamics ., Decisions have to be reproducible to allow for memory and learning ., On the other hand , a decision making ( DM ) system also has to be sensitive to new information from the environment ., These requirements are fundamentally contradictory , and current approaches 47–50 are not sufficient to explain the use of sequential activity for DM ., Here , we formulate a new class of models suitable for analyzing sequential decision making ( SDM ) based on the SHC concept , which is a generalization of Model 4 . A key finding in Decision Theory 51 is that the behavior of an individual shifts from risk-aversion ( when possible gains are predicted ) to risk seeking ( when possible losses are predicted ) ., In particular , Kahneman and Tversky 52 conducted several experiments to test decision making under uncertainty ., They showed that when potential profits are concerned , decision-makers are risk averse , but when potential losses are concerned , subjects become risk seeking ., Other classical paradigms assume that decision makers should always be risk averse , both when a potential profit and when a possible loss are predicted ., We have provided in this paper a theoretical description of the dynamical mechanisms that may underlie some cognitive functions ., Any theoretical model of a very complex process such as a cognitive task should emphasize those features that are most important and should downplay the inessential details ., The main difficulty is to separate one from another ., To build our theory we have chosen two key experimental observations: the existence of metastable cognitive states and the transitivity of reproducible cognitive processes ., We have not separated the different parts of the brain that form the cognitive modes for the execution of a specific function ., The main goal of such coarse grain theory is to create a general framework of transient cognitive dynamics that is based on a new type of model that includes uncertainty in a natural way ., The reproducible transient dynamics based on SHC that we have discussed contains two different time scales , i . e . , a slow time scale in the vicinity of the saddles and a fast time scale in the transitions between them ( see Figure 1 ) ., Taking this into account , it is possible to build a dynamical model based not on ODEs but on a Poincare map ( see for a review 5 ) , which can be computationally very efficient for modeling a complex system ., Winnerless competitive dynamics ( represented by a number of saddle states whose vicinities are connected by their unstable manifolds to form a heteroclinic sequence ) is a natural dynamical image for many transient cognitive activities ., In particular we wish to mention transient synchronization in the brain 54 , where authors have studied the dynamics of transitions between different phase-synchronized states of alpha activity in spontaneous EEG ., Alpha activity has been characterized as a series of globally synchronized states ( quasi-stable patterns on the scalp ) ., We think that this dynamics can be described on the framework of the winnerless competition principle ., From the theoretical point of view , a heteroclinic network between partially synchronized phase clusters has been analyzed in 55 , 56 ., The SHC concept allows considering transitions even between synchronized states with strongly different basic frequencies ( like gamma and beta frequencies ) ., Cognitive functions can strongly influence each other ., For example , when we model decision making we have to take into account attention , working memory and different information sources ., In particular , the dynamic association of various contextual cues with actions and rewards is critical to make effective decisions 57 ., A crucial question here is how to combine several reward predictions , each of which is based on different information: some reward predictions may only depend on visual cues , but others may utilize not only visual and auditory cues but also the action taken by a subject ., Because the accuracy of different reward predictions varies dynamically during the course of learning , the combination of predictions is important 58 ., In a more general view , the next step of the theory has to be the consideration of mutual interaction of models like Model 4 that represent the execution of different cognitive functions ., The dynamical mechanisms discussed in this paper can contribute to the interpretation of experimental data obtained from brain imaging techniques , and also to design new experiments that will help us better understand high level cognitive processes ., In particular , we think that the reconstruction of the cognitive phase space based on principal component analysis of fMRI data will allow finding the values of the dynamical model parameters for specific cognitive functions ., To establish a direct relation between model variables and fMRI data will be extremely useful to implement novel protocols of assisted neurofeedback 59–62 , which can open a wide variety of new medical and brain-machine applications ., We consider a system of ordinary differential equations ( M1 ) where the vector field X is C2-smooth ., We assume that the system M1 has N equilibria Q1 , Q2 , … , QN , such that each Qi is a hyperbolic point of saddle type with one dimensional unstable manifold that consists of Qi and two “separatrices” , the connected components of which we denote by ., We assume also that , the stable manifold of Qi+1 ., We consider now another system , say , ( M3 ) that also has N equilibria of saddle type Q1 , Q2 , … , QN with one dimensional unstable manifold , and with vi>1 , i\u200a=\u200a1 , … , N ., Denote by Ui a small open ball of radius ε centered at Qi ( one may consider , of course , any small neighborhood of Qi ) that does not contain invariant sets but Qi ., The stable manifold divides Ui into two parts: containing a piece of , and another one ., Assume that , and denote by the connected component of containing Qi and that ., Denote by the δ-neighborhood of in ℜd .

Introduction, Results, Discussion, Methods

The idea that cognitive activity can be understood using nonlinear dynamics has been intensively discussed at length for the last 15 years ., One of the popular points of view is that metastable states play a key role in the execution of cognitive functions ., Experimental and modeling studies suggest that most of these functions are the result of transient activity of large-scale brain networks in the presence of noise ., Such transients may consist of a sequential switching between different metastable cognitive states ., The main problem faced when using dynamical theory to describe transient cognitive processes is the fundamental contradiction between reproducibility and flexibility of transient behavior ., In this paper , we propose a theoretical description of transient cognitive dynamics based on the interaction of functionally dependent metastable cognitive states ., The mathematical image of such transient activity is a stable heteroclinic channel , i . e . , a set of trajectories in the vicinity of a heteroclinic skeleton that consists of saddles and unstable separatrices that connect their surroundings ., We suggest a basic mathematical model , a strongly dissipative dynamical system , and formulate the conditions for the robustness and reproducibility of cognitive transients that satisfy the competing requirements for stability and flexibility ., Based on this approach , we describe here an effective solution for the problem of sequential decision making , represented as a fixed time game: a player takes sequential actions in a changing noisy environment so as to maximize a cumulative reward ., As we predict and verify in computer simulations , noise plays an important role in optimizing the gain .

The modeling of the temporal structure of cognitive processes is a key step for understanding cognition ., Cognitive functions such as sequential learning , short-term memory , and decision making in a changing environment cannot be understood using only the traditional view based on classical concepts of nonlinear dynamics , which describe static or rhythmic brain activity ., The execution of many cognitive functions is a transient dynamical process ., Any dynamical mechanism underlying cognitive processes has to be reproducible from experiment to experiment in similar environmental conditions and , at the same time , it has to be sensitive to changing internal and external information ., We propose here a new dynamical object that can represent robust and reproducible transient brain dynamics ., We also propose a new class of models for the analysis of transient dynamics that can be applied for sequential decision making .

mathematics, neuroscience/cognitive neuroscience, computational biology/computational neuroscience, computational biology

journal.pgen.1003676

The Genome of Spraguea lophii and the Basis of Host-Microsporidian Interactions

Microsporidia are a diverse phylum of obligate intracellular parasites related to fungi ., Over 1300 species have been described in approximately 160 genera and , as is the case for other microbial eukaryotes , a vast undescribed diversity is thought to exist in the environment 1 ., Microsporidia are important pathogens of a broad range of animal groups: they can infect immunocompromized humans , such as those with HIV/AIDS , and are major pathogens of fish and invertebrates , representing a significant threat to sericulture 2 and fisheries 3 ., Their unusual lifecycle has also attracted attention , particularly the unique mechanism by which microsporidia gain entrance to host cells ., Outside the host cell , microsporidia exist as a resistant spore containing a coiled polar tube ., Upon coming into contact with a host cell , or appropriate stimulus , the spore rapidly everts this tube , penetrating the host cell membrane and delivering the spore contents to the host cytoplasm , where proliferation and the next round of spore production occurs 4 ., In addition to their importance as parasites of animals , Microsporidia have attracted much attention as eukaryotic model systems for reductive genome evolution ., The 2 . 9 Mb genome of the microsporidian Encephalitozoon cuniculi was one of the first eukaryotic genomes to be sequenced 5 ., Analysis of the E . cuniculi genome revealed a highly reduced and streamlined genome which had lost or simplified many biochemical pathways , had truncated genes , shortened intergenic spaces and had almost entirely lost introns and repetitive DNA 5; its close relative Encephalitozoon intestinalis has an even smaller genome , at 2 . 3 Mb 6 ., Interestingly , while microsporidian genomes are consistently smaller than those of their opisthokont relatives , there is a ten-fold difference in genome size within the phylum , with some genomes as large as 24 Mb 7 ., To date , it has proven difficult to relate this genomic variation to differences in parasite biology or host preference ., This is because all microsporidia sequenced so far share a broadly conserved core proteome , with differences in genome size due largely to changes in gene density , transposon content , and expansions of uncharacterized , lineage-specific or fast evolving protein families 8–10 ., As the main differences in coding capacity among sequenced microsporidian lineages , it seems reasonable to hypothesize that these lineage-specific or fast evolving proteins play a role in mediating host-parasite interactions ., However , because they lack any detectable similarity to genes from model eukaryotes , the functions of these proteins are difficult to predict using bioinformatics ., Combined with the current lack of a system for genetic manipulation in these parasites , this makes understanding the basis of host-microsporidian interactions extremely challenging ., Beyond identification of proteins of the spore wall and polar tube , very little known about molecular basis of spore germination and eversion of the polar tube , or the exact details of how the microsporidian sporoplasm is transferred into the host cell ., Even though microsporidia can have drastic effects on the organization of the host cell , we know little of the virulence factors and effector proteins that bring about these changes or how they are delivered into the host cell environment either directly or via the parasitophorous vacuole in human infective species such as E . cuniculi and E . intestinalis ., Spraguea lophii is a microsporidian that infects the monkfish Lophius piscatorius and Lophius budegassa , inhabiting both the North Atlantic and Mediterranean regions 11 ., Compared to those microsporidia that have been sequenced so far , S . lophii is an attractive model for identifying microsporidian effector proteins and investigating host-parasite interactions , despite the current lack of an in vitro culture system 12 ., Infection with S . lophii results in the formation of xenomas , large clusters of spore-filled cells in the vagal nerves of the fish that can be several centimeters in diameter and contain spores in various stages of development ( Figure 1 ) ., In the monkfish , no fitness effects are known to be associated with Spraguea infection , and the prevalence rate can be as high as 83% 13 , 14 ., However , xenoma formation in salmon infected with related microsporidia commonly localizes to the gills and is a considerable threat in aquaculture 15 ., S . lophii spores are easily purified from xenomas in large quantities , providing an opportunity to study germination and to perform experiments that are difficult or impossible in other microsporidia due to the difficulty of obtaining sufficient amounts of parasite material ., In addition , S . lophii is the first microsporidian parasite of fish to be sequenced , with the potential to provide new insights into host-parasite interactions in these economically important vertebrates ., Our aim here is to provide a genomic resource that will facilitate future work on this promising model microsporidian ., S . lophii was the first microsporidian to be explored with genome-scale sequencing , and 120 Kb of the genome has previously been published 16 ., Here we present 4 . 98 Mb of unique sequence from the S . lophii genome as determined by Illumina sequencing , representing 70–80% of the complete genome ( estimated at 6 . 2–7 . 3 Mb 17–19 ) and , based on our analyses , the great majority of the coding DNA ., We investigated the evolution of the S . lophii proteome , using OrthoMCL 20 to identify proteins that are unique to S . lophii and may therefore be associated with the unique xenoma formation seen in microsporidian infections of fish ., To explore the utility of S . lophii as a model for understanding core features of microsporidian biology , we combined complex mix proteomics with TruSeq transcriptomics to characterize the proteins expressed and secreted during spore germination , a key time point in the lifecycle of this intracellular parasite ., Our results highlighted the importance of microsporidia-specific and fast evolving proteins in germination and host interaction ., The sequence data are summarized in Table 1 ., Our sequencing resulted in 1392 contigs over 500 bp in length to give a total of 4982 Kb ( This Whole Genome Shotgun project has been deposited at DDBJ/EMBL/GenBank under the accession ATCN00000000 . The version described in this paper is version ATCN01000000 ) ., These contigs had a maximum length of 46788 bp and an N50 length of 5923 ., The average coverage across these contigs is 70× ( Mode\u200a=\u200a29× , Median\u200a=\u200a59× ) ., The overall GC content is low at 23 . 4% , rising slightly to 25 . 7% in protein coding regions ., The karyotype has been investigated both by pulse field electrophoresis and by using an ultrathin multiwire proportional chamber-based detector 17–19 ., This predicts a variable karyotype between isolates from different geographic regions , but the consensus is that there are 15 chromosomes , of which 10–13 are unique , and the overall genome size is estimated at 6 . 2 to 7 . 3 Mb ., In our assembly we identified 2 , 573 predicted open reading frames or fragments of , which made up 52% of our assembly ., This gives it an intermediate coding density amongst microsporidia , which is consistent with an emerging pattern for microsporidian genomes where coding density decreases with increasing genome size ., For example , Trachipleistophora hominis has 34% coding DNA and an estimated genome size of 8 . 5–11 . 6 Mb , while 86% of the 2 . 9 Mb E . cuniculi genome is made up of coding DNA 5 , 8 ., Our assembly covers 70–80% of the S . lophii genome based on size estimates , but is likely enriched for coding regions ., To evaluate the completeness of our assembly , we searched the S . lophii genome for the presence of genes involved in a range of core metabolic pathways as described previously 8 , 21 ( Table S1 ) ., Our assembly encodes most major metabolic pathways in full , including glycolysis and trehalose metabolism as well as a full complement of transfer RNAs ( tRNAs ) and protein kinases ( Table S1 ) ., We identified only a few absences , many of which are also absent in related microsporidia such as T . hominis , lending support to our coverage of the protein-coding component of the genome ., As an independent check on the completeness of our assembly , we compared the transcripts from our de novo transcriptome assembly ( see below ) to the genes predicted on the genome ., Based on BLAST similarity to genes from other microsporidia , there were only 20 additional S . lophii genes in the transcriptome that did not map to the genome assembly , 5 of which represent transcripts from LTR retrotransposons ., Taken together , these analyses suggest that our assembly represents a largely complete sampling of the coding component of the S . lophii genome ., To investigate the evolution of gene content in Spraguea , we mapped the taxonomic distribution of microsporidian gene families onto a cladogram ( Figure 2A ) ( derived from a multiprotein phylogeny - see below ) ., We built gene families using OrthoMCL 20 on a broad sampling of microsporidian genomes , with Homo sapiens and Saccharomyces cerevisiae as opisthokont outgroups ., Our analysis included genomes covering a broad taxonomic spectrum of sequenced microsporidia , including Nematocida parisii Ertm1 22 , T . hominis 8 , Nosema ceranae 23 , E . cuniculi 5 , and Enterocytozoon bieneusi 24 ., The results indicated that 19% of the predicted proteins are shared with all sampled opisthokonts , 1% are specific to sampled fungi , 4% are specific to microsporidia and conserved across the group , 3% are found in clusters of proteins only present in T . hominis and S . lophii ., 42% the 2499 analysed S . lophii proteins , do not cluster with proteins from any of the other organisms in our analysis and of these 30% cluster with other S . lophii proteins , indicating that they are part of multiprotein families within the S . lophii genome ., Table S2 gives a complete classification of the S . lophii proteome by OrthoMCL analysis ., E . cuniculi and other microsporidia have a reduced lipid metabolism repertoire 5 , 24 ., A key missing step is the initial reaction of fatty acid synthesis , the carboxylation of acetyl-CoA to malonyl-CoA by acetyl-CoA carboxylase ., Homologues of both acetyl-CoA carboxylase and the biotin-acetyl-CoA-carboxylase ligase that it depends upon are present in the S . lophii genome as well as in the T . hominis and N . parisii genomes , suggesting that they are capable of performing this reaction ., However , as in E . cuniculi , no fatty acid synthase is evident , though both a fatty acid elongase and desaturase are present ( Figure 2B ) ., Interestingly , the presence of these additional components in S . lophii was predicted on the basis of comparative liquid chromatography of the lipid composition of E . cuniculi and S . lophii spores which showed a higher level of docosahexaenoic acid , an unsaturated fatty acid , in S . lophii than E . cuniculi 25 ., In comparison to E . cuniculi , the S . lophii genome also encodes more enzymes for glycerophospholipid synthesis , allowing for a greater variety of interconversions between different types of phospholipid for membrane integration ., In contrast , and despite more complexity in some aspects of fatty acid metabolism , no components of the isoprenoid biosynthesis pathway were present in the S . lophii genome , though these are encoded in the E . cuniculi and N . ceranae genomes 5 , 23 ( Figure 2B ) ., These are also absent from our transcriptome data , and from the genomes of T . hominis , N . parisii and E . bieneusi ., Taken together , these results suggest that several lineages of microsporidia have independently lost the ability to biosynthesize isoprenoids , a capability that is otherwise conserved across the tree of life 24 ., This pathway has been shown to be essential in prokaryotes , and whilst some parasitic Apicomplexa have replaced the classical mevalonate biosynthesis pathway with the alternative MEP pathway 26 , it too is absent from these microsporidian genomes ., These data suggest that some microsporidia scavenge sterols from the environment ., This pathway has been lost in microsporidia with diverse hosts: E . bieneusi ( mammals , insects ) , T . hominis ( mammals , insects ) , N . parisii ( worms ) and S . lophii ( fish ) , suggesting that there is nothing specific about the host biochemical environment that is driving the loss of this pathway ., Key components of the RNAi system are encoded by the S . lophii genome , including a dicer protein , an argonaute protein and fragments of an RNA dependent RNA polymerase ., This is consistent with emerging genomic data for microsporidia with larger genomes such as N . ceranae and T . hominis that possess transposable elements 8 , 23 ., This suggests that RNAi was present in the common ancestor of microsporidia but has been secondarily lost in highly reduced genomes such as E . cuniculi and E . bieneusi 5 , 8 , 24 ., A comparable situation exists in ascomycete fungi , where RNAi has been lost from the compact S . cerevisiae genome but is conserved in other budding yeasts 27 ., Relatively few proteins with homology to characterized domains are found in S . lophii but no other microsporidia ., There are proteins annotated on the basis of similarity to PFAM domains such as kinases , phosphatases and acetyltransferases , which are difficult to relate to specific functions within the cell ., One notable exception is a glutamate-ammonia ligase domain containing protein , which can catalyze the generation of glutamine from glutamate and ammonia 28 ., This protein is nested between genes with homologs in other microsporidian genomes ( a DNA binding protein and acetyl-CoA carboxylase ) ( Figure S1 ) and in phylogenetic analysis does not fall into a clade with other fungi , but rather with prokaryotes meaning that it is not clear whether this gene was acquired by lateral transfer or by vertical inheritance ., Fish excrete their nitrogenous waste products as ammonia across gills , but glutamate-ammonia ligases are expressed in the brains of fish and other vertebrates to protect from fluctuations in ammonia levels 29 ., This protein may have a similar role in protection against ammonia stress in the microsporidian: Whilst the fish is alive the microsporidia may be protected by host ammonia defense mechanisms , however , once the fish dies , microbial degradation of the fish can increase ammonia levels 30 ., As the spores of S . lophii are embedded deeply within the nervous tissue of the monkfish , they may have to be liberated after the death of the fish , and this glutamate-ammonia ligase may allow the spores to survive fluctuations in ammonia levels in the decaying fish tissue ., Despite the relatively close relationship of T . hominis to S . lophii ( Figure 2A ) , these two species share few genes that are not found in other microsporidian genomes in our comparison , and most of these are uncharacterized , lineage-specific or fast evolving genes with no similarity to genes in other lineages ., A handful of genes shared between T . hominis and S . lophii have a function that can be predicted on the basis of homology to characterized proteins from model organisms ., These include an arsenite transporting ATPase , which might act as an efflux pump in the cell membrane and a cold shock domain protein , which could allow the cell to survive at lower than optimal temperatures 31 ., The largest protein family expansion present in S . lophii is a family of proteins containing LRRs ( Figure 3 ) ., Fungal genomes generally encode fewer LRR proteins than their animal relatives 32 though expansion of LRR protein families as pathogenicity factors is known in pathogenic fungi 33 ., The genome of E . cuniculi encodes just 9 LRR genes in total 5 , yet in stark contrast , we have found 97 ORFs encoding fragments of LRR proteins in the S . lophii genome , 52 of which appear complete ( that is , they have a predicted start and stop codon ) , and 35 of which appear in our transcriptome data ., We used PFAM and MEME 34 , 35 to identify common conserved motifs , SignalP 4 . 1 and TargetP 1 . 1 to look for presence or absence of a secretion signal , and TMHMM 2 . 0 36 , 37 to look for evidence of transmembrane domains that could anchor the proteins in the membrane of the parasite as a potential LRR receptor protein ., MEME analysis shows the presence of three different leucine-enriched motifs in the proteins ( Figure 3 ) ., We also found that the majority of the proteins ( 37/52 ) have a predicted signal peptide but no transmembrane domain , meaning that they are potentially a family of secreted parasite effector proteins ( Figure 3 ) ., Leucine rich repeat proteins often mediate protein-protein interactions , particularly through the formation of dimers 38 ., One possibility is that , if these microsporidian LRR proteins are secreted into the host , they could potentially interfere with the formation of dimers of host proteins , disturbing the functional dimer-monomer cycle by sequestering them into inactive dimers , a mechanism seen in mammalian cells 39 ., Perhaps surprisingly , similar sequences are also found in the distantly related microsporidian parasite of humans , Vittaforma corneae , but in no other microsporidian species for which there is an available genome sequence ( Table S3 ) ., A large family of leucine-rich proteins was recently reported in another microsporidian , T . hominis , although the two families are not related and no predicted secretion signals were reported for the T . hominis family 8 ., Although the S . lophii genome encodes 97 LRR-containing ORFs , we have evidence of expression for only 35 ., Therefore , we cannot exclude the possibility that some family members are never expressed and are pseudogenes ., In other eukaryotic parasites such as Trypanosoma and Giardia , large protein families contain many pseudogenes which provide the genetic variation for the ongoing process of antigenic switching 40 , 41 ., If microsporidian multigene families interact with the host , switching of expression from gene to gene may allow escape from the fish adaptive immune response over the course of infection 42 ., The cDNAs of artificially germinated spores were sequenced by the non strand-specific TruSeq approach using Illumina sequencing ., This Transcriptome Shotgun Assembly project has been deposited at DDBJ/EMBL/GenBank under the accession GALE00000000 ., The version described in this paper is the first version , GALE01000000 ., We used Trinity RNA-Seq 43 to assemble the spore transcriptome , and RSEM 44 to quantify the relative abundance of transcripts ., Our de novo transcriptome assembly contained 12 , 932 unique transcripts , of which 2 , 896 mapped to the S . lophii genome ., Although relatively small in number , these transcripts made up 67 . 7% of the transcriptome by abundance , and likely represent the majority of the S . lophii genes in the dataset ., 2 , 514 of these transcripts mapped to existing S . lophii genes , so that 1 , 598 of the predicted 2 , 539 open reading frames had at least one matching transcript ., A small proportion of transcripts ( 398 ) mapped to regions of the genome assembly without gene predictions; manual inspection of these cases provided evidence for 30 additional genes that had been missed by the initial annotation process ., The remaining third of transcripts that did not map to the S . lophii genome assembly might represent contaminants , unmapped S . lophii genes , or artifacts of the assembly process ., To distinguish between these possibilities , we searched these transcripts against the NCBI nr database using BLASTX 45 ., Many showed high sequence identity to Pseudomonas and Flavobacterium genes and likely represent contaminants ., We did , however , identify 20 additional genes with best BLAST hits among the Microsporidia , particularly T . hominis and Vavraia culicis; these are most likely S . lophii genes from the unassembled portion of the genome ( Table S4 ) ., Interestingly , this set of 20 genes included one LTR and four non-LTR retrotransposons with similarity to those found on the T . hominis genome 8 ., While the T . hominis retrotransposons are largely fragmented and pseudogenized , these results demonstrate that at least some of their homologues in S . lophii remain active ., Overall , our analyses of the S . lophii transcriptome provided support for the completeness of our genome assembly and for our gene calling approach , and also provided some new insights into microsporidian biology ., Of the 1 , 986 genes in our genomic data that have complete open reading frames , that is , they have a start and a stop codon , 265 have complete coverage and for these , the RNA transcript was on average 132 base pairs longer than the gene ., Five of these show more than one gene in the transcripts ., However , given the short read-length of Illumina sequences and the possibility that transcripts of adjacent divergent genes could erroneously assemble , it is not possible to say whether these transcripts are overrunning into downstream genes as seen in other microsporidian species 46 , 47 ., Interestingly , although the most abundant transcripts corresponded to 18S ribosomal RNA ( Table 2 ) , the most highly expressed protein in S . lophii spores is an uncharacterized ORF , with homologues found only in a limited number of other microsporidia ., Indeed , while the list of highly transcribed proteins contained many of the expected candidates ( ribosomal proteins , ATP-binding proteins , transcription factors , and proteins involved in energy metabolism ) , these were intermingled with a number of uncharacterized proteins annotated as hypotheticals ( see Table 2 ) , strongly suggesting an important role for novel , lineage-specific or very fast evolving proteins in S . lophii and microsporidian biology ., An interesting observation is that microsporidia-specific proteins conserved in multiple species make up a larger proportion of the transcriptome than Spraguea-specific proteins ( Figure 4 ) , and a larger proportion of the transcriptomic data than they do of the genomic data ., These potentially novel proteins originating in the common ancestor of microsporidia may be good targets for future experimental work on the maintenance of the parasitic lifecycle both in S . lophii and other microsporidian species ., Our de novo transcriptome assembly also enabled us to investigate the splicing of putative introns in S . lophii protein-coding genes ., The number of introns in microsporidian genomes is greatly reduced compared to their opisthokont relatives 48 ., E . cuniculi , N . ceranae , and T . hominis encode a relatively small ( 6–78 ) number of spliceosomal introns , which are largely confined to ribosomal proteins , while the Nematocida genus appears to have lost both introns and the splicing machinery entirely 22 ., S . lophii does encode conserved components of the splicing machinery , so we searched its coding sequences for introns using a two-step approach ., First , we scanned the genome with a consensus microsporidian intron motif built from comparisons of the introns in E . cuniculi and T . hominis 5 , 8 , 49 ., This search returned hits to 8 genes , 6 of which encode ribosomal proteins ( Figure 5 ) ; the two non-ribosomal proteins included genes encoding the DNA replication licensing factor Mcm1 and a poly ( A ) binding protein ., We then searched the S . lophii transcriptome for transcripts containing deletions relative to the genome assembly , which might also indicate the presence of introns ., Surprisingly , this analysis identified only two transcripts from which the predicted intron sequence had been spliced , corresponding to two of the eight genes identified by our motif scan ( ribosomal protein S23 and poly ( A ) binding protein , Figure 5 ) , transcripts for the other six genes still contained the putative intron motif ., A comparison of the sequences of the two actively spliced introns with those of the other intron-like sequences revealed three striking differences ( Figure 5 ) : the spliced introns are much longer , are out of frame with the coding sequence , and are located further downstream from the 5′ end of the gene ( 89 and 152 nucleotides 3′ of the start codon , as opposed to directly adjacent to that codon in all other cases ) ., Of the eight putative intron-containing genes we identified in Spraguea , five have orthologues in E . cuniculi that also contain an intron ( S17 , L27a , S24 , L5 and poly ( A ) binding protein ) , and the efficiency with which those introns are spliced parallels our results with the S . lophii transcriptome ., The introns in E . cuniculi S17 , L27a , S24 and L5 , for which we did not detect splicing in Spraguea , are also short 49 and are among the least efficiently spliced genes in E . cuniculi , with less than 15% of transcripts experiencing splicing ( a figure which drops to 5% for L5 ) 50 ., In contrast , the E . cuniculi orthologue of the actively-spliced poly ( A ) binding protein contains the longest and most frequently spliced intron in E . cuniculi , with over 80% of transcripts spliced ., Thus , it appears that the properties determining intron splicing efficiency are conserved between these two distantly related microsporidia; it will be interesting to see if they hold more generally for other intron-containing microsporidian genomes ., These observations raise the question of how genes containing intron-like sequences that are rarely , if ever , spliced can be adequately expressed in Spraguea ., The genes containing these motifs encode some of the most widely conserved and functionally important proteins in cellular life forms , including components of the ribosome and a DNA replication factor ., Several of these intron-containing proteins were identified in our whole cell protein analysis of germinated and non-germinated spores ., Ribosomal proteins S27 , S24 and L5 were present in our germinated sample and S27 and L5 were also present in the proteome of dormant spores ( Table S5 ) ., For two of the six genes ( S24 and L5 ) , inspection of the intron-like sequence suggests a simple explanation: indels in these sequences have caused a frameshift such that the intron can be read through from the upstream ATG without encountering an in-frame stop codon ., Thus , a full-length protein containing a short N-terminal insertion could be expressed from these transcripts in the absence of splicing ., The other four introns contain in-frame stop codons such that translation from the upstream ATG is not possible unless the intron is spliced ., We considered the possibility that translation could instead begin at an alternative start codon downstream of the intron ., However , initiation at the next available , in-frame ATG would result in substantial N-terminal deletions ( covering 25–37% of the coding sequence ) for three of these genes , and in the case of ribosomal protein S17 no suitable alternative start codon is available ., Thus , it remains unclear whether these genes can be expressed without splicing , or whether translation depends on a rate of splicing too low to be detected in our assay; it was recently suggested that low rates of transcript degradation might partially ameliorate this problem in E . cuniculi 50 ., Dissecting the interactions between microsporidia and their host requires an understanding of the process of spore germination , in which the spore leaves dormancy and rapidly expels a long polar tube , through which the spores cellular contents exit the spore and enter the host cell ., The specific host cell stimuli inducing microsporidian spore germination are unidentified and likely complex , however several successful methods for artificial spore germination in-vitro have been described in a range of species 51–53 ., At present , both the changes within the spore that trigger germination , and the identity of the secreted effector proteins used by the parasite gain entry into the host cell and control its biology are unknown ., Genetic manipulation is a powerful tool for identifying these factors in many pathogens , but is not yet available for any microsporidian ., However , S . lophii xenomas are densely packed with spores , providing an abundant source of parasite material for proteomic comparisons between the dormant and germinated spore stages ., To identify any proteins present in artificially germinated but not dormant spores , we analyzed whole protein extractions of both lifecycle stages with mass spectrometry of complex protein mixtures ., After pooling and filtering 3 biological replicates our analysis showed no consistent variation at the proteomic level between the two lifecycle stages ( Table S5 ) ., We did find components of many core pathways in germinated and non-germinated spores , such as histones , heat shock protein and ribosomal proteins ., We also find glycolytic enzymes in both germinated and non-germinated spores , which is consistent with recent work that glycolytic pathways are specifically active in the spore stage 8 , 54 ., Two components of the secretory pathway ( Sec23 and Sec24 ) were identified only in germinated spores , which may indicate its activation specifically upon germination , however these were not consistently found in all three replicates and overall we found a surprisingly conserved repertoire of proteins between the two samples ., It may be that germination happens too rapidly to allow for translation , with microsporidia pre-packaging the proteins needed for immediate use upon recognition of the germination stimulus and therefore that obvious changes in protein complement may come later in development in meront and sporogonial stages ., Alternatively it may be that the samples are dominated by highly expressed housekeeping proteins , and the proteins that vary between the two samples may be present at low levels not easily detectable by complex mix proteomics ., Next , we investigated the complement of proteins present in the extracellular medium after in-vitro germination ., Here , we consistently retrieved a small subset of proteins that were visualized by SDS-PAGE ( Figure S2 ) ., Importantly , no proteins were identified from the supernatant of non-germinated S . lophii spores , suggesting that the identified proteins are released specifically by the parasite upon germination ., These could be proteins released by the sporoplasms on early infection or by the spore during germination ., We retrieved 37 proteins from three quality-filtered replicates ( Figure 6 ) ., Of these , 11/37 proteins are predicted by SignalP 4 . 1 to have a secretion signal and 17 are predicted by TargetP 1 . 1 to be directed to the secretory pathway; in some cases these predictions overlap ( Figure 6 ) ., Proteins secreted to the extracellular medium range in size from 101 amino acids to 935 amino acids however there is no obvious correlation between size and the presence or absence of a predicted secretion signal ( Figure 6 ) ., Five proteins are consistently present in all three replicates ., Three of these have no similarity to any other proteins in the NCBI nr database ( e<1×10−5 ) ( SLOPH 477 , 723 , 762 ) , while two of the proteins are found in other microsporidia ( SLOPH 1766 , 1854 ) ., One of these has features which make it particularly interesting in the context of potential effector proteins: it is part of a multigene family whose members have predicted Ricin-B-lectin domains and is found in several copies in S . lophii , as well as in several other microsporidian genomes ( SLOPH 1766-see below ) ., The other secreted protein shared with other microsporidia has significant sequence similarity to a spore wall protein ( SWP7 ) in Nosema bombycis ( SLOPH 1854 ) ., Ten proteins are found in two of three replicates ., Six of these proteins have no similarity to

Introduction, Results, Discussion, Materials and Methods

Microsporidia are obligate intracellular parasites with the smallest known eukaryotic genomes ., Although they are increasingly recognized as economically and medically important parasites , the molecular basis of microsporidian pathogenicity is almost completely unknown and no genetic manipulation system is currently available ., The fish-infecting microsporidian Spraguea lophii shows one of the most striking host cell manipulations known for these parasites , converting host nervous tissue into swollen spore factories known as xenomas ., In order to investigate the basis of these interactions between microsporidian and host , we sequenced and analyzed the S . lophii genome ., Although , like other microsporidia , S . lophii has lost many of the protein families typical of model eukaryotes , we identified a number of gene family expansions including a family of leucine-rich repeat proteins that may represent pathogenicity factors ., Building on our comparative genomic analyses , we exploited the large numbers of spores that can be obtained from xenomas to identify potential effector proteins experimentally ., We used complex-mix proteomics to identify proteins released by the parasite upon germination , resulting in the first experimental isolation of putative secreted effector proteins in a microsporidian ., Many of these proteins are not related to characterized pathogenicity factors or indeed any other sequences from outside the Microsporidia ., However , two of the secreted proteins are members of a family of RICIN B-lectin-like proteins broadly conserved across the phylum ., These proteins form syntenic clusters arising from tandem duplications in several microsporidian genomes and may represent a novel family of conserved effector proteins ., These computational and experimental analyses establish S . lophii as an attractive model system for understanding the evolution of host-parasite interactions in microsporidia and suggest an important role for lineage-specific innovations and fast evolving proteins in the evolution of the parasitic microsporidian lifecycle .

Microsporidia are unusual intracellular parasites that infect a broad range of animal cells ., In comparison to their fungal relatives , microsporidian genomes have shrunk during evolution , encoding as few as 2000 proteins ., This minimal molecular repertoire makes them a reduced model system for understanding host-parasite interactions ., A number of microsporidian genomes have now been sequenced , but the lack of a system for genetic manipulation makes it difficult to translate these data into a better understanding of microsporidian biology ., Here we present a deep sequencing project of Spraguea lophii , a fish-infecting microsporidian that is abundantly available from environmental samples ., We use our sequence data combined with germination protocols and complex-mix proteomics to identify proteins released by the cell at the earliest stage of germination , representing potential pathogenicity factors ., We profile the RNA expression pattern of germinating cells and identify a set of highly transcribed hypothetical genes ., Our study provides new insight into the importance of uncharacterized , lineage-specific and/or fast evolving proteins in microsporidia and provides new leads for the investigation of virulence factors in these enigmatic parasites .

parastic protozoans, mycology, genomics, protozoology, host-pathogen interaction, microbiology, biology, proteomics, parasitology

journal.pntd.0004355

Multiplicity of Infection and Disease Severity in Plasmodium vivax

A common observation in many malaria endemic areas is that there are patients concurrently infected by more than one distinct parasite genotype ., These infections are usually referred to as multiclonal infections ., In addition to the frequency of multiclonal infections , molecular epidemiologists estimate the average number of lineages per infected individual or multiplicity of infection ( MOI ) ., Together , MOI and the frequency of multiclonal infections are measurements that relate to transmission intensity 1 , 2 ., Ecologically , multiclonal infections could be the result of two different processes , the co-transmission of different parasite variants ( co-infections ) or the overlap of genetic variants due to infectious contacts before the primary infection is resolved ( superinfections ) 1 , 2 ., Distinguishing between these processes is particularly laborious in field settings ., Beyond their expected association with transmission , multiclonal infections may indicate complex interactions between genetically distinct parasite lineages and their host 2 , 3 ., Broadly defined as intra-host dynamics , these interactions have been the subject of several theoretical and experimental studies ., These processes are considered related to disease severity and the fixation of mutations associated with drug resistance 2–7 ., In particular , mathematical models and data from experimental infections in rodent malaria observed that , as result of competition among genetically distinct lineages , multiclonal infections should be more virulent than single infections ., Furthermore , they could lead to an increase in virulence at the population level because natural selection will favor parasites that , by replicating more , outcompete less virulent variants 5 ., This hypothesis , however , had limited empirical support from field epidemiologic investigations 3 , 8 ., The estimation of MOI and the frequency of multiclonal infections as part of epidemiological studies have several technical limitations that hamper our ability to compare field observations with predictions made in terms of disease severity from laboratory models ( S1 Table ) ., First , MOI is usually reported as a single measurement on a locus or set of loci ., Most studies actually ignore whether there are undetectable genetic differences that are phenotypically relevant ., Second , there is no standardization on the loci used; thus , comparisons across studies are intrinsically difficult 8–13 ., The matter is further complicated by the fact that many MOI studies have been carried out using the fragment size polymorphisms of genes encoding antigens such as msp-2 in Plasmodium falciparum and msp3α in Plasmodium vivax ., Variation at these loci may be hard to interpret since multiple insertion-deletion mutations , recombination events , and/or convergence due to selection could generate alleles that differ at the sequence level but have the same fragment size or restriction fragment length polymorphism pattern 9 , 14 , 15 ., Finally , many studies lack suitable controls in terms of potential differences in parasite genotypes circulating among the group of patients compared ., Such comparison is important since observations from a rodent malaria model suggest that genotypes may differ in their competitive capabilities 5 , 16 , 17 ., Indeed , results from these experimental infections support the hypothesis that different genotypes may lead to different outcomes ., On top of these technical problems , the role of immunity and the actual temporal dynamics of how different genotypes interact within the host are factors that make any association between MOI and disease severity hard to detect in field studies 3 , 16 ., Regardless of these challenges , the hypothesized links between multiclonal infections and clinical outcomes have driven several molecular epidemiologic investigations 3 , 18 ., Not surprisingly , the data emerging from field studies on P . falciparum are contradictory 3 ., Some studies report associations between MOI and clinical endpoints 10 , 19–21 and others fail to find any or find that single clonal infections are actually associated with severe disease 12 , 22–27 ., Regretfully , there are only a handful of studies that include P . vivax or that are carried out in low transmission settings ( see S1 Table ) ., Here , the relationships between the frequency of multiclonal infections and MOI with complicated malaria were explored in areas with seasonal transmission in Colombia , South America ., These settings offer three advantages ., First , finding single infections is possible so they can be compared with multiple infections in the context of complicated/uncomplicated malaria cases ., Second , P . vivax can be compared with P . falciparum in the same population ., Finally , exposure to malaria is lower than in hyper-endemic areas in Africa , Southeast Asia , and Oceania/Pacific so a lower impact of acquired immunity is expected ., A problem in these low transmission areas , however , is that the number of reported complicated malaria cases is limited so studies need to be carried out for longer periods of time 28 , 29 ., In this investigation , the complexities of infections were compared in P . vivax and P . falciparum cases by using multiple species-specific microsatellite loci ., These loci offer the advantage of being neutral if they are not physically linked to a gene under selection ( e . g . mutations conferring drug resistance or antigens ) ., Furthermore , a multi-locus approach that incorporates fast evolving microsatellite loci can detect multiple infections even when the lineages co-infecting the patient are highly related , a phenomenon expected in low transmission areas 11 , 13 ., Although P . vivax may result in slightly more complex infections than P . falciparum ( higher MOI ) , no noticeable differences were found in terms of the average MOI between complicated and uncomplicated cases in these two parasites ., No detectable differences were found in P . vivax or P . falciparum in terms of specific multilocus genotypes infecting complicated and uncomplicated cases ., However , we found that multiclonal infections were associated with complicated malaria in P . vivax but not in P . falciparum ., A passive surveillance study was conducted between 2011 and 2013 in four malaria outpatient clinics located in areas with distinct transmission intensities and parasite distribution 29 ., A total of 1 , 328 symptomatic volunteers were passively recruited when visiting the health posts for malaria diagnosis ., Patients with malaria infection as determined by microscopic examination of Giemsa stained thick blood smears ( TBS ) received oral and written explanations about the study and , after free willingness to participate , were requested to sign an informed consent ( IC ) form previously approved by the Institutional Review Board ( IRB ) affiliated to the Malaria Vaccine and Drug Development Center ( MVDC , Cali-Colombia ) ., IC from each adult individual or informed assent ( IA ) from the parents or guardians of children <18 years of age was obtained ., Individuals between seven and 17 years of age were asked to sign an additional IA ., A trained physician of the study staff completed a standard clinical evaluation and a physical examination in all malaria symptomatic subjects ., All individuals were treated by the local health provider as soon as the blood sample was drawn , using the national antimalarial therapy protocol of the Colombian Ministry of Health and Social Protection ( MoH ) 29 ., Each individual received a unique code number to simplify data collection and identification ., Out of the 1 , 328 patients , 38 P . vivax and 24 P . falciparum cases were classified as clinically complicated following the criteria listed in Table 1 ., These complicated cases were scattered throughout the time of the study ., In order to control for temporal fluctuations of malarial genotypes , complicated cases were compared with a random subsample of uncomplicated cases that were diagnosed in a time window of up to 8 days around each complicated case and in the same locality ., As a result , 92 P . vivax and 57 P . falciparum uncomplicated cases were randomly subsampled and genotyped as described below ., Four localities in Colombia were selected due to their high prevalence of malaria but different average annual parasite incidence ( API ) between 2011 and 2013: Tierralta ( Department of Córdoba; API ~6 . 7 ) in the northern area , Quibdó ( Department of Chocó API ~25 ) , Buenaventura ( Department of Valle del Cauca; API ~1 . 9 ) and Tumaco ( Department of Nariño; API ~10 . 3 ) in the southeast area of the Pacific Coast ., Plasmodium vivax and P . falciparum are both transmitted in these regions in different proportions with an unstable endemic pattern , displaying differences in the relative importance of both parasites ., As in other areas of Latin America 30 , the incidence of P . falciparum has been declining in recent years across the sampled localities whereas P . vivax has shown to be a more resilient parasite ., In Tierralta ( ~90 , 000 inhabitants ) , 44 . 4% of the population lives in the rural areas ., Most inhabitants are mestizos and a small Amerindian indigenous community of Emberá Katío ., In this region , the predominant malaria parasite species is P . vivax ( ~85% ) ., Quibdó ( ~100 , 000 inhabitants ) , located on the Pacific Coast of Colombia close to the border with Panamá , has a population mainly consisting of Afro-descendants and Afro-Amerindians ., Most of the malaria cases are caused by P . falciparum ( ~70% ) ., In Buenaventura ( 350 , 000 inhabitants ) , most of inhabitants are Afro-descendants and mestizos and more than 90% of the population lives in the urban area with malaria mostly due to P . vivax ( ~75% ) ., Tumaco ( ~160 , 000 inhabitants ) is situated close to the border with Ecuador with a population predominantly Afro-descendants with an Amerindian indigenous community of Awá the predominant malaria parasite species in this region is P . falciparum ( ~79% ) 29 ., Approximately 100 μL of whole blood were collected by finger-prick ., Malaria diagnosis at enrollment was performed by Giemsa stained TBS and examined under oil immersion by an expert microscopist and the parasitemia was confirmed by a second experienced reader 29 ., Parasite density was counted after reviewing at least 200 leukocytes ., Total parasite load was expressed as the number of parasites/μL using the actual leukocytes counts for each patient ., Then , DNA was extracted using the PureLink Genomic DNA kit ( Invitrogen , USA ) and Real time PCR ( RT-qPCR ) was performed retrospectively as described elsewhere 31 to confirm the parasite species ., Standard P . falciparum and P . vivax DNA positive and negative controls were used in each batch of tests , including extraction of both negative and inhibition control ., A sample was considered negative if there was no increase in the fluorescent signal after a minimum of 40 cycles ., Regardless of the malaria parasite species , patients were classified as complicated malaria according to the clinical and laboratory criteria of the WHO and the Colombian Ministry of Health and Social Protection guidelines ( Table 1 ) 32 , 33 ., Uncomplicated malaria was defined as a clinical malaria case ( symptoms including fever >38°C , headache , chills and/or malaise and a positive TBS ) without severity criteria ., Clinical and parasitological findings have been reported 29 ., Genomic DNAs were used for microsatellites analyses ., Those samples with low parasitemia were amplified by whole genome amplification using the REPLI-g Mini Kit ( Qiagen Inc , CA , USA ) ., Genotyping was performed using fluorescently labeled PCR primers for a set of nine standardized microsatellite loci for P . vivax and nine for P . falciparum out of an extensive pool of loci that have been explored 11 ., In the case of P . vivax the following loci were included in the analyses: MS2 , MS5 , MS6 , MS15 34 and 14 . 185 , 8 . 332 , 2 . 21 , 10 . 29 , and 8 . 332 35 ., Loci POLYa , TAA60 , ARA2 , Pfg377 , TAA109 , TAA81 , TAA42-3 , TA40 , and PfPK2 were amplified for P . falciparum 36 ., Fluorescently labeled PCR products were separated on an Applied Biosystems 3730 capillary sequencer and scored using GeneMarker v2 . 6 . 3 ( SoftGenetics LLC ) ., After the microsatellite pattern was identified across samples , we scored all the alleles at a given locus if minor peaks were more than one-third the height of the predominant peak ., The finding of one or more additional alleles at any locus was interpreted as a multiple infection with two or more genetically distinct clones in the same isolate ( transmitted by one or several mosquitoes ) ., Single infections were those with only one allele per locus at all the genotyped loci; this method has been widely used 11 , 34–36 ., Missing data ( no amplifications ) were reported by locus but not considered for defining multilocus genotypes ., Suit of approaches was used to test whether there were differences in the circulating genotypes in complicated and uncomplicated malaria cases by exploring how microsatellite haplotypes clustered ., We used the Haplotype Analysis software v1 . 04 37 on all the multi-locus genotypes that we could unambiguously identify ., Thus , a limitation in these analyses is that complex infections with differences at more than two loci were not included because the haploid genotypes could not be inferred ., In particular , we estimated the number of different sampled multilocus genotypes ( SMG ) , number of unique genotypes ( G ) , number of private genotypes ( PG ) , and the Nei’s index of genetic diversity ( He ) estimated without bias 38 ., The He was defined as He = n/ ( n − 1 ) 1−∑i=1Lpi2 , where n is the number of isolates analyzed and pi is the frequency of the i-th allele ( i = 1 , … , L ) in the population ., He gives the average probability that a pair of alleles randomly selected from the population is different ., Then , a Bayesian model-based clustering algorithm was used as implemented in the Structure v2 . 3 . 4 software 39 ., This software uses a Bayesian clustering approach to assign isolates to K populations or clusters characterized by a set of allele frequencies at each locus ., This approach allows for the identification of groups or populations of parasites that could separate the group of complicated and uncomplicated malaria ., We evaluated the observed genetic diversity at different K values ( K = 2 to 10 for P . falciparum and K = 2 to 30 for P . vivax ) and each K value was run independently 10 times with a burn-in period of 10 , 000 iterations followed by 10 , 000 iterations ., For this analysis , we used a set of eight out of the nine microsatellites for both parasites ( without MS2 and PfPK2 ) in order to include as many samples as possible ., The admixture model was used in all the analyses that allow for the presence of individuals with ancestry in two or more of the K populations 39 ., We used Structure Harvester v0 . 6 . 94 to compute Delta K values from Structure 40 ., The program CLUMPP ( Cluster Matching and Permutation Program ) was used to facilitate the interpretation of population-genetic clustering results 41 , and then , distruct v1 . 1 was used to graphically display the clustering results 42 ., The posterior probability for each number of populations or clusters ( K ) is computed and the K value that better explains the genetic data is an estimate of the number of circulating clusters or populations circulating ., Finally , population genetic analyses were complemented by inferring the haplotype genealogies found in complicated and uncomplicated malaria cases for each Plasmodium species ., Those genealogies were inferred for eighth microsatellites by using the Global Optimal eBURST algorithm 43 , as implemented in PHYLOViZ 44 ., Using an extension of the goeBURST rules up to n locus variants level ( nLV , where n equals to the number of loci in our dataset: eight ) , a Minimum Spanning Tree-like structure was drawn to cluster the 100 sequence types ( STs ) for P . vivax and 18 for P . falciparum ( including uncomplicated and complicated malaria cases ) into a clonal complex ( CC ) based on their multilocus genotypes ( a total of 130 patients infected with P . vivax and 81 patients with P . falciparum , many sharing the same sequence types ) ., A Fisher exact test was performed for 130 P . vivax and 81 P . falciparum samples subdivided into uncomplicated and uncomplicated malaria cases ( Table 1 ) to test the hypothesis that the frequency of multiclonal infections differs between complicated and uncomplicated malaria cases ., Multiclonal infections were defined as those having more than one allele in at least one locus out of the nine loci genotyped ., A single infection is one with only one allele per locus at all the genotyped loci ., There were few cases of complicated malaria in these areas with low transmission 29 ., A total of 38 P . vivax and 24 P . falciparum cases were classified as clinically complicated following the criteria listed in Table, 1 . Uncomplicated cases were sub-sampled to create a control group that matched the complicate malaria cases ( CMC ) in terms of location and the time when the case was diagnosed ., The age , gender , ethnic composition , average MOI ( and range ) and the percentage of multiplicity of infection of the complicated and uncomplicated malaria cases are reported in Table, 2 . No noticeable demographic differences were observed between the complicated and uncomplicated malaria groups ( Table 2 ) and no association between gender and complicated and uncomplicated malaria cases was found ( Table 3 ) ., The total of malaria positive samples ( n ) , CMC and multiclonal infection ( % ) by parasite and population is reported in Table, 4 . Overall , 130 P . vivax and 81 P . falciparum samples were genotyped ( complicated and uncomplicated cases ) ., Most of the P . vivax cases were contributed by patients from Tierralta whereas most of the P . falciparum samples were from Tumaco ., This was expected due to the distinct geographic distribution of these parasites in Colombia ., Among the P . vivax cases , 47 . 7% of the 130 samples genotyped ( complicated and uncomplicated cases ) had infections with more than one lineage in at least one locus ., In contrast , only 14 . 8% out of 81 P . falciparum samples were found with multiclonal infections ( Tables 2 and 3 ) ., This difference translated into a slightly higher MOI in P . vivax ( 1 . 5 vs . 1 . 15 , Table 2 ) with overall more complex infections ( few loci with up to three alleles ) ., Overall , P . vivax loci harbored more alleles and exhibited higher heterozygosity than the loci genotyped in P . falciparum ., In particular , the minimum number of alleles in P . vivax was 11 at one locus whereas in P . falciparum it was two ( S2 Table ) ., We reported measurements of genetic diversity per locus by dividing the infections in two not mutually exclusive groups ., First , the genetic polymorphism per locus was calculated by considering all infections ( single and multiclonal ) ., The second group considered only infections that are monoclonal ( single ) or multiclonal infections at one locus only; those were the infections where multilocus genotypes could be reconstructed ., This comparison showed that some alleles were only found at multiclonal infections with multiple alleles at two or more loci ., The heterozygosity , however , was comparable in the two groups for the two parasites ( S2 Table ) ., We proceeded to analyze haplotypes that could be reconstructed for both parasites by using single infections or those multiclonal infections with highly related multilocus genotypes that differed at one locus only ., These analyses included 112 P . vivax samples out of the 130 and 76 of 81 P . falciparum samples ., As stated earlier , complicated and uncomplicated malaria cases were matched by time of collection by randomly subsampling among uncomplicated cases that were diagnosed in an interval of up to 8 days around each complicated case ., Our aim was to compare whether there were different genotypes circulating in the complicated and uncomplicated group ., The number of sampled multilocus genotypes ( SMG ) from the human specimens , the number of distinct genotypes ( G ) , the number of private genotypes ( PG ) , and the Nei’s index of genetic diversity ( He ) estimated for each population using Haplotype Analysis software v1 . 04 are shown in Table, 5 . Overall , the mean genetic diversity was high and similar in both parasites ( Pv-He: 0 . 969 and Pf-He = 0 . 822 ) ., We sampled a total of 118 private genotypes for P . vivax in terms of their geographic origin ( Table 5 ) ., In contrast , out of the 76 P . falciparum samples that could be phased , the three populations shared many genotypes with only 18 private genotypes found in terms of their geographic origin ., A minimum spanning tree for P . vivax samples is shown in Fig 1 , reflecting a genealogical relationship of 100 genotypes or sequence types ( STs ) at the 8LV level constructed using goeBURST with several potential putative primary founders ., Each ST is represented by a circle , and the size of the circle is logarithmically proportional to the number of strains represented by the ST . The color of each circle represents the locality of the origin of the ST ( Fig 1A ) and complicated versus uncomplicated cases ( Fig 1B ) ., Although this was not a study on the parasite geographic structure , it is worth noting that the minimum spanning tree did not reveal a clear geographic pattern ., However , some local diversification can be observed , e . g . genotypes that relate with other local genotypes ( Fig 1A ) ., Importantly , genotypes are shared between complicated and uncomplicated malaria cases showing that there is not a particular cluster of genotypes in the minimum spanning tree that could be associated with complicated cases ., Indeed , some completely identical genotypes ( 9 out of the 100 ) were found in both complicated and uncomplicated malaria cases ( Fig 1B ) ., Our analyses excluded complex multiclonal infections ., Given the high genetic diversity of P . vivax in these populations , our structure analyses failed to converge with this limited number of samples so we could not reliably assign isolates to K clusters and reveal the distribution of clusters in terms of complicated/uncomplicated malaria cases ., Thus , we only reported the minimum spanning tree for P . vivax samples in this study ., In the case of P . falciparum , the 18 STs were also grouped into one clonal complex ( CC ) at the 8LV level by goeBURST ( Fig 2 ) including three putative primary founders ( ST4 , ST5 and ST17 ) ., From these , ST5 was observed in the three populations ( Tierralta , Quibdo and Tumaco ) included in this analysis , whereas ST4 and ST17 were only sampled in Tierralta and Tumaco respectively ( Fig 2A ) ., Some P . falciparum genotypes ( 7 out of the 18 ) were found in both complicated and uncomplicated malaria cases ( Fig 2B ) and the primary founders ST5 and ST17 were also in both groups ( Fig 2B ) ., Using Structure v2 . 3 . 3 , three clusters were identified for P . falciparum for the three populations ( Fig 2C ) and there were not specific clusters linked to complicated or uncomplicated malaria cases ., The cases infected by each parasite species were categorized into complicated and uncomplicated ( see Table 1 ) ., Their infections , on the other hand , were categorized as single or multiclonal based on the set of microsatellites used in this investigation ., An infection was considered multiclonal if it harbored more than one allele in at least one locus ., We then explored the association between having a single or multiclonal infection with having a complicated or uncomplicated malaria event by using a Fisher exact test ., The Fisher exact test yielded a significant association ( p = 0 . 0035 ) between having a multiclonal infection and disease severity for P . vivax ., In contrast , no association was observed for P . falciparum ( p = 1 . 0000 ) ., Similar analyses were performed on an expanded set of samples ( Table 3 , n = 419 for P . vivax and n = 279 for P . falciparum ) regardless of time collection ., The association observed in P . vivax was also observed for this set ( p = 0 . 0268 ) with no association for P . falciparum ( p = 0 . 432 ) ., The pattern in P . vivax persisted even when we considered as multiclonal infections only those having multiple alleles in two loci or more ., The 2x2 contingency tables for both parasites are given in Table 3 ., The pattern in P . vivax cannot be explained by differences on the average parasitemia that affected our capacity to detect lineages ., No differences in parasitemia were observed between the complicated and the non-complicated malaria groups ( p = 0 . 712 , Mann Whitney test on medians ) ., There have been multiple epidemiological investigations aiming to explore the relationship between MOI and/or the frequency of multiclonal infections with variables of epidemiological interest , including but not limited to clinical endpoints ., Examples of such studies are shown in S1 Table ., The variation of the genetic markers used and the broad spectrum of epidemiological variables investigated hampered our ability to compare findings across studies ., Nevertheless , there were some emerging patterns ., For example , in the handful of studies where P . vivax was compared with P . falciparum , patients with P . vivax malaria harbored multiclonal infections more often than those with P . falciparum malaria 8 , 11 , 13 , 45 , 46 ( S1 Table ) ., Our findings are consistent with this global trend ., The observed higher frequency of multiclonal infections in P . vivax could be the result of hypnozoites accumulating in the liver yielding multiple relapses of distinct genotypes ., If this were the only factor , it would imply that patients received incomplete treatment with primaquine , a drug that is prescribed to treat uncomplicated P . vivax malaria in Colombia and other Latin-American countries ., Our observation , however , cannot be taken as evidence of lack of compliance with the local drug policy ., It is possible that P . vivax patients remained asymptomatic for a long period of time 47 facilitating superinfections because antimalarial treatment was not provided ., A factor that could also contribute to this pattern is that P . vivax has higher prevalence and genetic diversity in this region when compared to P . falciparum; thus , ecological differences in terms of transmission are easier to detect and could partially explain the higher frequency of multiclonal infections as a result of coinfections or superinfections 29 , 30 ., The differential contribution of these and other factors to the observed high frequency of multiclonal infections in P . vivax is a matter that requires additional investigations ., Many studies indicate that MOI is better explained by exposure as it correlates with age 48–51; these investigations have been carried out mostly in areas with higher transmission than the one surveyed in this study ., It may be possible that superinfections are more likely if the patients are subclinical for long periods of time due to acquired immunity; thus the frequency of multiclonal infections is expected to correlate with age ., Because our study design focused on contrasting complicated with uncomplicated cases in several localities , it did not allow us to properly test a relationship between age and the frequency of multiclonal infections ., In the context of disease severity , controlled epidemiologic investigations have found that the frequency of multiclonal P . falciparum infections is not associated with clinical symptoms or severity of malaria cases ., In particular , groups of severe and mild malaria cases have been compared independently in The Gambia 22 , Senegal 52 , Gabon 23 , Côte dIvoire 12 , and Thailand 53 with each study showing that the numbers of genotypes per infection were similar between groups ., These observations are consistent with our findings in P . falciparum with the caveat that we only had a few complicated malaria cases in this low transmission setting ., Contrary to the P . falciparum pattern , we found that having a multiclonal infection is associated with disease severity in P . vivax ., Our observations on P . vivax are consistent with those found in experimental infections using rodent malaria models 5 , 16 , 17 where multiclonal infections correlated with disease severity ., At this point it is worth noting that in some rodent malaria models ( Plasmodium chabaudi ) , multiclonal infections may lead to an increase on the average virulence at the population level since natural selection will favor highly competitive parasites 5 , 16 ., However , in other rodent malaria model ( Plasmodium yoelii ) this was not observed 17 ., Thus , having an association between multiclonal infections and disease severity does not necessarily indicate a selective advantage for more virulent parasites in that population ., Consistent with this scenario , we found no evidence indicating that there were particular parasites being selected toward “higher virulence” ., In particular , our haplotype networks did not detect differences in the genotypes circulating between complicated and uncomplicated cases in the two malarial parasites ., This suggests that any effect on disease severity may be due to differences in the host ( e . g . differences in acquired immunity ) or the actual composition of the infection ( multiclonal versus single infections ) rather than the genetic makeup of the circulating parasites ., A limitation of the haplotype network analyses as a proxy of the parasites genealogies , however , is that by excluding complex multiclonal infections we did not consider some specific genotypes in both groups of cases ( complicated and uncomplicated ) ., Importantly , the loci sampled were not linked to any known virulent factor ., A more comprehensive analysis that incorporates the parasites genealogies will require bigger samples sizes in terms of the number of complicated malaria cases and the use of approaches such as genotyping by sequencing ., Although differences in disease severity between P . vivax and P . falciparum are expected given their distinct biological characteristics , the observed association in P . vivax may also provide insights on the limitations that simple measurements such as MOI or the frequency of multiclonal infections may have when testing in the field predictions regarding disease severity derived from experimental models ., Experimental models control for specific variables because they aim to test evolutionary hypotheses; such controls are not possible to implement or are not considered in field settings ., As an example , multiclonal infections driven by genetically related parasite lineages ( siblings ) are expected to reduce virulence in the population 54–56 ., This prediction is consistent with the observation that many P . falciparum multiclonal infections were highly related ( siblings ) since they had multiple alleles at one locus only ., Unfortunately , the limited number of complicated cases in our field sites hampered our ability of performing any meaningful tests ., Notably , the association between multiclonal infections and disease severity in P . vivax holds even when we changed our definition of multiclonal infections to one requiring more than one loci with multiple alleles ., At this point , it seems that a major noticeable difference between the infections caused by the two parasites is that P . vivax multiclonal infections have lineages that were more distantly related among them ( e . g . more loci with multiple alleles ) than in the case of the P . falciparum ., It is also worth noting that the association found in P . vivax may reflect host differences rather than being a

Introduction, Materials and Methods, Results, Discussion

Multiplicity of infection ( MOI ) refers to the average number of distinct parasite genotypes concurrently infecting a patient ., Although several studies have reported on MOI and the frequency of multiclonal infections in Plasmodium falciparum , there is limited data on Plasmodium vivax ., Here , MOI and the frequency of multiclonal infections were studied in areas from South America where P . vivax and P . falciparum can be compared ., As part of a passive surveillance study , 1 , 328 positive malaria patients were recruited between 2011 and 2013 in low transmission areas from Colombia ., Of those , there were only 38 P . vivax and 24 P . falciparum clinically complicated cases scattered throughout the time of the study ., Samples from uncomplicated cases were matched in time and location with the complicated cases in order to compare the circulating genotypes for these two categories ., A total of 92 P . vivax and 57 P . falciparum uncomplicated cases were randomly subsampled ., All samples were genotyped by using neutral microsatellites ., Plasmodium vivax showed more multiclonal infections ( 47 . 7% ) than P . falciparum ( 14 . 8% ) ., Population genetics and haplotype network analyses did not detect differences in the circulating genotypes between complicated and uncomplicated cases in each parasite ., However , a Fisher exact test yielded a significant association between having multiclonal P . vivax infections and complicated malaria ., No association was found for P . falciparum infections ., The association between multiclonal infections and disease severity in P . vivax is consistent with previous observations made in rodent malaria ., The contrasting pattern between P . vivax and P . falciparum could be explained , at least in part , by the fact that P . vivax infections have lineages that were more distantly related among them than in the case of the P . falciparum multiclonal infections ., Future research should address the possible role that acquired immunity and exposure may have on multiclonal infections and their association with disease severity .

Previous studies on rodent malarias and mathematical models have postulated a link between multiclonal infections and disease severity ., This association has been tested in Plasmodium falciparum mostly in Africa with limited information on P . vivax ., Furthermore , there is a paucity of information from areas with low transmission ., Here , we used samples available from a passive surveillance carried out in Colombia , South America ., We found an association between multiclonal infections and disease severity in P . vivax but not in P . falciparum ., Although the number of complicated malaria cases is low , the contrasting pattern between these two species emphasizes their epidemiological differences ., We discuss how this pattern could be the result of a higher divergence among the P . vivax lineages co-infecting a patient ., We hypothesize that low levels of acquired immunity may play a role in the association between multiclonal infections and disease severity .

journal.pgen.1004028

MBD3 Localizes at Promoters, Gene Bodies and Enhancers of Active Genes

Since its discovery in the late 1990s by a number of investigators , the Mi-2/nucleosome remodeling and histone deacetylase ( NuRD ) complex has been proposed to regulate chromatin structure and promote transcriptional repression via its intrinsic nucleosome remodeling and histone deacetylation activities 1 ., Although this model provided a useful experimental framework , like all models , it has been challenged by subsequent data ., In particular , the depiction of NuRDs principal function as a regulator of a silent chromatin state has been questioned by genetic and molecular studies from Georgopoulos and colleagues 2 , 3 , 4 and Hendrich and colleagues 5 , which provide compelling evidence that NuRD can have both positive and negative impacts on gene expression ., zNuRD complex contains six core subunits which are invariably encoded by 2 or more gene paralogs , prompting the hypothesis that combinatorial assembly of subunits may contribute to functional specificity 6 ., The smallest complex subunit can be either MBD2 or MBD3 7 , members of the family of proteins that possess the methyl CpG binding domain ( MBD ) fold ., While MBD2 is a bona fide methyl CpG binding protein , mammalian MBD3 has lost the ability to selectively interact with methylated DNA 8 ., Recently , however , it has been suggested that MBD3 specifically binds another modified form of DNA , 5-hydroxymethylcytosine ( 5-hmC ) , leading to NuRD recruitment at 5-hmC marked loci in embryonic stem cells 9 ., Other investigators find that NuRD complexes are involved in different aspects of the transcription cycle , coupling its action to enhancers 10 or to recruitment of polycomb complexes 11 ., Currently , the field lacks consensus on the localization of MBD3 ( and by extension NuRD complex ) in the genome as well as its roles in modulating chromatin biology to facilitate gene regulation 12 ., Chromatin immunoprecipitation coupled to massively parallel sequencing , ChIP-seq , represents ‘gold standard’ methodology to identify sites of enrichment of a particular protein ( or modified protein ) across the genome ., High quality ChIP-seq data are dependent on a number of technical factors , including antibody quality , fixation conditions ( where ChIP is performed from fixed chromatin ) , chromatin shearing or cleavage with nuclease , and stringency of wash conditions for immune complexes ., Progress over the last decade has established the principle that conditions that produce high quality ChIP data for one protein may not necessarily be effective for others ., Performing ChIP on chromatin regulators , including NuRD complex , is particularly challenging 13 ., Given the technical issues , the lack of concordance of recent NuRD ChIP-seq studies may not be surprising ., It does , however , highlight a need for independent studies to provide additional data for comparison ., Here we have analyzed human MBD3 localization across the genome using two complementary genomic approaches ., We first used DNA adenine methyltransferase identification ( DamID ) , a methodology independent of antibodies , chromatin shearing and other technically challenging features of ChIP ., We also performed ChIP-seq studies of endogenous human MBD3 following optimization of antibody , cross-linking and chromatin shearing ., The results of these two techniques were in excellent agreement , suggesting they may not be unduly influenced by technical issues ., The resulting , composite location analysis revealed unexpected association of MBD3 with the active fraction of the genome ., MBD3 localized preferentially to active promoters characterized by histone marks associated with open chromatin and with genomic regions bearing the properties of enhancers , supporting the emerging evidence that models depicting NuRDs principal functions as a component of repressive chromatin require re-evaluation ., In addition , MBD3 coated gene bodies of actively transcribed genes , extending to the transcript end site ., Conversely , we also observed association of MBD3 with promoters of genes marked by repressive histone marks , albeit not with the frequency of active promoters , highlighting the likelihood that NuRD action is not unidirectional and is context dependent ., Finally , the largest category of loci bound by MBD3 has no obvious association with known chromatin features , underscoring the relative dearth of knowledge on NuRD localization and function ., We initiated location analysis of MBD3 in human cells using the DamID technique 14 ., To facilitate biological comparisons , we chose two human breast cancer cell lines with different biological properties ., MCF-7 cells provide a model for the luminal class of breast tumors , MDA-MB-231 ( MDA-231 ) cells model basal tumors 15 ., As a prelude to location analysis , we first confirmed that the cell lines chosen express comparable levels of endogenous MBD3 , and that the exogenously expressed MBD3-Dam fusion protein was expressed , nuclear , and could be incorporated into NuRD complex ( Figure 1 , Figure S1 ) ., We prepared two biological replicates of Dam alone and MBD3-Dam fusion for each cell line and performed 2 color hybridization using human promoter arrays ., The raw data were processed as described in Methods and the ratio of MBD3-Dam to Dam alone displayed in genome browser format ., Visual inspection of the data indicated multiple areas where the two cell types displayed highly similar patterns of localization as well as regions where localization differed by cell type ( Figure 1C ) ., We determined local enrichment in the two cell lines by defining peaks using a conventional peak-calling algorithm ( see Methods ) ., Peak localization was consistent across biological replicates ( greater than 75% overlap ) and differed across cell type ( less than 50% overlap ) suggesting the data were of high quality ., We identified putative NuRD target genes as those containing a peak within 3 kb of the annotated transcription start site , resulting in 7 , 064 putative targets in MCF-7 cells , 9 , 310 in MDA-231 cells ., Comparison of target genes across cell type indicated many common targets and many cell-type specific targets ( Figure 2A ) ., Collectively , these data indicate that MBD3 , and by extension NuRD complex , has a cell-type specific localization pattern , suggesting cell-type specific functions ., To assess the relationship of MBD3 localization to gene expression and histone modification patterns , we merged the biological replicates for each cell type , binned promoters into 20 bins from −7 to +3 kb relative to transcription start site ( TSS ) , and calculated occupancy for MBD3 and for H3K4me3 ( assessed by ChIP-seq ) as described in Methods ., We displayed the results in a heatmap with genes ordered based on MBD3 density; scores for H3K4me3 and for gene expression were displayed in the same order ., We observed a striking association of MBD3 DamID score with H3K4me3 density by ChIP-seq and with gene expression – regardless of cell type ( Figure 2B ) ., Most MBD3 bound genes were highly expressed and carried high levels of H3K4me3 in both MCF-7 and MDA-231 cells , suggesting MBD3 preferentially associates with open chromatin regions at actively transcribed genes ., To describe the binding pattern of MBD3-Dam with promoters , we constructed a composite analysis across all promoters ( −3 to +3 kb relative to TSS ) ., MBD3 was prominently enriched in two distinct peaks , each about 1 . 5 kb from TSS , with a prominent dip at TSS in both cell lines ( Figure 2C ) ., To ascertain whether this enrichment pattern was associated with promoter type , we subdivided our MBD3 data into three promoter classes 16 ., Regardless of cell line , we observed that MBD3 preferentially associates with CpG rich promoters with little obvious accumulation evident at CpG poor promoters ( Figure 2D ) ., To develop an understanding of the association of MBD3 with genic regions not represented on the promoter arrays , we performed MBD3 DamID using a tiling array covering human chromosomes 6 , 7 , and 8 ., We constructed a gene model ( see Methods ) , observing a gradient of MBD3 density from a peak around TSS that gradually declines across the gene body ., A second peak of MBD3 accumulation was noted roughly concurrent with the transcript end site ( TES ) ( Figure 2E ) ., The association of MBD3 with overlapping , but distinct , genes in MCF-7 and MDA-231 cells suggested that the MBD3-NuRD complex may play a role in cell-type specific patterns of transcription ., To address this issue , we performed Functional Analysis using Broad Institutes Molecular Signature Data Base ( MSigDB v 3 . 0 ) ., We specifically asked whether the MBD3-NuRD target genes ( as defined for Figure 2A ) were enriched for gene expression patterns diagnostic of the luminal and basal transcriptional programs in breast cancer ( Figure 2F , Table S1 ) ., Functional analysis demonstrated significant enrichment of luminal discriminatory genes , but not basal discriminatory genes , in the MCF-7 ( luminal ) specific MBD3-bound gene set ., MDA-231 ( basal ) specific MBD3-NuRD putative targets displayed the opposite pattern ., These data suggest that MBD3 , and by extension the MBD3-NuRD complex , may have a biological role in breast cancer subtype specification ., The DamID technique , while extremely useful , has relatively low resolution , relies on the presence of the GATC motif , and our experiments provided data only on the regions tiled in the microarray platform chosen ., Therefore , we opted to pursue MBD3 ChIP-seq to obtain a higher resolution map for MBD3 that included genic and intergenic regions not represented on the arrays ., We first optimized conditions for ChIP of endogenous MBD3 , finding that fixation conditions are critical to the success of robust MBD3 ChIP ., We applied a two-step crosslinking method , similar to our previous conditions 17 , 18 , crosslinking with disuccinimidyl glutarate followed by formaldehyde ( see Methods ) ., Using these optimized conditions , we prepared two biological replicates of MBD3 ChIP in MCF-7 and in MDA-231 cells ., Precipitated DNA was analyzed by massively parallel sequencing ., After initial filtering and mapping of the sequence data , we merged the biological replicates for each cell line prior to further analysis ( see Methods ) ., Visual assessment in genome browser format indicated multiple regions where enrichment appeared similar in both cell lines as well as many regions where enrichment was cell-type specific ( Figure 3A ) ., We assessed local enrichment of MBD3 more rigorously by defining peaks using SICER 19 ., We detected 35 , 165 and 23 , 880 peaks in MCF-7 and MDA-231 cells , respectively; peak overlap between the two cell lines was similar to the pattern observed in the DamID analysis ( Figure 3B ) ., Finally , we formally compared peaks detected in ChIP-seq to DamID and observed excellent concordance ( Table S2 , Figure S2 ) ., Like the DamID data , higher MBD3 density by ChIP was observed at a large number of promoter regions ., As with DamID , we visualized the MBD3 localization data by binning promoters ( 500 bp bins , −7 to +3 kb relative to TSS ) and ordering genes by MBD3 density ., We once again observed a strong association of MBD3 with actively transcribed genes marked by H3K4me3 ( Figure 4A ) ., As was the case with DamID , metagene analysis indicated prominent peaks of MBD3 localization flanking a prominent dip in density at the TSS with a preferential association with CpG rich promoters , although the resolution of ChIP-seq was clearly superior ( Figure 4B , 4C ) ., The ChIP-seq data permitted us to analyze regions not covered in depth by the DamID analysis ., We assessed the overlap of MBD3 peaks with genomic features , finding that MBD3 overlapped with promoters ( −3 to +3 kb relative to TSS ) and with TSS with high frequency ( Figure 4D ) ., As was the case with DamID , we also observed frequent peak overlap with TESs ., MBD3 tended to be more frequently associated with genes ( exons and introns ) than with intergenic regions , although localization between annotated genes is a common event ( Figure 4D ) ., Metagene analysis ( Figure 4E ) indicated that MBD3 peaks were found in abundance at or near the TSS with a 5′ to 3′ gradient across the gene body and a second , less pronounced , peak concurrent with the TES ., As was the case with DamID data , genes marked by MBD3 in MCF-7 cells were enriched in transcripts defining the luminal gene expression pattern in breast cancer; genes marked by MBD3 in MDA-231 cells were enriched in transcripts defining the basal transcriptional program ( Figure 4F , Table S3 ) ., Collectively , the ChIP-seq data indicate that MBD3 is predominantly found at actively transcribed genes with CpG island promoters and that the protein coats the gene body , extending to the TES ., We utilized publicly available data from the ENCODE project 20 collected in MCF-7 cells to ascertain the nature of chromatin bound by MBD3 ., Focusing first on transcript 5′ ends , we noted that transcripts in which an MBD3 peak overlaps the promoter region ( −3 to +3 kb relative to TSS ) were associated with a peak of H3K4me3 91 . 5% of the time ( Figure 5A ) ., MBD3 bound promoters were associated with H3K9me3 very infrequently , approximately 4 . 7% of the time ( although this does represent a substantial portion of H3K9me3 bound promoters −42 . 6% ) ., A significant number of MBD3 bound promoters were packaged in chromatin characterized by the presence of H3K27me3 ( 10 . 3% of MBD3 associated promoters , 48 . 3% of H3K27me3 associated promoters ) in agreement with data from Hendrich and colleagues in murine ES cells 11 ., Given the prevalence of MBD3 peaks in intergenic regions , we asked whether these loci bore chromatin signatures indicative of function ., We plotted the distributions of MBD3 and histone modifications ( H3K4me3 and H3K27ac ) relative to the MBD3 peak center ., Non-TSS MBD3 colocalized with H3K27ac but not with H3K4me3 , while MBD3 colocalized with both H3K4me3 and H3K27ac around TSS ( Figure 5B ) ., Thus , non-TSS MBD3 bound regions , on average , bear the chromatin signature of active enhancers , a genomic feature that has been associated with NuRD complex in ES cells 10 ., Next , we quantified the overlap of MBD3 with various patterns of histone marks across the genome by dividing the human genome into 1 kb windows and calculating the enrichment for several chromatin marks in each window ( see Methods ) ., Overall , we noted at least five different patterns of histone modification characteristic of MBD3 bound genomic regions ( Figure 5C ) ., Regions encompassing the transcription start and containing histones modified by H3K4me3 ( pattern 1; active promoters ) were very abundant in this dataset , accounting for 18 . 4% of MBD3 enriched regions ., Colocalization with H3K27me3 and TSS ( pattern 2; inactive promoters ) was present with some frequency ( 2 . 3% of all MBD3 enriched regions ) , while colocalization with H3K9me3 ( pattern 3; inactive promoters ) was rare ( about 1 . 4% ) in these data ., Regions enriched in both MBD3 and H3K27ac , but not overlapping TSS ( pattern 4; active enhancers ) , were the most frequent pattern observed in our dataset ( 38 . 6% ) ., Surprisingly , approximately one quarter ( 22 . 5% ) of MBD3 enriched genomic regions ( pattern 5 ) were found in chromatin lacking any of these patterns of histone marks , suggesting that much remains to be learned regarding NuRD complex enrichment relative to local chromatin features ., To clarify whether MBD3 localization has any correlation with DNA modification in this system , we queried DNA methylation status within MBD3 bound regions ., In murine ES cells , Yildirim and colleagues suggested a causal relationship between 5-hmC modification and NuRD localization 9 ., We assessed the methylation status of MBD3 bound CpG islands ( using ENCODE reduced representation bisulfite sequencing data in MCF-7 cells ) ., The majority of CpG islands overlapping a peak of MBD3 were hypomethylated with 60% of these islands falling within the lowest decile of DNA methylation ( Table 1 , Figure S3 ) ., MBD3 was enriched at islands falling in the lowest two deciles of DNA methylation and excluded from the highest 3 ( Table 1 ) ., These data indicate that MBD3 binds preferentially to unmethylated CpG islands in human breast cancer cells and agrees with recent biochemical studies indicating that MBD3 has no measurable biochemical preference for methylated cytosine 21 , 22 ., Distal enhancer elements physically interact with promoter regions and these interactions have a major role in gene regulation 23 ., A substantial portion of MBD3 bound loci have chromatin features consistent with function as enhancers ., The spatial architecture of the nucleus and proximity of distal regulatory elements to promoters relative to occupancy of a given protein is conveniently measured by ChIA-PET 24 ., Given the high frequency with which we observed MBD3 peaks at the TSS of active genes , we assessed the relationship of MBD3 occupancy to RNA polymerase II and to distal regulatory DNA by querying a Pol II ChIP-PET data set in MCF-7 cells ., We observed frequent occurrence of MBD3 enrichment at both ends of Pol II ChIA-PET pairs ., An exemplar locus , GATA3 is depicted in Figure 6A ., We used 3C technology 25 to validate whether a selected subset of MBD3 bound intergenic peaks coinciding with Pol II ChIA-PET ends , including loci in the vicinity of GATA3 , NR2F2 , NRIP1 , and MASTL in MCF-7 cells , are in proximity to their respective promoters in three-dimensional space ., At all 4 loci queried , we detected an interaction between a distal MBD3 bound peak and the promoter region ( Figure 6B , Figure S4 ) ., To extend these observations to the level of the entire dataset , we plotted MBD3 density at TSS for genes with a Pol II ChIA-PET pair anchored at TSS , ranking genes by MBD3 abundance ., Display of MBD3 abundance at the distal ChIA-PET pair in the same order revealed that genes with high level MBD3 at TSS tend to have high MBD3 at the distal region defined by Pol II ChIA-PET as being in proximity in three-dimensional space ( Figure 6C ) ., These data show that some intergenic MBD3/H3K27ac peaks are in physical proximity to core promoters in three dimensional space , consistent with action as enhancers ., Because MBD3 is a component of NuRD complex which contains chromatin remodeling factors ( CHD3 and CHD4 ) , we hypothesized that MBD3 regulates nucleosome occupancy at its binding sites ., To test this hypothesis , we mapped the nucleosome positions in MBD3 depleted and control MCF-7 cells ., We verified that MBD3 was efficiently depleted ( Figure S5 ) ., Native chromatin from control and MBD3 depleted cells were digested with micrococcal nuclease and the resulting mononucleosome-sized DNA fragments were collected and subjected to massively parallel sequencing ., We identified about 150 million nucleosomes for each group and performed a metagene analysis centered on TSS ( Figure 6D , Table S4 ) ., Control cells showed a regular nucleosome organization consistent with previous publications; a nucleosome depleted region ( NDR ) is observed around the TSS and well-positioned nucleosomes flank this NDR ., In MBD3 depleted cells , nucleosome phasing was similar to that in control cells but nucleosome occupancy was decreased - particularly at the NDR , and the −1 , +1 , +2 , +3 , and +4 nucleosomes ., To test whether this effect is MBD3 dependent , we ranked promoters based on MBD3 occupancy ( Figure S6 ) ., The changes at the NDR and +1nucleosome did not correlate with MBD3 occupancy and may be indirect ., However , the occupancy pattern at −1 , +2 , +3 , and +4 nucleosomes differed substantially upon MBD3 depletion at promoters in the highest quartile while these same nucleosomes did not change upon MBD3 depletion in the lowest quartile ., These data indicate that MBD3 regulates nucleosome organization , particularly near promoters and in gene bodies that have high MBD3 occupancy ., Chromatin regulators are critical integration points wherein biological signals are converted into alterations in gene expression ., These protein machines are essential to normal cell function , to development and to differentiation 26 ., A large number of chromatin regulators are mutated in cancer , highlighting the importance of their function in normal cells 27 ., Critical to understanding the biology of these regulators is determination of their sites of accumulation , and presumably of their action , within the genome ., Here , we have utilized two complementary techniques to address the localization of MBD3 , and by extension NuRD complex , arriving at a robust and reliable location map ., These data associate MBD3 with previously undescribed genomic features , including extensive colocalization with the bodies of active genes ., Further , an abundant category of MBD3 localization observed was not associated with any particular genomic feature , histone or DNA mark we analyzed , suggesting that the catalog of functions for NuRD is not yet exhaustive ., Finally , we assessed the contribution of MBD3 to chromatin organization , finding that MBD3 regulates nucleosome organization near promoters and within gene bodies - consistent with its localization ., The use of multiple techniques for location analysis of chromatin associated factors provides an opportunity to control for common technical problems inherent to a single protocol ., DamID involves expression of a fusion protein that modifies DNA in its genomic vicinity with detection relying on creation of novel restriction sites ., It suffers from poor spatial resolution relative to ChIP and the necessity of exogenous expression of a fusion protein that must faithfully recapitulate the biological properties of the unmodified factor ., Chromatin immunoprecipitation relies on biochemical fractionation of chromatin , in many cases following fixation ., High quality ChIP results are reliant on antibody affinity and specificity as well as on crosslinking conditions and biochemical fractionation methods ., Our results using these two techniques are in excellent agreement , suggesting they are converging on a robust genomic location map for MBD3 in the system chosen ., Given the poor concurrence and quality issues ( Table S5 ) of recent NuRD ChIP-seq data 12 , the convergence of location determined by independent techniques provides clarity to the question of where the enzyme is enriched in the genome ., Localization of MBD3 at promoter regions of active genes marked by H3K4me3 was unexpected given biochemical experiments documenting the failure of NuRD to productively interact with H3K4 methylated peptides 28 , 29 ., Importantly , the current data are in excellent agreement with CHD4 ChIP-seq experiments performed in thymocytes by Katia Georgopoulos and colleagues 4 and with ChIP-seq of exogenously expressed MBD3 in HeLa cells 30 and in murine ES cells 31 ., MBD3 enrichment with active histone marks at regions with the characteristics of enhancers underscores the surprising association of NuRD with the active fraction of the genome , in agreement with reports on CHD4 localization in K562 cells 13 and in murine ES cells 10 ., Given that the half-life of acetylated histones in the active fraction of the genome is less than 5 minutes 32 , it is not surprising that histone deacetylases , including NuRD , may accumulate there 33 ., Presumably , the dynamic equilibrium between the acetylated and deacetylated state for histones , or other chromatin associated factors , is an important determinant of promoter/enhancer function and its regulation ., While association of MBD3 with active promoters was abundant in our data , we also observed accumulation in regions with local chromatin marks diagnostic of transcriptional repression ., A significant number of promoters marked by H3K27me3 in MCF-7 cells also bore a peak of MBD3 , in agreement with ChIP-seq for CHD4 in murine ES cells 11 ., Somewhat surprisingly , we did not observe substantial colocalization of MBD3 with H3K9me3 ( although we did observe that a substantial proportion of promoters bound by H3K9me3 are also bound by MBD3 ) , despite extensive biochemical data documenting specific interaction of the PHD finger domains of CHD4 with this mark 34 ., While we do not completely understand the nature of this discrepancy , it may reflect the propensity of this histone mark to be localized in the repetitive fraction of the genome ., The biochemical data also indicate high affinity interaction of CHD4s PHD fingers with H3K9 acetylation 35 , which agrees very nicely with our ChIP-seq data ., It is interesting to speculate that association of the PHD1/2 domain of CHD4 with different modifications , both of which change the physical properties of a single lysine residue on histone H3 , may be instrumental in directing NuRD to regions of the genome with completely opposing functional states ., The methyl CpG binding domain family is intimately tied to cytosine modification 36 ., MBD3 is a most interesting member of this family , being a bona fide methyl-CpG binding factor in some , but not all , taxa 37 ., Whether MBD3 can sense cytosine modification remains a matter of some contention in the literature; some investigators describe interactions with 5-hydroxymethyl C 9 , others do not 21 , 22 , 31 ., Here , we describe enrichment for MBD3 at CpG islands that have extremely low levels of cytosine modification as measured by reduced representation bisulfite sequencing which does not distinguish between methylation and hydroxymethylation ., We interpret this data as supporting the model that mammalian MBD3 does not recognize cytosine methylation or hydroxymethylation 21 , 31 and is preferentially bound at CpG islands with low levels of cytosine modification ., Collectively , MBD3 localization supports roles for NuRD complex in regulation of chromatin structure and/or protein modification status at promoters of active genes , at enhancers , at stably repressed genes , and in bodies of actively transcribed genes ., Functional data reported here document a role for MBD3 , and by extension NuRD , in nucleosome organization , a critical determinant of chromatin structure ., Further , they predict novel functions that remain to be described ., These predictions clearly indicate that models describing NuRD as a static corepressor are inadequate in the face of emerging genomic data ., Rather , it seems likely that NuRD is involved at multiple levels in modulation of epigenetic features to facilitate chromatin biology ., Recently , whole-exome sequencing revealed high-frequency deletion of a short segment of chromosome 19 containing the MBD3 locus in uterine serous carcinoma and frequent point mutations in CHD4 in serous endometrial tumors , suggesting fundamental functions of NuRD in primary tumors 38 , 39 ., Future challenges for the field include defining modes of local enrichment at specific genomic features as well as functional studies to describe the nature and extent of enzymatic and non-enzymatic actions of NuRD complex on the chromatin fiber ., MCF-7 , MDA-MB-231 , and 293T cells were obtained from the American Type Culture Collection and cultured at 37°C , 5% CO2 in Dulbeccos Modified Eagle Medium/Nutrient Mixture F-12 ( DMEM/F-12 ) Media containing 10% fetal bovine serum supplemented with penicillin-streptomycin ., DamID lentiviral vectors , pLgw-RFC1-V5-EcoDam and pLgw-V5-EcoDam were kindly provided by Dr . Bas van Steensel , Netherlands Cancer Institute ., Human MBD3 cDNA ( BC043619 ) was amplified and cloned into pENTR/D-TOPO and then recombined by an LR-reaction into destination vector pLgw-RFC1-V5-EcoDam ( pLgw-MBD3-V5-EcoDam ) ., Retroviral knockdown constructs , pSMP-Luc ( Addgene plasmid 36394 ) and pSMP-MBD3_3 ( Addgene plasmid 36373 ) were obtained from Addgene ., All constructs were verified by DNA sequencing ., Lentivirus production and infection were performed as previously described 40 , using pLgw-MBD3-V5-EcoDam ( MBD3-Dam ) or pLgw-V5-EcoDam ( Dam-only ) ., Retrovirus production and infection were performed as described 41 , using pSMP-Luc or pSMP-MBD3_3 ., The DamID experiments were carried out as previously described 42 ., Briefly , MCF-7 or MDA −231 cells were seeded into 6-well plates ., Seventy-two hrs after infection , genomic DNA was isolated using Qiagen DNeasy tissue kit ., Genomic DNA ( 2 . 5 µg ) was digested with Dpn I followed by adaptor ligation ., The ligated product was digested with Dpn II and amplified by PCR ., One microgram amplified product was labeled using Dual-color DNA Labeling kit ( Nimblegen ) according to manufacturers protocol and then hybridized to Nimblegen 2 . 1M Deluxe promoter array or human 2 . 1 M Whole-Genome Tiling array ( Array 5 of 10 covering chromosomes 6–8 ) and washed following the manufacturers directions ., The slides were scanned using a DNA microarray scanner ( G2565BA; Agilent Technology ) and the images were processed with the Nimblegen software ., A two-step normalization approach was used , where the first step is designed to correct for GC bias and dye bias within a chip ( intrachip correction ) and the second step corrects for variations across chips ( interchip correction ) ., The first step was within-chip normalization ., First , all probes were binned according to their GC content ., The GC content was computed as a ratio of C and G nucleotides to the total number of nucleotides in the probe sequence ., The overall variability in GC content values was used to compute bin width according to zero-stage rule 43 , 44 ., These bin widths are proven to be approximate L2 optimal; i . e . , they minimize mean integrated square error ., The bins with fewer probes were then merged so that each bin contains at least 500 probes ., Within each bin , Lowess regression was used to predict log-transformed cy5 values as a smooth function of log-transformed cy3 values 45 , 46 ., The scaled ( median of absolute residuals is used for scaling ) difference between observed and predicted log ( cy5 ) values were used as normalized signal ., The second step was between-chip normalization ., Once the data were corrected for dye and GC bias as described in the first step , we employed quantile normalization independently for each histone mark and DNA methylation to correct for between-chip variation ., The differentially bound “peak” regions for each cell type comparisons were identified using modified ACME algorithm that allows for spooling data across replicates 46 ., This algorithm like ACME , depends on three user-specified tuning parameters: window size ( w ) , signal threshold ( s ) and p-value threshold ( p ) ., To identify MBD3 bound peaks , we first compute the number ( x ) of signal values within window of size w ( centered at probe ) that are greater than 100sth percentile across all replicates ., Next , we compute enrichment p-value for probe using hypergeometric distribution as followingwhere N denotes total number of probes , k denotes number of probes in window and r denotes the number of replicates ., Finally , the peaks are identified as runs of enrichment p-values that are less than p-value threshold ( p ) ., The analysis presented here correspond to signal threshold ( s\u200a=\u200a0 . 95 ) , window size ( w\u200a=\u200a2000 ) and p-value threshold ( p\u200a=\u200a0 . 001 ) with peaks containing less than six probes excluded ., For each set of differentially bound and cell-specific gene signatures we performed Fishers exact test to assess enrichment of gene-sets from Molecular Signature Database ( MSigDB , version3 . 0 , Broad Institute ) and other published Cancer gene sets ., The resulting significance p-values were subjected to Benjamin-Hochberg ( FDR ) multiple test correction ., We employed chromosome bound circular permutations test described in 46 to assess whether the observed overlap between two sets of genomic interva

Introduction, Results, Discussion, Methods

The Mi-2/nucleosome remodeling and histone deacetylase ( NuRD ) complex is a multiprotein machine proposed to regulate chromatin structure by nucleosome remodeling and histone deacetylation activities ., Recent reports describing localization of NuRD provide new insights that question previous models on NuRD action , but are not in complete agreement ., Here , we provide location analysis of endogenous MBD3 , a component of NuRD complex , in two human breast cancer cell lines ( MCF-7 and MDA-MB-231 ) using two independent genomic techniques: DNA adenine methyltransferase identification ( DamID ) and ChIP-seq ., We observed concordance of the resulting genomic localization , suggesting that these studies are converging on a robust map for NuRD in the cancer cell genome ., MBD3 preferentially associated with CpG rich promoters marked by H3K4me3 and showed cell-type specific localization across gene bodies , peaking around the transcription start site ., A subset of sites bound by MBD3 was enriched in H3K27ac and was in physical proximity to promoters in three-dimensional space , suggesting function as enhancers ., MBD3 enrichment was also noted at promoters modified by H3K27me3 ., Functional analysis of chromatin indicated that MBD3 regulates nucleosome occupancy near promoters and in gene bodies ., These data suggest that MBD3 , and by extension the NuRD complex , may have multiple roles in fine tuning expression for both active and silent genes , representing an important step in defining regulatory mechanisms by which NuRD complex controls chromatin structure and modification status .

Chromatin structure is tightly regulated by multiple mechanisms; its dysregulation is associated with developmental abnormalities and disease ., The Mi-2/nucleosome remodeling and histone deacetylase ( NuRD ) complex is proposed to regulate chromatin structure by changing the location and/or the chemical properties of the fundamental building block of chromatin , the nucleosome ., NuRD has been shown by genetics to be important for normal development , yet the detailed mechanism of how NuRD regulates chromatin structure is still unclear ., Here , we study the localization and function of MBD3 , a component of NuRD , in two human breast cancer cell lines using two independent genomic technologies ., Our data demonstrate that existing models , which associate NuRD with transcriptional repression , are not completely correct ., Rather , MBD3 showed cell-type specific localization at active genes ., Moreover , we found a previously unidentified localization of MBD3 across gene bodies and identified a regulatory role for MBD3 in nucleosome organization ., Our data provide a reliable starting point from which to address mechanisms by which NuRD controls chromatin structure and nuclear biology .

journal.pgen.1003255

Transposon Variants and Their Effects on Gene Expression in Arabidopsis

While transposable elements ( TEs ) constitute a large fraction of plant , animal and human genomes 1–3 , their contribution to genome size can change rapidly during evolutionary time ., In some taxa , TEs have been responsible for two-fold differences in genome size that arose over a few million years or less ., These rapid fluctuations , which may be due to TEs being either more active or more efficiently deleted in certain species , indicate that control of TEs can differ greatly between closely related plant species 4–7 ., The balance between TE transpositions and selection against TEs is influenced by factors ranging from mating system to silencing by short interfering RNAs ( siRNAs ) and chromatin modification ., Therefore the control of TE activity and the removal of transposed copies can be considered key factors in the evolution of genomes ., TEs are often regarded as genomic parasites due to the potentially detrimental effects of insertional inactivation of genes and ectopic recombination of DNA 8 ., Twenty-four nt long siRNAs are associated with most TEs as part of a ‘double-lock’ mechanism of siRNA-mediated DNA methylation that controls transposition via transcriptional repression , with a reinforcement loop between DNA methylation , histone methylation and siRNAs reviewed in 9 ., siRNAs are a robust proxy for DNA methylation at TEs , with unmethylated TEs generally lacking matching 24 nt siRNAs 10–13 ., Most plant TEs have cytosine methylation at CG , CHG and CHH sites , but a quarter is unmethylated and a further 15% have atypical methylation patterns ., In the TE-dense heterochromatin , DNA methylation can spread about 500 bp into neighboring unmethylated TEs 13 ., In the euchromatin , methylation spreads from TEs to approximately 200 bp beyond the siRNA target sites 13 , consistent with the effect of siRNAs on expression of proximal genes dissipating by 400 bp 14 ., siRNA-targeted , methylated TEs are , on average , located farther away from expressed genes than TEs that are not strongly methylated or associated with siRNAs 13 , 15 ., As expected from this correlation , siRNA-targeted TEs have more effects on nearby gene expression than those without 14 , 15 ., Most poorly methylated TEs are short and have few CG dinucleotides 13 ., This indicates a progression over evolutionary time from TEs that are active and targeted by siRNA-mediated DNA methylation , to inactive , degenerate relics that have changed through deletions and nucleotide substitutions initiated by deamination of methylated cytosines ., These inactive TEs are then no longer targeted by siRNA-mediated DNA methylation ., Presumably because of interference with cis-regulatory elements , Arabidopsis TEs reduce the average expression levels of adjacent genes , although the distance over which these effects are noticeable varies between A . thaliana and A . lyrata 14 ., Differences in TEs next to genes contribute to the divergence of gene expression levels between orthologs in these closely related species 14 , and gene expression is negatively correlated with the number of nearby siRNA-targeted , methylated TEs 15 ., In the selfing species A . thaliana , TEs account for only a fifth of the genome 7 , 13 , 16 , making it relatively depauperate of TEs ., Given that the A . thaliana genome is small relative to other members of the family and that its close relative A . lyrata , an outcrosser , contains approximately three times as many TEs 14 , deletion of TEs in A . thaliana is likely an ongoing , active process ., In accordance with this hypothesis , intraspecific polymorphisms and deletions in A . thaliana are disproportionately located within TEs and , to a lesser extent , intergenic regions 17–19 ., A reference-guided assembly approach has been applied to accurately characterize complex sequence variation in several A . thaliana accessions 19 ., Here , we exploit this information to examine TE variants and their effect on the expression of nearby genes in three divergent accessions ., We report that TEs are more likely to be located in polymorphic regions of the genome ., Where TEs are present in less polymorphic regions , they also tend to be less polymorphic themselves ., Although polymorphic TE variants are less abundantly targeted by siRNAs , uniquely mapping siRNAs targeting polymorphic TE variants are strongly correlated with the TE regions that vary between accessions ., These findings suggest a link between the ability to tolerate TE insertions , siRNA-mediated silencing and purging of TEs by deletion ., We annotated the sets of genes and TEs in three A . thaliana accessions: Col-0 , Bur-0 and C24 19 , 20 ., For reference accession Col-0 , we used the TAIR9 annotation of TEs and protein-coding genes ., Excluding centromeric sequences , 21 , 913 full-length and degenerate TEs and 26 , 541 genes were considered further ., We built genome templates of Bur-0 and C24 from re-sequencing data using the SHORE pipeline 21 ., The reference coordinates of TEs and genes were projected onto these genome templates , and variation in TEs and genes was determined based on single nucleotide polymorphisms ( SNPs ) , 1 to 3 bp insertions/deletions ( indels ) and larger deletions of 4 to 11 , 464 bp ( median 30 bp , mean 113 bp ) ., Larger insertions were not included because of the high false-negative rate 17 ., Comparison of polymorphism densities confirmed that coding regions were relatively depauperate of SNPs , indels and large deletions compared to intergenic regions and TEs ( binomial test , pCoding Regions/Intergenic Region\u200a=\u200a0 and pCoding Regions/TE\u200a=\u200a0 for SNPs , indels or large deletions ) ., Large deletions were significantly over-represented in TEs compared to intergenic regions , while SNPs and indels were not ( Figure S1a; binomial test , pTE/Intergenic Region\u200a=\u200a0 for large deletions ) ., Over 6% of reference TEs differed by at least 10% of total length in each of the two accessions , Bur-0 and C24 , compared to Col-0 ( Figure 1a and Figure S2 ) ., Almost all of this variation , 93% , was due to large deletions ( Figure S1b; for distribution of large deletion sizes see Figure S1c ) ., We defined TEs with at least 10% variation by length ( SNPs , indels and larger deletions combined ) , but not completely missing in Bur-0 or C24 , as TE variants or VarTEs ( please also see Figure S3 for abbreviation definitions ) ., Close to 40% of VarTEs were shared between Bur-0 and C24 ( Figure S4a ) ., TE density is highest in and next to the centromeres , where there are few genes ., The fraction of VarTEs and the average level of TE variation were higher in the pericentromeric regions than on the gene-dense chromosome arms ( Figure 1b; Mann-Whitney U MWU test , p<2×10−16 for Col-0 versus Bur-0/C24 , Table S1 and Figures S5 and S6 ) ., To examine whether gene proximity biases TE variation across the chromosomes , we calculated the distance between TEs and protein-coding genes for Col-0 ., TEs were separated into two subsets: TEs within 2 kb of any gene , subsequently called proximal TEs , and TEs at least 2 kb away from the closest gene , called distal TEs ., Distal TEs were on average more variable than proximal TEs ( Figure 1c; Figures S7 and S8; MWU pCol-0/Bur-0\u200a=\u200a0 . 001 , pCol-0/C24<6×10−5 ) ., Proximity to protein-coding genes may therefore influence TE variation , consistent with TEs closer to genes likely being under stronger selective constraint 15 , 22 ., The correlation between TE variation and proximity to genes was compared among TE superfamilies 23 , 24 ., For non-centromeric TEs , LTR retrotransposons were more distal from genes , while no significant difference in distance to genes was observed for other TE superfamilies ( Table S2 ) ., However , for proximal TEs there were differences among TE superfamilies in distance to genes and , as expected , TE superfamilies that are closer to genes ( e . g . CACTA , MITE ) were less variable than superfamilies located farther away from genes , e . g . non-LTR retrotransposons ( Table S2 ) ., To investigate the link between TE and proximal gene variation , we examined whether TE variation and location correlated with the polymorphism level of neighboring genes ., We used the small-scale mutations to calculate the polymorphism level of non-centromeric genes ., For each accession , genes were separated into two subsets; TE+ genes included genes within 2 kb of a TE and genes with TEs anywhere within the transcribed region , while TE- genes were at least 2 kb from the closest TE ( Table S3 ) ., To be conservative , any TEs in Bur-0 or C24 with predicted deletions of at least 10% of the reference length were annotated as deleted ., TE+ genes were on average more polymorphic than TE− genes in each accession ( Figure 2a; MWU p<2×10−16 for Col-0 , Bur-0 and C24 ) ., The same analysis was repeated for 80 resequenced A . thaliana accessions 17; we could confirm the correlations observed with Bur-0 and C24 in these accessions ., Since polymorphism levels vary enormously among gene families , we further investigated whether there is a correlation of TE proximity with gene family using small-scale mutations from the 80 A . thaliana accessions ( 20 , 61 ) , and Col-0 , C24 and Bur-0 ., Genes from highly polymorphic families such as those encoding NBS-LRR , F-box and Cytochrome P450s proteins were , on average , closer to TEs in all accessions ( Figure S9; distance is negatively correlated with gene polymorphism , Spearmans ρ ( Col-0 ) =\u200a−0 . 11 , ρ ( Bur-0 ) =\u200a−0 . 11 , ρ ( C24 ) =\u200a−0 . 10; p<2×10−16 ) , including a higher proportion of genes having proximal TEs ( Figure S10 ) ., TEs are therefore either more likely to insert into or near polymorphic genes , or are less efficiently purged from such regions ., To further examine the effects of TE variants on proximal genes , we divided TE+ genes into two subsets: genes where flanking TEs were <10% variant ( Invariant TEs: InvTE ) among the three accessions ( InvTE+ genes ) , and genes where at least one flanking TE showed ≥10% sequence ( VarTE ) variation between accessions ( VarTE+ genes; Table S3 ) ., Three quarters of VarTE+ genes were shared in comparisons between Col-0 and Bur-0 or Col-0 and C24 ( Figure S4b ) ., The VarTE+ genes were on average more polymorphic than InvTE+ genes ( Figure 2b; MWU p\u200a=\u200a0 . 005 ) , also in the 80 accessions dataset 17 ., We conclude that TEs close to genes are less polymorphic , while genes close to polymorphic TEs are themselves more polymorphic ., A correlation between polymorphism levels of TEs and nearby genes is insufficient to address whether this is a direct link as opposed to high directional selection pressure on the genomic region in general ., To address this question , we therefore compared the polymorphism level of TEs , the flanking regions and nearby genes ., TEs in highly polymorphic regions are themselves more polymorphic than TEs in regions of low divergence ( Figure S11a; binomial test , p\u200a=\u200a0 ) , with the exception that TEs in highly polymorphic regions with nearby lowly polymorphic genes show a similar level of divergence as TEs in regions of low polymorphism with no coding genes ., Moreover , TEs in gene-free regions show significantly higher divergence than TEs within 4 kb of a gene , especially if those genes are less polymorphic ., TEs are generally more polymorphic than their flanking sequences ( binomial test , p\u200a=\u200a0 ) , with the exception of TEs in highly polymorphic regions with lowly polymorphic gene ., The results for large deletions ( Figure S11b ) are consistent with our observation from Figure S1 that large deletions are over-represented in TEs compared to intergenic regions ., Notably , there is no significant difference in the level of small-scale mutations between TEs and flanking regions ( Figure S11c ) ., Taken together , TE variation through large deletions shows a positive correlation with flanking region polymorphism level , but is also strongly influenced by the conservation and presence/absence of nearby genes ., The frequency of large deletions is however generally higher in TEs than in the flanking regions , indicating positive selection for large deletions within TEs ., Genes that are close to TEs ( TE+ genes ) tend to have a lower expression average than TE− genes in the Col-0 reference accession 15 ., We set out to determine whether this was true for the accessions studied here as well ., Gene expression was measured using Affymetrix tiling arrays and RNA extracted from floral tissue of each accession ., We considered presence/absence of TEs in the flanking regions of genes , taking into account the number of linked TE insertions and the distance from each gene to the closest TE ., We confirmed the reported pattern for Col-0 15 , and found that it applies to Bur-0 and C24 as well ., In all three accessions , genes with proximal TEs ( TE+ genes ) were on average expressed at lower levels than those without proximal TEs ( TE− genes; Figure 3a; MWU p<2×10−16 for Col-0 , Bur-0 and C24 ) ., This effect was even stronger if TEs were located simultaneously within , upstream and downstream of the gene ( Figure 3a; MWU p≤2×10−14 for Col-0 , Bur-0 and C24 ) ., Moreover , the average expression level of neighboring genes was positively correlated with the distance to the nearest TE ( Figure 3b; Spearmans ρ ( Col-0 ) =\u200a0 . 15 , ρ ( Bur-0 ) =\u200a0 . 13 , ρ ( C24 ) =\u200a0 . 13; p<2×10−16 ) , and negatively correlated with the number of proximal TEs ( Figure 3c; df\u200a=\u200a55 , chi-square sums 915 , 588 and 553 for Col-0 , Bur-0 and C24 , respectively , p<2×10−16 ) ., Thus , gene expression is suppressed by proximal TEs , especially if they are close to the gene and numerous ., Since TE superfamilies may have different effects on proximal genes , we examined gene expression according to the TE superfamily of the closest proximal TE ., TE+ genes are expressed differentially depending on the TE superfamily of the proximal TE ., TE+ genes with DNA transposons are on average expressed at a higher level compared to TE+ genes surrounded by retrotransposons ( Figure S12; MWU , p\u200a=\u200a0 . 02 for Col-0 , Bur-0 and C24 ) ., However , this is solely due to the higher expression level of genes proximal to CACTA elements ., Indeed , we did not find evidence for CACTA TEs having any effect on gene expression ( Figure S12 , MWU , p ( CACTA TE+ genes/TE− genes ) =\u200a0 . 7 , 0 . 6 and 0 . 8 for Col-0 , Bur-0 and C24 , respectively ) , which may explain why they are on average closer to genes than TEs from other families ., Within the retrotransposons , LTR retrotransposons are younger on average than non-LTR retrotransposons and have a greater suppressive effect on proximal genes ( Table S2; 25 ) ., Therefore TE superfamilies can differ considerably in their effects on proximal genes ., TEs suppress the expression of neighboring genes at least partially through DNA methylation , which in turn is linked to 24-nt long siRNAs 12 , 15 , 22 , 26 , 27 ., To investigate the influence of siRNAs on TE silencing , we sequenced siRNAs from mixed inflorescence tissue ( shoot meristem plus flowers , stages 1–14 ) of each accession and mapped the reads to all possible positions of the respective genomes without any mismatches ., As expected from previous work , the density of siRNAs over TEs was about four times higher than the genome average ( Table S4; Figure S13 ) ., We have reported before that siRNA-targeted TEs are more effective in suppressing expression of neighboring genes than are non-siRNA-targeted TEs , and that they are farther from genes 15 ., We determined whether this held true in the current , more comprehensive dataset ., If at least one 24-nt siRNA mapped to a TE it was labeled as siRNA+ ( Table S5 ) ., siRNA+ and siRNA− TEs were overall similar in number , but retrotransposons were targeted by siRNAs more frequently than DNA transposons ( Figure S14; binomial test , p\u200a=\u200a0 for Col-0 , Bur-0 and C24 ) ., siRNA+ TEs were farther from genes ( Figure 4a; Figure S15a; MWU p<2 . 2×10−16 for Col-0 , Bur-0 and C24 ) , and this bias was consistent among TE superfamilies ( Figure S16 ) ., To examine the effects of siRNA-targeting on the expression of flanking genes , we classified genes by whether the nearest TE was siRNA+ or siRNA− ( Table S5 ) ., In each accession , genes flanked by siRNA+ TEs had lower average expression levels than genes with adjacent siRNA− TEs ( Figure 4b; Figure S15b; MWU pCol-0\u200a=\u200a0 . 0001 , pBur-0\u200a=\u200a0 . 002 , pC24\u200a=\u200a2×10−6 ) ., The effect of suppression was stronger if the closest siRNA+ TE was within 2 kb of the gene ( Figure 4b; Figure S15b; MWU p<2×10−16 for Col-0 , Bur-0 and C24 ) ., Therefore , as found previously for Col-0 , siRNA-targeting of TEs represses nearby genes and TEs that are close to genes are less likely to be targeted by siRNAs , either due to stronger selection for deletion of siRNA-targeted TEs close to genes or selection against siRNA-targeting of these TEs ., Because siRNAs that map to unique positions in the genome ( usiRNAs ) correlate more closely with DNA methylation than siRNAs that map to multiple positions ( msiRNAs; 12 ) , we investigated whether usiRNAs and msiRNAs target TEs differentially , and how usiRNA− and msiRNA-targeted TEs might affect the expression of nearby genes ., All TEs with at least one usiRNA were labeled as usiRNA+ ( Table S5 ) ., In both Bur-0 and C24 , over 83% of siRNA+ TEs were usiRNA+ , similar to what has been reported for Col-0 14 ., usiRNA+ TEs were farther away from genes than msiRNA+ TEs ( Figure 4a; Figure S15a; MWU pCol-0<2×10−16 , pBur-0\u200a=\u200a6×10−13 and pC24\u200a=\u200a2×10−6 ) ., We also observed that the average expression level of genes within 2 kb of usiRNA+ TEs was lower than the expression of genes within 2 kb of msiRNA+ TEs ( Figure 4b; Figure S15b; MWU pCol-0\u200a=\u200a3×10−6 , pBur-0\u200a=\u200a5×10−5 , pC24\u200a=\u200a0 . 01 ) ., Therefore , even though TEs targeted by usiRNAs and msiRNAs are on average farther from genes , they more strongly reduce expression of proximal genes compared to TEs targeted by only msiRNAs ., Overall , we confirmed that siRNA+ TEs , especially usiRNA+ TEs , suppress neighboring gene expression , consistent with a trade-off between reduced TE mobility and deleterious effects on neighboring gene expression 14 , 15 ., If TEs suppress the expression of adjacent genes , presence of gene-proximal TEs in the different accessions should be associated with differences in expression levels of proximal genes ., We found that expression of TE− genes varied less between accessions than TE+ genes , and further that expression varied less between genes proximal to invariant TEs ( InvTE+ genes ) than genes proximal to variant TEs ( VarTE+ genes; Figure 5a; MWU pTE−/TE+<2×10−16 , pInvTE+/VarTE+\u200a=\u200a2×10−5 ) ., However , because TEs , and especially VarTEs , are found more often next to polymorphic genes , these conclusions could be confounded by correlated differences in genic polymorphisms ., We therefore classified genes based on the extent of sequence variation ( Table S6 ) ., Regardless of degree of genic polymorphism , VarTE+ genes were the ones that varied most in expression between accessions ( Figure 5b ) , indicating that TE variation increases variance in gene expression ., We next determined whether differential siRNA-targeting influences gene expression ., To remove the potentially confounding effects of variation in TEs themselves , we focused on InvTE+ genes and grouped these based on whether siRNAs for the adjacent TE could be detected in either all or none of the three accessions , or whether accessions differed in siRNA-targeting of the adjacent TE ., We found that while variation in siRNA-targeting increased expression differences between accessions , this increase was not statistically significant ( Figure 5a ) ., It should be noted that in our analysis we could not distinguish between the effects of differential siRNA-targeting and any perturbations of cis-regulatory sequences ., Since each TE that differs in presence/absence or each siRNA-targeting variant between accessions represents a natural mutagenesis experiment , this offers an opportunity to study the effects on individual genes , to confirm the inferences drawn from averaging over all genes ., We selected siRNA+ TE+ genes in Col-0 that are siRNA− TE+ or TE− in Bur-0 or C24 and tested for differential expression between Bur-0 or C24 and Col-0 ., To remove the potential confounding effect of genic polymorphism , we excluded genes with a polymorphism level greater than 2% ., Overall 706 genes were retained for this analysis ., The effect of siRNA-targeting on gene expression was further verified by comparing expression profiles among wild-type , rdr2-1 and a ddc ( drm1drm2cmt3 ) DNA methyltransferase triple mutant 28 ., Fifteen genes out of 706 showed significant up-regulation ( top 5% ranking ) in Bur-0 or C24 and in at least one of the RNA silencing mutants ( Table S7 ) ., Although not statistically significant , this observation is consistent with siRNA-targeting and TE presence affecting gene expression ., Moreover , it is likely an underestimate of TE effects on gene expression , given our stringent selection criteria ., Because siRNA+ TEs suppress neighboring gene expression particularly efficiently , we asked whether targeting of different regions of TEs was reflected in the expression of adjacent genes ., We first investigated whether invariant and variant TEs ( InvTEs and VarTEs ) differed in siRNA-targeting , normalized by TE length , and whether there were differences between invariable and variable regions of VarTEs ( Figure 6a; Table S8 ) ., Fewer siRNAs mapped to siRNA+ VarTEs than to siRNA+ InvTEs ( Figure 6a; MWU p<2×10−16 for Col-0 versus Bur-0/C24 ) , but there were more siRNAs in variable regions than invariable regions of siRNA+ VarTEs in Col-0 ( Figure 6a; MWU pCol-0/Bur-0\u200a=\u200a1×10−5 , pCol-0/C24<2×10−16 ) ., Furthermore , usiRNAs were overrepresented in variable regions ( binomial test , pCol-0/Bur-0\u200a=\u200a7×10−18 , pCol-0/C24\u200a=\u200a0 ) , while msiRNAs were biased towards invariable regions ( pCol-0/Bur-0\u200a=\u200a1×10−6 , pCol-0/C24\u200a=\u200a0 ) ., Therefore , usiRNAs strongly correlate with variability of TE sequences and are over-represented in the variable regions of variant TEs ., This finding raised the question whether TE regions that varied between accessions and were targeted by siRNAs had a particularly large effect on expression of adjacent genes ., We therefore separated Col-0 genes within 2 kb of variable TEs into three subsets: genes next to siRNA− VarTEs ( siRNA− VarTE+ genes ) ; genes next to VarTEs with an siRNA-targeting bias towards invariable TE regions ( InvsiRNA+ VarTE+ genes ) ; and genes next to VarTEs with an siRNAs targeting bias towards variable TE regions ( VarsiRNA+ VarTE+ genes; Table S8 ) ., As expected , siRNA− VarTE+ genes had a higher average expression level compared to InvsiRNA+ VarTE+ genes ( Figure 6b; MWU pCol-0/C24\u200a=\u200a0 . 01 , pCol-0/Bur-0\u200a=\u200a0 . 01 ) or VarsiRNA+ VarTE+ genes ( MWU pCol-0/C24\u200a=\u200a9×10−5 , pCol-0/Bur-0\u200a=\u200a0 . 003 ) ., The InvsiRNA+ VarTE+ genes , however , were expressed on average more highly than the VarsiRNA+ VarTE+ set ( MWU pCol-0/C24\u200a=\u200a0 . 01 , pCol-0/Bur-0\u200a=\u200a0 . 04 ) ., This indicates that gene suppression by neighboring TEs may not only be influenced by siRNA presence or absence at the TEs , but may also depend on which TE regions are targeted by siRNAs ., We speculate that siRNA-targeting of particular TE regions suppresses the expression of nearby genes to such an extent that there is significantly higher selection pressure for these regions to be excised or mutated ., Alternatively , due to the skew of usiRNA mapping towards variable regions , and the greater correlation between usiRNAs and TE methylation , the lower expression level of VarsiRNA+ VarTE+ genes may reflect a higher degree of epigenetic silencing of these elements compared to InvsiRNA+ VarTE+ genes ., TEs may be prevented from reaching fixation within a population through negative selection , especially for gene-proximal , methylated TEs 13 , 15 , 34 ., Therefore , it is perhaps unsurprising that TEs are over-represented in analyses of structural variants among accessions and between species 17 , 18 , 35 , 36 , and that a recent comparison of 80 A . thaliana genomes reported evidence of structural variation in 80% of TEs 17 ., Similarly , Hollister and Gaut 15 found that 44% of over 600 TE insertions were polymorphic among 48 accessions ., Since most TEs in A . thaliana are relatively old 7 , the simplest way to explain these patterns is ongoing deletion of TEs , which is also consistent with TEs in A . thaliana being on average farther from genes than in the closely related but outcrossing A . lyrata 7 ., This may , however , be too simplistic an explanation as non-LTR retrotransposons are skewed towards an older insertion distribution than LTR retrotransposons 25 , even though they are not significantly more variable ( Table S2 ) ., While TE presence/absence polymorphisms in different accessions have been previously characterized 17 , we have shown that there is substantial sequence variation in about 6% of TEs when comparing accessions ( Figure 1a ) ., These TE variants are equally distributed throughout the genome ( Figure 1b ) ., TEs can affect the expression of proximal genes via mechanisms including disruption of promoter sequences , reduction of transcription through the spread of epigenetic silencing 13 , or read-though antisense transcription 37 ., Often TEs suppress the expression of proximal coding genes 15 , 22 , 38 however , TEs can also introduce new promoter sequences , leading to up-regulation of proximal genes 37 ., In both plants and animals , TE-derived sequences have been recruited to form regulatory sequences and have contributed to coding regions 8 , 39–42 ., Methylated TEs suppress expression of proximal genes in A . thaliana , regardless of insertion upstream or downstream of the coding region ., Purifying selection is therefore greatest for methylated TEs proximal to genes 15 ., Notably , the effects of siRNAs on expression of proximal genes can only be detected up to 400 bp 14 , while measurable TE effects extend to 2 kb 14 ., This supports the assertion that TEs either directly affect gene expression by disruption of positive regulatory sequences , or otherwise act through DNA structure and epigenetic marks to affect genes over longer distances ., We found that TEs that with variable siRNA-targeting do not affect proximal genes more strongly than TEs that are targeted in all three accessions ( Figure 5 ) ., It is possible that siRNA-targeting varies independently of TE sequence variation , as observed recently for DNA methylation 43 , and that such TEs mask more subtle differences between the TE classes examined ., However , the region of the TE targeted by siRNAs does seem to matter , with siRNA-targeting of TE sequences within an accession that are variant/absent in other accessions showing a greater suppression of proximal genes ( Figure 6 ) ., This agrees with the observation that genes close to usiRNA-targeted TEs have a lower expression average than those close to msiRNA-targeted TEs , and that usiRNAs are over-represented in the variable regions of transposons ., A recent study of hybrids between parents of different ploidy found that a reduction in 24 nt siRNAs is associated with up-regulation of more TE-associated genes than when there is no significant change in siRNA levels 44 ., This result supports the hypothesis that siRNAs , or linked epigenetic changes , can affect the expression of nearby genes , with deletion of the siRNA-targeted regions alleviating repression of adjacent genes ., While TEs in the euchromatin are often found close to genes , methylated TEs are underrepresented upstream of genes , likely because changes in the promoter more easily affect gene expression than variation in the 3′ region 13 ., In agreement , methylated TEs have a skewed distribution , with older elements farther from genes , but unmethylated TEs do not show such a bias 15 ., In a comparison of humans and chimpanzees , TE insertion site preference appears to be the main cause for TEs being found more often in the vicinity of genes with increased interspecific expression variation 45 ., This is reminiscent of what we have observed , with additive effects of polymorphism , TE presence and TE variance on the variability of orthologous gene expression ( Figure 2 and Figure 4 ) ., In a comparison of two rice subspecies , TE presence/absence polymorphisms were also found to be underrepresented in SNP deserts 35 ., There are several possible explanations for these observations: some genomic regions may suffer from generally elevated mutation rates TEs near highly conserved genes are more efficiently purged; or TE integration into more mutable genomic regions is favored ., In the latter case , new mutations may destabilize DNA packing and facilitate TE insertions , similar to the TE insertion preference for transcribed genomic regions 42 ., With our observation of TE deletions correlating with siRNA-targeting , we can expand the current model for TE evolution 15 ., Our model starts with the duplication of a TE that is already present and targeted by siRNAs within the genome ( Figure 7a and 7b ) , leading to all siRNAs produced by and targeting the original TE now being multiply-mapping siRNAs ( msiRNAs ) ., As the two copies of the duplicated TE gain mutations ( enhanced by deamination of methylated cytosines ) , uniquely-mapping siRNAs ( usiRNAs ) are produced in addition to msiRNAs ( Figure 7c ) ., Hollister and colleagues 14 noted that usiRNA-targeting increases with TE age , while msiRNA-targeting decreases , and that TEs are expressed at lower levels when also targeted by usiRNAs ., Furthermore , usiRNAs are more closely correlated with DNA methylation than are msiRNAs 12 and they are expressed at higher levels than msiRNAs 14 ., With usiRNAs , the duplicated TEs will therefore be more effectively silenced , probably with a concurrent increase in methylation , a further reduction in the expression level of proximal genes , and thus increased selection against the TEs ., usiRNA-targeting may then facilitate TE inactivation through preferential deletion of usiRNA-targeted regions ( Figure 6 and Figure 7d ) ., This may be actively promoted by the usiRNAs and attendant epigenetic marks , in a mechanism analogous to the siRNA-guided removal of “internal eliminated sequences” including TEs in Tetrahymena 46 , 47 ., In favor of such a scenario , small deletions within TEs have been shown to occur more frequently than ectopic recombination events at the LTRs 31 , 48 ., Ectopic recombination appears to be less important for TE elimination in A . thaliana , as TE density and recombination rate are not correlated in this species 48 , and because ectopic recombination is lower in homozygotes 49 ., No matter what the mechanism , deletions within TEs would reduce selection pressure by removing usiRNA target sites , inactivating TEs so they are no longer transposition-competent , and relieving proximal gene repression ., In apparent contrast to the majority of TEs , some are under positive selection 50 , 51 , and TEs can also contribute to new regulatory networks 52 ., Our model is only appropriate for TEs under neutral or negative selection ., Modeling of TE dynamics suggests that transposition events occur in a cyclical manner 53 , 54 , with some activation events creating new favorable genetic variants ., One such example is provided by transposition of a TE that is induced upon heat stress in genetic backgrounds impaired in siRNA

Introduction, Results, Discussion, Methods

Transposable elements ( TEs ) make up the majority of many plant genomes ., Their transcription and transposition is controlled through siRNAs and epigenetic marks including DNA methylation ., To dissect the interplay of siRNA–mediated regulation and TE evolution , and to examine how TE differences affect nearby gene expression , we investigated genome-wide differences in TEs , siRNAs , and gene expression among three Arabidopsis thaliana accessions ., Both TE sequence polymorphisms and presence of linked TEs are positively correlated with intraspecific variation in gene expression ., The expression of genes within 2 kb of conserved TEs is more stable than that of genes next to variant TEs harboring sequence polymorphisms ., Polymorphism levels of TEs and closely linked adjacent genes are positively correlated as well ., We also investigated the distribution of 24-nt-long siRNAs , which mediate TE repression ., TEs targeted by uniquely mapping siRNAs are on average farther from coding genes , apparently because they more strongly suppress expression of adjacent genes ., Furthermore , siRNAs , and especially uniquely mapping siRNAs , are enriched in TE regions missing in other accessions ., Thus , targeting by uniquely mapping siRNAs appears to promote sequence deletions in TEs ., Overall , our work indicates that siRNA–targeting of TEs may influence removal of sequences from the genome and hence evolution of gene expression in plants .

Transposable elements ( TEs ) are selfish DNA sequences ., Together with their immobilized derivatives , they account for a large fraction of eukaryotic genomes ., TEs can affect nearby gene activity , either directly by disrupting regulatory sequences or indirectly through the host mechanisms used to prevent TE proliferation ., A comparison of Arabidopsis thaliana genomes reveals rapid TE degeneration ., We asked what drives TE degeneration and how often TE variation affects nearby gene expression ., To answer these questions , we studied the interplay between TEs , DNA sequence variation , and short interfering RNAs ( siRNAs ) in three A . thaliana strains ., We find sequence variation in genes and adjacent TEs to be correlated , from which we conclude either that TEs insert more often near polymorphic genes or that TEs next to polymorphic genes are less efficiently purged from the genome ., We also noticed that processes that cause deletions within TEs and ones that silence TEs appear to be linked , because siRNA targeting is a predictor of sequence loss in accessions ., Our work provides insight into the contribution of TEs to gene expression plasticity , and it links TE silencing mechanisms to the evolution of TE variation between genomes , thereby linking TE silencing mechanisms to expression plasticity .

transposons, genomics, molecular cell biology, plant science, genome evolution, plant evolution, plant biology, plant genomics, biology, computational biology, epigenomics, molecular biology, genetics and genomics

journal.pntd.0001577

Analysing Spatio-Temporal Clustering of Meningococcal Meningitis Outbreaks in Niger Reveals Opportunities for Improved Disease Control

Meningococcal meningitis ( MM ) , caused by the bacterium Neisseria meningitidis ( Nm ) , is a major health problem in sub-Saharan Africa ., The highest incidences of the disease are observed in the so-called “African Meningitis Belt” where annual recurrent epidemics occur during the very hot , dry season 1 ., In Niger , reported meningitis cases varied between 1 , 000 and 13 , 000 from 2003 to 2009 , with case-fatality rates of 5–15% ., The factors involved in the spatio-temporal occurrence of meningococcal epidemics are only suspected and still poorly understood ., Surveillance and reactive vaccination are the predominant strategies for managing meningococcal meningitis outbreaks in the Belt , recently completed with a conjugate vaccine to prevent the carriage of Nm serogroup A . In Niger like in most sub-Saharan countries , surveillance is performed at the district level ., Quantitative morbidity and mortality data on meningitis are collected within a reporting network managed by the Direction for Statistics , Surveillance and Response to Epidemics ( DSSRE ) from the Ministry of Public Health ., Data from all health care facilities covering the entire Niger population are collected on a weekly basis by the district health authorities , which aggregate and forward their data to the regions and subsequently to the DSSRE ., These reported data include all suspected and probable cases , according to the standard clinical definition of meningococcal meningitis 2: A suspected case is any person with sudden onset of fever ( >38 . 5°C rectal or 38 . 0°C axillary ) and one or more of the following signs: stiff neck , altered consciousness or other meningeal sign; in patients under one year of age , a suspected case occurs when fever is accompanied by a bulging fontanelle ., A probable case is defined as a suspected case with turbid CSF or Gram stain showing Gram-negative diplococcus or petechial/purpural rash or ongoing epidemic ., Laboratory confirmation of meningitis is not required to report a case ., In parallel to this epidemiologic surveillance and in close collaboration with DSSRE , the Centre de Recherche Médicale et Sanitaire ( CERMES ) is in charge of the national microbiological surveillance of meningitis ., The CERMES collects the cerebrospinal fluid ( CSF ) samples taken from suspected cases of meningitis by health care workers or physicians and carries out the etiological diagnosis ( see Methods ) ., Based on this national surveillance system , the strategy applied in Niger to respond to meningitis outbreaks with limited amounts of available vaccines , is to initiate reactive vaccination in a district once weekly incidence exceeds the epidemic threshold defined by WHO ( see 3 and Methods for definitions ) ., Thus , early detection of epidemics is essential for an effective operational response ., Analysing spatio-temporal patterns of epidemics at a fine geographic scale could lead to a better understanding of the underlying causes of the disease and potential future prediction of outbreaks 4 ., One of the techniques to uncover spatial patterns of disease is cluster detection ., In epidemiology , a cluster is a number of health events situated close together in space and/or time 5 ., Identifying spatial and spatio-temporal clusters of cases could help:, ( i ) to generate new information for further etiologic studies;, ( ii ) to identify risk areas where to focus the surveillance and allocate the resources ( antibiotics , rapid diagnostic tests… ) ;, ( iii ) to develop cost-efficient vaccination strategies ., In sub-Saharan Africa , data from national disease notification have already been used at country , regional or district levels to study the geographical and temporal dynamics of epidemics and their correlation with environmental factors 6–12 ., However , little is known about MM emergence and distribution at a sub-district level ., Using a finer spatial scale such as health centre catchment areas ( HCCAs ) would have several advantages:, ( i ) it would capture heterogeneity in MM incidence at sub-district level;, ( ii ) epidemic thresholds would be studied at a more accurate scale , allowing for a more rapid and targeted public health response;, ( iii ) monitoring of the impact of the intervention would be performed at the same level as the intervention itself ., Therefore , we aimed to investigate the spatio-temporal distribution of MM epidemics in Niger at the health centre catchment area level , to identify the most frequently affected HCCAs requiring a particular attention from public health authorities ., The national microbiological surveillance database was used to perform two cluster detection methods in order to uncover spatial and spatio-temporal clustering of MM incidence from July 2002 to June 2009 ., Then , as a preliminary analysis to a more thorough etiologic study , we searched for ecologic correlation of MM incidence with human density and roads at the HCCA level ., Finally , to further investigate the benefit of using a finer spatial scale for surveillance and disease control , we compared timeliness of epidemic detection at the HCCA level versus district level ., This paper provides new insights into the spatio-temporal dynamics of MM epidemics and discusses the potential implications of our findings for meningitis control in sub-Saharan Africa ., The CERMES is the national laboratory in charge of the microbiological surveillance of meningitis in Niger ., This surveillance has been reinforced since 2002 13 , 14 by its extension to the whole country ( it was only effective in the capital city before 2002 ) and by the inclusion of a Polymerase Chain Reaction ( PCR ) assay for etiological diagnosis of meningitis to the DSSRE routine surveillance ., CSF samples were collected by health care workers or physicians from suspected cases of acute meningitis ., Each CSF was documented with an epidemiological form that included date of sample collection , clinical information and general characteristics about the patient ( age , sex , geographic origin such as region , district , HCCA and village ) ., The samples were kept either refrigerated or frozen in health facilities , or inoculated into a trans-Isolate ( TI ) medium ., The more remote health centres sent CSF samples ( frozen in a cool box ) on a voluntary basis to CERMES by mandated transport companies ., Additionally , CERMES carried out active collection of samples twice a day in Niamey , so that the samples remained suitable for culture , and every month within a radius of about 300 kilometres around Niamey , in the regions of Tillabery and Dosso ., Etiological diagnosis of MM was carried out by PCR for all CSF as described in 13 and 14 and by culture 15 for suitable CSF received promptly at CERMES ( fresh CSF and CSF inoculated into TI medium ) ., Questionnaire data and microbiological results were entered in a database managed by CERMES ., The data were used for a retrospective study on meningococcal meningitis cases between July 1 , 2002 and June 30 , 2009 ., All data were collected through the national routine surveillance system ., Therefore , written consent was not asked and approval from the national ethics committee was not needed ., However , patients were informed of the reason why their cerebrospinal fluid was sampled and confidentiality on patients identity was guaranteed ., In 2008 , in order to create a digitized National Health Map of Niger , CERMES mapped the countrys HCCAs , each of which included all villages served by the same health centre ., As projected data were required for the spatial statistics , all analyses were carried out with a projected version of the National Health Map , using the WGS84 – UTM32N projection ., The number of inhabitants per village was extracted from the 2001 census database of the Institut National de la Statistique ( INS ) and an annual population growth rate of 3% was applied ., A shapefile of primary roads was retrieved from the HealthMapper application of the World Health Organization ( WHO ) ., From July 1 , 2002 to June 30 , 2009 , a total of 15 801 CSF specimens from meningitis suspected cases were analysed at the CERMES laboratory ( table 1 ) ., 112 CSF ( 0 . 7% ) could not be tested ( depleted , broken tubes… ) and 79 ( 0 . 5% ) did not give conclusive results because of contamination ., Overall , biological specimens originated from 416 ( 61% ) of the 682 HCCAs mapped in 2009 ., Among these CSF , 6556 ( 41 . 5% ) were confirmed as bacterial meningitis cases , 82 . 2% of which were positive for Neisseria meningitidis ., Serogroup A was the predominant serogroup every year , except in 2006 ., The mean ( SD ) age of the MM cases was 9 . 6 ( 7 . 5 ) years and 58 . 8% were male ., Over the study period , MM cases were detected in 349 HCCAs ( 51 . 2% ) in all regions of Niger ( figure 1 ) , with contrasting incidence rates within districts ., The highest incidence rates were found in HCCAs of Niamey , Tillabery , Dosso , Tahoua , Maradi and Zinder regions ., As for the temporal distribution , 82 . 5% of the MM cases occurred from February to April ., Figure 2 depicts for each year the Kulldorffs spatial scan statistic results overlain on the Anselins Local Morans I results ., Over the seven years , the Local Morans I method identified 140 high-risk HCCAs ( 130 high-high and 10 high-low ) , with an annual number ranging from 11 ( in 2003 and 2007 ) to 31 ( in 2008 and 2009 ) ., The spatial scan method identified 58 significant spatial clusters altogether , with an annual number ranging from 3 ( in 2003 ) to 16 ( in 2009 ) ., The median number of HCCAs per cluster was 2 ( IQ range\u200a=\u200a1–5 ) and the median annual incidence rate of the clusters was 34 . 9 ( IQ range\u200a=\u200a20 . 5–72 . 3 ) cases per 100 , 000 ., Almost 80% of the high-risk HCCAs identified with the Local Morans I were included in clusters detected by SaTScan and 62% of the SaTScan clusters encompassed high-high or high-low HCCAs ., Spatial clusters generally occurred in different HCCAs from year to year over the study period , as shown by the low frequencies observed at the HCCA level ( figure 3 ) ., Among the HCCAs contributing to a cluster at least once over the study period , the median frequency was 1 ( range\u200a=\u200a1–4 ) for clusters detected by at least one method , and 1 ( range\u200a=\u200a1–3 ) for clusters detected by both methods ., Only four HCCAs were detected three or more times by at least one method and two or more times by both methods ., They were: Chare Zamna ( in Zinder urban community ) , Gazaoua , Doumega and Loudou ., Spatial clusters most frequently occurred within nine districts out of 42 , containing three or more times a cluster detected by at least one method , and two or more times a cluster detected by both methods ., These districts were: Tera and Say ( bordering Burkina Faso ) , Keita , Zinder and five districts bordering Nigeria , Doutchi , Madaoua , Guidan Roumji , Madarounfa and Aguie ., The median time interval between two clusters occurring in the same district was one year ., When a district contained a cluster detected by at least one method , only 13 . 3% ( median ) of its HCCAs contributed to that cluster , and 9 . 7% when a district contained a cluster detected by both methods ., No systematic spatio-temporal pattern for cluster emergence and epidemic spread was observed within the seven years of the study period ., Figure 4 shows the 66 significant spatio-temporal clusters detected with the SaTScan space-time scan ( except a 2009 northeast cluster in Dirkou , Bilma district , which is outside the displayed zone ) and the incidence rate observed for each HCCA of a spatio-temporal cluster during the time period associated to that cluster ., They essentially occurred between February and April , with an additional few at the beginning ( November–January ) and the end ( May ) of the epidemics ., In 2003 , the epidemic could be summarized in two western and eastern poles , with the western pole occurring before the eastern one ., In 2004 , the first cluster was detected in the west; then all clusters appeared in the east , ending with the northernmost one in Tanout district ., In 2005 , clusters were detected only in the eastern part ., In 2006 , two spatio-temporal poles were clearly distinguished , first in the east and then in the west ., In 2007 , the first three clusters were detected in the west , followed by one in the east , still another one in the west and a final northernmost one in Keita district ., In 2008 , between the eastern clusters at the beginning and the end of the epidemic season , other clusters essentially appeared in the centre ( Tahoua region ) and the west ( Tillabery and Dosso regions ) without a clear order , concluding again the northernmost cluster in Keita district ., In 2009 , from the first cluster in the east to the final one in the west , clusters appeared in all regions in between , but followed no clear geographical direction ., No significant correlation was found between MM incidence at the HCCA level and human density ( r\u200a=\u200a0 . 02 ) or distance to primary roads ( r\u200a=\u200a−0 . 07 ) ., Between 2003 and 2009 , 88 districts crossed the alert threshold ., For 42 ( 47 . 7% ) of them , the alert threshold was crossed earlier ( 4 weeks early in median ) in at least one HCCA of these districts ., Between 2003 and 2009 , 46 districts crossed the epidemic threshold ., For 15 ( 32 . 6% ) of them , the epidemic threshold was crossed earlier ( 3 weeks early in median ) in at least one HCCA of these districts ., To our knowledge , this is the first study using health centre catchment areas as spatial units for the spatio-temporal analysis of MM over a whole sub-Saharan country ., The studys first finding was the more frequent detection of spatial clusters within nine southern districts , mainly on the southern border with Nigeria ., Second , clusters most often encompassed only a few HCCAs within a district , without expanding to the entire district ., In addition , no consistent annual spatio-temporal pattern for cluster emergence and epidemic spread could be observed , thus precluding the capacity to predict where the next epidemic would break out , and what geographical direction it would follow ., These findings rely on laboratory-based data and have important public health implications as discussed hereafter ., The first asset of this study was the quality of the microbiological data ., We used laboratory-confirmed N . meningitidis cases data , coming from a surveillance system managed by CERMES and DSSRE throughout the country ., Most other spatio-temporal studies on meningitis epidemics in sub-Saharan Africa 6–12 , 19 are based instead on suspected cases reported in the framework of the national surveillance systems ., In our dataset , none of the three typical bacterial aetiologies ( N . meningitidis , S . pneumoniae and H . influenzae ) could be identified in almost 60% of the CSF analysed by CERMES over the study period ( see Table 1 ) ., Relying only on suspected cases would therefore introduce a large number of misclassified cases ., However , our system may suffer from underreporting from areas where performing a lumbar puncture and shipping the samples to CERMES may represent logistical difficulties ., Further analyses ( not shown here ) have documented that indeed the districts the most remote from CERMES ( in Maradi and Zinder regions ) were sending less CSF specimens than the closer ones , for a similar number of suspected cases notified to DSSRE ., However , the proportion of negative cases among the received CSF specimens was fairly similar among the healthcare centres ( outside the capital Niamey ) ., This suggested that the decision to take or not a CSF sample from a patient based on clinical criteria had no significant spatial variability ., Moreover , our cluster analyses enabled us to detect the importance of remote regions in the epidemic dynamics according to the recurrent clusters identified there ., Like in many other settings , the surveillance system may not cover the entire population of Niger affected by meningitis ., However , we can reasonably assume that most meningitis cases , because of their severity , end up reaching the healthcare centres , with or without prior self-treatment or consultation of a tradi-practitioner ., Moreover , free healthcare offered to all people suffering from meningitis in Niger probably reduces social and spatial disparities in care-seeking behaviours ., Thus , for all the reasons above , we are confident that the surveillance system is representative enough and that underreporting did not substantially affect the validity of our results , which are more likely to reflect the dynamism peculiar to meningitis than the spatial disparities in the surveillance system efficiency ., Incidence estimates were based on the 2001 census and constant population growth rates were applied ., We could not take into account possible variations of population growth rate over time and space , due to the difficulty in quantifying population migrations ., The second asset of this study was the use of HCCAs as spatial units for the spatio-temporal analysis of MM ., They represent a more accurate spatial unit of analysis than the district level on which reactive vaccination strategies and spatio-temporal studies are usually based 3 , 6 , 7 , 19 ., Analysing data at the HCCA level has greater relevance for understanding the epidemic dynamics , for making decisions in response to starting epidemics and for assessing control strategies ., Indeed , this study has shown that clusters most often included only a few HCCAs within a district ., This finding , previously suggested by 20 , is important for understanding meningitis epidemics and should encourage surveillance at the health centre level ., Clusters occurred in different HCCAs within the same districts in consecutive years , demonstrating strong intra-district heterogeneity and year-to-year variability of the affected HCCAs ., This could result from outbreaks limited to HCCAs without exceeding the threshold at the district level: the district is not vaccinated and may be affected by a large outbreak the following year ., Besides , waiting for the threshold to be reached at the district level to initiate reactive vaccination may incur unnecessary delays: we showed that a decision based on threshold estimated at the health centre level might lead to earlier detection of outbreaks , so more reactive and possibly more cost-effective vaccination strategies ., Thus , adding HCCA-level surveillance to the current district-level surveillance would improve the timeliness of epidemic detection ., With the introduction of a new meningococcal A conjugate vaccine ( MenAfriVac™ ) in the meningitis belt over the next few years , the use of the health centre catchment areas as spatial units can also help to monitor more accurately the vaccine supply at a finer spatial scale , saving doses that could be given inadequately , and to evaluate its impact and protective efficacy in the population ( herd immunity ) at the same level ., Although this vaccine brings new hope to the control of meningitis epidemics , reactive vaccination with polysaccharide vaccines and research to improve control strategies will still be needed in the coming years , since it will take several years to immunize against the A the vulnerable population across the belt and since other serogroups like W135 may replace meningitis A as the dominant serogroup 21 ., New decision criteria will have to be found for reactive vaccination ., With the additional use of a finer spatial scale like the HCCAs , an interesting strategy would be real-time cluster detection , with prospective space-time scan statistic 22 or other existing methods 23 ., In the context of a resource-limited country , this study can also assist public health authorities in their decision-making regarding resource allocation ., The spatial clusters detected in our study were located in different HCCAs from year to year , but nine of the 42 districts were more recurrently affected by clustering of MM cases ., Thus , these findings provide approaches to better adjust allocation of resources , including a ready supply of antibiotics and rapid diagnostic tests 24 , 25 , as well as additional health care personnel ., In order to reduce the reaction time of the vaccination , one may consider allocating vaccines to these districts hospitals prior to the meningitis season , provided the cold chain can be maintained ., Given cost and organizational constraints , further cost-effectiveness and feasibility analyses are needed to evaluate this strategy , before any policy recommendation ., Clusters were more often found in nine districts , including five bordering Nigeria within a 500 km distance between Doutchi and Aguie , most likely because of intense mobility of border populations 26 ., However , no consistent annual spatio-temporal pattern could be found over the study period; hence , no spread in a systematic geographical direction from a fixed source could be identified ., This is contrary to a study carried out in Mali , which highlighted a potential south-north spread , with Bamako and Mopti as probable sources 12 ., Instead , our results suggest the emergence of scattered sources , likely from a pool of carriers when conditions are favorable to the occurrence of the invasive disease ., Favorable conditions may include climatic conditions occurring during the dry season ( low absolute humidity and dust-laden Harmattan wind ) , which would damage the nasopharyngeal mucous membrane and increase the risk of bloodstream invasion by a colonizing meningococcus 27 ., In this study , we observed that the latest spatio-temporal clusters during the epidemic season were often the northernmost ones , which could be correlated with the northward advance of the Intertropical Front preceding the arrival of rains from the south , thus raising relative humidity ., However , climatic factors do not entirely explain these spatio-temporal epidemic patterns ., As suggested by Muellers hypothetical explanatory model 20 , their role may be limited to the hyperendemic increase during the dry season , while transition from a hyperendemic state to highly localized epidemics may be due to increased transmission , possibly caused by viral respiratory co-infections ., Moreover , in equivalent climatic conditions , an area in which the proportion of susceptible individuals is higher due to waning immunity ( acquired by infection or vaccination ) would be more prone to outbreaks 28 ., Recently , Irving et al 29 suggested that population immunity may be a key factor in causing the unusual epidemiology of meningitis in the Belt ., Although density and distance to primary roads were not individually correlated with MM incidence at the HCCA level , other socio-demographic factors ( poverty , overcrowded housing , migrations , markets… ) may also have an influence on local transmission of the bacteria and carriage and contribute to the risk of micro-epidemics of co-infections 20 ., Of note , one spatio-temporal cluster of four adult cases was detected in February 2009 in Bilma district ( see figure 1 ) , in the oasis town of Dirkou , located on an important south-north route of trans-Saharan trade and transit migration ., Meningococcal strain variations most likely play a role in the occurrence of epidemic waves 20 , 30 , 31 ., In this study , the spatio-temporal distribution of all N . meningitidis cases was analysed irrespective of the serogroups ., A subsequent analysis will differentiate serogroups of meningococci as their spatio-temporal patterns may significantly vary 32 , 33 ., Further etiologic studies are needed to explore causality of the spatio-temporal patterns highlighted in this paper ., Finally , our findings provide an evidence-based approach to reflect on public health policies and indicate a promising strategy to improve prevention and control of meningitis in sub-Saharan Africa ., They can serve as an example for other meningitis belt countries , illustrating what finer scale surveillance and spatial analyses can offer for prevention and control of meningitis ., Research efforts should now focus on investigating the role of dust , socio-demographic factors , co-infections and vaccination strategies on cluster occurrence at the HCCA level , and on developing an operational decision support tool to respond better to meningitis outbreaks with the introduction of the new conjugate vaccine .

Introduction, Methods, Results, Discussion

Meningococcal meningitis is a major health problem in the “African Meningitis Belt” where recurrent epidemics occur during the hot , dry season ., In Niger , a central country belonging to the Meningitis Belt , reported meningitis cases varied between 1 , 000 and 13 , 000 from 2003 to 2009 , with a case-fatality rate of 5–15% ., In order to gain insight in the epidemiology of meningococcal meningitis in Niger and to improve control strategies , the emergence of the epidemics and their diffusion patterns at a fine spatial scale have been investigated ., A statistical analysis of the spatio-temporal distribution of confirmed meningococcal meningitis cases was performed between 2002 and 2009 , based on health centre catchment areas ( HCCAs ) as spatial units ., Anselins local Morans I test for spatial autocorrelation and Kulldorffs spatial scan statistic were used to identify spatial and spatio-temporal clusters of cases ., Spatial clusters were detected every year and most frequently occurred within nine southern districts ., Clusters most often encompassed few HCCAs within a district , without expanding to the entire district ., Besides , strong intra-district heterogeneity and inter-annual variability in the spatio-temporal epidemic patterns were observed ., To further investigate the benefit of using a finer spatial scale for surveillance and disease control , we compared timeliness of epidemic detection at the HCCA level versus district level and showed that a decision based on threshold estimated at the HCCA level may lead to earlier detection of outbreaks ., Our findings provide an evidence-based approach to improve control of meningitis in sub-Saharan Africa ., First , they can assist public health authorities in Niger to better adjust allocation of resources ( antibiotics , rapid diagnostic tests and medical staff ) ., Then , this spatio-temporal analysis showed that surveillance at a finer spatial scale ( HCCA ) would be more efficient for public health response: outbreaks would be detected earlier and reactive vaccination would be better targeted .

Meningococcal meningitis ( MM ) is an infection of the meninges caused by a bacterium , Neisseria meningitidis , transmitted through respiratory and throat secretions ., It can cause brain damage and results in death in 5–15% of cases ., Large epidemics of MM occur almost every year in sub-Saharan Africa during the hot , dry season ., Understanding how epidemics emerge and spread in time and space would help public health authorities to develop more efficient strategies for the prevention and the control of meningitis ., We studied the spatio-temporal distribution of MM cases in Niger from 2002 to 2009 at the scale of the health centre catchment areas ( HCCAs ) ., We found that spatial clusters of cases most frequently occurred within nine districts out of 42 , which can assist public health authorities to better adjust allocation of resources such as antibiotics or rapid diagnostic tests ., We also showed that the epidemics break out in different HCCAs from year to year and did not follow a systematic geographical direction ., Finally , this analysis showed that surveillance at a finer spatial scale ( health centre catchment area rather than district ) would be more efficient for public health response: outbreaks would be detected earlier and reactive vaccination would be better targeted .

medicine, bacterial diseases, infectious diseases, geography, public health and epidemiology, computer science, epidemiology, earth sciences, public health, geoinformatics, human geography

journal.pcbi.1004122

Kinetically-Defined Component Actions in Gene Repression

The initial steps by which steroid receptors induce or repress target gene transcription are the same ., After steroid binding to the intracellular receptor , the resulting complex is activated/transformed to a form with increased affinity for DNA 1 and is concentrated in the nucleus , where it is recruited to DNA sequences that are usually near the regulated genes ., Cofactors and comodulators assist or impede the transcriptional activity of DNA-associated steroid receptors 2 , 3 ., Beyond this , it is currently not possible to predict the transcriptional outcome for any specific combination of gene , receptor , and cofactor/comodulator ., In most cells , a given steroid-bound receptor will induce one set of genes while repressing another set under otherwise identical conditions ., For certain genes , the same receptor-steroid complex activates transcription in one cell line while repressing it in another cell line 4 ., Similarly , selected cofactors increase the activity of one steroid receptor while reducing the activity of another receptor 5 ., In some cases , different cofactors cause the same gene to be induced or repressed 6 , 7 ., In other cases , the same cofactor may interact with the same steroid receptor to augment the induction of one gene but increase the repression of another gene 6–10 ., Thus no relationship between induction vs . repression and presence of particular promoter/enhancer bound factors and cofactors has yet emerged 11 ., The DNA sequence to which the receptor is recruited , either by direct DNA binding or by tethering to another DNA-bound molecule , can often indicate the resultant activity of induction or repression respectively 11–13 ., Even this categorization , though , is not precise as repression can occur from GR binding directly to DNA 14 , 15 and the outcomes can depend upon whether the cofactor binds to DNA-bound glucocorticoid receptor ( GR ) or GR is tethered to DNA-bound cofactor 16 ., An unresolved question is whether the underlying mechanism of each transcriptional component is the same or changes with the direction of gene expression output ( i . e . , increase in induction vs . decrease in repression ) ., The answers are vital because efforts to modify the responses with selected factor combinations , be it in isolated cells or human patients , will depend critically on whether the mechanism of each component is constant or varies with the specific mixture of factors ., Current attempts to address these issues have been inhibited by insufficiently precise methods of analysis ., Thus , many cofactors are classified as either coactivators or corepressors based solely upon their ability to increase or decrease respectively , the level of steroid-mediated gene expression 2 , 17 ., Unfortunately , while such descriptions are operationally useful , they are mechanistically uninformative ., It is well known from enzyme kinetics that an enzymatic inhibitor can increase the total response while an enzymatic activator can lead to decreased output 18–20 ., A more precise and quantitative understanding of the mechanisms of gene transcription is required to resolve these issues ., Mathematical modeling provides one solution; and , a theory has been developed recently to understand the underlying mechanisms of factor action during steroid-regulated gene induction ., The theory is based on the fact that the dose-response curve for gene induction is non-cooperative with a Hill coefficient of one 20 ., This shape of the dose-response curve has also been variously described as a Michaelis-Menten function , hyperbolic dose-response , first-order Hill plot , and first-order Hill dose-response ., The essential feature is that it is in the mathematical family of linear-fractional functions ., The theory enables one to determine the kinetically-defined mechanism of factor action and the position of factor action in the many steps of the overall signaling cascade ., This position is specified relative to both another competing factor and a steady-state analogue of a rate-limiting step called the concentration limited step ( CLS ) 20–27 ., The concentrations of bound factors after the CLS are negligible compared to their unbound concentrations ( which could be involved in other reactions as long as they are readily available ) ., A factor can act kinetically like an enzymatic activator , which we call accelerator , or an enzymatic inhibitor , which we call decelerator , depending on how the factor participates in the reaction 23 ( see Fig . 1 ) ., These classifications describe how the factor alters a specific reaction step independently of the direction of change in the observed product ., In all cases investigated so far , the reporter acts as an accelerator at the CLS and thus serves as an invariant positional landmark in the otherwise poorly defined landscape of reactions in steroid-regulated gene induction 23 , 25–27 ., Here , we extend the mathematical theory and model to GR-regulated gene repression as influenced by varying concentrations of competing cofactors ., The relevant mass-action equations have been used to relate the graphs of several reaction parameters derived from the dose-response to the kinetically-defined mechanism and position of action of each of the two competing cofactors ., Using the extensively characterized system of GR repression of AP1 induction in U2OS . rGR cells with a transiently transfected synthetic reporter 8 , 15 , 28 , 29 , we show that four factors ( the reporter gene , TIF2 , and two small molecules 30 ) have the same kinetically-defined mechanism and position of action as in GR-regulated gene induction ., The difference between induction and repression is the position of GR action ., This suggests that many transcriptional cofactors/comodulators are mono-functional and similarly modulate basic steps in gene expression irrespective of the directional change in gene product levels ., Experimentally , the dose-response of gene activity A in steroid-regulated repression has been found to be non-cooperative with a Hill coefficient of one ( see Fig . 2 ) : i . e ., A=Amax+Amin/IC50S1+1/IC50S, ( 1 ), where Amax is the maximal activity with no added steroid , Amin is the minimum value of activity with saturating steroid concentrations , and IC50 is the concentration of steroid for half-maximal suppression ., Added cofactors can change the parameters Amax , Amin and IC50 while preserving the shape of the dose-response ( 1 ) ., Our goal was to develop a theory for gene repression that explains why the dose-response has the shape given in ( 1 ) and what that implies for the actions of the added cofactors that change the parameters ., We use the fact that the linear-fractional shape of the dose-response curve puts severe constraints on the possible biochemical kinetic schemes involved in gene repression , as it did for gene induction 20 ., We then derive formulas that can be compared to the data to make predictions for the actions of cofactors ., Our data for the dose-response curve is based on the contributions from many copies of the induced gene in multiple cells ., Hence , it is possible that the dose-response for a single gene could be different from the averaged population response ., However , single-molecule imaging experiments for a gene induced by a nuclear receptor show that transcripts are produced in on-off stochastic bursts , where the bursting is well modeled as a stochastic process ( i . e . random telegraph model 31 , 32 ) and the burst probability is similar between cells ., The frequency ( stochastic intensity ) of the stochastic bursts of individual genes follows a non-cooperative dose-response with respect to the inducing agonist 31 , 32 ., Thus , the mean activity of a single gene follows the same non-cooperative dose-response and our theory explains the behavior of this mean ., We first present a theory for which gene induction has a non-cooperative dose-response since the theory for gene repression hinges directly on it ., Gene expression involves the binding of molecules , protein , and DNA into complexes that lead to transcription ., We model this as a sequence of complex building reactions , Yi−1+Xi↔qiYi , where we call the Y variables products , the X variables accelerators , and the q’s are equilibrium or affinity constants ., Decelerators of various types can also inhibit each of these reactions ., Fig . 1 shows the general reaction scheme at each step ., The accelerator X in Fig . 1 acts like an enzymatic activator and the decelerator D acts like an enzymatic inhibitor 23 ., The kinetic scheme is stochastic and characterized by a probability distribution for the reactants ., However , to simplify the calculations , we consider the mean field limit obeying the law of mass action and assume that the gene activity is proportional to the mean concentration of one or a set of products ., The dose-response curve is the gene activity as a function of the concentration of the initial product Y0 ( i . e . , agonist steroid ) ., The theory can be illustrated by an example of induction with three reactions ( i = 1 , 2 , 3 ) in the absence of deceleration ( which we introduce later ) ., Suppose the dose-response is given by Y3 as a function of Y0 ., The goal is to calculate this function and determine conditions for when it is non-cooperative with unit Hill coefficient ., In steady state , the concentrations obey the equilibrium conditions, Yi=qiXiYi−1 , i=1 , 2 , 3, ( 2 ), and the mass conservation conditions, X1+Y1+Y2+Y3=X1TX2+Y2+Y3=X2TX3+Y3=X3T, where XiT is the total concentration of accelerator i ., Together they form a system of 6 equations and 7 unknowns ., Therefore , any one concentration can be solved in terms of any other ., In general , the dose-response for this system will not have unit Hill coefficient 20 ., However , a non-cooperative dose response can arise if Y2 , Y3<<Y1 and Y3<<X3 so that the mass conservation equations become, X1+Y1=X1T ( 3a ) X2+Y2+Y3=X2T ( 3b ) X3=X3T ( 3c ), This form for mass conservation can be achieved if the concentration of X2 is limited with respect to its binding affinity , i . e . , q2X2<<1 , while the other factors are not limited ., This can be achieved biochemically if the products have short lifetimes , which has been observed experimentally 33–35 ., We call Step ( 3b ) the concentration-limited step ( CLS ) 20 ., All factors following the CLS are in excess ( i . e . , bound concentrations are negligible ) , implying that reactions after the CLS are pseudo-first order ., Hence , the CLS is a step where the accelerator concentration is limited with respect to its binding affinity but the accelerator ( s ) following it are not limited ., In the Methods , we give a detailed account of the CLS for an arbitrary number of reactions ., Substituting ( 2 ) into ( 3a ) – ( 3b ) gives, X1+q1X1Y0=X1TX2+qX2Y1+q3X3Tq2X2Y1=X2TX3=X3T, Each equation is bilinear in the accelerator and product concentrations ., When the accelerator concentrations are substituted back into the equilibrium Equations ( 2 ) , the results are linear-fractional functions between adjacent products:, Y1=q1X1TY01+q1Y0 , Y2=q2X2TY11+q2 ( 1+q3X3T ) Y1 , Y3=q3X3TY2, Linear-fractional functions form a group under function composition ensuring that the function of any product in terms of any other product is always linear-fractional 20 ., Successive substitution of these functions yields the dose-response:, A≡Y3=q1X1Tq2X2Tq3X3TY01+q1Y0+q2 ( 1+q3X3T ) q1X1TY0, where, Amax=q1X1Tq2X2Tq3X3Tq1+q2 ( 1+q3X3T ) q1X1T , EC50=1q1+q2 ( 1+q3X3T ) q1X1T, The dose-response can be derived for an arbitrary number of reactions as long as the mass conservation equations are bilinear as in ( 3 ) 20 , 22 ., Although , our reactions are reversible and obey detailed balance , the theory can also incorporate dissipative irreversible steps ( see Methods ) 36 ., Since Y3 is proportional to Y2 and this is true for all concentrations following the CLS , a more general form for the activity is the sum of all of these concentrations ., Biophysically , this implies that the final product can arise from each step after the CLS independently ., As shown in Fig . 1 , decelerators can interact with accelerators ., Consider the competitive decelerator D interacting with X1 via X1+D↔X′1 ., If D is in excess , the addition of D leads to one additional equilibrium condition , X′1=q′DX1 , and a modification to the mass conservation law for X1 ( 3a ) to X1+Y1+X′=X1T ., Solving the new equilibrium and mass conservation conditions then gives, Y1=q1X1TY01+q1Y0+q′D, The equations for Y2 and Y3 are unchanged ., Since Y1 remains a linear-fractional function of Y0 in the presence of inhibition by D , the dose-response will also remain a linear-fractional function ., Activity can also be repressed by a reaction following the third reaction , Y3+X4↔Y4 , which can suppress Y3 by diverting the product to a less productive pathway ., The new reaction changes mass conservation for X2 ( 3b ) to X2+Y2+Y3+Y4=X2T which gives, Y2=q2X2TY11+q2 ( 1+q3X3T ( 1+q4X4T ) ) Y1, This is again linear-fractional , yielding, Y2=q1X1Tq2X2TY01+q′D+q1Y0+q2 ( 1+q3X3T ( 1+q4X4T ) ) q1X1TY0, which is inhibited by X4T ., Since Y3=q3X3TY2 and Y4=q4X4TY3 , the most general expression of the activity that preserves non-cooperativity is the sum: A=∑i=24ai−1Yi ., The activity as a function of the agonist and any other factor is linear-fractional with the form, A=VY01+WY0, ( 4 ), where, V=q1X1Tq2X2T ( a1+a2q3X3T+a3q3X3Tq4X4T ) 1+q′D , W=q1+q2 ( 1+q3X3T ( 1+q4X4T ) ) q1X1T1+q′D, ( 5 ), V = Amax/EC50 and W = 1/EC50 are linear-fractional functions of the equilibrium constants and the concentrations of all factors ( total concentration of accelerators and free concentration of decelerators ) in the system ., From ( 4 ) and ( 5 ) , we see that activity can be repressed by the action of either a decelerator or an accelerator , provided the latter acts after the CLS and the contribution to the activity from Y4 is less than that from Y2 and Y3 ., We can write formulas for V and W for an arbitrary number of cofactors ., Their functional forms are distinguished by the types of the cofactors ( i . e . , accelerators or one of 6 types of decelerator ) and where they act in relation to each other and the CLS ., The formulas for V and W for all the possible combinations of 2 factors are calculated in Dougherty et al . 22 and shown in S1 Table ., They are always linear fractional functions of each accelerator or decelerator ., They can be used to make predictions of the mechanisms of added factors on the basis of the graphs of V and W vs . the cofactor just as the Lineweaver-Burk plot is used in enzyme kinetics ., Finally , it is commonly accepted that transcription factors sometimes form oligomers before they act 17 ., Our theory can be generalized to include factors acting through oligomers , provided that they do so non-cooperatively ., For example , suppose that an accelerator X first forms an oligomeric complex with another factor Z in the reaction X+Z↔rXZ prior to interacting with a product Yi-1 in a reaction Yi−1+XZ↔qYi before the CLS ., If Z is super-abundant compared to X then the concentration of the product as a function of the previous product has the non-cooperative form, Yi=rqXTZ1+rZYi−11+rqZ1+rZYi−1, Hence , for accelerators acting through a hetero-oligomer , the limited factor , X , acts as an accelerator in our current theory while the action of the superabundant factor , Z , saturates with sufficiently high Z ., A decelerator could likewise act through a hetero-oligomer ., We apply the theory to steroid-regulated gene repression by observing that gene activity in induction can be repressed by other factors and still maintain a linear-fractional form ., Hence , a linear-fractional dose-response ( 1 ) can arise in repression if the steroid-receptor complex ( GR ) acts as either a decelerator at any position or an accelerator after the CLS of a gene initiated by some other inducer ., We compute the dose-response for steroid-regulated gene repression ( 1 ) by substituting the steroid-receptor complex ( GR ) ( or some activated form of GR ) into the formulas for V and W in the dose-response ( 4 ) for a gene activated by another inducer ., Note that the experimentally measured dose-response of gene activity with respect to the inducer need not be linear-fractional for this theory to hold ., What is required is that downstream steps where the cofactors and GR act have the linear-fractional property ., It is well known that steroid binding to GR follows Michaelis-Menten kinetics in terms of S 20 ., Suppose that GR acts as D in the above example ., We substitute, D=GR=GSK+S, into Equation ( 5 ) to obtain, V=q1X1Tq2X2T ( a1+a2q3X3T+a3q3X3Tq4X4T ) ( K+S ) Y0K+S+q′GS , W= ( q1+q2 ( 1+q3X3T ( 1+q4X4T ) ) q1X1T ) ( K+S ) Y0K+S+q′GS, Substituting this into ( 4 ) and clearing the fractions results in an activity that is linear-fractional in S , which we can write as, A=T ( S ) U ( S ) =T ( 0 ) +T′∗SU ( 0 ) +U′∗S, ( 4’ ), where, T=q1X1Tq2X2T ( a1+a2q3X3T+a3q3X3Tq4X4T ) ( K+S ) Y0U=K+S+q′G S + ( q1+q2 ( 1+q3X3T ( 1+q4X4T ) ) q1X1T ) ( K+S ) Y0, and the prime signifies derivative with respect to S ., From this , we can thus surmise that, Amax=T ( 0 ) U ( 0 ) , Amin=T′U′ , IC50=U ( 0 ) U′, These three dose-response parameters are determined by the four quantities T ( 0 ) , U ( 0 ) , T′ , and U′ which implies that the combination parameter:, AmaxIC50Amin=T ( 0 ) T′, is also linear-fractional ., The theory predicts that these four dose-response parameters are always linear fractional and that there are four compatibility conditions between them:, a ) the numerator of Amax is equal to the numerator of Amax×IC50/Amin ,, b ) the numerator of Amin is equal to the denominator of Amax×IC50/Amin ,, c ) the denominator of Amax is equal to the numerator of IC50 , and, d ) the denominator of Amin is equal to the denominator of IC50 ., These properties are not expected for arbitrary linear-fractional functions and provide a validity check for the theory ., Suppose we are only interested in the influence of an accelerator after the CLS ( i . e . , X3T or X4T ) ., We can then write T and U above in terms of the accelerator XT , S , and effective constants that depend on the parameters of the hidden reactions:, T= ( B1+B2XT ) ( K+S ) U=K+S+qGS+ ( B3+B4qXT ) ( K+S ), From which we immediately obtain, Amax=B1+B2XT1+B3+B4XT, ( 6a ), Amin=B1+B2XT1+q′G+B3+B4XT, ( 6b ), IC50= ( 1+B+3B4XT ) K1+qG+B3+B4XT, ( 6c ), AmaxIC50Amin=K, ( 6d ), A cofactor can act like an accelerator or a decelerator before , at , or after the CLS ., For two cofactors , such as GR and one other cofactor , there are 5 possible configurations for their action when not acting together at the same step ( e . g . , both before the CLS , one before and one at the CLS , etc . ) ., There are 10 total positional combinations since GR can act before or after the other cofactor ., There are 3 more configurations where GR acts at the same position as the other cofactor if one is a decelerator while the other is an accelerator ., Thus , GR can act as a decelerator in all of these 13 configurations or as an accelerator after the CLS in 5 configurations ., This gives a total of 18 possible configurations of GR and one other cofactor ., A calculation for T and U can be made for each of these combinations and the results are in S2 Table ., What these calculations show is how the dose-response parameters change as a function of differing amounts of added cofactor ., The experimental dose-response can be fit to the predictions for each of the 18 cases to see which fits best ., However , many of the cases can be eliminated immediately based on qualitative properties of the curves ., The dose-response parameters will always be linear-fractional functions with the form, y=a+bxc+dx, Depending on the parameters , y can appear like a constant , a linear function , a Michaelis-Menten function , or a general linear-fractional function that increases or decreases with x ., We also know that y increases with x if ad<bc , decreases if bc<ad , and is a constant if ad = bc ., The x value for half-maximal y ( half-maximal concentration ) is c/d , and a/b for 1/y ., Using these properties , we see that Amin and Amax in Equations ( 6a ) and ( 6b ) for the three reaction example can either increase or decrease depending on the parameter values but if Amax increases then so must Amin ., IC50 in ( 6c ) is an increasing function because the denominator has an extra positive constant term ., The graph of Amax×IC50/Amin in ( 6d ) is a horizontal line ., Similar predictions for all the possible combinations of GR and one cofactor are summarized in Table 1 ., The graph properties in Table 1 represent some sufficient conditions for the predicted mechanisms and position of action and do not represent a comprehensive list of all possible predictions ., Our experimental paradigm for steroid-regulated repression is the well-documented GR inhibition of phorbol myristate acetate ( PMA ) induction of a reporter construct ( AP1LUC ) with the human collagenase-3 promoter 15 , 28 , 29 ., We usually measured the gene activity for four concentrations of the GR agonist Dex including EtOH in the presence of different concentrations of AP1LUC reporter plus one of three added cofactors: the plasmid for TIF2 or two small molecules ( NU6027 and phenanthroline ) recently identified in a high throughput screen as accelerators of GR transactivation 30 ., We present three lines of evidence to support the application and validity of the theory ., 1 ) Fig . 2 shows the dose-response curve for GR repression of PMA induction of AP1LUC without ( Fig . 2A ) or with added TIF2 ( Fig . 2B ) ., The curves ( including EtOH ) are well fit by the linear-fractional function in Equation ( 1 ) ( solid lines ) as required by the theory ., We found that excellent fits of the dose-response data to Equation ( 1 ) could be obtained with four points ( 3 concentrations of Dex plus EtOH ) ( R2 = 0 . 984 ± 0 . 026 S . D . , n = 160 randomly selected plots , median = 0 . 993 ) , from which we estimated the dose-response parameters Amax , Amin , and IC50 in the ensuing experiments ., 2 ) Figs ., 3–5 show plots of Amax , Amin , IC50 , and Amax×IC50/Amin determined from four steroid concentrations for varying amounts of AP1LUC reporter and each of the added cofactors ., The parameters are all well fit by linear-fractional functions ( solid curves ) as predicted by the theory ., 3 ) We tested if these parameter graphs satisfied the four predicted compatibility conditions using Bayesian model comparison as detailed in the Methods ., We found that the Bayesian Information Criterion ( BIC ) is lowest for the predicted model compared to two null models ( see Tables S3 , S4 , and S5 ) , which further validates the theory ., Given the confidence that the theory is applicable , we used it to make predictions for the mechanism and position of action for the added cofactors as well as for the reporter and GR ., Since the dose-response for repression is derived from the dose-response for induction , how a factor affects the Amax produced by the inducer in GR-regulated repression should be the same as how it alters Amax in GR-regulated gene induction even if the inducer is different in the two cases ., That this is so can be seen from the formulas for Amax in induction ( S1 Table ) and repression ( S2 Table ) ., In our system , the Amax in gene repression is the response to PMA alone and occurs in the absence of added steroid ., All the cofactors we considered ( other than the reporter ) were found to be accelerators after the CLS in steroid-mediated gene induction 22 , 30 ., The above example and S2 Table show that a graph of Amax vs . cofactor can inform us of where an accelerator acts in gene repression and these predictions are summarized in Table 1 ., We used four concentrations of both AP1LUC and TIF2 in our competition assay to analyze TIF2 action in GR-regulated gene repression in U2OS . rGR cells ., The graphs of Amax , Amin , IC50 , and Amax×IC50/Amin vs . TIF2 ( Figs . 3A-D ) all have linear-fractional shapes ( solid lines ) as predicted ., The data for Amax vs . TIF2 ( Fig . 3A ) are well fit by Michaelis-Menten functions that have an x-axis intersection coordinate of -46 . 2 ± 26 . 8 ng ( S . E . M . , n = 4 ) of TIF2 plasmid ., As is true in the competition assays for gene induction 22 , the interpretation of the graphs for gene repression requires that the x-axis values reflect the total amount of factor , i . e . , the sum of endogenous plus exogenous factor ( also see Methods ) ., From quantitative Western blots ( not shown ) , it was determined ( assuming 50% transfection efficiency of cells 22 ) that the endogenous TIF2 is equivalent to 2 . 7 ± 1 . 5 ng ( S . E . M . , n = 3 ) of plasmid ., Thus the point of zero endogenous TIF2 is at -2 . 7 ng TIF2 plasmid , which is much more positive than the intersection point of the curves at -46 , despite the large error range ., As seen in Equation ( 6a ) , S2 Table ( since T ( 0 ) >0 for XT=0 for all entries where k > CLS ) , and summarized in Table 1 ( entry 5 ) , this is consistent with TIF2 acting as an accelerator after the CLS ., Figs ., 4 and 5 show that the dose-response parameters are also well fit by linear-fractional functions for the small molecules NU6027 and phenanthroline 30 ., Amax and Amin versus both cofactors are lines that intersect at values more negative than the concentration of endogenous chemicals , which is zero ., This behavior is consistent with both compounds being accelerators after the CLS ( Figs . 4A&B and 5A&B and entries 5 and 10 of Table 1 ) ., The conclusions for TIF2 , NU6027 and phenanthroline being accelerators acting after the CLS in gene repression are consistent with what was observed in gene induction 30 ., The traces in the graphs of Amax vs . AP1LUC ( Fig . 3E ) for varying concentrations of TIF2 are all linear , intersecting at the origin ., The linear plot of Fig . 3E is preferred over a nonlinear plot ( BIC = 46 . 39 vs . 55 . 56 respectively ) ., As can be seen from the example above , in the formulas of S2 Table where k = CLS and summarized in Table 1 ( entry 2 ) , this is consistent with AP1LUC acting as an accelerator at the CLS ., Graphs of IC50 vs . AP1LUC ( Fig . 3F ) consist of near horizontal lines ( e . g . , a constant slope ) that decrease in position with added TIF2 ( average slope = -0 . 0016 ± 0 . 0038 , S . D . , n = 4 traces of graph ) ., These plots , summarized in Table 1 ( entry 17 ) , are also diagnostic of the reporter AP1LUC being an accelerator ( A ) at the CLS ., S1 A&B and S2 A&B Figs ., show that Amax and Amin versus AP1LUC for varying concentrations of NU6027 and phenanthroline are again linear through the origin ., The graphs of IC50 vs . AP1LUC have a constant zero slope with NU6027 ( = -0 . 0009 ± 0 . 0064 , S . D . , n = 4 traces ) and with phenanthroline ( = 0 . 0067 ± 0 . 0070 , S . D . , n = 4 traces ) ( S1 C and S2 C Figs ) ., These imply that AP1LUC is an accelerator at the CLS in the presence of both NU6027 and phenanthroline ., Hence , we find that the reporter is always an accelerator at the CLS in both gene induction and repression ., Generally , in order to determine the action of a factor , one measures the response to changes of that factor ., However , this was not possible with GR because we could only observe the robust repression needed for accurate graphical analyses with the high amounts of stably transfected GR in our experimental system ., However , we can still deduce the mechanism and location of GR by comparing the responses to changes in the cofactors to the formulas in S2 Table to see which behaviors for GR are compatible with the observed results ., Fig . 3 indicates that Amax increases while Amin , IC50 and Amax×IC50/Amin all decrease vs . TIF2 ., As we show in the Methods , this is mathematically possible only if GR acts as an accelerator after TIF2 and TIF2 is an accelerator after the CLS ., The results are also summarized in Table 1 ( entries 7 , 19 and 22 ) ., Furthermore , from our posterior parameter estimates of our Bayesian model comparison test ( see S3 Table ) , we find that the concentration of TIF2 at half-maximal Amax ( parameter 1 ) is greater than the same for Amin ( parameter 3 ) ., This condition is also true for 1/Amax ( parameter 2 ) and 1/Amin ( parameter 4 ) ., These conditions further support the above deductions that TIF2 is an accelerator after the CLS and GR acts as an accelerator after both the CLS and TIF2 ( Table 1 , entries 12 and 14 ) ., Figs ., 4 and 5 show that Amax and Amin are each augmented by increasing concentrations of both NU6027 and phenanthroline ., Hence , these cofactors cannot uniquely predict the action of GR ., However , they can still be used to test for consistency ., Figs ., 4C&D show that Amax×IC50/Amin and IC50 are both decreasing versus NU6027 ., According to entries 22 and 19 respectively of Table 1 , these graphs support the inference that GR acts as an accelerator after NU6027 , which acts as an accelerator after the CLS ., The concentrations of NU6027 at half-maximal Amax and 1/Amax are larger than those of Amin and 1/Amin respectively ( S4 Table ) , which is also consistent with the conclusion that NU6027 acts as an accelerator after the CLS and before GR ., Unlike TIF2 and NU6027 , Amax×IC50/Amin and IC50 versus phenanthroline do not exhibit any obvious trends ( Figs . 5C&D ) ., However , an examination of the formulas for Amax×IC50/Amin and IC50 in S2 Table shows that there are parameter regimes where Amax×IC50/Amin and IC50 vary so slowly that they would appear constant when the factor acts after the CLS ., There was also no significant difference between the half-maximal concentrations of Amax and Amin and their reciprocals for phenanthroline ( S5 Table , mean posteriors ) ., Hence , these data neither confirm nor contradict the above conclusion that GR acts as an accelerator after phenanthroline ., Therefore , in this system , the reporter ( AP1LUC ) and the added cofactors display the same kinetically-defined mechanisms of action , and at the same positions relative to the CLS , in GR-regulated gene repression and gene induction ( Fig . 6 ) ., The only difference is that the position , but not mechanism , of GR action changes in gene repression from that in gene induction ., We introduce a theory for GR-regulated gene repression that is based on first principles ., The theory is general but is mathematically solvable only when the dose-response curves for gene repression are linear-fractional ( Fig . 2 ) ., The theory accommodates any number of pathway steps , transcription factors , and cofactors that alter the Amax , Amin , and/or IC50 of GR-controlled gene repression ., The formation of multicomponent complexes is permitted as long as their concentrations are low or their lifetimes are short , which is biologically reasonable and has been observed for numerous factors 33–35 ., The theory could also be generalized to include the action of factors through preformed hetero-oligomeric complexes with other factors ., The competition assay for gene repression , like that for GR transactivation 20 , 22 , 23 , 27 , informs the kinetically-defined mechanism of action of each factor ( i . e . , accelerator vs . one of six decelerators ) and the position of factor action relative both to the other competing factor and to the CLS , which again appears to be an invariant marker in the overall reaction sequence ( see below ) ., The theory makes specific predictions regarding the graphs of Amax , Amin , IC50 , and Amax×IC50/ Amin vs . one factor with increa

Introduction, Results, Discussion, Methods

Gene repression by transcription factors , and glucocorticoid receptors ( GR ) in particular , is a critical , but poorly understood , physiological response ., Among the many unresolved questions is the difference between GR regulated induction and repression , and whether transcription cofactor action is the same in both ., Because activity classifications based on changes in gene product level are mechanistically uninformative , we present a theory for gene repression in which the mechanisms of factor action are defined kinetically and are consistent for both gene repression and induction ., The theory is generally applicable and amenable to predictions if the dose-response curve for gene repression is non-cooperative with a unit Hill coefficient , which is observed for GR-regulated repression of AP1LUC reporter induction by phorbol myristate acetate ., The theory predicts the mechanism of GR and cofactors , and where they act with respect to each other , based on how each cofactor alters the plots of various kinetic parameters vs . cofactor ., We show that the kinetically-defined mechanism of action of each of four factors ( reporter gene , p160 coactivator TIF2 , and two pharmaceuticals NU6027 and phenanthroline ) is the same in GR-regulated repression and induction ., What differs is the position of GR action ., This insight should simplify clinical efforts to differentially modulate factor actions in gene induction vs . gene repression .

While the initial steps in steroid-regulated gene induction and repression are known to be identical , the same cannot be said of cofactors that modulate steroid-regulated gene activity ., We describe the conditions under which a theoretical model for gene repression reveals the kinetically-defined mechanism and relative position of cofactor action ., This theory has been validated by experimental results with glucocorticoid receptors ., The mode and position of action of four factors is qualitatively identical in gene repression to that previously found in gene induction ., What changes is the position of GR action ., Therefore , we predict that the same kinetically-defined mechanism usually will be utilized by cofactors in both induction and repression pathways ., This insight and simplification should facilitate clinical efforts to maximize desired outcomes in gene induction or repression .

journal.pcbi.1005625

Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets

Many proteins assemble into large macromolecular complexes with essential cellular functions ., The three dimensional arrangement of proteins in a complex is vital to the complex’s function and knowledge of this arrangement would be highly valuable in understanding the mechanism of function ., Conserved protein complexes are estimated to number in the thousands but the vast majority of these are structurally elusive by traditional structural biology techniques ., Advances in proteomics technologies have allowed for the high throughput identification of protein complexes across the tree of life including large-scale affinity purification mass spectrometry ( AP-MS ) datasets 1–3 as well as high-throughput co-fractionation mass spectrometry ( CF-MS ) datasets comprising thousands of experiments across human , metazoan and prokaryotes 4–7 ., In the CF-MS approach , cellular lysate is biochemically fractionated by multiple , non-denaturing chromatographic methods and then complexes are inferred bioinformatically in a machine-learning framework using correlations of the resulting protein elution profiles as a prominent feature ., Although this approach has primarily been used to identify component subunits of complexes , we previously observed that the correlation structure of the protein elution profiles also revealed structural information about the complexes 6 ., This allowed for the identification of sub-complexes , which were accurate when compared to known structural models and when compared to known functions ., However , correlation did not consistently reveal the directly bound protein pairs that other experiments such as yeast two-hybrid 8 , 9 and chemical crosslinking 10–14 can reveal across large portions of the proteome ., Other computational approaches have been proposed to identify direct contacts by analyzing co-occurrence of proteins in mass spectrometry experiments but they have only been applied to AP-MS datasets 15 ., Protein sub-complexes are valuable in understanding the three dimensional arrangement of proteins in a complex but correlation often convolutes specific physical interactions between proteins with indirect interactions and non-physical relationships ., Removal of these spurious interactions from the correlation network is crucial to identifying which specific proteins directly contact each other ., A classical statistical approach to remove such interactions can be achieved with graphical models 16 ., Graphical models represent the conditional dependence structure of a set of random variables as a graph ., Unfortunately , classical statistical methods to estimate graphical models fail in scenarios where the number of variables ( e . g . , proteins ) greatly exceeds the number of samples , such as the case with co-fractionation profiles ., However , recent advances in the field of statistical analysis , specifically on the topic of sparse high-dimensional statistical inference , have led to new methods for addressing these underdetermined problems ( see , e . g . 17 and references therein ) ., In biology , these methods enabled a number of successful applications of graphical modeling , such as estimating interactions between genes from high-throughput expression profiles 18 , predicting contacts between amino acid residues from multiple sequence alignments 19 , and inferring associations of microbes from environmental sequencing data 20 , respectively ., Here , we apply a graphical model to identify direct protein interactions ( Fig 1 ) from one of the largest proteomic interaction datasets to date consisting of approx ., 3 , 000 published human CF-MS experiments 6 ., We make the assumption that conditional dependence is a proxy for direct protein interactions , which is consistent with the biochemical chromatography methods used in CF-MS experiments due to their separation of native complexes and sub-complexes ., We evaluated the performance of our predictions in a precision-recall framework on a benchmark of large protein complexes with known molecular structures and observe substantial improvement over correlation alone ., We also observe that the ranking of the learned conditional dependencies is insensitive to particular choices of the regularization parameter λ which balances model complexity and model fit ., We additionally characterize our method’s performance finding better predictions for well-observed complexes and validate our predictions with a whole cell lysate crosslinking dataset where we observe enriched overlap ., We therefore believe , in principle , these measures of conditional dependence could also be applied to additional proteomic datasets such as AP-MS as well as used in conjunction with other features of direct protein-protein contacts in supervised machine learning frameworks to further improve predictive performance ., We highlight predictions made for the 26S proteasome complex and demonstrate agreement with the true set of contacts ., We show new predictions for complexes without known structures , specifically the exocyst and tRNA multi-synthetase complex , to illustrate the utility of our approach ., Finally , in our predicted set of directly contacting proteins we show support for direct contact of a recently identified component of the human EKC/KEOPS complex ., Our results suggest that our predicted direct protein interaction edges will be a valuable constraint that can be used in structurally modeling the thousands of stable protein complexes in the human proteome inaccessible to current structure determination techniques , as we demonstrate with an improved 3D model of the EKC/KEOPS complex ., In order to identify direct physical interactions between proteins , we first organized a large , published dataset of human CF-MS experiments 6 ., CF-MS experiments consist of two steps , the first being to biochemically separate native protein complexes and sub-complexes along a specified gradient ( e . g . , hydrodynamic radius , charge , etc . ) using non-denaturing separation techniques that preserve intact complexes ., The second step is to identify and quantify the proteins that elute at each time point , providing a characteristic elution profile for each protein observed ., The aim of our approach is to use these elution profiles to reconstruct the physical interaction network of the proteins identified , and specifically find which proteins directly contact each other within complexes ., The dataset comprises d = 15 , 964 protein elution profiles each consisting of a vector of n = 2 , 989 protein abundance values ., Each protein abundance value is derived from 28 fractionation experiments using multiple , distinct biochemical separation techniques , including ion exchange chromatography , isoelectric focusing and sucrose gradients , analyzing native protein extracts isolated from HeLa cells ( 17 experiments ) , HEK293 cells ( 8 ) , glioma stem cells ( 2 ) and neural stem cells ( 1 ) ., Fractionation experiments consist of a series of collected fractions along a biochemical gradient of the applied chromatography method ., The number of fractions ranges between ~10 to ~200 per experiment depending on the method ., Each fraction of extract is then subjected to proteomic analysis using mass spectrometry producing observed protein abundances ., We use the pipeline described in Wan et al . 2015 6 , where the proteomic consensus identification tool , MSBlender 21 is used to identify proteins from mass spectra ., For peptide identifications , we use a false discovery rate of < 1% ., Missing values that arise when a protein is not identified in a given fraction are set to 0 . 0 ., This diverse set of experimental conditions allows for the analysis of a large fraction of the proteome and thorough separation of endogenous complexes ., We denote the resulting CF-MS data matrix by X∈R0d×n ., Each column Xi , i = 1 , … , n represents relative protein abundance data ( compositions ) and is normalized to sum up to 1 ., We next introduce a sparse graphical model learning framework to infer direct ( physical ) protein interactions from CF-MS data from the covariation pattern of the protein abundances ., Here , the nodes of the graph represent proteins and the edges approximate direct protein contacts ., We first note that components of the compositions Xi are not independent due to the unit sum constraint ., Thus , higher order statistics , such as covariance matrices of compositional data exhibit negative bias due to closure ., To alleviate this shortcoming we borrow a transformation technique from compositional data analysis 22 , the so-called centered log-ratio ( CLR ) transformation ., The CLR transformation is defined as CLR ( Xi ) =log\u2061Xig ( Xi ) , where g ( Xi ) denotes the geometric mean ., This transformation is particularly useful , as it is symmetric and isometric with respect to the original composition ., The CLR maps compositional data from the d-dimensional simplex to a ( d − 1 ) -hyperplane in d-dimensional Euclidean space ., A pseudo-count of 1 is added to all entries in X to ensure applicability of the transformation ., We denote the corresponding covariance matrix by Γ = cov ( CLR ( X ) ) ., Recent work 23 has shown that , in the sparse high-dimensional setting and under certain technical conditions , the covariance matrix Γ is a good estimator for the covariance matrix Σ ∈ ℝd×d of the unknown absolute abundances ., This observation is the basis for the proposed graphical model inference framework ., Following 20 , 24 , we propose to learn a sparse undirected graph G∈Rd×d representing node-node interactions via the following minimization problem:, G^ ( λ ) =argminG∈Rd×d , Gjj=012tr ( G⊺ΓG ) −tr ( G⊺Γ ) +λ‖G‖1, for all j = 1 , … , d where tr denotes the trace operator , ‖∙‖1 denotes the element-wise L1 norm , and λ > 0 is the regularization parameter ., Each of the d subproblems is equivalent to fitting a linear regression model with L1 penalization ( Lasso ) 25 to each protein profile , using the other profiles as predictors ., To relax any distributional dependencies of the regression , we also apply a non-paranormal ( copula ) transform to the data before the linear regression step 12 ., To symmetrize the graph , derived from the described node-wise regression ( or neighborhood selection ) algorithm , the OR rule is applied across all node neighborhoods , i . e . , an edge in the protein-protein graph is present if either node i is associated with node j or vice versa ., It has been shown in 24 that , under certain conditions , the non-zero entries G^ij≠0 of this symmetrized adjacency matrix are asymptotically identical to the non-zero elements Θij of the inverse covariance ( or precision ) matrix Θ = Σ−1 ., This allows a clear statistical interpretation of the edges in terms of partial correlation coefficients among the nodes 26 ., Thus , the procedure is able to remove transitive correlations among nodes by approximately learning the full conditional dependence among all nodes ., One of the key challenges in learning a sparse graphical model from data is the selection of the regularization parameter λ > 0 ., In the unsupervised setting , several methods have been proposed , including cross validation and information criteria 27 , 28 ., One state-of-the-art model selection scheme is the Stability Approach to Regularization Selection ( StARS ) 29 ., StARS selects the minimum amount of regularization that results in a graph that is sparse and comprises a stable edge set under random subsampling of the data at a prescribed stability level 1 − β 30 , 31 ., StARS typically selects N = 20 sub-samples of size b ( n ) =⌊10n⌋ and learns a graphical model from each subsample across the entire λ-path ( here , 30 values of λ are chosen between 0 and λmax ) ., StARS records for each edge in G^ ( λ ) the empirical frequency of edge presence Pij across the entire λ-path , stored in a list of matrices P ( λ ) ∈ 0 , 1d×d ., Standard StARS selects λ where the normalized sum of variances of the Pij in the corresponding P ( λ ) drops below β = 0 . 1 ., It has been shown in 31 that this selection can lead to sub-optimal regularization selection ., In the present application , we thus opted for an alternative semi-supervised selection procedure ., For all positive edges in the interaction graph , we interpreted the edge frequencies as ( protein ) contact probabilities and ranked edges in order of decreasing contact probability ., We compared these ranked predictions to a benchmark of physically interacting proteins determined from multi-protein complexes with known three-dimensional structures and selected the λ that maximized the area under the precision recall ( AUPR ) curve ., The selected λ corresponds to a more conservative StARS variability threshold of β = 0 . 005 ., We also note that , in our application , our introduced edge ranking based on the edge stability was insensitive to the precise selection of λ ., Finally , we filtered our reported direct contact predictions by protein interactions that are present in 896 complexes larger than 4 subunits from the human protein complex map , hu . MAP 32 ., This step was to ensure pairs of proteins are present in the same complex thereby increasing the likelihood of direct contact ., All computation was performed in R using the Hotelling package 33 for CLR transformation and the Huge 34 package for graphical modeling ., For comparison purposes , correlation analysis was applied to each pair of protein co-elution profiles in the human CF-MS dataset ., Profiles were first normalized by the total number of theoretical tryptic peptides for each protein and then a z-score was calculated for each value in the matrix relative to its corresponding fraction ( i . e . , column-wise standardization ) ., Pearson correlation coefficients were then calculated for each pair of proteins ., In order to evaluate the predictive performance of our direct contact prediction method we assembled a benchmark of 29 large non-redundant protein complexes with known structure ( S1 Table ) ., Due to the ease at which direct contacts can be predicted at random for small complexes , we restrict our benchmark to complexes having > 4 unique subunits ., Note , subunits from certain complexes may not be sampled in our data or have ambiguous ortholog mapping ., We process the reported biological assembly of each complex using the PISA tool 35 , which calculates macromolecular interface surface area ., All pairs of proteins within each complex with interfacial areas ( Å2 ) > 0 . 0 were considered physically contacting and marked a true contact , comprising benchmark positive examples ., Protein pairs with no interface area were considered not contacting , comprising benchmark negative examples ., Note that protein pairs that spanned two complexes ( e . g . , protein 1 in complex 1 and protein 2 in complex 2 ) were not considered ., For complexes whose structure was determined in an organism other than human , InParanoid 36 was used to identify human orthologs of the structurally solved subunit ., If no human ortholog could be found for a given subunit , interactions involving that subunit were not considered ., We split the benchmark into two sets , the first ( 10 complexes ) to evaluate λ selection and performance and the second ( 19 complexes ) to evaluate generality of the method ., The complete protein pair benchmark is provided in S2 Table ., We evaluated the overlap of our direct contact predictions with a set of identified inter-protein crosslink interactions from Liu et al . 10 ., Similar to the method described in 32 we collapsed all crosslink interactions to one interaction per pair of proteins ., We first generate a random overlap distribution by selecting random pairs of proteins from the crosslinking dataset and calculate the overlap with the direct contact predictions for 1000 repeated trials ., We then calculate a z-score for the overlap of the direct contact predictions and the reported crosslinking interactions with regards to random distribution ., We repeat the process for determining the enrichment of complexes from hu . MAP and the crosslinking interactions ., To construct a structural model of the human EKC/KEOPS complex , we built structural models of human EKC/KEOPS proteins based on available template structures in the Protein Data Bank ( PDB ) 37 and then aligned those models with existing co-complex structures ., Specifically , we used HHPred 38 to build alignments of the query protein and PDB sequences and then used MODELLER 39 to build homology models ., Homology models of human proteins were then structurally aligned to the homologous structures in yeast and archeal crystal structures 40–42 using DaliLite 43 ., Fig 1 shows a workflow of our direct contact prediction framework ., Native complexes represented by the true physical interaction network are biochemically fractionated and their proteins identified using mass spectrometry ., In order to find pairwise relationships between proteins in a given CF-MS dataset , prior work has relied on correlation analysis , which effectively reconstructs the subunit composition of complexes ( especially when used as features in a supervised machine learning framework , a case we do not consider here ) , but only partially indicates the direct binding relationships among those subunits 4 , 6 ., More specifically , using correlation to identify pairwise relationships results in a large fraction of indirect interactions ., For example , consider proteins A , B and C , where A directly binds B , B directly binds C , but A does not directly bind C . In this scenario , a network based on correlation would produce a spurious edge between proteins A and C due to the indirect relationship mediated by protein B . To address this issue , the inverse covariance matrix can be calculated , which represents a network of undirected edges between conditionally dependent nodes ., With respect to CF-MS data , the nodes represent proteins and the conditional dependence edges represent direct physical contacts ., The construction of this network has many theoretical solutions due to the limited number of samples and vast number of possible interactions , but methods are available to infer the inverse covariance matrix when the resulting network is expected to be sparse ., Sparsity is a safe assumption with respect to protein interactions , as estimates of the total number of expected human protein-protein interactions range between 150k – 650k , orders of magnitude less than the roughly 200–300 million possible interactions 44–46 ., As described in detail in the Methods , we analyzed a dataset of approx ., 3 , 000 co-fractionation / mass spectrometry experiments 4 , 6 , and restrict direct contact predictions to known co-complex interactions ., Specifically , we use complexes with structures in the PDB for evaluation and a set of 896 protein complexes larger than 4 unique subunits derived from >9000 published mass spectrometry proteomics experiments 1 , 3 , 4 , 6 in hu . MAP 32 , for all other predictions ., In all , we identified 2 , 434 potential interactions ( S3 Table ) ., To evaluate whether our direct contact prediction method accurately identifies true interactions , we compared our predictions to a benchmark of physically interacting proteins determined from multi-protein complexes with known three-dimensional structures ( S1 Table ) , as described in the Methods ., Fig 2A plots the precision recall curve of our direct contact prediction method relative to the set of 10 complexes used to select λ ., We observed high precision for the most confidently predicted contacts ., This performance is in contrast to correlation analysis , also plotted in Fig 2A , which has limited accuracy for high correlation coefficients ., Plotting precision-recall curves for the 29 alternative λ values considered during λ selection ( Fig 2A , gray curves ) confirmed that all predictions made with alternative λ values substantially outperformed correlation alone , demonstrating that this parameter was highly stable with regard to its selected value ., We further evaluated our direct contact predictions on an additional 19 complexes with known structure ( Fig 2B ) and observe consistent behavior of our method in terms of precision recall ., Interestingly , while correlation performs poorly relative to our method including all λ values on the first set of complexes , it performs substantially better on the second benchmark almost equal to our method ., The precision recall curve of the combined benchmark with both direct contact probability and correlation threshold markers can be found in Fig 2C ., We next asked if the ability to predict direct contacts was consistent across all complexes or if certain complexes performed better than others ., We therefore calculated the area under the precision recall curve ( PR AUC ) for each individual complex and plotted its distribution in Fig 2D ., For our direct contact predictions , we observe a large variance of PR AUC suggesting our method performs well for certain complexes and is limited for others ., We still find , however , direct contact predictions outperform correlation analysis and random predictions ., To further understand what types of complexes for which our method is appropriate , we investigated how much of an impact the amount of experimental observation affected the degree to which high confident direct contact predictions were made ., We first calculated the number of nonzero protein abundance measurements ( i . e . count of fractions ) for each observed protein and then computed the mean count for every complex in the structure benchmark ., Fig 3A shows the distribution of the mean counts for complexes that had at least one prediction with a direct contact probability > = 0 . 5 and those complexes which did not ., We observe a difference in the distributions suggesting that complexes that are well sampled in our dataset are more likely to have high confident predictions ., It is important to note that several complexes in our benchmark are not well sampled and our method errs on the side of false negatives so as to limit making false predictions ., We additionally plot all direct contact predictions in Fig 3B to better understand the relationship between the direct contact probability score and amount of experimental sampling ., We see that pairs of proteins that have high confidence predictions are more likely to have been well sampled suggesting that repeated observations of the proteins across many experiments are important ., This trend is likely due to our subsampling scoring procedure which is robust to spurious co-elutions from a single experiment ., Fig 4 shows the relationship between correlation and direct contact probability for four examples of complexes in our structural benchmark spanning a range of well observed to poorly observed ., Two of the complexes , the proteasome and spliceosome have high confidence predictions made by our method , while the other two , mitochondrial ribosome and mitochondrial super-complex have high-ranking correlation analysis predictions but lack high ranking predictions by our method ., The proteasome ( pdbid: 4CR2 ) is well observed with an average nonzero fraction count of ~356 ., Fig 4A shows our method makes many high confident true positive contact predictions for the proteasome ( i . e . top 9/10 are correct ) while protein pairs with high correlation coefficient have more of a mix of true positive and false positives ., The spliceosome ( pdbid: 5MQF , Fig 4B ) is moderately observed in the dataset with an average nonzero fraction count of ~182 and still shows good relative discrimination between true and false positive contacts ( i . e . top 5/10 are correct ) ., Most of the co-fractionation experiments were focused on identifying soluble cytosolic complexes and therefore membrane bound complexes as well as complexes in subcellular compartments have limited coverage ., For example , two mitochondrial complexes , the mitochondrial ribosome ( pdbid: 4CE4 , Fig 4C ) and the mitochondrial super-complex ( pdbid: 2YBB , Fig 4D ) are identified in a limited number of fractions , on average ~42 and ~105 nonzero fractions respectively ., Our method makes very few direct contact predictions for both complexes while correlation has a wide distribution of coefficients , many receiving high scores ., Interestingly , high correlation coefficients for the mitochondrial ribosome have a high false positive rate ( i . e . top 10 are all false positives ) while the mitochondrial super-complex performs better with 7 out of the top 10 pairs being true positives ., The poor performance on the mitochondrial ribosome by correlation analysis contributes to the substantial dip in performance seen in the precision recall curves ( Fig 2A ) ., These examples further demonstrate the ability of the direct contact prediction method to balance true and false positives and to accurately report contacts when sufficient data is available ., As more CF-MS experimental datasets are published , we anticipate an improvement in the coverage of moderately to lowly observed complexes ., To assess our direct contact predictions on an independent dataset different from protein structures , we compared to a human cell lysate mass spectrometry crosslinking dataset 10 ., The maximum Cα-Cα distance between cross-linked residues for the DSSO cross-linker reagent used is 23 . 4 Å , making an identified cross-linked subunit pair a reasonable proxy for directly contacting proteins ., Since our direct contact predictions are limited to co-complex subunits , we first compare the crosslinking dataset to the set of complexes with which we restricted our predictions ., Fig 5 shows that the overlap of complex edges and cross-linked subunits as well as the overlap of our conditionally dependent interactions and cross-linked subunits are both enriched compared to random pairs ., Further , we see a much larger enrichment in our conditionally dependent interactions as opposed to complex edges demonstrating the direct contact predictions are highly enriched for physically close and contacting proteins pairs in human cell lysate ., We next highlight our method’s ability to identify direct physical contacts among proteins by focusing on a specific protein complex with known structure ., The 26S proteasome makes for a clear example of the utility of conditional dependency inference over correlation analysis due to the availability of known three-dimensional structures of this complex 47–50 and the presence of well-defined sub-complexes ( e . g . , the 20S core and 19S cap ) ., Fig 6A shows the contacts from the known proteasome structure in the upper right portion of the matrix ., Interactions are observed amongst the PSMA1 through PSMA7 subunits and PSMB1 through PSMB7 subunits , representing the core , as well as PSMC1 through PSMC6 and PSMD1 through PSMD14 subunits , representing the cap ., Notably , not all subunits of the core contact each other , and there are relatively few contacts made between core and cap subunits ., These known contacts can be compared with the case shown in the lower left portion of the matrix in Fig 6A , which plots correlation scores from fractionation profiles ., While the correlation data exhibit a clear block structure with respect to the core and cap , they do not exhibit the more detailed structure observed in the true contact matrix ., The conditionally dependent interactions for these same data are plotted in the lower left portion of the matrix in Fig 6B , representing the method’s estimate of directly contacting subunits ., In contrast to the full block structure exhibited by the raw correlations , the direct contact predictions capture finer details of the true contact matrix ., Notably , many of the spurious indirect contacts predicted by the correlation matrix are successfully eliminated ., For example , the core subunit PSMA6 does not directly contact PSMA1 , PSMA7 or PSMB1-5 , but does directly contact PSMA2-5 and PSMB6-7 ., This binding specificity is at least partly captured by the direct contact predictions , but is completely missed by the correlation analysis ., Specifically , our method predicts no direct contacts between PSMA6 and PSMA7 or PSMB1-3 subunits , while correlation analysis produces high correlation coefficients for all core subunits ., This example exposes the inability of correlation to identify specific direct physical contacts amongst indirect contacts and demonstrates the capacity to remove spurious contacts based on identification of conditional independence ., We looked further into cases were we predicted high confidence direct contacts that were labeled as incorrect based on structure data ., We noticed an incorrect but high confidence direct contact prediction between two subunits of the spliceosome , SNRPD2 and SNRPD3 ( direct contact prob = 1 . 0 ) ., The electron microscopy structure of the spliceosome ( pdbid: 5MQF ) shows these two subunits within ~17 Å of each other and between the two subunits is an RNA molecule ., CF-MS is primarily a proteomics technique and does not observe other molecules such as RNA ., We therefore expect to have a degree of error with respect to complexes with structural RNA present , as CF-MS will not show co-elution profiles that discriminate RNA—protein sub-assemblies ., We do believe that when these data do become available , the direct contact prediction method is robust enough to identify direct contacts between RNA and protein molecules ., Thus , in this case , the high confidence prediction points to a close biological relationship between the two subunits ., Additionally , we predict a high confidence direct contact ( direct contact prob = 0 . 95 ) between two subunits of the eIF3 complex , specifically eIF3e and eIF3h ., The C-termini of these subunits participate in an octameric helical bundle at the center of the complex but do not directly contact in the structure used for evaluation ( pdbid 5A5T ) 51 ., In contrast , another structure of eIF3 ( pdbid: 3J8B ) 52 does have eIF3e and eIF3h directly contacting in the helical bundle ., Both structures have limited resolution and are not considered atomic-models suggesting that our data can inform in this discrepancy between models ., The prediction of direct contacts gives an opportunity to characterize the structural architecture of complexes that do not yet have a solved structure ., The exocyst complex , for example , is a hetero-octamer involved in tethering vesicles to the plasma membrane and is not well understood at the molecular level 53 ., Recent studies by Heider et al . 54 and Picco et al . 55 have attempted to resolve the yeast exocyst subunit connectivity map using co-purification and nanometer precision fluorescence microscopy , respectively ., Interestingly , Heider and colleagues identified two sub-complexes , sub-complex I consisting of Sec3/EXOC1 ( denoting yeast/human orthologs ) , Sec5/EXOC2 , Sec6/EXOC3 , Sec8/EXOC4 and sub-complex II consisting of Sec15/EXOC6 , Sec10/EXOC5 , Exo84/EXOC8 and Exo70/EXOC7 ., Our direct contact predictions , plotted in Fig 7A , support the presence of these two sub-complexes in addition to identifying inter-sub-complex contacts between EXOC4—EXOC7 , EXOC4—EXOC5 , and EXOC3—EXOC8 ., These contacts along with the highly confident direct contact predicted between EXOC3—EXOC4 ( also supported by the Heider et al . data ) suggests that EXOC3 and EXCO4 form the core subunits of sub-complex I and serve as a bridge to sub-complex II ., Likewise , the direct contacts predicted between EXOC5 , EXOC7 and EXOC8 suggest they form the core of sub-complex II and are reciprocally responsible for the bridge between sub-complexes ., In comparison to the correlation network shown in Fig 7B we observe a much denser network with fewer discriminating edges that help to identify the sub-complexes ., We also see a range of correlation coefficients that , empirically , have lower precision then their corresponding direct contact probab

Introduction, Methods, Results and discussion, Conclusion

Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes ., There are several methods for determining co-complex interactions between proteins , among them co-fractionation / mass spectrometry ( CF-MS ) , but it remains difficult to identify directly contacting subunits within a multi-protein complex ., Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes ., To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3 , 000 CF-MS experiments on human cell lines ., We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures ., We demonstrate the method’s value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure ( the exocyst and tRNA multi-synthetase complex ) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex , GON7/C14ORF142 , providing a more complete 3D model of the complex ., Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available .

Proteins physically associate into complexes in order to carry out the essential functions of life ., Knowing how proteins are physically arranged three dimensionally in these complexes provides clues towards how they work ., In principle , the associations between proteins in large-scale proteomics datasets should often reflect direct physical contacts between proteins in each complex ., Here , we describe a statistical method to discover which subunits within complexes directly contact each other based on their co-purification behavior in published co-fractionation mass spectrometry datasets ., Within our predictions , we recover many known protein-protein contacts , serving to validate our method , as well as unknown contacts that can inform future studies of these complexes ., Specifically , we observe confident contacts between subunits within the exocyst and tRNA multi-synthetase complexes , two complexes that have incomplete structural information ., Using our method , we further provide structural information for a previously missing subunit of the EKC/KEOPS complex ., We anticipate that this method and the associated predictions will help to better inform our understanding of the functions and structures of diverse protein complexes .

chemical bonding, protein interactions, protein structure prediction, protein structure, research and analysis methods, physical chemistry, proteins, biological databases, chemistry, cross-linking, proteomics, molecular biology, proteasomes, protein structure comparison, biochemistry, protein complexes, proteomic databases, database and informatics methods, biology and life sciences, physical sciences, macromolecular structure analysis

journal.pgen.1003104

Deciphering the Transcriptional-Regulatory Network of Flocculation in Schizosaccharomyces pombe

Flocculation is an inherent characteristic of yeasts involving asexual aggregation of cells into flocs that separate rapidly from the medium ( reviewed recently in 1 , 2 ) ., Individual yeast cells transition into this morphological state as an adaptation to various environmental stresses by shielding the inner cells of the flocs 3 ., The flocculent trait has also proven highly beneficial in industrial yeast applications by allowing efficient and cost-effective removal of cells 4 ., The ability of yeast strains to flocculate is dependent on the expression of specific cell surface glycoproteins known as flocculins ., Cell-to-cell adhesion occurs via binding between the flocculin and surface carbohydrates in a calcium-dependent manner 5 ., The bound carbohydrates consist of various sugars including mannose , glucose , and galactose that are specific to the type of flocculin and yeast species 6–8 ., There has been considerable interest in elucidating the genetic control of flocculation to better understand this phenomenon and generate biotechnological advances in yeast-based industries ., In Saccharomyces cerevisiae , a transcriptional-regulatory network composed of interactions between transcription factors and their flocculin gene targets is central in controlling flocculation ., The primary flocculins that function in flocculation are encoded by the FLO1 , FLO5 , FLO9 , and FLO10 genes 9–11 ., Overexpression of the individual FLO genes is sufficient to trigger flocculation 8 , 12 ., However , the degree of flocculation by FLO overexpression varies from FLO1 to FLO10 exhibiting the strongest to weakest flocculation , respectively ., The flocculin FLO11 also exhibits weak flocculation when overexpressed 8 , but its function is mainly in cell-to-surface adhesion 13 , diploid pseudohyphal growth 14 , and haploid invasive growth 15 ., The transcription factors required for flocculation include Flo8p and Mss11p , which primarily activate FLO1 transcription 16 ., The Sacc ., cerevisiae laboratory strain S288C containing a nonfunctional FLO8 gene is not able to flocculate , but flocculation is restored in this strain by the overexpression of FLO8 or MSS11 16 , 17 ., In addition , Sfl1p has been shown to inhibit transcription of FLO1 in the W303-1A strain and not in S288C , likely through interactions with the Ssn6p-Tup1p global repressor and components of Mediator 18 , 19 ., The control of flocculation is much less known in Schizosaccharomyces pombe ., The ability of the heterothallic wild-type strains 972 h− and 975 h+ to flocculate has not been observed presumably because the inducing environmental conditions have not been identified ., Phenotypic analysis of constitutive flocculent mutant strains show that flocculation is dependent on the presence of calcium , but unlike Sacc ., cerevisiae , the flocculin-carbohydrate interactions involve galactose rather than mannose and glucose residues 7 ., Moreover , the transcriptional-regulatory network governing flocculation in S . pombe remains poorly characterized ., Only a single interaction between the Mbx2 MADS box transcription factor and the gsf2+ flocculin gene is currently known 20 , 21 ., The gsf2+ gene was initially identified as highly upregulated in response to heterologous expression of FLO8 20 ., Overexpression of gsf2+ is sufficient to trigger flocculation while its deletion abrogates the flocculent phenotype of tup12Δ , lkh1Δ , and gsf1 mutants ., In addition , gsf2+ displays additional roles in cell-to-surface adhesion and invasive growth 20 ., The induction of gsf2+ during flocculation and invasive growth is mediated by Mbx2 21 ., Two other transcription factors implicated in flocculation have been reported ., The CSL transcription factors Cbf11 and Cbf12 play opposing roles in flocculation where mutant strains lacking cbf11+ or overexpressing cbf12+ flocculate 22 ., The direct targets of these transcription factors functioning in flocculation have not been identified , but could be several putative flocculin genes that show protein sequence homology to other yeast-related proteins 23 ., Indeed , these putative flocculin genes , as well as gsf2+ are transcriptionally upregulated in certain Mediator mutants that flocculate indicating that these genes are likely repressed by Mediator 24 ., Similar to Sacc ., cerevisiae , the global transcriptional regulators Tup11 and Tup12 function in flocculation but their influence on the expression of these flocculin genes has not been addressed 25 ., Importantly , it has not been directly demonstrated that these putative flocculin genes in S . pombe actually play a role in flocculation and the identity of the transcription factors that regulate them remains unknown ., In this study , we have initiated an extensive characterization of the transcriptional-regulatory network of S . pombe flocculation by identifying the relevant transcription factors and their flocculin gene targets ., Importantly , we have also determined that heterothallic wild-type S . pombe is able to flocculate when grown in rich medium containing ethanol and glycerol as a carbon source ., A screen of transcription factor deletion and overexpression strains for flocculent phenotypes revealed five novel transcriptional regulators of flocculation ( Rfl1 , Adn2 , Adn3 , Sre2 , Yox1 ) in addition to our independent finding of Mbx2 , Cbf11 , and Cbf12 ., The strongest flocculation was observed upon overexpression of mbx2+ and deletion of rfl1+ ( SPBC15D4 . 02 ) which encodes an uncharacterized fungal Zn ( 2 ) -Cys ( 6 ) transcription factor ., Microarray expression profiling of the mbx2OE and rfl1Δ strains revealed good overlap in the upregulation of several flocculin genes , while ChIP-chip analysis of HA-tagged Mbx2 and Rfl1 under control of the nmt41 promoter indicated that these transcription factors bound to some of the flocculin gene promoters ., Nine flocculin gene targets ( pfl1+–pfl9+ ) including gsf2+/pfl1+ were identified ., The single overexpression of these genes triggered flocculation to varying degrees and cumulative effects on flocculation were observed in double overexpression experiments ., Only loss of gsf2+ could abrogate the flocculent phenotype of all the transcription factor mutants indicating that gsf2+ encodes the dominant flocculin in S . pombe ., Interestingly , we discovered that certain cell wall-remodeling enzymes can also function in flocculation , and some of these genes are likely regulated by the LisH transcription factors Adn2 and Adn3 ., In addition to the identification of target genes within the transcriptional-regulatory network , autoregulatory and inhibitory feed-forward loops involving several transcription factors were also detected ., These results provide a significant insight into the transcriptional control of flocculation in S . pombe ., Our understanding of the transcriptional-regulatory network that governs flocculation in S . pombe remains limited ., To further decipher this network , we sought to systematically identify transcription factors that play a role in flocculation ., A list of 101 genes encoding sequence–specific transcription factors containing a bona-fide DNA-binding domain was assembled from 26 and GeneDB 27 ., From this gene list , we constructed 101 nmt1-driven overexpression strains and 92 nonessential deletions in which the entire ORF was replaced with the KanMX6/NatMX6 cassette ., A detailed description of the construction and phenotypic characterization of this transcription factor mutant collection will be described elsewhere ( unpublished data ) ., The transcription factor array of overexpression and deletion strains were screened for flocculation in EMM lacking thiamine and YES media , respectively ., We recovered a total of eight transcription factors in which four overexpression strains ( mbx2OE , adn2OE , adn3OE and cbf12OE ) and four deletions ( rfl1Δ , sre2Δ , yox1Δ and cbf11Δ ) exhibited flocculation ., These transcription factors represent positive and negative regulators of flocculation , respectively ., Among these transcription factors , only the overexpression of cbf12+ and mbx2+ and deletion of cbf11+ have been reported to cause flocculation 20 , 22 ., The strongest flocculation was observed in the mbx2OE and rfl1Δ strains ., The flocs of the rfl1Δ strain in YES medium were larger and sedimented faster than the flocs produced in the mbx2OE strain after 48 hour induction ( Figure 1A ) ., The mbx2+ gene encodes a MADS-box transcription factor which was originally isolated in a screen for genes functioning in the biosynthesis of cell surface pyruvated galactose residues 28 ., Recently , Mbx2 has been shown to function in flocculation and invasive growth by regulating the flocculin gene gsf2+ 20 , 21 ., The rfl1+ ( repressor of flocculation ) gene encodes an uncharacterized fungal Zn ( 2 ) -Cys ( 6 ) transcription factor ., The flocculation exhibited by these overexpression and deletion transcription factor mutants recovered from our screens could be abolished with the addition of galactose , but not mannose or glucose ( data not shown ) ., The amount of galactose required to completely deflocculate cells depended on the degree of flocculation ., For example , mbx2OE strain could be deflocculated with 2% galactose while rfl1Δ strain required 5–10 times more galactose to completely deflocculate ., Reflocculation of these strains was achieved in CaCl2 or in YES medium ( data not shown ) ., The growth conditions that trigger flocculation in heterothallic wild-type S . pombe are not well known ., To identify the inducing conditions , 972 h− and 975 h+ cells were tested on different carbon sources at different cell densities for flocculation ., We determined that heterothallic wild-type cells were able to flocculate when cultured for five days at an initial concentration of 1×106 cells/ml in medium containing 1% yeast extract , 3% glycerol and , 4% ethanol ( referred to as flocculation-inducing medium , Figure 1B ) ., The degree of flocculation was slightly enhanced in strains auxotrophic for leucine , uracil , and/or adenine indicating that nutrient status may also play a role in triggering flocculation ( data not shown ) ., However , these wild-type strains flocculated significantly less in flocculation-inducing medium than the mbx2OE and rfl1Δ mutants in EMM and YES media , respectively ., The weaker flocculation in these strains was more easily observed in petri-dishes incubated on an orbital rotator than in test tubes ., In contrast to wild type , deletion of mbx2+ did not produce any visible flocs in the flocculation-inducing medium ( Figure 1B ) ., Fungal genes that function in flocculation are usually associated with filamentous invasive growth 17 , 20 ., We hypothesized that the rfl1Δ strain would exhibit hyperfilamentous invasive growth because of its strong flocculent phenotype ., Indeed , the amount of cells resistant to removal from the agar by washing in the invasive assay on LNB medium with an underlayer of YE+ALU was much greater in the rfl1Δ strain than in wild type ( Figure 1C ) ., Under the microscope , the filamentous growth like those detected by Dodgson et al . 29 was observed below the agar surface for both wild type and rfl1Δ strain with the latter showing much larger and more frequent formation of filamentous growth ( data not shown ) ., Similarly , adn2+ and adn3+ which were previously observed to have defects in invasive growth when deleted were recovered in our screens as flocculent when overexpressed 29 ., The strongest flocculation observed in the mbx2OE and rfl1Δ strains indicated that these two genes encode the major regulators of flocculation ., Therefore , we initially focused on the characterization of these two transcription factors and proceeded to identify their target genes involved in flocculation ., The nmt41-driven mbx2-HA strain was subjected to microarray expression profiling with a custom-designed S . pombe 8×15 K Agilent expression microarray ( Table S2 ) ., The intermediate strength nmt41 promoter was sufficient for mbx2OE flocculation and was utilized in the microarray experiments in order to reduce possible secondary transcriptional effects compared to the strong nmt1 promoter ., To better distinguish the direct target genes , ChIP-chip was also carried out concurrently on the same strain using the S . pombe 4×44 K Agilent Genome ChIP-on-chip microarray ( Table S3 ) ., For the rfl1+ expression profiling and ChIP-chip experiments , the flocculent deletion mutant and nmt41-driven rfl1-HA strain were used , respectively ( Tables S4 and S5 ) ., The highly-induced putative target genes identified by microarray expression profiling of these transcription factor mutant strains were validated by qPCR ( Table S13 ) ., The list of genes that were induced at least two fold in the mbx2OE or rfl1Δ strain was subjected to gene ontology analysis using the Princeton GO Term Finder ( http://go . princeton . edu/cgi-bin/GOTermFinder ) ., These induced genes were highly enriched in cell wall components with p-values of 9 . 0e-9 and 6 . 3e-6 for the mbx2OE and rfl1Δ strains , respectively ., Strikingly , the most-induced genes in the mbx2OE strain encoded cell surface glycoproteins ., The cell surface glycoprotein genes up-regulated above two-fold were SPAC186 . 01 , gsf2+ , SPAC977 . 07c/SPBC1348 . 08c , SPCC188 . 09c , fta5+ , SPBC947 . 04 , SPBC359 . 04c , SPBC1289 . 15 , SPAPB2C8 . 01 , SPAC1F8 . 02c , SPAPB18E9 . 04c , SPCC553 . 10 , and SPBPJ4664 . 02 , which all but gsf2+ and the last 4 genes were predicted to be pombe adhesins based on BLAST sequence analysis ( Figure 2A; 23 ) ., SPAC977 . 07c and SPBC1348 . 08c are gene duplications with 100% sequence identity ., To our knowledge , these genes with the exception of gsf2+ have not been characterized further ., The induction of these genes in the mbx2OE strain ranged from 2 to 112-fold relative to the empty vector control ( Figure 2A , Table S13 ) ., In addition , several genes ( agn2+ , psu1+ , SPAC4H3 . 03c and gas2+ ) encoding cell wall-remodeling enzymes such as glucan glucosidases and a betaglucanosyltransferase were induced up to 91-fold compared to the empty vector control when mbx2+ was overexpressed ( Figure 2A ) ., In the rfl1Δ expression data , a similar set of cell surface glycoprotein genes were upregulated at a comparable level as the mbx2OE expression data except for SPAC1F8 . 02 , SPBC359 . 04c , SPAPB18E9 . 04c and SPBPJ4664 . 02 ( Figure 2A , Table S13 ) ., In contrast to the mbx2OE strain , the same genes encoding the cell wall-remodeling enzymes were not highly upregulated in the rfl1Δ strain ( Figure 2A ) ., Of the thirteen highly-induced cell surface glycoprotein genes in the mbx2OE expression data , nine of them were detected with ChIP-chip indicating that these genes are very likely the direct transcriptional targets of Mbx2 ( Figure 2A ) ., Four of the nine highly-induced cell surface glycoprotein genes in the rfl1Δ strain were detected with ChIP-chip confirming that these genes are probably direct transcriptional targets of Rfl1 ( Figure 2A ) ., For both Mbx2 and Rfl1 , gsf2+ , fta5+ and SPAPB2C8 . 01 were detected in the expression microarray and ChIP-chip experiments ( Figure 2A ) ., Next , we sought further evidence that these cell surface glycoprotein genes were targets of Mbx2 and Rfl1 by epistasis studies ., We decided to study a subset of these genes , which included the majority of the gene sequences analyzed by Linder and Gustafsson 23 , 24 ., The mbx2+ gene was overexpressed in single deletions of these putative target genes and their degree of flocculation was determined visually in petri-dishes , as well as quantitatively ( Table S14 ) ., The putative glycoprotein gene SPAPB15E9 . 01c was included in these studies , because even though the transcript was downregulated in both mbx2OE and rfl1Δ strains , ChIP-chip analysis detected Mbx2 and Rfl1 association with its promoter ( Figure 2A ) ., Deletion of gsf2+ decreased mbx2OE flocculation to the greatest extent while the reduction of flocculation was less extensive in the other single deletion mutants ( Figure 2B , Table S14 ) ., The degree of reduction in mbx2OE flocculation roughly corresponded to the pfl numbers , which were assigned based on the degree of flocculation when overexpressed ( see below ) ., Moreover , mbx2OE flocculation was completely abrogated in the gsf2Δ pfl9Δ double mutant indicating that the reduction of mbx2OE flocculation in these mutants were additive in some cases ( Figure 2B ) ., Similar experiments were performed for rfl1+ in which flocculation was assayed in the same putative target deletions in the rfl1Δ background ., The flocculation exhibited in the rfl1Δ strain was completely abolished by the deletion of gsf2+ , but not by the deletion of pfl9+ ( Figure 2C ) ., To further analyze the expression microarray datasets of Mbx2 and Rfl1 , the promoter regions of the differentially-expressed genes were subjected to the motif-finding algorithms RankMotif++ and MEME to identify their binding specificities 30 , 31 ., Mbx2 is a member of the MEF2-MADS box transcription factor family which has been shown to bind to the consensus sequence 5′- ( C/T ) TA ( T/A ) 4TA ( G/A ) -3′ 28 , 32 , 33 ., The Mbx2 binding specificity obtained by RankMotif++ closely resembled this known consensus sequence ( Figure 2D ) ., Similarly , RankMotif++ generated an Rfl1 binding specificity that resembled known consensus sequences of several members of the fungal Zn ( 2 ) -Cys ( 6 ) transcription factor family ( Figure 2E ) ., The binding specificity of Zn ( 2 ) -Cys ( 6 ) DNA-binding domains is composed of conserved GC-rich trinucleotides spaced by a variable sequence region differing in length among members of the transcription factor family 34 ., Analyses of the Mbx2 and Rfl1 expression microarray and ChIP-chip datasets by MEME did not generate any candidate DNA motifs ., Altogether , these results demonstrate that Mbx2 and Rfl1 are transcription factors responsible for regulation of flocculation in fission yeast by activating or repressing the transcription of candidate S . pombe flocculin genes , respectively ., Besides gsf2+ , the other putative target genes of Mbx2 and Rfl1 that encode for cell surface glycoproteins share some amino acid sequence homology with domains found in other fungal adhesins 23 ., However , the role of these glycoprotein genes in flocculation has not been demonstrated ., Overexpression studies were employed to the aforementioned set of putative flocculin target genes of Mbx2 and Rfl1 to determine whether they function directly in flocculation ., Each single overexpression of these flocculin genes was able to induce flocculation to varying degrees with the strongest flocculation observed in the gsf2OE strain which produced visible flocs within one day ( Figure 3A; Table S14 ) ., Weaker flocculation was observed from the overexpression of the other flocculin genes after total incubation of 2–7 days in EMM minus thiamine medium with sub-culturing into fresh medium in Day 3 ., The flocculation images of these overexpression strains shown in Figure 3A were captured after total of 7 days of induction ., As a result of these observations , we named these genes pfl+ for Pombe Flocculins and numbered them according to their degree of flocculation when overexpressed: pfl1+/gsf2+ ( referred as gsf2+ hereafter ) , pfl2+/SPAPB15E9 . 01c , pfl3+/SPBC947 . 04 , pfl4+/SPCC188 . 09c , pfl5+/SPBC1289 . 15 , pfl6+/SPAC977 . 07c , pfl7+/SPBC359 . 04c , pfl8+/fta5+ ( referred as fta5+ hereafter ) and pfl9+/SPAC186 . 01 ., Furthermore , we overexpressed some double combinations of the weaker flocculin genes to determine whether flocculation could be additive ., Indeed , the pfl4+ pfl9+ , pfl6+ pfl9+ , and fta5+ pfl9+ double overexpression strains flocculated earlier and formed larger flocs than their corresponding single overexpressors , thus , demonstrating the additive effect of these flocculins ( Figure 3B , Table S14 ) ., We next tested the single deletions of the pfl+ genes for their ability to flocculate in flocculation-inducing medium ., No visible flocculation was observed in the gsf2Δ strain while wild type was flocculent ( Figure 1B ) ., In contrast , flocculation still occurred in the pfl2Δ–pfl9Δ strains in the inducing medium indicating that gsf2+ encodes the dominant flocculin and the other flocculin genes are dispensable for flocculation ( data not shown ) ., These observations revealed that the contribution in flocculation by these pfl+ genes varied and certain combinations of pfl+ were additive ., The strength of flocculation by the single overexpression of pfl+ genes was directly correlated with the reduction of mbx2OE flocculation in the corresponding deletion strains ( Figure 2B and Figure 3A , Table S14 ) ., For example , the pfl2OE strain which produced larger flocs than the pfl3OE–pfl9OE strains exhibited a greater inhibition of mbx2OE flocculation when deleted ., Similarly , the flocculation of the rfl1Δ strain was completely abrogated by the deletion of gsf2+ , but not at all by the deletion of pfl9+ ( Figure 2C ) ., Consistent with the above results , the deletion of both gsf2+ and pfl9+ led to a greater abrogation of mbx2OE flocculation compared to each deletion alone ( Figure 2B ) ., In summary , we have demonstrated that these pfl+ genes encode for S . pombe flocculins and Gsf2 is the dominant flocculin ., Interestingly , ChIP-chip analysis also detected binding of Mbx2 and Rfl1 to their own promoters , as well as Rfl1 binding to the mbx2+ promoter ( Figure 2A ) , indicating autoregulation and mbx2+ regulation by Rfl1 within the transcriptional-regulatory network of S . pombe flocculation ., Mbx2 also appeared to be associated with the rfl1+ promoter , but this interaction was marginal as it was found just above the detection threshold for ChIP-chip ( Figure 2A ) ., To investigate the autoregulation of mbx2+ , the gene was C-terminal tagged with GFP at its native locus ( mbx2-GFP ) ., However , the GFP-tagged strain resulted in a hypermorphic allele that displayed constitutive flocculation and nuclear localization of Mbx2-GFP ( see below ) ., We speculated that the removal of the 3′-untranslated region of mbx2+ during the C-terminal tagging may be the cause of the hypermorphic allele ., To bypass this potential problem , we created an N-terminal GFP-tagged allele ( GFP-mbx2 ) with an intact 5′-untranslated region and approximately 1 kb of native promoter sequence ., In contrast to the C-terminal tagged hypermorphic allele , the N-terminal tagged GFP-Mbx2 expression was comparable to background levels and the strain did not exhibit constitutive flocculation ( Figure 4A ) ., Moreover , the GFP-mbx2 strain flocculated when grown in glycerol-inducing medium indicating that the tagged protein is functional ( Table S14 ) ., When nmt1-driven mbx2+ expression was induced for 9 hours in the GFP-mbx2 strain , nuclear GFP-Mbx2 expression was detected , indicating that Mbx2 can activate its own expression ( Figure 4A ) ., As expected , this strain was now flocculent ., Longer induction of nmt1-driven mbx2+ expression resulted in greater GFP-Mbx2 expression with multi-nucleated GFP foci ( data not shown ) ., The positive autoregulation of mbx2+ is likely to be direct as several putative MEF2-binding sequences ( e . g . 5′-TTAAAAATAG-3′ ) are located within 1000 bp upstream from the mbx2+ start codon ( data not shown ) ., To determine whether negative autoregulation occurs with rfl1+ , a C-terminal GFP-tagged strain under native control was generated ( rfl1-GFP ) ., The localization of Rfl1-GFP was nuclear in the rfl1-GFP strain ( Figure 4B ) ., The induction of nmt1-driven rfl1+ expression for 18 hours in the rfl1-GFP strain led to a reduced nuclear Rfl1-GFP signal and a slightly increased cytoplasmic Rfl1-GFP signal ( Figure 4B ) ., However , overall Rfl1-GFP expression in the cell was reduced when Rfl1 was overexpressed compared to the empty vector control ( Figure 4B; two-tailed t-test; p value<0 . 01 ) ., In contrast to our observations with the Rfl1-GFP protein expression , we found that there was no decrease of the Rfl1-GFP transcript when rfl1+ was overexpressed ( Table S13 ) ., These results indicate that although Rfl1 can bind to its own promoter , negative autoregulation appears marginal or may not be occurring ., The observation that Rfl1 is associated with the mbx2+ promoter by ChIP-chip suggests that Rfl1 may oppose Mbx2 function in flocculation by repressing its expression ., To test this hypothesis , we first examined the genetic interactions between mbx2+ and rfl1+ ., The mbx2Δ rfl1Δ double mutant did not display flocculation indicating that mbx2+ is epistatic to rfl1+ ( Figure 5A ) ., In addition , the flocculation associated with mbx2OE was abrogated by co-overexpression of rfl1+ ( Figure 5A ) ., These results are consistent with mbx2+ being downstream of rfl1+ and that rfl1+ opposes mbx2+ function in flocculation ., We next utilized the C-terminal and N-terminal GFP-tagged mbx2+ strains to further determine if Rfl1 represses mbx2+ expression ., First , Rfl1 was overexpressed in the hypermorphic C-terminal tagged mbx2-GFP allele which shows constitutive nuclear Mbx2-GFP expression and flocculation ., This resulted in the near-abolishment of both the GFP signal ( Figure 5B ) and flocculation ( data not shown ) in the hypermorphic mbx2 allele ., Second , when the N-terminal tagged GFP-mbx2 strain was crossed into the rfl1Δ background , the resulting strain displayed dramatic increase in nuclear GFP-Mbx2 expression ( Figure 5C ) and flocculation strength equivalent to the rfl1Δ strain ( data not shown ) ., These results support the hypothesis that mbx2+ expression is repressed by Rfl1 in non-flocculent cells ., Cbf12 , a member of the CSL transcription factor family has previously been reported to trigger flocculation when overexpressed 22 ., However , the target genes of Cbf12 that function in flocculation have not been identified ., To further elucidate the role of cbf12+ in flocculation , we took a similar approach to identify its direct target genes by concurrent expression microarray profiling and ChIP-chip analysis of the nmt41-driven cbf12-HA strain ( Tables S6 and S7 , respectively ) ., When cbf12+ was deleted and cultured in flocculation-inducing medium , flocculation was abolished ( Figure 6A ) ., In contrast , overexpression of cbf12+ by the nmt1 promoter triggered flocculation ( Figure 6C ) and produced a bowling pin–shaped phenotype after 24 hours in medium lacking thiamine ( data not shown ) ., Further induction of the nmt1-driven cbf12+ caused the strain to become sick and granulated , eventually leading to growth arrest ( data not shown ) ., To reduce the toxic effects of cbf12+ overexpression , an nmt41-driven cbf12-HA strain was used for concurrent expression profiling and ChIP-chip analysis ., Gene ontology analysis was carried out separately on the top 50 most highly-induced genes and all 160 promoter-occupied genes by Cbf12 with the Princeton GO Term Finder ., Functional enrichment of genes in cell surface ( p\u200a=\u200a1 . 8e-7 ) and plasma membrane ( p\u200a=\u200a5 . 7e-4 ) was detected for the highly-induced and promoter-occupied genes , respectively ., These genes included several flocculin genes , ( Figure 6B ) ., Both gsf2+ and pfl7+ were among the five highest induced genes ( 18 . 1 and 27 . 6-fold , respectively ) in the cbf12OE strain and were also detected by ChIP-chip ( Figure 6B ) suggesting that Cbf12 directly activates the transcription of gsf2+ and pfl7+ for flocculation ., The flocculation triggered by cbf12+ overexpression was completely abrogated in the gsf2Δ background , whereas deletion of pfl7+ had little effect ( Figure 6C , Table S14 ) ., This was consistent with the hypothesis that gsf2+ encodes the dominant flocculin ., In addition , loss of gsf2+ or pfl7+ did not alter the bowling-pin cell shape or the reduced fitness phenotypes of the cbf12OE strain indicating that these two phenotypes were not due to the upregulation of the flocculin genes ( data not shown ) ., The much weaker flocculation observed in the cbf12OE strain in comparison to the mbx2OE and gsf2OE strains may be attributed to additional defects in cell and nuclear division , which would cause early growth arrest before the full flocculation potential could be reached 22 ., Consistent with previous findings , C-terminal GFP-tagged Cbf12 under native control was expressed predominantly in the nucleus in stationary phase cells while expression in logarithmic cells was comparable to background ( Figure 6D; 22 ) ., Compared to logarithmic growth in rich medium , Cbf12-GFP nuclear expression increased in cells grown in flocculation-inducing medium , thus supporting its role in flocculation ( Figure 6D ) ., Interestingly , Cbf12 was also detected by ChIP-chip to bind to its own promoter ( Figure 6B ) ., Indeed , positive autoregulation appears to occur as native Cbf12-GFP expression increased greater than three-fold when nmt1-driven cbf12+ was ectopically expressed in logarithmically growing cells ( Figure 6E ) ., Recently , it was demonstrated that an N-terminal-truncated Cbf12 bound to probes containing a canonical CSL binding motif ( 5′-GTGGGAA-3′ ) by gel mobility shift assay 35 ., We next searched for a similar DNA binding sequence for Cbf12 from the expression microarray and ChIP-chip cbf12OE datasets by RankMotif++ and MEME ., RankMotif++ and MEME analyses of the expression microarray and ChIP-chip data , respectively , did not identify a binding specificity for Cbf12 ., However , when the promoters of up-regulated genes in the cbf12OE strain belonging to the cell surface GO category were subjected to MEME analysis , a motif closely matching the canonical CSL binding motif ( 6/7 nucleotide match ) was recovered ( Figure 6F ) ., These results demonstrate Cbf12 as part of the transcriptional-regulatory network of fission yeast flocculation by controlling the transcription of several flocculin genes including gsf2+ ., From our transcription factor screens , the deletion of yox1+ , sre2+ , or cbf11+ also resulted in flocculation , although the size of the flocs were smaller than observed in mbx2OE , cbf12OE and rfl1Δ strains ( Figure 7A , Table S14 ) ., Yox1 has been implicated in a negative autoregulatory loop to prevent inappropriate transcriptional expression of MBF gene targets , while the function of Sre2 , which shows homology to the human sterol regulatory element binding protein SREBP-1A remains largely unknown 36 , 37 ., A role of Yox1 and Sre2 in flocculation has not been reported ., In contrast , cbf11+ encodes a CSL transcription factor that plays a role in flocculation , but its target genes are not known 22 ., To elucidate the transcriptional flocculation program of yox1+ , sre2+ and cbf11+ , expression microarray profiling was conducted on the corresponding flocculent deletion strains in rich medium ( Tables S8 , S9 , S10 ) ., The expression microarray profiles of yox1Δ and sre2Δ most resembled each other compared to the other strains described in this study ( Figure 7B ) ., Genes upregulated by at least two-fold in the yox1Δ and sre2Δ strains showed enrichment for ribosomal subunits ( p\u200a=\u200a2 . 8e-31 and 7 . 4e-25 for yox1Δ and sre2Δ , respectively ) and mitochondrial membrane transporters ( p\u200a=\u200a7 . 5e-5 and 1 . 2e-3 for yox1Δ and sre2Δ , respectively ) ., These findings did not intuitively answer our questions as to how these two transcription factors might be related or associated with the flocculation pathway ., We next examined whether any of the flocculin genes and their putative regulators were induced in the yox1Δ and sre2Δ strains ., In the sre2Δ strain , gsf2+ , pfl3+ and fta5+ transcripts were upregulated 3 . 7 , 2 . 5 and 3 . 1-fold , respectively , indicating that the expression of these genes could be contributing to the flocculent phenotype ( Figure 7C ) ., In contrast , mbx2+ and cbf12+ transcripts were downregulated approximately 2-fold suggesti

Introduction, Results, Discussion, Materials and Methods

In the fission yeast Schizosaccharomyces pombe , the transcriptional-regulatory network that governs flocculation remains poorly understood ., Here , we systematically screened an array of transcription factor deletion and overexpression strains for flocculation and performed microarray expression profiling and ChIP–chip analysis to identify the flocculin target genes ., We identified five transcription factors that displayed novel roles in the activation or inhibition of flocculation ( Rfl1 , Adn2 , Adn3 , Sre2 , and Yox1 ) , in addition to the previously-known Mbx2 , Cbf11 , and Cbf12 regulators ., Overexpression of mbx2+ and deletion of rfl1+ resulted in strong flocculation and transcriptional upregulation of gsf2+/pfl1+ and several other putative flocculin genes ( pfl2+–pfl9+ ) ., Overexpression of the pfl+ genes singly was sufficient to trigger flocculation , and enhanced flocculation was observed in several combinations of double pfl+ overexpression ., Among the pfl1+ genes , only loss of gsf2+ abrogated the flocculent phenotype of all the transcription factor mutants and prevented flocculation when cells were grown in inducing medium containing glycerol and ethanol as the carbon source , thereby indicating that Gsf2 is the dominant flocculin ., In contrast , the mild flocculation of adn2+ or adn3+ overexpression was likely mediated by the transcriptional activation of cell wall–remodeling genes including gas2+ , psu1+ , and SPAC4H3 . 03c ., We also discovered that Mbx2 and Cbf12 displayed transcriptional autoregulation , and Rfl1 repressed gsf2+ expression in an inhibitory feed-forward loop involving mbx2+ ., These results reveal that flocculation in S . pombe is regulated by a complex network of multiple transcription factors and target genes encoding flocculins and cell wall–remodeling enzymes ., Moreover , comparisons between the flocculation transcriptional-regulatory networks of Saccharomyces cerevisiae and S . pombe indicate substantial rewiring of transcription factors and cis-regulatory sequences .

Flocculation is a process that involves yeast cells adhering to one another to form clumps called flocs ., This trait is important for industrial yeast applications as it provides a cost-effective and efficient method to remove yeast cells ., The adherence between cells occurs by the binding of glycoproteins known as flocculins and carbohydrate molecules located on the cell surface ., To better understand how flocculation works , the genes that encode for flocculins and the transcription factors that regulate their expression need to be identified ., In the fission yeast S . pombe , many of the flocculins and transcription factors that function in flocculation are not known ., To address this gap in knowledge , we have employed molecular genetics and functional genomic approaches to uncover transcription factors and their target genes that play a role in flocculation ., We discover that flocculation in S . pombe is regulated by a complex network of transcription factors that activate and repress themselves , as well as multiple target genes that encode for flocculins and cell wall–remodeling enzymes ., The comparison of the flocculation regulatory networks between fission and budding yeasts indicates that they mainly differ in the types of transcription factors and their binding sequences .

genomics, functional genomics, model organisms, gene expression, genetics, molecular genetics, biology, computational biology, yeast and fungal models, microbiology, genetics and genomics

journal.pgen.1007285

Global characterization of copy number variants in epilepsy patients from whole genome sequencing

Structural variants ( SVs ) are defined as genetic mutations affecting more than 50 base pairs and encompass several types of rearrangements: deletion , duplication , novel insertion , inversion and translocation ., Deletions and duplications , which affect DNA copy number , are collectively known as copy number variants ( CNVs ) ., SVs arise from a broad range of mechanisms and show a heterogeneous distribution of location and size across the genome 1–3 ., Numerous diseases are caused by SVs with a demonstrated detrimental effect 4 , 5 ., While cytogenetic approaches and array-based technologies have been used to identify large SVs , whole-genome sequencing ( WGS ) has the potential to uncover the full range of SVs both in terms of type and size 6 , 7 ., SV detection methods that use read-pair and split read information 8 can detect deletions and duplications but most CNV-focused approaches look for an increased or decreased read coverage , the expected consequence of a duplication or a deletion ., Coverage-based methods exist to analyze single samples 9 , pairs of samples 10 or multiple samples 11–13 but the presence of technical bias in WGS remains an important challenge ., Indeed , various features of sequencing experiments , such as mappability 14 , 15 , GC content 16 , replication timing 17 , DNA quality and library preparation 18 , have a negative impact on the uniformity of the read coverage 19 ., Epilepsy is a common neurological disorder characterized by recurrent and unprovoked seizures ., It is estimated that up to 3% of the population will suffer from a form of epilepsy at some point during their lifetime ., Although the disease presents a strong genetic component that can be as high as 95% , typical “monogenic” epilepsy is rare , accounting for only a fraction of cases 20 , 21 ., Genetic factors have been associated with epilepsy in the past such as rare genetic variations found by linkage studies as well as common genetic variations found by genome-wide association studies 22 , 23 For example , a meta-analysis combining multiple epilepsy cohorts found positive associations with the disease 24 , the strongest in SCN1A , a gene already associated with the genetic mechanism of the disease via linkage studies and subsequent sequencing 25 or more recently as harboring de novo variants 26 ., Thanks to array-based technologies , surveys of large CNVs ( >50 Kbp ) first associated CNVs in genomic hotspots such as 15q11 . 2 and 16p13 . 11 with generalized epilepsy 27 , 28 ., Other studies have further shown the importance of large and de novo CNVs as well as identified a few associations with specific genes 29–34 ., Rare genic CNVs were typically found in around 10% of epilepsy patients 30 , 34 , 35 and CNVs larger than 1 Mbp were significantly enriched in patients compared to controls 33 , 35–37 ., Unfortunately , small CNVs and other types of SVs could not be efficiently or consistently detected using these technologies , hence much remains to be done ., To more comprehensively characterize the role of CNVs in epilepsy , we performed whole-genome sequencing of epileptic patients from the Canadian Epilepsy Network ( CENet ) , the largest WGS study on epilepsy to date ., In the present study , we assessed the frequency of CNVs in epileptic individuals using 198 unrelated patients and 301 healthy individuals ., Using this data , we showed that technical variation in WGS remains problematic for CNV detection despite state-of-the-art intra-sample normalization ., To correct for this and to maximize the potential of the CENet cohorts , we developed a population-based CNV detection algorithm called PopSV ., Our method uses information across samples to avoid systematic biases and to more precisely detect regions with abnormal coverage ., Using two public WGS datasets 38 , 39 , and additional orthogonal validation , we showed that PopSV outperforms other analytical methods both in terms of specificity and sensitivity , especially for small CNVs ., Using this tool , we built a comprehensive catalog of CNVs in the CENet epilepsy patients and studied the properties of these potentially damaging structural events across the genome ., We sequenced the genomes of 198 unrelated individuals affected with epilepsy and 301 unrelated healthy controls ., Because CNV detection relies on read coverage we first investigated the presence of technical bias and the value of standard corrections and filters ( e . g . GC correction , mappability filtering ) ., The genome was fragmented in 5 Kb bins and we counted the number of uniquely mapped reads in each bin ., In contrast to simulated datasets , we found that the inter-sample mean coverage in each bin varied between genomic regions even after stringent corrections and filters ( Fig 1a ) ., Supporting this observation , the bin coverage variance across samples was also lower than expected and varied between regions ( S1 Fig ) ., We also observed experiment-specific biases ., In particular , some samples consistently had the highest , or the lowest , coverage across large portions of the genome ( S1 Fig ) ., These observations were not unique to our data and could also be observed in two public WGS datasets , and persisted even after correcting the GC bias and mappability using the more elaborate model from the QDNAseq pipeline 40 ( S2 Fig ) ., Our results across multiple samples suggest that existing GC bias and mappability corrections 40 cannot correct completely the technical variation in read coverage ., This fluctuation of coverage has implications for CNV detection approaches that assume a uniform distribution 9 , 10 , 41 after standard bias correction and will lead to false positives ., To better control for technical bias , we developed PopSV , a new SV detection method ., PopSV uses read depth across the samples to normalize coverage and detect change in DNA copy number ( Fig 1b ) ., The normalization step here is critical since most approaches will fail to give acceptable normalized coverage scores ( S1 Fig ) ., Moreover , with global median/variance adjustment or quantile normalization , the remaining subtle experimental variation impairs the abnormal coverage test ( S3 Fig ) ., The targeted normalization used by PopSV was found to have better statistical properties ( S3 Fig ) ., In order to assess the performance of our tool , we compared it to several algorithms 8–11 using a dataset that included monozygotic twins and also performed experimental validation of different types of predicted CNVs in the epilepsy cohort ( see below ) ., We found that PopSV performed as well or better in different aspects ., First , for several algorithms , a large proportion of the detected events in a typical sample were also identified in almost all samples ( 60% of the calls found in >95% of the samples , S4 Fig ) ., PopSV’s calls were better distributed across the frequency spectrum , hence more informative as we expect the relative frequency of disease-related variants to be rare ., In addition , the pedigree structure was more accurately recovered when the CNVs were used to cluster the individuals in the Twins dataset ( S5 Fig ) ., The agreement with the pedigree was computed by the Rand index after clustering the individuals with three hierarchical clustering approaches ( see S1 Text ) ., Looking at the replication between 10 pairs of monozygotic twins , PopSV detected more replicated CNVs compared to other methods , while maintaining similar replication rates ( Fig 1c ) ., The CNV calls were further filtered with gradually more stringent significance thresholds and PopSV remained superior in term of number of replicated calls ( S6 Fig ) ., When investigating the overlap of calls between different methods , we noticed that PopSV was better recovering calls from CNVnator 9 , FREEC 10 , cn . MOPS 11 or LUMPY 8 , especially if found by two or more methods ( S7 Fig ) ., For example , around 92% of the CNVs called by other methods were also found by PopSV when focusing on calls found in at least two methods ., Similar results were also obtained in a cancer dataset where we looked for replicated germline CNVs in the paired tumor ( S8 Fig ) ., Finally , we repeated the twin analysis using 500 bp bins and observed high consistency with the 5 Kbp calls ( S9 Fig ) ., These results suggest that PopSV can accurately detect around 75% of events that are as large as half the bin size used ( see S1 Text ) ., Having demonstrated the quality of the PopSV calls , we applied our tool to the epilepsy and control cohorts ., The epilepsy cohort comprises 198 individuals diagnosed with either generalized ( n = 160 ) , focal ( n = 32 ) or unclassified ( n = 6 ) epilepsy ., CNVs ranged from 5 Kbp to 3 . 2 Mbp with an average size of 9 . 98 Kbp ., We observed an average of 870 CNVs per individual accounting for 8 . 7 Mb of variant calls ( Fig 2a ) ., This is around 9 times more variants and considerably smaller than in typical array-based studies 42 , 43 , such as the previous epilepsy surveys 30 , 31 , 34 , 35 , although a similar size distribution was previously obtained using denser arrays 4 but were never applied to epilepsy ( S10 Fig ) ., Next , we annotated each variant using four public SV databases 13 , 44–46 as well as an internal database of the germline calls from PopSV in the two public datasets used earlier ( see S1 Text ) ., For each CNV , we derived the maximum frequency across these databases and defined as rare any region consistently annotated in less than 1% of the individuals ( Fig 2b ) ., In total , we identified 12 , 480 regions with rare CNVs in the epilepsy cohort including: 8 , 022 ( 64 . 3% ) with heterozygous deletions , 21 ( 0 . 2% ) with homozygous deletions and 4 , 850 ( 38 . 9% ) with duplications ., Although the overall amount of rare CNVs was not higher in epilepsy patients , the proportion of deletion was significantly higher compared to controls ( χ2 test: P-value 10−7 ) ., Next , we selected 151 CNVs and further validated them using a Taqman CNV assay and Real-Time PCR ., To explore PopSV’s performance across different CNV profiles , we selected variants of different types , sizes and frequencies ., We found that the calls were concordant in 90 . 7% of the cases ( Table 1 and S2 Table ) ., As expected , the estimated false positive rate was slightly higher for rare or smaller variants ( 12 . 1% for rare CNVs; 15 . 1% for CNV <20 Kbp ) ., Furthermore , we noted that calls supported by both PopSV and LUMPY ( when available ) had a similar validation rate as calls found by PopSV only ( 86 . 2% and 87 . 5% respectively ) ., To assess the role of CNVs in the pathogenic mechanism of epilepsy , we evaluated the prevalence of exonic CNVs in our epileptic cohort compared with healthy controls ., First , focusing on CNVs larger than 50 Kbp , we found no difference between epileptic patients and controls ( Fig 2c ) ., As expected , we observed fewer CNVs overlapping exonic sequence than expected by chance but similar levels for both groups ., The number of CNVs overlapping exonic sequences of genes intolerant to loss-of-function mutations 47 was even lower ., Interestingly , the coding regions of those genes were significantly more affected by CNVs in epileptic patients compared with controls ( permutation P-value<0 . 001 , Fig 2c and S11 Fig ) ., Because they are more likely pathogenic and of greater interest , we performed the same analysis using rare CNVs only ., Here , we observed the increased exonic burden described previously for large rare CNVs 35–37 ., In contrast to previous studies , we could also detect and compare small CNVs ( <50 Kbp ) in epileptic patients and healthy controls ., We found similar enrichment patterns than for large CNVs ( Fig 2c and S11 Fig ) , suggesting that small rare exonic CNVs are also associated with epilepsy ., Indeed , there was no significant difference between epileptic patients and controls when considering all small CNVs and all genes ., The exonic enrichment was significant for genes with predicted loss-of-function intolerance and for rare variants ( permutation P-value<0 . 001 , Fig 2c and S11 Fig ) ., In both cohorts , most of the rare exonic CNVs were private , i . e . present in only one individual ., However , we observed that rare exonic CNVs were less likely private in the epileptic patients ( permutation P-value<0 . 001 , S12 Fig ) ., We replicated this result using only individuals with a similar population background ( French-Canadians , S12 Fig ) ., Overall we concluded that rare CNVs were not only enriched in exons but also affected exons more recurrently in the epilepsy cohort as compared to controls ., We then sought to evaluate if there was an excess of CNVs disrupting epilepsy-related genes or nearby functional regions ., We first retrieved genes whose exons were hit by rare deletions or duplications and evaluated how many were known epilepsy genes based on a list of 154 genes previously associated with epilepsy 48 ( Fig 3a ) ., Because epilepsy genes tend to be large , we controlled for the gene size when testing for enrichment ( S13 Fig ) ., In the epilepsy cohort only , we noted a clear enrichment for epilepsy genes hit by rare deletions ( S13 Fig ) ., Moreover , the enrichment became stronger for rare CNVs ., For instance , the exons of 921 genes were disrupted in the epilepsy cohort when considering deletions completely absent from the public and internal databases , 17 of which were epilepsy genes ( P-value 0 . 015 , Fig 3b ) ., In addition , we observed significantly more epilepsy patients with a rare non-coding CNV close to an epilepsy gene compared to control individuals ( S14 Fig ) ., Interestingly , this enrichment was stronger for non-coding deletions ( S14 Fig ) ., We further explored the distribution of rare non-coding deletions by testing each epilepsy gene for a difference in mutation load between patients and controls ., The GABRD gene had the strongest and only nominally significant association with four non-coding deletions among the 198 epileptic patients and none in the 301 controls ., GABRD encodes the delta subunit of the gamma-aminobutyric acid A receptor and has been associated with juvenile myoclonic epilepsy 49 ., In our cohort , two of the four patients with a rare non-coding deletion close to GABRD had been diagnosed with this syndrome , including one patient with a 2 . 7 Kbp deletion located only 3 Kbp upstream of GABRD’s transcription start site ( S15 Fig ) ., Although none survived multiple testing correction , we noted that the strongest associations were all in the direction of a higher mutation load in the epilepsy cohort rather than in the control cohort ., To get a better idea of the functional regions close to epilepsy genes , we retrieved their associated eQTLs in the GTEx database 50 and the DNase hypersensitivity sites associated with their promoter regions 51 ., Notably , focusing on rare non-coding CNVs overlapping these functional regions , the enrichment in epileptic patients was greatly strengthened and clearly present up to 100 Kbp from an epilepsy gene ( Kolmogorov-Smirnov test: P-value 9 × 10−5 , Fig 3c ) ., Comparing epilepsy patients and controls , the odds ratio of having such a CNV at a distance of 100 Kbp or less from an exon was 1 . 33 and gradually increased the closer to the exon ( 2 . 9 for CNVs at 5 Kbp or less , S16 Fig ) ., These non-coding CNVs were rare even in the epileptic cohort , but collectively represented an important fraction of affected patients ., While 20 patients ( 10 . 1% ) had exonic CNVs in epilepsy genes that were not seen in any control or in the public and internal databases , this number rose to 57 patients ( 28 . 8% ) when counting non-coding CNVs in functional regions located at less than 100 Kbp of an epilepsy gene ., These non-coding CNVs were never seen in the controls nor the CNV databases and overlap with annotated enhancer of epilepsy genes ., Although their functional impact remains putative , we believe these CNVs to be of high-interest for the identification of disease causing genes ., Among these CNVs of high-interest , a duplication of a regulatory region 5 Kbp downstream of CSNK1E was detected and validated in two different patients but absent from our controls and the public and internal databases ( S15 Fig ) ., Another example is a short deletion of an extremely conserved region downstream of FAM63B , detected in one patient and overlapping expression QTLs for this epilepsy gene ( S15 Fig ) ., Next , we used an array of criteria to select the rare CNVs ( less than 1% in 301 controls ) with the highest disruptive potential in the epilepsy cohort ., Priority was given to exonic CNVs in genes already known to be associated with epilepsy ., For CNVs in other genes , we also prioritize recurrent variants and deletions in genes highly intolerant to loss-of-function mutations ., In total , we identified 21 such putative pathogenic CNVs ( Tables 2 and 3 and S3 Table ) ., Out of these , 8 directly affected a gene previously associated with epilepsy 48 ( Table 2 ) ., In particular , we identified a deletion resulting in the loss of more than half of the DEPDC5 gene in a patient affected with partial epilepsy ., A number of point mutations have previously been reported in this gene for the same condition 52 , 53 ., We also identified two deletions and one duplication in CHD2 gene ( see Fig 4 ) ., The first deletion is large and affects a major portion of the gene while the second is a small 4 . 6 Kbp deletion of exon 13 , the last exon of CHD2’s second isoform ( S17 Fig ) ., No exon-disruptive CNVs were reported in any individuals from the control cohort ., This gene was previously associated with patients suffering from photosensitive epilepsy 54 ., Interestingly , all three patients carrying the CNVs in CHD2 have been diagnosed with eyelid myoclonia epilepsy with absence , the same diagnosis that was largely enriched in the Galizia et al . study ., Other known epilepsy genes affected by deletions include LGI1 and the 15q13 . 3 region ., Four of the 21 putative pathogenic CNVs were found in more than one individual ( see Table 3 for precise numbers ) ., To assess their global prevalence we tested them in an additional cohort of 325 epileptic patients and 380 ethnically matched controls ( Table 3 ) ., Two regions were replicated: the first region in chromosome 2 consists of duplication of the genes TTC27 , LTPB1 and BIRC6 ., In total , 4 patients carried this duplication and it was not reported in any of the two sets of controls ., The second region was found on chromosome 16 and encompasses several genes ., Two deletions were found in epileptic patients for this region and 1 epileptic individual and 1 control were also carriers of a duplication in the same region ., This region corresponds to a genomic hotspot whose deletions were previously associated with epilepsy 30 and other neurological disorders ., Finally , the remaining putative pathogenic CNVs were also associated with a number of genes ( S3 Table ) ., However , as we lack additional evidence for those specific CNV regions , we propose that these genes should be assessed in independent epilepsy cohorts ., Of note , one patient had a rare 170 Kbp deletion encompassing three exons of the PTPRD gene which is predicted to be highly intolerant to loss-of-function mutations ( pLI = 1 ) 47 ., Rare deletions in this gene were previously found in four independent individuals with attention-deficit hyperactivity disorder 55 and associated with intellectual disability 56 ., In addition , de novo deletions were found in an individual with autism 57 and more recently in a patient with epileptic encephalopathy 32 ., A common intronic variant in PTPRD was also associated with remission of seizures after treatment in a clinical cohort of epilepsy patients 58 ., Although several tools exist for the detection of CNVs using WGS data , we found that none of them could efficiently account for technical biases , thus resulting in limited sensitivity ., To improve on this , we developed a new tool , PopSV , which we demonstrated was able to accurately detect CNVs , including rare and small events ., A key aspect of our approach is the use of a set of reference samples to identify abnormal read coverage ., In this context , the choice and number of reference samples will have an effect on the analysis ., Results from running PopSV using different reference cohort sizes suggest that CNV calls are consistent across runs but that a higher number of reference samples increases the sensitivity and robustness of the CNV detection ( S18 Fig ) ., Based on these results , we recommend PopSV when 20 samples or more can be used as reference ., In a given study , all samples can be used as a reference , or a subset of a few hundreds if the total sample size is extremely large ., Although variants with frequency around 50% might not be detected , PopSV excels at detecting less frequent variants , smaller variants or variants in challenging regions such as repeat-rich regions ., In a case/control design , the control samples could be used as reference in order to maximize the detection of case-specific variants ., In the current study we used both epilepsy patients and controls as reference in order to be able to directly compare the observed CNV distributions ., Finally , in a cancer project with paired normal and tumor samples , only normal samples should be used as reference such that PopSV can detect somatic CNVs of any frequency ., To maximize performance , the same library preparation , sequencing and data pre-processing should be employed on all the samples ., To identify potential batch effects , a principal component analysis of read coverage was implemented as part of the PopSV package and is recommended to assess the homogeneity of the reference samples ., The read length and aligner can lead to drastic changes in the read coverage and should be consistent across the cohort when analyzed with PopSV ., This is particularly important in repeat-rich regions ., Although the different datasets were produced by different sequencing and pre-processing protocols and showed varying degrees of technical bias ( Fig 1a , S1 and S2 Figs ) , the performance of PopSV was comparable when benchmarking the methods in the two public datasets and experimentally validating calls in the CENet cohort ., PopSV’s approach does not require a uniform read coverage and integrate the coverage variation separately in each studied region ., For these reasons , it would be straightforward to analyze targeted sequencing data , such as exome-sequencing ., PopSV could also be extended for the detection of other types of SVs such as balanced SVs ., To do this , instead of counting properly mapped reads , the method could be modified to test for an excess of discordant reads ., Finally , additional modules could be added to PopSV to help characterize the detected variants ., For instance , instead of computing a copy-number estimate from the average coverage in the reference , a HMM approach including all samples could provide a better genotyping strategy ., Similar to other approaches 9 , 16 , an additional step in the pipeline could explore the effect of the bin size on the variation in read coverage across the population and suggest an optimal bin size ., As in previous array-based studies 35–37 , we observed an enrichment of large rare exonic CNVs in patients compared to controls ., However , thanks to the resolution of WGS and PopSV , we found that the global distribution of small CNVs ( <50 Kbp ) in 198 unrelated epilepsy patients was also skewed towards rare exonic CNVs ., In addition , genes disrupted by rare deletions in patients were enriched for previously known epilepsy genes ., These observations support the association of small CNVs with epilepsy and could not have been detected in previous array-based studies ., We also observed a clear enrichment of non-coding CNVs in the neighborhood of previously implicated genes ., When focusing on CNVs seen only in the epilepsy cohort and around epilepsy genes , 10 . 1% of epilepsy patients have an exonic CNVs and our results shows that up to 28 . 8% of patients harbor non-coding CNVs of high-interest in the proximity of epilepsy genes ., These non-coding variants are present in the epilepsy cohort only and located in annotated regulatory regions associated to known epilepsy genes ., Although it is challenging to directly test their functional impact , their frequency and location suggest a putative importance in the genetic mechanism of epilepsy and should be further investigated in the future ., Finally , to better understand the impact of these findings on an individual scale , we selected CNVs with the highest pathogenic potential within our patients ., These CNVs highlighted known but also potentially new epilepsy genes ., Using a second epilepsy cohort , we were also able to identify two chromosomal regions that were recurrently disrupted by CNVs ., These findings highlight the benefits of having a comprehensive survey of CNVs when trying to understand the genetic causes of a disease ., This study was approved by the Research Ethics Board at the Sick Kids Hospital ( REB number 1000033784 ) and the ethics committee at the Centre Hospitalier Universitaire de Montréal ( project number 2003-1394 , ND02 . 058-BSP ( CA ) ) ., Before their inclusion in this study , patients or parents ( when needed ) had to give written informed consents ., Patients were recruited through two main recruitment sites at the Centre Hospitalier Universitaire de Montréal ( CHUM ) and the Sick Kids Hospital in Toronto as part of the Canadian Epilepsy Network ( CENet ) ., The main cohort of this study was constituted of 198 unrelated patients with various types of epilepsy; 85 males and 113 females ., The mean age at onset of the disease for our cohort was 9 . 2 ( ±6 . 7 ) years ., S1 Table presents a detailed description of the clinical features for the various individuals recruited in this study ., 301 unrelated healthy parents of other probands from CENet were also included in this study and used as a control cohort ., DNA was exclusively extracted from blood DNA ., Libraries were generated using the TruSeq DNA PCR-Free Library Preparation Kit ( Illumina ) and paired-end reads of size 125 bp were sequenced on a HiSeq 2500 to an average coverage of 37 . 6x ± 5 . 6x ., Reads were aligned to reference Homo_sapiens b37 with BWA 59 ., Finally , Picard was used to merge , realign and mark duplicate reads ., Raw sequence data has been deposited in the European Genome-phenome Archive , under the accession code EGAS00001002825 ., For more details , see S1 Text ., Two high-coverage public datasets were used to benchmark PopSV against existing methods ., A Twin study provided WGS sequencing data for 45 individuals , including 10 monozygotic twin quartets from the Quebec Study of Newborn Twins 38 ., All patients gave informed consent in written form to participate in the Quebec Study of Newborn Twins ., Ethic boards from the Centre de Recherche du CHUM , from the Université Laval and from the Montreal Neurological Institute approved this study ., DNA was extracted from blood and sequencing was done on an Illumina HiSeq 2500 ( paired-end mode , fragment length 300 bp ) ., The reads were aligned using a modified version of the Burrows-Wheeler Aligner 59 ( bwa version 0 . 6 . 2-r126-tpx with threading enabled ) ., The options were ‘bwa aln -t 12 -q 5’ and ‘bwa sampe -t 12’ ., Aligned reads are available on the European Nucleotide Archive under ENA PRJEB8308 ., The 45 samples had an average sequencing depth of 40x ( minimum 34x / maximum 57x ) ., A cancer dataset from a study of renal cell carcinoma 39 was also used ., 95 pairs of normal/tumor tissues were sequenced using GAIIx and HiSeq2000 instruments ., Paired-end reads of size 100 bp totaled an average sequencing depth of 54x ( minimum 26x / maximum 164x ) ., Reads were trimmed with FASTX-Toolkit and mapped per lane with BWA 59 backtrack to the GRCh37 reference genome ., Picard was used to adjust pairs coordinates , flag duplicates and merge lanes ., Finally , realignment was done with GATK ., Raw sequence data has been deposited in the European Genome-phenome Archive , under the accession code EGAS00001000083 ., More details can be found in Scelo et al . 39 ., To investigate the bias in read depth ( RD ) , we fragmented the genome in non-overlapping bins of 5 Kbp and counted the number of properly mapped reads ., In each sample , we corrected for GC bias and removed bins with extremely low or high coverage ( see S1 Text ) ., Then , read counts across all samples were combined and quantile-normalized ., Using simulations and permutations , we constructed two control RD datasets with no region-specific or sample-specific bias ., We computed the mean and standard deviation of the coverage in each bin across samples ., Next , to investigate experiment-specific bias , we retrieved which sample had the highest coverage in each bin ., Then we computed , for each sample , the proportion of the genome where it had the highest coverage ., The same analysis was performed monitoring the lowest coverage ., This analysis was performed separately on the CENet dataset , the Twin dataset and the normal samples from the cancer dataset ., On the Twin dataset , the same analysis was also run after correcting the read coverage following the QDNAseq pipeline 40 ( see S1 Text ) ., The main idea behind PopSV is to assess whether the coverage observed in a given location of the genome diverges significantly from the coverage observed in a set of reference samples ., PopSV was implemented in an R package ( see Data and code availability ) ., The genome is first segmented into bins and the number of reads with proper mapping in each bin is counted for each sample ., In a typical design , the genome is segmented in non-overlapping consecutive windows of equal size , but custom designs could also be used ., With PopSV , we propose a new normalization procedure which we call targeted normalization that retrieves , for each bin , other genomic regions with similar profile across the reference samples and uses these bins to normalize read coverage ( see S1 Text ) ., Our targeted normalization was compared to global approaches that adjust for the median coverage , or quantile-based approaches ., After normalization , the value observed in each bin is compared with the profiles observed in the reference samples and a Z-score is calculated ( Fig 1b ) ., False Discovery Rate ( FDR ) is estimated based on these Z-score distributions and a bin is marked as abnormal based on a user-defined FDR threshold ., Consecutive abnormal bins are merged and considered as one variant ., In PopSV’s R package , circular binary segmentation 60 can also be used to merge bins into variant regions ., Copy number was estimated by dividing the coverage in a region by the average coverage across the reference samples , multiplied by 2 ( see S1 Text ) ., We compared PopSV to CNVnator 9 , FREEC 10 and cn . MOPS 11 , three popular RD methods that can be applied to WGS datasets ., We also ran LUMPY 8 which uses an orthogonal mapping signal: the insert size , orientation and split mapping of paired reads ., For LUMPY , all the CNVs ( deletions and duplications ) and intra-chromosomal translocations ( labeled as ‘BND’ in Lumpy’s output ) larger than 300 bp were kept for the upcoming analysis ., These methods were run on the two publicly available datasets , using 5 Kbp bins for the RD methods ., First , we compared the frequency at which a region is affected by a CNV using the calls from the different methods ., To investigate the presence of systematic calls in each method , we compute how many of the calls in a typical sample are called at different frequencies in the dataset ., For example , on average , how many calls in one sample are called in more than 90% of the samples ., In th

Introduction, Results, Discussion, Materials and methods

Epilepsy will affect nearly 3% of people at some point during their lifetime ., Previous copy number variants ( CNVs ) studies of epilepsy have used array-based technology and were restricted to the detection of large or exonic events ., In contrast , whole-genome sequencing ( WGS ) has the potential to more comprehensively profile CNVs but existing analytic methods suffer from limited accuracy ., We show that this is in part due to the non-uniformity of read coverage , even after intra-sample normalization ., To improve on this , we developed PopSV , an algorithm that uses multiple samples to control for technical variation and enables the robust detection of CNVs ., Using WGS and PopSV , we performed a comprehensive characterization of CNVs in 198 individuals affected with epilepsy and 301 controls ., For both large and small variants , we found an enrichment of rare exonic events in epilepsy patients , especially in genes with predicted loss-of-function intolerance ., Notably , this genome-wide survey also revealed an enrichment of rare non-coding CNVs near previously known epilepsy genes ., This enrichment was strongest for non-coding CNVs located within 100 Kbp of an epilepsy gene and in regions associated with changes in the gene expression , such as expression QTLs or DNase I hypersensitive sites ., Finally , we report on 21 potentially damaging events that could be associated with known or new candidate epilepsy genes ., Our results suggest that comprehensive sequence-based profiling of CNVs could help explain a larger fraction of epilepsy cases .

Epilepsy is a common neurological disorder affecting around 3% of the population ., In some cases , epilepsy is caused by brain trauma or other brain anomalies but there are often no clear causes ., Genetic factors have been associated with epilepsy in the past such as rare genetic variations found by linkage studies as well as common genetic variations found by genome-wide association studies and large copy-number variants ., We sequenced the genome of ∼200 epilepsy patients and ∼300 healthy controls and compared the distribution of deletion ( loss of a copy ) and duplication ( additional copy ) of genomic regions ., Thanks to the sequencing technology and a new method that takes advantage of the large sample size , we could compare the distribution of small copy-number variants between epilepsy patients and controls ., Overall , we found that small variants are also associated with epilepsy ., Indeed , the genome of epilepsy patients had more exonic copy-number variants , especially when rare or affecting genes with predicted loss-of-function intolerance ., Focusing on regions around genes that have been previously associated with epilepsy , we also found more non-coding variants in epilepsy patients , especially deletions or variants in regulatory regions ., Finally , we provide a list of 21 regions in which we found likely pathogenic variants .

computer applications, medicine and health sciences, population genetics, catalogs, twins, genomic databases, developmental biology, genome analysis, population biology, research and analysis methods, epilepsy, computer and information sciences, monozygotic twins, genetic polymorphism, biological databases, heredity, neurology, database and informatics methods, genetics, biology and life sciences, genomics, evolutionary biology, genetic linkage, computational biology

journal.ppat.1007062

Metabolic reprogramming of Kaposi’s sarcoma associated herpes virus infected B-cells in hypoxia

Kaposi’s sarcoma associated herpesvirus ( KSHV ) is the etiological agent of Kaposi sarcoma , primary effusion lymphoma and multicentric Castleman disease 1–3 ., By altering the expression of core metabolic enzymes , KSHV infected cells acquire a metabolic strategy of aerobic glycolysis generally referred as to the Warburg effect where these cells drive a high rate of glycolysis even in the presence of molecular oxygen 4–8 ., This alteration of host metabolism mimicking Warburg effect by KSHV is believed to be necessary for the maintenance of latently infected cells 4 ., Similar to most cancer cells , the mitochondria is also an organelle targeted by KSHV in viral infected cells altering apoptotic pathways and metabolism , so necessitating up-regulation of glycolysis to compensate for the energy demands of rapidly growing cells 9–12 ., Metabolite profiling of KSHV infected cells suggest a wide difference between metabolite pools of KSHV infected cells when compared to control cells , including those which are common to anabolic pathways of most cancer cells 5 ., Interestingly , the metabolite changes are not limited to only carbohydrates , but also included fatty acids and amino acids where inhibition of key enzymes in this pathway led to apoptosis of infected cells 5 , 13 ., KSHV infection-mediated elevation of metabolites pools are due to enhanced anabolic activity rather than degradation from respective macromolecules 5 ., Previous attempts to identify the mechanism of such reprogramming confirm increased expression of host factors such as glucose transporters , as well as hypoxia inducible factor ( HIF1α ) which are prerequisites for such changes in KSHV infected cells ., In addition , decreased mitochondrial copy number and down regulated EGLN2 and HSPA9 have been reported upon over-expression of KSHV coded microRNAs , and are believed to be among the many KSHV factors involved in metabolic changes 14 ., Nevertheless , previous observations either do not support or was unable to determine the involvement of other KSHV-encoded factors involved in metabolic differences caused by KSHV infection ., Hypoxia and HIF1α play critical roles in pathogenesis of KSHV by modulating expression of KSHV genes as well as stabilizing several KSHV-encoded proteins 15 , 16 ., KSHV infection alone can mimic several physiological and metabolic changes due to hypoxia and those common to cancer cells ., Hypoxia on the other hand plays an important role in KSHV reactivation biology where HIF1α facilitates KSHV-encoded RTA-mediated reactivation by binding with LANA and up-regulating RTA expression 16 , 17 ., Hypoxia is also reported to enhance viral reactivation potential associated with 12-O-tetradecanoylphorbol-13-acetate 18 ., The role of hypoxia in maintenance of latency and KSHV associated pathogenesis is also crucial , where the promoter of the key latent gene cluster coding for LANA , vFLIP and vCyclin harbors hypoxia responsive elements and can be activated by HIF1α 15 ., Among other KSHV factors affecting the HIF1α axis , is the constitutively active G protein-coupled receptor ( vGPCR ) encoded by KSHV 19 , 20 ., vGPCR is a bonafide oncogenic protein and stimulates angiogenesis by increasing the secretion of vascular endothelial growth factor ( VEGF ) , which is a key angiogenic stimulator and a critical mitogen for the development of Kaposi’s sarcoma 21 , 22 ., KSHV-encoded vGPCR enhances the expression of VEGF by stimulating the activity of the transcription factor HIF1α , which activates transcription from a HRE within the 5-flanking region of the VEGF promoter 23 ., Stimulation of HIF1α by KSHV encoded vGPCR involves phosphorylation of its regulatory/inhibitory domain by p38 and mitogen-activated protein kinase ( MAPK ) signaling pathways , thereby enhancing its transcriptional activity 24 ., Specific inhibitors of the p38 / MAPK pathways are able to inhibit the transactivating activity of HIF1α induced by the KSHV-encoded vGPCR , as well as the VEGF expression and secretion from cells expressing this receptor 24 ., These findings suggest that the KSHV-encoded vGPCR oncogene subverts convergent physiological pathways leading to angiogenesis and provides the first insight into a mechanism whereby growth factors and oncogenes acting upstream of MAPK , as well as inflammatory cytokines and cellular stresses that activate p38 , can interact with the hypoxia-dependent machinery of angiogenesis 24 ., However , the role of vGPCR in modulating other physiological pathways is poorly explored ., In the present study , we investigated the role of stabilized HIF1α on the metabolic status of KSHV positive cells and compared the results with KSHV negative cells with the same genetic background under normoxic or hypoxic conditions ., We present data for differentially expressed KSHV-encoded genes when HIF1α is stabilized ., We then showed the changes in global transcription of cells growing in normoxia or hypoxia with HIF1α to identify the common targets of HIF1α and KSHV infection ., Our results showed enhanced induction of a tumorigenic metabolic phenotype in KSHV-positive cells growing in hypoxia compared to KSHV-negative cells growing under the same condition ., Further , we now identify a comprehensive list of metabolic genes differentially expressed on KSHV infection in the hypoxic environment ., These results provide new insights into the role of KSHV factors , in cooperation with hypoxia on the global metabolic status of KSHV positive cells ., KSHV infection is known to stabilize HIFs and this stabilization provides the cells a mechanism to survive in a hypoxic environment by up-regulating several cellular pathways involved in metabolism , survival and angiogenesis ., We wanted to determine how KSHV infected cells respond compared to their isogenic KSHV-negative counterparts in hypoxic environments , and their metabolic requirements ., There is no available control cell line with the same isogenic background for comparative studies in B-cells ., Therefore , we selected KSHV-negative BJAB cells and KSHV positive BJAB-KSHV stably infected with KSHV 25 ., We first characterized and confirmed the presence of full length KSHV in BJAB-KSHV cells at the level of the viral genome and transcriptome to determine if gross genomic alterations had occurred ., Amplification of 10 different KSHV genomic regions with KSHV specific primers confirmed the presence of a KSHV genome most likely intact in BJAB-KSHV cells ( S1A and S1B Fig ) ., The sequences of the primers used to characterize BJAB-KSHV cells are also provided in S1 Table ., The BJAB-KSHV cells were further characterized at the level of transcripts by amplifying the KSHV-encoded latent gene vCyclin from the cDNA made from BJAB-KSHV cells ( S1C Fig ) ., The isogenic background and authenticity of these two cell lines were also examined by short tandem repeat ( STR ) profiling ., The STR profiling results for these two cell lines were compared with each other as well as with BJAB cells STR profile obtained from ExPASy Bioinformatics Resource portal database ., The STR profile results confirmed the same origin and isogenic background of these cells ( S2 Table ) ., To study the effects of hypoxia , we proceeded with two different approaches to induce hypoxia in cell culture ., In the first approach , we treated the cells with CoCl2 , a chemical inducer of hypoxia to induce stabilization of HIF1α with minimal effects on the growth rate of the cells 26 ., In the second approach we grew cells in 1% O2 hypoxic condition ., Puromycin was omitted from the media of BJAB-KSHV and control cells for the entire treatment period ., HIF1α stabilization was confirmed by western blot using HIF1α specific antibody ( Fig 1A and 1B ) ., An estimation of glucose consumed by BJAB or BJAB-KSHV cells suggested that the bulk of glucose from medium was being consumed during the initial period of 24–48 hours , in which cells growing under normoxic conditions showed an exponential growth pattern ( Fig 1C & 1E ) ., The growth patterns of cells growing in either CoCl2 or 1% O2 were quite different and showed diminished proliferation rates ., Growing the cells in the same partially depleted medium showed a retarded growth in both normoxic as well as hypoxic environments , though hypoxic induction due to low oxygen showed a more drastic adverse effects on cell survival ( Fig 1C & 1E ) ., The estimation of glucose consumed by BJAB and BJAB-KSHV cells grown in normoxia and hypoxia showed a large difference in the consumption of glucose between these cells ., Within the initial 24 hours , BJAB-KSHV cells showed an almost 18% higher glucose consumption as compared to BJAB cells during the same time period ( 940 . 7mg compared to 773 . 8mg glucose per million cells ) ( Fig 1C & 1E ) ., The BJAB-KSHV cells showed a similar increase in uptake of glucose throughout the time points of 48 , 72 and 96 hours compared to BJAB cells ., A time dependent enhancement in the glucose uptake was observed for BJAB-KSHV cells when compared to BJAB cells growing in normoxic condition ., However , the diminished medium condition and hypoxia due to 1% oxygen led to a drastic reduction in cell survival and growth past 72 hours ., To rule out the possibility of an effect of puromycin pretreatment on glucose uptake , glucose consumption was also measured in cells growing either in the presence of puromycin , or its absence for 48 hours ., The results showed no significant difference in glucose consumption due to presence or absence of puromycin in culture of BJAB-KSHV cells ( S1D Fig ) ., The effect of puromycin due to hypoxic induction or its downstream target was also determined by measuring real time expression of HIF1α and VEGFA ., The results showed no effects of puromycin on expression of HIF1α nor VEGFA ( S1E Fig ) ., To estimate lactate released in medium by these cells a standard curve of lactate ranging from 0 to 10 nmol/μl was prepared followed by a pilot experiment to determine the range of lactate in the medium ., Here , different volumes of fresh culture medium ( 1μl and 10 μl ) and 1μl medium from growing cultures were used ( S1F Fig ) ., Based on the pilot experiment , 10 μl of a 10X diluted culture medium was used to estimate lactate released in medium by BJAB and BJAB-KSHV cells growing under normoxia or CoCl2/1%O2-induced hypoxia ( Fig 1D and 1F ) ., A pattern similar to glucose uptake was observed for lactate release in these cells under similar growth conditions suggesting a directly proportional relationship between glucose uptake and lactate release ., We also investigated whether this metabolic phenotype was mimicked in primary infection to peripheral blood mononuclear cells , KSHV infection of PBMCs was monitored growing them in the presence of CoCl2 or 1%O2 ., The infection of PBMCs with KSHV was confirmed by immuno-staining for KSHV latent protein LANA and the induction of hypoxia was confirmed by western blot to detect HIF1α ( Fig 1G and 1H ) ., The percentage of cells infected with KSHV was empirically calculated by LANA immune-staining ., The infection efficiency of PBMCs with KSHV was approximately 50% ., Estimation of glucose uptake and lactate release by infected PBMCs grown under conditions of normoxia or in CoCl2 or 1%O2 at 48 hours post-infection showed an enhanced glucose dependency and lactate release similar to BJAB and BJAB-KSHV cells ( Fig 1I and 1J ) ., As the 1% oxygen for induction of hypoxia showed highly adverse effects on cell survival , we performed RNA sequencing experiments on BJAB and BJAB-KSHV cells growing in normoxia or CoCl2-induced hypoxia showing a more relevant physiological response of HIF1α stabilization due to KSHV infection ., Analysis of RNA sequencing data for differential gene expression of KSHV encoded genes identified 42 transcripts coded by KSHV ( Fig 2A–2D ) ., A histogram for genes across the KSHV genome is provided in Fig 2A ., Statistical analysis revealed that expressions of 11 KSHV-encoded genes were significantly changed when grown under CoCl2–induced hypoxia compared to their normoxic counterpart ., Among these 11 genes , the viral G-protein coupled receptor ( vGPCR ) , which is a constitutively active homolog of human G-protein coupled receptor 24 , was found to be up-regulated by 3 . 62 fold ( Fig 2B ) ., Three other genes up-regulated due to hypoxia were K1 ( Immunoreceptor tyrosine-based activation motif containing signal transducing membrane protein ) , ORF2 ( homolog of cellular Dihydrofolate reductase ) , and ORF4 ( Complement binding protein ) with a fold change of 1 . 58 , 1 . 26 and 1 . 38 , respectively ( Fig 2B–2D ) ., Among the down-regulated genes , K12 , ORF 40 , and vFLIP were heavily down-regulated with a fold change of -4 . 1 , -3 . 58 and -2 . 68 , respectively ( Fig 2B–2D ) ., The levels of LANA and vCyclin transcripts were induced but not statistically significant due to possible differential efficiency of sequencing through these templates ( Fig 2B ) ., However , they were clearly induced as shown by RT-PCR of cells grown in CoCl2 and 1% O2 ( Fig 2B & 2C ) ., RTA transcripts were moderately increased as detected by sequencing , but was clearly increased when validated by RT-PCR in CoCl2 and 1% O2 ( Fig 2B & 2C ) ., Interestingly , all the four KSHV encoding interferon regulatory factors ( vIRFs ) were down-regulated with a fold change of -3 . 19 , -2 . 19 , -1 . 7 and -2 . 69 for vIRF1 , vIRF2 , vIRF3 and vIRF4 , respectively ( Fig 2B–2D ) ., To validate the results obtained from differential gene expression seen for KSHV-encoded genes by RNA-sequencing , real-time PCR was also performed for the individual genes using gene specific primers ., The primers used for real-time PCR are provided in S3 Table ., Similar results were obtained by real-time PCR assays where vGPCR showed the highest up-regulation and K12 as a greatest down-regulated gene ( Fig 2B & 2C ) ., Similarly , RTA was shown to be up-regulated by RT-PCR in CoCl2 and 1% O2 , as expected ( Fig 2B & 2C ) ., To further corroborate the differential gene expression of KSHV-encoded genes , we wanted to determine if a similar pattern was observed in low oxygen environment ., BJAB-KSHV cells grown in a hypoxic chamber with 1% oxygen were collected and real-time PCR analysis was performed on the KSHV-encoded genes ., The results showed a similar pattern of expression for the genes analyzed ., However , the magnitude of change was slightly lower for vGPCR while it was slightly higher for K1 as compared to their expression in CoCl2-induced hypoxia ( Fig 2C & 2D ) ., The expression of ORF2 , ORF4 , vFLIP , vCyclin , LANA and RTA was also observed with the same pattern as it was seen in CoCl2-induced hypoxia ( Fig 2C & 2D ) ., Interestingly , the expression of some of the vIRFs were slightly less than that observed in CoCl2-induced hypoxia suggesting that the expression of vIRFs are also dependent on the overall ATP pool ( Fig 2D ) ., To determine the physiological relevance of the differentially expressed KSHV-encoded genes in response to hypoxia , real time expression of vGPCR , K1 , vFLIP , vCyclin , LANA and RTA were also analyzed in the primary effusion lymphoma ( PEL ) cell line BC3 , grown in both CoCl2 as well as 1% O2 induced hypoxia ., The results strongly supported a universal effect of hypoxia on the expression of these KSHV-encoded genes ( Fig 2E & 2F ) ., These results led to further analysis of other critical KSHV-encoded genes when HIF1α was stabilized in the naturally infected KSHV positive cell line , BC3 ., We analyzed expression of 27 candidate KSHV-genes from BC3 cells grown under normoxic and CoCl2 induced hypoxic condition ., The primer sets used are included in S3 Table ., The resulting data showed that ORF9 ( DNA polymerase ) , ORF18 ( involved in late gene regulation ) , ORF25 and ORF26 ( major capsid protein ) , ORF27 ( Glycoprotein ) , ORF28 ( BDLF3 EBV homolog ) , ORF34 , ORF40 ( Helicase-primase ) , ORF57 ( mRNA export/splicing ) , and ORFK14 . 1 were significantly up-regulated in BC3 cells grown under hypoxic conditions ( S2A–S2C Fig ) ., Similarly , ORF11 ( predicted dUTPase ) , ORF31 ( nuclear ad cytoplasmic protein ) , ORF32 , ORF33 ( tegument proteins ) , ORF44 ( Helicase ) , ORF64 ( Deubiquitinase ) and ORFK14 ( vOX2 ) were significantly down-regulated in BC3 cells grown under hypoxic condition ( S2A–S2C Fig ) ., The expression of ORF6 ( ssDNA binding protein ) , ORF7 ( virion protein ) , ORF8 ( Glycoprotein B ) , ORF36 ( serine protein kinase ) , ORF54 ( dUTPase/Immunmodulator ) , ORF56 ( involve in DNA replication ) , ORF69 ( BRLF2 nuclear egress ) and ORFK8 . 1 ( Glycoprotein ) showed little or no significant change ( S2A–S2C Fig ) ., Based on the results showing HIF1α stabilization and up-regulated expression of vGPCR , we performed a bioinformatics analysis of the vGPCR promoter region for identification of possible hypoxia responsive elements ( HREs ) 16 ., A search for HREs consensus ( ASGT; where S = C/G ) within the vGPCR promoter identified 9 different HREs ( Fig 3A ) ., To determine the role of these HREs in directly regulating transcription of vGPCR in a HIF1α dependent manner , luciferase based reporter assays were performed ., In brief , 10 different clones from the promoter region of vGPCR were generated and the results from the luciferase activity showed that the HREs at the 3rd , 4th , 5th , 6th , and 7th positions were significantly responsive to HIF1α ( although not equally responsive ) ., The promoter region containing all 9 HREs showed the strongest response , followed by clone C6 containing the initial 5 HREs ( Fig 3C ) ., The primers used to generate the clones are provided in S4 Table ., Next , we wanted to determine if HIF1α knockdown in KSHV-positive cells can rescue the hypoxia associated expression of KSHV-encoded genes ., The ShControl and ShHIF1α-BC3 cells were generated by lentivirus based transduction ., Knock-down of HIF1α transcripts was confirmed at the transcript levels by real time PCR ( Fig 3D ) ., We confirmed the expression of HIF1α at the protein level by HIF1α western blot of lysates from ShControl and ShHIF1α BC3 cells grown under CoCl2 or 1% O2 induced hypoxia ( Fig 3E ) ., Real-time PCR analysis to determine the vGPCR and vFLIP expression in CoCl2 treated cells ., HIF1α knockdown cells showed a reversal of expression as treatment with CoCl2 did not show the effect in HIF1α competent cells ( Fig 3F and S2D Fig ) ., As vGPCR is a potent candidate for the activation of several proliferation pathways , we wanted to determine whether the metabolic phenotype observed for KSHV positive cells was only due to elevated HIF1α levels , or if vGPCR expression was sufficient to induce the metabolic changes ., We transfected HEK293T cells with an expression plasmid coding for KSHV-encoded vGPCR and compared it to that of cells transfected with empty vector ., We also compared the results with cells expressing HIF1α ., The results suggest that hypoxia or vGPCR can modulate the metabolic phenotype ( Fig 3G and 3H ) ., RNA sequencing on total RNA from BJAB and BJAB-KSHV cells grown under normoxic condition or CoCl2 induced hypoxic condition was analyzed to determine the differential gene expression profiles ., To increase our confidence in the differential gene expression data for host genes , the fold-change difference for VEGFA ( a known target of HIF1α ) was first analyzed ( Fig 4A ) ., Real time PCR validation was also performed for VEGFA transcripts from CoCl2 treated cells ( Fig 4B ) ., A comparative analysis was performed between BJAB vs BJAB-CoCl2 , BJAB vs BJAB-KSHV and BJAB vs BJAB-KSHV-CoCl2 cells ., The comparative analysis between BJAB cells grown under normoxia and CoCl2 induced hypoxia revealed major transcriptional changes between the two conditions ( Fig 4C ) ., CoCl2 treatment resulted in up-regulation of 2 , 182 transcripts ( p≤0 . 001; FC ≤2 or ≥ 2 ) ., Similarly 1882 transcripts were observed down-regulated due to CoCl2 treatment ( p≤0 . 001; FC ≤2 or ≥ 2 ) ., A volcano plot for the differentially expressed genes in BJAB-CoCl2 cells compared to BJAB cells is also presented showing that expression of a large number of genes was clearly modulated ( Fig 4C ) ., The top 10 up-regulated and top 10 down-regulated genes from the comparative group are provided ( Fig 4D ) ., Analysis of the RNA sequencing data for the differential gene expression in BJAB-KSHV cells compared to BJAB cells resulted in detection of 357 up-regulated transcripts ( p≤0 . 001; FC ≤2 or ≥ 2 ) ., Similarly , 233 transcripts were observed down-regulated in BJAB-KSHV cells compared to BJAB cells ( p≤0 . 001; FC ≤2 or ≥ 2 ) ., A volcano plot for the differentially expressed genes in BJAB-KSHV cells compared to BJAB cells is shown ( Fig 5A ) ., The top 10 up-regulated and top 10 down-regulated genes from the comparative group are provided ( Fig 5B ) ., To further corroborate the transcriptional profiles of CoCl2 induced hypoxia or the combinatorial effects of hypoxia and KSHV infection , a comparative analysis of RNA sequencing data between BJAB and BJAB-KSHV-CoCl2 was performed ., The results revealed a more enhanced effect of CoCl2 on transcription of host genes ., Compared to 2182 transcripts up-regulated in BJAB-CoCl2 cells , 2560 transcripts were up-regulated in BJAB-KSHV-CoCl2 cells ( p≤0 . 001; FC ≤2 or ≥ 2 ) ., Similarly , an enhanced effect on down-regulation of transcripts was also observed in BJAB-KSHV-CoCl2 cells where a total of 2 , 143 transcripts were observed down-regulated ( p≤0 . 001; FC ≤2 or ≥ 2 ) , compared to only 1 , 882 genes in BJAB-CoCl2 cells ., A volcano plot for the differential gene expression of BJAB vs . BJAB-KSHV-CoCl2 cells is shown in S3A Fig . The top 10 up-regulated and top 10 down-regulated genes from this comparative group is also provided in S3B Fig . KSHV infection is known to stabilize HIF1α 16 , 20 ., We wanted to determine which genes are common targets of KSHV infection , and CoCl2-induced hypoxia ., We also analysed the data to identify synergistic activation or suppression activities linked to the combination of KSHV and hypoxia ., A Venn diagram was prepared ( using Partek software ) for the differentially expressed genes ( p ≤0 . 001; FC ≤2 or ≥ 2 ) in BJAB-CoCl2 , BJAB-KSHV and BJAB-KSHV-CoCl2 cells compared to BJAB cells ( Fig 6A ) ., Among the 357 transcripts observed up-regulated in BJAB-KSHV cells and 2 , 182 transcripts up-regulated in BJAB-CoCl2 cells , 160 transcripts were common ( Fig 6A ) ., Similarly , among 233 down- regulated transcripts in BJAB-KSHV cell and 1 , 882 down-regulated transcripts in BJAB- CoCl2 cells , 60 transcripts were common ( Fig 6A ) ., Interestingly , 105 transcripts out of 357 up-regulated in BJAB-KSHV cells were specific for KSHV ., These transcripts were also up-regulated in BJAB-KSHV-CoCl2 cells ( Fig 6A ) ., Among the 233 down-regulated genes in BJAB-KSHV cells , 59 were observed to be specific for KSHV ., These genes were also down-regulated in BJAB-KSHV-CoCl2 cells ( Fig 6A ) ., Intensity maps of common up-regulated and down-regulated genes are provided in S4 Fig . We wanted to know if transcription regulatory genes were common targets of CoCl2-induced hypoxia , and KSHV infection ., Our analysis showed that the DNMTs , mainly DNMT3A and DNMT3B were two common targets for down-regulation induced by both hypoxia and KSHV infection ., Real-time PCR analysis for these DNMTs followed by western blot analysis for protein levels showed similar results as observed from our RNA sequencing , although DNMT3A was clearly more dramatic in its suppression ( Fig 6C , 6D and 6E ) ., 310 genes involved in glucose , fatty acids and amino acids metabolism were identified by reviewing genes involved in these processes ( S6 Table ) ., This list was used to identify differentially expressed genes in each group when compared to BJAB cells ., To optimize the number of genes differentially expressed in BJAB-KSHV , BJAB–CoCl2 or BJAB-KSHV-CoCl2 , the analysis stringency was maintained to allow for statistical significance ( p<0 . 05 ) ., A Venn diagram was created for the common genes from the 310 metabolic regulated genes which were differentially expressed ( up-regulated or down-regulated ) in each group above ., Compared to BJAB cells , a total of 16 metabolic genes were up-regulated in BJAB-KSHV cells ( Fig 7A ) ., These up-regulated genes predominantly belonged to either glycolysis or the pentose phosphate pathway ( ALDOA , ENO1 , ENO2 , HK2 , PDK3 , PDP2 , PFKL and PGK1 , PRPS1 , PRPS2 and RPE ) , which supports a direct role for KSHV-infection in elevation of glycolysis ., In addition , a subset of TCA cycle regulated genes ACLY , IDH3B , MDH1 and PCK1 were also up-regulated ( Fig 7C ) ., Interestingly , these genes were exclusively restricted to the KSHV-positive background , and were not up-regulated in cells grown under CoCl2-induced hypoxic condition ( Fig 7A & 7C ) ., Interestingly , in cells with elevated HIF1α due to CoCl2 treatment , activation of a different subset of metabolic genes from the glycolysis and TCA cycle pathways ( 15 genes for BJAB-CoCl2 and 16 genes for BJAB-KSHV-CoCl2 ) were identified ., The up-regulated genes due to CoCl2 treatment were ALDOA , ALDOB , BPGM , PDPR and PGM3 ( glycolysis ) and DLST and IDH1 ( TCA cycle ) ( Fig 7A & 7C ) ., In addition , a set of glycogen synthesis genes ( GSK3A , GSK3B , PHKG1 , PHKG2 and PYGM ) were also observed to be induced in CoCl2 treated cells ( Fig 7A & 7C ) ., Interestingly , HIF1α stabilization due to CoCl2 treatment appeared to be dominant over KSHV infection ., The expressions of these up-regulated genes were similar in both BJAB and BJAB-KSHV cells treated with CoCl2 ( Fig 7A & 7C ) ., A second set of evidence showing glycolytic up-regulation by KSHV infection , or CoCl2-induced HIF1α was visible from the set of down-regulated genes in the TCA cycle ( IDH2 , PDHB , SDHA and SUCLG2 ) , and glycogen metabolism ( AGL , GB1 , PCK2 and PGM1 ) ( Fig 7B ) ., These genes were down-regulated in both KSHV alone , and CoCl2 alone , in addition to their combination ., CoCl2 treatment in fact showed an additional set of genes which were down-regulated compared to BJAB or BJAB-KSHV cells ( Fig 7B & 7C ) ., The differentially expressed genes observed by RNA sequencing were further validated by real time PCR ( Fig 8A ) , using gene specific primers ( S5 Table ) ., Among the metabolic genes , Transketolase ( TKT ) and Succinate dehydrogenase subunit A ( SDHA ) were down-regulated by either KSHV-infection or CoCl2 treatment ., Interestingly , the expression of both TKT and SDHA were suppressed by KSHV infection and HIF1α stabilization ( Fig 8A and 8B ) ., As vGPCR over-expression was associated with global transcriptional regulation and generation of reactive oxygen species ( ROS ) 27 , we hypothesized that expression of these genes can be a consequence of induced vGPCR in the hypoxic environment ., To confirm the role of vGPCR in the down-regulation of TKT and SDHA , vGPCR knock down BJAB-KSHV cells were generated by lentivirus based transduction ( Fig 8C ) ., Real-time expression of TKT and SDHA was analyzed in Sh-vGPCR BJAB-KSHV cells grown under normoxia or CoCl2 induced hypoxic conditions ., The results showed clear involvement of vGPCR in regulating expression of these genes ., ShControl cells showed a significant down-regulation of both TKT and SDHA expression in the hypoxic environment , however , Sh-vGPCR cells did not show any significant down-regulation ( Fig 8D & 8E ) ., Importantly , we did not observe any strong up-regulation of TKT and SDHA in hypoxia ., However , in Sh-vGPCR cells their expression was definitely increased compared to wild type KSHV indicating a role in transcription regulation under hypoxic conditions ., To further corroborate a role for KSHV-encoded vGPCR in metabolic changes observed in the hypoxic environment , we investigated whether vGPCR knockout cells showed a reversal of the metabolic phenotype observed under hypoxia ., The KSHV-bacmid clone containing vGPCR-Frame shift knock out mutant ( KSHV-vGPCR-FS-KO ) or vGPCR-Frame shift mutant reversed ( KSHV-vGPCR-FS-R ) KSHV 28 were transfected into HEK293T cells to generate stable lines ., The transfected GFP positive cells were selected using hygromycin ( Fig 9A ) ., To confirm that the frame shift mutation and revertant was maintained , genomic DNA from stable cells were isolated and the KSHV region encompassing the insertion site , PCR amplified and sequenced ., The electropherogram showing the sequencing results confirmed the frame shift insertion and reversion to wild type ( Fig 9B ) ., KSHV reactivation of the vGPCR-Frame shift mutant or revertant was induced by treatment with TPA and Butyric acid followed by standard virus purification ., As expected , the vGPCR-Frame shift mutant showed a substantial decrease in lytic replication with significantly less yield in genome copies ., To determine if vGPCR was a critical factor for the observed metabolic changes in hypoxia , PBMCs were infected with the vGPCR knockout and revertant KSHV virus and cells were subjected to hypoxic induction by treating with CoCl2 ., KSHV infection was monitored by GFP signal and the induction of hypoxia was confirmed by western blot to detect HIF1α ( Fig 9D ) ., The percentage of cells infected with KSHV was empirically calculated by counts of GFP signals in the cell population ., The infection efficiency of PBMCs with KSHV was approximately 50% , although we observed slightly weaker GFP signal intensity from cells infected with the KSHV-vGPCR-FS-KO ( Fig 9C ) ., Cells were grown in normoxic , or CoCl2 induced hypoxia for 48 hours followed by media collection and measurement of glucose uptake ., The results suggest a clear role for KSHV-encoded vGPCR in contributing to the metabolic changes ., The PBMCs infected with the vGPCR kockout KSHV showed a significantly lower glucose uptake compared to the revertant ( Wild type ) KSHV infected cells ( Fig 9E ) ., As vGPCR has been shown to modulate transcriptional changes through the global signaling molecule i . e reactive oxygen species ( ROS ) , we wanted to determine if vGPCR mediated ROS had any role in the transcriptional regulation of genes which were differentially expressed in our study ., We first determined the levels of reactive oxygen species in PBMCs infected with revertant ( wild type ) KSHV treated with or without ROS scavenger , superoxide dismutase ( SOD ) by DCFH-DA staining ( Fig 9E and 9F ) ., As expected , the results showed a high level of reactive oxygen species in PBMCs infected by KSHV ., The level was significantly lower in infected cells treated with SOD ( Fig 9F ) ., RNA was isolated from these cells and reversed transcribed ., The expression of TKT was determined by real-time PCR of cDNA ., The results showed a reversal in the expression pattern of TKT upon SOD treatment , suggesting a possible role of vGPCR mediated ROS in transcriptional regulation of cellular genes ., Similar to infection with most of oncogenic viruses , KSHV infection leads to stabilization of HIFs in host cells by either preventing its degradation or by up-regulating its expression at the transcription level 24 , 29–33 ., The stabilized HIF1α alone or in conjugation with host and viral factors modulates several physiologic pathways supporting survival and growth of the infected cells 8 , 24 ., Further , stabilization of hypoxia inducible factors due to viral infection only partially mimic the in vitro experimental methods of inducing hypoxia in cell culture by growing the cells either under low oxygen or chemical induction by Cobalt Chloride ( CoCl2 ) /Deferoxamine mesylate ( DFO ) 34 , 35 ., The stabilization of hypoxia inducible factor due to viral infection activated the HIF1α dependent pathways , whereas hypoxia due to low oxygen led to activation of several other energy associated pathways such as the AMPK dependent pathways 36–38 ., Independent of the HIF1α stabilization mechanism , the interaction of stabilized hypoxia inducible factors with KSHV factors resulted in modulation of several pathw

Introduction, Results, Discussion, Materials and methods

Kaposi’s sarcoma associated herpesvirus ( KSHV ) infection stabilizes hypoxia inducible factors ( HIFs ) ., The interaction between KSHV encoded factors and HIFs plays a critical role in KSHV latency , reactivation and associated disease phenotypes ., Besides modulation of large-scale signaling , KSHV infection also reprograms the metabolic activity of infected cells ., However , the mechanism and cellular pathways modulated during these changes are poorly understood ., We performed comparative RNA sequencing analysis on cells with stabilized hypoxia inducible factor 1 alpha ( HIF1α ) of KSHV negative or positive background to identify changes in global and metabolic gene expression ., Our results show that hypoxia induces glucose dependency of KSHV positive cells with high glucose uptake and high lactate release ., We identified the KSHV-encoded vGPCR , as a novel target of HIF1α and one of the main viral antigens of this metabolic reprogramming ., Bioinformatics analysis of vGPCR promoter identified 9 distinct hypoxia responsive elements which were activated by HIF1α in-vitro ., Expression of vGPCR alone was sufficient for induction of changes in the metabolic phenotype similar to those induced by KSHV under hypoxic conditions ., Silencing of HIF1α rescued the hypoxia associated phenotype of KSHV positive cells ., Analysis of the host transcriptome identified several common targets of hypoxia as well as KSHV encoded factors and other synergistically activated genes belonging to cellular pathways ., These include those involved in carbohydrate , lipid and amino acids metabolism ., Further DNA methyltranferases , DNMT3A and DNMT3B were found to be regulated by either KSHV , hypoxia , or both synergistically at the transcript and protein levels ., This study showed distinct and common , as well as synergistic effects of HIF1α and KSHV-encoded proteins on metabolic reprogramming of KSHV-infected cells in the hypoxia .

Hypoxia inducible factors ( HIFs ) play a critical role in survival and growth of cancerous cells , in addition to modulating cellular metabolism ., Kaposi’s sarcoma associated herpesvirus ( KSHV ) infection stabilizes HIFs ., Several factors encoded by KSHV are known to interact with up or downstream targets of HIFs ., However , the process by which KSHV infection leads to stabilized HIF1α and modulation of the cellular metabolism is not understood ., Comparative RNA sequencing analysis on cells with stabilized hypoxia inducible factor 1 alpha ( HIF1α ) , of KSHV negative or positive cells led to identification of changes in global and metabolic gene expression ., Our results show that hypoxia induces glucose dependency of KSHV positive cells with high glucose uptake and high lactate release ., KSHV-encoded vGPCR was identified as a novel target of HIF1α regulation and a major viral antigen involved in metabolic reprogramming ., Silencing of HIF1α rescued the hypoxia associated phenotype of KSHV positive cells ., Analysis of the host transcriptome identified several common targets of hypoxia and KSHV-encoded factors , as well as other synergistically activated genes belonging to cellular metabolic pathways ., This study showed unique , common and the synergistic effects of both HIF1α and KSHV-encoded proteins on metabolic reprogramming of KSHV-infected cells in hypoxia .

sequencing techniques, cell physiology, carbohydrate metabolism, medicine and health sciences, pathology and laboratory medicine, oxygen, pathogens, microbiology, cell metabolism, glucose metabolism, viruses, dna viruses, hypoxia, molecular biology techniques, rna sequencing, herpesviruses, research and analysis methods, artificial gene amplification and extension, medical microbiology, gene expression, microbial pathogens, chemistry, kaposis sarcoma-associated herpesvirus, molecular biology, biochemistry, chemical elements, cell biology, polymerase chain reaction, viral pathogens, genetics, biology and life sciences, physical sciences, metabolism, organisms

journal.pcbi.1002737

Next-Generation Sequencing of Human Mitochondrial Reference Genomes Uncovers High Heteroplasmy Frequency

The first complete human ‘genome’ sequenced was that of the mitochondrion in 1981 1 ., Since then , over 8 , 250 complete human and 3 , 220 complete non-human vertebrate mitochondrial genomes have been sequenced ( http://www . ncbi . nlm . nih . gov ) ., These contributions have come from numerous laboratories , where obtaining the complete sequence of even the ∼16 . 5 kb circular mitochondrial genome has been labor intensive and expensive ., As an exemplar , it would be desirable to obtain this sequence on tens of thousands of samples in a simple , inexpensive , yet accurate manner ., Beyond enriching many aspects of human biology , this development could be considered as a prelude , or even as a prerequisite , to sequence-based individualized medicine ., Indeed , the mitochondrial genome , despite its unique structure and function , is an excellent ‘model system’ to identify and solve the technical , biological and medical problems that genomic medicine will encounter ., The mitochondrial genome ( mtgenome ) has multiple attractive structural and functional features ., First , it is small at 16 , 569 bp ( revised Cambridge Reference Sequence , rCRS ) 2 ., Second , it is divided into a small ( 6 . 8% ) non-coding displacement loop ( D-loop ) or control region which provides the origin for mtDNA replication , and a large ( 93 . 2% ) coding region compactly housing 37 genes ( 22 tRNAs , 13 proteins and 2 rRNAs ) that encode proteins critical to the electron transport chain 1 ., The unique biochemical functions of the mitochondria and its high functional content suggest that a higher fraction of mitochondrial , as compared to nuclear , mutations is likely to be functionally deleterious and have distinct phenotypes ., Consequently , we have an enhanced possibility of understanding the logic of how sequence variation affects biochemical functions and organismal phenotypes ., Third , depending on cell type , each cell contains hundreds or more of mitochondria , each mitochondrion harboring 2–10 genomes ., Thus , the functional consequences of mtgenome variation acutely depend on the tissue , and are thus a model for all genes ., Genetic variation in the mtgenome has been critical to demonstrating its unique features of matrilineal inheritance 3 , 4 , lack of recombination 5 , higher variability than the nuclear genome 6 , 7 and hypervariability within the D-loop as compared to the rest of the mtgenome 8 , 9 ., These features have allowed delineation of mitochondrial haplotypes and haplogroups along maternal lines of descent in different human populations , and greatly contributed to our current understanding of human population structure and evolution ., In turn , mitochondrial haplogroups have become a marker of an individuals ancestry ., A surprising aspect of the mitochondrial genome has been its unusually large impact on human disease given its small size , owing to its high coding ratio and high mutation rate ., The impact of mutations in the mtgenome on tissues with high-energy needs , such as muscle , has long been recognized in genetic disorders such as myoclonus epilepsy with ragged red fibers ( MERRF ) and Lebers hereditary optic neuropathy ( LHON ) 10 , 11 ., More broadly , mutations in the mtgenome have been identified in , or associated with , many complex disorders such as cancer , cardiovascular disease , neurodegeneration , diabetes and hearing loss 10 , 12–16; accumulation of mutations in the mitochondrial genome is a natural part of aging 17 , 18 and the development of tumors as well 12 ., Therefore , improved methods to sequence the mtgenome are of value to both biology and medicine ., The 100–1 , 000-fold higher mutation rate in mitochondria , as compared to the nuclear genome , is owing to the lack of a DNA repair system within the organelle 19 ., Thus , alterations in the mtgenome sequence occur frequently , visualized as two or more mitochondrial genomes of different sequence within a single human ., Such ‘heteroplasmy’ has long been considered rare but it is one major explanation for the variation in phenotypes between maternally related individuals with a deleterious mitochondrial mutation since different individuals within the same maternal lineage may harbor different proportions of wildtype to mutant mitochondria ., However , strictly on theoretical grounds , heteroplasmy must be common since each oocyte has multiple mitochondria , as compared to the single nuclear genome ., Therefore , any new mutation has a significant probability of being lost through mitochondrial segregation in the daughter cells after fertilization ( mitochondrial “drift” ) and needs to be balanced by additional mutations to allow variation ., This may be a second reason for the higher mitochondrial mutation rate observed through heteroplasmy in all tissues ., Indeed , some have proposed that , under the “mutation-drift-selection” scenario , heteroplasmy should be the default state for mtDNA in all tissues of the body from mitochondrial segregation of inherited variation or from somatic mutation 20 ., Indeed , all extant mitochondrial polymorphisms must have gone through a heteroplasmic state after their origin by mutation ., A number of studies have demonstrated heteroplasmy , but its mechanism and incidence in the general population remains unknown since the detection of heteroplasmy has been hindered by the resolution of available sequencing technologies ., While Sanger sequencing allows for complete coverage of the mtgenome , it is limited by the lack of deep coverage and low sensitivity for heteroplasmy detection when it is much less than 50% 21 ., The Affymetrix Mitochip Array 2 . 0 containing the full mtDNA sense and antisense sequences tiled on an array has been successfully used in our laboratory for full mtgenome sequencing with slightly improved heteroplasmy detection 22 , 23 ., However , neither of these technologies allows the assessment of individual mitochondrial molecules ., In contrast , next generation sequencing technology is an excellent tool for obtaining the mtgenome sequence and its heteroplasmic sites rapidly and accurately since it allows deep coverage of the genome through multiple independent sequence reads ., In fact , two recent studies demonstrate that the degree of heteroplasmy can vary across an order of magnitude ( typically <5% but occasionally >50% ) 24 and multiple sites with the mtgenome have heteroplasmy rates >10% 25 ., In this study , we present the complete mitochondrial genomic sequence and heteroplasmic status of 40 samples from the International HapMap Project 26 using the next-generation 454 GS FLX pyrosequencing platform ., The samples include 20 individuals from the CEU ( European ancestry ) and 20 individuals from the YRI ( African ancestry ) reference panels; these are mtgenome sequences isolated without any contamination from nuclear embedded numts ( see results ) and from publicly available reference samples ., The availability of such reference samples is critical as the samples could serve as a basis for reproducing and benchmarking new sequencing technologies ., To enable analyses , we developed novel sequence processing and analysis algorithms , both for mapping against the reference sequence and for de novo assembly , for confident determination of the mitochondrial sequence ., Our analyses demonstrate sequence accuracy of near 100% , nucleotide diversity of 1 . 6×10−3 for CEU and 3 . 7×10−3 for YRI , patterns of sequence variation consistent with earlier studies , but a high rate of heteroplasmy varying between 10% and 50% ., Twenty-two unique CEU ( European ancestry ) and twenty-two unique YRI ( African ancestry ) samples from the International HapMap Project 26 , including two sets of duplicates for each population ( CEU: NA10851 , NA10856; YRI: NA18500 & NA18503 ) , were sequenced ., The DNA used was enriched for mitochondrial sequences by long range PCR ( LPCR ) of three ∼5–6 kb segments using mtgenome-specific primers ., Although mitochondrial sequencing using total cellular DNA is possible and easy , and is being routinely performed with heteroplasmy detection 2728 , we avoided this approach because the human nuclear genome has >1 , 200 non-functional mtgenome fragments ( numts ) 29 and mitochondrial pseudogenes that complicate mtgenome sequence assembly and introduces numerous polymorphism and heteroplasmic artifacts ., Thus , despite its simplicity it is quite erroneous , as we will demonstrate ., LPCR reduced this possibility greatly since <5% of insertion sites are >5 kb ., Additionally , our primers are designed to avoid nuclear genome amplification; each primer set is specific for the mtgenome as verified by BLAST ( refer to methods ) ., We completed sequencing using the 454 GS FLX system by pooling 12 individually tagged samples into each lane of a 4-region gasket PicoTiterPlate ( PTP ) ., Two YRI samples ( NA19209 and NA19116 ) were discarded from analysis as both samples showed only one of three amplicons with an unusually high number of sites containing two different nucleotides at high frequencies; this could have arisen from a sample mixture ., In addition , two CEU samples ( NA12750 and NA12872 ) were removed due to suspected mislabeling ., The results presented are from the remaining 40 samples ., On average , each sample had 10 , 554 reads with a standard deviation of 2 , 652 reads ., The read length distributions were similar and consistent across all samples; the distribution across all 44 samples ( including duplicates ) show read lengths across a wide range but 93 . 7% of them are between 200–300 bp ., The average read length was 250 bp with a standard deviation of 36 bp ( Supporting Figure S1 ) so that the yield per sequencing run was ∼2 . 6 megabases ( mb ) ., Our approach for obtaining the mtgenome sequence was to map quality filtered reads against the reference sequence ( rCRS ) to identify homoplasmic and heteroplasmic variant sites ., We also introduce a novel method for de novo assembly of the reads into a circular genome ., An important consideration in our study was to obtain high accuracy of the resulting called bases ., We accomplished this by quantitative filtering of reads that were error prone ., We finally estimated an accuracy of the resulting sequence and an analysis of its genetic features ., The overall quality of the data is summarized in Figure 1 ., It portrays normalized coverage and the 0-centered ratio of forward/reverse reads at each position of the mtgenome ., The average coverage across all 40 samples in YRI and CEU was ∼120-fold ., However , the total number of reads varied per sample so that we normalized coverage by a samples total number of reads ., Second , we assessed the directionality bias in the reads by computing ρ\u200a= ( r−1 ) / ( r+1 ) where r is the ratio of forward to reverse reads at a position ., We present data on normalized coverage and read ratio as an average across the 20 samples for each population , YRI and CEU respectively ., This is displayed along the mitochondrial genome ( Figure 1 ) as a function of local GC-content , calculated using a sliding window of length 51 bp ( 25 bp before and after each position ) across the circular genome ., The figure also illustrates where the D-Loop and amplicons lie along the mitochondrial genome ., As can be seen , the average coverage falls and the read ratio spikes prior to the PCR amplicon overlap regions in both populations ., However , ρ fluctuations are not due to variations in GC content ., The human mitochondrial genome can be sequenced at very high accuracy and rapidly using next generation sequencing technology as we , in this study , and other recent studies 24 , 25 , have shown ., All of these studies have in common that they have uncovered patterns of sequence variation as has been described before but quantified the novel finding of a high rate of heteroplasmy in multiple individuals and across the mtgenome ., Our study , however , has made three additional and important contributions ., First , we have sequenced widely and publicly available biological samples so that our experiments can be replicated and provide a basis for future benchmarking and technology comparisons ., Second , our methodology for variant and heteroplasmy detection is quantitative and parametric so that the method can be further optimized with additional experiments and new data ., Third , we have developed a method for de novo sequence assembly of the mitochondrial circular genome with an internal test of sequence accuracy ( identity of antegrade and retrograde assembly along a circular genome ) ., Each of the above developments is significant for understanding mitochondrial biology and medicine ., First , DNA sequencing technology is advancing and new platforms that include single-molecule sequencing are on the horizon 35 ., The availability of multiple sequencing methods on publicly available biological samples , such as those we have used , is the only certain way for comparing different technologies and their relative advantages and disadvantages ., Second , we believe that the parameters we have used for identifying variants and heteroplasmy will need to be varied depending on the specific technology used and its features such as directional bias , read accuracy , difficulty in reading through homopolymeric tracts and coverage ., Consequently , our approach is general and generalizable ., Third , mapping reads against a reference suffers from the disadvantage of not being able to confidently identify insertions or inversions ., The de novo methods we have introduced can rectify this deficiency particularly since our preliminary exploration of 40 sequences suggests that it produces high-quality assemblies ., The problems associated with recovery of target mitochondrial DNA from a biological sample , its DNA sequencing using short reads , the assembly of these reads into an mtgenome and its interpretation of variation and heteroplasmy are invariably confounded ., We chose to recover the mtgenome in each individual by three distinct long-range PCR segments , analogous to Li et al . ( 2010 ) and in contrast to He et al . ( 2010 ) ., Our primers are designed to specifically target mtDNA and avoid introducing any artifacts from the numerous mitochondrial fragments ( numts ) in the nuclear human genome ., Even if there is indeed some contamination from numts , this effect is expected to be small since it is assumed that there are many more copies of the entire mtgenome than two numts copies per the >1 , 200 autosomal insertion sites ., However , specific fragments are present in >100 copies and can , and do , get amplified 29 ., We expect that single molecule sequencing will reduce or eliminate this potential technical artifact ., It is currently popular to extract and assemble the mitochondrial genome from whole genome sequencing of total cellular DNA 27; Picardi and Pesole ( 2012 ) have recently done so from off-target exome sequencing data ., But , these latter authors also show that ∼1% of all reads map to the mtgenome and not to known numts !, Consequently , extensive filtering may be necessary to derive the mtgenome but this might also lose the genome-specific features including heteroplasmic sites ., In other words , comparison of our data with those of others needs to consider how the mt DNA was isolated in the first place ., In this study , we have made no attempt to estimate the cost of sequencing a single mtgenome in any accurate way ., In any case , we have demonstrated that we can obtain such sequence rapidly and with an error rate <5 . 63×10−4 ., Our crude estimate is that each sequence can be obtained for ∼$50 at high throughput much of this cost being the cost of mt DNA recovery ., If so , studies of an entire cohort of individuals who have been measured for numerous medically relevant traits and are being followed for disease outcomes would be an ideal pilot experiment for individualized medicine ., Forty-four reference DNA samples of unrelated individuals from the International HapMap project were studied using 454 pyrosequencing technology ., The samples included 22 Yoruba samples from Nigeria ( YRI: NA18500 , NA18503 , NA18506 , NA18516 , NA18523 , NA18852 , NA18855 , NA18858 , NA18861 , NA18870 , NA18912 , NA19092 , NA19101 , NA19116 , NA19137 , NA19140 , NA19152 , NA19159 , NA19171 , NA19200 , NA19203 & NA19209 ) and 22 Utah residents of European ancestry ( CEU: NA06993 , NA06994 , NA07019 , NA10851 , NA10854 , NA10856 , NA10863 , NA11831 , NA11881 , NA11882 , NA11995 , NA12004 , NA12005 , NA12144 , NA12145 , NA12146 , NA12156 , NA12248 , NA12750 , NA12760 , NA12872 & NA12891 ) , four of which were studied in duplicate ( NA18500 and NA18503 from YRI; NA10851 and NA10856 from CEU ) ., Additionally , four of these samples were sequenced using Sanger sequencing and the Affymetrix Mitochip Array 2 . 0 ( NA06994 , NA12146 , NA18516 , and NA18523 ) for comparison ., We also evaluated the Standard Reference Material ( SRM ) 2394 developed by the National Institute of Standards and Technology ( NIST ) ., These are a set of eight mixtures ( mass percentages of 1% , 2 . 5% , 5% , 10% , 20% , 30% , 40% , and 50% ) of two 285 bp mitochondrial amplicons that differ in sequence by only one nucleotide and is obtained from two different human cell lines ., After QC checks that detected sample contamination , data from NA19209 , NA19116 , NA12750 and NA12872 were dropped from further analysis ., For pyrosequencing , we enriched for the mitochondrial genomic DNA by long range PCR ( ∼5–6 Kb ) for three overlapping amplicons using high-fidelity TaKaRa LA Taq ( TaKaRa Biomedicals ) in 50 µl reactions ( 50 ng gDNA , 1× LA PCR buffer , 0 . 3 µM of each primer , 400 µM dNTPs , 2 . 5 U LA Taq ) ., The primer sequences used were those described in Maitra et al ( 2004 ) ., Each primer set was blasted against the entire human genome to verify that there was no nuclear genome amplification ., In silico PCR also confirmed no nuclear genome targets amplification by any of the three distinct primer sets ., The success of the amplification reaction was checked by gel electrophoresis ., The PCR products were then cleaned using the QIAquick PCR purification kit ( QIAGEN ) following the column purification protocol and the DNA was eluted in 30 µl of Elution Buffer to obtain a higher concentration ., The actual concentration was determined using the Quant-iT PicoGreen dsDNA kit ( Invitrogen ) ., To obtain a uniform representation of the entire mtgenome , the amplicons were pooled in equimolar amounts ( amount per amplicon ng\u200a=\u200afraction of total x total amount needed ) ., Since the pyrosequencing protocol required more than 5 µg of total DNA at a concentration of 300 ng/µl we performed at least two PCR reactions per amplicon ., After pooling the three amplicons per reference sample in equimolar amounts , the samples were run through a QIAquick purification column to concentrate the pool to the desired 300 ng/µl concentration ., For Sanger sequencing , the mtgenome was amplified in 24 overlapping PCR fragments ( 800–900 bp ) as described in Rieder et al 1998 ., For easy detection during sequencing , M13 tags were added to all forward and reverse primer sets ., PCR reactions and cycling conditions were optimized across all primer sets and used 1× PCR Buffer , 200 µM dNTP , 0 . 5 U Taq2000 , 10 ng DNA , and 0 . 5 µM of each primer ., Confirmation of the reactions specificity was assessed by 2% agarose gel electrophoresis ., The final concentration of each amplicon was determined using the Quant-iT PicoGreen dsDNA kit ( Invitrogen ) ., All sequencing using Sanger chemistry were performed by a commercial entity ( Agencourt ) for each individual PCR product on an automated ABI3730xl platform using a concentration of 15–25 ng/µl in 30 µl of TE buffer; individual sequence traces were provided ., The Sanger sequence for each sample was assembled and analyzed in the SeqManII program from the DNASTAR Lasergene® v . 7 . 0 analysis software suite ., All sequencing reads for an individual sample were imported and assembled into one contiguous consensus sequence by aligning them to the revised Cambridge Reference Mitochondrial Sequence ( rCRS ) ., The variant bases for each sample were determined and used as the genotype for that sample for further analysis ., Peak intensities for each sequence variant identified by the program were manually reviewed ., For pyrosequencing of the 48 samples , including duplicates , we pooled the pooled long range PCR products per sample in four batches of 12 each using 454s Multiplex Identifiers ( MID ) that are molecular barcodes that serve as unique tags to identify each sample post-sequencing ., These mitochondrial DNA pools were sequenced on a 4-gasket PicoTiterPlate ( PTP ) using the GS FLX sequencing system ., Standard emPCR and sample preparation were followed as recommended by the manufacturer ( Roche Inc . ) As an additional precaution against misalignments , we developed an improved version of the BLAST algorithm ., BLASTN uses an affine gap costs model and allows control of gap opening , gap extension and mismatch penalties and are particularly problematic for homopolymer stretches due to undercalls and overcalls ., To accurately align these reads against a reference sequence , we needed an aligner that adjusts the gap penalties depending on the presence and length of the homopolymer sequence ., The standard Smith-Waterman algorithm for aligning two sequences can be extended to handle these situations as follows ., Let c ( n , m ) be the penalty for a n-length homopolymeric stretch of the reference appearing as an m-length stretch in the read ., Then , the dynamic programming algorithm was modified to consult the c matrix also when computing the optimal alignment of the sequences ., The entries of the c ( n , m ) matrix needed to be defined heuristically ., In the current study , we set c ( n , m ) such that in homopolymer stretches of length ≥5 , two gaps were ignored and the remaining penalized using the standard affine gap penalty model of BLASTN ., In homopolymeric stretches of length 4 , one gap was ignored ., Since the largest homopolymeric stretch in the mitochondrial sequence is only 8 bases long , these values in the c ( n , m ) table were sufficient to yield good results ., For performance reasons , we carried out alignment first using BLAST ., Portions of the resultant alignment that were likely to benefit from our homopolymer-aware aligner were identified and refined using a Perl implementation of the model described above ., We developed an independent de novo assembly of each mtgenome ., In our approach , we initially populate a database comprising all unique n-mers ( n\u200a=\u200a27 here ) and the frequency of each n-mer in the raw read data ., To populate the database we slide a window , n bases long , along each read and record the sequence within the window as the read is traversed ., Starting at the first base position , the n-mer comprising the first base and the subsequent n-1 bases is recorded ., The window position is then incremented 1 base at a time until all n-mers from the read have been entered into the database ., If an n-mer sequence already exists in the database , the number of occurrences ( multiplicity , m ) is incremented by 1 ., As an example , the distribution of m over all 454 reads for sample NA06993 is shown in Supporting Figure S9 ., The distribution is multimodal ., The peak at multiplicity m\u200a=\u200a1 comprises all n-mers that contain one or more 454 sequencing errors and that are not repeated as a group in any other read of the particular region of the genome ., The peak near m\u200a=\u200a50 is the mode of the local , n-mer matched , consensus coverage of the genome ., The high multiplicities in the tail of the distribution are due to genomic regions where the long PCR segments overlap ., To de novo assemble the mtgenome using the n-mer database data , we make the following minimal set of assumptions:, 1 ) there are no duplicated n-mers within the genome;, 2 ) there are no palindromic n-mers , i . e . , an n-mer on the L-strand of the mtGenome is not found in reverse complement form on the H-strand and vice versa , and, 3 ) for a short n-mer drawn from the genome , the sequence read of this n-mer is more likely to be correct than contain an error ., The third assumption depends on the sequence-context-dependent error rate of the 454 platform ., If we consider as a characteristic value , λ\u200a=\u200a0 . 005 , for the average 454 error rate per base , then for any n-mer , the probability that the n-mer is error free is given by p\u200a= ( 1−λ ) n ., If we choose n\u200a=\u200a27 , this gives p\u200a=\u200a0 . 87 ., This means that a majority of database n-mers are correct given that most of the mtgenome sequences are “average in content” ., This calculation assumes errors are uncorrelated along the n-mer , which is not the case for the sequence context of long homopolymeric runs ( see below ) ., Our choice of n\u200a=\u200a27 is a compromise value that seeks to ensure the validity of assumptions 1–3: the shorter an n-mer is , the more likely it is to be repeated in the mtgenome or be a palindrome; on the other hand , if the n-mer is chosen to be too long , the majority of n-mers derived from reads at a given genome position will contain an error somewhere within the n-mer ., The satisfaction of assumption 3 allows us to apply a “majority base wins” criterion as our basis for selecting sequences in our de novo consensus assembly ., The de novo assembly initially proceeds by searching the database for the n-mer matching at position 1 on the L-strand of the rCRS and ensuring the multiplicity m for L- strand and H- strand sequences at this position exceed 10 ., This starting condition was satisfied for all mtgenomes assembled ( i . e . , no genome contained a polymorphism with respect to the rCRS in this portion of the genome , otherwise successive positions along the rCRS could readily be probed until this condition was satisfied ) ., First , de novo assembly proceeds in the antegrade direction ( with increasing rCRS position ) ., We form the four possible candidates for the successive n-mer in the sequence and their respective reverse complements by dropping the first base of the n-mer at rCRS position 1 and adding A , T , C , or G to the end ., The database is then searched for each candidate n-mer and its reverse complement , and the sum of the respective forward and reverse n-mer multiplicities is recorded for each candidate n-mer ., According to assumption 3 ) , the appropriate choice of the subsequent n-mer is the one that is the most abundant in the database ., The selected new base is then added to the de novo assembly and the process is repeated until the starting n-mer sequence at rCRS position 1 is again encountered ( exploiting the circular nature of the mtgenome ) ., The antegrade de novo assembly is then complete ., To assign consensus coverage at each base position we form the n-mer from the antegrade assembly in which the position in question is at the center , with ( n_mer-1 ) /2 bases on either side ., The database is then searched for this n-mer and the sum of the L-strand and H- strand multiplicites , m , is recorded as the consensus coverage ., As a check on the antegrade de novo consensus assembly , the entire assembly process above is repeated by sequencing from rCRS position 1 in the retrograde direction using the same database ., Here , the base at the end of the L-strand n-mer is dropped and the candidate n-mers for the next position in the retrograde direction are formed by adding A , T , C , or G to the beginning of the n-mer ., The alternative assemblies in the antegrade and retrograde directions are subsequently compared to identify discrepancies for further investigation and curation ., Substitution heteroplasmy candidates , and their respective fractions with respect to the consensus sequence , can then be determined by replacing the central base at each position with the other three possible bases , and then summing the L-side and H-side multiplicities of the n-mers in the database ., Indel heteroplasmys with respect to the consensus can also be determined using a method aligning the unused n-mers in the database against the consensus .

Introduction, Results, Discussion, Materials and Methods

We describe methods for rapid sequencing of the entire human mitochondrial genome ( mtgenome ) , which involve long-range PCR for specific amplification of the mtgenome , pyrosequencing , quantitative mapping of sequence reads to identify sequence variants and heteroplasmy , as well as de novo sequence assembly ., These methods have been used to study 40 publicly available HapMap samples of European ( CEU ) and African ( YRI ) ancestry to demonstrate a sequencing error rate <5 . 63×10−4 , nucleotide diversity of 1 . 6×10−3 for CEU and 3 . 7×10−3 for YRI , patterns of sequence variation consistent with earlier studies , but a higher rate of heteroplasmy varying between 10% and 50% ., These results demonstrate that next-generation sequencing technologies allow interrogation of the mitochondrial genome in greater depth than previously possible which may be of value in biology and medicine .

This manuscript details a novel algorithm to evaluate high-throughput DNA sequence data from whole mitochondrial genomes purified from genomic DNA , which also contains multiple fragmented nuclear copies of mtgenomes ( numts ) ., 40 samples were selected from 2 distinct reference ( HapMap ) populations of African ( YRI ) and European ( CEU ) origin ., While previous technologies did not allow the assessment of individual mitochondrial molecules , next-generation sequencing technology is an excellent tool for obtaining the mtgenome sequence and its heteroplasmic sites rapidly and accurately through deep coverage of the genome ., The computational techniques presented optimize reference-based alignments and introduce a new de novo assembly method ., An important contribution of our study was obtaining high accuracy of the resulting called bases that we accomplished by quantitative filtering of reads that were error prone ., In addition , several sites were experimentally validated and our method has a strong correlation ( R2\u200a=\u200a0 . 96 ) with the NIST standard reference sample for heteroplasmy ., Overall , our findings indicate that one can now confidently genotype mtDNA variants using next-generation sequencing data and reveal low levels of heteroplasmy ( >10% ) ., Beyond enriching our understanding and pathology of certain diseases , this development could be considered as a prelude to sequence-based individualized medicine for the mtgenome .

population genetics, sequence assembly tools, algorithms, genome sequencing, genome analysis tools, population biology, mitochondrial diseases, sequence analysis, genetic polymorphism, biology, computer science, genetics, genomics, computational biology, genetics and genomics, human genetics

journal.pgen.1003807

Natural Genetic Variation of Integrin Alpha L (Itgal) Modulates Ischemic Brain Injury in Stroke

Stroke is the second leading cause of death and the most common cause of acquired adult disability worldwide 1 , 2 ., Ischemic stroke is caused by disrupted blood flow within the territory of an occluded blood vessel that results in death of brain cells ( infarct ) ., The severity of cerebral infarction primarily depends on the re-perfusion and response of the blood vessels , but neuronal cell death is also determined by intrinsic molecular cascades including excitotoxicity , oxidative stress , apoptosis , and inflammation 3 ., More recently , emerging data suggest that the dynamic interaction between vascular cells , glia , neurons and associated tissue matrix proteins – the neurovascular unit – plays a crucial role in the pathogenesis of ischemic brain injury 4 ., Although genome-wide association studies have made some progress in the identification of stroke susceptibility genes 5 , the genetic determinants for stroke outcome have yet to be fully explained ., Because variation in the anatomic location of the occluded artery , the extent and duration of occlusion , time until treatment , and other contributing factors cannot be controlled in patients , very few genetic factors have been identified that contribute to the severity of brain damage in human ischemic stroke ., By contrast , infarct volume in murine models of focal cerebral ischemia ( stroke ) has been shown to vary widely among inbred strains , suggesting strong genetic control 6–8 ., In a mouse model of ischemic stroke , we have previously demonstrated that infarct volume varies up to 30-fold in 16 common inbred mouse strains ., Using a forward genetic mapping analysis in an F2 intercross between C57BL/6J ( B6 hereafter ) and BALB/cByJ ( BALB/c ) strains , we have identified three distinct quantitative trait loci ( QTLs ) that modulate the volume of cerebral infarction ., In particular , a single locus mapping to distal chromosome 7 , Civq1 ( cerebral infarct volume QTL1 ) , accounts for the major portion of variation ( 56% ) in infarct volume 8 ., In the present study , through the use of multiple QTL mapping analyses , generation of sub-congenic mouse lines , genome-wide association across inbred strains , and ancestral SNP haplotype analyses , we have identified that genetic variation in integrin alpha L ( Itgal ) modulates ischemic brain injury in mice ., Using 16 common inbred mouse strains , we have previously demonstrated that infarct volume after permanent distal middle cerebral artery occlusion ( MCAO ) is under strong genetic control , and had mapped Civq1 on chromosome 7 as a major genetic determinant of infarct volume 8 ., To further explore the naturally occurring genetic variation in infarct volume , and provide additional statistical power and map resolution , we determined infarct volumes on additional 16 inbred strains , representing the priority strains for the Mouse Phenome Project ( http://phenome . jax . org ) ., We excluded wild-derived strains to avoid spurious false positive association 9–11 ., The ischemic damage was localized exclusively in the frontal and parietal cortex and infarct volumes were highly reproducible among individual animals of the same inbred strain ., Similar to our previous report 8 , we observed large variability in infarct volumes among strains ( Figure 1A , B ) ., Strains I/LnJ , LP/J , C3H/HeJ , AKR/J , A/J , BALB/c , SWR/J ( SWR ) , BUB/BnJ , MRL/MpJ , and C58/J exhibited marked sensitivity to ischemic injury and developed large infarct volumes ( >17 . 0 mm3 ) ., By contrast , strains C57BLKS/J , FVB/NJ ( FVB ) , C57BR/cdJ , CBA/J , NON/LtJ , DBA/2J , NZL/LtJ , RIIIS/J , 129X1/SvJ , NOD/ShiLtJ , SJL/J , DBA/1J , C57L/J , B6 , and 129S1/SvImJ were relatively resistant to cerebral ischemia , showing infarct volume smaller than 5 mm3 ( Figure 1B ) ., Mean infarct volume ranged from 0 . 9 to 43 . 0 mm3 between the strain pairs at the phenotypic extremes ( C57BLKS/J vs . C58/J ) , representing a 47-fold difference in the trait ., From these combined data on 32 total strains ( 255 total mice ) , we preformed genome-wide associations for this trait ., To correct for population structure and genetic relatedness among inbred strains of mice , we employed the Efficient Mixed Model Association ( EMMA ) 12 ., The association scan was carried out using the 4 million high-density SNP panel using the EMMA server ( http://mouse . cs . ucla . edu/emmaserver ) ., We identified a significant region of association ( Chr7: 132 . 35–134 . 81 Mb ) that co-localized within the 95% confidence interval of Civq1 that we previously mapped in linkage analyses ( Figure 2A ) 8 ., A total of 69 SNPs across the ∼2 . 5 Mb region reached the statistically significant threshold ( P<10−5 ) for cerebral infarct volume and the most significant association without any missing alleles was at a SNP ( rs32965660 , P\u200a=\u200a1 . 25×10−6 ) at 132 , 390 , 712 bp on the chromosome ., Our EMMA associated region for Civq1 on chromosome 7 covers approximately 2 . 5 Mb ( 132 . 35–134 . 81 Mb ) ., A previous report calculated a median distance of 3 Mb between the actual causal variant and the closest marker in EMMA analysis 11 , so we expanded our candidate interval for an additional 1 . 5 Mb region flanking either side of the associated SNPs ., There are 124 known genes in the expanded associated region ( 130 . 85–136 . 31 Mb ) on chromosome 7 ( Figure 2A ) ., Because of lack of precision in genome-wide association studies due to incomplete understanding of linkage disequilibrium in the mouse genome , statistical power is highly dependent on the number and genetic relatedness of the inbred mouse strains used 13 , 14 ., A previous study suggests that for a trait with a genetic effect contributing in the range of 30% to the total variance ( Civq1\u200a=\u200a56% ) , 30 strains or more are required for acceptable power 15 ., This suggests that our analysis is sufficiently powered , and that the causative gene for the Civq1 locus is located within the expanded 5 . 5 Mb region , and most likely , in the 2 . 5 Mb region , reduced by our EMMA analysis using 32 inbred strains ., Since we previously identified Civq1 in two different genetic crosses ( B6×BALB/c and B6×SWR ) , as well as in the Chromosome Substitution Strain 7 ( CSS7 ) where A/J chromosome 7 was introgressed into the B6 background , and each cross includes B6 as one of the parental strains 8 , it is possible that the sequence variant underlying Civq1 is unique to B6 , occurring in this strain only after it was separated from its last common ancestor with the other strains 16–19 ., To determine whether allelic variation at Civq1 is unique to the B6 strain or instead due to a sequence variant mapping within an ancestral murine haplotype block 20 , we performed an additional intercross between the large infarct strain , BALB/c , and a different small infarct strain FVB; two strains which exhibit a 10-fold difference in infarct volume ( Figure 1B ) ., By substituting FVB for B6 as the “small infarct” strain in this new cross , we could effectively determine whether FVB and B6 share the “protective” allele at Civq1 ., Because our goal was to determine whether we would remap Civq1 in this new cross , and to date , Civq1 had shown effect sizes in excess of 50% in other crosses , we surmised that if Civq1 was responsible for the difference between these two parental strains , the locus could be identified with a minimum number of F2 animals ., Even with only 35 F2 ( FVB×BALB/c ) mice , we identified a statistically significant locus ( LOD\u200a=\u200a5 . 2 ) that mapped to the identical position ( peak LOD at rs13479513 ) on chromosome 7 as that of Civq1 ( Figure 2B ) ., Interestingly , the locus identified in this F2 ( FVB× BALB/c ) cross exhibits a large effect size ( ∼85% ) , even stronger than observed in our original two crosses ( 56–57% ) ., In this cross , Civq1 accounts for nearly all of the phenotypic difference in infarct volume observed between FVB and BALB/c strains ( Figure S1 ) , and this may explain the highly significant LOD score obtained with only 35 F2 progeny ., These combined data further validate the importance of Civq1 in the determination of infarct volume across common inbred mouse strains ( Figure 2C ) ., More importantly , these data strongly suggest that the sequence variant underlying Civq1 is located within an ancestral haplotype block that has been inherited across multiple inbred mouse strains , rather than being unique to strain B6 ., This further supports the use of ancestral haplotype association mapping approaches to fine map the Civq locus , towards the identification of the causative gene variant ( s ) ., To validate the phenotypic effects of the isolated Civq1 locus , and to narrow down the critical region of the QTL , we created recombinant congenic mouse lines ( C . B6-Civq1 ) carrying different segments of the Civq1 region from B6 introgressed into the BALB/c background ., Congenic Line 1 contains approximately 22 . 6 Mb of the B6 region of Civq1 , and Line 3 is a fully nested sub-congenic line containing a smaller region completely contained within the larger congenic region ( C . B6-Civq1-1: 119 . 3–141 . 9 Mb and C . B6-Civq1-3: 126 . 2–135 . 8 Mb ) ., We also attempted to generate a reciprocal congenic line ( B6 . C-Civq1 ) containing the Civq1 region from BALB/c on the B6 background ( Figure 3A ) , but for unknown reasons , the reciprocal congenic line was embryonic lethal when crossed to homozygosity ( 0 homozygotes out of 52 progeny from a heterozygous congenic cross ) ., Nonetheless , since both Civq1 exhibit heterozygous effects in the mapping crosses , we retained the heterozygous congenic line ( B6 . C-Civq1 ( Het ) ) for analysis ( retaining approximately 16 Mb of BALB/c genomic from D7Mit238 to rs32420445 ) ., We first analyzed infarct volume of the congenic lines ., Both C . B6-Civq1-1 and -3 lines showed significantly reduced ( ∼30% ) volume of cerebral infarction compared to control BALB/c mice ( Figure 3B , C ) ., There was no significant difference in infarct volume between the two lines carrying the larger or fully nested , smaller , segment of B6 Civq1 region , providing evidence that the 9 . 6 Mb region ( 126 . 2–135 . 8 Mb ) located between the markers D7Mit238 and rs45999701 defines the critical interval for Civq1 ( Figure 3A ) ., In a B6×BALB/c intercross , a locus modulating the number of pial collateral arteries ( Collateral artery number QTL , Canq1 ) 21 was mapped that appears to coincide with our infarct volume locus , Civq1 ., This suggested that allelic variation in the same gene ( s ) might modulate the phenotypes of both infarct volume and collateral vessel formation in the brain ., We next determined the collateral artery phenotype of these same congenic lines , measuring pial collateral arteries connecting between MCA and ACA ( Figure 3D ) ., Consistent with previous reports 21 , BALB/c mice have on average less than one collateral artery per cerebral hemisphere , compared to an average of 10 in B6 mice ., Both Lines 1 and 3 of the ( C . B6-Civq1 ) congenic lines showed an approximately 50% increase in the number of collateral arteries when compared to control background BALB/c mice , and similar to the infarct volume data , there was no difference between C . B6-Civq1-1 and -3 lines ( Figure 3E ) ., Surprisingly , although the heterozygous reciprocal congenic mice ( B6 . C-Civq1 ( Het ) ) showed no difference in pial collateral number when compared with B6 controls ( Figure 3E ) , the infarct volume of the heterozygous congenic mice was significantly increased when compared to B6 mice ( Figure 3C ) ., Infarct volume of the congenic mice ( B6 . C-Civq1 ( Het ) ) was ∼3-fold larger than that of B6 mice ( 14 . 7 mm3 vs . 4 . 5 mm3 ) ., To examine whether the Civq1 locus confers a collateral-independent , tissue-intrinsic effect on cerebral infarction , we performed the widely used brain slice-based assay where transient oxygen-glucose deprivation ( OGD ) is used to induce neuronal cell death ., Neuronal degeneration was measured via biolistic transfection of the vital fluorescent reporter , YFP , which creates a dispersed ‘sentinel’ population of cortical pyramidal neurons that can be used to quantify neuronal vitality and numbers 22 , 23 ., We first examined neuronal cell death for parental B6 and BALB/c strains ., Subjecting YFP-transfected coronal brain slices to transient OGD resulted in the degeneration and clearance of a large proportion of cortical pyramidal neurons by 24 hr post OGD treatment ., Intriguingly , similar to the sensitivity to focal cerebral ischemia , cell viability in YFP-transfected brain slices in B6 mice was significantly higher than that in BALB/c mice ( Figure 3F , G ) ., Based on this finding , we next determined the phenotype of the C . B6-Civq1-3 congenic mice ., The congenic mice displayed a significantly increased number of YFP-positive live cortical neurons in OGD-treated brain slices when compared with control BALB/c mice ( 40%; Figure 3F , G ) ., There was no difference in YFP transfection efficiency and viability in non OGD-treated brain slices between these mouse strains ( Figure S2 ) ., In support of differential resistance to OGD in brain tissues between these strains , western blot analysis showed that the level of cleaved Caspase-3 was significantly reduced in lysates of brain slices from C . B6-Civq1-3 mice compared to control BALB/c mice after OGD treatment ( Figure 3S ) ., Taken together , these results show that the B6 allele ( s ) of at least one of the causal gene ( s ) underlying Civq1 provides non-vascular , tissue-intrinsic resistance to ischemic brain injury ., More importantly , although the sum of genetic evidence to date suggested that Civq1 , regulating infarct volume , and Canq1 , controlling collateral artery density , are mapped to the identical genomic region , the data from the congenic strain ( B6 . C-Civq1 ( Het ) ) and these ex vivo , OGD experiments using brain slice explants that lack functioning vasculature suggest that the Civq1 locus may be more complex than Canq1 , containing at least one gene variant that modulates ischemic brain injury independent of collateral artery density ., The congenic line ( C . B6-Civq1-3 ) reduces the critical QTL interval to a 9 . 6 Mb interval between D7Mit238 at 126 . 2 Mb and rs45999701 at 135 . 8 Mb , but this region still harbors over 200 genes ., Although genome-wide association analysis can be employed to significantly reduce a QTL interval for candidate gene identification 24 , the phenotype-associated EMMA interval in this region of chromosome 7 encompasses approximately 2 . 5 Mb , consisting of a genomic region of unusually high gene density , harboring more than 100 potential candidate genes ., To further dissect the interval , we compared ancestral SNP haplotype patterns across the inbred mouse lineages , specifically focusing on those strains for which we had generated independent genetic mapping information 25–27 ., Interval-specific SNP haplotype block analysis can reduce confidence intervals by identifying high-priority regions within a QTL interval that are likely to harbor the causal polymorphism 27 , 28 ., Because Civq1 was identified in three different genetic crosses ( B6×BALB/c , B6×SWR , and FVB×BALB/c ) and mapped more broadly to chromosome 7 using the CSS ( B6×A/J ) series , allelic variation at Civq1 is most likely harbored by a gene that maps within an ancestral haplotype block that is shared between BALB/c , A/J , and SWR ( large infarct volumes ) , but that is different from B6 and FVB strains ( small infarct volumes ) ., As illustrated in Figure 4 , defining a haplotype block to be three or more adjacent consecutive shared SNP alleles 25 , we identified all SNP haplotype blocks throughout the 3 . 3 Mb critical region of Civq1 ( 132 . 5–135 . 8 Mb ) , a region consistent with each of the 95% confidence intervals of the 4 independent linkage peaks for Civq1 ., Only 4 genes ( 4933440M02Rik , Fam57b ( 1500016O10Rik ) , Qprt , and Itgal ) fall within haplotype blocks matching the phenotype pattern of the mapping strains ( Figure 4 ) ., To identify the causal gene for Civq1 , we first sought the presence of non-synonymous coding SNPs in these genes ., Re-sequencing of the 4 candidate genes identified non-synonymous amino acid substitutions in Qprt and Itgal ., Qprt encoding quinolinate phosphoribosyltransferase harbors two coding SNPs ( E205K and D253N ) that segregate with the infarct volume phenotype ( Figure S4 ) ., These changes occur at residues that are not well conserved between mammalian species and that are predicted to be functionally benign by in silico amino acid substitution analysis in the three different databases , PolyPhen ( http://genetics . bwh . harvard . edu/pph/ ) , PMut ( http://mmb2 . pcb . ub . es:8080/PMut/ ) , and Panther ( http://www . pantherdb . org/ ) 29 ., Itgal ( CD11a ) encodes an α subunit of β2-integrin Lymphocyte Function associated Antigen-1 ( LFA-1 ) that mediates adhesion and migration of leukocytes ., The gene harbors two coding SNPs that create W972R and P978L polymorphisms located in the calf-2 extracellular domain of the protein ., Strains B6 and FVB that exhibit small infarcts , encode the W972 and P978 isoform , whereas BALB/c , A/J , and SWR strains that exhibit large infarcts , encode the R972 and L978 isoform ., Interestingly , despite a lack of conservation at these residues across other species , the W972R change is predicted to be deleterious to the protein in the three in silico databases ., No coding changes were identified in the two uncharacterized genes , 4933440M02Rik and Fam57b ., Next , to identify genes that show different levels of mRNA between the mapping strains , a surrogate measure of the effects of “regulatory” sequence variants ( broadly defined ) , we performed quantitative real time PCR ( qRT-PCR ) for the 4 candidate genes ., In adult brain cortex , only a single gene , Itgal , shows a strain-specific expression difference; an 8-fold higher transcript level in B6 cortex than seen in cortex from the large infarct strains ( BALB/c , CSS7 , and SWR ) ( Figure 5A ) ., Since the causative variant for the infarct phenotype would need to reside within the mapped Civq1 interval , we performed allele-specific gene expression analysis to determine whether the observed expression difference is due to cis-acting variation 30 ., Similar to the qRT-PCR results , allele-specific gene expression analysis confirmed that the level of B6 Itgal transcript is approximately 6-fold higher than BALB/c transcript in the adult cortex in F1 ( B6×BALB/c ) animals ( Figure 5B ) , providing further evidence that regulatory genetic variation in cis causes the difference in Itgal mRNA abundance in the brain ., These differences at the mRNA level were also seen at the protein level , as detected by flow cytometry of CD45-positive cells isolated from adult brain ., We found that the level of ITGAL protein is significantly higher in B6 than in BALB/c mice ( Figure 5C ) ., Because the Civq1 locus contains at least one gene that modulates infarct volume via effects on collateral artery formation 21 , we also examined the mRNA level for each of the 4 candidate genes in postnatal day 1 ( P1 ) cortex ( Table S1 ) , consistent with the time of development of these vessels 31 ., Itgal did not display allele-specific differential gene expression ( Figure S5 ) , consistent with a collateral-independent effect on infarct volume ., We also noted that neither Fam57b nor Qprt showed allele-specific expression , and we were unable to detect 4933440M02Rik in either P1 or adult cortices ., These other genes are therefore also unlikely to play a role in infarction via effects on collateral vessel anatomy ., While performing the complete re-sequencing of all of the coding exons ( including at least 50 bp of flanking intron ) for the 4 candidate genes , we found that the large infarct strains , BALB/c , SWR , and A/J , harbor a complex rearrangement in the distal region of the Itgal gene; a ∼150 bp deletion in intron 29 and multiple insertions and deletions ( indels ) in the coding and 3′-UTR of exon 30b of an alternative splice form of Itgal ( Itgal-003 , ENSMUST00000120857 ) ( Figure 6A , B ) ., Further sequencing of cDNA of the Itgal-003 transcript identified a 5-bp insertion in the coding region of exon 30b in BALB/c , SWR , and A/J strains causing a frameshift in the encoded protein , resulting in novel amino acid sequence and a shorter cytoplasmic tail of the protein , as compared to strains B6 and FVB ( Figure 6C ) ., We also found that the mRNA level of Itgal-003 markedly differed between the 5 mapping strains in the P1 cortex ., The mRNA level of this splice variant was substantially higher ( >11-fold ) in the large infarct strains ( BALB/c , SWR , and CSS7 ) than that of the small infarct strains , B6 and FVB mice ( Figure 6D ) ., Using the more accurate allele-specific expression analysis , the level of BALB/c Itgal-003 transcript is approximately 20-fold higher than B6 transcript in P1 cerebral cortex in F1 ( B6×BALB/c ) animals ( Figure 6E ) , indicating that sequence variation in cis leads to increased levels of the Itgal-003 transcript in the BALB/c strain ., Since the interplay between neurons , endothelial cells , and glial cells plays a crucial role in the early development of the neurovascular unit , and thus , the pathogenesis of cerebral ischemia 4 , we investigated the mRNA profile of Itgal-003 in both CD146 ( LSEC ) -positive endothelial cells and CD11b-positive brain macrophages isolated from F1 embryos ( E18 . 5 ) ., As illustrated in Figure 6E , BALB/c-specific Itgal-003 transcript is expressed 43 times and 25 times higher in endothelial cells and macrophages , respectively , than the B6-specific transcript in the allele-specific expression analysis ., Interestingly , the Itgal-003 transcript was not detected in the adult cerebral cortex by RT-PCR , suggesting that this splice variant is acting primarily or exclusively during brain development ., To date , 5 Itgal splice isoforms have been identified in mice ( Figure S6 ) , so we determined the relative allele-specific expression of all the other Itgal splice isoforms ., No difference was found in P1 and adult cerebral cortices for the other isoforms ( data not shown ) ., To further determine whether the strain specific level of Itgal-003 transcript is also reflected at the level of the protein , we generated an Itgal-003-specific antibody against the unique cytoplasmic tail peptide to assay protein expression ( Figure 6C ) ., Consistent with the mRNA level , western blot analysis of P1 cerebral cortex demonstrated markedly increased protein level in strain BALB/c compared to strains B6 and FVB ( Figure 6F ) ., To determine whether Itgal is involved in ischemic brain injury in vivo , we next examined the phenotype of Itgal knockout mice 32 ., A recent study reported that there was no difference in collateral vessel number or in infarct volume between Itgal knockout and control B6 mice 33 ., However , despite also finding no difference in number of collateral arteries ( Figure 7A ) , we observed that infarct volume in Itgal knockout mice ( n\u200a=\u200a30 ) of B6 background ( 17 generations backcrossed into B6 ) was ∼3-fold larger than that observed in B6 mice ( 15 . 5 mm3 vs . 4 . 7 mm3 ) ( Figure 7B , C ) ., We believe that this difference in infarct volume results from the use of different surgical techniques ., When performing permanent distal MCAO , care has to be taken to position the occlusion proximal to the lenticulo-striate branches 34 , 35 ., If the artery is occluded more distally , smaller and more variable infarcts are produced ., In support of this explanation for the difference between the studies , they report a difference in infarct volume of ∼3-fold between B6 and BALB/c strains 33 , whereas we routinely observe that infarct volume in BALB/c mice is ∼7-fold larger than that of B6 mice ( 4 . 7 mm3 vs . 34 . 2 mm3 ) ( Figure 1B ) ., Since the Civq1 locus retaining infarct volume phenotype displays both vascular and non-vascular neuroprotective effects on ischemic tissue injury , we hypothesized that the increased infarct volume in Itgal knockout mice might be related to neuroprotection after focal cerebral ischemia ., To examine this further , we determine the level of OGD-induced neuronal cell death in brain slices from Itgal knockout mice , again counting YFP-transfected cortical pyramidal neurons in brain slices ., As a control , there was no difference in YFP-transfection efficiency and viability in non OGD-treated brain slices between B6 and Itgal knockout mice ( Figure S2 ) ., Consistent with the infarct volume data , Itgal knockout mice showed an approximately 50% increase in neuronal cell death after transient OGD , compared to control B6 mice ( Figure 7D , E ) ., After OGD treatment , the level of cleaved Caspase-3 was also significantly increased in lysates of brain slices from Itgal knockout mice compared to B6 mice ( Figure S3 ) ., To further investigate whether reduced levels of Itgal modulate ischemic brain injury , and particularly whether these effects were collateral vessel-independent , we employed an ex vivo model of cerebral ischemia , using siRNA to knockdown Itgal expression in cortical brain slices ( where collateral circulation is no longer relevant ) ., Consistent with the increased neuronal cell death observed in Itgal knockout mice , after transient oxygen deprivation , Itgal siRNA-treated brain slices from B6 mice show markedly increased levels of cleaved Caspase-3 , a marker of neuronal cell death ( Figure 8 ) ., These results indicate that Itgal plays a protective role in ischemic brain damage , independent of tissue reperfusion through the collateral vessels ., It should be noted that the Itgal knockout mouse line used in our study was generated by replacing the exons 1 and 2 with a Neo-cassette 32 but more recent data shows that there are 5 known alternative splicing transcripts of the gene , including transcripts that do not include these two exons ( Figure S6 ) ., Thus , we examined whether this Itgal knockout mouse line generates null alleles for all of the splice variants of the gene ., We found that a splicing variant that uses a different initiation site ( Itgal-004 , ENSMUST00000118405 ) was detected in P1 and adult cortices by RT-PCR ( Figure S6 ) , suggesting that the phenotypes we observed in the knockout mice represent only partial knockout of the entire complement of Itgal gene transcripts/functions ., Thus , this well-established Itgal knockout line may retain residual or additional isoform protein functions , and in the sense of total Itgal gene function ( s ) , thus represents a hypomorphic allele ., The Itgal knockout allele was generated using 129Sv ( Stevens ) genomic DNA and a 129S7 ( 129S7/SvEvBrd-Hprtb-m2 ) ES cell line ., Thus , it is formally possible that the some of the differences in infarct volume that are seen in the Itgal knockout were contributed by 129 alleles flanking the deleted Itgal locus ., Although the original strains used in the knockout construction are not widely available , we have determined infarct volume and collateral artery number for the related 129S1/SvImJ strain which is derived from the original 129/Sv strain 36 ., Both infarct volume and collateral artery number of 129S1/SvImJ mice are not significantly different from those of B6 mice ( Figure S7 ) ., Thus , we conclude that the effects seen with the Itgal KO mice are primarily due to the loss of the Itgal gene , and not linked ( 129Sv or 129S7 ) polymorphisms , although we cannot rule out modest bystander effects ., Although several approaches have been proposed to reduce ischemic brain damage , including reperfusion , neuroprotection , and neuronal regeneration , for the vast majority of stroke patients , current therapies are limited ., We reasoned that given the multiplicity of mechanisms causing cell death in stroke , approaches that augment endogenous protective pathways might be more likely to lead to success ., Thus , we have attempted to identify novel genetic factors modulating stroke outcomes by exploiting naturally occurring endogenous genetic variation determining ischemic brain injury ., In crosses between inbred mouse strains that exhibit large differences in infarct volumes , we previously identified a QTL ( Civq1 ) mapping to distal chromosome 7 that determines more than 50% of the variation observed between the strains ., In this study , we present evidence that Itgal is one of the genes underlying the complex Civq1 locus ., Despite the almost routine detection of QTLs for important disease traits in both rodents and humans , identification of causal genes underlying QTLs remains a major obstacle , in large part due to the large confidence intervals for the typical QTL , often covering hundreds of candidate genes 26 ., To narrow the Civq1 interval we employed three different methods that capitalize on the structure of the mouse genome: ( 1 ) generation of interval-specific congenic lines , ( 2 ) genome wide association ( EMMA ) analysis across more than 30 inbred mouse strains , and ( 3 ) interval-specific SNP haplotype analysis using the 5 inbred strains from our experimental crosses ., The latter approach was most effective at reducing the list of candidate genes in the Civq1 interval to only 4 genes that clearly fall within shared SNP haplotype blocks ., Of these 4 genes , Itgal was the only gene that harbored non-synonymous coding SNPs and exhibited altered mRNA abundance , with both molecular phenotypes co-segregating with the infarct volume phenotypes in the 5 strains used in QTL mapping ., We also found that allelic variation in an alternative splicing variant of Itgal ( Itgal-003 ) resulted not only in differential transcript abundance , but also in a truncated cytoplasmic tail of the protein , consisting mostly of a novel amino acid sequence ., Itgal encodes the α subunit of LFA-1 ( αLβ2 ) integrin , which is highly expressed in microglia , spleen , bone marrow , and most immune cell populations ., The binding of intracellular proteins to the cytoplasmic tail of Itgal is essential to the activation of LFA-1 integrin ., The canonical splice isoform of Itgal ( Itgal-002 , ENSMUST00000117762 ) contains this important functional domain , conserved among all integrin family members ., Mutation of the cytoplasmic tail of Itgal ( Ital-002 ) has been shown to inhibit its interactions with intracellular proteins , destabilize integrin conformation , and disconnect to cytoskeleton 37 , 38 ., By contrast , the function of the unique , truncated sequence of the cytoplasmic tail found in Itgal-003 remains unknown ., The increased expression of the Itgal-003 in large infarct strains may interfere with the functions of the well-studied , reference isoform of Itgal , or with other α subunits ( αD , αM , and αX ) that can bind to the β2 subunit , resulting in inhibition of cell adhesion and migration during the development and/or tissue injury ., In addition to these differences in the cytoplasmic tail of Itgal-003 , the inbred mouse strains show two amino acid substitutions ( W972R and P978L ) in the calf-2 domain that segregate with the infarct volume phenotypic difference ., These two coding changes in ITGAL fall in relatively poorly conserved residues of the protein and the 972R BALB/c allele is shared with other species including cow , sheep , and opossum ., However , the lack of conservation at these residues itself does not allow us conclude that these changes are inconsequential because non-synonymous coding SNPs causing risk for complex genetic traits tend not to fall within highly conserved residues/regions of proteins 39 ., In point of fact , W972R is predicted to be deleterious by multiple in silico amino acid substitution databases ., Although the exact role of the calf-2 domain is not fully understood , a point mutation in the calf-2 domain of αvβ3 integrin in Glazmann thrombasthenia patients disrupts the normal contacts between α and β subunits , resulting in impai

Introduction, Results, Discussion, Materials and Methods

During ischemic stroke , occlusion of the cerebrovasculature causes neuronal cell death ( infarction ) , but naturally occurring genetic factors modulating infarction have been difficult to identify in human populations ., In a surgically induced mouse model of ischemic stroke , we have previously mapped Civq1 to distal chromosome 7 as a quantitative trait locus determining infarct volume ., In this study , genome-wide association mapping using 32 inbred mouse strains and an additional linkage scan for infarct volume confirmed that the size of the infarct is determined by ancestral alleles of the causative gene ( s ) ., The genetically isolated Civq1 locus in reciprocal recombinant congenic mice refined the critical interval and demonstrated that infarct size is determined by both vascular ( collateral vessel anatomy ) and non-vascular ( neuroprotection ) effects ., Through the use of interval-specific SNP haplotype analysis , we further refined the Civq1 locus and identified integrin alpha L ( Itgal ) as one of the causative genes for Civq1 ., Itgal is the only gene that exhibits both strain-specific amino acid substitutions and expression differences ., Coding SNPs , a 5-bp insertion in exon 30b , and increased mRNA and protein expression of a splice variant of the gene ( Itgal-003 , ENSMUST00000120857 ) , all segregate with infarct volume ., Mice lacking Itgal show increased neuronal cell death in both ex vivo brain slice and in vivo focal cerebral ischemia ., Our data demonstrate that sequence variation in Itgal modulates ischemic brain injury , and that infarct volume is determined by both vascular and non-vascular mechanisms .

Stroke is the second leading cause of death and the most common cause of acquired adult disability worldwide ., Ischemic stroke is caused by an interruption of blood flow in the cerebral arteries and results in neuronal damage ( infarct ) to the area of perfusion in the brain ., Although significant progress has been made in the identification of genetic risk factors for stroke susceptibility , identification of genetic factors determining the severity of tissue damage has proven more challenging ., By contrast , infarct volume varies widely among laboratory inbred mouse strains ., Using a well-established mouse model of stroke and complex genetic analysis , we have exploited these differences and identified Itgal as one of the candidate genes ., We further show that allelic variation in Itgal segregates with infarct volume among inbred mouse strains and deficiency of the gene increases ischemic neuronal cell death in stroke .

journal.pgen.1000672

Genetic Determinants of Circulating Sphingolipid Concentrations in European Populations

Sphingolipids are essential components of plasma membranes and endosomes and are believed to play critical roles in cell surface protection , protein and lipid transport and sorting , and cellular signalling cascades ., They are known to have roles in both health and disease 1 , 2 ., Several rare monogenic diseases associated with sphingolipid biosynthesis and turnover have been identified such as metachromatic leukodystrophy and GM1- and GM2-gangliosidosis , Niemann-Pick , Gaucher , Krabbe , Fabry , Farber , Tay-Sachs and Sandhoff diseases 3 ., Defective biosynthesis due to mutations in genes involved in sphingolipid metabolism ( e . g . serine palmitoyl transferase ( SPTLC1 ) 4; ceroid-lipofuscinosis , neuronal 8 ( CLN8 ) 5; and ceramide synthase ( LASS1 ) 6 ) can also lead to disease ., Moreover , natural fungal inhibitors of ceramide synthase can result in a broad spectrum of effects including equine leucoencephalomalacia , porcine pulmonary oedema syndrome and liver cancer in rats 7 , demonstrating the wide range of processes that include cell proliferation , differentiation and apoptosis underpinned by sphingolipid metabolism ., Identifying common genetic variants that influence the balance between individual sphingolipid concentrations represents an important step towards understanding the contribution of sphingolipids to common human disease ., To achieve this goal , we conducted a genome-wide association study ( GWAS ) on plasma levels of 33 major sphingolipid species ( 24 sphingomyelins and 9 ceramides ) in five European populations , both within and across populations ., The traits were analysed by individual species ( sphingomyelins ( SM ) , dihydrosphingomyelins ( Dih SM ) , ceramides ( Cer ) and glucosylceramides ( GluCer ) ) or aggregated into groups of species with similar characteristics ( e . g . unsaturated ceramides ) , and expressed as absolute concentrations or as molar percentages within sphingolipid classes ( mol% ) ., In addition we examined 43 matched metabolite ratios between the traits as a surrogate for enzyme activity 8 in separate clusters designed to examine sphingolipid metabolism ( 11 ratios ) , desaturation ( 16 ratios ) and elongation ( 16 ratios ) ., All traits displayed substantial heritabilities in that much of the observed variation in sphingolipid levels could be attributed to genetic variation among individuals in each population ., The GWAS for single species and matched metabolite ratios revealed a total of 32 SNPs in five distinct loci reaching genome-wide significance ( p values ranging down to 9 . 08×10−66 ) ( Table 1 , Figure 1 and Figure 2 , and Table S1 and Table S3 ) ., The direction and magnitude of the observed effect sizes for the 22 variants identified in the analysis of single species are summarized in Table 1 with full details in Table S1 ., For three of the regions ( chromosomal regions 4p12 , 14q23 . 2 and 19p13 . 2 ) , p values reached genome-wide significance in the largest cohort ( South Tyrol ) , and the effect was replicated in the other populations ., For two additional loci ( 11q12 . 3 and 20p12 . 1 ) , signals bordered on genome-wide significance in South Tyrol alone , were consistent between all 5 populations and reached genome-wide significance in the meta-analysis ., In the single species analysis , the strongest associations for three of the loci ( 11q12 . 3 , 14q23 . 2 and 19p13 . 2 ) were found with sphingomyelins and dihydrosphingomyelins ., The 4p12 locus showed the strongest association with serum glucosylceramides and the 20p12 . 1 locus showed the strongest association with serum ceramide concentrations ., Table S2 shows the p-values for the individual SNPs when included in a multiple regression model , and the fraction of single sphingolipid variance explained by sex , age and all SNPs in the model together ., Taken together , the SNPs explain up to 10 . 1% of the population variation in each trait ., Ratios of matched ( substrate/product ) pairs have been shown to reduce variation in the dataset and increase power of association several orders of magnitude 8 ., Analysis of 43 matched metabolite ratios ( Table S3 ) indeed increased power of association up to 10 orders of magnitude on some of the 22 variants above , and revealed an additional 10 SNPs over the same 7 genes reaching statistical significance ( see Table S3 ) ., Surprisingly no signals from new genes reached genome-wide significance , highlighting the fact that across the 5 cohorts analysed here , the 7 genes identified are the major genes associated with circulating sphingolipid concentrations ., Among the 32 significant individual SNPs ( Table S4 ) variants in LASS4 explain up to 7 . 5% of the variance in some ratios ( i . e . in SM16:0/SM18:0 ) , SGPP1 variants explain up to 12 . 7% of the variance ( i . e in SM14:0/SM16:0 ) , FADS1–3 variants explain up to 3 . 5% of the variance ( e . g . in SM16:0/SM16:1 ) , SPTLC3 variants explain up to 4 . 9% of the variance ( e . g . in SM14:0/SM16:0 and SM24:0/Cer24:0 ) , and ATP10D variants up to 4 . 2% of GluCer/Cer variance ., Combined effects of several genes ( i . e . SPTLC3 and SGPP1 ) explains up to 14 . 2% of the variance in medium chain SM ratios ( SM14:0/SM16:0 ) and , in combination with LASS4 , up to 11 . 2% of the variance in long-chain sphingomyelin ratios ( SM22:0/SM24:0 ) ., All SNPs within the associated chromosomal regions are located within or are in linkage disequilibrium ( LD ) with genes that encode enzymes involved in sphingolipid biosynthesis or intracellular transport ( Figure 2 ) ., The ATPase , class IV , type 10D ( ATP10D ) gene , located at chromosome 4p12 , encodes a putative serine-phospholipid ( phosphatidylserine , ceramide ) translocase 9 ., Three SNPs at this locus showed genome-wide significant associations with glucosylceramides ( C16:0 , C24:1 ) ( Table 1 , Table S1 ) , with an additional five variants revealed in the ratio analysis ( Table S3 ) ., SNP rs10938494 gave the strongest association in the single species analysis ( p-values of 1 . 68×10−9 in South Tyrol and 8 . 03×10−19 in the joint analysis ) , and was among the strongest association in the ratio analysis ( p\u200a=\u200a3 . 04×10−16 ) along with rs2351791 ( p\u200a=\u200a6 . 58×10−17 ) ., Three fatty-acid desaturase genes ( FADS1 , 2 and, 3 ) are located adjacent to one another in a cluster at the 11q12 . 3 locus ., The FADS1–3 genes encode enzymes that regulate the desaturation of fatty acids by the introduction of double bonds between defined carbons of the fatty acyl chain ., Seven SNPs at this locus , distributed in and around the three genes , reached statistical significance in the single species analysis for sphingomyelin 16∶1 levels in the joint analysis , with p-values ranging from 2 . 99×10−11 ( rs174449 , close to FADS3 ) to 6 . 60×10−13 ( rs1000778 , in FADS3 ) ( Table 1 ) ., The ratio analysis revealed an additional SNP at this locus within the FADS3 gene ( rs174450 , Table S3 ) , and improved association results for other SNPs several orders of magnitude ( e . g . rs1000778 p\u200a=\u200a1 . 29×10−15 ) ., Fatty acids are built into ceramides by the ceramide synthases ( e . g . LASS4 ) ., Unsaturated ceramides can be synthesized exclusively by the introduction of unsaturated fatty acids into the sphingosine/sphinganine chain ., The pivotal role of FADS1–3 in the synthesis of unsaturated ceramides is confirmed by the strong associations of SNPs in this cluster to the mono-unsaturated sphingomyelins 16∶1 , 18∶1 and 20∶1 , which are the end-products of the ceramide biosynthesis pathway ( Table 1 , Table S1 ) , and the ratios between these and their respective unsaturated precursors ( Table S3 ) ., Previous studies of sphingolipid metabolites and poly-unsaturated fatty acids ( PUFA ) have demonstrated associations to SNPs , including rs174537 , over the FADS1 and FADS2 genes in several populations 8 , 10 , 11 ., The sphingosine-1-phosphate phosphohydrolase 1 gene ( SGPP1 ) at the 14q23 . 2 locus belongs to the super-family of lipid phosphatases that catalyze the generation of sphingosine and , together with irreversible cleavage by sphingosine-1-phosphate ( S1P ) -lyase , strongly influences the pathway of S1P to ceramide ( Figure 3 ) ., Six SNPs in and around this gene demonstrate the most significant associations with circulating sphingomyelin C14–C16/C22–C24 and dihydrosphingomyelin concentrations ( Table, 1 ) in the single species analysis , with a further two SNPs revealed in the ratio analysis ., SNP rs7157785 showed the strongest association with sphingomyelin 14∶0 relative content ( molar percentage: mol% ) with genome-wide significant p-values in all five populations , particularly in the South Tyrol population ( p\u200a=\u200a2 . 53×10−28 ) and joint analysis ( p\u200a=\u200a9 . 08×10−66 ) , and demonstrated the most significant association in the ratio analysis ., Enhanced SGPP1 activity leads to elevated ceramide levels by shifting the stochiometric balance of SGPP1/S1P-lyase towards sphingosine and ceramide production ., Five SNPs at the 19p13 . 2 locus showed some of the strongest associations with sphingolipids and all lie within LASS4 , the gene encoding LAG1 longevity assurance homologue 4 ., In the single species analysis SNP rs7258249 showed the highest genome-wide significant association with sphingomyelin 18∶0 mol% ( South Tyrol p\u200a=\u200a1 . 04×10−15 and joint analysis p\u200a=\u200a2 . 28×10−27 ) ., Several LASS4 SNPs showed statistically significant association with the sphingomyelin species C18 to C20 and with ceramide C20∶0 ( Table 1 and Table S1 ) ., In the ratio analysis , however , associations strengthened by several orders of magnitude ( p value ) over those with these SNPs , with rs1466448 demonstrating the most statistically significant association ( p\u200a=\u200a4 . 05×10−35 ) ., LASS family members , six of which have been identified in mammals ( LASS1–6 ) , are de novo ceramide synthases ( CerS ) that synthesize dihydroceramide from sphinganine and fatty acid ( Figure 3 ) ., Moreover , LASS enzymes catalyze the re-synthesis of ceramide and phytoceramide from sphingosine and phytosphingosine respectively , which are cleavage products of alkaline ceramidase activity in endoplasmic reticulum ( ER ) membranes ., The 20p12 . 1 locus contains the serine palmitoyltransferase long chain base subunit 3 gene ( SPTLC3 ) encoding a functional subunit of the SPT enzyme-complex that catalyzes the first and rate-limiting step of de novo sphingolipid synthesis ., One SNP ( rs680379 ) demonstrated association for unsaturated ceramide in the South Tyrol population alone ( p\u200a=\u200a1 . 77×10−07 ) and was genome-wide significant in the joint analysis ( p\u200a=\u200a8 . 24×10−15 ) ., Significant association was observed also with C16 to C24 ceramides and the sphingomyelins 16∶1 and 17∶0 ( Table 1 and Table S1 ) ., The ratio analysis strengthened association at this variant ( p\u200a=\u200a3 . 3×10−20 for the metabolite ratio SM24:0/Cer24:0 ) and revealed two further significant variants at this locus ( rs3848751 and rs6078866 , Table S3 ) ., As matched metabolite ratios can serve as a proxy for enzyme activity 8 , in a complementary candidate gene approach , we investigated association signals in our combined single species and ratio datasets at 624 SNPs within or near 40 genes that encode enzymes involved in sphingolipid metabolism , in order to identify the most promising variants within these genes for further analysis ., Of these , a total of 70 variants in or near 23 of the genes demonstrate association p values of 10−4 or less ( Table S5 ) ., Sex and age adjusted single sphingolipids species displayed strong phenotypic correlations with circulating plasma lipoproteins especially with total cholesterol or LDL-cholesterol ( Table S6 , e . g . between the sum of saturated sphingomyelin species and total cholesterol: 0 . 788/0 . 717/0 . 794/0 . 733/0 . 773 in respectively NPHS/ERF/SOUTH TYROL/CROATIA/ORKNEY; or SM16:1 and total cholesterol 0 . 737/0 . 631/0 . 671/0 . 6/0 . 638 ) ., This is in agreement with recent lipid profiling of lipoprotein fractions , showing higher proportions of sphingomyelin and ceramides in the LDL fraction 12 ., However , among the GWAS hits uncovered in this analysis , only the FADS1–3 cluster overlaps with those reported in large meta-analysis of circulating serum lipoproteins levels ( strongest with total and LDL-cholesterol levels ) 13 ., Several of the variants reported here display suggestive associations with classical lipids in the EUROSPAN cohorts ( Table S7 ) ., All eight SNPs in the FADS1–3 cluster associate with HDL-cholesterol levels ( age-sex adjusted p values between 0 . 06 and 0 . 0041 ) similar to previous observations 8 ., Interestingly , the sex-specific age-adjusted results show that these associations seem driven by the association found in males ( lowest p\u200a=\u200a0 . 0037 at rs174546 ) ., Association with HDL-cholesterol in males is also seen with SNPs in ATP10D ( rs2351791 , p\u200a=\u200a0 . 01 ) and SPTLC3 ( rs3848751 , p\u200a=\u200a0 . 0047 ) ., SNPs at ATP10D also associate with LDL-cholesterol , albeit weakly in the total population ( rs469463 , p\u200a=\u200a0 . 034 ) ., In the males only , variants at LASS4 ( rs28133 , p\u200a=\u200a0 . 043 ) and SPTLC3 ( rs3848751 , p\u200a=\u200a0 . 022 and rs6078866 , p\u200a=\u200a0 . 02 ) also associate weakly with LDL-cholesterol levels ., Five variants in FADS1–3 and two in ATP10D associate with triglyceride levels , with lower p values in males than in the whole group ( p values from 0 . 017 to 0 . 009 in FADS1–3 and 0 . 0071 for rs17462424 in ATP10D ) ., Association of FADS variants with triglyceride levels has also been observed in other populations 8 ., As previously highlighted 8 , the p values for association with the sphingolipids species were orders of magnitude stronger than with these classical lipids ., Given the reported associations to classical lipids and cardiovascular disease with variants at the FADS1–3 locus 10 , 13 , 14 , and the evidence from functional studies of a role for sphingolipids in atherosclerotic plaque formation and lipotoxic cardiomyopathy 15 , we looked in silico in a series of three age- and sex-adjusted GWAS datasets of German myocardial infarction ( MI ) case-control studies ( Ger MIFS I 16 Ger MIFS II 17 and Ger MIFS III ( KORA ) , unpublished ) for evidence of association with the major variants associating with sphingolipid concentrations ., Variants within three of the genes ( ATP10D , FADS3 and SPTLC3 ) associate with MI in one or more of the studies ( Table 2 ) ., The protective odds ratios observed for variants in ATP10D and SPTLC3 are on alleles correlating positively with higher metabolite/lower ceramide ratios ( i . e . GluCer/Cer and SM/Cer ) , in support of evidence that increased enzyme/transporter activity that lowers ceramide levels might alleviate the pro-apoptotic effects seen with higher ceramide levels in cardiomyocytes 18 ., As previously hypothesised , carriers of FADS variants that are associated with higher desaturase activity may be prone to a proinflammatory response favoring atherosclerotic vascular damage 14 ., Direct experimental evidence indicates a role for sphingolipids in several common complex chronic disease processes including atherosclerotic plaque formation , myocardial infarction ( MI ) , cardiomyopathy , pancreatic beta cell failure , insulin resistance and type 2 diabetes mellitus ( T2D ) 15 ., Until now , the genetic variants that influence circulating sphingolipid concentrations in the general population have been characterized in relatively small cohorts 8 ., Here we identified genetic variation with a significant effect on the biosynthesis , metabolism or intracellular trafficking of some of the major sphingolipids species in a large diverse group of European population samples ., The SNPs showing association with circulating sphingolipids explain up to 10 . 1% of the population variation in each trait and 14 . 2% of some matched ratios ( Tables S2 and Table S4 ) ., Four of the five loci identified contain genes encoding proteins that are either responsible for de novo ceramide synthesis or for ceramide re-synthesis from sphingosine/sphinganine-phosphates or both ( SPTLC3 , LASS4 , FADS1–3 and SGPP1 ) ., Increases in all of these enzymatic activities are predicted to elevate the “ceramide-pool” ., The associations are observed not only with ceramides , but also with sphingomyelins , indicating that a considerable proportion of ceramide is converted into the large and more stable “sphingomyelin-pool” ., None of the genes involved in ceramide degradation or ceramide-related signaling is genome-wide significantly associated with the traits analyzed , indicating the primary role of genes related to ceramide production in the genetic control of ceramide levels ., Of these four loci , the FADS1–3 gene cluster has been the most frequently to be reported linked with disease in recent literature ., Variants within in this region have been associated with cardiovascular disease and classic lipid risk factors such as cholesterol levels 10 , 13 , 14 ., Reported variants demonstrating association in these reports ( rs174547 , rs174570 , rs174537 and rs174546 ) are within the FADS1 and FADS2 genes , but expression studies indicate complex regulation in this region , with the FADS1 SNP rs174547 showing correlation with expression of both FADS1 and FADS3 genes 19 , while the FADS1 SNP rs174546 correlates with FADS1 but not FADS2 expression 10 ., Our strongest associations with both sphingolipid levels and MI are in or nearest the FADS3 gene , with variants showing less marked association with cholesterol levels than that observed with variants over FADS1 and FADS2 genes ( Table S7 ) ., It is known that sphingomyelin and ceramides can modulate the atherogenic potential of LDL 20 ., Further functional studies will be necessary to determine whether the active mechanism is through FADS3 alone , or in concert with FADS1 , FADS2 or both ., Neurological phenotypes associated with FADS2 include attention-deficit/hyperactivity disorder 21 and the moderation of breastfeeding effects on IQ 22 ., Little is published regarding disease association with variants at the other four major loci described here ., However , a reported association between expression levels of SGPP1 with Schizophrenia 23 along with changes in SPTLC2 ( with variants identified in our candidate SNP search –Table S4 ) and ASAH1 , highlights the importance of testing variants in these genes with multiple neurological and psychiatric diseases ., Additional neurological associations with candidate genes listed in Table S4 include SGPL1 in Alzheimers disease 24 and GBA with Parkinsons disease and dementia with Lewy bodies 25 , 26 ., The wider possible involvement of genes within pathways of ceramide metabolism in Lewy body disease has also been recently reviewed 27 ., The fifth locus contains ATP10D , a cation transport ATPase ( P-type ) type IV subfamily member ., The type IV subfamily is thought to be an important regulator of intracellular serine-phospholipid trafficking however the exact function or transport specificity of ATP10D has not yet been described 9 ., A novel functional finding of this study is the specificity of the association of ATP10D SNPs to glucosylceramides ( among the species tested so far ) , which provides the first evidence for the functional involvement of ATP10D in intracellular transport of specific species of ceramide ( Figure 3 ) ., Impaired function of ATP10D may therefore lead to enhanced exposure of ceramide to glucosyltransferases , forming higher concentrations of glycosylceramides that are released into the plasma compartment or may elevate serum glucosylceramide concentrations by impaired transport of glycosylceramide to the trans Golgi network ., Mutations of ATP10D ( C57BL/6J ( B6 ) ) in mice result in low HDL concentrations and these mice develop severe obesity , hyperglycaemia and hyperinsulinaemia when fed on a high-fat diet 28 ., Based on the mouse model , increased circulating glucosylceramides in connection with ATP10D function would be one plausible mechanism of contributing to weight gain and early insulin resistance ., From the novel association of SNPs in ATP10D to MI ( Table 2 ) seen in German studies , further investigation of the specific role of glucosylceramides in MI and other cardiovascular diseases is warranted ., Thus , sphingolipids play a role in pathological processes leading to common complex diseases , and identification of genetic variants that influence the balance between individual sphingolipid species is an important first step into dissecting out the genetic components in such processes ., Associations between the SNPs identified in this study , some of which have a strong effect on the circulating plasma levels , and complex metabolic , cardiovascular , inflammatory and neurological diseases in which a role for a sphingolipid-dependent mechanism is implicated can now be investigated ., Modulation of sphingolipids in vivo has demonstrated that this may be a successful preventative strategy for diseases in which sphingolipids play a role , lending hope that , once such disease contributions are identified , successful therapeutic regimes may subsequently be identified ., All studies were approved by the appropriate Research Ethics Committees ., The Northern Swedish Population Health Study ( NSPHS ) was approved by the local ethics committee at the University of Uppsala ( Regionala Etikprövningsnämnden , Uppsala ) ., The ORCADES study was approved by the NHS Orkney Research Ethics Committee and the North of Scotland REC ., The Vis study was approved by the ethics committee of the medical faculty in Zagreb and the Multi-Centre Research Ethics Committee for Scotland ., The ERF study was approved by the Erasmus institutional medical-ethics committee in Rotterdam , The Netherlands ., The MICROS study was approved by the ethical committee of the Autonomous Province of Bolzano ., For the German MI studies ( GerMIFS-I , -II and –III ( KORA ) , local ethics committees approved the studies and written informed conset obtained as published previously ., The ERF study is a family-based study which includes over 3000 participants descending from 22 couples living in the Rucphen region in the 19th century ., All descendants were invited to visit the clinical research center in the region where they were examined in person and where blood was drawn ( fasting ) ., Height and weight were measured for each participant ., All participants filled out questionnaire on risk factors , including smoking ., The 800 participants included in the lipidomics studies consisted of the first series of participants ., The MICROS study is part of the genomic health care program ‘GenNova’ and was carried out in three villages of the Val Venosta on the populations of Stelvio , Vallelunga and Martello ., This study was an extensive survey carried out in South Tyrol ( Italy ) in the period 2001–2003 ., An extensive description of the study is available elsewhere 29 ., Briefly , study participants were volunteers from three isolated villages located in the Italian Alps , in a German-speaking region bordering with Austria and Switzerland ., Due to geographical , historical and political reasons , the entire region experienced a prolonged period of isolation from surrounding populations ., Information on the health status of participants was collected through a standardized questionnaire ., Laboratory data were obtained from standard blood analyses ., Genotyping was performed on just under 1400 participants with 1334 available for analysis after data cleaning ., All participants were included in the lipidomics studies ., The Swedish samples are part of the Northern Swedish Population Health Study ( NSPHS ) representing a family-based population study including a comprehensive health investigation and collection of data on family structure , lifestyle , diet , medical history and samples for laboratory analyses ., Samples were collected from the northern part of the Swedish mountain region ( County of Norrbotten , Parish of Karesuando ) ., Historic population accounts show that there has been little immigration or other dramatic population changes in this area during the last 200 years ., The Orkney Complex Disease Study ( ORCADES ) is an ongoing family-based , cross-sectional study in the isolated Scottish archipelago of Orkney ., Genetic diversity in this population is decreased compared to Mainland Scotland , consistent with the high levels of endogamy historically ., Data for participants aged 18 to 100 years , from a subgroup of ten islands , were used for this analysis ., Fasting blood samples were collected and over 200 health-related phenotypes and environmental exposures were measured in each individual ., All participants gave informed consent and the study was approved by Research Ethics Committees in Orkney and Aberdeen ., The Vis study includes a 986 unselected Croatians , aged 18–93 years , who were recruited into the study during 2003 and 2004 from the villages of Vis and Komiza on the Dalmatian island of Vis 30 , 31 ., The settlements on Vis island have unique population histories and have preserved their isolation from other villages and from the outside world for centuries ., Participants were phenotyped for 450 disease-related quantitative traits ., Biochemical and physiological measurements were performed , detailed genealogies reconstructed , questionnaire of lifestyle and environmental exposures collected , and blood samples and lymphocytes extracted and stored for further analyses ., Samples in all studies were taken in the fasting state ., Lipids were quantified by electrospray ionization tandem mass spectrometry ( ESI-MS/MS ) in positive ion mode as described previously 32 , 33 ., EDTA plasma ( serum for South Tyrol ) samples were quantified upon lipid extraction by direct flow injection analysis using the analytical setup described by Liebisch et al . 33 ., A precursor ion scan of m/z 184 specific for phosphocholine containing lipids was used for phosphatidylcholine ( PC ) and sphingomyelin ( SM ) 33 ., Ceramide and hexosylceramide were analyzed using a fragment ion of m/z 264 32 ., For each lipid class two non-naturally occurring internal standards were added and quantification was achieved by calibration lines generated by addition of naturally occurring lipid species to plasma ., Deisotoping and data analysis for all lipid classes was performed by self programmed Excel Macros according to the principles described previously 33 ., Nomenclature of sphingomyelin species is based on the assumption that d18∶1 ( dihydroxy 18∶1 sphingosine ) is the main base of plasma sphingomyelin species , where the first number refers to the number of carbon atoms in the chain and the second number to the number of double bonds in the chain ., DNA samples were genotyped according to the manufacturers instructions on Illumina Infinium HumanHap300v2 ( except for samples from Vis for which version 1 was used ) or HumanCNV370v1 SNP bead microarrays ., Four populations have 318 , 237 SNP markers in common that are distributed across the human genome , with Vis samples having 311 , 398 SNPs in common with the other populations ., Samples with a call rate below 97% were excluded from the analysis ., Sphingolipid measurements were available for analysis following quality control assessment for 4110 study participants ., Genome-wide association analysis was performed using the GenABEL package in R 34 ., A score test was used to test for association between the age- and sex-adjusted residuals of sphingolipid traits ( both as absolute concentrations and as relative content of the total sphingolipid pool: mol% ) and SNP genotypes using an additive model ., The Genomic Control procedure 35 was used to account for under-estimation of the standard errors of effects , which occurs because of pedigree structure present in the data 36 ., For the most interesting results and the species ratios , we re-analysed the data using “mmscore” function , a score test for family-based association 37 , as implemented in GenABEL ., The relationship matrix used in analysis was estimated using genomic data with “ibs” ( option weight\u200a=\u200a“freq” ) function of GenABEL ., This analysis , accounting for pedigree structure in an exact manner , allowed for unbiased estimation of the effects of the genetic variants ( adjusted for age and sex ) ., The results from all cohorts were combined into a fixed-effects meta-analysis with reciprocal weighting on standard errors of the effect-size estimates , using MetABEL ( http://mga . bionet . nsc . ru/~yurii/ABEL/ ) ., Thresholds for genome wide significance were set at a p value of less than 1 . 57×10−7 ( 0 . 05/318 , 237 SNPs ) for the individual populations ., For the overall meta-analysis we chose to use the conservative threshold of 7 . 2×10−8 38 ., Since many of the traits tested and especially the ratios demonstrate high degrees of correlation , introducing a suitable statistical correction the multiple testing of the 76 correlated traits would be complex ., Since Bonferroni correction ( unsuitable in this instance ) would lower thresholds to values between p\u200a=\u200a10−9 to 10−10 , and since all five genomic regions have variants with p values <10−10 , we report the age-sex corrected p values alone ., The threshold for replication of significant results from one population in other cohorts was set at a p-value less than 0 . 05 divided by the number of SNPs tested ., All significant variants reported are in Hardy-Weinberg Equilibrium , and effect directions are consistent across all five populations .

Introduction, Results, Discussion, Materials and Methods

Sphingolipids have essential roles as structural components of cell membranes and in cell signalling , and disruption of their metabolism causes several diseases , with diverse neurological , psychiatric , and metabolic consequences ., Increasingly , variants within a few of the genes that encode enzymes involved in sphingolipid metabolism are being associated with complex disease phenotypes ., Direct experimental evidence supports a role of specific sphingolipid species in several common complex chronic disease processes including atherosclerotic plaque formation , myocardial infarction ( MI ) , cardiomyopathy , pancreatic β-cell failure , insulin resistance , and type 2 diabetes mellitus ., Therefore , sphingolipids represent novel and important intermediate phenotypes for genetic analysis , yet little is known about the major genetic variants that influence their circulating levels in the general population ., We performed a genome-wide association study ( GWAS ) between 318 , 237 single-nucleotide polymorphisms ( SNPs ) and levels of circulating sphingomyelin ( SM ) , dihydrosphingomyelin ( Dih-SM ) , ceramide ( Cer ) , and glucosylceramide ( GluCer ) single lipid species ( 33 traits ) ; and 43 matched metabolite ratios measured in 4 , 400 subjects from five diverse European populations ., Associated variants ( 32 ) in five genomic regions were identified with genome-wide significant corrected p-values ranging down to 9 . 08×10−66 ., The strongest associations were observed in or near 7 genes functionally involved in ceramide biosynthesis and trafficking: SPTLC3 , LASS4 , SGPP1 , ATP10D , and FADS1–3 ., Variants in 3 loci ( ATP10D , FADS3 , and SPTLC3 ) associate with MI in a series of three German MI studies ., An additional 70 variants across 23 candidate genes involved in sphingolipid-metabolizing pathways also demonstrate association ( p\u200a=\u200a10−4 or less ) ., Circulating concentrations of several key components in sphingolipid metabolism are thus under strong genetic control , and variants in these loci can be tested for a role in the development of common cardiovascular , metabolic , neurological , and psychiatric diseases .

Although several rare monogenic diseases are caused by defects in enzymes involved in sphingolipid biosynthesis and metabolism , little is known about the major variants that control the circulating levels of these important bioactive molecules ., As well as being essential components of plasma membranes and endosomes , sphingolipids play critical roles in cell surface protection , protein and lipid transport and sorting , and cellular signalling cascades ., Experimental evidence supports a role for sphingolipids in several common complex chronic metabolic , cardiovascular , or neurological disease processes ., Therefore , sphingolipids represent novel and important intermediate phenotypes for genetic analysis , and discovering the genetic variants that influence their circulating concentrations is an important step towards understanding how the genetic control of sphingolipids might contribute to common human disease ., We have identified 32 variants in 7 genes that have a strong effect on the circulating plasma levels of 33 distinct sphingolipids , and 43 matched metabolite ratios ., In a series of 3 German MI studies , we see association with MI for variants in 3 of the genes tested ., Further cardiovascular , metabolic , neurological , and psychiatric disease associations can be tested with the variants described here , which may identify additional disease risk and potentially useful therapeutic targets .

neuroscience/motor systems, genetics and genomics/complex traits, genetics and genomics/genetics of disease, neuroscience/neurobiology of disease and regeneration, cardiovascular disorders/myocardial infarction, genetics and genomics/medical genetics, genetics and genomics/population genetics

journal.pgen.1003068

A Spatial and Temporal Gradient of Fgf Differentially Regulates Distinct Stages of Neural Development in the Zebrafish Inner Ear

Neurons of the VIIIth cranial ganglion , or the statoacoustic ganglion ( SAG ) , innervate sensory hair cells in the inner ear ., These bipolar neurons relay auditory and vestibular information to the hindbrain ., During development , SAG precursors ( neuroblasts ) originate in the floor of the otic vesicle during a relatively brief window of time ., Newly specified neuroblasts soon delaminate from the floor of the otic vesicle before continuing development outside the ear ., Neuroblast specification requires the bHLH transcription factor neurogenin1 ( neurog1 ) 1 , 2 ., Expression of neurog1 is transient and is followed by strong upregulation of neurod , which encodes a related bHLH transcription factor required for completing neuronal differentiation 1 , 3 ., After delamination , neuroblasts migrate a short distance to become situated between the hindbrain and otic vesicle and undergo a transient phase of proliferation to expand the precursor population 4–7 ., This phase , termed transit-amplification , is characterized by co-expression of neurod and proliferation markers 8 ., Neuroblasts eventually exit the cell cycle and differentiate into mature neurons ., Numerous studies suggest a role for Fgf in otic neurogenesis ., In chick , Fgf10 is expressed in the neurosensory domain of the otic placode and promotes neuroblast specification 4 ., Elevating Fgf2 or Fgf8 increases the number of SAG neurons 9 , 10 , though the mechanism of action in these cases has not been determined ., In mouse , Fgf3 is also expressed in the neurosensory domain , and SAG development is impaired in Fgf3 null mutants 11 ., In zebrafish , fgf3 and fgf8 are prominently expressed in the developing utricular macula adjacent to the neurogenic domain 12 , 13 , and impairment of fgf8 causes a reduction in SAG markers 12 , 14 ., Also in zebrafish , mutations that expand the domain of fgf3 expression in the hindbrain cause a corresponding expansion of anterior markers in the otic vesicle , including markers of the utricular macula and neurogenic domain 15 , 16 ., Unfortunately , interpretation of these mutant phenotypes in mouse and zebrafish is clouded because morphogenesis of the inner ear is significantly altered ., Additionally , previous studies have not been able to clearly distinguish effects of changing Fgf levels on different stages of SAG development ., Here we study the development of SAG and its regulation by Fgf by conditionally manipulating Fgf signaling levels ., We show that Fgf signaling differentially controls distinct stages of otic neurogenesis ., A moderate level of Fgf is necessary for the initial specification of neuroblasts in the floor of the otic vesicle , whereas high levels of Fgf inhibit specification ., During later stages of SAG development , Fgf5 expressed by mature SAG neurons serves two roles ., First , upon accumulation of sufficient mature neurons the phase of specification is terminated ., Second , ongoing Fgf signaling delays the differentiation of SAG precursor cells ., This ensures maintenance of progenitors and steady production of an appropriate number of mature neurons ., The paradigm for otic neurogenesis , as formalized in several recent reviews 17 , 18 , involves a sequential process of specification , delamination , proliferative expansion and differentiation of precursor cells to form the mature SAG ., The general features of this process appear to be conserved in zebrafish , shown schematically in Figure 1A ., In zebrafish , SAG neuroblasts are initially specified in the floor of the late placode/nascent vesicle as early as 16 hpf ( 14 somites ) and express neurog1 1 , 19 ., Neuroblasts begin to delaminate and accumulate outside the otic vesicle by 17 hpf ., The reiterative process of specification and delamination peaks at 24 hpf , continues at a more moderate pace through 30 hpf ( Figure 1D ) , then declines sharply and stops entirely by 42 hpf 20 ., Expression of neurog1 is only transient ., As neuroblasts delaminate they lose expression of neurog1 and initiate expression of neurod 1 , 21 ., At this point SAG precursors enter a phase of transit-amplification , as shown by co-labeling with neurod expression and BrdU incorporation ( Figure 1E , 1F ) ., neurod+ cells continue to proliferate through at least 4 days post fertilization ( dpf ) , the latest stage examined ( Figure 1F ) ., Surprisingly , staining with anti-phospho histone H3 shows that there are typically only 1–2 mitotic cells in the SAG at any time between 24 and 50 hpf ( Figure 1G , and data not shown ) , indicating that transit-amplifying cells cycle relatively slowly ., Summing neurod+ cells in serial sections revealed that the number of transit-amplifying cells remains relatively constant after 30 hpf , with a transient peak at 48 hpf followed by a return to steady state of 180–200 cells through 78 hpf ( Figure 1B ) ., As precursor cells begin to differentiate they exit the cell cycle and lose expression of neurod ( Figure 1E , 1F ) and initiate expression of isl1/2 genes ( Figure 1H , 1I ) 3 ., The first mature Isl1+ neurons appear by 20 hpf and almost immediately begin to extend processes to peripheral and central targets ( data not shown ) ., Co-staining for BrdU and Gfp in isl2b:Gfp transgenic embryos 22 confirms that relatively few mature neurons incorporate BrdU ( Figure 1H ) ., The number of Isl1+ neurons increases linearly at a rate of 2–2 . 5 neurons per hour through at least 72 hpf , despite the cessation of specification and delamination at 42 hpf ( Figure 1C ) ., The steady increase in mature neurons after 42 hpf presumably reflects ongoing differentiation from the slowly cycling pool of transit-amplifying cells ., The slow mitotic rate amongst precursors presumably counterbalances production of new neurons , thereby maintaining a relatively stable transit-amplifying pool ., To clarify the spatial relationship between transit-amplifying and mature SAG cells , we examined sections co-stained for neurod and Isl1 ( Figure 1I–1M ) ., The most mature neurons accumulate in immediate contact with the ventromedial surface of the ear , while neurod+ cells undergoing transit-amplification reside more distally ( Figure 1L , 1M ) ., By 36 hpf the SAG also develops a more complex spatial distribution , forming three distinct regions along the anterior-posterior axis: The anterior-most region abuts the front end of the otic vesicle and contains only neurod+ precursors ( Figure 1K ) , although mature neurons accumulate in this region at later stages ( see below ) ., The middle region forms a broad mass spreading mediolaterally beneath the utricular region and contains complementary domains of neurod+ cells and Isl1+ cells ( Figure 1L ) ., The posterior-most region forms as a narrow finger of Islet+ cells and abutting neurod+ cells extending along the medial surface of the otic vesicle to the level of the saccular macula ( Figure 1M ) ., Segregation of neurons into these three AP domains reflects emergence of the topological pattern of innervation of the inner ear: Specifically , Sapède and Pujades 23 reported that anteroventral SAG neurons ( corresponding to the anterior and middle regions reported here ) predominantly innervate the utricular macula and to a lesser degree anterior and lateral cristae , whereas posterior-medial SAG neurons ( corresponding to the posterior region reported here ) predominantly innervate the saccular macula and to a lesser degree the posterior crista ., The data presented in subsequent sections support a model in which changing levels of Fgf differentially affect SAG development: Initially , moderate Fgf from nearby cells promotes neuroblast specification in the otic vesicle ., Subsequently , Fgf levels rise in part because mature SAG neurons specifically express Fgf5 and accumulate just outside the otic vesicle ( Figure 2 ) ., Elevated Fgf then terminates further specification/delamination and also inhibits maturation of transit-amplifying precursors ., Manipulation of Fgf to test this model was achieved by specifically knocking down fgf5 ( described below ) and more generally by using two heat shock inducible transgenic lines , hs:fgf8 and hs:dnfgfr1 ( dominant-negative Fgf receptor ) , to increase or decrease Fgf signaling , respectively 24 , 25 ., To document the efficacy of these transgenic lines , we examined expression of the Fgf-feedback gene etv5b ( previously erm ) 26 , 27 following activation of the transgenes ., Strong activation of hs:fgf8 by heat shocking embryos at 24 hpf ( 30 minutes at 39°C ) led to a detectable increase in etv5b levels by the end of the heat shock period ( not shown ) , with maximal etv5b seen throughout the embryo by 26 hpf ( Figure 3B , 3E ) ., etv5b levels remained elevated through 30 hpf ( Figure 3H ) and subsequently returned to normal ., In contrast , strong activation of hs:dnfgfr1 at 24 hpf ( 30 minutes at 38°C ) led to marked reduction of etv5b expression throughout the embryo by 25 hpf ( not shown ) , and complete loss by 26 hpf ( Figure 3C , 3F ) ., Expression first began to return by 36 hpf , though levels were still well below normal at that time ( Figure 3I ) ., These transgenes were subsequently used to assess the effects of changing Fgf signaling levels at different stages of SAG development ., Several Fgfs expressed in tissues near the developing SAG have been implicated in establishing a neurogenic domain in the ear 28 ., In zebrafish , fgf3 is expressed in the adjacent hindbrain through placodal stages and later helps initiate expression of fgf3 and fgf8 in the nascent utricular macula by 18 hpf 15 ., We have hypothesized that sensory-neural patterning is spatially coordinated by a lateral gradient of Fgf , with high levels initiating sensory development in the medial half of the otic placode – e . g . closest to the Fgf source 13 , and lower levels specifying the neurogenic domain in laterally adjacent otic epithelium ., We previously documented a stringent requirement for Fgf in sensory development 13 and here we focused on the requirement for Fgf in neurogenic specification ., To bypass the early requirements of Fgf during otic induction we used the chemical inhibitor , SU5402 , to block Fgf signaling at later stages of otic development ., Embryos treated with 100 µM SU5402 from 14 hpf −18 hpf showed a strong reduction in neurog1 expression ( Figure 4A , 4B ) ., Likewise , impairment of Fgf signaling by strongly activating hs:dnfgfr1 25 ( 38°C for 30 minutes ) showed similar results ( Figure 4C ) ., Blocking Fgf from this early stage caused widespread cell death at later stages , precluding analysis of SAG maturation ., Nevertheless , these data confirm that normal specification of the neurogenic domain requires Fgf signaling ., To test the hypothesis that SAG neuroblasts are specified by a specific lower level of Fgf in a signaling gradient , we manipulated Fgf levels using hs:fgf8 25 ., The level of hs:fgf8 activity can be adjusted by heat shocking at different temperatures 29 ., To provide a broad shelf of low Fgf signaling , embryos were incubated at 35°C from 18 hpf to 24 hpf ., This caused a marked upregulation and expansion of neurog1 expression ( Figure 4D , 4E ) ., Additionally , there was a notable increase in the number of delaminating neuroblasts as seen by hmx3 expressing cells leaving the vesicle ( Figure 4F , 4G ) ., By 42 hpf , the number of Isl1+ cells in the mature SAG had increased by 37% over the control ( Figure 4H , 63±6 . 0 Isl1+ cells in control embryos compared to 86±3 . 6 in hs:fgf8 transgenic embryos , n\u200a=\u200a15 ) ., To evaluate the effects of a higher level of Fgf , hs:fgf8 embryos were maximally induced by heat shocking them at 39°C for 30 minutes beginning at 18 hpf ., Under these conditions , neurog1 expression was reduced for several hours following heat shock but recovered to near normal by 24 hpf ( data not shown ) ., However , the number of mature Isl1+ neurons at 42 hpf was reduced by 20% ( 51±4 . 2 Isl1+ cells , n\u200a=\u200a15; Figure 4H ) ., As summarized in Table 1 , these data support the idea that Fgf acts in a concentration-specific manner , with lower levels promoting neuroblast specification and higher levels inhibiting specification ., Because the rate of neuroblast specification and delamination peaks at 24 hpf , we examined the effects of Fgf misexpression during this stage ., As before , maximal activation of hs:fgf8 ( 39°C ) at 24 hpf reduced expression of neurog1 in the ear by 30 hpf ( Figure 4I , 4J ) ., However , in contrast to earlier stages , low level activation of hs:fgf8 ( 35°C ) at 24 hpf reduced neurog1 expression by 30 hpf ( data not shown ) ., Fully blocking Fgf by strong activation of hs:dnfgfr1 ( 38°C ) at 24 hpf also diminished neurog1 expression by 30 hpf ( data not shown ) , in keeping with the requirement for Fgf in neuroblast specification ., However , weak attenuation of Fgf signaling by activating hs:dnfgfr1 at a low level ( 35°C for 2 hours followed by incubation at 33°C ) expanded the neurog1 expression domain at 30 hpf ( Figure 4K ) ., Overall these data ( summarized in Table, 1 ) support the hypothesis that a specific low-to-moderate level of Fgf promotes neuroblast specification at both early and later stages , and either a high level of Fgf signaling or complete blockage of Fgf signaling impairs this process ., At later stages , however , the process of specification becomes increasingly sensitive to inhibition by elevated Fgf ., This likely reflects the finding that the level of Fgf increases during development , as described in the next section ., Because SAG specification becomes increasingly sensitive to inhibition by elevated Fgf , we hypothesized that the process of neuroblast specification is normally terminated by a developmental increase in local Fgf signaling ., To explore this possibility , we surveyed expression of all known fgf genes in zebrafish and identified fgf5 as a strong candidate for a feedback regulator of SAG development ., During mid-somitogenesis stages fgf5 is diffusely expressed throughout the neural tube and strongly marks the developing trigeminal ganglion ( not shown ) ., As mentioned above , fgf5 shows relatively specific expression in mature SAG neurons , and several other cranial ganglia , by 24 hpf and this pattern is maintained through at least 48 hpf ( Figure 2 ) ., No expression is detected in the otic vesicle or other nearby tissues ., We tested the role of Fgf5 by injecting morpholino oligomers to block translation ( fgf5tb-MO ) or to disrupt splicing at the intron1-exon2 splice junction ( fgf5i1e2-MO ) ., Injection of either MO yielded identical phenotypes: Morphants showed highly specific and fully penetrant enhancement of SAG specification and maturation , as described below , but otherwise there were no other detectable changes in embryo morphology nor was there a detectable increase in cell death ., For most experiments reported here , we show results obtained with fgf5i1e2-MO , which proved to be highly effective in reducing mature fgf5 transcript levels ( Figure 5A , 5B ) ., To address the role of fgf5 in neuroblast specification we examined neurog1 expression at various stages in fgf5 morphants ., At 24 hpf no obvious difference was observed between fgf5 morphants and control embryos ( not shown ) ., By 30 hpf , however , neurog1 expression was dramatically expanded in fgf5 morphants ( Figure 6A , 6B , 6E , 6F ) , including a pronounced mediolateral expansion of neurog1 in the floor of the otic vesicle ( Figure 6B , 6F ) ., Normally , neuroblast specification declines dramatically after 30 hpf 1 , 20 ( Figure 6C ) ., However , fgf5 morphants continued to show abundant neurog1-positive cells at 36 hpf , indicating a prolonged phase of robust specification and delamination ( Figure 6G ) ., Neuroblast specification/delamination finally ceased by 48 hpf in fgf5 morphants ( not shown ) ., Knockdown of fgf5 appeared to affect SAG development in a highly specific manner , as other regional markers in the otic vesicle were expressed normally and development of sensory hair cells was also normal at 32 hpf ( Figure 7 ) ., Additionally , the fgf5 morphant phenotype was rescued by strong activation of hs:fgf8 ( 39°C ) at 24 hpf such that neuroblast specification returned to normal ( Figure 6D , 6H ) ., Such rescue supports the idea that neuroblast specification relies on a proper balance of Fgf signaling , with the morpholino and transgene counter-balancing each other ., Overall , these data ( summarized in Table, 1 ) support the hypothesis that mature SAG cells become a source of elevated Fgf , which eventually exceeds a signaling threshold that serves to terminate neuroblast specification in a timely manner ., To test this model in another way , mature neurons marked by isl2b:gfp transgene expression 22 were killed by serial laser-ablation at 22 hpf and 25 hpf ( Figure 6I , 6J ) and neurog1 expression was examined at 30 hpf ., Expression of neurog1 was expanded on the ablated side relative to the unablated ( contralateral ) side ( Figure 6K , 6L , Table 1 ) ., Together , these data support the notion that as mature neurons expressing fgf5 accumulate within the SAG , overall levels of Fgf signaling increase and as a result neuroblast specification is terminated ., This also explains the increased susceptibility to misexpression of Fgf8 after 24 hpf , as described above ., We next examined the effects of Fgf on post-delamination stages of SAG development ., In these experiments heat shock transgenes were activated at high levels ( 38–39°C ) at 24 hpf and the effects on neurod+ ( transit-amplifying ) and Isl1+ ( mature ) populations were examined at 36 hpf and 48 hpf ., Summing neurod+ cells in serial sections of control embryos indicated that there are approximately 200–250 transit-amplifying cells in the SAG at these stages ( Figure 1B ) ., Because this approach proved laborious and was prone to occasional loss of tissue sections , changes in the neurod domain were assessed by measuring mean cross-sectional areas in the three AP regions of the SAG in transgenic and control embryos ., Strong activation of hs:fgf8 at 24 hpf ( 39°C for 30 minutes ) increased the neurod+ precursor domain by 31% in the largest , middle region of the SAG at 36 hpf ( Figure 8A , 8B , 8I ) ., A similar trend was observed in the anterior region , although the difference was not statistically significant ( Figure 8I ) ., Under these conditions , the smallest , posterior part of the SAG was truncated in hs:fgf8 embryos and therefore was nearly devoid of neurod+ cells in most specimens ( Figure 8I ) ., This is possibly because the posterior SAG forms later and elevated Fgf prematurely terminates specification of neuroblasts that might otherwise contribute to this region ., Despite , the increased population of transit-amplifying cells in the middle region , the total number of Isl1+ neurons in the SAG was reduced in hs:fgf8 embryos by 30% at 36 hpf ( Figure 8E , 8F , 8J ) and the hourly rate of neuron production between 24 hpf and 36 hpf was reduced by half ( Figure 8K ) ., For loss of function studies , hs:dnfgfr1 was activated at 24 hpf ( 38°C for 30 minutes ) to impose a strong block to Fgf signaling ., This resulted in a decrease of 26% in the neurod+ domain in the middle region at 36 hpf , and a decrease of 50% in the posterior region ( Figure 8C , 8I ) ., Again , the anterior region showed a similar but non-significant trend ., Under the same conditions , there was a 30% increase in the total number of mature Isl1+ SAG neurons ( Figure 8G , 8J ) ., The relative effects of hs:fgf8 and hs:dnfgfr1 on the transit-amplifying population persisted through at least 48 hpf ( Figure 8L ) ., Differences in the total number of mature neurons also persisted at 48 hpf ( Figure 8M ) ., However , most of the differences seen at 48 hpf appeared to reflect changes occurring before 36 hpf because the rate of production of new Isl1+ neurons after 36 hpf was nearly normal in hs:fgf8 and hs:dnfgfr1 embryos ( compare Figure 8K , 8N ) ., This presumably reflects the transient nature of transgene activity and gradual reestablishment of normal SAG regulation ., Note that under the conditions used here , we detected little or no cell death in the transit-amplifying or mature regions of the SAG as shown by staining with Acridine Orange or anti-Caspase 3 antibody ( not shown ) ., Likewise , we detected no changes in the number of mitotic cells in the SAG , nor in the proportion of cells incorporating BrdU ( data not shown ) , indicating that Fgf does not directly affect cell cycle dynamics ., Instead , the data ( summarized in Table, 2 ) suggest that Fgf slows the rate at which transit amplifying cells differentiate into mature SAG neurons , whereas blocking Fgf accelerates differentiation ., We next assessed the role of Fgf5 in restraining maturation of precursor cells ., In fgf5 morphants , the size of neurod+ domain was increased in both the middle and posterior regions of SAG in the embryos at 36 and 48 hpf ( Figure 8D , 8I , 8L ) ., Note that the increase in the transit-amplifying region seen in fgf5 morphants was different from what was observed following activation of hs:dnfgfr1 ., This is presumably because the prolonged phase of robust specification seen in fgf5 morphants ( Figure 6G , 6H ) continues to replenish the transit-amplifying population ., Additionally , fgf5 morphants also produced more Isl1+ neurons than normal ( Figure 8H , 8J , 8K , 8M , 8N ) ., However , despite the enlarged pool of precursors fgf5 morphants did not produce more mature neurons than did hs:dnfgfr1 embryos ( Figure 8J , 8M ) ., This is possibly because redundant factors ( possibly macular Fgfs ) continue to restrain the enlarged pool of progenitors in fgf5 morphants ., Finally , we observed that strong activation of hs:fgf8 ( 39°C ) at 24 hpf in fgf5-morphants restored neuron production to normal ( Figure 8J , 8K , 8M , 8N ) ., Thus , as during neuroblast specification , the rate of neuronal maturation is also regulated by a proper balance of Fgf signaling ., Moreover , these data ( summarized in Table, 2 ) support a role for Fgf5 as a feedback inhibitor released by mature SAG neurons to restrict the rate of neuronal differentiation ., To further explore regulation of SAG maturation , we assessed whether laser-ablation of mature SAG neurons affects the rate of new neuron production ., This analysis was conducted after 30 hpf to minimize the impact of neuroblast specification on overall cell number ., Using the isl2b:Gfp line , mature SAG neurons on one side of the head were targeted for serial ablation at 30 hpf and 32 hpf , with the contralateral side serving as a non-ablated control ., We observed that a single round of ablation was inefficient , allowing a substantial fraction of neurons to survive ., However , serial ablation successfully eliminated over 90% of mature neurons , as confirmed by anti-Isl1 staining just after the second ablation ( not shown ) ., Analysis of the transit-amplifying population revealed that the number of neurod+ cells declined by 10–20% on the ablated side during the first 12 hours following neuronal ablation , probably reflecting collateral damage , but the number returned to normal by 56 hpf ( 24 hours post-ablation ) ( data not shown ) ., Despite the initial decrease in transit-amplifying cells , new Isl1+ neurons accumulated at a rate comparable to the non-ablated contralateral side for the first 12 hours after ablation , ( Figure 9A ) ., The rate of neuron production briefly declined during the next 12-hour period , but then increased to a rate 60% greater than normal through at least 80 hpf ( Figure 9A , Table 2 ) ., Co-ablation of both mature and transit-amplifying cells ( the latter were targeted based on position and morphology ) nearly eliminated production of new Isl1+ cells through at least 56 hpf ( Figure 9A ) , confirming the vital importance of transit-amplifying cells for producing new mature SAG neurons ., Together these data suggest that loss of feedback inhibition from mature neurons leads to accelerated differentiation of cells from a pool of self-renewing progenitors ., We next examined whether altering Fgf signaling influences neuron production after 30 hpf , with and without laser-ablation of mature neurons ., Ablations were conducted in transgenic embryos carrying both isl2b:Gfp and either hs:fgf8 or hs:dnfgfr1 ., Again , isl2b:Gfp+ cells were serially ablated on one side at 30 hpf and 32 hpf , and embryos were then heat shocked at 38°C or 39°C ( strong activation ) at 34 hpf ., The contralateral side served as a non-ablated control ., On the non-ablated side , the effects of activating hs:fgf8 or hs:dnfgfr1 at 34 hpf were similar the effects of activating these transgenes at 24 hpf: Specifically , strongly elevating Fgf impaired production of new neurons whereas blocking Fgf accelerated production of new neurons ( Figure 9B , Table 2 ) ., On the ablated side , activation of hs:dnfgfr1 ( 38°C ) accelerated production of new neurons to more than twice the normal rate through 44 hpf , after which the rate flattened out as in non-transgenic ablations ( Figure 9B ) ., Moreover , the rate of neuron production in ablated hs:dnfgfr1 embryos was 50% greater than in non-ablated hs:dnfgfr1 embryos ., Surprisingly , strong activation of hs:fgf8 ( 39°C ) resulted in a rate of neuronal accumulation on the ablated side that was nearly normal ( comparable to the non-ablated control ) ., Thus , misexpressing Fgf8 counterbalances the effects of eliminating mature neurons ( and hence Fgf5 ) such that there is no net change in the rate of neuron production ., This is similar to the ability of hs:fgf8 to counterbalance the effects of fgf5-MO on neuroblast specification ( Figure 6H ) and maturation of SAG neurons ( Figure 8J , 8K , 8M , 8N ) ., Analysis of the neurod+ domains showed that transgene activity had no significant effect on the size of the transit-amplifying pool at these stages ( Table 2 ) ., Thus , blocking Fgf accelerates production of new neurons and enhances the effects of neuronal ablation whereas misexpressing Fgf8 offsets the effects of neuronal ablation ( summarized in Table 2 ) ., These data further support the hypothesis that Fgf5 from mature neurons acts as a feedback inhibitor to slow the rate of maturation of new SAG neurons ., Numerous studies support a role for Fgf in SAG neuroblast specification in the chick and mouse ., In chick , misexpression of Fgf8 or Fgf10 during placodal stages causes expansion of the neurogenic domain in the otic vesicle , whereas blocking Fgf signaling dramatically reduces the neurogenic domain 4 , 35 ., In mouse , knockout of Fgf3 or receptor isoform Fgfr-2 ( IIIb ) causes severe deficiencies of delaminating neuroblasts and neurons 11 , 36 ., Explant cultures of chick or mouse otocysts treated with exogenous Fgf2 produce 5- to 10-fold more delaminated neuroblasts compared to controls , whereas blocking Fgf2 with a neutralizing antibody severely reduces the number of neuroblasts 10 , 37 ., Thus the requirement for Fgf in neuroblast specification appears broadly conserved ., However , the spatial gradient of Fgf that we propose coordinates sensory and neural development in zebrafish is unlikely to operate in mammals ., Unlike the situation in zebrafish , in mouse the neurogenic and sensory domains overlap spatially but are specified at slightly different times ., Neuroblast specification occurs first , but as the phase of neuroblast specification/delamination begins to wane sensory epithelia begin to form in the same region ., The transition from neural to sensory development partly reflects mutual repression between Neurog1 and Atoh1 , the principal initiators the proneural and prosensory pathways , respectively 38 ., Whether Fgf also influences this transition is not known ., Despite the above studies showing a requirement for Fgf , it is not clear whether high levels of Fgf are inhibitory in birds and mammals as we have shown here , nor whether Fgf delays maturation of cells in the transit-amplifying pool ., In explants of chick or mouse otocysts , exposure to Fgf accelerates the appearance of mature neurons compared to cultures lacking exogenous Fgf 10 , 37 ., At first glance , these results appear to contradict our findings that Fgf delays differentiation ., However , Fgf levels used in the above explant studies were based on dose-response curves and were selected to optimize growth of the explant ., Hence potential inhibitory effects of higher doses of Fgf were not evaluated ., Furthermore , neuroblasts in culture disperse after delamination rather than accumulating against the otocyst wall where they might facilitate feedback inhibition ., This possibly explains why otic explants continue to produce neuroblasts for many days , far longer than during normal embryonic development ., In rodent embryos , differentiating auditory neurons express Fgf1 , Fgf2 , Fgf5 and Fgf10 36 , 39–42 , which could help mediate feedback inhibition ., Unfortunately , relevant functional studies are lacking ., In adult rodents , neuronal Fgf is thought to play a role in maintenance of the spiral ganglion ., Augmenting Fgf mitigates neural degeneration following nerve injury or noise-induced trauma 43 , 44 ., Additionally , conditional knockout of Fgf receptor genes Fgfr1 and Fgfr2 in glial cells in the spiral ganglion leads to progressive loss of auditory neurons beginning around 2 months of age , suggesting a role in promoting trophic support from glia 45 ., In cultures of spiral ganglion from adult mouse , exogenous Fgf2 can promote neuronal survival and neurite outgrowth 46 ., Unexpectedly , such cultures were also found to contain quiescent progenitors that could be induced to reenter the cell cycle by incubation with EGF and Fgf2 , with some cells differentiating into neurons after removal of EGF and Fgf2 46 ., These latter data are consistent with the possibility that Fgf maintains progenitors and inhibits neural differentiation , though it remains to be seen whether such a mechanism operates in vivo ., The developing SAG can be compared to the developing olfactory epithelium ( OE ) ., Fgf8 expression around the rim of the olfactory pit stimulates proliferation of OE progenitors , which differentiate into mature neurons deeper inside the pit away from the Fgf8 source 47 ., Conditional knockout of Fgf8 results in severe deficiency of neurons due to failure of progenitors to expand ., Development of the OE neurons is also regulated by feedback inhibition from mature neurons , though the mechanism differs from the SAG ., Specifically , mature OE neurons secrete the TGFβ factor GDF11 , which inhibits further proliferation of progenitors by antagonizing Fgf8 48 ., In the eye , too , GDF11 acts as a feedback inhibitor of retinal ganglion cells , though in this case GDF11 blocks further differentiation of progenitors rather than restricting proliferation 49 ., In numerous other settings , Fgf regulates the balance between growth and differentiation of neural progenitors ., In cultures of human or rat cortical progenitors , high levels of Fgf stimulate proliferation and block neuronal differentiation 50 , 51 ., In the developing midbrain-hindbrain region in mouse , conditional knockdown of Fgf receptors results in an increase in differentiated neurons and a concomitant loss of progenitor cells in the ventricular zone 52 ., During earlier stages of mouse development , Fgf induces embryonic stem ( ES ) cells to form epiblast , which begin to express Fgf5 ., Subsequently , Fgf maintains the epiblast as a stable intermediate by preventing reversion back to the ES ground state and blocking further differentiation into neural ectoderm 53 ., Thus , maintenance of stable progenitor pools by Fgf appears to be a broadly conserved mechanism utilized in many aspects of neural development ., A relatively novel aspect of SAG development is that Fgf coordinates the entire process , initially specifying neuroblasts and , at a hig

Introduction, Results, Discussion, Materials and Methods

Neuroblasts of the statoacoustic ganglion ( SAG ) initially form in the floor of the otic vesicle during a relatively brief developmental window ., They soon delaminate and undergo a protracted phase of proliferation and migration ( transit-amplification ) ., Neuroblasts eventually differentiate and extend processes bi-directionally to synapse with hair cells in the inner ear and various targets in the hindbrain ., Our studies in zebrafish have shown that Fgf signaling controls multiple phases of this complex developmental process ., Moderate levels of Fgf in a gradient emanating from the nascent utricular macula specify SAG neuroblasts in laterally adjacent otic epithelium ., At a later stage , differentiating SAG neurons express Fgf5 , which serves two functions: First , as SAG neurons accumulate , increasing levels of Fgf exceed an upper threshold that terminates the initial phase of neuroblast specification ., Second , elevated Fgf delays differentiation of transit-amplifying cells , balancing the rate of progenitor renewal with neuronal differentiation ., Laser-ablation of mature SAG neurons abolishes feedback-inhibition and causes precocious neuronal differentiation ., Similar effects are obtained by Fgf5-knockdown or global impairment of Fgf signaling , whereas Fgf misexpression has the opposite effect ., Thus Fgf signaling renders SAG development self-regulating , ensuring steady production of an appropriate number of neurons as the larva grows .

Neurons of the statoacoustic ganglion ( SAG ) , which innervate the inner ear , are derived from neuroblasts originating from the floor of the otic vesicle ., Neuroblasts quickly delaminate from the otic vesicle to form dividing progenitors , which eventually differentiate into mature neurons of the SAG ., Fgf has been implicated in initial neuroblast specification in multiple vertebrate species ., However , the role of Fgf at later stages remains uncertain , because previous studies have not been able to evaluate the effects of changing levels of Fgf , nor have they been able to clearly distinguish the effects of Fgf at different stages of SAG development ., We have combined conditional loss of function , misexpression , and laser-ablation studies in zebrafish to elucidate how graded Fgf coordinates distinct steps in SAG development ., Initially moderate Fgf in a spatial gradient specifies neuroblasts within the otic vesicle ., Later , mature SAG neurons express Fgf5 and , as additional neurons accumulate outside the otic vesicle , rising levels of Fgf terminate further specification ., Elevated Fgf also slows maturation of progenitors , maintaining a stable progenitor pool in which growth and differentiation are evenly balanced ., This feedback facilitates steady production of new neurons as the animal grows through larval and adults stages .

growth control, neurogenesis, neuroscience, cell differentiation, developmental biology, organism development, molecular development, morphogenesis, pattern formation, developmental neuroscience, biology, auditory system, neural stem cells, regeneration, signaling, sensory systems, cell fate determination

journal.pcbi.1003319

Cell-Based Multi-Parametric Model of Cleft Progression during Submandibular Salivary Gland Branching Morphogenesis

Branching morphogenesis is a specific type of tissue morphogenesis that is a crucial developmental process occurring in several organs , such as the mammary glands , lungs , kidney , and salivary glands to maximize epithelial surface area for secretion or absorption of fluids and gases 1 ., The process of branching morphogenesis is complex and dynamic , requiring reciprocal interactions between the epithelium and the mesenchymal cell types 2 , 3 ., Since many organs develop by branching morphogenesis , one strategy for a regenerative medicine-based restoration of diseased or damaged branched organs would be to reactivate the cellular and molecular mechanisms that produce these organs during development ., Deciphering the coordinated mechanisms driving branching morphogenesis is therefore relevant to the basic understanding of development and may be applicable to future regenerative medicine strategies ., Submandibular salivary gland ( SMG ) is one of the best-characterized organ systems for the study of branching morphogenesis 4 since the embryonic organs can be grown ex vivo and manipulated genetically 5 or pharmacologically 6–9 and monitored using time-lapse imaging 10 , 11 ., The gland starts to develop at embryonic day 11 ( E11 ) when the epithelium protrudes into the neural crest-derived mesenchyme ., At E12 , clefts , or indentations , initiate in the surface of the primary epithelial bud , which progress inward towards the interior of the epithelium , subdividing the primary bud into multiple buds by E13 ., Cleft progression is associated with proliferation of the epithelial cells causing tissue outgrowth 2 ., In successive days , embryonic development continues into postnatal development with continued cleft formation and bud outgrowth together with duct formation , thereby forming a highly arborized adult structure ., Cellular differentiation begins at E15 , concomitant with continued branching to create functional cell types , leading to saliva secretion 3 ., Since the salivary glandular structure is presumably important to facilitate its function , the question of how this ramified epithelial structure is established has been the subject of many biological studies and some recent computational modeling studies ., Analysis of the physics of complex systems has demonstrated that collective behaviors arising from ensembles of a large number of interacting components cannot be interpreted from behavioral analysis of individual components 12 ., Thus , several researchers have utilized various systems biology and computational modeling approaches as tools to try and understand salivary gland morphogenesis 13 ., Starting at the organ level , Lubkins group developed a 2D model for cleft formation during early salivary gland branching morphogenesis ., In this work , the epithelium and mesenchyme were both modeled as immiscible Stokes fluids , separated by an interface representing the basal lamina ., Using a 2D model , they predicted that mesenchymal viscosity drives a clefting force that affects the time required for branching and that the ratio of viscosities of the epithelium to mesenchyme affects the shape of clefts 14 ., In subsequent work , they developed a more complex 3D model that incorporated the mesenchyme-generated traction forces ., This model predicted that these mesenchymal traction forces were sufficient to drive cleft formation 15 ., Although these computational models were the first attempt in modeling complex tissue-driven forces and were able to successfully generate clefts , the cleft shape did not mimic the actual shape observed in the developing salivary glands ., Additionally , the 3D model could not explain how branching morphogenesis can occur in the absence of mesenchymal cells when epithelial rudiments are grown in an artificial basement membrane together with growth factors 10 , 16–19 ., The fact that branching morphogenesis can occur without mesenchymal cells indicates that a cell-based model system that focuses on epithelial cellular processes may have utility in modeling the process of cleft formation ., Previous experimental research using ex vivo embryonic organ explants and transgenic mouse models has made possible the identification of many molecules and cellular processes required for cleft formation in the submandibular salivary gland; however an integrated model for cleft formation does not exist ., Using a cell-based modeling environment we set out to incorporate as much of the experimental data as possible into a computational model ., Early work indicated that actin microfilaments are required for forming clefts 20 , 21 ., Since actin is known to regulate cell shape , a simple model for cleft formation was proposed where localized actin contraction at the basal cell surfaces alternating with contraction at the apical surfaces in the outer monolayer of epithelial cells bends this peripheral cell layer to generate clefts ., However , subsequent electron microscopy studies did not detect basal actin bundles 11 ., According to recent experimental work , cleft formation can be subdivided into four fundamental steps: initiation , stabilization , progression and termination ., While the events leading to cleft initiation remain unclear , recent studies indicate that cleft stabilization requires formation of cell-ECM adhesions containing active focal adhesion kinase ( FAK ) 7 ., Initiated clefts can only progress when they have been stabilized by an inside-out integrin signaling that promotes activation of focal-adhesion protein complexes that can overcome a presumed mechanochemical barrier to progression ., Cleft progression was shown to require Rho kinase I ( ROCK I ) -stimulated non-muscle ( NM ) myosin II/-mediated actomyosin contractility for basal fibronectin ( FN ) assembly in the cleft region and associated cell proliferation , at least part of which is stimulated by FN 6 ., FN assembly induced epithelial cell proliferation , which had a major impact on cleft progression and bud outgrowth but not on cleft initiation ., Explants treated with hydroxyurea , a known pharmacological S-phase inhibitor , showed a reduction of progressive clefts with no effect on number of initiated clefts as compared to vehicle control glands 6 ., With time-lapse imaging studies , Kadoya and Yamashina 11 showed that clefts progress with a very subtle replacement of cell-cell adhesions with cell-ECM adhesions with very little space between the cells on each side of the cleft ., They proposed that local folding of the plasma membrane near the base of the cleft produces a “shelf” containing an accumulation of actin filaments ., The shelf was proposed to be the contact point between the epithelium and matrix , and the cleft progressed in the groove between the shelf and the cleft cell walls , through retraction of the groove 11 ., Cleft formation was also found to be accompanied by accumulation of FN in the cleft bases and concomitant loss of adjacent E-cadherin based cell-cell junctions 5 ., This conversion of cell-cell adhesions to cell-matrix adhesions was found to be regulated transcriptionally through increases in BTB ( POZ ) domain containing 7 ( Btbd7 ) to activate a local epithelial-to-mesenchymal transition ( EMT ) found near the base of the cleft 22 ., Btbd7 is assumed to assist in separating the adjacent epithelial cells , while assembled FN keeps accumulating at the newly separated cleft base cells , promoting continuous cleft progression 23 ., These experimental studies point to a coordinated requirement for cell proliferation , actomyosin contractility , cell-cell adhesions and cell-matrix adhesions in cleft progression ., To develop a relevant cellular level model of morphodynamic pattern formation in developing salivary glands , we used a modeling environment that specifically attempts to simulate several cellular events including mitosis or cell proliferation , actomyosin contraction , cellular organization with cell-cell interactions and cell-matrix interactions and allows independent computational manipulation of each parameter within specific cell populations ., The Glazier-Graner-Hogeweg ( GGH ) model 24 , 25 was originally developed to model cellular rearrangements as a function of inter-cellular surface energy , cell membrane fluctuations and energy between cells and their external environment 26 ., The GGH model has been utilized to recapitulate cellular events during pattern formation and morphogenetic movements in several organisms and organ systems 27–31 ., The GGH model represents each cell as an aggregation of lattice points , or pixels , in a 2D space ., Each cell is assigned an energy signature denoting the probability of the cell to grow , move , adhere , and organize into different patterns ., GGH thus enables cell-centered modeling to simulate changes in collective ensembles of cells within tissues to facilitate the testing of how specific cell behaviors affect a larger morphological process ., In this study , we construct a GGH model of salivary gland cleft progression using CompuCell3D ( CC3D ) , an open-source implementation of the GGH model ., We developed both a single cleft and a whole epithelial tissue model , which include GGH-based representations of cellular adhesions , cellular contractility , cell-matrix adhesions and cell proliferation within the epithelial cells that are surrounded by a simplified mesenchymal compartment ., The whole tissue model demonstrated a mutual dependence of cleft progression on neighboring clefts , and the single cleft model was used to investigate the contribution of the cellular parameters to individual cleft progression ., We used morphometric quantification of cleft depths from time-lapse images of ex-vivo cultured glands to create a temporally and spatially accurate model ., The clefts obtained during the simulations were assessed for quality using three morphometric features – cleft depth , cleft spanning angle , and cleft tilt angle ., Comparisons with ex-vivo cultured glands were generated from image data that was measured using the same features ., Using the single cleft model we have been able to make the following predictions regarding the contributions of cellular parameters to branching morphogenesis:, ( i ) cleft progression requires an intermediate level of actomyosin contractility in the cleft region , and lower contractility is more detrimental to cleft progression than higher levels of contractility ,, ( ii ) proliferation rates and location of the proliferating cells affect cleft progression such that very low proliferation rates are required and an equal number or majority of the proliferating cells should be in the outer columnar epithelial layer rather than in the inner cells ,, ( iii ) low levels of cell-cell adhesion in the cleft promote progressing clefts , and, ( iv ) cell-matrix adhesions do not have as significant an effect on cleft progression as do cell-cell adhesions ., Since it is difficult to make assessments of the relative importance of cellular factors to branching morphogenesis using experimental methods , we used ex-vivo data-sets to formulate three classes of cleft progression and used classifiers to identify the most important factors during cleft progression ., Our results show that epithelial cell contractility in the cleft cells is the most influential factor during cleft progression , closely followed by mitosis rate and cell contractility ., This study involves CD1 mice and was approved by the University at Albany , SUNY IACUC under protocol numbers 09-013 and 12-013 ., The GGH model is built on the energy minimization-based Ising model , using imposed fluctuations via a Monte Carlo approach 24 ., The simulation space is divided into a lattice , which may be two- or three-dimensional , and cells are represented by groups of adjacent lattice points; each lattice point has an associated energy value that is assigned based on its interactions with other lattice points ., Energy is also assigned to cells based on cell-cell interactions , and the sum of energies across all lattice points and cells in the simulation space is the effective energy ., The energy assignment of a lattice point is based on functions representing biological behaviors or constraints , and the effective energy of the simulation can be written as a Hamiltonian equation , where each term represents the sum contribution of a particular energy function ., The model is based on the assumption that the most favorable state is the lowest energy state ., To develop the 2D GGH single cleft and tissue models of cleft progression , we used the following terms: Contact energy represents differential adhesion between model cells of different types by assigning an energy penalty to adjacent lattice points belonging to different cells ., Each possible pair of cell types ( τa , τb ) is enumerated and assigned an energy penalty J ( τa , τb ) , including same-type pairs ., Cell types that adhere to each other are assigned a lower energy penalty; the cell type τ of a particular lattice point i is given by τσ, ( i ) , where σ is the cell ID ., The contact energy penalty assigned to a pair of lattice points ( i , j ) is therefore given as J ( τσ, ( i ) , τσ, ( j ) ) ., To prevent lattice points within the same cell from being assigned a contact energy penalty , this is multiplied by ( 1−δσ, ( i ) , σ, ( j ) ) , where δ is the Kronecker delta ., The term for contact energy in the Hamiltonian equation across all pairs of lattice points ( i , j ) is therefore given as: ( 1 ) for all neighboring lattice sites i , j ., Area ( a ) represents cell volume in two dimensions ., It is a cell-based energy function that penalizes cells for deviating from a target size , simulating the biological tendency for cells to grow to and maintain a certain size ., It has two constants , a target area A , and a strength factor λ ., The term is thus: ( 2 ) for each cell σ and cell type τ Perimeter ( p ) is a representation of surface area in two dimensions ., Like area , it is a cell-based energy function , and it imposes an additional constraint on cell size based on the amount of plasma membrane available to a cell ., It also uses two constants , a target perimeter P , and a strength factor λ ., The energy term is given as: ( 3 ) for each cell σ and cell type τ Focal point plasticity is a cell-based energy term that assigns an energy penalty for linked cells that deviate from a target length L , based on the distance between the cell centroids ( l ) ., Although it was developed to simulate actomyosin-dependent contractility , it is used in our model to simulate the effects of actomyosin contractility-dependent FN assembly ., Since we are unable to represent the FN wedge as a physical structure , we reproduce its cleft-forming effects by exerting a separating effect on opposing cells of the cleft wall through FPP ., Within the cleft , the target distances between opposing cells are assigned based on depth , and represent the shape constraint imposed by the FN structure ., The λ value modulates the effect of focal point plasticity , and corresponds to the amount of actomyosin contractility present in the simulation ., The energy term is: ( 4 ) for linked cells σ and σ , and cell type τ The target distances that produce the characteristic shape of the cleft are assigned based on an inverse relationship with the depth; cells near the bottom of a cleft are assigned shorter target distances than the cells at the top of the cleft ., This relationship was determined through examination of images of progressed clefts from time-lapse images of embryonic day 12 ( E12 ) organ explants ., Additionally , we used a simplified two-cell model to investigate the effects of FPP relative to Cell-Matrix ( CM ) contact energy , λ , and target distance , for constant cell-cell contact energy ( CC ) value\u200a=\u200a10 to determine the values of λ to use in the model ( Figure S4 ) ., The full Hamiltonian equation for our simulation is thus given as the sum of these four equations: ( 5 ) Energy minimization is carried out by choosing pairs of adjacent lattice points from different cells , and an attempt is made to copy the cell ID from the first point to the second ., This copy attempt grows one cell , either by forcing another cell to shrink , or expanding into the medium ., The effective energy is calculated before and after the change , and if the new energy is lower , the change is made permanent ., However , if the resulting energy is higher , the change is only retained with some probability using a Boltzmann acceptance function , e−ΔH/T ., In the context of the GGH simulation , T is a constant that controls the intrinsic motility of the cell , corresponding to the amplitude of cytoskeletally derived membrane fluctuations ., Using T , we have allowed a certain amount of cell motility ., Allowing some amount of energy-raising lattice-copy events is important as it prevents the model from stalling at local energy minima ., A single step in the GGH model actually consists of N lattice copy attempts , where N is the total number of lattice sites in the simulation space ., These attempts are carried out through a Monte Carlo simulation using modified Metropolis dynamics , designated as Monte Carlo steps ( MCS ) 24 ., Cell proliferation in the GGH model is accomplished by dividing an existing cell into two equally sized new cells ., To simulate mitotic cells , a subset of cells is instructed to grow to twice their original size and divide every 100 Monte Carlo steps ( MCS ) , mimicking the growth and mitosis of biological cells ., Simplification: Although the GGH model is able to mimic parameters such as growth factor absorption kinetics , we have omitted these from this initial study to reduce complexity and focus on the cellular behaviors ., Similarly , we have simplified the basement membrane and mesenchymal compartment , which contains nerves and blood vessels 2 , 32 in addition to mesenchymal fibroblasts; surrounding the epithelium into a single compartment we call “matrix” and that is often called “medium” in GGH models ., The matrix compartment is essentially represented here as a single special GGH cell that is not subjected to area and perimeter constraints ., We have not included apoptosis in our model since there is currently no biological data to suggest that apoptosis is important in cleft progression ., Embryos from timed-pregnant female mice ( strain CD-1 , Charles River Laboratories ) at embryonic day 12 ( E12 ) ( with day of plug discovery designated as E0 ) , were used to obtain submandibular salivary gland rudiments ( SMGs ) following protocols approved by the University at Albany , SUNY IACUC committee ( protocols 09–013 and 12-013 ) , as reported previously 6 , 7 , 33 , 34 ., E12 SMGs that contain 1 primary bud were micro-dissected from mandible slices and cultured , as described previously ., For culturing ex-vivo organs , 13 mm , 0 . 1 µm pore size Nucleopore Track-Etch membrane filters ( Whatman ) were used ., The SMGs were floated on top of the filters that sit on 200 µL of 1∶1 DMEM/Hams F12 Medium ( F12 ) lacking phenol red ( Invitrogen ) in glass-bottomed 50 mm microwell dishes ( MatTek Corporation ) ., The medium was supplemented with 50 µg/mL transferrin , 150 µg/mL L-ascorbic acid , 100 U/mL penicillin , and 100 µg/mL streptomycin , to make complete DMEM/F12 medium ., Brightfield images were acquired on a Nikon Eclipse TS100 microscope equipped with a Canon EOS 450D digital camera at 4X ( Plan 4X/0 . 10 NA ) magnification ., Whole-mount immunocytochemistry was performed as previously described 6 , 7 , 33 , 34 ., E12 SMGs were fixed in 4% paraformaldehye ( PFA ) in 1X phosphate buffered saline ( 1XPBS ) containing 5% ( w/v ) sucrose for 20 min at room temperature ., SYBR Green I ( 1∶10000 , Invitrogen ) was used to detect nuclei and proliferating cells were detected using phospho-Histone H3 ( pHH3 ) antibody ( 1∶100 , Cell Signaling Technology ) ., Epithelium was detected using an antibody recognizing E-cadherin ( 1∶250 , BD Biosciences ) , F-actin was detected using Alexa Fluor 546 Phalloidin ( Invitrogen , 1∶350 ) , and mesenchyme was detected using an antibody recognizing PDGF receptor ( R ) -β ( 1∶100 , Epitomics ) ., Appropriate cyanine dye-conjugated AffiniPureF ( ab′ ) 2 fragments were used as secondary antibodies ( Jackson ImmunoResearch Laboratories , 1∶100 ) ., SMGs were imaged on a Zeiss LSM510 confocal microscope at 20X ( Plan Apo/0 . 75 NA ) , or 63X ( Plan Apo/1 . 4 NA ) magnification ., E12 SMG organ explants were treated with 200 µl of Hanks balanced salt solution ( HBSS lacking Ca2+ or Mg2+ , Life Technologies ) containing 0 . 4% ( v/v ) dispase ( Life Technologies ) for 25 min at 37°C , and the mesenchyme was physically removed by microdissection , as described in 10 ., The epithelial rudiment was cultured in a final concentration of 6 mg/mL Matrigel ( BD Biosciences ) diluted in DMEM/F12 containing 20 ng/mL EGF and 200 ng/mL FGF7 ( R&D Systems ) ., The gland was imaged using time-lapse microscopy with a 20X objective lens using a Zeiss 510 Meta Confocal microscope ., 120 images were captured as 5 µm sections at 10 minute intervals for a 20 hour time period using the MultiTime macro ., The 543 nm laser was used to capture a near-DIC image ., Images were captured at a 512×512 pixel resolution using a scan speed of 9 in line averaging mode ., A total of 30 glands were imaged for 20 hours in three separate sets and 40 clefts were measured using image analysis software ImageJ 35 ., The first frame and the last frame ( after 20 hours ) were used to measure the depth in pixels for each cleft and according to the scale , the distances were converted to micrometers ( µm ) ., To enhance the contrast of the grey-scale pHH3 images and the SYBR green images , we applied the contrast-limited adaptive histogram equalization algorithm ( CLAHE ) 36 to the image ., The CLAHE algorithm considers the image as a collection of smaller regions and applies histogram equalization on these regions ., The objective of histogram equalization is to transform the image so that the intensity histogram of the output image approximately matches a specified histogram; in our case we use a curved histogram ., The CLAHE algorithm evens out the distribution of used grey values and thus makes hidden features of the image more visible ., Noisy regions of the images are removed by considering regions of intensity greater than a pre-determined threshold ., For the E-cadherin marker images , we applied a Gaussian smoothing followed by the CLAHE algorithm , and then removed noisy regions based on a predetermined threshold ., Binary masks were created for the SYBR green and pHH3 histogram equalized images by applying an OR operation on the histogram equalized image and the E-cadherin marker ., The total area of the connected components in both images was calculated , and the ratio yielded the percentage of SYBR green-positive cells ( total cells ) that are in mitosis , or M phase , of the cell cycle ., Four values for mitosis rate ( MR ) , six values for contractility ( FPP λ ) , five values for cleft region adhesion ( CC ) , and five values for cleft-matrix adhesion ( CM ) were chosen from the hypothesis driven individual analyses , and 40 simulations were run for each of the 600 possible combinations ., Cleft simulations were classified as failed ( less than 17 . 8 µm ) , non-progressive ( 17 . 8 to 30 . 5 µm ) , progressive ( 30 . 5 to 40 . 7 µm ) , and super-progressive ( greater than 40 . 7 µm ) based on minimum , first quartile , and third quartile depths of ex-vivo cleft measurements ., Parameter combinations were assigned an overall class based on the cleft depths attained in a majority class within the 40 runs; in the case of a tie , the median depth was used to classify the parameter combination ., This resulted in 275 failed , 188 non-progressives , 85 progressive , and 52 super-progressive results ., To determine the importance of each GGH parameter in cleft progression , we formulated the problem as a supervised learning feature selection task , with each combination as a data point and the parameter values as features ., Samples were created by drawing 50 random points from each class ., For each of the 15 possible combinations of the four features , a 10-fold cross-validation using a radial basis kernel support vector machine ( SVM ) was performed on the sample , reporting the training and testing accuracies 37 ., A greater decrease in classification accuracy corresponds to a more important feature ., Additionally , analysis of the parameters resulting in progressive clefts was performed to confirm the importance of each parameter; parameters that were essential to progressive clefts were expected to be distributed around a particular value with low variance ., We chose to start our model at E12 , when the mouse SMG undergoes the first round of branching morphogenesis ., At E12 , the gland is a single epithelial mass , or bud , atop a stalk , surrounded by a condensed mesenchyme ( Figure 1A , 1B ) ., Clefts initiate as indentations in the epithelium , which progressively furrow interiorly ., Since cleft initiation and cleft progression are biochemically independent steps 6 and little biological information is available regarding mechanisms of cleft initiation , we chose to pre-specify an individual initiated cleft in the model and simulate only the stage of cleft stabilization and cleft progression ( Figure 1E ) ., At E12 , the epithelium expresses E-cadherin ( Figure 1C , 1D ) but later stage differentiation marker proteins are not yet expressed 38 , 39 ., We therefore assumed that the cell-cell adhesions present are E-cadherin-containing adherens junctions with an absence of tight junctions , as previously reported 38 , 39 ., The epithelium is surrounded by mesenchyme that expresses PDGFR-β , which can be used to distinguish the latter from the former ( Figure 1C , 1D ) ., Closely associated with the epithelial cells is the basement membrane , a specialized extracellular matrix ( ECM ) that forms a boundary between the epithelial and mesenchymal tissue compartments 5 , 40 ., Since we are focusing on epithelial cell parameters that control cleft progression , we modeled the basement membrane and the entire mesenchyme compartment as a simplified single cell , designated as “matrix , ” which lacks area and perimeter constraints ., At E12 , there are two structurally distinct epithelial precursor cell populations 34 , 38 ., The outer columnar cells ( OCCs ) that contact the basement membrane surround a cluster of less organized inner polymorphic cells ( IPCs ) ( Figure 2A ) , and this cell arrangement is maintained during 24 hours of ex-vivo culture ( Figure 2B ) ., The 6×6 pixel square cells were arranged in a homogenous grid , a simplification that approximates the initial cell distribution with OCCs labeled in dark green and IPCs in light green ( Figures 3A , 3B ) ., To calibrate the model with image data , we performed time-lapse imaging of multiple E12 mesenchyme-free SMG organ explants for 20 hours and measured the length of the resulting clefts ( Figures 3C , 3D , Video S1 ) ., Clefts achieved an average depth of 36 . 2 µm and a median depth of 35 µm ., Based on the cleft depths obtained from the time-lapse analysis , we defined normal cleft depth in the CC3D model as 36 pixels , using 6 cells per cleft , shown in light and deep blue ( Figures 3A , 3B ) ., To distinguish OCCs from IPCs , we use a baseline perimeter equivalent to the perimeter of a square for the initial cell area ., Relative to this baseline , we allow a marginal increase in the target perimeter for IPCs , which encourages them to take on more irregular shapes , whereas OCCs were confined to a smaller perimeter , encouraging them to maintain a more ordered columnar shape as they do in-vivo ., Cells exhibit differential adhesion that can drive complex tissue-level behavior 30 ., The IPCs demonstrated a slightly more diffuse distribution of the adherens junction protein E-cadherin than the OCCs , suggestive of reduced adherence of the IPCs to each other 38 ., To represent cell-cell adhesions in the GGH model , we start with a baseline contact energy penalty; increasing or decreasing the penalty simulates lower and higher adhesion , respectively , as explained by Eq ., 1 . Relative to this baseline , we designated increased cell-cell contact energy between IPCs to represent decreased adhesion properties and decreased contact energy between OCCs , simulating a possible increased adhesion that may help OCCs maintain their regular shape ., During cleft progression , contact energy between the OCCs representing the cleft walls is directed to increase relative to the baseline , while contact energy between cleft cells and the matrix is decreased ., This decrease in contact energy allows cell-matrix contacts to be established between the cleft cells ., The basement membrane is a dynamic structure that plays a critical role in branching morphogenesis , and cell-matrix adhesions are known to change dynamically during branching morphogenesis 5 , 6 , 8 , 10 ., In the GGH model , we represent basement membrane through the contact energy settings between the OCCs and the matrix , which is represented as a single homogenous cell not subject to area and perimeter constraints ., This contact energy is designated in our model as the “cell-matrix” contact energy and behaves as defined by Eq ., 1 . The actin cytoskeleton has long been known to be required for branching morphogenesis and was specifically shown to be required to maintain initiated clefts 20 , 21 ., In salivary gland epithelial cells , the actin cytoskeleton is organized primarily into cortical actin filaments at the cell perimeter ( Figure 2C , 2D ) in an E12 organ explant grown ex vivo for 0 or 24 hours ., Our subsequent work indicated that actin and non-muscle ( NM ) myosin II–mediated contraction are required to regulate cleft progression 6 ., The current model for cleft progression assumes that actomyosin contraction is required for assembling fibronectin through integrin activation 5 , 6 , 7 , which then stimulates local EMT through upregulation of Btbd7 and Slug and reduction of E-cadherin levels 22 ., Since EMT is one of the chief factors promoting cleft progression , we utilized variable cell-cell and cell-matrix contact energies to facilitate cleft progression ., Without any other energy factors affecting cleft progression , the resultant clefts were poorly formed ( Video S2 ) ., During early cleft formation , the cleft evolves as a thin opening between OCC cells , possibly primarily aided by random cell movements 5 , 11 and possibly from a hypothesized force generated by FN assembly 10 pushing assembled basement membrane into the cleft opening ., FN assembly , dependent on strength of actin contractility for integrin activation , might cause the two cleft-forming epithelial cell layers to separate ., FN assembly also stimulates proliferation 6 , presumably causing an outward force that emanates from inside the bud to counteract an inward cleft movement force produced by FN ., Since our model lacks specific structural representation of basement membrane assembly dynamics , we could not simulate the FN generated “cleft forming force” which was hypothesized to be the primary cause for progressive clefts 10 ., Therefore , we attempted to simulate the effect of this FN-actomyosin dependent “cleft forming” force through an energy function called focal point plasticity ( FPP ) ., This function establishes links between selected cells and regulates the distance between them , assigning an energy penalty for deviating from a target distance ., As noted in Eq ., 4 , the penalty varies based on the target distance , and the λ term ., To replicate the wedge-shaped cells in the cleft , we paired opposite cells on each side of the cleft , and set decreasing target distances for pairs deeper within the cleft ., These target distances were determin

Introduction, Material and Methods, Results, Discussion

Cleft formation during submandibular salivary gland branching morphogenesis is the critical step initiating the growth and development of the complex adult organ ., Previous experimental studies indicated requirements for several epithelial cellular processes , such as proliferation , migration , cell-cell adhesion , cell-extracellular matrix ( matrix ) adhesion , and cellular contraction in cleft formation; however , the relative contribution of each of these processes is not fully understood since it is not possible to experimentally manipulate each factor independently ., We present here a comprehensive analysis of several cellular parameters regulating cleft progression during branching morphogenesis in the epithelial tissue of an early embryonic salivary gland at a local scale using an on lattice Monte-Carlo simulation model , the Glazier-Graner-Hogeweg model ., We utilized measurements from time-lapse images of mouse submandibular gland organ explants to construct a temporally and spatially relevant cell-based 2D model ., Our model simulates the effect of cellular proliferation , actomyosin contractility , cell-cell and cell-matrix adhesions on cleft progression , and it was used to test specific hypotheses regarding the function of these parameters in branching morphogenesis ., We use innovative features capturing several aspects of cleft morphology and quantitatively analyze clefts formed during functional modification of the cellular parameters ., Our simulations predict that a low epithelial mitosis rate and moderate level of actomyosin contractility in the cleft cells promote cleft progression ., Raising or lowering levels of contractility and mitosis rate resulted in non-progressive clefts ., We also show that lowered cell-cell adhesion in the cleft region and increased cleft cell-matrix adhesions are required for cleft progression ., Using a classifier-based analysis , the relative importance of these four contributing cellular factors for effective cleft progression was determined as follows: cleft cell contractility , cleft region cell-cell adhesion strength , epithelial cell mitosis rate , and cell-matrix adhesion strength .

Branching morphogenesis is a complex and dynamic embryonic process that creates the structure of many adult organs , including the salivary gland ., During this process , many cellular changes occur in the epithelial cells , including changes in cell-cell adhesions , cell-extracellular matrix ( matrix ) adhesions , cell proliferation , and cellular contraction , resulting in formation of clefts in the epithelial cells of the organ ., A comprehensive understanding of the relative contributions of these cellular processes has crucial therapeutic implications for organ regeneration and functional restoration of organ structure in diseased salivary glands ., Here , we have developed a cell-based model of cleft progression and simulated cleft progression under conditions of altered cell-cell adhesions , cellular contractility , cell-matrix adhesion and cell proliferation to identify the optimum cellular conditions that cause clefts to progress ., The model predicts that cleft progression requires a moderate level of cleft cell contractility , a low epithelial proliferation rate , reduced cell-cell adhesion strength in the cleft and high cell-matrix adhesion strength also in the cleft region ., The results of our classification analysis demonstrate that cellular contractility in the cleft cells has a significant effect on cleft progression , followed by cell-cell adhesion strength , rate of cell proliferation , and strength of cell-matrix adhesion energies .

journal.pcbi.1002932

A Protein Turnover Signaling Motif Controls the Stimulus-Sensitivity of Stress Response Pathways

Eukaryotic cells must constantly recycle their proteomes ., Of the approximately 109 proteins in a typical mouse L929 fibrosarcoma cell , 106 are degraded every minute 1 ., Assuming first-order degradation kinetics , this rate of constitutive protein turnover , or flux , imposes an average half-life of 24 hours ., Not all proteins are equally stable , however ., Genome-wide quantifications of protein turnover in HeLa cells 2 , 3 and 3T3 murine fibroblasts 4 show that protein half-lives can span several orders of magnitude ., Thus while some proteins exist for months and even years 5 , others are degraded within minutes ., Gene ontology terms describing signaling functions are highly enriched among short-lived proteins 3 , 6 , 7 , suggesting that rapid turnover is required for proper signal transduction ., Indeed , defects in protein turnover are implicated in the pathogenesis of cancer and other types of human disease 8 , 9 ., Conspicuous among short-lived signaling proteins are those that regulate the p53 and NFκB stress response pathways ., The p53 protein itself , for example , has a half-life of less than 30 minutes 10 , 11 ., Mdm2 , the E3 ubiquitin ligase responsible for regulating p53 , has a half-life of 45 minutes 4 ., And the half-life of unbound IκBα , the negative feedback regulator of NFκB , is less than 15 minutes 12 , 13 ( see Figure S1 ) , requiring that 6 , 500 new copies of IκBα be synthesized every minute 13 ., Given the energetic costs of protein synthesis , we hypothesized that rapid turnover of these proteins is critical to the stimulus-response behavior of their associated pathways ., To test our hypothesis we developed a method to systematically alter the rates of protein turnover in mass action models without affecting their steady state abundances ., Our method requires an analytical expression for the steady state of a model , which we derive using the py-substitution method described in a companion manuscript ., From this expression , changes in parameter values that do not affect the steady state are found in the null space of the matrix whose elements are the partial derivatives of the species abundances with respect to the parameters ., We call this vector space the isostatic subspace ., After deriving a basis for this subspace , linear combinations of basis vectors identify isostatic perturbations that modify specific reactions independently of all the others , for example those that control protein turnover ., By systematic application of these isostatic perturbations to a model operating at steady state , the effects of flux on stimulus-responsiveness can be studied in isolation of changes to steady-state abundances ( see Methods ) ., We first apply our method to a prototypical negative feedback module in which an activator controls the expression of its own negative regulator ., We show that reducing the flux of either the activator or its inhibitor slows the response to stimulation ., However , reducing the flux of the activator lowers the magnitude of the response , whereas reducing the flux of the inhibitor increases it ., This complementarity allows the activator and inhibitor fluxes to exert precise control over the modules response to stimulation ., Given this level of control , we hypothesized that rapid turnover of p53 and Mdm2 must be required for p53 signaling ., A hallmark of p53 is that it responds to DNA damage in a series of digital pulses 14–18 ., These pulses are important for determining cell fate 19–21 ., To test whether high p53 and Mdm2 flux are required for p53 pulses , we applied our method to a model in which exposure to ionizing radiation ( IR ) results in oscillations of active p53 17 ., By varying each flux over three orders of magnitude , we show that high p53 flux is indeed required for oscillations ., In contrast , high Mdm2 flux is not required , but rather controls the refractory time in response to transient stimulation ., If the flux of Mdm2 is low , a second stimulus after 22 hours does not result in appreciable activation of p53 ., In contrast to p53 , the flux of NFκB turnover is very low , while the flux of its inhibitor , IκB , is very high ., Prior to stimulation , most NFκB is sequestered in the cytoplasm by IκB ., Upon stimulation by an inflammatory signal like tumor necrosis factor alpha ( TNF ) , IκB is phosphorylated and degraded , resulting in rapid but transient translocation of NFκB to the nucleus and activation of its target genes 22–24 ., Two separate pathways are responsible for the turnover of IκB 12 ., In one , IκB bound to NFκB is phosphorylated by the IκB kinase ( IKK ) and targeted for degradation by the ubiquitin-proteasome system ., In the other pathway , unbound IκB is targeted for degradation and requires neither IKK nor ubiquitination 25 , 26 ., We call these the “productive” and “futile” fluxes , respectively ., Applying our method to a model of NFκB activation , we show that the futile flux acts as a negative regulator of NFκB activation while the productive flux acts as a positive regulator ., We find that turnover of bound IκB is required for NFκB activation in response to TNF , while high turnover of unbound IκB prevents spurious activation of NFκB in response to low doses of TNF or ribotoxic stress caused by ultraviolet light ( UV ) ., As with p53 then , juxtaposition of a positive and negative regulatory flux govern the sensitivity of NFκB to different stimuli , and may constitute a common signaling motif for controlling stimulus-specificity in diverse signaling pathways ., To examine the effects of flux on stimulus-responsiveness , we built a prototypical negative feedback model reminiscent of the p53 or NFκB stress-response pathways ( Figure 1A ) ., In it , an activator “X” is constitutively expressed but catalytically degraded by an inhibitor , “Y” ., The inhibitor is constitutively degraded but its synthesis requires X . Activation is achieved by instantaneous depletion of Y , the result of which is accumulation of X until negative feedback forces a return to steady state ., The dynamics of this response can be described by two values: , the amplitude or maximum value of X after stimulation , and , the time at which is observed ( Figure 1B ) ., Parameters for this model were chosen such that the abundances of both X and Y are one arbitrary unit and X achieves its maximum value of at time , where the units of time are also arbitrary ., To address the role of these parameters in shaping the response of the activator , we first performed a traditional sensitivity analysis ., We found that increasing the synthesis of X ( Figure 1C ) , or decreasing the degradation of X ( Figure 1D ) or the synthesis of Y ( Figure 1E ) , all result in increased responsiveness ., However , these changes also increase the abundance of X . To distinguish between the effects caused by changes in flux and those caused by changes in abundance , we developed a method that alters the flux of X and Y while maintaining their steady state abundances at ., Using this method , we found that increasing the flux of X increases responsiveness ( Figure 1G ) , but not to the same extent as increasing the synthesis parameter alone ( Figure 1C ) ., In contrast , reducing the flux of Y yields the same increase in responsiveness as decreasing the synthesis of Y ( Figure 1E ) or the degradation of X ( Figure 1D ) ., These observations suggest that both the flux and abundance of X are important regulators of the response , as is the flux of Y , but not its abundance ., This conclusion is supported by the observation that when the abundance of Y is increased by reducing its degradation , there is little effect on signaling ( Figure 1F ) ., To further characterize the effects of flux on the activators response to stimulation , we applied systematic changes to the fluxes of X and Y prior to stimulation and plotted the resulting values of and ., Multiplying the flux of X over the interval showed , as expected , that the value of increases while the value of deceases ( Figure 2A ) ., In other words , a high activator flux results in a strong , fast response to stimulation ., If we repeat the process with the inhibitor , we find that both and decrease as the flux increases; a high inhibitor flux results in a fast but weak response ( Figure 2B ) ., This result illustrates that fluxes of different regulators can have different but complementary effects on stimulus-induced signaling dynamics ., Complementarity suggests that changes in flux can be identified such that is altered independently of , or independently of ., Indeed , if both activator and inhibitor fluxes are increased in equal measure , is held fixed while the value of decreases ( Figure 2C ) ., Increasing both fluxes thus simultaneously reduces the timescale of the response without affecting its magnitude ., An equivalent relationship can be found such that remains fixed while is affected ( Figure 2D ) ., Because an increase in either flux will reduce , to alter without affecting requires an increase in one flux but a decrease in the other ., Also , is more sensitive to changes in the inhibitor flux versus the activator flux; small changes in the former must be paired with larger changes in the latter ., This capability to achieve any value of or indicates that flux can precisely control the response to stimulation , without requiring any changes to steady state protein abundance ., Given that flux precisely controls the dynamic response to stimulation in a prototypical signaling module , we hypothesized that for p53 , oscillations in response to DNA damage require the high rates of turnover reported for p53 and Mdm2 ., To test this , we applied our method to a published model of p53 activation in response to ionizing gamma radiation ( IR ) , a common DNA damaging agent ( Figure 3A ) 17 ., Because the model uses arbitrary units , we rescaled it so that the steady state abundances of p53 and Mdm2 , as well as their rates of synthesis and degradation , matched published values ( see Table S1 ) ., We note that these values are also in good agreement with the consensus parameters reported in 16 ., Next we implemented a multiplier of Mdm2-independent p53 flux and let it take values on the interval ., For each value we simulated the response to IR using a step function in the production of the upstream Signal molecule , , as previously described 17 ., To characterize the p53 response we let be the amplitude of stable oscillations in phosphorylated p53 ( Figure 3B ) , and use this as a metric for p53 sensitivity ., Where , we say the module is sensitive to IR stimulation ., We find that is greater than zero only when the flux of p53 is near its observed value or higher ( Figure 4A ) ., If the flux of p53 is reduced by 2-fold or more , p53 no longer stably oscillates in response to stimulation , but exhibits damped oscillations instead ., Interestingly , repeating this analysis with a multiplier for the Mdm2 flux over the same interval reveals that Mdm2 flux has little bearing on p53 oscillations ( Figure 4B ) ., For any value of the multiplier chosen , ., As with p53 , this multiplier alters the Signal-independent flux of Mdm2 but does not affect Signal-induced Mdm2 degradation ., If oscillations are already compromised by a reduced p53 flux , no concomitant reduction in Mdm2 flux can rescue the oscillations ( Figure 4C ) ., We therefore conclude that the flux of p53 , but not Mdm2 , is required for IR-sensitivity in the p53 signaling module ., What then is the physiological relevance of high Mdm2 flux ?, In the model , signal-mediated Mdm2 auto-ubiquitination 27 is a major contributor to Mdm2 degradation after stimulation ., If Signal production is transient , Mdm2 protein levels must be restored solely via Signal-independent degradation ., We therefore hypothesized that if the flux of Mdm2 is low , Mdm2 protein levels would remain elevated after stimulation and compromise sensitivity to subsequent stimuli ., To test this hypothesis we again let the Mdm2 flux multiplier take values over the interval ., For each value we stimulated the model with a 2-hour pulse of Signal production , followed by 22 hours of rest , followed by a second 2-hour pulse ( Figure 3B ) ., We defined to be the amplitude of the first peak of phosphorylated p53 and to be the amplitude of the second peak ., Sensitivity to the second pulse is defined as the difference between and , with indicating full sensitivity ., As seen in Figures 4D and E , the flux of p53 has no bearing on the sensitivity to the second pulse while the flux of Mdm2 strongly affects it ., At one one-hundredth the observed Mdm2 flux – corresponding to protein half-life of 3-days – over 20 , 000 fewer molecules of p53 are phosphorylated , representing more than a two-fold reduction in sensitivity ( Figure 4E ) ., This result is robust with respect to the interval of time chosen between pulses ( Figure S2 ) ., If the sensitivity to the second pulse is already compromised by a reduced Mdm2 flux , a concomitant reduction in p53 flux fails to rescue it , while an increase in p53 flux still further reduces it ( Figure 4F ) ., We therefore conclude that the flux of Mdm2 , and not p53 , controls the systems refractory time , and a high Mdm2 flux is required to re-establish sensitivity after transient stimulation ., A second major stress-response pathway is that of NFκB ., NFκB is potently induced by the inflammatory cytokine TNF , but shows a remarkable resistance to internal metabolic perturbations or ribotoxic stresses induced by ultraviolet light ( UV ) 13 , or to triggers of the unfolded protein response ( UPR ) 28 ., Like p53 , the dynamics of NFκB activation play a major role in determining target gene expression programs 29 , 30 ., Although NFκB is considered stable , the flux of IκBα – the major feedback regulator of NFκB – is conspicuously high ., We hypothesized that turnover of IκB controls the stimulus-responsiveness of the NFκB signaling module ., Beginning with a published model of NFκB activation 13 , we removed the beta and epsilon isoforms of IκB , leaving only the predominant isoform , IκBα ( hereafter , simply “IκB”; Figure 5A ) ., Steady state analysis of this model supported the observation that almost all IκB is degraded by either of two pathways: a “futile” flux , in which IκB is synthesized and degraded as an unbound monomer; and a “productive” flux , in which free IκB enters the nucleus and binds to NFκB , shuttles to the cytoplasm , then binds to and is targeted for degradation by IKK ( Figure 5B ) ., These two pathways account for 92 . 5% and 7 . 3% of the total IκB flux , respectively ., The inflammatory stimulus TNF was modeled as before , using a numerically-defined IKK activity profile derived from in vitro kinase assays 30 ( Figure 5A , variable ) ., Stimulating with TNF results in strong but transient activation of NFκB ., A second stimulus , ribotoxic stress induced by UV irradiation , was modeled as 50% reduction in translation and results in only modest activity 13 ., As above , we let be the amplitude of activated NFκB in response to TNF and the time at which is observed ., Analogously , we let be the amplitude of NFκB in response to UV , and the time at which NFκB activation equals one-half ( see Figure 5C ) ., We then implemented multipliers for the futile and productive flux and let each multiplier take values on the interval ., For each value we simulated the NFκB response to TNF and UV and plotted the effects on and ., The results show that reducing the productive flux yields a slower , weaker response to TNF ( Figure 6A ) ., By analogy to Figure 2 , this indicates that the productive flux of IκB is a positive regulator of NFκB activation ., In contrast , the futile flux acts as a negative regulator of NFκB activity , though its effects on and are more modest ( Figure 6B ) ., Thus , similar to p53 , the activation of NFκB is controlled by a positive and negative regulatory flux ., In response to UV , a reduction in either flux delays NFκB activation , but reducing the futile flux results in a significant increase in while reducing the productive flux has almost no effect ( Figure 6C and D ) ., Conversely , while an increase in the futile flux has no effect on , an increase in the productive flux results in a significant increase ., If we now define NFκB to be sensitive to TNF or UV when or are ten-fold higher than its active but pre-stimulated steady state abundance , then TNF sensitivity requires a productive flux multiplier , while UV insensitivity requires a productive flux multiplier and a futile flux multiplier ., This suggests that the flux pathways of IκB may be optimized to preserve NFκB sensitivity to external inflammatory stimuli while minimizing sensitivity to internal metabolic stresses ., In contrast to p53 , the negative regulatory flux of IκB dominates the positive flux ., We hypothesized that this imbalance must affect the sensitivity of NFκB to weak stimuli ., To test this hypothesis we generated dose-response curves for TNF and UV using the following multipliers for the futile flux: , , , and ( see Methods ) ., The results confirm that reducing the futile flux of IκB results in hypersensitivity at low doses of TNF ( Figure 7 , Row 1 ) ., At one one-hundredth the wildtype flux , a ten-fold weaker TNF stimulus yields an equivalent NFκB response to the full TNF stimulus at the wildtype flux ., Similarly , a high futile flux prevents strong activation of NFκB in response to UV ( Figure 7 , Row 2 ) ., At and times the futile flux , UV stimulation results in a 20-fold increase in NFκB activity , compared to just a 2-fold increase at the wildtype flux ., We therefore conclude that turnover of unbound IκB controls the EC50 of the NFκB signaling module , and that rapid turnover renders NFκB resistant to metabolic and spurious inflammatory stimuli ., Previous studies have shown that the fluxes of p53 10 , 11 , its inhibitor Mdm2 31 , 32 , and the unbound negative regulator of NFκB , IκB 12 , are remarkably high ., To investigate whether rapid turnover of these proteins is required for the stimulus-response behavior of the p53 and NFκB stress response pathways , we developed a computational method to alter protein turnover , or flux , independently of steady state protein abundance ., For p53 , we show that high flux is required for sensitivity to sustained stimulation after ionizing radiation ( Figure 4A ) ., Interestingly , inactivating mutations in p53 have long been known to enhance its stability 33 , either by interfering with Mdm2-catalyzed p53 ubiquitination 34 , 35 , or by affecting p53s ability to bind DNA and induce the expression of new Mdm2 36–39 ., Inactivation of p53 also compromises the cells sensitivity to IR 40 , 41–43 ., Our results offer an intriguing explanation for this phenomenon , that p53 instability is required for oscillations in response to IR ., Indeed , IR sensitivity was shown to correlate with p53 mRNA abundance 44–46 , a likely determinant of p53 protein flux ., In further support of this hypothesis , mouse embryonic fibroblasts lacking the insulin-like growth factor 1 receptor ( IGF-1R ) exhibit reduced p53 synthesis and degradation , but normal protein abundance ., These cells were also shown to be insensitive to DNA damage , caused by the chemotherapeutic agent etoposide 32 ., Like p53 , increased stability of Mdm2 has been observed in human leukemic cell lines 47 , and Mdm2 is a strong determinant of IR sensitivity 48 , 49 ., Again our results suggest these observations may be related ., Activation of p53 in response to IR is mediated by the ATM kinase ( “Signal” in Figure 3 ) 50 , 51 ., Batchelor et al . show that saturating doses of IR result in feedback-driven pulses of ATM , and therefore p53 17 ., In Figure 4B we show that these are independent of Mdm2 flux ., However , sub-saturating doses of IR ( 10 Gy versus 0 . 5 Gy ) 52 , 53 cause only transient activation of ATM 54 , after which constitutive Mdm2 synthesis is required to restore p53 sensitivity ( Figure 4E ) ., This suggests that high Mdm2 flux is required for sensitivity to prolonged exposure to sub-saturating doses of IR ., Indeed , this inverse relationship between flux and refractory time has been observed before ., In Ba/F3 pro-B cells , high turnover of the Epo receptor maintains a linear , non-refractory response over a broad range of ligand concentrations 55 ., For NFκB , our method revealed that an isostatic reduction in the half-life of IκB sensitizes NFκB to TNF ( Figure 7A ) , as well as to ribotoxic stress agents like UV ( Figure 7B ) ., This observation agrees with previous theoretical studies using a dual kinase motif , where differential stability in the effector isoforms can modulate the dynamic range of the response 56 ., For NFκB , the flux of free IκB acts as a kinetic buffer against weak or spurious stimuli , similar to serial post-translational modifications on the T cell receptor 57 , or complementary kinase-phosphatase activities in bacterial two-component systems 58 ., In contrast , increasing the half-life of IκBα alone – without a coordinated increase in its rate of synthesis – increases the abundance of free IκBα and actually dampens the activity of NFκB in response to TNF 25 ., This difference highlights the distinction between isostatic perturbations and traditional , unbalanced perturbations that also affect the steady state abundances ., It also calls attention to a potential hazard when trying to correlate stimulus-responsiveness with protein abundance measurements: observed associations between responses and protein abundances do not rule out implied changes in kinetic parameters as the causal link ., Indeed static , and not kinetic measurements , are the current basis for molecular diagnosis of clinical specimens ., Thus while nuclear expression of p53 59–66 and NFκB 67–69 have been shown to correlate with resistance to treatment in human cancer , the correlation is not infallible 40 , 70–74 ., If stimulus-responsiveness can be controlled by protein turnover independently of changes to steady state abundance , then correlations between abundance and a therapeutic response may be masked by isostatic heterogeneity between cells ., For p53 and NFkB , we show that stimulus sensitivity can be controlled by a paired positive and negative regulatory flux ., We propose that this pairing may constitute a common regulatory motif in cell signaling ., In contrast to other regulatory motifs 75 , 76 , the “flux motif” described here does not have a unique structure ., The positive p53 flux , for example , is formed by the synthesis and degradation of p53 itself , while the positive flux in the NFκB system includes the nuclear import of free NFκB and export of NFκB bound to IκB ., For p53 , the negative flux is formed by synthesis and degradation of Mdm2 , while for NFκB it is formed by the synthesis , shuttling , and degradation of cytoplasmic and nuclear IκB ., Thus the reaction structure for each flux is quite different , but they nevertheless form a regulatory motif that is common to both pathways ( Figure 8 ) ., And since the mathematical models used here are only abstractions of the underlying network , the true structure of the p53 and NFκB flux motifs are in reality even more complex ., The identification of a flux motif that controls stimulus-responsiveness independently of protein abundances may prompt experimental investigation into the role of flux in signaling ., At a minimum , this could be achieved using fluorescently-labeled activator and inhibitor proteins in conjunction with tunable synthesis and degradation mechanisms ., The tet-responsive promoter system 77 , 78 , for example , could provide tunable synthesis , while the CLP-XP system 79 could provide tunable degradation ., For the two-dimensional analysis presented here , and to avoid confounding effects on signaling dynamics caused by shared synthesis and degradation machinery 80 , independently tunable synthesis and degradation mechanisms may be required ., If these techniques are applied to mutants lacking the endogenous regulators , this would further allow decreases in protein flux to be studied in addition to strictly increases ., Finally , in this study we have examined the effects of flux on stimulus-responsiveness , but in a typical signaling module , many other isostatic perturbations exist ., For example , the isostatic subspace of our NFκB model has 18 dimensions , of which only a few were required by the analysis presented here ., By simultaneously considering all isostatic perturbations , some measure of the dynamic plasticity of a system can be estimated , perhaps as a function of its steady-state ., Such an investigation can inform diagnosis of biological samples , and whether information from a single , static observation is sufficient to predict the response to a particular chemical treatment , or whether live-cell measurements are required as well ., As we have shown that protein turnover can be a powerful determinant of stimulus-sensitivity , we anticipate that kinetic measurements will be useful predictors of sensitivity to chemical therapeutics ., To begin , we assume that the system of interest has been modeled using mass action kinetics and that the steady state abundance of every biochemical species is a known function of input parameters ., In other words , such that ( 1 ) Equation 1 is the well-known steady-state equation; is a vector of independent parameters and is the vector of species abundances ., We use an overbar to denote a vector that satisfies Equation 1 ., For excellent reviews on mass action models and their limitations , see 81–83 ., For a method on finding analytical solutions to the steady state equation , see our accompanying manuscript ., Next , we wish to find a change in the input parameters such that the resulting change in the species abundances is zero , where is defined as Thus for , we require that The right-hand side of this equation can be approximated by a truncated Taylor series , as follows:where is the Jacobian matrix whose elements are the partial derivatives of each species with respect to each parameter ., Thus , for we require that In other words , must lie in the null space of ., We call this the isostatic subspace of the model – parameter perturbations in this subspace will not affect any of the steady-state species abundances ., If lies within the isostatic subspace , it is an isostatic perturbation vector ., Let be a matrix whose columns form a basis for the isostatic subspace ., Then a general expression for an isostatic perturbation vector is simply ( 2 ) where is a vector of unknown basis vector coefficients ., Finally , Equation 2 can be solved for a specific linear combination of basis vectors that achieves the desired perturbation ., In our case we identified those combinations that result in changes to protein turnover ., Our prototypical negative feedback model consists of two species , an activator “X” and an inhibitor “Y” , and four reactions , illustrated in Figure 1A ., Let denote the abundance of the activator and denote the abundance of the inhibitor ., An analytical expression for the steady-state of this model was identified by solving Equation 1 for the rates of synthesis , giving ( 3 ) ( 4 ) To parameterize the model we first let ., Degradation rate constants were then calculated such that at time , where again is the maximum amplitude of the response ., Activation was achieved by instantaneous reduction of to ., To modify the flux , we defined flux multipliers and such that and ., Note that by virtue of Equations 3 and 4 , values for and other than result in commensurate changes in and such that steady state is preserved ., See file “pnfm . sci” in Protocol S1 for details ., Figures 2A and 2B were achieved by letting and vary over the interval , then calculating the altered vector of rate constants and simulating the models response to stimulation ., Figure 2C required letting vary over this same interval while having ., Finally , Figure 2D was achieved by letting vary over the same interval , and for each value of , numerically calculating the value of that gave ., All species , reactions , and rate equations required by our model of p53 oscillations are as previously described 17 ., Our only modification was to scale the parameter values so that the rates of p53 and Mdm2 synthesis and degradation , as well as their steady-state abundances , matched published observations ( see Table S1 ) ., Specifically we let To derive a steady-state solution for this model , we solved Equation 1 for the steady-state abundance of Mdm2 and the rate of Mdm2-independent p53 degradation , giving To simulate the response to ionizing radiation we used the ( scaled ) stimulus given in 17 ., Namely , at time we let the rate of Signal production , , go to ., This stimulus was either maintained indefinitely ( Figure 4A–C ) or for just 2 hours , followed by 22 hours of rest , followed by a second 2 hour stimulation ( Figure 4D–F ) ., Changes in p53 or Mdm2 flux were achieved as above , by defining modifiers and such that ( 5 ) ( 6 ) ( 7 ) Prior to stimulation , we let one modifier take values on the interval while holding the other modifier constant ., Equations 6 and 7 ensure that the p53-independent flux of Mdm2 is modified without affecting its steady-state abundance ., Equation 5 , which is slightly more complicated , results in changes to the rate of Mdm2-independent p53 degradation , , by modifying the independent parameter , which controls the rate of p53 synthesis ., This yields the desired Numerical integration was carried out to time ., After each integration , we defined to be the minimum vertical distance between any adjacent peak and trough in phosphorylated p53 , and and to be the amplitudes of the first and second peak , respectively ., Details of this model can be found in the file “p53b . sci” in Protocol S1 ., For more information on the time delay parameters and , and their role in generating oscillations , see 84 , 85 ., Our model of NFκB activation is similar to the one described in 13 , except the beta and epsilon isoforms of IκB have been removed ., Our model has 10 species and 26 reactions , the majority of which are illustrated in Figure 5A ., Rate equations and parameter values are identical to those in 13 ., An analytical expression for the steady-state of this model was found by solving Equation 1 for the following dependent variables: , , , , and , and the rate constants , , and ., The precise expressions for these variables are extremely cumbersome but may be found in their entirety in the file “nfkb . sci” in Protocol S1 ., Activation of NFκB is achieved by either of two , time-dependent numerical input variables , and ., modifies the activity of IKK while modifies the efficiency of IκB translation ., Both have a finite range of and have unstimulated , wildtype values of and , respectively ., The inflammatory stimulus TNF is modeled using a unique function of derived from in vitro kinase assays 30 ., Since these assays only measured IKK activity out to 4 hours , we extended each stimulus by assuming the value of at 4 hours is maintained out to 24 hours ., Justification for this can be found in the 24-hour kinase assays in 86 , which shows no IKK activity between 8 and 24 hours after TNF stimulation ., UV stimulation is modeled using a step decrease in the value of from 1 . 0 to 0 . 5 for the entire 24 hours ., This mimics the 50% reduction in translational efficiency observed in 13 ., Steady-state analysis of this model revealed that over 99% of all IκB was degraded via either of two pathways , futile ( 92% ) and productive ( 7% ) ., See Figure 5B for the composition of these pathways ., To modify the flux through either pathway without altering any of the steady-state abundances , the algebraic method described above proved absolutely necessary .

Introduction, Results, Discussion, Methods

Stimulus-induced perturbations from the steady state are a hallmark of signal transduction ., In some signaling modules , the steady state is characterized by rapid synthesis and degradation of signaling proteins ., Conspicuous among these are the p53 tumor suppressor , its negative regulator Mdm2 , and the negative feedback regulator of NFκB , IκBα ., We investigated the physiological importance of this turnover , or flux , using a computational method that allows flux to be systematically altered independently of the steady state protein abundances ., Applying our method to a prototypical signaling module , we show that flux can precisely control the dynamic response to perturbation ., Next , we applied our method to experimentally validated models of p53 and NFκB signaling ., We find that high p53 flux is required for oscillations in response to a saturating dose of ionizing radiation ( IR ) ., In contrast , high flux of Mdm2 is not required for oscillations but preserves p53 sensitivity to sub-saturating doses of IR ., In the NFκB system , degradation of NFκB-bound IκB by the IκB kinase ( IKK ) is required for activation in response to TNF , while high IKK-independent degradation prevents spurious activation in response to metabolic stress or low doses of TNF ., Our work identifies flux pairs with opposing functional effects as a signaling motif that controls the stimulus-sensitivity of the p53 and NFκB stress-response pathways , and may constitute a general design principle in signaling pathways .

Eukaryotic cells constantly synthesize new proteins and degrade old ones ., While most proteins are degraded within 24 hours of being synthesized , some proteins are short-lived and exist for only minutes ., Using mathematical models , we asked how rapid turnover , or flux , of signaling proteins might regulate the activation of two well-known transcription factors , p53 and NFκB ., p53 is a cell cycle regulator that is activated in response to DNA damage , for example , due to ionizing radiation ., NFκB is a regulator of immunity and responds to inflammatory signals like the macrophage-secreted cytokine , TNF ., Both p53 and NFκB are controlled by at least one flux whose effect on activation is positive and one whose effect is negative ., For p53 these are the turnover of p53 and Mdm2 , respectively ., For NFκB they are the TNF-dependent and -independent turnover of the NFκB inhibitor , IκB ., We find that juxtaposition of a positive and negative flux allows for precise tuning of the sensitivity of these transcription factors to different environmental signals ., Our results therefore suggest that rapid synthesis and degradation of signaling proteins , though energetically wasteful , may be a common mechanism by which eukaryotic cells regulate their sensitivity to environmental stimuli .

cellular stress responses, signaling networks, mathematics, stress signaling cascade, regulatory networks, biology, nonlinear dynamics, systems biology, biochemical simulations, signal transduction, cell biology, computational biology, molecular cell biology, signaling cascades

journal.ppat.1004665

Recognition and Activation Domains Contribute to Allele-Specific Responses of an Arabidopsis NLR Receptor to an Oomycete Effector Protein

A critical step in the lifestyles of plant pathogens is the secretion of effectors—pathogen-encoded proteins that are translocated into the plant cell , where they manipulate the host and promote pathogen growth 1 ., Many effectors function to modulate basal immunity , but their presence in the plant cell may betray the pathogen and activate a second layer of effector-triggered immunity ( ETI ) if recognized by intracellular host immune receptors 2–4 ., Most effector recognition occurs via the NLR family of immune receptors ( for nucleotide-binding and leucine-rich repeat protein ) ., NLR activation results in an elevated immune response , characterized by generation of reactive oxygen species , activation of defense-associated genes , and a localized cell death known as the hypersensitive response ( HR ) 5 ., Multiple modes of effector-triggered NLR activation have been described ., Well-studied plant NLRs , such as RPS2 and RPM1 from Arabidopsis , recognize effectors indirectly 6–8 ., These NLRs are activated not by association with effectors themselves , but instead by recognizing their biochemical effects in the plant cell , leading to models of NLR activation in which the receptors recognize perturbation of “guarded” host proteins ., Guarded proteins can be true virulence targets or simply decoys inviting modification by the pathogen 9 , 10 ., In contrast , NLRs including RPP1 , L6 , Pi-ta , and others interact directly with recognized effector alleles , suggesting a second model in which direct effector-NLR interaction is required for immune activation 11–15 ., The modular domain architecture of NLRs allows for the integration of effector recognition and signaling activation by a switch-like mechanism 16 ., An N-terminal domain , usually either a coiled-coil or Toll/interleukin-1-receptor ( TIR ) domain , mediates downstream immune signaling 17 , 18 and is followed by a nucleotide binding ( NB ) domain and two helical ARC subdomains ( for Apaf-1 , R protein , CED-4 ) ., This composite NB-ARC domain functions as a switch through exchange of an internally bound ADP for ATP 19 , 20 , but may also participate in cis-regulatory interactions in order to maintain an ADP-bound “off” state 21–23 ., These intramolecular interactions typically occur with a series of C-terminal leucine-rich repeats ( LRRs ) , which are critical for auto-inhibition in the absence of effector 24 , 25 ., A second role for the LRRs is in effector recognition , where the protein-protein interaction capacity of LRRs can mediate effector binding 12 , 13 ., Consistent with this role , NLR recognition specificity can be expanded through LRR variation 26 , 27 ., Positively selected amino acids in Arabidopsis NLR proteins genome-wide are also disproportionately located in the LRRs , consistent with LRR co-evolution with effector proteins under selective pressure to evade recognition 28 ., Recognition of the oomycete effector ATR1 by the Arabidopsis NLR RPP1 is consistent with the direct interaction model of receptor activation , and serves as a model system for studying the molecular basis of NLR function 29 ., ATR1 is one of approximately 140 effectors expressed and secreted by the naturally-occurring Arabidopsis pathogen Hyaloperonospora arabidopsidis ( Hpa ) 30 , and is recognized specifically by the NLR protein RPP1 , leading to Hpa resistance 31 ., Diverse ATR1 alleles from Hpa strains encode effectors that are differentially recognized by RPP1 12 , 32 , and thus ATR1 can condition strain-dependent resistance on a given Arabidopsis ecotype 33 ., Variation in the RPP1 receptor also contributes to the spectrum of resistance phenotypes; for example , RPP1-NdA ( from the Niederzenz ecotype ) and RPP1-WsB ( from the Wassilewskija ecotype ) vary in recognition specificity , with RPP1-NdA recognizing a smaller subset of the ATR1 alleles recognized by RPP1-WsB 12 ., As Hpa is an obligate biotroph , surrogate systems are used to study the molecular basis for ATR1 recognition by RPP1 ., Alleles of ATR1 and RPP1 can be co-expressed in Nicotiana tabacum , resulting in a visible HR only for combinations in which RPP1 is able to recognize the ATR1 variant 12 ., Biochemical and genetic lines of evidence from this system support a direct interaction model of ATR1 recognition ., Co-immunoprecipitation of ATR1 alleles with RPP1-WsB correlates with HR activation capability 12 , suggesting that direct interaction with the effector leads to receptor activation and signaling for ETI ., Furthermore , the LRR domain of RPP1-WsB is sufficient for association with ATR1 , indicating a role for the LRRs in effector recognition ., ATR1 has no known virulence function , and adopts a WY-domain fold common to oomycete effector proteins 34 , specifically with an N-terminal three-helix bundle and two tandem WY-domains comprising the “head” and “body” of a seahorse-like structure , respectively 35 ., Single amino acid substitutions on both the head and body region can confer gain-of-recognition phenotypes to an unrecognized ATR1 allele , ATR1-Cala2 35 ., The surface-exposure of these substituted residues is consistent with these substitutions altering protein-protein interaction strength between ATR1 and RPP1 12 , 35 ., In this work , we address several outstanding questions regarding NLR receptor activation using the ATR1-RPP1 system ., We performed a random mutagenesis screen of unrecognized ATR1-Cala2 to generate combinations of ATR1 mutations that activate each RPP1 allele ., We show that different ATR1 mutants specifically associate with and activate resistance against either RPP1-NdA or RPP1-WsB , defining distinct recognition specificities for the two RPP1 alleles ., We then used these ATR1 mutants to probe recognition and activation domains of both RPP1 alleles ., Chimeric receptors revealed that while the LRRs were sufficient for recognition of ATR1 through molecular association , they were insufficient to recapitulate a receptor’s full range of specificity ., Instead , inclusion of the ARC2 subdomain is further required for effective receptor activation ., Previous work in our lab employed natural variation across ATR1 alleles to identify effector surfaces involved in recognition by RPP1 ., Amino acids conserved in recognized ATR1 variants were substituted into the distantly related and unrecognized allele , ATR1-Cala2 ., Four substitutions were each sufficient to give gain-of-recognition HR phenotypes by RPP1-WsB , but not RPP1-NdA , and combining all four substitutions led to a robust response with similar timing and intensity to the naturally recognized allele , ATR1-Emoy2 35 ., Here , we term this combined mutant ATR1-Cala2 WsB-GOF ( Gain-of-Function ) ., In combination with the crystal structure of ATR1 , these results indicated that surface-exposed residues on a central WY-domain were involved in recognition by RPP1-WsB 35 ., While single amino acid substitutions can confer RPP1-WsB recognition , no natural alleles or mutants of ATR1 exclusively activate RPP1-NdA ., To test whether other ATR1 surfaces could confer gain-of-recognition phenotypes , and whether allele-specific mutants could be obtained , we performed a random mutagenesis screen of ATR1-Cala2 for gain-of-recognition by either RPP1 allele ( Fig . 1 ) ., We transiently co-expressed 2 , 240 clones of Agrobacterium tumefaciens expressing the mutagenized effector domain ( Δ51 ) of ATR1-Cala2 with RPP1-NdA and RPP1-WsB in Nicotiana tabacum and screened for visible cell death HR , indicating NLR receptor activation ., Only two mutants were recovered from the screen: either a valine substitution at position 88 ( E88V ) or a set of three combined substitutions at positions 139 , 140 , and 142 ( S139T/Y140H/G142R ) was each sufficient to confer weak recognition by RPP1-NdA , but not RPP1-WsB at 48hpi , and both mutants developed stronger activation of RPP1-NdA by 72hpi ( Fig . 2A ) ., Allele-specific responses could be combined , as combining E88V with the previously described WsB-GOF substitutions led to recognition by both RPP1 alleles ( S1 Fig . ) ., HR strength varies by leaf age , and older , less sensitive leaves did not show similar gain-of-recognition phenotypes for either mutant ., However , combining all four substitutions into a single construct , termed ATR1-Cala2 NdA-GOF , allowed for more robust activation of RPP1-NdA than either individual substitution ( Fig . 2A right , alternate leaf ) , albeit still weaker than activation by ATR1-Emoy2 or ATR1-Cala2 WsB-GOF of their respective RPP1 alleles ( Fig . 2A left , 48 hpi ) ., All ATR1 mutants expressed to a similar level to the unrecognized WT ATR1-Cala2 allele ( Fig . 2B ) ., Thus , mutant variants of ATR1-Cala2 activate RPP1-NdA but not RPP1-WsB , defining unique recognition specificities for each RPP1 allele ., We next mapped the NdA-GOF mutations onto a homology model of the ATR1-Cala2 structure , using the solved ATR1-Emoy2 structure as a template ( Fig . 2C , see S2 Fig . for amino acid alignment ) ., All four substitutions are predicted to be completely or partially surface exposed ( >25 Å2 exposed ) , consistent with a predicted role in direct interaction with RPP1 ., All four substitutions also fall within the previously defined minimal region for RPP1 recognition 35 , further indicating that this helical region specifies recognition by RPP1-NdA ., E88V occurs on an N-terminal three-helix bundle ., S139T/Y140H/G142R occur on the first of two tandem WY-domain repeats , at the N-terminal portion of the α1 helix as defined across other RXLR-type oomycete effectors 34 , 36 ., Thus mutations on the specific surfaces on the “head” and “body” of the seahorse-like ATR1 structure can lead to specific activation of RPP1-NdA , but not RPP1-WsB , as summarized in Fig . 2D ., Three of the four constituent substitutions in the ATR1-Cala2 NdA-GOF mutant altered the predicted ATR1 surface charge ., These three mutations either substitute a positively charged side chain for a neutral side chain ( Y140H , G142R ) in the first WY-domain or a neutral side chain for a negative side chain ( E88V ) in the N-terminal three-helix bundle ., We hypothesized that any alteration in surface charge on these ATR1 surfaces would affect recognition by RPP1 ., This is further supported by the fact that one of the component NdA-GOF substitutions , Y140H , occurs at the same residue as a previously identified WsB-GOF substitution , Y140D 35 ., We generated alanine , lysine , and arginine substitutions at positions 88 and 140 in an ATR1-Cala2 background ., Only two of these substitutions , E88R and E88A , conferred weak gain-of-recognition by RPP1-NdA but not RPP1-WsB ( Fig . 3A ) ., This HR reaction was , however , weaker than the originally identified mutation , E88V , despite similar expression levels of all mutants ( Fig . 3B ) ., Thus , simple changes in surface charge do not provide a consistent pattern of recognition phenotypes against RPP1-NdA or RPP1-WsB ., Previously , the ability of ATR1 alleles to co-immunoprecipitate RPP1-WsB correlated with activation of HR upon transient co-expression 12 ., We tested whether the novel gain-of-recognition HR phenotypes of ATR1-Cala2 NdA-GOF and WsB-GOF also correlated with allele-specific RPP1 association ., ATR1-Cala2 WsB-GOF associated with RPP1-WsB at a level similar to the recognized allele , ATR1-Emoy2 , and did not associate with RPP1-NdA ( Fig . 4 ) ., ATR1-Cala2 NdA-GOF did not associate with RPP1-WsB , but associated with RPP1-NdA , although more weakly than ATR1-Emoy2 ( Fig . 4 ) , consistent with the weaker HR phenotype we observed for this mutant ( Fig . 2A ) ., Overall , the association of ATR1 mutants with RPP1 correlates with HR phenotypes in tobacco , consistent with direct interaction of these mutants with corresponding RPP1 proteins ., Although Hpa is an obligate biotroph and has not been successfully cultured or genetically manipulated 32 , surrogate approaches allow delivery of Hpa effectors into the Arabidopsis host by bacterial type three delivery to assay for induced host defense responses 37 , 38 ., We tested whether HR and association phenotypes for ATR1 GOF mutants observed in transient assays correlated with resistance phenotypes in Arabidopsis upon bacterial delivery ., ATR1 alleles and mutants were fused with the secretion signal of AvrRps4 , mediating delivery by the endogenous type-three secretion system ( TTSS ) of strain DC3000 of the virulent bacterium , Pseudomonas syringae pv ., tomato ., Strains delivering alleles and mutants of ATR1 were inoculated into the recombinant inbred Arabidopsis line HRI3860 , which lacks functional RPP1 32 , as well as transgenic HRI3860 lines expressing RPP1-NdA or RPP1-WsB ., While all strains grew to similar levels by 3 days post-inoculation ( dpi ) on HRI3860 plants , delivery of ATR1-Emoy2 strongly inhibited bacterial growth in transgenic lines expressing either RPP1 allele ( Fig . 5A ) ., Strains with either an empty vector or delivering ATR1-Cala2 were uninhibited in growth on either transgenic line ., ATR1-Cala2 WsB-GOF delivery strongly inhibited bacterial growth on the RPP1-WsB expressing line , while ATR1-Cala2 NdA-GOF weakly inhibited growth on the RPP1-NdA expressing line ( Fig . 5A ) ., Disease symptoms at 3 dpi correlated with growth inhibition ., While ATR1-Cala2 delivering strains remained visibly virulent on both transgenic lines , producing chlorosis and necrotic lesions , delivery of ATR1 GOF alleles led to RPP1-NdA or RPP1-WsB-specific avirulence , leading to a healthy phenotype similar to that observed after delivery of the fully recognized allele , ATR1-Emoy2 ( S3A Fig . ) ., We also tested whether gain-of-recognition alleles could elicit HR phenotypes in the same RPP1-expressing transgenic lines ., Delivery of different ATR1 alleles by Pseudomonas fluorescens ( Pf0 ) engineered to express a TTSS was previously shown to yield allele-specific HR in Arabidopsis , correlating with HR phenotypes in the transient tobacco assay 33 , 39 ., The ATR1-Cala2 WsB-GOF mutant elicited a visible HR on a RPP1-WsB transgenic line , but we were unable to detect activation of strong HR by Pf0 delivering ATR1-Cala2 NdA-GOF to an RPP1-NdA transgenic line ( S3B Fig . ) ., This weaker HR recognition phenotype of the NdA-GOF mutant is comparable to growth inhibition , Co-IP , and transient HR phenotypes ( Fig . 2A , Fig . 4 , Fig . 5A ) ., Despite the weak recognition and resistance phenotypes activated by ATR1-Cala2 NdA-GOF relative to those of ATR1-Cala2 WsB-GOF , the exclusivity of the mutants for activating either RPP1-NdA or RPP1-WsB allowed us to explore regions governing recognition and activation for each RPP1 protein ., We hypothesized that , since NdA-GOF and WsB-GOF mutations occurred on different ATR1 surfaces ( Fig . 2C ) , unique receptor regions might be responsible for recognition of each mutant ., We generated several chimeric RPP1 constructs to test the contribution of different NLR domains to activation ( Fig . 6A ) ., As the leucine-rich repeats ( LRRs ) of RPP1-WsB are sufficient for association with ATR1-Emoy2 12 , we first generated reciprocal chimeric constructs between RPP1-NdA and RPP1-WsB in which all predicted C-terminal LRRs were swapped ( see S4 Fig . for chimeric exchange points on pairwise sequence alignment ) ., Neither WsB LRRs in an NdA context ( NdA605WsB ) nor NdA LRRs in a WsB context ( WsB598NdA ) led to autoactivity or activation by ATR1-Cala2 , and both retained the conserved ability to recognize ATR1-Emoy2 ( Fig . 6B ) ., We next tested whether the LRRs from one RPP1 allele were sufficient to recognize the cognate ATR1-Cala2 NdA-GOF or WsB-GOF mutant ., As expected , NdA605WsB , a chimera with RPP1-WsB LRRs , was fully activated by ATR1-Cala2 WsB-GOF ( Fig . 6C ) ., To determine a potential cutoff point for recognition along the series of C-terminal LRRs , we constructed a series of LRR chimeras in which the chimeric exchange point was made every 4–5 repeats further C-terminal , based on homology-modeled LRRs 29 ., Most chimeric exchanges were completely inactivated: NdA825WsB and NdA916WsB were detected by Western blot , but , unlike WT RPP1-NdA and RPP1-WsB , did not recognize ATR1-Emoy2 ( S5A Fig . ) ., A third chimeric LRR construct , NdA1026WsB , was not expressed ( S5C Fig . ) ., One chimera , however , NdA708WsB , expressed ( S5C Fig . ) , recognized ATR1-Emoy2 , but showed diminished HR in response to ATR1-Cala2 WsB-GOF relative to NdA605WsB ( Fig . 6C ) , indicating that the first 4 LRRs of RPP1-WsB are required for full recognition of this mutant ., Surprisingly , a reciprocal chimera with RPP1-NdA LRRs , WsB598NdA , was only weakly activated by ATR1-Cala2 NdA-GOF ( Fig . 6D ) , only visible as mild HR on the backside of the leaf ( S5B Fig . ) ., This indicated that the RPP1-NdA LRRs are insufficient for recapitulating the full range of RPP1-NdA specificity ., We generated constructs with chimeric fusions further N-terminal relative to the LRRs , at the TIR-NBS , NBS-ARC1 , and ARC1-ARC2 domain junctions ., While a chimera exchanging the TIR domain ( WsB266NdA ) expressed but was inactive even in recognizing the conserved recognized ATR1 allele , ATR1-Emoy2 ( S5A Fig . ) , all other constructs recognized ATR1-Emoy2 with similar timing and intensity ( Fig . 6D , S4B Fig . ) ., Chimeras with RPP1-NdA C-termini comprising either the ARC2-LRR ( WsB473NdA ) or ARC1-ARC2-LRR ( WsB417NdA ) domains were able to activate in response to ATR1-Cala2 NdA-GOF ( Fig . 6D , S5B Fig . ) , with the strongest response generated by RPP1-NdA ARC2-LRRs in an RPP1-WsB context ( WsB473NdA ) ., Thus , while the RPP1-NdA LRRs are insufficient to allow activation by ATR1-Cala2 NdA-GOF , inclusion of an RPP1-NdA ARC2 domain in chimeric constructs allows for receptor activation and contributes to RPP1-NdA specificity ., The RPP1-NdA ARC2 domain could expand specificity in two possible ways—either the ARC2 domain is involved in recognition of the ATR1 mutant through allele-specific contacts , or it instead plays a role in facilitating activation upon ATR1 recognition by the LRRs ., We carried out experiments to distinguish between these binding versus activation roles ., A binding role would be supported by sufficiency of the RPP1-NdA ARC2 subdomain for both association and activation by ATR1-Cala2 NdA-GOF ., To test an association role , we expressed only the NB-ARC or ARC1-ARC2 domains ( amino acids 296–605 or 424–605 respectively ) and probed for co-immunoprecipitation with ATR1-Cala2 NdA-GOF or ATR1-Emoy2 ., Co-immunoprecipitation of either domain with ATR1 was not observed ( S6 Fig . ) , but the RPP1-NdA LRRs were also unable to interact with ATR1 in these experiments ( S7 Fig . ) ., We thus directly tested ARC2 sufficiency for activation by generating a double chimeric construct , WsB473NdA605WsB , with an RPP1-NdA ARC2 subdomain in an RPP1-WsB context ( see cutoffs in S4 Fig . ) ., This chimera did not activate in response to ATR1-Cala2 NdA-GOF ( Fig . 7A ) ., Thus the RPP1-NdA ARC2 domain is insufficient for activation by ATR1-Cala2 NdA-GOF , a finding inconsistent with a model where direct ARC2 contacts fully condition receptor activation ., An alternative , activation state model for expansion of specificity by the ARC2 domain is that the RPP1-NdA LRRs determine allele-specific recognition of ATR1 , but a corresponding RPP1-NdA ARC2 domain is required for its full activation capacity ., One prediction of this model is that RPP1-NdA LRRs should be sufficient to associate with the ATR1-Cala2 NdA-GOF mutant ., Unlike with RPP1-WsB LRRs , we were unable to co-immunoprecipitate RPP1-NdA LRRs ( amino acids 606–1164 ) even with the fully recognized allele , ATR1-Emoy2 ( S7 Fig . ) ., We were , however , able to investigate an LRR binding role by co-immunoprecipitating full-length chimeric constructs ., While WT ATR1-Cala2 did not co-immunoprecipitate any chimeric RPP1 constructs , the ATR1-Cala2 NdA-GOF mutant associated with RPP1 chimeras containing an RPP1-NdA LRR , despite different activation phenotypes in the HR assay ( Fig . 7B ) ., Although this interaction of WsB598NdA with ATR1-Cala2 NdA-GOF was weaker than with ATR1-Emoy2 , the level was similar to that of interaction with WT RPP1-NdA , suggesting that different receptor sensitivities , and not association strengths , condition the intensity of allele-specific HR ., Finally , the activation state model predicts that the threshold for specificity by a strongly recognized ATR1 allele will be lower for the desensitized WsB598NdA chimera ., We tested transient expression of wild-type and chimeric RPP1 constructs against a gradient inoculum of a second Agrobacterium strain expressing ATR1-Emoy2 ., From typical to low inoculum ( OD = 0 . 45 to OD = 0 . 03 ) , ATR1-Emoy2 activates all constructs evenly ( Fig . 7C ) ., However , lowering the inoculum to OD = 0 . 02 led to specific activation of an RPP1-NdA ARC2-LRR containing construct , WsB473NdA , but not an LRR-only construct , WsB598NdA , consistent with a sensitizing effect of an allelic RPP1-NdA ARC2 domain ., In summary , RPP1-NdA LRRs are sufficient for association with , but insufficient for full-strength activation by , an NdA-specific ATR1 mutant ., Thus RPP1-NdA LRRs condition recognition , but efficient receptor activation further requires an RPP1-NdA ARC2 activation domain ., A set of mutations arising from our random mutagenesis screen of unrecognized ATR1-Cala2 conferred exclusive recognition by RPP1-NdA , but not RPP1-WsB ( Fig . 2A ) ., These NdA-GOF mutations occurred on both the “head” region of ATR1 ( N-terminal three-helix bundle ) and on the “body” ( in the first of two tandem helical WY domains ) ( Fig . 2B ) ., This structural location is consistent with previously described mutations of a differentially recognized allele , ATR1-Maks9 , which is recognized by RPP1-WsB but not RPP1-NdA ., Both a “head” substitution , E92K , and a “body” substitution , D191G , conferred RPP1-NdA recognition to ATR1-Maks9 12 ., Site-directed substitution of positively charged and neutral sidechains on these surfaces in ATR1-Cala2 did not lead to consistent gain-of-recognition phenotypes ( Fig . 3 ) , suggesting that more intricately defined surface interactions mediate specificity for each allelic pair ., Nonetheless , the location of ATR1-Cala2 NdA-GOF substitutions is consistent with multiple surfaces contacting RPP1-NdA ., The GOF residues described here and previously 12 , 35 also have a higher degree of surface exposure than the overall molecule ( average of 81 Å2 relative to 65 Å2 for ATR1 overall ) , further consistent with surface contacts with RPP1 ., Molecule-level resolution data on these interacting surfaces , for example from crosslinking or crystallography experiments , will likely inform the basis of the recognition phenotypes described here ., The ATR1-Cala2 NdA-GOF mutant provides further evidence that direct activation of NLRs can show a gradient of response strength that depends on a variety of effector-NLR contacts ., First , the four ATR1-Cala2 NdA-GOF substitutions are additive in strength of transient HR ( Fig . 2A ) , consistent with multiple contact points with RPP1-NdA that quantitatively increase binding strength ., The ATR1 Cala2 NdA-GOF activated responses were also weaker compared to that activated by the fully recognized allele , ATR1-Emoy2 , in HR , co-immunoprecipitation , and bacterial growth inhibition assays ( Fig . 2A , Fig . 4 , Fig . 5A ) ., Second , three of four residues substituted in ATR1-Cala2 NdA-GOF are also conserved in the recognized allele , ATR1-Emoy2 ( S4 Fig . ) ., This suggests that RPP1-NdA recognition of the ATR1-Emoy2 allele occurs via interaction with different amino acid residues ., Together , these data indicate that RPP1-activated resistance is quantitative in strength and that different allelic combinations can depend on distinct ATR1-RPP1 contact points ., This flexibility of the interaction likely contributes to high levels of amino acid variation in both the recognition domain of ATR1 and the LRR region of RPP1 29 ., Quantitative NLR activation strength may also underlie the partial resistance phenotypes observed for many Hpa-Arabidopsis interactions 33 ., Formally , co-immunoprecipitation could indicate complex formation through an intermediate host factor ., However , the nature of all gain-of-function ATR1 mutations described—surface substitutions activating specific RPP1 alleles in an additive fashion—is more consistent with recognition conditioned by direct association ., Biochemical characterization of interaction strength between RPP1 and various ATR1 mutants may correlate affinity with subtle HR phenotypes described here , but these experiments await reliable methods for RPP1 protein expression and purification , which have been recalcitrant to date ., The ATR1-Cala2 NdA-GOF mutant also provides evidence for a high degree of specificity in the recognition spectra of RPP1 alleles ., RPP1-NdA only recognizes a subset of naturally occurring ATR1 alleles recognized by RPP1-WsB 12 , 33 , and thus prior to this study we could not distinguish between two competing hypotheses—either both alleles have distinct but overlapping recognition spectra , or RPP1-WsB is simply more sensitive to activation by a wider array of ATR1 variants ., Support for the former , specificity-based model comes from our random mutagenesis screen ., First , we did not recover any ATR1 mutants that activated RPP1-WsB; a more sensitive receptor might be expected to recognize a larger range of random mutants ., Second , the fact that the mutant described here , ATR1-Cala2 NdA-GOF , is recognized by and associates with RPP1-NdA but not RPP1-WsB disproves the hypothesis that the expanded RPP1-WsB recognition spectrum is due to increased sensitivity ., Rather , it is consistent with the closely related receptors having sophisticated , individual recognition abilities , in addition to their shared ability to recognize certain ATR1 variants ., “Arms race” co-evolution of effector and receptor 40 , 41 likely leads to the unique specificities of the two RPP1 alleles described here ., Pathogen-driven receptor diversity may also lead to autoimmune consequences for the host , as RPP1 variants from other Arabidopsis ecotypes can condition hybrid incompatibility through genetic interaction with other loci 42 ., Allele-specific activation by the ATR1-Cala2 NdA-GOF and WsB-GOF mutants allowed us to explore recognition regions in the respective recognizing RPP1 alleles ., A chimeric exchange placing RPP1-WsB LRRs in a RPP1-NdA context led to full ATR1-Cala2 WsB-GOF recognition ( Fig . 6C ) , consistent with a recognition role for the LRRs ., A smaller exchange excluding the first 4 LRRs gave a highly reduced recognition phenotype ( Fig . 6C , NdA605WsB vs . NdA708WsB ) , while still fully recognizing ATR1-Emoy2 ., Polymorphic residues between RPP1-NdA and RPP1-WsB in these 4 LRRs likely mediate specificity for recognition of ATR1-Cala2 WsB-GOF ( S8 Fig . , left ) , including several residues on a predicted concave β-sheet surface associated with ligand binding in other LRRs 43 ., Surprisingly , chimeric exchanges indicated a role of the RPP1-NdA ARC2 helical domain in activation ., LRRs from RPP1-NdA did not allow for complete activation by ATR1-Cala2 NdA-GOF ( Fig . 6D , WsB598NdA ) , but a further N-terminal chimeric exchange including the RPP1-NdA ARC2 domain ( WsB473NdA ) greatly strengthened the response ., Further experiments testing chimeric , full-length RPP1 constructs against this ATR1 mutant indicated that the ARC2 domain expands specificity by facilitating receptor activation rather than by associating with the effector ., The RPP1-NdA ARC2 domain in an RPP1-WsB context was insufficient for receptor activation by ATR1-Cala2 NdA-GOF , and an RPP1-NdA ARC2 domain was not required for its allele-specific ATR1 association ( Fig . 7A , B ) ., In addition , a chimera with RPP1-NdA ARC2 was able to activate in response to a lower inoculum of the fully recognized ATR1-Emoy2 allele ( Fig . 7C ) ., Thus the ARC2 domain of RPP1-NdA functions to expand its specificity by decreasing the threshold for activation of the receptor ., We speculate that polymorphisms between RPP1-NdA and RPP1-WsB in the ARC2 domain condition intramolecular allelic compatibility between domains of the receptor , possibly through interactions with the LRRs ., Several ARC2 polymorphisms occur on the predicted surface of a homology modeled ARC2 , and could be candidates for LRR interaction ( S8 Fig . , right ) ., The structural basis of plant NLR receptor activation remains unclear , but there is increasing evidence for a role of the NB-ARC domain not just as a nucleotide hydrolysis-based switch downstream of effector perception 19 , but as an active contributor to effector-triggered activation in combination with C-terminal LRRs ., For example , recent data from chimeric exchanges in animal intracellular NLRs , the mouse NAIPs ( NLR family , apoptosis inhibitory protein ) , corroborate a role for central helical domains in specificity ., NAIPs oligomerize and recruit Caspase-1 upon recognition of specific ligands 44 , and a series of chimeric exchanges between NAIP2 and NAIP5 indicated that specificity of oligomerization was determined by the second and third ARC-like helical domains rather than by the C-terminal LRRs 45 ., Examples from plant NLRs also support a role for ARC domains in specificity ., ARC helical domains of the flax receptor L can , in tandem with LRR polymorphisms , expand recognition of AvrL567 effector alleles 46 ., Recently , ARC2 domain mutations were described in the wheat NLR Pm3 that expand its specificity against previously unrecognized wheat powdery mildew strains 47 ., In contrast to inactivating or autoactivating mutations in other NLRs that map near the nucleotide binding pocket 24 , 48 , 49 , the Pm3 recognition-expanding mutations map to a region of the ARC2 subdomain predicted to be an exposed loop 47 , suggesting that the mutations affect intra- or intermolecular interactions ., Intramolecular contacts between the ARC2 and LRR domains are thought to maintain an “off” state 23 , 25 , 46 , 50 , and specific ARC2 amino acid substitutions can affect ARC2-LRR binding affinity 22 ., Here we provide data that the ARC2 subdomain is dispensable for effector association , but required for full-strength activation , in an allele-specific effector-NLR interaction ., Thus compatibility between ARC2 and LRRs , even in closely related NLR variants such as RPP1-NdA and RPP1-WsB , may be required for a full range of specificity by allowing efficient receptor activation ., We present a model of RPP1 function where cooperation between recognition by LRRs and activation by the ARC2 subdomain leads to a full-strength receptor response ( Fig . 8 ) ., In an “off” state , RPP1-NdA is not activated by the unrecognized ATR1-Cala2 effector protein ( Fig . 8A ) ., Substitutions on distributed surfaces of ATR1-Cala2 allow activation of RPP1-NdA but not RPP1-WsB ( Fig . 8B ) ., Specificity is a multi-stage process: stepwise increases in recognition and activation strength in response to the ATR1 mutant can be achieved by substituting the recognition domain ( LRRs ) and activation domain ( ARC2 ) from RPP1-NdA ., Complete activation strength is achieved with full intramolecular compatibility in the wild-type RPP1-NdA receptor ., While our data provide new insight into the roles for specificity domains of ATR1 and RPP1 , it remains to be seen precisely how molecular contacts between the effector and receptor relieve plant NLR autoinhibition ., Escherichia coli strain DH5α was used for cloning and propagation of pEarleyGate and pEDV3 constructs , and was grown at 37°C in LB or LB agar supplemented with 25 μg /mL kanamycin or 10μg/mL gentamycin ., Agrobacterium strain GV3101::pMP90 51 was propagated at 28°C in LB supplemented with 50 μg/mL gentamycin ., Pseudomonas strains were propagated at 28°C ., Pseudomonas fluorescens ( Pf0 ) was grown on Pseudomonas Agar solid medium supplemented with 50

Introduction, Results, Discussion, Materials and Methods

In plants , specific recognition of pathogen effector proteins by nucleotide-binding leucine-rich repeat ( NLR ) receptors leads to activation of immune responses ., RPP1 , an NLR from Arabidopsis thaliana , recognizes the effector ATR1 , from the oomycete pathogen Hyaloperonospora arabidopsidis , by direct association via C-terminal leucine-rich repeats ( LRRs ) ., Two RPP1 alleles , RPP1-NdA and RPP1-WsB , have narrow and broad recognition spectra , respectively , with RPP1-NdA recognizing a subset of the ATR1 variants recognized by RPP1-WsB ., In this work , we further characterized direct effector recognition through random mutagenesis of an unrecognized ATR1 allele , ATR1-Cala2 , screening for gain-of-recognition phenotypes in a tobacco hypersensitive response assay ., We identified ATR1 mutants that, a ) confirm surface-exposed residues contribute to recognition by RPP1 , and, b ) are recognized by and activate the narrow-spectrum allele RPP1-NdA , but not RPP1-WsB , in co-immunoprecipitation and bacterial growth inhibition assays ., Thus , RPP1 alleles have distinct recognition specificities , rather than simply different sensitivity to activation ., Using chimeric RPP1 constructs , we showed that RPP1-NdA LRRs were sufficient for allele-specific recognition ( association with ATR1 ) , but insufficient for receptor activation in the form of HR ., Additional inclusion of the RPP1-NdA ARC2 subdomain , from the central NB-ARC domain , was required for a full range of activation specificity ., Thus , cooperation between recognition and activation domains seems to be essential for NLR function .

Plants defend themselves against pathogens using specific multi-domain immune receptors , which are able to recognize secreted “effector” proteins from the pathogen , and thus activate an immune response ., Variants of the Arabidopsis immune receptor RPP1 recognize different alleles of the oomycete effector ATR1 through direct association ., RPP1 and ATR1 alleles from different ecotypes and strains show a spectrum of recognition phenotypes , reflecting coevolution by the plant and pathogen to evade and re-establish immunity ., In this work , we identified mutations in an unrecognized ATR1 allele that lead to allele-specific recognition by RPP1 ., Using chimeric constructs of the immune receptor , in which domains were swapped between two alleles , we were able to determine domains contributing to allele-specific activation ., Our data point to the involvement of two domains in specific activation of immune receptors—one to associate with the effector , and one to sensitize the receptor and facilitate activation ., We suggest that these domains must cooperate to efficiently and specifically recognize pathogen effectors ., As NLRs confer pathogen resistance in many crop species , characterizing specificity domains involved in effector recognition will inform future efforts to breed or engineer disease resistant varieties .

journal.pcbi.1006637

Dynamical anchoring of distant arrhythmia sources by fibrotic regions via restructuring of the activation pattern

Many clinically relevant cardiac arrhythmias are conjectured to be organized by rotors ., A rotor is an extension of the concept of a reentrant source of excitation into two or three dimensions with an area of functional block in its center , referred to as the core ., Rapid and complex reentry arrhythmias such as atrial fibrillation ( AF ) and ventricular fibrillation ( VF ) are thought to be driven by single or multiple rotors ., A clinical study by Narayan et al . 1 indicated that localized rotors were present in 68% of cases of sustained AF ., Rotors ( phase singularities ) were also found in VF induced by burst pacing in patients undergoing cardiac surgery 2 , 3 and in VF induced in patients undergoing ablation procedures for ventricular arrhythmias 4 ., Intramural rotors were also reported in early phase of VF in the human Langendorff perfused hearts 5 , 6 ., It was also demonstrated that in most cases rotors originate and stabilize in specific locations 4–8 ., A main mechanism of rotor stabilization at a particular site in cardiac tissue was proposed in the seminal paper from the group of Jalife 9 ., It was observed that rotors can anchor and exhibit a stable rotation around small arteries or bands of connective tissue ., Later , it was experimentally demonstrated that rotors in atrial fibrillation in a sheep heart can anchor in regions of large spatial gradients in wall thickness 10 ., A recent study of AF in the right atrium of the explanted human heart 11 revealed that rotors were anchored by 3D micro-anatomic tracks formed by atrial pectinate muscles and characterized by increased interstitial fibrosis ., The relation of fibrosis and anchoring in atrial fibrillation was also demonstrated in several other experimental and numerical studies 8 , 11–14 ., Initiation and anchoring of rotors in regions with increased intramural fibrosis and fibrotic scars was also observed in ventricles 5 , 7 , 15 ., One of the reasons for rotors to be present at the fibrotic scar locations is that the rotors can be initiated at the scars ( see e . g . 7 , 15 ) and therefore they can easily anchor at the surrounding scar tissue ., However , rotors can also be generated due to different mechanisms , such as triggered activity 16 , heterogeneity in the refractory period 16 , 17 , local neurotransmitter release 18 , 19 etc ., What will be the effect of the presence of the scar on rotors in that situation , do fibrotic areas ( scars ) actively affect rotor dynamics even if they are initially located at some distance from them ?, In view of the multiple observations on correlation of anchoring sites of the rotors with fibrotic tissue this question translates to the following: is this anchoring just a passive probabilistic process , or do fibrotic areas ( scars ) actively affect the rotor dynamics leading to this anchoring ?, Answering these questions in experimental and clinical research is challenging as it requires systematic reproducible studies of rotors in a controlled environment with various types of anchoring sites ., Therefore alternative methods , such as realistic computer modeling of the anchoring phenomenon , which has been extremely helpful in prior studies , are of great interest ., The aim of this study is therefore to investigate the processes leading to anchoring of rotors to fibrotic areas ., Our hypothesis is that a fibrotic scar actively affects the rotor dynamics leading to its anchoring ., To show that , we first performed a generic in-silico study on rotor dynamics in conditions where the rotor was initiated at different distances from fibrotic scars with different properties ., We found that in most cases , scars actively affect the rotor dynamics via a dynamical reorganization of the excitation pattern leading to the anchoring of rotors ., This turned out to be a robust process working for rotors located even at distances more than 10 cm from the scar region ., We then confirmed this phenomenon in a patient-specific model of the left ventricle from a patient with remote myocardial infarction ( MI ) and compared the properties of this process with clinical ECG recordings obtained during induction of a ventricular arrhythmia ., Our anatomical model is based on an individual heart of a post-MI patient reconstructed from late gadolinium enhanced ( LGE ) magnetic resonance imaging ( MRI ) was described in detail previously 20 ., Briefly , a 1 . 5T Gyroscan ACS-NT/Intera MR system ( Philips Medical Systems , Best , the Netherlands ) system was used with standardized cardiac MR imaging protocol ., The contrast –gadolinium ( Magnevist , Schering , Berlin , Germany ) ( 0 . 15 mmol/kg ) – was injected 15 min before acquisition of the LGE sequences ., Images were acquired with 24 levels in short-axis view after 600–700 ms of the R-wave on the ECG within 1 or 2 breath holds ., The in-plane image resolution is 1 mm and through-plane image resolution is 5 mm ., Segmentation of the contours for the endocardium and the epicardium was performed semi-automatically on the short-axis views using the MASS software ( Research version 2014 , Leiden University Medical Centre , Leiden , the Netherlands ) ., The myocardial scar was identified based on signal intensity ( SI ) values using a validated algorithm as described by Roes et al . 21 ., In accordance with the algorithm , the core necrotic scar is defined as a region with SI >41% of the maximal SI ., Regions with lower SI values were considered as border zone areas ., In these regions , we assigned the fibrosis percentage as normalized values of the SI as in Vigmond et al . 22 ., In the current paper , fibrosis was introduced by generating a random number between 0 and 1 for each grid point and if the random number was less than the normalized SI at the corresponding pixel the grid point was considered as fibroblast ., Currently there is no consensus on how the SI values should be used for clinical assessment of myocardial fibrosis and various methods have been reported to produce significantly different results 23 ., However , the method from Vigmond et al . properly describes the location of the necrotic scar region in our model as for the fibrosis percentage of more than 41% we observe a complete block of propagation inside the scar ., This means that all tissue which has a fibrotic level higher than 41% behaves like necrotic scar ., The approach and the 2D model was described in detail in previous work 24–26 ., Briefly , for ventricular cardiomyocyte we used the ten Tusscher and Panfilov ( TP06 ) model 27 , 28 , and the cardiac tissue was modeled as a rectangular grid of 1024 × 512 nodes ., Each node represented a cell that occupied an area of 250 × 250 μm2 ., The equations for the transmembrane voltage are given by, C m d V i k d t = ∑ α , β ∈ { - 1 , + 1 } η i k α β g gap ( V i + α , k + β - V i k ) - I ion ( V i k , … ) , ( 1 ), where Vik is the transmembrane voltage at the ( i , k ) computational node , Cm is membrane capacitance , ggap is the conductance of the gap junctions connecting two neighboring myocytes , Iion is the sum of all ionic currents and η i k α β is the connectivity tensor whose elements are either one or zero depending on whether neighboring cells are coupled or not ., Conductance of the gap junctions ggap was taken to be 103 . 6 nS , which results in a maximum velocity planar wave propagation in the absence of fibrotic tissue of 72 cm/s at a stimulation frequency of 1 Hz ., ggap was not modified in the fibrotic areas ., A similar system of differential equations was used for the 3D computations where instead of the 2D connectivity tensor η i k α β we used a 3D weights tensor w i j k α β γ whose elements were in between 0 and 1 , depending both on coupling of the neighbor cells and anisotropy due to fiber orientation ., Each node in the 3D model represented a cell of the size of 250 × 250 × 250 μm3 ., 20s of simulation in 3D took about 3 hours ., Fibrosis was modeled by the introduction of electrically uncoupled unexcitable nodes 29 ., The local percentage of fibrosis determined the probability for a node of the computational grid to become an unexcitable obstacle , meaning that for high percentages of fibrosis , there is a high chance for a node to be unexcitable ., As previous research has demonstrated that LGE-MRI enhancement correlates with regions of fibrosis identified by histological examination 30 , we linearly interpolated the SI into the percentage of fibrosis for the 3D human models ., In addition , the effect of ionic remodeling in fibrotic regions was taken into account for several results of the paper 31 , 32 ., To describe ionic remodeling we decreased the conductance of INa , IKr , and IKs and depending on local fibrosis level as:, G Na = ( 1 - 1 . 55 f 100 % ) G Na 0 , ( 2 ) G Kr = ( 1 - 1 . 75 f 100 % ) G Kr 0 , ( 3 ) G Ks = ( 1 - 2 f 100 % ) G Ks 0 , ( 4 ), where GX is the peak conductance of IX ionic current , G X 0 is the peak conductance of the current in the absence of remodeling , and f is the local fibrosis level in percent ., These formulas yield a reduction of 62% for INa , of 70% for IKr , and of 80% for IKs if the local fibrosis f is 40% ., These values of reduction are , therefore , in agreement with the values published in 33 , 34 ., The normal conduction velocity at CL 1000 ms is 72 cm/s ( CL 1000 ms ) ., However , as the compact scar is surrounded by fibrotic tissue , the velocity of propagation in that region gradually decreases with the increase in the fibrosis percentage ., For example for fibrosis of 30% , the velocity decreases to 48 cm/s ( CL 1000 ms ) ., We refer to Figure 1 in Ten Tusscher et al 25 for the planar conduction velocity as a function of the percentage fibrosis in 2D tissue and 3D tissue ., The geometry and extent of fibrosis in the human left ventricles were determined using the LGE MRI data ., The normalized signal intensity was used to determine the density of local fibrosis ., The fiber orientation is presented in detail in the supplementary S1 Appendix ., The model for cardiac tissue was solved by the forward Euler integration scheme with a time step of 0 . 02 ms . The numerical solver was implemented using the CUDA toolkit for performing the computations on graphics processing units ., Simulations were performed on a GeForce GTX Titan Black graphics card using single precision calculations ., The eikonal equations for anisotropy generation were solved by the fast marching Sethian’s method 35 ., The eikonal solver and the 3D model generation pipeline were implemented in the OCaml programming language ., Rotors were initiated by an S1S2 protocol , as shown in the supplementary S1 Fig . Similarly , in the whole heart simulations , spiral waves ( or scroll waves ) were created by an S1S2 protocol ., For the compact scar geometry used in our simulations the rotation of the spiral wave was stationary , the period of rotation of the anchored rotor was always more than 280 ms , while the period of the spiral wave was close to 220 msec ., Therefore , we determined anchoring as follows: if the period of the excitation pattern was larger than 280 ms over a measuring time interval of 320 ms we classified the excitation as anchored ., When the type of anchoring pattern was important ( single or multi-armed spiral wave ) we determined it visually ., If in all points of the tissue , the voltage was below -20 mV , the pattern was classified as terminated ., We applied the classification algorithm at t = 40 s in the simulation ., In the whole heart , the pseudo ECGs were calculated by assuming an infinite volume conductor and calculating the dipole source density of the membrane potential Vm in all voxel points of the ventricular myocardium , using the following equation 36, E C G ( t ) = ∫ ( r → , D ( r → ) ∇ → V ( t ) ) | r → | 3 d 3 r ( 5 ), whereby D is the diffusion tensor , V is the voltage , and r → is the vector from each point of the tissue to the recording electrode ., The recording electrode was placed 10 cm from the center of the ventricles in the transverse plane ., Twelve-lead ECGs of all induced ventricular tachycardia ( VT ) of patients with prior myocardial infarction who underwent radiofrequency catheter ablation ( RFCA ) for monomorphic VT at LUMC were reviewed ., All patients provided informed consent and were treated according to the clinical protocol ., Programmed electrical stimulation ( PES ) is routinely performed before RFCA to determine inducibility of the clinical/presumed clinical VT ., All the patients underwent PES and ablation according to the standard clinical protocol , therefore no ethical approval was required ., Ablation typically targets the substrate for scar-related reentry VT ., After ablation PES is repeated to test for re-inducibility and evaluate morphology and cycle length of remaining VTs ., The significance of non-clinical , fast VTs is unclear and these VTs are often not targeted by RFCA ., PES consisted of three drive cycle lengths ( 600 , 500 and 400 ms ) , one to three ventricular extrastimuli ( ≥200 ms ) and burst pacing ( CL ≥200 ms ) from at least two right ventricular ( RV ) sites and one LV site ., A positive endpoint for stimulation is the induction of any sustained monomorphic VT lasting 30 s or requiring termination ., ECG and intracardiac electrograms ( EG ) during PES were displayed and recorded simultaneously on a 48-channel acquisition system ( Prucka CardioLab EP system , GE Healthcare , USA ) for off-line analysis ., Fibrotic scars can not only anchor the rotors but can dynamically anchor them from a large distance ., In the first experiments we studied spiral wave dynamics with and without a fibrotic scar in a generic study ., The diameter of the fibrotic region was 6 . 4 cm , based on the similar size of the scars from patients with documented and induced VT ( see the Methods section , Magnetic Resonance Imaging ) ., The percentage of fibrosis changed linearly from 50% at the center of the scar to 0% at the scar boundary ., We initiated a rotor at a distance of 15 . 5 cm from the scar ( Fig 1 , panel A ) which had a period of 222 ms and studied its dynamics ., First , after several seconds the activation pattern became less regular and a few secondary wave breaks appeared at the fibrotic region ( Fig 1 , panel B ) ., These irregularities started to propagate towards the tip of the initial rotor ( Fig 1 , panel C-D ) creating a complex activation picture in between the scar and the initial rotor ., Next , one of the secondary sources reached the tip of the original rotor ( Fig 1 , panel E ) ., Then , this secondary source merged with the initial rotor ( Fig 1 , panel F ) , which resulted in a deceleration of the activation pattern and promoted a chain reaction of annihilation of all the secondary wavebreaks in the vicinity of the original rotor ., At this moment , a secondary source located more closely to the scar dominated the simulation ( Fig 1 , panel G ) ., The whole process now started again ( Fig 1 , panels H-K ) , until finally only one source became the primary source anchored to the scar ( Fig 1L ) with a rotation period of 307 ms . For clarity , a movie of this process is provided as supplementary S1 Movie ., Note that this process occurs only if a scar with surrounding fibrotic zone was present ., In the simulation entitled as ‘No scar’ in Fig 1 , we show a control experiment when the same initial conditions were used in tissue without a scar ., In the panel entitled as ‘Necrotic scar’ in Fig 1 , a simulation with only a compact region without the surrounding fibrotic tissue is shown ., In both cases the rotor was stable and located at its initial position during the whole period of simulation ., The important difference here from the processes shown in Fig 1 ( Fibrotic scar ) is that in cases of ‘No scar’ and ‘Necrotic scar’ no new wavebreaks occur and thus we do not have a complex dynamical process of re-arrangement of the excitation patterns ., We refer to this complex dynamical process leading to anchoring of a distant rotor as dynamical anchoring ., Although this process contains a phase of complex behaviour , overall it is extremely robust and reproducible in a very wide range of conditions ., In the second series of simulations , the initial rotor was placed at different distances from the scar border , ranging from 1 . 8 to 14 . 3 cm , to define the possible outcomes , see Fig 2 . Here , in addition to a single anchored rotor shown in Fig 1H we could also obtain other final outcomes of dynamical anchoring: we obtained rotors rotating in the opposite direction ( Fig 2A , top ) , double armed anchored rotors which had 2 wavefronts rotating around the fibrotic regions ( Fig 2A , middle ) or annihilation of the rotors ( Fig 2A , bottom , which show shows no wave around the scar ) , which normally occurred as a result of annihilation of a figure-eight-reentrant pattern ., To summarize , we therefore had the following possible outcomes:, Termination of activity A rotor rotating either clockwise or counter-clockwise A two- or three-armed rotor rotating either clockwise or counter-clockwise, Fig 2 , panel B presents the relative chance of the mentioned activation patterns to occur depending on the distance between the rotor and the border of the scar ., We see , indeed , that for smaller initial distances the resulting activation pattern is always a single rotor rotating in the same direction ., With increasing distance , other anchoring patterns are possible ., If the distance was larger than about 9 cm , there is at least a 50% chance to obtain either a multi-armed rotor or termination of activity ., Also note that such dynamical anchoring occurred from huge distances: we studied rotors located up to 14 cm from the scar ., However , we observed that even for very large distances such as 25 cm or more such dynamical anchoring ( or termination of the activation pattern ) was always possible , provided enough time was given ., We measured the time required for the anchoring of rotors as a function of the distance from the scar ., For each distance , we performed about 60 computations using different seed values of the random number generator , both with and without taking ionic remodeling into account ., The results of these simulations are shown in Fig 3 . We see that the time needed for dynamical anchoring depends linearly on the distance between the border of the scar and the initial rotor ., The blue and yellow lines correspond to the scar model with and without ionic remodeling , respectively ( ionic remodeling was modelled by decreasing the conductance of INa , IKr , and IKs as explained in the Methods Section ) ., We interpret these results as follows; The anchoring time is mainly determined by the propagation of the chaotic regime towards the core of the original rotor and this process has a clear linear dependency ., For distant rotors , propagation of this chaotic regime mainly occurs outside the region of ionic remodelling , and thus both curves in Fig 3 have the same slope ., However , in the presence of ionic remodelling , the APD in the scar region is prolonged ., This creates a heterogeneity and as a consequence the initial breaks in the scar region are formed about 3 . 5 s earlier in the scar model with remodeling compared with the scar model without remodeling ., To identify some properties of the substrate necessary for the dynamical anchoring we varied the size and the level of fibrosis within the scar and studied if the dynamical anchoring was present ., Due to the stochastic nature of the fibrosis layout we performed about 300 computations with different textures of the fibrosis for each given combination of the scar size and the fibrosis level ., The results of this experiment are shown in Fig 4 . Dynamical anchoring does not occur when the scar diameter was below 2 . 6 cm , see Fig 4 . For scars of such small size we observed the absence of both the breakup and dynamical anchoring ., We explain this by the fact that if the initial separation of wavebreaks formed at the scar is small , the two secondary sources merge immediately , repairing the wavefront shape and preventing formation of secondary sources 37 ., Also , we see that this effect requires an intermediate level of fibrosis density ., For small fibrosis levels no secondary breaks are formed ( close to the boundary of the fibrotic tissue ) ., Also , no breaks could be formed if the fibrosis level is larger than 41% in our 2D model ( i . e . closer to the core ) , as the tissue behaves like an inexcitable scar ., For a fibrosis > 41% the scar effectively becomes a large obstacle that is incapable of breaking the waves of the original rotor 37 ., Close to the threshold of 41% we have also observed another interesting pattern when the breaks are formed inside the core of the scar ( inside the > 41% region ) only and cannot exit to the surrounding tissue , see the supplementary S1 Movie ., Finally , note that Fig 4 illustrates only a few factors important for the dynamical anchoring in a simple setup in an isotropic model of cardiac tissue ., The particular values of the fibrosis level and the size of the scar can also depend on anisotropy , the texture of the fibrosis and its possible heterogeneous distribution ., To verify that the dynamical anchoring takes place in a more realistic geometry , we developed and investigated this effect in a patient-specific model of the human left ventricle , see the Method section for details ., The scar in this dataset has a complex geometry with several compact regions with size around 5-7 cm in which the percentage of fibrosis changes gradually from 0% to 41% at the core of the scar based on the imaging data , see Methods section ., The remodeling of ionic channels at the whole scar region was also included to the model ( including borderzone as described the Fibrosis Model in the method section ) ., We studied the phenomenon of dynamical anchoring for 16 different locations of cores of the rotor randomly distributed in a slice of the heart at about 4 cm from the apex ( see Fig 5 ) ., Cardiac anisotropy was generated by a rule-based approach described in details in the Methods section ( Model of the Human Left Ventricle ) ., Of the 16 initial locations , shown in Fig 5 , there was dynamical anchoring to the fibrotic tissue in all cases , with and without ionic remodeling ., After the anchoring , in 4 cases the rotor annihilated ., The effect of the attraction was augmented by the electrophysiolical remodelling , similar as in 2D ., A representative example of our 3D simulations is shown in Fig 5 . We followed the same protocol as for the 2D simulations ., The top 2 rows the modified anterior view and the modified posterior view in the case the scar was present ., In column A , we see the original location of the spiral core ( 5 cm from the scar ) indicated with the black arrow in anterior view ., In column B , breaks are formed due to the scar tissue , and the secondary source started to appear ., After 3 . 7 s , the spiral is anchored around the scar , indicated with the black arrow in the posterior view , and persistently rotated around it ., In the bottom row , we show the same simulation but the scar was not taken into account ., In this case , the spiral does not change its original location ( only a slight movement , see the black arrows ) ., To evaluate if this effect can potentially be registered in clinical practice we computed the ECG for our 3D simulations ., The ECG that corresponds to the example in Fig 5 is shown in Fig 6 . During the first three seconds , the ECG shows QRS complexes varying in amplitude and shape and then more uniform beat-to-beat QRS morphology with a larger amplitude ., This change in morphology is associated with anchoring of the rotor which occurs around three seconds after the start of the simulation ., The initial irregularity is due to the presence of the secondary sources that have a slightly higher period than the original rotor ., After the rotor is anchored , the pattern becomes relatively stable which corresponds to a regular saw-tooth ECG morphology ., Additional ECGs for the cases of termination of the arrhythmia and anchoring are shown in supplementary S2 Fig . For the anchoring dynamics we see similar changes in the ECG morphology as in Fig 6 . The dynamical anchoring is accompanied by an increase of the cycle length ( 247 ± 16 ms versus 295 ± 30 ms ) ., The reason for this effect is that the rotation of the rotor around an obstacle –anatomical reentry– is usually slower than the rotation of the rotor around its own tip—functional reentry , which is typically at the limit of cycle length permitted by the ERP ., In the previous section , we showed that the described results on dynamical anchoring in an anatomical model of the LV of patients with post infarct scars correspond to the observations on ECGs during initiation of a ventricular arrhythmia ., After initiation , in 18 out of 30 patients ( 60% ) a time dependent change of QRS morphology was observed ., Precordial ECG leads V2 , V3 and V4 from two patients are depicted in Fig 7 . For both patients the QRS morphology following the extra stimuli gradually changed , but the degree of changes here was different ., In patient A , this morphological change is small and both parts of the ECG may be interpreted as a transition from one to another monomorhpic ventricular tachycardia ( MVT ) morphology ., However , for patient B the transition from polymorphic ventricular tachycardia ( PVT ) to MVT is more apparent ., In the other 16 cases we observed different variations between the 2 cases presented in Fig 7 . Supplementary S3 Fig shows examples of ECGs of 4 other patients ., Here , in patients 1 and 2 , we see substantial variations in the QRS complexes after the arrhythmia initiation and subsequently a transformation to MVT ., The recording in patient 3 is less polymorphic and in patient 4 we observe an apparent shift of the ECG from one morphology to another ., It may occur , for example , if due to underlying tissue heterogeneity additional sources of excitation are formed by the initial source ., Overall , the morphology with clear change from PVT to MVT was observed in 5/18 or 29% of the cases ., These different degrees of variation in QRS morphology may be due to many reasons , namely the proximity of the created source of arrhythmia to the anchoring region , the underlying degree of heterogeneity and fibrosis at the place of rotor initiation , complex shape of scar , etc ., Although this finding is not a proof , it supports that the anchoring phenomenon may occur in clinical settings and serve as a possible mechanism of fast VT induced by programmed stimulation ., In this study , we investigated the dynamics of arrhythmia sources –rotors– in the presence of fibrotic regions using mathematical modeling ., We showed that fibrotic scars not only anchor but also induce secondary sources and dynamical competition of these sources normally results their annihilation ., As a result , if one just compares the initial excitation pattern in Fig 1A and final excitation pattern in Fig 1L , it may appear as if a distant spiral wave was attracted and anchored to the scar ., However , this is not the case and the anchored spiral here is a result of normal anchoring and competition of secondary sources which we call dynamical anchoring ., This process is different from the usual drift or meandering of rotors where the rotor gradually changes its spatial position ., In dynamical anchoring , the break formation happens in the fibrotic scar region , then it spreads to the original rotor and merges with this rotor tip and reorganizes the excitation pattern ., This process repeats itself until a rotor is anchored around the fibrotic scar region ., Dynamical anchoring may explain the organization from fast polymorphic to monomorphic VT , also accompanied by prolongation in CL , observed in some patients during re-induction after radio frequency catheter ablation of post-infarct scar related VT ., In our simulations the dynamics of rotors in 2D tissue were stable and for given parameter values they do not drift or meander ., This type of dynamics was frequently observed in cardiac monolayers 38 , 39 which can be considered as a simplified experimental model for cardiac tissue ., We expect that more complex rotor dynamics would not affect our main 2D results , as drift or meandering will potentate the disappearance of the initial rotor and thus promote anchoring of the secondary wavebreaks ., In our 3D simulations in an anatomical model of the heart , the dynamics of rotors is not stationary and shows the ECG of a polymorphic VT ( Fig 6 ) ., The dynamical anchoring combines several processes: generation of new breaks at the scar , spread of breaks toward the original rotor , rotor disappearance and anchoring or one of the wavebreaks at the scar ., The mechanisms of the formation of new wavebreaks at the scar has been studied in several papers 15 , 37 , 40 and can occur due to ionic heterogeneity in the scar region or due to electrotonic effects 40 ., However the process of spread of breaks toward the original rotors is a new type of dynamics and the mechanism of this phenomenon remains to be studied ., To some extent it is similar to the global alternans instability reported in Vandersickel et al . 41 ., Indeed in Vandersickel et al . 41 it was shown that an area of 1:2 propagation block can extend itself towards the original spiral wave and is related to the restitution properties of cardiac tissue ., Although in our case we do not have a clear 1:2 block , wave propagation in the presence of breaks is disturbed resulting in spatially heterogeneous change of diastolic interval which via the restitution effects can result in breakup extension ., This phenomenon needs to be further studied as it may provide new ways for controlling rotor anchoring processes and therefore can affect the dynamics of a cardiac arrhythmia ., In this paper , we used the standard method of representing fibrosis by placement of electrically uncoupled unexcitable nodes with no-flux boundary conditions ., Although such representation is a simplification based on the absence of detailed 3D data , it does reproduce the main physiological effects observed in fibrotic tissue , such as formation of wavebreaks , fractionated electrograms , etc 22 ., The dynamical anchoring reported in this paper occurs as a result of the restructuring of the activation pattern and relies only on these basic properties of the fibrotic scar , i . e . the ability to generate wavebreaks and the ability to anchor rotors , which is reproduced by this representation ., In addition , for each data point , we performed simulations with at least 60 different textures ., Therefore , we expect that the effect observed in our paper is general and should exist for any possible representation of the fibrosis ., The specific conditions , e . g . the size and degree of fibrosis necessary for dynamical anchoring may depend on the detailed fibrosis structure and it would be useful to perform simulations with detailed experimentally based 3D structures of the fibrotic scars , when they become available ., Similar processes can not only occur at fibrotic scars , but also at ionic heterogeneities ., In Defauw et al . 42 , it has been shown that rotors can be attracted by ionic heterogeneities of realistic size and shape , similar to those measured in the ventricles of the human heart 43 ., These ionic heterogeneities had a prolonged APD and also caused wavebreaks , creating a similar dynamical process as described in Fig 1 ., In this study however , we demonstrated that structural heterogeneity is sufficient to trigger this type of dynamical anchoring ., It is important to note that in this study fibrosis was modeled as regions with many small inexcitable obstacles ., However , the outcome can depend on how the cellular electrophysiology and regions of fibrosis have been represented ., In modeling studies , regions of fibrosis can also be represented

Introduction, Materials and methods, Results, Discussion

Rotors are functional reentry sources identified in clinically relevant cardiac arrhythmias , such as ventricular and atrial fibrillation ., Ablation targeting rotor sites has resulted in arrhythmia termination ., Recent clinical , experimental and modelling studies demonstrate that rotors are often anchored around fibrotic scars or regions with increased fibrosis ., However , the mechanisms leading to abundance of rotors at these locations are not clear ., The current study explores the hypothesis whether fibrotic scars just serve as anchoring sites for the rotors or whether there are other active processes which drive the rotors to these fibrotic regions ., Rotors were induced at different distances from fibrotic scars of various sizes and degree of fibrosis ., Simulations were performed in a 2D model of human ventricular tissue and in a patient-specific model of the left ventricle of a patient with remote myocardial infarction ., In both the 2D and the patient-specific model we found that without fibrotic scars , the rotors were stable at the site of their initiation ., However , in the presence of a scar , rotors were eventually dynamically anchored from large distances by the fibrotic scar via a process of dynamical reorganization of the excitation pattern ., This process coalesces with a change from polymorphic to monomorphic ventricular tachycardia .

Rotors are waves of cardiac excitation like a tornado causing cardiac arrhythmia ., Recent research shows that they are found in ventricular and atrial fibrillation ., Burning ( via ablation ) the site of a rotor can result in the termination of the arrhythmia ., Recent studies showed that rotors are often anchored to regions surrounding scar tissue , where part of the tissue still survived called fibrotic tissue ., However , it is unclear why these rotors anchor to these locations ., Therefore , in this work , we investigated why rotors are so abundant in fibrotic tissue with the help of computer simulations ., We performed simulations in a 2D model of human ventricular tissue and in a patient-specific model of a patient with an infarction ., We found that even when rotors are initially at large distances from the fibrotic region , they are attracted by this region , to finally end up at the fibrotic tissue ., We called this process dynamical anchoring and explained how the process works .

dermatology, medicine and health sciences, diagnostic radiology, engineering and technology, cardiovascular anatomy, cardiac ventricles, fibrosis, magnetic resonance imaging, developmental biology, electrocardiography, bioassays and physiological analysis, cardiology, research and analysis methods, scars, arrhythmia, imaging techniques, atrial fibrillation, electrophysiological techniques, rotors, mechanical engineering, radiology and imaging, diagnostic medicine, cardiac electrophysiology, anatomy, biology and life sciences, heart

journal.pcbi.1004878

Oscillations via Spike-Timing Dependent Plasticity in a Feed-Forward Model

It is generally believed that synaptic plasticity is the basis for learning and memory ., According to Hebbs rule 1 , which is considered the foundation for current views on learning and memory , the interaction strength between two neurons that are co-activated potentiates ., This rule has been extended to the temporal domain by taking into account the effect of the causal relation of pre- and post-synaptic firing on the potentiation and depression of the synapse , which is known as spike-timing dependent plasticity ( STDP ) ., STDP has been identified in numerous systems in the brain , and a rich repertoire of causal relations has been described 2–12 ., Considerable theoretical efforts have been devoted to investigating the possible computational implications of STDP 13–29 ., STDP can be thought of as a process of unsupervised learning ( for other views such as reward modulated STDP see 30 , for example ) ., It has been shown that certain STDP rules can give rise to the emergence of response selectivity at the level of the post-synaptic neuron 14 , 15 , 20 , whereas other STDP rules can provide a homeostatic mechanism that balances the excitatory and inhibitory inputs to the cell 25 , 29 ., Furthermore , STDP in combination with other plasticity rules has been shown to lead to structure formation in networks 31 , 32 ., Although spike-timing is explicitly emphasized by the term STDP , most theoretical studies have focused on very basic temporal structures of neuronal activity ., Many studies , for example , assume that neuronal firing follows homogeneous Poisson process statistics and that the correlations are instantaneous in time ( with a possible small time shift to reflect delayed reactions ) ., However , neuronal oscillatory activity has been reported in such cognitive processes as the encoding of external stimuli , attention , learning and consolidation of memory 33 ., Thus , although the specific role of these oscillations in the learning process remains to be determined , it is clear that neuronal oscillations are abundant in the central nervous system ., This raises the question of the mechanisms that generate these oscillations: are they genetically hard-wired into the system or can they be acquired via a learning process ?, The effect and possible computational role of oscillations on STDP has been addressed in several studies 34–42 ., However , in all of these studies the oscillatory activity was either an inherent property of the neuron or inherited via feed-forward connections from inputs that were oscillating and had a clear preferred phase ., A recent numerical study indicated that oscillations may emerge in a large scale detailed thalamocortical model with STDP 43 ., Nevertheless , it remains unclear whether STDP can give rise to the emergence of oscillatory activity by itself , and if so , under what conditions ., This paper is organized as follows ., First , we define the STDP rule and the architecture of the feed-forward model network ., Next , we examine the learning dynamics of a single plastic synapse onto a post-synaptic cell , for the case where both pre- and post-synaptic neurons are oscillating ., We then investigate the emergence of oscillations in a post-synaptic cell as a result of STDP in a population of feed-forward inputs each of which are oscillating , albeit in different phases such that their net contribution has no defined oscillatory behavior ., In this case , if the synaptic weights are uniform or random then the total input to the post-synaptic neuron will have none or very little oscillatory component ., We analyze the stability of the homogeneous solution , and show that when the homogeneous solution is not stable the post- synaptic neuron begins to oscillate ., However , the synaptic weights themselves do not converge to a fixed point , but rather to a limit cycle solution ., Finally , we discuss our results and suggest an intuitive explanation for the limit cycle solution ., For convenience , we adopt the STDP formalism presented in 14 , 25 , 26 ., Specifically , we explore the following STDP rule, Δw=λf+, ( w ) K+ ( Δt ) −f−, ( w ) K− ( Δt ) ,, ( 1 ), where Δw denotes the modification of the weight w of a synapse connecting a pre- and post- synaptic neurons , following either a pre- or post- synaptic spike , λ is the learning rate , and Δt = tpost −tpre is the time difference between the pre- and post- synaptic spikes ., For simplicity , we assume that all pre-post spike time pairs contribute additively to synaptic plasticity , where the contribution of each such pair follows eq ( 1 ) ., The STDP rule is written as the sum of two processes: potentiation ( + ) and depression ( - ) ., We further assume a separation of variables , such that potentiation and depression are given as the product of the synaptic weight dependence f±, ( w ) and the temporal kernel K± ( Δt ) of the STDP rule ., Specifically , for the synaptic weight dependence we use the Gütig et al . 14 model, f+, ( w ) = ( 1−w ) μ, ( 2 ), f−, ( w ) =αwμ ,, ( 3 ), where α characterizes the relative strength of depression and μ the non-linearity of the learning rule ( see Fig 1A and 1B ) ., Here , we focused on two families of temporal kernels for the STDP rule ., One is a temporally asymmetric exponential rule of the form, K± ( Δt ) =e∓HΔt/τ±τ±Θ ( ±HΔt ) ,, ( 4 ), where Θ, ( x ) is the Heaviside step function , and τ± denote the characteristic timescales of the LTP and LTD branches of the rule , respectively ., The parameter H allows to controls the nature of the learning rule , with H = 1 for a “Hebbian” rule , as in Fig 1C ( i . e . , potentiating at the causal branch , that is for Δt > 0 when the post fires after pre ) , and H = −1 for an “Anti-Hebbian” STDP rule , see e . g . , 12 , 44 ., The second family of STDP rules considered here uses a difference of Gaussians of the form, K± ( Δt ) =1τ±2πe−12 ( Δt−T±τ± ) 2, ( 5 ), as temporal kernels , where τ± , and T± are the temporal widths and temporal shifts of the rule respectively , see Fig 1D ., We first analyze STDP-driven dynamics of a single synapse ., We assume that both pre- and post- synaptic neurons are oscillating at the same frequency ν albeit with a possible different phase φ = φpre − φpost ., This is done in the limit of weak coupling , assuming that the modification of a single synapse has a marginal effect on the post-synaptic neuron activity ., We assume that the pre and post neuronal spiking activity can be described by an inhomogeneous Poisson process statistics with intensity parameter representing instantaneous expected firing rate, 〈ρpre/post ( t ) 〉:=rpre/post ( t ) =Dpre/post+Apre/postcos ( νt−φpre/post ) ,, ( 6 ), where ρpre/post ( t ) =∑iδ ( t−tipre/post ) denotes the spike trains of the pre/post synaptic cell , as represented by a linear combination of delta functions at the neurons spike times , with {tipre/post}i=1∞ being the spike times; rpre/post ( t ) is the instantaneous firing rate of the pre/post- synaptic cell; Dpre/post , Apre/post are the mean ( DC ) and amplitude modulation ( AC ) of the firing rate , respectively , and φpre/post is the phase of the pre/post- synaptic neuron ., The angular brackets 〈⋯〉 denote ensemble averaging ., The pre-post correlation structure is an important factor driving the synaptic dynamics ., In the limit of weak coupling , the pre and post firing can be modeled as independent Poisson processes ., Consequently , one obtains the following pre-post correlation structure, Γ ( Δ ) :=ν2π∫02πν〈ρpre ( t ) ρpost ( Δ+t ) 〉dt=Γ0 ( 1+Γrcos ( νΔ+φ ) ) ,, ( 7 ), where, Γ0=DpreDpost , Γr= ( Apre/Dpre ) ( Apost/Dpost ) /2 ., ( 8 ), Note that Γr ≤ 1/2 since the intensity parameter of a Poisson process must be non-negative ( Ax ≤ Dx ) ., We now want to consider in which ways oscillatory activity affects the STDP-driven dynamics of a synaptic population of N excitatory neurons that project onto a single post-synaptic neuron in a feed-forward manner , see Fig 3 ., All the excitatory synapses obey the same STDP rule ., STDP is known to be able to act as an unsupervised learning algorithm that learns prominent features of the input layer ( pre-synaptic population ) statistic; namely , the spatial structure of the correlations ., Via a local synaptic STDP learning rule , spatial ( or stimulus ) selectivity can emerge 14 , 15 , 17 , 20 , 47–49 ., Here , we focused on temporal aspects and showed how temporal selectivity may emerge ., Specifically , we found that although the net activity of the input layer ( pre-synaptic population ) was constant in time , the homogeneous state was not always stable and the post-synaptic neuron developed temporal phase preferences via a mechanism of spontaneous symmetry breaking ., This instability depends on the sign of the real part of the Fourier transform of the STDP rule ( potentiation minus depression ) which is time shifted by the post-synaptic delay d at angular frequency ν ( see eq ( 33 ) ) ., However , in contrast to previous studies , we found that in many cases this selectivity is not static , but rather it drifts in time ., One can view Fig 6B as a graphic illustration of a search for a self-consistent solution with zero drift ., Assume that the post-synaptic neuron oscillates at some constant phase , without loss of generality φpost = 0 ., The STDP will shape the synaptic weight profile symmetrically around φpost according to eq ( 40 ) , the black line in Fig 6B ., Accordingly , the synaptic weight profile and the input to the post synaptic neuron will have a phase of ψ = φpost = 0 , the vertical dashed gray line in Fig 6B ., However , the response of the post neuron to its inputs dictates φpost = ψ + dν ( shown by the vertical blue line ) , which is inconsistent with our initial assumption φpost = 0 ., In order to satisfy the self-consistent condition we need to introduce a temporal shift to the STDP rule ( eq ( 5 ) with T+ = T− ≡T ≠ 0 ) that will align the initial assumption of the post phase at φpost = 0 with ψ + dν , Fig 6C ., Further increases in the temporal shift T of the STDP rule will generate a ‘force’ that will pull the weight profile to the left and will result in a negative drift velocity , see Fig 6D ., A quasi-periodic behavior of synaptic weights has been observed in the past ., Gilson and colleagues 48 , studied the effect of general correlation structure on STDP-driven synaptic dynamics ., Analyzing the homogeneous fixed point they assumed the synaptic dynamics will flow in the direction of the strongest spectral component ( of the input layer correlations ) , and consequently will converge to a stable fixed point that will reflect the structure of the spectral component ., In the additive learning rule ( i . e . , μ = 0 ) or in the case where the neuronal response covariance is negative the existence of a stable fixed point is not guaranteed ., Thus , it was shown that for the special case of additive STDP synaptic dynamics may be dominated by eigenvalues with large imaginary part that will result in quasi periodic behavior ., This pathology disappears for any positive μ ., Here , the limit cycle solution is not a pathology but a robust feature of the synaptic dynamics ( see for example limit cycle solution with μ > 0 in Fig 4 ) ., The main difference from the work of Gilson and colleagues is that , typically , here there is no stable fixed point solution ( note that response covariance of input layer neurons can be negative ) ., Even in the special case where a non-homogeneous fixed point exists it is: One , Not generic but requires a set of parameters that will solve V = 0 ., Two , due to the inherent U ( 1 ) symmetry of the problem this solution will be only marginally stable ., Theoretical investigations of STDP typically derive non-linear equations for the dynamics of the synaptic weights ., In general , non-linear dynamics is known to give rise to a wide range of behaviors , including convergence to a fixed point , line attractors , oscillatory activity , and chaos ., However , to the best of our knowledge , the existing theoretical research describes STDP as a process that relaxes to a steady state fixed point ( except for pathological cases ) ., On the other hand , empirical findings show synaptic dynamics as highly volatile and even chaotic 50–58 ., This raises the question of how can the central nervous system retain its functionality in the face of constant remodeling of synaptic weights ?, Here we have shown an example in which synaptic weights did not converge to a stable fixed point , but rather remained dynamic ., Yet , functionality , in this case oscillating activity of the post-synaptic neuron , was maintained as an emergent property of global order parameters w¯ and |w˜| that converge to a stable fixed point .

Introduction, Results, Discussion

Neuronal oscillatory activity has been reported in relation to a wide range of cognitive processes including the encoding of external stimuli , attention , and learning ., Although the specific role of these oscillations has yet to be determined , it is clear that neuronal oscillations are abundant in the central nervous system ., This raises the question of the origin of these oscillations: are the mechanisms for generating these oscillations genetically hard-wired or can they be acquired via a learning process ?, Here , we study the conditions under which oscillatory activity emerges through a process of spike timing dependent plasticity ( STDP ) in a feed-forward architecture ., First , we analyze the effect of oscillations on STDP-driven synaptic dynamics of a single synapse , and study how the parameters that characterize the STDP rule and the oscillations affect the resultant synaptic weight ., Next , we analyze STDP-driven synaptic dynamics of a pre-synaptic population of neurons onto a single post-synaptic cell ., The pre-synaptic neural population is assumed to be oscillating at the same frequency , albeit with different phases , such that the net activity of the pre-synaptic population is constant in time ., Thus , in the homogeneous case in which all synapses are equal , the post-synaptic neuron receives constant input and hence does not oscillate ., To investigate the transition to oscillatory activity , we develop a mean-field Fokker-Planck approximation of the synaptic dynamics ., We analyze the conditions causing the homogeneous solution to lose its stability ., The findings show that oscillatory activity appears through a mechanism of spontaneous symmetry breaking ., However , in the general case the homogeneous solution is unstable , and the synaptic dynamics does not converge to a different fixed point , but rather to a limit cycle ., We show how the temporal structure of the STDP rule determines the stability of the homogeneous solution and the drift velocity of the limit cycle .

Oscillatory activity in the brain has been described in relation to many cognitive states and tasks , including the encoding of external stimuli , attention , learning and consolidation of memory ., However , without tuning of synaptic weights with the preferred phase of firing the oscillatory signal may not be able to propagate downstream—due to distractive interference ., Here we investigate how synaptic plasticity can facilitate the transmission of oscillatory signal downstream along the information processing pathway in the brain ., We show that basic synaptic plasticity rules , that have been reported empirically , are sufficient to generate the required tuning that enables the propagation of the oscillatory signal ., In addition , our work presents a synaptic learning process that does not converge to a stationary state , but rather remains dynamic ., We demonstrate how the functionality of the system , i . e . , transmission of oscillatory activity , can be maintained in the face of constant remodeling of synaptic weights .

learning, medicine and health sciences, action potentials, nervous system, population dynamics, membrane potential, social sciences, electrophysiology, neuroscience, learning and memory, synaptic plasticity, cognitive psychology, population biology, neuronal plasticity, developmental neuroscience, animal cells, cellular neuroscience, psychology, cell biology, anatomy, synapses, physiology, neurons, biology and life sciences, cellular types, cognitive science, neurophysiology

journal.ppat.1005045

Dimerization-Induced Allosteric Changes of the Oxyanion-Hole Loop Activate the Pseudorabies Virus Assemblin pUL26N, a Herpesvirus Serine Protease

The family Herpesviridae is divided into the three subfamilies alpha- , beta- and gammaherpesviruses ., These infectious agents cause a variety of diseases in many different hosts including humans ., Pseudorabies virus ( PrV ) is a neurotropic porcine alphaherpesvirus 1 and the causative agent of Aujeszkys disease ., The pig is the only susceptible species that can survive a PrV infection depending on the age of the animal and virulence of the virus , while most other mammals die within a few days ., Only higher primates including humans and equids are resistant to infection ., Due to its broad host range PrV has become an important model system to study herpesvirus biology in cell culture and in the natural host ., PrV genome organization and protein content exhibit significant homology to that of the human herpes simplex virus type 1 ( HSV-1 ) 2 , 3 , which is among the best-studied herpesviruses ., Capsid assembly of HSV-1 has been intensively analyzed ., However , since herpesvirus capsid proteins are well conserved , it is very likely that the process leading to mature , DNA-filled nucleocapsids is also similar ., The proteolytic activity of the serine protease is essential for this process 4 ., In HSV-1 and PrV , this protease is encoded by the UL26 gene 5 , which is the longest open reading frame in a family of in-frame overlapping genes 5–8 ., UL26 overlaps in frame with UL26 . 5 3 ., UL26 and UL26 . 5 possess identical 3-termini , which encode a scaffold protein while the unique 5-terminus of UL26 contains the protease domain ., There are at least two target sites for the protease in the full-length UL26 protein ( pUL26 ) 8 , 9 ., Autoproteolytic activity at the release site ( R-site ) results in release of the N-terminal protease domain ( pUL26N , also called VP24 or generic: assemblin ) and the C-terminal part containing the scaffold protein ( pUL26C , also called VP21 or generic: assembly protein ) 10 ., Due to the presence of a linker region pUL26C is 21 amino-acid residues longer than pUL26 . 5 ( Fig 1 ) ., Near the C-terminus of the scaffold protein is the maturational site ( M-site ) where pUL26 . 5 and pUL26C are cleaved 10 ., The scaffold protein binds to the major capsid protein pUL19 ( VP5 ) and directs it to the nucleus 11–17 ., During capsid assembly , the scaffold protein forms a scaffold core with the major capsid protein bound to the C-termini of pUL26/pUL26C/pUL26 . 5 17 ., When the capsid is fully assembled the scaffold is cleaved at its M-site releasing the ring-like scaffold structure from the capsid , which is then expelled during DNA packaging ., In contrast , the protease remains in the nucleocapsid 18 ., Without protease activity , the scaffold remains in the capsid resulting in capsids without viral DNA designated as B-capsids ., Upon activation of the protease , the B-capsids mature and subsequent steps of viral replication occur as shown with a temperature-sensitive HSV-1 pUL26 mutant 19 ., The function of pUL26 . 5 can be taken over by pUL26C but with reduced efficiency as well as loss of an apparent core structure in the resulting capsids 22 ., Proteases of several herpesviruses , such as human cytomegalovirus ( HCMV ) and Kaposis sarcoma-associated herpesvirus ( KSHV ) , have one or more internal cleavage sites to regulate activity or promote destabilization of the protease 23–25 ., For proteases of PrV , HSV-1 and herpes simplex virus type 2 ( HSV-2 ) no internal cleavage sites have been reported ., Herpesvirus maturational proteases exist in a monomer-dimer equilibrium ., Dimers are weakly associated with dissociation constants ( KD ) in the micromolar range 26 and are active , while monomers are almost inactive 27 ., It was shown that dimerization of the HCMV assemblin is dependent on protein concentration ., The fraction of dimeric protease at 0 . 2 μM was demonstrated to be ~0 . 3 increasing to ~0 . 7 at 4 . 5 μM 27 ., Additionally , dimerization is favored by high concentrations of kosmotropic compounds like glycerol 26 , 27 ., Activity of the herpesvirus protease has to be strictly regulated ., The enzyme is expressed as full-length pUL26 in the cytosol of infected cells where its concentration is low and thus the inactive , monomeric form is predominant 28 ., The major capsid protein is bound by pUL26 . 5 and translocated to the nucleus via the nuclear localization sequence within pUL26 . 5 29 ., Since pUL26 encompasses pUL26 . 5 , it also contains this nuclear localization sequence ., Therefore , autoproteolytic activity of the protease in the cytosol would prevent its localization to the nucleus ., When capsid assembly is completed , the protease has to become active to release the scaffold protein from the capsid and to allow DNA packaging ., In the capsids , the concentration of protease is much higher than in the cytosol , thus promoting dimerization 28 ., Additionally , it was proposed that the capsid environment itself might enhance proteolytic activity of the protease 30 ., Several structures of homologous assemblins of other herpesviruses have been published , revealing the overall fold , active site and biological assembly 31–38 ., The sequence identities of these structures to the PrV assemblin range from 60% ( alphaherpesviruses ) to 30% ( beta- and gammaherpesviruses ) 39 , 40 ., All assemblins consist of 6–9 α-helices surrounding a β-barrel formed by two β-sheets ., The catalytic triad is unique among the serine proteases and consists of one serine and two histidine residues ., The active site is solvent accessible and distal to the dimer interface ., Nevertheless , dimerization drastically influences the activity 27 ., There is evidence , that upon dimerization the oxyanion hole is formed by structural changes of a loop containing two strictly conserved arginine residues 41 , 42 ., Currently , crystal structures are available for native dimeric and covalently inhibited dimeric assemblins ., Recently , three structures of the truncated KSHV assemblin in complex with helical-peptide mimetics were published 43 , 44 ., These compounds bind to the dimerization area and disrupt the dimerization interface of full-length KSHV assemblin 45 ., Here , we report crystal structure analyses of the active dimer , the diisopropyl phosphate-inhibited dimer as well as the non-inhibited monomer of pUL26N from PrV ( 224 amino-acid residues ) ., The latter is the first-ever structure of an assemblin in its native monomeric state ., Comparison of the monomeric and dimeric forms provides insight into the regulation of protease activity by dimerization and a structural basis for rational design of therapeutic substances that trap the protein in the inactive monomeric state ., Additional details and information about herpesvirus proteases and its involvement in capsid maturation are reviewed elsewhere 21 , 46–52 ., The active site serine is solvent accessible and the catalytic residues are part of β-strands β5 ( Ser109 ) and β6 ( His128 ) and the β2-β3 loop ( His43 ) , like in earlier structures 31–37 ., In our inhibitor complex , covalent binding of diisopropyl fluorophosphate has formed a phosphate ester with the active-site serine in a manner also observed in crystal structures of homologous assemblins 34 ., In the native structure , a chloride ion occupies the oxyanion hole , which is formed by the β6-β7 loop ( residues 133–142 , further referred to as oxyanion-hole loop , OHL , Fig 3 ) ., Two consecutive arginine residues in this loop ( Arg136 and Arg137 ) are strictly conserved throughout all herpesvirus maturational proteases ( S3 Fig ) ., The backbone N-H of Arg136 provides the first hydrogen-bond donor of the oxyanion hole ., A water molecule as second hydrogen-bond donor supports anion stabilization , as does the positive local environment established by these two arginine residues ., For HCMV assemblin it was shown that this water molecule plays a role in catalysis 56 ., It is kept in place by a second water molecule that is positioned by hydrogen bonds of the peptide backbone oxygen of Leu10 ( loop β1-α1 ) and the peptide N-H of Leu110 ( β5 ) ., Both water molecules are present in the dimeric structures of assemblins from PrV ( this report ) , HSV-2 ( pdb entry 1at3 ) , KSHV ( pdb entries 1fl1 , and 2pbk ) and HCMV ( pdb entries 1cmv , 1wpo , 1id4 , 1iec , 1ied , 1ief , and 1ieg ) ., The positions of these water molecules seem to be conserved in all active assemblins ., Our findings for the composition of the oxyanion hole are consistent with those reported for HSV-2 34 ., An extensive network of hydrogen bonds stabilizes the OHL ( S4 Fig ) ., This network includes conserved water molecules and parts of the α1 region , α8 and β5 ., There are five strictly conserved residues in the OHL and each of these is involved in the network of hydrogen bonds that positions Arg136 and stabilizes the oxyanion hole ., The side chain of Arg137 forms hydrogen bonds to the peptide backbone of Leu20 ( α1 ) and Leu110 ( β5 ) , which are also conserved in assemblins ( S3 Fig ) ., The OH-group of the conserved Thr140 forms a hydrogen bond to the backbone oxygen of Arg137 whereas the backbone oxygen of Thr140 accepts a hydrogen bond from the backbone N-H of Ala108 ( β5 ) ., Inspection of other assemblin structures showed that these hydrogen bonds are present in all dimeric structures ., In the PrV assemblin , the peptide oxygen of Val138 is connected to the peptide N-H of Leu12 via hydrogen bonds mediated by a conserved water molecule ( found in HSV-2 and KSHV assemblins ) ., Most likely this water molecule and hydrogen bonding pattern are present in all herpesvirus maturational proteases ., Additionally , the OHL of PrV pUL26N is maintained by hydrogen bonds of the side chains of Asp16 ( α1 ) , Glu214 and Arg211 ( both from α8 ) with the peptide backbone of Val138 , Gly139 and Gly135 , respectively ., These hydrogen bonds are also present in HSV-2 assemblin with Glu214 replaced by Gln ., Identical residues are present in the VZV assemblin , but the side chains of Glu and Asp point in different directions and do not form the corresponding hydrogen bonds ., This ambiguity may result from the limited resolution of that structural model ( pdb entry 1vzv with a resolution of 3 Å , no structure factors were deposited ) ., Most likely , the loop is stabilized comparable to HSV-2 and PrV assemblins ., Diffraction datasets of dimeric pUL26N were collected from needle-shaped crystals and phasing by molecular replacement was successful either by using a monomer or the complete dimer as search models ., For datasets derived from morphologically different , plate-shaped crystals , molecular replacement was only successful when using a monomer as a search model ., In the resulting structure , two chains are present in the asymmetric unit ., These chains and their adjacent symmetry mates are not in a proper position to form the known dimer ., PDBePISA 53 suggests one assembly ., This putative dimer has no local dyad , which is unusual for biologically relevant dimers 57 , 58 ., The interface area ( 978 Å2 ) is much smaller than average for homodimers of this molecular weight ( ~1 , 500 Å2 ) 57 ., Furthermore , the helices forming the interface have B-factors ( 60–100 Å2 ) above average ( 52 Å2 ) ., Thus , the suggested dimeric interaction is a mere crystal contact and the crystal structure actually corresponds to monomeric pUL26N ., The region corresponding to helix α1 in dimeric PrV pUL26N is disordered in the monomeric form ., For ease of comparison , in this report numbering of the helices in the monomeric protease is adapted to that of the dimeric protease ., The β-barrel and distal side of the dimerization area of chain A align very well with chain B of the asymmetric unit ., The OHL and helices α3 , α7 and α8 of the dimerization area , on the other hand , differ in chain A and B ( Fig 4 ) ., The OHL has two alternative conformations in chain A with equal occupancies ., One conformation correlates to the OHL of chain B and the other one is shifted towards α8 due to crystal contacts ., Compared to chain B the helices α3 and α7 of chain A are bent and helix α8 is slightly tilted ., Accordingly , the overall r . m . s . deviation of chain A and B is 1 . 03 Å ( 207 aligned Cα atoms ) ., Furthermore , there is no electron density observed for residues 16–19 of chain A ( β1-α2 loop ) and very weak electron density for residues 14–18 and 194–196 of chain B ( β1-α2 loop and the short α7-α8 loop , respectively ) ., The core of the monomer is rigid as indicated by low B-factors of Cα atoms in the β-barrel , helix α4 and most of helix α8 of chain A ( Fig 4 ) ., In contrast , the periphery of both monomers is flexible as evidenced by increased B-factors of Cα atoms at the distal side of the dimerization area , the OHL , and the dimerization helices α3 and α7 as well as in helix α8 of chain B . B-factors around 100 Å2 are also observed at the N-termini of helices α8 in both chains ., Taken together , these observations show that the dimerization area and the parts necessary for formation of the oxyanion hole in dimeric pUL26N are flexible and not strictly ordered in monomeric pUL26N ., The tertiary structures of monomeric and dimeric PrV pUL26N are partially similar ., The r . m . s . deviations of inhibited dimeric pUL26N chain B with chain A and chain B of monomeric protease are 0 . 93 Å ( 192 aligned Cα atoms ) and 1 . 01 Å ( 190 aligned Cα atoms ) , respectively ., The β-barrel , the distal side of the dimer interface , and helix α4 of the dimer interface are almost identical in monomeric and dimeric pUL26N ( Fig 5 ) ., The r . m . s . deviations of these parts of inhibited dimeric pUL26N chain B with chain A and chain B of monomeric protease are 0 . 54 Å ( 151 aligned Cα atoms ) and 0 . 63 Å ( 149 aligned Cα atoms ) , respectively ., In contrast , significant differences are observed for the OHL , N- and C-termini of helices α3 , α7 and α8 of the dimer interface , and the α7-α8 loop ( Fig 5 ) ., In the monomer , the N-terminus of helix α8 is one and a half turn longer , but the C-terminus of helix α7 is one turn shorter ., Thus , the α7-α8 loop conformation is shifted towards α8 upon dimer formation ., In dimeric pUL26N helices α3 and α7 form a hydrophobic cleft that is occupied by helix α7 of the second monomer within the dimer ., This cleft is closed in the monomer by bending the C-termini of these helices towards each other ( Fig 5 ) ., In the monomeric pUL26N , the OHL is positioned near the N-terminus of α8 ., Thus , the activation of pUL26N relies on dimerization-induced allosteric changes , which shift the OHL towards the active site ( Fig 6A ) ., The top of the OHL ( Arg136Cα ) moves approximately 11 Å ., In the dimer , the N-terminus of α8 is unwound , so some residues which point towards the protein core in the monomeric pUL26N are buried by the adjacent monomer of the dimer ( Fig 6B ) ., However , the side chain positions of the catalytic triad are unchanged as suspected by Batra et al . 41 ., Comparison of the active and inactive conformations of the OHL reveals a key location in the structure that is occupied by alternative hydrophobic residues ., In dimeric PrV pUL26N , Ile134 is present at this position , whereas in the monomeric form Val138 takes its place ( Fig 6A ) ., The hydrophobic character of the Ile134 position is highly conserved in all sequences and three-dimensional structures of assemblins ( S3 Fig ) ., The hydrophobic character of Val138 is type-conserved in alphaherpesvirus sequences ., Both residues are kept in place by hydrophobic interactions with two side chains of helix α8 ( Leu207 , Val208 ) or the corresponding helices in related structures ., The hydrophobic character of these residues is conserved in herpesvirus proteases , although Thr and long , partially aliphatic side chains appear to be tolerated ( e . g . Thr in Epstein-Barr virus ( EBV ) assemblin , or Lys in KSHV assemblin , S3 Fig ) ., Another consequence of the different conformation of the OHL in the monomeric form is that the Asp16-containing region and α1 are disordered ., This loop , the OHL and major parts of the dimer interface area are flexible in monomeric pUL26N as indicated by partially disordered segments and high B-factors ., Thus , the crystallographically observed increase of order upon dimerization of PrV assemblin is in line with the disorder-to-order mechanism of dimerization previously proposed for KSHV assemblin 28 , 43 ., In our crystallization assays we used a construct of PrV pUL26N that was shortened by one C-terminal amino-acid residue ( Ala225 ) compared to the in vivo form ., The resulting overall fold , conformation of the OHL , and position and orientation of the residues of the catalytic triad are identical to those in previously determined dimeric full-length assemblin structures of related herpesviruses , strongly suggesting that the C-terminal deletion does not affect the activity or structure of the protease ., Moreover , native and inhibited dimers of PrV pUL26N crystallized isomorphously and the diisopropyl phosphate-ester of the active-site serine is quantitatively observed in the inhibited structure ., The inhibitor diisopropyl fluorophosphate reacts specifically with the active-site serine , but hydrolyzes in aqueous solutions with a half-life of one hour at pH 7 . 5 and even faster at higher pH 59 ., After incubation of PrV pUL26N with a sevenfold excess of diisopropyl fluorophosphate ( 5 mM ) in a buffer at pH 7 . 5 at room temperature for one hour , the protein was crystallized at pH 8 ., Based on these constraints quantitative inhibition of PrV pUL26N ( minimum 90% in the crystal ) prior to inhibitor hydrolysis can be calculated to occur at a second order reaction rate of at least 0 . 09 s−1 M−1 ., This is consistent with the known reaction rate of full-length HSV-1 assemblin with diisopropyl fluorophosphate of 1 s−1 M−1 at higher pH ( pH 8 . 0 ) and higher temperature ( 30°C ) 60 ., Thus , the catalytic site of PrV pUL26N remains fully reactive with respect to this substrate-like inhibitor ., Furthermore , earlier work has shown that proteolytic activity with respect to substrates containing the M-site is not significantly reduced by 3- or 8-residue C-terminal truncations 61 ., C-terminally extended assemblins on the other hand do show a considerable decrease in activity 37 , 61 , 62 , presumably because such an extension sterically interferes with the proper positioning of helix α8 , which is required to establish the correct conformation of the OHL ., The absence of Ala225 , however , does not interfere in any way with helix α8 , the conformation of the active site , and the OHL ., Indeed , in the crystal structure of VZV assemblin the C-termini are disordered 35 , further confirming that the C-terminal region does not have a substantial impact on the overall protein fold ., Structural properties and self-association behavior of PrV pUL26N in solution were characterized by small-angle X-ray scattering ( SAXS ) ., Data were recorded at protein concentrations between 0 . 5 mg/ml and 10 mg/ml ., Normalized SAXS intensities vary with protein concentration , suggesting the presence of more than one oligomerization state ., The experimental SAXS curves can be accurately described assuming a monomer-dimer equilibrium and using form factors for the individual states derived from the monomeric and dimeric crystallographic models ( a representative example is shown in S5 Fig ) ., Monomer volume fractions resulting from curve fitting with the program OLIGOMER 63 are shown in S5 Fig and S1 Table ., At the highest protein concentration investigated ( 10 mg/ml , which approximately corresponds to the starting concentration in our crystallization experiments ) , a considerable volume fraction ( 30% ) of pUL26N is present in the monomeric form ., This observation is consistent with the fact that crystals of the monomer could be obtained under these conditions ., In contrast , inclusion of 0 . 2 M MgCl2 in the buffer used for SAXS measurements markedly shifts the monomer-dimer equilibrium , resulting in nearly complete dimerization at a protein concentration of 10 mg/ml ., This result may explain why crystallization of the dimeric form of pUL26N required the presence of MgCl2 ., Particle shape under conditions that result in virtually complete dimerization according to the analysis with OLIGOMER ( i . e . 10 mg/ml protein in the presence of MgCl2 ) was also determined independently by means of an ab initio approach ., The size and oblong shape of the final bead model obtained with DAMMIN 64 are in good agreement with the crystallographic structure of the pUL26N dimer ( S5 Fig ) , further confirming the nature of the concentration-dependent self-association that is observed here ., The dissociation constants with and without MgCl2 can be estimated to 200 μM and 50 μM , respectively ( S5 Fig ) ., Higher oligomers than dimers are not observed ., Three structures of KSHV assemblin ( KA ) with helical-peptide mimetics ( HPMs ) have been published to date with pdb entries 3njq , 4p2t , and 4p3h 43 , 44 ., These HPMs disrupt dimer formation in full-length KA as shown by size-exclusion chromatography 45 ., In solution , both C-terminal helices of the monomeric KA are unfolded according to NMR- and CD-spectroscopic data 28 , 43 ., Therefore , 34 C-terminal residues of KA were truncated for crystallization of these HPM complexes ., One of these truncated helices is the major dimer-interface helix , so this truncated KA is obligate monomeric ., Consequently , the models of these HPM complexes were stated as monomeric 43 , 44 ., Inspection of these models led us to the conclusion , that the HPMs function as an artificial dimer interface for this truncated KA ., PDBePISA suggests dimers or higher association states for HPM complexes ( buried surface area of ~1 , 200 Å2 per monomer ) ., The monomer is termed as unstable in solution , because the hydrophobic HPMs would be heavily solvent exposed ., Compared to the native dimeric structure of full-length KA , one monomer is rotated approximately 80° around an axis roughly perpendicular to the dimer interface in the artificial , inactive HPM complexes of truncated KA ( S6 Fig ) ., Although artificial , the HPM-interface underlines the importance of hydrophobic interfaces as suitable drug targets ., The used HPMs were reported to bind to assemblin dimer interfaces of all herpesvirus classes ., The IC50-values for these HPMs against the representative alphaherpesvirus assemblin ( HSV-2 assemblin ) were significantly higher than those against HCMV , EBV and KSHV assemblins , indicating much weaker binding to HSV-2 assemblin 44 ., The C-terminal helices of alphaherpesvirus assemblins are likely ordered due to the hydrophobic key position being occupied by alternative conserved hydrophobic residues of the OHL ., Thus , the HPMs have to compete against the C-terminal helices for binding which explains the observed higher IC50-values ., Additionally , the most important residues for dimerization in KA , the so-called “hot spot” residues 65 , are Trp109 ( α4 ) 43 and maybe Phe76 ( α3 ) ., These correspond to Tyr ( α4 ) and Leu ( α3 ) , respectively , in PrV , VZV , HSV-1 and HSV-2 assemblins ( S3 Fig ) ., Neither Leu nor Tyr are considered as “hot spot” residues 65 and thus , binding of these HPMs is likely weakened ., Screening for suitable mimetics is necessary to achieve specific and efficient inactivation of alphaherpesvirus proteases ., As mentioned above the HPMs force an artificial assembly of truncated KA in contrast to our native monomeric PrV assemblin ., Therefore , a detailed comparison of monomeric PrV assemblin and HPM complexes of truncated KA is discussed in the supplement ( S1 Text and S7–S10 Figs ) ., A putative cation was found in both dimeric structures from PrV ., A distorted octahedral arrangement of the coordinated water molecules with mean distances of 2 . 1 Å strongly suggests the presence of a cation rather than water ., Since the crystallization buffers contain MgCl2 , it is highly probable that these cations are Mg2+ ions ., For verification , divalent cations with higher electron density were tested in the crystallization procedure ., Mn2+ was able to substitute for Mg2+ but the resulting crystals diffracted considerably less well ., The best dataset that we managed to collect at a wavelength near the Mn absorption edge had a resolution of 3 Å , but no significant anomalous signal was detected and electron density at the putative Mg2+/Mn2+ position was too weak to unambiguously confirm the presence of a Mn2+ ion ., Presumably , the presence of putative Mg2+ ions in the structures is a direct result of the crystallization conditions ( containing 400 mM MgCl2 ) and does not reflect functional Mg2+ binding by the native protein since the metal ions compensate negative charges from two adjacent dimers , rather than within one dimer ( S11 Fig ) ., Thus , Mg2+ ions or divalent cations with related properties are presumably necessary for the observed crystal packing of dimeric pUL26N from PrV ., For HSV-1 protease a temperature-sensitive ( ts ) phenotype was described by mutating Y30F and A48V 66 ., Mutating the corresponding residues in PrV protease ( Y13F/A30V ) , however , did not result in the desired phenotype ., Hence , these mutations may not sufficiently destabilize PrV pUL26N ., In HSV-1 protease , it is highly probable that the result of these mutations is a destabilization of the region around helix α1 ., This helix and surrounding loops stabilize the OHL in the active conformation ., In the PrV assemblin Arg24 is located at the N-terminus of α2 ., This residue stabilizes the α1 region , because its side chain forms a hydrogen bond to the peptide oxygen of Asp59 ( Fig 7 ) ., This hydrogen bond is missing in HSV-1 assemblin , since the corresponding residue is Pro42 ., Consequently , we propose a Y13F/R24P/A30V mutant for achieving the desired ts phenotype in PrV assemblin ., If the additional R24P exchange completely inactivates the protease , an R24K variant could also be taken into account ., The slightly shorter side chain of Lys may cause a varied hydrogen-bond network and a weaker hydrogen bond to the peptide oxygen of Asp59 ., During capsid assembly , pUL26 accumulates in the nascent capsids because of its C-terminal scaffold-protein part ., Thus , the local high concentration of the protease is promoting dimerization and autoproteolysis occurs to release the scaffold from capsids for DNA packaging ., In comparison with its dimeric structure , the monomer of pUL26N reveals changes at the dimerization area , in line with allosteric changes of a loop forming the oxyanion hole ., As previously anticipated , the core of the protease including the positions and orientation of the active-site residues remains unchanged , but the oxyanion hole is disrupted in the monomeric form 41 , 42 ., The monomeric structure presented here is not truncated and does not contain any inhibitors ., Thus , it constitutes the first reliable model for native monomeric structures of other assemblins ., Dimerization induces the following allosteric events: helix α7 of a monomer interleaves between helices α3 and α7 of a second monomer moving these helices farther apart ., At the same time , helix α7 is elongated by one turn at its C-terminus to the cost of one N-terminal turn of helix α8 ., This allosteric process forces the OHL to shift to the vicinity of the active-site serine and builds a far-reaching network of hydrogen bonds with side chains of helix α8 and the polypeptide of strand β5 ., Furthermore , residues 13–20 of the β1-α2 loop become ordered and form helix α1 in the dimer by getting involved in that network of hydrogen bonds ., In this position , the OHL forms the oxyanion hole and activity of the protease is established ., The extent of disorder at the dimerization area will vary in assemblins of different herpesviruses , but the general disorder-to-order mechanism of dimerization 28 , 43 will very likely hold for all assemblins ., The molecular structure of dimeric pUL26N will help to engineer a temperature-sensitive phenotype of the PrV protease ., A temperature-sensitive variant will be a powerful tool to observe subsequent steps of viral replication in a synchronous wave 19 ., This will provide valuable data on kinetics for cleavage , packaging of the DNA , nuclear egress and intracellular trafficking of the virions ., The structure of monomeric PrV assemblin is the paradigm for monomeric states of other assemblins , primarily from alphaherpesviruses ., Detailed knowledge of this structure , conformational changes and sequence specific contacts upon dimerization are a rational basis for the development of drugs binding to the dimerization area and , thus , trapping the inactive monomeric state 45 ., Inhibition of dimerization suppresses protease activity and therefore prevents the assembly of fully functional virus capsids ., Small-angle X-ray scattering ( SAXS ) of PrV assemblin in solution revealed that dimerization increases with protein concentration and in the presence of MgCl2 ., Dissociation constants in the micromolar range are comparable to those observed for other assemblins 26 , 27 ., Divalent cations like Mg2+ or Mn2+ are required for crystallization of the dimeric PrV assemblin , because these cations support crystal packing by compensating negative charges of neighboring dimers ., Since MgCl2 shifts the condition of equilibrium towards dimeric pUL26N , the concentration of the monomeric form is likely below the critical nucleation concentration resulting in crystals of the dimeric form only ., Accordingly , crystallization of the monomeric assemblin requires absence of divalent cations ., The monomeric fraction of ~0 . 3 is sufficient for nucleation and the monomer-dimer equilibrium provides a steady supply of monomeric pUL26N ., The monomeric form is increasingly favored since crystallization of monomeric pUL26N decreases the concentration of pUL26N in solution ., Similar cases with a minor monomeric fraction crystallizing from a monomer-dimer equilibrium were reported earlier 67 ., Full-length pUL26 cleaves itself at two positions , and therefore expression of the full-length protein leads to an inhomogeneous product that is unlikely to crystallize ., Accordingly , cleavage was prevented by cloning a stop codon behind Gln224 ., The resulting coding sequence contains the protease fraction of pUL26 ( pUL26N ) only ., N-terminally ( His ) 6-tagged pUL26N was expressed using a pET28a+ vector in E . coli strain BL-21 ( DE3 ) ., Cells were grown in LB medium to an OD600 of 0 . 5–0 . 8 at 37°C and then induced by addition of isopropyl β-D-1-thiogalactopyranoside to a final concentration of 1 mM ., Cells were lysed by sonication ., The protein was purified by performing immobilized metal-ion affinity chromatography using a Poros MC 20 column loaded with Ni2+ ions ( 0 . 5 M NaCl , 50 mM Tris/HCl pH 7 . 5 , 5% glycerol , eluted with a gradient of 0–250 mM imidazole ) ., The protein was checked for its purity by SDS-PAGE and then concentrated to ~20 mg/ml ., Aliquots were stored at −80°C ., The purified protein was not tested for enzymatic activity ., Crystals were obtained using the hanging-drop vapor diffusion method at 22°C ., First crystals grew in drops containing 1 μl pUL26N concentrate and 1 μl precipitant solution ( 0 . 1 M Hepes pH 7 . 5 , 20% PEG 8 , 000 ) within several days ., The quality and size of the crystals could be increased by optimization of the composition of the precipitant solution and the ratio of protein solution to precipitant solution ., The morphology of the crystals changed from plate-shaped to needle-shaped when MgCl2 was used as an additive in the crystallization procedure ., Plate-shaped crystals turned out to be monomeric pUL26N , whereas the dimer formed needle-shaped crystals ., For crystallization with inhibitor , the concentr

Introduction, Results and Discussion, Materials and Methods

Herpesviruses encode a characteristic serine protease with a unique fold and an active site that comprises the unusual triad Ser-His-His ., The protease is essential for viral replication and as such constitutes a promising drug target ., In solution , a dynamic equilibrium exists between an inactive monomeric and an active dimeric form of the enzyme , which is believed to play a key regulatory role in the orchestration of proteolysis and capsid assembly ., Currently available crystal structures of herpesvirus proteases correspond either to the dimeric state or to complexes with peptide mimetics that alter the dimerization interface ., In contrast , the structure of the native monomeric state has remained elusive ., Here , we present the three-dimensional structures of native monomeric , active dimeric , and diisopropyl fluorophosphate-inhibited dimeric protease derived from pseudorabies virus , an alphaherpesvirus of swine ., These structures , solved by X-ray crystallography to respective resolutions of 2 . 05 , 2 . 10 and 2 . 03 Å , allow a direct comparison of the main conformational states of the protease ., In the dimeric form , a functional oxyanion hole is formed by a loop of 10 amino-acid residues encompassing two consecutive arginine residues ( Arg136 and Arg137 ) ; both are strictly conserved throughout the herpesviruses ., In the monomeric form , the top of the loop is shifted by approximately 11 Å , resulting in a complete disruption of the oxyanion hole and loss of activity ., The dimerization-induced allosteric changes described here form the physical basis for the concentration-dependent activation of the protease , which is essential for proper virus replication ., Small-angle X-ray scattering experiments confirmed a concentration-dependent equilibrium of monomeric and dimeric protease in solution .

Herpesviruses encode a unique serine protease , which is essential for herpesvirus capsid maturation and is therefore an interesting target for drug development ., In solution , this protease exists in an equilibrium of an inactive monomeric and an active dimeric form ., All currently available crystal structures of herpesvirus proteases represent complexes , particularly dimers ., Here we show the first three-dimensional structure of the native monomeric form in addition to the native and the chemically inactivated dimeric form of the protease derived from the porcine herpesvirus pseudorabies virus ., Comparison of the monomeric and dimeric form allows predictions on the structural changes that occur during dimerization and shed light onto the process of protease activation ., These new crystal structures provide a rational base to develop drugs preventing dimerization and therefore impeding herpesvirus capsid maturation ., Furthermore , it is likely that this mechanism is conserved throughout the herpesviruses .

journal.pntd.0003284

Improvements on Restricted Insecticide Application Protocol for Control of Human and Animal African Trypanosomiasis in Eastern Uganda

African trypanosomes transmitted by tsetse flies ( Diptera: Glossinidae ) pose one of the biggest constraints to animal and human health and livestock-crop integration in Sub-Saharan Africa 1 , 2 ., They cause a debilitating disease in domestic animals ( nagana ) and humans ( sleeping sickness ) 3–5 ., In the south-eastern part of Uganda , cattle are the main reservoir of Trypanosoma brucei rhodesiense the causative agent of the acute form of African human trypanosomiasis ( HAT ) 6–9 ., The chronic form of the disease caused by T . b ., gambiense , whose main reservoir is yet unknown , exists in West Nile Districts of Uganda extending to most parts of South–Sudan 10 , 11 ., However , active case detection and management have been shown to be effective in T . b . gambiense control indicating that humans are very important in maintaining disease transmission 12–14 ., The distance between the two forms of HAT has been decreasing threatening a merger as a result of massive cattle restocking in south-eastern Uganda following 20 years of unrest in this region 10 , 15 , 16 ., This merger has recently been temporarily halted by the Stamp-out sleeping sickness ( SOS ) program-led preventive chemotherapy and pyrethroid insecticide spraying of about 0 . 5 million cattle 17 , 18 ., However , this halt remains temporary unless control efforts are sustained 18 , 19 ., The above notwithstanding , The World Bank estimates that about 25% of the population in Sub-Saharan Africa and Uganda in particular , subsists on less than US $ 1 . 25 per day ., This poverty level is compounded by food insecurity that affects over 34% of the population 20 , 21 and ill-health caused by HAT in addition to other endemic human diseases in this region ., However , the majority of the poor people in Ugandan communities afflicted by HAT own cattle 22 , 23 whose production is also constrained by AAT ., This implies that improving livestock production has potential to reduce poverty and improve food security 1 , 2 , 20 , 24 ., Before this can be achieved , there is need to put in place effective and sustainable HAT/AAT control methods ., Such control methods to be effective and sustainable in small holder crop-livestock production systems need to be commensurate to inelastic budgets of small holder livestock keepers ., In addition , they need to be environmentally benign and target more than one of the endemic diseases that are known to occur in these areas ., Previously , restricting pyrethroid insecticides to the belly and legs had proved cheap , environmentally benign and unequivocally effective on tsetse and trypanosomiasis control compared to other control methods 25–27 ., It has also been suggested that RAP is unlikely to disrupt endemic stability to tick-borne diseases ( TBDs ) ; an epidemiological equilibrium that is known to maintain a large population of cattle protected against TBDs 27 ., However , it had been suggested that RAP needs to be optimized in the field setup so as to further reduce its cost and make it commensurate to inelastic budgets of small holder livestock keepers 27 ., To this end , a cluster randomized trial was carried out to determine the smallest proportion of a village herd that needs to be sprayed by restricting pyrethroid insecticides to the bellies , legs and ears of cattle and effectively control HAT/AAT ., To achieve this , bovine trypanosome prevalences were determined by molecular techniques before and after spraying ( by RAP ) 0% , 25% , 50% , 75% of village cattle herds in 20 villages in Tororo , district; eastern Uganda ., RAP was initially developed basing on a body of research that indicated that tsetse land and feed mostly on legs , bellies and ears of the larger compared to smaller/younger cattle 27–29 ., Restricting insecticides to the legs , bellies and ears reduces the amount of the insecticide by 5 fold; reducing on the cost of application and environmental effects to the dung fauna that break down dung into manure 25 , 30 ., It is upon this background that we sought to further optimize RAP ., This study was carried out in Tororo district , south-eastern Uganda for 18 months between June 2012–December 2013 ., Glossina fuscipes fuscipes and Glossina pallidipes are the main tsetse fly vectors of trypanosomiasis in this area 9 , 31 ., The location , livestock production systems , climate and vegetation of Tororo district have been described elsewhere 32 , 33 ., The 20 intervention villages were selected from 57 villages of a larger survey of trypanosome ( D Muhanguzi; unpublished ) and T . parva 33 prevalence in Tororo district by molecular techniques ., Fifty seven villages were screened for eligibility and data collected on basic socio-demographics and trypanosome prevalence by molecular techniques ., Twenty-seven villages fulfilled the eligibility criteria of, i ) a cattle population of >\u200a=\u200a50 and, ii ) a trypanosome prevalence of >\u200a=\u200a15% ., A village cattle population of 50 was used so as to make sure that cattle population is large enough not to be depleted in 18 months of follow-up ., Baseline trypanosome prevalence of 15% was used for village inclusion in order to provide a wide enough range to be able to measure the effect of graded RAP on trypanosome prevalence ., In order to select 20 villages , 100 unique allocation sequences were generated which fulfilled the condition of a minimum distance of 2 km between neighbouring villages ., This was to minimize contamination effects from different intervention arms ., Finally , one allocation sequence was selected randomly ., Each of the 20 study villages was randomized to one of five different treatments ., All cattle in 20 study villages were ear tagged for ease of identification at follow-up ., They were then treated with a short acting diminazene diaceturate ( DA ) containing cyanocobalamin ( vitamin B12 ) and hydroxocobalamin ( Vitamin B12a ) ( Veriben B12; Ceva santé animale , France ) at the beginning of the trial ., Another DA dose was administered 40 days later to all cattle in the 20 study villages to clean them of residual trypanosome infections and be able to monitor the rate at which re-infection took place ., Diminazene diaceturate was administered at a dose of 0 . 01 g/kg live body weight ( bwt ) by deep intramuscular injection ., In order to assess , herd structure ( age , sex , breed , exits/entries ) at each sampling time , livestock-keepers , their household particulars ( village , parish , county ) and cattle demographics were entered on a herd structure register at the time of introduction into the intervention ., This register was updated once three monthly for 15 months ., In regimens 2–4; different proportions ( 25% , 50% and 75% ) of the village herd were sprayed once every 28 days in what is referred to here as graded RAP ., This was to determine the effect of spraying different proportions of a village cattle herd on the rate of transmission of different trypanosomes ., An emulsifiable deltamethrin concentrate ( Vectocid , Ceva Interchem , Tunis ) spray was applied in the recommended concentration of 1; 1000 ( Vectocid to water parts ) on legs , belly and ears as previously described 27 ., The first 25% , 50% and 75% of all the registered cattle to be presented in the respective RAP regimens were spayed at each of the monthly spraying ., Cattle in regimen 5 were in addition given an Albendazole 10% drench at a dose rate of 0 . 008 g/kg bwt once after three months ., This was to create a replica non-RAP regimen where a non tsetse and-trypanosomiasis effective treatment was administered as an incentive for farmers to present cattle for trypanosome testing for 18 months ., This was introduced in the design of this study in order to reduce the risk of excessive losses to follow-up in Regimen 1 ., As such , regimens 1 and 5 were planned control regimens for RAP regimens 2–4 ., Blood samples were taken 14 days post the last Veriben B12 injection and repeated once three monthly for 18 months of the trial in order to monitor the rate of re-infection with different trypanosomes ., For ethical reasons , all cattle in the non-RAP villages were administered with Veriben B12 injections at the end of the trial since they were at a higher risk of infection during the trial ., About 125 µl of blood were collected from the middle ear vein and applied onto designated sample area of the classic Whatman FTA cards ( Whatman Bioscience , Cambridge , UK ) avoiding cross contamination 34 , 35 ., Blood samples were then allowed to air-dry , labelled with cattle ear tag numbers , treatment regimen , sampling number , village name , parish , sub County , County and date of collection ., They were packed in foil pouches with a silica gel desiccant ( Sigma Aldrich , Co . , Life sciences , USA ) prior to shipping to the University of Edinburgh , UK for analysis ., DNA was extracted and eluted in Chelex-100 resin ( Sigma Aldrich , Co . , Life sciences , USA ) from five 3 mm FTA sample discs according to a previously described protocol 35 , 36 ., Eluted DNA samples were kept at −20°C for long-term PCR analyses or 4°C if they were to be analysed within a few days after extraction ., Eluted DNA samples were screened for different trypanosome species using a single pair of primers ( CR and BR ) and thermo cycling conditions as previously described 37 ., The ITS1- PCR was done in 25 µl reaction volume; 20 µl of which were the PCR master-mix and either 5 µl of the test sample or negative control eluate or positive control DNA ., The master-mix was made of 10×-reaction buffer ( 670 mM Tris-HCl pH 8 . 8 , 166 µM ( NH4 ) 2SO4 , 4 . 5% Triton X-100 , 2 mg/ml gelatin ) ( Fisher Biotech ) , 1 . 0 mM MgCl2 , 200 µM of each dNTP , 5 µM each of the CF and BR primers , 0 . 5 U of Taq DNA polymerase ( Fisher Biotech ) and 15 . 2 µl RNase-free ( molecular grade ) water ., To determine which samples were infected with either T . brucei or T . b ., rhodesiense , multiplex PCR 38 was carried out on each of the samples from which a 450 bp fragment was detected on ITS1-PCR ., Multiplex PCR was done in 25 µl reactions using primers and conditions as previously described 38 ., In order to determine the commonest T . congolense genotype circulating in Tororo district , all samples from which a ≥600 bp fragment was amplified on ITS1-PCR were initially tested for T . congolense savannah using a single pair of primers ( TCS1 & TCS2 ) and thermo cycling conditions as previously described 39 ., All samples that were positive for T . congolense DNA on ITS1-PCR were positive for T . congolense savannah ., For this reason , no more T . congolense genotype-specific ( Kilifi , Tsavo , forest ) PCRs were performed although a few co-infections with different T . congolense genotypes could have been possible ., The PCR was done in 25 µl reaction volume; 20 µl of which were the PCR master-mix and either 5 µl of the test sample or negative control eluate or positive control DNA ., The master-mix was made of 10×-reaction buffer ( 670 mM Tris-HCl pH 8 . 8 , 166 µM ( NH4 ) 2SO4 , 4 . 5% Triton X-100 , 2 mg/ml gelatin ) ( Fisher Biotech ) , , 4 . 5% Triton X-100 , 2 mg/ml gelatin ) ( Fisher Biotech ) , 0 . 75 mM MgCl2 , 200 µM of each dNTP , 12 . 5 µM each of the TCS1 & TCS2 primers , 1 U of Taq DNA polymerase ( Fisher Biotech ) and 13 . 05 µl of RNase-free water ., PCR products for the three sets of PCRs were electrophoresed in 1 . 5% agarose ( Bio Tolls Inc . Japan ) , stained in GelRed ( Biotium , Inc . , USA ) and visualised on a UV transilluminator for fragment size determination ., Pyramidal traps 40 were set in 161 locations by Tororo District Entomology Department between June and September 2012 ., Individual tsetse trap catches were used to determine pre-intervention FTD ., About 12 traps were set per Km2 to cover at least a square km ( km2 ) of each sub county ., Tsetse fly catches were monitored , emptied and species and sex determined after every 24 hours ., Apparent tsetse density was determined as the number of tsetse flies per trap per day ., The primary analysis investigated the impact of RAP on the incidence risk ratios of any trypanosome infection using generalized linear mixed models with a Poisson distribution and a logarithmic link function ., To account for correlation within clusters , villages were included as gamma distributed random effects ., The logarithm of the time under observation , i . e . the time period between the first and last time an individual animal was sampled , was included as offset variable ., To assess the intervention effect over time , prevalences after 12 and 18 months of follow up were compared using mixed models with binary outcome and logit link function ., Additional analyses at other sampling points are provided in supporting information S1 ., The original idea of modelling the proportion of animals treated with RAP as a dose response relationship was abandoned because incidence did not decrease with increasing proportion of treated animals ., Therefore , the results for the different treatment regimens compared to the control regimens are presented ., Apparent tsetse density was determined as the number of tsetse captured per trap per day ., To determine the spatial distribution of tsetse flies ( G . pallidipes and G . fuscipes ) in Tororo district an FTD map was generated using the Inverse Weighing Distance Extension ( IDW ) 41 of ArcMap 10 . 3 of 161 individual trap catches ., Interpolation was done at two spatial resolutions ( grid cell sizes of 1 km2 and 25 km2 ) and raster values were extracted for each village at each spatial resolution ., A default exponent value of 2 was chosen ., Although there was little evidence of spatial autocorrelation ( Morans I\u200a=\u200a−0 . 11 , −0 . 08 , −0 . 10; all P >0 . 2 , for baseline trypanosome prevalence and FTDs at 1 km2 and 25 km2 resolution , respectively ., The association between FTD and trypanosome prevalence was adjusted for potential spatial dependence ., We used a generalized least squares model with a Gaussian spatial correlation structure to quantify the effect ., Statistical analyses were performed using R v 3 . 0 . 2 ( packages ‘lme4’ , ‘nlme’ and ‘ape’ ) except Poisson random effect models which were performed in STATA v 12 . 1 ., This study was reviewed and approved by the Makerere University College of Veterinary Medicine Animal Resources and Biosecurity ( COVAB ) research and ethics committee for consistency to animal use and care ., Upon approval ( number VAB/REC/10/105 ) the COVAB research and ethics committee forwarded it to the Uganda National Council for Science and Technology ( UNCST ) and it was further approved and registered under registration number HS1336 ., Over all seven time points , eleven thousand blood samples ( 11 , 087 ) were collected from 3 , 677 cattle ., One thousand nine hundred eighty one cattle ( 54% ) were sampled 14 days post the second Veriben B12 injections and examined to determine trypanosome residual infections ., Almost half the investigated animals ( 46% ) were newly introduced into the herd during the 18 months of follow up ( Figure 1 ) ., Pre-intervention trypanosome prevalence ranged from 20–27% in different regimens ., The Boran and African short horn zebu hybrid was the most predominant cattle breed ( 98% ) and well balanced among the five treatment groups ( range 93%–100% ) ., Treatment groups were slightly imbalanced with respect to age and sex composition ., Roughly half of the animals were above 3 years of age ( Table 1 ) ., Fourteen days post the second dose of diminazene diaceturate ( denoted as time 0 ) , trypanosome prevalences generally increased in all regimens up to month 6 when they started decreasing ( Regimen 2 , 3 and 4 ) over time ., In regimens 1 and 5 trypanosome prevalences increased up to about 12 and 15 months respectively and started decreasing thereafter ., The slope of curves representing trypanosome prevalences over time in different regimens is in increasing order of Regimen 2<3<4<1<5 ( Figures 2& 3 ) ., T . vivax was the most predominant species detected in any regimen while T . brucei s . l . was the least predominant species detected over the study period ( Table 2 ) ., At the end of follow-up , we observed an incidence of 9 . 8 per 100 animal years in the RAP regimens which was significantly lower compared to the 25 . 7 in the non RAP regimens ( incidence rate ratio: 0 . 37; 95% CI: 0 . 22–0 . 65; P<0 . 001 ) ., Likewise , trypanosome prevalence after one year of follow up was 15% in the non-RAP regimens compared to 4% in the RAP animals ( OR: 0 . 20 , 95% CI: 0 . 08–0 . 44; P<0 . 001 ) ., The effect was lower but statistically significant after 18 months of follow up ( 9% vs 4%; OR: 0 . 38; 95% CI: 0 . 14–0 . 93; P\u200a=\u200a0 . 03 ) ., Adjustment for sex , age category , FTD at 1 km2 spatial aggregation ( FTD-1000 m ) and for animal treatment at baseline did not noteworthy change the estimates ( Table 3 ) ., There was some indication that FTD-1000 m had an impact on the treatment effect , but the association was only statistically significant in one of the 3 models ., Newly introduced animals had slightly lower risk but it was only marginally significant ., Of note , newly introduced cattle were generally younger ( median age 2 . 3 years compared to 4 . 0 years ) ., As such , trypanosome infections were persistently higher in isolated villages in central , northern and western parts of Tororo District especially in Kirewa , Nagongera and Paya sub counties ( Figure 4 ) ., Details of the models on the other sampling dates as well as time×treatment interaction are provided in supporting information S1 ., The relative risk of infection with any trypanosome species measured here by the incidence risk ratios was highest in regimen 5 over the 18 months of the study ., Cattle in regimen 2 presented with incidence of 5 . 1 per 100 animal years which was significantly lower compared to the 20 . 9/100 years observed in the control group ( regime, 1 ) ( IRR: 0 . 24; 95% CI: 0 . 11–0 . 52; P<0 . 001 ) ( Table 4 ) ., The risk of infection with different trypanosome species was in order of regimen 5>4>3>2 ( Table 5 ) ., Contrary to our expectation there was no evidence that protection increases with increasing proportion of animals treated ., The number of tsetse flies caught per trap per day were summarised into FTD which was highly variable between traps ( Figure 5 ) ., About 88% of all tsetse caught during the period were of G . f ., fuscipes while 12% were G . pallidipes from Paya and Mulanda sub counties ., G . pallidipes was localised at one site in Lwala Parish , Mulanda Sub County but fairly distributed in each of the 4 selected parishes of Paya Sub County ( Table 6 ) ., We observed a 2 . 7%-points increase in the baseline trypanosome prevalence with each 1 unit increase of FTD ( 95% CI: 0 . 6–4 . 7%-points , P\u200a=\u200a0 . 02 ) using the prediction of the 1 km2 spatial aggregation ., On a higher spatial aggregation level ( 25 km2 grid cell size ) the observed effect was with 1 . 7%-points smaller and statistically not significant ( 95% CI: −1 . 2–4 . 7%-points , P\u200a=\u200a0 . 26 ) ., In Table 7 the baseline prevalences and the corresponding FTD are presented for all villages ., This study complements the available literature to demonstrate that RAP is effective in controlling African trypanosomiasis ., To our knowledge , this study provides the first field based longitudinal study to demonstrate that spraying only as low as 25% of a village cattle herd in stable African trypanosomiasis transmission area is sufficient in the control of T . brucei s . l . In high tsetse challenge areas where tsetse mainly feed on cattle , control of nagana ( T . vivax and T . congolense ) would probably require increasing village RAP herd coverage to 50–75% without reducing RAP efficacy ., This is particularly important because T . vivax and T . c ., savannah persist under moderate ( <50% RAP ) tsetse control over a long period of time ., In such areas , treatment of all cattle with a curative trypanocide once yearly for the first 1–2 years of the control program would leverage tsetse control by reducing parasitaemia ., Reducing RAP coverage to 25% ( T . brucei s . l control ) or 50–75% ( T . vivax/congolense control ) would further reduce the amount of insecticides used compared to that used in whole body spraying ., This will further reduce cost of application of RAP and improve uptake by small holder farmers in many crop-livestock production systems ., Before these findings are integrated in routine tsetse control programs we recommend that the performance of different RAP herd coverage levels is evaluated in varied tsetse challenge , trypanosome transmission rates and management systems .

Introduction, Materials and Methods, Results, Discussion

African trypanosomes constrain livestock and human health in Sub-Saharan Africa , and aggravate poverty and hunger of these otherwise largely livestock-keeping communities ., To solve this , there is need to develop and use effective and cheap tsetse control methods ., To this end , we aimed at determining the smallest proportion of a cattle herd that needs to be sprayed on the legs , bellies and ears ( RAP ) for effective Human and Animal African Trypanosomiasis ( HAT/AAT ) control ., Cattle in 20 villages were ear-tagged and injected with two doses of diminazene diaceturate ( DA ) forty days apart , and randomly allocated to one of five treatment regimens namely; no treatment , 25% , 50% , 75% monthly RAP and every 3 month Albendazole drench ., Cattle trypanosome re-infection rate was determined by molecular techniques ., ArcMap V10 . 3 was used to map apparent tsetse density ( FTD ) from trap catches ., The effect of graded RAP on incidence risk ratios and trypanosome prevalence was determined using Poisson and logistic random effect models in R and STATA V12 . 1 respectively ., Incidence was estimated at 9 . 8/100 years in RAP regimens , significantly lower compared to 25 . 7/100 years in the non-RAP regimens ( incidence rate ratio: 0 . 37; 95% CI: 0 . 22–0 . 65; P<0 . 001 ) ., Likewise , trypanosome prevalence after one year of follow up was significantly lower in RAP animals than in non-RAP animals ( 4% vs 15% , OR: 0 . 20 , 95% CI: 0 . 08–0 . 44; P<0 . 001 ) ., Contrary to our expectation , level of protection did not increase with increasing proportion of animals treated ., Reduction in RAP coverage did not significantly affect efficacy of treatment ., This is envisaged to improve RAP adaptability to low income livestock keepers but needs further evaluation in different tsetse challenge , HAT/AAT transmission rates and management systems before adopting it for routine tsetse control programs .

Poverty , hunger and human ill-health aggravated by trypanosomiasis in Sub-Saharan Africa can only be reduced by developing and using cheap and effective tsetse control methods ., To further reduce the cost of tsetse control by restricting insecticides to the legs , belly and ears ( RAP ) we set out to determine the lowest RAP coverage that can effectively control tsetse ., Cattle in 20 south-eastern Uganda villages were randomly allocated to 5 treatment groups , ear-tagged for ease of follow-up and treated twice forty days apart with a trypanocide at the beginning of the trial ., Cattle in regimens 2–4 received monthly graded RAP ( 25% , 50% and 75% of village herd respectively ) , while those in regimens 1 and 5 received no more treatment and deworming once every three months respectively ., Molecular techniques were used to check for trypanosome infections , while tsetse apparent density was determined by traps at 161 locations in the district ., About 25% RAP coverage was effective at controlling T . brucei s . l . while 50–75% RAP coverage would need to be used for effective T . vivax and T . congolense nagana control ., Use of RAP at lower herd coverage is envisaged to reduce its cost , damage to the environment and improve its uptake in resource poor communities .

biotechnology, research design, infectious diseases, electrophoretic techniques, veterinary science, medicine and health sciences, epidemiology, mathematical and statistical techniques, molecular biology techniques, biology and life sciences, tropical diseases, extraction techniques, parasitic diseases, molecular biology, parasitology, research and analysis methods

journal.pgen.1008273

BCDIN3D regulates tRNAHis 3’ fragment processing

The 5’ ends of eukaryotic RNAs are vital for determining their processing and function 1 ., The most well-known 5’ end RNA modification is the m7G cap , which has multiple functions , including of protecting messenger RNAs from exonucleolytic degradation , and of marking them for nucleo-cytoplasmic export and translation 2 ., Additionally , a chemically simpler 5’ end modification by O-methylation occurs directly on 5’ phosphate ends , either on the γ-phosphate of nascent tri-phosphorylated snRNAs , or on the α-phosphate of processed monophosphate RNAs 3 ., These methylations are carried out by the Bicoid Interacting 3 ( BIN3 ) 4 family of methyltransferases , which are conserved from fission yeast to humans 5 ., In humans , two BIN3 related enzymes , MePCE and BCDIN3D , have been identified ., Jeronimo et al . , uncovered that MePCE methylates the γ-phosphate of the 7SK snRNA 6 , while we discovered that BCDIN3D methylates the 5’ monophosphate of two specific microRNA precursors , pre-miR-145 and pre-miR-23b , both in vitro and in cells , to inhibit their processing into mature miRNA by Dicer 5 ., Our initial analysis in MCF-7 cells also suggested that other microRNAs could be methylated by BCDIN3D 5 ., However , due to lack of methods to specifically enrich 5’ phospho-methylated RNAs , the methylation of other types of RNAs with 5’ monophosphates was not analyzed in our initial study ., MePCE forms a stable ribonucleoprotein complex with its 7SK small nuclear RNA target 6 ., Based on these findings , we hypothesized that the other member of the BIN3 family , BCDIN3D , may interact with at least a subset of its RNA targets in a similar manner ., Purification and sequencing of RNAs interacting with BCDIN3D revealed that BCDIN3D specifically interacts with mature tRNAHis and miR-4454 , which our results suggest to be a tRNAHis 3’ fragment derived from cleavage of mature tRNAHis ., Importantly , BCDIN3D downregulation results in increased levels of tRNAHis 3’ fragments in cells ., Overall , our results indicate that tRNAHis interaction with BCDIN3D plays non-canonical roles , one of which is to regulate the generation of tRNAHis 3’ fragments ., This may have important biological implications as miR-4454 has been identified as a potential biomarker in several human diseases 7–9 ., Moreover , given that tRNA 3’ fragments limit the mobility of transposable elements in mammalian cells 10 , BCDIN3D may impact genomic stability ., In order to purify RNAs interacting with BCDIN3D , we used HeLa-S3-FlpIn cells containing a single insertion at a FRT locus of BCDIN3D tagged with a FLAG tag at its C-terminus ( denoted BCDIN3Df or B3Df ) ., We first purified BCDIN3Df with a protocol that combines FLAG antibody co-immunoprecipitation , followed by FLAG peptide elution ., We previously used this same protocol to show an RNase A-sensitive interaction of BCDIN3Df with Dicer 5 ., We then extracted RNAs from FLAG eluates of Control and BCDIN3Df cells with a protocol that allows recovery of RNAs of all sizes ( see Methods ) ., Analysis of the extracted RNAs on a denaturing 15% polyacrylamide/urea gel stained with silver showed a prominent band of 70 to 90 nucleotides in the FLAG eluates from BCDIN3Df cells , along with lighter non-specific bands present in the FLAG eluates from both Control and BCDIN3Df cells ( Fig 1A ) ., In order to unbiasedly uncover the identity of the small RNAs interacting with BCDIN3D , we sought to use next generation sequencing ( NGS ) ., Commonly used small RNA-Seq protocols use RNA ligases to add RNA adaptors to each end of the RNA prior to reverse transcription into cDNA and NGS library amplification ., However , RNAs methylated by BCDIN3D cannot be amplified by these protocols because the dimethyl-phosphate ( 5’Pme2 ) is resistant to ligation by RNA ligases 5 ., In an attempt to circumvent the 5’Pme2 end ligation problem , we first used a small RNA-Seq protocol that employs T4 Rnl2tr ( T4 RNA ligase 2 , truncated ) to add an adaptor only on the 3’ end , followed by reverse transcription and cDNA circularization with CircLigase prior to amplification 11 , but our attempts were unsuccessful ., We then reasoned that RNA secondary structure could also prevent efficient amplification of BCDIN3D-interacting RNAs ., Therefore , we used a TGIRT-seq protocol that combines template switching to the RNA 3’ end and a highly processive Thermostable Group II Intron Reverse Transcriptase 12 for cDNA synthesis ( Fig 1B ) ., The use of this method resulted in successful library amplification ., Because the visible BCDIN3D-specific RNA band is 70 to 90 nt , we first focused on paired end reads longer than 50 nt ( Fig 1C and S1 Table ) ., The reads were dominated by mature tRNAHis , containing both the non-templated 5’ G-1 characteristic of tRNAHis , and the non-templated 3’ CCA tail 13 ( see WebLogo and IGV plot of obtained sequences in Fig 1C ) ., In addition to tRNAHis , the BCDIN3D-interacting RNA libraries also included other reads corresponding to RNAs >50 nt , but these were found at the same level in the Control libraries ( Fig 1C and S1 Table ) , and were consequently deemed background reads ., We confirmed our TGIRT-seq results by northern blotting with a specific tRNAHis probe ( probe #1 ) that is complementary to the TΨC arm ( Fig 1C and 1D and S4 Table ) ., Notably , this probe detected full-length tRNAHis molecules present exclusively in the BCDIN3Df FLAG eluates , while a U6 probe detected a background U6 RNA in both samples ( Fig 1D ) ., As mentioned above , our TGIRT-seq results show that BCDIN3D exclusively interacts with mature tRNAHis containing the non-templated 5’ G-1 ( Fig 1C ) ., Consistent with this observation , our in vitro RNA methyltransferase assays with recombinant BCDIN3D show that BCDIN3D has a marked preference for mature tRNAHis containing the 5’ G-1 , when compared to a pre-tRNAHis without 5’ G-1 ( Fig 1E ) , which as expected , is the preferred substrate of tRNAHis Guanylyltransferase 1 Like ( THG1L ) ( Fig 1F ) , the human homolog of yeast Thg1 that adds the non-templated 5’ G-1 on pre-tRNAHis 14 ., We next sought to determine whether the BCDIN3Df-interacting tRNAHis molecules are methylated on their 5’ ends ., Treatment of a synthetic pre-miRNA harboring different 5’ end modifications with an alkaline phosphatase originating from the Antarctic strain TAB5 , also called Antarctic Phosphatase 15 , can remove 5’P and 5’Pme1 but not 5’Pme2 from the 5’ ends of RNAs , as judged by a distinct electrophoretic mobility shift on the denaturing 15% polyacrylamide/urea gel shown on Fig 2A ., When we performed in vitro RNA methyltransferase assays with pre-miR-145-5’P using the radioactively labeled methyl group donor S-Adenosyl-Methionine 3H-S-SAM 5 , the small fraction of C3H3-methylated pre-miRNA-145 detected by autoradiogram neither disappeared , nor changed its mobility upon treatment with Antarctic Phosphatase ( Fig 2B ) , consistent with our previous results that BCDIN3D dimethylates pre-miRNAs 5 ., While this manuscript was in preparation , tRNAHis was reported to be monomethylated by BCDIN3D in 293T cells and in vitro , based on analysis of RNase A-digested fragments detected by mass spectrometry 16 ., Our in vitro RNA methyltransferase assay differed from the assay used in 16 in that ours omitted MgCl2 and included 5 mM DTT as a reducing agent 5 ., As also reported by Blazer et al . , our conditions ensure optimal BCDIN3D activity in vitro 17 ., Under our conditions , tRNAHis methylated by BCDIN3D in vitro becomes fully resistant to Antarctic Phosphatase ( Fig 2D ) ., Thus , our data suggest that tRNAHis methylated by BCDIN3D is not monomethylated , and is most likely phospho-dimethylated ., Furthermore , the migration of tRNAHis that co-purified with BCDIN3Df in the FLAG eluates likewise did not change upon treatment with Antarctic Phosphatase , suggesting that tRNAHis associated with BCDIN3Df is largely dimethylated in HeLa-S3 cells ( Fig 2E ) ., Our TGIRT-seq analysis revealed a significant number of reads with a length < 50 nt and specific to the BCDIN3Df FLAG eluates ( Fig 3A and S3 Table ) ., Some of these reads could be due to a reverse transcription roadblock at modified bases of tRNAHis ( S2 Fig ) ., For example , while a very large number of reads <50 nt stop just before the G37 residue , no faster migrating band of the expected signal intensity was detected by northern blot with a tRNAHis probe #1 ( Fig 1D ) ., Although not detected by mass spectrometry 16 , tRNAHis interacting with BCDIN3D appears to be partially methylated at G37 , as evidenced by the observed nucleotide misincorporation introduced by TGIRT at the G37 position ( Fig 1C ) ., This is also consistent with the fact that BCDIN3D interacts with TRMT5 , which is the enzyme responsible for m1G methylation at that tRNA position 18 ( S2 Table , S2C Fig ) ., As shown in Fig 3A and 3B , a significant number of < 50nt reads specific to BCDIN3Df FLAG eluates map to hsa-miR-4454 ., We validated this result with Reverse Transcription coupled to quantitative PCR ( RTqPCR ) with a custom Taqman probe for hsa-miR-4454 ( S3 Fig ) ., Moreover , a probe complementary to the sequence of this miRNA showed fast migrating bands in northern blots ( Fig 3C , tRNAHis probe #2 ) , but also a strong signal corresponding to the full-length tRNAHis ., The latter is simply due to the fact that except for a G residue at the position of the m1A modification in the tRNA , the annotated sequence of hsa-miR-4454 is identical to that of the 3’ end of mature tRNAHis that includes the 3’ CCA sequence ( Fig 3D ) ., Given the observed interaction between BCDIN3D and tRNAHis , we hypothesized that hsa-miR-4454 may be a tRNAHis 3’ fragment ., This hypothesis is particularly appealing for three major reasons:, 1 ) the annotated hsa-miR-4454 does not have an optimal miRNA stem-loop as defined by 19 ( S4 Fig ) ;, 2 ) the annotated hsa-miR-4454 genomic locus does not display any feature of transcriptionally active chromatin in HeLa-S3-FlpIn cells ( S4B Fig ) ; and, 3 ) the annotated hsa-miR-4454 sequence is the reverse complement of the primer binding site of a HERVH-int LTR-retrotransposon ( chr4:163093605–163096486 in hg38 genome assembly ) , which as its name indicates uses a tRNAHis for reverse transcription ( S4C Fig ) ., Close analysis of the hsa-miR-4454 reads from the BCDIN3Df FLAG eluates strongly supports our hypothesis that they originate from tRNAHis rather than the annotated genomic locus ., Indeed , some hsa-miR-4454 reads from the BCDIN3Df FLAG eluates display heterogenous 5’ ends that differ from the sequence at the annotated MIR4454 genomic locus , but match the sequence of tRNAHis ( Fig 3D , IGV plots of reads mapping to hsa-miR-4454 from two biological repeats ) ., About 48% of miR-4454 reads are ~18 nt long , and perfectly match tRNAHis with a CCA tail ( Fig 3D ) ., The rest of the reads are longer and show a high level of polymorphism at what would be the “G4” residue of the annotated hsa-miR-4454 stem loop ( Fig 3D and S4D Fig ) ., This residue aligns to the A57 residue of tRNAHis , which is m1A methylated 16 , 20 , leading to high levels of polymorphism introduced by TGIRT at that position of tRNAHis ( Fig 1C ) 21 , 22 ., The spectrum of incorporated nucleotides , dominated by T and G , is characteristic of TGIRT-III mis-incorporation at m1A 23 and is similar in both miR-4454 and full-length tRNAHis reads ( Fig 3D and S4D Fig ) ., Moreover , as mentioned above , a smaller fraction of reads go beyond the annotated hsa-miR-4454 stem loop , and the mismatched nucleotides perfectly align to tRNAHis ( Fig 3D ) ., Altogether , these observations indicate that the hsa-miR-4454 reads from BCDIN3Df FLAG eluates derive from tRNAHis ., Next , we sought to assess whether hsa-miR-4454 could be generated by Dicer cleavage of tRNAHis as previously reported for CCA ending tRNA fragments 24 ., In order to test our hypothesis , we performed in vitro Dicer processing assays with synthetic 5’P and 5’Pme2 tRNAHis in the presence of 1 mM MgCl2 , as previously described 5 ( Fig 4A ) ., These experiments produced several noteworthy observations ., The first observation was that incubation with Dicer reduces the amount of visible full-length 5’P-tRNAHis ( Fig 4A , SYBR Gold stained gel ) ., Interestingly , in contrast to what is observed with Dicer processing of pre-miR-145 into a miRNA duplex 5 , no clear RNAs of smaller size were visible in our denaturing polyacrylamide/urea gels stained with SYBR Gold ( Fig 4A , left panel ) in Dicer- vs mock-treated lanes , suggesting that tRNAHis fragments generated by Dicer may be more heterogenous than the miRNA duplex products ., Indeed , northern blot with tRNAHis northern blot probe #2 showed that Dicer produces a series of discrete 3’ fragments ( Fig 4A , tRNAHis northern blot ) of similar size to the tRNAHis 3’ fragments interacting with BCDIN3Df ( Fig 3C ) ., Quantification of the faster migrating band by ImageQuant clearly showed an inhibitory effect of 5’Pme2 on the processing of tRNAHis 3’ fragments by Dicer ( Fig 4A , graph at the right ) ., In order to examine in detail these effects , we performed TGIRT-seq of RNAs purified from two independent in vitro Dicer assays using synthetic tRNAHis-5’P and -5’Pme2 and 2 μl of Dicer at 1 μg/μl ., This setup corresponds to conditions where Dicer reduces the amount of visible full-length 5’P-tRNAHis by ~50% ., We first quantified the proportion of full-length tRNAHis reads present in Dicer-treated vs mock-treated samples ( Fig 4B ) ., As expected from the northern blot results , this analysis showed that Dicer treatment reduces the levels of full-length tRNAHis-5’P by approximately 54% ., Most importantly , this same analysis showed that tRNAHis-5Pme2 is significantly more resistant to Dicer cleavage than tRNAHis-5’P ( ** , p-value of ~0 . 0095 ) ., This parallels our previous finding that 5’Pme2 inhibits the processing of pre-miR-145 by Dicer 5 ., We then analyzed the TGIRT-seq data to quantify the effect of 5’Pme2 modification on the generation of Dicer cleavage products ., To avoid noise from reverse transcription stops and/or incomplete 3’->5’ chemical synthesis of tRNAHis , we analyzed at which nucleotide position in tRNAHis the reads end in mock- and Dicer-treated samples ( Fig 4C ) ., As expected , above 90% of reads in the mock-treated samples have 3’ ends corresponding to the last nucleotide of tRNAHis ( dotted lines in Fig 4C ) , and this proportion decreases significantly in the Dicer treated samples ( solid lines in Fig 4C ) ., Concomitantly , new read ends corresponding to Dicer cuts appear ( Fig 4C ) ., These Dicer-specific read ends form several discrete peaks that are shown on the tRNAHis sequence and diagram ( Fig 4C ) ., Interestingly , two of the major cut sites were significantly decreased in the Dicer-treated tRNAHis-5Pme2 compared to tRNAHis-5’P ., These two sites are located on the double stranded part of the TΨC arm of tRNAHis ., However , 5’Pme2 only marginally inhibited the generation of a minor cut site in the middle of the loop of the TΨC arm ., Thus , 5’ phospho-methylation of tRNAHis significantly affects some Dicer cuts , but not others ( Fig 4C ) ., As seen in Fig 3D , the reads of tRNAHis 3’ fragments interacting with BCDIN3D are also heterogenous and very similar in size to the tRNAHis 3’ fragments generated in vitro by Dicer ., Overall our results suggest that Dicer can generate tRNAHis 3’ fragments in a way that is sensitive to the methylation status of the 5’P ., In order to determine how BCDIN3D knockdown affects the levels of miR-4454/tRNAHis 3’ fragments , we analyzed microRNA-Seq data from MDA-MB-231shNC and shBCDIN3D cells ., The MDA-MB-231shBCDIN3D cells reduce BCDIN3D mRNA and protein levels by approximately 70% compared to shNC cells ( Fig 4D ) ., The dataset was produced from small RNAs < 34 nt extracted from a denaturing 15% polyacrylamide/urea gel ( see Methods ) ., When analyzed with a microRNA-Seq bioinformatic pipeline , very few reads mapped to hsa-miR-4454 ., However , most microRNA-Seq bioinformatic pipelines pre-filter reads mapping to rRNAs and tRNAs ., Therefore , we re-analyzed our data with our TGIRT-seq pipeline that does not pre-filter any small RNA reads ( S1 Fig ) ., Our analysis revealed a significant number of miR-4454/tRNAHis 3’ fragment reads in MDA-MB-231 cells , going up to 473 reads per million in the shBCDIN3D condition , which is comparable to miRNAs well-expressed in MDA-MB-231 cells , such as miR-23a ., Importantly , this analysis showed that BCDIN3D depletion significantly increased the levels of tRNAHis 3’ fragments ( Fig 4E ) , without affecting either the tRNAHis levels , or the steady-state level of tRNAHis aminoacylation detected by acidic gel and northern blot ( Fig 4F , quantified in Fig 4G ) in these cells ., Thus , our results suggest that BCDIN3D activity on tRNAHis could protect this tRNA from digestion by Dicer in cells , which is consistent with our observation that 5’Pme2 inhibits the generation of tRNAHis 3’ fragments by Dicer in vitro ( Fig 4C ) ., To determine how miR-4454/tRNAHis 3’ fragments are produced in cells , we performed Drosha and Dicer knock downs experiments in MDA-MB-231 cells ( S5A Fig ) ., Our results showed that miR-4454/tRNAHis 3’ fragments behave differently from canonical miRNAs in that their levels were unaffected by Drosha knock down ( S5A Fig ) ., Unfortunately , we were unable to formally show that Dicer is responsible for the generation of tRNAHis 3’ fragments in MDA-MB-231 cells , because we were unable to functionally knock-down Dicer in these cells ( S5A Fig ) ., However , while the tRNAHis 3’ fragment is not incorporated into Ago2 ( S5B Fig ) , analysis of tRNAHis 3’ fragments in the relational database of Transfer RNA related Fragments tRFdb 25 showed that the tRF with ID: 3013b , which corresponds to the tRNAHis 3’ fragment described here , is highly enriched in Ago3 and Ago4 PARCLIP ( S5C Fig ) ., These findings suggest that tRNAHis 3’ fragments may have regulatory function ( s ) in cells ., Our result showing increased levels of tRNAHis 3’ fragments in MDA-MB-231 cells depleted for BCDIN3D is reproduced in Hap1 cells that have a BCDIN3D knock-out and complete loss of tRNAHis methylation ( S6 Fig ) ., Thus , BCDIN3D regulates the levels of tRNAHis 3’ fragments in at least two different cell lines ., In the future , it will be of upmost importance to investigate the biological function of tRNAHis 3’ fragments , including in contexts where BCDIN3D function may be of clinical importance , such as cancer and metabolic disease 5 , 26 , 27 ., Based on our results , we hypothesize that BCDIN3D forms an RNP with mature tRNAHis that is phospho-methylated ( Figs 1 and 2 ) ., This could result from relatively slow product release as recently shown for the MePCE phosphomethyltransferase 28 ., Stable interaction with tRNAHis may regulate BCDIN3D activity towards its other targets , including precursor miRNAs 5 or other yet to be uncovered phospho-methylated RNAs , by affecting BCDIN3D structure and/or RNA target selection ., In this context , it will be of particular interest to investigate which cellular conditions disrupt BCDIN3D interaction with tRNAHis , and how in turn those conditions affect BCDIN3D methyltransferase activity towards its other RNA targets ., The gel shift assay for probing tRNAHis phospho-methylation status developed here ( Fig 2 ) provides a fast tool for testing tRNAHis methylation in cells ( S6 Fig ) , and iterations of this assay could be used for other BCDIN3D targets as well ., Our results also suggest that one of the molecular consequences of BCDIN3D-mediated methylation of tRNAHis is to inhibit its cleavage by Dicer ( Fig 4A–4E and S6 Fig ) ., In this context , it is intriguing that BCDIN3D downregulation or knock-out does not decrease the steady-state levels of full-length tRNAHis in cells ( Fig 4F , S6A Fig and 16 ) ., This may be due to compensation by higher levels of transcription at tRNAHis loci in shBCDIN3D cells ., Our discovery that BCDIN3D partially protects tRNAHis from Dicer cleavage may have evolutionary relevance ., We discovered BCDIN3D in our screen for previously uncharacterized methyltransferases with human homologs conserved in fission yeast but not budding yeast 5 ., Indeed , BCDIN3D and MePCE have a single homolog in fission yeast 5 ., Among the known RNAs targeted by BCDIN3D and MePCE ( pre-miRNA , tRNAHis and 7SK ) , only tRNAHis exists in fission yeast ., Interestingly , Dicer is also conserved in fission yeast but not budding yeast ., Dcr1 , the fission yeast Dicer homolog , does not process microRNAs , but has a role in generating small interfering RNAs 29 ., Thus , the function of tRNAHis protection from Dicer processing may be the most ancient function of the BIN3 family of 5’ phospho-methyltransferases ., BCDIN3D depletion significantly increases the levels of tRNAHis 3’ fragments in cells ( Fig 4E and S6B Fig ) ., It was recently shown that specific tRNA 3’ fragments limit the reverse transcription of LTR-retrotransposons in mammalian cells 10 ., In this context , it is plausible that the high levels of BCDIN3D observed in aggressive breast cancers 5 , 27 could decrease the levels of tRNAHis 3’ fragments to promote the mobility of these transposable elements and to enhance genomic evolution , which is a driving force promoting metastasis and drug resistance of cancer cells 30 ., A recent paper by Hasler et al . showed that in La knockdown that shifts the pre-tRNA-Ile2-TTA-2-3 towards a hairpin structure , the pre-tRNA is cleaved by Dicer to generate a small RNA that acts as a microRNA ( miR-1983 ) 31 ., Thus , analogously to the mode of action of La , BCDIN3D-mediated phospho-methylation may stabilize a tRNAHis three-dimensional structure that counteracts Dicer processing ., Finally , hsa-miR-4454 has been detected in a number of studies aiming to identify cancer and disease biomarkers including bladder cancer 7 , inflammatory bowel disease 9 , and osteoarthritis 8 ., Our results indicate that miR-4454 actually corresponds to a tRNAHis 3’ fragment ., Thus it will be of high interest to re-evaluate these translational studies in the light of hsa-miR-4454 being a tRNA 3’ fragment regulated by BCDIN3D and to explore its physiological functions ., HeLa-S3-FlpIn Parental and BCDIN3Df were previously described 5 ., These cells were grown in spinner flasks at 75 rpm in RPMI containing 10% fetal bovine serum ( FBS ) , 100 U/ml penicillin , 100 μg/ml streptomycin and 2 mM L-glutamine ( RPMI+10%FBS+PSQ ) and supplemented with 200 μg/ml of Zeocin ( parental ) or 400 μg/ml hygromycin ( BCDIN3Df ) ., MDA-MB-231shBCDIN3D were generated by transfection of MDA-MB-231 cells with pRS-shBCDIN3D TR317908C/TI368844 plasmid from Origene , while the matched MDA-MB-231shNC were generated by transfection with pRS-Scrambled TR30012 ., These cells were grown in DMEM+10%FBS+PSQ+1 μg/ml of puromycin ., The Hap1 Parental and BCDIN3D KO cells were produced by Horizon and were grown in IMDM+10%FBS+PSQ ., 2×107 Hela-S3-Flp-In and Hela-S3-Flp-In-BCDIN3Df cells grown to a density of 4–6×105 cells per ml were used per Co-IP ., The cells were washed twice with 25 ml of cold PBS , extracted with 0 . 6 ml of cold co-IP buffer ( 20 mM HEPES pH7 . 5 , 150 mM NaCl , 20% glycerol , 0 . 1% NP40 , 1 mM EDTA , 0 . 1 mM PMSF supplemented with EDTA-free Complete Protease Inhibitor cocktail from Roche ) for 1 h at 4°C and cleared by centrifugation for 10 min at 15 , 000 × g at 4°C ., The supernatant was incubated for 4 h with 40 μl of pre-washed anti FLAG M2 conjugated beads ( Sigma ) at 4°C ., The beads were washed 3 times with 0 . 6 ml of co-IP buffer , once with 0 . 6 ml of TBS , and eluted with 100 μl of TBS containing 150 ng/μl of 3xFLAG peptide for 30 min at 4°C ., Half of the FLAG eluates were used for protein analysis and quantification , and half for RNA purification and analysis ., RNA was purified using the Qiagen RNeasy MinElute Cleanup Kit with a modified protocol that allows recovery of RNAs of all sizes ., 50 μl of FLAG eluates was mixed with 50 μl of water , 350 μl of RLT buffer and 675 μl of 100% molecular grade Ethanol ., The mixture was passed through the Qiagen RNeasy MinElute column ., The column was successively washed with 500 μl of RPE buffer and 750 μl of 80% ethanol , dried by centrifugation and the RNA was eluted twice with 17 μl of water ., Proteins were migrated on a NuPAGE 4–12% Bis-Tris gel and stained with a Colloidal Blue Staining kit ., Specific bands present in BCDIN3Df but not in the Control FLAG eluates were cut out using a Gene Catcher tip and sent for LC-MS/MS analysis at the Taplin Mass Spectrometry Facility at Harvard Medical School ., RNA samples were separated on a denaturing 15% polyacrylamide/urea gel and stained using the FASTsilver Gel Staining Kit ( #341298 ) ., TGIRT-seq libraries were prepared with a modification of the Total RNA-seq method 32 ., Reverse transcription with TGIRT-III ( InGex ) was initiated from a DNA primer ( 5-GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTN-3 ) encoding the reverse complement of the Illumina Read2 sequencing primer binding site ( R2R ) annealed to a complementary RNA oligonucleotide ( R2 ) such that there is a single nucleotide 3’ DNA overhang composed of an equimolar mixture of A , G , C and T . The RNA oligonucleotide is blocked at its 3’ end with C3Sp ( IDT ) to inhibit template switching to itself ., Reactions contained purified RNAs , reaction medium ( 20 mM Tris-HCl , pH7 . 5 , 450 mM NaCl , 5 mM MgCl2 ) , 5 mM DTT , 100 nM starting annealed molecule and 1 μM TGIRT-III ., Reactions were pre-incubated at room temperature for 30 min and cDNA synthesis was initiated by addition of 1 mM dNTPs ( an equimolar mix of dATP , dGTP , dCTP and dTTP ) ., Reactions were incubated at 60°C for 15 min and were terminated by adding 5 N NaOH to a final concentration of 0 . 25 N and incubated at 95°C for 3 min to degrade RNAs and denature protein ., The reactions were then cooled to room temperature and neutralized with 5 N HCl ., cDNAs were purified by using a Qiagen MinElute Reaction Cleanup Kit and then ligated at their 3’ ends to a DNA oligonucleotide encoding the reverse complement of the Illumina Read1 primer binding site ( R1R ) using Thermostable 5’ AppDNA/RNA Ligase ( New England Biolabs ) ., Ligated cDNAs were re-purified with MinElute Reaction Cleanup Kit and amplified by PCR for 12 cycles using Phusion DNA polymerase ( Thermo Fisher Scientific ) with overlapping multiplex and barcode primers that add sequences necessary for Illumina sequencing ., PCR reactions were cleaned up with AMPure XP beads ( Beckman Coulter ) to remove adapter dimers ., Libraries were sequenced on a NextSeq 500 instrument ( 75-nt , paired-end reads ) at the Next Generation Sequencing Facility at MD Anderson Science Park ., 10 μg of total RNA was separated on a denaturing 15% polyacrylamide/urea gel and small RNAs < 34 nt were extracted from the gel ., The rest of the library preparation was performed as in Ingolia et al . 11 with minor modifications ., RNA methyltransferase assays with BCDIN3D were performed in a total volume of 100 μl in 50 mM Tris-HCl , pH8; 150 mM NaCl; 1 mM EDTA; 5 mM DTT; 20% glycerol; 4 μl of 3H-SAM ( PerkinElmer NET155250UC ) ; 1X EDTA-free Complete Protease Inhibitor cocktail from Roche , 80 U of RNaseOUT from Invitrogen with 1 μg of recombinant BCDIN3D and 1 μl of 100 μM synthetic RNA for 2h at 37°C ., tRNAHis guanylation assays with THG1L were performed in a total volume of 40 μl in 25 mM HEPES , pH7 . 4; 125 mM NaCl; 10 mM MgCl2 , 3 mM DTT; 3 mM ATP , 0 . 8 μM tRNAHis , 0 . 4 μM 32Pα-GTP and 0 . 4 μM recombinant THG1L for 1h30 at 25°C ., Treatments with Antarctic Phosphatase or T4 Polynucleotide Kinase ( New England Biolabs ) were performed on 100 pmol of synthetic RNAs or on 100 ng of RNA purified from FLAG eluates ., The reactions were performed in a total volume of 20 μl of 1X Antarctic Phosphatase Reaction Buffer ( 50 mM Bis-Tris-Propane-HCl , 1 mM MgCl2 , 0 . 1 mM ZnCl2 , pH 6 @ 25°C ) with 5 units of Antarctic Phosphatase or 20 units of T4 Polynucleotide Kinase or mock for 30 min at 37°C ., Total RNA and protein extraction was typically performed on ~ 5x105 cells using the RNA/Protein Plus purification kit from Norgen ( Product # 48000 ) ., Cells grown on 3 cm diameter dishes were washed with 2 ml of PBS and lysed with 300 μl of Lysis Buffer supplemented with 10 μl of β-mercaptoethanol per ml for 5 min on a rocking table ., RNA extraction was performed according to the manufacturer’s instructions ., The whole flow-through after the RNA binding step was used for protein purification ., The RNA and protein concentrations were measured with a Denovix device ., For the purification of charged tRNAs , 10^6 cells were resuspended in 300 μl of cold Lysis Buffer solution ( 0 . 3 M Sodium Acetate pH 5 . 2 , 10 mM EDTA ) and extracted with 300 μl of cold acetate-saturated Phenol/Chloroform ( pH 4 . 5 ) ., 250 μl of the aqueous upper layer was mixed with 675 μl of cold ethanol and centrifuged for 1 h at 18 , 600 rcf at 4°C ., The RNA pellet was resuspended in 50 μl of RNA Resuspension Solution ( 10 mM Sodium Acetate pH 5 . 2 , 1 mM EDTA ) ., To deacylate a portion of the tRNA , 1 μg of RNA was treated with 0 . 1M Tris-HCl , pH 9 . 5 for 30 min at 37°C ., Taqman RTqPCR was performed with the Taqman MicroRNA Reverse Transcription Kit from Applied Biosystems ., 500 ng of total RNA was used for reverse transcription of mRNAs with the SuperScript III First-Strand Synthesis System from invitrogen ., Real-time PCR analysis was performed on a StepOne Plus system ., The Northern Blots were performed as previously described 33 ., Small DNA ladders were used as markers in our denaturing 15% polyacrylamide/urea gels , either the 10 bp DNA Ladder ( #10821–015 ) from Invitrogen , or the ss20 ssDNA Ladder from Simplex sciences ., Please note that the ssDNA ladder bands are offset by ~10–20 nt compared to RNA ., ChIP-Seq experiments and bioinformatic analysis was performed as previously described 34 ., 20 pmol of synthetic RNA was incubated with the indicated volumes of human recombinant Dicer ( Invitrogen # K3600-01 or Origene TP319214 ) in a total volume of 15 μl in 100 mM KCl , 10 mM Tris-HCl , pH8 , 0 . 1 mM EDTA , 1 mM MgCl2 , 0 . 5 mM dTT supplemented with 0 . 5 U/μl RNaseOUT for 2h at 37°C ., The samples were mixed with 15 μl of Gel Loading Buffer II ( Ambion ) , heated for 15 min at 70°C and separated on a denaturing 15% polyacrylamide/urea gel ., The gels were stained for 5 min with 1x SYBR Gold and the signal was detected and analyzed with the G:Box gel doc system from Syngene ., X-RIP was performed as previously described 5 .

Introduction, Results, Discussion, Methods

5’ ends are important for determining the fate of RNA molecules ., BCDIN3D is an RNA phospho-methyltransferase that methylates the 5’ monophosphate of specific RNAs ., In order to gain new insights into the molecular function of BCDIN3D , we performed an unbiased analysis of its interacting RNAs by Thermostable Group II Intron Reverse Transcriptase coupled to next generation sequencing ( TGIRT-seq ) ., Our analyses showed that BCDIN3D interacts with full-length phospho-methylated tRNAHis and miR-4454 ., Interestingly , we found that miR-4454 is not synthesized from its annotated genomic locus , which is a primer-binding site for an endogenous retrovirus , but rather by Dicer cleavage of mature tRNAHis ., Sequence analysis revealed that miR-4454 is identical to the 3’ end of tRNAHis ., Moreover , we were able to generate this ‘miRNA’ in vitro through incubation of mature tRNAHis with Dicer ., As found previously for several pre-miRNAs , a 5’P-tRNAHis appears to be a better substrate for Dicer cleavage than a phospho-methylated tRNAHis ., Moreover , tRNAHis 3’-fragment/‘miR-4454’ levels increase in cells depleted for BCDIN3D ., Altogether , our results show that in addition to microRNAs , BCDIN3D regulates tRNAHis 3’-fragment processing without negatively affecting tRNAHis’s canonical function of aminoacylation .

We previously identified an important modification of human microRNAs , written by BCDIN3D , an RNA phospho-methyltransferase linked to triple negative breast cancer , obesity and type II diabetes ., Here , we employed a powerful sequencing method that overcomes RNA secondary structure and RNA modifications , to unbiasedly identify RNAs stably bound to BCDIN3D ., This analysis showed that BCDIN3D interacts with full-length phospho-methylated tRNAHis and with miR-4454 ., Close inspection of miR-4454 sequence revealed that it is identical to the tRNAHis 3’ end , and we present evidence that miR-4454 is in fact a tRNA fragment generated through Dicer cleavage ., Overall , our results indicate that tRNAHis interaction with BCDIN3D plays non-canonical roles , one of which is to regulate the generation of tRNAHis 3’ fragments ., This may have important biological implications as ‘miR-4454’ has been identified as a potential biomarker in several human diseases ., Our work also provides a compelling example of how RNA modifiers and RNA processing enzymes multi-task using different species of small RNAs , such as miRNAs and tRNAs .

sequencing techniques, transfer rna, molecular probe techniques, natural antisense transcripts, gene regulation, enzymes, geographical locations, enzymology, phosphatases, northern blot, micrornas, methylation, molecular biology techniques, rna sequencing, gel electrophoresis, research and analysis methods, electrophoretic techniques, electrophoretic blotting, proteins, gene expression, denaturation, chemistry, molecular biology, people and places, biochemistry, rna, antarctica, nucleic acids, genetics, biology and life sciences, chemical reactions, physical sciences, rna denaturation, non-coding rna

journal.pgen.1001133

Identification of Early Requirements for Preplacodal Ectoderm and Sensory Organ Development

Cranial placodes provide major contributions to the paired sensory organs of the head ., Examples include the anterior pituitary , the lens of the eye , the olfactory epithelium , the inner ear , and clusters of sensory neurons in the trigeminal and epibranchial ganglia 1–4 ., Though diverse in fate , all placodes are thought to arise from a zone of pluripotent progenitors termed the preplacodal ectoderm ., Preplacodal cells arise from the nonneural ectoderm immediately adjacent to neural crest ., Neural crest cells originate in the lateral edges of the neural plate and later migrate to placodal regions to contribute to the corresponding sensory structures 1 , 2 ., However , while neural crest has been analyzed extensively , little is known about the early requirements for preplacodal development ., Various preplacodal markers , including members of the eya , six and dlx gene families , are expressed at high levels along the neural-nonneural interface around the anterior neural plate near the end of gastrulation 1–7 ., How these genes are regulated is still unclear , but modulation of Bmp signaling appears to be critical ., In a classical model ( Fig . 1A ) , ectoderm is patterned during gastrulation by readout of a Bmp morphogen gradient ., Such a gradient could coordinate specification of preplacodal ectoderm and neural crest in juxtaposed domains , with preplacodal ectoderm requiring slightly higher levels of Bmp than neural crest 8–15 ., Numerous studies provide strong support for the notion that neural crest requires a specific low threshold of Bmp signaling ., In zebrafish mutations or inducible transgenes that weaken overall Bmp signaling can expand neural crest throughout the ventral domain 12 , 13 , 15 ., Similarly , development of neural crest in Xenopus is stimulated by misexpression of moderate but not high levels of Bmp-antagonists 11 ., In contrast , available data are ambiguous with regard to Bmps role in preplacodal specification ., A number of Bmp-antagonists expressed near the neural-nonneural interface late in gastrulation are required for normal preplacodal development 16 , 17 ., Similarly , high-level misexpression of Bmp antagonists expands preplacodal gene expression partway into the nonneural ectoderm 18–21 ., These findings have been alternately interpreted as support for either of two competing models: Some investigators have argued that Bmp-antagonists titrate Bmp signaling to a specific level appropriate for preplacodal specification , consistent with the Bmp morphogen model 18 , 19 ( Fig . 1A ) ., Others counter that these misexpression conditions are likely to fully block Bmp signaling 20 , 21 , leading to an alternative model in which preplacodal specification requires attenuation of Bmp ( Fig . 1B ) ., These opposing models invoke fundamentally different mechanisms: In the morphogen model Bmp is a positive requirement whereas in the attenuation model Bmp is an inhibitor that must be fully blocked to permit preplacodal development ., Notably , none of these studies has measured changes in the level of Bmp signaling associated with their experimental manipulations , making it impossible to distinguish between the opposing models ., A similar uncertainty applies to genetic studies in zebrafish , which suggest that neither of the models in Fig . 1 is fully adequate ., Mutations that strongly impair Bmp signaling eliminate preplacodal development 12 , 13 , revealing a definite requirement for Bmp ., However , none of the mutations that impair Bmp to a lesser degree expand preplacodal fate throughout the ventral ectoderm , in sharp contrast to neural crest 12 , 13 ., Although these data fail to support predictions of the Bmp morphogen model for preplacodal specification , it is possible that available mutations do not expand the appropriate range of Bmp signaling required for preplacodal ectoderm , if one exists ., Thus the status of Bmp signaling during preplacodal specification remains an important unresolved question ., In addition to differing requirements for Bmp , preplacodal ectoderm and neural crest appear to be specified at different times ., Recent studies in chick and zebrafish suggest that neural crest is specified by the beginning of gastrulation 15 , 22 ., In contrast , preplacodal ectoderm appears to be specified during late gastrula or early neurula stages , as suggested by studies in chick and Xenopus 20 , 21 ., This difference in timing is especially relevant for the Bmp-attenuation model ( Fig . 1B ) ., Specifically , the lag in preplacodal specification allows time to reshape the Bmp gradient without jeopardizing the earlier requirement of neural crest for Bmp ., There are currently no data to show when preplacodal specification occurs in zebrafish ., Other signals from dorsal tissues also appear critical for preplacodal development ., In chick and Xenopus , grafting neurectoderm into more ventral regions induces expression of preplacodal markers in surrounding host tissue 20 , 21 , 23 ., Moreover , combining misexpression of Bmp antagonists with Fgf8 , a relevant dorsal signal , is sufficient to induce at least some preplacodal markers; neither Fgf8 nor Bmp-antagonism is sufficient 20 , 21 ., Various transcription factors have also been implicated in preplacodal development , but most appear to act after preplacodal specification to influence fates of cells in different regions of this domain 2 , 3 ., Here we provide the first direct evidence for a 2-step model in which Bmp is required only transiently during blastula/early gastrula stage to directly or indirectly induce ventral expression of four transcription factors , Tfap2a , Tfap2c , Gata3 and Foxi1 , which establish preplacodal competence throughout the nonneural ectoderm ., In this context , Bmp does not act as a morphogen because it does not distinguish between preplacodal and epidermal ectoderm within the nonneural domain ., We initially focused on foxi1 , gata3 , tfap2a and tfap2c as potential competence factors because they show similar early expression patterns throughout the nonneural ectoderm and all have been implicated in later development of various subsets of cranial placodes 2 , 3 , 24–29 ., Once expressed , preplacodal competence factors no longer require Bmp for their maintenance ., Near the end of gastrulation , Bmp must be fully blocked by dorsally expressed Bmp-antagonists , which combined with Fgf , are necessary and sufficient to induce preplacodal development within the zone of competence ., To monitor early preplacodal development , we followed expression of dlx3b , eya1 and six4 . 1 ., dlx3b is the earliest marker , initially showing a low level of expression throughout the nonneural ectoderm at 8 hpf , with strong upregulation in preplacodal ectoderm and downregulation in ventral ectoderm by 9 hpf ( late gastrulation ) 5 ., Expression of six4 . 1 and eya1 first appear in preplacodal ectoderm by 10 hpf ( the close of gastrulation ) , and a low level of six4 . 1 is also seen in scattered mesendodermal cells in the head 6 , 7 ., For comparison , we also monitored the neural crest marker foxd3 , which is expressed specifically in premigratory neural crest by 10 hpf 30 , 31 ., To assess the role of Bmp in preplacodal specification , we treated embryos at various times with dorsomorphin ( DM ) , a pharmacological inhibitor of Bmp signaling 32 ., Although we used DM at higher concentrations than previously reported 32 , it did not appear to cause defects beyond the phenotypes associated with Bmp pathway mutants ( see below ) ., Thus , unintended non-specific effects of the drug , if present , are apparently mild and do not interfere with the ability to block Bmp signaling ., We initially performed a dose-response to assess the effects of DM when added at 5 , 6 or 7 hpf ( Table 1 ) ., As expected , embryos were increasingly dorsalized after exposure to increasing concentrations of DM , and earlier exposure caused greater dorsalization than later exposure ., Exposing embryos to 50 or 100 µM DM beginning at 5 hpf mimicked strong loss of function mutations in the Bmp pathway 8 , 12 , 13 , 33 and resulted in complete dorsalization ( Table 1 ) ., In confirmation , exposure to 100 µM DM at 5 hpf eliminated phospho-Smad1/5/8 staining within 15 minutes ( Fig . S1A ) , indicating rapid and complete cessation of Bmp signaling ., Additionally , mRNA for sizzled , a feedback inhibitor of Bmp 34 , decayed rapidly under these conditions , with only weak staining after 30 minutes and none after 1 hour ( Fig . S1B ) ., Because the role of Bmp in neural crest specification has been well characterized 11–13 , 15 , we tested whether DM could affect this tissue as predicted by these previous studies ., Adding 100 or 200 µM DM beginning at 4 hpf totally ablated neural crest formation ( Fig . 2A and data not shown ) ., However , adding 50 µM DM at 4 hpf led to ventral expansion of cranial neural crest to fully displace the nonneural ectoderm , similar to the effects of mutations that weaken overall Bmp signaling in zebrafish 12 , 13 ., These conditions are thought to create a broad plateau of low Bmp signaling appropriate for neural crest specification , providing strong support for the role of Bmp as a morphogen in specifying neural crest ., Interestingly , after initially treating embryos with 50 µM DM at 4 hpf , fully blocking Bmp with a super-saturating dose of DM at 5 , 6 , or 7 hpf does not prevent formation of cranial neural crest , though the domain is somewhat reduced when Bmp is blocked earlier ., These data are consistent with the effects of timed misexpression of Chordin 15 , showing that Bmp acts very early in cranial neural crest specification and is no longer needed after late blastula/early gastrula stage ., Analysis of preplacodal markers revealed a different pattern of Bmp-dependence ., First , preplacodal ectoderm ( Fig . 2B ) and epidermal ectoderm ( not shown ) are totally ablated by exposure to 50 µM DM , reflecting loss of all nonneural ectoderm ., Accordingly , this treatment eliminated expression of putative preplacodal competence factors foxi1 and gata3 , though tfap2a and tfap2c continue to be expressed ( Fig . 2C ) ., The latter two genes are also required in the lateral edges of the neural plate for neural crest development 29 , 35 ., Second , we found no dose of DM that caused expansion of preplacodal markers throughout the ventral ectoderm ., Instead , exposure to 25 µM at 4 hpf yielded two distinct responses; either preplacodal markers were lost entirely or preplacodal ectoderm was shifted ventrally but was still confined to two bilateral stripes bordering the neural plate ( Fig . 2B and data not shown ) ., Thus , there does not appear to be a specific level of Bmp that can expand the preplacodal ectoderm at the expense of more ventral ( epidermal ) ectoderm ., To characterize the temporal requirements for Bmp , embryos were treated with 100 µM DM at different times during late blastula and early gastrula stages and subsequently analyzed for expression patterns of various ectodermal markers ., As expected from the severe dorsalization caused by administering this dose at 5 hpf ( Table 1 ) , neural markers were expanded throughout the ectoderm and all nonneural markers were lost , including putative preplacodal competence factors ( Fig . 3D , E , G–J ) ., Additionally , definitive preplacodal markers dlx3b , eya1 and six4 . 1 were not expressed in these embryos ( Fig . 3A–C ) ., In contrast , exposure to 100 µM DM from 7 hpf resulted in only partial dorsalization ( Table 1 , Fig . 3D , F ) and all embryos expressed nonneural markers , albeit in diminished ventral domains ( Fig . 3E , G–J ) ., Preplacodal markers dlx3b , eya1 and six4 . 1 were expressed on time by 10 . 5 hpf ( Fig . 3A–C ) ., Moreover , all placodal derivatives were produced on time in embryos treated with 100 µM DM from 7 hpf , including the anterior pituitary , olfactory , lens , trigeminal , epibranchial and otic placodes ( Fig . 4B , E , H , K , N , Q , T , W ) 36–46 ., Adding 100 µM DM at 6 hpf yielded two classes of embryos , with roughly half being fully dorsalized and the rest resembling the partially dorsalized embryos obtained with 100 µM DM at 7 hpf ( Fig . S2 , Table 1 ) ., Adding 100 µM DM at 5 . 5 hpf eliminated eya1 and six4 . 1 expression in all embryos , though some embryos still expressed dlx3b in bilateral stripes ( Fig . S2 ) ., These data indicate that embryos make a transition around 5 . 5–6 hpf after which Bmp is no longer required for preplacodal development ., As with treatment during blastula stage , treatment with 100 µM DM during gastrulation eliminated phospho-Smad1/5/8 accumulation and sizzled expression , confirming loss of Bmp signaling 15 , 34 ( Fig . 3K , L ) ., Additionally , the effects of adding 100 µM DM at 7 hpf were identical to the effects of 500 µM DM , the highest dose tested ( data not shown ) , arguing that the block to Bmp signaling was saturated at these doses ., Nevertheless , to ensure that Bmp was fully blocked , we combined addition of 100 µM DM at 7 hpf with activation of heat shock-inducible transgenes encoding Chordin and/or dominant-negative Bmp receptor 15 , 45 ( Fig . 3M , N ) ., The effects on preplacodal specification and morphological development were identical to treatment with 100 µM DM alone ., These data show that Bmp is not directly required after the onset of gastrulation for preplacodal specification ., The data further show that Bmp signaling is required to induce expression of putative competence factors foxi1 , gata3 , tfap2a and tfap2c during blastula stage , but is not required to maintain them thereafter ( Fig . 3G–J ) ., We hypothesized that foxi1 , gata3 , tfap2a and tfap2c encode preplacodal competence factors because they are expressed early throughout the nonneural ectoderm yet are specifically required for later development of various subsets of placodes 24–29 ., To test the functions of these genes , we injected morpholino oligomers ( MOs ) to knockdown their functions ., Knockdown of any one gene had no discernable effect on preplacodal gene expression ( data not shown ) , though loss of foxi1 specifically impairs development of the otic and epibranchial placodes 27 , 28 ., Knockdown of both foxi1 and gata3 enhanced the otic placode deficiency ( data not shown ) , and caused a slight reduction in expression levels of dlx3b , eya1 and six4 . 1 ( Fig . 5A ) ., Knockdown of both tfap2a and tfap2c caused a stronger reduction in expression levels of preplacodal markers ( Fig . 5B ) ., Co-injecting either gata3-MO or foxi1-MO with tfap2a/c-MOs further reduced preplacodal gene expression ( data not shown ) whereas simultaneous knockdown of foxi1 , gata3 , tfap2a and tfap2c ( quadruple morphants ) resulted in complete loss of preplacodal gene expression ( Fig . 5C ) ., Moreover , development of all cranial placodes ( pituitary , olfactory , lens , trigeminal , otic and epibranchial ) was severely deficient or totally ablated in all quadruple morphants examined ( Fig . 4C , F , I , L , O , R , U , X ) ., Disruption of preplacodal development in quadruple morphants did not reflect general impairment of nonneural ectoderm , as the epidermal marker p63 46 , 47 was appropriately expressed in the ventral ectoderm ( Fig . 5D ) ., Additionally , quadruple morphants did not exhibit elevated cell death , as indicated by relatively normal levels of staining with the vital dye acridine orange 48 ( data not shown ) ., These data show that foxi1 , gata3 , tfap2a and tfap2c are specifically required for formation of preplacodal ectoderm and all placodal derivatives , and are partially redundant in this function ., Importantly , quadruple morphants retained a neural-nonneural interface ( Fig . 4R and Fig . 5D ) , the region normally associated with preplacodal specification ., Moreover , Bmp signaling also persisted in quadruple morphants as shown by continued ventral accumulation of phospho-Smad1/5/8 and expression of sizzled ( Fig . 5D ) ., Expression of fgf3 , fgf8 and the Fgf-target gene erm were also appropriately localized in quadruple morphants ( data not shown ) ., Thus , neither Bmp signaling , Fgf signaling , nor neural-nonneural interactions are sufficient for preplacodal specification in this background ., These data support the hypothesis that foxi1 , gata3 , tfap2a and tfap2c are required for preplacodal competence or early differentiation ., Although p63 is normally co-expressed with preplacodal competence factors and is only known to regulate epidermal development 46 , 47 , we examined whether it is required for preplacodal development ., Knockdown of p63 did not detectably alter preplacodal development , nor did it enhance the deficits in preplacodal gene expression or morphological development seen in foxi1-gata3 or tfap2a/c double morphants ( Fig . 5E , and data not shown ) ., This further shows that not all early Bmp-target genes are required for preplacodal development and that the requirement for foxi1 , gata3 , tfap2a and tfap2c is relatively specific ., We also investigated the requirements for foxi1 , gata3 , tfap2a and tfap2c in neural crest formation ., Knockdown of both foxi1 and gata3 did not alter expression of foxd3 ( data not shown ) , whereas knockdown of tfap2a/c completely eliminated expression of foxd3 as reported previously 29 , 35 ., Not surprisingly , foxd3 expression is also ablated in foxi1-gata3-tfap2a/c-quadruple morphants ( data not shown ) ., This likely reflects a cell-autonomous requirement for tfap2a/c in neural crest specification 29 , 35 ., To further test the functions of preplacodal competence factors , we generated constructs to misexpress foxi1 , gata3 and tfap2a under the control of the hsp70 heat shock promoter 49 ., We reasoned that if these genes provide preplacodal competence , then misexpressing them in dorsal ectoderm , where preplacodal inducing factors are normally expressed , should be sufficient to induce ectopic expression of preplacodal genes ., We performed transient transfections to introduce hs:tfap2a and hs:gata3 whereas a stable transgenic line was used for hs:foxi1 ( see Materials & Methods ) ., Global heat shock-activation of any one of these genes at 4 . 5 hpf ( late blastula ) or 5 . 5 hpf ( early gastrula ) resulted in scattered ectopic expression of preplacodal markers within the neural plate by 11 hpf ( Fig . 6A–C , and data not shown ) ., In most experiments , over half of embryos showed ectopic expression of preplacodal genes ., Co-activation of any two heat shock genes yielded more robust and widespread expression of preplacodal genes in the neural plate , with nearly complete penetrance in most experiments ., For reasons that are unclear , misexpression of competence factors at these stages caused widening of the neural plate and narrowing of the ventral Bmp signaling domain ( Fig . S3 ) ., Nevertheless , Bmp signaling and general DV patterning are still evident following activation of hs:foxi1 , hs:gata3 and/or hs:tfap2a ( Fig . S3 ) ., Importantly , we never observed ectopic expression of the epidermal marker p63 in the neural plate following misexpression of competence factors , indicating that preplacodal competence factors do not induce all nonneural fates in this domain ., Co-activation of all three transgenes at 4 . 5 hpf led to widespread expression of preplacodal genes , but also caused severe axial patterning defects during gastrulation , making results difficult to interpret ( data not shown ) ., However , mosaic misexpression of all three competence factors at 4 . 5 hpf avoided defects in axial patterning yet still led to dorsal expression of dlx3b and six4 . 1 in a subset of misexpressing cells ( Fig . 6D ) ., These data are consistent with the hypothesis that foxi1 , gata3 and tfap2a are sufficient to render dorsal ectoderm competent to express preplacodal genes in response to dorsally expressed inducing factors ., In addition to their role in preplacodal development , Tfap2a and Tfap2c are required for neural crest 29 , 35 , whereas Foxi1 and Gata3 are required for preplacodal ectoderm but not neural crest ., We asked whether these differing roles in neural crest could also be distinguished in misexpression experiments ., Similar to the effects of injecting tfap2a mRNA 29 , we found that misexpression of hs:tfap2a , either alone or in combination with other competence factors , resulted in ectopic foxd3 expression in the neural plate ( Fig . S4 ) ., In contrast , activation of hs:foxi1 and/or hs:gata3 did not induce ectopic foxd3 expression ( data not shown ) , but instead reduced expression of foxd3 in the endogenous neural crest domain ( Fig . S4 ) ., Importantly , these findings show that formation of ectopic preplacodal tissue is not always associated with neural crest , further arguing that preplacodal competence can be regulated independently from other ectodermal fates ., We next attempted to induce preplacodal development throughout the zone of competence in the nonneural ectoderm by providing appropriate inductive signals normally limited to dorsal tissue ., Previous studies have implicated dorsally expressed Bmp-antagonists and Fgfs as preplacodal inducers 16–21 ., To mimic such signals throughout the nonneural ectoderm , we used heat shock-inducible transgenic lines to misexpress Fgf3 or Fgf8 ( hs:fgf3 and hs:fgf8 ) while blocking Bmp with DM ., Using standard heat shock conditions ( 39°C for 30 minutes ) to activate hs:fgf8 combined with DM treatment at 7 . 5 hpf fully dorsalized the embryo and was not informative ., However , full dorsalization was avoided by prolonged incubation at more moderate temperatures , achieving a weaker level of transgene activation ., Incubating hs:fgf8/+ transgenic embryos at 35°C with 100 µM DM from 7 . 5–10 . 5 hpf resulted in expression of eya1 and six4 . 1 throughout the nonneural ectoderm in all embryos ( Fig . 7B , F ) ., Diffuse ectopic expression of erm confirmed that this heat shock regimen elevated Fgf signaling within nonneural ectoderm ( Fig . 7I–K ) ., Similar results were obtained with hs:fgf3/+ transgenic embryos incubated at 36°C with 100 µM DM from 7–10 . 5 hpf ( Fig . 7D , H ) ., Activation of hs:fgf3 or hs:fgf8 alone was not sufficient to activate ectopic preplacodal gene expression ( Fig . 7A , C , E , G ) ., These data show that the entire nonneural ectoderm is competent to express preplacodal genes in response to Fgf plus inhibition of Bmp ., We next titrated the dose of DM required for ectopic induction of preplacodal genes ., Incubating hs:fgf8/+ embryos at 35°C with 50 µM DM at 7 hpf led to ventral expression of preplacodal genes , but lower concentrations of DM were not sufficient ( Table 1 ) ., The finding that 25 µM DM is not sufficient indicates that even very low levels of Bmp signaling can block preplacodal gene activation ., To express inductive signals with greater spatial control , we generated mosaic embryos to locally co-misexpress Fgf8 and Chordin ., Donor cells carrying both hs:fgf8 and hs:chd transgenes were transplanted into non-transgenic host embryos at the mid-blastula stage to obtain a random distribution of misexpressing cells ., To achieve maximal transgene activation , mosaics were heat-shocked at 39°C for 30 minutes beginning at 7 hpf and then maintained at 33°C until tailbud stage ( 10 hpf ) ., Of 4 mosaic embryos harboring transgenic donor cells on the ventral side , all showed significant ventral expression of six4 . 1 in surrounding host cells ( Fig . 7N ) ., In another experiment , transgenic donor cells were transplanted directly to the ventral side at the early gastrula stage ( 6 hpf ) ., Following heat shock at 7 hpf , all mosaic embryos ( n = 4 ) showed ectopic six4 . 1 expression in surrounding host cells ( Fig . 7O ) ., In contrast , no ectopic six4 . 1 expression was seen following mosaic misexpression of hs:fgf8 alone ( n = 13 ) or hs:chd alone ( n = 10 ) ( Fig . 7L , 7M ) ., This confirms that both Fgf and Bmp-antagonists are required to induce expression of preplacodal genes ., Because preplacodal specification has been reported to occur near the end of gastrulation in frog and chick embryos 20 , 21 , we tested whether activation of hs:fgf8; hs:chd cells at later stages could also stimulate ectopic preplacodal gene expression ., Heat shock activation of ventrally transplanted transgenic cells at 8 . 5 hpf ( yielding peak transgene expression at 9 hpf ) led to robust ectopic expression of six4 . 1 in surrounding host ectoderm by 11 hpf ( Fig . 7P ) ., This suggests that in zebrafish , too , preplacodal specification occurs near the end of gastrulation ., Importantly , activation of hs:fgf8 and hs:chd did not lead to ectopic expression of the general neural plate marker sox19b nor the neural crest marker foxd3 ( Fig . 7Q , R ) ., Thus , induction of ectopic six4 . 1 expression did not result indirectly from ectopic formation of neural plate ., On the other hand , activating transgenic cells at 8 . 5 hpf caused downregulation of p63 , suggesting that nearby host cells lose epidermal identity in response to preplacodal specifying signals ., Finally , we reassessed the requirement for Fgf during normal preplacodal specification ., Previous studies have reported that expression of preplacodal markers does not require Fgf in zebrafish 50–53 ., We find that blocking Fgf by adding the pharmacological inhibitor SU5402 at 8 . 5 hpf did not block expression of preplacodal markers , but levels of expression were reduced ( Fig . S5 ) ., We speculated that Pdgf , which is also dorsally expressed near the end of gastrulation 54 and activates a similar signal transduction pathway , might provide redundancy with Fgf ., We tested this by applying another inhibitor , AG1295 , which blocks Pdgf activity in zebrafish 55 ., Treatment with AG1295 alone had little effect on preplacodal gene expression , but co-incubation with AG1295 and SU5402 from 8 . 5 hpf led to further reduction of preplacodal gene expression ( Fig . S5 ) ., Indeed , expression of eya1 was almost totally eliminated in the preplacodal domain , though robust expression continues in the cranial mesoderm ., These data support the hypothesis that Fgf and Pdgf are partially redundant dorsal factors required for preplacodal specification ., Using DM to finely control Bmp signaling , we show that Bmp regulates neural crest and preplacodal ectoderm by markedly different mechanisms ., In agreement with earlier genetic studies in zebrafish 12 , 13 , 15 , our data indicate that neural crest is specified by a discrete low level of Bmp signaling as predicted by the classical morphogen model ( Fig . 1A ) ., Adding DM at 4 hpf at a dose sufficient to fully block Bmp signaling ablates neural crest formation , whereas a slightly lower dose causes a dramatic ventrolateral expansion of neural crest to fully displace nonneural ectoderm ( Fig . 2A ) ., Fully blocking Bmp after the onset of gastrulation does not block neural crest , in agreement with studies involving timed misexpression of Chordin 15 ., These data suggest that cranial neural crest is already specified by early gastrula stage , after which it no longer requires Bmp ., In chick , too , neural crest is specified by early gastrula stage 22 ., Preplacodal ectoderm , marked by expression of dlx3b , eya1 and six4 . 1 , develops in two distinct phases with distinct signaling requirements , neither of which resemble the pattern shown by neural crest ., Preplacodal ectoderm requires a robust Bmp signal during late blastula/early gastrula , but unlike neural crest , there does not appear to be a specific range of Bmp signaling that uniquely specifies preplacodal fate ., We found no dose of DM that could expand the preplacodal ectoderm in a manner similar to neural crest ., Instead , increasing the concentration of DM ( lowering Bmp signaling ) either shifted discrete bilateral stripes of preplacodal ectoderm to a more ventral position or eliminated them altogether , depending on the degree of neural plate expansion ., Indeed , treatment with a single dose ( 25µM DM beginning at 4 hpf ) yielded both classes of embryo , with nothing in between ., Thus , DM cannot expand preplacodal ectoderm at the expense of epidermal ectoderm , indicating that changing Bmp levels do not distinguish between these fates ., The requirement for Bmp changes during the second phase of preplacodal development beginning soon after the onset of gastrulation ., Adding a full blocking dose of DM at 7 hpf does not block preplacodal specification , even if transgenic Chordin and dominant-negative Bmp receptor are also activated during this period ., Thus , Bmp is not required during gastrulation for preplacodal specification ., By extension , the requirement of preplacodal ectoderm for locally secreted Bmp-antagonists 16–21 cannot reflect a requirement for a specific low threshold of Bmp; instead Bmp-antagonists are presumably needed to fully attenuate Bmp ., This conclusion is further supported by our experiments showing that a full blocking dose of DM is required to induce ectopic preplacodal markers throughout the ventral ectoderm ( Fig . 7 , Table 1 , and see below ) ., We have found that Fgf combined with Bmp attenuation is sufficient to induce preplacodal markers in ventral ectoderm , as has been shown in chick and frog 20 , 21 , suggesting that this mechanism is broadly conserved ., Thus , using heat shock-inducible transgenes , we show that misexpression of Fgf combined with DM treatment is sufficient to induce ectopic preplacodal markers anywhere within the nonneural ectoderm ., This supports two important conclusions ., First , it demonstrates that the entire nonneural ectoderm is competent to form preplacodal ectoderm , even at the ventral midline far from the neural plate ., This is consistent with the expression domains of preplacodal competence factors ( see below ) ., Second , although Fgf and Bmp-antagonists likely constitute a small subset of signals associated with the neural-nonneural border , no other signals are needed to trigger preplacodal development ., Fgf and Bmp-attenuation induces ectopic expression of preplacodal markers in chick and Xenopus 20 , 21 , though this combination of signals also induces expression of general neural plate markers in those species ., By contrast , our experimental conditions do not induce formation of ectopic neural plate or neural crest , tissues that could themselves have induced ectopic preplacodal markers 20 , 21 , 23 ., Thus induction of ectopic preplacodal ectoderm appears to be a direct and specific response to Fgf combined with Bmp attenuation , at least in zebrafish ., In addition to being able to induce ectopic preplacodal markers , we have found that Fgf is required in zebrafish for normal preplacodal development , and furthermore that Pdgf acts partially redundantly in this process ., Fgf and Pdgf have been shown to regulate distinct aspects of gastrulation , with Fgf promoting dorsal fate specification and Pdgf promoting convergence towards the dorsal midline 55 , 56 ., Although Fgf is not absolutely required for expression of general preplacodal markers 50–53 , we find that treating embryos with the Fgf inhibitor SU5402 during the latter half of gastrulation reduces the level of expression of preplacodal markers ., Treating embryos with the Pdgf inhibitor AG1295 alone has no effect on preplacodal specification , but blocking both Fgf and Pdgf further reduces preplacodal gene expression , nearly eliminating eya1 expression ., Homologs of Fgf and Pdgf are preferentially expressed in dorsal tissues near the end of gastrulation 54 , 56 , 57 and likely activate the same signal transduction pathways required for preplacodal specification ., It is not known whether Pdgf regulates preplacodal development in other species , but Pdgf and Fgf are specifically required for induction of the trigeminal placode in chick 58 ., In this study we have not addressed the role of Wnt inhibitor

Introduction, Results, Discussion, Materials and Methods

Preplacodal ectoderm arises near the end of gastrulation as a narrow band of cells surrounding the anterior neural plate ., This domain later resolves into discrete cranial placodes that , together with neural crest , produce paired sensory structures of the head ., Unlike the better-characterized neural crest , little is known about early regulation of preplacodal development ., Classical models of ectodermal patterning posit that preplacodal identity is specified by readout of a discrete level of Bmp signaling along a DV gradient ., More recent studies indicate that Bmp-antagonists are critical for promoting preplacodal development ., However , it is unclear whether Bmp-antagonists establish the proper level of Bmp signaling within a morphogen gradient or , alternatively , block Bmp altogether ., To begin addressing these issues , we treated zebrafish embryos with a pharmacological inhibitor of Bmp , sometimes combined with heat shock-induction of Chordin and dominant-negative Bmp receptor , to fully block Bmp signaling at various developmental stages ., We find that preplacodal development occurs in two phases with opposing Bmp requirements ., Initially , Bmp is required before gastrulation to co-induce four transcription factors , Tfap2a , Tfap2c , Foxi1 , and Gata3 , which establish preplacodal competence throughout the nonneural ectoderm ., Subsequently , Bmp must be fully blocked in late gastrulation by dorsally expressed Bmp-antagonists , together with dorsally expressed Fgf and Pdgf , to specify preplacodal identity within competent cells abutting the neural plate ., Localized ventral misexpression of Fgf8 and Chordin can activate ectopic preplacodal development anywhere within the zone of competence , whereas dorsal misexpression of one or more competence factors can activate ectopic preplacodal development in the neural plate ., Conversely , morpholino-knockdown of competence factors specifically ablates preplacodal development ., Our work supports a relatively simple two-step model that traces regulation of preplacodal development to late blastula stage , resolves two distinct phases of Bmp dependence , and identifies the main factors required for preplacodal competence and specification .

Cranial placodes , which produce sensory structures in the head , arise from a contiguous band of preplacodal ectoderm surrounding the anterior neural plate during gastrulation ., Little is known about early regulation of preplacodal ectoderm , but modulation of signaling through Bone Morphogenetic Protein ( Bmp ) is clearly involved ., Recent studies show that dorsally expressed Bmp-antagonists help establish preplacodal ectoderm , but it is not clear whether antagonists titrate Bmp to a discrete low level that actively induces preplacodal fate or , alternatively , whether Bmp must be fully blocked to permit preplacodal development ., We show that in zebrafish preplacodal development occurs in distinct phases with differing Bmp requirements ., Initially , Bmp is required before gastrulation to render all ventral ectoderm competent to form preplacodal tissue ., We further show that four transcription factors , Foxi1 , Gata3 , Tfap2a , and Tfap2c , specifically mediate preplacodal competence ., Once induced , these factors no longer require Bmp ., Thereafter , Bmp must be fully blocked by dorsally expressed Bmp-antagonists to permit preplacodal development ., In addition , dorsally expressed Fgf and/or Pdgf are also required , activating preplacodal development in competent cells abutting the neural plate ., Thus , we have resolved the role of Bmp and traced the regulation of preplacodal development to pre-gastrula stage .

developmental biology/embryology, cell biology/cell signaling, cell biology/developmental molecular mechanisms, developmental biology/pattern formation, developmental biology/cell differentiation, genetics and genomics/gene function, developmental biology/neurodevelopment, neuroscience/neurodevelopment, developmental biology/molecular development, cell biology/gene expression

journal.pcbi.1000914

Regression Analysis for Constraining Free Parameters in Electrophysiological Models of Cardiac Cells

Mathematical modeling has become an increasingly popular and important technique for gaining insight into biological systems , both in physiology , where models have a long history 1 , 2 , and in biochemistry and cell biology , where quantitative approaches have gained traction more recently 3 , 4 ., However , as new models proliferate and become increasingly complex , analysis of parameter sensitivity has emerged as an important issue 5 , 6 ., It is clear that to understand a model requires not only knowing the output generated using the published “baseline” set of parameters , but also some knowledge of how changes in the models parameters affect its behavior ., During the development of a mathematical model , the choice of parameters is a critical step ., Parameters are constrained by data whenever this is possible , but direct measurements are frequently lacking ., Often , however , a situation exists in which values for many parameters are unknown , but a considerable amount is known about the systems emergent phenomena ., In such cases , experienced researchers narrow down the values of the unknown model parameters based on how the model “ought to behave . ”, Parameter sets that generate grossly unrealistic output are rejected whereas those that produce reasonable output are tentatively accepted until they fail in some important respect ., The emergent phenomena considered in this process can be switching or oscillatory behavior in the case of biochemical signaling models 3 , 4 , or outputs such as action potential ( AP ) and calcium transient morphology in models of ion transport 7–10 ., Computational studies , however , have revealed the limitations of this intuition-based procedure ., In particular , work in theoretical neuroscience has shown that when a single output such as neuronal firing rate is considered , many different combinations of model parameters can generate equivalent behavior 11–14 ., This general problem is illustrated in Figure 1A , which shows results from a popular mathematical model of the human ventricular action potential , that of ten Tusscher , Noble , Noble , and Panfilov ( TNNP; 15 ) ., Random variation of model parameters revealed that completely different parameter combinations could produce virtually identical AP morphology ., This result is analogous to studies by Prinz et al . examining firing rate in neuronal cell models 13 , 14 ., However , an interesting aspect of the simulation is as follows ., The two hypothetical cells , although generating nearly identical APs under normal conditions , exhibited intracellular Ca2+ transients that differed with respect to both amplitude and kinetics ( Figure 1B ) ., Theoretically , then , a justifiable choice between these two parameter combinations , while impossible based only on the results shown in Figure 1A , could be made by considering the additional information in Figure 1B ., Such distinctions are frequently made by researchers with experimental expertise , who either accept or reject models based on how well they recapitulate a range of observed phenomena ., This process , although somewhat arbitrary and potentially subject to bias , nonetheless reflects sound reasoning , since a “good” model should successfully reproduce many biological behaviors ., Based on results such as those shown in Figure 1 , we sought to formalize and place on a sound mathematical footing the process of choosing parameters by comparing model output with several sets of data ., In particular , our hypothesis was that examining a single model output , such as action potential duration ( APD ) , would fail to constrain parameters , but success would be more likely if the number of physiological outputs was similar to the number of free model parameters ., We demonstrate that this is true in the case of the TNNP model 15 through two methods ., The first , an extension of the use of multivariable regression for parameter sensitivity analysis 16 , consists of inverting a regression matrix and then using this to calculate the changes in model parameters required to generate a given change in outputs ., The second method employs Bayess theorem to estimate the probabilities that model parameters lie within certain ranges ., The results , which are generally applicable across different models and different biological systems , can be of great use when building new models , and also provide new insights into the relationships between model parameters and model results ., The overall hypothesis of our study was that if several physiologically-relevant characteristics of a models behavior were known , this information would be sufficient to constrain some or all of the models parameters ., We tested this idea using two approaches: one based on multivariable regression and the other based on Bayess theorem ., We began by generating a database of candidate models ., The parameters that define maximal conductances and rates of ion transport in the TNNP model 15 were varied randomly , and several simulations , defining how the candidate model responded to altered experimental conditions , were performed with each new set of parameters ., In general , the simulations reflected experimental tests commonly performed on ventricular myocytes , such as determining the threshold for excitation or changing the rate of pacing ., For the first approach , the results of these simulations were collected in “input” and “output” matrices X and Y , respectively ., Each column of X corresponded to a model parameter , and each row corresponded to a candidate model ( n\u200a=\u200a300 ) ., The columns of Y were the physiological outputs extracted from the simulation results , such as action potential duration ( APD ) and Ca2+ transient amplitude ., Complete descriptions of the randomization procedure and simulation protocols are provided in the Methods and Text S1 ., Outputs are listed in Table 1 and described in detail in Text S1 ., Multivariable regression techniques were used to quantitatively relate the inputs to the outputs ., In the “forward problem , ” a matrix of regression coefficients B was derived such that the predicted output Y ̂\u200a=\u200aXB was a close approximation of the actual output Y . This method has recently been proven useful for characterizing the parameter sensitivity of electrophysiological models 16 ., We reasoned that a similar approach could be used to address the question: if the measurable physiological characteristics of a cardiac myocyte are known , can this information be used to uniquely specify the magnitudes of the ionic currents and Ca2+ transport processes ?, Specifically , we hypothesized that if:, 1 ) Y ̂\u200a=\u200aXB was a close approximation of the true output Y , and, 2 ) B was a square matrix of full rank , then Xpredicted\u200a=\u200aYB−1 should be a close approximation of the true input matrix X . This argument is illustrated schematically in Figure 2 ., Figure 3A demonstrates the accuracy of the reverse regression method ., For four chosen conductances , the scatter plots show the “actual” values , generated by randomizing the baseline parameters in the published TNNP model , versus the “predicted” values calculated with the regression model ., The large R2 values ( >0 . 9 ) indicate that the predictions of the regression method are quite accurate ., Of the 16 conductances in the TNNP model , 12 could be predicted with R2>0 . 7 ., The four that were less well-predicted were the Na+ background conductance ( GNab ) , the rapid component of the K+ delayed rectifier conductance ( GKr ) , the sarcolemmal Ca2+ pump ( KpCa ) and the second SR Ca2+ release parameter ( Krel2 ) ., To verify that these encouraging results were not specific to the TNNP model , we performed similar analyses on additional models , the human ventricular myocyte model of Bernus et al . 17 , and the “Phase 1” ventricular cell model of Luo and Rudy 18 ., In either case ( Figures S3 and S4 , respectively ) , the reverse regression was highly predictive of most parameters , indicating that this approach is generally applicable ., The outputs used for these analyses , listed in Text S1 , differed somewhat from those used for the TNNP simulations because the Bernus et al . 17 and Phase 1 Luo and Rudy 18 models are relatively simple and do not consider intracellular Ca2+ handling in detail ., Figure 3B illustrates how the quantity and identity of the outputs in Y affected the accuracy of the predictions ., Bar graphs show R2 values for prediction of each model parameter obtained by performing the reverse regression in three ways:, 1 ) using all 32 outputs ( blue ) ,, 2 ) matrix inversion ( green ) , with the 16 best outputs as identified by the output elimination algorithm ( see Methods ) , and, 3 ) using only the 16 rejected outputs ( red ) ., The R2 values computed using the 16 best outputs were virtually identical to those obtained when all 32 outputs were used whereas R2 values for most conductances were substantially lower when only the 16 rejected outputs were included ., These tests validate the algorithm which selected the outputs for matrix inversion ., Moreover , since the 16 best outputs performed essentially as well as the full set of 32 outputs , this result implies that the model outputs were not fully linearly independent , and the 16 rejected outputs contained redundant information ., Figure 4 displays , as heat maps , the coefficients for both the forward and reverse regression problems ., The former indicate how model parameters influence outputs , whereas the latter specify how changes in model outputs restrict the parameters ., Parameter sensitivities for selected outputs and conductances are shown as bar graphs to the right ., As previously argued for the case of forward regression 16 , these parameter sensitivities help to illustrate the relationships between parameters and outputs ., For instance , forward regression coefficients indicate that diastolic Ca2+ is determined primarily by a balance between SR Ca2+ uptake and SR Ca2+ leak , with other parameters making only minimal contributions ., Conversely , for reverse regression , the maximal conductance of L-type Ca2+ current ( GCa ) depends on many model outputs including action potential duration , Ca2+ transient amplitude , and , in particular , how these are altered with changes in extracellular potassium ., This result underscores the centrality of intracellular Ca2+ regulation to many cellular processes ., The results shown in Figure 3 demonstrated that most of the model parameters used to generate the dataset could be reconstructed using the reverse regression procedure ., To provide evidence that this procedure may be more broadly useful , we applied the method to a novel test case by performing simulations with the most recent version of the Hund & Rudy canine ventricular model 19 ., Specifically , we considered changes in seven parameters corresponding to the condition of heart failure , as previously modeled by Shannon et al 20 ., Figure 5A shows that implementing these parameter changes dramatically alters both AP shape and Ca2+ transient amplitude ., After performing simulations under a range of conditions with both normal , healthy cells and pathological , failing cells ( see Methods and Text S1 ) , we asked how well the reverse regression matrix could calculate the parameter changes in the failing cells ., We found that this method constrained 5 out of 7 parameters with excellent accuracy , while changes in two parameters ( GKs and Kleak ) were overestimated somewhat by the regression algorithm ., This novel test cases validates our approach and suggests that it may indeed prove a useful method for developing new models based on experimental measurements ., The second approach for constraining model parameters is based on Bayess theorem ., In statistics , this celebrated result describes the conditional probability of one event given another in terms of:, 1 ) the conditional probability of the second event given the first , and, 2 ) the marginal probabilities of the two events:In this context , we consider event A that a model conductance lies within a given range , and event B that a model output is within a particular range ., When many simulations are performed with randomly varying parameters , the probability P ( A ) is fixed by the user , while the probabilities P ( B ) and P ( B|A ) can be estimated from the results ., This allows us to approximate P ( A|B ) , which reflects how well a model parameter is constrained by a particular simulation result ., Since our hypothesis was that multiple outputs needed to be considered to constrain model parameters , we were interested in extensions of Bayess theorem to more than two variables , e . g . P ( A|B∩C ) , where B and C are events related to two model outputs ., For instance , B and C could represent , respectively , that APD and Ca2+ transient amplitude are within particular ranges ., If the conditional probability of the parameter increases as additional outputs are considered , this validates the thinking underlying the approach ., The application of this strategy to our data set is illustrated in Figure 6 ., The two rows of histograms display distributions of GNa and GCa , which are typical of the 16 model parameters considered ., The leftmost histogram in each row shows the distribution of conductance values in the entire population , and the remaining columns show conductance values for sub-populations that satisfy constraints on one or more model outputs ., Successive columns from left to right show distributions with additional model outputs considered , as noted ., In either case , the distributions become progressively narrower , and the conditional probability is unity once 3 outputs are considered ., This procedure also provides insights into which specific outputs provide the greatest information about particular model parameters ., For instance , the distribution of GNa given a certain range of APD appears similar to the overall distribution of GNa because these two variables are not strongly correlated ( i . e . P ( B|A ) ≈ P ( B ) ) ., In contrast , inclusion of Vpeak , an output highly dependent on GNa , narrows the distribution significantly ., In the case of GCa , restricting APD to a particular range makes the distribution narrower , which is to be expected given the relatively strong correlation between the parameter and the output ., Thus , an approach based on Bayess theorem also supports the idea that model parameters can successfully be constrained if multiple model outputs are considered ., In this study we have presented two methods that can be used to constrain free parameters in complex mathematical models of biological systems ., The utility of these methods was demonstrated through simulations with models of ventricular myocytes 15 , 17–19 , but with modifications the strategies could also be applied to other classes of models ., For instance , these methods could be used to constrain parameters in models of the sinoatrial node 21 , 22 , but in this case more useful outputs would be metrics such as inter-beat interval , diastolic depolarization rate , and maximum diastolic potential 23 ., Our results show that model parameters are difficult to specify uniquely using a limited number of model outputs as “targets , ” but parameters can be constrained successfully if numerous model outputs are simultaneously considered 24 ., The premise underlying this strategy is therefore similar to ideas advanced by Sethna and colleagues in discussions of model “sloppiness” 25 , 26 ., Even if individual parameters are largely unknown or cannot be measured with precision , predictive models can still be built if care is taken to match the models output to diverse sets of experimental data ., The reverse regression method uses matrix multiplication to predict a set of parameters , in this case ionic current maximal conductances , that are most likely to recapitulate a given set of model outputs ., In a recent paper 16 , parameter randomization followed by regression was used to quantify parameter sensitivities in electrophysiological models ., The method presented here is an extension of this: we added outputs so that the regression matrix B could be inverted ., Each element of this inverted matrix , B−1 , therefore indicates how much a physiological output contributes to the prediction of a particular input conductance ( Figure 4 ) ., In experimental studies , metrics derived from data are frequently used as indirect semi-quantitative surrogates of ionic conductances ., For instance , conventional wisdom holds that action potential upstroke velocity reflects the availability of Na+ current 27 , and the prominence of the Phase 1 “notch” indicates the contribution of transient outward K+ current 28 , 29 ., Our reverse regression method is simply a mathematically more formal extension of this general strategy , whereby every output can conceivably influence the prediction of each model parameter ., When applied to the simulations with the TNNP model , reverse regression was able to generate accurate predictions of most conductances or rates of ion transport in the model ( R2>0 . 7 for 12 of 16 parameters ) ., Of the 4 parameters that were not predicted accurately , two , namely Na+ background conductance ( GNab ) and the sarcolemmal Ca2+ ATPase ( KpCa ) are considered to be relatively unimportant for normal cellular physiology ., The parameter Krel2 ( crel in the original TNNP model ) , was also predicted poorly , most likely because it is partially redundant with the parameter Krel1 ( arel in the original TNNP model ) , which was well constrained by the analysis ., The surprise in our simulations was the poor prediction of the rapid component of the delayed rectifier current , GKr , since this current contributes to AP repolarization 30 , 31 , and block of IKr is the primary cause of drug-induced long QT syndrome 32 , 33 ., It should be noted , however , that our prediction of the conductance corresponding to the slow delayed rectifier , GKs , was accurate ., This suggests that in the TNNP model , these conductances serve similar functions and perhaps compensate for each other ., A similar conclusion can be drawn from the simulations in which we used the reverse regression procedure to reconstruct the parameters corresponding to heart failure in the Hund & Rudy 19 model ( Figure 5 ) ., Five out of the seven parameters altered in the heart failure cell were predicted accurately by the reverse regression procedure ., The two that were not predicted accurately , Kleak , and GKs , have relatively minor effects in the Hund & Rudy model , although these are more important in some other models ., Thus , these methods are not only useful for constraining parameters; they can provide novel insight into the relative importance of particular model parameters in determining physiological function ., Two important factors influencing the accuracy of the conductance predictions are the number and quality of the outputs ., Mathematically , inversion of the regression matrix B requires that the columns be linearly independent , which in turn requires independence of the columns of Y , i . e . the outputs ., In contrast , linear dependence would imply that the outputs contain redundant information ., Since we did not know a priori which outputs would be informative and which would be partially redundant , we implemented an algorithm to remove outputs sequentially and find a set of 16 that yielded the best results ., This resulted in the unexpected elimination of seemingly important outputs such as the maximal upstroke velocity , a metric closely related to Na+ conductance ., However , it is important to note that this result does not argue against the usefulness of upstroke velocity as a metric , it merely indicates that the information contained in this output has already been captured by the 16 that were selected ., These considerations suggest a future application of these techniques , besides their obvious utility in the construction of new mathematical models ., Since the regression analyses provide insight into which physiological measures are independent and which are partially redundant , these types of simulation studies can be used to prioritize experiments ., Experimental studies consume the valuable resources of reagents , animals , and person-hours , and computational approaches that could reliably distinguish between more informative and less informative experiments would therefore be quite valuable ., For example , the pacing cycle length at which a myocyte begins to exhibit APD alternans ( BCLalt ) is an important quantity related to the arrhythmogenic potential of the cardiac substrate 34 , 35 ., Determining this threshold , however , requires time-consuming experiments in which myocytes must be paced at many different rates ., This output was rejected by our elimination algorithm , suggesting that , at least in the TNNP model , the information provided by this difficult experiment is not different from that contained in other , perhaps simpler , measurements ., Our current work is focused on formalizing these ideas and developing methods to quantify the relative information content of different experimental measurements ., We should note that the outputs chosen for our analysis are physiologically meaningful metrics that are measured routinely in isolated cardiac myocytes ., We purposely excluded measures that quantify how cellular behavior changes after application of a pharmacological agent ., Since the explicit purpose of adding a drug is often to deduce the importance of the drugs primary target , we felt that including these metrics would , for an existing model , make the parameter constraint problem fairly trivial ., In future studies , however , including these outputs will undoubtedly improve the predictive power of these methods ., Similarly , the addition of more columns to the matrix Y corresponding to results from voltage-clamp experiments should also improve the accuracy of the method ., These extensions will likely be necessary if maximal conductances are essentially unknown , or if ionic current kinetic parameters are also to be constrained ., In the field of cardiac electrophysiology , a few modeling studies have examined issues of parameter sensitivity 6 , 16 , 36 , 37 , parameter estimation 38 , 39 , and model identifiability 40 ., For example , Fink and Noble recently assessed the adequacy of whole-cell voltage clamp records for uniquely determining parameters in models of ion channel gating 40 ., These analyses suggested that optimized voltage clamp protocols might be more efficient for parameter identification than protocols currently used in experiments ., More studies that address these sorts of issues have been performed in computational neuroscience ., For instance , analogous to the results shown in Figure 1A , several studies have shown that different combinations of model conductances can produce seemingly identical behavior , either in isolated neurons 11 , 13 or in models of small neuronal networks 14 ., Olypher and Calabrese then generalized this result by demonstrating that , close to a particular location in parameter space , infinitely many parameter combinations can produce the same level of activity as the original location , and these authors derived 2×2 sensitivity matrices to demonstrate these compensatory changes 41 ., Our reverse regression approach is essentially an extension of this idea to multiple dimensions , with the implicit assumption that considering additional linearly-independent model outputs will increase the likelihood of determining parameters uniquely ., Given that parameters in neuronal models cannot be uniquely specified using only a metric such as firing rate , a few studies have combined genetic algorithms with more sophisticated data-matching strategies such as phase-plane analysis 11 or multiple objective optimization 42 ., Our methods offer both advantages and disadvantages compared with these alternative strategies ., The primary advantage here is that reverse regression is simple and intuitive , and the outputs considered are well-defined metrics that are readily obtainable in the laboratory ., We can therefore easily relate , in a way that other techniques do not allow , the observable characteristics of the cardiac myocyte to the membrane densities of the important ion channels ., The main drawbacks of our approach are:, 1 ) that we only perform a local search around the baseline model and, 2 ) that we assume a linear relationship between changes in parameters and changes in outputs ., While linear approximations to these input-output relationships have been shown to work well in cardiac models 16 , particularly when conductances are expressed in log-transformed units , this assumption may not hold in all classes of models 43 ., This limitation is evident in the simulations shown in Figure 6 in that:, 1 ) two parameters were poorly predicted by the regression model; and, 2 ) in these simulations , the parameter search was constrained to only seven possibilities rather than allowing any model parameter to contribute to the phenotype ., Future studies will likely improve on these strategies and combine aspects of several approaches to refine methods for determining parameters in complex models of biological processes ., In summary , we have presented new methods for constraining free parameters in mathematical models , and demonstrated their utility through analyses of a common model of the ventricular myocyte ., The approaches we describe have potentially broad implications ., Analysis tools such as these can be used to obtain new insight into the relationships between model parameters , model outputs , and experimental data ., The ideas offer hope that , even if some model parameters cannot be directly measured , a close comparison of data to model output can still discriminate between possibilities and produce a model with strong predictive power ., This computational study aimed to extend the use of regression to develop methods for constraining free parameters in mathematical models ., The ideas were tested through simulations using the TNNP model 15 of the human ventricular action potential ( described in more detail in the Supporting Information ) ., First , regression was used to derive a matrix ( B ) whose elements indicate how changes in input parameters , namely maximal ionic conductances , affect physiologically-meaningful model outputs ., The regression matrix was then inverted , thereby deriving a new matrix ( B−1 ) that specifies the ionic conductances required to produce a given set of model outputs ., In the first stage , the input matrix X was generated by randomly scaling 16 parameters in the TNNP model ., A total of 300 random sets of parameters were generated such that X had dimensions 300×16 ., To compute the output matrix Y , several simulations were performed with each of the 300 models defined by a given parameter set ., These simulations reflected standard electrophysiological tests such as the response of the myocyte to changes in pacing rate or extracellular potassium concentration ., The calculation of some of these outputs is illustrated in Figure S1 ., The 32 outputs computed from these simulations , listed in Table 1 , ranged from straightforward measures such as action potential duration ( APD ) and Ca2+ transient amplitude to more abstract metrics such as the minimum cycle length required to induce APD alternans 34 ., The 16×32 matrix B relates the inputs to the outputs such that Y ̂\u200a=\u200aXB is a close approximation of the true output matrix Y . To allow for inputs and outputs expressed in different units to be compared , values in X and Y were converted into Z-scores – i . e . each column was mean-centered and normalized by its standard deviation ., The results of the “forward” regression performed in the first stage are shown in Figure S2 ., The second stage of the computational experiment aimed to determine if the input matrix X could be inferred , assuming the output matrix Y was known ., Since Y ̂\u200a=\u200aXB≈Y , we reasoned that YB−1 should be a close approximation of X , provided that B is an invertible matrix ., We performed an iterative procedure to determine the 16 most appropriate outputs for this matrix inversion ., First , with the full 300×32 matrix Y , “reverse regression” was performed to derive a matrix B′ such that YB′≈X ., We then removed each of the columns of Y and performed the reverse regression with the remaining 31 outputs ., The output whose removal caused the smallest change in the prediction of X ( quantified by R2 ) was deemed the least essential and was removed permanently ., This procedure was repeated to reduce the number of outputs from 31 to 30 , etc . , until Y had dimensions 300×16 ., A further set of simulations was performed with the 2008 version of the Hund and Rudy model of the canine action potential 19 ., In these simulations , we sought to determine whether changes in model parameters in heart failure could be determined using the reverse regression procedure ., We simulated the changes in parameters used by Shannon et al to simulate heart failure in their model of the rabbit action potential 20 ., This involved alterations to seven model parameters: GK1 , GKs , Gto , KNCX , KRyR , KSERCA , and Kleak ., Simulations were performed under three conditions: normal extracellular K+o ( 5 . 4 mM ) , hypokalemia ( K+o\u200a=\u200a3 mM ) and hyperkalemia ( K+o\u200a=\u200a8 mM ) ., In these simulations , a total of 33 model outputs were calculated to constrain the parameters ( see Text S1 for full list ) ., Reverse regression was performed to map the 33 outputs from the simulated failing myocyte to the predicted 7 parameter changes ., In the second approach , based on Bayess theorem , we were interested in estimating P ( A|B ) from P ( B|A ) , P ( A ) , and P ( B ) ., In this context , A is that a parameter is in a particular range , and B is that a model output is in a specified range ., To estimate P ( B|A ) from the set of 300 simulation results , we sorted the values in each column of X and Y , then computed the percentile ranges ., This allowed us to easily select , for instance , 10% of the values of a particular output centered around a given value ., To generate histograms such as those shown in Figure 4 , we first plotted the distribution of all the tested values of a given conductance ., Then we selected the conductance values corresponding only to those trials for which APD fell within a particular range , and generated the histogram of this set ., From this subset of conductances , we then selected the conductance values corresponding to those trials for which Vrest was in a certain range , etc ., To allow for visual comparison , each histogram was normalized to the total number of values of the subset ., To ensure that this procedure found a set of conductances that actually existed in the data set , we first identified the “best” trial for which the difference between Y and Y ̂ was minimal ., The output ranges used to select the subsets of conductances all represented deviations of ±5% around these values ., A bundle containing the Matlab™ code used to generate the results presented in the manuscript has been uploaded as Protocol S1 in the Supporting Information .

Introduction, Results, Discussion, Methods

A major challenge in computational biology is constraining free parameters in mathematical models ., Adjusting a parameter to make a given model output more realistic sometimes has unexpected and undesirable effects on other model behaviors ., Here , we extend a regression-based method for parameter sensitivity analysis and show that a straightforward procedure can uniquely define most ionic conductances in a well-known model of the human ventricular myocyte ., The models parameter sensitivity was analyzed by randomizing ionic conductances , running repeated simulations to measure physiological outputs , then collecting the randomized parameters and simulation results as “input” and “output” matrices , respectively ., Multivariable regression derived a matrix whose elements indicate how changes in conductances influence model outputs ., We show here that if the number of linearly-independent outputs equals the number of inputs , the regression matrix can be inverted ., This is significant , because it implies that the inverted matrix can specify the ionic conductances that are required to generate a particular combination of model outputs ., Applying this idea to the myocyte model tested , we found that most ionic conductances could be specified with precision ( R2 > 0 . 77 for 12 out of 16 parameters ) ., We also applied this method to a test case of changes in electrophysiology caused by heart failure and found that changes in most parameters could be well predicted ., We complemented our findings using a Bayesian approach to demonstrate that model parameters cannot be specified using limited outputs , but they can be successfully constrained if multiple outputs are considered ., Our results place on a solid mathematical footing the intuition-based procedure simultaneously matching a models output to several data sets ., More generally , this method shows promise as a tool to define model parameters , in electrophysiology and in other biological fields .

Mathematical models of biological processes generally contain many free parameters that are not known from experiments ., Choosing values for these parameters , although an important step in the construction of realistic computational models , is frequently performed using an ad hoc approach that is a combination of intuition and trial and error ., We have developed a novel method for constraining free parameters in mathematical models based on the techniques of linear algebra ., We demonstrate this methods utility through simulations with a model of a human heart cell ., The underlying premise is that if the model is only asked to recapitulate one or a few biological behaviors , the values of the parameters may be ambiguous; however , if the model must simultaneously match many features of experimental data , the free parameters can be determined uniquely ., The results demonstrate that if computational models are to be realistic , they must be compared with several sets of data at the same time ., This new method should serve as a valuable tool for investigators interested in developing realistic mathematical models of biological processes .

cardiovascular disorders/arrhythmias, electrophysiology, and pacing, computational biology/systems biology, physiology/cardiovascular physiology and circulation

journal.pcbi.1000127

The Spatiotemporal Pattern of Src Activation at Lipid Rafts Revealed by Diffusion-Corrected FRET Imaging

Src is a protein tyrosine kinase which plays crucial roles in cell adhesion , migration and cancer invasion 1 ., In fact , epidermal growth factor ( EGF ) and its receptor EGFR has been well documented to couple with Src kinase to regulate cancer progression 2 ., Before stimulation , Src is localized at microtubule-associated endosomes around the nucleus 3–7 ., The SH3 and SH2 domains of Src kinase are coupled together by intramolecular interaction , and the catalytic kinase domain of Src is masked by the interaction with C-terminal tail , thus preventing its action on substrate molecules 8 ., Upon EGF stimulation , Src can translocate to focal adhesion sites and associate with actin filaments at cell periphery 4 , 5 , 9–12 , possibly through the Src N-terminal tail and SH3 domain , but not the catalytic domain 3 , 10 , 13 ., Recent evidence indicates that EGF can enhance the Src localization and activation at lipid rafts to regulate cancer development 14–16 ., However , the existence of the extremely small and dynamic lipid rafts , and the mechanism on how these lipid rafts function as docking sites to coordinate signaling molecules , remain controversial 17 , 18 ., It is also not clear how EGF activates Src spatially and temporally at lipid rafts to impact on cellular functions ., Genetically encoded biosensors based on fluorescence resonance energy transfer ( FRET ) are powerful tools for live cell imaging 19 , 20 ., A variety of such biosensors utilizing cyan fluorescence protein ( CFP ) and yellow fluorescence protein ( YFP ) have been developed to visualize the activities of important kinases in live cells , including epithelial growth factor receptor ( EGFR ) , Abl 21 , protein kinase A 22 , protein kinase B 23 , protein kinase C 24 , and insulin receptor 25 ., We have also developed a genetically-encoded FRET biosensor for monitoring Src activity in live cells 21 , 26 ., The investigations based on these biosensors have provided invaluable information about the spatiotemporal activation pattern of the molecules studied 27 , 28 ., However , the observed FRET signal reported by these biosensors at any given spot represents the combined effect of two main factors: ( 1 ) the local kinase activity acting on biosensors and ( 2 ) the signal of activated biosensors moving in the cell among locations ., The movement of these biosensors is not dependent on the motion of the targeting enzymes or their endogenous substrate molecules ., Hence , the rapid motion of the biosensors can artificially dissipate the cumulative signals engendered by the in situ enzymatic activity ., Therefore , it is essential to identify and subtract the effect of biosensor motility from the apparent FRET signals to allow an accurate reconstruction of the spatiotemporal activation map of the targeting kinase ., The fluorescence recovery after photobleaching ( FRAP ) analysis has been widely used to estimate the apparent diffusion coefficients and characterize the motion of fluorescent molecules in live cells 29–32 ., In classical FRAP analysis , the fluorescence recovery curve is obtained by monitoring the average fluorescence intensity in a small region after photobleaching ., Based on the recovery curve , the apparent diffusion coefficient of fluorescent molecules can be estimated by parameter fitting 29 ., However , this approach has specific requirements on the cell geometry , photobleached spot , and the actual photobleaching process 29–31 , 33 ., Most recently , FRAP analysis using numerical methods , such as the computational particle method , the finite difference method , and the Monte Carlo simulation , have been developed to address these limitations 34–40 ., Results from FRAP analysis have revealed the characteristics of transport kinetics for many important molecules 41–43 ., Nonetheless , there is a need to apply these methods to quantify and analyze live-cell FRET images ., The finite element ( FE ) method is well known for its flexibility in resolving the complex geometry of tissue and cellular structures 44 , 45 ., It has been used to estimate the apparent diffusion constant in inhomogeneous tissues 46 and for modeling protein transport in single cells 47 ., In this study , we have developed a new imaging analysis method based on FE and FRAP to evaluate the motility of different Src biosensors ., The results revealed that the motility of biosensors tethered to lipid rafts is governed by 2D diffusion ., After the effect of biosensor diffusion on FRET signals was subtracted from the apparent FRET images , the diffusion-corrected FRET signals revealed that , at lipid rafts , high Src activities upon EGF stimulation are concentrated at relatively stationary clusters around cell periphery ., Our FE-based imaging analysis method , integrated with FRAP and FRET technologies , can also serve as a general method to study the spatiotemporal kinetics of other enzymatic activity in living cells ., To assess the effect of biosensor diffusion on the apparent FRET images recorded in experiments ( Figure 1 ) , we developed a FE-based method to analyze protein diffusion in FRAP experiments ., Based on Ficks second law of diffusion , the change of molecular concentration in time is proportional to the second derivative of the concentration in space , i . e . , the Laplacian of concentration ., This can be expressed mathematically as following: ( 1 ) where represents the time derivative of the concentration u ( x , y , t ) at a given time and location in 2D space , Δu ( x , y , t ) denotes the Laplacian of u ( x , y , t ) and D represents the diffusion coefficient of the target molecule 33 ., After Eq ., ( 1 ) was discretized using the FE method , the apparent diffusion coefficient can be estimated by applying a linear regression procedure on the weighted discrete Laplacian of concentration ( WDLC ) and the weighted change of concentration in time ( WCCT ) ( Figure 2 , and see Materials and Methods , “Computational Simulation and Validation of the Diffusion Model” ) ., The FE-based image analysis method was validated by computational modeling of the diffusion process ( Figure 3 ) ., A designated cell geometry , an initial distribution of molecular concentration to mimic the fluorescence image after photobleaching , a diffusion coefficient of 29 µm2/sec ( the diffusion coefficient of XPA-GFP which has the same size as our cytosolic Src biosensor 39 ) were first assigned ., A sequence of concentration maps ( Figure 4A–4B ) was numerically generated and saved to mimic the real procedures in FRAP experiments and used for the computation of the fluorescence recovery curve ( Figure 4C ) ., Based on these simulated FRAP images , FE analysis was used to triangulate the cell geometry and discretize the diffusion equation ( Figures 3 and S1 ) ., Linear regression was then used to calculate the apparent diffusion coefficient ( Figure, 2 ) to be 30 . 3 µm2/sec , close to the assigned diffusion coefficient ., Because the simulated diffusion process is governed by Ficks law , the WDLC should be linearly correlated to WCCT ., The plot of WDLC vs . WCCT on each FE mesh-node verified a linear relationship between these two quantities ( Figure 4D ) ., All these results suggest that our method is accurate for modeling diffusion process ., A large portion of data points in Figure 4D clustered near the zero of WCCT , suggesting that there was no significant change of the concentration at many mesh nodes distant from the photobleached spot over one time-step ., Meanwhile , the noise in Figure 4D is likely due to image processing in the simulation to mimic the procedures of data processing in FRAP and FRET experiments ( saving and loading image files ) , since the same discretization method was used for simulating concentration maps and estimating diffusion coefficient ., These noises can indeed be eliminated by running the simulation without saving/loading images ( data not shown ) ., A Src FRET biosensor was previously modified and tethered at lipid rafts in plasma membrane through a myristoylation and palmitoylation tag at the N-terminal ( Lyn-Src ) ( see Figures S2 and 5 ) 26 , 48 ., We have further developed , analyzed , and compared two other versions of compartment-localized Src FRET biosensors as shown in Figure 5 21 , 26 ., One biosensor is targeted to membrane regions outside of lipid rafts through a geranylgeranylated tag at the C-terminal ( KRas-Src ) 48 and the other is located in the cytoplasm and the nucleus ( Cytosolic-Src ) ., To assess their mobility , the biosensors in a small region of a live cell were photobleached ., The post-bleaching images were monitored and then normalized by the pre-bleaching images to obtain concentration maps ., Subsequently , the FE analysis and linear regression method were applied on the concentration maps to estimate the apparent diffusion coefficient ( Figure 6 ) ., As shown in Figure 7A–7B and Movie S1 , the fluorescence intensity of the Lyn-Src biosensor localized at lipid rafts recovered in ∼15 min after photobleaching , with an estimated apparent diffusion coefficient of 0 . 11±0 . 01 µm2/sec ., To evaluate the accuracy of the diffusion model , the mobility of this Lyn-Src biosensor was simulated and compared with experimental results ., The simulation-predicted concentration map of the Lyn-Src biosensor at 1 min after photobleaching precisely matches the experimental result ( Figure 7C ) ., The linear relationship between WDLC and WCCT further confirmed that the motion of the Lyn-Src biosensor is dominated by diffusion and governed by Ficks law ( Figure 7D ) ., These results suggest that our diffusion model can accurately predict the motility of biosensor tethered at lipid rafts ., Similar approaches were employed to analyze the mobility of the KRas-Src and the Cytosolic-Src biosensors ( Figures 8–9 ) ., The fluorescence intensity of the Cytosolic-Src biosensor recovered in ∼4 min after photobleaching ( Figure 9A–9B ) ., The estimated apparent diffusion coefficient of the Cytosolic-Src biosensor was 0 . 93±0 . 06 µm2/sec , which is 4–8 folds higher than that of the membrane-bound Lyn-Src ( 0 . 11±0 . 01 µm2/sec ) and KRas-Src biosensors ( 0 . 18±0 . 02 µm2/sec ) ., These observations are consistent with previous reports that the diffusion rate of the molecules near the plasma membrane is 2- to 3-fold slower than that in cytoplasm 30 , 49 , 50 , possibly reflecting the different nature of diffusions in 2D ( membrane ) and 3D ( cytosolic/nucleus ) ., Error analysis procedures were designed to further evaluate the accuracy of the FE-based diffusion analysis for the three versions of Src biosensors ( Figure 10 , see Materials and Methods , “Error Analysis” ) ., For the Lyn-Src biosensor , the model prediction matches experimental result precisely ( Figure 10A_i ) , and the scattered linear plot of data shows high confidence with the model ( Figure 10B_i ) ., The KRas-Src biosensor also had relatively uniform distribution on plasma membrane ( Figure 5B ) , with reasonable agreement between experimental and simulation results ( Figure 10A_ii–10B_ii ) ., It is of note that the mobility of the KRas-Src biosensor appears slightly less well predicted than for the Lyn-Src biosensor ( Figure 10 ) ., On the other hand , the results for the Cytosolic-Src biosensor demonstrated an obvious disagreement between simulation and experiments ( Figure 10A_iii–10B_iii ) , which is attributable , at least in part , to the accumulation of a large fraction of the Cytosolic-Src biosensor in the nucleus in which molecules may have significantly different mobility from that in the cytoplasm ( Figure 5B ) ., To gain more insights about the molecular dynamics and kinetics in lipid rafts , we investigated and compared the kinetics of the Lyn-Src and the KRas-Src biosensors in cells with MβCD treatment , which extracts cholesterol and disrupts lipid rafts ., Without MβCD treatment , the Lyn-Src biosensor was found by FRAP analysis to move at a slower rate on the plasma membrane than the KRas-Src biosensor ., Since the Lyn-Src biosensor is tethered on the lipid rafts , which are subdomains of plasma membrane rich in cholesterol 48 , 50–53 , this finding corroborates previous observations that molecules move more slowly in the cholesterol-rich than cholesterol-poor model membranes 54 ., In fact , we found that the treatment with MβCD to disrupt cholesterol-associated rafts significantly increased the apparent diffusion coefficient of the Lyn-Src biosensor ( from 0 . 11±0 . 01 to 0 . 17±0 . 01 µm2/sec ) , but not the KRas-Src biosensor ( from 0 . 18±0 . 02 to 0 . 20±0 . 01 µm2/sec ) ( Figure 11A ) ., This result is also consistent with earlier findings that MβCD enhances the molecular motility of HRas-tagged green fluorescence protein ( GFP ) tethering on lipid rafts , but not KRas-tagged GFP 50 ., The large coefficient of determination ( R2\u200a=\u200a0 . 79±0 . 033 ) ( Figure 11B ) , which represents a high correlation between the experimental results and the simulated predictions by our diffusion model ( see Method , “Error Analysis” ) , suggests that the mobility of the Lyn-Src biosensor is dominated by diffusion and hence can be accurately predicted by the diffusion model ., This result is also consistent with the error analysis approach ( Figure 10 ) ., The mobility of the KRas-Src biosensor ( R2\u200a=\u200a0 . 56±0 . 06 ) is less well predicted by simulation , suggesting that transportation mechanisms other than 2D diffusion may also contribute to the mobility of biosensors tethered outside of lipid rafts ., The apparent FRET images of the Src biosensors represent the combinatory effects of spatiotemporal Src kinase activity and the re-distribution of mobile activated biosensors ( Figure 1 ) ., Hence the apparent FRET signals may be different from the actual distribution of Src activity or its actions on endogenous substrate molecules ., In fact , many prominent substrate molecules of Src kinase , e . g . , p130cas and paxillin , are localized at subcellular regions with limited mobility in adherent cells 55 , 56 ., Recent evidence indicates that lipid rafts serve as an integrated platform for Src activation 16 , 57 and the recruitment of P130cas and paxillin 58–60 ., However , there is a lack of knowledge on the spatiotemporal pattern of Src activation at lipid rafts or its accumulative effects on the relatively immobile substrate molecules ., To reconstruct the Src activation map at lipid rafts , the contribution of biosensor diffusion was simulated and subtracted from apparent FRET signals ., Error analysis has shown that the FE model of diffusion can precisely predict the movement of the Lyn-Src biosensor ., Control experiments suggest that the diffusion rate of the Lyn-Src biosensor does not differ significantly with or without EGF stimulation ( data not shown ) ., Hence a diffusion coefficient of the Lyn-Src biosensor calculated before EGF stimulation can be applied to simulate the diffusion process through the entire time course of FRET experiment in the same cell ., The subtraction of this simulated diffusion effect revealed discrete clusters of high Src activities at lipid rafts close to the cell edge , in contrast to the FRET images without diffusion subtraction which are relatively uniform ( see Figure 11C and Movie S2 ) 26 ., Immunostaining of the distribution of Src activity in fixed cells upon growth factor stimulation 5 , 55 also showed high Src activities concentrated at cell periphery , consistent with our observations ., It is of note that the locations with high Src activity at lipid rafts are relatively stationary upon EGF stimulation ( Figure 11C ) , suggesting that active Src remains localized without significant motion upon arrival at lipid rafts ., The timing and localization of molecular activities are crucial for their proper functions ., In this paper , we have integrated FE-based imaging analysis modeling , FRAP and FRET technologies , to reconstruct and visualize the spatiotemporal Src activity in lipid rafts upon EGF stimulation ., The mobility of the Src biosensor tethered in the lipid rafts of plasma membrane was shown to be dominated by diffusion ., The subtraction of this diffusion effect from FRET images has helped to reconstruct the Src activation map at lipid rafts , with high Src activity localized at stationary clusters proximal to cell edge ., Given the important roles of Src and lipid rafts in mediating EGF/EGFR-regulated cancer development 2 , 14 , our results should shed new lights on how cells coordinate molecular activities in space and time to orchestrate pathophysiological responses upon external stimulation ., The advantage of our live-cell imaging approach is further underscored by the controversial effect of non-ionic detergents used for isolating lipid rafts in traditional assays 17 , 61 ., Although the roles of Src in regulating downstream signaling pathways are well studied , the detailed mechanism of Src activation in response to EGF is not clearly elucidated 62 ., It has been shown that growth factors can induce the translocation of Src from perinuclear regions to cell periphery through RhoB and actin cytoskeleton 5 , 63 ., Our results suggest that Src can be transported and activated at lipid rafts ., The active Src molecules upon arrival at lipid rafts appear relatively stationary with sub-compartment localization since the activation pattern of Src biosensor showed clusters with increasing size , but little motion ( Figure 11C ) ., It has been shown that EGF can form complex with its receptor EGFR , which further binds to integrins 64 ., Since integrins are anchored to immobile extracellular matrix and well documented to coordinate the localization of lipid rafts and its associated signaling molecules 65 , 66 , it is possible that EGF and its ligation with EGFR induce localized Src activation at lipid rafts via integrins ., In fact , evidence has shown that integrin β3 can directly bind to Src through the interaction of β3 C-terminal tail and Src SH3 domain 67 ., Some evidence has shown that EGFR did not colocalize with caveolae at rest state 68 ., Hence it is also possible that either EGF receptor or Src is activated outside of lipid rafts and then sequestered inside lipid rafts ., Further studies are warranted to elucidate the underlying mechanism for this localized and stationary Src activity at lipid rafts in response to EGF stimulation ., The motility of the Lyn-Src biosensor is dominated by diffusion , as evidenced by the close match between experimental and simulated results , and by the strong linear correlation between WDLC and WCCT ( Figures 7C and 10B_i ) ., The mobility of the KRas-Src biosensor , however , displays some nonlinear features between WDLC and WCCT ( Figure 10B_ii ) , suggesting that it is not completely governed by 2D diffusion ., Intracellular molecule mobility is influenced by molecular interaction , diffusion , and active transportation 31 , 33 ., Hence , molecular interaction or active transportation may contribute to the motion of KRas-Src biosensor besides diffusion ., The mobility difference between KRas- and Lyn-Src biosensors may be attributable to the tight membrane-binding of the Lyn tag through deep insertion of side chains into the bilayer interior and the fluctuating membrane-binding of the KRas tag through electrostatic switches 69 ., Because the membrane-tethered biosensors extend appreciably into the cytoplasm , it is also possible that some of the restricted motion at the proximity of the plasma membrane may be due to the interaction of the biosensor with the cortical actin cytoskeletal network 70 ., These interactions may have particularly contributed to the motion of KRas-Src biosensor , which is not dominated by random diffusion ., Our estimated diffusion coefficient of the Cytosolic-Src biosensor is several-fold higher than those of the membrane-targeted versions ., One of the possible reasons for the difference between the diffusion coefficients of the Cytosolic-Src and membrane-targeted biosensors may be the difference in the physicochemical properties of local environment , e . g . the diffusion of the Cytosolic-Src biosensors is 3D in nature whereas that of the membrane-targeted biosensors is 2D ., While our diffusion model can be used to estimate the apparent diffusion coefficient and simulate diffusion process in principle , it cannot be directly applied to study the Cytosolic-Src biosensor ., The low coefficient of determination ( R2\u200a=\u200a0 . 33±0 . 1 , n\u200a=\u200a5 ) suggests that the mobility of a large portion of the Cytosolic-Src biosensors cannot be described by diffusion ., This is possibly because the Cytosolic-Src biosensors reside in different sub-compartments of the cell , e . g . , the nucleus vs . the cytoplasm , as shown in Figure 5B and evidenced by the results from our fluorescence loss in photobleaching ( FLIP ) experiments ( data not shown ) ., The movement of the Cytosolic-Src biosensor will likely be better described by a 3D and multi-compartment diffusion model ., The approach of evaluating and subtracting diffusion based on FRAP and FRET video images can also be implemented by employing other numerical methods including finite difference method , computational particle method , and Monte Carlo simulation ., We decided to choose the FE-based method because it has been well-established for modeling the diffusion processes with complex geometry in 2D and 3D 44 , 71 ., Since the FE methods have great flexibility in resolving the complex geometry of tissue and cellular structures 44–47 , no specific requirement on the cell geometry , the bleaching light beam , or the photobleaching process is needed in our new FRAP analysis method ., Further , efficient solvers 72 and parallel implementation on distributed computers have been extensively developed for FE methods 73 ., Thus , with the integration of 3D imaging techniques , e . g . confocal microscopy , our system can be conveniently extended to 3D analysis and parallel computing environment ., In summary , our FE-based method can successfully separate the effect of biosensor diffusion from the apparent FRET signals to reconstruct the diffusion-corrected spatiotemporal activation map of membrane-tethered Src kinase ., The results suggest that the EGF-induced Src activation at lipid rafts has localized and stationary patterns clustered at cell periphery ., This methodology can be conveniently utilized to reconstruct other molecular activation maps from those reported by indirect and diffusion-driven biosensors ., HeLa cells ( ATCC , Manassas , Virginia ) were cultured in a humidified 95% air , 5% CO2 incubator at 37°C ., The culture medium was Dulbeccos modified Eagles medium ( DMEM ) supplemented with 10% fetal bovine serum , 2 mM L-glutamine , 1 unit/ml penicillin , 100 µg/ml streptomycin , and 1 mM sodium pyruvate ., The cell culture reagents were obtained from Invitrogen ( San Diego , CA ) ., The gene for the Cytosolic-Src biosensor was constructed as described previously 26 ., In brief , this Cytosolic-Src FRET biosensor consists of a peptide derived from Src substrate molecule p130cas and a phosphotyrosine-binding domain ( SH2 domain derived from c-Src ) , bracketed by monomeric ECFP and Citrine ( an improved version of EYFP ) at the N- and C-termini ., The substrate peptide phosphorylated by a Src kinase can interact with the intramolecular SH2 domain , which results in a change of distance or relative orientation between ECFP and Citrine , as shown in Figure S2 ., The subsequent changes of FRET between ECFP and Citrine can be represented by the ECFP/Citrine emission ratio to monitor the Src activities ., The membrane-targeted ECFP was constructed by PCR amplification of the monomeric ECFP with a sense primer containing the codes for N-terminal amino acids from Lyn kinase to produce a Lyn-Src biosensor 48 ., For the KRas-Src biosensor , the monomeric YFP was amplified by PCR with an anti-sense primer containing the codes for C-terminal amino acids from KRas ( KKKKKSKTKCVIM ) ., For simplicity , we refer to the monomeric ECFP and Citrine by CFP and YFP respectively in text and figures ., The various plasmids were transfected into HeLa cells at 80% confluence using the lipofectamine method as described by the vendor ( Invitrogen , San Diego , CA ) ., For FRAP experiments , the YFP images were collected using MetaFluor 6 . 2 software ( Molecular Devices , Sunnyvale , California ) on epi-fluorescence microscopy ( Zeiss , Oberkochen , Germany ) with emission at 535DF25 and excitation at 495DF20 using 1% of the light source power ., During imaging , the cells were kept in CO2-independent medium without serum ( Invitrogen ) at 25°C; and the objective focus was aimed near the basal side of the cell ., The cells were monitored before photobleaching to confirm there was no detectable photobleaching during imaging ., Photobleaching was conducted by exciting YFP at 495DF20 in a region of interest with full power of the light source for 15 sec , after which the recovery process was imaged at 1-sec and 10-sec intervals for the cytosolic and membrane-targeted Src biosensors , respectively ., For FRET experiments , the HeLa cells expressing the desired Src biosensors were starved with 0 . 5% FBS for 36–48 hr before being subjected to EGF ( 50 ng/ml ) stimulation ., The images were collected with a 420DF20 excitation filter , a 450DRLP dichroic mirror , and two emission filters controlled by a filter changer ( 480DF30 for CFP and 535DF25 for FRET ) ., The pixel-wise images of CFP/YFP emission ratio were computed to assess the FRET signals , which represent the concentration of phosphorylated Src biosensor and hence Src activity in space and time ., The Src biosensors were assumed to diffuse freely inside the cytoplasm or in the membrane ., According to Ficks Law , the diffusion equation is given by Eq ., ( 1 ) ( Results , Computer Simulation and Validation ) ., Enclosed in the cell boundary , a triangular mesh was generated for the finite element discretization ( Figure S1 ) ., A two-dimensional model was used because the thickness of a spread cell is relatively small compared to its length and width , and the photobleached region is sufficiently big ( ∼2 µm ) such that the 3D profile of the light beam is negligible ., Using the FE method for discretizing the Laplacian operator and the Crank-Nicholson Scheme for approximating time derivative 74 , Eq ., ( 1 ) can be approximated by a discrete linear system ( for details see Text S1 , “The Formulation of the Finite Element Method” ) ( 2 ) where M represents the mass matrix , K the stiffness matrix , dt the discrete interval between each time step , un and un+1 the concentration of fluorescent molecules at the nth and ( n+1 ) th time step , respectively ., Here the matrices were assembled using the finite element method to incorporate the geometry of the cell ., Zero flux was assumed at cell boundary ., For a given initial fluorescent concentration un and an assigned diffusion coefficient , the fluorescent concentration at the next time step , un+1 , can be computed based on a simple transformation of Eq ., ( 2 ) : With the interval between each time step dt set to be 0 . 0313 sec , numerical convergence of the FE method was confirmed by comparing the estimated diffusion coefficients and simulated diffusion results with those on a higher resolution mesh and a smaller time step ., According to Eq ., ( 2 ) , there is a linear relationship between the weighted change of concentration in time ( WCCT ) , M ( un+1−un ) , and the weighted discrete Laplacian of concentration ( WDLC ) , −0 . 5dt·K· ( un+un+1 ) ., Therefore , based on the fluorescence concentration at two consecutive time steps , the diffusion coefficient can be estimated by linear fitting between these two quantities using the least square method ( Figure 2 ) ., The calculated diffusion coefficient is then compared with the originally assigned diffusion coefficient to assess the accuracy of our method ., The whole process of computational simulation to assess and verify the accuracy of our FE and diffusion model is illustrated in Figure 3 ., All the computer-simulated concentration images were processed using a median filter with a window sized at 10×10 pixels ( Figure 3 ) ., Similarly , the apparent diffusion coefficients of the Src biosensors in FRAP experiments were obtained by computing the least-square linear fitting between the WDLC and the WCCT of the concentration images ., The diffusion coefficients were then used to simulate and predict the fluorescence recovery maps for comparison with the experimental concentration images ( Figure 6 ) ., Different from the computer simulation which covers the entire cell , most of the FRAP images were captured with the 100× objective , so only part of the cell was captured in the image in some occasions ., Therefore there may be fluxes across the image boundary , which is not part of the cell boundary ., In these cases , instead of zero flux boundary conditions ( BCs ) , the BCs were computed with the apparent diffusion coefficient during linear fitting , by estimating both parameter D and r0 in Eq ., S7 46 ., Using this linear regression procedure , one estimated apparent diffusion coefficient can be computed with every pair of concentration maps ( FRET ratio ) un and un+1 ., The apparent diffusion coefficient was obtained by averaging the estimated diffusion coefficients of several time intervals ., This strategy bears some similarity with the classic FRAP analysis where one apparent diffusion coefficient is obtained by fitting the complete recovery curve ., In addition , it is required that we convert the experimental fluorescent intensity images to concentration maps , and reduce noise by smoothing the images at several stages , as described in details in Text S1 , “Pre-processing of FRAP Experimental Images” ., Two kinds of error analysis were used to evaluate the accuracy of our diffusion model at each time step ., First , the absolute value of the error , abs ( un−est_un ) , was used to show the difference between the simulated concentration map with experimental images ., Here est_un and un denote the simulated and experimental concentration maps at the nth time step , respectively ., The accuracy of our diffusion model was further evaluated by computing the coefficient of determination , which measures the percentile of total variation in the data that can be explained by the mathematical model 75 ., In our diffusion model , the coefficient of determination , R2 , is equivalent to the square of the linear correlation coefficient between WCCT {xi} and WDLC {yi} ., The linear correlation coefficient between these two data sets {xi} and {yi} is defined aswhere x̅ and y̅ are the mean values of {xi} and {yi} respectively ., To smooth the data and reduce the computational noise , the data set of WDLC {yi} and WCCT {xi} was divided into ten equal intervals along the x-axis and averaged at each interval before computing the coefficient of determination ., For statistical analysis of the estimated apparent diffusion coefficients and the coefficients of determination , we used the Bonferroni multiple comparison test of means at 95% confidence interval , which is provided by the multcompare function in the MATLAB statistics toolbox ( The MathWorks , Natick , MA ) ., The estimated apparent diffusion coefficients were selected based on the criteria described in Text S1 , “Including Estimated Coefficients in Statistical Analysis” ., The FRET ratio images ( CFP intensity/ YFP intensity ) were used to quantify the Src activity , or the concentration of phosphorylated Src biosensor ., As shown in Figure 1 , the FRET signals originated from the diffusion of the biosensor at any given time ( Figure 1C ) was simulated by using the FRET image of the previous time step ( Figure 1A ) and the apparent diffusion coefficient estimated by previous FRAP experiments of the biosensor ., This simulated FRE

Introduction, Results, Discussion, Materials and Methods

Genetically encoded biosensors based on fluorescence resonance energy transfer ( FRET ) have been widely applied to visualize the molecular activity in live cells with high spatiotemporal resolution ., However , the rapid diffusion of biosensor proteins hinders a precise reconstruction of the actual molecular activation map ., Based on fluorescence recovery after photobleaching ( FRAP ) experiments , we have developed a finite element ( FE ) method to analyze , simulate , and subtract the diffusion effect of mobile biosensors ., This method has been applied to analyze the mobility of Src FRET biosensors engineered to reside at different subcompartments in live cells ., The results indicate that the Src biosensor located in the cytoplasm moves 4–8 folds faster ( 0 . 93±0 . 06 µm2/sec ) than those anchored on different compartments in plasma membrane ( at lipid raft: 0 . 11±0 . 01 µm2/sec and outside: 0 . 18±0 . 02 µm2/sec ) ., The mobility of biosensor at lipid rafts is slower than that outside of lipid rafts and is dominated by two-dimensional diffusion ., When this diffusion effect was subtracted from the FRET ratio images , high Src activity at lipid rafts was observed at clustered regions proximal to the cell periphery , which remained relatively stationary upon epidermal growth factor ( EGF ) stimulation ., This result suggests that EGF induced a Src activation at lipid rafts with well-coordinated spatiotemporal patterns ., Our FE-based method also provides an integrated platform of image analysis for studying molecular mobility and reconstructing the spatiotemporal activation maps of signaling molecules in live cells .

Fluorescence biosensors have been widely used to report the spatial and temporal activity of target molecules in live cells ., However , biosensors can move independently of the target molecule and carry its signal to other subcellular locations ., Therefore , the observed images appear to be the combination of the target molecular activity and the artifacts introduced by the movement of the biosensors ( mainly due to diffusion ) ., The intriguing question is how to estimate and exclude the movement effect of biosensors from the observed fluorescent images and to reconstruct the real activity map of the target molecules ., The Src molecule plays important roles in cell adhesion , migration , and cancer invasion ., In this paper , we developed a novel computational method to analyze and simulate the movement of the Src biosensor , which was then subtracted from the original fluorescent images ., With this computational method , we observed discrete clusters of high Src activity at relatively stationary locations on the plasma membrane ., Therefore , our results highlight the coordination of molecular activities in space and time ., In addition to Src , our computational method can be used to reconstruct the activity map of other signaling molecules .

cell biology/cell signaling, biochemistry/bioinformatics, biotechnology/bioengineering, biophysics/cell signaling and trafficking structures, computational biology/systems biology

journal.pcbi.1005566

Chemomechanical regulation of myosin Ic cross-bridges: Deducing the elastic properties of an ensemble from single-molecule mechanisms

The myosin family includes at least 20 structurally and functionally distinct classes 1 , 2 ., Although they all exhibit a common chemomechanical cycle , myosin molecules have remarkably diverse functions-including intracellular transport , force production in muscles , and cellular migration-as well as important roles in sensory systems 3 ., To understand the emergence of these different functions , it is necessary to characterize the biophysical details of the chemomechanical cycle for each myosin class ., Myosin molecules transduce chemical energy into mechanical energy through the hydrolysis of adenosine triphosphate ( ATP ) ., The hydrolysis reaction and the subsequent release of inorganic phosphate ( Pi ) and adenosine diphosphate ( ADP ) induce structural changes that result in a power stroke and generate forces ., The biochemical reaction rates and the response to external forces determine the specific function of each myosin 3 ., On the basis of their biochemical and mechanical properties , myosins have been classified into four groups:, ( i ) fast movers ,, ( ii ) slow but efficient force holders ,, ( iii ) strain sensors , and, ( iv ) gates 4 ., Although single-molecule experiments and structural studies have vastly advanced our understanding of force-producing molecules , we still lack a consistent description that quantitatively relates cellular functions to the molecular details ., One prominent case is myosin Ic , which has been identified as a component of the adaptation motor of the inner ear 5 ., Hair cells in the inner ear transduce mechanical stimuli resulting from sound waves or accelerations into electrical signals ., On the upper surface of each hair cell stands a hair bundle comprising dozens to hundred of actin-filled protrusions called stereocilia ., Cadherin-based tip links connect the tip of each stereocilium to the side of the longest adjacent one ., When a mechanical force deflects the bundle , the resultant shearing motion raises the tension in the tip links ., This tension increases the open probability of transduction channels and allows ions to diffuse into the stereocilia , depolarizing the hair cell ., To retain sensitivity , a hair cell adapts to a prolonged stimulus by changing the tension in the tip links ., This adaptation has a fast component lasting a millisecond or less and a slow component of a few tens of milliseconds , the molecular details of which remain uncertain ., To explain slow adaptation , it has been proposed that an ensemble of myosin Ic molecules alternately step up or slide down the actin filaments inside the stereocilia to regulate the tension in the tip links ., Sliding of myosin is triggered by a locally elevated Ca2+ concentration ., This picture has been quantitatively supported by experimental studies on hair cells and complemented by mathematical descriptions 6–9 ., Fast adaptation describes the rapid reclosure of transduction channels after abrupt stimulation of the hair bundle ., This process is poorly understood and several possible explanations at a molecular level are debated 6 , 10 ., One promising mechanism is the release model , in which a component of the transduction apparatus becomes more flexible and abruptly releases some of the tension in the tip links , allowing the channels to close rapidly 11 , 12 ., Although myosin Ic has been implicated in both slow and fast adaptation and an ensemble of myosin Ic molecules is a good candidate for the element that releases 10 , the precise role of myosin Ic in adaptation has yet to be elucidated ., The rapid response of the transduction channels to a displacement of the hair bundle suggests a direct mechanical activation through the transformation of the deflection into a force by a spring 6 , 13 ., This mechanism underlies the gating-spring hypothesis that is the prevailing explanation for mechanotransduction by hair cells ., The elastic property of the gating spring is the most important parameter in setting the precise relation between the deflection of a hair bundle and the open probability of the ion channels ., Despite numerous studies of the molecular components of the hair bundle and their biophysical properties , we remain uncertain of the identity of the gating spring 14–18 ., Every molecule that lies in series with the tip link could in principle influence the elastic properties , including the ensemble of myosin Ic molecules ., These molecules bind and unbind from actin filaments and thereby change the elasticity dynamically ., In order to fully explain mechanotransduction by hair cells , it is important to understand how the dynamics of single myosin Ic molecules determines the elastic properties of an ensemble and how it is regulated ., Over the past few years , the biophysical properties of individual myosin Ic molecules have been characterized in optical traps , biochemical assays , and structural studies 19–24 ., Like other myosin isoforms , myosin Ic displays catch-bond behavior , a prolonged attachment to an actin filament in response to increased external force 19 , 25 ., The force-sensitive step in myosin Ic’s cycle is the isomerization following ATP binding , however , and not ADP release as in other slow myosins 19 , 20 ., To understand how this behavior relates to the molecule’s physiological function , we introduce a consistent mathematical description of myosin Ic’s cross-bridge cycle ., After the introduction of the basic framework by Huxley and Huxley , cross-bridge models have been widely used to describe the dynamics of myosin motors 2 , 26–35 ., However , these models often assume irreversible transitions at fixed nucleotide concentrations that determine the input of chemical energy ., In a seminal work , T . L . Hill showed how to couple a description of an enzymatic cycle to free-energy transduction in a thermodynamically consistent manner , an approach that has been applied to study muscle myosin 36–39 ., We build our cross-bridge cycle for myosin Ic on these concepts and furthermore include the catch-bond behavior ., Our description allows a quantitative analysis of the differences between in vitro and in vivo conditions , of Ca2+ regulation , and of cooperativity between force-producing molecules ., Here we introduce a thermodynamically consistent description of myosin Ic based on single-molecule data and focus on the responses to external force , to different nucleotide concentrations , and to the availability of actin ., We use this description to predict the elastic properties of an ensemble of myosin molecules and highlight the potential implication for the release model of fast adaptation ., As a functional description of myosin Ic we introduce a chemomechanical cycle consisting of five states: one state in which myosin is unbound from actin and four actin-bound states ., Because we primarily focus on the force-producing states , we consider only a single , effective unbound state that combines the actin-detached ADP⋅Pi and ATP states ., Each of the actin-bound states is associated with the nucleotide occupancy of the binding pocket of the myosin head ( Fig 1 ) ., Myosin Ic performs its main , 5 . 8 nm power stroke upon phosphate release; a smaller power stroke of 2 nm follows ADP release ., To account for the work done by these power strokes , we include a force dependence of the associated transition rates ., We consider an effectively one-dimensional description in which the force acts along the coordinate of the power stroke: a positive force is oriented in a direction opposite to the power stroke ., The nucleotide-binding rates depend linearly on the nucleotide concentrations and the actin-binding rates increase linearly with the actin concentration ., By cycling through the five states , myosin performs work whose magnitude is bounded by the free-energy input associated with the nucleotide concentrations ., We base our description on the free-energy transduction of enzymes and thus ensure thermodynamic consistency ., To incorporate myosin Ic’s unique force sensitivity , we include a simple force dependence of the rate of unbinding from the filament of myosin in the ATP state ., Under high forces , we expect myosin Ic to be trapped in the ATP state ., Therefore we consider the ADP state ( 3 ) , the nucleotide-free state ( 4 ) , and the ATP state ( 5 ) as strongly bound ., The remaining states are weakly bound or unbound ( Fig 1 ) ., Our description , which captures many of the characteristics of myosin Ic , incorporates as free variables the experimentally controllable quantities external force , nucleotide concentrations , and actin concentration ., This approach allows us to obtain analytic expressions for quantities that have been measured in experiments , then to use that information to determine the unknown parameter values of the model ., An overview of the parameters is given in Table 1 ., A mathematical description of the cross-bridge cycle and details of the estimation of parameter values are presented in the Methods section ., In a single-molecule experiment using an isometric optical clamp , the lifetime of the myosin Ic-actin bond was measured for different external forces and two sets of nucleotide concentrations 20 ., Because a rapid transit into and out of the weakly bound state ( 2 ) could not be resolved experimentally , this bound lifetime must be interpreted as the average time tsb that myosin Ic spends in the strongly bound states ., We determined an analytic expression for the unbinding rate t sb - 1 from the strongly bound states ( Eq 53 ) as functions of force and nucleotide concentrations and fit this function simultaneously to two sets of experimental data acquired for distinct nucleotide concentrations ., This unbinding rate is independent of the transition rate ω15 and of the actin concentration ., Both quantities determine how often the molecule binds to the filament rather than how long it remains bound ., From the average time that myosin Ic resides in the weakly bound states we estimate the binding rate ω15 for an actin concentration of 100 μM appropriate for the experiments ., A detailed explanation for the fitting procedure is given in the Methods section ., Fits of the unbinding rate t sb - 1 from the strongly bound states describe the experimental data well , indicating that our description is able to capture the force sensitivity of myosin Ic ( Fig 2a ) ., Although none of the transition rates can account individually for the plateau around zero force , their combined effect in the cycle clearly displays such a behavior , which is characteristic of myosin Ic ., The numerical values obtained in this way for the transition rates ω21 ≃ 164 s−1 and ω 51 0 ≃ 314 s - 1 suggest that in the absence of force , state ( 2 ) and state ( 5 ) are both configurations from which the myosin head rapidly detaches ., The force-distribution factors ( δ ) indicate that phosphate release is only weakly dependent on force ( δ1 ≃ 0 . 12 ) and ADP release not at all ( δ1 ≃ 0 ) ., The concentrations of nucleotides in cells differ from those in single-molecule experiments ., We can use our description to predict the behavior of myosin molecules for different nucleotide concentrations ., Although in single-molecule experiments the phosphate concentration usually remains low , the phosphate concentration in vivo is on the order of 1 mM 2 ., In cells the ATP concentration is also near 1 mM and the ADP concentration is around 10 μM 2 ., In the remainder of this study we refer to these numbers as the physiological nucleotide concentrations ., The unbinding rate does not significantly change for higher phosphate concentrations ( Fig 2a and 2b ) ., The main reason for this robust behavior is the very low rate constant for phosphate binding ( Eq 38 ) ., Even for a millimolar phosphate concentration the phosphate-binding rate ω32 is very small compared to the other transition rates in the cycle ., In contrast , increasing the ADP concentration decreases the overall binding rate because the molecule spends more time in the ADP state ., This effect can be counteracted by an increase in the ATP concentration ( Fig 2b ) ., Using the formulation given in the Methods section with the explicit solutions in Eqs 45–49 , we can determine the steady-state probability distribution for the cross-bridge cycle at different nucleotide and actin concentrations ( Fig 3 ) ., For physiological nucleotide concentrations and 100 μM of actin , myosin is trapped in the ATP state ( 5 ) under forces exceeding 2 pN ( Fig 3a ) ., Comparing only the strongly bound states , the molecule predominantly occupies the ADP state ( 3 ) for forces smaller than 1 . 5 pN ., According to our description , myosin Ic’s cycle through the strongly bound states is limited by ADP release for forces smaller than 1 . 5 pN and by ATP release for forces larger than 1 . 5 pN ., This result is consistent with experimental findings 19 , 20 ., In the stereocilium of a hair cell , myosin Ic is thought to extend between the crosslinked actin filaments of the cytoskeleton and the insertional plaque to which the tip link is anchored 5 , 6 , 40 ., To analyze the implications of an environment with a high concentration of actin , we determined the probability distribution for an actin concentration of 10 mM ( Fig 3b ) ., Because of the increased binding probability , the unbound state ( 1 ) is depopulated ., The weakly bound ADP⋅Pi state ( 2 ) dominates for forces smaller than 2 pN and the ATP state ( 5 ) for larger forces ., An increased ADP concentration of 250 μM traps the myosin head in the ADP state for forces smaller than 2 pN and larger than 4 pN ( Fig 3c ) ., In the intervening regime the ATP state predominates ., In our stochastic description without irreversible transitions , we define myosin’s effective velocity as the average number of forward power strokes minus the average number of reverse power strokes per time ., We refer to this definition as an effective velocity to emphasize that this quantity is neither the gliding velocity of an actin filament nor the ensemble velocity of several myosin Ic heads cooperating to produce a continuous movement ., Every time the myosin head traverses the states ( 2 ) → ( 3 ) → ( 4 ) it performs a net power stroke of size Δx1 + Δx2 ., In contrast , the reverse pathway ( 4 ) → ( 3 ) → ( 2 ) is associated with a reverse power stroke of size − ( Δx1 + Δx2 ) ., The effective velocity v is accordingly given in terms of the combined local excess fluxes ΔJij ( Eq 26 ) as, v ≡ Δ x 1 Δ J 23 + Δ x 2 Δ J 34 ., ( 1 ) An increasing actin concentration enhances the binding of myosin and therefore decreases its cycling time , which leads to a higher effective velocity ( Fig 4 ) ., The velocity saturates for an actin concentration above 1 mM ., For large forces the effective velocity decreases until it becomes negative for forces larger than the stall force ., According to our thermodynamic description the stall force, F s = k B T Δ x 1 + Δ x 2 ln ATP K eq ADP P i ( 2 ), arises directly from Δμ = Eme , the equality of the Gibbs free energy for the hydrolysis reaction and the mechanical output ., This relation reflects an implicit assumption that all of the chemical energy can be converted into mechanical energy ., To account for mechanical inefficiency , the description could be extended with a loss parameter ., Because we restrict our analysis to forces smaller than 6 pN , for which power strokes have been observed experimentally , we ignore the precise behavior for larger forces and consider the stall force for myosin Ic as an unknown quantity ., A widely accepted definition of the duty ratio is the fraction of the total duration of an ATPase cycle that myosin spends in the strongly bound states 3 , 41–43 ., Ignoring the weakly bound , actin-attached states or combining them into other states , the duty ratio is often defined as the fraction of the total cycle time during which myosin is attached to an actin filament 2 , 44–46 ., Because the initiation of myosin Ic’s power stroke is limited by phosphate release , myosin Ic can bind to actin in the ADP⋅Pi state but detach without proceeding through the cycle if it detaches prior to Pi release ., Such an event contributes to the attachment to the filament but not to the time that the molecule spends in the strongly bound states ., The time that the molecule spends in the strongly bound states therefore differs from that spent attached to the filament ., The probability Psb of occupying the strongly bound states accordingly differs from the probability Pon of being attached to actin ., Our complete cycle description allows us to explicitly calculate both probabilities and to compare them ., We determine Psb in terms of the fraction of the cycle that the molecule spends in the strongly bound states as, P sb ≡ t sb t sb + t wb = ∑ i = 3 5 P i , ( 3 ), in which tsb is the average time spent in the strongly bound states , twb is the average time spent in the weakly bound and detached states , and Pi is the steady-state probability ( Eqs 45–49 ) ., Similarly , we obtain Pon from the fraction of the total cycle time during which the myosin molecule is attached to the filament as, P on ≡ t on t on + t off = ∑ i = 2 5 P i , ( 4 ), in which ton is the average time that myosin is attached to the filament , toff the average time that myosin is detached , and Pi is again the steady-state probability ( Eqs 45–49 ) ., Whereas the former quantity is closely related to the duty ratio , the later quantity is important for estimation of the number of bound molecules in an ensemble ., The probabilities of being attached to actin and of occupying the strongly bound states depend on the ADP concentration , on the available actin , and on the external force ( Fig 5 ) ., In general , because of the catch-bond behavior an increasing force enhances the probability of attachment to actin ., An elevated ADP concentration likewise traps myosin Ic in the strongly bound ADP state and increases both probabilities ( Fig 5a and 5c ) ., An increased accessibility of actin enhances the binding of the myosin head , which results in a high-almost unity-probability of being bound to the filament at high actin concentrations ( Fig 5d ) ., In contrast , the probability of occupying the strongly bound states saturates at a high actin concentration , for entering these states is limited by phosphate release ( Fig 5b ) ., Although in vestibular hair cells myosin Ic activity is required for fast adaptation , the precise molecular details remain unknown 10 ., Here we focus on two aspects that might contribute to the mechanism: the cooperative unbinding of an ensemble of myosin heads under force and a qualitative Ca2+ dependence that changes the binding probability and the elasticity of individual myosin Ic molecules 23 , 24 ., In particular , we determine how these properties influence the overall elasticity of an ensemble ., The myosin heads contribute to the rigidity of the adaptation motor by crosslinking the insertional plaque to the actin cytoskeleton ., We think of each myosin head as a linear spring , arranged in parallel to the others , such that the overall stiffness is given by the sum of the actin-attached myosin heads multiplied by the stiffness of each myosin molecule ., Because the binding and unbinding of the heads depend on the force and the nucleotide and actin concentrations , these quantities also influence the overall elastic properties of the ensemble ., In general the binding process could be very complicated because of the geometry and possible steric interactions between the heads ., Furthermore the helical structure of the actin filaments provides binding sites with an appropriate orientation only about every 37 nm 5 ., These constraints change the number of myosin molecules that can potentially interact with actin ., In our description , the total number of myosin heads is thus an effective number of molecules that can potentially bind to actin ., To estimate the average number of bound myosin molecules in an ensemble , we use the attachment and detachment rates determined from our description of the chemomechanical cycle ., We assume that each myosin head can bind to the filament with a binding rate kon and unbind with an unbinding rate koff ., Both rates stem directly from our description , kon = ω12 + ω15 and koff from Eq 58 ., Because of the stochastic binding and unbinding , the number n of bound molecules fluctuates ., To describe the system as a Markov chain , we introduce a state space ( Fig 6a ) associated with the number of bound myosin heads 47 ., The effective transition rates between these states are, k on n ≡ ( N - n ) k on , ( 5 ), and, k off n ≡ n k off ., ( 6 ), Here kon depends on the actin concentration and koff on the nucleotide concentrations and on the force f per myosin molecule ., We assume that an external force F applied to the ensemble is distributed equally among the attached myosin molecules , resulting in the effective force f = F/n per attached head ., If one head releases from the filament then the force is redistributed among the remaining bound heads and the force per myosin molecule accordingly increases , which changes the unbinding rate koff ., In general this mechanism leads to cooperative effects because the unbinding rate depends on the number n of attached myosin heads ., In the case in which the myosin heads act independently , the transition rates of a single head are independent of the number of attached myosin molecules ., We determine the average number of bound myosin molecules from the linear Markov chain as explained in the Methods section ,, n = ∑ n = 0 N n 1 + ∑ l = 0 N - 1 ∏ i = 0 l k on i k off i + 1 - 1 ∏ j = 0 n - 1 k on j k off j + 1 ., ( 7 ), For the cooperative case in which koff = koff ( F/n ) , we evaluate this equation ., In the independent case , in which the unbinding rate koff is independent of the number of bound myosin heads , we can simplify this expression to, n = N 1 + k off / k on = N t on t on + t off = N P on ., ( 8 ), Note that Pon = Pon ( f ) is a function of the force acting on a single myosin head ., For the independent case , we estimate this force by f = F/N ., However , in this way we underestimate the magnitude of the force per molecule because we expect that N > n ., For a better approximation , we distribute the external force between the mean number of bound motors , f = F/〈n〉 , an approach that leads to an implicit equation for 〈n〉 that is not easy to solve ., For physiological nucleotide concentrations and for 100 μM actin , we notice in Fig 5d that 〈Pon〉 ≈ 0 . 5 ., Using this value , we estimate that in a group of 30 molecules about 〈 n ˜ 〉 ≃ 15 of them are bound on average ., We then approximate the average force on a myosin molecule as f = F / 〈 n ˜ 〉 for the independent case ., Note that in the independent case the force per myosin head does not depend on the number of bound heads , in contrast to the cooperative case ., The mean number of bound myosin heads is influenced by the cooperative release of the molecules and the three approaches are different for intermediate forces ( Fig 6b ) ., We calculate the average number of bound myosin heads as a function of force for different total numbers of myosin molecules ( Fig 7a ) ., In small ensembles , the force per head is higher and therefore more heads are bound as a result of the catch-bond behavior ., Increasing the concentration of available actin causes more myosin heads to attach to the filament ( Fig 7b ) ., To validate our effective description , we compare our analytic results to Monte Carlo simulations as detailed in the Methods ., In these simulations , each myosin head is represented as a spring that is attached to a rigid common structure ., At each time step of the simulation the extensions of all springs are calculated by solving Newton’s law of force balance ., In this way , we obtain for each myosin head a force that determines the transition rates of the chemomechanical cycle of that molecule ., There are important differences from the analytic approach ., Whereas in the simulation a myosin head proceeds stochastically through the five-state chemomechanical cycle , the heads only bind and unbind in the analytic description ., As a consequence the myosin molecules step stochastically and exert fluctuating forces on each other , which in turn influences their dynamics ., In our analytic model , the myosin heads are only indirectly coupled through the number of bound motors and not through an elastic interaction ., The simulations show reasonable agreement with the analytic results ( Figs 6b and 7 ) ., An increased coupling stiffness increases the forces between the myosin heads , which in turn result in a longer attachment because of the catch-bond behavior ( Fig 6b ) ., Especially for a high actin concentration , the agreement between the simulations and the analytic description is very good ., In the following , we will focus on this particular case and therefore consider only the analytic description ., These results show that the average number of bound myosin heads depends on the external force , the total number of myosin molecules , the actin concentration , and-not shown here-the nucleotide concentrations ., We expect that the mechanical properties of a cellular structure including myosin Ic molecules also depend on these quantities ., To investigate the elastic properties of an ensemble of myosin Ic heads , we determine the force-extension relation, F = n κ x , ( 9 ), in which κ is the spring constant of a single myosin head ., The underlying assumption of this approach is a linear force-extension relation of the individual myosin heads , for which we take the value of κ = 500 μN/m 21 ., Applying forces below 20 pN to the ensemble leads to an extension smaller than 5 nm ( Fig 8a ) ., A reduced total number N of myosin molecules increases the extension because the force per myosin head is larger and stretches it farther ., To test whether a mechanical release of myosin Ic molecules is related to fast adaptation , we investigate two qualitative effects of Ca2+ ., First , Ca2+ could decrease the binding probabilities of the myosin head to actin 23 ., Second , it could change the stiffness of myosin by initiating the dissociation of one or more calmodulin molecules from the light chains , allowing the myosin molecules to attain a more flexible conformation 24 ., We next consider the mechanical release owing to Ca2+ binding ,, Δ x ≡ F 1 κ Ca 2 + n Ca 2 + - 1 κ n ., ( 10 ), We first study the effect of a reduced binding probability on the mean number of bound myosin molecules and maintain their stiffness before and after Ca2+ binding , κ Ca 2 + = κ = 500 μ N/m ., We reduce the binding probability by the factor β and determine the resulting release for N = 10 or N = 20 myosin molecules ( Fig 8b ) ., A large decrease of the binding probability leads to fewer bound molecules and a larger release ., The release for a group of 10 myosin molecules exceeds that for an ensemble of 20 molecules: the force on each individual myosin head is higher and stretches the molecule farther ., However , the overall distance for forces smaller than 20 pN is still less than 20 nm ., When we add to the 100-fold decrease of the binding probability a tenfold decrease of myosin’s elasticity and determine the resulting release for different total numbers of myosin molecules ( Fig 8c ) , the displacement is of the order of several tens of nanometers and becomes almost insensitive to force for a group of 50 myosins ., An important goal of biology is understanding how the structures and interactions of molecules result in measurable functions of cells and organisms ., By combining findings on different spatial scales in a consistent manner , mathematical descriptions help us understand how physiologically relevant function is determined by the interplay of molecular components ., We have constructed a quantitative description of myosin Ic’s chemomechanical cycle and studied the resulting properties at both a single-molecule and an ensemble level , which allows us to discuss important implications on the physiological function of hair cells at the whole-cell level ., On the single-molecule level , it is important to understand how different members of the large myosin family display distinct biophysical properties despite a common general structure of the chemomechanical cycle ., To describe myosin Ic , we constructed such a cycle and chose as control parameter the nucleotide concentrations and the external force , both of which are experimentally accessible and biologically relevant ., Our simplified , one-cycle description reproduces many of the characteristic features of myosin Ic , especially the force-dependent exit from the strongly bound states ., The probabilities of occupying the different states indicate that myosin Ic’s strongly bound states are dominated by the ADP state for forces below 1 . 5 pN and by the ATP state for larger forces ( Fig 3 ) ., Although this behavior is in contrast to previous models in which the ADP state is the only force-sensitive state , it is nevertheless consistent with the role of myosin Ic in adaptation 5 , 21 ., Increasing the ADP concentration traps the myosin heads in the ADP state , bound to actin filaments ( Figs 3 and 2b ) ., This effect can be reversed by increasing the ATP concentration ( Fig 2b ) ., Such a behavior accords with recordings of transduction currents in hair cells isolated from the bullfrog: changing nucleotide concentrations alters the relative occupancy of the states in the cross-bridge cycle and thus the number of bound myosin molecules , which in turn controls the tension on the mechanically sensitive ion channels ., Indeed , in the presence of an ADP analog , adaptation disappears and the tension on the channels increases ., Both effects can be reversed by increasing the concentration of ATP 48 ., This qualitative agreement constitutes direct evidence that the model , although constructed from single-molecule measurements in vitro , captures important aspects of the behavior of living cells ., Our description suggests a low effective velocity for myosin Ic ., Although velocities of only tens of nanometers per second have been reported from motility experiments in vitro 10 , 49–51 , larger values have been discussed 5 ., In motility assays , multiple myosin molecules work together to create motion ., How the velocity measured in motility experiments is related to the effective rate of a cross-bridge cycle and to other biophysical parameters of the molecules is an open question 52–56 ., However , our stochastic simulations suggest that 200 elastically coupled myosin Ic molecules , each described by the five-state chemomechanical cycle , display a motility rate of 25 nm⋅s-1 which is in good agreement with the experimental values of 16–22 nm⋅s-1 51 ., In these experiments the myosin molecules where coupled through a membrane ., Greater speeds of 60 nm⋅s-1 have been reported in gliding assays , but the data were acquired at a temperature of 37°C 22 , whereas the numerical values of the biochemical rates of our model stemmed from experiments conducted at 20°C ., We conclude that our description of myosin Ic constrained by single-molecule data accords with the experimental data on a larger scale ., Speeds of tens of nanometers per second are too low to be consistent with rates estimated for the adaptation motor in the inner ear , which has been associated with the function of myosin Ic 5 , 10 ., Depending on the species , the velocity of the adaptation motor ranges from several hundred to a few thousand nanometers per second 5 , 57 , 58 ., The discrepancy between the velocities in vivo and in vitro might stem from several factors ., It is still unknown to what extent these rates relate to the speed of myosin Ic molecules and to relaxations of other elastic elements ., It has been suggested that the recoil of an elastic element located pa

Introduction, Results, Discussion, Methods

Myosin Ic is thought to be the principal constituent of the motor that adjusts mechanical responsiveness during adaptation to prolonged stimuli by hair cells , the sensory receptors of the inner ear ., In this context myosin molecules operate neither as filaments , as occurs in muscles , nor as single or few molecules , as characterizes intracellular transport ., Instead , myosin Ic molecules occur in a complex cluster in which they may exhibit cooperative properties ., To better understand the motor’s remarkable function , we introduce a theoretical description of myosin Ic’s chemomechanical cycle based on experimental data from recent single-molecule studies ., The cycle consists of distinct chemical states that the myosin molecule stochastically occupies ., We explicitly calculate the probabilities of the occupancy of these states and show their dependence on the external force , the availability of actin , and the nucleotide concentrations as required by thermodynamic constraints ., This analysis highlights that the strong binding of myosin Ic to actin is dominated by the ADP state for small external forces and by the ATP state for large forces ., Our approach shows how specific parameter values of the chemomechanical cycle for myosin Ic result in behaviors distinct from those of other members of the myosin family ., Integrating this single-molecule cycle into a simplified ensemble description , we predict that the average number of bound myosin heads is regulated by the external force and nucleotide concentrations ., The elastic properties of such an ensemble are determined by the average number of myosin cross-bridges ., Changing the binding probabilities and myosin’s stiffness under a constant force results in a mechanical relaxation which is large enough to account for fast adaptation in hair cells .

Myosin molecules are biological nanomachines that transduce chemical energy into mechanical work and thus produce directed motion in living cells ., These molecules proceed through cyclic reactions in which they change their conformational states upon the binding and release of nucleotides while attaching to and detaching from filaments ., The myosin family consists of many distinct members with diverse functions such as muscle contraction , cargo transport , cell migration , and sensory adaptation ., How these functions emerge from the biophysical properties of the individual molecules is an open question ., We present an approach that integrates recent findings from single-molecule experiments into a thermodynamically consistent description of myosin Ic and demonstrate how the specific parameter values of the cycle result in a distinct function ., The free variables of our description are the chemical input and external force , both of which are experimentally accessible and define the cellular environment in which these proteins function ., We use this description to predict the elastic properties of an ensemble of molecules and discuss the implications for myosin Ic’s function in the inner ear as a tension regulator mediating adaptation , a hallmark of biological sensory systems ., In this situation myosin molecules cooperate in an intermediate regime , neither as a large ensemble as in muscle nor as a single or a few molecules as in intracellular transport .

stiffness, cell motility, mechanical properties, actin filaments, classical mechanics, chemical compounds, phosphates, nucleotides, mechanical energy, molecular motors, actin motors, mathematics, statistics (mathematics), materials science, motor proteins, research and analysis methods, contractile proteins, proteins, mathematical and statistical techniques, chemistry, monte carlo method, physics, biochemistry, cytoskeletal proteins, cell biology, myosins, biology and life sciences, physical sciences, material properties, statistical methods

journal.ppat.1006702

Estimating virus effective population size and selection without neutral markers

Evolution in isolated populations results from the interplay between several forces , including mutation , selection , and genetic drift ., Mutation creates genetic diversity within a population ., Subsequent selection and genetic drift drive the evolution of diversity within the population ., Selection is a deterministic force that increases the frequency of the fittest variants at the expense of the weakest ones ., It can be characterized by the selection coefficient s , commonly calculated , at a specific locus , as the relative difference in fitness conferred by two alleles ., Genetic drift , unlike selection , acts equally on all variants ., It is the outcome of random sampling effects between generations , resulting in stochastic fluctuations in variant frequencies 1 ., The strength of genetic drift is frequently evaluated by determining the effective population size Ne 1 ., Ne is defined as the size of an ideal panmictic population of constant size with non-overlapping generations that would display the same degree of randomness in allele frequencies as the population studied 2 ., Ne is often much lower than the census population size 3 , 4 , but it can be seen as its evolutionary analog 5 ., When Ne is small , sampling effects are magnified between generations , and allele frequencies therefore fluctuate strongly ., For populations varying in size over time , the effective population size over a given number of generations can be approximated by the harmonic mean N ¯ e of effective population sizes at each generation ., This approximation holds provided that the number of generations is much smaller than N ¯ e 6–8 and that mutation can be neglected 9 ., Population size may vary over time due to bottlenecks , which are common in natural populations ., As they greatly decrease population size , they have a disproportionate effect on the overall value of N ¯ e 1 ., When selection and genetic drift act simultaneously , the probability of fixation of a new mutation ( with a selection coefficient s ) , and , more generally , its evolutionary dynamics , is controlled by the product Ne × |s| 1 , 10 ., If Ne × |s| ≪ 1 , then genetic drift predominates over selection and evolution is mostly stochastic ., If Ne × |s| ≫ 1 , then selection becomes effective and evolution is mostly deterministic 10 ., This rule of thumb can be applied to the evolutionary dynamics of pathogen variants of biomedical or ecological interest , during the course of infection of a single host , for microbe variants escaping the immune response of their host , or becoming resistant to drug therapy ( e . g . 11 ) or , in the case of plant pathogens , for variants adapting to host resistance genes ( e . g . 12 ) ., In this study , we combined high-throughput sequencing ( HTS ) with experimental evolution to measure the within-host dynamics of five variants of Potato virus Y ( PVY , genus Potyvirus , family Potyviridae ) in closely related plant genotypes 13 ., It remains challenging to unravel the effects of genetic drift and selection in the absence of neutral markers , in studies of the adaptation dynamics of pathogens ., This situation is frequently encountered for pathogens with small genomes , especially viruses 14 , 15 ., Various approaches based on moment 16 , 17 or likelihood 18–20 methods have been proposed for estimating Ne , but all require the genetic markers studied to be neutral ., Various methods have also been proposed for detecting selection and estimating selection coefficients ., These methods require at least some prior information about Ne ( e . g . 21 ) or assume that genetic drift is negligible ( e . g . 22 ) ., However , in the absence of neutral markers and without prior estimates of Ne , both selection and genetic drift must be taken into account , as these two forces act simultaneously on evolution ., This greatly complicates the estimation of Ne and s ., Only a few methods have been proposed for the joint estimation of Ne and s from time-sampled data ( see 11 and 23 for a review ) ., For large effective population sizes ( typically Ne > 5000 ) and small selection coefficients ( typically |s| < 0 . 01 ) , several likelihood methods based on diffusion approximations of the Wright-Fisher model 1 , 11 are available 24–27 ., In the situations in which these methods are valid , the ranges of Ne and s values obtained are rather restrictive for many microorganisms , particularly viruses 28–31 ., Foll et al . 32 recently proposed the use of approximate Bayesian computation ( ABC ) for the joint estimation of Ne and s in a Wright-Fisher model ., Their method can deal with both weak and strong selection regimes , but still requires multilocus genome-wide data with mostly neutral loci to estimate Ne accurately ., In this study , we investigated the evolutionary dynamics of five variants of PVY in 15 closely related pepper genotypes ., All plants were inoculated with the same mixture of virus variants and variant frequencies were determined with HTS in eight plants of each genotype at each of six sampling dates after inoculation ., A diverse range of evolutionary patterns was observed ., We developed a method for estimating the parameters of a multi-allelic Wright-Fisher model with selection and genetic drift , to investigate the underlying evolutionary processes ., This method has two main advantages: it applies to a large range of selection and genetic drift intensities and it works efficiently in the absence of neutral markers ., The parameters of the Wright-Fisher model ( i . e . selection coefficients for each virus variant and effective population sizes at given time points ) can be estimated by coupling maximum likelihood and ABC methods and applying them to a set of variant frequencies determined at several time points in independent hosts ., We tested the method with numerical simulations mimicking the datasets obtained with HTS in evolve-and-resequence experiments 33 ., The simulations covered an extensive range of Ne and s values ., We were then able to estimate the selection coefficient of each PVY variant in each pepper genotype and the changes in effective population size over time during the colonization of the plant by the virus ., Finally , by varying pepper genotypes and fixing the initial PVY population , we provided evidence that the effective population size of PVY is a heritable plant trait ., This finding paves the way for the breeding of plant cultivars exposing viruses to greater genetic drift and/or smaller selection effects ., We developed a method for estimating the parameters of a multi-allelic Wright-Fisher model with selection and genetic drift for a haploid population ., The parameters and state variables of the model and the observed variables are summarized in Table 1 ., Before using the estimation method on the datasets corresponding to the biological experiment , we performed several batches of simulations to assess its ability to infer effective population sizes and selection coefficients accurately ( see S2 Text for details ) ., Briefly , in experiment 1 , we first simulated the changes in frequencies of five virus variants under 750 selection and genetic drift regimes with a Wright-Fisher model for haploid individuals ., The simulations were designed to fit the experimental setup of our datasets ( 48 independent host plants regularly analyzed at 6 sampling dates ) ., For each of the 750 datasets obtained , the true parameters θtrue were known and could be compared with the estimated parameters θ ^ ., In experiment 2 , we assessed the sensitivity of the estimation method to the presence of a sixth undetected virus variant ., This sixth variant was selectively neutral ( its selection coefficient is zero ) , present in the inoculum at a frequency of 3% and still present at the last sampling date ( 34 dpi ) in all plants analyzed , at frequencies ranging from 1% to 6% ., It affected the dynamics of the five variants of interest in all plants but was not detected , so the variant frequencies measured by HTS ( and used to estimate θ ^ ) are noisy with respect to their true values ., In all , 350 simulated datasets were analyzed in this second test ., The frequencies of the five virus variants were assessed in completely isolated populations during the course of infection , in 15 different plant genotypes ., For each of these 15 pepper genotypes , 48 plants were inoculated with the same equimolar mixture of the five variants , and the frequencies of the virus variants were determined in eight plants at each of six sampling dates , from 6 to 34 days post-inoculation ( Fig 2 ) ., In a few cases , no viruses were detected in plant samples ( lacking bars in Fig 2 ) ., These negative samples may reflect the presence of an extreme bottleneck at inoculation , leading to virus population extinction , or a long time lag to systemic infection of the plant ( for measurements from 10 to 34 dpi ) , resulting in the sampling of leaves not yet infected ( e . g . DH line 2321 ) ., Negative samples were most frequent for the first two dates on which systemically infected leaves were analyzed , i . e . at 10 and 14 dpi , probably indicating a time lag to systemic infection in some DH lines ., Negative samples were observed in only four DH lines ( e . g . DH lines 219 and 2321 ) ., No infection was observed in a mean of 3 . 5 ( resp . 2 . 0 ) plant samples 10 ( resp . 14 ) dpi for the four DH lines concerned ., The virus populations present in all infected plants and in the common inoculum were analyzed by HTS , to determine the frequencies of the five PVY variants ., Inoculum analysis confirmed that all variants were present in roughly equimolar proportions , with 22 . 6% of variant G , 17 . 5% of N , 20 . 6% of K , 17 . 1% of GK and 22 . 2% of KN ., The raw data for variant frequency dynamics provided considerably different patterns between the 15 pepper genotypes ( Fig 2 , S2 and S3 Figs ) ., Variant frequencies were similar between virus populations sampled on the same date in some plant genotypes , consistent with weak genetic drift ( e . g . DH lines 240 and 2430 , Fig 2A and 2B ) , whereas they differed in other plant genotypes ( e . g . DH lines 2321 and 219 , Fig 2D and 2E ) ., Furthermore , the heterogeneity of variant frequencies between the eight plants analyzed fluctuated between dates , probably due to changes in effective population size during the course of infection ( e . g . DH line 2344 , Fig 2C ) ., The four pepper genotypes for which some samples were virus-negative were also characterized by the highest heterogeneity in variant frequencies , consistent with an extreme bottleneck at inoculation and/or during systemic movement of the virus ( see DH lines 2321 , 219 , 2256 and 2400 in Fig 2D and 2E , S2D and S3I Figs ) ., Selection regimes also differed between lines ., In some DH lines , all variants remained present at all dates ( e . g . DH line 240 , Fig 2A ) , whereas one variant ( e . g . DH line 219 , Fig 2E ) , or up to two variants ( e . g . DH lines 2430 , 2344 , 2321 , Fig 2B–2D ) became extinct in others ., Before its application to the experimental dataset , we validated the estimation method proposed by numerical simulations of a Wright-Fisher model with selection and genetic drift for haploid individuals ., We estimated the Ne ( t ) and ri of the PVY populations in each DH line with a Wright-Fisher model including selection and genetic drift ., By contrast to the numerical experiments , the evolutionary parameters underlying the true dynamics of virus populations in their hosts were unknown ., The Wright-Fisher model fitted the data very satisfactorily ( Fig 5 ) ., The best-fit line between observed and fitted mean variant frequencies ( averaged over all virus populations and sampling times ) was very close to the first bisector ( Fig 5A; slope = 0 . 92 , intercept = 0 . 01 , R2 = 0 . 92 ) ., This was also the case for the variability of variant frequencies between virus populations at each sampling date td ( Fig 5B; slope = 0 . 92 , intercept = -0 . 09 , R2 = 0 . 84 ) ., A Wright-Fisher model including selection and genetic drift accurately described the mean evolutionary dynamics of a virus population and the variability of these dynamics between hosts ., Due to an identifiability issue ( we observed the relative frequencies of variants rather than variant densities ) , we had to fix the number of generations per day γ ., We set this number to 1 , a value close to that reported by Khelifa et al . 40 ., Different γ values would change ri and Ne ( t ) estimates to r i 1 / γ and γNe ( t ) , but would have no effect on their ranking ., Relative fitness values ( ri ) ranged from 0 . 43 to 1 . 25 ( corresponding to |s|: 5% quantile = 0 . 004 , mean = 0 . 12 , 95% quantile = 0 . 27 ) and were associated with narrow 90% confidence intervals ( S3 Table ) ., The fitness ranks of the PVY variants were very similar in most DH lines ( Fig 6A and 6C ) ., Variant G was the weakest in all DH lines , followed by variant N in 13 DH lines ., Variant GK was the fittest variant in 13 DH lines , with variant K the fittest variant in the remaining two lines ( DH lines 2256 and 2430 ) ., Overall , variants K and GK were the two fittest variants in 12 DH lines; variants GK and KN were the two fittest in DH lines 2349 and 2321 , and variants N and GK the two fittest in DH line 219 ., The fitness difference between the weakest and the fittest variants ranged from 0 . 14 for DH line 219 to 0 . 81 for DH line 2349 ., We further estimated the dynamics of effective population size over the time course of the experiment , as modeled by a piecewise function Ne ( t ) , using a model selection procedure ., Four models with one to four parameters were considered ., The most general model M 4 distinguished four successive effective population sizes ( one in the inoculated organ and three during systemic infection ) ., M 4 was the model best supported by the data for five DH lines ( 2173 , 2321 , 2328 , 2344 and 2367 ) ., Model M 3 distinguished three successive effective population sizes ( one in the inoculated organ and two during systemic infection ) ., It was best supported by the data for five DH lines ( 219 , 221 , 2256 , 240 and 2430 ) ., Model M 2 , which distinguished two successive effective population sizes ( one in the inoculated organ and one during systemic infection ) , was selected for a single DH line ( 2426 ) ., Finally , with M 1 , the effective population size of the virus population remained constant ., This model was selected in the four remaining DH lines ( 2123 , 2264 , 2349 and 2400 ) ., The corresponding posterior probabilities of each model are shown in S4 Table , together with effective population size estimates and 90% credibility intervals ., At the first sampling date , considerable variability was observed ( Fig 6B and 6D ) , with effective population sizes ranging from 13 for DH lines 219 and 2256 to 1515 for DH line 240 ., This was not surprising , given that we chose the DH lines on the basis of the density of primary infection foci in inoculated organs 34 ( S1 Fig ) ., A much narrower range of effective population sizes , from 18 to 462 , was observed across all plant genotypes at 10 dpi , the first date on which systemic infection was observed ., From 6 to 10 dpi , effective population sizes decreased in eight DH lines ( Fig 6B ) , remained approximately constant in six DH lines ( Fig 6D ) and increased slightly in a single plant genotype ( DH line 2173 , Fig 6D ) ., Later on , from 10 to 34 dpi , effective population size increased in eight DH lines ( mostly DH lines displaying a bottleneck from 6 to 10 dpi , Fig 6B ) and remained approximately constant in the others ( mostly in DH lines with lower , i . e . < 500 , effective population sizes in the inoculated organ , Fig 6D ) ., By creating two dataset replicates of 24 randomly chosen plants for each DH line , we estimated the heritability of two plant traits corresponding to the evolutionary forces exerted by the plant on virus populations: selection and genetic drift ., These forces were estimated by, ( i ) intrinsic rates of increase in viral variants and, ( ii ) effective population sizes for PVY ., With 24 plants in each dataset , we used the function Ne ( t ) of model M 2 with two parameters ., In this approach , we used the contrasting behavior of PVY populations , which were fixed and identical at the time of inoculation in all plants , on different pepper genotypes to characterize the phenotype of each host ., Very high heritability estimates were obtained for the intrinsic rates of increase ( mean heritability over the five variant estimates: h2 = 0 . 94 ) ., Somewhat lower , but nevertheless substantial heritability estimates were obtained for effective population size in the inoculated organ ( mean heritability , h2 = 0 . 64 ) and for effective population size during systemic infection ( mean heritability , h2 = 0 . 63 ) ., The details of the calculation are provided in S3 Text ., We present here a method for the estimation of selection and genetic drift in a haploid and asexual organism , as modeled by a Wright-Fisher process ., As for any model-based approach , the population of interest must not be too far from an ideal Wright-Fisher population with suitable parameters 10 ., The estimation method did not require neutral markers ., It was validated for small effective population sizes ( Ne ≪ 100 ) and a wide range of both positive and negative selection coefficients ( weak ( |s| ≃ 0 . 01 ) or strong ( |s| ≃ 0 . 15 ) selection ) , using simulated datasets ., Recent reviews 23 , 32 have highlighted the small number of methods available for the inference of selection and genetic drift over the whole range of variation , particularly in the case of small effective population sizes ( Ne ≪ 1000 ) and strong selection coefficients ( |s| ≃ 0 . 1 ) ., Indeed , these conditions do not fulfill the hypotheses underlying most approximations of the Wright-Fisher model ., The classical approximation , with a standard diffusion process , requires both selection and genetic drift to be weak 23 ., Approximations based on Gaussian diffusion require the stochastic effects of genetic drift to decrease more rapidly than the effects of selection 23 ., The work of Foll et al . 11 , 32 constituted a major step forward , but their method requires a large proportion of the genetic markers studied to be neutral ., This assumption is not valid for many pathogens with small genomes , such as viruses ., For example , only 22 . 7% of 66 randomly chosen mutations in the genome of Tobacco etch virus ( TEV , genus Potyvirus ) , a plant RNA virus , were found to be consistent with neutrality 45 ., As the statistical power to detect departure from neutrality is limited , the true proportion of neutral mutations is probably much lower ., Similar results have been obtained for bacteria ( e . g . 46 ) ., The estimation method proposed does not require neutral markers , an appealing feature for studying pathogens with small genomes ., Lacerda and Seoighe 47 recently developed another method that does not require neutral markers ., Their method provided satisfactory estimates of both Ne and s ( estimated at a single locus ) for a relatively small effective population size of 1000 individuals and values of s up to 0 . 5 ., They did not test the performance of their method for Ne ≪ 1000 ., By comparison , the method developed here was effective for much lower Ne values , in the range of a few tens of individuals , and for inferring the time course of Ne over a few tens of generations ., However , although the range of selection coefficients s included cases of strong selection ( |s| ≃ 0 . 1 , as defined by Malaspinas 23 ) , none of the simulation experiments included values as high as 0 . 5 ., It may be possible to infer such high selection coefficients with the estimation method proposed , provided that the first generations are sampled more densely , typically every day after inoculation in our set-up ., Lacerda and Seoighe 47 , for example , used samples taken at each generation , for 20 generations ., This makes it possible to record the trajectories of variant frequencies before variant loss or fixation ., The use of the proposed estimation method requires observation of the evolution of isolated populations derived from the same parental population , each population being sampled only once ., This design is particularly suitable for studying within-host microbial evolution when several genetically-identical hosts ( 48 plants for each pepper genotype in our case study ) can easily be included in the experiment ., With this experimental design , we observed a set of variant frequencies at several time points , in independent hosts ., This set contained footprints of selection and genetic drift ., In the method developed , selection is evaluated from the mean trajectories of variant frequencies ., Genetic drift is evaluated at several time points , by assessing differences in variant frequencies between the replicated populations during the time-course of the experiment ., Even for populations with small effective sizes , for which genetic drift and selection have confounding effects on the fate of variants ( Fig 2 ) , a moderate number of replicates contains sufficient information to disentangle the two mechanisms ., Here , we estimated four selection coefficients and four effective population sizes ( i . e . 8 parameters ) with 48 samples ( 6 sampling dates × 8 replicates ) ., The proposed estimation method could be improved further ., It explicitly accounts for the technical sampling noise resulting from the assessment of variant frequencies from finite counts of virus sequences ., However , HTS also introduces sequencing errors , albeit at a low rate of about 1 substitution per 400 bases for MiSeq technology 48 , which were not explicitly accounted for in our framework ., Several models have been proposed for separating true genetic variation from technical artifacts 48 , and these models could be integrated into the method through a hierarchical Bayesian modeling framework 49 , for example ., Finally , the method could be extended to take mutation and recombination into account , particularly for experiments over longer periods , in which new variants might appear and displace those currently most abundant ., In our short-term experiment , we have already observed de novo substitutions in a few plants ( removed plant samples , see S1 Text ) ., The inclusion of recombination is not relevant for our case study , as the nucleotide positions differentiating the variants are located only a few codons apart ., Recombination can thus be ignored in this study 50 , particularly given the small number of generations considered 32 ., On the host side , our experiment involved 15 DH lines of pepper , all carrying the major resistance gene pvr23 , but differing in terms of their genetic backgrounds 12 ., These DH lines were derived from the F1 hybrid between two pepper lines , Perennial and Yolo Wonder ., Consequently , on average , any pair of DH lines have 50 percent of alleles in common for markers differentiating between Perennial and Yolo Wonder ., This is the first study , to our knowledge , to show such a high level of diversity in selection and genetic drift regimes experienced by virus populations from the same viral inoculum in closely related host genotypes ( Fig 2 , S2 and S3 Figs ) ., On the pathogen side , we used five virus variants: the G and N variants displayed weaker adaptation to pvr23 than the K , GK and KN variants ., The ranking of the selection coefficients of the five variants was mostly identical in the 15 plant genotypes ., We were therefore unable to identify any host genotype , among those tested , able to counterselect against the virus variants best adapted to pvr23 ., This may be due to, ( i ) the strong selective effect exerted by the major-effect resistance gene pvr23 , which is present in all the DH lines studied here and probably exceeds the additional selective effect of the plant genetic background and/or, ( ii ) the close genetic relatedness of the DH lines analyzed ., Other genetic resources for pepper should be explored , to identify genotypes capable of counterselecting against the K , GK and KN variants , which were the fittest in our study ., The best candidates for this would be pepper genotypes carrying pvr2 resistance alleles other than pvr23 , with a different specificity in the face of PVY diversity 51 , or pepper genotypes devoid of resistance alleles at the pvr2 locus , as shown by Quenouille et al . 12 ., Combinations of plant genotypes exerting opposite selective pressures on pathogen populations are particularly interesting for the sustainable management of plant resistance at landscape level , and can be implemented in cultivar rotations , mixtures or mosaics 52 ., However , in our study , the difference in fitness between the weakest and fittest variants differed between host genotypes ., The dynamics of selection for the fittest variants were under plant genetic control and could therefore be modulated by the choice of plant genotypes grown ., For example , growing the pepper DH lines with the smallest differential selection between the five PVY variants would be particularly useful for delaying PVY adaptation in pvr23-carrying plants , in which a two-step mutational trajectory may be required 12 ., Indeed , the G and N variants are most likely to appear initially , because they require transitions , whereas the K variant requires a transversion , and transitions are more frequent than transversions 53 ., However , an additional substitution , in a second step , is required to confer a sufficient level of fitness for the emergence of GK and KN variants ., These mutational trajectories were observed in PVY adaptation to the Perennial pepper genotype , the resistant parent of all the DH lines studied here 12 ., We also inferred the time course of the genetic drift experienced by the viruses in the 15 host environments during the experiment ., Genetic drift intensities were highly variable with time and between plant genotypes , revealing an unprecedented level of variability between closely related host genotypes ., Our estimates of Ne ( t ) ranged from 18 to 462 just after the colonization of apical leaves at 10 dpi , and from 13 to 1515 in the inoculated leaves four days previously ( at 6 dpi ) ., Eight of the 15 DH lines displayed a high Ne in the inoculated leaves at 6 dpi ( from 421 to 1515 ) , a decrease at 10 dpi ( Ne ( 10 dpi ) values of 1 . 5 to 83 . 5% of the value at 6 dpi ) and a subsequent increase ( Fig 6B ) ., This pattern suggests a founder effect , in which a new PVY population in apical leaves is set up by a few members of the original population in the inoculated leaf ., In the remaining seven DH lines , the Ne of the inoculated leaves at 6 dpi was much lower ( from 13 to 462 ) , and Ne values often remained low in the apical leaves ( Fig 6D ) ., However , an increase in Ne was observed in DH lines 219 and 2173 , after 14 dpi ., This result sheds new light on the importance of the within-host bottlenecks experienced by virus populations , as discussed in a recent article by Zwart et al . 54 , who reported that the Ne of TEV in the first systemically infected leaf of tobacco plants was determined largely by inoculum viral load ., They then hypothesized that genetic drift occurred mostly during the inoculation process ., Previous estimations of Ne for viruses did not focus on Ne dynamics at the whole-plant level as in this study ., Instead , they considered the multiplicity of infection ( MOI ) during cell-to-cell movement or Ne during the colonization of apical leaves ( for a comprehensive review , see Gutiérrez et al . 4 ) ., Direct comparisons with these studies are , therefore , not appropriate ., Gutiérrez et al . 55 recently showed that Turnip mosaic virus ( genus Potyvirus ) infections are characterized by a very low MOI ( ≃, 1 ) when cells are infected with virus particles moving in the plant vasculature , and a much higher MOI ( ≃ 30 ) during subsequent cell-to-cell movement in the mesophyll ., The general picture that emerges when we consider both these MOI patterns and plant growth dynamics is consistent with our observations ., Indeed , the lowest Ne values were observed at 10 dpi , corresponding to the onset of systemic infection , when plants were small and consisted essentially of a few infected leaves ., Ne tends often to increase with time , because, ( i ) increasing numbers of leaves are infected and behave as virus sources as the plant grows and, ( ii ) leaf areas increase , probably increasing the relative proportion of cell-to-cell , as opposed to long-distance , virus movement ., One of the key results of this study is the finding that the effective population size of PVY is a heritable plant trait ., The high heritability estimated for Ne ( partially due to the use of a DH progeny of pepper genotypes ) indicates that plant resistance could potentially be improved through breeding programs ., Indeed , our findings pave the way for the breeding of plant cultivars exposing viruses to greater genetic drift ., This would provide a twofold benefit against viruses ., First , in asexual populations , genetic drift favors the accumulation of deleterious mutations , decreasing viral fitness ( Muller’s ratchet ) 56 ., Second , genetic drift decreases the fixation probability of beneficial mutations , such as those responsible for overcoming plant resistance genes 57 ., Breeding for greater genetic drift in virus populations would thus constitute a novel approach to increasing the durability of resistance to plant viruses in agricultural landscapes 52 , 58 , 59 ., Another key result is the finding that the Wright-Fisher model accurately captures the major processes driving the within-host dynamics of a set of virus variants ( Fig 5 ) , despite being much simpler than the underlying mechanisms involved in the infection of highly structured hosts ., Over longer periods , mutation and recombination increase in importance and this can easily be encompassed in the Wright-Fisher model 60 ., This model can thus serve as a valuable cornerstone for linking the within- and between-host scales of disease dynamics and studying , for example , how breeding for greater genetic drift can delay the emergence of a new pathogen variant .

Introduction, Materials and methods, Results, Discussion

By combining high-throughput sequencing ( HTS ) with experimental evolution , we can observe the within-host dynamics of pathogen variants of biomedical or ecological interest ., We studied the evolutionary dynamics of five variants of Potato virus Y ( PVY ) in 15 doubled-haploid lines of pepper ., All plants were inoculated with the same mixture of virus variants and variant frequencies were determined by HTS in eight plants of each pepper line at each of six sampling dates ., We developed a method for estimating the intensities of selection and genetic drift in a multi-allelic Wright-Fisher model , applicable whether these forces are strong or weak , and in the absence of neutral markers ., This method requires variant frequency determination at several time points , in independent hosts ., The parameters are the selection coefficients for each PVY variant and four effective population sizes Ne at different time-points of the experiment ., Numerical simulations of asexual haploid Wright-Fisher populations subjected to contrasting genetic drift ( Ne ∈ 10 , 2000 ) and selection ( |s| ∈ 0 , 0 . 15 ) regimes were used to validate the method proposed ., The experiment in closely related pepper host genotypes revealed that viruses experienced a considerable diversity of selection and genetic drift regimes ., The resulting variant dynamics were accurately described by Wright-Fisher models ., The fitness ranks of the variants were almost identical between host genotypes ., By contrast , the dynamics of Ne were highly variable , although a bottleneck was often identified during the systemic movement of the virus ., We demonstrated that , for a fixed initial PVY population , virus effective population size is a heritable trait in plants ., These findings pave the way for the breeding of plant varieties exposing viruses to stronger genetic drift , thereby slowing virus adaptation .

A growing number of experimental evolution studies are using an “evolve-and-resequence” approach to observe the within-host dynamics of pathogen variants of biomedical or ecological interest ., The resulting data are particularly appropriate for studying the effects of evolutionary forces , such as selection and genetic drift , on the emergence of new pathogen variants ., However , it remains challenging to unravel the effects of selection and genetic drift in the absence of neutral markers , a situation frequently encountered for microbes , such as viruses , due to their small constrained genomes ., Using such an approach on a plant virus , we observed that the same set of virus variants displayed highly diverse dynamics in closely related plant genotypes ., We developed and validated a method that does not require neutral markers , for estimating selection coefficients and effective population sizes from these experimental evolution data ., We found that the viruses experienced considerable diversity in genetic drift regimes , depending on host genotype ., Importantly , genetic drift experienced by virus populations was shown to be a heritable plant trait ., These findings pave the way for the breeding of plant varieties exposing viruses to strong genetic drift , thereby slowing virus adaptation .

organismal evolution, plant anatomy, population genetics, variant genotypes, microbiology, genetic mapping, plant science, effective population size, microbial evolution, population biology, leaves, evolutionary genetics, viral evolution, genetic drift, population metrics, population size, virology, heredity, genetics, biology and life sciences, evolutionary biology, evolutionary processes

journal.pcbi.1003651

The Effects of Theta Precession on Spatial Learning and Simplicial Complex Dynamics in a Topological Model of the Hippocampal Spatial Map

Considerable effort has been devoted over the years to understanding how the hippocampus is able to form an internal representation of the environment that enables an animal to efficiently navigate and remember the space 1 ., This internal map is made possible , in part , by the activity of pyramidal neurons in the hippocampus known as place cells 2 , 3 ., As an animal explores a given environment , different place cells will fire in different , discrete regions of the space that are then referred to as that cells “place field” 2 , 3 ., Despite decades of research , however , the features of the environment that are encoded , the identity of the downstream neurons that decode the information , and how the spiking activity of hundreds of cells is actually used to form the map all remain unclear ., We recently developed a computational model for spatial learning , focusing on what information is available to the still-unidentified downstream neurons 4 ., We reasoned that the information they decode must be encapsulated in the temporal patterns of the place cell spike trains , specifically place cell co-firing 4 , 5 ., Because place cell co-firing implies that the respective place fields overlap , the resulting map should derive from a sequence of overlaps between parts of the environment ., The information encoded by the hippocampus would therefore emphasize connectivity between places in the environment , which is a topological rather than a geometric quality of space 4 ., One advantage of this line of reasoning is that a topological problem should be amenable to topological analysis , so we developed our model using conceptual tools from the field of algebraic topology and , in particular , persistent homology theory 6 , 7 ., We simulated a rat exploring several topologically distinct environments and found that the information encoded by place cell co-firing can , in fact , reproduce the topological features of a given spatial environment ., We also found that , in order to form an accurate spatial map within a biologically reasonable length of time , our simplified model hippocampus had to function within a certain range of values that turned out to closely parallel those obtained from actual experiments with healthy rodents ., We called this sweet spot for spatial learning the learning region , L 4 ., As long as the values of the three parameters ( firing rates , place field sizes , and number of active neurons ) remain within the learning region , spatial learning is reliable and reproducible ., Beyond the perimeters of L , however , spatial learning fails ., Several features of this model are intuitively appealing ., First , the size and shape of L vary with the difficulty of the task: the greater the complexity of the space to be learned , the narrower the range of values that can sustain learning and thus the more compact the learning region ., Second , there is a certain tolerance for variation among the three parameters within L: if one parameter begins to fall outside the sweet spot , spatial learning can still occur if there is sufficient compensation in the other two parameters ., Our model suggests that certain diseases ( e . g . , Alzheimers ) or environmental toxins ( e . g . , ethanol , cannabinoids ) disrupt spatial learning over time by gradually shifting mean neuronal function ( place cell firing , neuronal number , or place field size ) beyond the perimeter of the learning region ., This notion receives support from studies of mouse models that show a correlation between impairment in spatial cognition and larger , more diffuse place fields , lower place cell firing rates , and smaller numbers of active cells 8 , 9 ., All this corresponds well with our subjective experiences of learning: the complexity of the task influences learning time; when the task is difficult we can feel we are at or just beyond the limits of our capacity; disease or intoxication can reveal limits in our spatial cognition that would normally be compensated for ., In this paper we focus on analyzing the structure of the learning region itself ., We begin by making the computational model more physiologically accurate ., There is a θ ( theta ) component of subcortical LFP oscillations that occurs in the frequency range of 6-12 Hz and regulates spiking activity 10 ., The timing of place cell spiking in the hippocampus is coupled with the phase of θ-oscillations so that , as a rat progresses through a particular place field , the corresponding place cell discharges at a progressively earlier phase of each new θ-cycle 11 ., This phenomenon , called theta phase precession , reproduces short sub-sequences of an animals current trajectory during each θ-cycle 11 ., This has been construed to suggest that θ-phase precession helps the hippocampus remember the temporal sequence of the rats positions in a space ( i . e . , its trajectory ) 12 , 13 , thereby enhancing spatial learning and memory ., If this is the case , θ phase precession should enhance learning in our computational model ., Indeed , we find that it significantly improves and stabilizes spatial learning ., We also find that different temporal windows to define co-firing exert a pronounced influence on learning time , and the most efficacious window widths correspond with experimental predictions ., Finally , we analyze simplicial complex formation within the learning region , examining both the structure of the complexes and the dynamics of loop formation , and find an explanation for the poor efficiency of ensembles at the boundary of the learning region compared to peak-performing ensembles ., We will first briefly describe the fundamental concepts on which our model is based ( this section is an abbreviated version of the approach described in 4 ) ., Central to this work is the concept of a nerve simplicial complex , in which a space X is covered by a number of smaller , discrete regions 14 ., If two regions overlap , the corresponding vertices , vi and vj , are considered connected by a 1D link vij ( Figure 1 ) ., If three regions overlap , then vij , vjk , and vki support a 2D triangular facet or simplex σijk , and so on as the number of overlaps and links increase ., The structure of the simplicial complex approximates the structure of the environment: the complex N ( X ) obtained from a sufficiently dense cover of the space X will reproduce the correct topological indices of X ( see 4 for details ) ., For our model we developed a temporal analogue to the simplicial complex , i . e . , a simplicial complex that builds over time: when the animal is first introduced to the environment , there will be only a few data points from place cell firing , but as the animal explores the space the place cell firing data accumulate ., ( Rodent experiments indicate that place fields take about four minutes to develop 15 . ), As the animal explores its environment and more place cells fire ( and co-fire ) , the simplicial complex T grows with T ( time ) ( T\u200a=\u200aT ( T ) ) ., Eventually , after a certain minimal time Tmin , the spaces topological characteristics will stabilize and produce the correct topological indices , at which point the topological information is complete ., Tmin is thus the minimal learning time , the time at which a topologically correct map is first formed ., The correct topological indices are indicated by Betti numbers , which in turn are manifested in persistent cycles ( see 4 , 7 , 16 ) ., As the rat begins to explore an environment , the simplicial complex T ( T ) will consist mostly of 0-cycles that correspond to small groups of cofiring cells that mark contractible spatial domains ., As the rat continues to explore the environment , the co-firing cells will produce links between the vertices of T ( T ) , and higher-dimensional cycles will appear ., As T increases , most cycles in each dimension will disappear as so much “topological noise , ” leaving only a few persisting cycles that express stable topological information ( Figure 1C ) ., The persistent homology method 6 ( see 4 Methods ) enables us to distinguish between cycles that persist across time ( reflecting real topological characteristics ) and transient cycles produced by the rats behavior ( e . g . , circling in a particular spot during one trial or simply not venturing into one part of the space during early explorations ) ., The pattern of cycles is referred to as a barcode 16 that can be easily read to give topological information about a given environment ( Figure 1C ) 6 , 7 ., If theta precession serves to enhance learning , as has been predicted 17–19 , then it should enhance spatial learning in our model ., This could occur by any of several means ., First , theta precession might enlarge the number of ensembles capable of the task by expanding the scope of the parameters ( including firing rates or place field sizes normally out of the bounds of L ) ., Second , it might make the ensembles that are in L converge on the correct topological information more rapidly ., Third , it might make the same ensembles perform more reliably ( e . g . , succeeding in map formation a greater percentage of times in our simulations ) ., To test the effect of theta precession in our model , we compared the rates of map formation for those formed with and without θ-precession ., We tested 1710 different place cell ensembles by independently varying the number of place cells ( N; 19 independent values , from 50 to 500 ) , the ensemble mean firing rate ( f; 10 independent values , from 4 to 40 ) , and the ensemble mean place field sizes ( s; 9 independent values , from 5 to 30 ) Methods; see 4 and Methods therein for further details ., For statistical analysis , we simulated each map 10 times so that we could compute the mean learning time and its relative variability , ξ\u200a=\u200aΔ Tmin/Tmin , for each set of ( s , f , N ) values ., In the following we will suppress the bar in the notation for the mean f , s , N , and Tmin ., Figure 2 shows the results of these simulations in a 1×1 m space with one hole ., ( The size of the environment in this study is smaller than the ones used in 4 , for two reasons: to avoid the potential problem of place cells with more than one field , and to reduce computational cost; see Methods . ), The learning region L is small and sparse in the θ-off case , but notably larger and denser in the θ-on case ( Figure 2A ) ., Values that would be just beyond the learning region—N that may be too small , or place fields that are too large or too small , or firing rates too high or too low 4—thus become functional with the addition of θ-precession ., Two criteria reveal the quality of the map-forming ensembles: speed and consistency in converging toward the correct topological signature ., The fastest map formation times ( under 4 minutes ) are represented by blue dots; as the color shifts toward red , map formation times become longer and the error rate ( failure to converge ) increases ., The size of the dot represents the success rate: small dots represent ensembles that only occasionally converge on the correct information , large dots represent ensembles that converge most or all of the time ., θ-precession increases the probability of convergence across all ensembles that can form accurate maps at all ( Supplemental Figure S1 ) ., Since we were interested in understanding the dynamics of efficient learning , however , we created a more stringent definition of the learning region to focus on the core of L where map-formation is most rapid and reliable , as well as to make the results more legible ( L can be quite dense , as in Figure 2A and Supplemental Figure S1 ) ; if θ-precession truly enhances learning , its effect should be apparent even in the most successful ensembles , and indeed this was the case ., The point clouds in Figure 2B depict those ensembles that formed maps with a convergence rate of ρ≥0 . 7 ( i . e . , those that produced correct topological information at least 70% of the time ) and simultaneously had low relative variability of the Tmin values , ξ<0 . 3 ., Even within this more efficient core of L , the effect of θ-precession was pronounced ., The histograms of the computed mean learning times are closely fit by the Generalized Extreme Value ( GEV ) probability distribution ( Figure 2B ) ., The distributions show that θ-precession reduced the mean learning times Tmin: the mode of all the θ-on GEV distributions decreased by ∼50% compared with the θ-off case for the learning region as a whole ( Figure 2C ) and by ∼15% for the efficient ensembles at the core of L ( Figure 2D ) ., Moreover , the effects of adding θ-precession—reducing map formation time and decreasing the relative variability of the Tmin values—were manifested in all maps , not just those with high ( ρ ≥0 . 7 ) convergence rates ( Figure 2C , D ) ., The histograms for all maps ( all ρ -values ) fit by the GEV distribution reveal that the typical variability ( the mode of the distributions ) in the θ-on cases is about half the size of the θ-off case ( Figure 2E ) ., In our model , therefore , θ-precession strongly enhances spatial learning ., Since we do not know what features of θ-oscillations might be important 20 , we studied four different θ-oscillations , two simulated and two derived from electrophysiological experiments in wild-type rodents ., Specifically , we modeled the effect of theta precession on the topological map by coupling the place cells Poisson firing rates , λc , with the phase of the following four θ-oscillations:, 1 ) θ1 – a single 8 Hz sinusoidal wave ,, 2 ) θ4 – a combination of four sinusoids ,, 3 ) θM – a subcortical EEG signal recorded in wild-type mouse , and, 4 ) θR – a subcortical EEG signal recorded in a rat ( Supplemental Figure S2; see Methods ) ., The last three signals were filtered in the θ-domain of frequencies ( 6–12 Hz ) ., The distribution of the learning times , the histograms of the mean learning times , and the histograms of the relative variability , ξ , for all four different theta cases are shown in Supplemental Figures S3 and S4 ., To compare the θ-off and θ-on cases , we performed two-sample Kolmogorov-Smirnov ( KS ) tests for all pairwise combinations of the studied sample sets 21 ., This produced a 5×5 matrix of the p-values , pij , where i , j\u200a=\u200a0 ( no theta ) , θ1 , θ4 , θM , and θR ., Black squares signify a statistically significant difference between cases i and j ( p<0 . 05 ) ; gray squares signify no statistically significant difference ., The statistical difference diagrams for the sets of Tmin values ( Supplemental Figure S3 ) and for the learning time variability ( Supplemental Figure S4 ) indicate that the distributions of learning times in the various θ-on cases were very similar , but the difference between all of these and the θ-off case was statistically significant ., So far we have described the outcome of place cell ensemble activity in terms of the time at which the correct number of loops in the simplicial complex T emerges ., But the learning process can also be described by how spurious loops are handled in the system ., These loops are a fair representation of the subjective experience of learning ., It takes time to build a framework into which new information can be properly slotted: until that framework is in place—whether its a grasp of the layout of a neighborhood or the basic principles of a new field of study—we have incomplete hunches and many incorrect notions before experience ( more learning ) fills in our understanding ., Translating this into topological terms , as the knowledge gaps close , the spurious loops contract ., We therefore wanted to study the effects of theta precession on the dynamics of loop formation ., Does a “smarter” ensemble form more spurious loops or fewer ?, Does it resolve those loops more quickly ?, We concentrated on the 1D cycles , which represent path connectivity within the simplicial complex , because they are more numerous and thus produce more robust statistics than the 0D cycles ., Figure 3 shows that θ-precession shortened the duration of the spurious loops ., The KS test reveals a statistically significant difference between the lifetimes of spurious loops in the θ-off case and those in all the θ-on cases ( Figure 3B ) ., To simplify the presentation of the results produced by the statistically similar θ-on cases , we combined the data on spurious loop duration from all four θ-driven maps into a single histogram ., It is interesting to note that the probability distributions for loop dynamics are typically better fit by the gamma distribution ( Figure 3B–D ) ., In the θ-driven maps , a typical spurious loop persisted for 50% less time than it would without θ-precession ( Figure 3B ) ., It is worth noting that the spurious loops persisted longer at the lower boundary of the learning region , where the mean firing rates and place field sizes are smallest ., This makes sense , insofar as whatever information appears will take longer to be corrected ., Statistical analysis of the largest number of loops observed at any given point over the course of the map formation period also differentiated θ-driven from θ-off maps ., Curiously , θ-driven cases tended to produce a significantly higher mean number of spurious loops than the θ-off case ( Figure 3C ) , but with a lower peak number of loops ( Figure 3D ) ., This implies that θ-precession enhances the speed of spatial learning overall at the price of creating more ( transient ) errors; lots of spurious loops are formed early on , but they disappear faster ., The KS test shows that the distributions of the mean number of loops in all θ-on cases differ from one another; only the maps driven by the two simulated θ-signals gave statistically similar results ., In our model , spatial learning can be quantified by the time required for the emergence of correct topological information , but it can also be quantified by studying the simplicial complex itself ., We noted earlier that the structure of the simplicial complex approximates the structure of the environment ., Similarly , it is possible to conceive of a simplex as a mathematical analogue to a cell assembly ( a group of at least two cells that repeatedly co-fire and form a synapse onto a readout neuron ) , and to view the simplicial complex as analogous to the realm of possible connections within the hippocampus ., We were therefore curious: since it is in the interest of neural function to be efficient , how many cell assemblies ( simplices ) does it take to encode a given amount of information ?, We would predict that the fewer the connections , the better , for the sake of efficiency ., One of the major characteristics of a simplicial complex T is the number of n-dimensional simplices it contains , traditionally denoted as fn ., The list of all fn –values , ( f1 , f2 , … , fn ) , is referred to as the f-vector 22 ., Since the D-dimensional simplices in T correspond to ( D+1 ) -ary connections , the number of which depends on the number of vertices , N , we considered the fn values normalized by the corresponding binomial coefficients , which characterize the number of simplices connecting vertices in the complex T . We can consider η an index of the connectivity of the simplicial complex ., Since we model 2D spatial navigation , we analyzed the connections between two and three vertices , i . e . , the 1D and 2D simplices , of T ( the number of 0D simplexes normalized by the number of vertices in T is η0≡1 ) ., Figure 4 shows the distribution of the normalized number of simplices at the time the correct signature is achieved ( η1 and η2 , for 1D and 2D , respectively ) ., As expected , the number of simplices was smaller at the lower boundary of the learning region L ( the base of the point cloud ) and increased towards the top of L where place fields are larger and the firing rates are higher , each of which would produce more place cell co-firing events ., Remarkably , the number of simplices depended primarily on the mean place field size and on the mean firing rate of the ensemble , and not on the number of cells within the ensemble ., This suggests a certain universality in the behavior of place cell ensembles that is independent of their population size ., In the ensembles with smaller place fields and lower firing rates , about 1 . 5% of place cell pairs and 1 . 7% of the triplets were connected , and this was enough to encode the correct topological information , whereas in the ensembles with low spatial selectivity and higher firing rates , 25% of pairs and 8% of triplets were connected ., These ensembles , in which the place fields and spike trains will by definition have a lot of overlap , are forced to form many more 1D and 2D simplices in order to encode the same amount of information and are thus less efficient ( Figure 4 , third column ) ., According to our model , such ensembles and the hippocampal networks whose activity they represent are inefficient on two counts ., First , these larger , more complex temporal simplicial complexes ( analogous to a larger number of coactive cell groups ) will take longer to form correct topological information , if they can manage it at all ., Second , a larger number of coactive place cells would hamper the training of downstream readout neurons , thereby impeding reliable encoding of spatial information ., This is consistent with studies showing that the number of cells participating in a particular task decreases until it reaches an optimal number that fire at a slightly higher rate than their no-longer-participating neighbors 23 ., Our model depends on patterns of place cell co-firing , but we had not previously explored what the optimal temporal window for defining co-activity might be ., Experimental work supports the widely held assumption that the temporal unit for defining coactivity ranges between tens 24 and hundreds of milliseconds 25–27 ., Our model , however , enables us to approach the question of optimal width for the coactivity window theoretically ., Clearly , if the time window w is too small , then the spike trains produced by the presynaptic place cells will often “miss” one another , and the map will either require a long time to emerge or it may not be established at all ., One would thus expect large values Tmin ( w ) for a small w ., On the other hand , if w is too large , it will allow cells whose place fields are actually distant from one another to be grouped together , yielding incorrect topological information ., Theta rhythm itself will have a tendency to group sequential spike trains together , but clearly there must be limits to this , or else some place cells would be read downstream as co-firing when they actually are not ., Therefore , there should exist an optimal value of w that reliably produces a finite , biologically relevant learning time Tmin at which the learning region L is robust and stable ., We assume that the capability of a read-out neuron to detect place cell co-activity is specified by a single parameter , the width of the integration time window w , over which the co-appearance of the spike trains is detected ., ( We considered the possible effect of time bin position on co-activity , but found this did not affect outcome; see Methods . ), We defined cell coactivity as follows: if presynaptic neurons c1 and c2 send a signal to a read-out neuron within a certain time window w , their activity will be interpreted as contemporaneous ., The width of this time window may be positive ( c2 becomes active w seconds after c1 ) or negative ( c2 becomes active while c1 is still firing ) ., We studied window widths w for which the place cell spike trains would eventually be able to produce the correct topological signature ( the Betti numbers , see Methods in 4 ) ., In order to describe the dependence of learning times on the window width , Tmin ( w ) , we scanned an array of 24 values of w ( ranging from 0 . 1 to 5 θ-periods ) for each combination of the parameters ( mean s , f , and N ) and noted the width of the value wo , at which the map began to produce the correct topological signature ., We call this initial correct window width the “opening” value ., A typical result is provided by an ensemble with f\u200a=\u200a28 Hz , s\u200a=\u200a23 cm and N\u200a=\u200a350 , in which an accurate topological map emerges at a fairly small window width , wo∼25 msec ( Figure 5 ) ., The distribution of the opening window widths shows that wo may exceed 1 . 5 θ-periods ( ∼25 msec ) , which matches the slow γ-period 24 , 28 ( Supplemental Figure S5 ) ., Since at this stage γ-oscillations have not been explicitly built into the model , this correspondence is coincidental , if suggestive ., As expected , the values of learning times at wo were rather large: Tmin ( wo ) ∼20 minutes in θ-off case and Tmin ( wo ) ∼30 minutes in the θ-on case , and in some cases exceeding one hour ( mostly for the ensembles with low firing rates ) ., For small window widths , the value of the learning time Tmin ( w ) was very sensitive to variations in w ( Supplemental Figure S6 ) ., As w increased , however , the learning time reached a plateau around some larger value ws ., This implies that in order to produce stable values for Tmin that are biologically plausible , the values of the window widths should start around ws ., The distribution of the ws values demonstrates that in the θ-off case the stabilization is typically achieved at approximately one θ-period , and in the θ-on case at about ∼1 . 2–1 . 5 θ-cycles ( Figure 6 ) , which justifies our choice of a two θ-period window width for the computations and corresponds well with the predicted limit of 150 msec for θ-cycle cofiring in sequence coding 29 ., Further increasing the integration time window w did not significantly alter the learning time Tmin in L; instead , the rate of map convergence decreased until the maps completely fail to encode the correct topological information at w ζ 4 . 5 θ-periods ., From the perspective of our current model , the range of optimal window widths w is between 20–25 msec and 0 . 5 secs ., Finally , we sought to uncover a relationship between learning time and window width ., Our analysis suggests that Tmin is inversely proportional to a power of the window width ( Figure 5B , Supplemental Figure S7 ) ., Numerous experiments have demonstrated that θ precession is important for spatial learning ., θ-power increases with memory load during both spatial and non-spatial tasks in humans 30 , 31 and in rodents 32 , 33; spatial deficits correlate with a decrease in the power of theta oscillations in Alzheimers disease 34 and in epilepsy 35 , 36 ., If θ-signal is blocked by lesioning the medial septum ( which does not affect hippocampal place cell representations ) , it severely impairs memory 37 and the acquisition of new spatial information 38 ., Recent experiments demonstrate more directly that destroying θ precession by administering cannabinoids to rats correlates with behavioral and spatial learning deficits 17 , 39 ., But at what level , and through what mechanisms , does θ precession exert its influence ?, The effect of θ-precession on the structure of the spike trains is rather complex 40 ., On the one hand , it groups cell spikes closer together in time and enforces specific sequences of cell firing , which is typically interpreted as increasing the temporal coherence of place cell activity 41–43 ., One might predict that grouping spikes together would ( somehow ) speed up learning ., On the other hand , θ-precession imposes extra conditions on the spike timing that depend on θ-phase and on the rats location with respect to the center of the place field through which it is presently moving ., Since every neuron precesses independently , one could just as well predict that θ modulation would either restrict or enlarge the pool of coactivity events , which in turn would slow down learning at the level of the downstream networks , and that the beneficial effect of the θ rhythm is a higher-order phenomenon that occurs elsewhere in the brain ., Our results suggest that θ precession may not just correlate with , but actually be a mechanism for , enhancing spatial learning and memory ., The interplay of θ precession and window width , especially the extremely long learning times at the opening window width wo , is particularly illuminating here ., As noted , theta precession acts at both the ensemble and the individual neuron level: it groups spikes together , but each neuron precesses independently ., When the time window is sufficiently wide , the coactivity events are reliably captured , the first effect dominates , and the main outcome of theta precession is to supply grouped spikes to downstream neurons ., For very small time windows , however , the system struggles to capture events as coactive , and the extra condition imposed by phase precession acts as an impediment: detected coactivities are rarer , and learning slows down ., Put more simply , imagine in Suppl ., Figure S8 that the window is only one spike wide: in a train of 10 spikes that overlaps by one spike with another train , it will take 10 windows before the overlap is detected ., It is noteworthy that the presence of theta precession was clearly more important than the details of the oscillation ., Although theta precession enhanced learning in our simulations , learning times were relatively insensitive to the details of the theta precession chosen ., One might expect differences in spike train structure induced by the four different θ-signals studied to alter the dynamics of the persistent loops and thus learning efficiency ., Our results show , however , that differences that would matter at the level of individual cells are averaged out at the level of a large ensemble of cells ., Here again the model shows its particular strength: it allows us to correlate parameters of activity at the level of individual neurons with the outcome at the level of an ensemble of hundreds of cells , providing a framework for understanding how micro-level changes play out at the behavioral level ., Interestingly , we also saw a difference between the micro and macro levels when we considered whether the placement of a temporal window affected what would be considered co-activity ( see Methods and Supplementary Figure S8 ) ., In theory it should , but the effect at the macro level washes out and we found that only the temporal width of the window matters for learning time ., Beyond validating the model as a reliable way to study physiological aspects of spatial learning , we have gone further in this work to analyze the simplicial complex itself as a way of describing learning ., As a rat starts to explore an environment , some cells begin to form place fields ., Then , the co-firing of two or more place cells will define the respective places as connected in space and temporal experience and will create corresponding simplices in the simplicial complex T . With time , these simplices form a chain corresponding to the animals route through the space ., If the environment is bounded , the rat will discover new connections between the same places ( arriving to the same location via different routes ) ., As a result , the chains of connected simplices grow together to form loops ., Existing loops become thicker and may eventually “close up” and disappear , yielding surfaces ., The appearance of such surfaces is significant: the closing up of a D-dimensional surface corresponds to the contraction , or disappearance , of one of its boundaries , which itself is a D1-dimensional loop ., Eventually , the structure of the simplicial complex saturates such that no new simplices ( connections between places ) are produced and no more loops contract because all that could close have already closed ., At this point , the saturated simplicial complex T encodes not only the possible locations of the rat , but also connections between the locations , along with the information about how these

Introduction, Results, Discussion, Materials and Methods

Learning arises through the activity of large ensembles of cells , yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap ., We have taken spatial learning as our starting point , computationally modeling the activity of place cells using methods derived from algebraic topology , especially persistent homology ., We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments ( “learn” the space ) within certain values of place cell firing rate , place field size , and cell population; we called this parameter space the learning region ., Here we advance the model both technically and conceptually ., To make the model more physiological , we explored the effects of theta precession on spatial learning in our virtual ensembles ., Theta precession , which is believed to influence learning and memory , did in fact enhance learning in our model , increasing both speed and the size of the learning region ., Interestingly , theta precession also increased the number of spurious loops during simplicial complex formation ., We next explored how downstream readout neurons might define co-firing by grouping together cells within different windows of time and thereby capturing different degrees of temporal overlap between spike trains ., Our models optimum coactivity window correlates well with experimental data , ranging from ∼150–200 msec ., We further studied the relationship between learning time , window width , and theta precession ., Our results validate our topological model for spatial learning and open new avenues for connecting data at the level of individual neurons to behavioral outcomes at the neuronal ensemble level ., Finally , we analyzed the dynamics of simplicial complex formation and loop transience to propose that the simplicial complex provides a useful working description of the spatial learning process .

One of the challenges in contemporary neuroscience is that we have few ways to connect data about the features of individual neurons with effects ( such as learning ) that emerge only at the scale of large cell ensembles ., We are tackling this problem using spatial learning as a starting point ., In previous work we created a computational model of spatial learning using concepts from the field of algebraic topology , proposing that the hippocampal map encodes topological features of an environment ( connectivity ) rather than precise metrics ( distances and angles between locations ) —more akin to a subway map than a street map ., Our model simulates the activity of place cells as a rat navigates the experimental space so that we can estimate the effect produced by specific electrophysiological components —cell firing rate , population size , etc ., —on the net outcome ., In this work , we show that θ phase precession significantly enhanced spatial learning , and that the way downstream neurons group cells together into coactivity windows exerts interesting effects on learning time ., These findings strongly support the notion that theta phase precession enhances spatial learning ., Finally , we propose that ideas from topological theory provide a conceptually elegant description of the actual learning process .

computational neuroscience, biology and life sciences, computational biology, neuroscience, learning and memory

journal.pcbi.1006161

Logistical constraints lead to an intermediate optimum in outbreak response vaccination

In applied epidemiology , models are increasingly used to inform management decisions on effective responses to a variety of outbreaking diseases , e . g . cholera 1 , smallpox 2 , influenza 3 , Ebola 4 , measles 5 , and Zika fever 6 ., In general , these epidemic dynamic models are designed with an emphasis on the transmission process , which includes factors such as individual encounter rates resulting from social interaction structure 7 , cultural practices ( e . g . funerals 8 ) , municipal layout ( e . g . availability of hospitals 9 ) , transportation network 10; seasonal aggregation 11–12 , and vector behavior and abundance 13 ., These types of models are commonly used to evaluate the potential impact of various interventions at the population scale , e . g . vaccination parameters in SEIR model 14 , or local scale , e . g . probability of livestock on farms being culled 15 ., By comparison , the dynamics of interventions have received much less attention ., Here , we explicitly model vaccination response to an outbreak , and examine the interactions between disease dynamics and the impact of an intervention strategy ., Density-dependent pathogen transmission is a standard assumption in most disease models , represented by a positive correlation between local population density and the probability of individual infection ., Few studies , however , consider the effect of density-dependence on outbreak control ., In compartmental models of rubella ( e . g . 16–17 ) , vaccination has been described simply as a rate that individuals are removed from the total susceptible pool ., Similarly , in early individual-based , foot-and-mouth models that evaluate livestock culling strategies ( e . g . 18–19 ) , farms or infected premises are culled at a rate independent of the total number of farms to be culled or the number of its neighboring farms designated for culling ( but see 20–22 for the incorporation of logistical constraints ) ., We propose a simple control model that features a more realistic assumption: for a given amount of vaccination effort , it takes longer to reach all susceptibles in densely populated locations ., The utility of a disease response model arguably hinges on its potential to inform management decisions for uncertain future outbreak scenarios under known logistical constraints ., Realistic limitations on controls have been frequently neglected in disease transmission models in the interest of analytical simplicity , leading to assumptions of a spatially constant vaccination rate ( e . g . 23 ) , a nearly immediate and uniform coverage of control areas ( e . g . 24 ) , or zero wait-time before patients become vaccinated ( e . g . 25 ) ., Isolated features of control complexity can be found in a few key studies ., For example , Handel et al 26 considered time-varying vaccination strategies in a well-mixed model under vaccine limitation in which the strengths of the controls were adjusted to sustain a tolerable level of effective reproduction number and thus provide protection against future outbreaks ., This model considers temporal variation of the control process as a solution to disease recurrence rather than an intrinsic , logistical feature of vaccination in general ., Spatially heterogeneous interventions have been investigated in heuristic models of smallpox and cholera interventions 27–28 , however , vaccines are commonly assumed to be administered homogenously within the control zones ( e . g . rings , discrete patches ) and at rates invariant to local demands ., In contrast to heuristic representations of disease response , there exist large-scale stochastic simulation models such as NAADSM ( North American Animal Disease Spread Model ) , InterSpread Plus , and Exodis designed to replicate explicit control measures ( e . g . daily farm destruction capacity ) with delayed and spatially heterogeneous effects 29 ., These models are better able to account for realistic logistical limitations , but the high dimensionality of these models makes it difficult to abstract their context-specific results to general control settings ., Here , we explicitly model the general dynamics between transmission and vaccination intervention and account for their real time interactions ., Specifically , we consider a vaccination response that is both spatially constrained and density-dependent ., As with disease transmission , vaccination may be assumed to originate from a focal location from which the response is coordinated: e . g . a central distribution point for house-to-house campaign as is used in polio vaccination or village-to-village strategies used for measles immunization days 30–32 ., In the pressing case of an outbreak , the rate at which available response efforts and resources should be actively deployed into the surrounding area poses a practical scheduling challenge in operations research by public health organizations 33 ., In traditional models where this deployment process occurs instantaneously , the recommended response strategies inevitably fail to account for the differential delays in vaccine delivery to different segments of the susceptible population ., But in a response model with an explicit inclusion of spatial constraint , how to deploy vaccines into new , vulnerable territories may be recognized as a distribution problem for a spatially limited amount of control capital ( e . g . effort , resource ) ., Our model is developed within an original framework that integrates an agent-based simulation of disease spread with a partial differential equation that describes the spatiotemporal distribution of vaccination effort ., Prior to this study , spatial models of disease dynamics have often been developed using one of these two frameworks , which represent the Lagrangian and the Eulerian approaches , respectively ., We use agent-based simulations to describe the process of transmission as a collection of random infective contacts at the individual level ., Simultaneously , we characterized the vaccination response using a compartmental model based on a partial differential equation to describe the process of response as spatiotemporal variations in vaccination coverage at the population level ., The combination of these approaches allows us to predict the epidemiological consequences of implementing a robust management decision in response to any realization of a novel outbreak ., Our objective is to evaluate the performance of explicit vaccination strategies where the total vaccination effort is constrained on the landscape and its effectiveness varies with local population densities ., In particular , we aim to identify context-specific , “optimal” vaccination strategies that minimize expected outbreak size given constrained vaccination effort ., We consider a continuum of vaccination strategies for a fixed amount of vaccination effort ., On one end of this range , we have “fast and free” vaccination strategies with a high rate of radial diffusion , which cover a large area quickly , but leave few vaccinators per unit area thereby reducing local vaccination efficiencies ., On the other end , we have “slow and steady” vaccination strategies with low rates of radial diffusion , which result in high local coverage but introduce an opportunity cost by delaying the implementation of vaccination efforts in areas far from the initial focus ., In other words , by constraining total response effort , the former strategy ensures low intensity of vaccination broadly , while the latter results in high intensity of vaccination locally ., Here , we illustrate that the optimal strategy for vaccine distribution depends on both the population density and the promptness of the outbreak response ., We find that a “one size fits all” policy does not exist; there are situations where the optimal strategy for minimizing outbreak size is to concentrate vaccination effort locally to control disease spread , and others where a rapid spatial expansion of vaccination effort is crucial for reducing overall burden ., By extension , our context-specific results highlight a tension between direct and indirect protection: if a “slow and steady” strategy can contain an outbreak , then indirect protection can be realized broadly by direct protection concentrated in a small area; by contrast , a “fast and free” strategy distributes direct protection over a broad area , and the resulting indirect protection is local ( i . e . neighbors ) in scale ., We define our model on a two-dimensional discretized landscape that contains N randomly distributed susceptible individuals ., Because we consider outbreak and response dynamics on a single , fixed landscape , N is also a measure of population density ., The first infected individual is introduced at a central location x0 at time t = 0 , from which point disease transmission may occur at the end of every time step ., At each location x and time t , the probability of infection for a susceptible individual is assumed to increase asymptotically with transmission rate δ and the local density of infected individuals , such that the individual becomes infected with probability, φ ( x , t ) =1−exp−δI ( x , t ) ., ( 1 ), The local force of infection , I ( x , t ) , is defined by smoothing the distribution of infected individuals with a interaction kernel , K , which characterizes the likelihood of an interaction between two individuals as a function of their distance apart ., We assume that the interaction kernel is a bivariate Gaussian density function with mean zero and variance-covariance matrix α2I , where α−1 is the rate at which interaction between individuals decays as a function of their distance apart ., During each time step , an infected individual may recover with probability ρ ., In the numerical simulations , vaccination is carried out per time step τ immediately after disease transmission and before recovery ., The effort available for vaccination , e . g . the quantity of operational equipment or number of healthcare personnel , at a particular space and time , H ( x , t ) , is modeled as a probability surface that is initially distributed according to another bivariate Gaussian density function with mean at the disease epicenter x0 , which may also represent a central distribution point or medical facility from which the response originates , and variance-covariance matrix I ., We thus implicitly assume that vaccination effort is delivered to all areas within the control region ., The probability that an individual at a particular location is immunized is assumed to increase asymptotically with vaccine intensity ε and the amount of available vaccination effort at that location relative to the local density of individuals who must be vaccinated ., Therefore , analogous to the transmission process , a person becomes immunized at location x and time t with probability, ω ( x , t ) =1−exp−εH ( x , t ) /S ( x , t ) ,, ( 2 ), where local density of susceptibles , S ( x , t ) , is defined by smoothing the distribution of susceptible individuals using the previous interaction kernel K with parameter α and the distribution of vaccination effort , H ( x , t ) , is defined below ., We assume that the total available vaccination effort at the global scale remains constant over time but finite across space , such that ∫ΩH ( x , t ) dx = 1 , where Ω denotes the region over which the control may reach; here the full landscape ., We maintain this conservation constraint by numerically applying zero-flux boundary condition on the landscape borders , then validate based on the integral’s deviation from unity at the end of the simulation ., The process of vaccination deployment conceptually represents the collective movement activity of healthcare workers , which spreads radially into the susceptible population within the landscape boundaries ., We therefore model H ( x , t ) as a two-dimensional diffusion equation:, ∂H∂t ( x , t ) =∇2 ( μH ( x , t ) ) ,, ( 3 ), where diffusion rate μ defines the vaccination strategy ., The introduction of a constant diffusion coefficient in the time derivative of H ( x , t ) assumes that the successive movement distances of healthcare are exponential and their movement direction is described by a von Mises distribution with respect to the epicenter 34 ., Specifically , μ is proportional to the mean-squared displacement distance of vaccination effort per time step ., In our present model , which aims to provide a heuristic and generalized description of disease response , we implicitly assume for H ( x , t ) that vaccination activities are informed by perfect knowledge of the disease status of all individuals at all times , such that vaccination effort is expended entirely on susceptibles , avoiding vaccination of already infected or recovered individuals ., The potential consequence of information uncertainty in surveillance is beyond the scope this study ., Starting from t = 0 , with one infected individual centrally seeded , we advance the model by time step τ and simulate the sequential events of outbreak and response ., To obtain transient solutions of H ( x , t ) , we solve Eq 3 forward in time by τ , using FiPy 35 , a finite volume partial differential equation solver written in Python ., Transmission and vaccination processes continue in succession until either the number of infected or susceptible individuals in the population drops to zero ., Because the simulation terminates only when the above epidemiological condition is satisfied , the time frame is variable across replicates ., The model description follows the ODD ( Overview , Design concepts , and Details ) protocol 36–37 ( S1 Appendix ) ., We explore the effect of population density and the rate of radial expansion of a vaccination program on the number of individuals either directly protected by vaccination or indirectly protected by herd immunity ., On a 101 x 101 square grid , we ran 6000 simulation replicates of an outbreak per population density , ranging from 2000 to 10201 ( the latter gives an average density of one individual per grid cell ) individuals on the landscape ( Fig 1 ) ., Each replicate is initialized with randomly located susceptibles ., Vaccination response is determined by the chosen rate of radial expansion μ , varying from 0 . 5 to 50 ., To clarify , based on the constraint on total vaccination effort , a slow expansion at rate μ = 0 . 5 means more vaccination effort and more efficient local vaccination of susceptibles within the vaccine radius ( i . e . “slow-and-steady” ) compared to a fast expansion at rate μ = 50 ( i . e . “fast-and-free” ) ., We ran these simulations under the condition of “timely response” , for which both transmission and vaccine response begin at time step 0 ., The model is parameterized using the following values: time step τ = 0 . 1 , transmission rate δ = 2 , recovery rate ρ = 0 . 2 , vaccine intensity ε = 20 , and interaction scale α = 1 ( see Supplementary Information for sensitivity of results to these parameters ) ., We also performed an analogous set of simulations under the condition of a “delayed response” , where vaccine response begins 10 time steps ( i . e . 10τ ∙ ( τ/ρ ) −1 = 2 epidemic generations ) after the start of transmission ., Assuming that distributing a vaccination response more quickly incurs a larger logistical cost , we define the optimum rate as the slowest rate μ that achieves the minimal number of infected individuals ., Sensitivity analyses are performed by re-running the model after reducing and doubling the values of δ , ε , and α ( S3–S6 Figs ) ., In the absence of vaccination response , our model reflects density-dependence in the transmission process as expected ( Fig 2A ) ., At low density N < 5000 , the outbreak is self-contained for this set of transmission parameters; as there are large distances between individuals , transmission occurs infrequently and the disease may quickly burn out based on recovery rate alone ( indicated by the short outbreak durations and low outbreak sizes on the left side of Fig 2A ) ., As density increases , neighbor interactions increase , and the infection is able to linger in population patches by means of episodic infection events until it becomes an epidemic after a period of slow and stuttering advancement; thus both outbreak size and duration increase at intermediate densities ( Fig 2A ) ., However , above a critical density threshold ( N = 5500 ) , infection events begin to occur at accelerated rates , fueling larger outbreaks in shorter amounts of time ., When N > 7000 , the infection is able to spread even more rapidly through a global cluster of individuals , allowing relatively fewer susceptibles to avoid infection ., Therefore , we can infer that , if response measures are designed to control the spread of infection and the latter differs in efficiency across population densities , then proper response measures should also be density-dependent ., In the absence of disease transmission , the time required to achieve strong herd immunity ahead of the outbreak varies nonlinearly with vaccination diffusion rate in a pattern that reflects both spatial constraints and negative density-dependence in the response process ( Fig 2B ) ., When the population ( here , susceptible ) at a given location is low , the mean waiting time to vaccination per unit effort is short ., For a given focal area , regardless of population density , increasing vaccination diffusion rate from the lowest level results in faster time to local herd immunity ., For the lowest population densities ( e . g . N = 1000 ) , very few individuals must be immunized per unit area to achieve herd immunity and there is no cost to rapid diffusion; even effort spread as thin as possible is sufficient to deplete susceptibles quickly ., As population density increases , e . g . N > 1000 , a larger number must be vaccinated to achieve the local herd immunity threshold and thus the time needed to achieve herd immunity ( that is , a given proportion of susceptibles immunized ) in a focal area scales positively with population density N . As vaccination diffusion rate increases beyond an intermediate level , a larger fraction of the fixed effort is allocated outside the focal area , and the waiting time to vaccinate enough individuals to achieve herd immunity gets longer ., Thus , there is a direct cost to any given focal area of rapid vaccination diffusion because of effort distributed elsewhere ., Comparison of Fig 2A and 2B indicates a tradeoff between transmission and control potentials when managing outbreaks in a moderate-to-highly dense population: disease spreads broadly and rapidly as a result of increased population density; however , focal populations near the outbreak epicenter are more difficult to protect in time , especially when the response process is driven by a high diffusion rate ., In an alternative , less realistic scenario where the vaccination process is unaffected by density constraints , rapid diffusion to overtake the outbreak can be applied at no cost–herd immunity is possible in key areas despite a rapid outflow of local vaccination effort; therefore , the control objective is optimized with a maximization of the vaccination diffusion rate ., By accounting for the negative density-dependence of the control process , our model introduces an exponential cost to higher diffusion in the form of a waiting time ., Successful outbreak control , therefore , is implicitly contingent on a balance between vaccination coverage and local efficiency ., The combination of the two opposing density effects suggests the existence of optimal vaccination strategies that are moderately diffusive and context-dependent ., We first consider a timely vaccination response , for which vaccination begins at the same time as the outbreak ., At low population densities ( N < 5000 ) , infection is unlikely to spread ( Fig 2A ) , consequently , a majority of the population will be uninfected regardless of which μ defined vaccination strategy is implemented ( Fig 3A:i-iii ) ; outbreak durations are short-lived after the initial case of infection ( see S1 Fig ) ., Within the density range where infection readily establishes and spreads ( Fig 2A ) , the effectiveness of a response strategy depends on its capacity to stay ahead of the outbreak ., At intermediate population densities ( 5000 ≤ N ≤ 7000 ) , our simulations show that , under low vaccination diffusion rates , herd immunity cannot be established quickly enough around new infection foci , thus allowing the remaining infections to spread into the surrounding area that has not yet achieved high proportion vaccinated ., As a result , these strategies increase the probability of major outbreak events , defined as final infection prevalence ≥10% ( we chose this threshold as it clearly divides the two modes of the outbreak size distribution between small outbreaks characterized by stuttering chains of infection and broad outbreaks , see S2 Fig ) , and widespread epidemics when they occur ( Fig 3A:iv-vi ) ., In comparison , with increased rates of vaccination diffusion , the response effort proceeds ahead of the outbreak and has time to establish strong herd immunity farther from the epicenter ( Fig 2B ) ., These “faster-and-freer” strategies are therefore more effective at reducing the probability of major outbreaks and their sizes ( Fig 3A:iv-vi ) ., Because the marginal reduction in expected outbreak size converges to zero as μ increases , vaccination at an intermediate diffusion rate is sufficient to achieve the maximal benefit ( Fig 3A:iv-vi , Fig 4 ) ., We computed optimal diffusion rate as min ( argminμi{j ( μi ) |μi=0 . 5 , 1 , … , 50} ) , where j ( ∙ ) refers to the rounded values of a 4th order polynomial regression of the proportion infected as a function of vaccination diffusion rate in order to smooth over simulation noise ., At high population density ( N > 7000 ) , widespread epidemics are difficult to prevent regardless of the vaccination strategy chosen ( Fig 3A:vii-ix ) ; major outbreaks can occur with 95% probability and , when they do , they infect almost the entire population ., Within these dense populations , vaccination effort deployed by “slow-and-steady” strategies are prone to be immediately outstripped by the rapidly spreading outbreak ., “Fast-and-free” strategies , on the other hand , push vaccination effort well beyond the outbreak perimeter and administer vaccines to many more individuals in areas not yet reached by the outbreak ., If the diffusion rate is too high , then the per unit vaccination effort is low and infection can continue to spread through susceptible individuals who remain in the vaccination queue ., In contrast , intermediate vaccination rates ( 10 < μ < 20 ) achieve a balance between, ( a ) staying ahead of the outbreak and, ( b ) allocating sufficient effort to the core epidemic area to establish local herd immunity within the target control area ., The effectiveness of these strategies is shown by their ability to reduce the probability of major outbreak events to 40% , which in turn minimizes the expected outbreak sizes ., We verified that intermediate vaccination diffusion rates are optimal to control outbreaks in moderately-to-highly dense populations for a range of interaction scale , transmission rate , and vaccine intensity parameters ( see S3–S6 Figs ) ., We performed separate simulations across the same set of vaccination strategies assuming a timely response , varying only the value of a single parameter ., The population density at which the intermediate optimum arises , and the relative benefit of this optimum compared to the slowest and fastest distribution strategies are shown to vary ., For instance , the lowest density at which an intermediate diffusion rate is optimal negatively correlates with both the interaction scale and transmission rate ( S3 and S4 Figs ) ., When we isolate the sensitivity runs simulated in populations where outbreaks can occur ( 51 out of 81 parameter and population density combinations where the minimal outbreak size cannot be achieved equally by all vaccination strategies ( see Fig 3A:iv-ix ) ) , the optimal strategy generally emerged at an intermediate value ( S6 Fig ) ., In a few exceptions ( 3 out of 51 parameter combinations ) , the optimum is minimized at diffusion rate μ = 0 . 5; all 3 of these reflect settings at the highest population density with the highest transmission rate or interaction scale considered ., In no case was the highest vaccination rate optimal ., The remaining optima tend to occur in the lower half of the vaccine distribution rates that we considered , suggesting that there is frequently a cost to distributing vaccination faster , or more broadly , than necessary ., We also optimized vaccination strategies when the response is delayed , lagging behind the outbreak by 2 epidemic generations ., When population density N > 4000 , the simulation always results in fewer individuals being protected ( i . e . more are infected ) than with a timely response under identical strategies ( Fig 3B:iv-ix ) ., However , at all densities we considered , the optimal rate of vaccination diffusion is always higher than for a timely response ( Fig 4 ) ., For some densities , a delayed response can achieve a level of protection equivalent to a sub-optimal timely response ., For example , at density N = 6000 ( Fig 3B:v ) , if vaccination response is delayed by 2 epidemic generations , 80% of the population is expected to be protected by a strategy of μ = 20; the same result can be achieved by a strategy of μ = 1 with a timely response ., Here , we present a linked epidemic and vaccination distribution model that evaluates the performance of spatially explicit vaccination distribution strategies ., The model integrates an agent-based SIRV simulation of a stochastic outbreak with a continuous-time diffusion model that describes the process of vaccination diffusion ., Vaccination coverage is spatially constrained and individual vaccination rate depends both on the local density of susceptibles ( vaccine demand , or need ) and the area over which vaccination resources are distributed ., We found that , for a timely vaccination response , the optimal rate of vaccination diffusion depends on population density ., Under our model setting , at low densities ( N < 5000 ) , outbreaks typically self-terminate , independent of the rate at which vaccine is distributed; at intermediate densities ( 5000 < N < 8000 ) , outbreaks may be limited as long as the distribution of vaccination is fast enough to stay ahead of disease spread ., Here , intermediate and high diffusion rates are equivalent at limiting outbreak size , but intermediate diffusion rates are sufficient; thus , if there are logistical costs to a faster vaccination program ( e . g . more transportation effort necessary ) then an intermediate strategy that achieves the same outbreak size may be favored ., At high densities ( N > 8000 ) , outbreak size is minimized by vaccination strategies of intermediate diffusion rates that balance the need to stay ahead of the outbreak and to achieve high enough local coverage around the disease epicenter to slow spread ., In this regime , major outbreaks , if they occur , affect a significant fraction ( generally > 0 . 4 ) of the population ( Fig 3A ) ., While rapid strategies always reduce the mean size of these major outbreaks , on average , they are suboptimal because , by spreading vaccination resources too thin , the probability of a major outbreak occurring is higher ., Indeed , for vaccination diffusion rates above an intermediate threshold , the probability of major outbreaks spreading through the population that has yet to receive vaccination may equal or exceed that of the slowest vaccination diffusion rate ., However , when response time lags behind the outbreak by multiple epidemic generations , the optimal strategy is to vaccinate as broadly and swiftly as possible regardless of the number of susceptibles on the landscape ., Based on sensitivity analysis , the optimality of intermediate rates of vaccination in high-density contexts appears to be a general pattern that emerges from the dynamic interactions between transmission and response processes ., For all parameter combinations considered , we found a range of susceptible densities over which the outbreak size is minimized by vaccination diffusion at an intermediate rate ., Thus , we find that the emergence of an intermediate optimum diffusion rate as population density increases was consistent; however , the population density at which this intermediate optimum emerges and the relative benefit of the optimum depends on the epidemiological context ., By modeling vaccination as a dynamic process , our results address the inherent constraints and tradeoffs in deploying control over space and time ., Conventionally , management though vaccination is described in terms of the herd immunity threshold: the proportion of the population that needs to be immune in order to prevent epidemic spread ., For instance , this threshold is 1 − 1/R0 in the classic mean-field models 38 and , similarly , network models of human contact assess conditions for stopping epidemics in terms of the necessary reduction of nodes 39 ., Both classes of models investigate an important management question: what is the threshold proportion of susceptibles necessary to prevent an outbreak ?, However , these statements are made in terms of the equilibrium criteria to prevent epidemic spread and largely ignore the operational question of how to reach those control targets or how outbreak responders should be distributed ., Thus , these classic control objectives ignore the potential for transient phenomena that may arise from the process of achieving those objectives ., Here , we show that the transient dynamics of epidemic spread and vaccination during outbreak response can lead to counter-intuitive control recommendations ., By formalizing the spatial limitation and temporal ( i . e . transient ) dynamics of vaccine delivery in our model , we are able to find an optimal strategy in terms of the action to implement ( e . g . diffuse vaccination effort at a specific rate ) rather than an outcome to achieve ( e . g . total coverage ) ., Furthermore , our representation of an explicit vaccination process reveals the intrinsic tradeoff in the consequence of any action ( i . e . area of coverage versus treatment intensity ) , such that vaccinating a population as extensively as possible can be suboptimal in dense demographic settings ., If we consider that population density may change over time , for example when suburban , sparsely-inhabited areas grow , these results would further suggest that the optimal response strategy is likely to differ between outbreaks that happen at different stages of urban development , changing over time from “slow-and-steady” to intermediate rate vaccination ., An emergent pattern of our model is the effect of initial response time on the optimal vaccination diffusion strategy ., As expected , timely response surpasses delayed response in terms of the fraction of the population protected under all density conditions; the distribution of outbreak sizes for a timely response is consistently lower than that for a delayed response ., Interestingly , however , the recommendation from this model is that the best strategy for a timely response is different from that of a delayed response ., In particular , a timely response performs best when vaccination is distributed more slowly , with more effort initially concentrated in the local vicinity of the outbreak epicenter , thus areas ( or individuals ) far from the epicenter indirectly benefit from the stronger effort to locally contain the outbreak ., This containment was not possible with a delayed response in our simulations; therefore , the best option is to distribute protection as quickly as possible to the whole population ., To the extent that the slower vaccination diffusion rate recommended for a timely response is less expensive to implement , our results would suggest that the outcome could be

Introduction, Methods, Results, Discussion

Dynamic models in disease ecology have historically evaluated vaccination strategies under the assumption that they are implemented homogeneously in space and time ., However , this approach fails to formally account for operational and logistical constraints inherent in the distribution of vaccination to the population at risk ., Thus , feedback between the dynamic processes of vaccine distribution and transmission might be overlooked ., Here , we present a spatially explicit , stochastic Susceptible-Infected-Recovered-Vaccinated model that highlights the density-dependence and spatial constraints of various diffusive strategies of vaccination during an outbreak ., The model integrates an agent-based process of disease spread with a partial differential process of vaccination deployment ., We characterize the vaccination response in terms of a diffusion rate that describes the distribution of vaccination to the population at risk from a central location ., This generates an explicit trade-off between slow diffusion , which concentrates effort near the central location , and fast diffusion , which spreads a fixed vaccination effort thinly over a large area ., We use stochastic simulation to identify the optimum vaccination diffusion rate as a function of population density , interaction scale , transmissibility , and vaccine intensity ., Our results show that , conditional on a timely response , the optimal strategy for minimizing outbreak size is to distribute vaccination resource at an intermediate rate: fast enough to outpace the epidemic , but slow enough to achieve local herd immunity ., If the response is delayed , however , the optimal strategy for minimizing outbreak size changes to a rapidly diffusive distribution of vaccination effort ., The latter may also result in significantly larger outbreaks , thus suggesting a benefit of allocating resources to timely outbreak detection and response .

It has long been recognized that an epidemic of infectious disease can be prevented if a sufficient proportion of the susceptible population is vaccinated in advance ., This logic also holds for vaccine-based outbreak response to stop an outbreak of a novel , or re-emerging pathogen , but with an important caveat ., If vaccination is used in response to an outbreak , then it will necessarily take time to achieve the required level of vaccination coverage , during which time the outbreak may continue to spread ., Thus , one must consider the logistical and operational constraints of vaccine distribution to assess the ability of outbreak response vaccination to slow or stop an advancing epidemic ., We develop a simple mathematical framework for representing vaccine distribution in response to an epidemic and solve for the optimal distribution strategy under realistic constraints of total vaccination effort ., Focused deployment near the outbreak epicenter concentrates resources in the area most in need , but may allow the outbreak to spread outside of the response zone ., Broad deployment over the whole population may spread vaccination resources too thin , creating shortages and delays at the local scale that fail to prevent the advancing epidemic ., Thus we found that , in general , the best strategy is an intermediate optimum that deploys vaccine neither too slow to prevent escape from the outbreak epicenter , nor too fast to spread resources too thin ., The specific optimum rate for any given outbreak depends on the infectiousness of the pathogen , the population density , the range of contacts amongst individuals , the timeliness of the response , and the vaccine intensity ., This insight only emerges from linking an epidemic model with a realistic model of outbreak response and highlights the need for further work to merge operations research with epidemic models to develop operationally relevant response strategies .

vaccines, public and occupational health, preventive medicine, population metrics, infectious diseases, medicine and health sciences, cholera vaccines, immunity, epidemiology, disease dynamics, biology and life sciences, immunology, population biology, vaccination and immunization, infectious disease control, population density

journal.pntd.0005885

Modeling the environmental suitability of anthrax in Ghana and estimating populations at risk: Implications for vaccination and control

Anthrax is a soil-borne , zoonotic disease found on nearly every continent ( except Antarctica ) that primarily infects herbivorous animals while secondarily infecting humans through the handling or ingestion of contaminated meat or animal by-products 1 , 2 ., The geographic distribution of the disease appears to be limited by a combination of climatic ( e . g . precipitation and temperature ) and environmental ( e . g . alkaline soil pH ) conditions 3 , 4 ., Under the appropriate ecological conditions , which remain poorly understood , the causative agent of anthrax , Bacillus anthracis , can survive for long-periods of time in the environment , perhaps years 1 , 4 ., Although it has received much attention as a potential agent of bioterrorism , the World Health Organization ( WHO ) has listed anthrax as a neglected disease 5 ., Poor livestock keepers and their animals often experience a disproportionate burden of anthrax in the hyper-endemic regions of Central Asia and West Africa 5 , 6 ., Despite the effectiveness of regular animal vaccination and proper outbreak response following recommended guidelines in controlling anthrax in humans , underreporting of the disease often skews its true burden and geographic distribution making it difficult to implement adequate vaccination campaigns 1 , 7 ., In Ghana , anthrax outbreaks have been reported annually in humans associated with contact with infected livestock and their contaminated animal by-products ( e . g . meat or hides ) 8 ., Anthrax vaccine is manufactured locally by the Central Veterinary Laboratory in Pong-Tamale , Ghana and is fully subsidized by the government ., Despite this , animal outbreaks are documented annually , and primarily affect cattle ., Although both human and animal cases are reported , few human cases are linked to confirmed animal cases 9 ., As a result , surveillance data alone provide limited information to efficiently plan prevention activities ., Previous efforts to elucidate the environmental suitability of anthrax in Africa have been focused on southern countries , such as Zimbabwe 10 , or national parks 11 ., A recent study from West Africa also used a machine learning algorithm to map and model the distribution of anthrax and B . anthracis in Cameroon , Chad , and Nigeria , however , that effort was based on limited sample size and no comparable efforts have been carried out in Ghana 12 ., To support Ghana’s national anthrax control and assessment , we our study had the following objectives: ( 1 ) model the environmental suitability of anthrax; ( 2 ) identify environmental and climatic factors associated with the occurrence of anthrax; ( 3 ) describe seasonal patterns; and ( 4 ) estimate populations at risk ., This work was performed on nationally available data on anthrax outbreaks in livestock from the Ministry of Food and Agriculture in Ghana ., We constructed a GIS of livestock anthrax outbreaks using data collected by the Ghana Field Epidemiology and Laboratory Training Program ( GFELTP ) and the Ministry of Food and Agricultural Veterinary Services ., ( Fig 1 ) ., Outbreaks were mapped using GPS coordinates collected by field personnel responding to outbreaks or the center of the village where the outbreak occurred ., We included data on outbreaks from 2005 through the first 6-months of 2016 included information on the geographic coordinates , date , livestock species , and number of individual animals infected ( periodically recording mortality and survival status ) for each outbreak ., However , total livestock populations on affected properties was rarely reported ., For this study , an outbreak was defined as any location with one or more anthrax cases in animals ., We plotted the seasonality of anthrax outbreaks in relation to the average rainfall during 1991–2015 using data obtained from the Climate Change Knowledge Portal ( http://sdwebx . worldbank . org/climateportal/index . cfm ? page=country_historical_climate&ThisCCode=GHA ) ., We also obtained livestock anthrax vaccine administration data during 2005–2015 from the World Animal health Information Database Interface ( OIE; http://www . oie . int/animal-health-in-the-world/the-world-animal-health-information-system/data-after-2004-wahis-interface/ ) ., Mapping and spatial analysis was performed in Q-GIS version 2 . 14 ( www . qgis . org ) and the R statistical package ( https://www . r-project . org/ ) ., Final maps were produced in ArcGIS version 10 . 3 . 1 ( ESRI , Redlands , CA , USA ) ., We used a combination of environmental and climatic variables at a spatial resolution of 30-arcseconds ( approximately 1km x 1km ) that followed , in part , recent studies in West Africa 13 and Central Asia 14 ( Table 1 ) ., Five “bioclimatic” variables describing measures of temperature and precipitation were obtained from the WorldClim database ( www . worldclim . org ) 15 ., WorldClim variables are interpolated monthly measurements recorded at weather stations located worldwide between 1950 and 2000 ., WorldClim produces bioclimatic variable grids to describe annual trends , seasonality , and ecological parameters such as temperature of the coldest and warmest quarters ., We also used a combination of physical ( sand content ) , chemical ( soil pH ) , and taxonomic classifications of soil characteristics ( cancerous vertisols and humults ) ., Soil data were obtained from the SoilGrids1km database http://www . isric . org/explore/soilgrids ) 16 ., SoilGrid variables were created using spatial model predictions based on a global database of soil profiles and a combination of environmental covariates ., Furthermore , we used two normalized difference vegetation index ( NDVI ) variables describing average conditions and the amplitude of vegetation greenness , which were obtained from the Trypanosomiasis and Land Use in Africa ( TALA ) research group ( Oxford , United Kingdom ) 17 ., TALA variables were derived from temporal Fourier analysed ( TFA ) time series data of advanced very-high resolution radiometer ( AVHRR ) satellite measurements taken between 1992 and 1996 17 ., Mapped variables are shown in S1 Fig . Random Forest ( RF ) modeling 18 , 19 was used to identify environmental characteristics associated with the occurrence of anthrax outbreaks using the ‘randomForest’ package for R . Previous studies have used this approach to map and model the distribution of Anopheles spp ., mosquito vectors in Africa and Europe 20 and reservoirs of avian influenza 21 ., RF modeling has been described and compared to other modeling approaches in detail elsewhere 18 , 22 ., Briefly , RF is a non-parametric method derived from classification and regression trees that consists of a combination of trees built using randomly selected bootstrap samples of the training data ( used to build the model ) , with the number of bootstrap samples equal to the number of trees ( ntrees ) selected ., Each tree is split by randomly sampling a number of predictor variables to use ( mtry ) at each node and then choosing the best split ., Model error estimates are obtained by internal splits of the training data ( 63 . 2% for model building ) and then predicting the data not used to build a tree ( out-of-bag or OOB ) and aggregating these predictions for each ensemble of trees 18 ., Since internal validation of the OOB data is performed , no external testing data is required to validate the model , but testing splits ( external data withheld from the model ) of the data are routinely utilized to assess model performance ., Partial dependence plots and variable importance of RF models were assessed for covariates in the model ., We used an ensemble modeling approach that incorporated information from multiple random splits of our data into training ( 80% ) and testing ( 20% ) sets ., Since our data consisted of presence only records of anthrax outbreaks , we generated pseudo-absence data from all available background data ., Several studies have either relied on internal derivations of pseudo-absence in species distribution models 23 or user-defined generations such as in the modeling of the global distribution of dengue virus 24 ., The required number of user-defined background pseudo-absence draws for every presence location is not standardized ., It has been suggested that a 1:1 random draw of pseudo-absence to presence data in machine learning algorithms such as RF produces optimal results 25 , although variations of this ( 2:1 or 3:1 draws ) have been adopted successfully 24 ., Similarly , pseudo-absence data creation has been shown to influence results; thus , research has recommended filtering pseudo-absence data from locations that are known to fall within suitable habitat or that occur within a defined proximity threshold 25 , 26 ., We first filtered geo-located anthrax presence data in Ghana ( n = 61 ) using a 5km x 5km proximity threshold in order to improve model performance and avoid overfitting 27 ., We generated background pseudo-absence data ( n = 200 ) , from all available background 24 , at a ratio of four absence points to every one filtered presence point ( n = 50 ) , restricting pseudo-absence data to exclude landscape within 5km of presence locations ., We then generated 10 random draws each of 1:1 , 2:1 , and 3:1 pseudo-absence to presence data ( 30 total draws ) with replacement ., Each randomly generated pseudo-absence to presence draw ( n = 30 ) was randomly divided into training and testing data splits to validate model performance ., The final RF models were built using a mtry = 4 at each split and ntrees = 1000 with a combination of variables in which the ensemble list contributed to a mean decrease in accuracy >1% ., The 30 individual RF models were then combined into an ensemble prediction at a spatial resolution of ~1km x 1km and scaled from 0 ( low suitability ) to 1 ( high suitability ) ; uncertainty in the model prediction was calculated by taking the range in the 95% confidence intervals of the ensemble model scaled from 0 ( low uncertainty ) to 1 ( high uncertainty ) following Deribe et al . 28 ., The resulting output of our ensemble RF model represents the environmental suitability of anthrax in Ghana ., To estimate the number of livestock and poor rural livestock keepers at risk in anthrax suitable areas , we dichotomized the modeled environmental suitability into a suitable versus not suitable prediction using a probability threshold that maximized sensitivity and specificity ., We then overlaid a database of global livestock density at a spatial resolution of ~1km x 1km ( http://www . livestock . geo-wiki . org/ ) 29 with the dichotomized anthrax prediction to estimate the livestock populations ( cattle , sheep , goats , and swine ) at risk ., Livestock populations at risk were further stratified to estimate the population at risk within each of the livestock production zones of Ghana using the livestock production systems data version 5 ( http://www . livestock . geo-wiki . org/ ) 29–31 ., Furthermore , we estimated the number of low income rural livestock keepers at risk within each livestock production zone by overlaying the dichotomized anthrax suitable areas with estimates of the population of low income rural livestock keepers provided in Robinson et al . 31 and deriving the fraction of cells that were within our model prediction ., Uncertainty in the populations at risk and 95% confidence intervals were calculated by using the 2 . 5% ( lower ) and 97 . 5% ( upper ) bounds of the ensemble RF model prediction 28 ., Model performance and validation was conducted for each individual RF model and included the internal: OOB error classification , area under the receiver operating characteristics curve ( AUC ) , sensitivity , and specificity ., Additionally , we performed accuracy assessments on the external testing data , which consisted of thirty random subsets of 20% of the data sampled with replacement ., Mean values and 95% confidence intervals were estimated for each accuracy metric ., The AUC has been used extensively in species distribution modeling to measure the discriminatory performance of models 32; an AUC value of 1 indicates a perfect discrimination while values of >0 . 9 are outstanding , 0 . 8–0 . 9 excellent , 0 . 7–0 . 8 acceptable , and <0 . 7 indicate poor discriminatory performance 28 , 33 ., From 2005 through the first 6 months of 2016 , there were 67 reported anthrax outbreaks in livestock ( 61 that were geo-located ) ( Fig 1 ) ., Nationally , there was a mean of 6 ( 95% CI: 4 , 7 ) outbreaks per year with a peak in 2011 ( n = 12 ) and lull in reporting in 2009 ( n = 2 ) ( Fig 2 ) ., The geography of outbreaks shows a higher frequency of anthrax in northern Ghana in the Upper East and Northern regions ., Of the reported outbreaks , 4 ( 6% ) were comprised of two or more livestock types ., Domestic cattle were reported in 53% ( 35 ) of outbreaks , followed by sheep in 32% ( 21 ) , goats in 11% ( 7 ) , and swine in 5% ( 3 ) ., During 2005–2016 , cattle anthrax cases were reported every year except in 2009 ., Sheep cases were ubiquitous annually and were characterized by a large number of deaths in 2012 , the same year there was also a large number of swine cases ( n = 500 ) ( Table 2 ) ., The seasonality of anthrax outbreaks nationally and regionally are illustrated in Fig, 3 . Nationally , outbreaks were reported , on average , across seasons and in every month ( except November ) ., There was an increase in outbreaks in the late winter and early spring months , with February through April having the highest reported number of outbreaks ., On average , there outbreaks appeared to occur in the dry season before the onset of the rains ., Trends in livestock anthrax vaccination among livestock type are shown in Fig, 4 . From 2000–20015 , there was a median of 17 , 957 doses 0–175 thousand of anthrax livestock vaccine administered annually livestock vaccination occurred annually with a median number of doses administered of 19 , 709 range: 0–175 thousand doses , followed by a decline in vaccine administration during 2008–2015 ., No vaccination was administered during the years 2010 , 2012 , and 2013 ., During 2008–2015 , there was a median of 542 range: 0–147 thousand doses doses administered ., In response to ongoing outbreaks , there was a vaccination campaign in 2014 that resulted in nearly an 8-fold increase in the number of doses administered compared to the previous six years ., Among livestock types , cattle were most frequently administered vaccine , followed by sheep , goats and swine ( Fig 4 ) ., The ensemble RF model suggests a latitudinal gradient in the environmental suitability of anthrax in Ghana ( Fig 5A ) ., High environmental suitability was identified in the Northern , Upper East , and Upper West regions of Ghana that encompass seasonal livestock migration routes from Burkina Faso in the north ., Conversely , low or no environmental suitability was identified in southern Ghana among the more acidic soils in the Western , Ashanti , Central , and Eastern regions ., Uncertainty ( range: 0–0 . 20 ) in the model prediction was scaled from 0 to 1 and showed it was highest in the Upper West and Northern regions ( Fig 5B ) ., The internal OOB model validation indicated excellent discrimination with an AUC = 0 . 88 ( 95% CI: 0 . 87 , 0 . 89 ) ., The external validation of anthrax outbreak locations withheld from the model ( testing data ) also showed excellent discrimination ( AUC = 0 . 87 95% CI: 0 . 85 , 0 . 90 ) ., The final list of variables used in the ensemble model are shown in Fig 6 ., A combination of bioclimatic , environmental , and soil characteristics had the greatest impact on the OOB prediction errors ., The most important variables influencing accuracy were: soil pH , bio7 ( annual temperature range ) , and bio14 ( precipitation of the driest month ) ( S2 Fig ) ., The probability of the occurrence of anthrax increased in a step like manner in response to soil pH , increasing as the soil became more alkaline , between 5 . 5 and 6 . 5 , and again between 6 . 5 and 7 . 0 ., Annual temperature ranges between 16 and 20°C were also related to a greater probability of occurrence ., The occurrence of anthrax showed an affinity for low values of precipitation during the driest month ( 0 to 10 mm ) and then dropped off dramatically as precipitation increased from 10 to 40 mm ., Furthermore , as average NDVI ( wd0114a0 ) increased from 0 . 3 to 0 . 6 the probability of anthrax occurrence decreased linearly , with a more suitable range of vegetation greenness identified in the lower ranges between 0 . 1 and 0 . 3 ( Fig 6 ) ., To estimate livestock and human populations at risk , we dichotomized the environmental suitability prediction ( on a continuous probability scale ) into suitable versus non-suitable environments for anthrax based on the optimal threshold ( 0 . 46 ) that maximized sensitivity ( 0 . 78 ) plus specificity ( 0 . 89 ) ( Fig 7 ) ., The dichotomized prediction shows a marked north-south demarcation in the suitability of anthrax , with a majority of northern Ghana predicted as suitable within the accompanying upper ( 97 . 5% ) and lower ( 2 . 5% ) confidence bounds ., The national livestock population located in areas environmentally suitable for anthrax was estimated to be ≈ 2 . 2 ( 95% CI: 2 . 0 , 2 . 5 ) million ( Table 3 ) ., More than 50% of the livestock populations at risk were sheep and cattle ( 650 95% CI: 583 , 745 thousand and 480 95% CI: 434 , 527 thousand , respectively ) ., Among livestock production systems , semi-arid rain-fed , mixed crop livestock systems ( MRA ) contained the greatest number of livestock at risk > 1 . 2 ( 95% CI: 1 . 1 , 1 . 3 ) million ( Table 3 ) ., Nationally , there are approximately 3 million low income rural livestock keepers in Ghana ( Table 4 ) ., Our model suggests that ≈ 805 ( 95% CI: 519 , 890 ) thousand are located in areas suitable for anthrax , with the majority located in a humid and sub-humid , mixed crop livestock system production zone ( MRH ) ., Anthrax is a globally distributed neglected disease that is often underreported , particularly in West Africa where it is hyper-endemic 1 , 2 , 6 , 13 ., Given the reliance of control on the vaccination of livestock , understanding the occurrence of anthrax is crucial for identifying populations at risk in order to disseminate limited resources ., Here , we used data on the location of livestock outbreaks to identify seasonal patterns and model the environmental suitability of anthrax in Ghana ., In keeping with previous studies , our findings indicate a defined outbreak season with a combination of ecological constraints on the potential geographic distribution of anthrax 3 , 34 ., Our modeled prediction suggests a marked ecological divide separating the broad areas of environmental suitability in northern Ghana from the southern part of the country ., Additionally , we estimated that populations characteristically at high risk for anthrax , which included >3 million combined ruminant livestock and poor rural livestock keepers are situated within the predicted anthrax risk zone ., Based on our estimates , current anthrax vaccination efforts cover only a fraction of the livestock potentially at risk ., Hence , these findings can be used to better direct public health intervention strategies and inform surveillance ., Official reports of livestock anthrax in endemic areas often go undocumented for a number of reasons , including the inability or unwillingness to report , limited surveillance capacity , and a lack of local knowledge about the disease 1 ., In Ghana , livestock cases are likely underreported due to the slaughter and consumption of sick or dead animals 8 , 35 , consistent with findings in the Caucasus and elsewhere 1 , 6 , 36 , 37 ., This practice is often undertaken as a means of recouping economic losses from livestock mortality as well as providing food and a readily available source of protein 1 , 8 , 35 ., The livestock anthrax outbreak data we used in this study were concordant with data reported to OIE during the same time frame suggesting Veterinary Services in Ghana are compliant with international reporting requirements ( http://www . oie . int/wahis_2/public/wahid . php/Wahidhome/Home ) ., Despite the close proximity to the equator , we identified marked seasonality in anthrax reporting; outbreaks increased during the onset of the rainy season from February through April ., Similar patterns of anthrax outbreaks associated with the rainy-season have also been reported in Namibia 34 ., One hypothesis suggests that there is greater soil consumption among ruminants during with the rainy season 34 , although soil exposure during the dry season has also been hypothesized as a cause of anthrax outbreaks 1 ., Regardless , these findings suggest vaccination of livestock could be carried out in Ghana ahead of the peak outbreak season ( September–November ) ., Livestock anthrax control in Ghana follows a similar trend in many endemic regions of reactively vaccinating in response to anthrax outbreaks 1 , 38 ., In Ghana , the livestock population we identified at risk comprises approximately ≈ 25% of the total national livestock population 29 ., Based on official vaccination reports ( Fig 4 ) , our estimates of the livestock populations at risk indicates poor vaccine coverage; this finding is consistent with ongoing outbreaks in endemic communities in Ghana where vaccination has not been officially documented for at least a decade 39 ., Barriers to vaccine uptake such as practices of livestock keepers my also affect coverage 1 , 40 ., However , Ghana faces additional control challenges with the potential presence of B . cereus biovar ( bv ) anthracis and West Africa strains ( D and E Clades , respectively 41 ) ., The West African strains have been hypothesized to evade the Sterne vaccine , which is the vaccine used in Ghana and throughout much of the world 13 , 42 ., Further research is needed on vaccine efficacy and to understand what proportion of anthrax outbreaks are due to either insufficient application methods or the vaccine itself ., Research has suggested that soil pH >6 . 1 in conjunction with high calcium levels are a crucial component of B . anthracis spore survival 1 , 4 , 43 ., Alkaline soils were also found to be associated with the persistence of anthrax transmission over several years 43 , 44 ., In keeping with these findings , we identified an increasingly higher likelihood of anthrax occurrence in soils as pH increased from 5 . 5 to 7 . 0 and with an increasing level of calcareous vertisols ., The association of anthrax suitability with lower levels of precipitation in our model is in line with reports that have documented soil nutrient leaching in regions with high precipitation , which may lead to soil acidification 45 ., We predicted an area of environmental suitability for anthrax that encompasses ≈ 36% of Ghana’s total area ( Fig 7 ) ; this is demarcated by a south ( largely unsuitable ) to north ( highly suitable ) divide , which closely mirrors the ecotone transitions from southern tropical and deciduous forests to the northern Sudanian and Guinea Savanna ., Our study had several limitations ., As with all neglected zoonoses , our data likely represent an underestimation of the true burden of disease due to underreporting and limited resources for surveillance and testing ., To better address issues with diagnostic testing and reporting we used a more contemporary dataset of anthrax outbreaks recorded during the last decade ., Anthrax can also be transmitted from contaminated feed that is imported , and animal mortality may occur from livestock moved across long distances; however , we had no information on any outbreaks arising in these instances 1 , 46 ., The use of machine learning algorithms to model the distribution of environmental pathogens has been well described , but such approaches , by their definition in conjunction with the use of averaged climate data , may over-generalize the landscape that supports the occurrence of anthrax outbreaks ., Other factors not included in our models that may influence the occurrence of anthrax include the health and immune status of the livestock 47 ., In conclusion , the current anthrax situation in West Africa , and in particular Ghana , remains a public and veterinary health threat due to challenges with reporting , surveillance , and control ., Our findings suggest that broad areas of northern Ghana are environmentally suitable for anthrax ., Furthermore , based on recent vaccination efforts , our estimates indicate that only a fraction of livestock at risk are being vaccinated ., These findings can be used to help improve differential diagnostics , vaccine coverage estimates , and surveillance efforts ., Given the reliance on agriculture and the large population of low income rural livestock keepers at risk in the northern part of the country where predicted suitability was highest , future control efforts should focus on improving livestock vaccination coverage and public awareness of the disease , prioritizing communities in the predicted anthrax zone .

Introduction, Methods, Results, Discussion

Anthrax is hyper-endemic in West Africa ., Despite the effectiveness of livestock vaccines in controlling anthrax , underreporting , logistics , and limited resources makes implementing vaccination campaigns difficult ., To better understand the geographic limits of anthrax , elucidate environmental factors related to its occurrence , and identify human and livestock populations at risk , we developed predictive models of the environmental suitability of anthrax in Ghana ., We obtained data on the location and date of livestock anthrax from veterinary and outbreak response records in Ghana during 2005–2016 , as well as livestock vaccination registers and population estimates of characteristically high-risk groups ., To predict the environmental suitability of anthrax , we used an ensemble of random forest ( RF ) models built using a combination of climatic and environmental factors ., From 2005 through the first six months of 2016 , there were 67 anthrax outbreaks ( 851 cases ) in livestock; outbreaks showed a seasonal peak during February through April and primarily involved cattle ., There was a median of 19 , 709 vaccine doses range: 0–175 thousand administered annually ., Results from the RF model suggest a marked ecological divide separating the broad areas of environmental suitability in northern Ghana from the southern part of the country ., Increasing alkaline soil pH was associated with a higher probability of anthrax occurrence ., We estimated 2 . 2 ( 95% CI: 2 . 0 , 2 . 5 ) million livestock and 805 ( 95% CI: 519 , 890 ) thousand low income rural livestock keepers were located in anthrax risk areas ., Based on our estimates , the current anthrax vaccination efforts in Ghana cover a fraction of the livestock potentially at risk , thus control efforts should be focused on improving vaccine coverage among high risk groups .

Anthrax is a soil-borne zoonotic disease found worldwide ., In the West African nation of Ghana , anthrax outbreaks occur annually with a high burden to livestock keepers and their animals ., To control anthrax in both humans and animals , annual livestock vaccination is recommended in endemic regions ., However , in resource poor areas distributing and administering vaccine is difficult , in part , due to underreporting , logistical issues , limited resources , and an under appreciation of the geographic extent of anthrax risk zones ., Our objective was to model high spatial resolution anthrax outbreak data , collected in Ghana , using a machine learning algorithm ( random forest ) ., To achieve this , we used a combination of climatic and environmental characteristics to predict the potential environmental suitability of anthrax , map its distribution , and identify livestock and human populations at risk ., Results indicate a marked ecological divide separating the broad areas of environmental suitability in northern Ghana from the southern part of the country , which closely mirrors the ecotone transitions from southern tropical and deciduous forests to the northern Sudanian and Guinea Savanna ., Based on our model prediction , we estimated >3 million combined ruminant livestock and low income livestock keepers are situated in anthrax risk zones ., These findings suggest a low level of annual livestock vaccination coverage among high risk groups ., Thus , integrating control strategies from both the veterinary and human health sectors are needed to improve surveillance and increase vaccine dissemination and adoption by rural livestock keepers in Ghana and the surrounding region .

livestock, medicine and health sciences, immunology, geographical locations, vaccines, preventive medicine, bacterial diseases, infectious disease control, vaccination and immunization, africa, veterinary science, veterinary medicine, public and occupational health, infectious diseases, anthrax, veterinary diseases, zoonoses, livestock care, agriculture, people and places, ghana, biology and life sciences

journal.ppat.0040037

Nucleotide Biosynthesis Is Critical for Growth of Bacteria in Human Blood

Bacteremia , characterized by the presence of pathogenic bacteria in the bloodstream , is a major cause of morbidity and mortality worldwide ., Bacteremia often leads to sepsis and death 1 ., To survive in blood , bacterial pathogens must evade a multitude of host defense mechanisms such as complement-mediated lysis , phagocytosis and antimicrobial peptide-mediated killing ., The spectrum of complement resistance mechanisms of bacteria is very wide and includes different activities like antigenic variation , use of membrane proteins to block binding of complement proteins and capsule biosynthesis 2–5 ., Most extracellular pathogens avoid phagocytosis by synthesizing a protective capsule that helps to evade recognition 6 , 7 ., Direct degradation of antimicrobial peptides and modification of cell surface properties are the major strategies used by bacteria to resist the bactericidal activity of host antimicrobial peptides like the platelet-derived thrombocidins in blood 8 , 9 ., However , these immune-evasion strategies are mostly pathogen-specific and it is difficult to use the underlying mechanisms as targets for broad-spectrum antibiotics ., To proliferate in the various host niches that bacterial pathogens invade during the course of infection they need to adjust their metabolism to suit local nutrient availability ., For example , the amount of free iron in human blood is 10−18 M 10 ., Most pathogens need 10−6 to 10−7 M iron for growth and hence they use very complex and diverse strategies to acquire and store iron in order to grow in this iron-limiting environment of the hosts blood 11 ., The most common strategy of iron acquisition is the production of siderophores , high-affinity ( 1030 M−1 ) ferric iron-binding molecules that can sequester iron from the hosts iron-binding proteins ., Knockout of iron acquisition mechanisms has been shown to attenuate the virulence of many bacteria 12 , 13 ., However , the uniqueness of blood as a niche for bacterial growth extends far beyond iron limitation: low availability of certain nutrients may define the ability of bacteria to proliferate in blood ., Although the absolute abundance of various metabolites , such as amino acids and nucleotides , in human serum is known 14 , 15 , it is unclear which nutrients are freely available and sufficient and which are limiting for bacterial growth in human serum ., Several previous reports described the importance of amino acid or nucleotide biosynthesis by bacteria in the cause of infection ., For instance , certain auxotroph mutants of Salmonella 16–18 , Staphylococcus aureus 19 or Streptococcus pneumoniae 20 have been shown to be avirulent in murine models of infection ., These reports suggest that purines and some amino acids are scarce in vivo ., Also , the inactivation of a potassium transporter in Vibrio vulnificus 21 or of a manganese , zinc and iron transporter in Streptococcus pyogenes 22 have been shown to attenuate virulence of the respective pathogens , suggesting that acquisition of some metal ions is critical for growth in vivo ., Notwithstanding the fact that most of these genes were identified in non-exhaustive screens , these studies only provide evidence of the limited availability of the corresponding metabolites in the host ., They do not describe nutrient availability in various host compartments invaded by the pathogens during infection ., It is unclear whether the reduced virulence of these mutants can be attributed to their inability to grow specifically in blood ., The comprehensive identification of genes that are critical , specifically for the bloodstream growth of pathogens , has never been attempted ., Hence , crucial nutrient requirements that need to be fulfilled during bacterial growth in blood are essentially unknown ., Identification of the limiting nutrients and of bacterial genes that are critical for growth in blood can pinpoint biosynthesis and acquisition strategies that are crucial during the bacteremic stage of infection ., Enzymes critical for survival and proliferation of pathogenic bacteria in blood can provide potential targets for treatment of bloodstream infections ., To this end , we sought to identify genes required for the growth of bacteria in human blood ., We screened a comprehensive gene-deletion library of the model Gram-negative organism , Escherichia coli , for mutants unable to grow in human serum ., We found that de novo purine and pyrimidine biosynthesis is the key pathway critical for E . coli growth in serum , thereby revealing the limited availability of nucleotide precursors as the major limitation for bacterial growth in blood ., Salmonella enterica , an important Gram-negative pathogen , exhibited a similar requirement for de novo biosynthesis of purines and pyrimidines for growth in serum ., Deletion of the corresponding genes in the evolutionarily distant Gram-positive pathogen Bacillus anthracis demonstrated the universal need for these two biosynthetic pathways for bacterial growth in serum ., Our major goal was to identify genes that are critical for the survival and growth of bacteria in blood ., Specifically , we were interested in identifying genes that mediate adaptation to the unique nutrient composition of blood rather than those which facilitate immune evasion ., Hence , in our experiments we used human serum in which the function of the complement system was inactivated by heat-treatment ., E . coli is a major cause of Gram-negative bacteremia in hospitalized patients 23 ., We used E . coli as an experimental model for our initial experiments ., Specifically , we used a defined library of 3985 E . coli single-gene deletion mutants ( “Keio collection” ) , where all non-essential genes of an E . coli K12 laboratory strain BW25113 have been individually replaced by a kanamycin-resistance cassette 24 ., For the identification of genes required for the growth of bacteria in serum , we employed a genetic technique called MGK ( Monitoring of Gene Knockouts ) 25 ., MGK is a microarray-based approach that uses the chromosomal flanks of inactivated genes as hybridization targets for custom-designed oligonucleotide microarrays ., It allows simultaneous assessment of the relative abundance of thousands of mutants in a population and identification of genes whose inactivation is unfavorable for cell growth under selective conditions ., To apply MGK , mutants in the Keio collection were individually grown and mixed at a similar ratio ( see Protocol S1 for details ) ., The pooled library was grown in either serum or in LB for 5 h ( approximately 4 generations in serum ) ( Figure 1A ) ., Mutants lacking genes critical for growth in blood are expected to be underrepresented in the resulting population of cells grown in serum ., Harvested cells were allowed to re-grow in fresh LB medium in order to enrich the population for viable cells and minimize isolation of genomic DNA from dead cells ., “MGK targets” corresponding to the flanks of inactivated genes were generated as described in Protocol S1 using genomic DNA isolated from cells grown in the reference ( LB ) and the selective ( serum ) conditions ( Figure 1A ) , and co-hybridized to an oligonucleotide microarray ., Two independent MGK experiments ( with dye-swapping ) were performed ., Twenty-two mutants with a potential growth defect in serum were identified that consistently showed at least a 2-fold reduction in the hybridization signal of the serum sample as compared to the LB sample ., Strikingly , the majority of these mutants ( 15 out of 22 ) carried a deletion of a gene involved in biosynthesis of either purines or pyrimidines ( Table 1 and Figure 2 ) ., This result suggested that the de novo biosynthesis of purines and pyrimidines is crucial for the growth of E . coli in human serum and that the scarcity of nucleotide precursors is the major limiting factor for bacterial growth in blood ., In an MGK experiment , thousands of mutants are grown together in a mixed population and growth characteristics of each mutant can be potentially affected by metabolites secreted by other cells ., In addition , during re-growth in LB media , the mutants that had growth defects in serum could potentially catch up with the rest of the cells ., To exclude this scenario , we supplemented the MGK approach with an independent screen involving replica growing of the 3 , 985 individual mutants from the Keio collection , arrayed in a 96-well format , in serum and LB ., The inherent turbidity of serum prevents the use of optical density as a measure of bacterial growth ., Therefore , we used a dye , 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ( MTT ) , to detect viable cells 26 , 27 ., After overnight incubation of mutants in 96-well plates in LB or in human serum , MTT dye was added to the wells and the plates were incubated for another 4–5 h at 37°C ., Overnight incubation of the cells in human serum before addition of the MTT dye is expected to identify mutants that are severely impaired in growth in serum rather than mutants that show only modest growth defects ., Live , actively growing bacteria reduce MTT to a blue formazan precipitate resulting in a deep blue color of the live serum cultures ., The lack of a blue color served as a qualitative indicator of the inability of that mutant to grow in serum ( Figure 1B ) ., The MTT screen identified 21 mutants that failed to grow selectively in serum ( Table 1 and Figure S1A ) ., Of these , 17 mutants ( 81% ) carried deletions of genes involved in the nucleotide biosynthesis pathway ., Fifteen of these 17 mutants were also identified by the MGK analysis ( Figure 2 ) ., To verify the phenotypes of E . coli mutants identified by the MGK and MTT screens , we followed their survival and growth in human serum by determining the number of colony forming units ( cfu ) ., This rigorous verification showed that mutants which lacked genes belonging to pathways other than purine or pyrimidine biosynthesis ( gcvR , rpoN , lysS , ihfB and rseA identified only by the MGK analysis and nadB , panB , panC , iscS identified only by the MTT assay ) were apparently false positives ., Two mutants , ydaS and ydaT , carrying deletions in genes with unknown function , found only in the MGK screen , exhibited a mild growth defect ( ∼10-fold reduction in cfu ) and were not studied further ., Notably , however , all mutants carrying a deletion in one of the pur or pyr genes showed a significant ( 20- to ∼1 , 000-fold ) reduction in the viable cell number compared to wild-type E . coli after 24 h growth in serum ( Figure 3A ) ., These included 15 pur or pyr mutants identified by both MGK and MTT screens and two pur mutants ( guaA and guaB ) identified only by the MTT screen ( Figure 2 ) ., Importantly , after incubation in serum , the cfu counts of some mutants dropped below the initial inoculum suggesting that these mutants not only had a growth defect , but were actively dying in serum ( Figure S1B ) ., All pur and pyr mutants grew well in LB medium , indicating that the growth defect was serum-specific ( Figure S1B ) ., These results suggest that nucleotide biosynthesis is a critical metabolic function required for growth of E . coli in human serum and that the scarcity of nucleic acid precursors , but not other metabolites , is the key metabolic limitation for bacterial growth in human blood ., Some of the E . coli nucleotide biosynthesis genes are organized in operons ., Therefore , their replacement by the gene inactivation cassette could have a polar effect on the expression of downstream genes ., We tested and eliminated this possibility by genetic-complementation studies ., Structural regions of three operon-encoded E . coli genes carA , pyrE , and guaB ( the first genes in the respective operons ) ( Figure S1C ) , along with ∼300 bp upstream regions that include their native promoters , were cloned on a suitable vector and introduced into corresponding mutant strains ., The genetically complemented E . coli strains ( carA/pCarAEC , purE/pPurEEC , guaB/pGuaBEC ) thus obtained ( Table S1 ) were tested for growth in serum ., Similarly complemented two strains of gene deletions corresponding to monocistronic operons , purA and pyrE ( purA/pPurAEC and pyrE/pPyrEEC ) were also tested ., All complemented E . coli mutant strains grew as well as the wild type in serum ( Figure 3B ) ., This confirmed that the observed growth defect of the E . coli mutants in serum was due to the lack of the corresponding gene and not the manifestation of polar effects of gene replacement on downstream genes ., In order to determine whether the observed growth defect of the E . coli mutants in serum was indeed due to a limiting supply of nucleic acid precursors , mutant growth was tested in serum supplemented with an appropriate nucleobase , either adenine or uracil , for the purine or pyrimidine biosynthesis mutants respectively ., Addition of the appropriate nucleobase to serum rescued the growth of the mutants ( Figure 3C and 3D ) ., This result confirmed that the purine and pyrimidine deficiency of human blood forces invading bacteria to rely on the de novo biosynthesis of these metabolites ., Virulent E . coli strains are extracellular enteric pathogens ., In contrast , the Gram-negative pathogen , Salmonella enterica serovar Typhimurium ( S . typhimurium ) , can also replicate intracellularly in phagocytes ., Previous studies have shown that inactivation of nucleotide biosynthesis genes in S . typhimurium attenuates its virulence 16 , 28 , 29 ., This effect has been attributed solely to the inability of the mutants to colonize the intracellular niche ., Our finding that the inactivation of nucleotide biosynthesis genes prevents E . coli from growing in human serum prompted us to test whether Salmonella strains defective in purine or pyrimidine biosynthesis show growth defects in serum ., We tested the growth in human serum of 14 different S . typhimurium mutants in which pur or pyr genes were inactivated by transposon insertions ( Table S1 ) ., Most of the mutants showed a substantial growth defect with 10- to ∼100-fold or more reduction in viable cell counts after 24 h of growth in human serum as compared to the wild-type strain ( Figure 3E ) ., This result demonstrated that de novo nucleotide biosynthesis is required for growth of S . typhimurium in human serum and that the attenuated virulence of such mutants may be due , at least in part , to an inability of the pathogen to multiply in the hosts blood ., The systemic stage of the life-threatening anthrax infection is characterized by the rapid growth of B . anthracis in the hosts blood reaching up to108 bacteria/ml 30 ., Few treatment options are available for the late stages of anthrax infections ., Thus , it was of particular interest to investigate whether purine and pyrimidine biosynthesis is critical for the growth of Gram-positive B . anthracis in human serum , as was observed for Gram-negative E . coli and S . enterica ., Mutants with deletions in either pur ( purE and purK ) or pyr ( carA and pyrC ) genes were constructed in B . anthracis Sterne 34F2 strain ( pXO1+ pXO2− ) by allelic gene replacement ., In agreement with the observations made with Gram-negative pathogens , all of the B . anthracis mutants displayed a severe growth defect in human serum: a 50- to ∼1 , 000-fold decrease in viable cell counts as compared to the wild type after 24 h ( Figure 4A ) ., None of these mutants showed reduced growth in LB medium ( data not shown ) ., Introduction of the plasmid carrying the deleted gene or addition of appropriate nucleobases rescued growth of these mutants in serum ( Figure 4B and 4C and Table S1 ) ., This result demonstrates that the limiting amounts of purines and pyrimidines in serum restrict the growth of B . anthracis mutants and shows that de novo nucleotide biosynthesis is a conserved requirement for the growth of at least three bacterial species in human serum ., We hypothesized that the growth defect exhibited by B . anthracis nucleotide biosynthesis mutants in serum would manifest at the bacteremic stage of the infection resulting in attenuated virulence ., We used a murine model of anthrax infection to test this hypothesis ., For these experiments we employed the B . anthracis Sterne strain , which causes lethal infections in certain strains of inbred mice with pathologies similar to systemic anthrax infection in humans 31 , 32 ., Murine serum contains about 30-fold more cytidine as compared to human serum ( 3 μM compared to 0 . 1 μM , respectively ) 14 ., Therefore , it was not surprising that unlike in human serum , the pyrimidine biosynthesis mutants of B . anthracis did not show any strong growth defect in murine serum ., However , as expected , purine biosynthesis mutants purE and purK were defective for growth in murine serum ( Figure S3 ) ., The virulence of these two mutants was tested in a murine infection model in which mice were challenged by direct intravenous inoculation with increasing numbers of bacilli and observed for survival following this experimental bacteremia ., The purE knockout mutant showed a dramatic decrease in virulence as evidenced by ∼3 . 5 log increase in LD50 ( p = 0 . 002 ) and an increased survival of the challenged mouse cohort ( p < 0 . 001 ) as compared to mice challenged with the wild-type Sterne strain ( Figure 4D ) ., Mice challenged with the purE mutant remained healthy for the entire additional 2-week period of observation and no bacteria could be cultured from their blood or spleen ., In contrast , a purE mutant strain complemented with a plasmid carrying the purE gene ( purE/pPurEBA ) , was as virulent as the wild-type Sterne strain ., Unexpectedly , the purK mutant , which exhibited a similar growth defect in serum in vitro as the purE mutant was almost as virulent as the wild-type Sterne strain ( Figure 4D ) ., This result showed that purE , but apparently not purK , is essential for virulence of B . anthracis in mice and thus reveals PurE as a putative antibiotic target for treatment of anthrax bacteremia ., In this paper we demonstrate that de novo nucleotide biosynthesis is critical for survival and growth of bacteria in human serum ., A near exhaustive search using two independent screening strategies based on co-growth of E . coli gene knockout mutants ( MGK ) and analysis of individual mutants ( MTT assay ) applied to a comprehensive library of E . coli mutants consistently pointed to the importance of mainly pur and pyr genes for E . coli growth in human serum ., The overlap of the results of both screens identified 15 pur or pyr genes that are required for successful growth of E . coli in human serum ( Figure 2 ) ., This result was confirmed for two other pathogenic species of bacteria , Gram-negative S . typhimurium and Gram-positive B . anthracis ., Inactivation of most of the pur and pyr genes was detrimental to bacterial growth in serum ., Of the 13 non-essential genes involved in the purine biosynthetic pathway in E . coli , only two genes , purN and purT , were not identified as being critical for growth in human serum ( Figure 2 ) ., This is not surprising because purN and purT both encode 5-phosphoribosylglycinamide transformylases with partly overlapping specificities 33 , and their individual inactivation should have little effect on cell growth ., Of the 9 non-essential genes involved in the pyrimidine biosynthetic pathway our screens did not identify pyrI , pyrD , and ndk mutants ., Upon checking the growth of the pyrD mutant in serum , we observed a significant growth defect similar to that of the mutants identified by the two screens ( Figure S2A ) ., The other two mutants did not show strong phenotype in serum ( Figure S2A ) ., This result was expected because pyrI encodes the regulatory subunit of the aspartate carbamoyltransferase that is not critical for the function of the enzyme 34 , while Ndk is a nucleoside-diphosphate kinase whose function is partly redundant 35 ., Iron acquisition genes are known to be important for bacterial adaptation to growth in the iron-limiting environment of blood 10 ., Yet no such mutants were identified in our screens , and testing of several such individual E . coli mutants ( entA , fepE , fecA , and tonB ) showed that their growth in serum was not impaired ( Figure S2B ) ., One possible explanation for this result is that heat inactivation destroys transferrin-iron complexes and releases free iron that can be used by bacteria ., The virulence of B . anthracis pur mutants has been previously characterized in a murine peritoneal cavity infection model 36 ., Of all the mutants tested by Ivanovics et al , only those lacking PurA or PurB activity were found to be significantly attenuated in mice ., On the other hand , our study shows that the B . anthracis purE mutant is significantly attenuated in virulence in a murine bacteremia model ., These results suggest that only certain enzymes in the purine biosynthetic pathway can be potential targets for antibiotics ., The observed attenuation of the B . anthracis purE mutant not only reveals the PurE enzyme as a novel target for the development of anti-anthrax therapies , but also indicates that deletion of purE in a fully virulent B . anthracis strain could be a promising way to develop a live attenuated vaccine ., In contrast to the purE knockout strain , the purK mutant of B . anthracis remained virulent and killed mice with an LD50 that was only slightly higher than that of the wild-type Sterne strain ( Figure 4D ) ., This result is not too surprising ., PurK catalyses the carboxylation of aminoimidazole ribonucleotide ( AIR ) leading to an unstable intermediate , N5-carboxyaminoimdazole ribonucleotide ( N5-CAIR ) 37 , 38 , which is then converted by PurE to carboxyaminoimidazole ribonucleotide ( CAIR ) ., In vitro studies showed that a significant fraction of AIR can be non-enzymatically converted to N5-CAIR at a high concentration of bicarbonate 39 ., The small amounts of N5-CAIR produced spontaneously in the purK mutant might relieve the block in purine biosynthesis and rescue the auxotrophy 40 ., Our data strongly suggest that nucleotide biosynthesis is the key metabolic pathway which is critical for proliferation of bacterial pathogens in blood ., It might be argued that our conclusions could be deduced empirically from a known low concentration of free nucleobases in the human blood serum ., Indeed , some previous studies pointed to a relative scarcity of purines in blood 19 ., However , other studies suggested that purines and pyrimidines are sufficiently abundant in this niche 41 and the importance of nucleotide prototrophy for bacterial growth in human blood has never been clearly demonstrated ., Furthermore , the simple knowledge of the nutrient composition of blood serum is not sufficient to predict which biochemical pathway will be rate-limiting for bacterial growth in this niche ., Thus , although threonine , lysine , histidine , aromatic amino acid and riboflavin biosynthetic functions have been shown to be important for bacterial infections , none of these pathways appears to be critical for growth of bacteria in blood 19 , 42 , 43 ., Several previous studies have shown that pathogens require nucleotide biosynthesis to establish a successful infection 16 , 19 , 20 , 44 , 45 ., Our study , however , is the first to our knowledge to demonstrate the importance of the de novo synthesis of purines and pyrimidines for successful proliferation of pathogens specifically in blood ., Reproduction of the phenotypes associated with several identified gene knockouts in Gram-negative bacteria , E . coli and S . typhimurium , in a Gram-positive pathogen B . anthracis , suggests the universal importance of the nucleotide biosynthesis pathways for growth of bacteria in the bloodstream ., Indeed , as our data shows , nucleotide biosynthesis may be the only metabolic pathway that is universally required by bacterial pathogens invading the blood ., The corresponding enzymes thus appear as putative antibiotic targets for curbing bacterial growth in the bacteremia stage of infection ., Of the enzymes identified in this study as essential for growth of bacterial pathogens in human blood , two , PyrC and PurE , are especially attractive as targets for antibiotics ., PyrC is a dihydroorotase that catalyzes the reversible cyclization of carbamoyl aspartate to dihydroorotate 46 ., The known dihydroorotases fall into two major sequence classes ., Class I enzymes are conserved among fungi and most Gram-negative proteobacteria whereas higher eukaryotes use class II dihydroorotases 47 ., The limited sequence conservation between the two types of PyrC ( less than 20% ) 48 makes this enzyme an attractive target for the treatment of Gram-negative and fungal infections ., PurE shows a higher degree of overall conservation between bacteria and eukaryotes ( Table S2 ) ., However , the catalytic mechanisms of the bacterial and eukaryotic PurE enzymes are substantially different ., In bacteria , PurE utilizes N5-CAIR to make CAIR , whereas human PurE uses AIR and CO2 and does not recognize N5-CAIR as a substrate 49 , pointing to a significant difference in the structure of the catalytic centers in the bacterial and human enzymes ., Given that the B . anthracis purE mutant is avirulent , apparently due to inability of bacteria to proliferate in blood , PurE emerges as an attractive target for antibiotic therapies ., Nucleotides are important substrates not only for DNA synthesis but also for DNA repair ., Thus inhibitors targeting the nucleotide biosynthesis functions identified in this study can also impede the repair processes following bacterial DNA damage induced by the hosts reactive oxygen intermediates during infection ., Detailed exploration of PyrC and PurE as well as other enzymes of the nucleotide biosynthesis pathways as potential antibiotic targets may lead to development of new therapies for treatment of bacterial bloodstream infections ., The collection of gene knockout mutants of the E . coli strain BW25113 ( Keio collection ) was obtained from Nara institute of Science and Technology , Japan 24 ., Wild-type Salmonella enterica serovar Typhimurium LT2 and isogenic Tn5 transposon insertion mutants were obtained from the Salmonella Genetics Stock Center ( Alberta , Canada ) ( http://www . ucalgary . ca/~kesander/ ) ., B . anthracis Sterne ( pXO1+ , pXO2− ) wild-type strain 50 was used to construct pur and pyr biosynthesis mutants ., All the cloning procedures were carried out using the OneShot TOP10 chemically competent E . coli cells ( Invitrogen ) as the host ., E . coli strain GM 2163 ( New England Biolabs ) was used to obtain unmethylated plasmid DNA for transformation of B . anthracis ., The vector pKS1 was used to construct deletion mutants in B . anthracis 51 ., Genetic complementation was carried out using recombinant plasmids based on the pBAD22 vector for E . coli or pHT304 vector 52 and pHTPSAC vector ( H . Lee , unpublished data ) for B . anthracis ., Construction of the plasmids used for genetic complementation is outlined in Protocol S1 ., All primers used in this study are listed in Table S3 ., For all experiments involving growth of bacteria in serum , frozen serum Sterile filtered type-AB human serum , Cat No . H4522 ( Sigma ) was thawed at 37°C , heat-inactivated by incubation for 30 min at 56°C and buffered at pH 7 . 0 by addition of 1 M HEPES buffer ( pH 5 . 2 ) to the final concentration of 5 mM ., Control cultures were grown in Luria-Bertani medium 53 ., When necessary , antibiotics were added at the following final concentrations: kanamycin 30 μg/ml for E . coli or 100 μg/ml for B . anthracis , erythromycin 200 μg/ml for E . coli or 5 μg/ml for B . anthracis and ampicillin 100 μg/ml for E . coli ., The MGK selection was carried out essentially as described , by Smith et al 25 , with minor modifications specified in Protocol S1 ., Mutants from the Keio collection were replicated into 96-well plates containing 100 μl/well of LB supplemented with kanamycin ., Plates were incubated overnight at 37°C with shaking ., Cells were pelleted , washed once with PBS and resuspended in 100 μl PBS ., 1 μl culture from each well was inoculated into fresh 96-well plates containing either LB or heat-inactivated human serum and incubated overnight with shaking at 37°C ., The next day , 10 μl of 0 . 5% dye solution MTT; ( 3- ( 4 , 5-dimethylthiazol-2-yl ) -2 , 5-diphenyltetrazolium bromide ) in PBS was added to each well and the plates were incubated for 4–5 h at 37°C ., When quantitation was required , plates were scanned using a Hewlett Packard Scanjet 5300C and Adobe Photoshop was used to determine luminosity values in each well ( Figure S1A ) ., B . anthracis Sterne strain , the purE and purK knockout mutants , or genetically complemented mutant purE/pPurEBA ( Table S1 ) were grown in LB media , washed three times with PBS , and resuspended in PBS at a cell density of ∼5x108 cfu/ml ., Cohorts of adult ( 8–10 weeks old ) , female , NIH-Swiss mice , obtained from Frederick Cancer Research Center , were inoculated via tail vein with 0 . 2 ml of PBS containing serial 10-fold dilutions of vegetative B . anthracis cells ( 103-108 cfu/mouse; 4–6 mice per bacterial dose in the studies represented ) ., Mice were observed daily for 6 days for signs of fatal outcome , using humane end points approved by the UIC institutional animal care and use committee ., To confirm that the fatal infection was caused by an inoculated strain , the presence of the correct gene knockout was verified by PCR analysis of the bacterial colonies recovered from blood or spleen of diseased animals ., 50% lethal dose ( LD50 ) was estimated using the Spearman-Karber method 54 ., The LD50 data were converted to average latency survival ( ALS ) curves using the described transformation 55 ., Data were analyzed using SigmaPlot software and log rank test of the significance of differences in survival curves and t-test analysis of the significance of differences in LD50s among animal cohorts challenged with different bacterial strains .

Introduction, Results, Discussion, Materials and Methods

Proliferation of bacterial pathogens in blood represents one of the most dangerous stages of infection ., Growth in blood serum depends on the ability of a pathogen to adjust metabolism to match the availability of nutrients ., Although certain nutrients are scarce in blood and need to be de novo synthesized by proliferating bacteria , it is unclear which metabolic pathways are critical for bacterial growth in blood ., In this study , we identified metabolic functions that are essential specifically for bacterial growth in the bloodstream ., We used two principally different but complementing techniques to comprehensively identify genes that are required for the growth of Escherichia coli in human serum ., A microarray-based and a dye-based mutant screening approach were independently used to screen a library of 3 , 985 single-gene deletion mutants in all non-essential genes of E . coli ( Keio collection ) ., A majority of the mutants identified consistently by both approaches carried a deletion of a gene involved in either the purine or pyrimidine nucleotide biosynthetic pathway and showed a 20- to 1 , 000-fold drop in viable cell counts as compared to wild-type E . coli after 24 h of growth in human serum ., This suggests that the scarcity of nucleotide precursors , but not other nutrients , is the key limitation for bacterial growth in serum ., Inactivation of nucleotide biosynthesis genes in another Gram-negative pathogen , Salmonella enterica , and in the Gram-positive pathogen Bacillus anthracis , prevented their growth in human serum ., The growth of the mutants could be rescued by genetic complementation or by addition of appropriate nucleotide bases to human serum ., Furthermore , the virulence of the B . anthracis purE mutant , defective in purine biosynthesis , was dramatically attenuated in a murine model of bacteremia ., Our data indicate that de novo nucleotide biosynthesis represents the single most critical metabolic function for bacterial growth in blood and reveal the corresponding enzymes as putative antibiotic targets for the treatment of bloodstream infections .

Bacterial growth in the bloodstream is a common manifestation of a number of bacterial infections ., When growing in blood , bacteria not only have to evade the hosts immune response , but also adjust their metabolism to suit availability of nutrients ., Although the concentrations of various metabolites in human blood are known , it is difficult to predict which nutrients are abundant and which are scarce ., To proliferate in human blood , bacteria need to synthesize metabolites that are present in the limiting concentrations ., For that , they need to produce specific enzymes that are , thus , critical for the bacterial growth in the bloodstream ., We carried out a comprehensive , genome-wide search for Escherichia coli genes that are essential for growth in human serum ., We found that inactivation of nucleotide biosynthesis genes leads to a significant growth defect in human serum not only for E . coli but also for two other pathogens , Salmonella Typhimurium and Bacillus anthracis ., The results of this study demonstrate that the limiting amounts of the nucleotide bases in human serum force invading pathogens to rely on de novo nucleotide biosynthesis ., Hence , our findings reveal nucleotide biosynthesis enzymes as a possible target for the treatment of bloodstream infections .

infectious diseases, microbiology, eubacteria

journal.pgen.1002199

Genome-Wide Association Analysis of Incident Coronary Heart Disease (CHD) in African Americans: A Short Report

Coronary heart disease ( CHD ) is the leading cause of death worldwide 1 ., In the United States , African Americans are the most vulnerable population with regard to CHD risk factors and mortality ., A recent American Heart Association report showed that African Americans are twice as likely to die from a heart-related disease compared to other ethnicities 2 ., The presence of multiple CHD risk factors is 50% more likely in African Americans than in the population of European ancestry ., Hypertension and diabetes are more prevalent and the highest rate of obesity is found in African American women 2 ., The factors underlying these disparities are not well understood ., Socioeconomic status and health care accessibility play an important role 3 ., However , genetic factors are known to influence the risk of CHD 4 and population differences in the frequency and effects of these genetic factors also likely have a role ., Progress in the discovery of susceptibility genes for multiple chronic diseases and their risk factors has been made possible in recent years through genome-wide association studies ( GWAS ) ; African Americans were noticeably absent from most of these studies ., GWAS have the advantage of discovering new genetic variants underlying a disease without a priori knowledge of gene location or function ., More than 20 variants for CHD have been discovered in samples of European-descent so far 5–11 ., In this study , we took advantage of rich longitudinal data on CHD incident events in the Atherosclerosis Risk in Communities ( ARIC ) study and performed a GWAS of incident CHD events in African Americans ., Results that reached pre-specified genome-wide significance were investigated in African Americans from the Womens Health Initiative ( WHI ) 12 ., Genome-wide association analysis for incident CHD was carried out in African Americans from the ARIC study ., After 19 years of follow-up , 362 individuals developed CHD and 2 , 543 individuals were free of CHD ., Descriptive statistics for multiple cardiovascular disease risk factors at the baseline examination are provided in Table, 1 . Three loci reached the pre-specified genome-wide significance threshold ( p<5×10−8 ) ( Table 2 ) and were considered for replication in a sample of African American women from WHI ., Results are shown in Table, 2 . For all variants , the direction of effect was consistent in both studies , but only the variant rs1859023 reached statistical significance in WHI ( Table 2 ) ., The rs1859023 minor allele A ( frequency\u200a=\u200a0 . 31 ) had a protective effect on CHD risk with a hazard ratio ( HR ) of 0 . 57 ( 95% CI 0 . 46 to 0 . 69 , p\u200a=\u200a1 . 86×10−08 ) in ARIC and 0 . 81 ( 95% CI 0 . 70 to 0 . 93 , p\u200a=\u200a0 . 005 ) in WHI ., After adjustment for CHD risk factors ( smoking , diabetes , LDL , BMI and hypertension ) , the result was only slightly less significant ( HR\u200a=\u200a0 . 59 , 95% CI 0 . 48 to 0 . 73 , p\u200a=\u200a8 . 53×10−07 in the ARIC study ) ., After exclusions of coronary revascularization procedures from the ARIC case definition , the association became stronger ( HR 0 . 53 , 95% CI 0 . 43 to 0 . 65 , p\u200a=\u200a2 . 95×10−09 ) , possibly due to a more homogenous definition of CHD ., The pooled hazard ratio of incident CHD for combined ARIC and WHI data was 0 . 72 ( 95% CI 0 . 64 to 0 . 81 , p\u200a=\u200a1 . 29×10−08 ) ., rs1859023 was not significantly associated with any of the traditional CHD risk factors at the baseline ARIC examination ( LDL , HDL , SBP , DBP and BMI , data not shown ) ., However , average thickness of the carotid artery , a common measure of subclinical atherosclerosis , was significantly different among rs1859023 genotypes ( mean difference in carotid artery thickness per copy of the minor allele β\u200a=\u200a−0 . 0168 , SE\u200a=\u200a0 . 0047 , p\u200a=\u200a3 . 39×10−04 ) which suggest a role for this variant in the atherosclerotic process ., When including carotid artery wall thickness as a covariate in the analysis of incident CHD attenuated but did not abolish the association with rs1859023 ( HR\u200a=\u200a0 . 59 , 95% CI 0 . 48 to 0 . 73 , p\u200a=\u200a1 . 58×10−06 ) ., We attepted to replicate this observation by testing the assocation of rs1859023 and right coronary fatty streak area ( % of intimal surface area ) in the PDAY study consisting of 1 , 452 African Americans and 1 , 342 European Americans but the results did not reach statistical significance using a one-sided test ., In the ARIC European Americans , rs1859023 was not significantly associated with incident CHD ., The smallest p-value in the region surrounding rs1859023 ( ±50 kb ) was at rs17869240 ( p\u200a=\u200a0 . 01 , MAF\u200a=\u200a0 . 07 ) ., rs1859023 is located in an intergenic region in proximity to the PFTK1 , CLDN12 and GTPBP10 genes ( Figure 1 ) ., PFTK1 , also known as CDK14 , encodes a serine/threonine-protein kinase PFTAIRE-1 that acts as a cyclin-dependent kinase regulating cell cycle progression and cell proliferation 13 ., It is highly expressed in heart tissue 14 ., In the ARIC study , rs1859023 is in loose linkage disequilibrium ( LD ) with other SNPs in the region ( r2<\u200a=\u200a0 . 4 ) ., In the Yoruba HapMap data , there are only 3 SNPs with LD greater than r2\u200a=\u200a0 . 8 , all located within a very short distance of rs1859023 ( Figure 1 ) which implies that the tagging region of rs1859023 is very narrow in African-derived populations ., Given the location of the rs1859023 5′ to the PFTK1 gene , these data imply that rs1859023 may affect gene expression ., To test this hypothesis , we undertook expression QTL analyses using the resources provided at the SCAN – SNP and CNV Annotation Database ( http://www . scandb . org/newinterface/about . html ) 15 ., rs1859023 predicts the expression of 6 genes with p-values less than 10−4 ( Table 3 ) ., Interestingly , the evidence of rs1859023 predicting the gene expression is found only in the Yoruba population ., A variant close to PFTK1 , rs10499903 , located ∼60 kb from rs1859023 was associated with ankle brachial index ( ABI ) in European Americans from the Framingham Heart Study ( FHS ) 16 ., Given the different ethnic backgrounds of the two studies , and expected difference of LD patterns within the region between two samples , the Framingham result strengthens our finding and further suggests a role for the PFTK1 gene region in the atherosclerotic process ., Some of European-discovered CHD genes have been reported to also influence CHD in African Americans 17 ., However , this is the first reported GWAS finding of a CHD risk locus in African Americans ., Although the sample size is less than contemporary GWAS publications in European-Americans ( i . e . often exceeding 100 , 000 individuals ( e . g . 18 ) , we have combined all of the available well-powered incident CHD data in African Americans with genotype data and are able to present results based on 133 , 415 person-years of follow-up ., In addition , supporting evidence is provided by the association with subclinical atherosclerosis and expression QTL analyses ., In conclusion , we have identified a region near the PFTK1 gene as being associated with incident CHD and subclinical atherosclerosis in African Americans ., Further studies are needed to examine the cellular or metabolic mechanisms underlying this association , and large population-based studies of minority populations are necessary to more fully understand the impact of genetic factors on multiple phenotypes in those that bear a disproportionate burden of disease ., The ARIC ( Atherosclerosis Risk in Communities ) study is a population-based prospective cohort study of cardiovascular disease and its risk factors 19 ., ARIC includes 15 , 792 persons aged 45–64 years at baseline ( 1987–89 ) , randomly chosen from four US communities ., Of these individuals , 4 , 266 are self-reported African Americans ., Cohort members completed four clinic examinations , conducted approximately three years apart between 1987 and 1998 , and followed with annual phone interviews since 1987 ., Incident CHD in ARIC was ascertained by contacting participants annually , identifying hospitalizations and deaths during the prior year , and by surveying discharge lists from local hospitals and death certificates from state vital statistics offices for potential cardiovascular events 19 ., A CHD event was defined as a validated definite or probable hospitalized MI , a definite CHD death , an unrecognized MI defined by ARIC ECG readings , or coronary revascularization ., Participants were excluded from analyses if they had a positive or unknown history of prevalent stroke , transient ischemic attack/stroke symptoms , or CHD at the initial visit and/or being of non–African American ethnicity ., Real-time , B-mode ultrasound was used to evaluate the carotid arterial intima-media wall thickness as an indicator of atherosclerosis in the ARIC study and the detailed description of its measurement is described elsewhere 17 ., Genotyping was done in 15 , 020 ARIC participants using the Affymetrix Genome-Wide Human SNP Array 6 . 0 ., A total of 3 , 182 individuals remained after excluding individuals of non African American ethnicity , subjects who did not consent DNA use , unintentional duplicates with higher missing genotype rates , suspected mixed/contaminated samples , scans from one problem plate , samples with a mismatch between called and phenotypic sex , samples with genotype mismatch with 39 previously genotyped SNPs , suspected first-degree relative of an included individual , and genetic outliers based on average IBS statistics and principal components analysis using EIGENSTRAT ., SNPs were excluded due to having no chromosome location , being monomorphic , having a call rate <95% and HWE-p<10−5 ., In this analysis , we considered only variants with a MAF greater than 10% ., Cox proportional hazards models with adjustment for age , gender and the first three principal components derived from EIGENSTRAT were used to estimate CHD hazard rate ratios ( HRs ) over a 19-year period ( 362 cases ) under an additive genetic model ., These analyses were done using PLINK and an R application for survival regression analyses ., We define as “genome-wide significant” all associations with p<5×10−8 ., We define replication to be a significant ( p<0 . 05 ) and directionally consistent association in an independent sample ., The WHI has two major components: ( 1 ) a clinical trial that enrolled and randomized 68 , 132 women ages 50–79 into at least one of three clinical trials; and ( 2 ) an observational study that enrolled 93 , 676 women ages 50–79 into a parallel prospective cohort study 12 ., WHI participants were recruited from 1993–1998 at 40 clinical centers across the U . S . During follow-up , incident CHD events were adjudicated locally and centrally from medical records including hospital discharge summaries , ICD-9 codes , diagnostic , laboratory , surgical , and pathology reports by trained physicians blinded to randomized intervention and exposure status 20 ., In the WHI replication sample , CHD was defined as MI , coronary revascularization , hospitalized angina , or CHD death ., Definite and probable nonfatal MI required overnight hospitalization and was defined according to an algorithm based on standardized criteria using cardiac pain , cardiac enzymes and troponin levels , and ECG findings ., CHD death was defined as death consistent with underlying cause of CHD plus one or more of the following: hospitalization for MI within 28 days prior to death , previous angina or myocardial infarction , death due to a procedure related to CHD , or a death certificate consistent with underlying cause of atherosclerotic CHD ., Of a total of 26 , 045 ( 17% ) women from minority groups , 8 , 515 self identified African American women who had consented to genetic research were eligible for the WHI GWAS project ., Genotyping was performed on the Affymetrix 6 . 0 array ., After excluding samples due to genotyping failure , cryptic relatedness , and discrepancy between genetic ancestry and self-reported race , there were 8 , 421 WHI African Americans ., Participants were further excluded from analyses if they had a positive or unknown history of prevalent stroke , transient ischemic attack/stroke symptoms , or CHD at the initial visit ., A total of 862 incident first CHD events occurred among 8 , 155 eligible African American women without baseline CHD ( Table 4 ) ., The mean age at study entry was 61 . 6+/−7 . 0 years ( range 50–79 ) ., The mean baseline age of the cases was 64 . 2+/−7 . 2 , and the mean baseline age of the non-cases was 61 . 3+/−6 . 9 ., The mean time to CHD event was 5 . 29+/−3 . 19 years ., The mean age at CHD event was 69 . 5+/−7 . 5 years ., This study was approved by the participating institutional IRBs , and all ARIC and WHI participants provided written informed consent , involving the sharing of data with the scientific community ., The Pathobiolobical Determinants of Atherosclerosis in Youth ( PDAY ) study is composed of subjects who were 15 to 34 years of age when they died of non-CVD related causes ( accidents , homicides or suicides ) ., The purpose of PDAY was to evaluate early development of atherosclerosis 21 ., For this replication analysis , we genotyped rs1859023 in 2 , 794 individuals from PDAY - 1 , 452 African Americans and 1 , 342 European Americans and tested the association with fatty streak area in the right coronary artery .

Introduction, Results/Discussion, Materials and Methods

African Americans have the highest rate of mortality due to coronary heart disease ( CHD ) ., Although multiple loci have been identified influencing CHD risk in European-Americans using a genome-wide association ( GWAS ) approach , no GWAS of incident CHD has been reported for African Americans ., We performed a GWAS for incident CHD events collected during 19 years of follow-up in 2 , 905 African Americans from the Atherosclerosis Risk in Communities ( ARIC ) study ., We identified a genome-wide significant SNP ( rs1859023 , MAF\u200a=\u200a31% ) located at 7q21 near the PFTK1 gene ( HR\u200a=\u200a0 . 57 , 95% CI 0 . 46 to 0 . 69 , p\u200a=\u200a1 . 86×10−08 ) , which replicated in an independent sample of over 8 , 000 African American women from the Womens Health Initiative ( WHI ) ( HR\u200a=\u200a0 . 81 , 95% CI 0 . 70 to 0 . 93 , p\u200a=\u200a0 . 005 ) ., PFTK1 encodes a serine/threonine-protein kinase , PFTAIRE-1 , that acts as a cyclin-dependent kinase regulating cell cycle progression and cell proliferation ., This is the first finding of incident CHD locus identified by GWAS in African Americans .

In the United States , African Americans are at high risk for coronary heart disease ( CHD ) ., Although environmental and social factors have a role , genetic factors also contribute to CHD risk and mortality ., Research to identify genetic factors for CHD susceptibility has been carried out mostly in Europeans and European Americans and little has been done in African Americans ., Genome wide association studies ( GWAS ) provide a means to identify susceptibility loci without any a priori assumptions about the functional importance of a gene ., In this study , we used GWAS to identify a novel genomic region associated with incident CHD events in African Americans from the ARIC study and replicated this finding in a large sample of African American women ., This region contains several genes , including PFTK1 , that regulate cell cycle progression and cell proliferation ., This is the first report of a susceptibility locus for incident CHD identified by GWAS in African Americans .

medicine, biology