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Zika virus ( ZIKV ) is an emerging arbovirus belonging to the genus flavivirus that comprises other important public health viruses , such as dengue ( DENV ) and yellow fever ( YFV ) . In general , ZIKV infection is a self-limiting disease , however cases of Guillain-Barré syndrome and congenital brain abnormalities in newborn infants have been reported . Diagnosing ZIKV infection remains a challenge , as viral RNA detection is only applicable until a few days after the onset of symptoms . After that , serological tests must be applied , and , as expected , high cross-reactivity between ZIKV and other flavivirus serology is observed . Plaque reduction neutralization test ( PRNT ) is indicated to confirm positive samples for being more specific , however it is laborious intensive and time consuming , representing a major bottleneck for patient diagnosis . To overcome this limitation , we developed a high-throughput image-based fluorescent neutralization test for ZIKV infection by serological detection . Using 226 human specimens , we showed that the new test presented higher throughput than traditional PRNT , maintaining the correlation between results . Furthermore , when tested with dengue virus samples , it showed 50 . 53% less cross reactivity than MAC-ELISA . This fluorescent neutralization test could be used for clinical diagnosis confirmation of ZIKV infection , as well as for vaccine clinical trials and seroprevalence studies .
Zika virus ( ZIKV ) is a mosquito-borne flavivirus that belongs to the Flaviviridae family , and is closely related to dengue virus ( DENV ) . Flavivirus virions present a positive single-stranded RNA genome of approximately 11 Kb with a single open reading frame that encodes one polyprotein , which is further cleaved in 3 structural ( C , prM and E ) and 7 non-structural proteins ( NS1 , NS2A , NS2B , NS3 , NS4A , NS4B and NS5 ) [1] . ZIKV was first isolated from a sentinel monkey in Uganda in 1947 [2] and , until 2007 , it was considered endemic to Africa and Asia , when a small epidemic was reported in Yap State , Federated States of Micronesia [3] . In 2013 , another ZIKV outbreak was reported in French Polynesia [4] . In 2015 , ZIKV emerged in Brazil , and rapidly spread . By 2017 , 48 countries and territories in the Americas had confirmed autochthonous ZIKV transmission [5–7] . In previous outbreaks , the illness was characterized by rash , conjunctivitis , subjective fever , arthralgia , and arthritis; infection appeared relatively mild , self-limiting , and nonlethal [3] . However , in recent outbreaks , an association with Guillain-Barré syndrome and congenital brain abnormalities in newborn infants of mothers infected with ZIKV during pregnancy has been observed [6 , 8 , 9] . These evidences indicate that an unequivocal diagnosis of the illness is of utmost importance for correct clinical management , especially in the case of pregnant women . ZIKV diagnosis is based on clinical , epidemiological and laboratorial criteria . When samples are collected up to 5–7 days after the onset of symptoms , viral RNA can often be identified in serum or urine , and RT-PCR is the preferred test for ZIKV , and also for DENV and chikungunya virus ( CHIKV ) detection [10] . After this period , IgM antibodies may be detected by ELISA; however , flaviviruses have strong cross-reactivity , which may generate false positive results in serological tests [4 , 11] . This makes diagnosis of ZIKV infections quite a challenge , especially because the disease emerged in regions where other flaviviruses are endemic . Therefore , plaque-reduction neutralization test ( PRNT ) is indicated to measure virus-specific neutralizing antibodies and may be able to determine the etiology of infection [12] . Classical virus PRNT was first described in the 1950s and is considered the gold standard to measure neutralizing antibodies against viruses . Although being more specific , it is laborious and therefore not readily amenable to high-throughput , making it difficult to use for large-scale surveillance and vaccine trials . In this study , we describe a fast and robust test to measure neutralizing antibody against ZIKV , which is suitable for high-throughput screening of large collections of serum specimens . This new assay is based on quantitative immunofluorescence , allying the classical PRNT format with a modern readout method .
C6/36 Aedes albopictus cells ( ATCC CRL-1660 ) were grown in Leibovitz L-15 medium ( Gibco/Invitrogen , Grand Island , NY , USA ) supplemented with 5% fetal bovine serum ( FBS ) ( Gibco/Invitrogen , Grand Island , NY , USA ) , 0 . 26% tryptose ( Sigma-Aldrich , St . Louis , MO , USA ) and 25 μg/mL gentamicin ( Gibco/Invitrogen , Grand Island , NY , USA ) at 28°C . Human-derived hepatoma cells ( Huh 7 . 5 , ATCC PTA-8561 ) were grown in Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 ( DMEM/F-12 medium ) ( Gibco/Invitrogen , Grand Island , NY , USA ) supplemented with 10% FBS and 100 IU/μg/ml penicillin/streptomycin ( Gibco/Invitrogen , Grand Island , NY , USA ) at 37°C in a humidified , 5% CO2-controlled atmosphere . D1-4G2-4-15 hybridoma was cultivated in RPMI-1640 medium ( Gibco/Invitrogen , Grand Island , NY , USA ) with 25 mM HEPES and supplemented with 10% FBS , 1 mM sodium piruvate , 250 ng/ml amphotericin B and 100 IU/μg/ml penicillin/streptomycin . ZIKV strain ZV BR 2015/15261 was isolated from a patient with zika fever from Northeast Brazil in 2015 . Dengue viruses from four serotypes were used . DENV1- FGA/89 was isolated from a South American patient with dengue fever in 1989 ( GenBank: AF226687 ) . DENV2- ICC 265 and DENV3- BR DEN 97–04 ( GenBank: EF629367 ) were isolated in Brazil . DENV4- LRV13/422 ( GenBank: KU513441 ) was isolated from a non-fatal case of dengue with hemorrhagic manifestation . To obtain viral stocks , virus were propagated in C6/36 at the multiplicity of infection ( MOI ) of 0 . 01 and titrated by focus forming assay in C6/36 . A total of 226 sera were used in this study , which was approved by Fiocruz and the Brazilian National Ethics Committee of Human Experimentation ( CAAE: 42481115 . 7 . 0000 . 5248 ) , as well as the waiver of the Informed Consents . Specimens were divided as follows: 29 positive sera for ZIKV were confirmed by IgM ELISA and/or real time RT-PCR; 30 IgG sera positive for DENV confirmed with Panbio IgG indirect ELISA ( Alere , Brisbane , Australia ) ; 95 IgM sera positive for DENV ( from all serotypes ) , confirmed by IgM capture ELISA and RT-PCR; 5 sera from yellow fever virus vaccinated volunteers; and 14 negative sera . Additionally , a panel of 53 samples positive for other acute infections was tested . This panel included sera positive for Toxoplasmosis ( 5 samples ) , Epstein-Barr virus ( EBV ) ( 10 ) , Venereal Disease Research Laboratory test ( VDRL ) ( 17 ) , Cytomegalovirus ( CMV ) ( 10 ) , CMV/EBV ( 2 ) , Leptospirosis ( 7 ) , Hantavirus ( 2 ) . With exception of Zika positive sera , all samples were collected prior to ZIKV emergence in Latin America . Zika positive sera have been received in our laboratory since ZIKV outbreak in Brazil , when it was designated as a Sentinel Laboratory by the Brazilian Ministry of Health , thus working on ZIKV diagnosis in the South region . Huh 7 . 5 cells were plated in 24 well plates at a density of 1x105 cells , 16h previous to infection . Serum samples were inactivated at 56°C for 30 min , and then diluted 1/20 ( followed by serial 1/3 dilutions ) . An equal volume of virus suspension containing 210 plaque-forming units ( pfu ) was mixed with diluted samples and incubated at room temperature for 1h . After this step , each mixture was inoculated onto plates with cells and after incubating at 37°C for 1h; inoculum was discarded and an overlay ( 1 . 6% CMC and 10% FBS in DMEM/F-12 medium ) was added . Plates were left at 37°C for 6 days and then , cells were fixed with 3% paraformaldehyde and stained with 0 . 75% crystal violet . Plaques were counted and antibody titer was determined as the serum dilution that inhibited 90% of the tested virus inoculum ( PRNT90 ) . Huh 7 . 5 cells were plated in 96 well plates at a density of 1 . 5x104 cells , 16h previous to infection . Serum samples were inactivated at 56°C for 30 min , and then diluted as described above . An equal volume of virus suspension ( MOI of 0 . 4–300 pfu ) was mixed with diluted samples and incubated at room temperature for 1h . Then , each mixture was inoculated onto plates with cells and incubated at 37°C for 1h . Inoculum was replaced with fresh medium and plates further incubated at 37°C for 48h . Cells were fixed with cold methanol/acetone ( v/v ) and immunostained . Monoclonal antibody 4G2 ( 1/100 ) was used to stain virus envelope protein . It was diluted in blocking buffer ( PBS with 1% BSA ) and incubated at 37°C for 1h . Wells were washed three times with washing buffer ( PBS with 0 . 05% tween 20 ) and incubated with secondary antibody anti-mouse IgG Alexa Fluor 488 ( 1/400 ) ( Molecular Probes ) in blocking buffer . Cell nuclei were counterstained with 5 μM DRAQ5 ( Thermo Fisher Scientific ) and washed three times with washing buffer . Images were obtained with the Operetta High-Content Imaging System ( PerkinElmer ) with the objective 10x long WD . The number of images necessary to be representative for the entire well was defined and analyzed with Harmony High-Content Imaging and Analysis Software ( PerkinElmer ) ( S1 Fig ) . Percentage of infected cells were obtained and normalized in relation to positive and negative controls; antibody titer was determined as the serum dilution that inhibited 90% of viral infection ( NT90 ) . Zika IgM antibody capture enzyme-linked immunosorbent assay ( MAC-ELISA ) was performed accordingly to the guidelines from CDC [13] with minor modifications . A humanized monoclonal antibody ( mAb ) anti-flavivirus kindly provided by CDC was used as positive control . Antigens ( ZIKV or Mock ) were derived from β-propiolactone inactivated cell-culture supernatant from non-infected and ZIKV infected cells . For ZIKV genome detection , viral RNA was extracted from 140 μL of samples using QIAamp viral RNA mini kit ( Qiagen , Hilden , Germany ) . Real-time RT-PCR was performed as described by Lanciotti et al . ( 2008 ) [14] , using 5 μL of RNA and Go-Taq Probe 1-Step RT-qPCR System ( Promega ) . Assays were performed in the LightCycler 96 instrument ( Roche , Mannheim , Germany ) and human RNAse P was used as endogenous control [15] . Assay quality was assessed by Z’ = 1– [3 ( σp+σn ) / ( μp- μn ) ] , where σ is the standard deviation , μ is the mean of both positive ( p ) and negative ( n ) controls . Results were considered when Z’ was higher than 0 . 5 [16] . Neutralization curves were obtained using the software Prism ( GraphPad version 6 , USA ) and PRNT90 and NT90 were calculated by the log ( agonist ) vs . response–Find ECanything curve , with a hillslope of 1 .
To develop and validate the newly proposed fluorescent neutralization test as a potential substitute to the low throughput and labor intensive classical PRNT , we tested several parameters seeking for reproducible and faster results . ZIKV strain ZV BR 2015/15261 was chosen because it is a recent Brazilian clinical isolate and therefore a good representative to test serum samples from this region . Viral stocks were obtained from the second viral passage and by using low multiplicity of infection ( MOI of 0 . 01 ) in C6/36 cell line , due to its good infection rates and low cytotoxicity . A kinetic of virus growth was performed between the third and tenth day after infection ( Fig 1A ) to determine the time point to recover culture supernatants . Viral stocks were harvested at the fifth day after infection during the middle to end of the exponential phases of growth , to avoid high concentrations of defective interfering particles that could lead to falsely low neutralization titers . Huh 7 . 5 , a human-derived hepatoma cell line , was chosen for the neutralization assays , because it is permissive to ZIKV and other flavivirus infection and also can be automatically well segmented with a software tool . The appropriate cell seeding density was defined as 1 . 5x104 cells per well ( 34 mm2 ) , since it has a sufficiently high number of cells but with enough spatial distribution for proper identification and accurate analysis . A MOI of 0 . 4 was used for all experiments because this condition yielded around 70% of infected cells after 48h ( Fig 1B ) . Cell infection was visualized by an indirect immunofluorescence assay , with detection of ZIKV E protein by the 4G2 mAb and secondary anti-mouse IgG Alexa Fluor 488; nuclei were counterstained with DRAQ5 . Four images per well ( representative of the whole well ) were acquired with the Operetta High-Content Imaging System and analyzed with the Harmony Analysis Software ( PerkinElmer ) . After the standardization step , we proceeded to the neutralization assay . Serum specimens were heat inactivated to reduce the effects that complement factors may have on final results . Serum and virus samples were mixed to allow neutralization . After the incubation period the mixture was added to cells so infection could occur by non-neutralized virus . The neutralization titer that inhibits 90% of viral infection ( NT90 ) was used to analyze results ( Fig 2 ) . Some criteria were followed in order to accept a valid assay . Among them , a uniform number of cells per well , appropriate percent of infection of controls , no serum toxicity observed with low serum dilutions , and a Z’ higher than 0 . 5 . In order to observe inter-assay variability , one negative and one positive sample were tested in three independent assays . It was observed a low variation for the negative ( 0 . 93 ± 0 . 16 ) , and for the positive samples ( 188 . 46 ± 3 . 01 ) , showing the robustness of the test . The average Z’ observed for all plates was 0 . 61 . PRNT is the gold standard for measurement of flavivirus neutralization . Therefore , neutralizing titers obtained from 12 serum samples by using either the new fluorescent neutralization test and or classical PRNT were compared . Similar neutralization results were obtained with the two approaches , with a correlation of 0 . 88 ( Fig 3 ) . This demonstrates the robustness of the newly developed test and that it could be used as a replacement of the traditional test , using the same interpretation guidance suggested by CDC [17] . The new proposed test was validated with a set of serum samples previously tested . This panel included sera positive for flavivirus and non-flavivirus acute infections and negative serum from healthy donors ( Table 1 ) . Zika positive samples were collected during the disease outbreak in Brazil; all the other samples were collected previous to the ZIKV emergence in the country . All samples were submitted to the fluorescent neutralization test and the NT90 was calculated ( Fig 4 ) . Following recommended interpretation for neutralization results [17] , a titer higher than 10 is supposed to be considered positive . However , it was observed that several IgM positive samples for dengue would be erroneous considered positive for ZIKV . Therefore , a more restrictive result interpretation was employed as follows: samples were considered negative when NT90 <10 , inconclusive when NT90 ≥10 and <20 , and positive when NT90 ≥20 . Even with the higher cutoff value , it was possible to observe six DENV IgM samples that cross-reacted in ZIKV neutralization assay; while another 10 samples were inconclusive . No cross reactivity was observed when samples of DENV IgG , other acute infections or YFV vaccine were analyzed . Regarding the Zika positive panel ( Table 2 ) , samples were tested by Zika MAC-ELISA and/ or real time RT-PCR , and then divided into two groups: early infection ( serum PCR positive and variable anti-ZIKV IgM ) and late infection ( PCR negative and anti-ZIKV IgM positive samples ) . PCR positive samples presented low neutralizing titers ( <23 ) , while NT90 of PCR negative /IgM positive samples ranged from 20 . 98 to 581 . 80 . Paired samples ( presented in blue in Fig 4 ) were obtained from four patients; first collections were all RT-PCR positive and presented low NT90 titers , while second collections obtained 3 to 6 months after the first one had neutralization titers increased to levels a lot higher than the cut off value ( Fig 5 ) . To assess the cross reactivity between ZIKV and DENV infections in MAC-ELISA format assay and the fluorescent neutralization test , 95 DENV well-characterized positive samples were tested ( Table 3 ) . Neutralization titers of these samples are presented in Fig 4 . This sample panel was obtained between the years of 2004 and 2006 in Venezuela , thus before the emergence of ZIKV in the region . It is worth mentioning that the panel is composed by paired samples and viral isolation during acute phase of infection was used as “gold standard” for DENV infection . Among the 95 IgM DENV positive samples tested by MAC-ELISA , 39 cross-reacted and presented false positive results for ZIKV and 25 were inconclusive or undetermined . On the other hand , only six samples presented false positive results for ZIKV and 10 were inconclusive in the neutralization test ( Table 4 ) . Thus , the novel neutralization test presented 50 . 53% less cross reactivity than MAC-ELISA , and the rate of correct identification of ZIKV negative serum increased from 32 . 63% to 83 . 16% . The fluorescent neutralization test format can be expanded to other diseases . As a proof of concept , the test was adapted to identify neutralization antibodies to dengue virus . The same standardization steps used previously were employed to develop a test for the four serotypes of DENV . Optimal harvest time for viral stocks was between the 6 and 7th day after infection and , in order to obtain around 70% of infection after 48h , a MOI of 0 . 1 was used . DENV fluorescent neutralization test was able to identify neutralization antibodies against the four serotypes of the virus in all DENV IgM positive samples tested ( Table 5 ) . However , it was not possible to identify which DENV serotype was responsible for the current infection according to fluorescent neutralization assay results , indicating a probable secondary DENV infection .
Since ZIKV emerged in South America causing a number of outbreaks with reported cases associated with Guillain-Barré syndrome and congenital brain abnormalities in newborn infants , a great effort to develop specific and reliable diagnosis tests has been made [18–20] . A definitive ZIKV diagnosis is achieved by detecting viral RNA in patient serum , or other samples like urine , semen and placenta . Although RT-PCR assay is trustworthy and with good sensitivity and specificity , viremia among ZIKV-infected patients are relatively low and detectable for only a few days after the onset of symptoms [14] . For individuals beyond this viremia window , a serologic test must be employed . The most common used method is the detection of reactive IgM antibodies by ELISA . A disadvantage in using this option for ZIKV is the high number of false positive results due to cross-reaction with antibodies against DENV , and the low sensitivity of most existing immunoassays [11 , 21] . A novel ELISA based on recombinant ZIKV non-structural protein 1 ( NS1 ) was able to eliminate cross-reactions with antibodies to DENV and other flaviviruses , although it presented low sensitivity in the IgM format [18] . In order to overcome this issue , a CDC diagnostic guideline recommends that presumptive positive or equivocal MAC-ELISA result for ZIKV needs to be verified with a confirmatory PRNT [10] . As previous stated , although PRNT is the gold standard for flavivirus serological test , a number of limitations prevents its use in large scale to test a great number of samples , as required during outbreaks or to perform serological surveys . In addition , there is a recommendation for serological testing of asymptomatic pregnant women with history of travel to ZIKV endemic regions or those living in areas with active viral transmission [22] . This study describes the development and validation of a novel image based neutralization test for ZIKV that overcomes restrictions presented by PRNT . Previous studies have developed assays for replacement of DENV PRNT . Vorndam and Beltran ( 2002 ) developed and evaluated a microneutralization test to measure anti-dengue antibodies using an in situ ELISA [23] . Additionally , a 96-well format flow cytometry-based neutralization assay was proposed , and similar neutralization patterns were observed when compared to classical PRNT [24] . The disadvantages observed included the high intra-assay variability and the need to remove adherent cells from wells . Recently some alternative assays to ZIKV PRNT have also been reported . A MTT-based cell viability assay for ZIKV neutralizing antibodies quantification has been developed , and although it does not require expensive equipment or costly reagents , it depends on virus-induced cytopathic effect [25] . Shan et al . ( 2017 ) developed a reporter virus neutralization test ( RVNT ) , based on ZIKV and DENV luciferase reporter viruses . The assay maintained relative specificity of traditional PRNT and was further evaluated with 258 clinical serum specimens , displaying a 93 . 1% agreement with the traditional ZIKV PRNT titers [26 , 27] . Furthermore , a neutralization assay in which the endpoint is measured by real-time PCR was proposed [28] . The novel fluorescent neutralization assay developed here combines the classical neutralization protocol with a new automatized readout method , employing a high-content imaging system . From seeding cells to obtaining results , the new test takes around 72h , in contrast to PRNT that can take up to 8 days [29] , and also depends on manual counting of plaques , which can vary from person to person . Besides that , this new assay is able to test at least ten serum samples against a virus on a single 96-well plate , with dilutions performed via multichannel pipetting devices that increase assay capacity . Maistriau et al . ( 2017 ) also proposed a fluorescent neutralization test using a high-throughput image acquisition system . However , it is based on the translocation of the transcription factor IRF3 in response to infection [30] , thus requiring a careful selection of cell lines according to the virus of interest . In contrast , we propose a robust and simple method that can be easily set up to investigate other flavivirus infections . In this study , a curve fitting method from several serum dilutions was used to calculate neutralization titers , which allows a more precise result , in contrast to simply report the reciprocal of the last serum dilution that shows 50 or 90% reduction of infection . The neutralization titer which inhibits 90% of viral infection ( NT90 ) was used because it is indicated for epidemiological studies or diagnostic purposes in endemic areas , decreasing background serum cross-reactivity among flaviviruses [29] . It has been reported that people exposed to secondary DENV infections develop broadly neutralizing antibodies that neutralize different serotypes other than the one responsible for current infection [31] , as it was also demonstrated in the DENV fluorescent neutralization test ( Table 5 ) . Additionally , sera from patients with secondary DENV infection exhibit potent cross-reactivity against ZIKV [11] . In this context , cross reactivity between ZIKV and DENV is quite expected , as the viruses envelope proteins share a high degree of homology with a sequence identity of 54% and nearly identical structures . The fusion loop , that is an important antibody target , is 100% conserved between the two viruses [11] . Therefore , increasing specificity of serological tests is particularly relevant , since ZIKV emerged in flavivirus endemic regions . Aiming to reduce false positives results , samples were considered ZIKV positive when NT90 ≥20 , while NT90 <10 samples were scored as negatives . When NT90 ranged from ≥10 to <20 , results were recorded as inconclusive . Other studies have also used a higher cut off PRNT90 value [3 , 28] . Based on those parameters , higher specificity was achieved when compared to MAC-ELISA , yielding in less ZIKV false positive results for DENV IgM positive serum samples . Only 10 . 53% of inconclusive and 6 . 32% of false positive results were observed with these settings . This result is particularly remarkable , since in another assay , up to 100% of cross reaction with ZIKV was observed when acute and convalescent sera from nine Thai patients with confirmed DENV infection by RT-PCR were tested , both in binding and in neutralization assays [11] . Another study using RVNT for anti-ZIKV antibodies detection , showed 20% of erroneous results in the presence of anti-DENV antibodies , although no false positive results with Yellow fever and West Nile positive samples were observed [27] . The real-time PCR neutralization assay also reported significant cross-reactivity when testing a serum specimen from a patient with proven current ZIKV infection which had a background of DENV infection [28] . It is noteworthy that higher cut off values may reduce assay sensibility , i . e , some samples of ZIKV early infections can become inconclusive . In those cases , a molecular diagnosis can be employed and/ or a second serum collection should be tested , since antibodies might not have yet reached detectable levels . This was observed when paired samples were tested and an increase in neutralization titers was observed . As a conclusion , the developed fluorescent neutralization test offers significant advantages over classical PRNT . It is faster , prompt to high throughput adaptation , has automated reading of results , and is more specific than MAC-ELISA assay . As expected it also presents some limitations , as it does not discriminate between antibody classes , requires expensive equipment and can be performed only in selected laboratories . Nevertheless , it will make it possible to test simultaneously a large number of samples and against different viruses , assisting the correct management of suspected patients or asymptomatic pregnant woman and be employed in seroprevalence surveys . | Since 2015 , DENV’s cousin known as ZIKV has been in the spotlight . It caught researchers’ attention because it rapidly spread worldwide and ZIKV infection has been associated with Guillain-Barré syndrome cases and congenital brain abnormalities in newborn infants . For being so closely related , differentiation between DENV or ZIKV infection is challenging . Among the assays used in viral serological diagnosis , the plaque-reduction neutralization test ( PRNT ) that was described in the 1950s seems to be more specific , although longstanding , very laborious and not capable to test large number of samples . Therefore , we developed an image based neutralization test for ZIKV that overcomes restrictions presented by PRNT . This new test is faster , robust and able to test many samples simultaneously . It was successful in distinguish ZIKV infection from other infections , such as dengue and yellow fever . This may be especially relevant to solve cases such as congenital disorders in newborns and also to elucidate the agents involved in neuropathological outcomes such as Guillain-Barré syndrome . It also can be useful in serological surveys and vaccine studies . | [
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"trop... | 2018 | Development and evaluation of a novel high-throughput image-based fluorescent neutralization test for detection of Zika virus infection |
The planar cell polarity ( PCP ) pathway is a cell-contact mediated mechanism for transmitting polarity information between neighboring cells . PCP “core components” ( Vangl , Fz , Pk , Dsh , and Celsr ) are essential for a number of cell migratory events including the posterior migration of facial branchiomotor neurons ( FBMNs ) in the plane of the hindbrain neuroepithelium in zebrafish and mice . While the mechanism by which PCP signaling polarizes static epithelial cells is well understood , how PCP signaling controls highly dynamic processes like neuronal migration remains an important outstanding question given that PCP components have been implicated in a range of directed cell movements , particularly during vertebrate development . Here , by systematically disrupting PCP signaling in a rhombomere-restricted manner we show that PCP signaling is required both within FBMNs and the hindbrain rhombomere 4 environment at the time when they initiate their migration . Correspondingly , we demonstrate planar polarized localization of PCP core components Vangl2 and Fzd3a in the hindbrain neuroepithelium , and transient localization of Vangl2 at the tips of retracting FBMN filopodia . Using high-resolution timelapse imaging of FBMNs in genetic chimeras we uncover opposing cell-autonomous and non-cell-autonomous functions for Fzd3a and Vangl2 in regulating FBMN protrusive activity . Within FBMNs , Fzd3a is required to stabilize filopodia while Vangl2 has an antagonistic , destabilizing role . However , in the migratory environment Fzd3a acts to destabilize FBMN filopodia while Vangl2 has a stabilizing role . Together , our findings suggest a model in which PCP signaling between the planar polarized neuroepithelial environment and FBMNs directs migration by the selective stabilization of FBMN filopodia .
The Planar Cell Polarity ( PCP ) signaling pathway is best understood as a cell contact dependent mechanism for generating and maintaining polarity in the plane of an epithelium [1 , 2] . Its function was first described in the static epithelial cells of the fly where the molecular asymmetry of “core” PCP proteins results in the morphological asymmetry of a single actin-rich hair at the distal side of each wing cell [3–5] . Subsequently , planar polarity established by the core pathway has been shown to be a characteristic of many epithelial tissues in vertebrates and invertebrates alike [6–10] . The core PCP pathway is comprised of two protein complexes that localize to distinct cell membranes . In the fly wing , the transmembrane protein Frizzled ( Fz ) is confined to distal apical cell junctions along with the cytosolic proteins Disheveled ( Dsh ) and Diego ( Dgo ) , while the transmembrane protein Van Gogh ( Vang ) ( Strabismus ( Stbm ) ) and the cytosolic protein Prickle ( Pk ) are proximally localized . This molecular asymmetry of PCP promotes actin polymerization at the distal side of the cell , downstream of Fz and Dsh [11–13] . While the factors that initially polarize PCP components are context dependent [14] , the asymmetric localization of PCP proteins is maintained within polarized cells via intracellular destabilizing interactions between the Vang complex and the Fz complex [15 , 16] . This polarization of PCP proteins is coordinated between cells by the formation of intercellular stabilizing interactions between Vang and Fz complexes across cell junctions [17–21] . In spite of the antagonistic roles of Vang and Fz complexes , loss of function of any core PCP component results in a loss of polarity . While PCP is well known for its role in stable epithelia [22–24] , core PCP components have also been implicated in dynamic cellular processes such as cell migration . How PCP controls directed cell movements is best , though incompletely , understood in coherently migrating cells such as those undergoing convergent extension [25–37] . However , independently migrating cells also require PCP [38–44] . Here , as our model we use the stereotyped and conserved migration of cranial motor neurons in the vertebrate hindbrain [45–47] . This enabled us to study in vivo how PCP can regulate the migration of non-coherent cells and to determine how PCP signaling between different cell types , the migrating neurons and the cells through which they migrate , can modulate migratory cell behaviors . The PCP pathway drives the stereotyped tangential migration of facial branchiomotor neurons ( FBMNs ) in the vertebrate hindbrain . FBMNs are a subset of cranial branchiomotor neurons that originate ventrally in rhombomere ( r ) 4 and undergo a posterior migration to r6 where they form the facial motor nucleus , whose axons exit the hindbrain in r4 and innervate muscles derived from the second branchial arch [45 , 47] . Forward genetic screens in the zebrafish have identified multiple core PCP components ( Vangl2 , Pk1b , Fzd3a , Celsr2 and Scribble ) as being required for FBMN migration [31 , 48–51]; this PCP requirement has also been shown for mouse FBMN migration [52–54] . Unlike the cell migrations mentioned above , screens have failed to identify a role for Wnts or other chemotactic cues . Although it is clear that many components of the PCP pathway are required for tangential FBMN migration , how these components regulate this highly dynamic process is unknown . As a first step in answering this question we defined the cell types participating in PCP signaling during FBMN migration , as previous studies using a range of approaches have yielded conflicting results [31 , 48 , 49 , 51 , 55] . Using the Gal4/UAS system to systematically disrupt PCP in a cell-type and rhombomere-specific manner , we demonstrate the dual requirement for PCP within FBMNs and the planar-polarized r4 neuroepithelial environment in which they arise , and identify reciprocal PCP-dependent interactions between FBMNs and the planar-polarized floorplate as being sufficient , though not required , to promote migration . Since cell migration results from the contact-dependent stabilization of cellular protrusions and PCP signaling is known to regulate actin dynamics , we examined the protrusive activity of single FBMNs using high-resolution single-cell time-lapse microscopy in chimeric embryos and demonstrate opposing functions for the PCP core components Fzd3a and Vangl2 in regulating FBMN filopodial protrusive activity in vivo . Within FBMNs we show that Fzd3a is required to stabilize filopodia while Vangl2 has an antagonistic , destabilizing role . However , in the migratory environment we show that Fzd3a is required to destabilize filopodia while Vangl2 has a stabilizing role . In spite of having antagonistic roles at the cellular level , Vangl2 and Fzd3a mutants have the same FBMN migration phenotype . These findings are thus reminiscent of the intracellular antagonistic versus intercellular stabilizing roles that core PCP proteins perform in stably polarized epithelia . Consistent with a role for Vangl2 in regulating filopodial dynamics , we show that Vangl2 localizes transiently to the tips of retracting FBMN filopodia; consistent with a role for Vangl2 and Fzd3a in the microenvironment , we show planar polarized localization of these proteins in the adjacent floorplate . Together , our findings support a model in which canonical interactions between PCP components within FBMNs and between the FBMNs and their planar polarized neuroepithelial environment promotes migration via the selective stabilization of FBMN filopodia .
Initial chimeric analyses suggested that the PCP components Vangl2 , Fzd3a , Celsr2 and Scrib primarily act non-cell-autonomously to regulate FBMN migration [31 , 48 , 49] . An additional cell-autonomous role for Vangl2 and Scrib in FBMN migration has been demonstrated [51] , but refuted by others [55] . To determine whether PCP signaling is required cell-autonomously within FBMNs for their migration , we expressed a dominant negative ( DN ) form of the PCP core component Dvl specifically in branchiomotor neurons using the islet-1 ( isl1 ) CREST enhancer ( Fig 1A , 1B and 1C ) [56] . Dvl is the branching point between multiple Wnt signaling pathways , and the overexpression of its individual domains exert pathway-specific DN properties [57] . Work in multiple vertebrate systems has demonstrated that Xdd1 and Dvl-DEP , two truncated forms of Dvl , act as PCP-specific DNs [25–27] . In previous studies mRNA injection of these DNs failed to disrupt zebrafish FBMN migration [31 , 54] . We reasoned that this could be due to decreased DN mRNA levels or activity by the time of FBMN migration . To stably express DN forms of Dvl in FBMNs we raised stable Tg ( isl1:Dvl-DEP-GFP ) zebrafish in which FBMNs express Dvl-DEP-GFP . In wild type embryos , FBMNs fully migrate to r6 by 48 hours post fertilization ( Fig 1B ) . However Dvl-DEP-GFP expressing FBMNs largely fail to migrate , with 31/35 of Tg ( isl1:Dvl-DEP-GFP ) embryos displaying FBMN migration defects where most FBMNs ( >75% ) remain in in r4 ( Fig 1C ) . This demonstrates that PCP signaling within FBMNs is required for their migration . To further confirm this , and to test specifically whether the core transmembrane PCP component Fzd3a , like Vangl2 [51] , is required within FBMNs for migration , we used chimeric analysis to assess the ability of fzd3arw689 mutant FBMNs to migrate in a normal planar polarized neuroepithelium . In these experiments we prevented host FBMN migration using a pk1b morpholino since it is well known that migrating FBMNs can carry other FBMNs with them independent of PCP signaling , complicating the interpretation of chimeras [51 , 58] . pk1b morphants precisely phenocopy pk1b mutants in which FBMNs fail to migrate even though the surrounding neuroepithelium can support wild type FBMN migration [50 , 51 , 59] . While 70 . 9% of wild type FBMNs migrate out of r4 in a pk1b morphant environment , only 19% of fzd3a mutant FBMNs do so ( S1 Fig ) . This suggests that Fzd3a is required within FBMNs for migration . The requirement for the core PCP components Vangl2 , Fzd3 and Celsr1-3 is conserved in mouse FBMN migration [52 , 54 , 60] . In order to confirm a FBMN-autonomous requirement for PCP signaling we employed tissue-specific knockout of Vangl2 in mouse FBMNs using a floxed Vangl2 allele and Isl1-driven Cre recombinase [61 , 62] . In the mouse embryo , FBMN migration occurs between E10 . 5 and E14 . 5 with neurons reaching r6 by E12 . 5 [45] . In E12 . 5 homozygous floxed animals ( Vangl2LoxP/LoxP ) lacking Isl1Cre FBMNs migrate to r6 and the mean length of the migration stream is 618μm ( Fig 1D and 1F; N = 6 ) . In contrast FBMNs in Vangl2LoxP/LoxP; Isl1Cre animals are significantly blocked in r4 with FBMNs occupying an average of 383μm along the hindbrain ( Fig 1E and 1F; N = 9; p = 0 . 0003 ) . Taken together , the disruption of migration due to FBMN-restricted DN expression , our chimeric analysis of fzd3a-/- and previous chimeric analysis of vangl2-/- FBMNs [51] and the failure of FBMN migration after FBMN-specific disruption of Vangl2 in the mouse confirms a FBMN-autonomous requirement for PCP signaling in migration . While these data support a cell-autonomous requirement for PCP signaling in FBMN migration , PCP signaling in FBMNs is not sufficient for their migration . Indeed , a non-autonomous requirement for PCP signaling in FBMN migration has been well established in chimeras in which wild type FBMNs are unable to migrate in vangl2 , fzd3a , celsr2 or scrib mutant hosts [31 , 48 , 49 , 51] . Since PCP is a cell-contact mediated signaling pathway in which the same transmembrane protein components are required in both contacting cells [2] , an attractive hypothesis is that FBMNs receive PCP cues from cells in their environment that promote or direct their migration . Thus we sought to determine where PCP signaling is required in the FBMN migratory path for migration . To block PCP signaling in distinct compartments of the hindbrain , we used the Gal4/UAS system to drive rhombomere-restricted expression of Tg ( UAS:Xdd1-GFP ) as well as a C-terminally truncated Fzd3a Tg ( UAS:Fzd3aΔC-GFP ) that lacks its cytoplasmic region , which has been shown to function as a potent PCP DN tool in zebrafish [49] . We used Tg ( egr2b:KalTA4 ) to drive expression throughout r3 and r5 starting at 12 hpf [63] and Tg ( hoxb1a:Gal4 ) [64] to drive expression throughout r4 starting at 10 hpf ( Fig 1A ) . Expression of Xdd1-GFP or Fzd3aΔC-GFP along the migration path in the r5 neuroepithelium does not affect migration ( Fig 1G and 1H ) . In contrast , r4-restricted expression of Xdd1-GFP or FzdΔC-GFP completely blocks FBMN migration ( Fig 1I and 1J ) . This suggests that PCP signaling is required at the onset , but not throughout the course of FBMN migration . However we note that r5 expression of Xdd1-GFP or Fzd3aΔC-GFP using egr2b:KalTA4 comes on slightly later than r4 expression using hoxb1a:Gal4 ( 12 hpf compared to 10 hpf ) , so the caveat remains that PCP signaling is not fully disrupted in r5 at the time of migration with the available tools . It was not surprising that FBMNs fail to migrate in Tg ( hoxb1a:Gal4 ) ; Tg ( UAS:DN-GFP ) embryos given that FBMNs arise in r4 , and thus express hoxb1a throughout their early development , and we had already shown a cell-autonomous requirement for PCP signaling within FBMNs . To assess whether PCP signaling plays a role in the r4 neuroepithelium outside of FBMNs , we transplanted wild type Tg ( isl1:mRFP ) donor FBMNs into the presumptive ventral hindbrain of Tg ( hoxb1a:Gal4 ) ; Tg ( UAS:Xdd1-GFP ) embryos and assessed the positions of donor-derived FBMNs at 48 hpf . In control hosts , 87% ( 328/378 ) of wild type donor-derived FBMNs migrated out of r4 . In contrast , in hosts expressing Xdd1-GFP in r4 , only 17% ( 33/190 ) of donor-derived wild type FBMNs migrate out of r4 ( Fig 1K , 1L and 1M ) . Thus , expression of Xdd1-GFP throughout r4 significantly hinders wild type FBMNs from initiating migration ( p<0 . 0001 , χ2 = 207 . 8 ) ( Fig 1M ) . This demonstrates that there is a non-autonomous requirement for PCP signaling for FBMN migration in r4 . Having established that PCP signaling is required both within FBMNs and their r4 neuroepithelial environment for migration to occur; we asked when this signaling is required . PCP signaling polarizes neuroepithelial progenitors before FBMNs differentiate [7 , 65 , 66] . It is possible that this early neuroepithelial polarity is maintained in FBMNs to orient their initial migration . Alternatively , PCP signaling active at the time of migration initiation may promote migration . We reasoned that in the former case a planar polarized environment would not be required for migration after FBMNs had differentiated while in the latter case PCP function in the r4 environment would continue to be essential for migration . To determine when PCP signaling is required for FBMN migration , we transplanted a small number ( 1–5 ) of pre-migratory but post-mitotic FBMNs directly from r4 of a Tg ( isl1:GFP ) donor into r4 of a stage-matched wild type or vangl2m209 mutant Tg ( isl1:mRFP ) host ( Fig 2A ) . During this extraction procedure , transplanted cells round up and become separated in the transplant pipette and are unlikely to retain polarity information . Nevertheless , 28% ( 48/174 ) of surviving post-mitotic FBMNs transplanted into a wild type host r4 migrated to r6 ( Fig 2B and 2D ) . To rule out the possibility that the transplanted FBMNs are simply being carried by migrating host FBMNs , we transplanted post-mitotic FBMNs into a pk1b mutant host , which has normal neuroepithelial PCP but no host FBMN migration; in this environment 11% , ( 17/152 ) of transplanted FBMNs migrated ( S2 Fig ) . This suggests that host neurons do contribute to migration [51 , 58] , but that post-mitotic transplanted FBMNs can migrate without contribution from migrating host neurons . Importantly , 0% ( 0/73 ) of FBMNs migrated to r6 after being transplanted into a vangl2 mutant host ( Fig 2C and 2D ) . Together , these results suggest that post-mitotic FBMNs engage PCP signaling as they initiate their migration out of r4 . FBMNs migrate in the ventral neural tube adjacent to the floorplate ( Fig 1A , [49 , 67] ) making the floorplate a potential source of PCP signaling for FBMN migration . A recent report found that floorplate expression of Vangl2 is both necessary and sufficient for FBMN migration [55] . Here , to investigate whether PCP signaling in the floorplate is required for FBMN migration , we generated a Tg ( shh:Gal4 ) line ( see Methods ) to drive Xdd1-GFP or Fzd3aΔC-GFP expression in the notochord and floorplate ( S3A , S3B and S3C Fig ) . In order to determine if dominant negative expression does indeed disrupt floorplate planar polarity , we quantified the anterior-posterior position of the basal body in single floorplate cells as the ratio of its distance from the anterior membrane to the full anterior-posterior cell length ( S3G Fig ) . Basal bodies in wild type floorplate cells are planar polarized to the posterior membrane ( average positon = 78% of cell length , S3D and S3H Fig , [6] ) . Conversely , basal body planar polarization is significantly disrupted in floorplate cells expressing Xdd1-GFP or Fzd3aΔC-GFP ( average position = 63% and 59% of cell length respectively; S3E , S3F and S3H Fig ) . By comparison , floorplate cells in vangl2 mutants display a complete loss of basal body planar polarity ( average position = 47% of cell length , [51] ) ( S3H Fig ) . With the caveat that this effect on floorplate planar polarity was scored after FBMN migration was complete ( 48 hpf ) rather than at the onset of migration ( 18–24 hpf ) , DN expression in the floorplate in the floorplate had no effect on FBMN migration ( Fig 3A and 3B ) . To confirm this , we specifically knocked Vangl2 out in the mouse floorplate using the floxed Vangl2 allele described above [61] and Shh-driven Cre recombinase [68] . We found that Cre-mediated deletion of Vangl2 in the mouse floorplate does not disrupt FBMN migration ( S4 Fig ) . These results suggest that PCP signaling in the floorplate is not required for FBMN migration , and point to the possibility that loss of PCP in the floorplate can be compensated for by other planar polarized cells in the r4 neuroepithelial environment . Floorplate PCP could nevertheless be sufficient to rescue FBMN migration as has been suggested [55] . We tested the sufficiency of Vangl2 in the floorplate for FBMN migration in two ways . We expressed a GFP-Vangl2 fusion protein specifically in the floorplate of vangl2 mutants and wild type siblings using stable Tg ( shh:Gal4 ) driver and Tg ( UAS:GFP-Vangl2 ) transgenic lines ( vangl2m209/m209; Tg ( shh:Gal4 ) ; Tg ( UAS:GFP-Vangl2 ) ) . Although GFP-Vangl2 was expressed broadly in the floorplate in these otherwise mutant embryos starting at 14 hpf , and exhibits planar-polarized localization ( S3A Fig and see below ) , it neither disrupted FBMN migration in a wild type embryo nor rescued migration in a vangl2 mutant embryo ( Fig 3C and 3D ) . Since a caveat of this experiment is that Vangl2 over-expression can itself disrupt planar polarity , we used targeted transplantation of wild type cells into the floorplate of vangl2 mutants to test whether floorplate Vangl2 is sufficient to rescue FBMN migration . We never observed rescue of host FBMN migration in vangl2 mutant Tg ( isl1:mRFP ) hosts with wild type donor-derived cells in the hindbrain floorplate ( Fig 3E ) . This includes 9 cases with 10 or more wild type floorplate cells in rhombomere 4 . This is contrary to the findings of Sittarmane et al . ( 2013 ) [55] who found that a single wild type floorplate cell in r4 of a vangl2 mutant could rescue FBMN migration . Together , our findings suggest that Vangl2 function in the floorplate is not sufficient for FBMN migration . In these transplant experiments we noted that FBMNs as well as floorplate cells differentiate from donor-derived cells . This is not unexpected , given the close proximity of floorplate and branchiomotor neuron progenitors in the early embryo [70] . Interestingly , we observed that unlike the mutant host FBMNs , wild type donor-derived FBMNs sometimes migrate ( Fig 3F ) , and their ability to do so correlates with the number of wild type cells in the hindbrain floor plate ( R2 = 0 . 244; p = 0 . 005 ) . We conclude that Vangl2 function in the floorplate is not sufficient for FBMN migration , but that Vangl2 function in the floor plate can support the migration of vangl2+ FBMNs in an otherwise vangl2 mutant neuroepithelium . Taken together , we conclude that the floorplate can serve as a source of PCP signals for FBMN migration , but other cells in the r4 environment , which are also planar polarized ( see below ) can compensate for the loss of normal floorplate PCP signaling . Thus far , we have shown that PCP signaling in FBMNs and their immediate neuroepithelial/floorplate r4 environment can drive migration . The localization of core PCP components is known to be crucial for many PCP mediated processes [2 , 22] . Therefore , to better understand how PCP signaling might be used in neuronal migration we asked where PCP proteins localize within FBMNs and in their neuroepithelial microenvironment . Using a polyclonal antibody against zebrafish Vangl2 , we observed localization of Vangl2 to cell membranes throughout the hindbrain neuroepithelium ( S5 Fig ) . In the r4 floorplate , we noted a 1 . 6-fold enrichment of Vangl2 protein at anterior/posterior membranes of floorplate cells compared to their lateral membranes ( Fig 4A and 4B ) . Co-staining with ZO1 shows that this staining is sub-apical , at the level of the tight junctions ( Fig 4A’ ) . In order to distinguish anterior from posterior membrane localization we mosaically expressed GFP-Vangl2 in the floorplate so we could visualize Vangl2 localization in isolated floorplate cells . This revealed that Vangl2 is specifically enriched at the anterior subapical membrane ( Fig 4C and 4E ) . The normalized mean fluorescent intensity ratio of GFP-Vangl2 at the anterior membrane versus the posterior membrane in expressing floorplate cells is 2 . 2 ( std . deviation 0 . 9; N = 29 cells in 8 embryos ) . Conversely , Fzd3a-GFP is enriched at the posterior membrane ( Fig 4D ) . These findings for PCP protein localization in the floorplate are consistent both with the requirement for PCP core components in the posterior localization of the floor plate primary cilium [6] , and with a conserved deployment of PCP core components in vertebrate and invertebrate epithelia . While the regular organization of floorplate cells makes it easy to visualize their planar polarization , our findings suggest that the primary source of environmental PCP signaling in FBMN migration comes from neuroepithelial progenitor cells outside of the floorplate . Previous studies demonstrated a planar polarization of GFP-Pk and GFP-Vangl2 in neuroepithelial progenitor cells during zebrafish neurulation [7 , 65] , and of endogenous Vangl2 in the Xenopus neural plate [66] . We asked whether neuroepithelial progenitor cells display planar polarization of Vangl2 in r4 at the time of FBMN migration . Using the Tg ( hoxb1a:Gal4 ) line we mosaically expressed GFP-Vangl2 and observed a subtle but significant asymmetry of GFP-Vangl2 to the anterior sub-apical side of r4 neural progenitors . While GFP-Vangl2 polarization is subtle and not detectable in all expressing neuroepithelial progenitors , in blinded experiments we were able to correctly guess the A-P orientation of embryos based exclusively on GFP-Vangl2 localization in r4 progenitors in 18/23 mosaically expressing embryos ( p = 0 . 004 that 18/23 correct guesses were due to chance alone ) . The normalized mean fluorescent intensity ratio of GFP-Vangl2 at the anterior membrane versus the posterior membrane in cells where asymmetry is detectable is 1 . 82 ( std . deviation 0 . 47 , N = 17 embryos , 23 cells . ) ( Fig 4F ) . Thus both the r4 neuroepithelium and floorplate exhibit planar polarized Vangl2 localization at the time of FBMN migration . We next sought to determine where Vangl2 localizes in migrating FBMNs . Endogenous Vangl2 in FBMN membranes and the membranes of surrounding cells could not be resolved using the anti-Vangl2 antibody and , unlike static floorplate cells and neuroepithelial progenitors , FBMNS are highly dynamic , extending primarily filopodia-like protrusions as they migrate [51 , 71] . Reasoning that Vangl2 localization would be similarly dynamic , we mosaically expressed GFP-Vangl2 in FBMNs and visualized localization using spinning disc time-lapse imaging . We found that GFP-Vangl2 localizes throughout the membrane as well as in putative cytoplasmic vesicles , as is predicted for a transmembrane protein ( Fig 5A ) . However , in addition to its membrane localization , we observe transient enrichment of GFP-Vangl2 at the tips of a subset filopodia immediately preceding filopodia retraction ( Fig 5A’–5C and S1 Movie ) . Before enrichment the mean fluorescent intensity ratio of GFP at the filopodia tip versus the filopodia base is approximately 1 ( 0 . 99 ± 0 . 01 ) , as is the case for mRFP ( background membrane marker ) ( 0 . 92 ± 0 . 02 ) . During the enrichment event , this ratio for GFP-Vangl2 increased to 1 . 31 ± 0 . 05 while the ratio for mRFP remained close to 1 ( 0 . 97 ± 0 . 02 ) ( Fig 5B ) . Since the ratio for mRFP remained close to 1 , this suggests that the enrichment of GFP-Vangl2 correlates with increased Vangl2 protein levels at filopodia tips and not simply condensation of the membrane due to retraction . Furthermore , as described below , addition of exogenous GFP-Vangl2 in FBMNs results in a reduced filopodial lifetime which is opposite to the effect of loss of Vangl2 in FBMNs . This suggests that exogenous GFP-Vangl2 is functioning in FBMNs and that this observed localization of Vangl2 at the tips of filopodia is correlated with retraction . This enrichment of GFP-Vangl2 in filopodia never lasted for more than one time-point ( images were taken at 30–45 second intervals ) and was only detected in a subset of filopodia ( N = 11/84 filopodia on 8 neurons in 7 embryos ) ; it is likely that due to the transient nature of enrichment events and the constraints of our imaging rate we failed to observe many enrichment events . Importantly , however , the enrichment events we captured invariably preceded filopodial retraction; filopodia never extended further after an enrichment event ( Fig 5C ) . Consequently , we infer that Vangl2 may function in FBMN filopodia to signal retraction events . Our findings that PCP signaling is required within FBMNs for migration , and that Vangl2 localizes transiently to the tips of retracting filopodia , suggested the possibility that PCP signaling influences filopodial dynamics in migrating neurons in vivo . In order to determine the cellular basis of FBMN migration defects in PCP mutants , we imaged the protrusive dynamics of single mutant FBMNs at high resolution in vivo . Previous studies have described membrane protrusions in fixed or live embryos expressing cytoplasmic GFP or membrane-RFP in bulk FBMNs at low time resolution , however the overlap between FBMNs allows only a subset of protrusions to be visualized and their dynamics could only be inferred from distant time points [31 , 55 , 58 , 71] . To visualize the protrusive activity of single FBMNs at high time resolution , we utilized cell transplantation to generate embryos in which one or a few FBMNs express membrane-localized teal fluorescent protein ( Tg ( isl1:mTFP ) ) , and imaged protrusion dynamics of single FBMNs at 30-second intervals , the shortest interval at which we could acquire comprehensive z-stacks on our instruments . We focused on the function of Vangl2 and Fzd3a , the mutually antagonistic transmembrane core components , whose localized activity is both the hallmark and the driver of classical epithelial planar polarity [2] . Time-lapse imaging of FBMN membranes and f-actin dynamics revealed that filopodia are the prevalent protrusion type in FBMNs ( Fig 6A–6E and S2 Movie ) . To determine whether PCP affects the polarized orientation of filopodia on migrating cells , we quantified the positions of filopodia on single isl1:mTFP-expressing FBMNs by measuring the angle from the anterior-posterior axis of a vector from the center of the cell to the base of each filopodium ( S6A Fig ) . When wild type FBMNs are in r4 , protrusive activity is largely radial 46 . 4% ( 13/28 ) of filopodia are located in the anterior half of the neuron , while 53 . 6% ( 15/28 ) of filopodia were on the posterior side ( S6A and S6B Fig ) . Once neurons are migrating through r5 and r6 , membrane protrusive activity becomes highly enriched posteriorly , in the direction of migration with 84 . 6% ( 44/52 ) of filopodia on the posterior side of the cell ( S6C and S6D Fig ) . However , filopodia on vangl2 mutant FBMNs fail to polarize . Time-lapse images were collected at a developmental time-point at which wild type neurons would have already migrated out of r4 . Similar to wild type neurons in r4 , filopodia in vangl2 mutant FBMNs are fairly evenly distributed along the anterior-posterior axis with 47 . 5% ( 29/61 ) of filopodia located anteriorly and 52 . 5% ( 32/61 ) located posteriorly ( S6E and S6F Fig ) . These findings suggest that PCP signaling through Vangl2 is required to properly localize cytoskeletal dynamics in FBMNs . To characterize protrusive membrane dynamics we quantified filopodial lifetime ( number of seconds each filopodium is present during a 15 minute time-lapse period ) and filopodial maximum length ( the greatest length during the lifetime of filopodia lasting 90 seconds or longer ) of Tg ( isl1:mTFP ) FBMNs . Wild type FBMNs generate filopodia with an average lifetime of 224 . 5 ± 18 . 66 ( SEM ) seconds and an average maximum length of 3 . 6 ± 0 . 3 μm ( Fig 6A , 6F and 6G and S3 Movie ) . This filopodial lifetime is comparable to that observed in other vertebrate cells both in vivo and in culture [72–75] . When compared to wild type , FBMNs in vangl2 mutant embryos have much more stable filopodia with a longer average lifetime of 537 . 3 ± 81 . 78 seconds ( Fig 6F , S4 Movie; p = 0 . 0059 ) . Filopodia of these vangl2 mutant FBMNs also reach a greater average maximum length of 6 . 4 ± 1 . 1 μm ( Fig 6G; p = 0 . 0406 ) . We saw a similar trend when we used microinjection rather than transplantation to mosaically express mTFP in FBMNs in wild type and vangl2 mutant embryos . These results suggest that Vangl2 is required to destabilize FBMN membrane protrusions . Previous studies have demonstrated that FBMNs in vangl2 mutants move more slowly than wild type FBMNs and in random directions , which is consistent with Vangl2 being required to destabilize protrusions [31] . Since Vangl2 is required within FBMNs and their r4 microenvironment for migration , we sought to determine where Vangl2 functions to regulate filopodia dynamics . To determine the cell-autonomous function of Vangl2 , we transplanted vangl2 mutant FBMNs into a wild type host . Donor embryos carried the Tg ( isl1:mTFP ) transgene to visualize FBMNs and contained rhodamine dextran to track other donor-derived cells so we could ensure that donor-derived FBMNs were in fact in a genetically chimeric environment ( S7 Fig ) . We found that vangl2 mutant FBMNs in a wild type environment have longer , more stable filopodia with a mean lifetime of 432 . 0 ± 55 . 65 seconds and a maximum length of 6 . 8 ± 0 . 8 μm , similar to vangl2 mutant FBMNs in a vangl2 mutant host ( Fig 6B , 6F and 6G and S5 Movie; p = 0 . 005 and p = 0 . 0078 respectively ) . To further test if Vangl2 functions cell-autonomously to control FBMN protrusive dynamics , we mosaically expressed GFP-Vangl2 in wild type FBMNs . FBMNs expressing GFP-Vangl2 in wild type embryos have less stable filopodia compared to wild type FBMNs , with an average lifetime of 123 . 4 ± 14 . 27 seconds ( S1 Movie; N = 6 embryos , 7 neurons , 42 filopodia , p = 0 . 0013 ) . Together , these loss- and gain-of-function findings suggest that Vangl2 functions within FBMNs to destabilize filopodia , since filopodia are affected in vangl2 mutant and GFP-Vangl2-expressing FBMNs regardless of the genotype of cells in their microenvironment . Fzd3a , like Vangl2 , is required cell-autonomously and cell non-autonomously for FBMN migration ( S1 Fig , [49] ) . To determine whether Fzd3a has a cell-autonomous role in FBMN protrusive activity , we transplanted fzd3a mutant FBMNs into a wild type host . We found that filopodia of fzd3a mutant FBMNs are significantly less stable than filopodia of wild type neurons , with a mean lifetime of 163 . 3 ± 8 . 006 seconds ( Fig 6C and 6F and S6 movie; p = 0 . 0092 ) . However , mean maximum filopodia length ( 3 . 4 ± 0 . 4 μm ) was not significantly different than that of wild type FBMNs ( Fig 6G ) . This suggests that Fzd3a normally functions within FBMNs to stabilize filopodia protrusions , consistent with a conserved role for Fzd in actin polymerization [11–13] . Taken together , our results suggest that Vangl2 and Fzd3a function antagonistically within FBMNs to regulate filopodial stability . Given that we observed this cell-autonomous function for Vangl2 and Fzd3a in regulating FBMN membrane protrusions , we asked whether these proteins regulate FBMN protrusive dynamics independently of cells in the migratory environment . To address this question , we analyzed the protrusive dynamics of isolated FBMNs in primary culture . We found that cultured FBMNs display altered filopodial dynamics compared to FBMNs in vivo . Cultured wild type FBMNs have a mean lifetime of 537 . 5 ± 32 . 28 seconds ( during a 600 second time-lapse ) and an average maximum length of 6 . 0 ± 0 . 5 μm ( S9 Fig ) . Furthermore , there is no difference in filopodial dynamics between cultured wild type and cultured vangl2 mutant FBMNs ( S9 Fig ) . This suggested to us that the cell-autonomous functions we observe for Vangl2 and Fzd3a in vivo depend on interactions with cells in the migratory environment . Since Fzd3a and Vangl2 are also required non-autonomously for FBMN migration and since FBMN protrusive dynamics depend on cells in the migratory environment , we asked whether cells in the FBMN environment influence FBMN protrusive activity in a PCP-dependent manner . In order to assess the role of Vangl2 in the environment , we imaged protrusion dynamics of wild type FBMNs in a vangl2 mutant environment . Interestingly we found that wild type FBMNs have less stable filopodia in a vangl2 mutant environment compared to a wild type environment , with a mean lifetime of 143 . 3 ± 18 . 28 seconds ( Fig 6D and 6F and S7 Movie; p = 0 . 0056 ) . The decrease in the average filopodia lifetime of wild type FBMNs in a vangl2 mutant environment is largely due to these neurons having a larger proportion of filopodia present for only one ( 30 seconds ) or two ( 60 seconds ) time-points ( S8 Fig ) . The mean average length however was not different between wild-type FBMNs in a wild type environment and wild type FBMNs in a vangl2 mutant environment ( Fig 6G ) ( 3 . 1 ± 0 . 2 μm ) . Removing Fzd3a from the migratory environment had the opposite effect on FBMN filopodia . Wild type neurons in a fzd3a mutant environment generate dramatically more stable filopodia compared to those in a wild type environment , with a mean lifetime of 363 . 8 ± 51 . 12 seconds ( Fig 6E and 6F and S8 Movie; p = 0 . 0273 ) . Together our results suggest that the core PCP components Vangl2 and Fzd3a antagonize each other’s activity to control filopodial dynamics during neuronal migration in vivo and they do so by functioning both within FBMNs and in cells in their migratory environment .
Our in vivo observations of filopodial dynamics in genetic chimeras demonstrate an antagonistic intracellular relationship between Vangl2 and Fzd3a in migrating FBMNs that regulates the stability of filopodium-like protrusions . While occurring in the context of a highly dynamic structure , this antagonistic relationship of Vangl2 and Fzd3a is reminiscent of the situation in stably polarized epithelia , where mutual intracellular antagonism between Fzd and Vangl complexes sets up polarized actin dynamics within the cell , with Fzd activating actin polymerization distally and Vangl suppressing it proximally [11–13 , 77 , 78] . This conserved interaction between Fzd promoting and Vangl suppressing actin growth may be common to other migratory cells . In metastatic breast cancer cells induced by stromal Wnt11-containing exosomes , Fzd6 and Vangl1 exhibit mutually exclusive localizations , with Fzd6 on the leading edge of cell protrusions and Vangl1 on non-protrusive cell surfaces , and knock-down of either protein decreases cell motility [44] . Similarly in migrating leukemia cells , Dvl-3 ( part of the Fzd complex ) localizes to the leading edge while Vangl2 localizes to the trailing edge [79] . During mesodermal and neuroectodermal convergence , mediolaterally oriented cell surfaces exhibit increased actomyosin contractility [33 , 80] that correlates with the asymmetric localization of PCP components Dvl and Pk ( part of the Vangl complex ) [7 , 81] , suggestive of a conserved intracellular antagonism of these complexes mediating actin dynamics . In contrast , in commissural growth cones , Vangl2 promotes Fzd3-dependent outgrowth induced by diffusible Wnt5a by antagonizing a non-canonical inhibitory interaction between Dvl1 and Fzd3 identified in that context [41] . These examples show that core PCP components localize to discrete domains of moving cells and we have shown in vivo for the first time that this results in opposing effects on filopodial stability . Filopodia are commonly associated with the promotion of directed cell migration , although in some instances , axons and cells can achieve proper targeting and guidance without filopodia [82–84] . Due to their dynamics and long thin architecture , filopodia are capable of probing a wide area around cells , and they can contain receptors for diverse diffusible and membrane-bound signals and extracellular matrix molecules [85] . Thus , it is thought that the primary function of filopodia is as “antennae” that cells use to sense their microenvironment to orient directed cell migration [86] . Indeed it has been demonstrated that elimination of filopodia in axon growth cones does not impair axon outgrowth , but instead impairs growth cone turning in response to environmental cues [87–89] . This sensing role for filopodia has also been demonstrated in cell migration [84 , 90] . In addition to a sensing role , filopodia are thought to contribute directly to cell motility , as cells lacking filopodia tend to migrate more slowly due to the absence of filopodial adhesion molecules which could induce traction and also through force generated by actin streaming in filopodia [82 , 90–93] . In the context of FBMN migration , filopodia extend in all directions from neurons when they initiate their migration , and we see no bias in the orientation of the filopodia that are affected in PCP mutants . We hypothesize that filopodia act as sensors of asymmetrically localized cell-surface PCP components on the neuroepithelial cells through which they are migrating and that this sensing fine tunes filopodium dynamics such that these filopodia can promote migration by acting as force generators or appropriately sensing other , as-yet unidentified environmental cues . In other migrating cells , several effectors have been identified as possible links between PCP signaling and cytoskeletal regulation [33 , 94 , 95] . While our work does not elucidate how those signals are transduced to the filopodial actin cytoskeleton in FBMNs , our previous work identified the WAVE-homology domain containing actin regulator Nhsl1 as localizing to FBMN filopodia and being required cell-autonomously for FBMN migration [51 , 96]; we hypothesize that PCP signals may be transduced to the actin cytoskeleton in FBMNs via Nhsl1 . A more surprising finding than opposing cell autonomous roles for Fzd3a and Vangl2 in FBMN filopodial dynamics and migration is that the same PCP components function in the FBMN environment to influence filopodial dynamics but in the opposite way: Fzd3a in the environment destabilizes filopodia while Vangl2 in the environment stabilizes them . These non-autonomous functions for Fzd3a and Vangl2 in filopodial dynamics correlate with their non-autonomous roles in FBMN migration [31 , 49] . Again , this is reminiscent of classical planar-polarity , where localized Fzd activity depends on the presence of Vangl in adjacent cells in the epithelium and vice versa; this is the mechanism by which PCP is coordinated across an epithelium [17–21] . We hypothesize that the cell-autonomous activities of Fzd3a and Vangl2 are activated in different filopodia when they contact Vangl2 and Fzd3a domains of neuroepithelial cells in the r4 environment ( Fig 7 ) , with consequences on the actin dynamics regulating filopodial stability , leading to changes in signaling and/or adhesion . We have shown here that Vangl2 and Fzd3a exhibit planar polarized localization in the r4 neuroepithelium and floorplate at the time of FBMN migration . In PCP mutants , this polarized information is absent and/or cannot be correctly interpreted by filopodia resulting in a failure of directional cell migration . We note that the cell-autonomous filopodial phenotypes appear to be dominant , since in constitutive mutants filopodia have the cell-autonomous phenotype ( long and stable in vangl2 mutants; unstable in fzd3a mutants ) . Together our findings suggest that conserved intracellular and intercellular interactions between PCP core components can have divergent effects on actin dynamics and consequently on cell behaviors . While the similar effects on filopodial dynamics when Vangl2 is depleted from FBMNs and when Fzd3a is depleted from their environment suggest that the two proteins are working together , environmental PCP may also influence filopodia dynamics of FBMNs through an indirect mechanism . For instance , core PCP proteins have been implicated in the trafficking and regulation of membrane levels of cadherins in fly and in vertebrate epithelial cells [97–99] . Therefore , Vangl2 and Fzd3a in the migratory environment may modulate FBMN filopodia dynamics by regulating N-cadherin levels at the surface of neuroepithelial cells . Another potential mechanism by which PCP in the migratory environment may regulate FBMN filopodial dynamics is through regulation of membrane type-1 matrix metalloproteinase ( MMP14 ) , which are known to degrade extracellular matrix proteins . During zebrafish gastrulation , an increase in Mmp14 activity was observed in vangl2 mutant embryos [100] . Thus , the decreased FBMN filopodial stability observed when Vangl2 is absent in the migratory environment could be due to decreased extracellular matrix . Which cells in the environment of FBMNs are the source of PCP cues for filopodial dynamics and migration ? We have shown that disruption of PCP signaling in the r4 environment prevents FBMN migration , demonstrating that PCP signaling is required to initiate directional migration . It was recently reported that Vangl2 expression even in a single cell in the r4 floorplate is sufficient to rescue FBMN migration in a vangl2 mutant [55] . In contrast , our results show that floorplate Vangl2 is neither required nor sufficient for FBMN migration . Neither the widespread presence of GFP-Vangl2 expressing cells or of wild type donor-derived cells in the floorplate of vangl2 mutants rescued the migration of vang2 mutant FBMNs . The rescue of FBMN migration observed by Sittaramane et al . ( 2013 ) may have been due to undetected early expression of Vangl2 outside of the floorplate driven by the Gal4 genetrap line used in their experiments [101 , 102] . We did , however , note that the presence of wild type cells in the floorplate could partially rescue the migration of wild type FBMNs in an otherwise vangl2 mutant embryo . This suggests that bidirectional PCP signaling between the planar-polarized floorplate and the FBMNs can promote migration . However this rescue was incomplete , indicating that other planar polarized cells in the r4 environment normally contribute to the pro-migratory environment . Consistent with this hypothesis , we found that disrupting the planar polarization of the floorplate alone in both fish and mouse embryos was insufficient to prevent FBMN migration ( Fig 3 and S4 Fig ) . We conclude that the planar polarization of the entire r4 environment surrounding the FBMNs is required to effectively initiate migration . We were unable to confirm this by rescuing FBMN migration in a vangl2 mutant with r4-restricted expression of GFP-Vangl2 , likely because the over-expression of PCP components disrupts planar polarity as efficiently as their loss [28 , 31 , 103 , 104] . Our study provides new insights into the role of the planar cell polarity pathway in neuronal migration by identifying when and where PCP signaling is required and how it affects the dynamic cell behaviors of migrating neurons in vivo . Our data suggests that a planar polarized hindbrain rhombomere 4 neuroepithelium serves to promote FBMN migration through the selective stabilization and destabilization of FBMN filopodia using conserved intra- and intercellular interactions between the PCP components Vangl2 and Fzd3a . Whether neuroepithelial planar polarity directs posterior migration or simply enables it , and through what effectors PCP signaling regulates filopodial dynamics in vivo are important questions to be answered in future work .
Experiments using animals were performed under the Fred Hutchinson Cancer Research Center Institutional Animal Care and Use Committee protocols #1392 ( zebrafish , approved on 3/31/2015 ) and #50857 ( mice , approved 4/1/2015 ) . The Fred Hutchinson Cancer Research Center Institutional Animal Care and Use Committee ( IACUC ) follow the guidelines of the Office of Laboratory Animal Welfare and set its policies according to The Guide for the Care and Use of Laboratory Animals . Fred Hutchinson Cancer Research Center maintains full accreditation from the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) and has letters of assurance on file with OLAW . The IACUC routinely evaluates the Fred Hutchinson animal facilities and programs to assure compliance with federal , state , local , and institution laws , regulations , and policies . The OLAW Assurance number is A3226-01 . Zebrafish ( Danio rerio ) were raised at the Fred Hutchinson Cancer Research Center , and animal care and experiments were approved by the Institutional Animal Care and Use Committee . All animals were maintained according to standard procedures [105] and staged as previously described [106] . All mutant lines used were previously described and are registered at The Zebrafish International Resource Center ( ZIRC ) : fzd3arw689 ( oltrw689 ) [49] , prickle1bfh122 [50] , and vangl2m209 ( trim209 ) [31] . Previously described transgenic lines used were as follows: Tg ( isl1:GFP ) rw0 [56] , Tg ( isl1CREST-hsp70l:mRFP ) fh1 [67] , TgBAC ( hoxb1a:RFP ) fh3 [67] , Tg ( egr2b:KalTA4 ) [63] and Tg ( hoxb1a ( β-globin ) :Gal4VP16 ) um60 [64] . The following transgenic lines were generated for this study: Tg ( shh:Gal4VP16 ) fh445 , Tg ( isl1:Gal4VP16 ) fh452 , Tg ( isl1-hsp70:mTFP ) fh350 , Tg ( isl-hsp70:dvl-DEP-GFP ) fh444 , Tg ( 10XUAS:xdd1-GFP ) fh446 , Tg ( 10XUAS:fzdΔC-GFP ) fh447 and Tg ( 10XUAS:GFP-vangl2 ) fh453 . The Gal4VP16 sequence was obtained from the Nonet Lab ( http://pcg . wustl . edu/nonetlab/ResourcesF/Zebrafish . html ) and the 10XUAS plasmid was obtained from the Tol2 kit ( http://tol2kit . genetics . utah . edu/index . php/List_of_entry_and_destination_vectors ) [107] . The mTFP construct was obtained from Alleleustrious , Inc ( Cat# ABP-FP-TFA1000 ) . To generate Tg ( shh:Gal4VP16 ) fh445 , the ar-B enhancer element of zebrafish sonic hedgehog ( shh ) [108 , 109] was amplified from a plasmid ( gift from Uwe Strähle ) . For the Gal4 lines , the shh and isl1 enhancers were inserted upstream of the gata2 minimal promoter element [110] . The Xdd1 and full-length Xenopus Dvl are described in Sokol et al . ( 1996 ) [25] . Transgenic elements were cloned using the Gateway ( Life Technologies ) system using the primer sequences listed in S1 Table . Final DNA constructs were assembled in the pDESTpBHR4R3 plasmid ( gift from the Brockerhoff Lab ) or the CG5 Tol2 expression vector [107] . Transgenic embryos were generated by Tol2 transposase RNA co-injection with each plasmid at the single cell stage [111] . All mice were maintained at Fred Hutchinson Cancer Research Center under Institutional Animal Care and Use Committee approved guidelines . For general colony maintenance , all mouse lines were crossed into the C57BL/6J background ( The Jackson Laboratory strain 00064 ) . The Vangl2Loxp and Vangl2ΔTM lines were a gift from the Deans laboratory [61] , the Isl1Cre ( Isltm1 ( cre ) Sev ) line was a gift from the Evans laboratory [62] and the Shh:gfp-cre ( Shhtm1 ( EGFP/cre ) Cjt ) line was purchased from The Jackson Laboratory ( strain 005622 ) . Chimeric embryos were generated by transplantation at the blastula or gastrula stage as previously described [51 , 112] . To track transplanted cells , donor embryos carrying the Tg ( isl1:GFP ) rw0 , Tg ( isl1:mRFP ) fh1 or Tg ( isl1:mTFP ) fh350 transgene were injected with 1% cascade blue-dextran or rhodamine-dextran ( for live imaging ) and 1% biotin-dextran ( for imaging after fixation ) ( 10 , 000 mw , Life Technologies ) . Host embryos were then processed and imaged for all donor-derived cells , donor-derived FBMNs or floorplate cells , and host FBMNs . Host and donor embryo genotypes were identified either by observing body axis elongation defects ( for vangl2 mutant hosts ) , by examining FBMN location at 48 hpf or by genotyping ( for fzd3a mutant hosts ) . For transplantation of post-mitotic FBMNs , cascade blue-dextran labeled donor embryos and unlabeled host embryos were mounted in agar on coverslips at the 15-somite stage . The head of each animal was exposed by careful removal of agar with insect pins , and a hole was cut in the skin overlying the forebrain to enable entry of a thin ( 10 μm diameter ) transplant pipette . Pre-migratory FBMNs ( visualized by isl1:GFP or isl1:mRFP expression ) were removed from r4 of a donor embryo and transplanted into r4 of a host embryo using a Zeiss AxioSkop fixed-stage microscope fitted with a 40X long working-distance water-immersion ( “dipping” ) lens . During this process some non-isl1:GFP/mRFP-expressing neuroepithelial progenitor cells were inevitably co-transplanted but these usually died shortly after transplantation; any surviving donor-derived cells that were not FBMNs were detected by the presence of the cascade blue dye . Due to the disruptive approach , which removes nascent axons and other processes , even wild type FBMNs transplanted into a wild type environment do not migrate as well as FBMNs transplanted at earlier stages . Anesthetized zebrafish embryos were fixed in 2% trichloroacetic ( TCA ) acid for 3 hours or 4% paraformaldehyde ( PFA ) / 4% sucrose in PBS for 1 hour at room temperature . Dissected mouse hindbrains were fixed in 4% paraformaldehyde ( PFA ) / 4% sucrose in PBS overnight at 4°C and permeablized in PBS + 1% TritonX100 . Fixed tissue was washed in PBS + 0 . 5% TritonX100 followed by standard blocking and antibody incubations . Following staining , brain tissue was dissected , cleared step-wise in a 25% , 50% , 75% glycerol series and mounted for confocal imaging . The following antibodies were used: rabbit anti-zebrafish Vangl2 ( 1:250 , Anaspec Cat# AS-55659 ) , mouse anti-islet1 39 . 4D5 ( 1:10 for zebrafish tissue and 1:100 for mouse tissue , Developmental Studies Hybridoma Bank ) ; chicken anti-GFP ( 1:500 , Abcam Cat# ab13970 ) ; rabbit anti-ZO-1 ( 1:1000 , Zymed Cat# 61–7300 ) ; mouse anti-Cc2d2a ( 1:100 , [113] ) ; rabbit anti-RFP ( 1:1000 , Abcam Cat# ab62341 ) . For analysis of chimeric embryos after fixation , host embryos were additionally stained with a fluorescently conjugated streptavidin ( Life Technologies Cat# S32351 ) to enhance the detection of Biotin-Dextran-containing donor-derived cells . Primary cultures of FBMNs were prepared from 24 hour post fertilization Tg ( isl1:mTFP ) ; Tg ( hoxb1a:RFP ) embryos . The hindbrains of embryos were micro-dissected and dissociated as previously reported [114] . Cells were plated on a chambered coverglass ( Sigma Z734756 ) coated with 5μg/mL poly-D-lysine ( Sigma L8021 ) and 5μg/mL laminin ( Sigma L2020 ) at a density of 4–5 hindbrains per 1 . 7 cm2 . FBMNs were distinguished from other Tg ( isl1:mTFP ) -expressing hindbrain motor neurons by virtue of Tg ( hoxb1a:RFP ) expression , which is restricted to hindbrain r4 and r4-derived neurons . Live imaging of explanted neurons was performed 5 hours after plating . Imaging was performed using a Zeiss 700 confocal microscope or a Zeiss spinning disc microscope with a QuantEM EMCCD camera for live time-lapse imaging . For timelapse imaging , Z-stack images at 1μm steps were captured every 30 seconds for 15 minutes for in vivo time-lapse images and every 5 seconds for 10 minutes for cultured neurons . Filopodia were defined as long thin protrusions , less than 0 . 2 μm in diameter and more than 0 . 75 μm in length , measured from the cell body margin to the protrusion tip . In vivo filopodia lengths , lifetimes and fluorescent intensities of mRFP and GFP-Vangl2 were quantified using Zeiss Zen 2012 software . For cultured neurons , filopodium quantification was performed semi-automatically using Imaris FilamentTracer software ( http://www . bitplane . com/imaris/filamenttracer ) . Mean anti-Vangl2 fluorescent intensity for all cell membranes were measured in user-drawn regions of interest using Zeiss Zen 2011 software or ImageJ's "Plot Profile" tool . Anterior/posterior GFP-Vangl2 fluorescent intensity ratios for each cell were normalized by dividing this value by the anterior/posterior ZO-1 fluorescent intensity ratio . Graphs were generated and statistics were computed using GraphPad Prism software . All statistical analyses were performed using a 95% confidence interval . In most cases significance was determined using an unpaired , two-tail t-test with Welch’s correction . For the anti-Vangl2 staining quantification significance was determined using a paired two-tail t-test with Welch’s correction . Differences in FBMN distributions were analyzed using a Chi-square test where the distribution of FBMNs in control animals served as the expected frequencies or null hypothesis to determine if the observed frequencies were significantly different . Circular plots were generated using Oriana 4 software . Figure images were created using Adobe Photoshop and Adobe Illustrator . | Planar cell polarity ( PCP ) is a common feature of many animal tissues . This type of polarity is most obvious in cells that are organized into epithelial sheets , where PCP signaling components act to orient cells in the plane of the tissue . Although , PCP is best understood for its function in polarizing stable epithelia , PCP is also required for the dynamic process of cell migration in animal development and disease . The goal of this study was to determine how PCP functions to control cell migration . We used the migration of facial branchiomotor neurons in the zebrafish hindbrain , which requires almost the entire suite of PCP core components , to address this question . We present evidence that PCP signaling within migrating neurons , and between migrating neurons and cells of their migratory environment promote migration by regulating filopodial dynamics . Our results suggest that broadly conserved interactions between PCP components control the cytoskeleton in motile cells and non-motile epithelia alike . | [
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"cell... | 2016 | PCP Signaling between Migrating Neurons and their Planar-Polarized Neuroepithelial Environment Controls Filopodial Dynamics and Directional Migration |
A relatively unexplored nexus in Drosophila Immune deficiency ( IMD ) pathway is TGF-beta Activating Kinase 1 ( TAK1 ) , which triggers both immunity and apoptosis . In a cell culture screen , we identified that Lysine at position 142 was a K63-linked Ubiquitin acceptor site for TAK1 , required for signalling . Moreover , Lysine at position 156 functioned as a K48-linked Ubiquitin acceptor site , also necessary for TAK1 activity . The deubiquitinase Trabid interacted with TAK1 , reducing immune signalling output and K63-linked ubiquitination . The three tandem Npl4 Zinc Fingers and the catalytic Cysteine at position 518 were required for Trabid activity . Flies deficient for Trabid had a reduced life span due to chronic activation of IMD both systemically as well as in their gut where homeostasis was disrupted . The TAK1-associated Binding Protein 2 ( TAB2 ) was linked with the TAK1-Trabid interaction through its Zinc finger domain that pacified the TAK1 signal . These results indicate an elaborate and multi-tiered mechanism for regulating TAK1 activity and modulating its immune signal .
Conjugation of Ubiquitin ( Ub ) and formation of polyubiquitin chains on proteins can stimulate the assembly of reversible , short-lived signalling centres [reviewed in 1] . The most studied of the different polyubiquitin chain types are the K48-linked and K63-linked chains . K48-linked polyubiquitin chains target a protein for proteosomal degradation while polyubiquitin chains linked by K63 function in processes including signal transduction , DNA repair and transcription , through a degradation-independent mechanism [2] . Ubiquitin-mediated signalling is particularly important for both activating and restricting the activity of nuclear factor-κB ( NF-κB ) during innate immune responses where deregulation leads to chronic inflammation and cancer [reviewed in 3] . In Drosophila , the IMD pathway , which shows striking similarities to the ones stimulated by members of the mammalian TNF-receptor super-family , is strongly triggered by DAP-type peptidoglycan , a cell wall component of Gram-negative bacteria and Gram-positive bacilli [reviewed in 4] . It is assumed that fragments of peptidoglycan released by these bacteria bind the peptidoglycan recognition proteins LC or LE at the cell surface or the cytosol respectively leading to their multimerization [5] . The signal is then transduced through a receptor-adaptor complex comprising Imd itself ( homologous to the mammalian Receptor Interacting Protein RIP , minus the kinase domain ) [6] , dFADD ( FAS-associated death domain protein homologue ) [7] , [8] and the caspase-8 homologue Dredd ( death-related Ced-3/Nedd2-like protein ) [9] . DREDD is K63-linked ubiquitinated by the Drosophila Inhibitor of apoptosis-2 ( dIAP-2 ) , which acts as an E3-ligase promoting DREDD activation [10] . DREDD then cleaves Imd thus unmasking a domain of interaction with dIAP-2 for further dIAP-2-dependent Ub-conjugation this time on Imd itself [11] . Through its RING domain , dIAP-2 ubiquitinates and stabilises Imd , which then acts like a scaffold for the recruitment of downstream components [11] . These components may include the Drosophila TGF-beta-activating-kinase 1 ( dTAK1 ) , together with adaptor TAK1-associated Binding Protein 2 ( dTAB2 ) [11]–[13] . TAK1/TAB2 play a critical role in activating the Drosophila IκB kinase ( IKK ) complex and also transiently activate the c-Jun-N-terminal kinase ( JNK ) pathway [14] . In this IKK/JNK dichotomy , the IKK complex represents the branch of the pathway that phosphorylates the NF-κB transcription factor Relish [15] . It is probable ( but not proven ) that Dredd cleaves the inhibitory C-terminal domain of phosphorylated Relish helped by an IKK scaffold [15] , [16] thus liberating the N-terminal portion to move to the nucleus and regulate expression of transcriptional targets such as the antimicrobial peptide ( AMP ) gene diptericin ( dipt ) . IMD signalling shows acute phase profile in terms of AMP triggering where induction is rapid following infection [17] . Negative regulation plays an important role in restricting the response both inside as well as outside of the cell in epithelia and systemic infection . Outside of the cell Peptidoglycan Recognition Proteins ( PGRPs ) with an amidase activity act to down-regulate the pathway following microbial sensing [18] . Inside the cell , Pirk negatively regulates the receptor PGRP-LC [19]–[21] while dUSP36 inhibits Imd itself [22] and CYLD the IKK complex [23] . Relish plays a crucial role in limiting the signal through proteosomal degradation of dTAK1 [24] . Nevertheless , it is still unclear how dTAK1 is activated although both Dredd and K63-polyubiquitin chains may be involved [25] , [15] . Here we report the discovery of Trabid as a novel component of the IMD pathway and a negative-regulator of dTAK1 . Trabid altered K63-linked polyubiquitination in dTAK1 through its OTU and NZF domains attenuating the immune-related but not the JNK-related signalling output of dTAK1 . We found Lysine 142 of dTAK1 to be critical for its function in the pathway as the probable K63 polyubiquitin acceptor site . Further , K156 functioned as a potential K48 Ub acceptor site . In addition , dTAB2 was found to interact with Trabid and modulate dTAK1 activity through its Zinc finger domain . Together , our findings indicate an elaborate and multi-tiered process modulating dTAK1 signalling activity , during Gram-negative bacterial infection .
In mammals , K63-linked polyubiquitination of TAK1 at Lys158 is critical for activating several signalling cascades including Tumor Necrosis Factor alpha ( TNFα ) , Interleukin-1beta ( IL-1beta ) -induced IkappaB kinase ( IKK ) /nuclear factor-kappaB ( NF-κB ) and c-Jun N-terminal kinase ( JNK ) /activator protein 1 ( AP-1 ) pathway [26] . We aligned the sequences of human TAK1 ( hTAK1; 1–230 amino acids ) and its fruit fly orthologue ( dTAK1 ) to determine the corresponding Lys in Drosophila ( Figure 1A ) . Based on the sequence of residues around Lys158 in hTAK1 , Lys142 of dTAK1 was identified as the most probable candidate . Previous studies had also implicated Lys34 and 209 as Ub acceptor sites in hTAK1 [27] . Based on sequence alignments we identified Lys194 as the Drosophila equivalent of Lys209 in hTAK1 . However , the equivalent of Lys34 in humans is not present in Drosophila ( Figure 1A ) . It was also plausible that the K63 & K48 putative Ub acceptor sites would be in close proximity to both each other and to the kinase activation domain . Hence , Lys134 , 156 and 189 were also selected as likely candidates . Mutation constructs dTAK1K142R-V5 , dTAK1K134R-V5 , dTAK1K156R-V5 , dTAK1K189R-V5 & dTAK1K194R-V5 were made changing the Lys to Arg at these sites . These point mutations maintained the positive charge but could not serve as an acceptor site for Ub modification . The constructs were then screened for their ability to activate IMD immune signalling in cell culture using quantitative real time PCR ( qPCR ) to measure induction of dipt 48 hrs post-transfection . While over expression of dTAK1 activated IMD signalling as previously observed [14] , concomitant overexpression with dTAB2 resulted in increased activation ( Figure 1B ) . In this screen , dTAK1K142R and dTAK1K156R showed significantly reduced dipt induction when compared to wild-type dTAK1 while all other mutants activated dipt at wild type levels ( Figure 1B; Figure S1 ) . However , the fact that the signalling capacity of dTAK1K156R and dTAK1K142R was significantly reduced could have been due to the two mutant proteins not folding properly and being therefore , non-functional . As a result , they would be targeted for degradation by the proteasome . If this were the case , using a proteasome inhibitor one could show increased accumulation of non-degraded mutants dTAK1K156R and dTAK1K142R in comparison to wild type dTAK1 . A time-course expression analysis was performed after treatment with 26S proteasomal inhibitor MG132 ( 75 µM for 8 hrs ) at concentrations that are known to block proteasome activity as previously described [16] . Results showed that expression profiles of wild type dTAK1 , and those of dTAK1K142R and dTAK1K156R were similar , indicating that the mutant proteins were not accumulating more than wild type dTAK1 and thus were presumably folding correctly ( Figure S2 ) . Therefore , dTAK1K142R and dTAK1K156R were selected for further analysis with the working hypothesis that the mutated Lysines were essential for dTAK1 immune activity . We next sought to determine whether there was a difference in the ubiquitination profile of dTAK1 and dTAK1K142R mutant . Co-overexpression of hTAK1 and TAB1 in cell culture results in hTAK1 polyubiquitination [26] . This assay was modified for Drosophila S2 cells . Expression vectors encoding for C-terminally V5 tagged dTAK1 or dTAK1K142R were co-transfected with dTAB2-HA and cMyc-Ub into S2 cells . Cells were lysed 48 hrs post-transfection , immunoprecipitated with anti-V5 antibody , resolved on 10% SDS PAGE and immunoblotted with anti-cMyc antibody ( Figure 2A ) . In contrast to human TAK1 , where mutation of the Lys158 Ub acceptor site to Arginine resulted in failure of TGF-β-induced ubiquitination [26] , [27] , ubiquitination of dTAK1K142R was dramatically increased . Examination of the cell lysates showed greater degradation of dTAK1K142R when compared with wild type dTAK1 ( Figure 2A , see dTAK1/dTAK1K142R panel ) . We then analysed the linkage type of these polyubiquitination chains to distinguish whether they were K48 or K63-linked chains . We co-transfected dTAK1-V5 or dTAK1K142R-V5 and dTAB2-HA together with cMyc-Ub mutants having only one Lys residue at position 48 or 63 ( UbK48 or UbK63; see experimental procedures ) . Wild type dTAK1 showed primarily K63-linked polyubiquitination ( Figure 2B ) . As expected from the degradation seen in the cell lysates of Figure 2A , elimination of Lys142 severely compromised the ability of dTAK1 to form K63-linked polyubiquitination chains , although it retained the ability to form K48-linked chains ( Figure 2B ) . Therefore , in agreement with results on human TAK1 , there appeared to be two separate Ub acceptor sites for K48 and K63-linked polyubiquitination chains in Drosophila TAK1 with Lys142 being the probable K63 Ub acceptor site . We then sought to determine the K48 Ub acceptor site and asked whether there was a difference in the Ub profile of dTAK1 and dTAK1K156R . Expression vectors encoding dTAK1-V5 or dTAK1K156R-V5 were transiently transfected together with dTAB2-HA and cMyc–Ub into S2 cells and Ub assays performed as above . Results showed that overall ubiquitination in the K156R mutant was greatly reduced in comparison to wild-type dTAK1 ( Figure 3A ) . We next identified , which type of polyubiquitination ( i . e . whether K48 or K63 ) had been affected by the K156R mutation . Expression vectors encoding dTAK1-V5 or dTAK1K156R-V5 were transiently transfected together with dTAB2-HA and either cMyc – UbK48 or cMyc-UbK63 into S2 cells , in combinations shown and Ub assays performed ( Figure 3B ) . Results showed that K48-linked polyubiquitination was significantly diminished in dTAK1K156R . Moreover , K63-linked polyubiquitination was not reduced in dTAK1K156R ( Figure 3B ) . The caveat in the above is that results have been obtained through overexpressing the relevant proteins and looking at ubiquitination . However , these data suggest that K63-linked ubiquitination must precede TAK1 activation whereas 48K-linked ubiquitination must in its turn follow to dampen the signal . We have observed such a sequence of events in a time-course experiment where we precipitated endogenous TAK1 with an antibody against it [15] and blotted for cMyc-UbK63 or cMyc-UbK48 following addition of peptidoglycan ( PG ) . Our results show that 2 h following challenge with PG from E . coli there is a bias towards K63-linked ubiquitination , which is gradually shifted towards K48-linked ubiquitination at the end of the 6 h time course ( Figure S3A ) . Moreover , activation of AMP-related immune responses with PG was comparable to TAK1 overexpression through transient transfection ( Figure S3B ) . As implied from the results presented so far , K63-ubiquitination played a critical role in activating dTAK1 . It follows therefore , that de-ubiquitination would be required for terminating the dTAK1 signal . In mammals , the zinc-finger protein A20 ( also known as TNFAIP3 ) has been identified as negative regulator of NF-κB transcription factors in both TNF and IL-1 signalling through its deubiquitinating ( DUB ) and ubiquitin editing functions . In this model , K63-linked chains are cleaved and re-arranged to form K48-linked Ub chains , thereby tagging the protein for proteosomal degradation [28] , [ reviewed in 29] . Trabid ( Trbd ) was originally discovered as a positive regulator of both the mammalian and Drosophila Wnt pathway with a remarkable preference for binding to , and cleaving , K63-linked ubiquitin chains [30] . Trbd is also a representative of the A20 OTU family in fruit flies [30] . To determine whether Trbd also functioned in IMD signalling , we tested for its interaction with dTAK1 . Co-immunoprecipitation assays showed that dTrbd bound to dTAK1 ( Figure 4A ) . Trbd also bound TAB2 as shown by relevant co-immuno-precipitation experiments ( see below ) . We then explored the in vivo effects on IMD signalling of a trbd deletion generated by homologous recombination [30] . We assayed AMP expression in 3–6 days old whole animals without an infection . We used flies homozygous for the trbd deletion; flies homozygous for a mutation in pirk [pirkEY00723 , ref 21]; yellow-white ( yw ) flies as the genetic background used for both the initial trbd targeting construct [30] and by Gene Disruption Project [31] , which generated pirkEY00723; flies mutant for both trbd and pirk; white118 flies ( w118 ) as an additional control . We observed a significant increase in expression of attacinD , drosocin ( dro ) , dipt and CecropinA1 in both the trbd and pirk; trbd flies relative to the yw and w1188 controls as well as to the pirk single mutant ( Figure 4B ) . This meant that removal of trbd ( or both pirk and trbd ) would increase the levels of AMPs in a systemic fashion leading to a chronic response in the absence of infection . Injection of bacteria did not increase AMPs further and following systemic infection with E . coli 1106 the levels of dipt or dro gene expression between control and trbd or pirk;trbd flies were statistically inseparable ( Figure 4C; Figure 4D ) . No further increase in whole fly AMP levels following systemic infection was observed in mutants of other negative regulators ( e . g . CYLD , Nubbin ) [23] , [32] . Further increases maybe tissue specific ( gut; see below ) and would therefore fail to be detected in whole fly preps . The Drosophila IMD pathway is also the mediator of local immune responses in the gut . In contrast to whole animals , we observed that the gut of heterozygous trbd flies showed a 2-fold increased expression of dipt over and above the wild type levels of induction following infection with Erwinia carotovora carotovora ( Ecc15 ) and assaying using qPCR 24 h later ( Figure 4E ) . Loss of both copies of trbd resulted in dipt induction 3 times as much as the wild type control . Finally , concomitant loss of pirk and trabid led to a 5-fold induction of dipt over and above wild type activation levels ( Figure 4E ) . This was suppressed in a dredd mutant background ( Figure 4E ) . Over-activation of dipt was not due to a delay in bacterial clearance as exemplified by measuring Colony forming Units ( CFUs ) following oral Ecc15 infection . Clearance in trbd and pirk; trbd mutants was statistically indistinguishable from wild type controls or pirk single mutants ( Figure S4 ) . Interestingly , bacterial clearance following systemic infection in trbd and pirk; trbd was statistically significantly faster than wild type and pirk flies using both a low ( approx . 400 cells; Figure S5A ) and a high ( approx . 4000 cells; Figure S5B ) dose of E . coli 1106 . These differences were just below the limit of statistical significance following Ecc15 systemic infection with the same doses ( Figure S5C and S5D ) . Nevertheless , the E . coli 1106 result correlated with the observation that trbd and trbd;pirk flies had a much higher level of AMPs to begin with ( Figure 4B ) , suggesting a protective effect . This potential protective effect however , had a cost . We found that deletion of trbd severely compromised the life span of flies ( in the absence of infection ) . Our results are shown in Figure 5 . 50% of flies heterozygous for both pirk and trbd survived the 30-day mark ( LT50 = 32; Figure 5A and 5C ) . A similar effect was observed in flies deficient for pirk in a trbd heterozygous background ( LT50 = 27; Figure 5A and 5C ) . Just deleting trbd ( in a pirk heterozygous background ) had serious consequences , as 50% of flies were dead by 18 days ( LT50 = 18; Figure 5A and Figure 5C ) . More significantly however , the double mutant pirk; trbd had a dramatic reduction in life span compared to either single mutant ( +/pirk; trbd or pirk; +/trbd ) or double heterozygote ( +/pirk; +/trbd ) since 50% of flies were dead before the 14-day mark ( LT50 = 14; Figure 5A and 5C ) . This phenomenon was suppressed in dredd; pirk; trbd flies ( LT50 = 37 ) where IMD was inactive ( Figure 5A; Figure 5C ) . Interestingly , there was no statistically significant change ( LT50 = 14 to LT50 = 18 ) when pirk; trbd flies were grown on germ-free conditions showing that it was largely the host rather than a disturbance in microbiota that was the cause for the reduction in life span ( Figure 5B; Figure 5C ) . Crucially for this assumption , flies that had a blocked IMD pathway ( dredd; pirk; trbd ) lived as long as wild type ( yw ) flies in germ-free conditions ( Figure 5B; Figure 5C ) . The above result meant that chronic activation of IMD in the absence of infection ( seen in Figure 4B ) was the cause of life span reduction . Moreover , gut homeostasis was disrupted . Upd3 is a ligand secreted by stressed enterocytes , which activates the JAK-STAT pathway in intestinal stem cells to promote both their division and differentiation [33]–[35] . In comparison to wild type flies , we observed a significantly higher level of JAK-STAT activity in the guts of unchallenged trbd and even more in pirk;trbd flies as monitored by the expression of upd3 and the JAK-STAT target gene Socs36E in qPCR assays ( Figure S6A; Figure S6B , respectively ) . The presence of dredd fully suppressed this elevated JAK-STAT activity observed in pirk;trbd flies , demonstrating that excessive Imd pathway activation must cause gut damage , which in turn induces epithelium renewal ( Figure S6A; Figure S6B ) . From the above experiments we concluded therefore , that 1 ) dTrbd physically interacted with dTAK1 , 2 ) negatively regulated the IMD signal in vivo and 3 ) its deletion had an impact on life span and gut homeostasis due to chronic activation of immune signalling . Moreover , reduction of life span in trbd deletion flies was enhanced when Pirk , a known negative regulator of the IMD pathway was also mutated . A20 proteins ( including Trbd ) belong to the ovarian tumour ( OTU ) family of DUBs . The OTU is a conserved cysteine protease domain that possesses DUB activity [reviewed in 36] . Trbd possesses three N-terminal NZF ( Npl4 zinc finger ) domains and a C-terminal OTU domain , which shows DUB activity with a preference for K63-linked ubiquitin [30] . Further , two or more NZF domains are required for binding to K63-linked Ub chains . The catalytic residue in the OTU domain in humans is C443 . Sequence alignment of hTrbd and dTrbd showed that C518 was most probably the corresponding catalytic residue in Drosophila ( Figure 6A ) . Two constructs were made: dTrbdC518S ( where C518 was changed to S ) and dTrbdC518S+3xNZFDel , where along with C518S the first Cys residue of each of the 3 NZF domains namely , C13 , C94 and C238 , were mutated to Ala . To determine their functional importance , these mutations , dTAK1 and dTAB2 were co-transfected along with dTrbdC518S and dTrbdC518S+3xNZFDel in S2 cells and relative dipt expression assayed using qPCR . As expected , over-expression of dTrbd significantly decreased the TAK1/TAB2-mediated dipt induction although it did not completely abrogate it ( Figure 6B ) . The C518S mutation substantially relieved the suppressive effect of wild type Trbd on dipt expression levels , with the C518S+3xNZFDel showing no suppression whatsoever ( Figure 6B ) . Expression levels of puckered ( a target of the JNK cascade also induced through TAK1/TAB2 activity ) revealed that the pathway was not affected , indicating that dTrbd functioned solely in the IMD pathway ( Figure S7 ) . It has been suggested that it is Plenty of SH3 ( POSH ) , which terminates the JNK-related dTAK1 signal [37] . To determine the relative contribution of the OTU and NZF domains in the ability of dTrbd to cleave K63-linked Ub chains [30] we tested the overexpression of dTrbd as well as dTrbdC518S and dTrbdC518S+3xNZFDel on dTAK1 K63-linked ubiquitination . As expected , dTrbd substantially reduced K63-linked ubiquitination of dTAK1 when co-transfected with dTAB2 ( Figure 6C ) . Ubiquitination was moderately affected by dTrbdC518S and not at all by dTrbdC518S+3xNZFDel ( Figure 6C ) . We also tested the effects of dTrbd , dTrbdC518S and dTrbdC518S+3xNZFDel on K48-linked ubiquitination . Results showed that K48-ubiquitination on TAK1 , was not affected by dTrbd wild type , or dTrbd mutants ( Figure S8 ) . We finally wanted to explore the tripartite relationship between TAK1 , TAB2 and Trbd . In mammals , TAB2 and TAB3 function as adaptors , which link TRAF2 and TRAF6 to TAK1 , facilitating complex formation and activation of TAK1 in IL-1 and TNF-induced NF-kB activation [38] , [39] . Both TAB2 and TAB3 contain a highly conserved C-terminal novel zinc finger domain , which binds preferentially to K63-linked polyubiquitin chains . Mutations in this domain abolish the ability of TAB2 and TAB3 to bind polyubiquitin chains , as well as their ability to activate TAK1 [39] , [40] . We generated a dTAB2 mutant ( dTAB2ZnFDel see Figure 7A ) where the first Cys residues in the C-terminal Zinc Finger ( ZnF ) motif were changed to Ala ( C769A and C772A ) as previously performed with human TAB2 [40]–[42] . Thereafter , the effect of this mutation on IMD signalling was assayed in cell culture using qPCR . Drosophila TAK1 was transfected either alone or together with dTAB2 or dTAB2ZnFDel into S2 cells and dipt expression assayed 48 h post infection using qPCR . Interestingly , signalling intensity was doubled in the presence of dTAB2ZnFDel when compared with wild type TAB2 ( Figure 7B ) . Increased dipt induction may be the result of greater dTAK1 activation . Therefore the level of K63-linked ubiquitination of dTAK1 was examined in an S2 cell-based Ub assay in the presence of either TAB2 or dTAB2ZnFDel . Our results showed that , K63-linked ubiquitination of dTAK1 was increased in the presence of dTAB2ZnFDel ( Figure 7C , left lane ) . These results suggested therefore that the mutation of the dTAB2 ZnF domain might stabilise dTAK1 by enhancing its 63K-linked ubiquitination . This led to an increase in signalling capacity seen by increased dipt induction . It appeared therefore that the TAB2 C-terminal ZnF domain restricted dTAK1 activity and thereby the IMD signal . Interestingly , dTAB2ZnFDel interacted more strongly ( judging from the intensity of the signal ) with dTrabid than wild type dTAB2 ( Figure 7D ) . This might indicate that the tighter dTrbd bound to dTAB2 the higher the signalling capacity of dTAK1 .
Our working model is that of a tripartite relationship involving dTAK1 , dTAB2 and Trabid . TAB2 is needed to activate TAK1 but through its ZnF domain it modulates TAK1 signalling and the TAB2-Trbd interaction . The latter is important for turning down the immune branch of TAK1 signalling and thereby the IMD pathway during gut epithelia responses and keeping IMD in check systemically in the absence of infection . Mutations in the dTAB2 ZnF domain enhance the TAB2-Trabid interaction and result in a more stable TAK1 presumably by keeping away Trbd from its target . An alternative scenario would be that through its ZnF domain TAB2 recruits an additional protein . This hypothesis would predict the presence of a protein that would act in concert with Trbd sharing some of its characteristics ( e . g . DUB activity ) . More work is needed to distinguish between these two possibilities .
Drosophila S2 cells ( Invitrogen ) were maintained at 25°C in Schneider's Drosophila Medium ( BioWhittaker/Lonza ) , supplemented with 10% heat-inactivated FBS and antibiotics 100 U/ml penicillin G and 100 µg/ml streptomycin sulfate – all Invitrogen ) . Cells were transfected with 2 ug plasmid DNA using Effectene Transfection Reagent ( Qiagen ) according to the manufacturer's protocol . Empty pAc5 . 1/HA-His vector was used to ensure equal amounts of DNA were delivered in each transfection . Cells were lysed 48 hrs post-transfection in RIPA buffer ( Sigma-Aldrich ) supplemented with Complete Mini Protease Inhibitor Cocktail tablets ( Roche Applied Science ) and Benzonase Nuclease ( Sigma-Aldrich ) . Cell lysates were incubated rocking with 50 µl of Anti-V5 or Anti-HA Agarose Affinity Gel ( Sigma-Aldrich ) for 2 hours at 4°C . Antibody beads were pre-blocked in RIPA buffer supplemented with 0 . 2% BSA ( NEB ) at 4°C for 2 hrs . Immunoprecipitates were washed with 600 µl CoIP wash buffer 900 ( 50 mM Tris-HCl [pH 8 . 0] , 900 mM NaCl , 5 mM EDTA [pH 8 . 0] , 0 . 5% Igepal CA-6030 ) four times for 10 minutes each at room temperature , followed by a final wash with 600 µl CoIP wash buffer 150 ( 50 mM Tris-HCl [pH 8 . 0] , 150 mM NaCl , 5 mM EDTA [pH 8 . 0] , 0 . 5% Igepal CA-6030 ) . Immunoprecipitates were eluted in 1X SDS sample buffer , resolved on 10% SDS-PAGE and transferred to PVDF membranes . Blots were probed with mouse anti-V5 ( 1∶5000 , Invitrogen ) , mouse anti-c-Myc peroxidise ( 1 µg . ml−1 . Roche Applied Science; Clone 9E10 ) or rat anti-HA antibodies ( 200 ng . ml−1 , Roche Applied Science; Clone 3F10 ) . Between probing with a different antibody , blots were stripped with Restore PLUS Western Blot Stripping Buffer ( Pierce ) . | Chronic activation of immune responses results in health problems including gastrointestinal infections , metabolic imbalances and inflammatory bowel diseases that may lead to colorectal cancer . Central to this , is the balance of activation/restriction of nuclear factor-κB ( NF-κB ) during innate immune responses . To study signaling through NF-κB , we use the fruit fly Drosophila melanogaster as a genetically tractable model system that reflects human biology ( due to the evolutionary conservation between innate immunity in flies and mammals ) , while reducing the complexity of the human disease of interest . We have found a new negative regulator of the Drosophila NF-κB pathway named Trabid . Its loss released the pathway and resulted in constitutive immune activation both in the gut as well as in the whole fly . This spontaneous immune activation reduced life span in the absence of infection , especially when it was combined with loss of another known negative regulator of the same pathway , a protein named Pirk . Stem cell activity in the gut in a pirk;trabid double mutant was found to be significantly increased , as the gut was trying to balance enterocyte loss . Trabid was acting at the level of TGF-beta Activating Kinase 1 ( TAK1 ) , which triggers both immunity and cell death . | [
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] | 2014 | Loss of Trabid, a New Negative Regulator of the Drosophila Immune-Deficiency Pathway at the Level of TAK1, Reduces Life Span |
Genome sequencing of the 5 , 300-year-old mummy of the Tyrolean Iceman , found in 1991 on a glacier near the border of Italy and Austria , has yielded new insights into his origin and relationship to modern European populations . A key finding of that study was an apparent recent common ancestry with individuals from Sardinia , based largely on the Y chromosome haplogroup and common autosomal SNP variation . Here , we compiled and analyzed genomic datasets from both modern and ancient Europeans , including genome sequence data from over 400 Sardinians and two ancient Thracians from Bulgaria , to investigate this result in greater detail and determine its implications for the genetic structure of Neolithic Europe . Using whole-genome sequencing data , we confirm that the Iceman is , indeed , most closely related to Sardinians . Furthermore , we show that this relationship extends to other individuals from cultural contexts associated with the spread of agriculture during the Neolithic transition , in contrast to individuals from a hunter-gatherer context . We hypothesize that this genetic affinity of ancient samples from different parts of Europe with Sardinians represents a common genetic component that was geographically widespread across Europe during the Neolithic , likely related to migrations and population expansions associated with the spread of agriculture .
The Tyrolean Iceman is a well-preserved natural mummy that was discovered on a glacier near the Austrian-Italian border in 1991 . Subsequent analyses revealed that the remains were from a ∼45-year-old male , with a 14C dating estimated age of 5 , 300 years , making it the oldest human mummy discovered in Europe to date [1]–[3] . This spectacular discovery provided a unique view of the cultural and socio-economic context of the Central European Alpine region during the late Neolithic and Copper Age ( e . g . [4] ) . More recently , the publication of the Iceman's nuclear genome sequence allowed us to address the question of his origin from a genomic perspective , revealing a surprising genetic affinity with individuals from Sardinia [5] . The Iceman's Y-chromosome lineage was determined to be G2a-L91 , which is found at appreciable frequencies only in Corsica and Sardinia but is rare elsewhere in Europe [5] . Furthermore , in a principal component analysis including more than 1 , 300 Europeans , the Iceman clustered outside of the genetic variation of the continental Europeans and close to individuals from Sardinia . However , although a previous study on bone and tooth stable isotope compositions concluded that the location of the Iceman's origin could be restricted to a few valleys close to the discovery site [6] , the question of whether this signal reflects an ancestry component more widespread in Europe during the Neolithic could not be answered from the analysis of a single individual . Most studies of the genetic structure of prehistoric European populations to date have described patterns of variation in the mitochondrial DNA ( mtDNA ) , primarily due to the limitations of sequencing the nuclear genome from ancient samples with low endogenous DNA contents . Nevertheless , the limitation of only investigating the maternal lineage has been offset by the capacity to investigate larger sample sizes , both spatially and temporally distributed , which has yielded considerable insight into the genetic history of Europeans [7]–[12] . More recently , and subsequent to the publication of the Iceman's genome , two additional studies of ancient Europeans have expanded the view to include autosomal loci [13] , [14] . The publication of these autosomal datasets , together with recently available whole-genome sequencing datasets from modern Europeans , therefore allows a re-examination of the Iceman's ancestry together with these new data . In particular , under the hypothesis that this Sardinian-like ancestry component was more widespread in Neolithic Europe , we would expect that individuals from a similar time period as the Iceman but a different geographic origin would show a comparable pattern of relatedness to Sardinians . To address this hypothesis , we compiled and analyzed datasets from modern and ancient Europeans , including previously unpublished whole genome sequencing data from 452 Sardinians , as well as ancient DNA data from two 2 , 500-year-old ancient Thracian individuals from Bulgaria , which we will refer to as Thracians . These datasets allow us to address the question of the Iceman's ancestry and its implications with respect to the genetic structure of Neolithic Europe .
We compiled several sets of single nucleotide polymorphism ( SNP ) genotype data for our analysis . For each dataset , data from the ancient genomes was merged into a reference set of modern populations , using only SNPs present in the modern data . Three complementary reference datasets were used: SNP array genotype data from the Human Genome Diversity Panel ( HGDP ) ; high coverage whole-genome sequencing data from Complete Genomics ( CG ) ; and low coverage whole-genome sequencing data from the 1000 Genomes project and Sardinians ( 1000G/Sardinia ) . For the 1000G/Sardinia dataset only population-level allele frequency data was used , to circumvent lower genotype accuracy in the low-coverage experimental design . Although the CG dataset does not include individuals from Sardinia , we included it to investigate the relationship of the ancient individuals with Eurasians using high-coverage whole-genome data . The ancient genomes we used comprised both previously published and newly generated data . In addition to the Iceman genome , we included partial genomic DNA data from five individuals distributed across Europe ( Figure 1A ) . We broadly classified all ancient samples into hunter-gatherers ( HG ) or farmers ( F ) , based on the context of their respective cultural attributes . The HG group included a 7 , 000-year-old Mesolithic individual ( brana1 ) from the Iberian Peninsula [14] , as well as a Neolithic Swedish individual ( ajv70 ) from Sweden [13] , both previously published . Both of these studies included data from more than one HG individual in their analyses . However , given that the results in both studies suggested comparable ancestry for the HG samples , we focused only on the individual with the highest coverage for our analysis . The remaining four individuals were classified as farmers . The F group included the Iceman , a Neolithic Swedish farmer ( gok4 ) from the same study as the Swedish hunter-gatherer [13] , and new DNA sequencing data from two ancient Thracian Iron Age individuals from Bulgaria ( P192-1 , K8 ) [15] . A summary of the final datasets is shown in Table 1 . We used the unsupervised clustering algorithm ADMIXTURE [16] to investigate the relationship of the Iceman with 11 European , Middle Eastern and North African populations in HGDP , using only SNPs discovered in non-European populations to avoid biases from differential relatedness of European populations to the discovery populations . Ancestral clusters were inferred for the modern samples only , with the number of ancestral clusters k ranging from k = 2 to k = 8 ( Figure S1 ) . We then determined the most likely cluster proportions of the Iceman using the inferred ancestral cluster allele frequencies from the modern samples . At smaller k values , estimated ancestry proportions of the Iceman match those of other individuals from Southern Europe , including a small estimated proportion of Middle East-related ancestry ( Figure S1 ) . For higher values of k , the majority of the Iceman's ancestry is composed of the Sardinian-related cluster , with a small proportion of the Basque-related cluster ( Figure 1B ) . Principal component analysis ( PCA ) recapitulates this picture , with the Iceman clustering close to the Sardinian individuals , although somewhat shifted towards the Northern Italian individuals ( Figure S2 ) . To determine the relationship of the Iceman with the CG genomes , we quantified the rate of derived allele sharing with each of the modern samples ( Figure 2A , Figure S3 ) . The highest sharing was found with European genomes ( TSI , CEU ) , consistent with the European origin of the Iceman . However , within Europe we did not observe a noticeable difference between the Southern European TSI and the Northwestern European CEU . To further investigate this result , we computed the D-statistics [17] , [18] of the configuration D ( Outgroup , Iceman; Population 1 , Population 2 ) , where populations 1 and 2 correspond to a pair of modern populations . When we test the configuration D ( O , Iceman; TSI , CEU ) we find no significant deviation from zero ( D = −0 . 007 , Z = −2 . 07 ) , indicating that neither CEU nor TSI are more closely related to the Iceman ( Figure S4 ) . Interestingly , the European ancestry segments from the Mexican genomes ( MXL ) show the highest overall derived allele sharing within Europe ( Figure 2A ) . Combining across all MXL European tracts , the D tests of the form D ( O , Iceman; MXL . EUR , CEU/TSI ) are both significantly different from zero ( ZCEU = −7 . 16 , ZTSI = −5 . 14 ) ( Figure S4 ) , indicating a closer relatedness of the Iceman with the MXL European segments than either CEU or TSI . One possible explanation for this observation would be that the source population for the European admixture in Mexicans was indeed more closely related to the Iceman than either the TSI or the CEU . However , we do not find a difference in relatedness between modern Iberians and either the TSI or CEU in the 1000G/Sardinia dataset described below . Gene flow from a more distantly related population into TSI or CEU after their split from the ancestral population of the MXL European segments would also cause a decrease in matching with the Iceman . A related explanation is that the excess sharing results from comparing only double European segments in the Mexican individuals to those of the whole genome in CEU and TSI . Since our local ancestry inference masks segments that are not clearly of European ancestry , any non-European ancestry in the CEU or TSI would lead to excess sharing of the European tracts in the MXL and the Iceman . For example , we and others have previously reported a gradient of North African ancestry within Europe and this may be a source of differential non-European ancestry among the CEU , TSI , and double European segments in the MXL . North African gene flow into Europe likely occurred after the death of the Iceman , so he would not carry many North African segments and thus would appear more closely related to the high-confidence European segments in the MXL . Another potential source of non-European ancestry in modern day Europeans is gene flow from other parts of Asia ( e . g . , the Finns appear to carry significant amounts of non-European Eurasian ancestry - see below ) . We then investigated the relationship between the Iceman and all pairs of European populations in the 1000G/Sardinia dataset as described above . We find that all significant tests fall into one of two categories , involving either the Finns ( FIN ) or Sardinians ( Table S1 , Figure S5 ) . In all tests involving the FIN the Iceman is significantly more closely related to the non-Finnish population , irrespective of which population is used . Furthermore , the Iceman is always significantly more closely related to Sardinians than any other European population ( Figure 2B ) , consistent with a more recent shared ancestry with Sardinians . To further investigate this scenario , we modeled the relationship of the Iceman with the modern populations using TreeMix [19] . The maximum-likelihood tree without migration edges shows the expected relationships corresponding to the three continental groups included in this dataset ( Figure 3A ) . Within the European clade , we find a North-to-South pattern of population splits starting with the FIN as outgroup to all other Europeans . Importantly , the Iceman consistently forms a clade with the Sardinians within the group of Southern Europeans ( 98% bootstrap ) , in agreement with the results of the D-test . Bootstrap support is high ( >95% ) for all other splits , with the exception of the edge grouping TSI with Sardinians and the Iceman , which is slightly less supported ( 89% ) . The long branch to the Iceman is likely a consequence of higher error rates and/or reduced heterozygosity in the ancient sample , mimicking a population exhibiting increased genetic drift and differentiation ( e . g . see also [20] ) . Examination of the residuals ( Figure 3A inset ) indicates some population relationships that are not fully resolved by the simple tree model ( e . g . , FIN and Asian populations ) . Allowing for up to five migration edges on the tree , we recover mixture events consistent with previous results ( Figure 3B; see below for discussion ) . Nevertheless , the clade Iceman/Sardinians remains strongly supported ( >96% bootstrap ) for all models irrespective of the number of mixture events included ( Figure S6 ) . To gain further insight into the demographic history of the Iceman , we used a coalescent-based method [21] to estimate divergence times between the Iceman genome and the genomes of the individuals in the CG and 1000G/Sardinia datasets . We find that the estimated divergence times between the Iceman and the CG genomes are within the expected range for an individual with European ancestry , and in line with recent results based on the whole-genome sequencing data [22] , [23] ( Table S2 ) . Within Europe , most individuals show estimates between 3 , 000 and 4 , 500 YBP , roughly corresponding to the age of the Iceman . In addition , a recent study on genetic ancestry in Europeans inferred from patterns of identity-by-descent across the genome also indicates a signal of demographic events in a shared ancestral population with a comparable age [24] . For the 1000G/Sardinia dataset , we estimated divergence times comparing the Iceman to population-representative genomes , constructed by randomly sampling alleles proportional to their frequency in each population . The divergence times we obtain are in line with the times estimated from the CG genomes , with higher estimates for Northern European populations , in particular the FIN ( 9 , 196 YBP; Table S3 ) . In Southern Europe , both Sardinians and TSI show very low estimates ( TSI 1 , 709 YBP; Sardinia 2 , 321 YBP ) . Surprisingly , we found that the divergence from Iberians ( IBS ) was higher than the FIN from Northern Europe ( 13 , 806 YBP; CI 1 , 957–29 , 739 ) ) . A possible explanation could be the presence of non-European ancestry in the IBS , through gene flow from North Africa as previously described [25] and discussed above . The results described in the previous section strongly suggest that among contemporary European populations , Sardinians are most closely related to the Iceman . To investigate whether other ancient Europeans show a similar pattern , we compiled genomic data from five additional individuals of varying ages and cultural contexts and merged them with the Iceman datasets ( Table 1 ) . We first inferred ancestral clusters for these individuals using the HGDP ADMIXTURE methodology described above . We find that at low values of k , ancestry proportions are similar among the different ancient samples , and comparable to other European populations . We note that at k = 3 , populations from Southern Europe show a small proportion of ancestry related to Middle Eastern populations , which is absent from the two hunter-gatherer ( HG ) individuals , but present in the farmer ( F ) individuals at a comparable level ( Figure S1 ) . At k = 6 , the HG group can be distinguished from the farmers by a smaller proportion of the Sardinian component , which is also the dominant component in both the Iceman and the Swedish farmer ( gok4 ) from the F group ( Figure 1B ) . Within the F group , gok4 has a slightly lower proportion of the Sardinian cluster than the Iceman , and most closely resembles the Tuscans and Northern Italians , in agreement with the results of Skoglund et al . [13] . Both of the HG individuals have previously been shown to cluster with Northern European populations ( 16 , 17 ) , and in concordance with these results we find the highest proportion of Northern European/Russian ancestry in those individuals . Interestingly , the Iberian HG ( brana1 ) , which was discovered geographically close to contemporary Basque populations , also shows the highest proportion of Basque ancestry . For the Thracian individuals from Bulgaria , no clear pattern emerges . While P192-1 still shows the highest proportion of Sardinian ancestry , K8 more resembles the HG individuals , with a high fraction of Russian ancestry . Because those two individuals also had the lowest number of SNPs , we wanted to investigate the accuracy of cluster assignments from the modern individuals . We inferred ancestry proportions for each modern individual for all SNP subsets corresponding to the ancient samples , and calculated the root mean squared error ( RMSE ) per population and k value compared to the full dataset . We find that the accuracy is very high for the Iceman ( RMSE< = 0 . 01 ) and remains high for gok4 and the HG samples ( RMSE< = 0 . 05 for most clusters; Figure S7 ) . Both ancient Thracians show RMSE values between 0 . 05 and 0 . 10 , suggesting greater uncertainty in the estimates for those samples , also seen in the PCA results for those samples ( Figure S2 ) . We then used the 1000G/Sardinia dataset to gain further insight into the relationship of the other ancient genomes with Sardinia . Using the D-test as described above , we observe a strong differentiation between HG and farmer individuals in their affinity with the modern European populations . We find that the HG individuals are always significantly closer to Northern European populations ( FIN , CEU , GBR ) ( Figure 2 , Figure S5 ) , whereas the farmer individuals generally show a closer relationship with Sardinia than with the other European populations . For gok4 the pattern is particularly striking , closely resembling the Iceman in its relationship with Sardinians . Results for P192-1 also follow this pattern , although some comparisons fail to reach significance , likely because of reduced power due to a lower number of SNPs . However , results are inconclusive for the other Thracian individual ( K8 ) , where all but one comparisons are non-significant ( Z< = 3 ) ( Figure S5 ) , again likely due to a smaller number of SNPs in this sample . Results from the TreeMix analysis for each ancient genome show a similar picture . In the HG group , the Iberian brana1 forms an outgroup to all other European populations ( 100% bootstrap ) , whereas ajv70 splits after the FIN but before other Northern Europeans ( 95% bootstrap ) ( Figure S8 ) . In the F group , both gok4 and P192-1 form a clade with Sardinians like the Iceman , although with less bootstrap support ( gok4 83% , P192-1 56% ) . Finally , although K8 clusters with Northern European populations , its position in the tree is not resolved ( 3% Bootstrap ) . We note however that despite the reduced number of SNPs for K8 , the relationships among the modern populations are consistent with the full dataset and generally well supported ( bootstrap >90% ) , except within the Southern European group ( minimum bootstrap 53% ) . It is therefore possible that the inconclusive pattern for K8 either reflects a possible higher level of modern DNA contamination ( see Table S4 in [15] ) or a more complex relationship to the modern populations included in the analysis . To further investigate the history of the populations in the 1000G/Sardinia dataset , we investigated patterns of admixture . We first turned to the TreeMix analysis of the merged dataset with the Iceman described above , allowing up to five mixture events . The first four inferred edges are highly significant ( p<10−10 for m = 1 to m = 4 ) , the last one however to a much lesser extent ( p = 0 . 009 ) . In addition , both the amount of variance explained and the residuals do not change substantially after m = 4 , indicating possible issues with overfitting the model at m = 5 . We therefore report estimates of the tree with four inferred mixture events in the remainder of this section . The first inferred edge corresponds to sub-Saharan African admixture in Southern European populations ( edge weight w = 0 . 027 ) , which is consistent with previous estimates of 1%–3% sub-Saharan African ancestry in those populations via North African gene flow [25] , [26] . We also infer that the FIN trace around 7% of their ancestry ( w = 0 . 075 ) to present-day Japanese ( JPT ) , consistent with evidence of circumpolar gene flow [27] . A higher similarity of Finns with Asians than other European populations has also been observed previously , in particular in Eastern Finns [28] . At m = 3 migrations , an edge is added between the ancestral Europeans and the TSI , with a surprisingly high proportion ( w = 0 . 35 ) ( Figure 3B ) , which is somewhat more difficult to interpret . The last edge added corresponds to a mixture of an Iceman-related population and the Bantu-speaking Luhya ( LWK ) from Eastern Africa ( w = 0 . 03 ) . The LWK have previously been reported as showing a signal of gene flow of possible Neolithic Middle Eastern or European origin [29] , which would be consistent with the observed signal ( see also [30] , [31] ) . We proceeded to investigate these signals of admixture in a less parameterized way , using f3 statistics [18] . We calculated f3 for all populations in the 1000G/Sardinia dataset as targets , using two sets of combinations of source populations . In the first set , we use all pairs of remaining modern populations as potential sources , whereas in the second analysis , we always use the ancient samples as one of the source populations . We find a large number of significant tests ( Z≤−3 ) , which we group based on interpretation ( Table S4 , S5 ) . A large fraction of these reflect the migration edges inferred in the TreeMix analysis . We find evidence for admixture in the LWK using any of the non-African population as source populations ( max Z = −9 . 7 ) , which remains significant if we replace the non-Africans with either HG or F ancient samples ( max Z = −3 . 4 ) . The TSI show the signature of African admixture as described when using CEU , GBR or Sardinia as European source populations ( max Z = −4 . 4 ) , whereas for the IBS only YRI and GBR as source populations gives a significant result ( Z = −3 . 4 ) . We also confirm the Asian-related admixture in the FIN when using the other Northern European populations as one source ( max Z = −5 . 8 ) . The remainder of the significant results within Europe follows the pattern of a south-to-north gradient of genetic similarity , with Sardinians and Finns at either ends of the gradient ( Sardinia – IBS/TSI – CEU/GBR – FIN ) , consistent with a model of isolation by distance as previously described [32]–[34] , For example , all f3 tests using Sardinians as one source population and TSI/IBS as the target population are significant irrespective of which Northern European population is used as the other source ( Table S4 ) , whereas for the CEU/GBR , all tests of the form f3 ( CEU/GBR;Europe S , FIN ) are significant . A complementary explanation suggested by recent results would be a scenario of increasing levels of admixture between arriving farmers and local HG populations during the Neolithic transition [13] , [18] . In the case of this second scenario , we would predict that replacing one modern European source population with its corresponding ancient sample of HG or farmer origin should show comparable results in the f3 test . This is indeed what we observe in the majority of the cases . For example , tests using either TSI or IBS as target population remain largely significant if we replace Sardinians with the Iceman as one of the source populations . In addition , in tests with CEU or GBR as target populations , the HG samples ajv70 and brana1 can replace the FIN , whereas the Iceman and P192-1 from the farmer groups can replace the southern source populations ( Table S5 ) . However , while the strongest signal using the modern populations is observed with the Sardinians as southern source ( e . g . f3 ( CEU;Sardinia , FIN ) , Z = −25 . 4 ) , tests using the HG samples only reach significance with the TSI as the second source population . Taken together , these results suggest that the observed patterns can at least in part be explained by different levels of HG ancestry proportion in Northern versus Southern Europe , as previously suggested [13] , [18] . The HGDP dataset also contains genotype data for Neanderthal , Denisova , and Primate samples in addition to modern humans . We therefore used this dataset to perform PCA as previously described [35] . Specifically , we first built the PCA space using only data from Denisova , Neanderthal and Chimpanzee , and we then projected all modern individuals as well as the Iceman onto the first two inferred principal components . In agreement with previously reported results , modern human samples separate into three distinct clusters ( Figure S9 ) . We see that all non-African populations are shifted towards the Neanderthal in PC space , whereas populations from Oceania form their own cluster with an additional shift towards the Denisovan . The Iceman clearly falls within the cluster of the non-African populations ( except Oceania ) , indicating a shared signal of Neanderthal admixture with other European and Asian samples . To confirm this signal , we merged data from both the Neanderthal and the Denisovan genome with the CG dataset and carried out D-tests as described above . In particular , we calculated all D-statistics of the form D ( O , archaic;Iceman , modern ) , where ‘archaic’ represents either Neanderthal or Denisova and ‘modern’ represents all modern populations in the CG dataset ( Figure S10 ) . To eliminate potential confounding due to shared DNA damage patterns between the Iceman and the archaic hominins , we excluded all transition sites for this analysis . We find that both Neanderthal and Denisovan are significantly more closely related to the Iceman than to African populations , consistent with previously reported results for modern non-Africans [36] . However , all D-tests involving another non-African population do not significantly deviate from zero , suggesting that the Iceman genome contains levels of archaic ancestry that are comparable to that of other non-African populations .
The discovery of a genetic affinity between the Iceman and modern-day Sardinians showcased both the power and some of the remaining limitations of the availability of nuclear genomic data from historical human samples [5] . While the result was unexpected with respect to the known history and geographic location of the individual at the time , the broader interpretation of the finding was hindered by the question of how representative this single individual really was for the Central Alpine region . Partial genomic sequences from ancient individuals published after the Iceman's genome have shed additional light onto the genetic structure of Neolithic Europe , in particular with respect to the relationship between the hunter-gatherer populations descending from the first Europeans and individuals associated with the spread of farming during the Neolithic transition [7] , [8] , [13] , [14] . Here , we compiled large-scale genomic datasets , including both recently published and newly generated data from ancient Europeans , to reexamine the evidence for the Iceman's relationship to Sardinia and to address the question of the broader historical context of this relationship . Our results using both the HGDP array data and whole-genome sequencing data confirm the Iceman's genetic relationship with Sardinian populations . For example , in the 1000G/Sardinia dataset , which we expect to provide the highest statistical power due to the large number of markers , we found that the Iceman consistently forms a clade with Sardinians , irrespective of the number of migration edges we allow in the model . The less parameterized D-test results also confirm this , with all pairwise comparisons involving Sardinians being highly significant ( Figure 2; −10 . 2≤Z≤−17 . 7 ) . The results of the analyses including additional ancient genomes provide mounting evidence that the Iceman's genetic affinity with Sardinians reflects an ancestry component that was widespread in Europe during the Neolithic . Despite their different geographic origins , both the Swedish farmer gok4 and the Thracian P192-1 closely resemble the Iceman in their relationship with Sardinians , making it unlikely that all three individuals were recent migrants from Sardinia . Furthermore , P192-1 is an Iron Age individual from well after the arrival of the first farmers in Southeastern Europe ( more than 2 , 000 years after the Iceman and gok4 ) , perhaps indicating genetic continuity with the early farmers in this region . The only non-HG individual not following this pattern is K8 from Bulgaria . Interestingly , this individual was excavated from an aristocratic inhumation burial containing rich grave goods , indicating a high social standing , as opposed to the other individual , who was found in a pit [15] . However , the DNA damage pattern of this individual does not appear to be typical of ancient samples ( Table S4 in [15] ) , indicating a potentially higher level of modern DNA contamination . On the other hand , the Swedish and the Iberian hunter-gatherers show congruent patterns of relatedness to the modern populations of Northern Europe , which is consistent with the previous results using those samples . Sardinians have long been recognized as forming a distinct outlier within contemporary European genetic diversity ( e . g . [37] ) , often interpreted as a consequence of genetic isolation and/or founder effects in the demographic history of the island . It is thought that permanent settlement of the island was established by around 10 , 000 YBP , and recent results from both genetic and cranial morphological data suggest population continuity since the Neolithic [38]–[40] . Our results support this continuity and indicate that gene flow from mainland Europe during the time of the spread of agriculture in Europe contributed significantly to the present Sardinian gene pool . It is important to point out that this does not imply that early Palaeo-Mesolithic settlers did not contribute to the genetic diversity observed in Sardinia today . In fact , D'Amore et al . argue for a substantial contribution of Mesolithic settlers , but also find evidence for a later contribution of people from the mainland during the Bronze and early Copper Ages , which is compatible with the age of the farmer individuals analyzed here [38] . We therefore hypothesize that Sardinia , by remaining largely isolated from the later events that shaped genetic variation in mainland Europe , provides a modern-day “snapshot” of the genetic structure of the people associated with the spread of agriculture in Europe ( see also [18] ) . Building on the results of previous studies , we outline a simplified scenario for the demographic history of Europe during the Neolithic in Figure 4 . It is important to note that this illustration is a simplification and certainly not adequate to explain the full complexities of European demographic history . For instance , a recent study using mtDNA data from 364 prehistoric Europeans suggests a number of marked shifts in the genetic makeup of Neolithic Central Europeans , particularly during the Middle and Late Neolithic stages ( <6 , 000 YBP ) [41] . It nevertheless provides a reasonable outline consistent with the broad patterns of ancestry presented in this and other recent studies , in particular with respect to the observed Sardinian affinity of the early farmers . In agreement with previous studies , we find that both HG individuals closely resemble each other in their relationships with modern Europeans despite considerable geographic distance , suggesting relative homogeneity in the HG gene pool prior to the spread of farming in Northern and Western Europe ( Figure 4A ) [7] , [42] . Furthermore , there is a large body of evidence ( reviewed in [42] ) suggesting a genetic contribution of early Neolithic farmers originating in the Middle East during the initial spread of agriculture into Southeastern Europe . While we do find that a small proportion of a Middle Eastern ancestry component distinguishes the farmer individuals from the HG in the ADMIXTURE analysis ( Figure 1A; Figure S1 , k = 3 ) , they generally resemble Southern European populations rather than Middle Eastern ones . A possible explanation for this observation could be that there was an initial wave of migration from the Middle East into Southeastern Europe during the early Neolithic transition , followed by a subsequent expansion of a derived Southern European population with some fraction of Middle Eastern ancestry towards Central and Northern/Western Europe ( Figure 1B and 1C ) . The secondary expansion would include substantial migration to Sardinia , leading to the observed genetic affinity of the ancient farmers descending from these migrants to present-day Sardinians . Interestingly , a recent study of mitochondrial genomes from 39 ancient individuals from Central Europe [11] suggests that early Neolithic mtDNA lineages in Central Europe introduced by the first farmers from the Middle East were largely replaced by events during the Middle Neolithic starting around 4000 BC , which would be consistent with this scenario . Differential levels of admixture of the expanding farmers with local hunter-gatherer populations then establish the main pattern of genetic diversity observed in Europe today , together with additional migration events ( such as the ones suggested in [41] as mentioned above ) , but excluding Sardinians due to their subsequent isolation ( dashed line , Figure 4D ) . For example , one such later migration event would be the expansion of the Bell Beaker culture described in Brotherton et al . [11] , where we also find that the IBS show a closer relationship with the CEU/GBR than the TSI ( Figure 3 ) . On the other hand , a recent preprint analyzing newly sequenced ancient European genomes suggests three ancestral populations for modern Europeans [43] , a hypothesis that was not addressed in our study given the limitation of the ancient samples available . Nevertheless , the main results in that study are largely consistent with the results presented here . For example , the authors suggest that early farmers from Central Europe already show evidence of admixture with European hunter-gatherers , and that Sardinians derive ∼90% of their ancestry from this early farmer population , consistent with our basic model described above [43] . Finally , an important caveat with analyses based on allele sharing such as the D-test is their sensitivity to confounding factors such as correlated errors in a pair of samples used in the test [44] . This is particularly a problem when using one ancient sample as an ingroup in the D-test , since correlated DNA damage patterns , or differences in sequence quality between the ancient samples , can lead to an over- or underestimation of allele sharing between them , respectively ( e . g . , also discussed in [44] ) . The exclusion of all transition sites from the analysis is commonly used to circumvent this problem; however , other more subtle biases shared among the ancient samples cannot be ruled out . For example , after excluding all transition sites , we find that the test D ( O , Iceman;gok4 , Sardinia ) is significantly negative ( D = −0 . 05 , Z = −3 . 0 ) , indicating that the Iceman is more closely related to the Swedish farmer . However , the test D ( O , Iceman;brana1 , Sardinia ) shows an even stronger signal in the same direction ( D = −0 . 09 , Z = −3 . 5 ) , which would suggest that the Iceman is also more closely related to the Iberian hunter-gatherer than to Sardinians . Given the observed discontinuity in the relationship of hunter-gatherers and farmers with modern Europeans , we believe that this pattern is at least in part due to unaccounted biases as described above . We therefore did not analyze the relationship of more than one ancient sample combined with modern samples using D-tests throughout the study . The aforementioned issues also highlight the need for improved statistical methods that will allow the analysis of ancient and modern samples in joint fashion , while at the same time accounting for differences in data quality and biases due to DNA damage and differences in sequencing technology . The incorporation of these methods with additional genomic and archaeological data from key spatial and temporal points will be crucial in the quest to disentangle the complex population history of the European continent .
The two individuals from Bulgaria used in this study originate from two separate excavations , both associated with Iron Age Thracian culture . The first individual ( P192-1 ) was excavated from a pit sanctuary near Svilengrad , Bulgaria , dated to 800–500 BCE . The other individual ( K8 ) was found in the Yakimova Mogila Tumulus in southeastern Bulgaria , dated to 450–400 BCE . DNA was extracted from teeth using clean room procedures to prevent contamination , following an established protocol [45] . Following the extraction , end-repair and dA-tailing of purified DNA was performed using the Next End Prep Enzyme Mix ( New England Biolabs ) and following the manufacturer's instructions . Illumina paired-end adapters were ligated , purified and amplified using standard protocols . Ancient DNA in the library preparation was subsequently enriched with WISC , a novel capture protocol using RNA probes transcribed from a modern human DNA library [15] . Pre-capture libraries were then sequenced on the Illumina HiSeq ( 2×90 bp paired end ) , whereas post-capture libraries were sequenced on the Illumina MiSeq ( 2×150 bp paired end ) . The ancient genome data used for this study come from a number of different sources , which differ both in sample preparation as well as sequencing technology . In order to minimize biases due to these differences as much as possible , we applied very stringent quality control filters during the processing of the different datasets , using the same processing pipeline for each sample when possible . For all samples , pileup files from the aligned reads ( reference genome build hg18 ) were obtained using the ‘mpileup’ command of SAMtools [46] , removing PCR duplicates ( http://picard . sourceforge . net ) as well as low quality alignments ( MQ<30 ) and bases ( base quality<30 ) . Due to the higher coverage of the Iceman genome , we performed diploid SNP genotype calling using the Bayesian algorithm implemented in ‘bcftools’ of the SAMtools suite . For all other ancient samples , haploid genotypes were obtained by randomly sampling reads if positions were covered by multiple reads . For all analyses involving only one ancient sample , we excluded DNA damage sites ( C>T/G>A transitions ) where the ancient sample shows the damage allele ( T/A ) . For analyses including multiple ancient samples , all transitions were excluded . For all population genetic analyses , data from the ancient genomes was merged with three different reference datasets of present-day human populations , using only autosomal data . We followed the approach of Skoglund et al . [13] and used only variants found in the respective reference dataset , and removing any genotypes with a mismatching alternative allele in the ancient samples . The unsupervised maximum-likelihood clustering algorithm implemented in ADMIXTURE [16] was used to cluster each ancient genome with populations of European and Middle Eastern origin in the HGDP dataset . In order circumvent the problem of differing overlap of variants of the ancient samples with HGDP , we first inferred ancestral clusters for the contemporary populations only , and subsequently determined the most likely cluster memberships for each ancient sample using the ancestral allele frequencies of all overlapping . An additional advantage of this strategy is that the genotypes of the ancient samples do not influence the clustering solution for the modern individuals . Initial clustering of the 263 contemporary individuals was performed for k = 2 through k = 8 ancestral clusters by running ADMIXTURE with default settings . In order to maximize the accuracy for the initial clustering , we used all SNPs where a genotype was observed for any ancient sample prior to removal of damage SNPs . For each value of K , ten replicate runs were performed , and the run with the greatest likelihood was selected for further analysis . The cluster membership proportions for each ancient sample were then obtained by maximizing the log-likelihood function ( equation 2 in [16] ) using the subset of SNPs with genotype data in the respective ancient sample , and the corresponding ancestral allele frequencies inferred from the modern samples ( the P matrix of the ADMIXTURE output ) . The optimization was implemented using the FRAPPE EM algorithm ( equation 4 in [16] ) . In order to evaluate the accuracy of the inferred clusters for the ancient samples , we repeated the procedure described above for all 263 modern samples and for each subset of SNPs corresponding to the six ancient samples . Cluster assignment accuracy was then determined by calculating the root mean standard error ( RMSE ) for all combinations of modern population , number of ancestral clusters and ancient sample SNP subset used . In addition , we also performed principal component analysis ( PCA ) , separately for each ancient genome together with the 263 contemporary individuals , using only non-missing SNPs of the respective ancient sample . The initial PCA was performed using the modern samples only , followed by projection of the ancient samples onto the inferred principal components . All analyses were carried out in R using singular value decomposition ( ‘svd’ function ) on the genotype matrix . Patterns of admixture among ancient and modern populations were inferred using a number of related approaches . For the individual-level datasets ( CG ) , we quantified the amount of derived allele sharing between the ancient and modern genomes . For each ancient genome , we extracted all variants showing the derived allele and calculated the proportion of variants with matching alleles for each of the modern genomes . The sharing rate was normalized by the rate for the individual with the lowest sharing rate with the Iceman ( NA18508 , YRI ) , for easier comparison among different ancient genomes . For population-level datasets ( 1000G/Sardinia ) , we used TreeMix [19] to infer a maximum-likelihood population tree and putative admixture events . The program was run separately for each ancient genome together with the contemporary populations , using only SNPs without missing data in the ancient sample . For the HGDP dataset , only the Iceman was used due to the low number of SNPs for the other ancient samples . The maximum number of migration edges added was 5 , and we selected the run with the maximum likelihood from 10 replicate runs for each migration edge parameter . The number of SNPs used for the LD correction was adjusted to the total number of SNPs for each ancient sample ( Table S6 ) . Additionally , we used formal tests of admixture as implemented in the three- and four-population tests [17] , [18] for all datasets . Standard errors for the statistics were determined using a weighted block jackknife , with a window size of 5 Mb . Divergence times of the Iceman with contemporary genomes was estimated using a previously described coalescent method [13] , [21] , based on the number of concordant and discordant genealogical topologies . For the CG dataset , divergence time was calculated in turn for each genome , using the two gene copies of the modern genome at each SNP to determine concordant/discordant status . Heterozygous sites were randomly assigned to the two resulting haploid genomes . For the 1000G/Sardinia dataset no individual-level genotypes were available , so we used a slightly modified approach . For each modern population , two virtual haploid genomes were generated by randomly sampling alleles with probability equal to their derived allele frequency at each SNP , and divergence time was estimated as above . Under the assumption of panmixia in each population , the resulting times can be interpreted as the divergence of an average individual from that population to the Iceman . The resulting coalescent times were converted to time in years using population-specific effective population sizes for African , European and Asian population from Gronau et al . [23] ( Africa: 29 , 300 ( 23 , 900–35 , 200 ) ; Europe: 5 , 800 ( 900–11 , 500 ) ; Asia: 3 , 000 ( 300–6 , 600 ) ) , assuming a generation time of 25 years . For the lower and upper bounds of the converted divergence time we combined the uncertainty of both our divergence estimates and the effective population size estimated by Gronau et al . [23] . | The analysis of the genome of the Tyrolean Iceman , a 5 , 300 year old mummy from Central Europe , revealed a surprising recent common ancestry with modern Sardinians for this ancient genome . However , this study was limited both by the availability of data from Sardinians and by a lack of genomic data from other ancient European samples . Here , we use genomic data from modern Sardinians and from ancient European individuals from different geographic regions and cultural contexts , to demonstrate that this ancestry component is shared among individuals associated with the onset of agriculture in Europe . Our results thus suggest that the Iceman's Sardinian ancestry actually reflects a more widespread genetic component related to the migration of people during the Neolithic transition in Central Europe . | [
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"biology",... | 2014 | Population Genomic Analysis of Ancient and Modern Genomes Yields New Insights into the Genetic Ancestry of the Tyrolean Iceman and the Genetic Structure of Europe |
Biological networks are powerful tools for predicting undocumented relationships between molecules . The underlying principle is that existing interactions between molecules can be used to predict new interactions . Here we use this principle to suggest new protein-chemical interactions via the network derived from three-dimensional structures . For pairs of proteins sharing a common ligand , we use protein and chemical superimpositions combined with fast structural compatibility screens to predict whether additional compounds bound by one protein would bind the other . The method reproduces 84% of complexes in a benchmark , and we make many predictions that would not be possible using conventional modeling techniques . Within 19 , 578 novel predicted interactions are 7 , 793 involving 718 drugs , including filaminast , coumarin , alitretonin and erlotinib . The growth rate of confident predictions is twice that of experimental complexes , meaning that a complete structural drug-protein repertoire will be available at least ten years earlier than by X-ray and NMR techniques alone .
Large biological networks have been used previously to suggest protein-protein interactions [1] , phosphorylation events [2] and most recently drug-protein interactions . New drug-protein relationships have been proposed from the analysis of shared side-effects [3] , by comparing sets of protein targets according to drug pairs [4] , or sets of targets for particular drugs [5] , [6] . Though not always considered as such , the database of protein three-dimensional ( 3D ) structures is also a large network , where links are physical associations between molecules within structurally determined complexes . The network contains many thousands of protein-protein and protein-chemical interactions , of which several hundred involve drugs . In this paper we explored this large network systematically to predict new potential protein-chemical interactions . We exploited the basic premise that if two proteins in the network share one bound chemical they are likely to share others . Considering protein-chemical interactions alone would lead to many thousands of predictions including mostly false positives . However , we profit here from the use of 3D structures , where we can use physicochemical criteria to remove false predictions . A single prediction candidate ( Figure 1 ) involves combining three protein-chemical complex structures , two of which involve two distinct proteins ( P1 and P2 ) binding a common ligand ( La ) and a third where one protein ( P1 ) binds another ligand ( Lb ) . By superimpositions based on the common protein and the common ligand , we obtain an additional putative complex ( P2 with Lb ) . We then used several criteria to decide whether or not these new complexes were structurally viable and evaluated the statistical significance using a p-value ( see Methods ) . From 10 , 842 complexes forming the network of known structures , we identified 907 , 827 potential interactions , of which 20 , 067 ( including 19 , 578 novel structures and 489 complexes with a previously determined structure ) were significant ( p≤0 . 05 ) . Note that we ignored trivial candidates where the two proteins ( P1 and P2 ) shared ≥80% identity ( i . e . where ligand transference would be very likely due to orthology ) . The predictions include enzyme/substrate , enzyme/product , target/inhibitor and target/activator structures ( Table S1 in Text S1 ) .
For the benchmark , we selected those protein-chemical complexes of known structure that could , in principle , be predicted by superimpositions of chemicals and proteins in a non-obvious fashion . Specifically , we considered only pairs of proteins with less than 80% sequence identity; and non-identical chemicals . Predictions made with identical ( or very similar ) chemicals or proteins are less interesting as they represent cases where transference is more obvious: for instance testing a chemical inhibitor of a human protein in a mouse orthologue , or inferring that a slightly modified chemical compound might have a similar activity . The number of these complexes is small relative to the total number of predictions owing to these requirements . That is , it is currently relatively unlikely that these combinations exist given the frequency of chemicals solved in complex with multiple distinct protein structures . There were thus only 376 complexes of known structure that were also predicted significant ( p≤0 . 05 ) by our method using different protein-chemical complexes in the network . For 270 ( 72% ) of them , the ligand in the predicted complex had an RMSD <2 Å when compared to the known structure , which matches standards acceptable for docking solutions ( e . g . [7] ) . There are several revealing instances where interactions already of known structure are predicted correctly via complex paths . For instance , we accurately predicted the complex of Pneumocystis carinii dihydrofolate reductase ( DHFR ) with trimethoprim . This prediction was made since both this DHFR and a remote homolog ( 36% identity ) from Mycobacterium tuberculosis have known structures in complex with methotrexate , and the latter has been solved with trimethoprim ( Figure 2A ) . We also made accurate predictions using radically different proteins that nevertheless share a common ligand . For example , we successfully reconstruct the complex of the endoplasmic reticulum paralog of the chaperone Hsp90 ( GRP94 ) with its high-affinity inhibitor radicicol , by exploiting the known complexes of pyruvate dehydrogenase kinase ( PDK3 ) with radicicol and both proteins with ATP ( Figure 2B ) . This prediction is remarkable in that neither the protein sequences nor the chemicals involved are detectably similar ( though PDK3 is a remote homologue of GRP94 , detectable only via structure comparison ) . The difficulty in establishing false-positive rates in molecular interaction studies is well established ( e . g . ref [8] , [9] ) , and is principally due to a lack of known negatives: pairs of molecules known not to interact . We suffer from the same situation here , as there is currently no standardized set of protein-chemical interactions known not to interact . We can , however , get a set of such interactions from screening studies , where it is at least known that the chemicals and the proteins did not interact under the conditions used . Extracting these from PubChem gives 63 , 218 , 098 potential negative interactions , of which only 172 overlap with the predictions made here . The low number is due to the fact that very few of the screens in PubChem can be unambiguously assigned to a single protein structure , and that chemicals both in screens and known structures are very often unique . We obtained positive predictions for just 17 of these giving a false positive rate of 9 . 9% . A natural question is how conformational flexibility impacts on our predictions . We do not attempt to explore alternative conformations , instead letting the structural integrity filter implicitly test for this – predictions will only be made when the conformation from the original structure fits into the new . In practice , the method tends to predict best in situations where ligands are similar in size to that in the original structure and/or conformationally rigid . There are many well-established protein-chemical interactions lacking a 3D structure in databases including STITCH [10] , DrugBank [11] , BindingDB [12] and ChEMBL [13] . The nature of the chemicals and proteins contained in these databases differs greatly from those in our set , being mostly dedicated to drugs and mammalian proteins , and including many membrane proteins , thus leading to a potential overlap of at most a few hundreds of drug/target pairs . Nevertheless , 222 of 312 predictions ( 71% ) have a p-value≤0 . 05 for STITCH , 131 of 185 ( 71% ) for DrugBank , 52 of 71 ( 73% ) for BindingDB , and 573 of 975 ( 59% ) for ChEMBL . The numbers improve considerably , when we adopt a more lenient p-value≤0 . 2 ( 86% , 85% , 94% , and 85% predictions , respectively ) suggesting that the p-value threshold ≤0 . 05 might be too stringent and thus miss some true positives . Overall , majority of predicted structures overlapping with these databases are significant , providing further support for our approach . As the number of solved structures grows , we expect the overlap with the potential complexes to grow accordingly . Both known structures and predictions are biased against proteins that are difficult to solve , most notably membrane proteins . This is in sharp contrast with the representation of certain protein classes , such as GPCRs or Ion channels , among the drug targets [14] ( Table S2 , Figure S1 in Text S1 ) . However , new innovations in the solution of membrane proteins will likely make this disparity diminish over time . A natural question is whether or not our approach can tell anything about the relative or absolute affinity of protein-chemical interactions predicted . Here we are limited by the fact that the protein databank contains protein-chemical interactions at a broad range of affinities: from millimolar to picomolar , and affinities for all protein-chemical structures are not systematically available . We thus believe that we are predicting essentially whether a crystal structure of a protein/chemical interaction is possible , which means we expect our predictions to have a similar range of affinities . This has some potential impact on our attempts to predict selectivity ( see below ) . Among predictions involving drugs are several established relationships that lack an experimental structure . For example , we predicted complexes between DNA topoisomerase 2 and radicicol [15] ( Figure 3A ) , and between pentoxifyline and human chitotriosidase , a recently established and surprising finding for this and several other methylxanthine drugs [16] . Here again predictions could be made using disparate routes , for instance the prediction of the known interaction [17] between flurbiprofen and aldo-keto reductase C3 ( 3-alpha-hydroxysteroid dehydrogenase ) was made via the non-homologous protein prostaglandin G/H synthase 2 and the very dissimilar chemical indomethacin . There are 19 , 578 new predicted interactions , including 7806 involving known drugs . Of the total , 4738 were made using intermediate proteins sharing ≥30% identity to the target protein or one in the same Pfam [18] family . These predictions can be considered easier , since the protein sequences can be readily aligned , and the resulting structure could be obtained by conventional modeling techniques . Similarly there were 3 , 782 predictions made using chemicals that were ≥90% identical to the intermediate , which could also be made by simple chemical similarity searching and superimposition . Lastly , 1 , 200 novel predictions involved solvents and common buffer components . Ignoring these three cases left 10 , 668 non-trivial predicted complexes , of which 4 , 240 involved drugs . We interrogated this list for interactions supported by literature or other evidence , though for the majority the relative obscurity of the chemicals means that no evidence could be found . The full list of predictions is given in Table S1 in Text S1; we discuss several highlights below . Several predictions are made using what appear to be convergently evolved binding pockets . That is , the proteins sharing the common ligand share no sequence or structural similarity , and we exploit the common ligand ( and thus the two binding pockets ) to predict a new ligand for one of the proteins . More exactly , there are 112 , 546 total protein pairs from the potential predictions that share <30% sequence identity and for which a SCOP [19] fold assignment is available , of which 14 , 931 ( 13% ) are significant . Of these 94 , 955 ( 10 , 883 , 11% , significant ) pairs do not share any SCOP fold assignment . There are , thus , more predictions made using weakly homologous proteins , but nevertheless convergences still play an important role . This is perhaps not surprising considering the number of compounds , such as ATP analogs , that are known to bind distinct ATP binding folds . We predict a complex between the heart-specific fatty acid binding protein ( FABP3 ) and alitretinoin , using structures of FABP3 with stearic acid and of mouse RXRα with strearic acid and alitretinoin . There is only indirect evidence in support of this interaction: proteins in the wider superfamily of lipid binding proteins show some ligand promiscuity [20] . However , the structural fit of the alitretinoin into FABP3 is striking ( Figure 3B ) . Elsewhere , we predicted a complex between orphan retinoic acid receptor ( ROR ) β and α-linolenate made by virtue of a complex between this protein and stearic acid , which also binds to maize non-specific lipid-transfer protein , which in turn binds to linolenate ( Figure 3C ) . The natural ligand of RORβ is not known; stearate was observed in complex fortuitously owing to the expression of the protein in E . coli [21] . In contrast to RORα , the expression of RORβ is highly restricted to parts of the brain , the retina , and pineal gland [22] . α-linolenate is an essential fatty acid and in humans is a precursor for eicosapentaenoic acid ( EPA ) and docosahexaenoic acid ( DHA ) , and deficiencies in dietary α-linolenate result in various problems , including learning [23] or vision [24] . Although these observations could be coincidental , they support the possibility of an interaction between linolenate and RORβ . Several predictions involve anti-viral compounds in complex with the E . coli transporter Tsx , mostly based on structures of herpes virus thymidine kinase with thymidine , which also binds to Tsx . The kinase has been solved in complex with 12 anti-viral compounds , of which eight fit well into the Tsx structure ( e . g . , HBPG , Figure 3D ) . The structure of E . coli Tsx was proposed [25] to be a possible model for drug transport via the eukaryotic equilibrative nucleoside transporters [26] . Our predictions support this possibility , though obviously additional structures of eukaryotic equivalents in complex with model compounds are needed . There are also hundreds of predictions involving drugs that apparently lack supporting evidence from the literature . These include the phosphodiesterase inhibitor filaminast binding to 5′-AMP-activated protein kinase , zanamivir binding to mammalian sialoadhesin , and coumarin binding to dipeptidyl peptidase 4 ( see Table S1 in Text S1 ) . Protein-drug selectivity is an important issue , since non-selective drugs can have undesired side-effects . The problem is particularly acute for drugs designed to target one member of a large homologous family of proteins , such as GPCRs or protein kinases . We predicted many protein-kinase inhibitor complexes ( 12% of confident predictions ) . They offered an opportunity to test whether our approach could say anything about inhibitor selectivity . There are currently 766 unique human protein kinase-inhibitor crystal structures ( 284 kinases and 627 inhibitors ) . We compared our predictions with systematic screens for 127 human kinases and 33 inhibitors . The overlap of kinase/compound pairs in the screens ( i . e . whether interacting or not ) and the known or predicted structures is low: only 21 complexes overlap with our set of potential structures , and we get significant predictions for six of these ( see Supplementary information ) . Despite the low overlap , we saw a correlation between the tendency for an inhibitor to be predicted to bind many kinases and the tendency to interact with many kinases in the screens , even if the particular kinases differ ( see Table S3 in Text S1 ) . For example , known promiscuous inhibitors , such as staurosporine are both predicted and observed to bind dozens of kinases , in contrast to imatinib where despite 70 potential predictions , none are significant . This set also contains numerous predictions of kinase-inhibitor complexes that have not , to our knowledge , been tested , for instance binding of nilotinib to KIT and LCK , or of erlotinib to SRC , HCK or PKR . Data related to the predictions presented here are available as an online resource at http://pcidb . russelllab . org/ .
We have demonstrated that using protein and chemical superimpositions and structural compatibility screens can reproduce known protein-chemical interactions , and suggest many novel relationships . The prospect of using databases or networks of known biomolecular interactions to predict additional relationships is not new ( e . g . [3] , [5] ) though to our knowledge this is the first attempt to use 3D structures in the network context in this way to link disparate chemicals and targets . Like other methods based on large experimentally determined networks , the approach here has the advantage that it will improve in terms of coverage and accuracy as the number of interactions grows . The number of protein-chemical interactions of known 3D structure has been growing exponentially since the late 1980s , and with the increasing number grows the potential to infer new relationships . The growth in the number of confident predictions is steeper than that for known structures ( Figure S2 in Text S1 ) . This raises the question as to when we will we be able to predict most protein-chemical interactions confidently based on available data . For such an estimate , it is simplest to consider a smaller subset of interactions , and for this purpose , we considered the set of human protein-drug interactions . There are currently 4 , 774 distinct drugs known ( in DrugBank [11] ) , with 14–44 new drugs appearing each year ( see http://www . vfa . de/en/statistics/innovation/ ) . Range estimates as to the average number of proteins to which a typical drug will bind can come from known 3D structures ( 3 proteins per compound ) , or drug-target databases such as SuperTarget/Matador [27] ( 4 . 8 ) or DrugBank [11] ( 2 . 7 ) . As we know these are either conservative or based on missing data , we also considered an upper figure of 15 proteins . We thus estimate between roughly 20 , 000 and 80 , 000 protein-chemical interactions within the human system , a number that would be reached by 2022 according to the extrapolation in Figure 4 , or perhaps later if to the apparent decrease in the growth rate in the last two years holds . This estimate presumes that protein-chemical structures are similar in terms of the ease with which they are solved; something that is obviously false for membrane proteins that make up more than 40% of drug targets [14] . Thus the estimate is probably over-optimistic , though we anticipate that additional breakthroughs in structural biology will also ultimately make membrane protein – ligand complexes more commonplace . Regardless of the precise details of this estimate , it is not fanciful to imagine a time when there will be sufficiently determined 3D structures to predict accurately most known protein-chemical interactions using methods like that described here . Twenty years ago the prospect of having structural information for most globular protein domains seemed very distant , but today , thanks to structural genomics and modeling techniques , it is difficult to find proteins for which structural information is unavailable [28] . Predicted protein-chemical structures will have limitations in the same way that modeled individual structures are not as accurate as those experimentally determined , but they will similarly provide a great deal of useful information . As with all estimates of this sort , we suspect that advances in structure determination methods will probably make the time shorter , though it is also likely that the number of known protein-chemical interactions will also increase greatly . It is clear that the continued study of the structural database by approaches like that discussed here will deliver a growing set of novel and highly relevant protein-chemical complexes for use in biomedicine , biotechnology and beyond .
Given three complexes , involving two common proteins and two common ligands as detailed in Figure 1 , we superimpose the two complexes involving P1 using structure superimposition [29] . The resulting transformation produces a superimposition of the two non-identical ligands La and Lb . We then use ligand La to superimpose the complexes it makes with P1 and P2 , producing a superimposition of the non-identical proteins . We combine these two superimpositions by re-centering on the common ligand La , producing a final superimposed complex , involving all proteins and ligands , and including the new complex P2:Lb . We considered seven structural criteria to judge the predicted complexes: a ) the clash volume; b ) the number of ligand-protein contacts; c ) the number of potential hydrogen bonds; d ) the number of potential Van der Waals contacts; e ) the number of un-satsified ligand hydrogen bond donors and acceptors; f ) the number of ligand carbon atoms not involved in Van der Waals contacts; and g ) the number of potential hydrogen donor and acceptor atoms exposed to the solvent . The raw values were normalized to ranges from 0 to 1: for a specific value of the parameter ξi for the model i , we calculate the fraction si of values ξ<ξi , where ξ represents values of the same parameter in the negative dataset composed of random complexes ( see below ) . For a , e , f and g we take the fraction of ξ>ξi , as these terms are detrimental to binding . We sum si for all the parameters and obtain a combined score Si that can range from 0 to 7 , greater scores corresponding to better models . We convert the scores into p-values by considering scores for a negative dataset of 100 , 000 random structures ( see below ) . For a given model score Si we calculate p as p = ( #σ: σ>Si ) /#σ , where σ are the scores from the negative dataset . Note , that only 24% of real structures , when scored by this scheme , have p≤0 . 05 , which we believe not to be due to crystal packing or non-specific binding of buffer components , as a solvent-free subset , and a subset where promiscuous small molecules ( those seen in more than 20 structures ) were removed gave similar ratios . Instead , we believe this to reflect the stringency of our approach; p<0 . 2 clearly separates the positive and negative datasets ( see below ) but produces many false positives and predicted complexes with poor RMSDs in the benchmark ( Figure S3 in Text S1 ) . When limiting the set of negatives to structures where the modeled random ligand has the same number of atoms as the cognate , and/or to modeled ligands having the same number of hydrogen bond donors and acceptors , we see little differences in the score distributions compared to the initial negative set ( correlations R = 0 . 99 , p = 0 and R = 0 . 94 , p = 0 respectively ) . We consider all pairwise complexes extracted from the Protein Data Bank [30] that consists of a protein annotated in Uniprot [31] and a compound annotated in PubChem [32] , as of mid-2009 . Only contacting pairs were considered , i . e . at least two heavy atoms , one from the proteins and one from the compound , were required to be closer than 5 Å . There were thus 45 , 455 protein-small-molecule complexes of known structure as the positive dataset . If a structure included more than one instance of either protein chain or chemical , we considered each chain-small molecule pair as a separate entry . We constructed a negative dataset of 100 , 000 complexes by randomly selecting two complexes from the positive dataset , and substituting their ligands according to their geometrical centers ( without any rotation to optimize binding ) . We then removed all complexes involving either protein-ligand clashes , or those lacking protein-ligand contacts . This set represents random fits of small molecules into protein pockets . For the benchmark dataset and the dataset of potential complexes , we selected all combinations of three complexes as shown in Figure 1 . We excluded instances where the proteins were ≥80% identical or the ligands had a Tanimoto score of 1 . This set includes all possible predictions ( even those that are wrong ) made using our approach . For 907 , 827 potential structures , we reconstructed 194 , 317 3D predicted complexes , of which 20 , 067 scored with a p-value≤0 . 05 . Within this set , we defined the benchmark dataset as those 376 significantly predicted complexes for which a structure for the predicted complex was already of known structure . For 703 of those we reconstructed a 3D structure using superimposition . We applied our scoring system to these complexes and excluded all predictions with a p-value>0 . 05 , thus yielding 376 significant predictions . We also excluded predictions where the number of heavy atoms in La and Lb differs more than two fold . | Predicting drug-target interactions is a hot topic , and many efforts have been undertaken to do this , many using large interaction networks . We take a novel approach using protein-chemical interactions derived from 3D structures . The basic premise is that two proteins sharing a common bound chemical will likely share others . We use protein and chemical superimpositions and physical tests of chemical-protein compatibility to identify the most likely candidates among the nearly one million potential interactions . We show for a benchmark that known protein-chemical structures are reconstructed with good accuracy and sometimes via very different proteins and chemicals . We make thousands of confident predictions , including structures for known protein-drug interactions lacking a structure ( e . g . topoisomerase-2/radicicol ) and many new interactions . The number of confident predictions grows faster than the number of known structures , suggesting that this approach will play a key role in completing the protein-chemical interaction repertoire . | [
"Abstract",
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] | 2011 | Combinations of Protein-Chemical Complex Structures Reveal New Targets for Established Drugs |
The gastrointestinal pathogen , Clostridioides difficile , initiates infection when its metabolically dormant spore form germinates in the mammalian gut . While most spore-forming bacteria use transmembrane germinant receptors to sense nutrient germinants , C . difficile is thought to use the soluble pseudoprotease , CspC , to detect bile acid germinants . To gain insight into CspC’s unique mechanism of action , we solved its crystal structure . Guided by this structure , we identified CspC mutations that confer either hypo- or hyper-sensitivity to bile acid germinant . Surprisingly , hyper-sensitive CspC variants exhibited bile acid-independent germination as well as increased sensitivity to amino acid and/or calcium co-germinants . Since mutations in specific residues altered CspC’s responsiveness to these different signals , CspC plays a critical role in regulating C . difficile spore germination in response to multiple environmental signals . Taken together , these studies implicate CspC as being intimately involved in the detection of distinct classes of co-germinants in addition to bile acids and thus raises the possibility that CspC functions as a signaling node rather than a ligand-binding receptor .
Clostridioides difficile , previously classified as Clostridium difficile , is a Gram-positive , spore-forming , obligate anaerobe that is a leading cause of health-care associated infections and gastroenteritis-associated death worldwide [1] . In the United States ( US ) alone , C . difficile causes over 500 , 000 infections per year , leading to ~29 , 000 deaths and over $5 billion in medical costs [2] . While immunocompetent individuals are usually protected from C . difficile infection by the intestinal microflora , antibiotic treatment can render individuals susceptible to C . difficile infections due to disruption of the protective gut microbiota [3–5] . C . difficile infections are characterized by high rates of disease recurrence: approximately one in five patients that recover from a C . difficile infection will acquire a second infection within three months [2 , 6 , 7] . Both C . difficile’s vegetative cell and spore form contribute to recurrent infections [1 , 8]: the vegetative form of C . difficile antagonizes growth of the protective microbiota by producing inflammation-inducing toxins [5] , while its dormant spore form , which can resist commonly used disinfectants and harsh conditions [9 , 10] , allows C . difficile to outlast antibiotic treatment and persist in the environment for long periods of time [11] . Since the vegetative form of C . difficile cannot survive outside the anaerobic environment of the gastrointestinal tract , C . difficile’s aerotolerant , dormant spore form is its major infectious particle [9] . Consequently , C . difficile infections depend upon its spores germinating . When ingested C . difficile spores reach the small intestine , they sense mammalian-specific bile acids , which initiate a signaling cascade that allows spores to exit dormancy during germination [12–14] . Germinating spores outgrow to form the vegetative , toxin-producing cells that are responsible for C . difficile disease symptoms , which can range from severe diarrhea to pseudomembraneous colitis , toxic megacolon , and death [1 , 6] . Since germination is required for C . difficile to initiate infection , therapeutics that inhibit germination to prevent C . difficile infection are currently being developed [15–18] . However , the molecular mechanisms underlying the C . difficile germination signaling cascade are poorly understood . Indeed , recent studies indicate that C . difficile’s germination pathway differs significantly from other spore forming organisms [19 , 20] . Almost all spore-forming organisms studied to date encode transmembrane germinant receptors of the Ger family [21 , 22] , which sense nutrient germinants , like amino acids , nucleic acids , and sugars [23] . In contrast , C . difficile does not encode Ger family receptors . Furthermore , the primary germinants for C . difficile are cholate-derived bile acids ( especially taurocholate [24] ) , which are not known to be a nutrient source . Instead , genetic data suggest that C . difficile uses a soluble pseudoprotease , CspC , to directly sense bile acids [12] . CspC was identified in a genetic screen for mutants that germinate in response to the bile acid , chenodeoxycholate [12] , which typically acts as a competitive inhibitor of germination [25] . Remarkably , a single point mutation in CspC ( glycine 457 to arginine , G457R ) expanded C . difficile’s germinant specificity to permit germination in response to chenodeoxycholate as well as taurocholate [12] . Since point mutations in CspC that prevent spore germination were also identified , CspC was proposed to be the bile acid germinant receptor [12] . Interestingly , C . difficile CspC is a catalytically inactive member of the Csp family of proteases , which were first identified in Clostridium perfringens as being responsible for proteolytically activating the cortex hydrolase , SleC [26 , 27] . Active SleC degrades the thick protective cortex layer [28 , 29] , a step that is essential for all spores to exit dormancy [23] . C . perfringens strains encode one or more of three Csps , CspA , CspB , and CspC [30 , 31] , which likely have redundant functions during germination [31] . We previously solved the crystal structure of CspB from C . perfringens and confirmed that Csps are structurally similar to other subtilisin-like serine proteases [32 , 33] . CspB protease activity depends on a catalytic triad consisting of Asp , His , and Ser , and the prodomain of CspB acts as an intramolecular chaperone that is auto-processed upon proper folding of CspB’s subtilisin-like serine protease domain [32] . However , unlike other subtilisin-like serine proteases studied to date , the prodomain of CspB stays associated with the subtilase domain after cleavage and sterically occludes CspB’s active site [32] . While C . difficile strains encode homologs of all three Csp proteins , only CspB has an intact Asp , His , and Ser catalytic triad , since CspA and CspC carry mutations in two of the catalytic residues that render them pseudoproteases [30] . As a result , only CspB can proteolytically activate SleC during C . difficile spore germination [32] . Furthermore , CspB is produced as a fusion to CspA in sporulating C . difficile cells that subsequently undergoes interdomain processing to release CspB and CspA as separate proteins , with the individual proteins being detected in mature spores [30 , 32] . Notably , the CspBA fusion protein and the pseudoprotease nature of CspA and CspC appear to be unique to C . difficile and the Peptostreptococcaceae family [34] , since members of the Clostridiaceae and Lachnospiraceae family exclusively produce Csp family proteases as individual proteins with intact catalytic triads [30] . In C . difficile , the CspA and CspC pseudoproteases both play critical and unique roles during spore germination . CspA controls CspC’s incorporation and/or stability in mature spores [30 , 35] , and as described earlier , CspC is the proposed bile acid germinant receptor [12] . To gain insight into the molecular mechanisms by which C . difficile spores sense germinant , we solved the crystal structure of C . difficile CspC to 1 . 55 Å resolution . This structure revealed unique features of the C . difficile CspC pseudoprotease when compared to the C . perfringens CspB protease . Structure-function analyses identified several flexible residues critical for CspC function that confer either hyper- or hypo-sensitivity to bile acid germinant . Further analyses revealed that some of these mutations alter sensitivity to amino acid and/or calcium co-germinants , which potentiate bile acid-induced germination in C . difficile . Since the mechanism by which co-germinants are sensed by C . difficile spores was previously unknown [19 , 20] , our study reveals for the first time , to our knowledge , that C . difficile CspC integrates multiple germinant and co-germinant signals to induce spore germination and raises important new questions regarding how these signals are specifically sensed and transduced .
To determine how C . difficile CspC directly senses bile acid germinants , we attempted to crystallize recombinant C-terminally His6-tagged CspC ( both the wild-type and the G457R variant , the latter of which was previously shown to expand the germinant specificity of C . difficile spores to include chenodeoxycholate [12] ) alone or in the presence of either taurocholate or chenodeoxycholate . While crystals were observed in all three conditions using wild-type CspC , no crystals were obtained in our screening with the G457R variant . Furthermore , diffraction quality crystals were only obtained with wild-type CspC in the absence of bile acids . Determination of the crystal structure of the C . difficile CspC pseudoprotease revealed that it shares a conserved three domain architecture with the C . perfringens CspB protease ( Fig 1A ) , consisting of an N-terminal prodomain , subtilase domain , and jelly roll domain ( Fig 1A , [32] ) . The jelly roll is a ~130 amino acid insertion in the subtilase domain that forms a β-barrel domain that appears unique to the Csp family of subtilisin like-serine proteases . This jelly roll domain provides CspB with remarkable structural rigidity , conferring thermostability and resistance to proteolysis [32] . In both CspB and CspC , the jelly roll domain emerges from the subtilase domain and extensively interacts with both the subtilase domain and the prodomain . Overall , the secondary structure of these proteins is highly similar ( Fig 1B ) , with a 1 . 03 Å rmsd over 1200 atoms of the backbone trace . Despite this shared secondary structure , C . difficile CspC does not autoprocess its prodomain due to substitutions in its catalytic triad . In addition , CspC’s prodomain is more closely associated with the subtilase and jelly roll domains due to a clamping loop shown in the surface representation ( Fig 1A ) . This loop resides between beta strand 4 and helix 4 within the subtilase domain ( residues 203–216 ) and interacts with the jelly roll domain via packing of tyrosine 209 and tyrosine 210 as well as hydrogen bonds from arginine 213 to backbone carbonyls in the 402–407 residue loop of the jelly roll domain ( Fig 1C ) . In CspB , the equivalent subtilase domain loop is shorter ( residues 214–225 ) and in a different conformation , and the jelly roll domain loop is disordered , possibly due to the autoprocessing of the prodomain by CspB’s subtilase domain ( Fig 1C ) . The prodomain of CspC aligns more closely with the canonical Tk-SP subtilisin prodomain [36] than to CspB because CspB’s prodomain is larger due to a helical insert ( Fig 1D ) . Nevertheless , both prodomains share a similar orientation relative to the active ( or degenerate ) site residues ( Fig 1E ) . As a result , leucine 64 of CspC ( Fig 1E ) is equivalent to the autocleavage site of the CspB perfringens prodomain , serine 96 [27 , 32] ) , and would be positioned to undergo autoprocessing if CspC’s catalytic triad were intact . Interestingly , the degenerate site residues of CspC’s pseudotriad share the same orientation as the catalytic triad of CspB and other subtilisin-like serine proteases ( Fig 1E ) . Despite these similarities , CspC appeared to bind its prodomain more tightly than CspB . When the interfaces between each of the three subdomains were evaluated by buried surface area analysis , a larger contact area is observed between each of the domains in CspC as compared to CspB ( S1 Table , 3200 Å2 for CspC vs . 2270 Å2 for CspB , calculated with PDBe PISA[37] ) . Surprisingly , even with the extensive interactions between the different sub-domains of CspC , residues in both the jelly roll domain and prodomain have higher B-factors than nearby residues in the subtilase domain ( Fig 2A ) . Since B-factors measure the movement of a residue around its average position , C . difficile CspC would appear to have greater flexibility in the jelly roll domain and prodomain than in the subtilase domain . In contrast , the previously solved crystal structure of C . perfringens CspB showed little flexibility in terms of its B-factors and was highly resistant to proteolysis [32] . CspC’s flexibility was particularly unexpected given that the free energy of binding of the prodomain to the rest of the protein was -25 . 2 ΔG kcal/mol relative to -20 . 0 ΔG kcal/mol for CspB ( S1 Table ) . To determine if the high B-factors in C . difficile CspC result in high conformational flexibility relative to C . perfringens CspB , we subjected purified C . difficile CspC and C . perfringens CspB to limited proteolysis . Consistent with prior work , purified C . perfringens CspB was highly resistant to proteolysis by chymotrypsin , whereas purified C . difficile CspC was considerably more sensitive ( Fig 2B ) . These results indicate that C . difficile CspC is more conformationally dynamic than C . perfringens CspB under the conditions used for crystallization and limited proteolysis , although within the C . difficile spore , these proteins likely experience different environmental conditions and possibly different relative mobilities . To assess whether the relative mobility of C . difficile CspC is important for its function , we targeted residues exhibiting conformational flexibility in the structure for mutagenesis . Both arginine 358 in the jelly roll and glutamate 43 in the prodomain have higher average B-factors ( 49 . 1 and 55 . 8 Å2 , respectively , vs . an average of 27 . 2 Å2 ) . These two residues form a salt bridge adjacent to another salt bridge between arginine 374 in the jelly roll and glutamate 57 in the prodomain , which have B-factors of 43 . 9 and 33 . 6 Å2 , respectively ( Fig 3A ) . To test if the flexible Arg358 and Glu43 residues , or the neighboring salt bridge residues , are important for CspC function , we generated strains producing alanine substitutions of Glu43 , Glu57 , Arg374 , and Arg358 and determined the impact of these mutations on spore germination efficiency . Purified spores were plated on BHIS media containing 0 . 1% taurocholate germinant , and the number of colony forming units ( CFUs ) that arose from germinating spores relative to wild type and the wild-type cspC complementation strain were determined . Similar to the phenotypes of other germination-receptor mutants and our own previously published results [35 , 38–40] , ΔcspC spores exhibited low levels of “spontaneous” germination when plated on BHIS media alone . However , none of the salt bridge mutants exhibited statistically significant germination defects when plated on rich media containing 0 . 1% taurocholate ( Fig 3B ) , although germination was slightly lower ( 2 . 5-fold ) in the R358A mutant . Interestingly , when the salt bridge mutants were tested in an optical density-based assay for spore germination , the R358A variant exhibited a severe defect in spore germination compared to wild type spores during the 90 min assay length ( p < 0 . 0001 , Fig 3C ) . In contrast , the other salt bridge mutants tested exhibited germination responses similar to wild type , albeit slightly slower for the E43A and R347A mutant spores ( p < 0 . 01 ) . The optical assay measures the decrease in optical density of a population of germinating spores exposed to 1% taurocholate in rich media over time as the cortex is hydrolyzed and the core hydrates [41] . Since the time scale for the optical density assay was much shorter than the CFU-based germination assay , during which spores are constantly exposed to 0 . 1% taurocholate on rich media , we speculated that the R358A mutant might be less sensitive to germinant than wild type spores . To test this possibility , we plated the salt bridge mutant spores on rich media containing increasing amounts of germinant ( from 0 to 0 . 1% ) . Notably , the R358A mutants exhibited ~25-fold reduced sensitivity to germinants relative to the wild type when plated on media containing lower concentrations of germinant ( i . e . 0 . 001% and 0 . 01% taurocholate , p < 0 . 01 ) , whereas the other salt bridge mutants , E43A , E57A , and R374A , exhibited wild-type germinant sensitivity ( Fig 3D ) . We next assessed whether additional substitutions of Arg358 would affect the ability of C . difficile spores to sense germinant by generating strains that produced CspC with a negatively charged residue at residue 358 , R358E , or a larger neutral residue at residue 358 , R358L . The R358E substitution resulted in ~10-fold sensitivity to taurocholate at 0 . 001% and 0 . 01% relative to wild type , although this difference was not statistically significant , while the R358L mutation resulted in 30-100-fold decreases in germinant sensitivity at these concentrations relative to wild type . Since none of the substitutions at Glu43 , Glu57 , Arg358 , or Arg374 affected CspC levels in purified spores ( Fig 3B ) , the Arg358 substitutions would appear to specifically disrupt CspC function and reduce the sensitivity of C . difficile spores to taurocholate germinant . Nevertheless , since mutation of Arg358’s salt bridge partner Glu43 ( or the neighboring salt bridge ) did not affect CspC function , the ability to Arg358 to bind Glu43 does not appear to affect CspC function . Instead , the physical properties of Arg358 , including potentially its conformational flexibility , would appear to be critical for CspC signaling . Two additional residues exhibited considerable conformational flexibility in the CspC structure: arginine 456 and glycine 457 , which lacked sufficient electron density to be included in our model for CspC ( Fig 4A ) . As described earlier , Gly457 had previously been shown by Francis et al . to affect the germinant specificity of C . difficile spores , with a G457R substitution enabling C . difficile spores to germinate in response to chenodeoxycholate [12] , which typically inhibits C . difficile spore germination [25] . This observation led to the hypothesis that CspC directly recognizes bile acids using glycine 457 [12] . Interestingly , Gly457 and Arg456 likely sit at the top of a pocket formed on the surface of the subtilase domain that could potentially accommodate a molecule of taurocholate . To test the importance of these residues , we individually mutated Gly457 to arginine ( G457R ) and Arg456 to Gly ( R456G , Fig 4A ) . These mutations generate either two consecutive arginines or two consecutive glycines in the unstructured region , respectively . Before characterizing the germinant sensitivity and specificity of these strains , we first tested if the G457R mutation in the 630Δerm strain background would permit germination in response to chenodeoxycholate as previously described for a G457R mutant in the UK1 strain background [12] . To this end , we monitored the change in optical density of germinating spores in response to taurocholate and chenodeoxycholate as previously described [12] . In our study , the cspCG457R allele was expressed from the 630Δerm chromosome , while the previous work expressed this mutant allele from a multicopy plasmid in strain UK1 . CspCG457R mutant spores germinated faster than wild-type spores in response to 10 mM taurocholate ( ~0 . 5% ) , suggesting that the glycine to arginine substitution at residue 457 increases the responsiveness of spores to taurocholate ( Fig 4B ) . In contrast with the prior report [12] , CspCG457R spores exposed to 5 mM ( ~0 . 5% ) chenodeoxycholate failed to decrease in optical density over time ( Fig 4B ) . Furthermore , no evidence of spore germination was observed by phase-contrast microscopy , since CspCG457R and control wild-type , ΔcspC , and ΔcspC/cspC spores remained phase-bright during a 20 min exposure to chenodeoxycholate , whereas the majority of CspCG457R , wild-type , and ΔcspC/cspC spores germinated and became phase-dark , indicative of core rehydration , upon exposure to taurocholate ( S1A Fig ) . We suspect that the decrease in optical density of CspCG457R mutant spores reported by Francis et al . is caused by chenodeoxycholate forming a precipitate in the presence of C . difficile spores . We could recapitulate the optical density drop for CspCG457R mutant spores when chenodeoxycholate was re-suspended in an older stock of DMSO; this DMSO stock resulted in a precipitate collecting at the bottom of the wells ( S1B Fig ) . However , since this optical density decrease was also observed for the negative control , ΔcspC spores , as well as wild-type and ΔcspC/cspC spores , chenodeoxycholate can cause non-specific effects on optical density depending on the assay conditions . To avoid these experimental artifacts , we assessed the ability of CspCG457R spores to germinate in response to pre-treatment with either taurocholate or chenodeoxycholate and form colonies when plated on media lacking germinant ( BHIS ) . Colonies that form on BHIS alone arise from spores that germinated during the transient pre-treatment with bile acids . Wild-type and wild-type complementation spores formed colonies only after pre-treatment with taurocholate and not chenodeoxycholate ( Fig 4C and S2 Fig ) . CspCG457R mutant spores formed colonies in response to pre-treatment with both taurocholate and chenodeoxycholate , albeit ~100-fold less efficiently with chenodeoxycholate . However , CspCG457R mutant spores formed similar number of colonies with no bile acid pretreatment ( water pretreatment ) as with chenodeoxycholate pretreatment , indicating that these spores are germinating independent of the bile acid pretreatment . Since the rich BHIS media used in the experiments detailed above is poorly defined , we tested whether CspCG457R spores would also germinate on a more minimal medium ( C . difficile defined-media , CDDM , [42] ) . Pre-treatment of wild-type , ΔcspC , ΔcspC/cspC , and ΔcspC/G457R mutant spores with either water , taurocholate , or chenodeoxycholate followed by plating on CDDM alone yielded results nearly identical to that obtained on BHIS alone ( S2 Fig ) . Thus , CspCG457R mutant spores can germinate independently of a bile acid signal and appear to sense something present in both undefined rich media ( BHIS ) and minimal media ( CDDM ) . It should be noted that these mutant spores nevertheless still respond to taurocholate as a germinant , since CFUs increase by ~2-logs upon taurocholate pre-treatment ( Fig 4 and S2 Fig ) . To test whether bile acid-independent germination would also be observed with the R456G substitution , we measured the germination sensitivity of CspCR456G and CspCG457R spores on BHIS lacking germinant and with increasing amounts of taurocholate . The R456G and G457R mutant spores exhibited increased taurocholate-independent germination with a >1 , 000-fold increase in CFUs on BHIS alone relative to wild-type ( Fig 5A ) . CspCR456G and CspCG457R spore germination steadily increased in response to increasing taurocholate and reached maximum germination at 0 . 01% taurocholate , whereas wild-type and ΔcspC/cspC spore germination reached maximum germination at 0 . 1% taurocholate ( Fig 5A ) , with 0 . 1% taurocholate being the maximum concentration tested . Since the G457R mutation , and the R456G substitution result in consecutive arginines or glycines , respectively , we tested whether we could revert the taurocholate-independent germination phenotype back to wild-type levels by generating a R456G-G457R double mutant . This mutation reverses the order of amino acids at residues 456 and 457 relative to the wild-type protein . Interestingly , CspCR456G/G457R spores exhibited an intermediate phenotype between CspCR456G and CspCG457R spores when plated on media with varying levels of taurocholate ( Fig 5A ) , with CspCR456G-G457R spores forming colonies on media lacking germinant or containing 0 . 001% taurocholate ~3-fold less efficiently than CspCG457R spores , although this difference was not statistically significant . In contrast , CspCR456G spores formed colonies ~10-fold less efficiently than CspCG457R spores on this media ( p < 0 . 02 ) . Regardless , all three mutants produced spores with wild-type amounts of CspC as determined by western blotting , suggesting that the CspC variants have enhanced signaling properties ( Fig 5B ) . We next questioned what effect the identity of the residue at position 457 had on spore germination . To this end , we mutated Gly457 to another small , neutral amino acid ( alanine ) , a polar residue ( glutamine ) , a negatively charged residue ( glutamic acid ) , and a smaller , positively charged amino acid ( lysine ) . CspCG457A exhibited wild-type germination responses on BHIS alone and in response to different concentrations of taurocholate ( S3A Fig ) . CspCG457E and CspCG457Q spores exhibited an intermediate phenotype between that of wild-type and CspCG457R spores at lower taurocholate concentrations , while CspCG457K spores behaved identically to CspCG457R spores ( S3A Fig ) . Western blot analysis indicated that none of these mutations affected CspC levels in mature spores ( S3B Fig ) . Taken together , polar and charged amino acid substitutions of Gly456 ( glutamic acid , lysine , and arginine ) result in increased levels of taurocholate-independent germination , with positively-charged amino acids having the greatest effect . Since high basal levels of germination in the absence of taurocholate made it difficult to distinguish in the plate-based taurocholate titration assay if the Arg456 and Gly457 mutations increase germinant sensitivity , we assessed their germinant sensitivity using the optical density-based germination assay . CspCR456G and CspCG457R spores were exposed to increasing concentrations of taurocholate in the presence of BHIS , and the decrease in optical density was measured compared to wild-type spores and ΔcspC spores . Both CspCR456G and CspCG457R spores germinated more quickly at all concentrations of taurocholate tested compared to wild-type spores ( S4 Fig ) . While the difference between wild-type and CspCG457R spores was statistically significant for all taurocholate concentrations tested , CspCR456G spores exhibited statistically significant increases in germination for the 0 . 5% ( 10 mM ) taurocholate concentration , although it approached significance at the lowest taurocholate concentration tested ( 0 . 125% , ~2 mM ) . Notably , no decrease in optical density was observed when CspCR456G and CspCG457R spores were incubated in BHIS alone , suggesting that no germination occurs during the 90 min time period analyzed in the optical density assay for these mutant spores in contrast with the plate-based germination assays . However , this apparent discrepancy could result from the optical density assay lacking sufficient sensitivity to detect changes in <5% of the population [43] or because the germination of these spores in the absence of bile acids occurs on a slower time scale than the 90 min analyzed ( Figs 4 and 5 and S4 Fig ) . Thus , in addition to having increased taurocholate-independent germination ( Fig 5 ) , the R456G and G457R variants have increased sensitivity to taurocholate germinant . The Arg456 and Gly457 residues are located on one edge of a pocket that could potentially bind small molecules , like taurocholate , so we tested if other residues in this region impact sensitivity to taurocholate . To this end , we made substitutions at residues on the other side of this pocket in aspartate 429 and glutamine 516 . The substitutions either introduced a bulkier residue or reversed the charge of the native residue to potentially occlude ligand binding by this pocket ( Fig 6A ) . Of the four substitutions tested , only CspCD429K exhibited a germination profile similar to CspCR456G and CspCG457R spores , with CspCD429K spores germinating at ~100-fold higher levels than wild type when plated on BHIS alone ( Fig 6B , p < 0 . 0001 ) . Although germination saturated at 0 . 01% taurocholate like CspCR456G and CspCG457R spores , the shape of its germinant sensitivity curve resembled that of wild type in that its germination on 0 . 001% TA was only ~2-fold higher than in the absence of TA . In contrast , CspCR456G and CspCG457R spore germination increased by ~8-fold , when comparing the germination levels on BHIS alone to BHIS containing 0 . 001% TA ( Fig 5A ) . Consistent with these observations , CspCD429K spores did not exhibit greater sensitivity to taurocholate in the optical density assay than wild-type spores ( S4 Fig ) , in contrast with CspCR456G and CspCG457R spores , indicating that the D429K allele results in a different germinant profile than the R456G and G457R alleles . Importantly , none of the amino acid substitutions affected CspC levels in mature spores relative to wild-type ( Fig 6C ) . Although CspCR456G and CspCG457R spores differed from CspCD429K spores in their sensitivity to taurocholate ( S4 Fig ) , it was unclear why all three mutant spores germinated significantly better on plates in the absence of taurocholate germinant than wild-type spores ( Figs 5 and 6 ) . Since Sorg and Sonenshein previously established that efficient C . difficile spore germination requires a second co-germinant signal [24] , namely amino acids [44] , we considered whether mutations in CspC could confer differential responsiveness to components within both rich and defined media . These small molecules enhance sensitivity to taurocholate without causing germination on their own [19 , 20] . Recently , Kochan et al . determined that divalent cations , particularly calcium , also potentiate taurocholate-induced germination and that amino acid and calcium co-germinant signals can synergize to further enhance taurocholate-induced germination [45 , 46] . Since it is currently unknown how C . difficile spores sense and respond to these co-germinants , but both amino acid and calcium co-germinants are present in the BHIS media used in our germination assays , we tested the sensitivity of CspCR456G , CspCG457R , and CspCD429K spores to various co-germinants individually in the optical density assay using a PBS-based buffer rather than BHIS . We first tested the sensitivity of CspCR456G , CspCG457R , and CspCD429K spores to the most potent amino acid co-germinant , glycine , and an amino acid co-germinant with a mid-range activating concentration ( EC50 ) , arginine [44] in the presence of constant amounts of taurocholate ( 1% , 19 mM ) . Importantly , wild-type , CspCR456G , CspCG457R , and CspCD429K spores did not germinate in PBS with 1% taurocholate alone ( Fig 7 ) or in PBS containing glycine alone ( S5 Fig ) , just as BHIS alone did not induce germination in the optical density assay ( S4 Fig ) . Thus , both germinant and a co-germinant must be present to detect germination in this assay . CspCG457R spores germinated in lower concentrations of glycine ( 0 . 4 mM ) than wild-type , CspCR456G , and CspCD429K spores in the presence of 1% taurocholate and germinated faster than wild-type and CspCD429K spores through all glycine concentrations tested ( Fig 7A , p < 0 . 05 ) . At 2 mM glycine in the presence of 1% taurocholate , CspCR456G spores germinated faster than wild-type and CspCD429K spores ( Fig 7A , p < 0 . 0001 ) . CspCG457R and CspCR456G spores also germinated faster than wild-type and CspCD429K spores at 11 . 1 mM arginine with 1% taurocholate ( Fig 7B , p < 0 . 01 ) , with CspCG457R spores germinating the fastest and at a lower arginine concentration ( 3 . 7 mM , Fig 7B ) , although this difference was not statistically significant . Both CspCR456G and CspCG457R spores reached a maximum germination rate at 33 . 3 mM arginine with 1% taurocholate , while wild-type and CspCD429K spores germination rates were still increasing at 100 mM arginine with 1% taurocholate ( Fig 7B ) . Taken together , these data indicate that CspCR456G and CspCG457R spores are more sensitive to glycine and arginine co-germinants in the presence of taurocholate , while CspCD429K spores respond to these amino acid co-germinants similarly to wild-type spores . We next tested the sensitivity of these CspC mutants to calcium co-germinant . In PBS buffer with CaCl2 added , the spores clumped together , so we used Tris buffer to test the sensitivity of the CspC mutants to calcium . We also decreased the taurocholate concentration to 0 . 25% to determine the sensitivity of the spores to Ca2+ , since spores germinated too rapidly in 1% taurocholate supplemented with CaCl2 to accurately measure the change in optical density in a plate reader . As observed with amino acid co-germinants , the optical density of all spores tested did not change in Tris buffer containing 0 . 25% taurocholate and no calcium ( Fig 8 ) or Tris buffer with 60 mM Ca2+ ( S5 Fig ) . However , CspCG457R spores were highly sensitive to calcium ions in the presence of taurocholate , germinating at near maximal rates at the lowest calcium concentration tested ( 2 . 22 mM CaCl2 , Fig 8 , p < 0 . 0001 ) . CspCD429K spores were also very sensitive to calcium , with the entire population germinating in response to 20 mM Ca2+ ( p < 0 . 0001 ) . In contrast , wild-type and CspCR456G spores did not germinate appreciably in response to 60 mM Ca2+ ( Fig 8 ) , a result that differs slightly from that of Kochan et al . , who first identified calcium as a co-germinant [46] . However , increasing the taurocholate levels to 1% in Tris buffer resulted in similar responses as Kochan et al . The different germinant sensitivities observed between the two studies could result from differences in the sporulation media ( Clospore broth [46] vs . 70:30 plates ) and spore purification methods . Regardless , our results indicate that CspCR456G , CspCG457R , and CspCD429K spores exhibit differential sensitivity to one or more classes of co-germinants . This heightened sensitivity correlates with their increased taurocholate-independent germination on plates .
Unlike the nutrient germinants used by most spore-forming bacteria , the nosocomial pathogen , C . difficile , uses mammalian-specific bile acids as the signal for initiating germination . C . difficile also lacks the membrane-bound germinant receptors common to almost all spore-forming bacteria [22 , 47] and instead employs the soluble , Peptostreptococceae-specific CspC pseudoprotease to sense bile acid germinants [12] . By solving the X-ray crystal structure of CspC , we identified residues , particularly residues located in flexible regions , that are critical for CspC’s signaling function . Our mutational analyses surprisingly revealed that C . difficile CspC responds not only to bile acid germinant but also to two different classes of co-germinants: amino acids and calcium ( Figs 7 and 8 ) . Since the mechanism by which co-germinants are sensed by C . difficile was previously unknown , these observations provide important insight into how C . difficile spores integrate multiple environmental signals to induce germination . In contrast with a prior report [12] , we also observed that a G457R substitution in CspC does not result in spores that germinate in response to chenodeoxycholate ( Fig 4 and S1 Fig ) . Instead , the G457R mutation allows C . difficile spores to germinate on media in the absence of added bile acids and heightens the sensitivity of spores to taurocholate , amino acids , and calcium ions . Mutations of Gly457 to charged residues potentiated bile acid-independent spore germination ( S3 Fig ) , suggesting that charged residues may promote interactions of CspC with ( co ) -germinants and/or signal transduction proteins . As we discuss later , these findings raise the possibility that C . difficile CspC may not bind bile acid germinants directly . Our structure-guided mutational analyses identified additional residues that regulate CspC function beyond Gly457 , namely the conformationally flexible residues , Arg358 and Arg456 . These residues all shared the property of exhibiting greater than average mobility within the CspC crystal structure . Mutation of these residues altered C . difficile CspC’s sensitivity to bile acid germinants ( Fig 3 and S4 Fig ) , suggesting that conformational mobility is important for CspC function . This finding was somewhat surprising given that a unique hinge and clamp region in CspC provides a larger contact surface area between the prodomain and subtilase domain than in CspB ( Fig 1A and S1 Table ) . Nevertheless , despite this larger contact surface , CspC was surprisingly more flexible than CspB in limited proteolysis analyses ( Fig 2 ) . Mutation of the conformationally flexible residue , Arg358 , in the jelly roll domain to chemically distinct residues ( alanine , glutamatic acid , and leucine ) resulted in hypo-sensitivity to taurocholate germinant and a dramatically reduced germination rate ( Fig 3 ) . Although Arg358 can form a salt bridge with the nearby Glu43 , this interaction appears dispensable for CspC function , since an E43A mutation did not affect germinant sensitivity ( Fig 3 ) . Thus , Arg358’s ability to interact with the surrounding environment likely determines CspC’s ability to sense germinant and/or transduce the germinant signal . Mutation of the flexible residue , Arg456 , to glycine resulted in bile acid-independent spore germination ( Fig 5 ) as well as increased sensitivity to taurocholate germinant ( S4 Fig ) and amino acid co-germinants similar to the G457R mutation ( Fig 7 ) , although unlike the G457R allele , the R456G allele did not enhance the sensitivity of C . difficile spores to calcium ion co-germinant . We also identified D429K as an additional mutation that confers bile acid-independent germination to C . difficile spores . Asp429 borders a shallow pocket next to the predicted positions of R456 and G457 ( Fig 6 ) ; however , unlike the R456G and G457R mutations , the D429K mutation did not increase responsiveness to taurocholate or amino acids and instead markedly increased spore germination in response to the calcium ion co-germinant ( Figs 7 and 8 , S4 Fig ) . Taken together , the D429K mutation increases sensitivity to calcium co-germinant alone; the R456G mutation increases sensitivity to taurocholate germinant and amino acid co-germinants; and the G457R mutation exhibits the highest sensitivity to all three of these small molecule types ( Figs 7 and 8 , S4 Fig ) . Since the G457R mutation increases sensitivity to both classes of co-germinants and bile acids , it may adopt the “activated” conformation of CspC more readily than wild-type CspC analogous to a hair-trigger . Interestingly , of the mutants we characterized , only those that were more sensitive to amino acid co-germinants were more sensitive to taurocholate germinant . Amino acid co-germinants were recently reported to act synergistically with divalent cation co-germinants , but neither class of co-germinants synergizes with members of the same class [45] . The authors hypothesized that amino acid co-germinants only synergize with divalent cation co-germinants because the different classes of co-germinants are sensed by different receptors or potentially even different pathways [20 , 45] . Our current data do not distinguish between whether C . difficile spores use two separate receptors , the same receptor , or CspC itself , to sense amino acid and divalent cations , but it is interesting that the Gly457 , Arg456 , and D429 residues all line the edge of a pocket that could bind taurocholate and/or small molecules like amino acids . While Ca2+ is a co-factor in some subtilisin-like proteases [33 , 48] , we did not observe evidence for Ca2+ binding in the structure . Indeed , the mechanism by which CspC senses and/or integrates the signals from these chemically distinct classes of molecules ( bile acids , amino acids , and divalent cations ) is unknown . CspC may bind bile acids , amino acids , and divalent cations directly or indirectly . If CspC indirectly senses these molecules , it presumably interacts with the direct receptors for bile acids and co-germinants . In this scenario , the mutations we identified would enhance CspC’s association with these receptors . Notably , while this manuscript was under review , Shrestha and Sorg reported the identification of specific mutations within the pseudoprotease CspA that allowed C . difficile spores to germinate exclusively in response to taurocholate and independent of either amino acid or calcium co-germinants [49] . This recent finding suggests that CspC and CspA could directly interact during germinant and co-germinant sensing . Alternatively , CspC could function through some combination of these models by directly binding germinant and a co-germinant receptor ( s ) or vice versa . A final model that has been proposed is that CspC alters the permeability of germinants to their “true” receptors in the cortex or core [20] . It is currently unknown whether bile acid germinants and Ca2+ co-germinants can pass through the outer spore membrane to reach the cortex where CspC and CspB are hypothesized to be located [19 , 28 , 38] . Thus , CspC may facilitate the transport of germinants and/or co-germinants to the cortex region . While more work needs to be done to distinguish between these models , our findings also raise the question as to how CspC activation by taurocholate and co-germinants ( directly or indirectly ) leads to activation of the CspB protease . CspC could activate CspB through a direct interaction [19 , 35 , 38] , as some subtilisin-like proteases form dimers [48 , 50] . This mechanism would be consistent with the observation that allosteric interactions between pseudoenyzmes can activate their cognate enzymes [51–56] . A possible model for CspB activation by CspC is that direct or indirect binding of germinants and co-germinants induces a conformational change in CspC that allows CspC to bring CspB into an active conformation through a direct interaction . Our observation that CspC is conformationally flexible may reflect the need for CspC to adopt a different conformation in response to germinant and/or co-germinant signal ( s ) that then allows CspC to activate CspB . Further work will need to be done to test these hypotheses . In addition to providing insight into the function of CspC , the mutations we have identified could help answer one of the major questions in the field regarding how sensitivity to germinant affects the ability of C . difficile to cause disease . Epidemic strains of C . difficile vary in the severity of the disease they cause and their sensitivity to bile acid germinant [57–61] . However , since C . difficile strains exhibit high genetic diversity , it has not been possible to directly correlate germinant sensitivity to disease severity . High sensitivity to bile acid germinants could increase the effective infectious dose of C . difficile and thus lead to more severe disease , or lower sensitivity to germinant could ensure that C . difficile spores germinate closer to the colon , the primary site of infection , and thus cause more severe disease [57 , 60–62] . Testing the ability of our hypo- and hyper-sensitive mutants to colonize and cause disease in animal infection models could help answer this outstanding question in the field about C . difficile pathogenesis .
All C . difficile strain manipulations were performed with 630ΔermΔcspCΔpyrE [35] as the parental strain using pyrE-based allele-coupled exchange ( ACE [63] ) , which allows for single copy complementation of the ΔcspC parental mutant from an ectopic locus . C . difficile strains are listed in S2 Table; they were grown on brain heart infusion media ( BHIS ) supplemented with L-cysteine ( 0 . 1% w/v; 8 . 25 mM ) , taurocholate ( TA , 0 . 1% w/v; 1 . 9 mM ) , thiamphenicol ( 5–15 μg/mL ) , kanamycin ( 50 μg/mL ) , or cefoxitin ( 8 μg/mL ) , as needed . Cultures were grown at 37°C under anaerobic conditions using a gas mixture containing 85% N2 , 5% CO2 , and 10% H2 . Escherichia coli strains for HB101/pRK24-based conjugations and BL21 ( DE3 ) -based protein production are listed in S2 Table . E . coli strains were grown at 37°C , shaking at 225 rpm in Luria-Bertani broth ( LB ) . The media was supplemented with chloramphenicol ( 20 μg/mL ) , ampicillin ( 50 μg/mL ) , or kanamycin ( 30 μg/mL ) as indicated . Plasmid constructs were cloned into DH5α and sequence confirmed using Genewiz . To construct the cspC mutant complementation constructs , flanking primers , #2189 and 2242 ( S3 Table ) , were used in combination with internal primers encoding a given point mutation with ΔcspBA genomic DNA template . The resulting cspC complementation constructs carry 282 bp of the cspBA upstream region along with the ΔcspBA sequence and the intergenic region between cspBA and cspC . This slightly extended construct was necessary to generate wild-type levels of CspC when the constructs were expressed from the pyrE locus as previously described [35] . The primers encoding each CspC point mutation are provided in S3 Table , where all primers used are listed . For example , the G457R substitution was constructed using primer pair #2189 and 1365 to amplify a 5’ cspC complementation construct fragment encoding the G457R mutation at the 3’ end , while primer pair #1364 and 2242 were used to amplify a 3’ cspC complementation construct encoding the G457R mutation at the 5’ end . The individual 5’ and 3’products were cloned into pMTL-YN1C digested with NotI/XhoI by Gibson assembly . In some cases , the two PCR products were used in a PCR SOE [64] prior to using Gibson assembly to clone the cspC construct into pMTL-YN1C digested with NotI and XhoI . The resulting plasmids were transformed into E . coli DH5α , confirmed by sequencing , and transformed into HB101/pRK24 . To generate the recombinant protein expression constructs for producing CspC-His6 variants , primer pair #1128 and 1129 was used to amplify a codon-optimized version of cspC using pJS148 as the template ( a kind gift from Joseph Sorg ) . The resulting PCR product was digested with NdeI and XhoI and ligated into pET22b cut with the same enzymes . The ligation mixture was used to transform DH5α . The G457R variant was cloned using a similar procedure except that primer pair #1128 and 1361 and primer pair #1360 and 1129 were used to PCR the 5’ and 3’ fragments encoding the G457R mutation . The resulting PCR products were joined together using PCR SOE and flanking primer pair #1128 and 1129 . E . coli BL21 ( DE3 ) strains 981 and 1721 ( S2 Table ) were used to produce codon-optimized CspC ( wild-type and G457R , respectively ) using the autoinduction method . Briefly , the indicated strains were struck out onto LB plates containing ampicillin and used to inoculate a 20 mL culture of LB containing 100 μg/mL ampicillin . The culture was grown for ~4 hrs after which it was used to inoculate 1:1000 of Terrific Broth ( Affymetrix ) supplemented with 5052 sugar mix ( 0 . 5% glycerol , 0 . 05% glucose , 0 . 1% alpha-lactose ) and ampicillin for 60 hrs at 20˚C [65] . The cells were pelleted and then resuspended in lysis buffer ( 500 mM NaCl , 50 mM Tris [pH 7 . 5] , 15 mM imidazole , 10% [vol/vol] glycerol ) , flash frozen in liquid nitrogen , thawed and then sonicated . The insoluble material was pelleted , and the soluble fraction was incubated with Ni-NTA agarose beads ( 5 Prime ) for 3 hrs , and eluted using high-imidazole buffer ( 500 mM NaCl , 50 mM Tris [pH 7 . 5] , 200 mM imidazole , 10% [vol/vol] glycerol ) after nutating the sample for 5–10 min . Four elution fractions were pooled and then buffer exchanged into gel filtration buffer ( 200 mM NaCl , 10 mM Tris pH 7 . 5 , 5% [vol/vol] glycerol ) using Amicon 30 kDa cut-off filters . The buffer-exchanged protein was concentrated to 20 mg/mL or less , and gel filtration chromatography was performed using a Superdex 200 ( GE Healthcare ) column . Fractions containing CspC-His6 were pooled , concentrated to ~30 mg/mL , and flash frozen in liquid nitrogen . Purified protein was buffer exchanged into 25 mM Hepes pH 7 . 5 , 100 mM NaCl , 1 mM TCEP with 10% [vol/vol] glycerol and concentrated to 22 mg/ml . Crystallization was performed via hanging drop vapor diffusion method by mixing of equivalent volumes of protein to crystallization reagent solution . Crystals grew in a broad range of conditions but suffered from severe twinning . This challenge was addressed by pre-incubation of the protein with 0 . 5 M guanidinium hydrochloride prior to mixing with crystallization reagent . Crystals grew with a reagent containing 0 . 2 M MES pH 6 . 5 and 2 . 5 M ammonium chloride equilibrating over a well containing 2M NaCl and incubated at room temperature . Cryoprotection was achieved by serial transfer from the growth condition to a final solution containing 1 . 0 M ammonium sulfate and 2 . 0 M lithium sulfate prior to cryo-cooling into liquid nitrogen . Data were collected at 12 KeV on GM/CA beamline 23-ID-D at the Advanced Photon Source ( Argonne National Laboratory ) using a Pilatus3 6M detector with data processed to 1 . 55 Å using HKL2000 [66] ( Table 1 ) . The crystals were of space group C222 ( 1 ) with a single molecule in the asymmetric unit . A molecular replacement search model was prepared from the subtilase-like domain of CspB ( 4I0W ) [32] using a sequence alignment imported into Chainsaw [67] followed by truncation to a c-alpha only trace . A clear molecular replacement solution ( TFZ of 10 . 3 ) was achieved using Phaser [68] within Phenix [32] and submitted to density modification using Solve/Resolve [69] . Clear density for both the prodomain and jelly roll domains was observed with model completion performed by iterative rounds of manual building , automated building using Autobuild and final refinement using Phenix with a final Rfree of 18 . 6% . The following residues were not included in the model due to ambiguous electron density: 1 , 89–92 , 456–457 , 506–508 , 556–558 . Purified C . perfringens CspB or C . difficile CspC proteins were diluted to 15 μM in 10 mM Tris pH 7 . 5 buffer . The protein solution was aliquoted into 0 . 2 mL tubes . A 1 mg/mL solution of chymotrypsin in 1 mM HCl was serially diluted to generate 10-fold dilutions for use in the assay . The serially diluted chymotrypsin solutions were added to the aliquoted protein solutions for a final concentration of chymotrypsin ranging from 40 μg/mL to 0 . 0004 μg/mL . 1 mM HCl was added to protein samples as an untreated control . The protein and chymotrypsin mixture was incubated at 37˚C for 1 hr . Chymotrypsin activity was quenched by the addition of NuPAGE 4X LDS Sample Buffer ( Invitrogen ) and boiled at 98˚C for three minutes . 10 μL aliquots were resolved using a 15% SDS-PAGE gel and visualized by Coomassie staining . Complementation strains were constructed as previously described using CDDM to select for recombination of the complementation construct into the pyrE locus by restoring uracil prototrophy [41] . Two independent clones from each complementation strain were phenotypically characterized . C . difficile strains inoculated from glycerol stocks were grown overnight on BHIS plates containing taurocholate ( TA , 0 . 1% w/v , 1 . 9 mM ) . The resulting colonies were used to inoculate liquid BHIS cultures , which were grown to early stationary phase before being back-diluted 1:50 into BHIS . When the cultures reached an OD600 between 0 . 35 and 0 . 75 , 120 μL of this culture were spread onto 70:30 agar plates and sporulation was induced as previously described [70] for 21–24 hrs . Sporulating cells were harvested into phosphate-buffered saline ( PBS ) , and cells were visualized by phase-contrast microscopy . After inducing sporulation on 70:30 agar plates for 2–3 days as previously described [71] , the samples were harvested into ice-cold , sterile water . A sample was removed to examine the sporulation cultures using phase-contrast microscopy . The spore samples were then washed 6 times in ice-cold water and incubated overnight in water at 4˚C . The following day , the samples were pelleted and treated with DNase I ( New England Biolabs ) at 37°C for 30–60 minutes , and purified on a 20%/50% HistoDenz ( Sigma Aldrich ) gradient . The resulting spores were washed 2–3 times in water , and spore purity was assessed using phase-contrast microscopy ( >95% pure ) . The optical density of the spore stock was measured at OD600 , and spores were stored in water at 4°C . Germination assays were performed as previously described [72] . For each strain tested , the equivalent of 0 . 35 OD600 units , which corresponds to ~1 x 107 spores , were resuspended in 100 μl of water , and 10 μL of this mixture were removed for 10-fold serial dilutions in PBS . The dilutions were plated on BHIS-TA , and colonies arising from germinated spores were enumerated at 20–24 hrs . Germination efficiencies were calculated by averaging the CFUs produced by spores for a given strain relative to the number produced by wild type spores based on analyses of spores from three independent preparations ( i . e . three biological replicates ) . Statistical significance was determined by performing a one-way analysis of variance ( ANOVA ) on natural log-transformed data using Tukey’s test . The data were transformed because the use of three independent spore preparations resulted in a non-normal distribution . Germination assays with chenodeoxycholate or taurocholate pretreatment were performed essentially as previously described [30] . Briefly , ~3 x 107 spores ( 1 . 05 OD600 units ) were resuspended in 150 μL water . 150 μL of BHIS was added to the spore suspensions . Aliquots of the spore suspensions were exposed to 1% taurocholate , 5% chenodeoxycholate , or water ( untreated ) and incubated at 37˚C for 20 minutes . 10 μL of this mixture was removed for 10-fold serial dilutions in PBS and the dilutions were plated on BHIS and BHIS with 0 . 1% taurocholate or CDDM and CDDM with 0 . 1% taurocholate . Colonies arising from germinated spores were enumerated at 20–24 hrs . Data presented are the averages of CFUs enumerated from three independent spore preparations . Statistical significance was determined by performing a one-way analysis of variance ( ANOVA ) on natural log-transformed data using Tukey’s test . The data were transformed because the use of three independent spore preparations resulted in a non-normal distribution . For OD600 kinetics assays with chenodeoxycholate in 96-well plates , ~1 . 6 x 107 spores ( 0 . 55 OD600 units ) for each condition tested were resuspended in BHIS and 180 μL were aliquoted into three wells of a 96 well flat bottom tissue culture plate ( Falcon ) for each condition tested . The spores were exposed to 10 mM taurocholate ( ~0 . 5% ) , 5 mM chenodeoxycholate ( ~0 . 2% ) , or 50% DMSO ( untreated ) in a final volume of 200 μL . The OD600 of the samples was measured every 3 minutes in a Synergy H1 microplate reader ( Biotek ) at 37˚C with constant shaking between readings . The OD600 for each technical triplicate was averaged at each time point and the OD600 of a blank measurement ( BHIS with 10 mM taurocholate , 5 mM chenodeoxycholate , or 50% DMSO alone ) was subtracted from the OD600 of the appropriately treated spores at each time point . The change in OD600 over time was calculated as the ratio of the OD600 at each time point to the OD600 at time zero . For OD600 kinetics assays with varying concentrations of taurocholate germinant , ~2 . 3 x 107 spores ( 0 . 8 OD600 units ) for each condition tested were resuspended in BHIS and 900 μL were aliquoted into a well of a 24 well suspension culture plate ( CellStar ) for each condition tested . The spores were then exposed to 2-fold dilutions of 1% taurocholate ( 19 mM ) or water ( untreated ) in a total volume of 1 mL . The OD600 of the samples was measured every 3 minutes in a Synergy H1 microplate reader ( Biotek ) at 37˚C with constant shaking between readings . The change in OD600 over time was calculated as the ratio of the OD600 at each time point to the OD600 at time zero . For OD600 kinetics assays with varying concentrations of co-germinants , ~2 . 3 x 107 spores ( 0 . 8 OD600 units ) for each condition tested were resuspended in either 1 . 5X PBS buffer or 50 mM Tris HCl pH 7 . 5 and aliquoted into a well of a 24-well plate for each condition tested . As spores clumped when CaCl2 was added to spores resuspended in 1 . 5X PBS , 50 mM Tris HCl pH 7 . 5 was used to measure OD600 kinetics in response to calcium as previously described [46] . 5-fold serial dilutions of 1 M glycine , 3-fold serial dilutions of 1 M arginine , 3-fold serial dilutions of 6 M CaCl2 , or 1 . 5X PBS or 50 mM Tris HCl ( untreated ) were added to spores resuspended in the appropriate buffer to a final volume of 900 μL . The spores were then exposed to 1% taurocholate ( 19 mM ) in a total volume of 1 mL and the OD600 of the samples was measured every 3 minutes in a Synergy H1 microplate reader ( Biotek ) at 37˚C with constant shaking between readings . The change in OD600 over time was calculated as the ratio of the OD600 at each time point to the OD600 at time zero . All assays described above were performed at least five times on three independent spore preparations . Data shown are averages from three replicates performed on a single spore preparation that is representative of data obtained from independent spore preparations . The additional replicates performed can be found in the Supplementary materials , S6–S9 Figs . Samples for immunoblotting were prepared as previously described [73] . Briefly , sporulating cell pellets were resuspended in 100 μL of PBS , and 50 μL samples were removed and freeze-thawed for three cycles . The samples were resuspended in 100 μL EBB buffer ( 8 M urea , 2 M thiourea , 4% ( w/v ) SDS , 2% ( v/v ) β-mercaptoethanol ) and boiled for 20 min , pelleted , and resuspended again . A small amount of sample buffer was added to stain samples with bromophenol blue . C . difficile spores ( ~1 x 107 ) were resuspended in EBB buffer and processed as above . The samples were resolved by 7 . 5% ( for sporulating cell analyses of CspBA and CspC ) or 12% SDS-PAGE gels and transferred to Millipore Immobilon-FL PVDF membrane . The membranes were blocked in Odyssey Blocking Buffer with 0 . 1% ( v/v ) Tween 20 and probed with rabbit polyclonal anti-CspB [32] , anti-CspA ( a generous gift from Joe Sorg , Texax A&M University ) , or anti-CotA [38] antibodies and/or mouse monoclonal anti-pentaHis ( ThermoScientific ) , anti-SleC [32] , anti-CspC [30] , or anti-SpoIVA antibodies [74] . The anti-CspB and anti-CspC antibodies were used at 1:2500 dilutions , the anti-SleC antibody was used at a 1:5000 dilution , and the anti-pentaHis , anti-SpoIVA , anti-CotA , and anti-CspA antibodies were used at a 1:1000 dilution . IRDye 680CW and 800CW infrared dye-conjugated secondary antibodies were used at 1:20 , 000 dilutions . The Odyssey LiCor CLx was used to detect secondary antibody infrared fluorescence emissions . Results shown are representative of analyses of three independent spore preps . Germination with chenodeoxycholate or taurocholate was performed as previously described above [30] by incubating spores with either water , taurocholate , or chenodeoxycholate for 20 min at 37˚C . The spores were pelleted to remove the inducers , re-suspended in PBS , mounted on glass slides , and analyzed by phase-contrast microscopy for evidence of germination ( i . e . to monitor the transition from phase-bright to phase-dark spores ) . | The major nosocomial pathogen Clostridioides difficile depends on spore germination to initiate infection . Interestingly , C . difficile’s germinant sensing mechanism differs markedly from other spore-forming bacteria , since it uses bile acids to induce germination and lacks the transmembrane germinant receptors conserved in almost all spore-forming organisms . Instead , C . difficile is thought to use CspC , a soluble pseudoprotease , to sense these unique bile acid germinants . To gain insight into how a pseudoprotease senses germinant and propagates this signal , we solved the crystal structure of C . difficile CspC . Guided by this structure , we identified mutations that alter the sensitivity of C . difficile spores to not only bile acid germinant but also to amino acid and calcium co-germinants . Taken together , our study implicates CspC in either directly or indirectly sensing these diverse small molecules and thus raises new questions regarding how C . difficile spores physically detect bile acid germinants and co-germinants . | [
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... | 2019 | The CspC pseudoprotease regulates germination of Clostridioides difficile spores in response to multiple environmental signals |
Schistosomiasis , a neglected tropical disease , owes its continued success to freshwater snails that support production of prolific numbers of human-infective cercariae . Encounters between schistosomes and snails do not always result in the snail becoming infected , in part because snails can mount immune responses that prevent schistosome development . Fibrinogen-related protein 3 ( FREP3 ) has been previously associated with snail defense against digenetic trematode infection . It is a member of a large family of immune molecules with a unique structure consisting of one or two immunoglobulin superfamily domains connected to a fibrinogen domain; to date fibrinogen containing proteins with this arrangement are found only in gastropod molluscs . Furthermore , specific gastropod FREPs have been shown to undergo somatic diversification . Here we demonstrate that siRNA mediated knockdown of FREP3 results in a phenotypic loss of resistance to Schistosoma mansoni infection in 15 of 70 ( 21 . 4% ) snails of the resistant BS-90 strain of Biomphalaria glabrata . In contrast , none of the 64 control BS-90 snails receiving a GFP siRNA construct and then exposed to S . mansoni became infected . Furthermore , resistance to S . mansoni was overcome in 22 of 48 snails ( 46% ) by pre-exposure to another digenetic trematode , Echinostoma paraensei . Loss of resistance in this case was shown by microarray analysis to be associated with strong down-regulation of FREP3 , and other candidate immune molecules . Although many factors are certainly involved in snail defense from trematode infection , this study identifies for the first time the involvement of a specific snail gene , FREP3 , in the phenotype of resistance to the medically important parasite , S . mansoni . The results have implications for revealing the underlying mechanisms involved in dictating the range of snail strains used by S . mansoni , and , more generally , for better understanding the phenomena of host specificity and host switching . It also highlights the role of a diversified invertebrate immune molecule in defense against a human pathogen . It suggests new lines of investigation for understanding how susceptibility of snails in areas endemic for S . mansoni could be manipulated and diminished .
Schistosomiasis is one of the world's most tenacious neglected tropical diseases , infecting an estimated 207 million people , mostly children [1] . The persistence of schistosome parasites stems in part from their use of freshwater snails for their larval development and transmission . Snails are often abundant and difficult to control , and it is in snails that the cercariae infective to humans are produced in prolific numbers . It takes only a single schistosome miracidium to establish a snail infection capable of producing hundreds of cercariae on a daily basis for months [2] . The amplification of schistosomes that occurs within snails creates a reoccurring problem for control efforts and is a significant obstacle for sustained prevention . It highlights the importance of understanding the dynamics of schistosome infections in snails and is the reasoning behind studies focused on characterizing the mechanistic basis for snail resistance to schistosome infection . If we could understand the underlying factors that enable snails to resist schistosome infection , then we could better understand the basis of compatibility in field snails . The level of compatibility exhibited will directly influence both transmission dynamics and control efforts . We could also potentially exploit resistance to favor development of more sustainable control strategies that go beyond today's largely one-dimensional control programs that depend primarily on treatment of infected people with praziquantel [3] . Not all snails are created equal: some are susceptible and some resistant to schistosome infection . Resistance is genetically controlled and affects immunological factors [4] , [5] that vary among snail species , strains or age categories . For example , the human parasite Schistosoma mansoni infects only certain species of Biomphalaria ( such as B . glabrata ) . Furthermore , only some strains of B . glabrata are compatible with this parasite . Many studies have focused on characterizing the transcriptional profiles of schistosome resistant strains compared to susceptible counterparts , and have identified a number of putative resistance-associated factors in the process [6] , [7] . Amongst these molecules are the fibrinogen-related proteins ( FREPs ) , members of a multi-gene family that undergo somatic diversification and point mutation events . FREP proteins couple together fibrinogen and immunoglobulin superfamily domains , to generate a protein that is unique as far as presently known to gastropod molluscs [8] . FREPs are capable of precipitating secretory/excretory products from digenetic trematode sporocysts [9] , and binding to diversified glycoproteins produced by parasites [10] . One individual FREP , FREP3 , has been singled out for further study because of its role in the snail defense response against the trematode Echinostoma paraensei [4] . FREP3 , like other FREPs , is a lectin-like molecule that recognizes a number of monosaccharides and is able to enhance the phagocytic uptake of targets , acting as an opsonin [11] . Knockdown of FREP3 in a normally resistant snail phenotype , and subsequent challenge of those snails with E . paraensei resulted in a significant proportion of the snails becoming infected with E . paraensei [11] . Trematode infection of a snail host is achieved , in part , by evading and suppressing the snail defense response . This provides a window for establishment of infection and then preventing the immune response from interfering with parasite development . These immune-evasion strategies can be observed in vitro [12] , and also by transcriptional analysis [7] , which suggests that many of the transcripts expressed by resistant snails during successful defense are suppressed in susceptible snails that become infected [11] . Immunosuppression is especially strong following exposure to E . paraensei , a parasite that can alter snail hemocyte morphology and interfere with hemocyte function [12] , and that can suppress the expression of important immune molecules almost immediately upon entry into the snail [7] . One of the factors we identified as being suppressed by E . paraensei during infection is FREP3 [11] . This observation prompted us to use , in one of the experiments described below , a protocol first employed by Lie and Heyneman [13] in which pre-exposure of schistosome-resistant snails to E . paraensei was used to abrogate resistance to subsequent schistosome infection . We hypothesized specifically that this treatment would interfere with FREP3 expression ( and likely with expression of other immune components as well ) , as compared to schistosome-resistant control snails not exposed to E . paraensei . In this study , we report on the results of two different manipulations undertaken with the intention of abrogating resistance to S . mansoni in the naturally resistant BS-90 strain of B . glabrata . We first examined the effects of knocking down FREP3 using RNAi on the subsequent ability of BS-90 snails to support S . mansoni development . Secondly , we also expressly repeated the classic experiment of Lie et al . ( 1977 ) [14] , using both BS-90 snails and accompanying microarray monitoring for the first time . We first exposed BS-90 snails to radiation-attenuated miracidia of E . paraensei , and then assessed their resistance level to S . mansoni as compared to snails not pre-exposed to E . paraensei . Radiation-attenuated E . paraensei parasites do not establish long-term , proliferative infections in snails , avoiding the potential complication that persistent larvae of this species would prevent the potential development of S . mansoni . It is known , however , that irradiated E . paraensei larvae , during their brief lifespan , exert a potent immunosuppressive effect just as do normal E . paraensei larvae [7] , [11] , [13] . Infection with S . mansoni of FREP3 knockdown snails and those first exposed to irradiated E . paraensei was also assessed by histological examination as well as by checking for shedding S . mansoni cercariae , which were tested for infectivity to mice . We compared the transcriptional profiles of BS-90 snails exposed only to irradiated E . paraensei to those exposed to irradiated E . paraensei and then challenged with S . mansoni . Our study seeks to demonstrate the involvement of a specific molecule in snail resistance to S . mansoni infection , and to provide a plausible natural mechanism by which trematode-mediated immunosuppression of the defense responses of a snail could facilitate infection by a parasite that it would normally successfully resist .
BS-90 and M-line strain Biomphalaria glabrata ( B . g . ) snails , and Schistosoma mansoni ( S . m . ) and Echinostoma paraensei ( E . p . ) were maintained as previously described [15] . Four independent 27 nucleotide oligos were designed to specific regions of FREP3 that displayed high conservation within the known diversified FREP3 transcripts . These oligos were combined and diluted in sterile snail saline at a final total concentration of 2 µg/µl , which was then injected into snails in a 5 µl volume . BS-90 snails were separated into two groups , the first to be injected individually with FREP3-specific siRNA oligos , and the second as a control , with GFP-specific oligos [16] , siRNA oligo design and injection techniques have been previously described [11] . Four hours later , all snails were exposed individually to 30 S . m . miracidia . Snails were collected for histology at 2 , 8 , 18 , 21 , and 28 dpe . Snails were examined for the presence of infection ( presence or absence of primary and secondary sporocysts ) as described above for signs of infection at 21 , 28 , 34 , 41 , 48 , and 54 dpe . Snails that shed cercariae were collected for histology and the rest were dissected to look for infections . Knockdown of FREP3 was confirmed by RT-PCR and western blot analysis both of which have been previously described [11] . Specific knockdown of FREP3 protein levels was confirmed by probing the same samples with a FREP4 specific antibody . For both Western blot analyses , 100 µg of cell free plasma was loaded into each well of an SDS acrylamide gel . FREP3 was detected using a FREP3 specific antibody , and the Western blot was developed using the Supersignal West Femto Chemiluminescent Substrate ( Pierce ) . FREP4 was detected using a FREP4-specific antibody and the Western Blot was developed using alkaline phosphatase . Injection of siRNA oligos and challenge of both FREP3 knockdown and GFP knockdown snails with S . mansoni resulted in similar mortality in both groups of snails . 36% of the FREP3 knockdown snails and 31% of the GFP knockdown snails died as a result of treatment . In addition to RT-PCR and Western blot confirmation of FREP3 knockdown using FREP3-specific siRNA oligos as previously described [11] , we confirmed the specificity of FREP3 knockdown using microarray analysis . BS-90 snails ( 4–8 mm ) were injected with either FREP3 or GFP-specific siRNA oligos , and 2 hours later exposed to 30 S . m . miracidia . At 2 and 4 dpe , ten snails from each group were collected , RNA was extracted and then used to generate template for the microarray as previously described [15] . Ten arrays were completed . Each array was probed with template from an individual FREP3 knock-down snail labeled with Cy5 and an individual GFP knock-down snail labeled with Cy3 . Hybridization , scanning , and analysis of the microarrays were previously described [7] , using a significance cutoff of +/−log 1 . 5 , and a false detection rate of 5% . Microarray results were submitted to GEO under the accession number GSE33525 . The microarray revealed that indeed FREP3 expression was reduced at the transcriptional level by 2 . 4 fold at 2 dpe and by 5 . 1 fold at 4 dpe . The only other significant results from that array revealed a slight reduction in FREP13 expression by 1 . 2 fold at 2 dpe and 2 fold at 4 dpe and a slight up-regulation of TGFR-1 at 1 . 8 fold at 2 dpe and 2 . 6 fold at 4 dpe ( Fig . S1 ) . 18 other transcripts including FREP2 and FREP6 displayed slight alterations in expression however these changes were not considered statistically significant; none of these other 16 transcripts were FREPs . To confirm viability of the cercariae produced from the FREP3 knock-down BS-90 snails that shed at 31 dpe we collected all cercariae produced ( ∼150 ) , and exposed one mouse , using standard procedures as previously described [17] . Seven weeks post-exposure , the mouse was injected with a heparin solution and perfused by cutting the hepatic portal vein and injecting a standard RPMI medium into the heart . S . mansoni adult worms were collected and the liver was homogenized to collect S . mansoni eggs . The presence of adult worms confirmed the cercariae isolated from BS-90 snails were viable and the miracidia hatched from the eggs were also viable , being able to infect M-line B . glabrata snails ( data not shown ) . Size ( 4–8 mm shell diameter ) matched snails were distributed into five groups: 1 ) . BS-90 exposed to 25–30 irradiated E . p . miracidia at day 0 and secondarily challenged 4 days later with 15 S . m . miracidia , 2 ) . BS-90 exposed to 25–30 irradiated E . p . only at day 0 , 3 ) . BS-90 unexposed control , 4 ) . BS-90 exposed to only 15 S . m . miracidia at day 4 , and 5 ) . M-line exposed to only 15 S . m . miracidia at day 4 to confirm S . m . infectivity . For groups 2 and 3 , RNA was collected at 1 , 2 , and 4 days post-exposure ( dpe ) to E . p . RNA was collected from groups 1 , 2 and 4 at 1 , 2 , 4 , and 8 dpe to S . m . and snails were collected for histology from groups 1 and 4 at 2 , 8 , 18 and 28 dpe to S . m . Snails from group 5 at 18 dpe to S . m . were also collected for histology . At days 18 , 28 , and 34 post-exposure to S . m . , all remaining snails were placed into large tissue culture wells with artificial spring water and examined for the presence of developing primary sporocysts in the head foot or mantle , or secondary sporocysts in the mantle or digestive gland/ovotestes . Snails that were shedding S . m . cercariae were collected for histology . All snails that did not shed cercariae , were individually placed in snail saline , dissected and examined with the aid of a dissecting microscope for any signs of infection ( sporocysts , germ balls , cercarial embryos ) which dissection of known infected snails indicates can be seen under the 40× magnification used . Irradiation of E . p . , and RNA extraction were previously described [15] . RNA was collected from whole snails at 2 and 4 dpe to 15 S . m . miracidia from groups 1 and 2 above , and was used to generate template for the microarray as previously described [15] . Each array was probed with RNA from an individual snail from the experimental group ( 1 from above ) , labeled with Cy5 and with RNA from a snail from control group 2 , labeled with Cy3 . There were twelve arrays in total , six from 2 dpe and six from 4 dpe , as a previous study revealed a great differentiation in transcription between these two time points [7] . Hybridization , scanning , and analysis of the microarrays were previously described [7] , using a significance cutoff of +/−log 1 . 5 , and a false detection rate of 5% . Microarray results were submitted to GEO under the accession number GSE28293 . Snails were collected and placed whole into tubes containing Railliet-Henry's fixative ( 930 ml H2O , 50 ml formalin , 20 ml acetic acid , and 6 g NaCl ) to both fix the tissue and dissolve the shell . Any remaining shell was removed before the tissue was transferred into 10% buffered formalin . All tissue processing , sectioning , mounting , and hematoxylin and eosin staining was performed by TriCore Reference Laboratories in Albuquerque , New Mexico . The images generated from these sections were taken using a Nikon D5000 SLR camera attached to a Zeiss Axioskop compound microscope with an MM-SLR adapter and T-mount by Martin Microscope Company .
Specific siRNA-mediated suppression of FREP3 expression in BS-90 snails was confirmed at both the transcriptional ( Fig . 1A ) and protein levels ( Fig . 1B ) using RT-PCR and Western blot respectively . To assess whether FREP3 participated in an anti-S . mansoni defense response a total of 70 S . mansoni-resistant BS-90 strain snails were injected with FREP3-specific siRNA oligos to assess the impact of FREP3 knockdown on the subsequent ability of S . mansoni to develop . Knockdown of FREP3 resulted in cercariae-producing S . mansoni infections in 15 ( 21 . 4% ) of these normally resistant snails ( Fig . 1C ) . In contrast , none of 64 control BS-90 snails receiving GFP specific siRNA oligos shed cercariae . As a check of the viability of the S . mansoni miracidia used in this experiment , over 85% of schistosome-susceptible M-line snails exposed to infection in both trials became infected , a level of infection typical for exposure of such snails ( Fig . 1C ) . Histological observations revealed that S . mansoni miracidia penetrated snails receiving either FREP3 or GFP siRNA oligos . The early stage mother sporocysts ( from 2 to 4 days post-infection ) we observed were not conspicuously encapsulated in either group of snails . In most of the FREP3 knockdown snails that shed cercariae , shedding was light and intermittent over a 1–2 week observation period , after which they were fixed for histology at 31 days post-exposure to S . mansoni . Histological examination of S . mansoni-challenged FREP3 knockdown BS-90 snails revealed a small number of large sporocysts in the head-foot of each of these snails ( Fig . 2 B , C ) . No disseminated daughter sporocysts were found in the digestive glands of these snails , however ( Fig , 2A ) . The head-foot sporocysts had clearly grown considerably in size beyond that of young mother sporocysts , and whether they represented mother , or ectopic daughter sporocysts could not be determined . They were not encapsulated by hemocytes , nor were hemocytes prominently found near them . Developing cercariae were not seen within them but the sporocysts were of a size that easily could have supported cercariae development . One of the infected FREP3 knock-down snails more persistently released cercariae over a 2 week observation period . Histological examination revealed this snail to have daughter sporocysts disseminated throughout the digestive gland . Hemocytes were conspicuous around them and encapsulation responses were noted ( Fig . 2D ) . Only one of eight control BS-90 snails injected with GFP-specific siRNA oligos and sectioned at 28 days post-exposure to S . mansoni was observed to contain S . mansoni sporocysts , but they had not grown and did not contain germ balls . BS-90 snails exposed to irradiated E . paraensei miracidia were challenged with S . mansoni miracidia 4 days later . After another 35 days , the snails were checked for shedding of viable S . mansoni cercariae , an indication that the infection was successful . Of 48 snails , 22 ( 46% ) shed S . mansoni cercariae , compared to 0% ( n = 35 ) of control BS-90 snails exposed to only S . mansoni ( Fig . 3 ) . To confirm the infectivity of the S . mansoni used , 22 M-line B . glabrata were challenged and 82% were successfully infected ( Fig . 3 ) . Histological comparison of S . mansoni cercariae-shedding BS-90 snails to normal resistant control BS-90 snails ( Fig . 4A ) showed they had disseminated S . mansoni sporocysts throughout the digestive gland ( Fig . 4B , C ) typical of normal infections . Snails exposed to irradiation-attenuated E . paraensei only did not develop disseminated E . paraensei infections , as expected . Degenerating E . paraensei sporocysts could be observed in the hearts of the sensitized snails , including those subsequently challenged with S . mansoni ( Fig . 4E ) . To confirm the viability of the E . paraensei cohort used , BS-90 snails were exposed to non-irradiated control miracidia from the same cohort that was irradiated and were successfully infected by 28 dpe , as expected ( not shown ) . BS-90 snails first exposed to irradiated E . paraensei miracidia were challenged four days later with S . mansoni miracidia . Microarray analysis was then undertaken on individual snails either 2 or 4 days post-exposure to S . mansoni . Schistosome-specific markers on the array were used to indicate whether each snail had been successfully infected with S . mansoni , or if it had resisted the challenge . At both 2 and 4 days post S . mansoni challenge , 50% of the snails assayed using the array were positive for S . mansoni infections ( 3 positive , and 3 negative for S . mansoni for each time point ) . Immunosuppression ( as indicated by the greater number of down-regulated than up-regulated features ) resulting from exposure to irradiated E . paraensei miracidia was noticeable for all 12 snails studied with the arrays ( Fig . 5A ) . However , snails negative for S . mansoni markers displayed increased expression of a variety of known and putative defense-related factors ( Fig . 5B ) . For some factors ( FREP3 , Dermatopontin , Heat shock protein 70 , Superoxide dismutase 1 Cu-ZnA , Serpin B4 , and Matrilin-1A ) increased expression in snails negative for S . mansoni was contrasted by a suppression of expression in snails positive for this parasite . Other factors ( FREP2 , Coagulation factor XI , Dual oxidase , Galectin 4 , Migration inhibition factor , Peroxiredoxin , and SOD Cu-Zn B ) increased in expression in snails not infected by S . mansoni , but remained unaltered as compared to control values in snails that were successfully infected . FREP4 expression differed from other putative resistance molecules in that it was increased compared to control levels in both snails positive or negative for S . mansoni ( Fig . 5B ) .
Schistosome parasites , including those that infect people , continue to thrive the world over , in no small measure owing their success to their productive use of snails as intermediate hosts . Particularly given that schistosome infection is harmful to the snail and results in its castration [18] , it is reasonable to expect that the snail would mount defense responses to prevent infection . Although schistosomes obviously frequently prevail and establish long-term , infections , it is likely that many schistosome-snail encounters in the field result in failed infections . Such failures go overlooked but may well have a significant impact on transmission . Furthermore , the efficacy of present-day chemotherapy-based control operations could potentially be enhanced if we could also exploit snail resistance responses to further limit the number of new snail infections that arise . After all , it is in snails where cercariae - the source of reinfections in people that so frustrate control efforts - are produced in such prodigious numbers . To fully understand the potential impact of snail defenses on schistosome transmission to people , we need to achieve a better understanding of the mechanistic basis of snail defenses to infection , and how these defenses are overcome by schistosomes . In the process , we will also learn a great deal about the general nature of invertebrate ( snail ) defense mechanisms and the intimate interplay between host and parasite . With respect to the immune responses of snails , our studies have lead us to focus on fibrinogen related proteins , or FREPs . One of the most noteworthy aspects of their biology is that two FREPs ( first shown for FREP3 , then FREP2 ) have been shown to undergo somatic diversification driven by gene conversion events and point mutations , creating a diversity of expressed sequences from a limited number of germ-line source sequences [10] , [11] , [19] . Recently , functional assessment of FREP3 demonstrated that it is capable of binding to carbohydrates and acts as an opsonin to enhance phagocytosis of targets by snail hemocytes . RNAi-mediated knockdown of FREP3 in snails resistant to the digenetic trematode Echinostoma paraensei resulted in an abrogation of resistance , resulting in one third of the snails developing established E . paraensei infections . Additionally , this study identified that FREP3 , while increased in expression in resistant snails challenged with S . mansoni or E . paraensei , was suppressed in snails that were successfully infected by either parasite [11] . FREP2 , another FREP that has the capacity for diversification , has been co-immuno-precipitated with S . mansoni polymorphic mucins , suggesting that this complex family of diversified parasite molecules may be the targets for FREPs [10] . Building on these earlier studies , here we demonstrate that FREP3 also plays a role in defense against S . mansoni infection . Knockdown of FREP3 resulted in 21% of the resistant BS-90 strain B . glabrata snails becoming successfully infected ( shedding cercariae ) with S . mansoni . In contrast , none of the 64 snails injected with GFP siRNAs shed cercariae . As previously hypothesized , FREP3 is likely working in combination with other defense mechanisms to manifest the resistant qualities of the BS-90 snails . However , this study clearly demonstrates that it is an important component of defense against S . mansoni . Examination of sectioned snails revealed that S . mansoni miracidia penetrated both control GFP and experimental FREP3 knockdown snails , but observations of sporocysts at 2 and 8-days post-infection did not yield obvious evidence in either group of snails of sporocysts under conspicuous attack by hemocytes including within multilayered hemocyte capsules . Rather , sporocysts were found with only loose aggregates of hemocytes in their vicinity . This is compatible with observations reported by Galvan et al . , 2000 [20] who noted that mother sporocysts of S . mansoni could remain viable in BS-90 snails for as long as 33 days . However , none of the S . mansoni-exposed BS-90 snails that they observed , nor any that we have observed over the years prior to this study , have ever shed cercariae . Our observations suggest that the inability of S . mansoni to thrive in BS-90 snails - at least in some cases - may be more dependent on inhibitory humoral factors than on overt hemocyte aggression and dismemberment . For example , humoral factors might serve to inhibit S . mansoni larval development or nutrition acquisition . In all but one of the BS-90 FREP3 knockdown snails from which cercariae were shed , cercariae production must have originated from a small number of sporocysts in the head-foot of the snail . This likely explains why cercariae were produced by them in small numbers and intermittently . The daughter sporocysts producing these cercariae were either within or adjacent to the mother sporocyst that produced them . As these snails were fixed for histology , it is not clear how long they might have persisted in shedding cercariae . We suggest FREP3 knockdown in these snails allowed sporocysts to persist and enlarge , but was insufficient to enable them to proliferate and establish disseminated infections in the digestive gland . Hemocytes were not prominent around the head-foot sporocysts suggesting they had acquired some ability to protect themselves from attack . In snails receiving GFP siRNAs , in only one snail examined could sporocysts be found . They were small and showed no evidence of germ ball development . Based on these results , one possibility is that FREP3 plays a role in suppressing development of S . mansoni sporocysts in BS-90 snails , and if its effects are temporarily reduced , sporocysts may be released from this inhibition sufficiently well to enable some sporocyst development and multiplication to occur . As the knock-down effects inevitably wane , then the sporocysts may be prevented from further development such that proliferative infections do not usually result . For the one FREP3 knockdown snail noted to have a disseminated infection , hemocytes accumulated in the digestive gland and in some cases were seen to be encapsulating daughter sporocysts . This is reminiscent of what was noted by Lie et al . [21] in some of the 10-R2 B . glabrata snails they observed in which resistance to S . mansoni had been broken down by pre-exposure of these snails to irradiated miracidia of E . paraensei . In about 30% of these snails , “self-cure” was eventually noted , characterized by hemocyte reactions to daughter sporocysts . Results of both experiments imply that snails inherently resistant to S . mansoni can reinvigorate an effective resistance response later in the course of infection , even though their ability to prevent establishment and development of infection had been earlier compromised by experimental manipulation . This suggests that the machinery for generating resistance is still intact . Furthermore , even though their collective biomass is large , daughter sporocysts may not be as effective as newly-penetrated ( and much smaller ) mother sporocysts in preventing effective responses . As noted in the previous paragraph , and initially documented in studies by Lie and co-workers ( 13 , 25 ) , both normal and irradiated sporocysts of E . paraensei have a potent ability to interfere with the resistance of B . glabrata to trematode infection . Their classic work has since stimulated a number of studies to reveal the underlying mechanisms of immunosuppression . Hemocytes collected from B . glabrata infected with E . paraensei exhibited reduced adhesive , spreading and phagocytic capacity compared to uninfected controls [22] , [23] . B . glabrata hemocytes exposed to live E . paraensei sporocysts in vitro actively move away from the parasite [12] , and eventually lose adherence to the substrate if exposed to parasite excretory/secretory ( ES ) products [24] . Furthermore , ( ES ) products of a related parasite , Echinostoma caproni , significantly impact the functional capacity and behavior of snail hemocytes , including a loss of adhesion , spreading and phagocytosis [25] . As these effects seem to be specific to suitable snail hosts , not extending to echinostome-resistant snail strains [25] or species [12] , [26] , the mechanism of their effects must be tailored to specific aspects of the defense system of compatible snails . To pursue the molecular basis of E . paraensei-induced immunosuppression , we have followed the transcriptional responses of exposed snails using microarrays . By as early as 12 hours post-exposure , E . paraensei provokes down-regulation of snail defense responses , including FREP3 expression [7] . Many of the targets of E . paraensei immunosuppression are putative or known resistance-associated factors such as FREPs 1 , 3 , 5 , 8 , 9 , and 10 [8] , migration inhibition factor [27] , dermatopontin [28] , alpha-2- macroglobulin receptor [29] , mannose receptor [30] , peroxiredoxin [31] , and galectins 4 and 7 [32] . Reduction in the presence of these factors theoretically would impact many aspects of defense function such as activation and phagocytosis by hemocytes ( FREPs , mannose receptor , alpha-2- macroglobulin receptor ) , intra- and extra-cellular killing ( peroxiredoxin ) , hemocyte adhesion and encapsulation ( dermatopontin , migration inhibition factor ) , and coagulation ( galectins ) . Based on these results , we sought to repeat the basic design of the experiment of Lie et al . [14] , to see if we could use pre-exposure of irradiated miracidia of E . paraensei to interfere with resistance of BS-90 snails to S . mansoni . Our experiment represents the first repeat of their classic experiment that has been accompanied by molecular ( microarray ) studies , and it is the first experiment to employ the naturally resistant BS-90 snails as hosts as opposed to other resistant B . glabrata snails of the 10-R2 or 13-16-R1 strains that were bred and selected for resistance [14] . We show that irradiated E . paraensei sporocysts suppress the BS-90 defense response sufficiently to allow 46% of the snails so treated to develop patent S . mansoni infections . When snails that permitted S . mansoni development were compared with those that did not using the B . glabrata microarray , we observed a number of immune-relevant transcripts that exhibited expression patterns indicating S . mansoni contributed to the suppression as well . FREP 2 and 3 , coagulation factor IX , dermatopontin , dual oxidase , galectin 4 , MIF , peroxiredoxin , superoxide dismutase Cu-Zn , and heat-shock protein 70 all exhibited increased expression in snails that successfully resisted infection compared to those that were infected by S . mansoni . Thus , we confirm previous hypotheses [7] suggesting that S . mansoni also utilizes a program targeted at suppressing the expression of important defense factors involved in killing larval parasites . This past work indicates that S . mansoni and E . paraensei differ in the timing and targets suppressed , E . paraensei beginning aggressive immunosuppression by 12 hours post challenge , S . mansoni beginning between 2 and 4 days post challenge [7] . Our results also suggest that irradiated echinostome larvae are more effective than our FREP3 knockdown protocol in protecting S mansoni sporocysts in resistant snails . This may be because irradiated echinostomes provide more persistent down-regulation of FREP3 , and also have effects on other immune factors as well . The irradiated echinostome experiment indicates that if S . mansoni sporocysts are sufficiently protected , they can reliably develop disseminated infections in resistant snails . This may be because irradiated echinostomes provide S . mansoni sporocysts a longer interval to acquire and express their own immunosuppressive effects . Our studies indicate that both echinostomes and schistosomes employ means of immunosuppression to colonize snails , and that this property can be manipulated to increase the breadth of strains of a single species , B . glabrata , that can be colonized . This work also bears on two important related general issues in parasitology , host specificity and host switching . Even though most digenetic trematodes are very host specific with respect to their choice of snail hosts , phylogenetic studies suggest that host-switching with respect to snails has been common in the history of trematodes like schistosomes [33] . The suppression we document offers one potential mechanism to resolve this apparent paradox: down-regulation of defense responses by one parasite may open the door for colonization of another parasite normally incompatible with that host . Field studies indicating the ability of one trematode to facilitate infection with another are consistent with this possibility [34] , [35] . Cercariae produced in this study , both from BS-90 B . glabrata infected by S . mansoni due to reduced FREP3 , or E . paraensei-mediated immunosuppression , were viable and able to infect mice . Thus , there is the potential for continuation of a trematode life cycle from a normally resistant snail host . It remains to be seen whether eggs produced from these mice have improved success at infecting BS-90 B . glabrata . We suggest that this study provides proof of principle that parasite-induced immunosuppression improves the chances that normally incompatible parasites can be successful in new , and hostile host environments . Furthermore , it provides a specific mechanism and molecules to target for future studies aimed at experimentally studying host specificity and host switching . Another potential application of this work relates to the role of FREP3 in resistance of wild B . glabrata to infection with S . mansoni . Although it is clear that other factors are involved in resistance , this line of work suggests efforts to up-regulate FREP3 expression in snails from natural populations could have the effect of diminishing S . mansoni infections . We now must focus our efforts on understanding whether snails from endemic areas mount FREP3 responses following exposure to natural schistosome infections . It also raises the question as to whether snails differ in their inherent FREP3 responsiveness , and if this trait can be manipulated or favored to diminish natural schistosome infections . | Schistosomiasis , a neglected tropical disease , owes its continued success to freshwater snails that support production of prolific numbers of human-infective cercariae . Encounters between schistosomes and snails do not always result in the snail becoming infected , in part because snails can mount immune responses that prevent schistosome development . Understanding the factors important for snail resistance to schistosome infection will facilitate new lines of investigation to 1 ) understand the underlying basis of compatibility between schistosomes and snails in endemic areas and how this affects transmission dynamics and control efforts; and 2 ) to reveal ways to manipulate natural snail populations to enhance their resistance to schistosome infections . Here , we present the first evidence that a snail immune molecule , fibrinogen related protein 3 ( FREP3 ) , is important for successful defense against schistosome infections in Biomphalaria snails . In addition , we demonstrate that FREP3 is a target suppressed by trematode parasites to facilitate their establishment within the snail . | [
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] | 2012 | A Somatically Diversified Defense Factor, FREP3, Is a Determinant of Snail Resistance to Schistosome Infection |
Deworming HIV-1 infected individuals may delay HIV-1 disease progression . It is important to determine the prevalence and correlates of HIV-1/helminth co-infection in helminth-endemic areas . HIV-1 infected individuals ( CD4>250 cells/ul ) were screened for helminth infection at ten sites in Kenya . Prevalence and correlates of helminth infection were determined . A subset of individuals with soil-transmitted helminth infection was re-evaluated 12 weeks following albendazole therapy . Of 1 , 541 HIV-1 seropositive individuals screened , 298 ( 19 . 3% ) had detectable helminth infections . Among individuals with helminth infection , hookworm species were the most prevalent ( 56 . 3% ) , followed by Ascaris lumbricoides ( 17 . 1% ) , Trichuris trichiura ( 8 . 7% ) , Schistosoma mansoni ( 7 . 1% ) , and Stongyloides stercoralis ( 1 . 3% ) . Infection with multiple species occurred in 9 . 4% of infections . After CD4 count was controlled for , rural residence ( RR 1 . 40 , 95% CI: 1 . 08–1 . 81 ) , having no education ( RR 1 . 57 , 95% CI: 1 . 07–2 . 30 ) , and higher CD4 count ( RR 1 . 36 , 95% CI: 1 . 07–1 . 73 ) remained independently associated with risk of helminth infection . Twelve weeks following treatment with albendazole , 32% of helminth-infected individuals had detectable helminths on examination . Residence , education , and CD4 count were not associated with persistent helminth infection . Among HIV-1 seropositive adults with CD4 counts above 250 cells/mm3 in Kenya , traditional risk factors for helminth infection , including rural residence and lack of education , were associated with co-infection , while lower CD4 counts were not . ClinicalTrials . gov NCT00130910
Worldwide , more than 2 billion people are infected with at least one helminth species . [1] The majority of these infections occur in resource-limited settings , where over half of the population may harbor infection . [2] Given the significant geographic overlap of HIV-1 and helminth infections , a large proportion of HIV-1 infected individuals are likely to be co-infected with at least one helminth species . [3] , [4] A pooled analysis of trials of deworming in HIV-1 infected individuals suggests significant benefit of deworming on both CD4 counts and plasma viral load . [5] Deworming has been estimated to reduce HIV-1 RNA by as much as 0 . 50 log10 copies/ml . [3] , [4] , [6] Modeling studies suggest that this magnitude of effect could delay HIV-1 disease progression by up to 25% and delay time to the development of AIDS by as much as 3 . 5 years . [7] , [8] Despite HIV-1 prevalence rates exceeding 10–20% in some countries , widespread helminth prevalence surveys among HIV-1 infected individuals have not been conducted . [9] Because deworming may be a useful intervention in HIV-1 treatment programs , it is important to determine burden of helminth infection and cofactors for helminth infection in HIV-1 infected adults . However , there are limited surveillance data on helminth prevalence in HIV-1 infected adults in diverse geographic settings . As part of a randomized trial of deworming , we determined prevalence and correlates of helminth infection among 1 , 541 HIV-1 infected adults attending ten geographically distinct HIV Care and Treatment Clinics in Kenya . We also determined factors associated with soil-transmitted helminth clearance and persistence/re-infection among 91 individuals who received albendazole as part of the clinical trial ( Figure 1 ) .
Antiretroviral-naïve HIV-1 seropositive adults not meeting WHO criteria for HAART initiation were screened by stool microscopy . Study participants were recruited from existing HIV Care and Treatment programs at ten sites geographically dispersed throughout Kenya using a mobile study team . Study sites were District Hospitals in Homa Bay , Kerugoya , Mbagathi , Thika , Kisumu , Kisii , Machakos and Kilifi , as well as the Kibera AMREF/CDC Clinic and the Coptic Hope Clinic ( Nairobi ) ( Figure 2 ) . The study was approved by the Kenya Medical Research Institute Ethical Review Board and the University of Washington Institutional Review Board . All participants provided written informed consent . The study was registered under Clinical Trials Registration identifier: NCT00130910 . Potentially eligible study participants were identified at each site by clinic staff and referred for helminth screening . Patients were eligible for helminth screening if they were HIV-1 seropositive , at least 18 years of age , non-pregnant and not eligible for initiation of antiretroviral therapy based on World Health Organization guidelines ( CD4 cell count <250 cells/µL , WHO Stage 4 and Stage 3 , other than for treated tuberculosis ) . Some individuals were enrolled based on a CD4 count obtained in the preceding three months and were subsequently found to have a CD4 count less than 250 cells/mm3 from the sample collected at enrollment . Exclusion criteria included having ever used antiretroviral drugs , having taken medicine for helminth infection in the preceding six months , having evidence of active tuberculosis ( TB ) , and having clinical signs of severe anemia . Potential participants were informed of the aims and procedures of the study and assessed for eligibility . Participants were provided with stool collection containers , instructions on specimen collection , and were requested to collect stool within six hours of the screening visit . All female participants were asked to provide a fresh urine sample for β-human chorionic gonadotropin testing to exclude pregnant women . Baseline demographic information and a medical history were obtained from participants at the time of screening . Individuals with evidence of co-infection with albendazole-treatable soil-transmitted helminths ( A . lumbricoides , T . trichiura or hookworm sp . ) were enrolled into a randomized , double-blind , placebo controlled trial , details of which have been previously published . [3] Individuals with schistosomiasis or infection with Taenia species were treated with open-label praziquantel ( and albendazole if co-infected with other helminths ) and were not enrolled . Individuals with no evidence of helminth infection were counseled on basic hygiene and avoidance of helminth exposure , but were not enrolled in the randomized trial . Individuals who participated in the clinical trial were randomized to albendazole , ( 400 mg a day for three days ) or placebo and followed for twelve weeks . CD4 counts and plasma HIV RNA assays were collected at baseline and at the twelve-week visit ( Figure 1 ) . Each participant provided a single stool sample separated into a plain collection vial ( AlphaTec , California , USA ) and a vial containing preservative ( ProtofixCLR , AlphaTec ) . Each stool sample was processed within 6 hours of collection and read within 30 minutes of processing . Stool samples were evaluated using wet preparation , Kato-Katz and formol-ether concentration techniques by an experienced laboratory technician . The presence of protozoa or helminth eggs was recorded and the burden of infection , based on number of eggs per gram of stool , was calculated according to WHO standards . [10] Helminth infection was defined as the presence of ova or grossly visible helminth on any single examination . All randomized participants underwent repeat HIV-1 serologic testing using Determine™ rapid test qualitative immunoassay ( Abbott , Japan ) and had CD4 counts and plasma viral loads determined at enrollment and follow-up . Randomized participants had CD4 counts measured at the time of enrollment using Multiset™ software on a FACSCalibur machine ( Becton Dickinson , USA ) . Plasma HIV RNA was quantified on samples from randomized participants using the Gen-Probe HIV-1 viral load assay , which has been shown to quantify the subtypes of HIV-1 prevalent in Kenya . [11] CD4 count data from within the previous 3 months were abstracted from clinic records for participants who were screened for helminth infection but not enrolled in the randomized trial . No plasma viral load data were available for participants who were not enrolled in the randomized trial . Analyses were performed using Stata version 9 . 2 ( College Station , Texas , USA ) . All HIV-1 seropositive adults who were screened for helminth infection for the randomized clinical trial were included in these analyses . Primary analyses identified correlates of helminth infection among HIV-1 infected adults , which included; age , gender , geographic area , education , employment , sanitation , water access , and CD4 count . Household development index , a composite measure combining education , sanitation , water source and occupation , was developed based on similar indexes in the literature . [12]–[14] The association between these factors and being infected with helminths at the time of screening was evaluated using relative risk regression . [15] , [16] Multicollinearity was assessed and a multivariate model of the risk of infection with any helminth was developed using relative risk regression to assess the independent effects of the various correlates . Chi-square tests were used to compare differences in the proportions of persistent or new infections at the 12 week follow-up visit after treatment with albendazole . Fisher's exact test was used if the expected value in any cell was less than 5 . Comparisons of median CD4 counts and median log10 plasma HIV RNA between species were done using a Kruskal Wallis test . Stratified relative risk regression was used to determine the association between the correlates identified above and the risk of infection for each species . Because of the large number of analyses conducted for each species , only statistically significant results ( p<0 . 05 ) are presented .
Between March 2006 and June 2007 , stool from 1 , 541 HIV-1 infected antiretroviral-naïve adults at ten geographically distinct sites in Kenya was screened for evidence of helminth infection . The mean age of the participants was 35 . 5 years ( range 18–80 ) and 74 . 5% percent were female . Most had less than a secondary school education ( 63% ) and about half ( 51 . 5% ) reported an income-generating occupation other than farming . Few individuals had access to piped water ( 12 . 3% ) , although most reported access to some form of latrine or toilet ( 93 . 6% ) . Individuals were recruited from urban clinics within Nairobi ( 44 . 7% ) , peri-urban clinics surrounding Nairobi ( 27 . 7% ) , rural clinics in western Kenya ( 13 . 8% ) or a rural coastal clinic ( 13 . 8% ) . Based on eligibility criteria , individuals in this cohort were relatively immunocompetent ( median CD4 count 410 cells/mm3 , IQR 297-586 ) . Median HIV-1 RNA was 5 . 0 log10 copies/mL , IQR: 4 . 3-5 . 4 ) in the subset of participants enrolled in the clinical trial . A total of 298 ( 19 . 3% ) individuals had evidence of infection with at least one species of helminth and 234 were enrolled into the randomized trial . Hookworm species was the most prevalent helminth identified , 56 . 4% ( n = 168 ) , followed by Ascaris lumbricoides , 17 . 1% ( n = 51 ) , Trichuris trichiura , 8 . 7% ( n = 26 ) , Schistosoma mansoni , 7 . 3% ( n = 21 ) , and Stongyloides stercoralis , 1 . 3% ( n = 4 ) . Mixed infection with at least two different helminth species was identified in 28 individuals ( 9 . 4% ) . Using WHO criteria , the majority of the infections were classified as being “light burden” with only 5 “moderate” and 2 “heavy burden” infections identified[10] . The geographic distribution of helminth infection in Kenya is displayed in Figure 2 . Individuals in rural areas were more likely to be infected with at least one species of helminth compared to individuals in urban areas ( RR 1 . 85 , 95% CI 1 . 51–2 . 26 ) . Demographic characteristics including age , gender , education and occupation were evaluated as correlates for risk of helminth infection by univariate analysis ( Table 1 ) . There was a trend towards a lower risk of helminth infection with increasing age by decade ( RR 0 . 88 , 95% CI: 0 . 78–1 . 00 ) . No statistically significant differences were found in the gender distribution between helminth infected and uninfected individuals . Lack of education was associated with an increased risk of helminth infection . Compared to individuals who completed secondary education , individuals with a primary school education were 29% more likely to be infected with any helminth ( RR 1 . 29 , 95% CI 0 . 99–1 . 68 ) and those with no education had nearly twice the risk of infection ( RR 1 . 90 , 95% CI 1 . 34–2 . 69 ) . There was an increased risk of helminth infection among individuals who reported farming as an occupation compared to those with a non-farming occupation ( RR 1 . 59 , 95% CI 1 . 22–2 . 06 ) . Lack of access to clean water and adequate sanitation were also predictors of helminth co-infection . Compared to individuals with access to piped water in their homes , individuals obtaining water from a lake , river or pond had almost 2 . 5-fold increased risk of helminth infection ( RR 2 . 43 , 95% CI 1 . 55–3 . 81 ) . When compared to individuals with access to a flush toilet , individuals with a pit latrine were twice as likely to be co-infected with helminths ( RR 1 . 99 , 95% CI 1 . 40–2 . 83 ) , and those with no latrine were >3-fold more likely to have a helminth infection ( RR 3 . 39 , 95% CI 2 . 26–5 . 08 ) ( Table 1 ) . Individuals with the poorest household development index ( HDI ) had more than 2 . 5 times the risk of having a soil-transmitted helminth infection ( RR 2 . 58 , 95% CI 1 . 17–5 . 70 ) when compared to individuals with the most resources ( Table 2 ) . However , HDI was not associated with risk of infection from S . mansoni . Baseline CD4 count was correlated with risk of helminth infection in this cohort . The study aimed to recruit pre-HAART individuals with CD4 counts greater than 250 cells . Participants were allowed to give CD4 count measurements from their prior HIV Care and Treatment Clinic visit if these fell within 3 months of the screening date . However , when these CD4 counts were confirmed , many people had lower CD4 counts than recorded by history . Eighty-nine participants had CD4 counts between 0–199 cells/µL3 and 256 participants had CD4 counts between 200–349 cells/µL3 . Compared to individuals with CD4 counts less than 349 cells/µL , those with higher CD4 counts ( ≥350 cells/µL ) were 40% more likely to be infected with any helminth ( RR 1 . 40 , 95% CI: 1 . 12–1 . 76 ) ( Table 1 ) . HIV-1 RNA levels were not available for individuals not enrolled in the randomized trial ( those without helminth infection ) , precluding an analysis of HIV-1 RNA as a correlate of helminth infection . In a multivariate model including CD4 count , rural residence , education , water source , sanitation and occupation , higher CD4 count ( RR 1 . 36 , 95% CI: 1 . 07–1 . 73 ) , rural residence ( RR 1 . 40 , 95% CI: 1 . 08–1 . 81 ) , and having no education ( RR 1 . 57 , 95% CI: 1 . 07–2 . 30 ) remained independently associated with risk of helminth infection . Species specific analyses were performed on all individual species with the exception of S . stercoralis ( due to the small sample size ( n = 4 ) ) . Correlates associated with species-specific infections are presented in Table 3 . Hookworm species accounted for 56% of all infections in this population , and correlates associated with hookworm infection were similar to the correlates associated with any helminth infection in the overall cohort , with the exception that hookworm infection was not associated with farming occupation ( p>0 . 05 ) . Farming occupation was associated with increased risk of infection with A . lumbricoides ( RR 2 . 02 , 95% CI: 1 . 05–3 . 88 ) , S . mansoni ( RR 8 . 62 , 95% CI: 2 . 78–26 . 8 ) , and multiple species ( RR 2 . 82 , 95% CI: 1 . 03–7 . 67 ) when compared to individuals employed in non-farming occupations ( Table 3 ) . In addition , individuals living in Western Kenya were more likely to be infected with A . lumbricoides ( RR 2 . 10 , 95% CI: 1 . 09–4 . 07 ) and S . mansoni ( RR 7 . 53 , 95% CI: 1 . 90–29 . 8 ) when compared to individuals living in urban Nairobi . There were no differences in the median CD4 counts of individuals infected with different helminth species ( p = 0 . 27 ) ( Figure 3 ) . Plasma log10 HIV RNA levels were similar between helminth species in the subset of individuals with available HIV-1 RNA levels ( p = 0 . 10 ) ( Figure 3 ) . At the twelve-week follow-up visit , 179 individuals ( 76 . 5% ) of the 234 individuals enrolled in the clinical trial provided stool for analysis ( 88 placebo arm vs . 91 treatment arm ) . Individuals in the treatment arm were significantly more likely to have evidence of cleared helminth infection at follow up than those in the placebo arm ( 68 . 1% vs . 51 . 1% , p = 0 . 02 ) . Of those with evidence of helminth infection at follow-up ( n = 72 ) , 61 . 1% ( 44/72 ) had hookworm infection , 16 . 7% ( 12/72 ) had A . lumbricoides , 11 . 1% ( 8/72 ) had mixed infection , 6 . 9% ( 5/72 ) had T . trichiura and 4 . 2% ( 3/72 ) had S . mansoni infection . Baseline median CD4 counts were similar between those with infection at follow-up and those without ( 489 vs . 474 , p = 0 . 99 ) , as were baseline median log10 HIV RNA levels ( 5 . 1 log10 vs 4 . 9 log10 HIV RNA , p = 0 . 29 ) . The distribution of initial and follow-up species-specific infections by treatment arm is displayed in Figure 4 . There were no differences noted in the age , gender , educational level , occupation , access to sanitation or clean water between those who cleared their helminth infection and those who were infected at follow-up .
This study describes the prevalence and correlates of helminth co-infection among antiretroviral naïve , HIV-1 infected adults in Kenya . Helminth infection was common in HIV-1 infected adults in this study , with distinct geographic and sociodemographic risk factors for infection with different helminth species . Rural residence and education were independently associated with risk of helminth infection in this cohort and are similar to risk factors described in HIV uninfected individuals from similar settings . In addition , higher CD4 counts were also independently associated with risk of helminth infetion . In a smaller cohort of albendazole-treated individuals , helminths were detected relatively frequently 12 weeks after treatment , although region , CD4 count , and educational level were not associated with persistence/re-infection . It has been estimated that more than a third of all individuals in sub-Saharan Africa are infected with at least one species of helminth , with considerable overlap in the prevalence of helminths and HIV-1 . [17] , [18] The prevalence and species distribution of helminth infection in our study were within the range of previously published reports of helminth infection in Africa , although lower than some previous estimates for Sub-Saharan Africa , where individual species prevalence has been estimated as 25% for Ascariasis , 29% for hookworm , and 24% for trichuriasis . [1] , [17] , [19] , [20] The lower rates of infection in this cohort may reflect lower prevalence in older age groups and/or the use of a single stool specimen for diagnosis , which is less sensitive than the use of three samples used in many surveys . [21] A 24 . 9% prevalence of helminth infection was reported in 297 HIV-1 infected individuals in an urban setting in Zambia , which was higher than noted in population-based helminth surveys from the same urban population . However , in studies directly comparing helminth prevalence between HIV-1 infected and uninfected individuals in Africa , no consistent correlation between HIV-1 infection and increased risk of infection with any helminth species has been reported . [22]–[25] Previous studies have been limited by relatively small sample sizes ( 60-582 individuals ) and have assessed helminth prevalence at a single site . Our study had a large sample size ( 1541 individuals ) derived from geographically diverse urban and rural sites , providing fairly comprehensive assessment of helminth infection in HIV-1 infected adults in urban and rural throughout Kenya . Our study suggests that helminth prevalence in HIV-1 infected individuals is similar to general population prevalence in endemic settings . In this cohort of HIV-1 infected adults in Kenya , rural residence , lack of education , poor sanitation , use of environmental sources of water and farming occupation were associated with helminth infection . However , in multivariate analyses , only education and rural residence remained predictive of risk of infection , suggesting that many of these cofactors share determinants . Poverty , lack of access to safe water and sanitation , agricultural exposure , household crowding , toileting practices , and education level are well described risk factors for infection with soil transmitted helminths . [26]–[29] Our observations differ from those reported in a previous study examining co-factors for helminth infection among HIV-1 infected adults in Lusaka , Zambia , which failed to find significant associations between water source , sanitation or occupation and helminth infection status . [30] The Zambian study was conducted in an urban setting , where sanitation , access to potable water and occupational exposure were likely different than the population reported in our study . In addition , the relatively small sample size included in the Zambian study ( 297 individuals ) may have limited the power to detect associations . Our study suggests that correlates of helminth infection are similar among individuals with and without HIV-1 co-infection . Helminth infected individuals in this cohort had significantly higher CD4 counts when compared to helminth uninfected individuals . It is possible that individuals with lower CD4 counts may be at lower risk of exposure , for instance by being less active and less likely to engage in agricultural work . There are conflicting data on the relationship between HIV-1 related immunosuppression and the acquisition of helminth infection . [31] Among HIV-1 infected pregnant women in Uganda , CD4 count was inversely associated with risk of hookworm infection ( OR for hookworm infection per 100 cells/uL increase in CD4 count of 1 . 16 ( 95% CI 1 . 02–1 . 31 ) ) . [32] However , declining CD4 counts have been associated with higher burdens and greater severity of Strongyloides infection . [33] In addition , significant reductions in the prevalence of A . lumbricoides , T . Trichiura , hookworm sp . , and Strongyloides have been reported following the introduction of highly active antiretroviral therapy ( HAART ) , suggesting that HAART may result in improved immunologic protection and control of helminth infection . [33]–[40] In this study , only individuals with helminth infection were eligible to be enrolled in the randomized trial , and therefore would have had more recent CD4 data than helminth uninfected participants who were not eligible for the clinical trial . The resulting measurement bias would be expected to result in lower CD4 counts in those with helminth infection , not higher counts as observed . It is also important to note that the study included antiretroviral naïve individuals with CD4 counts greater than 250 cells/mm3 and without WHO Stage 3 or 4 disease , and therefore may not be representative of individuals with severe HIV-1 related immunosuppression or those on antiretroviral therapy . Despite these limitations , our study suggests that moderate immune suppression does not increase susceptibility to helminth acquisition . The variable geographic distribution between different species of soil-transmitted helminths has important implications for treatment and control programs , particularly because the impact of helminth infection on HIV-1 may be species-specific . [41] We previously reported that albendazole treatment in A . lumbricoides co-infected individuals resulted in significant CD4 benefit but this effect was not seen for other helminths . [5] Previous studies also suggest that treatment of A . lumbricoides , S . mansoni , and lymphatic filiariasis may result in delaying HIV-1 disease progression , while treatment of T . trichiura and hookworm species may not . [3] , [6] , [42] These differences may be attributable to significant differences in the level of tissue invasiveness , antigen burden , duration of infection and host immune response between species . [42] , [43] We found that approximately one-sixth ( 17 . 1% ) of all helminth infections among HIV-1 infected adults in Kenya and 11 . 1% of detectable infections following albendazole treatment were due to A . lumbricoides . The prevalence of ascariasis likely reflects the age of the included population and is consistent with prior studies in the general population , which showed that A . lumbricoides prevalence peaks before age 10 and then decreases with age . [20] , [44] The most prevalent species we detected was hookworm ( 56 . 4% of infections ) . This is higher than in population-based prevalence surveys in Kenya where hookworm accounts for 36-46% of infections , but consistent with reported higher prevalence rates of hookworm with increasing age[20] , [45] , [46] Despite suboptimal detection techniques for schistosomal infections in this cohort , S . mansoni was prevalent among individuals screened in western and central Kenya , where it has previously been reported to be endemic . [47] Almost a third ( 32 . 1% ) of the individuals receiving three doses of albendazole in this trial returned with helminth infection after 12 weeks . It is not clear whether the infections detected at follow-up represent treatment failures or repeat infections . While albendazole is not completely effective for the treatment of helminth infections , individuals also returned to the same home environments following treatment , suggesting continuous environmental exposure to infection risk . Our findings suggest that repeated treatment , in combination with health education programs to minimize exposure risk , may be needed to maximize the efficacy of deworming in HIV-1 infected individuals . Strengths of this study include the screening of a large number of HIV-1 infected individuals from geographically distinct sites in Kenya for helminth infection , the use of a combination of screening techniques to increase the sensitivity of helminth diagnosis , and the systematic measurement of outcome measures with a randomized trial . However , this study also had limitations . While the combination screening methods used in this study are widely accepted , they have poor sensitivity for the detection of soil-transmitted helminth species . [48] The variability in sensitivity of stool diagnostic techniques is evident in the failure to detect helminth infection in half of individuals with previously detected helminths who received placebo . In addition , individuals were not screened with sensitive detection methods for schistosomiasis or strongyloidiasis . It is likely that we underestimated the true prevalence of helminth infection . This misclassification would have decreased the detection and the strength of reported associations . Approximately forty-five percent of study participants were recruited in Nairobi , which may have over-sampled individuals residing in urban populations . However , living conditions in areas of Nairobi ( such as Kibera , Kerugoya or other urban slums ) often include dirt floors , open sewage and unsafe water sources , all potential environmental sources of helminth infection . Finally , we excluded individuals with CD4<250 cells/mm3 , which limited ability to determine effects of severe immunosuppression . In summary , co-infection with HIV-1 and helminths is common among adults in Kenya and is associated with rural residence and lack of education . Moderate immunosuppression does not appear to increase the risk of helminth acquisition in HIV-1 infected adults . A combination of repeated deworming with anti-helminthics , as well as education on helminth-prevention , may be necessary to eradicate helminths in HIV-1 infected adults . Given the relatively high prevalence of helminth infection documented in this study and the available data suggesting possible benefit of deworming on markers of HIV-1 disease progression , measures to eradicate and prevent helminth infections may be a feasible public intervention that complements other interventions to delay immunosuppression in HIV-1 infected individuals . | Over one-third of people worldwide are currently infected with parasitic worms . The majority of these infections occur in sub-Saharan Africa , where over half of the population may be infected with at least one type of parasitic worm . HIV infection is also common in many of these countries , and there is significant geographic overlap in the presence of HIV and worm infection . Several studies have suggested that treatment of worm infections in people with HIV may delay the progression of HIV disease . Treatment has been shown to both decrease levels of the HIV virus in the blood of people with HIV and to increase the number of immune cells ( CD4 cells ) targeted by HIV . It is important to determine which populations of HIV-infected individuals are at greatest risk of worm infection in order to develop potential interventions for the treatment and prevention of worm infection in HIV-infected individuals . We report findings from a large study examining the prevalence and associated co-factors for worm infection among individuals at ten sites in Kenya . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
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"infection",
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"aids",
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"diseases",
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] | 2010 | Prevalence and Correlates of Helminth Co-infection in Kenyan HIV-1 Infected Adults |
Understanding the mechanisms that coordinate cell proliferation , cell cycle arrest , and cell differentiation is essential to address the problem of how “normal” versus pathological developmental processes take place . In the bristle lineage of the adult fly , we have tested the capacity of post-mitotic cells to re-enter the cell cycle in response to the overexpression of cyclin E . We show that only terminal cells in which the identity is independent of Notch pathway undergo extra divisions after CycE overexpression . Our analysis shows that the responsiveness of cells to forced proliferation depends on both Prospero , a fate determinant , and on the level of Notch pathway activity . Our results demonstrate that the terminal quiescent state and differentiation are regulated by two parallel mechanisms acting simultaneously on fate acquisition and cell cycle progression .
A high degree of coordination between cell proliferation , cell cycle arrest and cell differentiation is essential for proper development . Disruption of this coupling can lead to malformations and eventually cancer . Cell cycle progression relies primarily on the activity of cyclin-dependant kinases ( Cdk ) that are regulated by their association with factors like cyclins or cyclin kinase inhibitors ( CKI ) and by phoshorylation or dephosphorylation [1] . In worms and vertebrates , this mechanism is redundant and inactivation of certain cell cycle factors can be compensated by the activation of others . This has been observed for Cyclin-E ( CycE ) , which modulates Cdk2 activity and controls the transition from the G1 to S phase . In mouse and C . elegans , cell divisions are not completely blocked after genetic ablation of cycE [2] , [3] . However , cycE−/− mouse cells are resistant to oncogenic transformations suggesting that normal and oncogenic proliferation have different requirements for CycE [3] . In Drosophila , the core mechanism of the cell cycle is not redundant and down-regulation of CycE arrests the cell cycle . Thus , CycE appears to be the most important G1 cyclin in all Drosophila cell divisions studied so far . In addition , it has been shown that ectopic expression of CycE after terminal mitosis induces re-entry into the S-phase resulting in additional cell cycles [4]–[6] . This shows that terminal cells continue to respond to CycE , and suggests that , as in vertebrates , Drosophila CycE seems to be central to the generation of ectopic rounds of cell divisions after cell cycle deregulation . In the Drosophila bristle lineage , which produces the external mechanosensory organs called microchaetes , cell cycle progression and cell determination are intimately related [7] . Each adult microchaete is composed of four cells: two outer cells , the socket cell and the shaft cell , and two inner cells , the neuron and the sheath cell [8] . Each cell differs from the other by its size , localisation in the cluster and expression of specific markers ( Figure 1 ) . All four cells arise from a unique precursor cell , pI , after four asymmetric cell divisions occurring during early pupal development . At each division , one daughter cell ( N-off ) acts as a Notch ligand-producer and the other ( N-on ) as a Notch signal-receiver [9] , [10] . The bias in the activation of the N-pathway assured the acquisition of different fates by both daughter cells . During the first round of division , the pI cell divides at about 16 h after puparium formation ( APF ) and generates two secondary precursor cells , pIIa and pIIb . During the second round of mitosis , the pIIb cell divides prior to the pIIa cell giving rise to a glial cell and a tertiary precursor cell pIIIb . The division of pIIa generates the socket and the shaft cell . Finally , the pIIIb cell divides to produce the neuron and the sheath cell [11] . Later in development , between 21 to 24 h APF , the glial cell undergoes apoptosis [12] . Thus , only four cells of the bristle lineage form each sensory organ . Upon completion of the lineage , cells stop proliferating and terminally differentiate . This stereotyped lineage has become an excellent model to analyse the relationship between cell cycle and cell determination . In particular , to address questions as to how cells maintain their terminal quiescent state or whether all terminal cells are responsive to proliferative signals . To analyse these issues , we tested the capacity of post-mitotic cells to re-enter the cell cycle in response to the overexpression of CycE that mimicked the cell's response to a proliferative condition . Surprisingly , not all cells in the lineage are sensitive to this overexpression and we show that the responsiveness to ectopic proliferation depends on both Prospero ( Pros ) , a transcription factor , and the level of Notch ( N ) pathway activity .
In order to analyse the mechanisms that maintain arrest in terminal cells of the bristle lineage , we forced proliferation by specific overexpression of CycE using the UAS/Gal4 system [13] . The neuralized p72-GAL4 line ( neur ) was used as a driver to simultaneously overexpress CycE and Histone2B::YFP ( H2B::YFP ) , which highlights the DNA [14] . After CycE overexpression , 84% of the organs contained two sockets as revealed by scanning electron microscopy ( Figure 2A ) . At the cellular level , each sensory organ cell was identified by the expression of H2B::YFP ( see Materials and Methods ) , socket cells by the accumulation of high levels of Suppressor of Hairless ( Su ( H ) ) , sheath cells by the presence of Prospero , and neurons by the presence of ELAV or 22C10/Futsch ( see Figure 1 ) [9] , [11] , [12] , [15] . At 28 h APF , we observed three types of clusters . 13 , 5% of the clusters were wild-type and formed by four cells ( Figure 2B , upper row , n = 22 , and Figure 2C ) , 70% of the clusters contained one additional socket cell ( Figure 2B , middle row , n = 115 , and Figure 2C ) , and 16 , 5% of the clusters exhibited two additional cells namely , an extra-socket cell and an extra-neuron ( Figure 2B , bottom row , n = 28 , and Figure 2C ) . We never observed clusters with more than one shaft and one sheath cell . When overexpression of CycE was carried out at 30°C , where the GAL4/UAS system is more efficient , cluster composition was similar to that of pupae maintained at 25°C ( Figure 2C , 92% of the clusters showed duplicated socket cells , n = 71 , and 58% showed multiple neurons , n = 131 ) . Similar results were obtained when cluster composition was analysed up to 48 h APF showing that after a time-lapse longer than six cell-cycles neither extradivisions nor apoptosis occurred ( Figure S1 and data not shown ) . These data indicate that the duplication of neurons and socket cells are reproducible events induced after CycE overexpression and suggest that bristle lineage cells have differential sensitivities to proliferating signals . To identify the origin of these additional differentiated cells , we carried out in vivo imaging to follow the formation of the bristle lineage in neur>H2B::YFP , PON::GFP ( see supplementary data in [16] and Video S1 ) and in neur>CycE , H2B::YFP , PON::GFP pupae ( Video S2 ) . We used the PON::GFP fusion protein to identify the normal set of sensory cells during the time-lapse recording . Precisely , PON::GFP was inherited by the pIIb cell , the glial cell , the shaft cell and by the neuron [17] . Overexpression of CycE did not affect the sequence or the asymmetry of the first four divisions . Thereafter , up to two supplementary divisions were observed . One involved a pIIa daughter cell identified as the future shaft cell by its position in the cluster and its inheritance of Pon::GFP during pIIa mitosis ( observed in 80% of clusters analysed , n = 124 ) . The second involved a pIIIb daughter cell identified as the future neuron by its position and its inheritance of Pon::GFP during pIIIb mitosis ( observed in 25% of clusters analysed , n = 124 ) ( see Video S2 and Figure S2 ) . This supplementary division was always observed in clusters where the pIIa daughter cell had also undergone an extra-division . Consistently , overexpression of CycE at 30°C led to 94 , 5% of the shaft cells and 45% of the neurons undergoing an extra-mitosis ( n = 102 ) . To confirm the identity of the ectopically dividing cells , triple stainings were performed labelling metaphasic cells ( phospho-ser10 histone H3 ( PH3 ) immunoreactivity ) together with bristle lineage cells , neurons or socket cells . Figure 3A shows a mitotic cell in a five-cell cluster from a pupae at around 22 h APF . This mitosis is the first additional division that we observed with time-lapse imaging . The mitotic cell was identified as the future shaft cell by its nuclear size , position and its lack of Su ( H ) accumulation . The second additional mitosis was observed in clusters from pupae at around 24 h APF . In this case , the mitotic cell corresponded to the neuron since it expressed ELAV ( Figure 3B ) and 22C10/futsch ( Figure 3C ) markers . The fact that in five-cell clusters an extra socket cell was observed suggests that the extra division of the future shaft cell gives rise to a shaft and a socket cell . Similarly , in later clusters , two neurons were observed suggesting that the extra-division of the future neuron gives rise to two neurons . These observations were confirmed in in vivo time-lapse followed by immunodetection experiments ( not shown ) . Taken together these data indicate that the shaft cell and neuron are the only two cells in the terminal bristle lineage to undergo an ectopic division when CycE is overexpressed ( Figure 3D ) . To further analyse cell behaviour in response to CycE overexpression , we sought to determine the critical period during which cells were competent to undergo additional mitosis . To do so , CycE was expressed under the control of a heat-shock promoter at different moments in the cell lineage . Antibody staining was carried out twelve hours after heat shock to distinguish socket cells and neurons . Figure 3E shows the percentage of clusters with duplicated socket cells or neurons following heat shock treatment at different times APF . Two socket cells were generated only when CycE was overexpressed at the time of pIIa mitosis ( between 18 h and 19 h45 APF ) . In contrast , the period in which CycE overexpression was able to trigger a division of the neuron was longer ( from 19 h45 to 26 h APF ) . Since axogenesis starts at about 23 h30 APF [12] , ectopic neuronal divisions can occur at a time when neurons appear to be fully differentiated . This is shown in Figure 3C in which a dividing cell was positive for 22C10/Futsch , a late neuronal marker that reveals differentiated neurons [15] . Interestingly , in some cases we observed that the future neuron underwent two consecutive rounds of supplementary divisions ( not shown ) . These data indicate that neurons retain their competence to divide late into differentiation . The data presented above indicate that the first extra mitosis is asymmetric and generates two different cells ( a shaft and a socket cell ) while the second one is symmetric giving rise to two identical cells ( two neurons ) . In the normal bristle cell lineage all mitosis are asymmetric . The cell-fate determinants , Numb and Neuralized ( Neu ) , co-segregate to one daughter cell , where both act to bias the Notch-mediated fate decision [18] , [19] . We analysed the distribution of these factors in cells undergoing extra divisions following CycE overexpression . During the mitosis of the future shaft cell , Numb and Neu were detected and formed a crescent at the anterior pole of the mitotic cell ( n = 14 , Figure 3A , arrowhead and data not shown ) . The differential segregation of these factors was in agreement with the unequal repartition of the Pon::GFP fusion protein observed during in vivo recordings ( see Video S2 and Figure S2 ) . In contrast and in accordance with the symmetric characteristic of this division , Numb was never detected during the extra division of the future neuron ( n = 9 , Figure 3B ) . This is in agreement with in vivo recording data showing that Pon::GFP seems to be distributed uniformly between both daughter cells ( Video S2 ) . These data indicate that when cell divisions are forced in the pIIa sub-lineage , the future shaft cell behaves as its mother cell , pIIa , dividing asymmetrically and giving rise to another shaft and socket cell . The extra division of the future shaft cell was not correlated to different levels of CycE overexpression ( Figure S1 ) . Moreover , both cells responded similarly to CycE overexpression , exhibiting precocious and synchronous entries into the S phase [20] . This suggests that certain factors favours the progression of the mitotic cell cycle in the shaft cell or alternatively , prevent division in the socket cell . As the N pathway is off in the shaft cell , its capacity to respond to CycE overexpression could be related to the absence of N pathway activation . To analyse this possibility , we controlled the activation of the N pathway concomitantly in both pIIa daughter cells with CycE overexpression ( neur>CycE ) . N pathway was inhibited by expressing Numb under the control of a thermo-inducible promoter in both pIIa daughter cells ( 30 min pulse at 38°C at 20 h APF , [21] ) . Pdm1 was used as a marker of all pIIa progeny [16] and socket cells were identified by their accumulation of Su ( H ) ( Figure 4A–4D ) . The percentage of clusters containing 2 , 3 or 4 pIIa daughter cells obtained under different experimental conditions is shown in Figure 4E . In each case , the proportion of clusters harbouring 1 or 2 socket cells is indicated . Importantly , under these conditions Numb overexpression induced a very low rate of socket to shaft cell transformation ( Figure 4E , hs-numb column 2 ) . When CycE was overexpressed together with mild induction of Numb , we observed that 28% of the clusters harboured four pIIa daughter cells with two socket cells and two shaft cells ( Figure 4C , E hs-numb , neur>CycE column 4 ) . Under these same conditions , in vivo analysis showed that both pIIa daughter cells divided ( not shown ) . Such clusters were never observed when CycE was overexpressed alone ( Figure 4E , neur>CycE column 4 ) . Thus , when N activity was reduced and CycE overexpressed , the future socket cell divided asymmetrically giving rise to a shaft and a socket cell . This suggests that , similarly to the shaft cell , the socket cell retains pIIa features shortly after birth . Taken together , these data show that full activation of the N pathway is necessary to prevent the future socket cell from entering mitosis upon CycE overexpression . To further analyse the action of N on the proliferative capacity of terminal cells , we performed reciprocal experiments and activated the N pathway in the shaft cells . This pathway was activated after overexpression of the intracellular domain of the N receptor ( Nintra ) [22] . The expression of Nintra was induced at 20 h APF by a 30 min pulse at 34°C . Under these conditions , only 45% of clusters were transformed ( clusters harbouring two socket cells , Figure 4E , hs-Nintra , column 2 ) . We took advantage of this mild penetrance to analyse the effect of CycE overexpression together with weak Notch dependant transformation . We observed that the number of clusters containing three pIIa daughter cells ( two of which were socket cells ) was reduced from 91% under CycE overexpression alone to 38% under conditions of N and CycE overexpression ( Figure 4E compare neur>CycE column 3 and hs-Nintra , neur>CycE column 3 ) . This reduction was associated with an increase in the number of clusters having a normal set of pIIa daughter cells ( a socket and a shaft cells , Figure 4D and 4E , hs-Nintra , neur>CycE column 2 ) . This indicates that the extra divisions induced by CycE overexpression were blocked by mild activation of the N pathway . These data show that in the pIIa sub-lineage , activation of the N pathway is involved in maintaining the future socket cell in a quiescent state . As a consequence , CycE overexpression induced cell division exclusively in the future shaft cell . A similar anti-mitotic action of the N pathway has been reported in Drosophila follicle cells . In these cells , lack of N activity has been shown to induce extra mitoses at the expense of endocycles [23] , [24] . Similar to the situation in the pIIa sub-lineage , only the N-on cell of the pIIIb sub-lineage , namely the sheath cell , did not undergo extra mitosis upon CycE overexpression . To test whether activation of the N pathway was necessary to prevent ectopic division of the sheath cell , we reduced the activation of this pathway by overexpressing Numb ( 30 min heat shock at 38°C , at 21 h30 APF ) together with CycE ( neur>CycE ) . Under these conditions , the overexpression of Numb was not sufficient to modify cell identity , since both an ELAV and a Pros positive cell ( neuron and sheath cell respectively ) were present in all clusters . Surprisingly , even though the conditions of Numb overexpression were similar to previous experiments , we observed no change in the number of clusters with duplicated sheath cells when CycE was overexpressed ( 0% vs 4% in neur>CycE ( n = 126 ) and in hs-numb , neur>CycE ( n = 118 ) respectively ) . Similarly , no change was observed in the number of clusters with duplicated neurons ( 19% vs 22% in neur>CycE ( n = 126 ) and in hs-numb , neur>CycE ( n = 118 ) respectively ) . The fact that the response of pIIIb daughter cells to CycE overexpression was invariant after Numb overexpression suggests that factors other than N can prevent mitosis in these cells . One candidate that may impede extra mitoses is the cell determinant Pros , as loss of function of pros results in ectopic mitotic activity in the Drosophila central nervous system ( CNS ) [25] , [26] . In the bristle lineage , Pros is detected in the pIIIb cell during its division , is inherited by the neuron where it disappears rapidly and , at the same time , is expressed in the sheath cell ( Figure 1 ) [11] . To analyse the putative anti-mitotic role of Pros in the bristle lineage , CycE was overexpressed in a pros17/+ heterozygous background . Results depicted in Figure 5A show that 26% of the clusters contained duplicated sheath cells in pros17/+ , neur>CycE ( n = 129 ) compared to 1% in neur>CycE alone ( n = 71 ) . Interestingly , we also observed an increase in the percentage of clusters containing multiple neurons ( 86% n = 129 ) . These results suggest that with CycE overexpression , Pros affects the proliferative properties of the sheath cell and the neuron even if in this later Pros is transiently expressed . Similar results were obtained by overexpressing CycE using a hs-CycE construct at the time of pIIIb division ( 22 h APF ) in a pros17/+ background ( data not shown ) . To determine the origin of the duplicated cells , we combined time lapse imaging to follow the pattern of cell division and immunostaining to identify the fate of pIIIb daughter cells . Within the 25 lineages followed , three types of extra divisions were observed: ( i ) in 64% of the cases , only one pIIIb daughter cell underwent an ectopic mitosis , giving rise to two neurons; this is similar to what was observed upon overexpression of CycE alone ( Figure 3 ) ; ( ii ) in 8% of the cases , three neurons were observed , the third one resulting from an extra cell division of a duplicated neuron ( not shown ) ; ( iii ) in 24% of the cases , both pIIIb daughter cells divided , giving rise to two neurons and two sheath cells respectively . An example of an in vivo recording of such a lineage is shown Figure 5B and Video S3 . These data show that both pIIIb daughter cells underwent a symmetric division , giving rise to two neurons and two sheath cells respectively . The symmetric nature of these divisions was confirmed by the lack of Numb staining during these additional mitoses ( Figure 5C ) and Pros was equally distributed between both daughter cells ( Figure 5D ) . These data indicate that in a pros17/+ background , both pIIIb daughter cells are able to undergo an ectopic and symmetric mitosis in response to CycE overexpression . To determine whether loss of function of pros alone could also induce extra mitoses , we analysed the bristle lineage in pros null clones . Although CycE staining of pIIIb daughter cells inside pros clones was more intense , we failed to detect supplementary cell divisions ( Figure S3 ) . The absence of extra mitoses inside the pros null clones could be explained by the absence of Dacapo downregulation , Dacapo ( Dap ) being a CycE inhibitor . Taken together , these data indicate that Pros has a dual function in the bristle lineage . In addition to its involvement in neuron and sheath fate determination [27] , Pros acts as a cell cycle regulator in these two cells since it prevents extra cell divisions under conditions conducive to proliferation . The data presented above indicate that activation of the N pathway prevents mitosis of the socket cell upon CycE overexpression . A similar role is played by Pros in the sheath cell . Since the N pathway is active in the sheath cell , N and Pros could act redundantly to prevent extra divisions in this cell . To analyse this possibility , we overexpressed CycE ( neur>CycE ) while downregulating N activity ( hs-numb ) in a pros17/+ background . Induction of Numb expression ( 30 min heat shock at 38°C ) was performed under visual control on living pupae followed by immunostaining to identify sheath cells and neurons . Only clusters heat shocked within one hour following the pIIIb division were analysed . We observed a significant increase in the proportion of sheath cells that underwent an extra division ( Figure 6A , black box ) : 62% in hs-numb , pros17/+ , neur>CycE ( n = 26 ) versus 19% in pros17/+ , neur>CycE ( n = 72 ) and 4% in hs-numb , neur>CycE ( n = 104 ) . These data indicate that decreasing N pathway activity in a pros17/+ mutant background favours the division of the future sheath cell upon CycE overexpression . The joint action of Notch and Pros on the maintenance of the quiescent state of pIIIb daughter cells has been revealed in a sensitized background of CycE overexpression . In order to analyse whether both N and Pros act similarly under normal conditions , we studied the composition of sensory clusters inside null pros17 clones in conditions of down regulation of the N activity . This downregulation was obtained using pupae heterozygous for the thermosensitive allele of N , Nts-1 , maintained at restrictive temperature of 30°C [28] . In control conditions ( at a permissive temperature of 18°C ) and using Cut immunoreactivity to identify lineage cells , we always observed that all sensory clusters inside pros17 clones were composed by four cells , two pIIa and two pIIIb daughter cells , the former harbouring a nucleus bigger than the latter ( Figure 6B , [12] ) . Similar results were observed under restrictive temperature ( 30°C ) in heterozygous Nts-1 pupae alone or in pros17 heterozygous tissue ( data not shown and arrows in Figure 6C–6D ) . However , in 8% of clusters inside null pros17 clones , when N-function was reduced ( at 30°C ) , we observed a supplementary pIIIb daughter cell identified by its small nucleus ( arrowheads in Figure 6C–6D ) . In half of these cases , the supplementary cell was ELAV–positive indicating a neuronal identity . In the other half , the ELAV-negative cell probably corresponded to a sheath cell . These results suggest that ectopic cell divisions occurred when both Pros and Notch activities were reduced without forcing cell divisions by CycE overexpression . In the future sheath cell , the action of Pros appears predominant since reduction of the activity of the N pathway alone did not induce extra divisions after CycE overexpression .
In this study we have shown that in the bristle lineage terminal cells , the shaft cell and the neuron , but not the socket and sheath cells , undergo supplementary cell divisions after CycE overexpression . These supplementary cell divisions were not due to a cell-specific differential expression by neur>Gal4 of cycE . After CycE overexpression , CycE was detected in all cells of the cluster during the entire period analysed in neur>cycE pupae . This is in contrast with the absence of CycE detection in terminal cells under normal conditions [20] . However , between clusters , the level of CycE detected was variable in each cell type suggesting that the CycE accumulation was very dynamic ( Figure S1 ) . Nevertheless , we never observed a correlation between these variations and the cell-specific extradivisions reported . This absence of correlation was also found under the different genetic backgrounds used ( Figure S1 ) . In addition , we observed a similar cell-specificity using a heat-shock promoter construction to drive CycE . Taken together , these data show that the cell-specificity of the extra cell divisions observed was not due to an un-even CycE level driven by neur>Gal4 . Furthermore , in previous studies , we have shown that all terminal cells are arrested in G1-phase [20] . This indicates that resistance to cycE overexpression was not due to a differential cell-cycle arrest in terminal cells of the sensory cluster . Finally , an ectopic S-phase was induced after CycE overexpresion even in cells that did not undergo extra divisions ( [20] , data not shown ) . This shows that the level of CycE driven by neur-Gal4 was sufficient to force cell-cycle progression in all cells . This suggests that inhibitory role of CKI , like Dacapo , was overridden by the accumulated levels of CycE . As such , these extra cell divisions are due to a bone fide behavioural difference of the shaft cell and the neuron . Indeed , we show that this difference is due to the action of the Notch pathway and/or Prospero in maintaining cell cycle arrest in the socket and the sheath cells . After CycE overexpresssion , ( 1 ) supplementary cell divisions were observed only in those terminal cells in which the Notch pathway is not endogenously activated , ( 2 ) supplementary cell divisions were observed in sheath cells and neurons in a pros17/+ background; ( 3 ) activation of the N pathway blocked the ectopic division of the shaft cell; ( 4 ) socket cells underwent an extra division after reduction in N pathway activity and ( 5 ) additional sheath cells underwent extra divisions when both N and Pros activity was reduced . In this study , the proliferative capacity of terminal cells was analysed when cell divisions were forced by CycE overexpression under different Notch and Prospero backgrounds . The analysis was performed under conditions in which either no cell transformation per se was observed ( as in pros null background or pros heterozygous pupae ) or in which only a small proportion of cells were transformed ( as after mild overexpression of Numb or Nintra ) . As such , the observed effects were considered to be the result of N and Pros acting directly on the maintenance of the state of cell cycle arrest rather than a modulation of cell fate acquisition . Furthermore , we never observed changes in bristle cell identities after CycE overexpression . This is in contrast to Drosophila neuroblasts , in which CycE seems to control cell identity independently of its role in the cell cycle [29] . Thus , in the bristle lineage system , CycE seems to act exclusively as a cell cycle regulator . When N-activity was impaired and cell divisions were forced , both pIIa daughter cells divided like their mother , pIIa , each producing a shaft and a socket cell . These observations reveal that , just after birth , pIIa daughter cells are not yet committed to their final fate and retain pIIa characteristics . These results suggest that cell fate in pIIa daughter cells is acquired in a sequential manner . Initially , both pIIa daughter cells appear to be equivalent . Then , the N-pathway is rapidly activated in one daughter cell due to a N-signal originating from its sister cell [30] . The activation of the N-pathway arrests the cell cycle and this cell then acquires a socket terminal fate . Later on , the future shaft cell is committed , by a cell cycle independent and as yet unknown mechanism , to its terminal cell fate and becomes post-mitotic . Taken together , our results suggest that initially , precursor cell division is self-renewed and that an active process is required to commit both daughter cells to their final identities . Our results show that 15 minutes after birth , pIIa daughter cells are committed to their normal fate and do not respond to CycE overexpression . This suggests that extra divisions observed in these cells result from a delay in the cell cycle exit rather than a re-entry into the cell cycle . In contrast , cycE expression induced extra mitosis even in fully differentiated neurons that were identified by 22C10/Futsch immunodetection [15] . Interestingly , these extra mitoses were always symmetric and gave rise exclusively to neurons . Similarly , extra divisions of sheath cells , under conditions that impaired the activity of Pros and N , were mainly symmetric producing two sheath cells . This suggests that sheath cells , like neurons , can also re-enter the cell cycle long after being committed to their final fate . Similar mitotic capacity has been also observed in other differentiated cells , in particular in neurons , suggesting that terminal differentiation and cell cycle exit are distinct events [31]–[35] . Why pIIIb daughter cells only divide symmetrically and how this characteristic is related to the capability of these cells to retain proliferative capacities are open questions . The fact that CycE induced extra divisions in terminal cells suggests that these cells are arrested in G1 . Two main mechanisms trigger G1-arrest: repression of Cyclin/Cdk2 activity by CKI and repression of E2F activity by RB proteins ( reviewed in [36] ) . Both of these mechanisms are involved in the maintenance of a quiescent state . Our data show that in both neurons and future shaft cells of the sensory organs , a high level of CycE alone is sufficient to induce extra mitoses . This is in contrast to differentiated neurons of the eye and the anterior wing margin where extra mitoses after sustained cycE expression were induced only in a rbf1 mutant background [6] . We suggest that , in our conditions CycE overexpression override the action of downregulateurs like Rbf1 . Alternatively , factors other than Rbf1 can regulate cell cycle exit and the quiescent state maintenance in neurons and sheath cells . Interestingly , sensory organs containing two neurons were observed in null dap background ( AA unpublished results ) . Thus , Dap is involved in neuronal cell arrest . Since Dap is also expressed in shaft cells [20] , this factor probably plays a similar role in these cells . Our data show that Prospero , together with N , cooperate in maintaining a quiescent state in sheath cells . Without overexpression of CycE , ectopic cell divisions occurred resulting in supplementary pIIIb daughter cells when Pros and N activity was reduced . Since the future neuron is a N-off cell , we expected that this cell will not be affected by the reduction in the N-function . As such , we anticipate that the future sheath cell undergoes a supplementary division . Thus , clusters containing two sheath cells and those containing two neurons would result from symmetric and asymmetric cell divisions respectively . Since these data were obtained in a non-sensitized condition , these results suggest that Pros and Notch are actively involved in maintaining a quiescent state in terminal cells during the normal development of bristles . Between Pros and Notch , the former appears to predominate over the later to restrict cell proliferation . Indeed , in pIIIb daughter cells , CycE overexpression induced few extra divisions when N activity was reduced and significantly increased extra divisions in pros heterozygous cells . A role for Pros in maintaining cell cycle arrest is in agreement with the observation that neuronal proliferation was increased in pros loss of function embryos . It has been shown that this proliferation was associated with both an upregulation of cycE , stg and e2f genes [25] and a delay in the appearance of dap transcripts [26] . In our system , cell cycles were not resumed in pros null clusters per se . In addition , we observed only a mild increase in CycE expression and no decrease in Dap expression inside pros null clones . It is likely that in the absence of Dap downregulation , the increase in CycE levels is insufficient to induce extra mitosis . N-signalling is pleiotropic and either promotes or represses cell cycle progression depending on the cellular context [37] . Thus , in Drosophila eye development , N-activity is necessary and sufficient to trigger cell cycle progression in G1 arrest in cells of the morphogenetic furrow , by derepressing the inhibition of E2F1 by RBF1 [38] , [39] . Furthermore , glial precursor cells are maintained in an undifferentiated proliferative state by both N pathway activation and Dap downregulation [40] . In contrast , in follicle and wing cells , the N pathway has an anti-mitotic action [24] , [41] . In follicle cells , N activity stops mitotic cycles and promotes endocycles by repressing the expression of both String/cdc25 and Dap and upregulating Fizzy-related/Cdh1 expression [23] , [24] . In wing disc cells , N promotes G1-arrest by reducing E2F activity [41] , [42] . The mechanism involved in this G1-cell cycle arrest seems to involve downregulation of the dmyc proto-oncogene and the bantam micro-RNA , both of which act positively on E2F activity [42] . If a similar mechanism is involved in the N-mediated G1-arrest observed in socket and sheath cells , we anticipate that dmyc and bantam would be downregulated exclusively in N-on bristle cells , in particular in pIIa , sheath and socket cells . However , the analysis of dmyc and bantam expression in sensory organ cells was not consistent with this idea . Unexpectedly , bantam and dMyc were detected in N-on cell such as pIIa , socket or sheath cells ( AA , not shown ) . Thus , the mechanisms by which N and Prospero maintain cell cycle arrest in terminal cells of the bristle organ remain to be elucidated . In conclusion , our results demonstrate that fate determination factors such as Notch and Prospero participate in maintaining a quiescent state in terminal cells . Under normal conditions , several mechanisms act in concert to ensure cell cycle arrest . Dap appears as a plausible candidate to ensure this role in N-off cells like the neuron and the shaft cell . N cooperates to maintain a quiescent state in sheath and socket cells and , finally , Pros acts only on the sheath cell . Consequently , the terminal quiescent state and cell differentiation do not seem to be regulated by mutually exclusive mechanisms . We favour the notion that these phenomena are regulated by parallel mechanisms involving factors having dual actions on fate acquisition and cell cycle progression .
The neur-GAL4 driver was used to specifically express H2B::YFP [14]; Partner Of Numb::GFP ( PON::GFP , [17] ) , and CycE were expressed using the UAS/Gal4 system [13] . CycE overexpression was carried out using a line harbouring two copies of the UAS-CycE construct , one on the second and one on the third chromosome [5] . To increase viability of the neur>CycE and neur>CycE , pros17/+ pupae , overexpression was performed using a strain bearing GAL80ts ( gift from D . Coen ) . Fly crosses , embryonic and larval development were carried at 18°C , and white pupae were transferred to 30°C to allow the expression of GAL4 . Overexpression induced by heat-shocks were performed using the hs-CycE ( Bloomington ) ; hs-numb [21]; hs-Nintra [22] lines . In order to precisely stage pupae , pupae were collected at puparium formation and timed while considering that the developmental time at 18°C was twice longer than that at 25 or 30°C . Heat shocks were performed at 34°C or 38°C for 30 min and pupae were kept at 25°C for recovery . Somatic clones were obtained using the FLP/FRT recombination system [43] . The FRT82B pros17 [27] line alone or combined with Nts-1 [28] was crossed to the y , w , UbxFLP; FRT82B ubi-nls::GFP ( gift of J . Knoblich ) to generate pros-null somatic clones . Live imaging of the bristle lineage in neur>CycE , UAS-H2B::YFP , UAS-PON::GFP pupae was carried out as described previously [11] . Images were acquired every 3 minutes on a confocal microscope ( 20× or 40× objective ) driven by Metaview ( Universal Imaging ) . Temperature was maintained at 25°C . Time-lapse movies were assembled using ImageJ ( free software ) . Sensory clusters were identified according to their relative positions on the thorax . For each mitosis , asymmetric localisation of the PON::GFP fusion protein allowed the identification of the daughter cells . Live imaging in experiments combining time-lapse imaging and immunodetection was realised using an spinning disc microscope ( Ropert Scientific France ) ( 20× or 40× objective ) driven by Metaview ( Universal Imaging ) . Images were acquired every 4 minutes . Temperature was controlled by a thermo-regulated chamber ( home-made ) . Pupal nota were dissected between 17 h and 35 h APF and processed as previously described [9] . Primary antibodies were: mouse anti-Cut ( DSHB , 1∶500 ) ; rat anti-CycE ( gift from H . Richardson , 1/1000 ) ; rabbit anti-Dap ( gift from C . Lenher , 1∶300 ) ; rat anti-ELAV ( DSHB , 1∶10 ) ; mouse anti-ELAV ( DSHB , 1∶100 ) ; mouse anti-Futsch ( 22C10 ) ( DSHB , 1∶100 ) ; rabbit anti-GFP ( Interchim , 1∶1000 ) ; mouse anti-GFP ( Roche , 1∶500 ) ; mouse anti-Pros ( gift from C . Doe 1∶5 ) ; rat anti-Su ( H ) ( gift from F . Schweisguth , 1∶500 ) ; rabbit anti-phospho-Histone H3 ( Upstate , 1∶10000 ) . Alexa 488- and 568-conjugated secondary anti-mouse , anti-rat , anti-rabbit , anti-guinea pig were purchased from Molecular Probe and used at 1∶1000 . Cy5 conjugated antibodies anti-mouse , -rat or -rabbit were purchased from Promega and were used at 1∶2000 . In addition to antibody immunodetection , we also used other criteria to identify cells . ( 1 ) Nuclear size , bigger in pIIa daughter cells than in pIIb daughter cells . ( 2 ) Cell location relative to both the antero-posterior axis and the cell arrangement into the cluster , socket cell posterior to shaft cell and both cells posterior to pIIb daughter cells . ( 3 ) Relative YFP intensity , neuron nucleus is less intense than that of the sheath nucleus , ( 4 ) Small and bright YFP staining , reflecting apoptotic DNA condensation , to distinguish the glial cell . Images were processed with ImageJ and Photoshop . Counting of terminal cells was carried on-fixed pupae and was restricted to the clusters forming the two middle rows ( 24 and 28 h APF or after one night recovery when a heat shock was applied ) . | Despite substantial progress that has been made , we still know little about how single precursor cells undergo a limited number of cell divisions before arrest . Discovering the mechanisms by which terminal cells maintain cell division arrest is essential for understanding “normal” development , as well as the origin of pathological deregulations . Using the bristle cell lineage , a model system widely employed to analye cell identity acquisition , we observed that only two out of four terminal cells in this lineage are unable to re-enter the cell cycle and proliferate . Our study shows that in these cells , cell division arrest is maintained by the action of the transcription factor Prospero and the signalling pathway Notch . Since both of these factors also control cell identity in this lineage , this finding demonstrates that common elements acting simultaneously and in parallel regulate the terminal quiescent state and differentiation . This system provides a unique animal model in which to understand how the mechanisms involved in cell fate acquisition and those controlling cell division intermingle to produce cell lineages resulting in terminal cells in the right number and at the right place and time . | [
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] | 2009 | Notch and Prospero Repress Proliferation following Cyclin E Overexpression in the Drosophila Bristle Lineage |
Nasal colonization is a major risk factor for S . aureus infections . The mechanisms responsible for colonization are still not well understood and involve several factors on the host and the bacterial side . One key factor is the cell wall teichoic acid ( WTA ) of S . aureus , which governs direct interactions with nasal epithelial surfaces . We report here the first receptor for the cell wall glycopolymer WTA on nasal epithelial cells . In several assay systems this type F-scavenger receptor , termed SREC-I , bound WTA in a charge dependent manner and mediated adhesion to nasal epithelial cells in vitro . The impact of WTA and SREC-I interaction on epithelial adhesion was especially pronounced under shear stress , which resembles the conditions found in the nasal cavity . Most importantly , we demonstrate here a key role of the WTA-receptor interaction in a cotton rat model of nasal colonization . When we inhibited WTA mediated adhesion with a SREC-I antibody , nasal colonization in the animal model was strongly reduced at the early onset of colonization . More importantly , colonization stayed low over an extended period of 6 days . Therefore we propose targeting of this glycopolymer-receptor interaction as a novel strategy to prevent or control S . aureus nasal colonization .
The nasal cavity is the major reservoir of Staphylococcus aureus , which asymptomatically colonizes the anterior nares of 20% of the normal human population persistently [1] , [2] . The reasons for the underlying predisposition and the tropism for the human nose are still unclear . If carriers are infected with S . aureus , the strain found in the nose is usually also responsible for the infection [1] , [2] . Since S . aureus is able to cause a variety of severe diseases , the carrier status is an important risk factor in both the community and the healthcare system [1] , [3] . Despite the importance of nasal colonization , its molecular basis has still remained largely elusive . Some studies showed that S . aureus colonizes mostly the anterior parts of the nares and interacts very efficiently with squamous , keratinized cells [4] . However , there is clear evidence that S . aureus also interacts with living ciliated cells in deeper areas of the nasal cavity or even the throat [5] , [6] , [7] , [8] and might be equally abundant in all parts of the nasal cavity [9] , [10] . In addition , S . aureus is even able to persist intracellularly in nasal epithelial cells of patients suffering from recurrent sinusitis [5] . S . aureus nasal colonization is a multifactorial process [11] and host factors [12] , as well as S . aureus factors like the polysaccharide capsule [13] , an array of surface protein adhesins [4] , [14] , [15] , [16] , [17] , and cell wall teichoic acids ( WTA ) [6] , [18] , [19] have been implicated in nasal colonization . One of the important surface protein adhesins with a role is nasal colonization is the cell wall anchored clumping factor B ( ClfB ) . It binds to cytokeratin and keratinized nasal cells [4] and the squamous cell envelope protein loricrin . The impact of this molecular interaction on nasal colonization , has been demonstrated in a mouse model [20] . In a “state of the art” cotton rat model of nasal colonization , protein adhesins of S . aureus mainly impact the late stages of nasal colonization whereas the non-protein adhesin WTA played a key role in the initial stages [6] . This is in line with the fact that expression of WTA biosynthesis genes is high during early and later stages of experimental colonization , whereas expression of protein adhesins like ClfB is low in the early stages but high in the late stages of colonization [21] , [22] . WTA is a surface-exposed polyanionic cell wall glycopolymer ( CWG ) composed of about 40 ribitolphosphate repeating units which are modified with D-alanine and N-acetylglucosamine and covalently linked to the peptidoglycan [23] , [24] ( Figure S1 ) . Several lines of evidence indicate a direct interaction of WTA with receptors on nasal epithelial cells [6] , [18] . In vivo , WTA is a key factor of nasal colonization since a WTA-deficient S . aureus mutant was severely abrogated in colonizing cotton rat noses [18] . Therefore , it is of great importance to understand the molecular basis of WTA-mediated adhesion to the epithelial lining of the inner nasal cavity . We introduce here SREC-I as a receptor for WTA on nasal epithelial cells . SREC-I , a type F scavenger receptor with six extracellular EGF-like domains , has first been detected on endothelial cells , where the receptor is responsible for the uptake of calreticulin [25] and acetylated low density lipoproteins [26] . Recently , expression of SREC-I was also described in several epithelial cell lines , such as END1 , HELA , and Chang [27] epithelial cells . In addition expression , albeit at low levels , was also detected in primary human bronchial epithelial cells ( HBEC ) [28] . We present here the first evidence for a key role of SREC-I in the early phases of S . aureus colonization and thus a novel target for decolonization strategies that can possibly help to protect individuals from S . aureus infections .
In differential pull down experiments of solubilized membrane proteins from epithelial cells with cell wall preparations from wild-type S . aureus , a mutant without WTA ( tagO ) and a mutant with structurally altered ( negatively charged ) WTA ( dltA ) , SREC-I was found as a potential WTA binding partner ( data not shown ) . To confirm SREC-I expression in primary nasal epithelia of human and cotton rat origin we isolated cotton rat primary epithelial cells ( CRNECs ) ( Figure S2A ) and used primary human nasal epithelial cells ( HNECs ) ( Promocell ) . We evaluated the presence of SREC-I by reverse transcriptase PCR ( RT-PCR ) , FACS analysis and confocal microscopy in these cells . For detection of SREC-I by FACS analysis we used an anti-human SREC-I antibody and appropriate isotype controls . Both HNECs and CRNECs stained positive for the receptor with this antibody ( Figure S2B and Figure 1 ) . RT-PCR Primers were designed based on the mouse and human genome sequence for SREC-I . For both cell types SREC-I mRNA expression could be detected ( Figure S2C ) . Confocal microscopy , employing the same primary antibodies used in the FACS analysis , revealed a weak but specific signal for SREC-I on HNECs and CRNECs ( Figure S3 ) . We therefore conclude that SREC-I shows considerable surface exposition on both CRNECs and HNECs . To demonstrate a direct molecular interaction of SREC-I and WTA we employed several experimental setups . Due to the chemical nature of WTA ( loss of D-alanine esters during covalent coupling ) it was not possible to use a surface plasmon resonance ( SPR ) based approach on the Biacore platform ( data not shown ) . Therefore , we first studied SREC-I-WTA interaction in a microtiterplate based binding assay . A biotin-labeled soluble human SREC-I Fc-chimera , containing the entire extracellular domains of SREC-I , bound in a dose-dependent and saturable manner to immobilized WTA ( Figure 2A ) . Using a function-blocking SREC-I antibody we were able to block interaction of WTA with SREC-I in a dose-dependent manner ( Figure 2B ) . In addition , we detected a dose dependent interaction of SREC-I with WTA in a ligand blot assay ( Figure 2C and Figure S4 ) . In this assay system binding of an SREC-I Fc-chimera to immobilized WTA is detected with the help of an infrared dye-labeled anti-Fc antibody . The SREC-I Fc-chimera bound wild-type ( wt ) WTA significantly better than negatively charged WTA isolated from a dltA mutant . Furthermore , we directly labeled the SREC-I Fc-chimera with FITC and measured binding of the conjugate to whole bacterial cells in suspension ( Figure 2D ) . We found that wild-type S . aureus ( wt ) bound 41%±12 of the SREC-I in solution whereas a mutant lacking all WTA ( tagO ) bound only 19%±8 . The complemented tagO mutant bound 51%±15 . When we added MgCl2 in different concentrations , we could partially decrease the interaction of wild-type S . aureus with the FITC labeled SREC-I Fc-chimera to the level of the tagO mutant which lacks all WTA . The diminished interaction of the dltA mutant , which exhibits negatively charged WTA , was partially restored to wild-type levels at high MgCl2 concentrations . In addition we could decrease the interaction of wild-type S . aureus to the FITC labeled SREC-I Fc-chimera with WTA purified from wild-type S . aureus but not with WTA purified from the dltA mutant ( Figure S5A ) . To test the specificity of WTA/SREC-I interaction we used another FITC labeled scavenger receptor in this assay system . The CD36 FITC conjugate showed considerable binding to wild-type S . aureus , however we could not detect a considerable decrease in binding of the CD36 FITC conjugate to the tagO and dltA mutants as we could detect with the FITC labelled SREC-I Fc-chimera ( Figure S5B ) . To study the role of WTA-SREC-I interaction in adhesion to epithelial cells , we performed microscopical assays with fluorescently labeled bacteria . We chose the S . aureus strain background Sa113 for most in vitro and in vivo assays because it colonized the nasal cavity of cotton rats ( see below ) significantly better than more virulent strains like e . g . USA300 or USA400 which also altered the integrity of epithelial monolayers in adhesion assays under static conditions ( data not shown ) . WTA exerted an important role in the adhesion to HNECs as well as CRNECs , since especially under shear stress conditions a mutant lacking WTA ( tagO ) exhibited a strong defect in adhesion ( Figure 3 ) . This phenotype could be complemented by a wild-type copy of the tagO gene on a plasmid . With HNECs ( Figure 3A ) we detected a 54% reduced adhesion of tagO bacteria under static conditions when compared to wild-type bacteria at an MOI of 10 . This phenotype could also be complemented . Under shear stress conditions the adhesion of the tagO mutant was reduced by 77% . Complementation of the tagO mutant nearly restored wild-type levels . With CRNECs we detected a 55% reduction of the tagO mutant adhesion when compared to wild-type S . aureus under static conditions ( Figure 3B and Figure S6 ) . Again , this reduction could be complemented by expressing a wild-type copy of the tagO gene in the tagO mutant . Under shear stress conditions adhesion of the tagO mutant was reduced by 67% when compared to the wild-type bacteria . Complementation of the tagO mutant did nearly restore wild type levels of adhesion ( Figure 3B ) . With HNECs the adhesion of whole S . aureus bacterial cells could be partially blocked under static ( Figure 4A ) and shear stress condition ( Figure 4B ) using Fab2-fragments of a function blocking SREC-I antibody . Fab2-fragments of an isotype control had no impact on the adhesion of whole bacteria to HNECs . Under static conditions ( Figure 4A ) , preincubation with the SREC-I Fab2-fragments decreased the adhesion of the wild-type by 67% . In contrast , incubation with the isotype control did not change the level of adhesion . The reduced level of tagO mutant adhesion was not further influenced by SREC-I antibody or isotype control preincubation . Under shear stress conditions ( Figure 4B ) preincubation with the SREC-I Fab2-fragments decreased adhesion of the wild-type to HNECs by 81% . Incubation with the isotype control did not significantly change the level of adhesion when compared to the tests without Fab2-fragments . The level of tagO mutant adhesion was not influenced by SREC-I antibody or isotype control preincubation . The SREC-I Fab2-fragment inhibited adhesion of whole S . aureus bacteria on CRNECs in a similar manner and adhesion decreased by 54% ( Fig . 4c ) . Furthermore , the adhesion of additional S . aureus strains ( Newman , SH1000 , USA100 , USA300 and USA400 ) could be blocked with the SREC-I FAb2-fragments , but not with isotype control Fab2-fragments ( Fig . 4D ) . Specific , WTA-mediated , adhesion to epithelial cells was demonstrated with WTA-coated fluorescent beads . Wild-type WTA-coated beads bound in a dose-dependent manner to HNECs as demonstrated before [18] and CRNECs ( Fig . 5C ) while beads coated with structurally altered dltA WTA ( negatively charged ) showed no specific adhesion to HNECs ( Fig . 5A and [18] ) and CRNECs ( Fig . 5C ) . In line with bacterial adhesion assays , the function blocking SREC-I antibody revealed the impact of this receptor on WTA-dependent adhesion also in assays employing WTA-coated , fluorescently labeled latex beads ( Figure 5A and B ) . The antibody blocked adhesion of WTA-coated beads to HNECs significantly while an isotype control had no impact on adhesion . In more detail , adhesion of wild-type WTA-coated beads to HNECs ( Figure 5A ) , was reduced by 72% after SREC-I antibody preincubation while the isotype control had no influence on adhesion . Beads incubated with structurally altered dltA WTA lacking D-alanine side chain esters exhibited a decrease in adhesion by 78% . The SREC-I antibody and isotype control did not significantly influence adhesion of dltA WTA-coated beads . Control beads without WTA exhibited only a weak binding of 7 . 2% when compared to wild-type WTA-coated beads . With CRNECs the same SREC-I antibody blocked adhesion of WTA-coated beads in a similar manner ( Figure 5B ) . Preincubation with the SREC-I antibody decreased adhesion by 67% . The isotype control had no significant impact on adhesion of WTA-coated beads to CRNECs ( Figure 5B ) . Experiments with CHO epithelial cells expressing or not expressing functional SREC-I clearly underscore the role of WTA-SREC-I interaction in S . aureus adhesion to epithelial cells ( Figure 6 ) . Under flow conditions presence of SREC-I led to a strong increase in adhesion of wild-type S . aureus whereas no difference in the significantly lower adhesion of WTA-deficient S . aureus ( tagO mutant ) could be detected , irrespective of whether SREC-I was present or not ( Figure 6A ) . Expression of full length SREC-I in CHO cells led to a statistically significant 2 . 3 fold increase in adhesion of wild-type bacteria . With SREC-I expressing CHO cells the tagO mutant exhibited a severely decreased adhesion ( 72% reduction ) when compared to the wild-type . This residual adhesion was increased by a factor of 1 . 7 when SREC-I was expressed in the CHO cells although this increase wasn't statistically significant . The complemented mutant exhibited an adhesion profile that resembled the wild-type . To localize the region of the SREC-I receptor-mediating interaction with WTA we used CHO cells expressing SREC-I with truncated EGF-like domains ( Figure 6B ) . We assayed the adhesion of S . aureus wild-type , WTA-lacking tagO mutant and the complemented mutant under static condition . On CHO cells that express no SREC-I we detected 42±21 , 37±17 and 42±24 bacteria/0 . 1 mm2 CHO cells for wild-type , tagO mutant and complemented mutant , respectively . Adhesion to CHO cells expressing SREC-I lacking the EGF domains until domain 4 was not altered when compared to full length SREC-I expressing cells . However , in CHO cells expressing a truncated SREC-I lacking the first 4 EGF-like domains we detected a significant drop in adhesion levels of 61% and 63% for wild-type and complemented mutant strain . The residual adhesion of the tagO mutant was not affected . These experiments , with CHO cells expressing successive truncations of SREC-I EGF-domains , led to the conclusion that the WTA interaction interface of SREC-I and WTA is located in the EGF domains 3 and 4 or between these domains ( Figure 6B ) . This is in line with the presence of a stretch of charged amino acids ( Figure S7 ) that could spatially alternate on both sides of a possible binding groove for WTA and therefore potentially represents the interaction surface of SREC-I and WTA . Interestingly preincubation of wild-type S . aureus with MgCl2 led to a decrease in adhesion to CRNECs which again argues for an impact of the WTA charge on SREC-I interaction ( Figure S8A ) . In order to demonstrate a possible impact of the WTA-SREC-I interaction on nasal colonization , we employed a cotton rat in vivo model . The cotton rat model of nasal colonization is a preferred , well established model for S . aureus since the nasal cavity of cotton rats shares histological properties with the human nasal cavity and permits long-term colonization experiments [29] . Most importantly , cotton rats , as opposed to mice , are susceptible to many human respiratory pathogens , and experimental diseases develop similar to those observed in humans [30] . We tested the impact of a function-blocking antibody and a corresponding isotype control in the early phases and late phases of nasal colonization ( Figure 6C ) . At early time points ( 8 h ) upon exposure to S . aureus the impact of adhesion factors that modulate primary adhesion should be obvious . We could detect a significant decrease in colonization after preincubation with Fab2-fragments of the SREC-I antibody but not with an isotype control . The decrease in S . aureus CFU after preincubation with the function-blocking antibody was comparable with the decrease in colonization observed before with a WTA-deficient tagO mutant [6] , [18] . After 6 days , colonization in SREC-I antibody treated animals did not recover to the level observed in non treated animals or isotype control treated animals . The same effect could be observed in a different strain background . USA100 colonization of the cotton rat nares was significantly abrogated after pretreatment with Fab2 fragments of the SREC-I antibody ( Figure S8B ) .
An integral step in the establishment and persistence of nasal colonization is the adhesion of bacteria to nasal epithelial cells and surface components of epithelial surfaces . Histological evidence led to the conclusion that S . aureus seems to predominantly colonize the anterior part of the nasal cavity ( vestibulum nasi ) , which is lined by a stratified , keratinized , non-ciliated squamous epithelium [31] , [32] . The upper cell layers in the anterior part comprise mostly anucleated , squamous cells termed corneocytes , which are highly keratinized and are surrounded by a proteinaceous structure containing loricrin and involucrin [33] , [34] . However , these cellular layers are constantly shed , which should contribute to the clearing of attached bacteria . Therefore , interaction with the epithelial surface in the anterior part of the nasal cavity cannot fully explain persistent colonization . This idea is backed by newer evidence that demonstrates S . aureus interaction with living ciliated cells in deeper areas of the nasal cavity or even the throat [5] , [6] , [7] , [8] . In addition bacterial numbers seem to be similar in all parts of the nasal cavity [9] , [10] . In this line of thought , we investigated the molecular details of S . aureus adhesion to cells of the inner nasal cavity . Thus , we concentrated on primary growing cells isolated from the nasal cavity of humans and cotton rats . We detected expression of SREC-I , which is a member of the type F scavenger receptor family [25] , in these cells . Our study is the first report of SREC-I expression in nasal epithelial cells . SREC-I exhibits six extracellular EGF-like domains , binds acetylated lipoproteins as a natural substrate [26] , and has been identified as an important co-receptor for Neisseria gonorrhoeae adhesion to epithelial cells [27] . Our experiments revealed an impact of this receptor on S . aureus adhesion to nasal epithelial cells in vitro and S . aureus colonization in a cotton rat model of nasal colonization in vivo . S . aureus nasal colonization is a multifactorial process and surface proteins of S . aureus , such as ClfB , show a considerable impact in animal models of nasal colonization [4] , [14] , [35] , [36] , [37] . In the cotton rat model of colonization these proteins seem to be more important for long-term colonization , whereas WTA impacts on the early stages while colonization is established [6] . This is in line with ex vivo analysis of the transcriptional level of WTA biosynthesis genes ( tagO and tarK ) and surface protein expressing genes [21] , [22] which reveals a late stage upregulation of surface protein expressing genes . Interestingly , WTA shows no influence on S . aureus interaction with keratinized cells in the anterior region of the nasal cavity but clearly governs part of the adhesion to growing epithelial cells isolated from deeper parts of the nasal cavity [6] , [16] . We could show here that WTA specifically binds to SREC-I and that this facilitates adhesion of S . aureus to growing epithelial cells of the inner nasal cavity . In binding assays with immobilized WTA a functional SREC-I Fc-chimera , containing the extracellular domains of SREC-I fused to Fc-fragements , bound WTA in a dose dependent manner . This binding could be blocked by a SREC-I antibody . In adhesion assays with whole bacteria , S . aureus mutants that lacked WTA showed a significant defect , and the adhesion of wild-type S . aureus could be blocked with anti-SREC-I Fab2-fragments . The effect was especially pronounced under mild shear stress conditions . These conditions can be found in the nasal cavity where mucus movement , due to cillial activity and airstream , constantly creates shear stress conditions at the interface of bacteria and epithelial surfaces . To understand the molecular details of SREC-I-WTA interaction we expressed truncated versions of SREC-I in CHO cells . Adhesion studies with these cells revealed a WTA binding interface on EGF-domains 3 and 4 of SREC-I . Wild-type WTA usually exhibits both positively and negatively charged residues in the ribitol-phosphate repeating units . Interestingly , experiments with negatively charged , dltA mutant derived WTA that lacks all D-alanine esters , indicate a role of the D-alanine modification in WTA/SREC-I interaction . Since we could only partially restore WTA-SREC-I interaction with the addition of Mg2+ , WTA charge is important but can not be the only determinant of binding affinity . In line with this notion , inhibition experiments with purified WTA from wild-type and dltA mutant again demonstrate the role of the presence of D-alanine esters in the WTA structure . We therefore postulate that both the zwitterionic charge of the repeating units and the actual spacing of the charges are required for SREC-I binding . Charged amino acid residues in the WTA binding interface of SREC-I are most likely responsible for this interaction . Mutational studies of SREC-I are part of the ongoing research in our laboratory and will reveal the structural requirements for the WTA-SREC-I interaction . WTA is one of the most abundant molecules in the staphylococcal cell wall and therefore might facilitate multiple interactions of lower affinity which could create kinetically more favourable conditions for S . aureus binding proteins . Thus , we postulate that WTA modulates the strength of an initial interaction between bacterium and an epithelial surface , thereby facilitating binding protein activity . In the cotton rat model the function blocking anti-SREC-I antibody had a significant impact on the initial colonization of the nasal cavity with S . aureus wild-type bacteria . Therefore , it is tempting to conclude that in the early stages of colonization , the initial interaction of S . aureus with nasal surfaces partially takes place at other sides than the very anterior part of the nasal cavity . In addition , during later stages and persistent colonization , the inner part of the nasal cavity likely represents one of several habitates in the nose [9] . Keeping in mind that cells of the very anterior epithelial linings are shed and permanently removed from the nasal cavity , bacteria that enter deeper zones of the nasal cavity might have better chances to overcome physiological barriers , proliferate and then disseminate again into other areas of the nasal cavity , including the anterior ( Figure 7 ) . In line with this idea , histological evidence and in vitro studies suggest that S . aureus can also bind to nasal epithelial cells deeper inside the nasal cavity [6] , [38] and is also taken up by and able to persist within these cells in vivo [5] . Taken together we identified SREC-I as a receptor for WTA on nasal epithelial cells of human and cotton rat origin . SREC-I binds WTA most likely through charge interactions . This WTA-SREC-I interaction plays a key role in the initial stages of nasal colonization in a cotton rat model . This first report of a bacterial surface polymer-type-F scavenger receptor interaction could spark a new interest in charged bacterial surface polymers and their impact on bacterial interaction with the host . In the particular case of S . aureus it is worthwhile to speculate that the WTA-SREC-I interaction might be an interesting target for substances , like the antibody we used in this study , which could be used to combat S . aureus nasal colonization . At least it should be possible to use such substances as topical treatments , to prevent re-colonization after eradication of S . aureus in risk patients that require extended hospitalization . Such an approach could be also used in combination with vaccination therapy that targets other factors involved in S . aureus nasal colonization [37] . This could significantly reduce invasive S . aureus infections during hospitalization . In addition , further studies are now required to understand the impact of SREC-I on nasal colonization from an epidemiological perspective . We are currently trying to identify putative functional polymorphisms in SREC-I and correlate these with nasal carrier status . However , since nasal colonization is a multifactorial process , polymorphisms in SREC-I will be only one possible genetic determinant that has to be investigated in conjunction with other modulators of nasal colonization .
S . aureus SA113 ( ATCC 35556 ) is a frequently used laboratory strain [39] . The mutant ΔtagO [18] was generated in this strain background by replacing the tagO gene with an erythromycin resistance cassette . This mutant is devoid of WTA [18] . Plasmid pRBtagO contains a wild-type copy of tagO and its promoter , and reconstitutes wild-type level WTA biosynthesis along with all relevant phenotypic properties [18] . An isogenic S . aureus ΔdltA mutant [40] was used as a control in the affinity blotting experiments . Strains Newman ( NCTC 8178 ) [41] and SH1000 [42] are frequently used laboratory strains . We also included strains USA100 [43] , USA300 [44] and USA400 [44] in selected experiments . The GFP-tagged version of SREC-I was constructed by amplifying the cDNA-clone of wt-SREC-I ( IRATp970A0758D6 , RZPD: Deutsches Ressourcenzentrum für Genforschung GmbH ) with primers SREC-I sense 5′-GACTAGATCT GACGAACCCG AGCGCTGCCA CCG-3′ and SREC-I antisense 5′-GACTGAATTC GTTCTGTTGG CCTGGAGATGG-3′ . The resulting PCR fragments were cloned into pGEM-T easy TA-vector system ( Promega ) and transferred by BglII/EcoRI digestion and ligation into pEGFP-N1 ( Clontech ) resulting in GFP fused to the carboxy-terminus of the expressed proteins . Truncated SREC-I constructs were generated as follows: the amino-terminal domain including the signal peptide was amplified with SREC-I sense primer and SREC-SP-antisense ( 5′-GACTCTCGAG TTGATCCTTC TGCCTCCAGC CTG-3′ ) . The C-terminal domain was sequentially truncated by PCR amplification using SREC-I antisense primer together with the different sense primers SREC-I-1EGF ( 5′-GACTCTCGAG TCCGCTGCCC GGCCCAGTAC TG-3′ ) , SREC-I-2EGF ( 5′-GACTCTCGAG TTCCCGTGCG CCTGCGGCCC CC-3′ ) , SREC-I-4EGF ( 5′-GACTCTCGAG CTGCCCTGCC CGGCAGGCAG CC-3′ ) or SREC-I-5EGF ( 5′-GACTCTCGAG GGACCCCTGC CCCACTGGTA CC-3′ ) , respectively . The resulting PCR fragments were cloned into pGEM-T easy TA-vector system . DNA fragments containing either the signal peptide or truncated versions of SREC-I were prepared by restriction with BglII/XhoI or XhoI/EcoRI , respectively and were ligated into pEGFP-N1 . CHO cells were transfected with the resulting constructs as well as with the empty vector pEGFP-N1 . Stable cell clones were isolated and maintained in Ham's F12 medium ( Gibco ) supplemented with 10% FCS and 400 µg/ml G418 . Recombinant SREC-I Fc-chimera was purchased from R&D-Systems . The molecule was reconstituted in PBS to a concentration of 100 µg/ml . EZ-link Sulfo-NHS-LC-Biotin ( Pierce ) was used at a concentration of 50 µg/ml . The biotinylation reaction was performed in a modified PBS-buffer ( pH = 8 . 5 ) for 30 min at 37°C . The reaction was stopped by extensively dialyzing against PBS at a pH = 7 in a Slide-A-Lyzer dialysis cassette ( Pierce ) . In addition SREC-I was labelled with FITC . Therefore 100 µl of a FITC-solution ( 40 µM ) in carbonate-buffer ( pH = 8 . 5 ) was mixed with a SREC-I at a concentration of 100 µg/ml and allowed to react for 1 h at 37°C . After that time period the reaction was stopped by dialysis against PBS in a Slide-A-Lyzer dialysis cassette ( Pierce ) . We also labeled human recombinant CD36 ( R&D systems ) with the same protocol . Primary human nasal epithelial cells ( HNECs ) were purchased from Provitro ( Berlin ) and cultivated in airway epithelial growth medium ( Provitro ) . HNECs were used up to passage 6 . Primary cotton rat nasal epithelial cells ( CRNECs ) were isolated from cotton rat noses . Cotton rats noses were digested ON in DMEM with 10% FCS ( Sigma ) , penicillin ( 100 U/ml ) , streptomycin ( 100 µg/ml; Gibco , ) , and Collagenase Type VIII ( Sigma ) . Then tissue was transferred in DMEM culture medium containing 10% FCS , penicillin ( 100 U/ml ) , and streptomycin ( 100 µg/ml ) . After a significant amount of cells were detectable ( 72 h ) cells were detached with Trypsin ( Sigma ) and subcultured in DMEM supplemented with 10% FCS ( Sigma ) . Growth rate and morphology was constantly monitored and cells were used up to passage 7 . Studies were performed as described before [45] . Cells were detached by scraping in PBS supplemented with 1% FCS . SREC-I was detected by using an anti human SREC mouse IgG2B antibody ( R&D ) . Cells were blocked with 10% FCS in PBS for 20 min at room temperature following incubation with the anti-SREC antibody for 45 min at 4°C . Subsequently , cells were washed twice and incubated for 45 min at 4°C with secondary antibody ( anti-mouse IgG –PE ) . Flow cytometric analysis was performed using a BD FacsCalibur . As an isotype control we used a mouse IgG2B ( R&D systems ) . For immunofluorescence detection of SREC-1 on HNECS and CRNECs , cells were cultivated in Lab-TekII-Slides ( Thermo ) and were fixed with 100% MeOH pa for 10 min at -20°C . 1% BSA ( in PBS ) was added to the cells to block nonspecific staining . The cells were incubated at room temperature for 1 h . Then the cells were incubated with 10 µg/ml SREC-1 antibody ( SREC-I/SR-F1 goat IgG polyclonal antibody; R&D ) , 10 µg/ml isotype control ( Normal goat IgG; R&D systems ) , or blocking buffer ( 1% BSA in PBS ) for 1 h at room temperature and washed three times with PBS . A donkey anti-Goat IgG-Cy5 ( Milipore ) was used as secondary antibody ( diluted 1∶75 ) . Nuclei were stained with 1 µg/ml DAPI ( Applicam ) and cytoskeleton was stained with phalloidin-TRITC ( SIGMA ) for 5 min at room temperature . After three washing steps with PBS the cells were mounted with Mowiol . Microscopy was performed on a Zeiss LSM-710 NLO with Zen 2011 software . The SREC gene in CRNECs and HNECs was detected via RT-PCR . RNA was isolated with the RNeasy Mini Kit ( Qiagen ) . To exclude DNA contamination , RNA was digested with 10 U of RNase-free DNase I ( Roche ) and 40 U of RNasin ribonuclease inhibitor ( Promega ) for 30 min at room temperature . DNase I treatment was stopped using DNase Inactivation reagent ( Ambion ) . RNA was transcribed using First Strand cDNA Synthesis Kit ( Fermantas ) and 200 ng of random hexamer primers . PCR for detection of the SREC gene this cDNA was amplified with following primers . The sequence primers designed using the mouse SREC-I sequence were SREC-m1 fwd 5′AGCTGCCTTGCAACCCTGGA and SREC-m1 rev5′AGGTGCCTGCAGGACAT GGC , respectively . The second primer pair were SREC-m2 fwd 5′TGGGACTAGAGCTGGTGTTCT and SREC-m2 re5′CAGATGGGGATGGTGCA TTCT . The primers designed based on the human SREC-I sequence were SREC-h1 fwd 5′TGAAGCCGGGCCTCTGTCGA and SREC-h1 rev5′ CAGATGGGGATGGTG CATTCT as well as SREC-h2 fwd 5′CC TGCCAGAAAGACGAGGTG and SREC-h2 rev5′CCAGGCTTGCATCGACAGAG . Purified WTA was blotted on a nitrocellulose membrane by pipetting . 5 µl of a WTA solution in 20 mM NaAcetat containing 200 , 100 , 50 , 25 and 12 . 5 nmol Pi were spotted onto the membrane . The blots were blocked in TBS containing 5% BSA . After 3 washes with TBS-0 . 1% Tween20 the blots were incubated with 500 mg/ml SREC-I Fc-chimera ( R&D systems ) in TBS at room temperature for 30 min . After 1 wash with TBS-0 . 1% Tween20 the blots were incubated with an anti-human IgG antibody coupled with a 800 nm emitting infrared dye ( LI-COR ) for 45 min . The blots were washed again once with TSB-0 . 1% Tween20 and once with PBS and SREC/WTA interaction was detected on a LI-COR Odyssey system . Bacteria were cultured over night in MHB media and subcultured until mid-logarithmic growth phase in IMDM . The Bacteria were washed 3 times with PBS . The assay was performed in a 300 µl scale and 1*108 bacteria were incubated with 50 µg/ml FITC labeled SREC-I Fc-chimera ( R&D systems ) in PBS . After 30 min at 37°C and slow shaking , the bacteria were harvested by centrifugation at 10000 rpm for 10 minutes . The bound fluorescence was calculated by subtracting the unbound fluorescence from the total fluorescence of the FITC labeled SREC-I solution . In some assays different MgCl2 concentrations or purified WTA were added to test the charge dependency of the WTA/SREC-I interaction . As a specificity control FITC labeled human CD36 ( R&D systems ) was used at 50 µg/ml . CRNECs were seeded in 24 well culture plates at 2 . 5–5×104 cells/well in Dulbecco's Modified Eagle Medium ( DMEM; PAA Laboratories ) with 10% FCS , 100 U/ml penicillin , and 100 µg/ml streptomycin , while HNECs ( Provitro ) were seeded at 5×104 cells/well in airway epithelial growth medium ( Provitro ) . The Plates were incubated at 37°C under 5% CO2 . Binding assays where performed as described before [18] . When blocking experiments were performed , 10 µg/ml anti-human SREC-I/SR-F1 antibody ( R&D systems ) or 10 µg/ml isotype control ( normal goat IgG; R&D systems ) were used in adhesion assays with WTA coated latex beads ( WTA passively adsorbed to amine modified latex beads ( Sigma ) as described before [18] ) . The plates were incubated for 30 min at 37°C . The antibody concentrations were evaluated by dose dependency experiments ( data not shown ) . WTA-coated latex beads ( wild type WTA and negatively charged dltA WTA ) and the control sample ( only beads ) were diluted in RPMI 1640 . The fluorescence of the WTA-coated beads and the control sample was adjusted accordingly . Then , the samples were used in adhesion assays on epithelial cells with bead/epithelial cell ratios of 200∶1 , 20∶1 , 2∶1 for dose dependency experiments . In antibody blocking assays a ratio of 20∶1 was used . The plates were incubated for 1 h at 37°C . After a washing step with PBS the relative fluorescence at 520 nm ( emission ) was quantified using a fluororeader ( BMG Labtech ) . At least five assays were run in triplicate . When bacteria where used , bacteria were prepared and labeled with fluorescein isothiocyanate ( FITC ) as described before [18] . For Cell numbers were adjusted using a Neubauer chamber . 5 independent assays were performed under static conditions . When blocking experiments were performed , 10 µg/ml Fab2-fragments of anti-human SREC-I/SR-F1 antibody ( R&D systems ) or 10 µg/ml isotype control ( normal goat IgG; R&D systems ) were used . Fab2-fragments were produced with a Pierce kit according to the manufacturer's description . When assays under flow conditions were performed epithelial cells were seeded in chamber slides ( μ-Slide VI0 . 4; Ibidi ) . For infection peristaltic pumps ( Amersham ) were used with flow conditions mimicking 0 . 5 dynes , according to the manufacturer's instructions with a MOI of 20 ( at 37°C under 5% CO2 ) for 30 min . 6 independent assays were performed under flow conditions Chinese hamster ovary ( CHO ) epithelial cells were seeded in chamber slides ( μ-Slide VI0 . 4; Ibidi ) at 1×105 CHO cells/well and in DMEM/F12-GlutaMaxTM-I medium ( GIBCO ) supplemented with 10% fetal calf serum ( FCS ) , 400 µg/ml geneticin ( G418 ) , 100 U/ml penicillin , and 100 µg/ml streptomycin and grown at 37°C under 5% CO2 . Bacteria were prepared and labeled with fluorescein isothiocyanate ( FITC ) as decribed before [18] . Adhesion to CHO cells containing the empty vector pEGFP-N1 , the vector with the full length SREC-I receptor , or the vector with a truncated SREC-I receptor was assayed with FITC labeled Staphylococcus aureus SA113 wild-type , S . aureus SA113 ΔtagO , or S . aureus SA113 ΔtagO pRBtagO at a MOI of 10 . After the infected cells were incubated for 1 hour at 37°C under 5% CO2 the cells were washed three times with phosphate-buffered saline ( PBS ) and then fixed with 4% paraformaldehyde ( PFA ) . Adherent bacteria/0 . 1 mm2 were counted using a fluorescence microscope ( Zeiss ) . Three assays were run in duplicate . Before assays under flow conditions were performed epithelial cells were seeded in chamber slides ( μ-Slide VI0 . 4; Ibidi ) at 6×104 CHO cells/well and cultivated as described above . For infection peristaltic pumps were used with flow conditions mimicking 0 . 5 dynes according to the manufacturer's instructions with a MOI of 20 ( at 37°C under 5% CO2 ) for 30 min . Adherent bacteria/100 epithelial cells were counted using a fluorescence microscope . Three independent assays were performed . The cotton rat model was used as described earlier [18] . Cotton rats were anesthetized and noses were preincubated for 15 min with 2 µg of anti-human SREC-I Fab2-fragment ( R&D ) per nose . Afterwards , the noses were instilled intranasally with 10 µl of 1×109 colony forming units ( CFU ) of S . aureus ( SA113 ) . 8 hours after bacterial instillation the animals were euthanized and noses were removed surgically . The noses were vortexed in 1 ml of PBS containing 0 . 5% Tween for 30 sec . Samples were plated on appropriate agar plates and the bacterial CFU was determined . All animals received drinking water with 2 . 5 mg/ml streptomycin continuously , starting 3 days prior before beginning the experiment to reduce the natural nasal flora . Animal experiments were performed in strict accordance with the German regulations of the Society for Laboratory Animal Science ( GV-SOLAS ) and the European Health Law of the Federation of Laboratory Animal Science Associations ( FELASA ) . The protocol was approved by the Regierungspräsidium Tübingen ( Permit Numbers: T1/10 ) ) . Statistical analyses were performed with Graphpad Prism , using appropriate statistical methods as indicated . P values≤0 . 05 were considered significant . | About 20% of the human population is colonized by Staphylococcus aureus . The reservoir of S . aureus is mainly the human nose . Usually , colonization does not lead to infection and is therefore without symptoms . However , when hospitalized patients exhibit a suppressed immune system , they are at risk of getting infected by their own nasal S . aureus strain . Therefore , it is important to understand the events and mechanisms underlying colonization . Until now S . aureus nasal colonization is only partially understood . One bacterial key factor is a sugar polymer of S . aureus , termed cell wall teichoic acid ( WTA ) , which is involved in S . aureus adhesion to cellular surfaces in the inner part of the nasal cavity . We show here that a receptor-protein , which is expressed on such cells , binds WTA and is thereby involved in adhesion of S . aureus to nasal cells . This mechanism has a strong impact on nasal colonization in an animal model that resembles the situation in the human nose . Most importantly , inhibition of WTA mediated adhesion strongly reduces nasal colonization in the animal model . Therefore we propose that targeting of this glycopolymer-receptor interaction could serve as a novel strategy to control S . aureus nasal colonization . | [
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... | 2014 | A Nasal Epithelial Receptor for Staphylococcus aureus WTA Governs Adhesion to Epithelial Cells and Modulates Nasal Colonization |
Activation and/or recruitment of the host plasmin , a fibrinolytic enzyme also active on extracellular matrix components , is a common invasive strategy of bacterial pathogens . Yersinia pestis , the bubonic plague agent , expresses the multifunctional surface protease Pla , which activates plasmin and inactivates fibrinolysis inhibitors . Pla is encoded by the pPla plasmid . Following intradermal inoculation , Y . pestis has the capacity to multiply in and cause destruction of the lymph node ( LN ) draining the entry site . The closely related , pPla-negative , Y . pseudotuberculosis species lacks this capacity . We hypothesized that tissue damage and bacterial multiplication occurring in the LN during bubonic plague were linked and both driven by pPla . Using a set of pPla-positive and pPla-negative Y . pestis and Y . pseudotuberculosis strains in a mouse model of intradermal injection , we found that pPla is not required for bacterial translocation to the LN . We also observed that a pPla-cured Y . pestis caused the same extensive histological lesions as the wild type strain . Furthermore , the Y . pseudotuberculosis histological pattern , characterized by infectious foci limited by inflammatory cell infiltrates with normal tissue density and follicular organization , was unchanged after introduction of pPla . However , the presence of pPla enabled Y . pseudotuberculosis to increase its bacterial load up to that of Y . pestis . Similarly , lack of pPla strongly reduced Y . pestis titers in LNs of infected mice . This pPla-mediated enhancing effect on bacterial load was directly dependent on the proteolytic activity of Pla . Immunohistochemistry of Pla-negative Y . pestis-infected LNs revealed extensive bacterial lysis , unlike the numerous , apparently intact , microorganisms seen in wild type Y . pestis-infected preparations . Therefore , our study demonstrates that tissue destruction and bacterial survival/multiplication are dissociated in the bubo and that the primary action of Pla is to protect bacteria from destruction rather than to alter the tissue environment to favor Y . pestis propagation in the host .
Plague killed millions of humans during pandemics of the past and is still entrenched in regions of Asia , Africa and the Americas [1 , 2] . The last decades have witnessed resurgences and geographical extensions of the disease , leading WHO to categorize it as a re-emerging health problem [3 , 4] , and there are concerns that future climatic changes might further increase the occurrence of plague outbreaks in existing or new foci [2] . Bubonic plague is the most frequent form of the disease and results from intradermal injection by an infected flea of the Gram-negative bacterium Yersinia pestis [5 , 6] . Bacteria proceed then , via lymphatic draining , to the proximal lymph node and expand in this organ to high numbers of widespread and infiltrating extracellular organisms [7–11] . At this stage , the swollen and highly painful draining lymph node ( dLN ) is referred to as a “bubo” . Without treatment , bubonic plague most often progresses to fatal septicemia [12 , 13] . The 50% lethal dose ( LD50 ) of Y . pestis in mice is <10 and ~20 colony forming units ( cfu ) by the subcutaneous ( sc ) and intradermal ( id ) routes , respectively [6 , 14–16] . Y . pestis is a clonal species recently emerged from the foodborne enteropathogen Y . pseudotuberculosis [17] , which causes self-limiting gastrointestinal diseases in humans [18 , 19] and has an LD50 in mice of 105−107 cfu following oral or sc inoculation [20 , 21] . Therefore , although the two species are genetically nearly identical [22] , they display dramatically different pathogenic potentials . In a previous work , we used the Y . pestis/Y . pseudotuberculosis pair to explore the pathophysiology of bubonic plague [16] . Comparison of the diseases induced upon id injection of the two species showed that the dermal portal of entry newly acquired by Y . pestis is not the key to its increased virulence . The study also revealed specific histology features in the dLN within 2 days of infection . In buboes , plague was characterized by high bacterial loads , poorly contained bacterial infiltrates and widespread tissue destruction; in Y . pseudotuberculosis-infected dLNs , bacteria formed dense colonies enclosed in a focal and organized inflammatory reaction while the overall architecture of the dLN was preserved . At this stage , it was not clear if and how the characteristics of the mature bubo were connected in a pathophysiological pathway . The severe tissue damage might have been a consequence of the high bacterial burdens , through the action of bacterial or immunological components , and conversely , destructions of the lymph node parenchyma , by removing tissue barriers , could have been a facilitating factor for the free diffusion and expansion of Y . pestis within the organ . The Y . pestis pPla plasmid is one of the few genetic determinants that differentiate the plague bacillus from its Y . pseudotuberculosis ancestor [22] . Since this plasmid has been shown to participate in bacterial dissemination in vivo and to play a major role in Y . pestis virulence [15 , 23–25] , it was a potentially useful tool to analyze the relationships between the various features that specify a Y . pestis-infected dLN and to relate these features to virulence . pPla encodes Pla , a protease of the omptin family [26 , 27] , which cleaves mammalian plasminogen into active fibrinolytic plasmin and inactivates several inhibitors of the fibrinolytic pathway [28] . Pla also degrades antibacterial factors [15 , 23 , 29] , manipulates in vivo the pro-inflammatory response through interference with the FasL/caspase-3 pathway [30] , and confers adherence to various cell lines and extracellular matrix components , either directly [31–36] , or through processing of the YapE adhesin [37] . The pla gene encoding Pla is abundantly expressed in the dLN of Y . pestis-infected mice [38] . In heterologous expression systems , the proteolytic action of Pla at the bacterial surface is hampered by the O-antigen ( O-Ag ) fraction of the outer membrane lipopolysaccharide [39] . However , Pla is fully active in Y . pestis because genes encoding the O-Ag synthesis enzymes are naturally non-functional in this species [40] . The aim of this work was to use Y . pestis and Y . pseudotuberculosis strains differentially expressing Pla to analyze three defining characteristics of the bubo: infiltrating and diffuse pattern of bacterial invasion , extensive and disrupting tissue damage , and elevated bacterial titers . This approach helped us dissect the bubonic plague complex phenotype , uncovering new insights into the causal links between the various manifestations of the disease and their relation to outcome .
Animals were housed in the Institut Pasteur BSL3 animal facility accredited by the French Ministry of Agriculture to perform experiments on live mice ( accreditation B 75 15–01 ) , in compliance with French and European regulations on care and protection of Laboratory Animals ( EC Directive 86/609 , French Law 2001–486 issued on June 6 , 2001 ) . Protocols were approved by the Institut Pasteur Veterinary Staff and performed in compliance with the NIH Animal Welfare Insurance #A5476-01 issued on 02/07/2007 . Bacterial strains and plasmids used in this study are listed in Table 1 . Bacteria were grown using Luria-Bertani medium ( LB ) , supplemented ( Yersinia ) or not ( Escherichia coli ) with 0 . 002% ( w/v ) hemin ( LBH ) , at 28°C and 37°C , respectively . Selection for trimethoprim resistance was done using Müller-Hinton medium . Chloramphenicol ( 25 μg . ml-1 ) , kanamycin ( Km: 30 μg . ml-1 ) or trimethoprim ( Tmp; 100 mg . ml-1 ) was added to the media as necessary . All experiments involving Yersinia strains were performed in a BSL3 laboratory . Single nucleotide replacement in pla was performed by PCR amplification of the whole 9 . 6 kb pPlaTmp plasmid from strain Yptb* ( Pla ) ( Table 1 ) , using the divergent non-overlapping primers 896 and 897 harboring the required mutation ( S1 Table ) and a high-fidelity DNA Polymerase ( Phusion , NewEnglandBiolab ) [41] . The 80 μl reaction mix , containing 0 . 125 mM dNTP , 0 . 1875 μM of primers and 0 . 5 μl of Taq Polymerase , was incubated for 5 min at 98°C and subjected to 30 cycles of denaturation ( 30 s at 98°C ) , annealing ( 30 s at 60°C ) , and extension ( 3 min 30 s at 72°C ) , and a final cycle of extension ( 7 min at 72°C ) . The resulting PCR product was digested with DpnI to cleave the methylated template and subjected to electrophoresis . The band with the expected size was excised from the gel , extracted using the QIAquick Gel Extraction Kit ( Qiagen ) , and introduced into E . coli DH5α by transformation . Recombinant clones were selected on Tmp plates and plasmids were extracted using the Plasmid maxi kit ( Qiagen ) . Sequencing of the entire replicon , using the primers listed in S1 Table showed that it was 100% identical at the nucleotide level to the pPla template , except at position 7 , 341 in pla where the A was replaced by a C at the second position of the codon , leading to the expected replacement of an aspartic amino acid by an alanine at position 206 of the Pla protein sequence . The recombinant plasmid , designated pPlaD206A , was introduced by electroporation into Yp ( ∆Pla ) . Recombinant Y . pestis , designated Yp ( PlaD206A ) ( Table 1 ) , were selected on Tmp agar plates and the presence of the Tmp cassette was verified by PCR with primer pair 260B/C ( S1 Table ) . To confirm the presence of the recombinant pPlaD206A plasmid , the PCR product encompassing this region ( primers 839A/B ( S1 Table ) ) was digested with BlpI , which only cleaves the A/C mutated site . In order to obtain a pPla plasmid labeled with an antibiotic cassette other than Tmp ( already present on pPlaD206A ) , the dfr locus of the pPlaTmp plasmid was replaced by a Km cassette using the short flanking homologous regions ( SFH ) -PCR procedure [42] . Briefly , the Km locus was PCR amplified from the pGP704N-Km template [43] with primer pair 954A/B ( S1 Table ) that encompass the extremities of the Km cassette and 50 bp of the dfr gene . This PCR product was introduced into Yp ( pKOBEG-sacB ) ( pPlaTmp ) [21] by electroporation . KmR colonies were tested for correct allelic exchange between the PCR product and the target site by PCR with primers 932/933 ( S1 Table ) and 260B/C [21] , located at each end of the kan cassette and outside the dfr gene , respectively . All colonies contained both pPlaTmp and pPlaKm . To obtain Y . pestis clones containing pPlaKm , but devoid of pPlaTmp , the plasmids were extracted with the QuickLyse Miniprep kit ( Qiagen ) and introduced by electroporation into Yp ( ΔPla ) . KmR clones , designated Yp ( pPlaKm ) ( Table 1 ) were selected . The presence of the pPlaKm and the absence of pPlaTmp were confirmed by PCR with primer pairs 932/933 and 260B/C . Transfer of pPlaKm to Yp ( PlaD206A ) after plasmid extraction and electroporation resulted in the generation of Yp ( PlaD206A ) ( pPlaKm ) ( Table 1 ) . The presence of both the wild type and the mutated versions of pla was verified by amplification of the region encompassing the A -> C replacement on pla with primers 839A/B ( S1 Table ) , followed by digestion with BlpI . The presence of three bands ( one corresponding to the uncut wild type version of pla , and the other two to the fragments generated by the BlpI cleavage ) , confirmed the presence of the two forms of pPla in the strain . The assay was performed as described [21 , 44] , with slight modifications: bacteria were incubated at 37°C in Ca/Mg-free phosphate buffer containing 4 μg human Glu-plasminogen ( American Diagnostica ) and 30 μl of a 3 mM solution ( in H20 ) of the chromogenic plasmin substrate S-2251 ( Chromogenix ) in a total volume of 200 μl . Breakdown of the chromogenic substrate was monitored by serial measurements of absorbance at 405 nm ( A405 ) using a microtiter-plate reader . Experiments were performed in duplicate and at least twice . Eight-week old female OF1 mice ( Charles River , France ) were anesthetized by intraperitoneal injection of 10 mg . kg-1 Xylazine ( Rompun 2% , Bayer , Germany ) + 100 mg . kg-1 Ketamine ( Imalgène 1000 , Merial , France ) and infected as described [16 , 45] . Briefly , bacteria grown overnight on LBH agar medium were adjusted to the desired concentration in saline , based on A600 measurement , and 10 μl ( 5 , 000 cfu ) were injected id into the mouse ear pinna . Cfu counts were verified by plating on LBH . Control mice received the same volume of saline without bacteria . Mice were followed up for 21 days for survival . For cfu enumeration or histology examination of the LN , infected mice were euthanized by cervical dislocation at 48h post-infection ( pi ) , unless otherwise specified , and the ipsilateral superficial parotid LN [46] , which drains the injection site [16] , was harvested . LNs were first collected in a Zinc-based preservative [47 , 48] and then embedded in low melting-point paraffin ( Poly ( ethylenglycol ) diesterate , Aldrich ) . Four μm sections were stained with standard hematoxylin-eosin ( HE ) staining [49] . For immunohistochemistry , sections were treated for endogenous peroxidase activity by incubation for 20 min in 0 . 3% ( v/v ) H2O2 , and for 20 min in normal serum from the appropriate animal host ( dilution 1:10 in PBS ( pH 7 . 4 ) containing 1% ( w/v ) milk powder ) prior to incubation for 1 h with one of the following antibodies: rabbit polyclonal antiserum against the Y . pestis F1 Ag or the Y . pseudotuberculosis serotype I O-Ag ( produced by the French Reference Center for Yersinia , Institut Pasteur ) , or a rat anti-mouse CD45R ( B220 clone , Caltag ) . After three washes in PBS-1% ( w/v ) milk powder , LN sections were incubated for 1 h with the following secondary antibodies or reagent ( Dako ) : EnVision+System HRP-labeled anti-rabbit ( undiluted ) , streptavidin-peroxidase conjugate ( diluted 1:600 ) , or rat-specific biotinylated Ig ( diluted 1:400 ) followed by streptavidin-peroxidase conjugate . Bound peroxidase activity was detected using 3-amino-9-ethylcarbazole ( AEC ) substrate ( Sigma ) . Tissues were counterstained with Harris’ hematoxylin . Sections were photographed with a DMX1200 or DS-Fi1 Nikon Camera connected to an E800 or E600 Nikon microscope equipped with 2× to 100× Plan Apochromat objectives , or scanned with a MiraxScan Z1 from Zeiss . Statistical analyses were performed with the Prism version 5 for Mac software ( GraphPad Software , San Diego California ) , using the Student’s t test or Mann-Whitney U test , depending on the distribution of the data , to compare bacterial loads in organs and the log-rank test to compare mortality rates . Clustering analysis of the LN lesional patterns: histology sections were scored according to previously described criteria of histology lesions in the lymph node [16] . Each sample was scored as « 0 » or « 1 » for each of the criteria related to tissue damage and inflammatory reaction , so that a « 0 / 1 » table gathering the lesional patterns of all samples was created . The table was computed by the BioNumerics software , version 6 . 6 ( Applied Maths , Kortrijk , Belgium ) , using the Minimum Spanning Tree approach to analyze and display the similarity of the patterns .
Following injection of Y . pestis into the dermis , bacteria disseminate to the dLN . They first settle and spread in the subcapsular sinus , from which multifocal bacterial extensions subsequently penetrate into the cortex [11 , 16] . In contrast , the closely related species Y . pseudotuberculosis forms in the dLN discrete peripheral clumps separated from the parenchyma by an inflammatory cell mantle [16] . To determine whether the Y . pestis-specific plasmid pPla was responsible for the infiltrating behavior of Y . pestis , we used a set of four strains: ( i ) wild type Y . pestis ( Yp . wt ) , ( ii ) its derivative cured of the pla-bearing plasmid pPla ( Yp ( ΔPla ) ) , ( iii ) a Y . pseudotuberculosis strain in which O-Ag production has been abrogated ( Yptb* ) [21] , and ( iv ) its derivative in which pPla was introduced ( Yptb* ( Pla ) , [21] ( Table 1 ) . The use of an O-Ag-deprived strain of Y . pseudotuberculosis was necessary because the activity of Pla is inhibited in the presence of the lipopolysaccharide side chains [39] . We previously showed that the plasminogen activator activity of the recombinant Yptb* ( Pla ) strain is similar to that of Yp . wt [21] . Each of the four strains was inoculated in the mouse ear pinna at a dose of 5 , 000 cfu . This loading dose was found in an earlier work to produce well-developed lymphadenites exhibiting distinct features of Y . pestis versus Y . pseudotuberculosis infection [16] . Two days after infection , the animals were euthanized and the ipsilateral superficial parotid dLN was taken for microscopic examination . Immunostaining of dLNs infected with either Yp . wt or Yp ( ∆Pla ) evidenced diffuse and infiltrating bacterial projections towards the LN center in both cases ( Fig 1A ) . The similar pattern of bacterial infiltration between pPla-positive and -negative Y . pestis was confirmed at higher magnification ( Fig 1B ) . In contrast to Y . pestis , Yptb* and Yptb* ( Pla ) assumed a less diffuse spatial distribution , forming compact bacterial patches located at the dLN periphery ( Fig 1A ) . However , at higher magnification , Pla-expressing Y . pseudotuberculosis bacteria were often found to form less densely packed colonies than Yptb* ( Fig 1B ) , suggesting that pPla might slightly increase the diffusing potential of Y . pseudotuberculosis . Therefore , although pPla may participate in the infiltrating pattern that typifies a bubo , its role appears to be minor . This plasmid is not responsible for the difference in bacterial containment observed between Y . pestis and Y . pseudotuberculosis , and is not a requisite for Y . pestis infiltration of the dLN . We previously showed that on day 2 pi , the dLNs of mice infected id with Y . pestis exhibited destructive lesions leading to alterations of the tissue density and a breakdown of the functional organization of the organ , while dLNs of mice infected with Y . pseudotuberculosis displayed peripheral abscesses but kept an otherwise normal architecture [16] . To estimate the contribution of pPla to the destruction of the LN architecture , mice were infected with Yp . wt , Yp ( ΔPla ) , Yptb* and Yptb* ( Pla ) and LN histopathology was examined at 48h pi . Hematoxylin-eosin ( HE ) staining of the dLNs infected with either Yp . wt or Yp ( ΔPla ) revealed comparable heterogeneous tissue structures with zones of tissue depletion and necrosis ( Fig 2 , HE ) . Disruption of the LN follicular organization by both pPla-positive and -negative Y . pestis was also seen on sections immunostained to reveal B lymphocytes: these cells no longer occupied the outermost regions of the organ , as in normal LNs . They composed instead fragmented islets that were not restricted to the periphery ( Fig 2 , BL ) . LNs infected with Yptb* or Yptb* ( Pla ) ( Fig 2 ) displayed in both cases preserved tissue density ( Fig 2 , HE ) and the follicular architecture was also conserved , as B cells were located at the organ periphery or homogeneously forced inward by an organized and contained inflammation ( Fig 2 , BL ) . A grouping analysis of the histology lesions confirmed that the lesional profiles clustered according to the infecting species , and not to the presence of Pla ( S1 Fig ) . These findings thus show that histological features characteristic of the plague bubo , i . e . disappearance of the organ functional architecture , destructive alterations of the tissue , and lack of an organized innate cell response [10 , 11 , 16] , are specific for the plague bacillus , but independent of pPla . Therefore , pPla is neither required nor sufficient for the destruction of the LN structure that characterizes a Y . pestis infection . Overwhelming bacterial loads in mature buboes represent another distinctive feature of Y . pestis infections [11 , 12 , 16] . This characteristic has been linked to the presence of pPla by histological observations [15 , 24] , but no quantification of the effect of pPla on bubo bacterial loads has ever been performed . To quantitatively assess the impact of pPla on the bacterial ability to multiply in the dLN , 5 , 000 cfu of Yp . wt , Yp ( ∆Pla ) , Yptb* and Yptb* ( Pla ) were inoculated id and cfu enumerations in the dLN were carried out on day 2 pi . As shown in Fig 3 , loss of pPla by Y . pestis resulted in a reduced proportion of infected dLNs ( 87 . 5% for Yp . wt versus 65% for Yp ( ∆Pla ) ) , and in dLNs that were infected the amount of pPla-cured bacteria was on average ~1 , 000 fold lower than that of the wild type , confirming that pPla is required for achievement of high Y . pestis loads in the bubo . Furthermore , in the presence of pPla mean Y . pseudotuberculosis bacterial titers increased significantly to reach levels similar to those of Yp . wt ( Fig 3 ) . Thus pPla , whether in Y . pestis or a Y . pseudotuberculosis , enhances bacterial expansion in the dLN . Since Pla is a multifunctional protein , we wanted to determine whether its capacity to promote bacterial multiplication in the dLN was due to its proteolytic activity . We constructed strain Yp ( PlaD206A ) ( Table 1 ) , which differs from Yp . wt by a single point mutation that was previously shown to abolish the proteolytic action of Pla [44] . In an E . coli expression system [44] as well as in Y . pestis [25] , the D206A Pla mutation rendered the bacteria unable to activate plasminogen in vitro . We confirmed here that the D206A mutation in Pla abolishes its plasminogen activator activity ( S2 Fig ) . Upon id injection of Yp ( PlaD206A ) , the dLN bacterial burden of the mutant strain was strongly decreased , to a level similar to that of the pPla-cured derivative ( Fig 3 ) . To ensure that this impaired growth in the dLN was caused by the mutation , a Km-labeled pPla plasmid carrying the wild type pla allele was introduced into strain Yp ( PlaD206A ) . The bacterial load of the complemented strain Yp ( PlaD206A ) ( pPlaKm ) reached levels comparable to those of the wild type Y . pestis strain ( Fig 3 ) . Therefore , the D206A mutation alone had the same impact on the capacity of the bacteria to survive and multiply in the draining lymph node as that of loss of the whole plasmid , highlighting the prominent role of the Pla catalytic activity for bacterial expansion in the dLN during bubonic plague . Pla is a major virulence factor of various Y . pestis strains upon id or sc infections [14 , 15 , 23 , 24 , 51] . However , some strains , such as Pestoides F and strain 358 , do not require pPla for full virulence [15 , 52] . In this study we used Y . pestis strain 6/69 because a pPla-cured derivative was available in our strain collection . To evaluate the contribution of pPla to the pathogenic potential of strain 6/69 , mice were infected id with 5 , 000 cfu of Yp . wt or Yp ( ∆Pla ) and monitored for 21 days after injection . The survival curves showed that the high virulence of the parental strain , which killed 83% of mice within 4 days , was strongly attenuated in the absence of pPla , with most of the animals being still alive ( Fig 4A ) and apparently in good health at the end of the observation period . This confirms that Pla is a major virulence factor also of Y . pestis 6/69 . When the D206A mutation was introduced into pla , the Yp ( PlaD206A ) resulting strain was as attenuated as the Yp ( ∆Pla ) derivative following injection in the ear pinna ( Fig 4A ) . Reintroduction of a pPla plasmid carrying a functional pla gene restored the pathogenicity of the mutant strain ( Fig 4A ) . Thus , in a bubonic plague model , host mortality strongly depends on the proteolytic activity of Pla . Furthermore , since we showed in this study that Pla is not required for Y . pestis 6/69 to produce large-scale tissue damage and bacterial infiltrating pattern , our data demonstrate that histopathological lesions and outcome are dissociated during bubonic plague . In contrast , production of heavy bacterial loads in the dLN correlates to the death of the animals , consistent with the possibility that reaching a sufficient level of bacterial infection in the proximal LN is a decisive step for plague pathogenicity . To further explore the pathophysiological repercussions of high bacterial titers in the dLN , we took advantage of the fact that pPla was able to boost Yptb* expansion in the dLN to levels as high as those achieved by Yp . wt , and we asked whether these increased bacterial titers would result in an enhanced virulence of the complemented strain . Accordingly , mice were infected id with Yptb* ( Pla ) and followed up for survival . The pPla-complemented strain killed more mice and more rapidly than did the parent strain , but the difference failed to reach statistical significance ( Fig 4B ) . The fact that Yptb* ( Pla ) was not as virulent as Yp . wt although the two strains reached similar bacterial loads in the dLN prompted us to explore the ability of the two strains to disseminate beyond the dLN . Spleen cfu titers at 48h did not significantly differ between Yp . wt and Yptb* ( Pla ) ( log10 medians = 6 . 1 and 5 . 3 , respectively , p = 0 . 0952 ) . Therefore , neither high bacterial loads in the dLN nor the ability to disseminate to the bloodstream are sufficient to cause a fatal outcome . Other Y . pestis-specific pathophysiological mechanisms must play a role during bubonic plague to promote efficient host killing . Pla-mediated bacterial expansion in the LN at 48h may result from: ( i ) a higher capacity to reach the dLN , ( ii ) an increased multiplication rate and/or iii ) a lower death rate of the bacteria in the organ . Cfu enumerations of Pla-negative and wild type Y . pestis bacteria at an early time point ( 24h ) pi showed that they were present in similar amounts in the dLN ( S3 Fig ) , indicating that pPla is not required for the initial colonization of the dLN and that the difference in bacterial loads observed at 48h pi results from mechanisms that take place after translocation to and multiplication in the LN . High magnification images of 48h dLN preparations immunostained to highlight bacteria revealed differences between wild type and Pla-deficient Y . pestis infections that were not visible at lower scale . In dLNs infected with Yp . wt , large clusters of bacteria with an intact shape were visible ( Fig 5 ) . In contrast , sections of dLN infected with Yp ( ∆Pla ) displayed a fragmented staining pattern , made of irregular dots suggestive of bacterial debris ( Fig 5 ) . Similar observations were made on Yp ( PlaD206A ) -infected dLN sections ( S4 Fig ) . A consistently low number of intact bacteria per section ( 0 to 12 ) could be seen across all ( N = 26 ) Yp ( ∆Pla ) - or Yp ( PlaD206A ) -infected dLN preparations examined . The massive difference in bacterial integrity between wild-type and Pla-deficient Y . pestis was highly consistent . No exceptions were found among all infected LN preparations examined . These results thus indicate that a major role of Pla in the dLN is to prevent bacterial destruction , presumably by counteracting the host immune system and/or overcoming nutritional limitations .
During the evolutionary process of its emergence from Y . pseudotuberculosis , Y . pestis acquired the multifunctional protein Pla , which is a powerful determinant of virulence and dissemination [23 , 25] . The best-characterized function of Pla in vitro is the up regulation of the host fibrinolytic system by both proteolytic transformation of the precursor plasminogen to active plasmin and inactivation of plasmin inhibitors [28 , 53] . Plasmin , a broad-spectrum serine protease , is the main fibrin-clot degrading enzyme and is thus central to the coagulation/fibrinolysis balance . Because its targets include procollagenases and structural proteins of interstitial matrices and basement membranes , it is also important in connective tissue homeostasis [54] . Direct or indirect activation of the host plasminogen is a common invasive strategy among pathogenic bacteria belonging to diverse genera , such as Streptococcus , Staphylococcus , Borrelia , Helicobacter , Bacillus , Salmonella and Leptospira [26 , 55–57] . This strategy was also found in parasites and fungi [58–60] . It has been suggested that a function of bacterial plasminogen activating systems is to destabilize host barriers created by fibrin and extracellular protein networks to enable bacterial expansion [53 , 55 , 61 , 62] , in a way reminiscent of the use of plasmin by metastatic cancer cells [63] . Although there is little in vivo evidence to support this “bacterial metastasis” hypothesis , there have been several reports of bacterial plasminogen activators promoting the crossing of reconstituted extracellular matrices ( ECM ) and basal membranes in vitro [55 , 61 , 64] . The dramatic LN disruption observed in bubonic plague , associated with exceedingly high amounts of bacteria infiltrating the organ , led us to speculate that , within the frame of the bacterial metastasis model , these two features were pathogenetically linked . However , using Y . pestis mutants lacking Pla or its proteolytic activity in a mouse bubonic plague model , we found that extensive tissue damage and uncontrolled bacterial burdens are uncoupled . The destructive alterations of the bubo characteristic of Y . pestis infections do not require the Pla-encoding plasmid pPla , which is nonetheless key to bacterial outgrowth in the organ . Likewise , introduction of pPla in Y . pseudotuberculosis increased the bacterial load up to wild type Y . pestis levels , but did not result in severe histological alterations of the dLN . Hence , the mode of action of Pla underlying its virulence potential is not primarily to extensively disorganize the dLN matrix protein network to clear the way for bacterial spread . Conversely , in the absence of pPla the tissue breakdown of the Y . pestis-infected dLN is not sufficient to promote bacterial accumulation . Since Pla proteolysis is not involved , the mechanisms leading to dLN damage remain to be determined . Inflammatory responses to infections are normally accompanied by tissue destruction in and around infectious foci , chiefly owing to the release of various polymorphonuclear neutrophil ( PMN ) toxins , among which matrix metalloproteases and the serprocidin family of antibiotics proteins degrade most of the ECM components [65–67] . Therefore the PMN response visible in Y . pestis-infected LNs , although disorganized , could at least in part account for the observed tissue alterations . However , Y . pseudotuberculosis-infected dLNs are abundantly infiltrated by PMNs [16] without exhibiting Y . pestis-like destructive lesions . Apoptosis of immune cells , a hallmark of severe sepsis [68 , 69] , may be another cause of the profound cell depletion of wild type Y . pestis-infected buboes , but is unlikely to play an important role during the less severe infection caused by the Pla-deficient strains . While pPla is not the determinant of LN destructions , our observations confirm quantitatively its critical importance in the formation of bacteria-ridden buboes . We previously observed [16] and we confirm here that Y . pseudotuberculosis is significantly less abundant than Y . pestis in the infected dLNs at 48h pi . This difference in bacterial load between Y . pestis and Y . pseudotuberculosis is completely abolished when pPla is introduced into Y . pseudotuberculosis . pPla is thus a key genetic element to endow the bacteria with the capacity to survive and multiply in the dLN , and we further show that this capacity is due to the proteolytic activity of Pla . This protein could act on the bacterial load either by protecting the bacteria from the bactericidal action of innate immune defenses , or by providing them with an environment favorable for their growth . Our observation that the LNs of mice infected with Y . pestis strains lacking Pla catalytic activity contained many bacterial debris with few intact Y . pestis cells , reveals that Pla protects bacteria from undergoing lysis in the host . It is likely that most of the bacterial destruction takes place in the dLN , instead of the bacterial debris being drained from the dermis , because on day 1 pi , live Yp ( ∆Pla ) cells are present in quantities comparable to those of wild type Y . pestis , their titers subsequently declining between 24h and 48h . Among the innate bacteriolytic factors that have been tested , Yp ( ∆Pla ) is resistant to complement [23] but sensitive to the cationic antimicrobial peptides ( CAMPs ) human LL-37 and murine CRAMP [29] . CAMPs are small amphipathic molecules that bind to lipid components ( hydrophobic region ) and phospholipid groups ( hydrophilic region ) of the bacterial cell membranes , thereby causing disintegration of the lipid bilayer structure [70] . It has been reported that some CAMPs are targets of Pla and other bacterial omptins in vitro , and the proteolytic function of Pla prevents bacteriolysis by LL-37 and CRAMP [29 , 71–73] . This protection , however , was only effective in the absence of the F1 capsule , which is normally expressed in the bubo . This implies that degradation of the above CAMPs is not likely to account for the dramatically different survival rates in the bubo between wild-type and Pla-defective Y . pestis strains . Other possible mechanisms of Pla-mediated protection against in vivo bacteriolysis include targeting of other factors of the immune system or providing essential nutrients requiring a proteolytic degradation to become available to bacteria in the LN environment . It is also worth noting that removal of pPla from Y . pestis or inactivation of the proteolytic activity of Pla impaired the bacterial load in the dLN to an extent higher than that of the naturally pPla-negative Y . pseudotuberculosis strain . Y . pseudotuberculosis , the ancestor of Y . pestis , is an enteropathogen that has a tropism for lymphatic tissues and in particular for the mesenteric lymph nodes during its transit through the intestinal tract . Numerous genes were either lost or inactivated in Y . pestis after it evolved from Y . pseudotuberculosis [22 , 74] . It is thus possible that during its evolution , Y . pestis lost some of the Y . pseudotuberculosis ancestral functions involved in bacterial survival in LNs , while acquiring pPla , which conferred a higher capacity to survive and multiply in these lymphoid organs . Interestingly , the same phenomenon was recently reported in a study involving Y . pestis Pestoides F , a pPla-negative intermediate between Y . pseudotuberculosis and modern Y . pestis . In a murine model of pneumonic plague , Pestoides F was more fit than ∆Pla-Y . pestis to colonize the lung , suggesting that Pestoides F was still harboring the Y . pseudotuberculosis ancestral functions subsequently lost during the evolution of Y . pestis [75] . Another consequence of this evolution may be the bottleneck effect that restricts access to the dLN from the dermis [76] . It is interesting that in our work and in other studies [76–78] a consistent 10% fraction of animals showed no bacteria in the dLN following id challenge with Y . pestis , while we detected bacteria in the dLN of all mice that had received Y . pseudotuberculosis cells in the ear pinna . This suggests that the bottleneck effect is linked to the loss of one or several Y . pseudotuberculosis function ( s ) involved in the access to lymphoid tissues , and that the acquisition of pPla did not fully restore the ability to overcome this effect . In conclusion , this study unraveled pathophysiological relationships between components of the host response to Y . pestis infection and the role of pPla in this process . While high bacterial titers , tissue destructions and virulence were expected to be tightly linked , we show here that the extensive histological lesions observed in the bubo do not require Pla and are not associated with mortality . In contrast , bacterial loads in the dLN correlate with mortality and are Pla-dependent . However , achieving a high bacterial burden in the dLN is not sufficient for full pathogenicity . It has been proposed that Pla contribution to virulence was to catalytically break down protein barriers that would confine the bacteria and restrain their spread . However , since Pla is involved neither in the initial colonization of the organ nor in dLN destruction , this mechanism is unlikely to be essential for the development of the bubo . Our results show that achieving high bacterial burdens in the LN might be a critical step in plague pathogenesis and that one major role of Pla is to protect Y . pestis cells from the bactericidal action of the dLN environment . | The hallmark of bubonic plague , a disease that ravaged Medieval Europe and is still prevalent in several countries , is the bubo , a highly inflammatory and painful lymph node , which is characterized by high concentrations of bacteria within a severely damaged organ . Yersinia pestis , the causative agent , expresses a surface protease , Pla , critical to the development of bubonic plague . This multitarget protease has the potential to activate the fibrinolytic pathway and to promote destruction of extracellular protein networks within tissues . Hence , it was expected that Pla was responsible for the tissue destructions of the bubo , and consequently , for bacterial propagation and virulence . However , we found , using various engineered Yersinia strains in a mouse model of bubonic plague , that Pla proteolytic activity was dispensable for lymph node alteration , but was required to achieve high bacterial loads in the organ . Further analysis showed that Pla is essential for preventing the bacteria from being destroyed in the host . Therefore , the role of Pla as a virulence factor is to protect Y . pestis survival and integrity in the host , rather than to assist its spread through tissue destruction . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Dissociation of Tissue Destruction and Bacterial Expansion during Bubonic Plague |
The fatty acid amide hydrolase ( FAAH ) regulates the endocannabinoid system cleaving primarily the lipid messenger anandamide . FAAH has been well characterized over the years and , importantly , it represents a promising drug target to treat several diseases , including inflammatory-related diseases and cancer . But its enzymatic mechanism for lipid selection to specifically hydrolyze anandamide , rather than similar bioactive lipids , remains elusive . Here , we clarify this mechanism in FAAH , examining the role of the dynamic paddle , which is formed by the gating residues Phe432 and Trp531 at the boundary between two cavities that form the FAAH catalytic site ( the “membrane-access” and the “acyl chain-binding” pockets ) . We integrate microsecond-long MD simulations of wild type and double mutant model systems ( Phe432Ala and Trp531Ala ) of FAAH , embedded in a realistic membrane/water environment , with mutagenesis and kinetic experiments . We comparatively analyze three fatty acid substrates with different hydrolysis rates ( anandamide > oleamide > palmitoylethanolamide ) . Our findings identify FAAH’s mechanism to selectively accommodate anandamide into a multi-pocket binding site , and to properly orient the substrate in pre-reactive conformations for efficient hydrolysis that is interceded by the dynamic paddle . Our findings therefore endorse a structural framework for a lipid selection mechanism mediated by structural flexibility and gating residues between multiple binding cavities , as found in FAAH . Based on the available structural data , this exquisite catalytic strategy for substrate specificity seems to be shared by other lipid-degrading enzymes with similar enzymatic architecture . The mechanistic insights for lipid selection might assist de-novo enzyme design or drug discovery efforts .
Fatty acid amide hydrolase ( FAAH—Fig 1 ) [1–3] and monoacylglycerol lipase ( MAGL ) [4] are two hydrolytic enzymes that mainly regulate the endocannabinoid system . These enzymes act on the endocannabinoid signaling mostly through hydrolysis of the endogenous substrates anandamide and 2-arachidonoylglycerol ( 2-AG ) , respectively . [5–7] Both FAAH and MAGL can also hydrolyze other lipids although less efficiently . [8–10] Regulation of the endocannabinoid system is a promising strategy for treating pain , cancer , and other inflammatory-related diseases , suggesting both FAAH and MAGL as effective drug targets . [11–18] It is therefore crucial to decipher the mechanisms for substrate selection and catalysis , which might help in the rational design of new therapeutics that act by modulating the endocannabinoid system . Building on our own and other relevant studies on FAAH catalysis and inhibition , [19–33] we provide here an elucidation of the main structural and kinetic features involved in substrate selection during FAAH catalysis . Over the last decade , a wealth of experimental data has been generated on the structural properties and catalytic activity of FAAH . [5 , 7] FAAH is a homodimeric enzyme that can accommodate its substrate into a complex architecture of the catalytic site , which is characterized by three binding channels ( Fig 1 ) . [1] Substrates are thought to reach the catalytic site via a membrane access ( MA ) channel where two charged residues ( Asp403 and Arg486 ) may favor the entrance of the polar head groups of fatty acid molecules . The catalytic action of FAAH occurs in the core of the binding site where an unusual catalytic triad ( Ser241–Ser217–Lys142 ) performs the hydrolysis of the substrate , while an oxyanion hole ( Ile238–Gly239–Gly240–Ser241 ) keeps the substrate properly oriented for hydrolysis . Tightly connected to the catalytic region , a cytosolic port ( CP ) allows the exit of the leaving group after substrate hydrolysis . A third acyl-chain binding ( AB ) cavity , adjacent to the MA channel , seems to contribute to the proper accommodation of the substrate during catalysis . [1 , 7] The enzymatic activity of FAAH has been measured using different enzyme preparations and substrates , [9 , 10] showing that the enzyme displays a preferential hydrolytic activity for arachidonoyl substrates ( 20:4 ( Δ5 , 8 , 11 , 14 ) ) , such as anandamide . Other substrates such as oleamide or palmitoylethanolamide , which contain a lower degree of unsaturation , are hydrolyzed at significantly slower and time-dependent rates ( ~50 to 100 times slower than anandamide after 5 minutes of incubation ) . [10] However , the structural and kinetic properties that regulate the preference of FAAH for its main substrate anandamide are still largely unknown . Based on structural data of FAAH , Mileni et al . originally proposed that the MA/AB boundary residue Phe432 , in cooperation with the flexible residue Trp531 , could act as a dynamic paddle that directs and orients the substrate during catalysis . [34] This mechanistic hypothesis was further corroborated by our recent computational study , which suggested that anandamide is not fully locked into the AB channel during catalysis , as previously supposed . [7 , 25] Rather , our study suggested that anandamide assumes hydrolysis-prone conformations by moving its flexible arachidonoyl chain between the MA and AB cavities interceded by the dynamic paddle residues that act as a gate between these two binding cavities . To test this hypothesis and to elucidate the enzymatic strategy for substrate selectivity , we carried out long-timescale molecular dynamics ( MD ) simulations of FAAH embedded in a realistic membrane/water environment in complex with three substrates with different hydrolysis rates ( anandamide > oleamide > palmitoylethanolamide ) for both wild type and double mutant ( Phe432Ala and Trp531Ala ) systems . These unbiased microsecond MD simulations were accompanied by corresponding mutagenesis and kinetic experiments , which further validated the crucial role of Phe432 and Trp531 for substrate specificity . The integration of our theoretical and experimental results suggests indeed that lipid selection is attained through interplay of substrate and protein flexibility regulated by the dynamic paddle . In particular , the selective binding of specific lipid substrates seems to be regulated by the dynamic paddle residues that act as a gate between multiple binding pockets and that actively favor the formation of pre-reactive conformations for the preferred fatty acid substrate anandamide .
We considered six model systems , each based on the X-ray structure of rat FAAH in complex with the anandamide analogue methyl arachidonoyl fluorophosphonate ( MAFP ) , solved at 2 . 8 Å resolution ( PDB code: 1MT5 ) [1] . As in our previous computational studies of FAAH , [24 , 25] these systems include the trans-membrane residues ( 9-29 ) and the N terminus , which were built by homology modeling . Three of these systems are formed by the wild type ( wt ) FAAH protein in complex with anandamide , oleamide , and palmitoylethanolamide ( PEA ) and are labeled wtFAAH/anandamide , wtFAAH/oleamide , and wtFAAH/PEA , respectively . The dynamic paddle residues ( Phe432 and Trp531 ) were both mutated into alanine , obtaining the three corresponding double mutant systems ( mut ) mutFAAH/anandamide , mutFAAH/oleamide , and mutFAAH/PEA . The initial binding mode of anandamide within the FAAH active site was taken from our previous studies . [24 , 25] Oleamide and PEA were docked using the Autodock 4 . 2 package . [35] The acyl chain of all three substrates was initially located in the MA channel , as suggested in refs . [1 , 7] Full details on the docking calculations are reported in the S1 Text . The six FAAH/substrate systems were embedded into an explicit membrane environment formed by 480 1-palmitoyl-2-oleoyl-phosphatidylethanolamine ( POPE ) lipids . [1] Phosphatidylethanolamine is the major phospholipid of Escherichia coli membranes , [36] which was used as an expression system to produce purified proteins for the crystallization of the rat FAAH protein . [1] Each protein/membrane complex was hydrated with TIP3P[37] waters and 8 Cl- counterions were added to neutralize the total charge . The size of the final systems was approximately ~145 Å x ~95 Å x ~140 Å , with ~35 , 500 water molecules and ~480 lipids , resulting in a total number of ~200 , 000 atoms for each system . The all-atom AMBER/parm99 force field was adopted for the FAAH protein . The anandamide , oleamide , and PEA lipids were treated with the General Amber Force Field ( GAFF ) [38] and the atomic charges were derived via the RESP fitting procedure . [39] Force field parameters for the lipid bilayer were taken from our previous studies on FAAH catalysis . [24 , 25] Force field parameters for the non-standard residues were carefully validated via electronic structure calculations , confirming the accuracy of the force field parameters used here . [25] The LINCS[40] algorithm was used to constrain covalent bonds involving hydrogens , allowing a time integration step of 2 fs . All the simulations were performed using GROMACS 4 . [41] Long range electrostatic interactions were calculated with the particle mesh Ewald method with a real space cutoff of 10 Å . Periodic boundary conditions in the three directions of the Cartesian space were applied . The systems were coupled to a Nosé-Hoover thermostat[42 , 43] at a reference temperature of 310 K , and to an isotropic Parrinello-Rahman barostat[44] at a reference pressure of 1 bar both with a coupling time of 1 ps . The following simulation protocol was adopted: the systems were minimized using a steepest descent algorithm and then slowly heated up to 310 K in 1000 ps . This approach has been shown to be efficient for the equilibration phase of large biological systems ( ∼200 , 000 total atoms ) . [25 , 45 , 46] Under these conditions , POPE is a liquid–crystalline bilayer , [47 , 48] ensuring a realistic environment for the FAAH protein . Crucial membrane properties for the pre-equilibrated POPE membrane used here were carefully analyzed and have been published in our previous paper . [25] The simulations were performed with deprotonated Lys142 , as proposed for the catalytic mechanism of FAAH . [1 , 49] Standard protonation states were maintained for the other protein residues . Approximately ~500–550 ns of MD simulations were collected in the NPT ensemble under standard conditions , for each of the six systems , resulting in a total of ~3–3 . 5 μs of dynamics . Coordinates of the systems were collected every 10 ps , for a total of ~50 , 000 frames for each run . Statistics were collected for the equilibrated systems after ~150 ns . Binding free energies ( ΔGBind ) for the three ligands in the wt and mut FAAH systems were estimated by the Molecular Mechanics/Poisson Boltzmann Surface Area ( MM/PBSA ) [50 , 51] approach implemented in the Amber 12 package . [52] Full details are given in the S1 Text . Conformational and statistical analyses ( see below ) were performed over the equilibrated trajectories ( last ~350 ns of MD ) for all six simulations systems ( ~35 , 000 frames for each system ) . In all cases , both monomers yielded highly similar averages indicating that the system was well equilibrated ( see S1 Text and S2-S3 Tables in S2 Text ) . Statistics were thus accumulated over both monomers resulting in an aggregate total sampling time of ~700 ns per system ( ~70 , 000 frames were considered for each system , with a total of ~420 , 000 analyzed frames ) . Data for each separate monomer of all the studied systems are also reported in S1 Text , S8 and S9 Figs , and S2 and S3 Tables in S2 Text . The root-mean-square-deviation ( RMSD ) after the equilibration time ( ~150 ns ) was used as stability indicator , with respect to the crystal structure ( S3–S6 Figs ) . The location of the substrates in either the MA or AB channel during the trajectories was identified by calculating the minimum distances d between the center of mass of the last three atoms of each substrates and the centers of mass of residues of the MA channel [ ( Asp403 , Ile407 , Arg486 , Ile530 ) —d-MA] , of the AB channel [ ( Tyr335 , Glu373 , Arg428 , Phe527 ) —d-AB] , and of the MA/AB transition region [ ( Phe381 , Phe432 , Trp531 ) —d-T] , as in Palermo et al . [25] In detail , the substrates were located in MA if d-MA < 6 Å and d-AB > 6 Å; and in AB if d-MA > 6 Å and d-AB < 6 Å . If both these conditions were false and if d-T < 5 Å , the substrate’s acyl chain was considered to be located in the T region . The cutoff distances were chosen considering that the distance connecting the center of masses of the MA and AB channels is ~16/17 Å . Within this distance , ~6 Å each are occupied by the MA and AB channels , respectively ( for a total of ~12 Å ) . The remaining ~4/5 Å therefore are considered as MA/AB interface region . The g-mindist tool of the GROMACS 4 package for MD analysis was used ( S8 and S9 Figs ) . Full details on the substrate location are reported in S1 Text . Conformational changes of the unsaturated lipids anandamide and oleamide were classified using the Applegate and Glomset notation . [53] Accordingly to the latter , unsaturated lipids assume different conformations that can be grouped in three major shapes: ( i ) “elongated”; ( ii ) “hooked” , and ( iii ) “curved” . [54 , 55] Due to the absence of double bonds in the palmitoyl chain of PEA , conformational changes were followed in this case by considering the change of the lipid length ( end-to-end distance ) with respect to the initial configuration . This allowed identification of “elongated” , “hooked” , and “curved” conformations too . Full details on the conformational analysis of the FAAH substrates considered in this study can be found in the S1 Text . Conformational changes of the key Phe432 and Trp531 residues within the FAAH binding site were characterized by using the torsion angle φ along their Cα-Cβ axis , namely φF for Phe432 and φW for Trp531 . The pre-organization of the FAAH active site to perform substrate hydrolysis was assessed via the definition of catalytically significant conformational states ( for simplicity , here referred to as “pre-reactive states” ) of the FAAH/substrate complex . These substrate conformations are those characterized by optimal distances and orientations of key structural parameters involved in the enzymatic reaction , as explained in detail in Palermo et al . [25] The structural parameters used here ( S1 Text and S7 Fig ) were identified based on several computational and crystallographic studies , [1 , 22 , 27 , 31 , 32 , 34 , 56] including our recent quantum mechanics/molecular mechanics ( QM/MM ) study of anandamide hydrolysis in FAAH . [24] It is important to mention that our definition of pre-reactive states only concerns the predisposition of the substrate to undergo hydrolysis , given the proper relative orientation of the substrate with respect to the catalytic residues in the binding pocket of FAAH . Finally , to analyze the role of the dynamic paddle in pre-reactive conformations , we report the trend of the φF and φW angles with respect to the location of the anandamide acyl chain in the MA , T , and AB regions , using polar coordinates . Acetonitrile was purchased from Sigma Aldrich ( Italy ) . High purity standard anandamide and PEA were purchased from Cayman Chemical ( Ann Arbor , MI , USA ) . The rat ( r ) FAAHΔTM ( 97-1722bp ) cDNA was amplified by PCR from the cDNA clone 7370226 purchased from Open Biosystem ( Thermo Scientific ) using the following primer pair: forward 5’-GGGAATTCCATATGGGGCGCCAGAAGGCCC-3’; reverse 5’-ATAGTTTAGCGGCCGCTCAATGATGATGATGATGATGAGGGGTCATCAGCTG-3’ containing the NdeI and NotI restriction sites . A ( 6x ) Histidine tag was introduced in the reverse primer sequence ( bold ) . The amplified rFAAHΔTM was then cloned in pMALc5x vector in frame with the N-terminal MBP and finally introduced into Escherichia coli Rosetta gami 2 ( DE3 ) -pLysS strain . The F432A and W531A mutants were generated by site-directed mutagenesis using the QuickChange II Site-Directed Mutagenesis Kit ( Agilent Technologies , Santa Clara , CA , USA ) and the construct MBP-rFAAH-6xHis pMALc5x as template . The following primers were designed to introduce the single point-mutations Phe432Ala in rFAAH: forward 5’-CCTCGGCTGGCAGCCGCTCTCAACAGTATGCGTC-3’ and reverse 5’-GACGCATACTGTTGAGAGCGGCTGCCAGCCGAGG-3’; Trp531Ala in rFAAh forward 5’-GGCTACTTTGGGATATCGCGGACATCATCCTGAAG-3’ and reverse 5’-CTTCAGGATGATGTCCGCGATATCCCCAAAGTAGCC-3’ . Overexpression of the MBP-rFAAH-6xHis proteins was achieved in E . coli strain Rosetta gami 2 ( DE3 ) pLysS ( Novagen ) by growing cells in LB medium at 37°C to an OD600 of 0 . 6 , followed by induction with 0 . 25 mM isopropyl β-D-thiogalactopyranoside for 16 hours at 25°C . Cells were then harvested by centrifugation , resuspended in buffer [50 mM sodium phosphate pH 7 . 4 , 0 . 2 M sodium chloride , 10 mM imidazole] , and lysed by sonication . The lysate was incubated for 1h at 4°C with benzonase nuclease , 2 μM MgCl2 and 1% Triton-X100 . After centrifugation at 14 , 000 rpm for 30 min , the supernatant was incubated for 2 hours with NiNTA Agarose ( Qiagen GmbH , Hilden , Germany ) and washed with buffer containing increasing concentrations of imidazole ( 20 mM , 50 mM ) . Elution was performed with buffer containing 0 . 25 M imidazole . The buffer of the eluted sample was exchanged to 20 mM phosphate pH 7 . 4 , 200 mM NaCl , 0 . 07% chaps . Enzymes ( wt and mutants ) were dissolved in Tris-HCl 100 mM , pH 7 . 4 buffer and preincubated at 37°C for 10 minutes . Each substrate was then individually incubated at different concentrations ( 3 . 3 , 6 . 25 , 12 . 5 , 25 , 33 , 50 μM ) with the enzymes . The highest substrate concentration was 50 μM and 33 μM for anandamide and PEA , respectively , due to limited substrate solubility . The final enzyme concentration was kept at 10 nM . Reaction was stopped after 30 minutes at 37°C by addition of cold acetonitrile , assuming that a steady state was reached ( Michaelis Menten condition ) . After mixing and centrifugation , an aliquot of the supernatant was used for UPLC-MS/MS analysis . Each experiment was run in triplicate . Enzyme velocity was calculated as pmoles of substrate consumed per minute per μg of enzyme and plotted versus the concentration . Origin Pro 8 . 6 ( OriginLab Corporation ) was used to fit the velocity/concentration profiles and to determine the Michaelis Menten kinetic parameters ( Vmax and Km ) . Enzymes ( wt and mutants ) were dissolved at 10 nM concentration in Tris-HCl 100 mM , pH 7 . 4 buffer and preincubated at 37°C for 10 minutes . The reaction was then started by adding the substrates ( anandamide and PEA ) simultaneously up to a final 10 μM concentration . Final enzyme to substrate molar ratio was then 1 to 500 . At different time points ( 0 , 5 , 15 and 30 minutes ) an aliquot of the mixture was taken and the reaction was stopped by adding 4 volumes of cold acetonitrile . After mixing and centrifugation , an aliquot of the supernatant was used for UPLC-MS/MS analysis . Each experiment was run in triplicate . The incubation with rat liver microsomes ( Tebu-Bio , Le Perray-en-Yvelines , France ) was also prepared to run additional competition experiments ( final concentration 0 . 1 mg/ml in the buffer ) . Anandamide and PEA levels were measured by LC-MS/MS on a Xevo-TQ triple quadrupole mass spectrometer coupled with a UPLC chromatographic system . Analytes were separated on a reversed phase BEH C18 column , using a linear gradient of acetonitrile in water . Column , UPLC , and MS were purchased from Waters Inc , ( Milford USA ) . Quantification was performed monitoring the MRM transitions of the analytes . Analyte peak areas were compared with a standard calibration curve prepared in the 1 nM to 10 mM concentration range .
Here , we considered five model systems together with the one reported recently in Palermo et al . ( see Methods section ) . [25] Thus , the six model systems used for the comparative analysis are: wild type ( wt ) and mutated ( mut ) FAAH in complex with either anandamide , oleamide , or palmitoylethanolamide ( PEA ) . The three mutFAAH systems are lacking the dynamic paddle residues ( i . e . with both mutations Phe432Ala and Trp531Ala ) . After equilibration ( ~150 ns for each system ) , FAAH is stable in all simulations , meaning that the backbone RMSD of the protein with respect to the initial crystallographic structure oscillates around 3 ± 0 . 1 Å for all six systems ( see detailed data in SI ) . Interestingly , these extended simulations evidenced different conformations and locations of the three lipids within the FAAH active site , as induced by the presence/absence of the key Phe432/Trp531 dynamic paddle . In our analysis , these different substrate configurations are related to the propensity of FAAH to perform substrate hydrolysis , according to the definition of catalytically significant conformations ( i . e . pre-reactive states of the FAAH/substrate complex—see the Methods section ) . [25] As discussed in detail in our previous study , [25] when initially located in the MA channel , anandamide reversibly transfers its arachidonoyl chain to the adjacent AB channel ( Fig 2 ) , without ever showing the arachidonoyl chain fully locked into the AB cavity . After the equilibration time , 69% of the total anandamide configurations are located in the T region ( where T stands for Transition region , which is located between the MA and AB pockets , Fig 3 ) , while the population of the MA and AB channels is statistically less important ( 24% and 7% , respectively ) . Interestingly , pre-reactive conformations ( 27% of the production run ) are mostly sampled while the lipid acyl chain is located in the T region ( 72% ) and fewer conformations are located in the MA ( 21% ) and AB ( 7% ) channels . The anandamide’s tail preferentially assumes “curved” conformations due to the van der Waals interactions between its Δ 8/ Δ 11/ Δ14 double bonds and the aromatic rings of Phe432 and Phe381 ( Figs 3 and 4 ) . Phe432 and Trp531 trigger the MA<–>AB transitions of anandamide , assuming different configurations that open and close the MA channel ( Fig 2 ) . This mechanism favors the proper location of pre-reactive conformations of anandamide between the two channels , as evidenced by the polar plot of the φ angles of Phe432 ( φF ) and Trp531 ( φW ) with respect to the location of the substrate in pre-reactive states ( Fig 5 ) . In detail , for pre-reactive conformations in the MA channel ( red plot ) , the φF ( green dots ) ranges from ~120° to ~180° with the opening of MA . During the MA<–>AB transfer ( cyan plot ) , the φF shows a bimodal distribution , given the rotation of φF from ~150° ( “open” MA channel ) to ~60° ( “closed” MA channel , as observed in the X-ray structure ) , which permits the MA<–>AB transfer of the arachidonoyl chain . Trp531 contributes to this transfer , rotating φW by about ~35/40° ( magenta dots ) . When pre-reactive conformations are in AB ( yellow plot ) , Phe432 mainly closes the MA channel ( φF ~65° ) , opening the adjacent AB channel , while Trp531 rotates φW from ~145° to ~180° . For the mutant form of FAAH , we detect several MA<–>AB transitions of the arachidonoyl chain ( S9 Fig ) . After equilibration , the percentage of total anandamide conformations within the MA and AB channels is 58% and 37% , respectively , while only 5% of total anandamide conformations are found in the T region ( Fig 3 ) . This is primarily due to the absence of van der Waals interactions between the anandamide Δ14 double bond and Phe432 ( in this system mutated to Ala ) , which instead are present in the wt-system . As a result , the arachidonoyl chain is mainly “elongated” , whereas it was mainly “curved” in wtFAAH ( Fig 4 ) . As a consequence , pre-reactive conformations are not sampled in the mutFAAH system , as anandamide never locates in the T region assuming the specific “curved” conformations that characterize the pre-reactive states in the wt-system ( Figs 3 and 4 ) . In this system , oleamide reversibly transfers its acyl chain from the MA to the AB channel , resembling the behavior observed for anandamide in the wt-system ( Fig 2 ) , although with less frequent transfers . In both enzymatic subunits , Phe432 shows several dihedral transitions , thus assuming two different configurations that lead to the “open” ( φF ~160° ) and “closed” ( φF ~65°—X-ray ) MA channel configurations , whereas Trp531 does not undergo any conformational transitions . After equilibration , the percentage of oleamide conformations within the MA channel is 75% , while the T region and the AB channel are poorly populated ( 24% and 1% , respectively—Fig 3 ) . Pre-reactive conformations are sampled for 16% of the total equilibrated trajectory , therefore much less than for anandamide . The 78% of these conformations are located in the MA channel , 21% in the T region , and only a few are sampled in the AB channel ( 1%—Fig 3 ) . The different preferential location for oleamide in MA , compared to anandamide , can be explained by the presence of only one double bond ( Δ9 ) of the oleoyl chain , which results in weaker interactions with the aromatic residues Phe381/Phe432 ( Fig 3 ) . This also explains the formation of “hooked” configurations of the oleoyl chain ( Fig 4 ) . When oleamide is in a pre-reactive state and located in MA , φF shows a bimodal distribution ( red plot in Fig 5B—φF angle is shown in green dots ) , therefore opening ( φF ~180° ) and closing ( φF ~60° ) the MA channel . This bimodal behavior of the dynamic paddle mechanism ( which is similarly detected for pre-reactive conformations in T and AB ) allows the proper location of the shorter oleoyl chain in the MA channel , via the formation of van der Waals interactions with the Δ9 double bond of the lipid and Phe432 ( Fig 3 ) . During the simulations , oleamide never transfers its acyl chain from the MA channel into the adjacent AB channel ( S9 Fig ) . This explains that 70% of the total oleamide configurations are located in the MA channel , while only a few conformations of the lipid are located in the T region ( 3% ) and no conformations are detected in the AB channel ( Fig 3 ) . When located within the mutFAAH active site , oleamide is preferentially “elongated” , as the oleoyl acyl chain is not bent by Phe432 ( Fig 4 ) . As in the mutFAAH/anandamide system , pre-reactive conformations are not sampled for oleamide in complex with the mutFAAH protein . This highlights the crucial role of the Phe432/Trp531 gating residues in inducing specific conformations for hydrolysis of these lipids . Interestingly , oleamide spontaneously unbinds from the FAAH active site and locates within the lipid bilayer for 27% of the overall production run . Oleamide unbinding occurs in both FAAH subunits ( at ~425 ns in mnr-A and at ~350 ns in mnr-B ) and via the same mechanism . As previously suggested , [1 , 7 , 25] two charged residues ( Asp403–Arg486 ) facilitate the passage of the substrate through the MA channel , H-bonding to the polar head group of the substrate ( S10 Fig ) . Surrounded by lipids , oleamide mainly assumes “hooked” conformations ( Fig 4 ) , in agreement with the proposal that FAAH substrates need to adopt a closed “hairpin-like” conformation to be transported across the membranes . [57 , 58] The mutation of Phe432 , which in the wt-system interacts with the oleamide Δ9 double bond , causes a destabilization of the oleoyl chain within the active site . In addition , oleamide is a primary amide that , therefore , does not have the ability to form H-bond interactions with the CP residue Thr236 , which is critical for leaving group departure , after substrate hydrolysis . [1 , 7 , 25] A detailed description of oleamide unbinding in the mutFAAH protein is reported in S1 Text and S10 Fig . The lipid remains mainly located in the MA channel in both the enzyme subunits , for the whole simulation time ( Fig 2C ) and no MA<->AB transfers of the PEA acyl chain occur . Phe432 assumes different conformations via the rotation of φF from ~65° to ~160° , allowing an optimal fit of the long palmitoyl chain into the MA channel . Trp531 does not undergo dihedral transitions during the simulation , further stabilizing the palmitoyl chain in the MA channel . After the equilibration time , 91% of the total PEA configurations are located in the MA channel and 9% locate in the T region ( Fig 3 ) . Pre-reactive conformations are observed for 11% of the production run . Most of these conformations are located in the MA channel ( 86% ) , whereas fewer conformations locate in the T region ( 14% , Fig 3 ) , and none in AB . The palmitoyl tail preferentially assumes “elongated” shapes ( Fig 4 ) , given the absence of unsaturation within the lipid . This prevents PEA from establishing specific interactions with Phe381/Phe432 and catalyzing the MA<–>AB switch , which is similar to the process observed with oleamide and anandamide . Therefore , the lipid acyl chain does not undergo any bending , which explains the fully elongated shapes and the absence of MA<–>AB transfers . Here , PEA transfers its palmitoyl chain from the MA to the AB channel in the early phase of the equilibration ( ~10/20 ns ) , in both FAAH monomers ( S9 Fig ) . After equilibration , conformations of PEA detected within the MA channel are statistically irrelevant ( 0% ) , whereas 5% of PEA conformations are located in the T region and 95% within the AB channel ( Fig 3 ) . The lipid remains anchored to the end of the AB channel throughout the simulations , strongly interacting with Tyr225 and Phe527 . Meanwhile , the head of the substrate is H-bonding with Thr236 at the top of the active site . These interactions favor the formation of “elongated” conformations ( Fig 4 ) . Pre-reactive conformations are sampled for 15% over the whole production run . Most of the pre-reactive conformations are sampled when the palmitoyl acyl chain is located in the AB channel ( 84% ) and in the T region ( 16% ) . These conformations are not sampled in the MA channel ( Fig 3B ) . To further characterize the proposed mechanism for substrate selection during FAAH catalysis , we expressed and purified the recombinant wt rat FAAH protein ( MBP-rFAAH-6xHis construct ) and we also introduced a single point mutation for each of the two paddle residues ( Phe432Ala and Trp531Ala ) . The activity of the purified wt and two mutant proteins was tested using enzyme kinetic experiments in the presence of different concentrations of each substrate ( anandamide and PEA ) , as reported in the method section . The enzymatic reactions were quenched after 30 minutes when a steady-state equilibrium was reached ( i . e . , Michaelis Menten condition ) . The Km value obtained for the wt protein in the presence of the anandamide substrate was equal to 5 . 26 μM , in excellent agreement with the results reported by Labar G . et al . ( Km = 5 . 31 μM ) [59] for the recombinant MBP-FAAH construct . The overlay of the kinetic curves obtained for anandamide shows that the enzyme velocity ( pmol of substrate consumed per minute per μg of protein ) is only slightly higher for the wt protein compared to the two mutant proteins ( Table 1 ) . Thus , neither of the two mutations seems to significantly affect the affinity of FAAH for the anandamide substrate under steady-state conditions . With the PEA substrate , we find an enzyme affinity that is 2-fold lower then the one for anandamide , with a Km equal to 12 . 53 μM for the wt protein . Here too , each of the two mutations only marginally affects the affinity of the enzyme for the PEA substrate ( Km values in Table 1 ) . Overall , steady-state conditions confirm that FAAH has better affinity for anandamide , over PEA , for wtFAAH . [9] We further performed competition experiments under non-equilibrium conditions , which is often reported to be the case for biochemical reactions in vivo . [60–62] These are performed on a mixture of the three proteins , i . e . the wtFAAH and the two Phe432Ala and Trp531Ala mutants , in the presence of both anandamide and PEA . The enzymatic reaction was quenched at different time points , within the initial 30 minutes of reaction , and the products were then analyzed by UPLC-MS/MS ( see Methods section ) . As reported in Fig 6 , for the wtFAAH recombinant protein , the rate of anandamide hydrolysis is 5 . 6 times faster than for PEA ( S6 Table in S2 Text ) . Single point mutants ( Phe432Ala and Trp531Ala ) showed similar decay rates for both anandamide and PEA substrates ( Fig 6 and S6 Table in S2 Text ) . We also tested the validity of our competition assay , described in the Methods section , on a rat liver microsomes preparation . As expected , we found that FAAH preferentially cleaves its main substrate , anandamide , rather than PEA ( S11 Fig; S7 Table in S2 Text ) in rat liver microsomes . This is in agreement with Desarnaud et al . , [10] who reported that the FAAH hydrolytic function is more efficient for anandamide than for PEA in rat brain microsomes . A consistently faster rate of hydrolysis for anandamide , compared to PEA , was observed in both the recombinant form of FAAH and in rat liver microsomes . This suggests that the microsomal membrane may not critically affect substrate selectivity in FAAH catalysis .
Here , long time-scale classical molecular dynamics simulations have been integrated with mutagenesis and kinetic experiments in order to clarify the molecular basis for substrate selectivity in FAAH catalysis . Extensive MD simulations of FAAH in complex with its main substrate anandamide have been compared with simulations where FAAH is in complex with less efficiently hydrolyzed substrates ( oleamide and palmitoylethanolamide ) . This comparative study has revealed that FAAH selectively accommodates anandamide into a multi-pocket binding site , and properly orients it in pre-reactive conformations for efficient hydrolysis . Mutagenesis and kinetic experiments have further highlighted the importance of Phe432 and Trp531 for substrate selection in competition assays in non-equilibrium conditions . The interplay between ligand and protein structural flexibility seems crucial for lipid selection during catalysis in FAAH , as mediated by the gating residues Phe432 and Trp531 that form the dynamic paddle , which facilitates the formation of pre-reactive conformations of the substrate/enzyme complex . Based on existing structural data , we propose that our results could be extended to other lipid-processing enzymes where the presence of multiple binding cavities and gating residues have been indicated to be relevant for enzyme selectivity and function . One example is MAGL , another endocannabinoid enzyme , which primarily hydrolyzes 2-arachidonoylglycerol . A broader validation of this structural framework for lipid selection , with additional experimental and/or theoretical investigations , would be very informative and applicable to de-novo enzyme design and drug discovery efforts . [79] | We describe a new structural enzymatic framework to regulate substrate specificity in lipid-degrading enzymes such as fatty acid amide hydrolase ( FAAH ) , a key enzyme for the endocannabinoid lipid signaling that hydrolyzes a variety of lipids , however with different catalytic rates . The identified novel mechanism and key features for lipid selection in FAAH are then analysed in the context of other relevant lipid-degrading enzymes . Through the integration of microsecond-long molecular dynamics simulations with mutagenesis and kinetic experiments , our study suggests that structural flexibility , gating residues and multiple cavities in one catalytic site are keys to lipid selection in the endocannabinoid system . Our results suggest that the structural framework proposed here could likely be a general enzymatic strategy of other lipid-degrading enzymes to select the preferred lipid substrate within a broad spectrum of biologically active lipids . This new , and likely general , structural framework for lipid selection in FAAH could therefore now encourage additional experimental verifications of the role of ligand and structural flexibility , as regulated by key gating residues at the boundaries of multiple cavities forming a single catalytic site , as observed in several other lipid-degrading enzymes . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | Keys to Lipid Selection in Fatty Acid Amide Hydrolase Catalysis: Structural Flexibility, Gating Residues and Multiple Binding Pockets |
The primary role of Actin-Depolymerizing Factors ( ADFs ) is to sever filamentous actin , generating pointed ends , which in turn are incorporated into newly formed filaments , thus supporting stochastic actin dynamics . Arabidopsis ADF4 was recently shown to be required for the activation of resistance in Arabidopsis following infection with the phytopathogenic bacterium Pseudomonas syringae pv . tomato DC3000 ( Pst ) expressing the effector protein AvrPphB . Herein , we demonstrate that the expression of RPS5 , the cognate resistance protein of AvrPphB , was dramatically reduced in the adf4 mutant , suggesting a link between actin cytoskeletal dynamics and the transcriptional regulation of R-protein activation . By examining the PTI ( PAMP Triggered Immunity ) response in the adf4 mutant when challenged with Pst expressing AvrPphB , we observed a significant reduction in the expression of the PTI-specific target gene FRK1 ( Flg22-Induced Receptor Kinase 1 ) . These data are in agreement with recent observations demonstrating a requirement for RPS5 in PTI-signaling in the presence of AvrPphB . Furthermore , MAPK ( Mitogen-Activated Protein Kinase ) -signaling was significantly reduced in the adf4 mutant , while no such reduction was observed in the rps5-1 point mutation under similar conditions . Isoelectric focusing confirmed phosphorylation of ADF4 at serine-6 , and additional in planta analyses of ADF4's role in immune signaling demonstrates that nuclear localization is phosphorylation independent , while localization to the actin cytoskeleton is linked to ADF4 phosphorylation . Taken together , these data suggest a novel role for ADF4 in controlling gene-for-gene resistance activation , as well as MAPK-signaling , via the coordinated regulation of actin cytoskeletal dynamics and R-gene transcription .
The actin cytoskeleton is an essential , dynamic component of eukaryotic cells , involved in numerous processes including growth and development , cellular organization and organelle movement , and abiotic and biotic stress signaling [1] . Underpinning these processes in plants is a tightly regulated genetic and biochemical mechanism driven by the function of more than 70 actin-binding proteins ( ABPs ) , which through their coordinated activity , regulates the balance of free globular ( G ) -actin versus filamentous ( F ) -actin , of which nearly 95% is unpolymerized in plants [2] , [3] . As a consequence of this large pool of free G-actin , the potential exists for explosive rates of polymerization following elicitation by a broad range of external stimuli , including pathogen infection [1] . Among the numerous ABPs in plants responsible for modulating the balance of G- to F-actin , one subclass , Actin-Depolymerizing Factors ( ADFs ) , both sever and disassemble F-actin . In addition to its primary role in modulating host cytoskeletal architecture , a role for ADFs in defense signaling following pathogen infection is emerging [4] , [5] , [6] . The initiation of innate immune signaling in plants relies on multiple pre-formed and inducible processes to surveil , respond , and activate defense signaling following pathogen perception [7] , [8] . In total , these responses can be cataloged based on two primary nodes of defense signaling: pathogen-associated molecular pattern ( PAMP ) -triggered immunity ( PTI ) and effector-triggered immunity ( ETI ) [7] . In the case of PTI , perception and activation is typically mediated by extracellular plasma membrane-localized pattern recognition receptors ( PRRs ) , which are responsible for the recognition of conserved pathogen motifs ( i . e . , PAMPs; e . g . , flagellin , LPS , chitin ) . Recognition of PAMPs by PRRs initiates downstream signaling , including the activation of the Mitogen-Activated Protein Kinase ( MAPK ) signaling cascade , the generation of reactive oxygen species , and transcription of pathogen-responsive genes [9] . Arguably the best-characterized example of PTI signaling in plants is the activation of signaling associated with FLS2 ( Flagellin Sensitive-2 ) , a receptor-like kinase containing a serine/threonine kinase , which recognizes flagellin as well as the 22-amino acid peptide flg22 via the extracellular leucine rich repeat ( LRR ) domain [10] , [11] . Activation of FLS2 by flg22 results in the association of FLS2 with BAK1 ( BRI1-associated receptor kinase ) , as well as the phosphorylation of both FLS2 and BAK1 [12] . FLS2 ligand binding and association with BAK1 has been shown to activate the MAPK signaling pathway resulting in dual phosphorylation of conserved tyrosine and threonine residues of Arabidopsis ( Arabidopsis thaliana ) MAP kinases MPK3/6 [13] , which in turn leads to transcription of PTI-related genes including FRK1 ( Flg22-induced receptor kinase 1; [14] ) . The expression of FRK1 , however , is believed to be both MAPK dependent and independent [14] . As a counter to the activation of PTI , many plant pathogens deploy secreted effector proteins , which induce a host response ( e . g . , ETI ) - an enhanced PTI-like response , as well as a more robust , programmed cell death-like , response known as the hypersensitive response ( HR ) that is initiated via the direct or indirect recognition of pathogen effectors by host resistance ( R ) proteins [7] . As expected , numerous virulence targets of pathogen effectors identified thus far are components of PTI signaling pathways – with the hypothesis being that targeting PTI-components can lead to increased virulence of the pathogen [9] , [15] . Among the best-characterized signaling pathways leading to the activation of ETI , as well as a mechanistic example of the functional overlap between PTI and ETI , is the recognition of the bacterial effector protein AvrPphB by the Arabidopsis resistance protein RPS5 ( resistance to Pseudomonas syringae 5 ) [7] . RPS5 is a member of the coiled-coil ( CC ) nucleotide-binding-site ( NBS ) LRR R-gene family , required for recognition of Pseudomonas syringae pv . tomato DC3000 ( Pst ) expressing the cysteine protease effector protein AvrPphB [16] , [17] . RPS5-mediated resistance signaling is dependent upon AvrPphB cleavage of the receptor-like cytoplasmic kinase ( RLCK ) AvrPphB-Susceptible 1 ( PBS1 ) , which in turn results in the activation of ETI [18] . Recently , it has been suggested that the virulence target of AvrPphB may in fact be another RLCK , the PTI component BIK1 ( Botrytis-induced kinase; [15] ) . This hypothesis is based on the observation that not only does AvrPphB cleave BIK1 , as well as other RLCKs , including PBL1 ( PBS1-like 1 ) , but also that cleavage in the absence of RPS5 results in a significant reduction in PTI responses . It should be noted , that while the bik1/pbl1 double mutant does have significant reductions in many PTI responses , bik1/pbl1 does not exhibit reduced MPK3/6 phosphorylation upon flg22 stimulation [15] , [19] . In the current study , we report the identification of a reduction in the expression and accumulation of RPS5 mRNA in the absence of ADF4 . In total , our data demonstrate that this reduction results in the down-regulation of PTI-signaling in the presence of the bacterial effector AvrPphB . Additionally , we demonstrate this reduction in PTI-signaling is due in part to an ADF4-dependent abrogation of the MPK3/6 branch of the MAPK pathway . From the standpoint of cellular dynamics and the activation of ETI , expression of RPS5 was restored in an ADF4 phosphorylation-dependent manner , demonstrating a link between ADF4 phosphorylation , activity ( e . g . , F-actin binding ) , RPS5 mRNA accumulation and subsequent resistance signaling . In addition to elucidating the signaling cascade from perception through MAPK activation , we identified a link between reduced actin cytoskeleton co-localization of ADF4 and the activation of RPS5-mediated resistance in a phosphorylation-dependent manner . In total , the work presented herein represents the first identification of link between the actin cytoskeleton , the dynamic control of ADF4 , and the regulation of a resistance gene transcription .
Previous work has shown that Arabidopsis Actin-Depolymerizing Factor-4 ( ADF4 ) is required for resistance to Pst AvrPphB , however , the biochemical and genetic mechanism ( s ) associated with activation were largely undefined [4] . To elucidate the signaling cascade leading from the recognition of AvrPphB to the activation of resistance , we first investigated the expression of the resistance ( R ) gene ( i . e . , RPS5 ) required for the recognition of AvrPphB . As shown in Figure 1A , we found a significant reduction ( ∼250-fold ) in the accumulation of RPS5 mRNA in the adf4 mutant compared to wild-type Col-0 . It was further determined that there is no significant alteration in the expression of ADF4 in Col-0 during the course of infection with Pst AvrPphB ( Figure S1 ) . To address the possibility of positional effects in the adf4 T-DNA SALK line , Tian et al . [4] demonstrated that complementation of the adf4 mutant with native promoter-driven ADF4 restored resistance to Pst AvrPphB . Similarly , these lines also showed a restoration in mRNA expression of RPS5 ( Figure 1B ) . The expression of RPS5 in a second ADF mutant , adf3 , was not altered ( Figure 1B ) , confirming that the loss of resistance is specific to ADF4 , as previously reported [4] . To confirm that the loss of RPS5-mediated resistance in the adf4 mutant is specific to RPS5 , we transformed the adf4 mutant with a RPS5-sYFP ( adf4/35S:RPS5-sYFP; [20] ) to uncouple RPS5 expression from native regulation . As shown in Figure S2 , RPS5 mRNA ( Figure S2A ) and HR-induced cell death following AvrPphB recognition ( Figure S2B ) was restored . Taken together , this data demonstrates a direct and specific requirement of ADF4 for RPS5-mediated resistance . To determine the specificity of the ADF4-RPS5 genetic interaction , we investigated if the mRNA expression of additional Arabidopsis R-genes are altered in the adf4 mutant . To this end , we examined the expression of RPS2 [21] , RPM1 [22] , RPS4 [23] and RPS6 [24] . As an additional measure , we monitored the mRNA accumulation of NDR1 ( non race-specific disease resistance-1; [25] , [26] , [27] ) , a required component of most CC-NB-LRR defense signaling pathways in Arabidopsis , including RPS5 . As shown in Figure S3 , we did not observe a reduction in the resting levels of these mRNAs in the adf4 mutant . To confirm that increased susceptibility and the loss of the HR in the adf4 mutant is due to altered expression of RPS5 ( i . e . , mRNA reduction ) and not a reduction in the expression of the AvrPphB cleavage target PBS1 [16] , [17] , [28] , [29] , [30] , the expression of PBS1 mRNA was also measured . As shown in Figure 1C , we did not detect a significant difference between PBS1 expression in the adf4 mutant and Col-0 . Additionally , there was no alteration of RPS5 mRNA expression in the functional PBS1 mutant , pbs1-2 ( [30]; Figure 1B ) . Our data present a role for ADF4 in the expression of RPS5 , but not for the expression of PBS1 , suggesting the loss of ETI in the adf4 mutant may be a direct result of reduced RPS5 expression ( Figure 1A , Figure 1C ) . However , whether a role for AvrPphB in the down-regulation of RPS5 expression exists is unknown . In order to address this question , we measured the expression of RPS5 in both Col-0 and the RPS5 point-mutant , rps5-1; the rationale being that if AvrPphB negatively regulates the expression of RPS5 , its expression should be reduced in the absence of the activation of ETI . In support of this hypothesis , as shown in Figure 1D , we observed a significant reduction in RPS5 expression in rps5-1 at 24 hpi following inoculation with Pst AvrPphB . Based on our observations above , we hypothesize that absence of RPS5-derived ETI in adf4 is most likely due to the reduced expression of RPS5 . Based on this , and given the significant overlap in signaling of ETI and PTI , particularly with regard to AvrPphB activity [9] , [15] , [31] , we asked if PTI signaling is affected in the adf4 mutant . To address this question , we first monitored the activation of FRK1 expression , a transcriptional marker for FLS2 activation [14] , in wild-type ( WT ) Col-0 , adf4 and rps5-1 . As shown in Figure 2A , when Col-0 , adf4 and rps5-1 plants were treated with flg22 , no significant changes in FRK1 mRNA expression were observed , and mock infiltration did little to activate FRK1 ( Figure 2A , Figure 2B ) . As a second , complementary analysis of the fidelity of PTI-based signaling responses in the adf4 mutant , we also monitored root growth inhibition in the presence of flg22 ( Chinchilla 2007 , same as in the methods section ) . As shown in Figure S4 , we did not observe a significant difference in root growth in adf4 in the presence of flg22 as compared to Col-0 . In total , these data demonstrate that flg22-induced PTI-signaling is functional in both the rps5-1 and adf4 mutants . As an additional measure to ensure that the technique employed in Figure 2A and B did not have an adverse effects on RPS5 mRNA expression in either Col-0 or adf4 , RPS5 mRNA was monitored following hand-infiltration with either flg22 or mock ( i . e . , buffer alone ) . As shown in Figure S5A , we observed that flg22-induced expressional changes of RPS5 mRNA was similar to that of mock , thus assuring the observed activation of FRK1 in Col-0 and adf4 ( Figure 2A ) can be attributed specifically to flg22 , and is independent of the infiltration technique ( Figure 2B ) , or changes in RPS5 expression ( Figure S5A ) . Recent work from Zhang et al . [15] suggests that FRK1 mRNA accumulation is reduced in the rps5-1 mutant following flg22 treatment of protoplasts expressing AvrPphB . This raises the question of the relationship between the activation of PTI-signaling in parallel with the activation of ETI . To investigate the downstream signaling response ( s ) associated with the activation of RPS5-mediated resistance , we measured the expression of FRK1 mRNA accumulation in Col-0 , adf4 , and rps5-1 when inoculated with Pst AvrPphB . As shown in Figure 2C , we observed a significant decrease in FRK1 mRNA expression in both the adf4 and rps5-1 mutants , as compared to Col-0 , at 6 hpi with Pst AvrPphB . Coupled with the results of Zhang et al . [15] , this would suggest that the adf4 mutant has a decreased level of RPS5 . In support of this , we did not detect a significant difference between FRK1 expression in the adf4 and rps5-1 mutants when inoculated with flg22 ( Figure 2A ) , demonstrating that the mutants had equivalent signaling potential following to FLS2 activation , and that ultimately , the reduction in FRK1 expression is a direct result of a loss in ETI , most likely due to a reduction in RPS5 mRNA expression and accumulation ( Figure 1A ) . It is possible that our observations described above could be an indirect result of cross-talk of PTI response signaling pathways in adf4 and rps5-1 in the presence of Pst . To test this , FRK1 mRNA expression in Col-0 , adf4 and rps5-1 following inoculation with the type three secretion system ( T3SS ) mutant Pst hrpH− was assessed to differentiate PTI from ETI in the ADF4-RPS5 signaling node . As shown in Figure 2D , we detected no difference in FRK1 mRNA expression between Col-0 , adf4 or rps5-1 . Additionally , RPS5 mRNA expression following Pst hrpH− inoculation ( Figure S5B ) and elf18-induced PTI-signaling in Col-0 and adf4 ( Figure S6 ) further supports these observations . When challenged with Pst expressing the catalytically inactive AvrPphB-C98S isoform [16] , [18] , both WT Col-0 and the adf4 mutant showed increased expression levels of FRK1 mRNA , in agreement with previously published data [15] ( Figure S7A ) . A loss of induction of the HR in Col-0 , adf4 and rps5-1 when challenged by Pst AvrPphB-C98S variant [18] confirms the catalytic inactivity of AvrPphB-C98S ( Figure S7B ) . At this point , we reasoned that altered FRK1 expression in both the rps5-1 and adf4 mutants is due to a specific block in the MAPK signal cascade , most likely a function of the virulence activity of AvrPphB in the absence of ETI . To examine MAPK activation in the presence of both flg22 and AvrPphB , in the absence of pathogen , Col-0 , adf4 and rps5-1 plants were transformed with an estradiol-inducible AvrPphB construct ( i . e . , Col-0/pER8:AvrPphB , adf4/pER8:AvrPphB and rps5-1/pER8:AvrPphB ) to enable us to monitor the interplay between flg22 perception ( i . e . , PTI ) and AvrPphB ( i . e . , ETI ) . As shown in Figure 3A and Figure 3C , when phosphorylation of both MPK3 and MPK6 was measured in response to flg22 , a significant reduction in adf4/pER8:AvrPphB was observed as compared to Col-0 at 10 minutes; this reduction was not observed in adf4 , and Col-0/pER8:AvrPphB . Interestingly , no significant reduction of MPK3 and MPK6 was observed in the rps5-1/pER8:AvrPphB 10 minutes after flg22 treatment ( Figure 3B and Figure 3C ) . This observation suggests a potential combinatory role for ADF4 in both the expression of RPS5 ( Figure 1A ) , resulting in reduced PTI-signaling ( Figure 2C ) , as well as in the proper regulation of MAPK-signaling in the presence of AvrPphB ( Figure 3A and Figure 3C ) . Estradiol induction of AvrPphB is shown in Figure S8 . ADF4 mediated actin depolymerization is regulated in large part by the phosphorylation status of ADF . Indeed , previous work has demonstrated that mammalian cofilin/ADF activity is regulated by phosphorylation at serine-3 , and that de/phosphorylation at this residue is responsible for the regulating the activation of actin depolymerization [32] . In plants , a direct correlation between the phosphorylation status of ADF and its function has not been demonstrated; however , ADF4 function is presumed to be regulated in a manner similar to that of mammalian cofilin [32] , [33] , [34] . Herein , we demonstrate for the first time that Arabidopsis ADF4 is indeed phosphorylated at serine-6 , and that the phosphorylation status directly correlates with its activity and function of actin cytoskeletal dynamics . ADF4 and the phospho-null ADF4_S6A ( i . e . , serine-6 to alanine ) plant lines were generated by expressing T7:ADF4 and T7:ADF4_S6A in the adf4 mutant under the control of a constitutive promoter ( adf4/35S:ADF4 and adf4/35S:ADF4_S6A ) . As shown in Figure 4A , after 2D isoelectric focusing ( IEF ) and SDS PAGE , native ADF4 shows a differential IEF profile than the phospho-null ADF4_S6A . In order to determine if phosphorylation of ADF4 affects RPS5 expression , an additional phosphorylation isoform line was generated: a phospho-mimic isotype , reflecting a serine to aspartic acid change at amino acid position 6 ( i . e . , S6D ) expressed in the adf4 mutant background ( adf4/35S:ADF4_S6D ) . As shown in Figure 4B , the phosphomimetic isoform , adf4/35S:ADF4_S6D , restored RPS5 mRNA expression , while the phospho-null isoform , adf4/35S:ADF4_S6A , did not . A second independent transgenic Arabidopsis line expressing the ADF4 phosphorylation mutants were generated and tested for RPS5 expression to ensure that altered mRNA expression was not due to a positional transgene insertion effect ( Figure S9A ) . To confirm that the ADF4 phosphomimetic constructs were functional in their ability to restore resistance in the adf4 mutant , the induction of HR and disease phenotypes , as well as bacterial growth were assessed to determine the relationship between ADF4 phosphorylation and resistance activation through AvrPphB-RPS5 . As shown in Figure 4C and 4D , inoculation of adf4 mutant plants expressing the phosphomimetic ( ADF4_S6D ) with Pst AvrPphB restored the WT Col-0 resistance phenotype , both in terms of HR ( Figure 4C , top panel ) , disease symptoms ( Figure 4C , lower panel ) , and bacterial growth at 4 dpi ( Figure 4D ) . Conversely , inoculation of the phospho-null-expressing plants ( i . e . , adf4/35S:ADF4_S6A ) with Pst AvrPphB resulted in the absence of HR ( Figure 4C , top panel ) , the development of disease symptoms ( Figure 4C , lower panel ) , and an increase growth of the pathogen ( Figure 4D ) , similar to that observed in the adf4 mutant . As a control , to correlate transgene expression levels with our observations , the relative expression levels of both ADF4_S6A and ADF4_S6D were assessed by western blot to confirm that the observed restoration of RPS5 with the phosphomimetic isoform was in fact due to the phosphorylation status and not an artifact of expression ( Figure S9B ) . In total , our data confirms a restoration in resistance , as well as supports the hypothesis that phosphorylated ADF4 is required for resistance to Pst AvrPphB . Similarly , and in agreement our phosphorylation data , expression of FRK1 following Pst AvrPphB inoculation in the adf4/35S:ADF4_S6D mutant was similar to that observed in Col-0 , whereas the adf4/35S:ADF4_S6A plants had an FRK1 expression pattern similar to the adf4 mutant ( Figure S10 ) . As shown above , phosphorylated ADF4 is required for the accumulation of RPS5 mRNA , as well as for resistance signaling in response to Pst AvrPphB ( Figure 4 ) . Previous work has demonstrated the potential for nuclear localization of ADFs , supportive of a role for actin and ADFs in regulating gene transcription [35] , [36] , [37] . To this end , we sought to determine if translocation of ADF4 into the nucleus is dependent upon the phosphorylation status of ADF4 . As shown in Figure 5A , we found that ADF4 , ADF4_S6A and ADF4_S6D are all present in the nucleus . This data would suggest that perturbation of RPS5 expression in the adf4/35S:ADF4_S6A plants is not due to an inability of phospho-null ADF4 to enter the nucleus . However , the phospho-null ADF_S6A ( ds-Red_ADF4 ) does show an increased co-localization with the actin cytoskeleton ( filamentous Actin Binding Domain 2-GFP; fABD2-GFP ) , as well as the formation of filamentous like structures in the ADF4_S6A panel ( Figure 5B ) . Conversely , phosphomimetic ADF4_S6D is more diffuse within the cytosol and has reduced co-localization with the actin cytoskeleton ( Figure 5B ) . To confirm our observations of a phosphorylation-specific alternation in the co-localization of our ADF4 isoforms ( i . e . , S6A versus S6D ) with the actin cytoskeleton , we next performed a red-green analysis on the collected images , calculating the overlap coefficients , according to Manders ( R ) . In short , this analysis will determine the actual overlap of the red/green signals in our collected images [38] , providing an in vivo quantification of the co-localization of ADF4 with the actin cytoskeleton . As shown in Figure 5C , both ADF4_S6A and ADF4_S6D were found to have a significant R-value , 0 . 697±0 . 009 and 0 . 701±0 . 009 respectively , with significant differences in co-localization of ADF4_S6A and ADF4_S6D based on co-localization coefficients m1 and m2 . For a red-green pairing , such as was preformed in our analysis , m1 refers to the fraction of red pixels co-localized with green pixels , while m2 is the fraction of green pixels co-localized with red pixels . The m1 values for ADF4_S6A and ADF4_S6D are 0 . 604±0 . 032 and 0 . 485±0 . 033 respectively , while the m2 values are 0 . 250±0 . 028 and 0 . 353±0 . 030 ( Figure 5C ) . The co-localization coefficients suggest a significant co-localization of ADF_S6A with fABD2 , but not for ADF4_S6D . In total , these observations are in agreement with previous reports of phosphorylated cofilin having reduced binding to both G- and F-actin [39] .
Understanding the mechanism ( s ) of pathogen effector recognition , as well as elucidating the putative virulence function ( s ) of these secreted proteins , provides the foundation for our understanding of innate immune signaling in plants [8] . Using a combination of cell biology , biochemical , and genetics-based approaches , we show that ADF4 is required for the specific activation of RPS5-mediated resistance . In both plants and animals , the actin cytoskeletal network plays a broad role in numerous cellular processes , including cell organization , growth , development and response to external stimuli , including pathogen infection . Herein , we propose a mechanism through which the expression of the R-gene RPS5 is under the control of the actin binding protein ADF4 , in a phosphorylation dependent manner , independent of nuclear localization , which subsequently affects co-localization with actin , suggesting a possible cytoskeletal role in gene transcription ( Figure 6 ) . In animal cells , a complex signaling network involving Rho-GTPase activation , actin cytoskeletal dynamics , and the interplay between pathogen virulence has been extensively characterized [1] . In plants , however , the elucidation of the genetic link between pathogen virulence and the regulation of actin cytoskeletal dynamics has only recently been described [4] , [5] . In plant-pathogen interactions , the effects of modulation to the host actin cytoskeleton have been best characterized using a combination of pharmacological and cell biology-based approaches to monitor focal orientation of F-actin filaments to the site of infection during fungal pathogenesis [6] , [40] , [41] , [42] , [43] . As a first step towards elucidating the mechanism of activation of RPS5-meditated resistance , we examined the expression levels of Arabidopsis genes associated with resistance to Pst AvrPphB . We observed a marked reduction in mRNA levels of the R-gene RPS5 , while the protein kinase PBS1 was not affected ( Figure 1B , Figure 1C ) . Additionally , the mRNA levels of R-genes unrelated to the recognition of AvrPphB were not affected in the adf4 mutant ( Figure S2B ) . From these data , we conclude that ADF4 is specifically required for the expression of RPS5 and subsequent resistance to Pst AvrPphB . The initiation of resistance signaling in plants following pathogen infection engages a multitude of processes , including PRR activation [12] , MAPK signaling [14] and transcriptional reprogramming [44] . In the current study , our observation of a reduction in PTI-signaling in the adf4 mutant supports our hypothesis that RPS5 mRNA levels correlate with reduced levels of RPS5 protein . In support of this , we observed a reduction in FRK1 transcript accumulation in the presence of AvrPphB in both the adf4 and rps5-1 mutants . This observation is in agreement with recent reports , including a study demonstrating a physical interaction between FLS2 and RPS5 , which would suggest that PTI and ETI signaling is more interdependent than previously hypothesized [45] . Subsequent analysis of upstream MAPK components partially attributed diminished FRK1 mRNA levels to a reduced activation of MPK3/6 . Herein , we did not detect a significant reduction in flg22-induced phosphorylation of MPK3/6 in either Col-0/pER8:AvrPphB or rps5-1/pER8:AvrPphB; however , in adf4/pER8:AvrPphB plants , a significant reduction in MPK3/6 phosphorylation following flg22 treatment was observed ( Figure 3 ) . MAPK signaling is often primarily associated with PTI ( i . e . flagellin activation of the FLS2 receptor ) ; however , many reports have demonstrated the necessity of these components for ETI . For example , in tomato ( Solanum lycopersicum ) and tobacco ( Nicotiana tabacum ) the requirement of MAPK signaling-components for AvrPto- and N-mediated ETI has been well documented [46] , [47] , [48] . Our data would suggest that in the case of AvrPphB , R-Avr activation does not specifically induce MPK3/6 within 48 hours of estradiol-induced expression of AvrPphB ( Figure 3B ) . Furthermore , the absence of perturbation to MPK3/6 in the rps5-1/pER8:AvrPphB suggest that while it appears recognition is important for aspects of PTI-signaling i . e . FRK1 mRNA expression ( Figure 2C ) , MAPK-signaling specifically is independent of the need for recognition ( Figure 3B ) . One possible explanation for reduced MAPK-signaling in the absence of ADF4 reflects the virulence activity of AvrPphB . Indeed , recent work has demonstrated a physical interaction between BIK1 and the FLS2 receptor upon ligand activation – an association that is required for the activation of PTI-signaling [15] . As a mechanism linking with the virulence activity of AvrPphB with both PTI and ETI , cleavage of BIK1 by AvrPphB results in reduced PTI-signaling in the absence of recognition ( i . e . the rps5-1 mutant ) . Our observation of a reduction in MPK3/6 phosphorylation in adf4 , but not Col-0 nor rps5-1 , would suggest an additional role for ADF4 in regulation of MAPK-signaling , while the reduced FRK1 in adf4 and rps5-1 as compared to Col-0 , supports the aforementioned potential virulence activity of AvrPphB , as well as a possible role for recognition ( i . e . ETI ) in the protection/recovery of the targeted PTI-signaling pathway . Although the mechanism ( s ) utilized by Arabidopsis to preserve the integrity of the MAPK- and PTI-signaling pathway are not yet fully understood , it is possible that ETI-induced SA accumulation , which has been demonstrated to prime and enhance accumulation of MPK3/6 , can be partially responsible for the recovery of MAPK signaling in Col-0 [49] . Another possible contribution to the reduction in PTI-signaling associated with loss of ETI is the aforementioned direct association of FLS2 with RPS5 [45] . In plants , ADF localization is intimately associated with actin reorganization [50] . At present , a full understanding of how translocation of ADFs into the nucleus occurs has not been defined [51]; moreover , the precise function within the nucleus is unclear [36] . The current hypothesis is the translocation of ADFs , as well as other ABPs , into the nucleus may serve a chaperone function [39] . In support of this , actin , as well as several actin-binding proteins ( including ADFs ) , has recently been shown to be present in the nuclei of Arabidopsis [36] . This data support the hypothesis that in addition to actin , ABPs and actin-related proteins ( ARPs ) may have specific functions within the nucleus , including chromatin assembly and remodeling , as well as participation in various steps of RNA transcription and processing [36] , [52] . It is quite possible that ADF4 either facilitates nuclear translocation of specific actin isoforms required for processes related to the expression of RPS5 , or , ADF4 itself is required for gene expression ( i . e . , transcription ) , as has been demonstrated to be the cased for other ARPs . Mechanistically , however , it is unclear how ADF proteins are translocated into the nucleus . Plant ADFs do not have a conserved nuclear localization signal sequence , as is found in the vertebrate ADFs/cofilins; however , plant ADFs do have two regions with basic amino acids which are similar to domains in other plant proteins that function together as a nuclear localization signal ( NLS ) [53] . To date , the function of these domains has not been explored . Our data , as well as a recent study by Kandasamy et al . [36] , suggests that these two regions of basic amino acids may be both sufficient for translocation to the nucleus , which is not affected by the phosphorylation status of ADF4 at serine-6 ( Figure 5 ) . In the current study , we demonstrate that ADF4 phosphorylation influences both actin cytoskeletal localization , and ultimately , RPS5 mRNA expression ( Figure 4 , Figure 5 ) . In total , our data provide prima facie evidence for an actin-based regulatory mechanism controlling R-gene expression , and further support the emerging hypothesis that there are critical physiological roles for phosphorylated ADFs in plants [39] . Phosphorylation of cofilin , the predominant ADF found in animal cells , is regulated in part through the action of LIM kinase [54] , and results in a reduced affinity of cofilin for F-actin . To this end , ADF phosphorylation has commonly been viewed as an inactivation mechanism , however , recent data suggest that this is not the case [39] . In plant-pathogen interactions , numerous defense-associated processes are regulated by kinase phosphorylation [15] , [18] , [55] , [56] . Conversely , the regulatory mechanisms controlling the phosphorylation , and subsequent regulation of actin dynamics , have not been well established , nor has the crosstalk between ADF regulation and innate immune signaling been fully defined . One obvious disconnect in the link between innate immune signaling and kinase activity in plants and animals is that plants do not have a kinase family homologous to mammalian LIM kinases [54] , and thus , ADF phosphorylation is likely mediated by the activity of additional kinase ( s ) , such as calcium dependent protein kinases [33] . One interesting hypothesis in support of the work described herein is that the kinase responsible for the phosphorylation of ADF4 may be a virulence target of AvrPphB . This hypothesis is supported in part by Figure 1D , in which RPS5 expression is significantly reduced in the rps5-1 point mutant following inoculation with Pst AvrPphB . Additionally , the observed requirement of ADF4 for MAPK-signaling in the presence of AvrPphB ( Figure 3A ) lends support for the idea of ADF4 , or the kinases required for its regulation as potential virulence targets . In this regard , such a mechanism would further solidify a link between the virulence function and activity of AvrPphB and the role of the actin cytoskeleton in controlling RPS5 transcription and disease signaling .
Arabidopsis plants were grown in a BioChambers walk-in growth chamber ( model FLX-37; Winnipeg , Manitoba , Canada ) at 20°C under a 12-hour light/12-hour dark cycle , with 60% relative humidity and a light intensity of 100 µmol photons m−2s−1 . Transformation of Arabidopsis , as well as selection of transformants , was performed as described by Clough and Bent [57] . Pseudomonas syringae pv . tomato DC3000 ( Pst ) strains were grown as previously described [4] . Four-week-old plants were used for bacterial inoculations . For growth assays and qRT-PCR analyses , whole plants were dip inoculated into bacterial suspensions of 3×108 colony-forming units ( cfu ) mL−1 in 10 mM MgCl2 containing 0 . 1% Silwet L-77 . Three 0 . 7 cm diameter leaf disks were collected from three plants for bacterial growth assays , as previously described [4] . The hypersensitive response ( HR ) was analyzed by hand infiltrating bacterial suspension in 10 mM MgCl2 at 5×107 cfu mL−1 and scoring leaves for tissue collapse 20 to 24 hours post inoculation . flg22 infiltration was performed at a concentration of 1–10 µM in 10 mM MgCl2 , as previously described [27] . Col-0 and adf4 plants were grown upright on plates containing MS media for 10 days±10 nM flg22 in a GC8-2H growth chamber ( Environmental Growth Chambers LTD . , Winnipeg , Manitoba , Canada ) at 20°C under a 12-hour light/12-hour dark cycle , with 60% relative humidity and a light intensity of 120 µmol photons m−2s−1 . Analysis of flg22 inhibition of root growth was performed as previously described [12] . The native promoter driven pMD1-g:ADF4 ( g:ADF4 ) was constructed as described in Tian et al . [4] . Primer sequences 5′-GCGGTCGACATGGCTAATGCTGCGTCAGGAATGG-3′ ( forward ADF4 ) , 5′-GCGGTCGACATGGCTAATGCTGCGGCAGGAATGG-3′ ( forward ADF4_S6A ) , 5′-GCGGTCGACATGGCTAATGCTGCGGACGGAATGG-3′ ( forward ADF4_S6D ) and 5′- GCGGTCGACATGGCTAATGCTGCGTCAGGAATGG -3′ ( reverse for all 3 ) were used to add SalI restriction enzyme sites ( underlined ) for cloning ADF4 and its phospho-mutants into pMD1:35S:T7 [27] . Nuclei isolations were conducted as described in Kandasamy et al . [36] . Approximately 1 g of 2- to 3-week old adf4/35S:ADF4 , _S6A , and _S6D Arabidopsis seedlings , grown upright on MS medium plates were used for each nuclear extraction . The isolated nuclei were fixed on chrome alum slides , permeabilized , and incubated with primary antibody T7-monoclonal ( EMD Chemicals , Gibbstown , NJ , USA ) , secondary anti-mouse IgG-FITC ( Sigma-Aldrich ) and DAPI ( Sigma-Aldrich ) before imaging [36] . Isolated nuclei and transiently expressed dsRed-ADF4 constructs , and fABD2-GFP generated using Agrobacterium tumefaciens-mediated transient expression in Nicotiana benthamiana , were imaged using laser confocal scanning microscopy using a 60×/1 . 42 PlanApo N objective on an Olympus FV1000 ( Olympus America Inc , Center Valley , PA ) , as described in Tian et al . [58] . Co-localization was preformed utilizing FluoView FV1000 ( System Analysis Software , Olympus ) . An area of each image was selected for analysis containing <50% fABD2-GFP occupancy in order to examine true co-localization and not artificial co-localization due to over abundance of fABD2-GFP . Thresholds were set manually to account for background , and overlap coefficient according to Manders ( R ) , and co-localization coefficients m1 and m2 were generated by the FV1000-ASW . Co-localization coefficient equations used can be found in Table S1 . Total RNA was extracted from leaves using the PrepEase Plant RNA Spin kit ( USB Affymetrix , Santa Clara , CA , USA ) . First-strand cDNA was synthesized from 1 µg total RNA using the First-Strand cDNA Synthesis kit ( USB Affymetrix ) . Primers used for quantitative real-time PCR ( qRT-PCR ) are listed in Table S2 . qRT-PCR was performed using the Mastercycler ep Realplex system ( Eppendorf AG , Hamburg , Germany ) , as previously described [27] , using the Hot Start SYBR Master mix 2× ( USB Affymetrix ) . Ubiquitin ( UBQ10 ) was used as an endogenous control for amplification . Fold Col-0 was determined using the following equation: ( relative expression ) / ( relative expression of Col-0 untreated ) , where “relative expression” = 2 ( −ΔCt ) , where ΔCt = Ctgene of interest−CtUBQ10 . All data were analyzed using GRAPHPAD PRISM Software ( San Diego , California , USA ) . Values are represented as mean ±SEM . All statistical analysis was performed using two-way ANOVA , followed by the Bonferroni post-test as compared to Col-0 . In Figure 2C , a two-way ANOVA , followed by the Bonferroni post-test was performed in order to determine if there is a significant difference between rps5-1 and adf4 . In Figure S1 , an unpaired student t-test with a 95% confidence interval was performed to determine if change over time was significant . P values≤0 . 05 are considered significant , where *p<0 . 05; **p<0 . 01 and ***p<0 . 005 . Western blot analysis of phosho-MPK3/6 was performed using 40 µg total protein , utilizing anti-pTEpY ( Cell Signaling Technology , Danvers , MA , USA ) , while analysis of adf4/35S:ADF4_S6A and adf4/35S:ADF4_S6D was preformed using 20 µg total protein , utilizing anti-T7-HRP ( EMD Chemicals , Gibbstown , NJ , USA ) , as previously described [26] . 2D IEF was preformed on 500 mg of total lysate from adf4/35S:ADF4 and adf4/35S:ADF4_S6A . The lysates were precipitated using chloroform∶methanol ( 1∶4 ) and reconstituted in Urea buffer ( 7 M Urea , 2 M Thiourea , 2% CHAPS , 2% ASA-14 , 50 mM DTT , 0 . 2% Biolyte ampholytes and 0 . 1% bromophenol blue ) . Isoelectric focusing was conducted according to manufacturing guidelines at the proteomics core at Michigan State University Research Technology Support Facility ( Bio-Rad ) . Immunoblot analysis was preformed as above . | The activation and regulation of the plant immune system requires the coordinated function of numerous pre-formed and inducible cellular responses . Following pathogen perception , plants not only activate specific defense-associated signaling , such as resistance ( R ) genes , but also redirect basic cellular machinery to support innate immune signaling . Within each of these processes , the actin cytoskeleton has been demonstrated to play a significant role in structural-based defense signaling in plants in response to pathogen infection . Most notably , the actin cytoskeleton of plants has been shown to play a role in structural-based defense signaling following fungal pathogen infection . Recent work from our laboratory has demonstrated that the actin cytoskeleton of Arabidopsis mediates defense signaling following perception of the phytopathogenic bacterium Pseudomonas syringae . Using a combination of genetic and cell biology-based approaches , we found that ADF4 , a regulator of actin cytoskeletal dynamics , is required for the specific activation of R-gene-mediated signaling . By analyzing the activation of signaling following pathogen perception , we have identified substantial crosstalk between recognition of pathogen virulence factors ( e . g . , effector proteins ) and the regulation of R-gene transcription . In total , our work highlights the intimate relationship between basic cellular processes and the perception and activation of defense signaling following pathogen infection . | [
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] | 2012 | Arabidopsis Actin-Depolymerizing Factor-4 Links Pathogen Perception, Defense Activation and Transcription to Cytoskeletal Dynamics |
Dengue disease is an increasing global health problem that threatens one-third of the world's population . Despite decades of efforts , no licensed vaccine against dengue is available . With the aim to develop an affordable vaccine that could be used in young populations living in tropical areas , we evaluated a new strategy based on the expression of a minimal dengue antigen by a vector derived from pediatric live-attenuated Schwarz measles vaccine ( MV ) . As a proof-of-concept , we inserted into the MV vector a sequence encoding a minimal combined dengue antigen composed of the envelope domain III ( EDIII ) fused to the ectodomain of the membrane protein ( ectoM ) from DV serotype-1 . Immunization of mice susceptible to MV resulted in a long-term production of DV1 serotype-specific neutralizing antibodies . The presence of ectoM was critical to the immunogenicity of inserted EDIII . The adjuvant capacity of ectoM correlated with its ability to promote the maturation of dendritic cells and the secretion of proinflammatory and antiviral cytokines and chemokines involved in adaptive immunity . The protective efficacy of this vaccine should be studied in non-human primates . A combined measles–dengue vaccine might provide a one-shot approach to immunize children against both diseases where they co-exist .
Dengue fever is a mosquito-borne viral disease that threatens the health of a third of the world's population . During the last twenty years , the four serotypes of dengue virus spread throughout the tropics due to the presence of the mosquito vector Aedes aegypti in all urban sites and to the major demographic changes that occurred in these regions . This global re-emergence shows larger epidemics associated with more severe disease [1] . Dengue is a major worldwide public health problem with an estimated 100 million annual cases of dengue fever ( DF ) and 500 , 000 annual cases of dengue hemorrhagic fever ( DHF ) , the severe form of the disease , resulting in about 25 , 000 fatal cases , mainly in children under the age of 15 . Although global prevention appears the best strategy to control dengue expansion , there is still no licensed vaccine available . Dengue viruses ( DV ) are enveloped , positive-stranded RNA viruses ( Flaviviridae family ) . Four antigenically distinct viral serotypes exist ( DV1-4 ) . The surface of virions is composed of the major envelope glycoprotein ( E ) and a small membrane protein ( M ) . Very little information is available concerning the role of the 75-amino acid long M protein . We previously reported that ectopic expression of the 40-residue intraluminal ectodomain of M ( referred hereafter as ectoM ) is able to induce apoptosis in mammalian cells , suggesting that M might play an important role in the pathogenicity of flaviviruses [2] . The envelope E protein , which is exposed on the surface of viral particles , is responsible for virus attachment and virus-specific membrane fusion . Anti-E antibodies inhibit viral binding to cells and neutralize infectivity . A primary DV infection is believed to induce life-long immunity to the infecting serotype , while heterologous cross-protection against other serotypes lasts only a few weeks , allowing re-infection by another serotype . A number of clinical and experimental data demonstrated the implication of the immune response in the pathogenesis of severe forms of dengue , possibly through an antibody-dependant enhancement ( ADE ) phenomenon based on the cross-reactivity of DV antibodies [3] , [4] . The molecular structure of the ectodomain of E glycoprotein has been determined [5] . It is folded in three distinct domains I , II and III . The C-terminal immunoglobulin-like domain III ( EDIII ) can be independently folded as a core protein through a single disulfide bond and contains major serotype-specific neutralizing epitopes [6] . On the opposite , epitopes inducing antibodies that cross-react between serotypes have been located within the domain II , which contains the fusion peptide [7] . Therefore , EDIII has emerged as an antigen of choice to develop a dengue vaccine eliciting serotype-specific rather than cross-reactive antibodies . Indeed , recent studies have demonstrated that immunization with EDIII , either encoded by a plasmid or as a recombinant protein in fusion with a bacterial carrier , elicited neutralizing antibodies to DV [8] , [9] , [10] , [11] , [12] . A preventive dengue vaccine needs to protect unexposed individuals against all four serotypes of DV . It must be tetravalent , safe for 9–12 months children and provide long-lasting protective immunity . It must be produced at low cost and scaled up at million doses . To address these challenges , we evaluated the immunogenicity of a live recombinant vector derived from pediatric measles vaccine ( MV ) expressing a DV antigen designed to induce neutralizing and non cross-reactive antibodies . MV vaccine is a live-attenuated negative-stranded RNA virus proven to be one of the safest , most stable , and effective human vaccines developed so far . Produced on a large scale in many countries and distributed at low cost through the Extended Program on Immunization ( EPI ) of WHO , this vaccine induces life-long immunity after a single injection [13] , [14] , [15] and boosting is effective . We previously developed a vector derived from the live-attenuated Schwarz strain of MV [16] that expressed stably different proteins from HIV and induced strong and long-term specific humoral and cellular immune responses [17] , [18] . Based on this approach a program of clinical trials was initiated in collaboration with an industrial vaccine manufacturer with funding from the EC , to evaluate the safety and immunogenicity in humans of MV encoding an HIV antigen . We also demonstrated that recombinant MV could protect against flaviviruses , since MV expressing the secreted form of the E protein from West Nile virus ( WNV ) induced sterilizing humoral immunity against WNV in a mouse model [19] . In the present work , we evaluated the immunogenic potential of a MV vector expressing a DV1 soluble antigen composed of the EDIII fused with the ectoM . In a mouse model of MV infection this vector induced serotype-specific , virus-neutralizing antibodies against DV1 . Consistent with this observation , we showed that infection of human monocyte-derived dendritic cells ( DCs ) resulted in up-regulation of co-stimulatory molecules as well as robust secretion of cytokines and chemokines that are identified as playing a pivotal role in establishment of anti-viral immune responses .
Vero ( African green monkey kidney ) cells were maintained in DMEM-Glutamax ( Gibco-BRL ) supplemented with 5% heat-inactivated fetal calf serum ( FCS , Invitrogen , Frederick , MD ) . Helper 293-3-46 cells ( a gift from M . A . Billeter , Zurich University ) used for viral rescue [20] were grown in DMEM/10% FCS and supplemented with 1 . 2 mg of G418/ml . The human monocytic cell line U937 ( ATCC CRL 1593 , American Type Culture Collection , Rockville , Md . ) was maintained in complete RPMI ( Gibco-BRL ) supplemented with 10% FCS ( Invitrogen ) , sodium pyruvate , non-essential amino acids , penicillin G ( 100 IU/ml ) , and streptomycin ( 100 µg/ml ) . Clinical-grade DCs were prepared as described elsewhere [21] , [22] . DCs were maintained in AIMV medium containing 500 U/mL GM-CSF ( Gentaur , Brussels , Belgium ) and 50 ng/mL IL-13 ( Peprotech , Tebu-bio , Rocky Hill , NJ ) . The plasmid pTM-MVSchw , which contains an infectious MV cDNA corresponding to the anti-genome of the Schwarz MV vaccine strain , has been described elsewhere [16] . The genomic RNA of DV-1 strain FGA/89 [23] ( Genbank accession number AF 226687 ) was extracted from purified virions and reverse transcribed using Titan One-Step RT-PCR kit ( Roche Molecular Biochemicals ) according to the manufacturer's instructions . The coding sequence for PrM/E ( amino acids 1-395 ) was cloned into pMT/Bip/V5-His A plasmid ( kindly provided by Erika Navarro-Sanchez ) and used as a template for further cloning . A PCR fragment encoding the EDIII ( aa 295-394 ) from the E protein was amplified by High Fidelity Polymerase ( PCR expand High Fidelity , Invitrogen ) using the forward primer 1EDIII 5′-AATTAAGATCTAAAGGGATGTCATATGTGATGTG-3′ containing a BglII restriction site ( underlined ) , and the reverse primer 2EDIII 5′-TTAAGCGGCCGCTATCGCTTGAACCAGCTTAGTTTC-3′ containing a NotI restriction site ( underlined ) and a stop codon ( in bold ) . The sequence encoding the EDIII from the E protein ( aa 295-394 ) linked to the ectodomain of the membrane M protein ( aa 1-40 ) by the original furin-like cleavage site RRDKR , was generated as follows . The FGA/89 EDIII ( Genbank accession no . AF 226687 ) was amplified using the forward primer 1EDIII and the reverse primer 3EDIII 5′-CGGAACGTTTGTCTCGTCGGAACCAGCTTAGTTTCAAAGC-3′ containing the reverse complement sequence of the furin site of the DV ectoM protein ( underlined ) and in 3′ the reverse complement sequence of the 3′ EDIII end . The PCR product was used as primer and template to amplify by a second PCR the chimeric sequence EDIII-ectoM ( Genbank accession no . CS479843 ) using 1EDIII primer and the reverse primer 5EDIII 5′-TTAAGCGGCCGCTATCATGGGTGTCTCAAAGCCCAAG-3′ that contains a NotI restriction site ( underlined ) and a stop codon ( in bold ) . The cloned sequences respect the “rule of six” , which stipulates that the number of nucleotides into the MV genome must be a multiple of 6 [24] . A shuttle plasmid ( pTRE2-ssCRT ) containing the human calreticulin signal sequence was generated by transferring the calreticulin-derived endoplasmic reticulum targeting signal sequence from the pEGFP-RE vector ( Clontech ) to the pTRE2-Hyg plasmid ( Clontech ) . The DV-1 cDNAs were introduced into pTRE2-ssCRT using BglII/NotI digestion . After sequencing , the 384 bp coding for the EDIII and 516 bp coding for the EDIII-ectoM antigens were inserted into BsiWI/BssHII-digested pTM-MVSchw-ATU2 , which contains an additional transcription unit ( ATU ) between the phosphoprotein ( P ) and the matrix ( M ) genes of the Schwarz MV genome [16] , [18] , [19] . The resulting plasmids were designated as pTM-MVSchw-EDIII and pTM-MVSchw-EDIII-ectoM . Rescue of recombinant Schwarz MV from the plasmids pTM-MVSchw-EDIII and pTM-MVSchw-EDIII-ectoM was performed as previously described [16] using the helper-cell-based rescue system described by Radecke et al [20] and modified by Parks et al . [25] . The titers of MV-EDIII and MV-EDIII-ectoM were determined by an endpoint limit-dilution assay on Vero cells . The TCID50 was calculated by use of the Kärber method . The EDIII and EDIII-ectoM PCR products described above were cloned into pMT/Bip/V5-His A plasmid ( Invitrogen ) between BglII and NotI restriction sites . The clones were validated by sequencing . Drosophila S2 cells ( Invitrogen ) were transfected by these plasmids using the Calcium Phosphate Transfection Kit ( Invitrogen ) . Transfected cells were selected by adding 25 µg/ml blasticidin . The EDIII and EDIII-ectoM protein production was induced by adding 750 µM CuSO4 . Cell culture supernatant was filtered on 0 . 2 µM filters before concentration on 10 , 000-MWCo Vivaspin columns ( Vivasciences ) eluted with PBS . Recombinant proteins were semi-quantified by Western blot using the MAb 9D12 reactive to EDIII from DV [26] . The following DV strains from the collection of Institut Pasteur were used: strain FGA/89 French Guiana for serotype DV1 [27] , Jamaica/N . 1409 for DV2 [28] , PaH881/88 Thailand for DV3 and 63632/76 Burma for DV4 . The E . coli strain BL21 ( DE3 ) and SB medium have been described [29] . Plasmids pLB11 , pLB12 , pLB13 , pLB14 , coding respectively for EDIII from DV serotypes 1 , 2 , 3 , 4 ( residues 296-400 ) with an hexahistidine tag in C-term , under control of the T7 promoter and pelB signal sequence , were constructed by insertion of RT-PCR products obtained with primers specific for EDIII , in plasmid pET20b+ ( Novagen ) ( O . Lisova et al . , in preparation ) . The DV-EDIII-H6 recombinant proteins were produced from these plasmids in E . Coli BL21 ( DE3 ) . Bacteria were grown at 24°C in SB medium with ampicillin ( 200 µg/mL ) until A600nm = 1 . 5 to 2 . 0 and then induced for 2 . 5 hours with 1 mM IPTG to obtain the expression of the recombinant genes . The purification of DV-EDIII-H6 proteins from bacteria's periplasmic fluid was performed by chromatography on a NiNTA resin ( Qiagen , Hilden ) and concentration determined by absorbance spectrometry as previously described [30] . The protein fractions were analyzed by SDS-PAGE in reducing conditions . The fractions that were homogeneous at >95% , were pooled , dialyzed against 50 mM Tris-HCl , pH 8 . 0 , 50 mM NaCl , snap frozen in liquid nitrogen , and stored at −80°C . A synthetic 40-residue long peptide corresponding to the ectoM sequence from FGA/89 strain ( SVALAPHVGLGLETRTETWMSSEGAWKQIQKV ETWALRHP ) was synthesized with a purity of at least 80% ( Genecust , France ) . Protein lysates from Vero cells or U937 cells infected with recombinant viruses were fractionated by SDS-PAGE gel electrophoresis and transferred to cellulose membranes ( Amersham Pharmacia Biotech ) . DV1 EDIII ( 5 ng ) produced in drosophila cells was loaded as a positive control . The blots were probed with a murine monoclonal antibody mAb4E11 directed against the E EDIII of DENV1 ( Hybridoma cells producing 4E11 raised against the envelope protein of DV1 were kindly provided by Dr Morens [30] ) . A goat anti-mouse immunoglobulin G ( IgG ) -horseradish peroxidase ( HRP ) conjugate ( Amersham ) was used as a secondary antibody . Peroxidase activity was visualized with an enhanced chemiluminescence detection kit ( Pierce ) . Cells were recovered by pipetting , centrifuged for 3 min at 1200 rpm , washed once in PBS and resuspended in FITC-labeled annexinV/propidium iodide ( PI ) according to the manufacturer's instructions ( Becton Dickinson , Apoptosis Detection kit ) . Labeled cells were analyzed by flow cytometry using a FacsCalibur ( BD Biosciences , San Diego , CA ) , with CellQuest software ( Becton Dickinson , Lincoln Park , NJ ) . The percentage of apoptotic cells was determined as the percentage of AnnexinV positive and propidium iodide negative cells . Immunofluorescence staining was performed on infected cells , as described elsewhere [31] . Cells were probed with mouse anti DV-1 EDIII 4E11 antibody , mouse anti DV-1 HyperImmune Ascitic Fluid [32] or rabbit anti human MHC-II dimer ( kindly provided by Neefjed J . ) antibodies . Cy3-conjugated goat anti mouse IgG antibody Cy3 conjugated ( Jackson Immunoresearch laboratories ) , FITC-conjugated goat anti-mouse IgG antibody ( Chemicon ) , or and FITC-conjugated goat anti-rabbit IgG antibody ( Amersham Pharmacia Biotech ) were used as secondary antibodies respectively . Cell-surface staining was performed at 4°C for 30 minutes using anti-CD86-PE ( BD Pharmingen ) , CD83-APC ( BD Pharmingen ) , CD80-PE-Cy5 ( Immunotech , Marseille ) in 1% BSA and 3% human serum-PBS . Isotype-matched mAbs were used as negative controls . Labeled cells were analyzed by flow cytometry using a FacsCalibur ( BD ) , with FlowJo software ( Tree Star , Ashland , OR ) . Supernatants were harvested after 16 h or 24 h of DC incubation with MV-EDIII or MV-EDIII-ectoM at an MOI of 1 . Aliquots of 200–300 µl were stored at −80°C . Production of cytokines/chemokines was analyzed in 50 µL supernatant with a human cytokine 25-plex antibody bead kit ( Biosource , CA , WA , cat . LHC0009 ) which measures IL-1α , IL-1Ra , IL-2 , IL-2R , IL-4 , IL-5 , IL-6 , IL-7 , IL-8 , IL-10 , IL-12p40/70 , IL-13 , IL-15 , IL-17 , TNF-α , IFN-α , IFN-β , GM-CSF , MIP-1α , MIP-1β , IP-10 , MIG , Eotaxin , RANTES and MCP-1 by using a Luminex 100 instrument ( Luminex Corp . , Austin , TX , USA ) . CD46-IFNAR mice susceptible to MV infection were produced as previously described [16] . Mice were housed under specific pathogen-free conditions at the Pasteur Institute animal facility . Six-week-old CD46-IFNAR mice were inoculated intraperitoneally ( i . p . ) with 104 or 105 TCID50 of recombinant MV . Boosting was performed using 10 µg of recombinant EDIII-ectoM protein in Alugel adjuvant . To detect the anamnestic response generated by immunization , immunized mice were i . p . inoculated with 107 FFU of live FGA/NA d1d variant of DV1 ( Genebank accession number AF 226686 ) . This strain was previously generated by adaptation of a clinical isolate to growth in newborn mouse brain [23] . All experiments were approved and conducted in accordance with the guidelines of the Office of Laboratory Animal Care at Pasteur Institute . To evaluate the specific antibody responses , mice were bled via the periorbital route at different time after inoculation . Sera were heat inactivated at 56°C for 30 min and the presence of anti-MV antibodies was detected by ELISA ( Trinity Biotech ) . HRP-conjugated anti-mouse immunoglobulin ( Jackson Immuno Research ) was used as secondary antibody . Anti-DV antibodies were detected by ELISA using 96-wells plates coated with either highly purified FGA/89 DV1 particles , recombinant EDIII proteins from DV1 , DV2 , DV3 , DV4 produced in E . Coli . or synthetic ectoM peptide . HRP-conjugated anti-mouse immunoglobulin was used as secondary antibody . The endpoint titers of pooled sera were calculated as the reciprocal of the last dilution giving twice the absorbance of sera from MV inoculated mice that served as negative controls . Anti-DV neutralizing antibodies were detected by a focus reduction neutralization test ( FRNT ) on Vero cells previously described [19] using 50 FFU of Vero-adapted DV1 Hawaï ( WHO reference strain , Genbank accession no . AF226687 ) , DV2 Jamaica ( Genbank accession no . M20558 ) , DV3 H97 ( WHO reference strain , Genbank accession no . M93130 ) or DV4 63632 . The endpoint titer was calculated as the highest serum dilution tested that reduced the number of FFU by at least 50% ( FRNT50 ) or 75% ( FRNT75 ) . For the neutralization tests in presence of recombinant EDIII or synthetic peptides ectoM peptides serum samples were pre-incubated ( in 50 µl medium ) with 5 µg , 500 ng or 50 ng of recombinant EDIII ( produced in Drosophila S2 cells ) or synthetic ectoM peptide before performing FRNT .
We cloned from DV1 viral RNA the sequence encoding the EDIII ( E295-394 ) fused in C-term to the ectoM ( M1-40 ) , using as a linker the original prM/M furin-like cleavage site RRDKR . This combined dengue antigen was cloned downstream the cellular calreticulin ( ss-CRT ) signal peptide sequence in order to allow the disulfide bond formation and therefore the correct folding of EDIII and to address the antigen in the secretion pathway . As a control of proper folding and effective secretion of EDIII , we also generated a similar construct without ectoM . The resulting ss-CRT-EDIII-ectoM and ss-CRT-EDIII constructs were inserted into MV vector ( pTM-MVSchw plasmid ) , which contains an infectious MV cDNA corresponding to the anti-genome of the Schwarz MV vaccine strain [16] ( Figure 1A ) . The recombinant measles viruses MV-EDIII-ectoM and MV-EDIII were rescued by transfecting the pTM-MVSchw-EDIII-ectoM and pTM-MVSchw-EDIII plasmids into helper cells and propagation on Vero cells , as previously described [16] . We analyzed the expression of DV antigens by recombinant MV in infected Vero cells by immunofluorescence using a monoclonal neutralizing anti-DV1 EDIII antibody ( 4E11 , [33] ) and anti-DV1 Hyper Immune Ascitic Fluid ( HMAF ) ( Figure 1B ) . In both cases , the antigens were clearly detected indicating that the EDIII was expressed and that the epitope of 4E11 neutralizing antibody was present and accessible . The presence of EDIII in lysates and supernatants of infected Vero cells was further confirmed by Western blot using 4E11 antibody ( Figure 1C ) . DV1 recombinant EDIII polypeptides ( rEDIII and rEDIII-ectoM ) secreted from stable S2 cell lines were used as positive controls ( Figure 1D ) . DV1 EDIII antigen was detected both in lysates and supernatants from cells infected by MV-EDIII-ectoM and MV-EDIII vectors . The EDIII was clearly detected in unconcentrated supernatant , despite that the supernatant volume was 100 times larger than the lysate volume . Thus , secretion was efficient . The intracellular EDIII shows a higher molecular weight on the western blot than EDIII secreted in the supernatants , because of the presence of the peptide signal , which was cleaved during secretion . Similarly , the positive control ( rEDIII from S2 cells ) has a higher molecular weight because it contains a poly-histidine tag . Cells infected by MV-EDIII-ectoM produced cleaved EDIII and uncleaved EDIII-ectoM antigens both in cell lysates and medium , indicating that the furin-like cleavage site was accessible . Taken together , these data show that MV-EDIII-ectoM vector is able to produce secreted forms of EDIII , EDIII-ectoM , and by assumption , ectoM ( not detected because of the lack of specific antibodies to ectoM ) . We analyzed the replication of MV-EDIII-ectoM and MV-EDIII viruses on Vero cells using the same MOI ( 0 . 01 ) than for MV production . The growth kinetics of both recombinant viruses were similar to that of control MV and the final titer was slightly higher for MV-EDIII-ectoM ( Figure 2A ) . We then investigated whether the presence of ectoM in MV-EDIII-ectoM virus could increase apoptosis of infected cells [2] . Since human monocytes constitute a determinant target of MV infection for initiation of immune responses , we addressed this question by infecting human monocytic cells ( U937 leukemic monocyte lymphoma cell line ) . Growth kinetics of MV , MV-EDIII and MV-EDIII-ectoM in U937 cells ( MOI 1 ) show that these cells are permissive to MV infection and that MV-EDIII-40 growth was slightly delayed as compared to MV and MV-EDIII ( Figure 2B ) . We quantified apoptotic cells ( annexin V positive/propidium iodide negative cells ) after infection at different MOI 0 . 1 , 1 and 10 . While we did not observe apoptosis up to 42 hours post-infection when using MOIs of 0 . 1 or 1 with the 3 viruses , increasing the MOI to 10 with MV-EDIII-ectoM virus eventually induced apoptosis in 15% of cells ( Figure 2C ) . Apoptosis was related to the activation of caspase 3 pathway ( not shown ) and was dependent on virus replication , since UV inactivated virus did not trigger apoptosis . We examined the ability of MV-EDIII-ectoM recombinant virus to raise specific anti-DV1 neutralizing antibodies in genetically modified mice susceptible to MV infection [34] . These mice express CD46 , the human receptor for vaccine MV strains , and lack the INF-α/β receptor ( IFNAR ) [16] , [18] , [35] , [36] . They have previously been used as a model to evaluate the immunogenicity of recombinant MV [16] , [17] , [18] , [19] , [36] . Six-week-old CD46-IFNAR mice received two intraperitoneal ( ip ) injections within one month of either 104 or 105 TCID50 of MV-EDIII-ectoM . As a control , CD46-IFNAR mice were immunized with MV-EDIII and empty MV vector . Specific antibody responses were analyzed by ELISA one month after the second injection ( Table 1 ) . All immunized mice raised antibodies to MV at similar titers . Specific anti-DV1 and anti-rEDIII antibodies were mounted in mice immunized with MV-EDIII-ectoM ( titers 3 , 000 and 10 , 000 respectively ) . Surprisingly , no anti-DV antibodies were detected in sera of mice immunized with MV-EDIII . An ELISA test using ectoM as coated viral antigen showed that immune sera had no detectable levels of anti-M antibodies . We evaluated the anti-DV1 neutralizing activity of immune sera by using a focus reduction neutralization test ( FRNT ) that allows to determine the highest serum dilutions able to reduce by at least 50% or 75% the number of DV1 focus forming units ( FFU ) on Vero cells . Again , whereas immunization with MV or MV-EDIII did not induce neutralizing antibodies to DV1 , immunization by MV-EDIII-ectoM raised FRNT50 titers to 320 and FRNT75 titers to 40 ( Table 1 ) . Altogether , these data show that EDIII-ectoM is able to elicit humoral immunity to DV . Although CD46-IFNAR mice used in this study did not allow to evaluate the protection conferred by immunization , we tested the ability of live DV1 peripheral inoculation to stimulate long-term anamnestic humoral response against MV-EDIII-ectoM . To assess first the susceptibility of CD46-IFNAR mice to DV1 replication , we inoculated mice intraperitoneally with 107 FFU of DV1 strain FGA/NA d1d . No symptoms or mortality were observed and virus replication could not be detected in mice serum by direct plaque assay on mosquito cells ( not shown ) . However , all groups of mice seroconverted after live DV1 inoculation ( Table 1 ) . Interestingly , the anamnestic memory induced by immunization with recombinant MV-EDIII-ectoM was remarkably boosted by live DV1 inoculation , whereas animals immunized with MV-EDIII or empty MV did not show any boost ( Table 1 ) . Both ELISA and FRNT neutralizing titers against DV1 were strongly increased in mice immunized with MV-EDIII-ectoM ( 30–100 times increase ) , showing evidence of an efficient anamnestic response . To test the longevity of this memory , another group of mice was primed with two injections of MV-EDIII-ectoM vector ( Table 2 ) . Six months later , they were boosted by injecting 5 µg of adjuvanted rEDIII-ectoM protein purified from supernatants of transfected drosophila S2 cells . Protein boost increased the neutralizing titer from 10 to 200 . However , the titer decreased rapidly to 40 , indicating a transient boost . As a control , mice inoculated only with the recombinant protein remained negative even after three injections ( not shown ) . At 9 months post priming , mice were i . p . inoculated with 107 FFU of live FGA/NA d1d DV1 . One month after DV1 inoculation , we again observed a 100 times increase in the level of antibodies to DV1 and to EDIII as well as DV1 neutralizing titers ( Table 2 ) . This experiment shows that neutralizing antibodies to DV EDIII induced by MV-EDIII-ectoM are efficiently boosted upon live DV exposure 9 months after priming , and demonstrates the induction of a durable B-cell memory . However , the protection against DV infection needs to be evaluated in a more appropriate non-human primate model . To assess the DV serotype specificity of antibodies induced , we tested mice sera from experiment presented in table 2 by ELISA against rEDIII proteins from DV1 , 2 , 3 and 4 , respectively . We also evaluated the anti-DV1 , anti-DV2 , anti-DV3 and anti-DV4 neutralizing activity of immune sera by FRNT50 . Antibodies induced by recombinant MV-EDIII-ectoM were specific to DV1 and did not cross-react with the EDIII from the other serotypes of DV ( Table 3 ) . This confirms that EDIII antigenic surface is serotype specific . To determine whether the antiviral neutralizing activity induced by MV-EDIII-ectoM was specifically directed against the EDIII , even after live DV1 inoculation , we performed neutralization tests in presence of increasing concentrations of either rEDIII protein produced in drosophila cells or synthetic ectoM peptide ( Figure 3 ) . Increasing concentrations of rEDIII protein strongly reduced the antiviral activity of sera collected before and after live DV1 inoculation , whereas ectoM peptide was ineffective . This experiment demonstrates that the neutralizing antibodies induced by immunization were specifically directed against EDIII epitopes essential for virus infectivity . Moreover , the antiviral activity of sera collected after live DV1 inoculation was also completely inhibited by rEDIII protein , indicating that live DV1 inoculation increased the level of antibodies already induced by immunization , but did not raise new antibodies directed to other neutralizing epitopes than EDIII . Altogether , these experiments demonstrate that recombinant MV-EDIII-ectoM virus induced specific antibodies to DV1 EDIII that did not cross-react with other DV serotypes and that neutralized specifically DV1 infection . The EDIII alone expressed by recombinant MV was poorly immunogenic , although it was expressed and secreted at levels similar to EDIII-ectoM . The presence of the ectoM was determinant to its immunogenicity , raising the question of the mechanism of this effect . Does the presence of ectoM in C-term of the EDIII sequence improve the conformation of EDIII to make it biologically active , or adjuvant its immunogenicity through an indirect mechanism ? The EDIII secreted from cells infected by MV-EDIII was recognized by neutralizing antibody 4E11 , which binds to the active receptor-binding form of EDIII [33] The same EDIII sequence secreted by drosophila cells was able to efficiently compete with the neutralizing activity of antibodies induced by the MV-EDIII-ectoM virus , thus suggesting that EDIII was biologically functional , at least able to present neutralizing epitopes . To address the question of adjuvantation , we compared in vitro the effect of MV-EDIII-ectoM and MV-EDIII infection on human immature monocyte-derived DCs ( MDDCs ) in terms of activation/maturation and cytokine/chemokine secretion . Upon virus infection , immature dendritic cells ( DCs ) undergo maturation , and transport the virus to regional lymph nodes , where viral antigens are presented to lymphocytes to initiate immune response [37] . DCs are permissive to MV infection [38] leading to the up-regulation of co-stimulatory molecules [39] , [40] , [41] . To evaluate in vitro the effect of recombinant MV-DV on DC activation , we cultivated human immature monocyte-derived DCs ( MDDCs ) in presence of MV-EDIII-ectoM or MV-EDIII viruses . After 17 hours of infection , DCs expressed the DV1 EDIII as detected by immunofluorescence ( Figure 4 ) . We then analyzed the kinetics and levels of expression of costimulatory molecules on the surface of DCs . The three viruses MV-EDIII-ectoM , MV-EDIII and MV promoted the up-regulation of CD86 , CD83 and CD80 molecules as compared to mock-treated DCs ( Figure 5 ) . Remarkably , MV-EDIII-ectoM up-regulated these molecules more extensively and at earlier time points than MV-EDIII and MV . Viral replication and de novo synthesis of EDIII-ectoM were required since UV-inactivated recombinant viruses or synthetic ectoM peptide had no effect ( not shown ) . To determine if the increased capacity of MV-EDIII-ectoM to activate DCs correlated with phenotypic functional changes , we analyzed the secretion of 23 cytokines/chemokines in the supernatant of DCs cultivated for 16h and 24h in presence of MV-EDIII-ectoM or MV-EDIII . As a control , DCs were mock-infected or cultivated in presence of LPS . We found that infection with MV-EDIII or MV-EDIII-ectoM induced a panel of cytokines and chemokines consistent with other reports on DC infection by MV ( Table 4 ) [42] . Remarkably , some cytokines and chemokines were significantly enhanced and/or induced at earlier time points by MV-EDIII-ectoM as compared to MV-EDIII ( Table 4 , Figure 6 ) . Among them , IFN-α ( 1000 pg/ml at 16 h ) , IL1RA ( 750 pg/m ) , IL4 ( 13 pg/m ) and the proinflammatory cytokines IL-6 ( 1250 pg/m ) and TNF-α ( 1700 pg/m ) were induced much more rapidly after MV-EDIII-ectoM infection and at levels 8–10 times higher than after MV-EDIII infection ( Figure 6 ) . Such a strong enhancement in production of these cytokines is expected to accelerate the establishment of immune response and to favor humoral immunity . Similarly , remarkable levels of MIP-1α chemokine were induced by MV-EDIII-ectoM at early time points ( 9 , 000–20 , 000 pg/ml ) . A similar robust and early secretion was observed for RANTES , MIP-1β and MCP-1α . The production of these chemokines by maturing DCs may promote the recruitment of other antigen presenting cells ( APC ) such as immature DCs to enhance and sustain antigen sampling , and polarization of the immune response [43] , [44] , [45] . Thus , adding the ectoM protein in fusion with EDIII resulted in a stronger and faster maturation of human DCs and activated the secretion of higher levels of inflammatory and antiviral cytokines as well as chemokines determinant for the establishment of specific immune responses .
The objective of this study was to evaluate the immunogenicity of a dengue vaccine candidate based on a pediatric measles vaccine expressing a minimal dengue antigen . This strategy provides a recombinant vaccine that might protect children simultaneously from measles and dengue and that might be affordable to populations through the EPI program in the regions affected both by dengue and measles infections . An efficient pediatric dengue vaccine is supposed to elicit durable protective humoral immune responses against all four dengue serotypes without risk of ADE [46] . Regarding this objective , we assembled covalently the antigenic domain III from the DV1 envelope E glycoprotein and the pro-apoptotic ectodomain of DV-1 M protein to generate a dengue combined antigen , EDIII-ectoM . In the fusion construct , the N-terminal calreticulin peptide signal sequence directs EDIII-ectoM to the secretory pathway . The furin-dependent cleavage site of prM/M which links ectoM to EDIII allows the processing of the antigen by specific proteases throughout the Golgi apparatus . Expressed by recombinant MV vector , the EDIII-ectoM antigen induced in mice susceptible to MV specific antibodies to DV1 EDIII that did not cross-react with other DV serotypes and that neutralized DV1 infection in vitro . Immunization primed a long-term memory that was vigorously boosted when animals were inoculated with live DV . Although DV disease pathogenesis and protection mechanisms are not fully clarified , disease severity is correlated with viremia levels and neutralizing antibody is generally used as a marker of vaccine effectiveness [47] . Experimental mouse models of DV infection have been reported showing that adult AG129 mice , which are deficient for IFN α/β/γ receptors develop a dose-dependant transient viremia after peripheral injection of unadapted or mouse-adapted DV , whereas A129-IFNAR mice , which are deficient only for IFN-α/β receptor are less sensitive to DV infection [48] , [49] , [50] . However , AG129 mice are not sensitive to MV infection and the prototype DV1 Hawaï strain did not replicate in these mice [50] . Suckling mice develop lethal encephalitis after DV intracerebral inoculation , but in our study mice were 3–4 month-old after two MV immunizations and intracranial inoculation could not be performed . Moreover , this model is far from the human situation since DV does not infect the nervous system , nor lead to encephalitis in humans . The CD46-IFNAR mouse model sensitive to MV infection that we used did not allow documenting in vivo protection from DV replication . Therefore , to demonstrate the induction of anamnestic neutralizing antibody response upon live DV exposure , mice were peripherally inoculated with DV a long time after immunization . These experiments showed that neutralizing antibodies induced by immunization with MV-EDIII-ectoM were strongly boosted by live DV inoculation , thus suggesting a protective capacity . Indeed , the available vaccines against yellow fever , Japanese encephalitis and tick-borne encephalitis viruses have proven that anamnestic neutralizing antibodies play an essential role for protection against flaviviral infections [47] . The EDIII without ectoM was poorly immunogenic in the context of MV expression . It appeared , therefore , that the 40-residue long ectodomain of M plays a critical role in its immunogenicity . DV EDIII has been previously shown to be immunogenic in the form of recombinant chimeric proteins [8] , [9] , [10] , [12] or expressed from a plasmid [51] or from adenovirus vector [52] , [53] . To determine whether the EDIII sequence inserted into MV vector was able to present neutralizing epitopes , we produced in E . coli recombinant EDIII proteins from DV1 , 2 , 3 and 4 corresponding to the same sequence and we coated plates with these proteins . Tested by ELISA on these plates , a neutralizing HMAF specific to DV1 recognized specifically the DV1-EDIII , but not the other serotypes ( data not shown ) , indicating its specificity . This DV-1 HMAF recognized also specifically by immunofluorescence the DV1 EDIII expressed in cells infected by MV-EDIII , indicating the capacity of EDIII to expose serotype-specific epitopes . The neutralizing monoclonal antibody 4E11 recognized also the EDIII expressed by MV-EDIII infected cells , indicating that the epitope specific of this antibody is accessible within the EDIII expressed by MV . This epitope has been mapped and shown to be exposed on the native form of EDIII [33] . Furthermore , we expressed the same EDIII sequence as a secreted protein by drosophila cells and showed that it was able to efficiently compete with the neutralizing activity of antibodies induced by the MV-EDIII-ectoM virus . Altogether , these observations suggest that EDIII expressed by MV was able to present a conformationally active neutralizing epitope . Recent studies evaluating the immunogenicity of West-Nile virus ( WNV ) EDIII showed that a high amount of EDIII was necessary to induce neutralizing antibodies , while EDIII fused to TLR ligands was immunogenic and conferred protection at lower doses [54] , [55] . Therefore , the low immunogenicity of DV EDIII in our hands might be due to the lower amount of antigen expressed by recombinant MV as compared to the high protein or DNA doses administered by others . To increase the level of expression by MV , EDIII can be cloned upstream the N gene , as MV genes are expressed as a gradient from the 3′ to the 5′ end of the genome . This small ectoM protein , which is highly conserved among the four serotypes of DV , has pro-apoptotic properties [2] . High titers of MV-EDIII-ectoM induced apoptosis of infected U937 monocyte-like cells that was not observed at standard titers . This critical point in terms of safety needs to be evaluated further in the development of this vaccine candidate . Indeed , recombinant MV vector has to keep the high safety level of standard MV vaccine . However , this property might be determinant to the immunogenicity of EDIII because apoptotic infected cells express Toll-like receptor ( TLR ) ligands that increase the cross-presentation of viral epitopes by antigen presenting cells [56] , [57] . Indeed , we observed that ectoM , in the context of MV replication , increased human DCs maturation and triggered the release of cytokines and chemokines determinant for the establishment of specific adaptive immunity . Therefore , its capacity to adjuvant EDIII might be still more efficient in humans than in CD46-IFNAR mice . Further studies are needed to address the mechanism of action at the molecular level . In conclusion , we have produced a minimal antigen from DV1 able to induce long-term specific neutralizing antibodies to DV1 with no cross-reactivity with other serotypes . We have shown that the remarkable adjuvant capacity of ectoM to EDIII immunogenicity was correlated to its capacity to mature primary DCs and to activate the secretion of a panel of proinflammatory and antiviral cytokines , as well as numerous chemokines determinant for the establishment of specific adaptive immunity . The immunogenicity of this antigen was demonstrated through its expression by a recombinant MV vector , thus making the proof-of-concept of this strategy for dengue vaccine development . Using MV as a vaccination vector presents a number of advantages : vaccination against measles is mandatory , vaccine strains are genetically stable , MV does not recombine or integrate genetic material , and vaccine does not persist or diffuse . MV-specific CD8 T cells and IgG are detected in vaccinees up to 25–34 years after a single MV vaccination [14] and boosting increases this memory [15] . Using MV as a recombinant vaccine to immunize simultaneously against measles and dengue might be particularly attractive in areas where both diseases threaten children every year , such as Africa and South America . Taking advantage of the capacity of MV vector to express large amounts of heterologous genetic material very stably [58] , we generated tetravalent dengue antigenic constructs inserted into single MV vectors that are currently characterized . Such a strategy should avoid the stability and interference problems encountered with tetravalent formulation of four attenuated viruses , as well as the reactogenicity problems [59] . These new candidates will be evaluated in a much more appropriate non-human primate model . | Dengue is a tropical emerging disease that threatens one-third of the world's population , mainly children under the age of 15 . The development of an affordable pediatric vaccine that could provide long-term protection against all four dengue serotypes remains a global public health priority . To address this challenge , we evaluated a strategy based on the expression of a minimal dengue antigen by live attenuated measles vaccine ( MV ) , one of the most safe , stable , and effective human vaccines . As a proof-of-concept , we constructed a MV vector expressing a secreted dengue antigen composed of the domain III of the envelope glycoprotein ( EDIII ) , which contains major serotype-specific neutralizing epitopes , fused to the ectodomain of the membrane protein ( ectoM ) from DV-1 , as an adjuvant . This vector induced in mice durable serotype-specific virus-neutralizing antibodies against DV1 . The remarkable adjuvant capacity of ectoM to EDIII immunogenicity was correlated to its capacity to mature dendritic cells , known to initiate immune response , and to activate the secretion of a panel of cytokines and chemokines determinant for the establishment of specific adaptive immunity . Such strategy might offer pediatric vaccines to immunize children simultaneously against measles and dengue in areas of the world where the diseases co-exist . | [
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"im... | 2007 | Pediatric Measles Vaccine Expressing a Dengue Antigen Induces Durable Serotype-specific Neutralizing Antibodies to Dengue Virus |
Cytoplasmic Polyadenylation Element Binding ( CPEB ) proteins are translational regulators that can either activate or repress translation depending on the target mRNA and the specific biological context . There are two CPEB subfamilies and most animals have one or more genes from each . Drosophila has a single CPEB gene , orb and orb2 , from each subfamily . orb expression is only detected at high levels in the germline and has critical functions in oogenesis but not spermatogenesis . By contrast , orb2 is broadly expressed in the soma; and previous studies have revealed important functions in asymmetric cell division , viability , motor function , learning , and memory . Here we show that orb2 is also expressed in the adult male germline and that it has essential functions in programming the progression of spermatogenesis from meiosis through differentiation . Like the translational regulators boule ( bol ) and off-schedule ( ofs ) , orb2 is required for meiosis and orb2 mutant spermatocytes undergo a prolonged arrest during the meiotic G2-M transition . However , orb2 differs from boule and off-schedule in that this arrest occurs at a later step in meiotic progression after the synthesis of the meiotic regulator twine . orb2 is also required for the orderly differentiation of the spermatids after meiosis is complete . The differentiation defects in orb2 mutants include abnormal elongation of the spermatid flagellar axonemes , a failure in individualization and improper post-meiotic gene expression . Amongst the orb2 differentiation targets are orb and two other mRNAs , which are transcribed post-meiotically and localized to the tip of the flagellar axonemes . Additionally , analysis of a partial loss of function orb2 mutant suggests that the orb2 differentiation phenotypes are independent of the earlier arrest in meiosis .
Proteins in the Cytoplasmic Polyadenylation Element Binding ( CPEB ) family were first identified in Drosophila ovaries and Xenopus oocytes [1]–[4] . In both organisms the CPEB proteins function in the localization and translational regulation of mRNAs encoding key developmental and polarity determinants as well as factors controlling the process of egg maturation . Since then CPEB family proteins have been implicated in many other biological contexts . These include translational regulation of embryonic cell division [5] , [6] , regulation of p53 expression [7] , [8] , synaptic plasticity in the rat hippocampus [9] , long-term memory in Aplysia [10] , [11] and spermatogenesis in the worm [12] . The CPEB proteins bind to CPE elements in the 3′ UTRs of target mRNAs and can both repress and activate translation . Translation activation typically involves the phosphorylation of the CPEB protein and the subsequent recruitment of a cytoplasmic poly A polymerase which extends the poly A tail [13] . Most animals have two or more CPEB genes . Completed genome sequences reveal that humans , mice , and C . elegans have four genes , while there are only two CPEBs , orb and orb2 , in Drosophila . The homology between the CPEBs is largely restricted to the C-terminal region of the protein , where two RNA-Recognition Motif ( RRM ) domains are found , while the N-terminal domain is highly divergent even amongst closely related species . Phylogenetic trees indicate that the CPEB genes fall into two different subgroups . One subgroup includes Drosophila orb , mouse CPEB1 and the canonical Xenopus CPEB , while the other subgroup contains the second Drosophila CPEB gene , orb2 , as well as mammalian CPEB 2 , 3 , and 4 [12] , [14] . The Drosophila orb gene has been extensively characterized . Its expression appears to be restricted to the germline as neither mRNA nor protein can be detected in somatic tissues of the embryo , larvae and adult . While a male-specific Orb isoform is expressed in the male germline , its activity is not absolutely essential since the fertility of orb null males is reduced but not eliminated ( Agunwamba and Xu , unpublished ) . In contrast , orb plays a central role in the process of oogenesis . orb expression is first activated during the mitotic divisions that ultimately generate an egg chamber containing 15 nurse cells and an oocyte . At this stage orb activity is required for the proper specification of the oocyte . Subsequently , orb is required for establishing the anterior-posterior and dorsal-ventral axes of the egg and embryo . Amongst the key orb mRNA regulatory targets are the polarity determinants oskar and gurken [15]–[18] . Unlike orb , the second Drosophila CPEB gene , orb2 , is broadly expressed in both the soma and germline . The highest levels of Orb2 are in the embryonic , larval and adult CNS , and in the germ cells of the male testes [19] . There are two Orb2 isoforms , one of 75 kD and the other of 60 kD . The larger isoform is expressed in somatic tissues and the germline of both sexes , while the smaller isoform is found in testes but is not detected elsewhere . The isoforms share a 542 C-terminal amino acid sequence , but have unique N-termini of 162 and 9 amino acids respectively . Included in the common region is the conserved C-terminal CPEB signature RRM type RNA binding and zinc finger domains . The N-terminal half of both isoforms has short conserved sequences rich in serine or histidine interspersed with poorly conserved sequences containing poly-glutamine or poly-glycine repeats [19] . As might be expected from its broad expression pattern , orb2 has a number of somatic functions . During embryogenesis it is required for asymmetric cell division of neuroblast and muscle precursor stem cells and appears to function by promoting the localized accumulation of atypical Protein Kinase C ( aPKC ) [19] . In addition , orb2 mutants have substantially reduced viability , a shortened life span , and defects in behavior and long-term memory [14] , [19] , [20] , [21] . Here we report that orb2 is essential for spermatogenesis , and that it functions in programming the orderly and sequential progression of spermatogenesis from meiosis through differentiation .
In situ hybridization and antibody staining were used to examine orb2 expression in the testes . While there is little if any orb2 mRNA ( Figure 1B , 1B′ ) or protein ( Figure 1C , 1C ( I ) ) in stem cells , low levels are detected in the mitotic cysts . After mitosis is finished and the interconnected spermatocytes begin to grow , there is a substantial upregulation in both mRNA and protein . This period of growth corresponds to the stage when many gene products needed for subsequent steps in spermatogenesis are synthesized [22] , [23] . Though Orb2 protein is found throughout the spermatocyte cytoplasm , higher levels of protein are concentrated in a ring around the nucleus ( Figure 1C ( II ) , 1D ) . Orb2 expression peaks as the mature spermatocytes go through meiosis and high levels of Orb2 are found in 32 and 64 cell spermatid cysts ( Figure 1C ( III ) ) . Orb2 persists after the spermatids in the 64 cell cysts start differentiation and begin flagellar axoneme elongation ( Figure 1C ( IV ) ) . As the axonemes begins to elongate , the 64 spermatid nuclei bundle together and then begin to condense into needle-like structures ( Figure 1F , inset , Figure 1A ) . Though Orb2 is distributed along the entire axoneme bundle , the highest concentrations are found in a prominent band ( Figure 1E , arrowhead ) close to the distal tip of the growing flagellar axonemes ( Figure 1A ) . The leading edge of the axonemes is just in front of the Orb2 band and this region contains small clumps of Orb2 ( Figure 1E , arrow ) . In the region behind the band , Orb2 is organized into a series of striated lines that extend towards the sperm nuclei at the proximal ( basal ) tip of the spermatid ( Figure 1E , bracket ) and presumably correspond to individual flagellar axonemes in the spermatid bundle . While Orb2 is present in elongating spermatids that have not yet completed nuclear condensation ( * in Figure 1F ) , it disappears once elongation and nuclear condensation are completed ( o in Figure 1F ) . To confirm this , we compared the accumulation patterns of Orb2 and Don Juan-GFP ( DJ-GFP ) . While DJ-GFP is highly expressed once the nuclei have condensed and individualization begins , it is not found in spermatids that are still undergoing elongation [24] , [25] . As expected , we did not observe spermatids that simultaneously had Orb2 and DJ-GFP . Moreover , since some fully elongated spermatids with condensed nuclear bundles have neither Orb2 nor DJ-GFP ( x in Figure 1F ) , there seems to be a delay between the disappearance of Orb2 and DJ-GFP expression . This suggestion is supported by a comparison of the Orb2 and Orb expression patterns . orb is transcribed post-meiotically and orb mRNAs localize in a band at the distal tip of elongating spermatids [3] , [26]; however , the localized mRNAs does not appear to be translated until the end of the elongation phase after Orb2 begins to disappear . Figure 2A–2D show that high levels of Orb are found in the tips of elongated spermatids that have neither Orb2 nor DJ-GFP . On occasion we observed spermatids that have activated Orb translation but still retain some residual Orb2 ( Figure 2B , arrowhead ) . While meiosis and differentiation require different gene products for execution and have their own regulators , there is a class of genes that control both aspects of spermatogenesis . Included in this group are always early ( aly ) , spermatocyte arrest ( sa ) , meiosis I arrest ( mia ) and cannonball ( can ) which encode testes specific TAFs ( TATA Box Protein associated factors ) [22] , [27] . Mutations in these testes specific TAFs cause spermatocytes to arrest at the G2-M transition of meiosis I and block the expression of factors needed for differentiation [28] . However , though these genes encode factors essential for Pol II activity , the effects of mutations are not limited to general transcription . For example , twine ( twe ) mRNA is expressed in tTAF mutants , but is not properly translated [28] . Figure S1 shows that mutations in these four genes have two effects on Orb2 protein expression in the testes . First , Orb2 levels were substantially reduced ( Figure S1A ) . Second , there was a noticeable reduction in the electrophoretic mobility of the larger Orb2 isoform . As illustrated for the sa mutation , treatment of the testes extract with lambda phosphatase removes the Orb2 signal with slow electrophoretic mobility and indicates that phosphorylation is responsible for the reduced mobility of Orb2 in the mutant testes ( Figure S1B ) . As might be expected , these tTAF mutations do not seem to affect Orb2 in somatic tissues such as the head ( Figure S1A ) . To better understand how orb2 functions in spermatogenesis , we examined the effects of mutations . Previously we characterized a collection of 5 transposon insertions in the orb2 locus [19] . As shown in Figure 3 , two of the transposons , 1556 and 4965 have no effect on Orb2 expression . This is expected as 1556 is inserted upstream of the orb2-1 promoter , while 4965 is located downstream of the Orb2 protein coding sequences . Two of the transposons , 6090 and 1925 , are inserted downstream of both the orb2-1 and orb2-2 promoters and interfere with expression of orb2 mRNAs encoding the 75 kD isoform in the testes and head ( Figure 3 , Figure S2 and [19] ) . In contrast , the 1793 insertion , which is located farther upstream in between the orb2-1 and orb2-2 promoters , affects 75 kD expression in the testes , but not in the head , suggesting that orb2-1 is more heavily used in the testes , while orb2-2 is more heavily used in the head ( Figure S2 ) . As expected from their insertion sites , none of the transposons affect the 60 kD isoform . On the other hand , the reduction in the 75 kD isoform in 6090 , 1925 and 1793 is accompanied by a small but reproducible increase in the 60 kD isoform ( Figure 3B ) . This raises the possibility that a negative feedback loop might regulate the levels of the two isoforms . Consistent with an important role for Orb2 in spermatogenesis , we find that the fertility of homozygous 6090 , 1925 , and 1793 males is substantially impaired ( not shown ) . When trans to deficiencies that uncover orb2 , 1925 is completely sterile , while 6090 and1793 occasionally give fertile males ( Figure 3C ) . In contrast , the two insertions , 1556 and 4965 , that have no effect on the expression of the 75 kD isoform , are fully fertile . That sterility is due specifically to the loss of the Orb2 75 kD isoform is supported by the finding that excision of the transposon insertions restores the expression of this isoform and reverts the sterility phenotype ( 6090−1 , Figure 3 ) . Since the mutants still expressed the 60 kD isoform , along with residual 75 kD isoform , they could retain some orb2 function . For this reason , we generated orb2 nulls using FLP recombination ( Figure S2 ) [29] , [30] . Two upstream piggyBac insertions ( 1556 and 1925 ) contain correctly oriented FRT sites for deleting the orb2 protein coding sequence when paired with the downstream 4965 insertion . The resulting deletions , orb27 ( 1925×4965 ) and orb236 ( 1556×4965 ) , eliminate orb2 mRNA and protein expression ( Figure 3 ) . They have substantially reduced viability ( data not shown ) , while the surviving males are completely sterile ( Figure 3 ) . To exclude possible background effects , we combined the two null alleles with three different third chromosome deficiencies that remove small parts of the third chromosome including orb2 ( Df ( 3L ) ED4421 , Df ( 3L ) ED4415 , and Df ( 3L ) ED4416 ) . These trans combinations also have reduced viability and are completely male sterile ( Figure 3C; not shown ) . Similar results were obtained for an independently generated null allele , orb2Δ [14] . Since all null alleles behave the same in our assays , we used orb236 in the experiments described below . Overall testes morphology and the pre-meiotic stages of spermatogenesis appear normal in orb236 and other orb2 mutants . The spermatogonia undergo the sequential mitotic divisions generating 16 interconnected spermatocytes , and the spermatocytes mature as in wild type . However , subsequent stages of spermatogenesis are abnormal . In wild type , the products of meiosis , the spermatids in the 64 cell cysts , have two characteristic spherical structures when observed by phase contrast microscopy: a light nucleus and a dark mitochondrial Nebenkern ( Figure 4A ) . While pseudo-spermatids are present in orb236 , the cells and their nuclei are unusually large and they have a poorly contrasted Neberken , which is abnormally shaped and sometimes fragmented ( Figure 4B ) . As the overall DNA content is also increased ( Figure 4C , 4D ) , it seems likely that the orb2 spermatids have replicated their DNA as in wild type , but failed to complete meiotic divisions . Consistent with this possibility , we never observe products of the first and second meiotic divisions , the 32 and 64 cell cysts respectively , in orb236 testes . By contrast , 32 and 64 cell cysts are seen in wild type . To further characterize the meiotic defects , we examined chromosome morphology . During the prolonged G2 before the spermatocytes enter meiosis I , the three large chromosomes segregate into 3 domains and start the process of condensation . As illustrated in Figure 4I , the spermatocyte chromosomes initially coalesce into irregular rod-like structures located at vertices of a triangle ( Figure 4E ) . They subsequently condense into 3 sharp dots ( Figure 4F ) before congressing to the metaphase plate in preparation for the first meiotic division ( Figure 4G ) [31] . In orb2 , the spermatocyte chromosomes segregate into three domains , and start the process of condensation . However , condensation is incomplete and the chromosomes don't congress to the metaphase plate ( Figure 4H ) . These findings suggest that orb2 spermatocytes arrest meiosis at a step prior to the first meiotic division . To analyze the meiotic arrest further we examined Cyclin A accumulation . In wild type testes , Cyclin A accumulates in the cytoplasm during G2 . However , just prior to the meiosis I G2 to M transition , Cyclin A is targeted to the spermatocyte nucleus , and then quickly degraded as meiosis proceeds [31] , [32] . Since nuclear localization is only transient , cysts with nuclear Cyclin A are rarely seen in wild type ( Figure 5A ) . However , in orb236 and orb236/Df ( 3L ) 4416 , most cysts in the middle of the testes have high levels of nuclear Cyclin A ( Figure 5B ) . These orb2 meiotic phenotypes are similar to the phenotypes reported for mutations in boule ( bol ) and off-schedule ( ofs ) [32]–[34] . bol encodes a homolog of mammalian DAZ fertility factor , while ofs encodes a testes eIF4G . Like orb2 , bol and ofs mutant spermatocytes arrest meiosis prior to the first meiotic division and the cysts have high levels of nuclear Cyclin A . The fact that all three proteins are needed for meiosis suggested that they might function together . To explore this possibility , we first tested whether Orb2 and Bol associate with each other in testes extracts . As shown in Figure 5G , Orb2 and Bol are in an RNase resistant immunoprecipitable complex . We also examined the pattern of Bol accumulation in orb236 testes . In wild type spermatocytes , Bol localizes in a perinucleolar dot during spermatocyte maturation; however , once meiosis begins , Bol is relocalized to the cytoplasm where it is thought to promote the translation of target mRNAs [35] . Figure S3A , S3A′ , S3B , and S3B′ show that both phases of Bol localization are observed in orb2 mutant testes . Also as in wild type , Bol is present in “post-meiotic” ( see below ) orb236 spermatids even though they haven't undergone meiosis ( Figure S3C , S3C′ , S3D and S3D′ ) . As for Ofs , we were unable to demonstrate an association with Orb2 in testes extracts ( Xu: unpublished data ) . One reason that bol and ofs mutants are blocked in meiosis at the G2/M transition is that both factors are required for translation of twine ( twe ) mRNA [33] , [34] , [36] . twe encodes Drosophila Cdc25 phosphatase . In order for meiosis to proceed twe must remove an inhibitory phosphorylation on tyrosine 15 of Cdc2 ( Ck1 ) [37] , [38] . In bol testes , twe mRNA is present but it is not translated . In the absence of Twe protein , phosphorylated Cdc2 on Tyr15 accumulates and meiosis arrests at the G2/M transition [38] . Since our results indicate that orb2 also arrests meiosis at the G2/M transition , we anticipated that orb2 activity would be required to translate twe mRNA . To test this hypothesis , we first determined whether twe mRNA levels are normal . The RT-PCR experiment in Figure 5I shows that twe mRNA levels in orb2 testes are similar to wild type . We next used a chimeric twe-lacZ translational reporter to ascertain whether twe mRNA is translated in orb2 mutants . The reporter has sequences encoding β-galactosidase inserted in frame into the twe gene and expresses a chimeric mRNA including the twe 3′ UTR [36] . While we anticipated that the translation of the chimeric twe-lacZ mRNA would be blocked in orb2 testes as in bol ( and ofs ) , this is not the case . Instead , Twe-lacZ expression in orb2 exceeds even wild type . Figure 5E and 5F show that the pattern of Twe-lacZ expression differs in several respects from wild type . First , compared to wild type ( E ) there are many more cysts in orb2 testes ( F ) that express Twe-lacZ . Second , the amount of lacZ is typically much higher than in wild type ( compare purple arrows in E and F ) . Third , while residual Twe-lacZ is degraded in wild type once meiosis is complete and the spermatids begin differentiation , it persists in elongating orb36 spermatids ( green arrow in Figure 5F ) . Finally , we sometimes observe that Twe-lacZ is precociously expressed in immature spermatocytes that normally would not have Twe protein ( orange arrows in Figure 5F ) . Meiosis arrests at the G2-M transition in bol mutants because CDC2 remains phosphorylated on Tyr15 in the absence of Twe [37] , [38] . This should not be the case in orb2 because high levels of Twe-lacZ and presumably Twe accumulate . To confirm this prediction we compared CDC2 Tyr15-P in wild type , bol and orb2 testes . As expected the ratio of phosphorylated to unphosphorylated CDC2 is elevated in bol mutants compared to wild type , while it is reduced in orb2 ( Figure 5H ) . This finding indicates that CDC2 is activated in orb2 mutants and that meiosis I must be blocked at a subsequent step in the G2-M transition . To further pinpoint the meiosis block we examined the expression of Cyclin B ( Cyclin B ) . In wild type testes Cyclin B is expressed in primary spermatocytes when chromosome condensation starts . It persists during metaphase and is abruptly degraded at the beginning of anaphase [28] . Like Cyclin A , Cyclin B's transient nuclear accumulation is seen only very infrequently . Previous studies have shown that the upregulation of Cyclin B expression during chromosome condensation doesn't occur in ofs mutants . But other than that , Cyclin B expression and degradation seem normal [33] , [34] . In bol testes , Cyclin B is found in the cytoplasm ( Xu , unpublished data ) . In orb2 mutant testes , Cyclin B initially accumulates in the cytoplasm as in wild type ( Figure 5C , 5D , arrow ) . However , instead of transiently accumulating in the nuclei and then disappearing , we find many orb2 cysts with high levels of nuclear Cyclin B ( Figure 5D , arrowhead ) . In older orb2 cysts we often observe many small Cyclin B speckles in the cytoplasm . Taken together with the effects on twe expression and CDC2 phosphorylation , these findings place the meiosis arrest in orb2 at a step later than in bol and ofs . Even though orb2 spermatocytes fail to undergo meiosis , the spermatids in the older cysts eventually exit the meiotic cycle and begin the process of differentiation . One of the first steps in differentiation is the elongation of the flagellar axonemes . In wild type , the elongating bundle of flagellar axonemes extends towards the apical tip in a roughly straight and smooth line ( Figure 6A ) . In contrast , the elongating flagellar axonemes in orb2 zigzag back and forth and are much shorter than wild type . The individual axonemes also often splay out from each other instead of remaining in a tight bundle ( Figure 6B ) . In addition , rather than having a smooth , regular internal morphology , their internal morphology is rough and irregular . This phenotype likely arises from underlying defects in the assembly or localization of axonemal proteins . One protein that is not properly localized is the meiosis regulator Bol . In wild type , Bol co-localizes with the prominent Orb2 band near the tip of the elongating flagellar axoneme bundle . In the region distal to this band extending towards the spermatid nuclei , there is a lower level of Bol and Orb2 and both are distributed uniformly along the individual axonemes ( Figure 6C1–6C3 ) . In orb2 testes , the prominent Bol band at the tip of the axoneme is missing , while in the remainder of the axoneme bundle , Bol is dispersed in an irregular fashion , and unlike wild type , its association with individual axonemes is difficult to discern ( Figure 6D1–6D3 ) . At the end of meiosis just as spermatid elongation commences , the 64 spermatid nuclei cluster together and begin the process of condensation , eventually forming a cap-like structure ( Figure 7A , arrowhead ) [39] . This doesn't happen in orb236 , and instead of coalescing into a tight bundle , the spermatid nuclei usually end up spread out along the partially elongated flagella axonemes ( Figure 7B ) . The process of individualization begins once elongation is complete . In wild type testes , individualization is accomplished by a special structure called the Individualization Complex ( IC ) . The IC is comprised of 64 individual actin cones that assemble around each nucleus in the condensed spermatid nuclear bundle ( Figure 7C , inset ) and then travels down the bundled axonemes , ensheathing each in a plasma membrane and pushing the excess cytoplasm into a waste bag [40] , [41] . The IC is never assembled in orb236 testes and individualization never takes place . However , we do observe scattered triangular shaped actin cones ( Figure 7D , inset ) . Based on the observed defects , the steps involved in organizing actin filaments into individual actin cones might be comparatively normal , while the subsequent assembly of the cones into the larger IC ensemble is not . Consistent with this idea , Myosin VI , a component of the Actin cone [39] , is present in the orb236 cones ( Figure 7E1 , 7E2 , 7F1 , 7F2 ) . As the bundled and condensed spermatid nuclei are believed to provide the scaffolding for assembling the IC [41] , the defects in orb236 could be due to the failure in spermatid nuclei bundling and condensation . Alternatively or in addition , orb2 may be regulating genes directly involved in assembling the IC . As might be expected from the failure in IC assembly , Don-Juan GFP is not expressed in orb236 testes ( Figure 7C , 7D ) and mature sperm are never observed ( Figure 6A , 6B ) . orb mRNAs are expressed after meiosis is complete and localize to the tip of the elongating axoneme close to the band of Orb2 protein [3] , [26]; however , these localized mRNAs don't appear to be translated until Orb2 protein begins to disappear at the end of the elongation phase ( Figure 2A–2D ) . These observations suggested that the localized orb mRNAs might be a target of Orb2 repression . To test this hypothesis , we first probed Western blots of wild type and orb2 testes extracts with Orb antibodies . Figure 8A shows that Orb levels are elevated in orb2 mutants . In addition to this increase in Orb protein , orb mRNA translation appears to be ‘prematurely’ activated in orb2 mutant spermatids . As shown in Figure 2E and 2F , Orb protein is expressed in incompletely elongated orb2 mutant spermatids . Finally , the expression of Orb protein is not properly restricted to the tip of the elongated flagellar axoneme as in wild type . Instead , Orb is found throughout the mutant spermatid axonemes . The orb mRNAs in the two sexes differ at their 5′ and 3′ ends . The male transcripts begin at an internal promoter and encode a protein that has a different N-terminus from the female Orb . At the 3′ end , the male UTR is only about 200 bases in length , while the female UTR is over a thousand [3] . While the male 3′UTR lacks most of the critical sequences for orb mRNA localization and translational regulation in ovaries , there are two CPE elements . Thus , it seemed possible that orb2 might repress orb mRNA translation by a mechanism that involves an association between Orb2 protein and orb mRNA . To test this idea we reverse transcribed RNA isolated from Orb2 immunoprecipitates of wild type and orb236 testes extracts , and then used primers specific for the orb male 3′ UTR for PCR amplification . Figure 8B shows that orb mRNA is readily detected in the Orb2 immunoprecipitates from wild type but not orb236 testes . In control experiments ( not shown ) , neither boule nor twine mRNA was found in Orb2 immunoprecipitates . Taken together , these findings are consistent with the idea that Orb2 represses orb mRNA translation directly , rather than by regulating some other intermediate . More than twenty other mRNAs are transcribed post-meiotically and localize to the tip of the elongating spermatid flagellar axonemes [26] . In addition to having similar expression and localization patterns to orb several of these mRNAs have CPE-like elements in their 3′ UTRs and could be regulatory targets of orb2 . Consistent with this possibility we found that two of the CPE containing mRNAs , scotti and f-cup , can be immunoprecipitated with Orb2 antibody from wild type but not orb2 mutant testes ( Figure S4 ) . While the function of f-cup is unknown , Barreau et al . [26] found that scotti mutant males are sterile . The primary defect appears to be at a late step in spermatogenesis and involves the assembly or maintenance of the IC structure . Although the experiments above show that orb2 is required for spermatid differentiation , it could be argued that the differentiation defects are the indirect consequence of the failure to undergo meiosis rather than because orb2 has special functions in this stage of spermatogenesis . To address this problem , at least in part , we took advantage of the hypomorphic orb2ΔQ allele , which has a small deletion that removes an N-terminal poly-Q domain ( Figure S2A , Figure 3 ) [14] . As shown in Figure 3B , both the large isoform and the smaller , testes specific isoform are abundantly expressed in orb2ΔQ testes; however , they migrate more rapidly than the corresponding wild type isoforms due to the loss of the poly-Q domain . We examined spermatogenesis in orb2ΔQ homozygous flies . In contrast to the mutants that reduce or eliminate expression of the 75 kD isoform , there are no meiosis defects in orb2ΔQ . Instead , like wild type , 32- and 64-cell cysts are observed , and each of the spermatids in the 64-cell cysts has a normal looking nucleus ( Figure 9C ) and Nebenkern ( not shown ) . Also unlike orb236 , elongating orb2ΔQ flagellar axonemes have a seemingly normal morphology and as in wild type the mutant Orb2 protein and Bol accumulate together in a prominent band near the growing tip of the flagellar axoneme ( Figure 6E1–6E3 ) . Likewise the assembly of the spermatid nuclei into a bundle and their condensation appear to be normal ( not shown ) . On the other hand , the process of differentiation is not normal in orb2ΔQ . In wild type testes , elongation of the flagellar axoneme stops before the tail reaches spermatogonia region ( Figure 9D , arrow points to end of elongation ) . This is not true in orb2ΔQ . About 70% of the mutant testes have over-elongated flagellar axonemes that extend into the spermatogonia region . Elongation doesn't seem to arrest even at this point . Overgrowth of spermatid axoneme results in the swelling of testes tip region and an over-sized testes tip is often observed in orb2ΔQ testes ( Figure 9E , compare d in Figure 9E and d′ in Figure 9D ) . In some cases , the elongating flagellar axonemes push against the testes wall and cause the muscle layer encasing the apical tip of the testes to rupture ( not shown ) . On other occasions , when the flagellar axoneme bundle reaches the spermatogonia region , it changes direction and begins elongating towards the side of the testes or even reverses direction and elongates towards the base of the testes ( Figure 9F ) . Another differentiation defect is in the assembly and functioning of the IC . While fully elongated cysts with scattered actin cones are occasionally observed in wild type testes ( ∼6% ) , 35% of the fully elongated cysts in orb2ΔQ testes have scattered actin cones ( Figure 9A , 9G , 9H ) . The IC defects range from actin cones that are not fully coalesced into the IC structure ( Figure 9G , arrow ) to completely dispersed actin cones ( Figure 9H , arrows ) . These IC phenotypes resemble the phenotypes reported for scotti [26] . In the testes that have IC defects , there is always a mixture of both wild type and defective ICs ( Figure 9H arrow and arrowhead ) , which may explain why orb2ΔQ males are still fertile . Also by comparison , all ICs in orb236 testes are defective . There is also a reduction in the number of ICs in orb2ΔQ testes . In wild type flies , over 90% of the testes have more than 19 ICs . In contrast , orb2ΔQ testes , 44% testes have less than 19 IC ( Figure 9B ) . The fact that meiosis is normal in orb2ΔQ , but there are clear defects in both spermatid elongation and individualization , would provide further support for the idea that orb2 activity is required not only for meiosis , but also for proper differentiation .
Although CPEB family proteins play critical roles in germline development in many species , their germline functions differ between proteins within an organism and also between proteins in different organisms . For example , in C . elegans , Fog-1 and the Orb2-like CPB-1 function in the male germline and are required for sex determination and meiosis respectively . A third , Orb-like CPEB , CPB-3 is required for meiosis in females [42]–[44] . Similar functional specializations are evident for orb and orb2 . While orb is essential for oogenesis , it is not absolutely required for spermatogenesis as orb mutant males produce functional sperm and their fertility is reduced but not eliminated . The opposite sex specificity is exhibited by orb2 . Though genetic interaction studies ( suppression of orb haploinsufficiency in the gurken dorsal-ventral polarity pathway: see for example [17] , [55] ) suggest that orb2 may negatively regulate orb in the ovary , orb2 females are fertile and oogenesis appear to be comparatively normal ( Nathaniel Hafer , PhD thesis ) . In contrast , orb2 plays an essential role in the male germline , and is required for programming the orderly progression of spermatogenesis from meiosis through differentiation . How CPEB proteins regulate meiotic progression is best understood in Xenopus oocytes . During oocyte maturation , CPEB1 acts as a repressor , blocking translation of mRNAs containing CPE motifs . However , after progesterone stimulation , CPEB1 is converted into an activator by Aurora kinase phosphorylation , initiating translation by stimulating the Gld-2 dependent polyadenylation of target mRNAs . Amongst the targets are mRNAs encoding Mos and the Cyclins B2 and B5 . These cyclins activate Maturation Promoting Factor ( MPF ) which mediates entry into metaphase I . Although CPEB1 is degraded during metaphase I , it induces expression of CPEB4 , which is a member of the second CPEB family . CPEB4 subsequently controls the transition to metaphase II by regulating Cyclins B1 and B4 expression [45]–[49] . Interestingly , though mouse CPEB1 is also essential for meiosis in both sexes , it controls meiosis at an earlier step by regulating mRNAs encoding synaptonemal complex proteins [50] . Since there is no recombination in Drosophila males , the function ( s ) of orb2 in meiosis are necessarily different from those of mouse CPEB1 [48] . Additionally , its role is distinct from that of Xenopus CPEB1 . While Xenopus CPEB1 promotes meiotic progression by activating translation of Cyclin B mRNAs , orb2 pre-meiotic cysts accumulate high levels of Cyclin B . orb2 also differs from the fly translation factors ofs and bol . The meiotic phenotypes of mutations in these two genes suggest that they regulate different targets and likely function at earlier steps in meiotic progression than orb2 . Unlike orb2 mutants , Cyclin B levels aren't properly upregulated during G2 in ofs mutants . However , it is not clear whether ofs regulates Cyclin B mRNA translation directly , or whether the defects are an indirect consequence of incomplete spermatocyte maturation [33] , [34] . bol seems to function at a step after ofs , controlling the onset of metaphase I by activating twe mRNA translation . In bol mutants Twe is not expressed and meiotic progression is blocked because CDC2 remains phosphorylated and inactive . orb2 mutations have a very different effect on Twe . First , Twe is precociously expressed in cysts containing spermatocytes that have not fully matured . Second , very high levels of Twe accumulate in mature cysts that are arrested prior to metaphase I . Moreover , as would be expected , a substantial fraction of Cdc2 in orb2 testes is dephosphorylated . Finally , Twe persists in differentiating spermatids . These phenotypes , together with the high levels of the A and B Cyclins , argue that orb2 regulates meiotic progression at a step that is likely later than either ofs or bol . Additionally , these findings indicate that meiotic progression in male flies does not depend upon a single critical step or “switch” such as turning on twe or cyclin mRNA translation . Rather , it would appear that multiple steps in meiotic progression are subject to translational regulation , and that these steps are controlled by different translation factors . One simple model for Twe ( Twe-LacZ ) misexpression is that orb2 represses the translation of twe mRNA , perhaps by antagonizing Bol dependent activation . However , there are complications with this model . For example , the high levels of Twe-LacZ that accumulate in cysts arrested before metaphase I could be the consequence of a prolonged arrest at a point after Bol activation of twe translation rather than a failure to repress twe mRNA translation . While an indirect effect of this type would not explain why Twe-LacZ is precociously expressed in immature orb2 spermatocytes , we were unable to demonstrate an association between Orb2 and twe mRNA . Additionally , twe 3′ UTR doesn't contain any obvious CPE-like recognition sequences . With the caveat that these are negative results , an alternative possibility is that the effects on Twe-LacZ expression are indirect . The onset of spermatid differentiation in wild type normally proceeds only after the completion of meiosis . However , as is seen for twe , ofs and bol , differentiation becomes uncoupled from meiotic progression and the mutant cysts ultimately exit the pre-metaphase I arrest and begin the process of spermatid differentiation [32]–[34] . In all of these mutants the differentiation process is abnormal , with some steps being initiated , but not properly executed , while other steps are not even initiated . One of the key events in spermatid differentiation is the elongation of the flagellar axoneme . Little or no elongation is evident for bol , while twe and ofs spermatids begin elongating but quickly abort [32]–[34] . While the spermatid flagellar axonemes elongate in orb2 mutants , the axonemes don't extend straight back towards the stem cells at the tip of the testes , but instead zigzag irregularly and prematurely halt elongation . They also have an abnormal internal morphology and though they express Bol , they lack the prominent Bol band , which in wild type testes co-localizes with the Orb2 band near the tip of the elongating axonemes . Since Bol is essential for elongation , the absence of the Bol band is likely to be relevant to the elongation defects in orb2 . While we didn't detect any association between Orb2 and bol mRNA , RNA independent Orb2-Bol proteins complexes are found in testes extracts . Thus , a plausible idea is that localization of Bol to the axonemal band is mediated by interactions with Orb2 . Once elongation is completed in wild type , the spermatid nuclei condense and coalesce into a nuclear bundle and this structure provides a scaffold for assembling the IC . In orb2 the spermatid nuclei don't properly condense and never coalesce into a tight nuclear bundle . Though the process of IC assembly is initiated and actin cones are generated , a complete IC is never formed . The individualization marker Don Juan is also not expressed in orb2 testes . Interestingly , though spermatid differentiation appears to be much less complete in ofs than in orb2 , Don Juan is expressed in ofs testes [33] . An important question is whether the defects in differentiation evident in orb2 testes reflect functions for orb2 during this stage of spermatogenesis or are the indirect and perhaps non-specific consequence of the earlier meiotic arrest . Arguing against the later possibility is the fact that ofs , bol , and orb2 mutants have quite distinct differentiation phenotypes , yet all three fail to undergo meiosis . In the case of orb2 , other lines of evidence point to functions at specific steps in differentiation . First , orb2 appears to be required for repressing the post-meiotic expression of Orb until after spermatid elongation is complete . In wild type , the orb gene is transcribed post-meiotically , but orb mRNA is not translated until after spermatid elongation is nearly complete . Since the timing of orb mRNA translation is correlated with the disappearance of Orb2 , a plausible idea is that Orb2 represses orb mRNA translation . Consistent with this hypothesis , the levels of Orb protein are elevated in orb2 mutant testes , and it is expressed prematurely in incompletely elongated spermatids . In addition , instead of being expressed only at the tip of the flagellar axonemes , Orb is distributed all along the axonemes . As orb mRNA contains two CPE elements and can be detected readily in Orb2 immunoprecipitates , it seems possible that Orb2 could directly repress orb mRNA translation . As noted above , a role in repressing orb mRNA translation is also suggested by genetic interaction studies in females ( Nathaniel Hafer , PhD thesis ) . Second , orb mRNA does not seem to be the only post-meiotic orb2 regulatory target . We found that scotti and f-cup , which are also expressed after meiosis and thought to encode proteins involved in differentiation , are found in Orb2 immunoprecipitates of testes extracts . Moreover , there could be additional targets besides these three mRNAs . Several of the other post-meiotically expressed genes have CPE-like elements in their 3′ UTRs [26] . Similarly , the mRNA encoding gld2 poly ( A ) polymerase , which is thought to be an Orb co-factor , also has a CPE-like element in its 3′ UTR and resembles Orb in that Gld2 protein preferentially accumulates near the tip of elongated flagellar axonemes [51] . Third , the hypomorphic poly Q deletion mutant , orb2ΔQ , makes it possible to separate meiotic arrest from at least some steps in differentiation . Meiosis appears to be completely unaffected by the ΔQ mutation; however , as is seen for orb236 there are defects in both flagellar axoneme elongation and IC assembly . On the other hand , since the differentiation defects in orb2ΔQ are much less severe than those in the null , the possibility remains open that the failure in meiosis interferes with some process ( es ) critical for proper differentiation . For example , the defects in chromosome condensation and spermatid nuclear bundle formation could be due to the fact that the orb2 spermatid nuclei have a large excess of DNA . In turn it could be argued that the failure in IC assembly in orb2 is due to the absence of a coalesced spermatid nuclear bundle . However , the fact that IC assembly is also defective in orb2ΔQ would argue that orb2 must have IC specific functions that are independent of any IC assembly steps that require completion of meiosis . Consistent with this possibility , mRNAs encoding Scotti , which has also been implicated in IC function , are found in orb2 immunoprecipitates . Finally , our studies provide some insights into the functional properties of the N-terminal region of the Orb2 protein . First , the very modest phenotypes observed not only in the soma [14] , [19] but also in the male germline for orb2ΔQ suggest that the prion forming poly-Q domain , which is present in both the 75 kD and the 60 kD isoforms [10] , [11] , is dispensable for most orb2 functions . Second , even though the testes differ from the soma in that there are readily detectable levels of the 60 kD isoform , it is not clear what function if any this isoform has in spermatogenesis . In the insertion mutants that reduce expression of the 75 kD isoform there are even higher levels than normal of the 60 kD isoform , yet these mutants exhibit meiotic and differentiation defects that resemble those seen for the orb2 deletions . Though their phenotypes appear less severe than the deletion mutants , this could be attributed to the fact that all express some residual 75 kD protein . Third , the 162 N-terminal sequence that is unique to the 75 kD isoform is critical for orb2 function in programming the orderly development of the male germline from meiosis through the process of spermatid differentiation . Since there is little if any of the 60 kD isoform in somatic tissues , it isn't certain at this point whether the smaller isoform would be able substitute for the 75 kD in the soma . Additional tools will be required to determine whether the smaller isoform has any role in spermatogenesis and also to further dissect how orb2 functions at different points in meiosis and differentiation .
We obtained P-element/Piggybac insertion ( f01556 , c06090 , e01925 , d01793 , f04965 ) from the Exelexis collection maintained at Harvard [29] . Deficiency stocks Df ( 3L ) ED4421 , Df ( 3L ) ED4415 , Df ( 3L ) ED4416 and the dj-GFP stock were obtained from the Drosophila stock center ( Bloomington ) . bol1 is a kind gift from Steven Wasserman [37] . orb2Δ and orb2ΔQ are provided by Barry Dickson [14] . All twine-lacZ flies , twine , aly , sa , can , and mia mutants are kindly provided by Minx Fuller ( Stanford ) . 20 individual males were placed with two w1118 females each in food vials for 5 days , after which adults were removed . Presence of larvae , pupae and adults were examined after another 2 weeks . Those with presence of larvae are considered fertile . piggyBack ( pBac ) transposon insertions with FRT sites near the orb2 gene used to generate orb2 null alleles are: FRT1 ( f01556 ) , FRT2 ( d01925 ) , FRT3 ( f04965 ) ( Figure S2 ) . The FRT sites are used in combination with FLP to create targeted deletions of genomic DNA ( method as described in [29] , [30] ) . Deletions were confirmed using PCR primers specific for pBac sequences flanking the deletion site and within the gene region . We recovered and established several independent deletion stocks from each transposon pair and they behave similarly . Experiments described here use deletions from f01556–f04965 , which we named orb236 . There are also deficiencies in the region that uncover the orb2 locus and have been mapped molecularly ( Df ( 3L ) ED4421 , Df ( 3L ) ED4415 , Df ( 3L ) ED4416 ) . They behave the same when combined with orb236 . In the text , Df ( 3L ) ED4416 is used and referred to as 4416 . Western blotting was essentially performed as in [19] . Antibodies used were as follows: mouse anti-Orb2 2D11 ( 1∶25 ) , mouse anti-Orb2 4G8 ( 1∶25 ) , mouse anti-Snf 4G3 ( 1∶2000 ) , rabbit anti CDC2 ( PSTAIR ) ( 1∶2000 , millipore ) , rabbit anti CDC2Tyr15 ( IMG668 ) ( 1∶2000 , IMGENEX ) , mouse anti-Orb 6H4 ( 1∶60 ) , mouse anti-Orb 4H8 ( 1∶60 ) [3] , [4] , goat anti-mouse conjugated HRP ( 1∶1000- Jackson Immunoresearch ) . Blots were then washed 4×10 minutes in TBST and developed with ECL-plus ( Amersham ) . In situ hybridization was performed as described in [52] . Fluorescent antisense probes for orb2 were synthesized by Biosearch Technologies ( www . biosearchtech . com ) . Forty non-overlapping 17 bp probes targeted at orb2 mRNA sequence from cctggacgatcagatgt to atatgttatttaatctcac were synthesized and labeled with Quasar 670 and used at 1∶100 dilution . Detection is done on an inverted Zeiss LSM510 confocal microscope . Whole mount staining is performed as in [33] . Antibodies used were as follows: mouse anti-Orb2 2D11 and 4G8 IgG ( undiluted ) , rabbit anti-Bol ( 1∶1000 , a gift from Steven Wasserman ) , mouse anti-Myosin VI 3C7 1∶25 ( a gift from Kathryn Miller ) , monoclonal anti-β-Tubulin E7 1∶50 ( Developmental Studies Hybridoma Bank ) , monoclonal anti-Orb 6H4 and 4H8 1∶30 . DNA was stained with Hoescht ( 1∶1000 ) . Actin was stained with Alexa488-phalloidin , Alexa546-phalloidin ( Invitrogen , Carlsbad , CA ) . Secondary antibodies used were goat anti-mouse IgG Alexa 488 or 546 , goat anti-rabbit Alexa 488 or 546 ( Molecular Probes , Inc . ) Samples were mounted in Aqua-polymount on slides for an inverted Zeiss LSM510 confocal microscope . Testes live squash and phase contrast was performed as described in [41] . β-galactosidase activity assay was performed as described by [28] . Immunoprecipitation was performed essentially as described by [53] , except the followings: crude monoclonal anti-Orb2 antibodies 2D11 and 4G8 were affinity purified with Orb2 coupled HiTrap NHS-activated HP column ( GE healthcare ) before used for immunoprecipitation; purified Orb2 antibodies were mixed with testis extract for 0 . 5 h–2 h at room temperature before protein-A/G beads ( Calbiochem/Millipore ) were added in; the mixture was then incubated at 4 C° for 2 h to overnight . Putative Orb2 target mRNAs with CPE binding sites were predicted using software described in [54] . RT-PCR was done according to [19] . Primers used were as follows: orb2 common exon among RA , B , C , D: CAACAGTGCCACCAGCAGTGC and GCGCAGACTAACTTCGTCGTT . Cg5741: ATGAGCAAAGCTCCGTTGAAAGCC and TATCCGGATTAACCGTGTTCCGCA . orb :CAAGCCCTTGACTCGCAACTCC and CTCCGCCATATTTCTACGTCGCCTAC scotti: AAGAACCTCTCTTGGACCTCGGAA and AATGGGATGCATATCGGCTGGTTG f-cup: AACCAGCTGAGCACTTTGCCCAAT and AGATGAACTGTGGCACATAGCCGA Phosphorylation assay was done as in [55] . Testis were squashed in cold PBS and treated with λ protein phosphatase for 1 hour at 30°C followed by Western blots . | Cytoplasmic Polyadenylation Element Binding ( CPEB ) proteins bind and recognize CPE sequences in the 3′ UTRs of target mRNAs and can activate and/or repress their translation depending on the mRNA species and the biological context . Drosophila has two CPEB family genes , orb and orb2 . orb is expressed in the germline of both sexes and has critical functions at multiple steps during oogenesis; however , it plays only a limited role in spermatogenesis . Here we show that the second CPEB family gene orb2 has the opposite sex specificity in germline development . While it appears to be dispensable for oogenesis , orb2 has essential functions during spermatogenesis . It is required for programming the orderly and sequential progression of spermatogenesis from meiosis through differentiation . orb2 mutants fail to execute the meiotic G2-M transition and exhibit a range of defects in the process of sperm differentiation . | [
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... | 2012 | The CPEB Protein Orb2 Has Multiple Functions during Spermatogenesis in Drosophila melanogaster |
DNA mismatch repair suppresses gastrointestinal tumorgenesis . Four mammalian E . coli MutL homologues heterodimerize to form three distinct complexes: MLH1/PMS2 , MLH1/MLH3 , and MLH1/PMS1 . To understand the mechanistic contributions of MLH3 and PMS2 in gastrointestinal tumor suppression , we generated Mlh3−/−;Apc1638N and Mlh3−/−;Pms2−/−;Apc1638N ( MPA ) mice . Mlh3 nullizygosity significantly increased Apc frameshift mutations and tumor multiplicity . Combined Mlh3;Pms2 nullizygosity further increased Apc base-substitution mutations . The spectrum of MPA tumor mutations was distinct from that observed in Mlh1−/−;Apc1638N mice , implicating the first potential role for MLH1/PMS1 in tumor suppression . Because Mlh3;Pms2 deficiency also increased gastrointestinal tumor progression , we used array-CGH to identify a recurrent tumor amplicon . This amplicon contained a previously uncharacterized Transducin enhancer of Split ( Tle ) family gene , Tle6-like . Expression of Tle6-like , or the similar human TLE6D splice isoform in colon cancer cells increased cell proliferation , colony-formation , cell migration , and xenograft tumorgenicity . Tle6-like;TLE6D directly interact with the gastrointestinal tumor suppressor RUNX3 and antagonize RUNX3 target transactivation . TLE6D is recurrently overexpressed in human colorectal cancers and TLE6D expression correlates with RUNX3 expression . Collectively , these findings provide important insights into the molecular mechanisms of individual MutL homologue tumor suppression and demonstrate an association between TLE mediated antagonism of RUNX3 and accelerated human colorectal cancer progression .
Colorectal cancer ( CRC ) is one of the common malignancies in industrialized countries . Lynch syndrome , a highly penetrant disorder that confers predisposition to cancer of the colorectum , endometrium and other extra-colonic sites [1] , is caused by germline mutations in DNA Mismatch Repair genes ( MMR ) . Including sporadic forms , defective MMR underlies ∼12–15% of CRC [2] . MMR plays critical roles in the maintenance of genomic stability in both prokaryotes and eukaryotes [3] . The study of model organisms has yielded great insights into the mechanisms through which MMR prevents cancer [1] , [3] , [4] , [5] , [6] , [7] , [8] . Briefly , there are nine mammalian MMR genes ( MLH1 , MLH3 , PMS1-2 , MSH2-6 ) . The mammalian E coli MutS homologues ( MSH ) directly contact DNA , scanning along the genomic DNA for mismatches analogous to a “sliding clamp” until they encounter a base-pair containing a mismatch [9] , [10] . MSH2-MSH6 primarily recognizes single-base substitutions and 1 base-pair insertion-deletion loop ( IDL ) mutations , while MSH2-MSH3 recognizes 1–4 base-pair insertion-deletion mutations [1] , [3] . The IDL repair deficiency is commonly referred to as Microsatellite Instability ( MSI ) . The MSH proteins interact with multiple proteins including the mammalian E coli MutL homologues ( MLH ) and yeast post-meiotic segregation ( PMS ) homologue proteins ( which have significant amino acid identify and structural similarity to the MLH proteins ) , as well as RPA , EXO1 , RFC , HMGB1 , POLDC and other proteins [1] , [8] , [11] , [12] . MLH1-PMS2 is the primary MutL complex that interacts with both MSH2/6 and MSH3 complexes . MLH1–MLH3 is less well characterized , but is believed to participate in IDL repair [13] , [14] , DNA damage response [13] , and possibly single-base point mutation repair ( SBR ) [15] . MLH1-PMS1 exists in mammalian cells but currently has no clearly defined roles in processes related to cancer prevention [16] , [17] . To study the precise mechanisms through which MMR suppresses carcinogenesis in vivo , we and others [16] , [18] , [19] , [20] , [21] , [22] , [23] , [24] previously developed several mouse models carrying mutations in different MMR genes . Mlh1−/− and Msh2−/− mice develop early onset GI epithelial cancers , lymphomas and other types of cancer . Pms2−/− mice develop lymphomas , but not GI epithelial cancers . Mlh3−/− mice develop GI and extra-GI tumors , have decreased survival when compared with Wt mice , but with later onset than Mlh1−/− [13] . Mlh3−/−;Pms2−/− mice have increased cancer incidence , resistance to apoptosis and MSI [13] . However , the precise mechanisms in which Mlh3 and Pms2 participate to suppress GI epithelial tumorigenesis and progression remain poorly characterized . Germ-line mutations in tumor suppressor gene APC lead to familial adenomatous polyposis ( FAP ) [25] , [26] . Mutations in APC are found in the majority of sporadic CRC and many Lynch syndrome tumors [27] , [28] . APC complexes with AXIN and CK1/2 and destabilizes β-Catenin by enhancing proteasomal destruction . Mutated APC proteins are unable to down-regulate β-Catenin , and the stabilized β-Catenin translocates into the nucleus where it acts as a transcriptional coactivator of the DNA binding protein TCF-4 [29] , [30] . More than 95% of APC germ-line mutations are truncating or nonsense mutations and most of the pathogenic mutations are located within the first 1500 codons . Apc mutations cooperate with MMR deficiency in both tumorigenesis and tumor progression . Apc1638N mice are a well characterized model that develops GI cancer [31] . Mlh1−/−;Apc1638N mice showed significantly increased GI tumor multiplicity and accelerated progression to adenocarcinoma compared to either mutation separately . Analyses of GI tumors from Mlh1−/−;Apc1638N and Msh3−/−;Msh6−/−;Apc1638N mice revealed that both single-base substitutions and MSI induced frameshift mutations in repetitive sequences were responsible for most mutations found in the remaining wild-type ( Wt ) Apc allele [32] , [33] . In contrast , tumor-associated Apc mutations found in the Wt Apc allele in Msh6−/−;Apc1638N tumors were predominantly single-base point mutations . To understand more precisely the mechanistic roles that Mlh3 and Pms2 play in GI tumor suppression , we generated Mlh3−/−;Apc1638N ( MA ) and Mlh3−/−;Pms2−/−;Apc1638N ( MPA ) mice . We show that in vivo Mlh3 mutations significantly increase frameshift mutation rates in Apc , and increase GI tumorigenesis . Unlike typical MSI-induced mutations , Mlh3 deficiency also results in frameshift mutations in non-repetitive sequences , a unique mutational signature among MMR deficient mice found only in Mlh3 deficient mice . Consistent with the role of Pms2 in SBR , combined Mlh3 and Pms2 mutations proportionally increase point mutations and show a sequence preference for a CpG mutation hotspot also previously seen in Mlh1−/− mice . Because MPA mutant mice also have significantly increased rates of GI adenocarcinomas vs . Apc1638N or MA mice , we investigated mechanisms of tumor progression . Using array-CGH , we identified a recurrent 5-Mb amplification on chromosome 12 in GI tumors from MPA mice . We defined the amplicon critical interval and demonstrated that it contains a previously uncharacterized member of the Transducin enhancer of Split ( TLE ) /Groucho family of transcriptional co-regulators , Tle6-like , that contributes to tumor progression . Tle6-like overexpression in colon cancer cell lines increases cell proliferation , colony-formation ability , cell migration and xenograft tumorigenicity . Human TLE6D , an alternatively spliced isoform of TLE6 , with a domain structure similar to Tle6-like , has functional activity similar to Tle6-like . Both Tle6-like and TLE6D interact with GI tumor suppressor , RUNX3 [34] , and antagonize RUNX3 gene target tranactivation . TLE6D is overexpressed in multiple human microsatellite stable ( MSS ) and microsatellite unstable ( MSI-H ) CRCs , and TLE6D expression levels correlate with RUNX3 expression levels . Collectively , these findings provide important insights into the molecular mechanisms through which MMR-deficiency contributes to GI tumorigenesis and implicate a novel association between TLE6 isoforms and antagonism of RUNX target gene expression in CRC tumor progression .
By 9 . 5 months of age , MA mice develop >50% more tumors than Apc1638N mice ( P<0 . 001; Mann-Whitney ) ( Figure 1A and C ) . However , the relative ratios of GI adenomas to carcinomas in Apc1638N mice ( 65% and 35% respectively ) were very similar to that seen in MA mice ( 70% and 30% respectively ) and overall survival is not significantly affected ( 9 . 5 vs . 10 . 5 months ) . No significant effect was seen on extra-GI cancer incidence or progression . These data suggest the primary role of Mlh3 is in suppression of GI tumor initiation and not tumor progression . To study the effects of combined Mlh3 and Pms2 mutations in vivo , we generated MPA mice . MPA mice had significantly shorter survival vs . Apc1638N or MA mice ( P<0 . 01 , Mann-Whitney test; Figure 1A , C ) and developed significantly more adenocarcinomas than MA or Apc1638N mice ( Figure 1B , C ) ( P = 0 . 022 MPA vs . MA and p = 0 . 0003 MPA vs . Apc1638N ) . These are consistent with a role for Mlh3;Pms2 combined loss both to increase GI tumor initiation and accelerates progression . However , mean overall survival of MPA mice is longer than that previously seen in Mlh1−/−;Apc1638N mice [35] . In vitro studies have alternatively suggested that Mlh3 participates in either IDL repair [13] or SBR [15] . To understand the role of Mlh3 in these processes , we used the wild type Apc allele as a tumor-associated in vivo reporter gene to analyze the mutation spectrum from MA GI tumors . A total of 49 tumors from MA mice and 28 tumors from Apc1638N littermates were analyzed for Apc truncation mutations by IVTT analysis . Truncated Apc products were detected in 27 of 49 ( 55% ) MA tumors while only 9 of 28 ( 32% ) were found in Apc1638N tumors . The current observed incidence of Apc somatic mutations of Apc1638N tumors is in agreement with the previous results ( 7 of 22 , 32% ) [36] , hence for better understanding of mutational differences between the two strains , this and the previous data for Apc1638N tumors were combined and used for further comparisons . This 23% increase in somatic Apc mutations in MA mice was significant ( P<0 . 0048; Fisher exact test ) and was attributable to increased small insertion/deletion frameshift mutations ( 62 . 5% ) vs . Apc1638N ( 33 . 3% ) mice ( P<0 . 001; Fisher exact test; Figure 2B and Tables 1 and 2 ) . MA mice had one recurrent insertion/deletion mutation “hotspot” also observed in Mlh1;Apc1638N mice ( amino acid 1464 ) ( Figure 2A ) . Furthermore , examination of the sequences surrounding each Apc mutation site in MA tumors showed that , unlike in other mismatch repair deficient tumors such as Mlh1−/−;Apc1638N or Msh6−/−;Msh3−/−;Apc1638N [32] , [33] , about 40% of frameshift mutations occurred at non-repetitive sequences within the Apc coding region . These data are consistent with a primary in vivo role for Mlh3 in DNA repair of small insertion/deletion mutations in GI epithelial cells . We also studied the tumor-associated Apc mutations in GI tumors from MPA mice . The overall incidence of Apc truncation mutations in MPA tumors were similar to that observed in MA tumors , yet the nature of mutations characterized was distinct . Compared with MA mice ( 37 . 5% ) , combined Mlh3;Pms2 deficiency caused a significant increase in the proportion of single-base point mutations ( 57 . 2% , P<0 . 01; Figure 2 and Table 2 ) . Within the types of single-base point mutations , MPA tumors showed higher frequency of C∶G→T∶A transition mutations ( 12 of 16 , 75% ) compared to MA tumors ( 7 of 12 , 58 . 3% ) . However , this high frequency was not as prominent as that of Mlh1;Apc1638N tumors which showed the majority ( 23 of 25 , 92% ) of base substitutions to be transition mutations[32] . The C∶G→T∶A transition mutations found in tumors , irrespective of genotypes , occurred at either CpG dinucleotides or CpNpG sites , typical targets for DNA methylation . Among these , Apc codon R854 seems to be a preferential target for base substitution mutation , which was not only demonstrated to be a mutational hotspot in Mlh1;Apc1638N mice [32] but also in MPA mice . Apc mutation is thought to be an early event in CRC carcinogenesis . The significantly increased number of adenocarcinomas vs . adenomas seen in MPA vs . MA or Apc1638N mice suggested that MPA tumors have accelerated tumor progression . While there is extensive evidence that increased mutation rates and decreased apoptosis contribute to MMR defective CRC , it is likely that additional mechanisms participate in tumor progression as well . Because chromosomal and segmental aneuploidy has been described in a subset of MMR deficient adenocarcinomas [37] , [38] , [39] , we performed array comparative genomic hybridization ( aCGH ) analyses of GI tumor vs . E18 . 5 C57BL/6 embryonic control DNA from Apc , MA , and MPA mice to identify specific genetic changes that accelerate MPA GI tumor progression . Comparison of aCGH profiles revealed a recurrent 5-Mb base pairs amplification on chromosome 12F2 ( 66 . 7%∼83 . 3%; see Table 3 for detail; Figure 3A and B ) in MPA GI tumors not seen in Apc1638N or MA tumors ( Figure S1 ) . To define the critical interval for this amplification on chromosome 12F2 we bred a new cohort of MPA mice and quantified copy number variation in the tumor using real-time quantitative PCR ( qPCR ) ( Figure 3C and Table 4 ) . Using qPCR with primer sets for the six genes within the amplified region and two flanking genes , we identified one gene that showed recurrent increased level of genomic DNA in tumor tissues ( Figure 3C ) , Transducin-like enhancer protein 6-like , ( Tle6-like ) . TLE family members act as transcriptional corepressors [40] , [41] without any intrinsic DNA-binding activity . They are recruited to specific gene regulatory sequences in a context-dependent manner by forming complexes with different DNA-binding transcription factors . Two evolutionarily conserved domains define the TLE gene family: an N-terminal glutamine-rich ( Q ) domain that mediates TLE family member heterodimerization , and a C-terminal domain of WD motif repeats that mediates direct interactions with sequence specific DNA binding transcription factors ( Figure 4 ) [40] , [41] . Previously TLE family members have been described containing only the Q domain , such as Grg1-S [42] , or only the WD repeat motif , such as Grg6/Tle6[43] . Tle6-like similarly contains only the C-terminal WD repeat domain and had highest amino acid identity ( 84 . 4% ) with TLE6 ( Figure 4A and Figure S2 ) . To understand the impact of gene amplification on Tle6-like expression , we isolated total RNA from tumor and normal tissues from MPA mice and used qPCR to quantify relative Tle6-like mRNA expression . As a result of copy number amplification , Tle6-like mRNA levels were significantly increased in tumors compared with adjacent normal GI tissue ( Figure 3D ) . To understand whether Tle6-like protein levels are subsequently increased , we generated anti-Tle6-like specific antisera . Western blot analysis with this antisera demonstrated that Tle6-like protein levels are significantly increased in GI tumors compared to surrounding normal GI epithelial tissue from MPA mice ( Figure 3E ) . Overall , these data suggest increased genomic DNA copy number of Tle6-like causes increased mRNA and protein expression of Tle6-like in MPA tumors . Gene diversity can be generated by several mechanisms , including gene duplication and paralogue evolutionary divergence , and the generation of alternative mRNA splice isoforms that modify coding sequence . The mouse Tle6-like-containing amplicon is syntenic to human chromosome 14q33 , but amplification of this chromosomal region is not associated with CRC . Upon further analysis , we discovered that 14q33 contains no human ortholog of mouse Tle6-like , or any other TLE family member . However , when we analyzed TLE6 mRNAs bioinformatically , we identified a previously identified alternative spliced isoform of TLE6 ( TLE6D ) ( Genbank Accession #BX375733 ) that contains only the C-terminal WD repeat domain of TLE6 , and therefore has the same domain structure as mouse Tle6-like ( Figure 4B ) To understand expression of TLE6A ( full-length isoform ) and TLE6D in human CRC , we generated three sets of RT-PCR primers: one for the TLE6D N-terminus , one crossing the splice junction that is specific for TLE6D and one that detects TLE6A but not TLE6D ( Figure 4B ) . We then calculated expression of these transcripts in 40 human CRC samples and normal tissue . Compared to adjacent normal tissue , the TLE6D-specific and TLE6 C-terminus qPCR showed significantly increased expression in a subset of human CRCs ( Figure 5A ) , but not for the TLE6 N-terminal or TLE6A qPCR ( data not shown ) . These data suggest that the TLE6D isoform specifically is overexpressed in a subset of human CRCs . Because GI tumors from MPA mice showed increased number of adenocarcinoma than Apc1638N or MA mice , we evaluated whether increased levels of Tle6-like can contribute to mechanisms that underlie tumor progression . We generated stable cell 293 cell lines that express Tle6-like or TLE6D . For both Tle6-like and TLE6D overexpressing cell lines , cell proliferation rates were significantly increased compared with vector-transfected control cells ( Figure 6A ) . Similar results were also seen in HCT116 and 3T3 cells ( data not shown ) . We next tested the effect of Tle6-like/TLE6 expression on the ability to form colonies in vitro . Mouse embryonic fibroblasts transfected with Tle6-like or TLE6D significantly increased colony formation ( four-fold and two-fold , respectively ) compared with empty vector-transfected control cells ( Figure 6B and C ) . We also tested the mobility of the cells transfected with Tle6-like/TLE6D by in vitro migration assay . Cell lines stably expressing Tle6-like or TLE6D were able to migrate a significantly longer distance when compared with control cell lines expressing only the vector ( Figure 6D ) . In contrast , no effect of Tle6-like or TLE6D ectopic expression was seen on induction or resistance of apoptosis induced by serum-depletion in culture medium ( data not shown ) . In summary , these results are consistent with a proliferation and migration advantage for tumor cells expressing Tle6sh or TLE6D . Because Tle6-like or TLE6D ectopic expression increased cell proliferation and migration in vitro , we evaluated their impact in vivo . We injected HCT116 cells stably expressing Tle6-like , TLE6D or vector s . c . into nude mice and quantified tumor growth . As expected , HCT116 cells transfected with vector formed xenograft tumors . In parallel , HCT116 cells expressing Tle6-like and TLE6D formed significantly larger tumors ( Figure 7 ) . These results suggest that Tle6-like and TLE6D expression increases CRC cell proliferation and growth , in vivo . RUNX genes encode transcription factors that activate or repress transcription of key regulators of growth , survival and differentiation pathways [44] , [45] . This gene family is defined by the Runt domain , which mediates both protein-DNA and protein-protein interactions with transcriptional co-regulators . TLE proteins interact with , and regulate the function of , RUNX proteins through direct interactions between the TLE WD domain and the Runt domain and the interactions antagonize RUNX-mediated transactivation [44] , [45] , [46] , [47] , [48] . RUNX3 has been shown to play important roles in GI epithelial cell development and tumorgenesis . Loss of Runx3 predisposes knockout mice to gastric hyperplasia , indicating a tumor suppressor-like role for this gene [34] , [49] , [50] , [51] , [52] . In human gastric cancers , hypermethylation of RUNX3 , hemizygous deletion and truncating point mutations have been observed [34] , [52] , [53] , [54] , [55] , [56] , [57] , [58] . To test whether Tle6-like/TLE6D interact with RUNX3 , we first evaluated sub-cellular localization using immunofluorescence staining in 293 cells co-transfected with Tle6-like or TLE6D and native RUNX3 ( Figure S3 ) . Using anti-Myc , anti-Xpress and anti-RUNX3 antibodies , we observed that highest levels of Tle6-like and TLE6 and are in the nucleus overlapping with nuclear RUNX3 staining . Furthermore , in 293 cells , transiently transfected with Tle6-like or TLE6D , endogenous RUNX3 co-immunoprecipitated with anti-Myc or anti-Xpress antibodies ( Figure 8A and B ) , suggesting an interaction between Tle6-like/TLE6D and RUNX3 . Similar co-localization and co-immunoprecipitation results were seen in HCT116 and 3T3 cells ( data not shown ) . Finally , to evaluate the functional consequences of Tle6-like/TLE6D interaction on RUNX3 transcriptional regulation we used a well characterized RUNX3 transactivation on promoter target , osteocalcin ( OC ) , fused to a luciferase reporter gene [47] . As expected , transfected RUNX3 activated luciferase expression in 293 , Hela or HCT116 cells ( Figure 8C , lane 1 and 2 ) . Co-transfection of Tle6-like or TLE6D decreased RUNX3 transcriptional reporter activity in a dose-dependent manner ( Figure 8C ) , whereas Tle6-like/TLE6D transfection had no effect on promoters lacking RUNX3 binding sites , such as the TOPFLASH/FOPFLASH system ( data not shown ) . Taken together , these results are consistent with a model whereby Tle6-like/TLE6D expression antagonizes RUNX3 GI tumor suppressor mediated target gene transactivation through an interaction between the Tle6-like/TLE6D and RUNX3 , providing a selective growth advantage for cell proliferation and migration . In gastric cancer , RUNX3 activity is most commonly reduced through a mechanism involving RUNX3 promoter hypermethylation and subsequently decreased mRNA expression . However , its expression levels in CRC have not been well characterized . We therefore used qPCR to evaluate RUNX3 expression in 40 human CRC and matched normal GI epithelial samples , normalized to GAPDH expression . In many CRCs , RUNX3 expression is low , consistent with a role in GI tumor suppression . However , in a subset of CRCs RUNX3 expression is paradoxically increased ( Figure 5B ) . To test whether elevated TLE6D expression is associated with RUNX3 activation , we used qPCR to analyze TLE6D expression levels in the same matched sets of CRCs and normal mucosa . We observed a clear correlation of RUNX3 and TLE6D expression levels ( R = 0 . 723; Figure 5C ) . However , at the same time no clear correlation was seen for RUNX3 and TLE6D expression levels with regard to MSI-H/MSS status or for expression levels of the full length TLE6 and RUNX ( data not shown ) . Overall , in combination with the functional antagonism of RUNX3 activity by TLE6D observed in colon cancer cells , the correlation of RUNX3 and TLE6D expression in human CRCs suggests that TLE6D may interact with the RUNX3 GI epithelial tumor suppressor and inactivate RUNX3 in a subset of CRCs independent of MSI status . However , further experiments will be required to analyze the association between RUNX3 and TLE6D expression levels and functional interactions in more detail .
Because APC is a common mutation target in MMR-deficient CRC , we created novel mouse models combining different mutations in these genes to analyze their roles in MMR-deficient GI carcinogenesis and progression . The observation that MA mice have increased tumor multiplicity but no accelerated tumor progression or decreased survival vs . Apc1638N mice suggests a primary role for the Mlh1–Mlh3 heterodimer in suppression of GI tumor initiation . While previous in vitro studies have alternatively suggested that Mlh1–Mlh3 participates in IDL repair [13] and SBR[15] , [59] , our study provides the first in vivo evidence that Mlh3 deficiency significantly increases IDL mutation frequency . This type of mutation occurred both at repetitive and non-repetitive Apc sequences , implicating its role in repair of both types of IDL ( Figure 2 ) . Previous studies of Pms2−/−;ApcMin mice have shown a primary role for Mlh1-Pms2 in GI tumorgenesis suppression but not tumor progression[60] . We therefore combined these mutations to create MPA mice . Like Mlh1−/−;Apc1638N mice , MPA mice have significantly increased GI tumor multiplicity , accelerated tumor progression and decreased overall survival[61] . MPA tumors harbor proportionally more C∶G→T∶A ( at either CpG or CpNpG sites ) transition mutations than MA tumors , showing recurrence in certain arginine codons , one of which was at Apc codon 854 , a SBR hotspot that was also previously seen in Mlh1−/−;Apc1638N mice . In addition to Mlh1-Pms2 and Mlh1–Mlh3 , several lines of evidence from our study suggest a potential role for Mlh1-Pms1 in suppression of GI tumorigenesis . First , MPA mice have later mean GI tumor onset compared to previous studies of Mlh1−/−;Apc1638N mice[32] . Second , the multiplicity of GI tumors is decreased vs Mlh1−/−;Apc1638N mice . Third , two Apc insertion/deletion mutation hotspots seen in Mlh1−/−;Apc1638N mice have not been detected in MPA tumors . These data are consistent with previous studies of yeast Mlh2p ( orthologue of mammalian PMS1 ) that demonstrate a minor role for this protein in IDL repair [62] . Because the combination of Mlh3 , Pms2 and Apc mutations accelerates tumor progression , we searched MPA GI tumor specific genetic changes associated with progression using high-resolution aCGH . MPA tumors contained a recurrent 5-Mb amplicon with a critical interval containing a novel , poorly characterized member of the TLE family of transcriptional co-repressors , Tle6-like . Unexpectedly , this MPA recurrent amplification hotspot is not detected by aCGH in GI tumors from Mlh1−/−;Apc1638N mice ( data not shown ) . The reason for this difference is unclear , but again suggests that Mlh1-Pms1 may play a role in causing chromosomal instability . TLE genes are the mammalian homologues of Drosophlia groucho that play critical roles in a wide range of developmental and cellular pathways [40] . TLE proteins are transcriptional corepressors for specific families of DNA-binding transcription factors , including RUNX proteins[48] . In addition , Tle1/Grg1 has been shown to act as a lung-specific oncogene in a transgenic mouse model [63] . Mouse Tle6/Grg6 has been shown to synergize with the E2A-HLF oncoprotein in antagonism of Runx1 transactivation in murine pro-B cells , causing acute leukemogenesis [64] . Tle6/Grg6 also participates in developmental mechanisms of neurogenesis [43] . Here , we provide data that a previously uncharacterized TLE family member containing only the WD repeat domain , Tle6-like , has amplified gene copy number , mRNA and protein levels in GI epithelial tumors from MMR deficient/Apc mutant mice , and is associated with accelerated tumor progression . Consistent with this observation , in functional studies Tle6-like/TLE6D enhances cell proliferation , colony-formation , migration and xenograft tumorgenicity . While TLE family members have previously been shown to repress Wnt/β-catenin signaling [42] , [65] , [66] , [67] , we were unable to demonstrate any Tle6-like/TLE6D protein-protein interactions with β-catenin or effect of Tle6-like/TLE6D overexpression on β-catenin reporter gene activity using TOPFlash in transient transfection in colon cancer cell lines ( data not shown ) , suggesting that Tle6-like/TLE6D might not be involved in canonical Wnt pathway . RUNX family genes regulate lineage and stage specific gene transcription by direct binding to DNA promoters and enhancer elements [44] , [45] . Loss of Runx3 in the mouse results in the development of gastric mucosal hyperplasia , decreased apoptosis and attenuated TGF-β anti-proliferative signaling . Consistent with previous observations of interactions between RUNX3 and TLE family members mediated through the Runt and WD repeat domains , respectively [46] , [48] , we detected an interaction between RUNX3 and Tle6-like/TLE6D by co-immunoprecipitation . Furthermore , we demonstrated that Tle6-like/TLE6D antagonized RUNX3 regulated transcriptional targets . However , while these experiments show an association between RUNX3∶TLE6D interactions and tumor progression , they do not demonstrate mechanistically the functional importance of this interaction in accelerating tumor progression . Alternative mRNA splicing allows multiple gene products to be produced from a single coding sequence , and through this mechanism a higher diversity of mammalian genes is generated [68] . Several distinct TLE/Grg gene alternative splice forms , such as Grg-1s , QD of TLE4 , and Grg3b [42] , [69] , [70] , have been reported . While the human genome does not encode a TLE6-LIKE ortholog , a structurally equivalent protein , TLE6D , is generated through alternative splicing . The observation that GI adenocarcinomas from both humans and mice use two very distinct mechanisms to amplify Tle6-like/TLE6D activity suggests a strong growth advantage and selective pressure for this TLE isoform in tumor progression . Similarly , the correlation between TLE6D and RUNX3 expression in human CRC suggests a model whereby RUNX3 inactivation by TLE6D could be an important factor driving this growth advantage in both MSI-H and MSS CRC . Future studies will be required to understand the mechanistic implications of the interaction between these two proteins in CRC progression in more precise detail .
Wild-type ( Wt ) , Pms2+/− and Mlh3+/− mice were maintained on the 129 Sv/Ev genetic background and intercrossed to generate Mlh3+/−;Pms2+/− mice as described before [13] . Apc1638N mice were backcrossed four times to 129 Sv/Ev and subsequently intercrossed with Mlh3+/−; Pms2+/− to generate Mlh3−/−;Apc1638N and Mlh3−/−;Pms2−/−;Apc1638N mice . Kaplan-Meier survival curves were generated and statistical significance between genotypes was determined using the Log Rank test as previously performed [13] . All lines of mice were necropsied when they became morbid or moribund . Sacrificed mice were surveyed for tumors and suspicious masses were histology analyzed as previously performed . Statistical analyses of tumor onset and incidence among the different mouse lines were performed using the Mann-Whitney test as previously described [23] , [32] , [33] , [35] , [71] , [72] , [73] , [74] , [75] , [76] . Tumors from stomach , small intestine , and colon were cut into two parts . One part of the tumor was processed for histopathological analysis and the other part was used for DNA/RNA extractions . Genomic DNA samples were extracted using Puregene DNA Isolation kit ( Gentra Systems , Minneapolis , MN ) and subjected to mutational analysis of Apc gene between codons 677–1674 as previously described [33] . Genomic DNAs were isolated from tumor tissue and tail tissue from each mouse using PUREGENE DNA Isolation kit ( Gentra Systems , Minneapolis , MN ) . DNAs were digested with DpnII and subsequently purified using the QIAquick PCR Purification kit ( Qiagen ) . The quality of the DNA samples was evaluated using the Agilent 2100 BioAnalyzer . The purified fragmented DNA samples were random-prime labeled with either Cy5 or Cy3 and hybridized as previously described [77] Briefly , for each labeling reaction , 2 µg of purified digested DNA were used . Each sample was dye-swap labeled for hybridization to mouse 70-mer oligonucleotide microarrays ( Agilent Technologies , Palo Alto , CA ) containing 20 , 281 clones . After hybridization , the arrays were scanned using an Agilent Microarray DNA scanner ( Agilent Technologies ) and the spot intensity was extracted from slide images using Agilent Feature Extraction Software 7 . 0 . The data were further analyzed using the procedures of Automatic Data Analysis Pipeline ( ADAP ) . Only spots with fluorescence intensities statistically different from the surrounding background ( P<0 . 001 ) were considered reliable , taking up >85% of total spots on the chip . For further analysis the fluorescence intensity values of reliable spots were transformed to log2 . To minimize the effect of the variations , the log2 intensity ratios of remaining spots were subjected to normalization by Lowess fitting . Gene copy number changes for each sample was calculated by taking the median of the normalized log2 intensity ratios of dye-swapped chip experiments for the corresponding sample . The gene copy numbers were ordered along chromosomes by the map positions of corresponding genes . To eliminate systematic noise , gene copy number changes ( log2Ratios ) along the chromosomes were smoothed by taking a moving median of symmetric 5-nearest neighbors , followed by Lowess fitting ( f = 0 . 2 ) . The mean and standard deviation ( SD ) of smoothed log2Ratios for all genes in all the samples were calculated . The copy number profiles of at least 5 consecutive genes that deviated significantly above mean+3SD were interpreted as regional gains , below mean-3SD as regional losses . The threshold for whole chromosomal gain/loss was mean±2SD . The ideograms of chromosomal aberrations were drawn using mapping information of cytogenetic bands to the mouse genome ( NCBI Mapview Build 32 ) . For RNA extractions , Trizol reagent ( Invitrogen ) was used to isolate total RNA . RNA were further digested with RNAse-free DNAseI ( Promega ) and cleaned with RNeasy Mini kit ( Qiagen ) . High Capacity cDNA Archive kit from Applied Biosystems was used to make cDNA from the RNA samples . Real-time quantitative PCR was performed with either SYBRGreen PCR master mix or Taqman PCR master mix ( Applied Biosystems ) following the manufacture's protocol on ABI 7900 machine . Primers used for SYBR Green assays are listed in Table 1 . Each gene was normalized to the internal control gene Gapdh and then compared to a known single copy gene ( Alkbh ) , which is located on non-amplified region on chromosome 12 D3 in the MPA tumors . The whole Tle6-like gene ( encoded 240 amino acids ) was cloned in to pET28b vector and Tle-6like protein was induced and purified from E . coli . Rabbit anti-serum was raised against Tle6-like protein . The anti-serum was further purified using affinity column , in which Tle6like protein was covalently bound to CNBr-activated Sepharose 4B ( Sigma ) . The purified antibody was used in immunoblotting at 1∶100 dilutions . HCT116 , 293 , Hela or 3T3 cells were maintained in DMEM with 10%FBS and transfected using Lipofectamine 2000 ( Invitrogen ) . The human isoform TLE6D cDNA clone was purchased from Invitrogen ( Full-length Human Clones CS0DC017YC05; Accession number BX375733 ) . Tle6-like was cloned from cDNA samples from MPA mice . We subcloned Tle6-like and TLE6D into either Xpress-epitope-tagged pcDNA6/HisA vector ( Invitrogen ) or Myc-tagged pCS2+MT vector . Cells were transfected with following plasmids: pcDNA6/HisA , pcDNA6/HisA-Tle6-like , pcDNA6/HisA-TLE6D , pCS2+MT , pCS2+MT-Tle6-like , pCS2+MT-TLE6D . Stable cell lines from each transfectant were generated with the selection medium containing 10 µg/ml blasticidin ( Calbiochem ) for 10 days . The pooled populations of cells that survived were used in the experiments for MTT assay and cell mobility assay . The transient-transfected cells were used for colony formation assay , immunoprecipitation , and reporter assay . For the cell proliferation assay , 4000 cells were plated in 96-well plates and MTT assay were used to determine the cell numbers in a time-course experiment . Briefly , cells were washed with PBS and treated with 5 µg/ml MTT ( [3- ( 4 , 5-dimethylthiazol-2-yl ) - diphenyltetrazolium bromide]Sigma , St . Louis , MO ) for 5 hours . After removal of MTT , DMSO was added to dissolve the dark purple formazam crystals in the viable cells and absorbance of 600 nm were determined by a multiwell scanning spectrophotometer . The cell numbers were calculated with a control standard curve . For colony-formation assay , MEF cells were seeded in 6 well plates and transient-transfected with 1 µg of the respective plasmids in the next day . After 24 h , cells were trypsinzed , transferred to 10-cm plates and allowed to grow with the selection medium containing 10 µg/ml blasticidin for 2 weeks . Survived cells were fixed in 30% ethanol and stained with 0 . 25% methylene blue . Colonies containing more than 50 cells were counted . Both assays were repeated three times in three independently-derived cell lines . The monolayer “wounding assay” was used to demonstrate the in vitro cell migration . Human colon cancer HCT116 cells stably expressing corresponding plasmids were plated on glass microscopy slides and cultured to confluence . A “wound” was generated by scratching the slide with a razor blade , clearing a portion of adherent cells on the slide . Photo documentation was taken at day 4 and the migration of cells from the cut edge of the monolayer into the clear portion of the slides was assessed . Two independently-derived stable cell lines for each plasmid were used in this assay . Transient-transfected 293 cells in 10-cm plate were lysed with 1 ml of NP-40 lysis buffer and prepared as described before [13] . Five hundred µl of lysates were pre-cleared with 50 µl ProteinA/G agarose beads ( Santa Cruz ) for 1 h . After spinning down the ProteinA/G beads , the collected supernatants were incubated with 5 µg anti-Xpress or anti-myc monoclonal antibody ( Invitrogen ) and 50 µl ProteinA/G beads overnight at 4°C . The next day , the beads were washed with NP-40 buffer 5 times and incubate with 4× protein loading dye ( Invitrogen ) 10 min at 95°C to elute the binding proteins . These samples were resolved by SDS-PAGE and the immunoblotting was used as previously described to detect the corresponding proteins . The antibodies used in immunoblotting are: mouse monoclonal anti-Xpress and anti-myc ( 1∶2000 , Invitrogen ) , rabbit anti-RUNX3 ( 1∶1000 , Abcam ) and goat anti-β-actin ( 1∶1000 , Santa Cruz Biotechnologe ) . 293 , Hela or 3T3 cells were transient-transfected accordingly with the Flag-RUNX3 ( a kind gift from Dr . Yoshiaki Ito ) and rat Osteocalcin promoter fused to luciferase reporter construct ( OC-Luci , a kind gift from Dr . Gary Stein ) , and plasmids as described above . Luciferase activities were determined using Dual-Luciferase reporter assay systems kit ( Promega ) on the luminemeter . Female 6-week-old nude mice ( Charles River Laboratories , Wilmington , MA ) were divided into four experimental groups , five for each . One million HCT116 cells stably transfected with vectors ( pCS2+MT or pCDNA6/HisA ) , pCS2+MT-Tle6sh , or pCDNA6/HisA-TLE6D were injected subcutaneously in the flanks of each mice . Mice were monitored daily for palpable tumors . Because of rapid growth , tumors were dissected out 3 weeks after injection and were analyzed . | Approximately one million people every year are diagnosed with colorectal cancer worldwide , and about five hundred thousand of these people subsequently perish from the disease . Colorectal cancer is thought to develop through a series of early and later stages ( called cancer initiation and progression , respectively ) . Deaths from colorectal cancer are particularly tragic because the disease can usually be cured if discovered before full-blown progression . However , our knowledge of how these tumors progress remains very limited . DNA mismatch repair is known to be an important process in preventing ∼15% of colorectal cancer initiation . In this study we describe how two of these genes ( Mlh3 and Pms2 ) that have partial functional redundancy and therefore individually are rarely mutated are also important in preventing colorectal cancer progression . Additionally , we describe a new gene ( Tle6-like ) that , when overactive , makes these cancers progress more rapidly . The overall goal of this study is to understand colorectal cancer progression better so that we can come up with new ways to block it at the later stage . | [
"Abstract",
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"oncology/gastrointestinal",
"cancers"
] | 2008 | Novel Roles for MLH3 Deficiency and TLE6-Like Amplification in DNA Mismatch Repair-Deficient Gastrointestinal Tumorigenesis and Progression |
A small focus of hemorrhagic fever ( HF ) cases occurred near Cochabamba , Bolivia , in December 2003 and January 2004 . Specimens were available from only one fatal case , which had a clinical course that included fever , headache , arthralgia , myalgia , and vomiting with subsequent deterioration and multiple hemorrhagic signs . A non-cytopathic virus was isolated from two of the patient serum samples , and identified as an arenavirus by IFA staining with a rabbit polyvalent antiserum raised against South American arenaviruses known to be associated with HF ( Guanarito , Machupo , and Sabiá ) . RT-PCR analysis and subsequent analysis of the complete virus S and L RNA segment sequences identified the virus as a member of the New World Clade B arenaviruses , which includes all the pathogenic South American arenaviruses . The virus was shown to be most closely related to Sabiá virus , but with 26% and 30% nucleotide difference in the S and L segments , and 26% , 28% , 15% and 22% amino acid differences for the L , Z , N , and GP proteins , respectively , indicating the virus represents a newly discovered arenavirus , for which we propose the name Chapare virus . In conclusion , two different arenaviruses , Machupo and Chapare , can be associated with severe HF cases in Bolivia .
The family Arenaviridae is composed of largely rodent-borne viruses which are divided into Old World and New World complexes [1] , [2] . Lassa and lymphocytic choriomeningitis ( LCM ) viruses are considered the most important of Old World arenaviruses due to their association with severe disease . The New World complex is divided into 3 major Clades ( A , B and C ) , with Clade B containing all the hemorrhagic fever ( HF ) associated viruses [3] , [4] , [5] , [6] . These are Junín , Machupo , Guanarito and Sabiá viruses , the cause of Argentine , Bolivian , Venezuelan , and Brazilian HF , respectively [1] . Three of these viruses , Junín , Machupo , and Guanarito , can be associated with large HF outbreaks and untreated case fatalities can be in excess of 30% . The clinical picture is similar for each of these diseases . Onset of symptoms follows an incubation period of 1–2 weeks . Initial symptoms often include fever , malaise , myalgia and anorexia , followed approx . 3–4 days later by headache , back pain , dizziness , nausea , vomiting , and severe prostration . Hemorrhagic and neurologic symptoms , including petechiae and bleeding gums , tremors , and lethargy are common . About a third of untreated cases go on to develop more severe neurologic and/or hemorrhagic symptoms , with diffuse echymoses , and bleeding from mucous membranes or puncture sites , and/or delirium , coma and convulsions . Machupo virus , vectored by Calomys callosus rodents [7] , is the only known pathogenic arenavirus found in Bolivia , although another arenavirus , Latino virus , has also been isolated from Calomys callosus in Bolivia [8] . Despite broad distribution of this rodent host , which is thought to include the lowlands of Bolivia , east-central Brazil , Paraguay and northern Argentina [9] , Machupo virus-associated HF cases have originated only in the Beni department in northeastern Bolivia ( Figure 1 ) . We report here the investigation of a fatal HF case which occurred near Cochabamba , Cochabamba Department , Bolivia in December , 2003 , and identify the associated arenavirus as a unique newly discovered virus , Chapare virus .
In late 2003 reports were received of a small cluster of HF cases in a rural area near the Chapare River , close to Cochabamba , Bolivia in the eastern foothills of the Andes ( Figure 1 ) . Exact details of the number of cases and verification of symptoms were difficult to obtain . However , a clinical description and blood specimens were available for one fatal case . This patient , a 22 year old male , had lived for the last 4 years in Samuzabeti , a small town located 35 km northeast of Villa Tunari . He was a tailor and also a farmer . Coca is the main crop in this area . He had no history of travel and no contact with any case with compatible illness for at least 4 weeks prior to his disease onset on January 3rd , 2004 . In addition , no members of case household or other close contacts were affected . His clinical course included fever , headache , arthralgia , myalgia and vomiting with subsequent deterioration and multiple hemorrhagic signs and death on January 17th , 2004 ( 14 days post onset ) . Based on these symptoms , the patient was initially suspected of having yellow fever or dengue HF . However , initial tests for these agents were negative . Initial IgM , IgG , antigen capture and RT-PCR testing for Machupo virus or related arenaviruses were also negative . Patient specimens were sent to the biosafety level 4 ( BSL4 ) containment laboratory at the Special Pathogens Branch in Atlanta where virus isolation attempts could be performed . These specimens consisted of 4 acute phase sera , collected on days 4 , 7 , 9 and 14 post onset of disease . Both the day 7 and day 9 sera yielded a non-cytopathic virus by growth in Vero E6 cells . These were identified by immunofluorescent antibody ( IFA ) staining with rabbit polyvalent hyperimmune serum raised against South American arenaviruses previously known to be associated with HF ( Guanarito , Machupo , and Sabiá ) . RT-PCR analysis of the virus isolate RNAs amplified a 481 bp fragment which yielded nucleotide sequence related to known New World Clade B arenaviruses ( which includes all the South American HF associated arenaviruses ) . Full length virus genome sequences were successfully determined for the virus isolated from the day 9 post onset bleed ( designated strain 810419 ) by RT-PCR and sequence analysis followed by primer walking utilizing newly derived sequence information . The full length S segment was amplified by using the 19C primer designed based on the conserved RNA termini of New World Arenaviruses [10] , whereas the L segment was amplified in multiple sections using a variety of primers ( sequences available on request ) . Sequence analysis of the complete S and L segments confirmed that this virus , proposed name Chapare , was a unique member of the Clade B New World arenaviruses [3] , [4] , [5] , [10] , [11] . The virus was found to be most closely related to Sabiá virus , but with 26 and 30% nucleotide difference in the complete S and L segments , and 26 , 28 , 15 and 22% amino acid differences for the L , Z , N and GP proteins , respectively ( Tables 1 and S1 ) . The genetic differences between Chapare virus and other Clade B viruses range from 36–40% for the complete S segment and 39–40% for the complete L segment ( data not shown ) . These nucleotide and amino acid sequence divergence levels are in excess of those seen among strains of the same species of New World arenavirus ( Tables 2 and S1 ) [12] , [13] , [14] . For instance , the greatest difference seen between complete S segments of virus strains is 14% ( within Allpahuayo virus strains ) and 10% for the complete L segment ( among Machupo virus strains ) [15] . Chapare virus was found to be monophyletic with Sabiá virus on phylogenetic analysis of the nucleotide or encoded amino acid sequences of the complete S or L segment ( Figure 2 ) , or NP , GP , L or Z ORFs ( data not shown ) . No evidence of reassortment or recombination between Chapare virus and other arenaviruses was found . There is no overall change in the structure of the trees except for the previously described [4] , [5] switch of the Clade A/Rec viruses from Clade A for the NP gene to Clade B for the GP gene ( data not shown ) . The pathogenicity of the New World Clade B viruses correlates with the efficient interaction of their GP1 surface glycoproteins with the human cellular receptor , transferring receptor 1 ( TfR1 ) [16] , [17] . Presumably , Chapare virus will be found to have similar TfR1 binding properties , but this remains to be confirmed . Even assuming this to be true , the diversity of the GP1 amino acid sequences of Junín , Machupo , Guanarito , Sabiá and Chapare viruses is such that one cannot easily discern the GP1 domain involved in high efficiency binding to TfR1 solely on the basis of amino acid sequence alignments . The relationship of Chapare virus from Bolivia to Sabiá virus from Brazil is intriguing . Both these virus clearly cause HF similar to that seen with Junín , Machupo and Guanarito viruses . The single HF case associated with a naturally acquired Sabiá virus infection was reported in the community of Sabiá , in Sao Paulo , Brazil in 1990 [18] . The exact site of exposure was unclear , as was the rodent reservoir . Yellow fever was the initial suspicion in the Sabiá case and that associated with the Chapare virus infection as both had associated extensive liver necrosis . More extensive liver involvement may be a feature shared between these viruses , as it is not commonly observed with HF associated with the other New World arenaviruses ( although it is occasionally seen ) . Due to the difficulties of working in this resource poor rural region , initial follow up efforts in the Chapare area , did not yield a more precise description of the reported cluster of cases with similar illness , and a limited ecological study did not reveal the rodent reservoir of this virus . It is hoped that more extensive studies in the area will reveal the extent to which Chapare virus poses a public health problem in this area , and shed light on the source of human infection . In summary , three arenaviruses are now known to be present in Bolivia , namely Machupo and Latino viruses ( both hosted by Calomys callosus ) and Chapare viruses ( reservoir unknown ) . Furthermore , both Machupo and Chapare viruses are agents of fatal hemorrhagic fever in Bolivia .
Initial virus genetic detection and analysis was conducted on total RNA extracted from infected Vero E6 cells , using Tripure Isolation Reagent ( Roche Applied Science , Indianapolis , IN ) in a ratio of 1∶5 and incubated at room temperature for a minimum of 10 min . Total RNA was isolated by using the RNaid Kit following the manufacturer's recommendations ( Qbiogene Inc . , Carlsbad , CA ) , and the extracted RNA was reconstituted in 50 µL H2O . Broadly reactive Arenavirus primers used for initial identification were designed for the L polymerase gene on the L segment ( L4160F , GCA GAR TTY AAA TCI AGA TT; L4393R , CCR TYI ASC CAR TCT ITI ACA TC; L4292F , GAT CAT TCI RTY GCI AAT GG; L4841R , CAI AII CCT ATA AAI CCW GAT G ) [19] and the glycoprotein gene on the S segment ( GP878+ , GAC RTG CCW GGI GGI TAY TG; GP1126- , TAC CAA AAT TTG TGT ART TRC ART AIG G; GP1153+ , CCT TAY TGY AAY TAC ACI AAA TTT TGG T; GP1396- , ATG TGY CTR TGI GTI GGI AW ) . Reverse Transcription ( RT ) was done using 2 . 5 µL of RNA in a 25 µL total reaction volume and AMV RT ( Promega Biosciences , San Luis Obispo , CA ) at 42°C for 1 hr . Subsequent PCR amplification using FastStart Taq DNA Polymerase with GC-rich solution ( Roche ) was performed using 5 µL of the completed RT reaction in a 25 µL reaction volume with the following cycling conditions: 2 min at 95°C , ( 36 cycles of 1 min at 95°C , 1 min at 45°C , 2 min at 72°C ) , and a final elongation of 10 min at 72°C . Resulting DNA products were visualized and purified using a 1% agarose gel , and the Qiagen Gel Extraction Kit ( Qiagen , Valencia , CA ) . PCR products were sequenced directly ( without cloning ) using the corresponding primers in a BigDye Terminator v3 . 1 reaction on a 3100 Genetic Analyzer ( Applied Biosystems , Foster City , CA ) . Sequence was further analyzed using Sequencher ( Gene Codes Corporation , Ann Arbor , MI ) . To obtain full length sequence for each segment , alignments of all New World arenavirus complete genomes were used to design primers for the conserved regions ( available upon request ) . The full length S segment was generated following the Thermoscript RT-PCR system's directions ( Invitrogen , Carlsbad , CA ) and using the 19C primer [10] . Reverse transcription was conducted at 55°C , while the PCR profile was the same as stated above with an increased extension time of 4 minutes . Different fractions of the full-length L RNA were amplified using 2-step or 1-step RT-PCR protocols and following the manufacturer recommendations . Briefly , cDNA was synthesized in the first approach using 10 µl of purified RNA , specific primers , dNTPs and Superscript III ( Invitrogen ) in 20 µl reactions . Amplification reactions were done using 5 µl of cDNA , specific primers , dNTPs and Platinum Taq DNA polymerase High Fidelity ( Invitrogen ) in 50 ul reactions . Alternatively , 1-step RT-PCR were performed using 5 µl of RNA , dNTPs and the enzyme blend provided by the SuperScript III One-Step RT-PCR System with Platinum Taq High Fidelity ( Invitrogen ) in a 50 µl reactions . Amplification reactions were analyzed in TBE/agarose gels and DNA bands purified using QIAquick Gel Extraction Kit ( Qiagen ) . Sequencing reactions were done as described above . All full length S and L segment sequences available in Genbank were used to compute pairwise uncorrected genetic distances using PAUP 4 . 0b10 ( Sinauer Associates ) for the following viruses: Allpahuayo , Amapari , Chapare , Flexal , Guanarito , Junín , Machupo , Paraná , Pichindé , Pirital , Sabiá , Tacaribe , Tamiami , and Whitewater Arroyo . A representative sub-set of full length sequences ( omitting multiple near identical variants of the same virus ) were included in a Bayesian phylogenetic analysis . Sequence alignments were done with ClustalX [20] with manual adjustments and phylogenetic analysis was done with MrBayes3 . 1 . 2 [21] using the GTR+I+G model in 2 runs of 500 , 000 generations using the sequence of Pichindé virus as the outgroup . | Four rodent-borne arenaviruses are known to cause hemorrhagic fever ( HF ) in the New World . These include Junín , Machupo , Guanarito , and Sabiá viruses , which are found in rural areas of Argentina , Bolivia , Venezuela , and Brazil , respectively . In December 2003 and January 2004 , a small number of HF cases were reported in rural Bolivia in an area outside the known Machupo HF endemic zone , and sera from one fatal case was available for laboratory testing . The man had symptoms similar to those seen with other arenaviral HF cases—acute febrile illness beginning with headache , joint and muscle pain , and vomiting—and rapidly progressed to shock , bleeding , and death at 14 days post onset of illness . Virus was isolated from two of the patient's serum samples and identified as an arenavirus by reaction of virus infected cells with arenavirus-specific antibodies and by genetic detection techniques ( PCR ) . Subsequent complete genome analysis of the virus showed the virus to be a distinct newly discovered member of the arenavirus family , and the name Chapare virus was proposed . The virus is phylogenetically related to other arenaviruses that naturally cause hemorrhagic fever in South America , particularly Sabiá virus . Physicians should consider Chapare virus as a potential etiologic agent when encountering HF cases in the region . | [
"Abstract",
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"Results/Discussion",
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] | [
"genetics",
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"microbiolo... | 2008 | Chapare Virus, a Newly Discovered Arenavirus Isolated from a Fatal Hemorrhagic Fever Case in Bolivia |
Affinity maturation is a Darwinian process in which B lymphocytes evolve potent antibodies to encountered antigens and generate immune memory . Highly mutable complex pathogens present an immense antigenic diversity that continues to challenge natural immunity and vaccine design . Induction of broadly neutralizing antibodies ( bnAbs ) against this diversity by vaccination likely requires multiple exposures to distinct but related antigen variants , and yet how affinity maturation advances under such complex stimulation remains poorly understood . To fill the gap , we present an in silico model of affinity maturation to examine two realistic new aspects pertinent to vaccine development: loss in B cell diversity across successive immunization periods against different variants , and the presence of distracting epitopes that entropically disfavor the evolution of bnAbs . We find these new factors , which introduce additional selection pressures and constraints , significantly influence antibody breadth development , in a way that depends crucially on the temporal pattern of immunization ( or selection forces ) . Curiously , a less diverse B cell seed may even favor the expansion and dominance of cross-reactive clones , but only when conflicting selection forces are presented in series rather than in a mixture . Moreover , the level of frustration due to evolutionary conflict dictates the degree of distraction . We further describe how antigenic histories select evolutionary paths of B cell lineages and determine the predominant mode of antibody responses . Sequential immunization with mutationally distant variants is shown to robustly induce bnAbs that focus on conserved elements of the target epitope , by thwarting strain-specific and distracted lineages . An optimal range of antigen dose underlies a fine balance between efficient adaptation and persistent reaction . These findings provide mechanistic guides to aid in design of vaccine strategies against fast mutating pathogens .
Upon infection or vaccination , antibodies ( Abs ) are generated through affinity maturation ( AM ) , a Darwinian process occurring in a short time ( Fig 1 ) . Affinity maturation mainly takes place in germinal centers ( GCs ) , which are dynamic structures in secondary lymphoid tissues that arise and dissolve in response to antigen ( Ag ) stimulation [1 , 2] . GCs house B cells and T helper cells , as well as resident f ollicular dendritic cells ( FDCs ) that present antigens to B cells . Somatic hypermutation diversifies the Ag receptors of B cells as they replicate . Mutated B cells that bind Ag sufficiently strongly can internalize it and present short peptides derived from pathogenic proteins bound to major histocompatibility complex ( MHC ) class II molecules on their surface . T helper cells can potentially bind to these peptide-MHC molecules to provide a survival signal . While Ag displayed on FDCs is the fuel that sustains GC reactions ( GCRs ) , limited T cell help drives competition between B cells . Through rounds of mutation and selection , GC B cells can enhance the Ag affinity of their receptors up to 103 folds within a few weeks [3] . Most selected B cells are recycled for further rounds of mutation and selection [4 , 5] . The rest differentiate into memory and plasma cells . Soluble forms of the B cell receptors ( BCRs ) secreted by plasma cells are called Abs . Highly mutable complex pathogens , such as HIV , have evolved mechanisms to evade immune recognition as well as divert immune responses , such that they can persist in a circulating population and diversify . Therefore , a protective Ab response must cover a very diverse pool of viral strains . Recently , an increasing number and variety of broadly neutralizing antibodies ( bnAbs ) have been isolated from chronically infected patients [6–10] . These bnAbs can individually recognize a vast majority of global viral isolates . Notably , potent monoclonal bnAbs have dramatic effects on blocking viral transmission [11 , 12] and controlling ( though transiently ) established infection [13 , 14] in non-human primates . These findings have renewed the hope for an effective HIV vaccine because they provide proof that the human immune system can evolve such Abs . But attempts to elicit HIV bnAbs efficiently by vaccination have so far been unfruitful [6 , 8 , 9 , 15] . Recent longitudinal tracking of HIV-1 bnAb lineages and the co-evolving virus , in infected individuals over more than a few years has suggested that a broadening reactivity of elicited Abs relies on an expanding diversity of the encountered Ag [16–20] . This observation proves that AM can indeed lead to broad Abs , but also emphasizes a tardy development of bnAbs in a non-protective amount by natural immunity . Importantly , it points to the necessity of using multiple Ag variants as immunogens . This finding has triggered a shift in focus from AM against a single Ag , extensively studied by experiment [3 , 5 , 21–30] and models [4 , 31–37] , to investigating how AM occurs when multiple distinct Ag variants are present [38–41] . The latter situation is poorly understood . Vaccination with multiple variants of a complex Ag provides an opportunity ( and also a daunting combinatorial complexity ) to design the selection forces acting on B cell populations during AM , and to arrange them in an optimal temporal sequence . Our recent work [39] on vaccine strategies against HIV has shown that sequential immunization with Ag variants ( that share a set of conserved residues , but whose variable residues are separated by relatively large mutational distances ) is favored over a simultaneously administered cocktail of the same variants in inducing bnAbs . This is in line with previous immunization studies [42 , 43] and was tested in model experiments in mice [39] . New experiment [44] provides further support for the sequential strategy . The key lies in temporal separation of potentially conflicting selection forces represented by the variant Ags which can frustrate affinity maturation . Apart from its remarkable variability , HIV has complex molecular features that present unique challenges to vaccination . The protective effect of an Ab is determined by how well it binds to a set of residues , the epitope , on the proteins that make up the spike on the surface of the virion . The HIV-1 envelope glycoprotein trimer ( Env ) is the sole target of known HIV-1 neutralizing Abs . These trimeric spikes are made of proteins that are among the most mutable in the viral proteome . But they do contain highly conserved residues that are essential for mediating viral entry by binding to particular receptors expressed on host cells ( e . g . HIV has to bind to CD4 receptors to infect helper T cells ) . However , these conserved residues are often surrounded by highly variable regions and masked by glycans and variable protein loops that hamper access to the conserved residues [45–49] . Recent modeling studies involving multiple Ags [38 , 41 , 50] have assumed independent epitopes , either purely conserved or entirely variable , which occupy separate locations on the viral protein . Computational [41] and analytical [50] studies on co-evolution of Abs and HIV viruses using such a model have indicated that increasing the initial diversity of Ag variants and the antigenic distances between them makes it more likely that a bnAb lineage establishes . We believe that this result , at least in part , emerges from the assumption that epitopes are either entirely conserved or entirely variable . It is crucial to consider concurrent conserved and variable residues in the target epitope as this reflects reality and poses an additional challenge to bnAb induction . Despite the apparent similarity in successive presentations of distinct Ag variants , there are important differences between the dynamics of natural infection and vaccination . Infection by mutating viruses presents a continuously varying selective pressure , which sustains the GC reaction as new viral mutants establish before their ancestors get cleared . In contrast , upon vaccination by distinct Ag variants well separated in time , GCs are likely to dissolve prior to the arrival of a new variant . Reseeding of GCs then raises a new question: to what extent is B cell passed across successive periods of immunization , and how this affects bnAb evolution . As has recently been demonstrated , properly designed immunogens can induce germline B cells that target epitopes containing the conserved residues on the HIV viral spike proteins [51–53] . But , it seems likely that to evolve bnAbs one would have to subsequently immunize with variant Ags that resemble the viral spike ( such as variants of SOSIP [47 , 48] ) . In addition to the target epitope containing the conserved residues , such immunogens ( and the Env spikes ) also present numerous distracting epitopes that are easily accessible , highly immunogenic and yet do not contain conserved residues [54] . Abs directed towards these epitopes are not able to recognize even close variants of them and thus will have very narrow reactivity . How such distraction impacts the efficacy of bnAb induction in various vaccination schemes is important , and yet unexplored . Aiming to design vaccination strategies to favor HIV bnAb evolution , we extend our previously developed model of AM driven by variant Ags to explicitly account for the process of GC reseeding upon immunization with new variant Ags , and the existence of distracting epitopes . This stochastic dynamic model enables a detailed investigation of the evolutionary mechanisms behind divergent maturation outcomes in various immunization schemes . We describe how different temporal arrangements of selection forces shape evolutionary dynamics of competing B cell lineages that in turn determine the predominant mode of Ab responses that emerge . We find that sequential immunization with mutationally distant yet related Ag variants can induce bnAbs , and is also most robust to distraction . Unexpectedly , diversity loss in between periods of immunization may even favor bnAb evolution . Our results also highlight the prominent role of Ag dose in balancing the seemingly incompatible demands of adaptation efficiency and persistence of maturation . Therefore , this study provides mechanistic guides that may aid the design of vaccination strategies that can induce bnAbs against highly mutable complex pathogens .
We employ a coarse-grained stochastic model of B cell AM in the GC ( Fig 1 ) . Naïve B cells acquire genetically diverse receptors via recombination events ( VDJ recombination ) , which create a diversity in their ability to bind different epitopes . Upon activation , Nsd = 3 distinct naïve clones seed each GC and replicate without mutation to ∼1500 cells [24] . This mimics the growth stage before the enzyme responsible for hypermutation of the BCR variable region genes turns on . Cyclic action of mutation and selection ensues , driving the evolution of B cells . Point mutations are introduced uniformly at a rate of 0 . 5 per sequence per division [21] . The probability that a mutation is functionally silent ( no change in affinity ) is pS = 0 . 5 , the probability of a lethal mutation ( e . g . non-folding ) is pL = 0 . 3 , and the rest are affinity-affecting mutations ( probability pA = 0 . 2 ) [32] . Affinity change due to point mutations is drawn from an asymmetric bounded distribution , with deleterious mutations that reduce affinity more likely [37 , 39] . We model the dynamics of B cell populations as branching processes , which naturally accommodate time-varying GC sizes . Population bottleneck ( BN ) arises because overwhelming detrimental mutations first lead to population decline , and only after rare beneficial mutations emerge and establish , population growth starts , giving rise to even more favorable mutants that populate a GC . We assume all the maturing B cells in a GC are synchronized in replicating , making mutations , competing for survival and recycling or output . GC reaction ends either when all B cells die , when a threshold number of B cells survive ( i . e . , ∼1500 cells ) , or when a maximum duration is reached ( 120 days or 240 GCR cycles ) , whichever comes first . The last two termination conditions reflect Ag uptake by the maturing GCs or Ag decay , respectively . As a consequence , mature GCs may differ in population size and duration of reaction . Persistent maturation at intermediate population sizes allows B cells to sample and accumulate the large number of mutations required for high affinity and large breadth . B cell survival requires success in two stochastic processes . Mutated B cells can internalize Ag if their BCR binds sufficiently strongly to it [27] . The probability of this event is modeled as obeying Langmuir equilibrium at a physiological temperature T: P a i = ∑ j = 1 n A C j e ( E j i - E a ) / k T 1 + ∑ j = 1 n A C j e ( E j i - E a ) / k T , ( 1 ) where E j i is the affinity of B cell i to Ag of type j and Ea denotes the activation threshold . Cj is the concentration of Ag of type j presented on the FDCs . The sum over the Ag index j runs through nA distinct types that B cell i can potentially interact with simultaneously during an encounter with a FDC bearing multiple Ag variants . Activated B cells internalize the Ag and present derived short peptides bound to MHC molecules on their surface to attract attention from helper T cells . B cells with higher affinity for Ag obtain and process a greater amount of Ag in a given period of time and thus outcompete the surrounding B cells by presenting more peptide-MHCs to helper T cells . There has been increasing evidence showing that competition between B cells for limited T cell help is essential for establishment and persistence of GC reactions [5 , 29] . It is clear that probabilities of being activated ( Pa ) and of receiving T cell help ( PTh ) are both dictated by the binding affinity of a B cell to its encountered Ag . Thus , we model the probability of a B cell to succeed in receiving T cell help by P Th i = W i W i + R ⟨ W i ′ ⟩ i ′ ( ≠ i ) . ( 2 ) Here W i = ∑ j = 1 n A C j e E j i / k T is in proportion to the probability of B cell i internalizing Ag , which is compared to the average probability , 〈Wi′〉i′ ( ≠i ) , over all the competing B cells . The parameter R is a dimensionless quantity of order 1 , which is related to the total Ag concentration and the ratio between B cells and T helper cells . Since the precise dependence is unknown , we compute it as R = ( ∑ j = 1 n A C j ) - 1 , which accounts for the observation that a higher Ag dose triggers a stronger response of T helper cells , thus making it more likely that B cells receive T cell help . The level of spatial heterogeneity of Ag display on FDCs remains unclear . We consider two scenarios of Ag presentation: ( 1 ) Ag variants are homogeneously distributed on the FDCs , and thus B cells interact simultaneously with all types of variants in each contact with the FDCs , namely “See all Ag”; ( 2 ) Ag distribution is heterogeneous and each B cell interacts with only one type of Ag variant randomly chosen in each round of selection , which we call “See 1 Ag” . As a minimal set of immunogens to mimic the vast genetic diversity of HIV-1 , we consider 3 Ag variants , G , v1 and v2 , distinguished by the set of epitopes they carry ( schematic in Fig 2A ) . Each variant has a unique target epitope ( T epitope: G , T1 or T2 ) , characterized by a maximum number of non-overlapping mutations between the variants ( rings of different colors in Fig 2A ) . Importantly , target epitopes also contain an identical conserved part where mutations are not tolerable ( the red oval area ) . The presence of a conserved core , surrounded by variable elements and glycans that limit access to the conserved residues , is a defining feature of the HIV epitopes that are targeted by broad and potent Abs [20 , 55–57] . Note the variant G represents a reference strain which activates the desirable germline B cells [51–53] that recognize the G epitope , i . e . , the autologous version of the target epitope . To account for the molecular features of HIV-1 Env that do not contain conserved elements , we introduce 3 distracting epitopes ( D epitopes , D1 , D2 and D3 ) shared between the variants v1 and v2 ( colored spherical regions in Fig 2A ) . Recognition of D eptiopes confers no breadth . D epitopes are constructed to be sufficiently distant from T epitopes in the sequence space that a B cell is unlikely to become cross-reactive to both . The number 3 is chosen to represent the greater abundance of D over T epitopes but is otherwise arbitrary . We assume the same set of D epitopes to be shared between v1 and v2 to present a most favorable situation for D-targeting clones , in which they have a constant supply of antigen and experience no frustration as v1 and v2 alternate . Therefore , this corresponds to a most distracting scenario for T-targeting clones . Either introducing distinct D epitopes to different Ag variants or raising the quantity of D epitopes would potentially frustrate D-targeting clones and reduce their distracting effect . Now that each Ag variant carries multiple epitopes , target or distracting , the affinity of a B cell to its encountered Ag is determined by the epitope it binds most strongly to , among the available ones . This particular epitope is named the selecting epitope of the B cell . A B cell is noted as T-targeting if its selecting epitope is one of the target epitopes T1 , T2 or G , otherwise D-targeting , by having maximum affinity to one of the distracting epitopes D1 , D2 or D3 . We assume individual B cells can always find their selecting epitopes , by performing diffusive search on the Ag surface to locate the best complementarity . Each epitope residue is treated in a coarse-grained manner , such that it is either the wild-type ( WT ) amino acid or a mutant . The strength of interaction of a residue ( k ) on BCR ( i ) with a residue on the selecting epitope is denoted by h k i . The binding affinity , E j i , between a B cell clone h → i and an Ag variant s → j is modeled as E j i h → i , s → j = ∑ k = 1 M h k i s k j + ∑ k = M + 1 N h k i . ( 3 ) The first M interactions are with variable residues on the selecting epitope , where sk can be either 1 ( WT ) or −1 ( mutated ) . The other ( N − M ) sites are conserved residues on the epitope with sk = 1 . The interaction strength hk is drawn from a continuous and uniform distribution within a bounded range . For target epitopes , we assume an equal amount of conserved and variable residues ( i . e . , M = N/2 ) , whereas for distracting epitopes , there are no conserved residues ( i . e . , M = N ) . As before [39] , through pairwise correlated changes in hk ≤ M and hk > M , we account for the fact that BCRs that reduce interactions with shielding or blocking variable residues are more likely to be able to access and make contacts with the protected conserved residues on the target epitope , as indicated in recent structural studies of bnAb-lineage members in complex with HIV-1 Env [20 , 57] . Such non-local coupling effect pertinent to the three-dimensional structure of Abs provides a potential mechanism for maturing B cells to amplify their gain in affinity attained by point mutations and make large steps in affinity landscapes . Germinal centers are transient microstructures that disassemble once the supply of FDC Ag is exhausted , either by immune clearance or via self degradation . Therefore , upon successive exposures to distinct Ag variants well apart in time , new GCs arise in response to each new variant . This suggests that founder B cells of each nascent GC should be sampled from the memory pool formed against earlier variants . Upon GC re-seeding , diversity bottleneck [25 , 26] occurs prior to the onset of hypermutation . Since the extent of diversity loss is hard to probe experimentally and may vary from GC to GC , we consider the limiting cases of “full seeding” versus “subset seeding” ( Fig 2B ) which should encompass the biological conditions . “Full seeding” implies that the memory B cell pool from the previous round of maturation against variant Ags is passed on to the next maturation period in its entirety . Whereas for “subset seeding” , a modest number Nsd of seeding clones are either generated anew ( i . e . naïve B cells reactive to D epitopes ) or sampled from the most recent memory and evenly expanded to sum up to the initial GC size; here dramatic loss in diversity and marked changes in clonal composition may occur , since rare clones could be amplified by chance in newly formed GCs . Naïvely we might expect that diminishing diversity would slow adaptation , however as we will see , quite surprisingly , reducing the dominance of ancestral clones could allow a broader search for more advantageous mutants and even favor the evolution of bnAbs by sustaining the GC reaction . We consider 3 immunization schemes as before [39]: Scheme I—a cocktail of three variants in one dose , G+v1+v2 ( see 1 Ag or see all Ag ) , Scheme II—the germline-targeting variant followed by a mixture of two mutants , G|v1+v2 ( see 1 Ag or see both Ag ) , and Scheme III—sequential administration of three variants , G|v1|v2 . However , the composition of seeding clones now differs; since B cells reactive to D epitopes on v1 and v2 could also seed or join a GC when a new variant arrives ( Fig 2C ) , Ab responses might be distracted from the target epitopes . For the Nsd seeding clones , we sample a random fraction from the most recent memory repertoire , and generate naïve cells for the rest that bind any of the D epitopes with above-threshold affinity . The degree of distraction in matured GCs turns out to vary significantly between schemes .
Sampling a handful of seeding clones from a diverse mature pool could dramatically alter the clonal composition of a GC population , from that shaped by evolution against previous Ag variant ( s ) . By chance , B cells with rare binding patterns , lying in the tail of the affinity distribution in individual GCs , might get amplified through this initial partial sampling . Thus on the ensemble level , subset seeding could lead to stronger GC-to-GC variations compared to full seeding , as well as weaker dominance of ancestral clones . To focus on the effect of diversity loss alone , we examine these expectations and their implications in the absence of distracting epitopes . If each mature GC population is passed on to the next immunization period as is ( full seeding ) , two schemes can elicit bnAbs with high probabilities–G|v1+v2 , see 1 Ag and G|v1|v2 ( filled bars in upper panels of Fig 3 ) . Although Abs resulting from these two schemes exhibit similarly large breadth , they develop distinct patterns of interaction with the variable residues via disparate evolutionary paths ( S1 Fig ) . In scheme G|v1+v2 , see 1 Ag , due to the conflict between selection forces and uncertainty in Ag encounter , almost all the surviving GCs evolve highly specific B cells for one of the variants ( Fig 4C lower panels , symbols along either axis and two blobs far from the diagonal on either side ) —some are barely responsive to the unfavorable variant ( see an example in S1A Fig bottom panel ) while others are not reactive at all , similar to those developed in scheme G|v1+v2 , see both Ag ( S1B Fig bottom panel ) . Here large breadth comes from strong binding to common mutations in the test panel sequences . One caveat is that we use a binary representation of the panel sequences which might lead to overestimate of the breadth of the Abs that are specific for particular mutations ( S1 Text; S2 Fig ) . Therefore , the actual Ab breadth achievable by scheme G|v1+v2 , see 1 Ag is upper bounded by the values shown in Fig 3A . Another consequence of specific interaction with variable residues is loss of prior specificity during the course of maturation; as B cell clones specific for different Ag variants compete for dominance in a GC , this polyclonal population can partially lose its reactivity to earlier variants as new specificity develops ( marked by asters in S1A Fig middle panel ) . Whereas in scheme G|v1|v2 , since conflicting selection forces are separated in time , maturing B cells progressively weaken interactions with one set of variable residues and then another , so they acquire recognition of new variants without losing responsiveness to old ones , i . e . , cross-reactivity expands in range ( S1C Fig middle and bottom panels ) . Resulting B cells bind to both variants with comparable affinities well-above the activation threshold ( Fig 4D lower panels , a single blob close to the diagonal ) . Here moderate interactions with all the variable residues along with strong contact to the conserved core give rise to a large breadth . Curiously , however , loss in initial diversity ( subset seeding ) has opposite effects on breadth development in these two bnAb-producing schemes ( Fig 3 ) . In scheme G|v1+v2 , see 1 Ag , diversity loss broadens the distribution of breadth toward the lower end ( Fig 3A , filled yellow bars to filled purple bars ) , whereas in scheme G|v1|v2 , an even narrower distribution toward the largest breadth results ( Fig 3B , yellow to purple bars ) . Interestingly , these opposite trends originate from departing from versus approaching to persistent GC reactions . Effective affinity enhancement and breadth development both occur at intermediate population sizes , as in G|v1+v2 , see 1 Ag with full seeding ( Fig 5A filled symbols ) and in G|v1|v2 with subset seeding ( Fig 5B open symbols ) , where efficient and sustained maturation brings about increasingly more potent and broad mutants . To the contrary , too small GC sizes limit clonal diversity and strong genetic drift slows the incorporation of large-effect beneficial mutations into the population ( G|v1+v2 , see 1 Ag , subset seeding , Fig 5A open symbols ) , whereas too large GC sizes only allow brief maturation times before the Ag is consumed and the GC reaction wanes ( G|v1|v2 , full seeding , Fig 5B filled symbols ) , so neither situation is likely to evolve bnAbs . GC dynamics shows consistent trends ( Fig 4 ) : For G|v1+v2 , see 1 Ag ( Fig 4A ) , compared to full seeding ( upper panel ) , subset seeding ( lower panel ) leads to slightly stronger mean affinity at the population bottleneck ( BN ) , yet much weaker affinity when GCs mature ( Final ) , due to slow adaptation at small population sizes . In contrast , in the v2-period of G|v1|v2 ( Fig 4B ) , while for full seeding ( upper panel ) mean affinity only improves modestly when GC reactions end , subset seeding ( lower panel ) sustains maturation , resulting in significant affinity enhancement . Positive correlations between the efficacy of bnAb induction ( affinity and breadth of resulting Abs ) and the characteristics of surviving GCs ( size and duration ) , as shown in Fig 5 , make evident that persistent maturation plays a central role in evolving bnAbs , in harmony with the observation that potent bnAbs only arise after years of evolution driven by escaping viruses in chronically infected patients [16–20] . Rather than impede adaptation , diversity loss upon GC reseeding might provide a natural mechanism that encourages rare or de novo B cell clones and prolongs GC reactions in response to distant yet related variant Ags that arrive well apart in time . Therefore , loss in seeding diversity renders scheme G|v1+v2 , see 1 Ag even less efficacious due to too strong frustration , whereas reduced dominance of memory to past variants in the sequential scheme G|v1|v2 drives enduring adaptation and promotes acquisition of breadth-conferring mutations in a more effective manner . How strongly a GC population is distracted from its target depends on how the selection forces represented by multiple variant Ags are temporally arranged . As we discuss in detail below , this interesting behavior stems from competition between the entropic advantage of D-targeting clones due to the constant presence and greater abundance of D epitopes , and the energetic advantage of T-targeting clones that accumulates through AM in a favorable environmental history . In the next section , we construct a simple analytical model to describe the competitive evolution of T- and D-targeting lineages , by translating the entropy-energy competition into trade-offs between average gain and variations in fitness . Here we focus on how the presence of distracting epitopes influences Ab breadth development using our individual-based simulations . When 3 variants are presented in a cocktail ( scheme I—G|v1+v2 ) , T clones have no energetic advantage to start with , and suffer from a lower effective Ag concentration compared to D clones , since the T epitopes differ between variants . As a result , all the surviving T-targeting B cells are derived from GCs that are exclusively seeded by T clones . In these GCs , T lineages survive by alternately encountering G Ag and their favored variant among the two . Moderate interactions with the conserved residues and considerable footprint on the variable region of the target epitope leave the resulting Abs with small breadth ( Fig 6A blue bars ) . In contrast , the majority of D lineages readily survive by binding to their selecting epitope present on both variants . Consequently , D clones win over T clones whenever they coexist in the seeding stage , resulting in dominance of D-targeting B cells in the mature pool ( red bar at zero breadth in Fig 6A ) . Moreover , the overall GC survival rate dramatically increases compared to having only the T-epitopes , since D-targeting clones which win the competition and take over the population could save an otherwise deemed-to-collapse GC . This suggests that distraction might underlie the observation that Ab responses that emerge early in HIV infection are not broadly neutralizing [54] . If maturation first proceeds against G Ag ( scheme II—G|v1+v2 ) , T targeting clones would have acquired an energetic advantage by the time they face the variants , which alleviates their susceptibility to distraction ( Fig 6B ) as compared to in the cocktail scheme ( Fig 6A ) . However , an initial advantage does not guarantee future success . Spatial heterogeneity in Ag distribution on the FDCs makes the number and type of encountered Ag uncertain for individual B cells in successive cycles of selection—each B cell can only see one type of Ag ( see 1 Ag ) if two variants tend to segregate , yet can encounter both ( see both Ag ) if two variants are well-mixed . The uncertainty in Ag encounter can be detrimental or even fatal to T-targeting lineages , if the fitness fluctuations due to v1-v2 alternation render T-targeting clones less fit than D-targeting clones sooner or later; as seen in S3A Fig , the worst possible mean affinity of T-targeting seeding clones often falls below that of D-targeting clones , which occurs when most T-targeting clones encounter their unfavorable variants . Surviving T-targeting lineages are the lucky ones that keep seeing their activating Ag in successive selection cycles , thus generating highly specific T clones . In addition , unfrustrated D lineages ( i . e . having constant access to selecting epitopes ) raise the risk of extinction for T lineages which , on their own , might have struggled through the bottleneck and produced neutralizing Abs upon maturation . This results in stronger susceptibility to distraction as well as lower affinity of T-targeting clones ( S4A versus S4B Fig ) , if individual B cells encounter one type of Ag as compared to encountering both ( a higher zero-breadth bar in Fig 6B than in Fig 6C ) . When both variants are accessible , all the B cells can see their selecting epitopes and optimize interactions iteratively . Most surviving GCs are dominated by T targeting B cells specific for mutated residues on their selecting epitopes , thus producing Abs with limited breadth ( blue bars in Fig 6C ) . When 3 variants are administered sequentially ( scheme III—G|v1|v2 ) , T-targeting clones also gain constant access to their activating variants in every encounter . In this case , as long as T and D clones coexist in the seeding population , D clones will be driven to extinction before reaching the bottleneck , thanks to sufficient fitness advantage of T clones accumulated in a serial fashion ( panels C and D in S3 and S4 Figs ) . Upon immunization with v1 , in most T-winning GCs , T clones only have modestly higher affinity than D clones to start with ( S3C Fig ) . By the time v2 is administered , however , T lineages have acquired much enhanced energetic advantage via efficient maturation ( S4C and S4D Fig upper panels ) , shown as a shoulder at appreciable mean affinity differences between T and D clones ( red arrow in S4D Fig ) , which further suppresses distraction in subsequent competitive evolution ( S4C and S4D Fig lower panels ) . This efficient maturation stems in part from non-local coupling effect; T-targeting clones that shrink their footprint on the variable region and simultaneously strengthen contact to the conserved core would achieve greater affinity gain thus outcompeting specific clones that suffer affinity penalties as they enhance binding to variable residues . Owing to a lack of conserved elements on the D epitopes , D-targeting clones do not benefit from the coupling effect either . As a result , the mature B cell repertoire is enriched with cross-reactive T-targeting clones ( blue bars in Fig 6D ) with significant enhancement in mean affinity and affinity variation ( S4D Fig upper panel ) . A small fraction of surviving GCs are filled with zero-breadth antibodies ( red bar in Fig 6D ) . These GCs are exclusively seeded by D clones , which exhibit limited affinity variation and moderate mean affinity when GCR ends ( S4D Fig lower panel ) . Our simulations also showed that , if GCs start with a greater number of distinct seeding clones ( e . g . of order 10 rather than a few ) , as has recently been suggested by imaging and sequencing studies of GC dynamics [30] , distraction would be slightly weaker in all schemes . However , the relative degree of distraction between the schemes remains similar ( S5 Fig ) . In brief , temporal patterns of conflicting selection forces determine the level of frustration that T-targeting clones have to cope with in the absence of distraction , which in turn dictates how likely D-targeting clones can invade and win in various situations . We speculate that , in the presence of distraction , there should still be an optimum level of frustration [39] that corresponds to persistent and efficient maturation and hence a maximum likelihood of evolving bnAbs . In schemes G|v1+v2 , see both Ag and G|v1|v2 , all the competing lineages evolve in an identical antigenic environment that remains unchanged during each immunization period . In scheme G|v1+v2 , see 1 Ag , however , each B cell lineage can experience a distinct environmental history , by randomly encountering one of the two variants in each mutation-selection cycle . Competition between T and D targeting lineages arises because , while G-matured T-targeting clones could have higher mean fitness than D clones to start with , they may also suffer from uncertainty in subsequent Ag encounter that leads to considerable fluctuations in fitness along the evolutionary trajectories . To understand how environmental fluctuations influence the outcome of competitive dynamics between T and D targeting lineages , we consider a simple evolutionary model of two competing species in a rapidly fluctuating environment , similar to Refs [60–63] . This would allow us to identify the dominant mode of Ab response that emerges from the competition and verify the behavior in various regimes with our individual-based simulations . Population dynamics of the two species is described by the following stochastic differential equations: N T ˙ = ( r T - N K ) N T + r T + N K N T ξ T ( t ) + N T σ T η T ( t ) , N D ˙ = ( r D - N K ) N D + r D + N K N D ξ D ( t ) + N D σ D η D ( t ) . ( 4 ) Environmental variability is incorporated by a time modulation of the birth rates , rT and rD , of T and D clones , modelled as Gaussian white noises of strength σS , i . e . , rS ( t ) = rS + σS ηS ( t ) , where 〈ηS ( t ) ηS ( t′ ) 〉=δ ( t−t′ ) , with S = T , D being the species label . Death rate is assumed identical for both species , increasing with total population size N = NT + ND . The logistic growth of the population is bounded by its steady state size , N⋆ = K[rT x + rD ( 1 − x ) ] , where x = NT/N is the proportion of T clones . This saturation may account for density dependent ecological factors , such as exhaustible Ag on FDCs and limited T cell help , in the context of GC reaction . In addition to the environmental noise , demographic fluctuations originated from the discreteness of the evolving entities yield the term ( r S + N / K ) N S ξ S ( t ) , where 〈ξS ( t ) ξS ( t′ ) 〉 = δ ( t − t′ ) , with the variance being the sum of deterministic birth and death rates . We obtain the 2D Fokker-Planck equation equivalent to the Langevin description ( Eq 4 ) and marginalize it with respect to the total population size , by assuming that selection acts on a much longer time scale than that of population growth . This leads us to a 1D Fokker-Planck equation for P ( x , t ) ( derivation in S1 Text ) that describes the evolution of population composition in the presence of environmental and demographic fluctuations: ∂ ∂ t P ( x , t ) = - ∂ x s - σ T 2 x + σ D 2 ( 1 - x ) - ϵ σ T σ D ( 1 - 2 x ) x ( 1 - x ) P ( x , t ) + ∂ x 2 σ T 2 + σ D 2 - 2 ϵ σ T σ D 2 x ( 1 - x ) + 1 K x ( 1 - x ) P ( x , t ) . ( 5 ) Here s ≡ rT − rD denotes selection strength and the coefficient ϵ = 〈 η T η D 〉 / 〈 η T 2 〉 〈 η D 2 〉 characterizes the correlation of environmental noises experienced by the two competing species . Note that even though T and D clones have independent histories of Ag encounter , i . e . , environmental noises of the two species are uncorrelated ( ϵ = 0 ) , drift v ( x ) = s - σ T 2 x + σ D 2 ( 1 - x ) and diffusion D ( x ) = ( σ T 2 + σ D 2 ) x ( 1 - x ) / 2 + 1 / K both depend on environmental variability of T and D clones ( σT and σD ) and population composition ( x ) . Scenarios of interest in scheme G|v1+v2 , see 1 Ag correspond to s = rT − rD ≠ 0 and σT > σD = 0 . Fig 7 shows the distribution of T-clone abundance at different times ( blue to purple: early to late ) obtained from numerical integration of Eq ( 5 ) . It starts from a Gaussian distribution centered at x0 = 0 . 5 , i . e . , similar proportion of two subpopulations . Absorbing boundary conditions are applied , so that vanishing to the right ( left ) boundary corresponds to fixation ( extinction ) of T clones . As shown , an increasing amplitude of environmental fluctuations ( increasing σT from A to C ) elevates the risk of extinction for T clones and speeds up relaxation . The condition s - σ T 2 x 0 = 0 ( panel B ) represents the boundary between the regimes where either T or D clones fixate; balanced bias and fluctuations result in strong uncertainty in clonal composition and most likely coexistence . Possible clonal fate observed in our individual-based in silico model can be identified with the three regimes marked in Fig 8: Regime I ( 60% of surviving GCs , corresponding to s - σ T 2 x 0 > 0 ) : T clones win with a higher mean affinity ( see example in Fig 9A and 9B ) than D clones ( s > 0 ) and vanishing fluctuations ( σT = 0 ) . In this regime , seeding T clones have a higher mean affinity than D clones ( Fig 9A ) , and they rapidly take over the population in approach to the population bottleneck . This is achieved by persistent encounters with the favorable variant ( upper panel of Fig 9B ) , thereby closely tracing the best possible affinity ( solid blue curve in Fig 9A ) , in a subset of T-targeting lineages . Yet the GC size remains very small ( lower panel of Fig 9B ) since T-targeting lineages that experience fluctuating environments are outcompeted and absent from the recycled population—even if T clones win . Thus , resulting Ab titers are low . Regime II ( 17% of surviving GCs , corresponding to s - σ T 2 x 0 > 0 ) : T clones win with a higher best affinity ( see example in Fig 9C and 9D ) than D clones ( s > 0 ) and steady environments ( σT = 0 ) . In this regime , seeding T clones have slightly lower mean affinity than D clones ( Fig 9C ) , and in particular , the worst possible affinity ( dashed line ) is constantly below the activation threshold . Nonetheless , as long as a subset of T lineages keep seeing their favorable variant , D lineages are quickly driven to extinction . Again the GC size stays small ( lower panel of Fig 9D ) , since in each GCR cycle a considerable fraction of T-targeting lineages encounter the non-activating variant and apoptose . Regime III ( 23% of surviving GCs , corresponding to s - σ T 2 x 0 < 0 ) : T clones lose due to strong environmental variabilities in spite of a modest selective advantage for persistent best affinity . Here the environmental fluctuations lead to a sudden catastrophe at early times ( blue curve in Fig 9E ) , reducing the reproduction rate of T-targeting clones to a value which can no longer sustain a steady ( sub ) population ( blue curve in Fig 9F ) . In this regime , seeding D clones have slightly higher mean affinity , and even the best possible affinity of T clones ( solid blue curve in Fig 9E ) is barely above the activation threshold . Therefore , once most T-targeting lineages encounter their non-activating variants , D clones take over the population . Since alternate encounter of v1 and v2 ( upper panel of Fig 9F ) incurs no affinity cost to the D subpopulation , it grows to the GC capacity ( red curve in the lower panel of Fig 9F ) . T-D coexistence is not observed in any mature GC from our simulations so far , because the simulated system is located in a strong-selection regime with a deep population bottleneck: Strong selection discerns small affinity differences between B cell clones , whether they target the same or different epitopes , leading to the dominance of a single subpopulation that targets a particular epitope . Nonetheless , inter-GC variations provide an overall diversity of Ag specificities in the mature B cell repertoire . In reality , however , T- and D-targeting clones may well coexist in maturing GCs . Indeed , as we weaken selection by increasing Ag concentrations as well as decreasing the number of neighboring B cells competing for T cell help , simultaneous maturation of T and D lineages appear in all schemes ( S6 Fig ) . This is because higher Ag concentrations effectively lower the activation threshold for B cells and also trigger a stronger response of helper T cells , whereas a smaller number of competitors for each B cell in getting T cell help makes it easier for mediocre B cells to survive , as they manage to survive selection by not competing with the strongest binders . Note that each subpopulation targeting a particular epitope contains multiple clones ( S6 Fig bottom row ) , and that specificity loss mostly occurs during the population bottleneck ( S6A and S6C Fig middle panels ) . The sequential scheme is not unconditionally superior—its efficacy relies on sustained maturation against each variant , which only occurs within a window of Ag concentration , where GC survival and Ab breadth both peak and coincide [39] . For a given number of Ag variants with properly chosen mutational distances separating them , Ag dose controls the size and duration of GCs and hence the efficiency of B cell adaptation . At an optimal Ag concentration , maturation takes place in a slowly yet steadily expanding population following the population bottleneck ( S1C Fig upper panel ) : a modest rate of Ag consumption allows enough time for beneficial mutations to enter the population and in the meanwhile , an intermediate population size promotes efficient selective spread of beneficial mutations without causing much clonal interference . As a result , cross-reactive clones that have accumulated the large number of beneficial mutations emerge before Ag is exhausted and GCs disassemble ( arrows in S1C Fig ) . In this manner persistent reaction and efficient adaptation can be achieved simultaneously . To make these arguments more concrete , we relate population dynamics of a GC to the expected selection probability as follows . If each B cell divides r times per cycle with a probability pL to acquire lethal mutations , then the population- and time-averaged selection probability P ¯ s e l can be estimated by [ 2 r × ( 1 - p L ) × P ¯ s e l ] t f - t B N = N f / N B N , where tf ( tBN ) and Nf ( NBN ) denote the time and GC size at the end ( population bottleneck ) of a maturation period , respectively . As described above , at an optimal Ag dose , B cell populations undergo sustained adaptation following the bottleneck and reach GC capacity Nmax at the maximum GCR duration tmax , i . e . , Nf = Nmax at tf = tmax . This gives the expression of the corresponding optimal selection probability P s e l ⋆ = [ N m a x N B N ] 1 t m a x - t B N / [ 2 r × ( 1 - p L ) ] . If r = 2 , pL = 0 . 3 , and a 100-fold increase in population size occurs during 70 GCR cycles , we would have P s e l ⋆ ≃ 0 . 38 . This value is very close to that computed from our stochastic simulations of the sequential scheme , based on the selection probability of individual B cells in each cycle P s e l i ( t ) = P a i ( t ) · P T h i ( t ) , where P a i and P T h i are given by Eqs ( 1 ) and ( 2 ) . This matching indicates that Ag concentrations used in our in silico study indeed lie within the optimal range leading to persistent maturation . Therefore , an appropriate range of Ag dosage would invoke an optimal strength of selection . In addition to facilitating breadth development , an optimal Ag dose also serves to suppress distraction . In synergy with the sequential procedure , it helps to enhance the selective advantage of increasingly more focused T clones , thus driving D lineages to extinction prior to the population bottleneck . In contrast , if Ag concentration is higher than optimal , as seen in S6 Fig , D-targeting lineages are more likely to survive the bottleneck and coexist with T lineages that have acquired little fitness advantage during brief AM in earlier periods . Therefore , GC populations are more susceptible to distraction even when environments do not fluctuate , and T lineages are more prone to extinction as a new Ag variant arrives .
In facing highly mutable complex pathogens , such as HIV , affinity maturation of Abs is not only frustrated by conflicting selection forces , but also distracted by immunodominant variable epitopes . Design of immunogens and efficient immunization protocols to elicit bnAbs that target the conserved part of the pathogen requires a detailed understanding of the selection forces that arise from multiple Ag variants each carrying multiple epitopes and follow various temporal procedures . To this end , we develop a stochastic dynamic model of affinity maturation driven by distinct yet related Ag variants with complex epitopes , identify the evolutionary conditions that favor the selection of potent bnAbs and deduce immunization strategies that can potentially resolve conflict and minimize distraction at the same time . Protecting against highly mutable pathogens is likely to require multiple immunizations with distinct immunogens . Two new aspects become important to understand—loss of B cell diversity in between periods of immunization , and the presence of distracting epitopes that might lead to seeding or invasion of GCs by D-targeting B cells . Surprisingly , our in silico results suggest that loss in B cell diversity , upon sampling from the memory pool to seed nascent GCs , does not necessarily impede adaptation; instead , it can facilitate a broader search of the sequence space and prolong the consumption of FDC-Ags , which allows persistent evolution of favorable mutations that confer breadth . An underlying broad distribution of intermediate GC sizes is shown to lead to large breadth and great diversity of the mature B cell repertoire , benefiting from efficient adaptation ( if too small GCs , slow adaptation ) and sustained reaction ( if too large GCs , brief maturation ) . Although Ag is not self-renewing in immunization settings , appropriate temporal arrangements of selection forces can serve to maintain effective adaptation . We find that , in the presence of distracting epitopes , how selection forces are arranged in time determines the relative selective advantage between T and D lineages that compete for survival , and thus the susceptibility of a GC population to distraction . D-targeting clones have the best chance of invasion in the cocktail scheme , where T-targeting clones are severely frustrated by mutationally distant T epitopes present on the three Ag variants , and the dominance of D-targeting clones leads to very narrow ( autologous ) Ab response . If one primes with G Ag and then boosts with a mixture of two variants , highly strain-specific Abs result from T lineages that have constant access to their selecting epitopes . But Ab titers are not high , because in each cycle of mutation and selection , a large fraction of T clones encounter the non-activating Ag and go extinct . Only when Ag variants are administered sequentially , bnAbs are likely to evolve efficiently . On the one hand , by separating conflicting selection forces in time , it favors cross-reactive lineages over specific ones . On the other hand , by aiding in serial enhancement of selective advantage for increasingly more focused T clones , it thwarts distracted lineages . Therefore , the same set of beneficial mutations that confer breadth to T clones also grant them affinity superiority over D clones , which suggests sequential immunization with an optimal design should make possible simultaneous achievement of large breadth and little distraction . Experimental measurement of the relative abundance and affinity of T-epitope and D-epitope directed Abs in the serum following different immunization schemes may be able to test the predictions that we report in this paper . An optimal range of Ag dose is important for the effectiveness of the sequential scheme . For a given set of variant Ags , an optimal dose enhances GC survival while preventing cross-reactive memories from rapidly occupying nascent GCs . Even if the range of optimal Ag concentration is relatively narrow , since Ag distribution is heterogeneous within and between GCs , an appreciable fraction of B cells are likely to experience an optimal dose . In other words , spatial heterogeneity of Ag concentration could effectively broaden the range of optimality . Ag dose also influences the degree of frustration when multiple variants are present . In this study , we give each B cell one shot to bind and internalize Ag . If multiple shots are allowed and the variety of Ag is small , frustration will not arise . This resembles the “See all Ag” case , which should usually result in strain-specific Abs [39] . However , reducing Ag concentration while allowing multiple shots will frustrate the system again in the “See 1 Ag” case . In contrast to past works which assume that conserved and variable elements constitute separate epitopes that occupy different locations on the Env protein , we model the target epitope ( e . g . CD4 binding site of HIV-1 ) as containing simultaneously conserved and variable residues in physical proximity , such that low-affinity precusor B cells must evolve to evade the variable occluding elements in order to access the conserved core . Reducing interactions with variable residues compensated by enhancing contact to the conserved elements is critical for breadth development . However , these correlated changes of interactions take many random mutational trials to occur and fixate . Very often , potentially broad lineages are purged by distracted or specific ones early in response . In order to grant bnAb precusors immediate advantage over distracted ones , we may create an environment where T-clones can emerge and establish in the first place . This can be realized by priming with solely G Ag , an engineered scaffold of the Env trimer that only presents the target epitope in its non-mutated form and activates the desirable germline T clones . To further favor broad T clones over specific ones , a sequential presentation of Ag variants with shared conserved part yet distant variable part of the T epitope would selectively expand cross-reactive T lineages while filtering out specific clones . An appropriate choice of Ag dose and antigenic distances between the variants would prolong adaptation of T lineages with minimal extinction; energetic advantage of T clones thus accumulates across successive maturation periods , beating the entropic disadvantage . In sum , in the sequential scheme , desirable germline T clones get activated and undergo sustained maturation against G Ag to develop cross-reactivity to v1 and outcompete naïve D clones , followed by effective adaptation to respond to v2 , thereby winning over naïve and v1-matured D clones , as well as v1-specific T clones . This flexible in silico model offers ample room for optimization of the immunization strategies in future studies . One immediate direction is a potential combination strategy that integrates sequential and mixture schemes: after activating the appropriate germline B cells , if we are allowed multiple doses of different mixtures , we can imagine gradually increasing the mutational distance between the variants in consecutive mixtures , which likely mitigate frustration at early stages of affinity maturation and enable a smoother broadening of cross-reactivity with even better success rates , compared to well-separated variants one at a time . A new report [44] on sequential immunization with distant variants followed by a mixture of closer variants suggests an alternative approach . To better guide the choice of immunogens and to identify the optimal conditions for a combination strategy , further computational investigations are needed to complement experimental studies . Practically , the results of this paper should have useful implications for immunization against other highly mutable pathogens ( e . g . malaria ) that might require multiple vaccinations . Conceptually , this work poses interesting theoretical questions in evolutionary dynamics for future exploration , namely , how to focus evolutionary paths under the constraints of diversity of selection forces and distracting selection pressures . | Highly mutable pathogens pose significant challenges to vaccine design , mainly owing to the vast antigenic diversity they present to the immune system . Recently an increasing variety of broad antibodies that can recognize diverse strains have been isolated from patients , but how to induce them by vaccination is largely unknown . In particular , how affinity maturation , the Darwinian process that evolves potent antibodies , proceeds under multiple stimulations by distinct antigen variants is not well understood . We use computer simulations and evolutionary models to examine realistic new aspects important for vaccine development: loss of B cell diversity in between immunization periods and the existence of distracting molecular features that do not contain conserved elements . We find counterintuitive impact of these factors on antibody breadth development , which depends crucially on temporal arrangements of selection forces . Our findings provide guides for optimal vaccination strategies and reveal their evolutionary basis . | [
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"immunodeficiency... | 2017 | Optimal Sequential Immunization Can Focus Antibody Responses against Diversity Loss and Distraction |
Kaposi's sarcoma-associated herpesvirus ( KSHV ) is the etiological agent of Kaposi's sarcoma ( KS ) , a malignancy commonly found in AIDS patients . Whether KS is a true neoplasm or hyperplasia has been a subject of intensive debate until recently when KSHV is unequivocally shown to efficiently infect , immortalize and transform rat primary mesenchymal precursor cells ( MM ) . Moreover , KSHV-transformed MM cells ( KMM ) efficiently induce tumors with hallmark features of KS when inoculated into nude mice . Here , we showed Smad1 as a novel binding protein of KSHV latency-associated nuclear antigen ( LANA ) . LANA interacted with and sustained BMP-activated p-Smad1 in the nucleus and enhanced its loading on the Id promoters . As a result , Ids were significantly up-regulated in KMM cells and abundantly expressed in human KS lesions . Strikingly , genetic and chemical inhibition of the BMP-Smad1-Id pathway blocked the oncogenic phenotype of KSHV-transformed cells in vitro and in vivo . These findings illustrate a novel mechanism by which a tumor virus hijacks and converts a developmental pathway into an indispensable oncogenic pathway for tumorigenesis . Importantly , our results demonstrate the efficacy of targeting the BMP-Smad1-Id pathway for inhibiting the growth of KSHV-induced tumors , and therefore identify the BMP pathway as a promising therapeutic target for KS .
Kaposi's sarcoma-associated herpesvirus ( KSHV ) is the etiological agent of Kaposi's sarcoma ( KS ) , which is the most common malignancy in AIDS patients [1] . The KSHV-infected proliferating spindle cells are the driving force of KS [2] . KSHV mainly displays latency in spindle cells . Viral latent genes were reported to promote cell proliferation and inhibit apoptosis through various mechanisms . In particular , latency-associated nuclear antigen ( LANA ) , a multifunctional major viral latent protein , is responsible for maintaining viral episome , inhibiting viral reactivation , and promoting cell proliferation by targeting p53 , pRb and GSK-3β , etc ( reviewed in [3] , [4] ) . We have also shown that LANA contributes to cell proliferation by promoting intracellular Notch ( ICN ) accumulation through inhibition of Sel10-mediated ICN degradation [5] , [6] . Due to the lack of in vitro KSHV cellular transformation model and the lack of KS cell lines , the roles of KSHV-deregulated signaling pathways in KSHV-induced cellular transformation remain unclear . The recent development of a robust model of KSHV-induced cellular transformation and tumorigenesis has made this possible [7] . Specifically , KSHV can efficiently infect , immortalize and transform primary rat embryonic metanephric mesenchymal precursor ( MM ) cells . KSHV-transformed MM cells ( KMM ) efficiently induce tumors with virological and pathological features of KS . This work has paved a way for studying the intrinsic oncogenic pathways underlying the tumorigenesis driven by KSHV latent genes . Using this system , KSHV-encoded miRNAs and vCylin were recently demonstrated to play critical roles in KSHV-induced cellular transformation and tumorigenesis [8] , [9] . Bone morphogenetic proteins ( BMPs ) belong to the transforming growth factor β ( TGF-β ) superfamily . BMP signaling pathways play critical roles in diverse developmental phases [10] . In recent years , BMP signaling pathways have increasingly been the focus in cancer research , since these developmental pathways are frequently disrupted in cancer [11] . BMP signaling pathways are involved in both promotion and inhibition of cancer progression depending on the context , which is similar to the TGF-β pathway [12] . Inhibitors of DNA-binding ( Id ) family are major downstream targets of BMP signaling , and belong to the helix-loop-helix ( HLH ) family of transcription factors . There are four known members of the Id family in vertebrates ( called Id1 , Id2 , Id3 and Id4 ) [13] . Id proteins do not possess a basic DNA binding domain and functions as a dominant-negative regulator of basic HLH proteins [14] . Recent evidence has revealed that Id proteins , especially Id1 , are able to promote cell proliferation and cell cycle progression . Moreover , up-regulation of Id1 has been found in many types of human cancers and its expression levels are also associated with advanced tumor stage . [15] . Id1 was once reported to be up-regulated in KSHV-infected endothelial cells and in KS tissues [16] , however , the mechanism and implication of Id1 up-regulation remains unclear . In this study , Smad1 was identified as a novel LANA-binding protein . LANA up-regulated Id expression through constitutively sustaining the activation of the BMP-Smad1-Id signaling pathway , and thus contributed to the oncogenicity of KMM cells in vitro and in vivo . These studies have identified a novel viral oncogenic signaling pathway , and our data indicate that small inhibitors targeting BMP-Smad1-Id signaling pathway could be promising candidates for the treatment of KS .
In order to explore the novel function of LANA , we utilized Strep-Flag ( SF ) -tag based tandem affinity purification ( SF-TAP ) method to identify novel LANA-binding proteins ( Fig . 1A ) [17] . Smad1 , a critical transducer of BMP signaling [18] , was one of the hit proteins co-purified by SF-LANA [19] . We confirmed that LANA physically interacted with Smad1 in 293T cells by reciprocal co-immunoprecipitation ( Co-IP ) ( Fig . 1B , C ) . We further confirmed their interaction in KSHV-infected cells ( Fig . S1 ) . LANA is predominantly located in the nucleus [20] , while Smad1 shuttles from cytosol to nucleus in complex with Smad4 resulting in the transcription of BMP target genes following phosphorylation at C terminus S463/465 ( SXS motif ) by type I BMP receptor [18] . To determine the compartment of LANA-Smad1 interaction , 293T cells were transfected with LANA and Smad1 , then treated with BMP2 and harvested for cell fraction . Co-IP assay was performed with cytoplasmic fraction and nuclear fraction respectively . As expected , LANA-Smad1 interaction was only detected in the nuclear but not in cytoplasmic fraction ( Fig . 1D ) . Moreover , Smad1 pulled-down by LANA was recognized by a p-Smad1/5/8 antibody ( Fig . 1D ) . Since LANA did not bind to Smad5 ( Fig . S1 ) , these results suggested that LANA interacted with BMP-activated p-Smad1 in the nucleus . We further mapped out the Smad1-binding domain of LANA . Smad1 could be pulled down by Myc-tagged full length LANA1–1162 and N-terminus LANA1–432 , but not by C-terminus LANA762–1162 , negative control Intracellular Notch1 ( ICN ) nor control vector ( Fig . S1 ) . Therefore , N-terminus LANA1–432 is responsible for Smad1-binding . Next , we mapped out the LANA-binding domain of Smad1 . Smad1 has highly conserved N- and C-terminal regions known as Mad homology ( MH ) 1 and MH2 domains , respectively , which are linked by a linker region with a highly variable structure [18] . HA-tagged full length Smad1 , Smad1-C ( Linker+MH2 ) , Smad1-MH2 , but not Smad1-N ( MH1+Linker ) were pulled down by LANA ( Fig . 1E ) . Therefore , Smad1 MH2 domain is responsible for LANA-binding . To narrow down the LANA-binding domain within Smad1 MH2 domain , we constructed a series of MH2 truncation mutants , termed as MH2-N , MH2-M and MH2-C respectively . Deletion of neither C-terminus of MH2 ( MH2-N ) nor N-terminus of MH2 ( MH2-C ) totally abolished its binding to LANA while the center part of MH2 ( MH2-M ) retained LANA binding activity ( Fig . 1F ) . Therefore MH2-M ( Smad1308–407 ) was critical for LANA-binding . We then asked whether LANA-Smad1 interaction depended on the phosphorylation of SXS motif of Smad1 . The Smad1 mutant with the SXS motif deleted ( ΔC3 ) , inactivated ( AVA ) or constitutively activated ( DVD ) [21] bound to LANA as efficiently as the wild type Smad1 ( Fig . 1G ) . The differences of the apparent molecular weight between the wild type Smad1 and Smad1 mutants in SDS-PAGE were due to tag sizes . These results indicated that the nuclear location but not the phosphorylation of Smad1 is the restriction factor for the LANA-Smad1 interaction . BMP signaling regulates fundamental biological processes during embryonic development , postnatal development , as well as tumorigenesis [22] . The Smad1 MH2 domain is responsible for sensing BMP signaling , oligomer formation with other Smads , interaction with various DNA-binding proteins , and transcriptional activation of BMP downstream targets [18] . We wondered whether LANA modulated BMP-Smad1 signaling by regulating the expression and/or function of p-Smad1via interaction with Smad1-MH2 domain . To address this hypothesis , 293T cells were transiently transfected with LANA or a control vector and then treated with BMP2 . 293T cells were harvested at different times and subjected to immunoblotting for the levels of p-Smad1 and BMP downstream target Id1 . The levels of p-Smad1 activation and Id1 were normalized to their expression levels at 0 hour in two groups , respectively . Activation of p-Smad1 started to decline at 3 hours post BMP2 treatment and reached the basal level at 24 hours in the vector-transfected 293T cell while activation of p-Smad1 did not start to decline until 15 hours and continued to maintain at a relatively higher level at 24 hours in the LANA-transfected 293T cells ( Fig . 2A , B ) . Therefore , BMP-induced p-Smad1 expression was significantly sustained by LANA ( Fig . 2A , B ) . Consistent with these results , the induction of the canonical BMP downstream target Id1 was significantly potentiated in the LANA-transfected cells than the vector-transfected control cells by BMP2 ( Fig . 2A , C ) . Id1 was once reported to be up-regulated in KSHV-infected endothelial cells and in KS tissues; moreover , expression of LANA and vCyclin seemed to up-regulate Id1 expression in post-transcription level [16] . Since Id1 was well-recognized for its roles in tumorigenesis [13] , [23] , we sought to determine whether LANA up-regulated Id1 expression through the BMP-Smad1 pathway . As previously reported , we showed that Id1 was up-regulated in KSHV-infected human primary endothelial cells ( Fig . S2 ) . However , LANA but no other KSHV latent genes significantly up-regulated Id1 expression in 293T cells ( Fig . S3 ) . Meanwhile , LANA did not obviously alter Id1 protein stability . These results indicated that LANA regulated Id1 expression mainly at transcription level ( Fig . S3 ) . Consistent with these results , Id1 transcription was up-regulated more than two fold in LANA-transfected 293T cells ( Fig . 3A ) . Treatment with noggin , which inhibited BMP signaling , abolished LANA induction of Id1 expression ( Fig . 3A ) ; while treatment with BMP2 further enhanced LANA induction of Id1 expression ( Fig . 3B ) . We then asked whether LANA was directly involved in Id1 transcription regulation . In a promoter reporter assay , LANA increased the activity of Id1 promoter reporter Id1-985 , which contained a Smad1 binding site or BRE ( BMP-responding element ) , but not that of the mutant reporter Id1-956 lacking the BRE [24] ( Fig . 3C ) . Knock-down of Smad1 abolished LANA activation of the Id1-985 promoter reporter ( Fig . 3D , Fig . S3 ) . Therefore , LANA up-regulation of Id1 transcription depended on the BMP-Smad1 signaling pathway . We showed that LANA was directly recruited to the Id1 promoter , together with Smad1 in ChIP-PCR assay ( Fig . 3E ) . Moreover , LANA significantly enhanced the enrichment of Smad1 binding to the Id1 promoter after BMP2 treatment ( Fig . 3F ) . Collectively , these results indicated that LANA promoted Smad1-mediated Id1 transcription activation through sustaining p-Smad1 expression , and probably facilitating and extending the loading of Smad1 on Id1 promoter . Interestingly , we found that other Id family members , including Id2 and Id3 were also up-regulated in LANA-transfected 293T cells at both mRNA and protein levels ( Fig . S4 ) , whereas Id4 was not detected in our system . Furthermore , we showed that Id2 and Id3 were up-regulated in KSHV infected human primary endothelial cells ( HUVECs ) as Id1 ( Fig . S5 ) . Knockdown of Smad1 significantly impaired the expression of Id1 , Id2 and Id3 in KSHV infected HUVECs , which suggested that Ids were mainly regulated by BMP-Smad1 pathway in those cells . We also showed that BMP signaling inhibitor Dorsomorphin dramatically repressed Id1 , Id2 and Id3 in iSLK . 219 cells ( Fig . S5 ) . Based on our data , we believed that LANA might generally up-regulated the transcription of Id family members through BMP-Smad1-Id signaling pathway in KSHV infected cells . Since Ids were important oncogenic proteins , we sought to determine whether Ids were aberrantly expressed in KS tissues . We examined the expression of Id proteins and LANA in 10 cases of classical KS tissues and 5 cases of normal skin tissues by immunohistochemistry . As shown , there were weak to modest staining signals of Id1 , Id2 and Id3 only in the basal cells of epidermis and around the hair follicle of dermis in normal skin tissues ( Fig . 4 ) . There was no LANA staining in any normal skin tissues ( Fig . 4 ) . In sharp contrast to normal skin tissues , there were strong staining signals of Id1 , Id2 and Id3 in the spindle cells in the KS lesions . By staining for Ids and LANA in consecutive sections , positive signals of Ids were only observed in the spindle cells in KS lesions , which were also positive for LANA staining . No staining of Ids was observed in the adjacent tissues , which were negative for LANA ( Fig . S6 ) . Because of the small sample size , we were not able to perform a valid correlation analysis between Ids expression and the stage of the tumors . Nevertheless , our data suggested that Ids were aberrantly regulated in KS tumors and might be relevant to the development of KS . Because the p-Smad1 antibody was not suitable for immunohistochemical staining , we were not able to examine the expression of p-Smad1 in these KS lesions . Nevertheless , we showed that there was strong staining of Smad1 in the KS lesions but not in adjacent tissues ( Fig . S6 ) indicating that BMP-Smad1-Id signaling might be involved in the aberrant expression of Ids in KS . KSHV can efficiently infect and transform primary rat embryonic metanephric mesenchymal precursor ( MM ) cells [7] . KSHV-transformed MM cells ( KMM ) efficiently induce tumors with virological and pathological features of KS [7] . We asked whether Id family members were up-regulated by LANA in KMM cells . We detected significantly higher levels of Id1∼Id3 ( about 3 fold ) in KMM cells than in MM cells at both mRNA and protein levels ( Fig . 5A , B ) . Knock-down of LANA dramatically suppressed the expression of Id1 , Id2 , and Id3 in KMM cells ( Fig . 5C ) . These results indicated that LANA was responsible for the up-regulation of Ids in KMM cells . Id proteins , especially Id1 , are able to promote cell proliferation and cell cycle progression . To determine if LANA deregulation of the BMP-Smad1-Id pathway could contribute to KSHV-mediated tumorigenesis [7] , we established KMM-shId1 cell lines with high Id1 knockdown efficiency and determined the effect on cellular transformation ( Fig . 6A ) . Knock-down of Id1dramatically decreased the proliferation of KMM cells ( Fig . 6B ) and inhibited the formation of foci in culture ( Fig . 6C , D ) , formation of colonies in soft agar ( Fig . 6E , F ) , and induction of tumors in nude mice ( Fig . 6G , H , I ) In contrast , knockdown of Id1 in MM cells only slightly decreased the proliferation of MM cells ( Fig . S7 ) . We also established KMM-shId2 and KMM-shId3 cell lines ( Fig . S8 ) . Knockdown of Id2 and Id3 inhibited anchorage-independent growth of KMM cells in soft agar ( Fig . S8 ) . Moreover , knockdown of either LANA or Smad1 also severely impaired the anchorage-independent growth of KMM cells ( Fig . S9 ) . These results indicated that Ids were required for maintaining the oncogenic phenotype of KMM cells . We further asked whether Ids were the driving force for KSHV-mediated cellular transformation . We overexpressed Id1 in MM cells , however , no direct cellular transformation was observed as expected ( Fig . S10 ) . Nevertheless , ectopic expression of Id1 in KMM cells ( Fig . S11A ) further accelerated cell proliferation ( Fig . S11B ) , and increased the formation of foci in culture and formation of colonies in soft agar ( Fig . S11C , D , E , F ) . Collectively , our data provided evidence that LANA increased BMP-Smad1-Id signaling and this pathway was required for KSHV-induced tumorigenesis . Based on the above findings , we speculated that inhibitors of the BMP pathway might be potential therapeutic agents of KS . Dorsomorphin potently inhibits BMP-mediated Smad1/5/8 phosphorylation [25] , while WSS25 disrupts the interaction between BMP and BMP receptor [26] , [27] . Indeed , treatment with these two molecules dramatically inhibited BMP2-stimulated p-Smad1 expression ( Fig . 7A ) , and inhibited the anchorage-independent cell growth of KMM cells in soft agar assay ( Fig . 7B ) . Importantly , compared to MM cells , Dorsomorphin showed preferential toxicity to KMM cells ( Fig . 7C ) , indicating Dorsomorphin selectively targeted KSHV-transformed cells . Furthermore , we found that Dorsomorphin dramatically inhibited the expression of Ids ( Fig . 7D ) while ectopic expression of Id1 significantly rescued Dorsomorphin induced G2/M arrest [28] , cellular toxicity in KMM cells ( Fig . 7E , F and Fig . S12 ) , and partially rescued Dorsomorphin inhibition of anchorage-independent colony formation ( Fig . 7G ) . These results indicated that Dorsomorphin mainly inhibited the oncogenicity of KMM cells through targeting the BMP-Smad1-Id pathway . To strengthen our conclusion , we showed that overexpression of Id1 significantly rescued Dorsomorphin-induced cellular toxicity in 293T cells in a dose-dependent manner ( Fig . S13 ) . Finally , we determined the efficacy of Dorsomorphin in inhibiting in vivo tumor growth of KMM cells . We subcutaneously injected 1×106 KMM cells into BALB/c nude mice . When tumor volume reached about 50∼100 cm3 , the nude mice were randomly divided into 2 two groups . One group was intraperitoneally injected with a single dose of Dorsomorphin at 10 mg/Kg [29] while the other group was injected with vehicle control . Impressively , single treatment with Dorsomorphin was sufficient to significantly inhibit the tumor growth of KMM cells ( Fig . 7H ) . Immunohistochemical staining showed that Dorsomorphin inhibited Id1 , Id2 , Id3 and Ki67 expression and activated caspase 3 in the tumors ( Fig . 7I ) . Interestingly , LANA remained positive in the Dorsomorphin-treated tumors ( Fig . 7I ) , suggesting that the antitumor activity of Dorsomorphin was not dependent on the inhibition of KSHV infection and replication in KMM cells .
Our results showed that KSHV LANA interacted with BMP-activated p-Smad1 in the nucleus , sustained p-Smad1 expression , and facilitated its loading on the Id promoter leading to aberrant expression of Ids , which were indispensable driving forces for KSHV-induced tumorigenesis . Thus , KSHV hijacks and converts a developmental pathway into an oncogenic pathway , which is essential for KSHV-induced transformation . Furthermore , our results have shown that small inhibitors targeting BMP-Smad1-Id signaling pathway may serve as potential candidates for the treatment of KS ( summary in Fig . 8 ) . Although KSHV exerts multiple mechanisms to promote cell survival by repressing TGF-β signaling [30]–[32] , little is known whether KSHV manipulates BMP signaling and contribute to the pathogenesis of KSHV-induced malignancies . Previously , KSHV lytic gene K5 was reported to inhibit BMP signaling by down-regulating BMPR-II through ubiquitination-mediated degradation [33] . However , KSHV is predominantly maintained in the latent state of replication in KS spindle tumor cells and KMM cells , in which K5 is usually not expressed . In this context , we believe that KSHV hijacks the BMP-Smad1-Id pathway to promote tumorigenesis . We previously reported that Smad1 was not detected in PEL cells [31] and LANA did not interact with Smad5 . It is unlikely that LANA is involved in Id regulation in PEL cells . We showed that Id1∼3 were expressed in KSHV-positive BCBL1 and BC3 cells at levels similar to KSHV-negative BJAB cells ( Fig . S14 ) . Since BJAB was not an ideal control for BCBL1 and BC3 cells , we further compared the expression of Ids in BJAB and KSHV stably transfected BJAB ( KSHV-BJAB ) cells . We found that Id2 and Id3 , but not Id1 was decreased by about 50% in KSHV-BJAB cells compared to BJAB cells ( Fig . S14 ) . Since Id2 and Id3 but not Id1 were reported to be down-regulated in the vFLIP-transfected cells [34] , vFLIP might be the main viral gene that regulates the expression of Ids in PEL cells . Since Id1∼3 are significantly up-regulated in KS lesions compared to adjacent tissue and normal skin and in KMM cells compared to MM cells , up-regulation of Ids by LANA through LANA-Smad1-Id signaling is likely the principal mechanism that KSHV regulates the expression of Ids in KS tumor cells and in KSHV-transformed KMM cells . Interestingly , Id1 and Id3 were induced by EBV latent protein LMP1 [35] , [36] . LMP1 inactivates the function of Foxo3a leading to up-regulation of Id1 . Id1 increased cell proliferation and conferred resistance to TGFβ-mediated cell cycle arrest in nasopharyngeal epithelial cells [37] . Therefore , Id proteins may serve as conserved targets for oncogenic herpesviruses . Ids inhibit apoptosis and promote cell proliferation through distinct mechanisms [13] . For example , Id1 had been shown to inhibit E-protein and Ets-protein-mediated activation of the p16/INK4a [38] , [39] . Id2 has been found to reverse cellular growth inhibition by the retinoblastoma protein ( pRb ) through direct interaction with pRb [40] . How individual Ids promote KSHV-mediated oncogenesis remain to be further clarified . Our data showed that LANA up-regulated BMP-Smad1-Id signaling was required but not sufficient for KSHV-induced tumorigenesis . It is likely that additional oncogenic signaling pathways are involved in KSHV-induced cellular transformation and tumorigenesis . Discovery of these additional pathways could help better understanding of how KSHV induces tumorigenesis . Dorsomorphin is known to potently inhibit the expression of Ids through suppressing BMP-induced Smad1 phosphorylation . Our results showed that Dorsomorphin dramatically inhibited the growth of KMM cells in vitro and tumor growth in vivo . Even though Dorsomorphin might also target other kinases [41] , our results showed that Id1 was capable of rescuing Dorsomorphin-induced G2/M arrest and cellular toxicity in KMM cells . Therefore , we have demonstrated that , by targeting the KSHV-deregulated BMP-Smad1-Id pathway , Dorsomorphin inhibits KSHV-induced tumorigenesis . Dorsomorphin might be a promising lead compound for KS therapy . Currently , it is still unknown how LANA sustains p-Smad1 activation through their interaction . In the basal state , Smad1 constantly shuttles between cytoplasm and nucleus through its N-terminal nuclear localization signal ( NLS ) motif and C-terminal nuclear export signal ( NES ) [42] . Upon activation by BMP , the C-terminal of Smad1 , which is phosphorylated at SXS motif , undergoes conformation change , and creates an acidic knob to form a trimer with the homologous MH2 domain of another Smad1 molecule and MH2 domain of Smad4 [43] . The ligand-induced phosphorylation promotes the accumulation of the hetero-oligomer in the nucleus by inhibiting the nuclear export and enhancing its import [42] . In the nucleus , Smad1/Smad4 complexes bind to other co-transcription factors and initiate target gene transcription . The signal undergoes rapid termination through dephosphorylation in its C-terminal SXS motif by PPM1A [44] and/or SCP ( small C-terminal domain phosphatase ) family of nuclear phosphatases [45] or degradation via polyubiquitylation and proteasome-mediated degradation by Smurf1/2 [46] or CHIP [47] . In this study , we have demonstrated the interaction between the N-terminal of LANA and MH2 domain of Smad1 . This interaction may have several effects on sustaining the activated Smad1 in the nucleus: 1 ) it may stabilize the heteromeric complex between the phosphorylated Smad1 and the common mediator Smad4 , thus masking the NES motif; 2 ) it may protect Smad1 from dephosphorylation caused by PPM1A or SCPs; 3 ) it may disrupt the rapid Smad1 turnover via Ubiquitin-Proteasome Pathway mediated by Smurf1/2 or CHIP . Thus , LANA facilitates the loading of functional p-Smad1 on the Id promoter and ultimately leads to aberrant expression of Ids . However , additional works are required to confirm any of these speculations . In a summary , our study has revealed that BMP-Smad1-Id signaling pathway is positively regulated by LANA and serves as an intrinsic oncogenic pathway of KSHV-induced tumorigenesis . More importantly , we have shown that the BMP-Smad1-Id pathway is a potential therapeutic target for KS .
The clinical section of the research was reviewed and ethically approved by the Institutional Ethics Committee of the First Teaching Hospital of Xinjiang Medical University ( Urumqi , 127 Xinjiang , China; Study protocol # 20082012 ) . Written informed consent was obtained from all participants , and all samples were anonymized . All participants were adults . The animal experiments were approved by the Institutional Animal Care and Use Committee of the Institut Pasteur of Shanghai , Chinese Academy of Sciences ( Animal protocol # A2013010 ) . All animal care and use protocols were performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals approved by the State Council of People's Republic of China . Rat embryonic metanephric mesenchymal precursor cells ( MM cells ) , KSHV-transformed MM cells ( KMM ) , 293T cells were maintained in DMEM ( HyClone ) supplemented with 10% fetal bovine serum ( HyClone ) . HUVEC was maintained in EGM ( Lonza ) . KMM/shsmad1 , KMM/shId1 , KMM/shLANA , and KMM/shControl , KMM/Id1 , KMM/Vector , 293T/shSmad1 , 293T/shControl , 293T/SF-LANA and 293T/SF-Puro cell lines were established by infection of indicated lentivirus according to the manufacturer's instructions ( System Bioscience ) . LANA truncation plasmids were previously reported [48] . pCDH-SF-LANA was constructed by sub-cloned full-length LANA into pCDH-SF-EF1-Puro by EcoRI and BamHI sites . Reporter plasmids pGL3-Id1-985 and pGL3-Id1-956 were constructed as previously reported [24] . HA-Smad1and Flag-Smad1 , were provided by Dr . Naihe Jing ( Shanghai Institutions of Biological Sciences ) . ShLANA was previously reported [49] . shId1 , shId2 , shId3 and shSmad1 were constructed in pLKO . 1 using the following targeting sequence: Id1 ( AAGGTCACATTTCGTGCTTCT ) ; Id2 ( CAGCACGTCATCGATTATATC ) ; Id3 ( GTGATCTCCAAGGACAAGAGG ) ; Smad1 ( CGGTTGCTTATGAGGAACCAA ) . Truncated or SXS motif mutated Smad1 plasmids were constructed by cloning the indicated sequence into pCMV-HA vector using the following primers: Smad1-N ( F: 5′-CGCGTCGACAATGAATGTGACAAGTTTATT-3′ , R: 5′-CGCCTCGAGTTAAGCAACCGCCTGAACATCTC-3′ ) ; Smad1-C ( F: 5′-CGCGTCGACAATGCCTGTACTTCCTCCTGTGCT-3′ , R: 5′-CGCCTCGAGTTAAGATACAGATGAAATAGGAT-3′ ) ; Smad1-MH2 ( F: 5′-CGCGTCGACAATGTATGAGGAACCAAAACACTG-3′ , R: 5′-CGCCTCGAGTTAAGATACAGATGAAATAGGAT-3′ ) ; Smad1-DVD ( F: 5′-CGCGTCGACAATGAATGTGACAAGTTTATT-3′ , R: 5′-CGCCTCGAGTTAATCTACATCTGAAATAGGATTA-3′ ) ; Smad1-AVA ( F: 5′-CGCGTCGACAATGAATGTGACAAGTTTATT-3′ , R: 5′-CGCCTCGAGTTAAGCTACAGCTGAAATAGGATT-3′ ) ; Smad1-ΔC3 ( F: 5′-CGCGTCGACAATGAATGTGACAAGTTTATT-3′ , R: 5′-CGCCTCGAGTTAAGCTACAGCTGAAATAGGATT-3′ ) . Expression plasmids of truncated Smad1-MH2 were constructed by cloning the indicated sequence into pEGFP vector using the following primers: MH2-N ( F: 5′-cgcAGATCTTATGAGGAACCAAAACACTG-3′ , R: 5′-cgcGGATCCTTAATGATGGTAGTTGCAGTTCC-3′ ) MH2-M ( F: 5′-cgcAGATCTCGTTTCTGCCTTGGGCTGCT-3′ , R: 5′-cgcGGATCCTTATGTAAGCTCATAGACTGTCTCA-3′ ) MH2-C ( F: 5′-cgcAGATCTGGATTTCATCCTACTACTGTTTGC-3′ , R: 5′-cgcGGATCCTTAAGATACAGATGAAATAGG-3′ ) MH2-F ( F: 5′-cgcAGATCTTATGAGGAACCAAAACACTG-3′ , R: 5′-cgcGGATCCTTAAGATACAGATGAAATAGG-3′ ) . The antibodies and reagents were used as follows: anti-LANA ( 1B5 , prepared in our lab ) , anti-Smad1 ( Santa cruz , sc-7965x ) , anti-pSmad1/5/8 ( Cell signaling technology , #9511 ) , anti-Id1 ( Santa cruz , sc-488 ) , anti-Id2 ( Santa cruz , sc-489 ) , anti-Id3 ( Santa cruz , sc-490 ) , anti-Ki67 ( Novocastra , NCL-Ki67p ) , anti-cleaved Caspase-3 ( Cell signaling technology , #9661 ) . Anti-Flag M2 affinity gel ( Sigma , A2220 ) , Strep-Tactin sepharose ( IBA , 2-1201-010 ) , desthiobiotin ( IBA , 2-1000-001 ) , BMP2 ( Sigma , B3555 ) , Cycloheximide ( Sigma , C1988 ) , Dorsomorphin ( Sigma , P5499 ) and WSS25 were kindly provided by Dr . Kan Ding from Shanghai Institute of Materia Medica [26] . TAP of SF-LANA was done as previously described [19] . Briefly , 293T-SF-LANA or 293T-SF-Puro cells were harvested and subjected to nuclear extraction as previously described [50] . Dialyzed nuclear extract was loaded into a column of prewashed Strep-Tactin Superflow ( 0 . 5 ml bed volume , IBA ) . The column was washed with 10 bed volume of Buffer W ( 50 mM Tris pH 7 . 9 , 100 mM KCl , 10% Glycerol , 0 . 2 mM EDTA , 0 . 5 mM DTT , 0 . 1% Triton-X100 , 0 . 2 mM PMSF ) and eluted with 3 bed volume of Buffer E ( Buffer W containing 2 . 5 mM D-desthiobiotin ) . The elute was then subjected to second round of affinity purification by anti-Flag M2 affinity gel for 2 hours at 4°C . The beads were washed with Buffer W for 5 times and eluted with 3×Flag Peptide in Buffer W . The elute was monitored by SDS-PAGE and subjected to mass spectrometry . Cells were lysed in radio immunoprecipitation assay ( RIPA ) buffer ( 50 mM Tris [pH 7 . 6] , 150 mM NaCl , 2 mM EDTA , 1% Nonidet P-40 , 0 . 1 mM PMSF , 1×phosphatase inhibitors [Phospho-Stop , Roche] ) for 1 h on ice with brief vortexing every 10 min . The lysate were incubated with antibody or affinity beads as indicated overnight at 4°C . The immunoprecipitations were separated by SDS-PAGE and analyzed by immunoblotting . For cytoplasmic protein and nuclear protein fractionation , cells were harvested and extracted as described [51] . Cells were collected and lysed in Trizol buffer ( Life technology ) , and RNA was isolated according to the manufacturer's instructions . Reverse transcription was performed with a cDNA Reverse Transcription Kit ( Toyobo ) . Real-time RT-PCR was performed with a SYBR green Master Mix kit ( Toyobo ) . Relative mRNA levels were normalized to Actin and calculated by ΔΔCT method . The primers were listed below: Id1 ( F: 5′-CTGCTCTACGACATGAACGG-3′ , R: 5′-GAAGGTCCCTGATGTAGTCGAT-3′ ) ; Id2 ( F: 5′-GCTATACAACATGAACGACTGCT-3′ , R: 5′-AATAGTGGGATGCGAGTCCAG-3′ ) ; Id3 ( F: 5′-GAGAGGCACTCAGCTTAGCC-3′ , R: 5′-TCCTTTTGTCGTTGGAGATGAC-3′ ) ; Actin ( F: 5′-GCACGGCATCGTCACCAACT-3′ , R: 5′-CATCTTCTCGCGGTTGGCCT-3′ ) . Chromatin immunoprecipitation ( ChIP ) was performed as previously described . Briefly , 5 µg correspondent antibody ( anti-HA mAb , anti-Flag-mAb ) or control mouse immunoglobulin ( IgG ) was added into each group of lysate at 4°C overnight . Then 50 µl proteinA/G beads , which had been precleared with binding buffer containing 0 . 2 mg of salmon sperm DNA per ml for 6 h , were added into each sample at 4°C for 2 h for immunoprecipitation . To extract the DNA fragment , TE buffer with 1% SDS and proteinase K ( Beyotime ) was added to the washed precipitates . After incubation at 65°C for at least 6 h , the eluted solution was subjected to DNA extraction kit ( Bio-Dev ) . Specific primers used for chromatin immunoprecipitation ( ChIP ) DNA amplification matched the Id1 promoter region were: Id1-F: 5′-CAGTTTGTCGTCTCCATG-3′; Id1-R: 5′-TCTGTGTCAGCGTCTGAA-3′; GAPDH-F: 5′-TACTAGCGGTTTTACGGGCG-3′; GAPDH-R: 5′-TCGAACAGGAGGAGCAGAGAGCGA-3′ . MTT assay for cell proliferation or toxicity was conducted according to the manufacturer's instructions ( Beyotime ) . For cell proliferation , 1000 cells were seeded per well in 96-well plates as indicated; for toxicity , 4000 cells were seeded per well in 96-well plates with DMEM containing Dorsomorphin of indicated concentrations . Cell cycle assay was conducted according to manufacturer's instructions ( Beyotime ) . KMM-Vector and KMM-Id1 cells were treated with DMSO or 5 mM Dorsomorphin for 48 hours . Then the cells were harvested and subjected to PI staining and cell cycle analysis by Mod Fit software . Soft agar assay: Six-well plates were covered with a bottom layer of 1% agar ( Invitrogen ) in DMEM containing 10% FBS . Then 10000 cells were prepared in DMEM containing 10% FBS and 0 . 4% agar and seeded onto the solidified bottom layer . After two weeks of cell culture , colonies were photographed by microscopy and stained with 0 . 005% crystal violet . The number of colonies was analyzed by Quantity One . Colony formation assay: 1000 cells were prepared in DMEM containing 10% FBS and seeded in six-well plates . After two weeks of cell culture , colonies were photographed by microscopy and stained with 0 . 005% crystal violet . The number of colonies was analyzed by Quantity One . The clinical tissue specimens from 10 patients with KS were collected from Xinjiang province , northwestern of China . The clinical section of the research was reviewed and ethically approved by the Institutional Ethics Committee of the First Teaching Hospital of Xinjiang Medical University ( Urumqi , 127 Xinjiang , China; Study protocol # 20082012 ) . The expression of LANA , Id1 , Id2 , Id3 , Smad1 , Ki67 , and activated caspase 3 were analyzed by IHC as described [52] . 1×106 KMM-shCtrl cells or KMM-shId1 cells were subcutaneously injected into BALB/c Nude mice . There were 5 mice in each group . The size of tumor was measured every 3 day . Tumor volume was calculated by the formula: ( length×width2 ) /2 . Nude mice were sacrificed at the same time when the size of tumors in shCtrl group reaches about 2000 mm3 . In another xenograft assay with drug treatment , 1×106 KMM cells were subcutaneously injected into BALB/c Nude mice . When tumor volume reached about 50∼100 cm3 , nude mice were divided into 2 two groups randomly . There were 5 mice in each group . One group was intraperitoneal injection with a single dose of Dorsomorphin ( 10 mg/Kg ) , the other group was injected with vehicle . Tumor volume was monitored daily and calculated by the formula: ( length×width2 ) /2 . These animal experiments were approved by the Institutional Animal Care and Use Committee of the Institut Pasteur of Shanghai , Chinese Academy of Sciences ( Animal protocol # A2013010 ) . Data were analyzed by Student's t test . P<0 . 05 was considered to be significant ( two tailed ) . Error bars represent standard error of mean ( s . e . m . ) . Gene IDs: BMP2: 650 Smad1: 4086 Smad5: 4090 Id1: 3397 Id2: 3398 Id3: 3399 LANA: 4961527 | Although KSHV exerts multiple mechanisms to promote cell survival by repressing TGF-β signaling , little is known whether KSHV manipulates BMP signaling and contributes to the pathogenesis of KSHV-induced malignancies . In the present study , we have identified Smad1 as a novel binding protein of LANA by tandem affinity purification . We demonstrated that LANA up-regulated Id transcription through BMP-Smad1-Id signaling pathway . Id proteins were significantly up-regulated in KSHV-transformed MM ( KMM ) cells , and were abundantly expressed in human KS lesions; therefore , they were probably relevant to the development of KS . Importantly , we have shown that Ids are required to maintain the oncogenic phenotype of KMM cells in vitro and in vivo . These findings illustrate a novel mechanism by which a tumor virus hijacks and converts a developmental pathway into an indispensable oncogenic pathway for tumorigenesis . Furthermore , we showed that BMP signaling inhibitors dramatically hampered the tumorigenicity of KMM cells in vitro and in vivo . Our results demonstrate that small inhibitors targeting BMP-Smad1-Id signaling pathway are promising candidates for the treatment of KS . | [
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] | 2014 | Oncogenic Herpesvirus KSHV Hijacks BMP-Smad1-Id Signaling to Promote Tumorigenesis |
In Ethiopia guidelines for diagnoses and treatment of leishmaniases are available , but only a few hundred people are diagnosed and receive treatment . A field study has been carried out to determine the status and environmental determinants of cutaneous leishmaniasis ( CL ) and assess the degree of awareness of the rural communities in affected areas in Tigray , northern Ethiopia . Following a reconnaissance survey that identified endemic foci , a cross sectional door-to-door survey was conducted in 2009 in five rural communities around the towns of Adigrat and Hagereselam in Tigray . In total 9 , 622 residents of 1 , 721 households were clinically screened and household heads interviewed regarding the determinants of infection . The χ2 test and logistic regression were used to determine differences in prevalence between localities , age and sex , and to identify environmental determinants of infection . The overall prevalence of localized CL was 2 . 3% ( highest 4 . 7% ) , with marked inter-village differences . Another 20 . 9% had scars from previous infections . While risk was sex-independent , prevalence was significantly higher in the 0–9 ( 4 . 5% ) and 10–19 ( 2 . 5% ) age groups and predominantly involved the face ( 82 . 1% ) and upper limbs ( 13 . 1% ) . Nearly 11% of the households had one or more cases of CL and this was associated with proximity to hyrax habitats . All interviewees were knowledgeable about the lesions but ignorant of the disease’s mode of transmission and its association with hyraxes . The study established that CL is an important public health problem in the study communities , and has been so for a while , as demonstrated by the widespread presence of scars . CL in Tigray appeared to be predominantly of zoonotic nature , mainly transmitted in peri-domestic habitats in proximity to hyrax habitats . Integrated interventions , including awareness creation , are highly recommended .
Ethiopia is among the 98 leishmaniasis endemic countries of the world with both the visceral and cutaneous forms prevalent in the country [1] . An estimated 0 . 2 to 0 . 4 million visceral leishmaniasis ( VL ) cases and 0 . 7 to 1 . 2 million cutaneous leishmaniasis ( CL ) cases occur each year globally [2] . CL due to Leishmania aethiopica has long been the most widespread skin disease in the highlands of Ethiopia [3 , 4 , 5] principally affecting the poor rural communities . A recent study indicated that an estimated 29 million people are at high risk of CL in the central highlands of the country [6] . Estimates of the annual incidence range from 20 , 000 to 50 , 000 cases yearly [7 , 8] , but only a few hundred cases are actually reported [8] . Stable foci are maintained by hyraxes ( small herbivorous mammals Procavia capensis and Heterohyrax brucei ) , and the parasite is transmitted among hyraxes and humans by female phlebotomine sandflies ( Phlebotomus longipes and P . pedifer ) that feed mainly on hyraxes and share their habitat [9 , 10 , 11] . There are three clinical forms of CL due to L . aethiopica: localized CL ( LCL ) , mucosal cutaneous leishmaniasis ( MCL ) , and diffuse cutaneous leishmaniasis ( DCL ) . Although not fatal , persistent LCL , MCL and DCL are disfiguring [12] and may bring long-term psycho-social problems , particularly in young women . CL is the most neglected even among the neglected tropical diseases ( NTDs ) in the country . Exact figures on the magnitude of CL in Ethiopia are lacking both nationally and by regional state . The first guideline for diagnosis , treatment and prevention of VL was produced in 2006 , and updated with the inclusion of CL in June 2013 . However , only very few health centres ( eight ) diagnose leishmaniasis and as of 2014 , only 342 CL cases were treated in VL treatment centres [13] . Diagnosis of CL involves clinical assessment and confirmation with microscopic examination of skin lesion sample . Antimonials are approved for CL treatment in the selected health centres , but most cases are treated traditionally using plants and local application of heat ( with hot iron or charcoal fire as per local practice ) [8] . There is no leishmaniasis vector control program . Distribution of insecticide-treated nets ( ITNs ) and insecticide spraying in the context of malaria control may have some impact on phlebotomines in lowland localities where VL is also endemic . On the whole , there is limited evidence , nor control efforts of CL in the country . Outbreaks of CL are not uncommon [14] . The risk of HIV / CL co-infection is also a serious threat as it increases the burden of CL by causing severe forms that are more difficult to manage [15] . Absence or limited access to diagnosis and treatment for CL further increases the urgency for epidemiological surveillance of the disease in the country . Reports pertaining to CL in Ethiopia date back to 1913 , but the disease appears to be around much longer considering the presence of vernacular names in every language where the disease is endemic . Despite its long recognized endemicity [1 , 3] , information on the epidemiology of CL in Ethiopia is still incomplete [16] . Tigray is one of the regions in northern Ethiopia where the status of CL is still unknown . This is mainly due to absence of epidemiological field studies in the region . Due to absence of diagnosis and drugs to treat CL , even reports from health facilities indicating its mere presence in the region have been scarce [15] . This study was initiated following our observation of a steady flow of severe CL cases to a newly established ( 2005 ) Italian dermatological unit that started to provide treatment at Ayder Referral and University Teaching Hospital in Mekelle , the capital of the region . With the aim to identify active CL foci and map the pattern of distribution of the disease in the region , a large-scale study has been in progress since late 2008 . Part of this study was a door-to-door survey ( see Supporting information S1 STROBE checklist ) undertaken in five rural communities of Tigray to assess the extent of CL presence and in high risk areas identify potential environmental risk factors , the results of which are presented here .
The study was approved by the Research Ethics Review Committee ( RERC ) of College of Health Sciences at Mekelle University under reference number CHS/790/DN-16 . Permission from the district and respective village authorities was also obtained . Informed oral consent was obtained from the head of the household selected for the study and for those with active lesions signed consent was sought from the guardians . The data were anonymised before analysis by replacing birth dates by age range and household details by name of the subdistrict . The study was carried out in March and April of 2009 in villages around the towns of Adigrat and Hagereselam in the Tigray National Regional State in northern Ethiopia . The region covers a surface area of about 50 , 000 km2 and borders with the Sudan in the west and Eritrea in the north ( Fig 1 ) . Its nearly 4 . 3 million people are predominantly rural ( 80 . 5% ) and engaged in subsistence rain-fed agriculture [17] . The region has a diverse topography , with peak highlands ( 8% ) , midlands ( 39% ) and lowlands ( 53% ) . Its altitude varies from about 200 meters above sea level ( masl ) in the north east to almost 4165 masl in the south west . The climate is semi-arid , and the rainfall pattern is mainly unimodal ( June to September ) but erratic ( 200 to >1000 mm annually ) . The regional average annual temperature is about 18 . 0°C but varies greatly with altitude [18] . The first study site lies to the east of the town of Adigrat . Adigrat is the administrative centre of the eastern zone , located at about 900 km north of Addis Ababa and 120 km north of Mekelle , the regional capital . Locally known for its CL endemicity , the study area comprises of three adjacent subdistricts ( kebeles , peasant associations ) demarcated by huge gorges: Golea-Genahti , Sasun-Bethawariat , and Kumasubuha ( Table 1 ) . The altitude of the study sites ranges from 2248–2650 masl with mild to high temperatures and rainfall is on average 400 mm per year . The area is characterized by deeply incised plateaus , dominated by limestone and sedimentary rock formations providing ideal habitats for hyraxes . The area has moderate to high density population with heavily deforested plains , predominantly scattered bush , and acacia trees . Cactus grows wild in the backyards of most homes . The soils are sandy and of low fertility . The second study site is located around the market town of Hagereselam ( altitude 2650 masl ) , the administrative centre of Degua-Tembien district 50 km west of Mekelle . It consists of villages belonging to two subdistricts: Mahbereslassie and Michael-Abya ( Table 1 ) . The district is about 1033 km2 with an average population density of about 108 persons/km2 [17] . The area has a stepped landscape where flats alternate with steep escarpments . As part of an environmental rehabilitation and reforestation programme nearly 10% of the total area of the district is closed off from people and livestock . Such ‘exclosures’ are mostly located on steep and degraded slopes . The climate of the district is locally classified as Dega ( lower highland; 2200–2800 masl ) , receiving an average annual rainfall of around 700mm . The mean annual temperature is about 15–16°C . The epidemiological status of leishmaniasis in the study area is not known , owing to the absence of field-based research and limited access to treatment . Based on university hospital records between February 2005 and February 2009 , 21 cases of CL were reported from the Adigrat area and 23 cases from the study villages around the town of Hagereselam . VL has never been reported from these highland localities . The subdistrict villages were selected based on information from medical records of Ayder Referral Hospital and personal experience . In absence of reliable boundary and demographic data at the time , the largest possible sample size was sought by considering every other household in each selected subdistrict . Accordingly , a total of 1721 households were surveyed from the study areas , namely: 437 in Golea-Genahti , 378 in Sasun-Bethawariat , 511 in Kumasubuha , 210 in Mahbereslassie , and 185 in Michael-Abya . Household members’ socio-demographic , clinical and household data as well as information pertaining to environmental determinants , knowledge on mode of transmission and prevention methods of CL were recorded by a regularly supervised research team consisting of trained health officers guided by a community member knowledgeable on CL . The questionnaire is added as supporting information ( S1 Text ) . The skin lesions of CL are well identified by the community in its vernacular name “Gizwa” . The required information was sought from the household head or eligible adult present during the visit . Information , collected at each household , included housing type and demographic variables such as age , sex , occupation , duration of residence , and number of animals owned ( livestock , dogs ) . We also observed and recorded the geographic features of the household , including proximity to caves , gorge , and hyrax habitats . Household heads were also asked for the presence of members with active skin lesions or scars due to CL or other causes . When present , members with evidence of CL were summoned and examined for lesions and scars . For each case , information was recorded on the number , type ( LCL , MCL , and DCL ) and location of lesions and scars , as well as determinants of infection , such as history of travel and outdoor sleeping . Healed lesions that are depressed , none-or hypo-pigmented , but shiny at the periphery and with rubbery borders , were diagnosed as past CL and referred to as ‘scars’ . Papular , nodular or ulcerative lesions with or without satellite lesions and mostly located in uncovered parts of the body were clinically diagnosed as LCL ( ‘lesions’ ) . Lesions involving the mucosa was considered as MCL while multiple non-ulcerative nodular lesions , often bigger in size from those lesions of LCL were operationally defined as DCL . People with lesions were considered as cases of active CL and this was used to calculate prevalence . Samples of skin snips ( n = 51 ) were taken from a subsample of active cases , smeared on microscope slides , stained with Giemsa and examined for presence of Leishmania amastigotes . The remaining active cases were referred for treatment to Ayder Referral Hospital in the capital and reports of tissue smears of those who visited the hospital were sought from the laboratory records . This confirmed that active CL was caused by Leishmania parasites , most probably L . aethiopica . Frequencies and proportions were used for the descriptive analysis of the data . The χ2 test was used to determine any statistically significant difference in disease prevalence between age groups , sexes and areas and a P-value <0 . 05 was considered significant . Any association between the presence of lesions and environmental and host factors was sought using logistic regression . As the flight range of most sandflies is estimated at 300m from their breeding sites [22] , this distance was taken as the cut off point for the analysis of environmental factors such as proximity to caves , gorges and hyrax habitats . The data were analysed using SPSS ( Statistical Package for Social Sciences , version 16 ) . The full data set is added as supporting information ( S1 Data ) .
A total of 9622 inhabitants in 1721households were surveyed in the selected subdistricts . Males ( 50 . 01% ) and females ( 49 . 99% ) were represented almost equally . All study participants resided in the area for more than three years and none of them travelled out of their area within the last 6 months during the study period . However , three people with active CL claimed to have travelled to neighbouring CL endemic subdistricts prior to the appearance of lesions . Prevalence of active CL was 2 . 3% , with an additional 20 . 9% of the population showing scars . All active lesions observed during the study were of the localized type ( LCL ) . Of the 1721 households sampled , nearly 11% ( 188 ) had one or more cases of active CL , with a total of 60% that had either lesions or scars . Of the 188 households with active CL , 159 ( 84 . 6% ) households had one case , 25 ( 13 . 3% ) had two cases , and 4 ( 2 . 1% ) had three or more cases . There was a marked difference in prevalence between the study localities . The highest prevalence ( active lesions ) was observed in Mahbereslassie subdistrict ( 4 . 7% ) in Degua-Tembien district and in Kumasubuha ( 2 . 7% ) in Saesie-Tsaedaemba district ( Table 2 ) . Most scars , indicative of past infections , were found in Kumasubuha ( 34 . 4% ) . Statistically significant differences were observed between the five study sites , both in the prevalence of lesions ( χ2 = 45 . 860; df = 4; p < 0 . 001 ) and scars ( χ2 = 628 . 080; df = 4; p < 0 . 001 ) . In the present study , males showed slightly higher rates of active lesions ( 2 . 5%; 119/4812 ) than females ( 2 . 1% ) , but the difference was not statistically significant ( χ2 = 1 . 17 , df = 1 , p = 0 . 762 ) . Age specific active prevalence was significantly higher in the 0–9 years olds ( 4 . 5%; χ2 = 86 . 96; df = 3 , p < 0 . 001 ) than for those more than 10 years old ( average 1 . 6%; χ2 = 86 . 96; df = 3 , p < 0 . 001 ) . Of those with active lesions , 71 . 6% ( 159/222 ) were under 15 years of age and nearly 20% ( 44/222 ) were less than 6 years . The youngest person with active CL was eleven months old . The majority ( 82 . 1%; 207/252 ) of lesions were found on the face , in which the cheeks and nose were the most affected ( Table 3 ) . A similar pattern of distribution was observed with regard to scars . The number of lesions or scars per individual ranged from 1 to 7 ( Fig 2 ) . Nearly 90% ( 199/222 ) of active CL cases had single lesions and 93 . 5% ( 1879/2009 ) of those with healed lesions had single scars . Most of the single lesions ( 90% ) were of the ulcerative type , with indurated margins and necrotic base , appearing as reddish plaques with irregular borders covered by a firmly adherent crust . Nearly 82% of the active lesions had developed less than two years ago . Out of the 51 active CL skin scraping smears examined , amastigotes were found in 31 ( 60 . 8% ) of them . All tissue smears , of twenty individuals with lesions who visited the hospital , were found to be positive for amastigotes raising the total percentage to 71 . 8% . Several environmental and host related factors were assessed for their association with active CL cases using univariate and multivariate logistic regression ( Table 4 ) . Significant variations were observed in prevalence of active CL among age groups , study villages , and physical features of household location , including the presence of hyraxes , caves , and gorges within 300 m of the residence . Individuals in the age group of 0–9 years were nearly five times ( OR = 4 . 71; 95% CI: 3 . 13–7 . 1 ) and those aged 10–19 years were 2 . 5 times ( OR = 2 . 54; 95% CI: 1 . 66–3 . 89 ) more likely to have CL compared to individuals in the age group of 30 years and above . However , those in the age group 20–29 years were 18% ( OR = 0 . 82; 95% CI: 0 . 4–1 . 69 ) less likely to have active CL . Livestock ownership ( OR = 1 . 65; 95% CI: 1 . 001–2 . 71 ) , presence of hyraxes ( OR = 4 . 15; 95% CI: 2 . 64–6 . 53 ) , gorges ( OR = 3 . 63; 95% CI: 2 . 39–5 . 52 ) , and caves ( OR = 3 . 22; 95% CI: 2 . 32–7 . 47 ) in the vicinity were highly associated with the presence of active CL . Accordingly , participants who lived near hyrax colonies were 4 . 2 times and those living near caves were 3 . 2 times more likely to be infected by CL compared to those living far away . Similarly , those living within 300 m of a gorge were found to be 3 . 6 times more likely to get CL than those far away . Besides , those who slept outdoor were two times ( OR = 2 . 04; 95% CI: 1 . 29–3 . 21 ) more likely to get CL than those who slept inside . Similarly , individuals residing in households with livestock were found to be 65% times more likely to be infected with CL than those without . When compared to those residing in Michael-Abya subdistrict , inhabitants living in Mahbereslassie were found to be nearly 5 . 3% ( OR = 5 . 26; 95% CI: 2 . 58–10 . 71 ) times more likely to be infected with CL; in Kumasubuha this was 3% ( OR = 2 . 96; 95% CI: 1 . 48–5 . 91 ) , in Golea-Genahti 2 . 1% ( OR = 2 . 1; 95% CI: 1 . 01–4 . 24 ) , and 70% ( OR = 1 . 70; 95% CI: 0 . 81–3 . 57 ) in Sasun-Bethawariat subdistrict ( Table 4 ) . Except for livestock ownership , multivariate logistic regression analysis also showed the significant effect of the environmental and host factors on the odds of being positive for CL ( Table 4 ) . Accordingly , the age groups 0–9 years ( AOR = 4 . 67; 95% CI: 3 . 10–7 . 04; p< 0 . 001 ) and 10–15 years ( AOR = 2 . 48; 95% CI: 1 . 62–3 . 81; p<0 . 001 ) , outdoor sleeping ( AOR = 2 . 67; 95% CI: 1 . 17–2 . 67; p = 0 . 007 ) and location of residences in proximity to caves ( AOR = 2 . 62; 95% CI: 1 . 26–2 . 62; p = 0 . 001 ) , gorges ( AOR = 2 . 43; 95% CI: 1 . 57–3 . 75; p < 0 . 001 ) and hyrax habitats ( AOR = 2 . 35; 95% CI: 1 . 43–3 . 86; p = 0 . 001 ) was highly associated with the presence of active CL . All interviewed household heads were aware of CL and the great majority ( 98 . 7% ) identified CL lesions . However , they were invariably ignorant of the disease’s association with hyraxes and its mode of transmission . Nearly all ( 99 . 8% ) respondents claimed that CL is treated using traditional medicine , such as herbs , holy water ( “Tsebel” ) and local heat application , in that order ( Table 5 ) . Only 4 ( 0 . 2% ) of the participants claimed the presence of modern treatment for CL . In some villages , farmers made use of the huge piles of hyrax pellets as manure , yet , all the interviewed said that hyraxes eat crops and vegetables and so hyraxes are considered an agricultural pest .
The present study revealed that the localities around the towns of Adigrat and Hagereselam are important CL foci in the region . All cases were of the localized type ( LCL ) and occurred mostly in the face , with the cheeks in particular . Leishmania aethiopica should be the etiologic agent as in a follow-up study conducted in a neighbouring subdistrict it was isolated as the main Leishmania species causing CL in the area [23] , as in other parts of the country . The overall prevalence rate of active LCL in the study communities ranged from 0 . 9–4 . 7% , for scars this was 3 . 9–34 . 4% ( Table 2 ) . This is indicative of the public health significance of CL in these areas previously and currently . Despite good awareness and recognition of CL , understanding the mode of transmission lacked . This may have contributed to the high prevalence of the disease in the study villages and holds a risk of further spread in the future , since no active detection and treatment strategies are in place . Moreover , knowing that CL is transmitted by biting sandflies does not necessarily lead to adequate prevention strategies [24] . Integration with malaria control by distribution of ITNs and indoor residual spraying is unlikely as CL is predominantly prevalent in the malaria free highland areas . The observed absence of gender sensitivity to infection by CL is consistent with several studies conducted elsewhere in the country . The predominance of CL in the young ( 0–9 years old ) is also well known and resonates with countrywide data ( e . g . [25 , 26] , who found 8 . 5% and 7 . 1% respectively ) . In established endemic areas , CL prevalence typically increases with age up to 15 years , after which prevalence levels off , presumably because of the acquisition of immunity . In this study , the occurrence of the disease almost in equal proportion in both sexes , with large numbers of women and children infected , including those under one year , probably reflects that CL transmission may have occurred in peri-domestic habitats , where sandfly exposure is most equally distributed among individuals . This also explains why sleeping outdoors is an important ( peri-domestic ) risk factor . This is further corroborated by the fact that Phlebotomus longipes , the proven vector of CL in the Ethiopian highlands [9] , was collected both from indoor and predominantly outdoor locations ( compounds of houses ) in a follow-up study conducted in the neighbouring subdistrict [23] . In peri-domestic settings , sandflies rest in cool , dark and humid corners of animal shelters or human dwellings [27 , 28] . As with rodent burrows , peri-domestic areas also provide ready access to bloodmeals in addition to shelter and suitable breeding grounds in decaying organic matter ( manure ) . In line with previous reports from different parts of the country [9 , 23 , 29 , 30 , 31] , the presence of CL cases in households was closely associated with the presence of hyrax colonies in the vicinity , indicating that the disease is mainly of zoonotic nature . The intimate ecological association of rocky hyraxes and the sandfly species Phlebotomus longipes and P . pedifer , the proven vectors of L . aethiopica induced CL in Ethiopia , is well established [30 , 31] . Two species of hyrax ( Procavia capensis and Heterohyrax brucei ) are the widely incriminated reservoir hosts of L . aethiopica in the country [30] . Livestock was also a risk factor , as P . longipes readily feeds on cows [9] and manure provides breeding habitat . In the present study , some of the most important environmental determinants for CL occurrence were location of households near gorges and on rocky hillsides and presence of caves nearby . This is consistent with previous reports by Ashford [9] and Lemma et al . [3] , who pointed out that gorges and escarpments , rock cliffs and mountainous areas constitute favourable environments for the reservoir host ( hyrax ) . These features result in steep slopes , identified as a risk factor for CL by Seid et al . [6] . The presence of livestock and their manure further creates favourable breeding grounds for the sandfly vectors in peri-domestic environments . Overall , this study establishes that important foci of CL exist in Tigray , northern Ethiopia , with children and young adults being the most affected . The data further highlight that the disease is predominantly of zoonotic nature , and mainly transmitted in peri-domestic habitats where hyraxes prevail in the vicinity . Apart from their role as reservoirs of CL , the status of hyraxes as agricultural pests needs to be determined . This offers perspectives for environmental transmission control complementing minimal efforts in passive and active case detection , treatment , reporting , and data analysis . Reservoir control , i . e . small-scale eradication of hyraxes in the proximity of dwellings , would be effective , especially combined with fogging of their habitats to reduce sandfly densities . Vector control alone is unlikely to be effective . In addition , regular education to children and adults on the transmission and prevention of CL is recommended . All control efforts should be evaluated periodically , with their impact on incidence , building on essential active case detection and monitoring . Finally , although the study was carried out in 2009 , we believe that our results still pertain to the present situation of CL in Ethiopia as there has been no progress in advancing treatment or prevention activities . Following our study , an awareness creating international consultative meeting was held by WHO and Federal Ministry of Health in 2011 and the first National Neglected Tropical Disease ( NTD ) Master Plan was launched In June 2013 , to achieve WHO NTD elimination and control targets by 2020 . As of September 2015 , although the Federal Ministry of Health has managed to mobilize support to implement mass drug administration in 84% - 100% of the endemic districts for other NTDs ( trachoma , onchocerciasis , lymphatic filariasis , soil-transmitted helminths and schistosomiasis ) , there has been no progress in advancing treatment or prevention activities in CL , owing to the absence significant domestic or international donors to support CL intervention activities . A follow-up study in 2013 ( not part of this investigation ) and further studies in progress indicate an even greater magnitude both in terms of spread and in level of prevalence of CL in the region [23] . Unfortunately , today CL remains neglected even among the NTDs . | Cutaneous leishmaniasis ( CL ) is a skin infection , transmitted by sandflies . It is most common in Ethiopia , but so far only a few hundred people have received treatment . Five rural villages in Tigray Region , in the north of Ethiopia , were visited to assess the status and determinants of CL . In a door-to-door survey 9 , 622 residents of 1 , 721 households were examined and interviewed . A total of 222 had active lesions , an average prevalence of 2 . 3% CL . Children ( up to 9 years old ) and teenagers ( age 10–19 ) were more affected than other groups . Most active lesions were found in the face and on arms . Almost 11% of the households had one or more cases of CL and this was associated with proximity to habitats of hyrax , intermediate hosts of the disease . A total of 2009 people ( 20 . 9% ) showed scars from earlier infections . The findings show how widespread the disease is in the north of Ethiopia and provide some first insights into the environmental factors that influence transmission . | [
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"grou... | 2019 | Prevalence and environmental determinants of cutaneous leishmaniasis in rural communities in Tigray, northern Ethiopia |
Microbial minimal generation times range from a few minutes to several weeks . They are evolutionarily determined by variables such as environment stability , nutrient availability , and community diversity . Selection for fast growth adaptively imprints genomes , resulting in gene amplification , adapted chromosomal organization , and biased codon usage . We found that these growth-related traits in 214 species of bacteria and archaea are highly correlated , suggesting they all result from growth optimization . While modeling their association with maximal growth rates in view of synthetic biology applications , we observed that codon usage biases are better correlates of growth rates than any other trait , including rRNA copy number . Systematic deviations to our model reveal two distinct evolutionary processes . First , genome organization shows more evolutionary inertia than growth rates . This results in over-representation of growth-related traits in fast degrading genomes . Second , selection for these traits depends on optimal growth temperature: for similar generation times purifying selection is stronger in psychrophiles , intermediate in mesophiles , and lower in thermophiles . Using this information , we created a predictor of maximal growth rate adapted to small genome fragments . We applied it to three metagenomic environmental samples to show that a transiently rich environment , as the human gut , selects for fast-growers , that a toxic environment , as the acid mine biofilm , selects for low growth rates , whereas a diverse environment , like the soil , shows all ranges of growth rates . We also demonstrate that microbial colonizers of babies gut grow faster than stabilized human adults gut communities . In conclusion , we show that one can predict maximal growth rates from sequence data alone , and we propose that such information can be used to facilitate the manipulation of generation times . Our predictor allows inferring growth rates in the vast majority of uncultivable prokaryotes and paves the way to the understanding of community dynamics from metagenomic data .
Maximal growth rates are central to microbial life-history strategies [1]–[9] . Among host-associated bacteria , competition often results in increased virulence through selection for higher growth rates as these have an outstanding role in the trade-off between rapid horizontal dissemination and slow clearance from the host [10] , [11] . Highly infectious bacteria are associated with high maximal growth rates , e . g . enterobacteria , whereas bacteria producing chronic infections , e . g . mycobacteria , typically grow slowly under optimal conditions . The rapidity of spread of some bacteria poses a problem of urgency in antibiotic treatment , rendered more difficult by arising multiple resistances [12] . But slow growing bacteria sometimes also pose a therapeutic problem , as many antibiotics are ineffective in very slow growing cells [13] . Among free-living bacteria there is also a trade-off between fast growth in copiotrophs and scavenging potential in slow-growing oligotrophs [4] , [14] , [15] . Copiotrophic bacteria tend to have low affinity transporters and abundant gene expression machinery allowing fast growth in periods of feast , while enduring starvation in periods of famine where much of the protein synthesizing machinery is degraded [16] . Slow growing oligotrophs have high affinity transporters allowing them to thrive even under very small nutrient concentrations , but these become saturated or even toxic at high nutrient concentrations leading to their selective exclusion by fast growers in rich environments [17] . Because growth rates are outcomes and constraints of microbial life-history strategies , it is important to understand the mechanisms allowing fast growth and how they are imprinted by natural selection in genomes . Inversely , it would be extremely useful to predict maximal growth rates from sequence alone . This would allow establishing generation time predictions for the vast numbers of unknown or uncultivated bacteria for which we lack such information . Classical studies in E . coli physiology have uncovered the physiological changes concomitant with fast growth ( reviewed in [18] ) . When E . coli's generation time decreases from 100 to 24 min , cellular RNA polymerases ( RNAP ) are multiplied by 15 and ribosomes by 10 . A large fraction of the additional transcription capacity is used to produce stable RNA ( rRNA and tRNA ) . While the rate of synthesis also increases , it does so at much more moderate rates , e . g . elongation is faster by 40% for RNAP and 75% for ribosomes , which then attain maximal translation capacity . Thus , high growth rates result more from the increase in the production of the gene expression machinery than from its increasing productivity . At high growth rates , about 74% of all E . coli transcription concerns the production of stable RNA . To allow for such high levels of expression stable RNA genes tend to be in multiple copies in fast growing bacteria [19] . This multiplicity of rRNA operons constitutes a metabolic burden at lower growth rates [20] . In fast growing E . coli B/r , a replication round starts every 20 minutes , corresponding to the cell's minimal doubling time . Yet , replication of the chromosome takes ∼45 minutes [21] . This is possible because multiple rounds of replication can occur concurrently . The start of a new replication round before the previous one has finished doubles the number of regions around the replication origin in the cell . In cells with three simultaneous rounds of replication , genes the near the origin are thus 8 times more abundant in the cell than the genes near the terminus of replication . In the absence of negative feedback regulatory control , replication associated gene-dosage effects result in higher gene expression levels near the origin of replication [22]–[24] . Since genes coding for the translation and transcription machineries are under particularly strong demand at times of fast growth , there is a strong selection for their positioning near the origin of replication in fast growing , but much less so in slow growing , bacteria [25] . Even if tRNA concentration in the cell increases with growth rates , the tRNA/ribosome ratio decreases by 50% when comparing slow and fast growing E . coli [26] . The tRNA pool becomes limiting at very high growth rates . Thus , its quick turnover at ribosomes is under strong selection . This can be optimized if codons of highly expressed genes under fast growth recruit the most abundant tRNA in the cell [27] . Such codon usage bias , i . e . differential preference of some synonymous codons over others , is therefore as strong as the gene is highly expressed [28] , [29] . It is also stronger for fast growing bacteria because of the above-mentioned decrease of tRNA/ribosome at higher growth rates and because in these conditions the few percent most highly expressed genes account for a larger fraction of all gene expression . Codon usage bias is thus thought to result from selection for accurate and fast translation by maximizing the recruitment of the most abundant tRNAs into ribosomes [30] . The highly significant role of translation and its machinery in the cell budget of fast growing bacteria makes codon usage bias a good predictor of gene expression levels under exponential growth [31] , [32] . There have been studies on the association between maximal growth rates and rRNA operon [1] , [19] , [33] , [34] and tRNA [35] , [36] multiplicity , replication-associated gene dosage [25] , [37] and codon usage biases [35] , [38] . All these factors are thought to imprint genomes in accordance with the microbe's maximal growth rates . Previous studies focused on only one of the traits in one or few genomes and sometimes using coarsely binned growth data . To understand the relative role and importance of each factor and be able to manipulate growth rates more integrative studies are required . Unfortunately , the paucity of physiological data for the vast majority of microbes precludes the use of mechanistic models that can only be parameterized in E . coli [39] . Hence , we decided to use an empirical approach to answer the following questions: What is the association of each growth-related trait with maximal growth rates ? How inter-correlated are they ? What is their predictive power ? Can we use the growth-related genomic traits to test ecological hypothesis with metagenomic data ?
Following a previous work [35] , we extracted from primary literature 214 minimal generation times ( d ) of species of bacteria and archaea ( Table S1 ) . We used this data to assess how genomic traits correlate with minimal generation times . We started by analyzing its correlation to genome size . Historically , microbial genomes have been viewed as short and compact due to selection for rapid replication and fast growth . In agreement with previous work [40] , [41] , we found no evidence for a positive correlation between minimal generation time and genome size or genome density ( Spearman correlations ρ = −0 . 10 and −0 . 08 , p-value = 0 . 13 and 0 . 24 ) . The reasoning that smaller genomes allow for quicker replication is belied by the observation that replication can be initiated before the previous rounds have finished . There is thus no necessity for a direct correlation between genome size and minimal generation time , as observed . As expected , we found an increase in copy number of rRNA ( Figure 1 ) and tRNA genes ( Figure S1 ) with decreasing minimal generation times ( ρ = −0 . 59 and ρ = −0 . 51 , all p-value<0 . 0001 ) . The multiplicity of the subset of nearly ubiquitous tRNAs ( ubi-tRNA , listed in Table S2 ) , which in most species match the most favored codons [35] , is more correlated with d than the other tRNA genes ( ubi-tRNAs and non-ubi-tRNAs respectively , ρ = −0 . 54 and ρ = 0 . 13 , p-value<0 . 0001 and p-value = 0 . 06 , Figure S1 ) . While many enterobacteria contain two copies of the highly expressed elongation factor Tu [42] , we found no systematic trend for duplication of highly expressed protein coding genes in fast growers . Since each mRNA is translated ∼100 times [18] , multiple copies of ribosomal protein coding genes would only be required to match the expression of rRNAs if the latter was present in excess of 100 copies . However , in our dataset , and in the rRNA Operon Copy Number Database [43] , the maximal number of rRNA operon copies is 15 for Photobacterium profundum . As described above , gene dosage of highly expressed genes can be increased transiently when these genes are located near the origin of replication in fast growing cells . Indeed , a positive correlation was found between minimum generation time and the relative distance to the origin of replication of rRNA genes ( ρ = 0 . 36 , Figure 1 ) , RNA polymerase genes ( ρ = 0 . 42 ) , ribosomal proteins coding genes ( ρ = 0 . 42 ) , tRNA ( ρ = 0 . 35 ) and ubi-tRNA ( ρ = 0 . 41 ) genes ( Figure S2 ) ( all p-values<0 . 0001 ) . Hence , our data supports previous work suggesting that high growth rates are correlated with high transient or stable gene dosage in highly expressed genes associated with translation and transcription [25] . The importance of gene multiplicity , based on gene deletion studies , has been attributed to selection for quick start of exponential growth , not for its maintenance [1] , [2] , [19] , [44] . These two effects are tangled in genome organization because selection for fast growth is usually associated with selection for quick start of exponential growth in copiotrophic bacteria enduring feast and famine regimes [1] , [16] , [45] . Once replication has started , the replication-associated gene dosage effect ensures that rRNAs are in much higher copy number in the cell than expected given their gene multiplicity . This makes the 7 copies of rRNA genes in E . coli to effectively increase in the cell by a factor of 5 under maximal growth [18] . Thus , gene multiplicity and replication-associated gene dosage can be seen as complementary , with the former being essential for the start of exponential growth and affecting stable RNA genes , and the latter ensuring high cellular concentration of translation and transcription-associated highly expressed genes under stable growth , thus affecting both RNA and protein coding genes . Finally , two previously proposed indices of codon usage bias in highly expressed genes ΔENC′ [35] and S [46] correlate negatively with d ( respectively , ρ = −0 . 64 and ρ = −0 . 54 , p-value<0 . 0001 , Figure 1 ) . For the calculation of these indices we used the ribosomal proteins as the set of highly expressed genes under exponential growth ( see Materials and Methods ) , as this is frequently done [29] , [32] , [35] . The ubiquity and high conservation of ribosomal proteins facilitate the identification of this set of genes in the subsequent metagenomic analyses . We tested that the results remained qualitatively similar when using other highly expressed genes under exponential growth , such as elongation factors or RNA polymerase genes ( data not shown ) . Although ΔENC′ corrects for the influence of the G+C content of the genome on codon usage bias , we verified that G+C content is not correlated with minimal generation time ( ρ = 0 . 06 , p-value = 0 . 39 ) nor with ΔENC′ ( ρ = 0 . 09 , p-value = 0 . 24 ) . Incidentally , genomic G+C content correlates with genome size ( ρ = 0 . 61 , p-value<0 . 0001 ) [47] , [48] . The correlation between codon usage bias and minimum generation time is attributable to the selective pressure acting on highly expressed genes for the use of translationally optimal codons in these genomes where few genes correspond to the vast majority of gene expression . While experimental work has shown the advantages of optimizing codon usage bias for expression of heterologous proteins [49] , our results suggest that optimization of highly expressed genes should lead to higher growth rates . Phylogenetic dependencies between species may introduce a potentially important confounding factor in our analysis . If doubling times have important phylogenetic inertia then closely related genomes are bound to have similar growth rates and similarly important growth-related traits because their last common ancestor is too recent for these genomes to have diverged significantly . Hence , similarity in growth-related traits would not represent independent adaptive processes [50] . To test the effect of phylogenetic dependences we made an independent contrast analysis using a 16S-based phylogenetic tree ( see Materials and Methods ) . All but one variable remained highly significantly correlated with minimal generation times after control for phylogenetic dependencies ( Table 1 ) . We have no explanation for the only exception , corresponding to the distance to the origin of replication of ubi-tRNA genes . We then analyzed how the difference in minimal generation times between two genomes increased with evolutionary distance ( Figure 2A ) . This shows that when genomes are distant more than 0 . 2 substitutions/nt in our alignment there is no correlation between the two variables . Less than 8% of all pairs of genomes are distant by less than this threshold distance . This shows that evolutionary inertia on minimal growth rates is indeed low , often limited to the genera . We then performed the same analysis for all other variables ( Figure 2B ) . This shows that even at low evolutionary distances , the minimal generation time has the lowest evolutionary inertia . It is thus tempting to speculate that changes in minimal growth rates tend to pre-date changes in growth-related traits , and not the other way around . In summary , low minimal generation times are associated with the optimization of the translation machinery through: codon usage bias , an increased number of rRNA and tRNA gene copies by gene amplification , and the transient replication associated gene dosage of highly expressed genes under exponential growth . This information could be useful to reprogram growth rates in prokaryotes by synthetic biology approaches because modification of these traits should modify minimal generation times . Indeed , lower growth rates result from deletion of rRNA operons and from inversions decreasing gene dosage effects [19] , [51] . Similarly , lower codon usage bias leads to lower growth rates in viruses [52] . Naturally , not all traits are equally easy to manipulate . While insertions of extra rRNA operons , e . g . using plasmids , are relatively straightforward , extensive changes in codon usage bias are only viable if the whole sequence is synthesized in vitro . This is now possible for viruses and even small bacterial genomes [53] , [54] . Having delimited a range of 10 variables that correlate significantly with maximal growth rates ( column Individual R2 in Table 1 ) , we estimated their predictive power using stepwise forward regressions . This allows to iteratively introduce in the model the most contributing variables while minimizing the number of variables in the model by excluding the ones without significant explanatory power [55] . For this analysis , we only used the 188 species for which we could retrieve an origin of replication ( out of 214 ) . To normalize the data we used a box-cox transformation Φλ ( d ) , which in this case approximates to the commonly used log-transformation ( Figure S3 ) . We focused on the increase in explained variance given by the inclusion of each variable ( column Cumulative R2 in Table 1 ) . The highest contributing variables are ΔENC′ , S and the relative distance of the rRNA genes to the origin of replication ( R2 contribution column in Table 1 ) . Prokaryotic genes often cluster in operons . We therefore tested if there were changes in the results if we had used operons instead of genes . We did this in the most significant positional variable , rDNA , and found no differences in the correlation with doubling time ( ρ = 0 . 37 for genes and ρ = 0 . 36 for operons , both p-values<0 . 0001 ) . Although rRNA operon multiplicity has a high individual explanatory power , it doesn't add new information into the model when codon usage bias , which has higher explanatory power , is already included . Hence , adaptation to fast growth is very strongly correlated in terms of gene multiplicity and codon usage bias , possibly because both are essentially associated with the optimization of translation . Genome organization around the origin of replication is less correlated with codon usage bias , possibly because it reflects the impact of replication rates on transcription: faster DNA polymerases lead to lower gene dosage effects for a similar generation time . We then tested if the phylogenetic information could be a good predictor of minimal generation times . For this we made a stepwise regression where we added one more variable: the generation time of the most closely related genome . This variable adds little additional information ( R2 = 0 . 65 versus R2 = 0 . 61 without the variable ) . The first variable to enter in the stepwise regression is still ΔENC′ ( Table S3 ) . This result is consistent with the abovementioned low phylogenetic inertia of minimal generation times . Since phylogenetic information is not as amenable to mechanistic interpretation as the other variables we didn't include it in the final predictor . ΔENC′ and S both measure the intensity of selection for optimization of the translation of highly expressed genes . However , because they do it differently they both carry significant predictive power . These are the only genomic traits mentioned above that can be calculated from partial genome sequences , an undeniable advantage for the construction of a sequence-based predictor of minimum generation time . Evaluation of the codon usage bias does not require prior knowledge about the origin of replication , we can thus build our predictor on the full dataset ( N = 214 ) . Since together ΔENC′ and S have larger explanatory power than individually ( R2ΔENC′ = 0 . 44 , R2S = 0 . 33 , R2both = 0 . 49 , p-value<0 . 0001 ) , we combined them using principal component analysis . The first component , explaining 47% of the variance of minimum generation times , was called F ( ρ = −0 . 66 , p-value<0 . 0001 ) . A preliminary linear predictor of Φλ ( d ) in function of F was obtained by a least squares regression ( N = 214 , R2 = 0 . 47 ) : ( 1 ) The fit of the model showed that psychrophiles and thermophiles are systematically grouped above and below the prediction line , respectively ( Figure 3 ) . This suggests that part of the deviation from the model is biologically relevant and not a mere product of poor modeling or measurement errors . The residuals of the regression , representing the deviations to the model , are negatively correlated with optimal growth temperature ( ρ = −0 . 37 , p-value<0 . 0001 , Figure 4 ) . Naturally , we used minimal generation times obtained at optimal growth temperatures , therefore this result does not reflect slower growth at low temperatures of species with higher optimal growth temperature . This is also not an indication of higher growth rates at optimal growth temperatures in thermophiles . In fact , there is no significant difference of minimal generation times between thermophiles , mesophiles and psychrophiles ( p-value>0 . 05 for ANOVA and Wilcoxon tests ) . This is also not caused by the over-representation of archaea among thermophiles , since archaea and bacteria do not have significantly different deviations to the model ( p-value>0 . 1 , Wilcoxon test ) . The association between deviations to the model and optimal growth temperature indicates that psychrophiles ( thermophiles ) are slower ( faster ) growers than expected given their genome growth-associated traits . While the above residuals are from a regression where only codon usage bias was used , we found similar patterns while analyzing the residuals of regressions using only information on gene multiplicity or replication associated gene dosage effects ( data not shown ) . Hence , the association of deviations of the growth-related traits with optimal growth temperature is not exclusive to codon usage bias . Since there are no differences in minimal generation times between the different groups this suggests that for a given minimal generation time the psychrophiles require more structured genomes than mesophiles and these more than thermophiles . Fast-growth associated traits are probably under weak selection , therefore subject to mutation-selection-drift balance . These results could then be interpreted as a sign of negative temperature dependence of selection for growth-related traits . At high temperature there would be less selection for optimization of these traits than at lower temperatures . Accordingly , mutations disrupting these traits are under strong purifying selection in psychrophiles and relaxed selection in thermophiles . For example , Desulfotalea psychrophila , Methylobacillus flagellatus and Pyrococcus furiosus present very similar genomic trends of adaptation to a minimum generation time of ∼3 hours ( F = −0 . 23 , −0 . 20 and −0 . 25 respectively ) . However , their respective observed minimum generation times are of 27 , 2 and 0 . 6 hours for optimal growth temperatures of 7 , 36 and 100°C . The temperature dependence of the deviations to the model could also result from differences in effective population sizes in the different groups , if effective population size decreases with optimal growth temperature . We don't have data allowing the test of such a hypothesis . Instead , it is tempting to associate the effect of optimal growth temperature on the degree of genome optimization for fast growth with the dependence of enzymatic activity on temperature . At higher temperatures diffusion increases , water viscosity and activation energy decrease , facilitating rapid reactions [56] and could thus lead to lower requirements for growth-associated traits . As a case in point , psychrophiles have the highest multiplicity of rRNA and tRNA genes [57] , whereas even fast-growing thermophiles have few copies , with a maximum of 4 rRNA operons in Thermoanaerobacter tengcongensis and Carboxydothermus hydrogenoformans . High temperatures possibly increase the catalytic rates of translation-associated reactions , and also the tRNA diffusion into ribosomes , allowing quick start and maintenance of exponential growth with fewer genes . This leads to weaker selection for gene multiplicity , lower codon usage bias and lower replication associated gene dosage effects . Hence , while we find no evidence that psychrophiles grow slower than other prokaryotes , they do show a tendency to strongly select for growth-related traits . After derivation , our predictor of minimal generation times ( d in hours ) ( N = 214 , R2 = 0 . 58 ) including optimal growth temperature ( OGT in °C ) becomes: ( 2 ) See Materials and Methods for a detailed derivation of the equations . For mesophilic organisms this simplifies to ( N = 187 , R2 = 0 . 59 , Figure 5 ) : ( 3 ) We made a program to compute the expected minimal generation time given sequences of highly expressed genes and other genes in genomes . The program is publicly available at http://mobyle . pasteur . fr/cgi-bin/portal . py ? form=growthpred . The information on ribosomal proteins for all the genomes and metagenomes used in this work can be found at the same site . We next investigated the genomes of mesophiles deviating most from the model . The highest positive residuals , corresponding to genomes with lower than expected maximal growth rates , are from the genomes of Sodalis glossinidius morsitans and Mycobacterium leprae , with observed generation times ∼18 and 35 times slower than expected . These genomes have the highest number of pseudo-genes within our data set ( respectively , 49% and 50% of non-coding DNA ) , resulting from an ongoing process of genome reduction [58] , [59] . It has been estimated that pseudogenes in M . leprae have an average age of ∼9 million years and have accumulated ∼15% of changes since then [60] . Naturally , synonymous positions of functional genes should evolve at least as slowly . Thus , even if selection for biased codon usage decreases , the slow pace of accumulation of synonymous substitutions by drift takes a long time to lower the bias down in highly expressed genes to the new value expected by the mutation-selection equilibrium for the new maximal growth rate . The lower phylogenetic inertia of minimal generation times , compared with other traits , namely codon usage bias ( Figure 2B ) , justifies why the highest positive residues are among the genomes that have higher pseudogene density , in agreement with suggestions of a recent dramatic shift in lifestyle . Indeed , S . glossinidius and M . leprae grow much slower than the other closely related mycobacteria and free-living enterobacteria [61] , [62] . Genomes that have endured slow growth for a long period of time such as Buchnera aphidicola , Rickettsia typhi or Mycoplasma pneumoniae have now lost any putative ancient organization related to high growth rates . These genomes thus conform to the predictions of maximal growth rates based on genome analysis . We adapted the codon usage bias indices to make them computable from partial genomic and metagenomic data ( see Materials and Methods ) . Measuring these variables on small sets of genes inevitably introduces some uncertainty in the estimation of the parameters . To evaluate the associated error , we sampled sets of genes of varying cardinality from mesophilic genomes for which we know the doubling time . We did this for non-highly expressed genes comparing them with the whole dataset of highly expressed genes ( HEG ) , and inversely . The resulting ΔENC′ and S values were then subject to principal components analysis , of which the first component ( Fa ) was compared with the one obtained from the whole genome . The results for 3 organisms ( fast , slow and intermediate growers ) are represented in Figure 6 for the first set of experiments and in Figure S4 for the latter . As expected , the estimates of Fa are less accurate with decreasing sample size . We then varied the sample size of both populations of genes and found that the analysis still had a remarkable power even when considering only 5 highly expressed and 5 non-highly expressed genes . In this case , a discrete classification of the mesophilic species ( see Materials and Methods ) into very fast , fast , intermediate and slow resulted in 50% exact classifications ( expected 25% ) and 89% approximate or exact classifications ( prediction matching the same observed class or the adjacent ones , expected 59% ) . Even in this extremely small set of 10 genes , we only found 7% of slow growers predicted as fast or very fast or vice-versa ( expected 29% ) . Therefore , a robust coarse qualitative assessment of minimal generation times can be made even with as few as ten genes ( see Table S4 for a comparison of the results of discrete classification with the total set of genes , 40 genes , 20 genes and 10 genes ) . Such genome samples are easily accessible in metagenomic data from low diversity environments . For the other environments , the increase in coverage or the use of large-insert bacterial artificial chromosome libraries will also produce sufficiently large contigs [63] . Given the possibility of inferring minimum generation times from partial genomic data , we selected published metagenomic datasets to test 2 hypotheses: First , that environmental factors such as presence of toxic contaminants or resource availability influences the growth rate strategies of the resident microbial populations . Second , that fast growers are favored during the colonization phase of a new niche . Environmental samples can be interpreted either as collections of pseudo-genomes or as metagenomes . In the former approach sequences putatively assigned to one same species can be put together in pseudo-genomes . In this approach , a large fraction of the data is lost because most species genomes are not sequenced and because genomes are so diverse in terms of gene repertoires that some genes will not match a template genome of the same species [64] . This approach has the advantage that if species are well known we can make more informed interpretations and we can control for phylogenetic dependencies . In the latter approach the sequences are all put together and treated as a great single meta-genome . This has the advantage of using all the data , including all the elusive non-cultivated prokaryotes , and accounts for the different availability of different species by their different quantitative contributions to the sample . However , it does not allow controlling for phylogenetic dependencies . We have preferred to use the second approach because we wanted to account for uncultivated species and relative frequencies of each species . We then confirm the results using the first approach . Our results show that minimal generation times imprint genome organization and sequence of Bacteria and Archaea . They also show that such information allows the prediction of maximal growth rates from sequence alone . Naturally , organisms rarely grow at maximal growth rates because they rarely meet ideal growth conditions . As a result , our data does not allow predicting growth rates in specific environments . Yet , information on the maximal growth rates coupled with biochemical modeling can eventually lead to prediction of growth rates in particular media [78] . The optimization of growth related traits allows the quick start of exponential growth upon favorable environmental changes and allows faster growth also under sub-optimal conditions . In this sense , maximal growth rates are proxies of the capacity of the species to rapidly produce biomass , to quickly change growth rates and to take advantage of rich media . If such traits were not important , then random mutations erasing codon usage bias , genome organization and gene multiplicity would not be selected against and none of these traits would be found . Instead , we have shown that the majority of genomic traits that correlate significantly with maximal growth rates are also strongly correlated among themselves . This is a consequence of a shared selective pressure leading to the adaptation of the cellular machinery for high growth potential . We found that some unexpected variables have strong influence in genome optimization for growth , notably ongoing genome reduction and optimal growth temperature . The slow pace of substitutions is likely to explain the higher than expected codon usage bias in reducing genomes . The association of optimal growth temperature with deviations to expected growth rates might result from the enzymatic rate dependence on temperature , but that remains openly speculative until comparative data on the translation biochemistry of psychrophiles and thermophiles becomes available . Other variables may also influence maximal growth rates and genome optimization . We detail three types . First , while we made exhaustive searches in primary literature to collect minimal generation times , there is substantial incertitude on these . We may have missed some publications with lower generation times , but more importantly , current growth conditions are still far from optimal for many prokaryotes . This introduces a bias in the analysis , since slow-growers are much less studied than fast-growers . For example , a search in the PubMed database of the number of articles citing each of the species we analyzed showed that this number is highly correlated with the minimal generation times ( ρ = −0 . 45 , p-value<0 . 0001 ) . Hopefully , our data will be of use to pinpoint the species for which a revision of growth times will be most likely to be fruitful , since the largest residuals that are not explained by temperature or ongoing genome reduction might concern prokaryotes for which generation times are less accurate . Secondly , other measures of within genome bias in gene expression such as strength of ribosome binding sites , promoters , operon organization and genome structure might improve our predictor [79]–[82] . Yet , since our 10 growth-associated traits were all highly correlated , increasing the number of growth-associated traits in the analysis is unlikely to add much information . Thirdly , environmental variables that can affect growth can have more important , and for the moment unforeseeable roles , especially if they affect enzymatic activity . As the database grows larger we will be able to better pinpoint them by systematic analysis of deviations from the predicted values , as we found for optimal growth temperature . Along the discussion of our results we have systematically interpreted deviations from the model in a selective perspective . This is based on the extensive literature showing the physiological effects of selection for growth-related traits in exponentially growing cells . Yet , most growth related traits , e . g . codon usage bias , are expected to be under weak selection thus liable to genetic drift depending on the effective population size ( Ne ) . If Ne is independent of maximal growth rates this will only result in increased variance in our predictor . But if Ne is negatively correlated with minimal generation times then fastest growing organisms could have more growth-related traits than slow growers because of higher selection coefficient for these traits and/or because of more efficient selection , ie higher Ne . In this case , our predictor for growth rates would also be a predictor of effective population size . While selection for growth related traits is not under dispute , systematic deviations from the model could be strongly influenced by the effective population size . For example , if Ne were negatively correlated with optimal growth temperature it might explain the deviations we observe . Unfortunately , we have no way of systematically computing Ne for our sample of prokaryotes . Lynch [83] computed Ne . u , where u is the mutation rate , for 11 bacteria , all mesophiles . Assuming similar mutation rates the 3 slowest growing bacteria are in the 4 top positions . The highest Ne is for Prochorococcus marinus , by far the slowest-growing bacteria in the set and thought to be one of the most abundant species on earth [84] . Also of relevance , the recent application of a model for predicting trophic lifestyle to marine metagenomic data has shown that copiotrophs dominate free-living microbial populations [85] . These results suggest that among free-living bacteria slow-growing species tend to outnumber fast-growing ones . On the other hand , highly reduced symbiotic genomes , supposedly with very low Ne , tend to have high minimal generation times , with some exceptions among Mollicutes . These contradictory trends suggest no obvious correlation between growth rates and effective population size . There is also little evidence for a correlation between maximal growth rates and absolute population sizes . This is because population sizes result from average , not maximal , growth rates and are moderated by the rates of cell death . While most free-living slow-growers lack growth-related traits because they do not endure selection for fast growth , it is possible that bacteria with sudden contractions of population sizes will endure a degradation of growth-related traits leading to lower growth rates . The availability of population data for a growing number of genomes will hopefully allow understanding the evolution of growth-related traits in a population genetics framework . Besides contributing to the understanding of genome evolution at different maximal growth rates , our results open two important avenues of further research . First , we find that a composite index of codon usage bias allows for the accurate prediction of the type of growth expected from a given prokaryote . Surprisingly , this can be done even with very few genes paving the way for the understanding of a key physiological parameter from partial sequence data alone . This will be of use in the incoming surge of metagenomic data that contains sequences of species about which we ignore everything . Aggregation of metagenomic data into phylotypes will also allow analyzing the diversity of communities in terms of minimal generation times . Second , our data will also be useful in the delineation of experiments aiming at increasing or lowering growth rates in synthetic biology . The production of many metabolites of industrial interest is in conflict with the cell capacity to replicate . Our results point some ways in which prokaryotes can be engineered to grow slower , e . g . by decrease in codon usage in ribosomal proteins , deletion of rRNA operons or ubi-tRNAs . If it is of interest to maximize the production rate of biomass , then inverse interventions , conjugated with experimental evolution , may significantly accelerate the pace at which a lineage acquires the capacity to grow faster . It would be naïve to think that just changing rRNA expression will necessarily result in higher growth rates . In fact , slow growing bacteria often show higher than needed ribosome concentrations [86] , [87] . To change growth rates one probably needs to use design growth-related traits optimized genomes and then use experimental evolution to select for high growth rates in environments more favorable to growth than the natural one . Our work , by ranking the information provided by the different traits , provides guidelines for the relevance of each trait in such design . Third , the proposed predictor of minimum generation times applied to metagenomic datasets allows testing central theories in microbial ecology associated with growth rates . Metagenomic datasets give a unique access to whole microbial communities , regardless of their cultivability . As metagenomics develops , longer scaffolds will be available , with enough information to predict the growth rate of the corresponding species . Also , key genomes for specific niches are being sequenced , with example of the Human Microbiome Project sequencing 1000 microbial reference genomes . The emergence of all this new material will open new avenues of research in microbial ecology and evolution .
We retrieved 214 genome sequences , 1 per species , from GenBank Genomes ( ftp://ftp . ncbi . nih . gov/genomes/Bacteria/ ) . Genes were extracted from annotation data and pseudo-genes were ignored . Genes of the transcription/translation machinery ( RNA polymerase , rRNAs , ribosomal proteins ) were identified by the annotation fields , or , when not possible , by homology from the genomes of closely related species . A pair of genes were regarded as orthologous if they were reciprocal best hits with more than 40% sequence similarity and less than 20% difference in protein length , as measured by a end-gaps free sequence alignment . tRNAs were searched with tRNAscanSE [88] using the default parameters for bacteria or archaea . When the tRNA anticodon matched a previously published list of nearly ubiquitous tRNAs [35] it was included in the list of ubi-tRNAs . Optimal growth temperatures ( OGT ) were retrieved for 204 of the 214 organisms from the DSMZ database ( http://www . dsmz . de/microorganisms/ ) . Psychrophiles and thermophiles were defined as organisms whose OGT is under 15°C and over 60°C , respectively . We extracted from primary literature the minimal generation times ( d ) for the 214 species of bacteria and archaea ( Table S1 ) . The contigs from the 3 metagenomic datasets used in Figure 7 were retrieved from GenBank ( http://www . ncbi . nlm . nih . gov/books/bv . fcgi ? rid=metagenomics ) , including the acid mine drainage biofilm ( AADL01000001–AADL01002534 ) , the Waseca County Farm Soil ( AAFX01000001–AAFX01139340 ) , and the human distal gut microbiome ( AAQK01000001–AAQK01010488 , AAQL01000001–AAQL01012020 ) . The contigs from the 13 healthy humans gut microbiomes of the Human Metagenome Consortium Japan ( HMGJ; http://www . metagenome . jp/ ) were also retrieved from GenBank under the following accession numbers: subject F1-S ( BAAU01000001–BAAU01028900 ) , subject F1-T ( BAAV01000001–BAAV01036326 ) , subject F1-U ( BAAW01000001–BAAW01016539 ) , subject F2-V ( BAAX01000001–BAAX01036455 ) , subject F2-W ( BAAY01000001–BAAY01030198 ) , subject F2-X ( BAAZ01000001–BAAZ01031237 ) , subject F2-Y ( BABA01000001–BABA01035177 ) , subject In-A ( BABB01000001–BABB01020226 ) , subject In-B ( BABC01000001–BABC01009958 ) , subject In-D ( BABD01000001–BABD01037296 ) , subject In-E ( BABE01000001–BABE01020532 ) , subject In-M ( BABF01000001–BABF01016164 ) and subject In-R ( BABG01000001–BABG01034797 ) . Predicted origins of replication were retrieved from DoriC database ( http://tubic . tju . edu . cn/doric/ ) [89] . Archaea often have multiple and difficult to assess origins of replication [90] . Therefore , archaea were excluded from the calculation of distances to the origin of replication and subsequent correlations to growth rate . Relative distance to the origin of replication is calculated as the smallest circular distance of the gene to the origin of replication divided by half of the chromosome size . Hence , 0 corresponds to the origin of replication , 0 . 5 to half the replicon and 1 to the position opposite to the origin , typically the terminus . We used two different measures to assess the difference in codon usage biases between the average and the highly expressed genes: the ΔENC′ and the S indices . ΔENC′ is an empirical estimator of the strength of selection acting on codon usage bias in highly expressed genes [35] . For each genome , the ENC′ value [91] was calculated separately for the concatenation of all the coding sequences ( ENC′all ) and for the concatenation of the ribosomal protein genes ( ENC′rib ) , using the average coding nucleotide frequency . The ΔENC′ was then calculated as: ( 4 ) S is also an estimator of the strength of selection acting on codon usage bias , but based on the mutation-selection balance between pairs of codons , where one is fitter . Following Sharp , we compute S using the frequency of codons for four amino acids: Phe ( C1 = UUC , C2 = UUU ) , Ile ( C1 = AUC , C2 = AUU ) , Tyr ( C1 = UAC , C2 = UAU ) , Asn ( C1 = AAC , C2 = AAU ) . Codons C1 and C2 are recognized by the same tRNA . By Watson-Crick rules , the codon-anticodon interaction between C1 and the anticodon is better . Hence , C1 should be favored in genes having translation-associated codon usage bias . For each of the 4 amino acids mentioned above , we calculated the frequency of the optimal codon P = C1/ ( C1+C2 ) in all proteins ( Pall ) and in ribosomal proteins ( Prib ) . The S component for each amino acid is then given by: ( 5 ) S is the weighted mean of the Si values [46] . As alternatives to ΔENC′ and S , we also tested the use of the genome ENC′ and of ribosomal proteins ENC′ . The former was a very bad predictor of growth rates ( R2 = 0 . 12 ) , the latter was as good predictor as ΔENC′ ( respectively R2 = 0 . 53 and R2 = 0 . 54 , for the mesophiles ) , but correlated with the genome G+C content , suggesting that while the genome ENC′ has little informative power it calibrates for compositional biases when it's included in the computation of ΔENC′ . Both ΔENC′ and S calculations were adapted to use gene-level information ( ΔENC′a and Sa ) instead of the genomic-level information ( concatenation of the genes as previously done ) . When analyzing metagenomes , concatenating all of the sequences would erroneously increase the mean effective number of codons ( ENC′ ) of the dataset , because each organism might have a different codon usage bias ( i . e . a different set of preferred codons ) . This is not the case for the calculation of S [46] , which only takes into account the codon usage for 4 amino acids , for which the optimal codon is the same in all species . The problem of analyzing a mixture a sequences from different species can be circumvented if ENC′ is calculated gene by gene . Thus , we calculated for each gene separately , ENC′ and P ( P = C1/C1+C2 , for the 4 amino acids indistinctly ) ( C1 and C2 codons are listed above in the ‘Codon usage bias’ section ) . Then , we calculate the average ENC′ and P for the set of genes coding for ribosomal proteins and for the all the genes separately ( and , and ) . Afterwards , we compute ΔENC′a and Sa using: ( 6 ) ( 7 ) AWK and R scripts and C source of the programs to compute ENC′ and P calculations for each gene are available from the authors . For the set of all genes , open reading frames ( ORFs ) with a minimum size of 450bp were retrieved using EMBOSS function getorf . For the set of highly expressed genes , ribosomal proteins were retrieved by similarity with a database of ribosomal proteins of all sequenced genomes available to date ( e-value<10−5 ) . The error bars of average growth rates of environmental metagenomes correspond to the standard deviation of the predictions generated with 1000 bootstraps on the metagenome dataset of all genes and highly expressed genes independently and simultaneously . In order to compare the average predicted minimal doubling time of different metagenomes , we computed the difference between the predictions of pairs of environments , for each bootstrap iteration . The significance ( p-value ) of the comparison of averages of different metagenomes was calculated as the proportion of the differences that didn't match the expectation . For example , for the acid mine ( AM ) and the farm soil ( FS ) , we calculated for each iteration dAM-dFS . The acid mine's average doubling time is larger than the farm soil . The significance of this difference has a p-value p‰ if one finds p out of 1000 iterations where dAM-dFS<0 ( e . g . 10 iterations<0 give a p-value = 0 . 01 ) . If no such iteration is found we mark p<0 . 001 . The observed minimum generation times ( d ) of the mesophilic species were discretized into four classes: very fast ( d<1h , N = 46 ) , fast ( 1h<d<2h , N = 26 ) , intermediate ( 2h<d<5h , N = 41 ) and slow ( d≥5h , N = 74 ) . The predicted continuous values for the 187 species were obtained with the mesophilic predictor , using 5 highly expressed genes and 5 other genes ( both randomly chosen in the complete sets , 1000 random experiments ) . These were discretized in the same way and compared to the observed ones . The accuracy of the classification was evaluated from the proportion of exact , approximate and wrong classifications ( % ) , respectively defined as the proportion of 1 ) predictions matching the same observed class , 2 ) predictions matching the same observed class or the adjacent ones ( e . g . predicted ‘fast’ when actually ‘very fast’ ) and 3 ) slow growers predicted as fast or very fast and inversely . The Box-Cox power transformation aims at ensuring that the usual assumptions for linear models hold [55] . We used it to linearize the relation between minimum generation time ( d ) and the other variables . For example , in the association between d and F , a Box-Cox transformation was applied to d: ( 8 ) In order to retrieve the most relevant information of ΔENC′ and S combined , a PCA was performed and the first principal component , which was highly correlated to growth rate , was named F . ( 9 ) By linear regression , the following relation between the transformation of minimum generation time ( eq . 8 ) and the first principal component ( F ) of codon usage bias indices ΔENC′ and S ( eq . 9 ) :Replacing F ( eq . 9 ) , we obtain:Reversing the transformation of minimum generation time ( eq . 8 ) , we obtain our predictor ( eq . 3 ) : We build a phylogenetic tree using the 16S rDNA subunit for each species . We made a multiple alignment of the 16S sequences with MUSCLE [92] , followed by manual correction with SEAVIEW [93] . The tree was computed by maximum likelihood with PHYML [94] using the model HKY+Γ ( 4 ) +I . Pairwise phylogenetic distances were computed from the distance matrix . Phylogenetic contrast analysis was done with the ape package in R using generalized estimation equations ( GEE ) [95] . Pairwise differences of minimum doubling time Δd were calculated for the 214 prokaryotes . The difference of the box-cox transforms of doubling times for the pair of species were normalized by the maximum observed difference in the 22791 pairs . ( 10 ) The relative pairwise differences in codon usage bias indices ΔENC′ , S , F and G+C content were calculated the same way , for the 188 prokaryotes with known origins of replication . We mapped each protein of a given metagenome dataset in a given template genome . Template genomes were taken among 601 completely sequenced genomes . For each species we chose one single strain to avoid statistical bias . By default we used the first published strain . Mapping was done as follows: 1 ) for each protein of the metagenome dataset we find highly similar homologues within every proteome using quickhit , a companion of swelfe [96] , that allows to quickly find highly similar protein sequences . 2 ) The hits were then aligned using exact end-gap free Needleman-Wunsch alignments . 3 ) A given protein was added to one , and only one , pseudo-genome if it matched the corresponding template genome , if this was the best among all matches and if the protein similarity was higher than 95% . | Microbial minimal generation times vary from a few minutes to several weeks . The reasons for this disparity have been thought to lie on different life-history strategies: fast-growing microbes grow extremely fast in rich media , but are less capable of dealing with stress and/or poor nutrient conditions . Prokaryotes have evolved a set of genomic traits to grow fast , including biased codon usage and transient or permanent gene multiplication for dosage effects . Here , we studied the relative role of these traits and show they can be used to predict minimal generation times from the genomic data of the vast majority of microbes that cannot be cultivated . We show that this inference can also be made with incomplete genomes and thus be applied to metagenomic data to test hypotheses about the biomass productivity of biotopes and the evolution of microbiota in the human gut after birth . Our results also allow a better understanding of the co-evolution between growth rates and genomic traits and how they can be manipulated in synthetic biology . Growth rates have been a key variable in microbial physiology studies in the last century , and we show how intimately they are linked with genome organization and prokaryotic ecology . | [
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"computatio... | 2010 | The Systemic Imprint of Growth and Its Uses in Ecological (Meta)Genomics |
Access to an accurate diagnostic test for Buruli ulcer ( BU ) is a research priority according to the World Health Organization . Nucleic acid amplification of insertion sequence IS2404 by polymerase chain reaction ( PCR ) is the most sensitive and specific method to detect Mycobacterium ulcerans ( M . ulcerans ) , the causative agent of BU . However , PCR is not always available in endemic communities in Africa due to its cost and technological sophistication . Isothermal DNA amplification systems such as the recombinase polymerase amplification ( RPA ) have emerged as a molecular diagnostic tool with similar accuracy to PCR but having the advantage of amplifying a template DNA at a constant lower temperature in a shorter time . The aim of this study was to develop RPA for the detection of M . ulcerans and evaluate its use in Buruli ulcer disease . A specific fragment of IS2404 of M . ulcerans was amplified within 15 minutes at a constant 42°C using RPA method . The detection limit was 45 copies of IS2404 molecular DNA standard per reaction . The assay was highly specific as all 7 strains of M . ulcerans tested were detected , and no cross reactivity was observed to other mycobacteria or clinically relevant bacteria species . The clinical performance of the M . ulcerans ( Mu-RPA ) assay was evaluated using DNA extracted from fine needle aspirates or swabs taken from 67 patients in whom BU was suspected and 12 patients with clinically confirmed non-BU lesions . All results were compared to a highly sensitive real-time PCR . The clinical specificity of the Mu-RPA assay was 100% ( 95% CI , 84–100 ) , whiles the sensitivity was 88% ( 95% CI , 77–95 ) . The Mu-RPA assay represents an alternative to PCR , especially in areas with limited infrastructure .
Buruli ulcer ( BU ) is a neglected tropical disease caused by M . ulcerans . The pathogenesis of BU is linked to the production of a polyketide toxin known as mycolactone which is cytotoxic and has immunomodulatory properties [1] . The disease affects mostly children and adults of all ages and presents as nodules , plaques , ulcers and oedema . There is a wide differential diagnosis ranging from lipomas , ganglion , onchocerciasis nodules , and fungal lesions for non-ulcerated lesions to tropical , diabetic or vascular ulcers in the case of ulcerated lesions so it is vital that accurate diagnosis should be available close to patients in rural West Africa [2 , 3] . Currently , there are no preventive strategies against BU as the mode of transmission remains unknown and there is no vaccine . However , antimicrobial therapy with a combination of rifampicin and streptomycin or clarithromycin has proven effective in healing all forms of the disease and reducing the recurrence rate to less than 2% [4–6] . As a result , early diagnosis of clinically suspected cases has become a critical step in the clinical management of BU in order to prevent misdiagnosis and administration of unnecessary antibiotics [7] . The gold standard diagnostic tool for BU is PCR for the repeat sequence IS2404 , which is specific to M . ulcerans [8] . Microscopy for acid fast bacilli and culture for M . ulcerans have low sensitivity and histopathology is rarely available in endemic areas . Although PCR has high sensitivity ( up to 95% ) it has to be performed in a reference laboratory , often far from the endemic area , due to the need for a sophisticated laboratory setup and skilled personnel which may not be available in endemic communities [9] . Difficulties with sample collection and transportation may lead to a slow turnaround resulting in delayed treatment and an increase in costs . A field friendly diagnostic tool would bring diagnosis closer to the patients , thereby reducing costs and bringing forward the start of treatment . Recently , isothermal amplification techniques such as loop-mediated amplification ( LAMP ) have been proposed as an alternative to PCR for diagnosis of BU [9 , 10] . Unlike PCR , isothermal amplification techniques do not require a thermocycler and yield readily readable results within a short turnaround time . Recombinase polymerase amplification ( RPA ) has emerged as a novel isothermal technique in molecular diagnosis of various infectious diseases [11] including tuberculosis [12 , 13] and paratuberculosis ( Johne’s disease ) [14] . Compared to PCR and other isothermal techniques RPA is more rapid ( less than 20 min ) and simpler to run as it requires a lower temperature ( 37–42°C ) [11] . This technique opens the door to extending molecular diagnosis in fieldwork and at the point of care . In this study , we developed a real-time isothermal RPA assay for the detection of M . ulcerans as an alternative to PCR . The efficiency of the assay as a diagnostic tool was determined by testing its sensitivity and specificity with clinical samples from suspected patients .
To generate a molecular standard that will be used as positive control and a calibration template with known copy numbers , a 451 bp fragment of IS2404 sequence covering the nucleotides 96540 to 96990 ( Genbank accession no . CP000325 . 1 ) was synthesised by GeneArt Gene Synthesis ( Invitrogen , Regensburg , Germany ) . A dilution range of 100 to 106 copies/μl of the standard was prepared . Genomic DNA was derived from mycobacterial strains obtained from Belgian Co-Ordinated Collections of Micro-organisms , Institute of Tropical Medicine ( BCCM/ITM Mycobacteria Collection , Antwerp , Belgium ) . DNA was extracted from pure colonies following culture on Lowenstein-Jensen slant Medium ( BD , Franklin Lakes , NJ , USA ) for 4–6 weeks . Colonies were suspended in 700 μl of Cell Lysis solution ( CLS ) ( Qiagen , Hilden , Germany ) and stored in a fridge until ready for extraction . Similarly , swabs and fine-needle aspirates obtained from ulcerative and non-ulcerative lesions respectively were collected directly into 700 μl CLS . M . ulcerans DNA was extracted using the Gentra Puregene DNA isolation kit ( Qiagen , Hilden , Germany ) following manufacturer’s instructions with minor modifications as previously described [15] . The QIAamp DNA Mini Kit ( Qiagen ) was used for isolation of DNA from 5 bacterial species frequently colonizing human skin following manufacturer’s instruction . The amount of DNA was measured with a DeNovix DS-11 Spectrophotometer ( DeNovix Inc . , Wilmington , USA ) . Real-time PCR was run on a Rotor-Gene Q ( Qiagen , Hilden , Germany ) using the Hot FIREPol Probe qPCR Mix Plus to amplify a 59 bp long fragment ( Nucleotide 96627 to 96685 of the GenBank accession number CP000325 . 1 ) ( see Table 1 ) . The PCR reaction volume was 20 μl containing 1 μl each of 10 μM IS2404 TF and IS2404 TR , 1 μl of 5 μM IS2404 TP2 , 4 μl of 5 U/μl qPCR Mix Plus , 2 μl of 10x Exo IPC Mix , 0 . 4 μl Exo IPC DNA and 8 . 6 μl Molecular grade H2O as well as 2 μl of the DNA template . The PCR cycling conditions were adopted from a published assay [16] as follows: Initial denaturation for 15 minutes at 95°C , then 40 cycles of 95°C for 15 seconds and 60°C for 60 seconds with fluorescence activation . Each batch of samples run included negative extraction control , no template control ( NTC ) and positive control . All the negative extraction controls and NTC did not show an amplification curve . This indicated that there were no contamination during preparation of the PCR master mixes or during the DNA extraction process . All samples that did not show an amplification curve above the set threshold were considered negative . Similarly , the internal positive control ( IPC ) and the positive controls showed exponential amplification curves . All samples with an exponential amplification curve with the cycle threshold ( CT ) less than 40 , was considered positive . The Mu-RPA assay was designed to target the insertion sequence IS2404 which has been shown to have high sensitivity and specificity for diagnosing BU . Primers were designed using the recommendations given in the TwistDx instruction manual [17] and Primer-BLAST available at http://www . ebi . ac . uk/ena/data/sequence/search combining Primer3 and BLAST global alignment . Oligonucleotide primers and probes were synthesized by TIB MOLBIOL ( Berlin , Germany ) . Preliminary screening of 3 forward primers and 3 reverse primer combinations were tested with the Twist Amp Basic “Improved Formulation” kits according to the manufacturer’s instructions ( TwistDx Ltd . , UK ) in a final reaction volume of 50 μl . Briefly , 29 . 5 μl Rehydration buffer , 8 . 2 μl H2O , 2 . 4 μl of 5 μM of both forward and reverse primer and 5 μl of the DNA template were added to a freeze dried reaction pellet . Water was used for the negative control . The RPA reactions were incubated at 42 °C for 15 minutes following the addition of 2 . 5 μl 280 mM MgAc . The template used was 1 ng/μL of Mu strain ( ITM 063846 ) . One primer pair producing a 217 bp fragment of gene IS2404 sequence covering nucleotides 96641–96857 ( Genbank accession CP000325 . 1 ) was selected ( Table 1 ) after analysis of amplicons by agarose gel electrophoresis ( AGE ) . In the case of real-time RPA detection , TwistAmp Exo “Improved Formulation” kit ( TwistDX Ltd , Cambridge , UK ) was used according to the protocol described dx . doi . org/10 . 17504/protocols . io . vvve666 . Fluorescence detection at 570 nm for FAM channel was measured and a threshold set by increasing the fluorescence above the 3 standard deviations over the background detected in the first minute of incubation . We programmed the T8- fluorometer using the T8-ISO Desktop application ( Axxin Pty Ltd , Victoria , Australia ) to detect the lowest dilutions that met criteria for distinguishing positive samples from negative controls based on serial dilutions of the molecular standard . All tests were run at 42 °C for 900 seconds ( 15 minutes ) with mixing after 4 minutes . To be considered positive , a sample required either an amplitude or gradient of at least 900 mV over a 40 second sliding window during the amplification phase of the test ( 300–900 seconds ) . To determine the analytical sensitivity , 5 μl and 2 μl of a dilution range 106–100 copies/μl of quantitative IS2404 DNA fragment standard was tested six times in triplicates with Mu-RPA and the real-time PCR , respectively . The threshold time ( TT ) and the cycle threshold ( CT ) values were plotted against the number of molecules detected . Non-regression analysis and probit analysis was done by GraphPad Prism ( GraphPad software , San Diego , USA ) . The limit of detection in 95% of the dilutions was extrapolated from the sigmoid curve . To assess the specificity of the Mu-RPA assay for the detection of M . ulcerans , 1 ng/μL DNA from closely related mycobacterial species and bacterial species contaminating the human skin were tested . RPA was evaluated with DNA extracts isolated from samples previously obtained from patients referred to BU treatment centres in Ghana . Samples were collected as part of routine diagnostic procedure . Swabs were obtained from ulcers and fine needle aspirates from non-ulcerative lesions using standard guidelines [18] . In total , a panel of 79 DNA extracts from 69 clinically suspected BU patients and 12 clinically confirmed non BU ulcers . All samples were tested with both the real-time PCR and the Mu-RPA assay to determine the clinical sensitivity and specificity of the assay using real-time PCR as the reference test . Both tests were conducted independently by two different investigators and were blind to the results of the other test . Microsoft Excel 2016 was used for data management . GraphPad Prism v . 6 ( GraphPad software , San Diego , USA ) was used to calculate a semi-log regression of the dataset of repeated amplification runs of RPA and real-time PCR by plotting the mean threshold time ( TT ) and cycle threshold ( CT ) respectively against molecules detected of the standard DNA dilutions ( 106−100 copies/μl ) . A probit regression analysis was performed to determine the limit of detection ( LOD ) in 95% of dilutions for both assays using GraphPad Prism . Descriptive statistics were used to obtain general descriptive information such as median and interquartile ranges from the data . Contingency tables and receiver operating characteristics ( ROC ) curve analysis were employed to calculate the sensitivity , specificity and the predictive values in evaluating the assay . Ethical approval for this study was obtained from the Committee on Human Research , Publication and Ethics ( CHRPE/AP/122/17 ) School of Medical Sciences , Kwame Nkrumah University of Science and Technology . A total of 79 samples were included in this study under ethical consideration . All samples were handled anonymously . Mycobacterium ulcerans Agy99 , Complete genome–Genbank accession no . CP000325 . 1 .
Primers and probes were designed to target the insertion sequence , IS2404 , a region that has been shown to have high sensitivity for diagnosing BU using PCR . The region chosen for the RPA primer design was overlapping with already published primer binding sites of M . ulcerans PCR [19] . Designed primers and probes were screened using BLASTN and the NCBI nucleotide database to ensure that target sequences for the designs were exclusive for M . ulcerans strains . The RPA assay was developed in single tube reactions to screen primers and test different reaction conditions . Among 3 forward primers , 3 reverse primers and one probe ( P ) only F1+R2 and probe ( Table 1 ) were able to amplify down to 10 DNA molecules per reaction . The target region was subjected to a BLAST search ( blast . ncbi . nlm . nih . gov ) to check for cross-reactivity with non-Mu sequences and no non-Mu sequences were identified . The detection limit of the Mu-RPA assay was determined by six independent runs of serial dilutions ( 106–100 copies/μl ) with the molecular standard ( Fig 1 ) . Rapid detection was observed; 5 minutes for the detection of 106 copies , 7 minutes for 103 copies and 12 minutes for 10 copies . The data were used in semi-regression and probit regression analyses . The assay was reproducible showing good correlation between copy number and time to detection ( Fig 2 ) but with less efficiency compared to PCR ( R2 0 . 92 versus 0 . 99 respectively ) . The limit of detection in 95% of dilutions was 45 copies ( Fig 3 ) . To determine whether there was any cross-reactivity of the Mu-RPA assay for other microorganisms other than M . ulcerans , DNA extracts from a panel of 7 non-M . ulcerans mycobacterial species , 5 bacterial species that commonly colonise the skin and 7 M . ulcerans strains were tested . No amplification signals were obtained from any of the bacterial DNAs other than those of the Mu strains ( Table 2 ) . The assay performance as a diagnostic tool was assessed using a panel of 67 DNA samples collected from suspected cases of BU during routine care by an expert and 12 non-BU lesions clinically confirmed to have other diseases ( ie . 3 diabetic foot ulcers , 4 traumatic injuries , 3 cellulitis and 2 surgical site infections ) . The samples were obtained from patients presenting with varied lesions: 12 ( 15% ) nodules , 9 ( 11% ) plaques , 1 ( 2% ) edema and 57 ( 72% ) ulcers . It was made up of 24 ( 30% ) fine needle aspirates ( FNA ) and 55 ( 70% ) swabs . The characteristics of cases and the type of samples collected are summarized in Table 3 . All samples were tested by both RPA and real-time PCR . Fifty-eight of these samples were confirmed by PCR as BU . Of the 58 confirmed cases , 51 were correctly identified by the RPA assay with 7 false negative results giving a sensitivity of 88% ( 95% CI , 77–95 ) . The 21 PCR negative samples were all negative by RPA , specificity of 100% ( 95% CI , 84–100 ) and a 100% ( 95% CI , 93–100 ) positive predictive value ( PPV ) with a Youden’s index of 88% ( 95% CI , 61–95 ) . When the analysis was stratified by type of sample , the sensitivity and specificity of the RPA for swabs in comparison to PCR were 92% ( 95% CI 78–98 ) and 100% ( 95% CI , 82–100 ) respectively with a 100% ( 95% CI , 89–100 ) PPV . Similarly , the sensitivity and specificity of FNA samples were 82% and 100% ( 95% CI , 81–100 ) respectively ( See Table 4 ) . The specificity was 100% for all forms and severity of lesions analyzed . To further assess the reliability of the assay , we plotted the receiver operating characteristic ( ROC ) curve ( Fig 4 ) . The area under the curve ( AUC ) was 88 ( 95% CI , 70–96 ) .
A real-time RPA assay was developed for the rapid and accurate detection of M . ulcerans DNA with high sensitivity , specificity and reproducibility comparable to real-time PCR . It was significantly faster than available real-time PCR methods for detecting M . ulcerans with a run time of 15 minutes , compared to almost 2 hours for real-time PCR . Potentially the Mu-RPA can be used in a low resource setting closer to the patients when combined with a fast DNA extraction method . | Current diagnostic methods to detect M . ulcerans suffer from delayed time-to-results in most endemic countries by the prolonged period of time for the shipment and storage of samples to a distant , centralized laboratory . The M . ulcerans recombinase polymerase amplification assay ( Mu-RPA ) is a new , rapid diagnostic test developed for the detection of M . ulcerans infection , known commonly as Buruli ulcer , a chronic , debilitating , necrotizing disease of the skin and soft tissues . This assay is suitable for use on a portable detection device , with the potential to be used for quick diagnosis at the point of need , providing timely results to health workers at Buruli ulcer treatment clinics . | [
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"bacteri... | 2019 | Rapid detection of Mycobacterium ulcerans with isothermal recombinase polymerase amplification assay |
In sub-Saharan Africa the recommended strategy to control schistosomiasis is preventive chemotherapy . Emphasis is placed on school-aged children , but in high endemicity areas , preschool-aged children are also at risk , and hence might need treatment with praziquantel . Since a pediatric formulation ( e . g . , syrup ) is not available outside of Egypt , crushed praziquantel tablets are used , but the efficacy and safety of this treatment regimen is insufficiently studied . We assessed the efficacy and safety of crushed praziquantel tablets among preschool-aged children ( <6 years ) in the Azaguié district , south Côte d'Ivoire , where Schistosoma mansoni and S . haematobium coexist . Using a cross-sectional design , children provided two stool and two urine samples before and 3 weeks after treatment . Crushed praziquantel tablets , mixed with water , were administered at a dose of 40 mg/kg . Adverse events were assessed and graded 4 and 24 hours posttreatment by interviewing mothers/guardians . Overall , 160 preschool-aged children had at least one stool and one urine sample examined with duplicate Kato-Katz thick smears and a point-of-care circulating cathodic antigen ( POC-CCA ) cassette for S . mansoni , and urine filtration for S . haematobium diagnosis before and 3 weeks after praziquantel administration . According to the Kato-Katz and urine filtration results , we found high efficacy against S . mansoni ( cure rate ( CR ) , 88 . 6%; egg reduction rate ( ERR ) , 96 . 7% ) and S . haematobium ( CR , 88 . 9%; ERR , 98 . 0% ) . POC-CCA revealed considerably lower efficacy against S . mansoni ( CR , 53 . 8% ) . Treatment was generally well tolerated , but moderately severe adverse events ( i . e . , body and face inflammation ) , were observed in four Schistosoma egg-negative children . Crushed praziquantel administered to preschool-aged children at a dose of 40 mg/kg is efficacious against S . mansoni and S . haematobium in a co-endemic setting of Côte d'Ivoire . Further research is required with highly sensitive diagnostic tools and safety must be investigated in more depth . Controlled-Trials . com ISRCTN53172722
Schistosomiasis is still a major public health problem in many parts of the developing world , especially in sub-Saharan Africa [1]–[5] . Indeed , more than 200 million people are infected , with about half of them suffering from morbid sequelae , including hematuria , dysuria , nutritional deficiencies , anemia , and delayed physical and cognitive development [1] , [5]–[9] . The anthelmintic drug praziquantel is the cornerstone for morbidity control with millions of people treated every year [10]–[14] . Morbidity control is emphasized since the mid-1980s , and this strategy has been reinforced in 2001 by the World Health Assembly ( WHA ) resolution 54 . 19 , which urged member states to regularly de-worm at least 75% and up to 100% of school-aged children at risk of schistosomiasis and soil-transmitted helminthiasis [10] , [15] . Preschool-aged children ( individuals below the age of 5–6 years ) are currently excluded from preventive chemotherapy control campaigns . The main reasons for this exclusion are that preschool-aged children are believed to be at low risk of schistosomiasis [16] , and that there is insufficient data documenting the safety and efficacy of praziquantel in this age group [17] , [18] . However , recent studies carried out in different parts of East and West Africa showed that in high endemicity areas a considerable proportion of preschool-aged children are already infected with Schistosoma , and hence treatment might need to be extended to younger age groups in such high-risk areas [19]–[29] . With regard to morbidity control of schistosomiasis using praziquantel , it is important to note that an appropriate pediatric formulation for treating preschool-aged children is currently not available outside of Egypt ( i . e . , praziquantel syrup , Epiquantel , manufactured by the Egyptian International Pharmaceutical Industries Co . A . R . E . , Cairo , Egypt ) . Hence , a common approach in high endemicity areas is to use praziquantel tablets ( 600 mg ) , crush them between two spoons , mix with water or fruit juice , and then administer orally to preschool-aged children at a dose of 40 mg/kg [17] , [23] , [24] . Recent studies using Epiquantel in preschool-aged children revealed similar efficacy than crushed praziquantel tablets [28] , [30] . The study reported here was designed to assess the efficacy and safety of crushed praziquantel tablets in preschool-aged children ( <6 years ) in an area where Schistosoma mansoni and S . haematobium coexist [31] , [32] . Our findings , along with other investigations pertaining to the epidemiology of schistosomiasis in preschool-aged children , may be useful to further optimize the control of schistosomiasis in a currently neglected age group .
Ethical approval was granted by the Ministry of Health and Public Hygiene of Côte d'Ivoire ( reference no . 4248/2010/MSHP/CNER ) . The trial is registered with ClinicalTrial . gov ( identifier: ISRCTN 53172722 ) and the protocol is available as Supporting Information ( Protocol S1 ) . Local authorities of the Azaguié district were informed about the purpose , procedures , and potential risks and benefits of the study . In the absence of recent census data , an exhaustive door-to-door survey was carried out to identify all preschool-aged children ( <6 years ) in the two selected villages . Parents or guardians of eligible children were informed about the objectives of the study and asked to provide written informed consent on behalf of their children . Only those preschool-aged children who had written informed consent from their parents/guardians were included . Participation was voluntary and parents/guardians could withdraw their child from the study anytime without further obligation . At the end of the study , anthelmintic drugs ( i . e . , praziquantel against schistosomiasis and albendazole against soil-transmitted helminthiasis ) were offered free of charge to all community members . The study was conducted between June and November 2011 in two villages located in the district of Azaguié , southern Côte d'Ivoire: Azaguié Makouguié ( geographical co-ordinates 05°37′33″N latitude , 04°09′04″W longitude ) and Azaguié M'Bromé ( 05°39′42″N , 04°08′38″W ) . Recent studies have shown that S . mansoni and S . haematobium are co-endemic in Azaguié [31]–[33] . Villagers are mainly engaged in subsistence farming . Both villages lack access to clean water and improved sanitation . We pursued an intervention study ( i . e . , praziquantel administration ) , including children of both sexes below the age of 6 years . Children's infection with S . mansoni and S . haematobium was assessed during a baseline cross-sectional survey and again 3 weeks posttreatment using standardized , quality-controlled methods . The STROBE checklist is available as Supporting Information ( Checklist S1 ) . With the overarching goal to further the understanding of the epidemiology , diagnosis , and control of schistosomiasis in preschool-aged children , we aimed at a sample of about 200 individuals , as recommended by statistical textbooks in health studies [34] . Our population census carried out in June 2011 revealed 367 preschool-age children . We assumed that the prevalence of S . mansoni in preschool-aged children would be around 20% ( i . e . , a quarter of the 80% S . mansoni infection prevalence observed in school-aged children in Azaguié in 2010 [31] ) and that about 70% of the preschool-aged children would comply ( lower rate than among school-aged children due to the difficulty to obtain biological samples in this younger age group ) . Aiming for a precision of 5% , we finally decided to include all 367 registered preschool-aged children . We adhered to the following inclusion criteria: ( i ) preschool-aged children ( <6 years ) ; ( ii ) written informed consent by parents/guardians; ( iii ) submission of at least one sufficiently large stool sample for duplicate Kato-Katz thick smears , and one urine sample for a 10 ml filtrate and a single point-of-care circulating cathodic antigen ( POC-CCA ) cassette test; ( iv ) no abnormal medical condition , as judged by the study physician on the day of the treatment; ( v ) no recent anthelmintic treatment ( within the past 4 weeks ) according to a parental questionnaire; and ( vi ) no participation in any other clinical trial . The door-to-door census carried out in June 2011 to establish up-to-date census data generated lists of preschool-aged children , including their name , age , sex , and geographical coordinates of the household . Mothers/guardians of the preschool-aged children were provided with plastic containers labeled with unique identifiers ( IDs ) and they were asked to obtain a fresh stool and urine sample of their child . Stool and urine samples were collected at any time of the day due to the difficulty of collecting biological samples in this age group . Our aim was to obtain two stool and two urine samples over two consecutive days from each participating child . Stool and urine samples were transferred to a nearby laboratory in Azaguié town and worked up on the day of collection . For the diagnosis of S . mansoni and soil-transmitted helminths , duplicate Kato-Katz thick smears were prepared from each stool sample ( i . e . , quadruplicate Kato-Katz thick smears per child ) , using 41 . 7 mg templates [35] . The Kato-Katz thick smears were allowed to clear for at least 30 min before examination under a microscope by experienced laboratory technicians . The number of S . mansoni and soil-transmitted helminth eggs were counted and recorded for each species separately . For the diagnosis of S . haematobium , urine samples were subjected to a filtration method , as described elsewhere [36] . Briefly , a single filtration was performed with each urine sample ( i . e . , two urine filtrations per child over two consecutive days ) . Urine samples were vigorously shaken and 10 ml pressed through a small-meshed filter ( aperture: 30 µm ) and a drop of Lugol's solution was added on the filter paper , which was then placed onto a microscope slide . Slides were examined under a microscope and the number of S . haematobium eggs counted by experienced technicians . For quality control , 10% of the Kato-Katz thick smears and the urine filter slides were re-examined by a senior technician . In case of disagreement , the results were discussed with the concerned technician and discordant slides re-read until agreement was reached . An additional approach for the diagnosis of S . mansoni was employed , namely a POC-CCA cassette , that is based on a commercially available lateral flow immuno-chromatographic test [31] . The POC-CCA cassette ( batch 33112 ) was performed according to the manufacturer's instructions . In brief , a drop of urine was added to the well and once fully absorbed a drop of buffer was added . The tests were read within 20–25 min . In case the control band failed to develop , the test was considered invalid and the respective urine sample retested with a new POC-CCA . Valid tests were scored as negative or positive , the latter stratified into trace ( very light color band ) , 1+ , 2+ , and 3+ according to the visibility of the color reaction . The tests were scored independently by two investigators . In case of conflicting results , a third investigator was consulted , and the results discussed until agreement was reached [31] . Children were treated with crushed praziquantel tablets ( 600 mg; Biltricide , Bayer ) at a dose of 40 mg/kg [10] . Children were weighed using an electronic balance ( Evolis; Rumily , France ) . The appropriate number of praziquantel tablets ( e . g . , half a tablet for a child weighing 7–8 kg ) were crushed between two spoons , mixed with tap water in a clean soup spoon before oral administration . Treatment was given by the mothers/guardians of the children under close supervision of trained medical personnel . Children were closely monitored by medical staff for 4 hours . In case vomiting occurred within 1 hour after treatment , a second dose of praziquantel was administered . Treatment-related adverse events were assessed 4 and 24 hours posttreatment . Mothers/guardians were asked to report unusual behavior of their children since drug intake , and whether any of the following adverse events occurred: abdominal pain , allergic reaction , diarrhea , dizziness , fatigue , fever , headache , nausea , and vomiting . Adverse events were graded ( i . e . , light , moderate , severe , or life threatening ) , as described elsewhere [37] . Three weeks after praziquantel administration , stool and urine samples were collected again , using the same procedures . Treatment efficacy was determined by means of cure rate ( CR , percentage of children positive at the pretreatment cross-sectional survey who became egg-negative 3 weeks after treatment , as assessed by the Kato-Katz technique for S . mansoni and urine filtration for S . haematobium ) and egg reduction rate ( ERR , reduction in the group's geometric mean fecal egg count for S . mansoni or the group's geometric mean S . haematobium egg count in 10 ml of urine comparing the before and after treatment situation ) . Data were double entered into an Excel spreadsheet , transferred into EpiInfo version 3 . 2 ( Centers for Disease Control and Prevention; Atlanta , USA ) and cross-checked . Statistical analyses were done with Stata version 10 ( Stata Corp . ; College Station , USA ) . Preschool-aged children who had at least one stool sample examined with duplicate Kato-Katz thick smears , a single POC-CCA cassette for S . mansoni diagnosis , and one urine sample subjected to a filtration method for S . haematobium diagnosis before and after treatment were included in the final analysis ( per-protocol ) . Continuous data ( e . g . , schistosome egg counts ) are presented as geometric mean , whereas dichotomous data ( e . g . , presence or absence of an infection ) are presented as proportion . Infection intensities were stratified according to cut-offs proposed by the World Health Organization ( WHO ) [10] . There are three intensity classes for S . mansoni: ( i ) light ( i . e . , 1–99 eggs/gram of stool ( EPG ) ) ; ( ii ) moderate ( 100–399 EPG ) ; and ( iii ) heavy ( ≥400 EPG ) . S . haematobium infections were categorized as light ( 1–49 eggs/10 ml of urine ) and heavy ( ≥50 eggs/10 ml of urine ) .
Figure 1 shows the adherence of preschool-aged children to the study protocol . The village census revealed 367 children aged below 6 years , all of whom were invited to participate . Sixty-three children were absent during the baseline survey and 16 had no written informed consent by their parents/guardians . From the 288 children participating at the baseline cross-sectional survey , seven were excluded due to incomplete parasitological data ( e . g . , insufficiently large stool sample for duplicate Kato-Katz thick smears ) . Among the remaining 281 children , 234 were administered crushed praziquantel . Three weeks posttreatment , we were able to re-examine at least one sufficiently large stool and urine sample from 160 children . Our final study cohort consisted of 82 ( 51 . 3% ) boys and 78 girls with an average age of 3 . 2 years ( range: 5 months to 5 years ) . Boys were slightly younger than girls ( average , 3 . 0 years; 95% confidence interval ( CI ) , 2 . 7–3 . 3 years versus average , 3 . 3 years; 95% CI , 3 . 0–3 . 6 years ) . Table 1 shows the pretreatment S . mansoni and S . haematobium infections , stratified by children's sex and diagnostic approach . According to at least duplicate Kato-Katz thick smears , 35 children of our per-protocol population ( 21 . 9% ) were found S . mansoni-positive , with a geometric mean infection intensity of 1 . 2 EPG ( Table 2 ) . According to the POC-CCA results , there were 128 S . mansoni infections ( 80 . 0% ) when considering ‘trace’ results as positive , and 78 ( 48 . 7% ) considering ‘trace’ results as negative . With regard to S . haematobium , the urine filtration method revealed 18 infections ( 11 . 2% ) . The geometric mean infection intensity was 1 . 0 eggs/10 ml of urine . Eleven children ( 6 . 9% ) were co-infected with S . mansoni and S . haematobium . The Kato-Katz technique also allows detection of soil-transmitted helminth eggs . The observed prevalence of Trichuris trichiura , hookworm and Ascaris lumbricoides was 10 . 9% , 5 . 9% and 3 . 8% , respectively . Table 2 summarizes CR and ERR , stratified by diagnostic approach . According to the Kato-Katz technique , at the 3-week posttreatment follow-up , four children ( 2 . 5% ) were identified with S . mansoni eggs in their stool with a geometric mean infection intensity of 0 . 04 EPG . The CR and ERR was 88 . 6% and 96 . 7% , respectively . The POC-CCA results only allowed estimating CR . Including or excluding ‘traces’ as positive results , revealed 79 and 36 S . mansoni-infected children , respectively , at the 3-week posttreatment follow-up . The respective CRs were 38 . 3% and 53 . 8% . With regard to S . haematobium , two children ( 1 . 3% ) had a positive urine filtration at the 3-week posttreatment follow-up with a geometric mean infection intensity of 0 . 02 eggs/10 ml of urine . The CR and ERR were 88 . 9% and 98 . 0% , respectively . Figure 2 shows infection intensity categories of S . mansoni and S . haematobium at the baseline and posttreatment surveys . At baseline , among 35 S . mansoni-infected children , 23 , nine and three children had light , moderate and heavy infections , respectively . With regard to S . haematobium , 17 children and one child were lightly or heavily infected , respectively . At the 3-week posttreatment follow-up all children who were still Schistosoma-positive had light infections . Table 3 shows the incidence of adverse events 4 and 24 hours after praziquantel administration among the 234 treated children . Overall , 43 children reported to have adverse events . Among these children , 12 ( 27 . 9% ) were coinfected with S . mansoni and S . haematobium , 17 ( 39 . 5% ) had S . mansoni single infection , no child was infected with S . haematobium only , and 14 children ( 32 . 6% ) were not infected at all . We stratified adverse events into two independent groups . The first group designated children with adverse events reported within 4 hours posttreatment and the second group was made up by children with adverse events reported by their mothers/guardians 24 hours posttreatment . Most of the adverse events were observed within the first 4 hours after treatment ( n = 32 , 74 . 4% ) , including abdominal pain ( n = 7 ) , diarrhea ( n = 6 ) , nausea ( n = 5 ) , vomiting ( n = 4 ) , dizziness ( n = 3 ) , fever ( n = 2 ) , fatigue ( n = 2 ) , face and body inflammation ( n = 2 ) , and headache ( n = 1 ) . More than one adverse event was observed in three ( abdominal pain and diarrhea ) , two ( vomiting and nausea ) and one ( fever and dizziness ) children within 4 hours posttreatment . Twenty-four hours posttreatment , 11 ( 25 . 6% ) children reported adverse events , including diarrhea ( n = 3 ) , abdominal pain ( n = 2 ) , fatigue ( n = 2 ) , face and body inflammation ( n = 2 ) , dizziness ( n = 1 ) , and headache ( n = 1 ) . At this time point , none of the children reported multiple adverse events . Adverse events were considered of light severity , with the only exception of face and body inflammation that was graded as a moderately severe . Mothers/guardians with children experiencing body and face inflammation sought advice from the study physician who assured the mothers that this adverse event is transient and self-limiting . Indeed , within 24 hours , children's conditions resolved to normal . The four children complaining of body and face inflammation were all Schistosoma egg-negative according to Kato-Katz and urine filtration results , but one had a positive POC-CCA test results ( 1+ ) .
The anthelmintic drug praziquantel is the cornerstone for morbidity control due to schistosomiasis [2] , [10]–[14] . Emphasis is placed on school-aged children , whereas preschool-aged children ( individuals below the age of 5–6 years ) are usually excluded from preventive chemotherapy . However , in highly endemic areas , a considerable amount of preschool-aged children is already affected by schistosomiasis [19]–[29] . For that reason , there is ongoing discussion whether preventive chemotherapy with praziquantel should be extended to preschoolers [17] , [24] , [25] , [28]–[30] , [38] , [39] . However , absorption and metabolism of drugs are age-dependent [18] and it is not well understood whether the developmental changes in the physiology during biological maturation from newborns to adolescence influence the efficacy and toxicity of praziquantel . We assessed the efficacy and safety of crushed praziquantel among preschool-aged children in an area of south Côte d'Ivoire where S . mansoni and S . haematobium coexist [31] , [32] . Our study confirms that preschool-aged children are at risk of schistosomiasis . Indeed , we found that more than 20% of the children before their sixth birthday were infected with either S . mansoni or S . haematobium , or both species concurrently ( 7% ) . Using a commercially available POC-CCA cassette test , more than half of the children showed positive S . mansoni antigen reactions . Our study reveals that crushed praziquantel ( 40 mg/kg ) administered to preschool-aged children is highly efficacious with CRs around 90% and ERRs above 95% , when standard diagnostic methods ( Kato-Katz for S . mansoni and urine filtration for S . haematobium ) were used . There are two limitations of our study worth highlighting . First , we initially aimed to obtain two stool samples ( for quadruplicate Kato-Katz thick smears ) and two urine samples ( for duplicate urine filtration and duplicate POC-CCA cassette test ) from each participant before and after praziquantel administration . However , it proved difficult to obtain multiple stool and urine samples in this age group , and hence we finally included preschool-aged children who had at least duplicate Kato-Katz thick smears and a single urine filtration before and after treatment . In view of this sampling effort and diagnostic approach , it is clear that we have missed some infections , particularly those of light intensity . Second , a considerable proportion of preschool-aged children were lost to follow-up . However , comparing our per-protocol population ( n = 160 ) with children with incomplete parasitological data , who were absent at treatment , or lost to follow-up ( n = 121 ) , we found comparable prevalence estimates and infection intensities with S . mansoni and S . haematobium . In spite of the aforementioned limitations , our investigation provides new insight into the efficacy and safety of praziquantel among a neglected population group in a S . mansoni-S . haematobium co-endemic area . Our findings support previous studies conducted in other parts in Africa . For example , a recent study carried out in preschool-aged children in Uganda , using quadruplicate Kato-Katz thick smears ( two stool samples , each examined by duplicate Kato-Katz ) , reported a slightly lower efficacy against S . mansoni ( CR , 80 . 2%; ERR , 87 . 9% ) [17] . Mutapi and colleagues in a study done in Zimbabwe with children aged 1–5 years found high CR ( 92% ) and very high ERR ( 99% ) of crushed praziquantel against S . haematobium [40] . Of considerable concern are recent findings from Ugandan preschool-aged children , as the overall CR among 305 S . mansoni egg-patent individuals was only 56 . 4% , with particularly low CR observed in preschoolers with a history of previous praziquantel treatments ( CR 41 . 7% ) [29] . It should also be noted that the process of crushing praziquantel tablets is time consuming , and hence poses a challenge for large-scale control programs . It is encouraging to note that efforts are under way to develop a pediatric formulation of praziquantel , for example oro-dispersable tablets or minitablets that might be more convenient to administer to small children [30] . In our study , we not only used standard diagnostic tests ( i . e . , Kato-Katz and urine filtration ) but also a more recently developed and now commercially available POC-CCA cassette applied to urine for the diagnosis of S . mansoni ( Rapid Medical Diagnostics , Pretoria , South Africa ) . Considering POC-CCA results from the pre- and posttreatment surveys , including or excluding ‘trace’ results as positive , we found low CRs of 38 . 0% and 53 . 8% , respectively . These results are worrying and reasons explaining the differences in CR according to the diagnostic technique might be explained as follows . First , S . mansoni eggs might have been missed by the Kato-Katz technique , particularly at the posttreatment follow-up when the remaining positive children had very low infection intensities . Indeed , it is widely acknowledged that the Kato-Katz technique lacks sensitivity in areas characterized by low S . mansoni infection intensities , which is common after treatment [41] . Second , perhaps CCA might still be detectable in urine 3 weeks after treatment [42] , [43] , while the recommended time for S . mansoni assessment after treatment is 15–20 days [44] . Third , while children might have been cured from patent S . mansoni infection , praziquantel is largely refractory against young developing stages of the worms [12] , [13] , and hence antigens might still be present in the urines of young children . Fourth , the POC-CCA cassette might lack specificity after praziquantel administration . Further investigations are therefore needed to determine whether or not a POC-CCA cassette can be utilized for determining praziquantel efficacy , including the most appropriate time point posttreatment . Three weeks posttreatment might be too short for assessing praziquantel efficacy in preschool-aged children , but the longer one waits , the higher the risk for confounding factors ( e . g . , schistosomula fully developed into adult worms , and reinfection ) . In our study , we also thoroughly assessed the safety of crushed praziquantel given to preschoolers . We observed similar frequencies of adverse events in preschool-aged children as reported by other groups [26] , [40] , and as observed in school-aged children [45]–[47] . Recently , Namwanje and colleagues showed that praziquantel alone and in combination with mebendazole in the treatment of S . mansoni and soil-transmitted helminths in preschool-aged children showed similar safety profiles [48] . However , in the current study , inflammation of the body and the face was observed in four children ( 2 . 5% ) . Interestingly , these children were Schistosoma egg-negative at the baseline survey before drug administration and only one child showed a light positive POC-CCA result ( 1+ ) . Inflammation of body and face has been observed in previous studies [40] , raising concern with regard to the inclusion of preschool-aged children in preventive chemotherapy campaigns , which has been proposed by different authors [17] , [24] , [25] , [29] . While preschool-aged children with a confirmed Schistosoma infection must be treated [49] , we feel that further research is still required , including development of an appropriate pediatric formulation , dose-finding , detailed pharmacokinetic investigations , and in-depth safety studies , before preventive chemotherapy be extended from the school-aged population to preschoolers [18] . In conclusion , our study documents that preschool-aged children are at risk of schistosomiasis in the Azaguié area , south Côte d'Ivoire , with 7% of our per-protocol population patently infected with S . mansoni and S . haematobium concurrently . Crushed praziquantel is efficacious against both species in preschool-aged children . In view of unwanted adverse events in non-infected children following praziquantel administration , we suggest that only parasitologically confirmed preschool-aged children should be given praziquantel . New research is needed to accurately determine the frequency and severity of adverse events after praziquantel administration against schistosomiasis in the preschool-aged population . There is a need to develop a safe and user-friendly formulation of praziquantel so that infected children can be treated at an early stage of infection in order to prevent any harmful damage in later life . | Schistosomiasis is a parasitic worm infection that plagues more than 200 million people in the developing world , particularly in sub-Saharan Africa . The current strategy to control schistosomiasis is to regularly administer the deworming drug praziquantel to school-aged children . Younger children before reaching school-age are not included in these deworming campaigns , because they are considered at low risk of schistosomiasis , and because the amount of available data to evaluate the safety of praziquantel in young children is insufficient . We conducted a study in two villages in southern Côte d'Ivoire and examined the stool and urine of more than 250 children ( <6 years ) for schistosome eggs and antigens . Children were treated with crushed praziquantel tablets ( 40 mg/kg ) and the efficacy of this treatment was determined 3 weeks after treatment . The safety of the treatment was assessed by interviewing mothers of treated children for adverse events ( e . g . , abdominal pain , diarrhea , and headache ) . Complete data records were available for 160 children . Praziquantel cleared most of the infections . The treatment was generally well tolerated , but we observed four children who were not infected at the baseline survey who developed face and body inflammation that required close supervision by the study physician . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"biology"
] | 2012 | Efficacy and Safety of Praziquantel in Preschool-Aged Children in an Area Co-Endemic for Schistosoma mansoni and S. haematobium |
Although multiple studies have documented the expression of over 70 novel virus-encoded microRNAs ( miRNAs ) , the targets and functions of most of these regulatory RNA species are unknown . In this study a comparative bioinformatics approach was employed to identify potential human cytomegalovirus ( HCMV ) mRNA targets of the virus-encoded miRNA miR-UL112-1 . Bioinformatics analysis of the known HCMV mRNA 3′ untranslated regions ( UTRs ) revealed 14 potential viral transcripts that were predicted to contain functional target sites for miR-UL112-1 . The potential target sites were screened using luciferase reporters that contain the HCMV 3′UTRs in co-transfection assays with miR-UL112-1 . Three of the 14 HCMV miRNA targets were validated , including the major immediate early gene encoding IE72 ( UL123 , IE1 ) , UL112/113 , and UL120/121 . Further analysis of IE72 regulation by miR-UL112-1 with clones encoding the complete major immediate early region revealed that the IE72 3′UTR target site is necessary and sufficient to direct miR-UL112-1-specific inhibition of expression in transfected cells . In addition , miR-UL112-1 regulation is mediated through translational inhibition rather than RNA degradation . Premature expression of miR-UL112-1 during HCMV infection resulted in a significant decrease in genomic viral DNA levels , suggesting a functional role for miR-UL112-1 in regulating the expression of genes involved in viral replication . This study demonstrates the ability of a viral miRNA to regulate multiple viral genes .
microRNAs ( miRNAs ) are a species of regulatory RNA molecules that are involved in the control of a variety of cellular processes [1–3] . miRNAs are small single-stranded RNA species of approximately 20–24 bases in length and are initially expressed in the nucleus , where they form defined hairpin loop structures within longer primary transcripts [4] . The hairpin loop sequence containing the miRNA is recognized and cleaved by the RNAseIII Drosha complex [5] and transported to the cytoplasm by exportin 5 [6 , 7] . Additional processing by a second endonuclease , Dicer , produces a double-stranded RNA from which one strand is loaded into the RNA-induced silencing complex [8–11] . Targeting of transcripts by the miRNA RNA-induced silencing complex relies on complementarity between the miRNA and the target transcript . In cases of complete homology , the target transcript is cleaved , while partial homology can lead to RNA cleavage or translational inhibition [12–14] . The precise nucleotide requirements for functional binding of a miRNA to a target sequence are not fully understood . However , binding within the first 10 bases of a miRNA , especially within bases 2 to 7 of the miRNA known as the seed region , is considered particularly important [15–17] . miRNAs are ubiquitous among multicellular eukaryotic organisms and have been identified in species as diverse as plants and higher mammals [15] . The expression of miRNAs is also a phenomenon common to many DNA viruses [18] , and bioinformatic and direct cloning approaches have led to the identification of over 70 novel miRNAs expressed in DNA viruses [19] . The majority of these viral miRNAs have been identified in the herpesvirus family , although SV40 and adenoviruses also encode their own miRNAs [20–33] . Herpesviruses belong to a large family of enveloped , double-stranded DNA viruses disseminated throughout nature that are able to maintain a persistent or latent infection during the lifetime of the host [34] . The herpesviruses are divided into three groups ( alpha , beta , and gamma ) , and members of all three groups encode miRNAs , indicating that herpesviruses have utilized RNA interference ( RNAi ) throughout their evolution . miRNAs identified in α- and γ-herpesviruses are located within clusters in and around genomic regions associated with latent transcription . Three α-herpesviruses , herpes simplex virus-1 ( HSV-1 ) and Marek disease virus-1 and 2 ( MDV-1 and MDV-2 ) , have been shown to encode miRNAs close to and within the minor latency-associated transcript , a non-coding RNA detected during latent infections of all three viruses [20 , 21 , 24 , 27] . Multiple miRNAs have been identified within two genomic regions of the γ-herpesvirus Epstein–Barr virus and are expressed during latent infection of transformed B cell lines [23 , 29 , 30] . In murine γ-herpesvirus-68 ( MHV-68 ) , tRNA-like transcripts previously identified as latency markers were found to encode a number of miRNAs , whereas the majority of the miRNAs expressed by Kaposi sarcoma-associated herpesvirus ( KSHV ) are processed from a single transcript also associated with latent gene expression [29] . In contrast to α and γ herpesviruses , miRNAs encoded by the β-herpesvirus human cytomegalovirus ( HCMV ) are located throughout the genome and were identified and detected during acute infection [25 , 26 , 29] . Following infection of human fibroblast cells , HCMV miRNAs accumulate as the infection progresses and in the majority of cases are expressed with early kinetics . Of the nine miRNAs encoded by HCMV , four are encoded antisense to open reading frames ( ORFs ) , with the remaining encoded within intergenic regions , including UL36–1 , which is encoded within the intron of the anti-apoptosis gene UL36 [35] . Validated target transcripts and regulatory functions for the vast majority of viral miRNAs remain unknown . Two likely scenarios for viral miRNA function include targeting of cellular gene expression to induce a more favorable environment for the virus , or the regulation of viral genes to establish precise temporal or tissue-specific regulation of viral gene expression . To date , viral miRNAs do not demonstrate complete homology to any known cellular transcript , suggesting that targeting of cellular genes is unlikely to occur through direct cleavage . Two recent reports have suggested that cellular genes can be regulated through incomplete binding of viral miRNAs to target sequences within cellular 3′UTRs . Gupta et al . identified a miRNA encoded by HSV-1 , which the authors suggest protects infected cells against apoptosis by inhibiting the translation of the cellular genes , TGF-β1 and SMAD3 [27] . In addition , Samols et al . identified the cellular gene THBS-1 as a target for KSHV miRNAs [36] . Surprisingly , the authors found that all ten of the KSHV miRNAs targeted this gene , suggesting a remarkably high level of redundancy . Viral miRNAs have also been reported to regulate expression of viral genes . SV40 regulates the expression of its large T antigen via two miRNAs encoded directly antisense to the gene [33] . Expression of the miRNAs leads to cleavage of the large T antigen transcript and reduced levels of large T antigen during infection . There are , however , no reports of trans-regulation of viral gene expression by viral miRNAs through binding to target sequences with incomplete homology . The aim of the current study was to identify and characterize functional targets of the HCMV-encoded miRNA miR-UL112-1 . Using a bioinformatics approach in combination with luciferase assays , we identified three HCMV gene targets for miR-UL112-1 , including the mRNA encoding the major immediate early ( MIE ) trans-activating protein IE72 . IE72 , and the additional MIE transactivator , IE86 , are the most abundant transcripts expressed from the complex MIE region of the viral genome , are known to be critical for the induction of early and late gene expression , and are required for efficient viral replication [37] . These studies provide the first description to our knowledge of a viral miRNA that regulates multiple viral genes through binding to the 3′ untranslated region ( UTR ) of viral transcripts and inhibition of translation .
To determine whether miR-UL112-1 targets HCMV genes , we used a bioinformatics approach to predict potential miRNA binding sites within the 3′UTRs of HCMV transcripts . Currently , there is limited data available regarding the precise 3′ ends of HCMV transcripts . Therefore , we established a putative 3′UTR database by including sequences from the 3′ end of each annotated ORF to the first canonical AATAAA polyadenylation signal following that 3′ end . Using the online miRNA target identification algorithm RNAhybrid [38] , the 3′UTR database was searched for potential miR-UL112-1 target sites . Thirty-two unique target sites for miR-UL112-1 were identified in 37 different HCMV ORFs . The total number of sites increased to 63 when we included repeated sites within overlapping 3′UTRs of neighboring ORFs . To further refine this approach , we also predicted target sites for the closely related chimpanzee cytomegalovirus ( CCMV ) in a parallel manner . Since miRNAs encoded by HCMV are highly conserved in CCMV and in the case of miR-UL112-1 the sequences are identical between the two viruses , we predicted that miR-UL112-1 would target the same transcripts in both HCMV and CCMV . Therefore , miRNA target sites conserved within similar 3′UTR sequences have a higher probability of being functional . Following analysis of the CCMV genome , 34 3′UTRs were predicted to contain target sites for miR-UL112-1 with 14 3′UTRs containing target sites in both HCMV and CCMV ( Figure 1 ) . A list of the 14 identified genes , along with a brief description of their functions , if known , is shown in Table S1 [39] . To determine whether the predicted target sequences represent functional target sites for miR-UL112-1 , each of the sequences was cloned at the 3′ end of the luciferase reporter construct in pMIR ( Ambion ) . When the 3′UTR sequence was too large to clone in its entirety , the target sequence along with 500 bases of flanking sequence was used . If neighboring 3′UTR sequences contained overlapping target sites , a single representative sequence containing the targets was used . Nucleotide coordinates of the cloned regions are listed in the Materials and Methods section . To express miR-UL112-1 , the predicted coding sequence for miR-UL112-1 pre-miR was cloned into the vector pSIREN ( Clontech ) and expression of mature miR-UL112-1 was confirmed by northern analysis ( Figure S1 ) . 3′UTR constructs were co-transfected with either pSIREN miR-UL112-1 or the same vector expressing a randomized negative hairpin RNA supplied with the pSIREN plasmid . As shown in Figure 2 , three out of the 14 luciferase constructs containing potential HCMV 3′UTR miR-UL112-1 target sequences , IE72 , UL120/121 , and UL112/113 , directed specific inhibition of luciferase activity in the presence of miR-UL112-1 . These observations demonstrate that these three HCMV 3′ UTR sequences are functional miR-UL112-1 target sites ( Figure 2A ) . Schematic representations of predicted mir-UL112-1 binding to the Ul112/113 and UL120/121 and IE72 target sequences are shown in Figure 2B . The identification of functional target sites clustered within the 3′UTRs of the MIE region of the virus was particularly interesting . One site was identified within the 3′UTR of UL123 , which encodes IE72 , with two overlapping sites within the 3′UTR of ORFs UL120 and UL121 ( Figure 3 ) . Intriguingly , the site downstream of IE72 of CCMV maintained less homology to miR-UL112-1 than the equivalent site in HCMV , and would presumably form a less effective binding site . However , a second site was identified further upstream that was not found in the HCMV counterpart , suggesting that CCMV has evolved two less effective binding sites in comparison to the more homologous single site found in HCMV ( Figure S2A and S2B ) . Interestingly , both IE72 and UL112/113 encode gene products important for viral replication , and although the function of UL120/121 is unknown , it has been suggested that UL120/121 may encode exons within the MIE family of transcripts [40] . Since IE72 serves an important regulatory role during HCMV replication , we examined the regulation of this immediate early ( IE ) gene product by miR-UL112-1 in more detail . To confirm that miR-UL112-1-mediated inhibition of luciferase activity in the presence of the IE72 3′UTR was due to the predicted target site , a deletion mutation encompassing the 3′ half of the target ( Figure 4A ) was analyzed for luciferase activity . The deletion mutation completely abrogated the inhibitory effect of miR-UL112-1 ( Figure 4B ) . Expression of the HCMV miRNA miR-UL22A-1 , which is not predicted to target IE72 , caused only a minor reduction in luciferase activity , and miR-UL112-1 also did not target a construct containing the 3′UTR of IE86 ( unpublished data ) . These results indicate that miR-UL112-1 regulation of IE72 is specific and requires the predicted target sequence identified in the 3′UTR . Following confirmation of the specific regulation of a reporter construct containing the IE72 3′UTR , we examined the effect of miR-UL112-1 on IE72 protein expression in the context of the HCMV genomic clone pSVH-1 [41] that encodes the complete IE region , including the MIE promoter driving expression of IE72 and IE86 . In these experiments pSVH-1 was co-transfected with either pSIREN miR-UL112-1 or the control plasmid pSIREN UL22A-1 into 293 cells . IE86 expression provides an ideal control since expression of this IE protein is regulated by the same promoter as IE72 , but the IE86 3′ UTR does not contain the miR-UL112-1 target sequence . Total cellular protein was extracted at 24 , 48 , and 72 h post transfection and examined by western blot analysis using a polyclonal antibody that recognizes both IE72 and IE86 . As shown in Figure 4C , co-expression of miR-UL112-1 in the context of pSVH-1 significantly reduced the levels of IE72 , but not IE86 , expression in comparison to a vector expressing a miRNA not targeting this 3′UTR . These observations are consistent with the luciferase results , suggesting that the negative regulatory effects of miR-UL112-1 are specific to messenger RNAs containing the predicted miRNA target site . However , the level of IE72 reduction was much greater than in the luciferase reporter experiments , with a decrease in protein expression of approximately 14-fold as determined by densitometry ( Figure 4D ) . This difference may reflect inherent variations in the assays or more effective inhibition of the endogenous IE72 viral transcript compared to the artificial luciferase reporter transcript . In addition , the inhibitory effects of miR-UL112-1 were not observed using a pSVH-1 construct deleted for the target sequence ( Figure 4C and 4D ) . Additional smaller mutations within the target region also disrupted the inhibitory effect of miR-UL112-1 , including mutations within the seed sequence ( Figure S3 ) . Interestingly , point mutations that introduced G:U base pairing within the seed region did not disrupt the inhibitory effects of miR-UL112-1 , which is consistent with recent data [42] indicating that Watson and Crick base pairing within the seed region is not essential for target function ( Figure S3 ) . Recent studies have suggested that degradation of target transcripts may occur even if the target site contains only partial homology to the miRNA . Degradation is thought to occur through the accumulation of miRNA-targeted transcripts in proximity to cytoplasmic RNA processing regions called P bodies [43–45] . To determine whether the reduction in IE72 levels following expression of miR-UL112-1 was due to translational inhibition , or the result of mRNA degradation , IE72 and IE86 transcript levels were determined by reverse transcriptase ( RT ) -PCR following co-transfection of pSIREN UL112–1 with pSVH-1 or the pSVH-1 deletion mutant . As seen in Figure 4E , IE72 RNA levels were unaffected by miR-UL112-1 expression , indicating that the reduction in IE72 protein levels occurs primarily through a post-transcriptional mechanism . Finally , to determine whether the predicted IE72 target sequence was sufficient to direct inhibition of gene expression through miR-UL112-1 , the predicted 21-base target sequence was introduced into the 3′UTR of IE86 in the IE72 target deletion background of pSVH-1 . As shown in Figure 4F , expression of miR-UL112-1 led to a reduction in IE86 protein levels , although not to the same degree previously shown with IE72 . This suggests that complete function of the target sequence may require additional flanking sequences . Thus , we have demonstrated that in the context of either a luciferase reporter construct containing the IE72 3′UTR or with a genomic expression vector containing the complete MIE region , miR-UL112-1 specifically targets the expression of IE72 through a conserved target site within the IE1 3′UTR . The target site is necessary and sufficient to direct miR-UL112-1 specific inhibition of expression , and this effect occurs through translational inhibition rather than degradation of the transcript . Although the effect of miR-UL112-1 on the down regulation of IE72 expression from a genomic vector is clear , the importance of this inhibition during viral infection is unknown . We have previously shown that during the early stages of HCMV infection , when IE72 expression levels are high , miR-UL112-1 levels are low . miR-UL112-1 does not accumulate to significant levels until late in infection [26] . Furthermore , previous studies have demonstrated that disruption of IE72 expression decreases efficient viral replication at low multiplicities of infection [46 , 47] . Therefore , we predicted that expression of miR-UL112-1 prior to infection with HCMV would lead to a block in IE72 expression , potentially leading to disruption of efficient HCMV replication . To determine the effect of pre-expressing miR-UL112-1 on production of IE72 as well as on viral DNA replication during infection , we used a commercially produced synthetic miR-UL112-1: a short double-stranded RNA with the exact sequence of mature endogenous miR-UL112-1 . To first demonstrate that the synthetic miR-UL112-1 can inhibit IE72 expression as effectively as the pSIREN miR-UL112-1 construct , U373 cells were co-transfected with pSVH-1 and either synthetic miR-UL112-1 or a random sequence negative control pre-miR ( Ambion ) . As shown in Figure S4 , synthetic miR-UL112-1 RNA effectively inhibits IE72 expression . We next determined whether transfection of U373 with synthetic miR-UL112-1 RNA cells prior to infection with HCMV could effectively block IE72 expression . U373 cells were therefore transfected with either the synthetic miR-UL112-1 or the negative control RNA followed by infection with an AD169 strain containing an eIF1-α driven green fluorescent protein ( gfp ) cassette inserted into the UL21 . 5 gene . Cells were infected at a multiplicity of 0 . 5 plaque-forming units per cell . Total protein was harvested between 24 and 72 h post infection , and levels of viral immediate early proteins were determined through western blot analysis using a polyclonal antibody that recognizes IE72 , IE86 , and two other products of the MIE region . As seen in Figure 5A , miR-UL112-1 significantly inhibited the expression of IE72 . However , additional proteins encoded by the IE region were also decreased in expression . This result is in contrast with the specific regulation of IE72 observed following transfection with pSVH-1 and does not reflect the phenotype observed for IE72 knock-out viruses . This observation is not entirely surprising , as we have previously shown that two additional viral genes other than IE72 contain functional target sites for miR-UL112-1 . We also predict that other viral targets exist that were not identified through our bioinformatics screen . Expression of miR-UL112-1 is therefore likely to have pleiotropic effects on viral infection , resulting in a phenotype distinct from IE72 knock-out viruses . Targeting of IE72 by miR-UL112-1 therefore likely contributes to the phenotypic effects shown in this report , but is unlikely to be the only factor involved . An attenuation of viral replication caused by an inhibition of IE72 expression may also contribute to a global reduction in viral gene expression due to decreased levels of viral template . Consistent with this model , the levels of gfp fluorescence expressed by the virus were significantly reduced in cells transfected with synthetic miR-UL112-1 ( Figure 5B ) , suggesting attenuation in viral replication or infection . To confirm whether transfection of miR-UL112-1 results in a reduction in the efficiency of viral replication , viral DNA levels were analyzed by real-time PCR . Viral DNA levels were consistently lower in cells transfected with miR-UL112-1 than in control cells at later time points , indicating that the miR-UL112-1 has a significant negative effect on acute replication of the virus ( Figure 5C ) . Importantly , transfection of cells with miR-UL112-1 had no effect on DNA replication of HSV-1 , indicating that the reduction in HCMV DNA replication caused by miR-UL112-1 is not due to a general attenuation of DNA viral replication ( Figure 5D ) . Together , these results demonstrate that pre-expression of miR-UL112-1 has pleiotropic effects on HCMV and that this regulation can inhibit viral DNA replication .
The MIE region of HCMV is known to express a number of regulatory proteins crucial for the efficient replication of the virus and coordination of viral gene expression . Although driven by the same promoter , differential splicing leads to the expression of multiple transcripts and proteins . The most abundant of these proteins are the major trans-activators IE72 and IE86 , which are expressed from five exons within the MIE region . The first three exons are common to both transcripts , with differential splicing resulting in the fourth exon encoding IE72 , and the fifth exon encoding IE86 [49–51] . This differential splicing allows each transcript to encode individual 3′UTRs , which results in specific regulation of IE72 by miR-UL112-1 and may contribute to the independent expression kinetics of IE72 and IE86 . Both proteins are thought to be important in promoting early and late viral transcription , although only IE86 is essential for virus replication [41 , 46 , 47 , 52 , 53] . However , disruption of IE72 expression results in a significant attenuation of viral replication following low multiplicity infections [46] . Although the precise mechanism of action of IE72 is not fully understood , it is thought that the viral protein promotes transcription through interaction with cellular factors . Studies have shown that IE72 causes disruption of ND10 domains within the nucleus [54 , 55] and can directly interact with cellular basal transcription factors [56] . The functional relevance of IE72 regulation by miR-UL112-1 is currently unclear , but this mechanism could affect viral replication in one of several ways . Expression of IE72 is down regulated late in infection , while IE86 expression continues at a high level . miR-UL112-1 may therefore be required to achieve regulation of IE72 translation during acute replication and may be important to establish the correct kinetic progress of viral gene expression . In support of this , a recent publication by White et al . has demonstrated that over expression of IE72 during acute replication correlates with a drop in efficient viral replication in fibroblast cells [53] . Regulation of IE72 by miR-UL112-1 may also be important for the establishment and maintenance of latent and persistent infection in vivo [37] . Although studies on HCMV latency are limited by the lack of effective tissue culture or animal models , studies on the related murine cytomegalovirus ( MCMV ) support a role for gene expression from the MIE region during latent or persistent infections [57] . IE1 and IE3 of MCMV are thought to be the functional homologs of IE72 and IE86 , respectively . Long-term expression of MCMV IE1 is detected following establishment of a latent/persistent infection in the lungs of mice , and immune escape of the virus results in increased expression of IE3 , which may represent an initial step in reactivation . Since IE72 is required for efficient replication of the virus and promotes acute replication by trans-activating early and late viral genes , negative regulation by miR-UL112-1 could therefore potentially contribute to establishing and maintaining a gene expression pattern appropriate for latent or persistent infection . Furthermore , γ-herpesviruses express multiple miRNAs during latent infection , suggesting a functional role during this stage of viral infection [23 , 29] . Experiments to determine the function role of IE72 regulation by miR-UL112-1 are in progress . Given the relative complexity of gene expression in large DNA viruses such as herpesviruses , the potential to regulate multiple genes through a single miRNA is attractive . miRNAs require minimal genomic space and are not thought to induce innate or adaptive immune responses , a particularly attractive characteristic for viruses that maintain long-term infections of hosts . It is probable that additional herpesvirus miRNAs are dedicated to the regulation of viral transcripts , and it will be interesting to determine whether miRNAs encoded by other viruses target major trans-activating genes such as IE72 . During the submission of this manuscript , a recent study demonstrated that miR-UL112-1 also targets the expression of the cellular gene MICB , a receptor for activated natural killer cells . Interestingly , this would suggest that single viral miRNAs have evolved the capacity to target both cellular and viral gene expression [58] . Finally , the possibility that viral miRNAs may have evolved as a mechanism to inhibit viral gene expression and viral replication presents a particularly attractive avenue for potential antiviral therapy strategies . Unlike many antivirals that artificially block viral processes , delivery of endogenous viral miRNAs could exploit the virus's own mechanisms to subdue replication . Not only might this approach be effective , but it could also be less prone to the problems of viral escape and resistance as the virus has evolved to maintain these mechanisms .
The annotated genome sequences for AD169 were downloaded from NCBI . Additional 3′UTR sequences deleted from AD169 were generated from the sequence of the HCMV clinical strain TR genome . 3′UTRs in the database consist of sequence from the 3′ end of each annotated ORF to the first canonical AATAAA polyadenylation sequence following that 3′ end . The online target prediction algorithm RNAhybrid ( http://bibiserv . techfak . uni-bielefeld . de/rnahybrid/submission . html ) was used to identify potential target sites from the 3′UTR database [38] . The algorithm was run using default settings with the additional constraints of full Watson Crick base pairing through nucleotides 2 to 7 . The predicted miRNA target base pairing schematics were produced using the online RNA folding program mfold ( http://frontend . bioinfo . rpi . edu/applications/mfold/cgi-bin/rna-form1 . cgi ) . Normal human dermal fibroblast ( NHDF ) cells ( Clonetics ) and human U373 cells ( American Type Culture Collection ) were cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal calf serum and penicillin-streptomycin-L-glutamine . HCMV strain AD169gfp was constructed using the AD169 BAC genome [59] . In brief , the cellular EF1-α promoter drives the gfp cassette and is inserted in the sense orientation into the UL21 . 5 gene at nucleotide position 39389 . Luciferase reporter construct pMIR was acquired from Ambion and was modified by replacing the ampicillin cassette with the kanamycin cassette from pdsRED2-N1 vector ( region 1874 to 2806 ) into the NotI and ScaI site of pMIR . Renilla firefly control plasmid pRL was acquired from Promega . UL112/113 , IE72 , and IE86 3′UTR sequences were cDNA cloned from RNA harvested from HCMV-infected NHDF cells . Additional 3′UTR sequences were cloned directly from AD169 BAC DNA . Sequence coordinates as follows: UL10 19598–20548; UL29i 31018–31466; UL29ii 28993–29404; UL31 45826–46154; UL33 44399–46012; UL46 58472–58874; UL47i 68421–68734; UL47ii 64519–64903; UL112/113 162801–162914; UL114 160772–161167; UL120/121; IE72 170906–171005; IE86 169363–169275; US10 198659–199026 . Genomic sequences were cloned into SpeI and HindIII sites of the pMIR multiple cloning region . The miRNA expression vector pSIREN was acquired from Clontech . miR-UL112-1 and miR-UL22A-1 were PCR cloned directly from AD169 BAC DNA , sequence coordinates miR-UL22A-1 27611–27750; miR-UL112-1 163123–163321 . pSVH-1 contains the genomic region 168817–175524 of HCMV , which includes the MIE promoter and exons encoding IE1 and IE2 . The construction of this plasmid has previously been described [41] . The pSVH-1 deletion mutant contains a 39-base deletion between AD169 nucleotide coordinates 170968–170912 and was constructed by site-directed mutagenesis . pSVH-1 IE2tar was constructed using site-directed mutagenesis . The predicted 21-base miR-UL112-1 target sequence from the 3′UTR of IE72 was inserted at AD169 nucleotide coordinate 169336 of pSVH-1 deletion within the 3′UTR coding sequence of IE2 . The synthetic miR-UL112-1RNA and negative control were purchased from Ambion . Assays were conducted in a 96-well format . pMIR constructs ( 0 . 2 μg ) were co-transfected into 293T cells along with 0 . 2 μg miR-UL112-1 expression plasmid and 0 . 2 μg control renilla plasmid pRL using Lipofectamine 2000 according to the manufacturer's guidelines . Twenty-four hours post transfection , cells were harvested and luciferase levels measured using the Dual luciferase reporter assay system ( Promega ) according to the manufacturer's guidelines . Cells were lysed in buffer containing 50 mM Tris ( pH 8 . 0 ) , 150 mM NaCl , 1% NP-40 , and 1% sodium deoxycholate . Proteins were resolved by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) and transferred to Immobilon-P membranes ( Millipore ) . The following antibodies were used: HCMV IE2 rabbit polyclonal antibody 638—antibody was raised against the entire IE2 protein—and horseradish peroxidase conjugated anti-rabbit ( Amersham ) . Blots were visualized by Supersignal West Pico chemiluminescent substrate ( Pierce ) according to the manufacturer's protocol . U373 cells were transfected with pre-miR RNAs using a modified version of the Oligofectamine protocol . A 20-μM stock ( 2 . 5 μl ) of pre-miR was transfected into 24-well dishes of U373 cells in Optimem media according to the manufacturer's guidelines . Cells were transfected three times and allowed to recover for 2 h in complete media between transfections . Cells were infected at a multiplicity of 0 . 5 for 3 hs with AD169 , then washed and overlayed with fresh complete media . Supernatant was removed and cells harvest in buffer containing 50 mM Tris ( pH8 . 0 ) , 150 mM NaCl , 1% NP-40 , and 1% sodium deoxycholate and subjected to western blot analysis . RNA was isolated from cells using TRIzol Reagent ( Invitrogen ) according to the manufacturer's protocol . Total RNA ( 2 μg ) was reverse transcribed using Omniscript RT ( Qiagen ) and 1 μM random hexamer primers . One-tenth of the RT reaction was used as a template for real-time PCR using TaqMan Universal PCR master mix ( Applied Biosystems ) on an ABI prism 7700 Sequence detector . Sequences of primers and dual-labeled TaqMan probes are IE86 forward primer ATGTCCTGGCAGAACTCGGT; IE86 reverse primer GCTGCAAGAGTGGGTTGTCA; IE86 probe VIC-CCAGTAGCACCGGCCCCACG-TAMRA; IE72 forward primer AGTGACCGAGGATTGCAACG; IE72 reverse primer CCTTGATTCTATGCCGCACC; IE72 probe VIC-AAAGATGTCCTGGCAGAACTCGTCAAACA-TAMRA . DNA was isolated from infected cells using the Qia-Amp DNA isolation kit ( Qiagen ) according to the manufacturer's recommendations . Real-time PCR was carried out using primers to the IE72 gene . Sequences of primers and dual-labeled TaqMan probe are: forward primer TTACCGAGTTCTGCCAGGACA; reverse primer CTGTTTCCAGAGTTGGCCGA; probe VIC-TCTCGTTGCAATCCTCGGTCACTTGTT-TAMRA . Reactions were carried out using TaqMan Universal PCR master mix ( Applied Biosystems ) on an ABI prism 7700 Sequence detector .
The National Center for Biotechnology Information ( http://www . ncbi . nlm . nih . gov/ ) annotated genome sequences discussed in this paper are AD169 ( X17403 ) and CCMV ( NC_003521 ) . Additional 3′UTR sequences deleted from AD169 were generated from the sequence of the HCMV clinical strain TR genome ( AC146906 ) . | Our ability to understand the biology of viruses depends not only on functional analysis of genes they encoded but also on specific regulation of those genes during viral infection . In herpesviruses , viral gene regulation is highly complex and plays a significant role in determining the viral life cycle during acute , latent , or persistent infection . The discovery that many herpesviruses express small regulatory RNAs , known as microRNAs , has opened up a whole new area of research in regulation of gene expression . In this paper we demonstrate that a microRNA expressed by human cytomegalovirus is able to regulate multiple viral genes , including one gene thought to be crucial for both acute and latent stages of viral infection in the host . Expression of this microRNA results in a significant reduction in viral replication . This work therefore demonstrates that viral microRNAs can regulate multiple viral genes and can have significant effects on the replication of a virus . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Supporting",
"Information"
] | [
"viruses",
"molecular",
"biology",
"homo",
"(human)",
"virology"
] | 2007 | A Human Cytomegalovirus-Encoded microRNA Regulates Expression of Multiple Viral Genes Involved in Replication |
The incidence and severity of dengue in Latin America has increased substantially in recent decades and data from Puerto Rico suggests an increase in severe cases . Successful clinical management of severe dengue requires early recognition and supportive care . Fatal cases were identified among suspected dengue cases reported to two disease surveillance systems and from death certificates . To be included , fatal cases had to have specimen submitted for dengue diagnostic testing including nucleic acid amplification for dengue virus ( DENV ) in serum or tissue , immunohistochemical testing of tissue , and immunoassay detection of anti-DENV IgM from serum . Medical records from laboratory-positive dengue fatal case-patients were reviewed to identify possible determinants for death . Among 10 , 576 reported dengue cases , 40 suspect fatal cases were identified , of which 11 were laboratory-positive , 14 were laboratory-negative , and 15 laboratory-indeterminate . The median age of laboratory-positive case-patients was 26 years ( range 5 months to 78 years ) , including five children aged <15 years; 7 sought medical care at least once prior to hospital admission , 9 were admitted to hospital and 2 died upon arrival . The nine hospitalized case-patients stayed a mean of 15 hours ( range: 3–48 hours ) in the emergency department ( ED ) before inpatient admission . Five of the nine case-patients received intravenous methylprednisolone and four received non-isotonic saline while in shock . Eight case-patients died in the hospital; five had their terminal event on the inpatient ward and six died during a weekend . Dengue was listed on the death certificate in only 5 instances . During a dengue epidemic in an endemic area , none of the 11 laboratory-positive case-patients who died were managed according to current WHO Guidelines . Management issues identified in this case-series included failure to recognize warning signs for severe dengue and shock , prolonged ED stays , and infrequent patient monitoring .
Dengue is a major public health problem throughout the tropics and subtropics [1] . During the last decade , both the incidence and severity of dengue in Central and South America , Mexico , and the Caribbean have increased substantially [2] . In Puerto Rico , dengue virus ( DENV ) was first isolated during a large epidemic in 1963 [3] . Since then , there have been several large island-wide epidemics of dengue with dengue hemorrhagic fever ( DHF ) , including two epidemics in 1998 and 2007 that involved the simultaneous transmission of all four DENV [4] , [5] . Despite the well-publicized island-wide epidemic in 2007 and an increasing trend in severe disease [5] , the true incidence of fatal dengue is likely under-estimated because of underreporting and under-recognition [6] , [7] , which has included failure to designate dengue as an underlying cause of death on death certificates [8] . Primary prevention of dengue through vector control activities has had limited success worldwide [9] . Currently , no vaccine exists to prevent dengue nor is there an anti-viral treatment . However , secondary prevention to reduce mortality through improved clinical case management has substantially lowered the mortality rate for severe dengue from 10–20% to <1% in some countries over the past two decades [10] , [11] . To begin to understand patient care and management issues related to dengue associated deaths including under-recognition of severe dengue , we performed a review of medical records from the case-series of all laboratory-positive fatal cases in Puerto Rico that occurred during the 2007 epidemic .
Suspected deaths due to dengue with onset of illness in 2007 were identified from three sources: 1 ) the passive dengue surveillance system ( PDSS ) maintained by the Puerto Rico Department of Health ( PRDH ) and Centers for Disease Control and Prevention ( CDC ) Dengue Branch , 2 ) death certificates filed at the Demographic Registry of Puerto Rico , and 3 ) hospital-based infection control nurse dengue surveillance system ( ICNDSS ) as previously described [4] , [6] . Complete medical records from all hospitalizations , emergency room and clinic visits for suspected dengue deaths were obtained and reviewed by physician investigators using a standardized instrument to collect demographic , clinical , and laboratory data . This project underwent institutional review at the CDC and it was determined not to be subject to formal institutional review board review requirements as defined by US regulations ( i . e . , Title 45 Code of Federal Regulations Part 46 ) . Suspected dengue cases are reported to PDSS by health care providers who submit a serum specimen for diagnostic testing accompanied by a Dengue Case Investigation Report ( DCIR ) . For fatal cases , autopsy tissue is occasionally sent with the serum specimen and DCIR . The DCIR includes patient demographic , clinical , travel , vaccination , and disease outcome data including whether the illness resulted in hospitalization , death or both . DCIR data are entered into an electronic data base . Deaths reported to PDSS are immediately confirmed by calling the reporting hospital . Hospitalized suspected dengue cases are reported to the ICNDSS by nurse epidemiologists or infection control nurses . Because of a steady decline in participation , data from this reporting system are only used to augment PDSS data . When an ICNDSS case report indicated a patient death these cases were confirmed and investigated . Death certificates with “dengue” included as a cause or contributing factor in the death were obtained on a monthly basis . The PDSS database was queried to determine if a diagnostic specimen was received for the case . All serum specimens were tested by DENV serotype-specific , real-time , reverse transcriptase polymerase chain reaction ( RT-PCR ) [12] , [13] . Specimens were also tested for anti-DENV IgM with an IgM antibody-capture enzyme-linked immunosorbent assay ( MAC-ELISA ) [14] . Specimens with borderline results were retested against a standard negative serum . A quantitative IgG ELISA was performed to detect anti-DENV IgG in all specimens [15] . As human West Nile virus ( WNV ) infections were identified for the first time in Puerto Rico in early 2007 ( i . e . , among healthy blood donors ) , [16] all fatal cases were tested by anti-WNV MAC-ELISA and if positive , WNV specific RT-PCR [17] and plaque reduction neutralization test ( PRNT90 ) assays were performed to differentiate between DENV and WNV infections [18] . All serum specimens with sufficient volume remaining after dengue diagnostic testing was completed were also screened for IgM antibodies to Leptospira at the CDC Bacterial Zoonoses Branch , Zoonoses and Select Agent Laboratory using the rapid dipstick ELISA ImmunoDOT kit ( GenBio , Inc . , San Diego , CA ) . Specimens with positive or borderline ELISA results were further tested using the microscopic agglutination test ( MAT ) using 20 Leptospira reference antigens representing 17 serogroups [19] . Autopsy tissue was sent to CDC Infectious Diseases Pathology Branch for identification of DENV antigen by immunohistochemical ( IHC ) microscopy and DENV-specific RT-PCR . If microscopic examination of the tissue or the clinical history was suggestive of leptospirosis ( e . g . , interstitial nephritis , pulmonary hemorrhage ) , IHC microscopy was conducted using 16 polyclonal anti-Leptospira spp . antibodies . Depending on the clinical presentation and histopathology of the tissue specimens submitted , additional testing with histochemical stains , immunohistochemistry , and/or additional molecular assays was performed to determine the infecting pathogen . A suspected dengue case is a dengue-like , acute febrile illness in a person with that clinical diagnosis and a specimen submitted for dengue diagnostic testing . A laboratory-positive dengue case is a suspected dengue case with any of the following: ( 1 ) detection of DENV RNA in serum , cerebrospinal fluid , or tissue by RT-PCR; ( 2 ) identification of DENV antigen in tissue by IHC assay; ( 3 ) IgM anti-DENV seroconversion or demonstration of a fourfold or greater increase in anti-DENV IgG titers in paired serum specimens; or ( 4 ) positive anti-DENV IgM in a single serum specimen . A laboratory-negative dengue case is a suspected dengue case for which no anti-DENV IgM is detected in a serum specimen collected >5 days after fever onset ( i . e . , a negative convalescent phase specimen ) , and no anti-DENV IgM , DENV RNA or DENV antigen is detected from serum collected ≤5 days after onset ( i . e . a negative acute phase specimen ) or tissue ( if submitted ) . Laboratory-negative dengue cases are hence considered to have the diagnosis of dengue ‘ruled out’ . A laboratory-indeterminate dengue case is a suspected dengue case that subsequently had no DENV RNA or anti-DENV IgM detected in specimen collected ≤5 days after fever onset , and no convalescent specimen submitted for diagnostic testing . A laboratory-confirmed leptospirosis case is a suspected dengue case with any of the following laboratory results: 1 ) isolation of Leptospira from a clinical specimen , 2 ) fourfold or greater increase in MAT titer between acute- and convalescent-phase serum specimens studied at the same laboratory , 3 ) demonstration of Leptospira in tissue by immunohistochemistry , or 4 ) MAT titer ≥800 in a serum specimen . A presumptive leptospirosis case is a suspected dengue case with any of the following laboratory results: 1 ) presence of MAT titer >200 but <800 in a serum specimen , or 2 ) demonstration of Leptospira in a clinical specimen by darkfield microscopy . A terminal event is defined as the first event in a patient's clinical course that resulted in the need for cardiopulmonary resuscitation ( e . g . , a hypoxic seizure , an intracranial bleed ) . A hemorrhagic manifestation is defined by the presence of any of the following: petechiae , purpura , ecchymosis , epistaxis , gingival bleeding , hematuria , menorrhagia , hemoptysis , hematemesis , melena , or an intracranial bleed . Dengue fever ( DF ) is any suspected dengue case that meets the 1997 World Health Organization ( WHO ) case definition [20] , which include acute onset of fever plus two or more of the following sign or symptoms: headache , retro-orbital pain , myalgia , arthralgia , rash , hemorrhagic manifestation , and leucopenia . Leucopenia was defined by white cell count <5 . 0×109/L . Dengue hemorrhagic fever ( DHF ) is any suspected dengue case that meets the following WHO criteria [20]: ( 1 ) fever or recent history of fever , ( 2 ) any hemorrhagic manifestation , ( 3 ) platelet count of ≤100 , 000/mm3 , and ( 4 ) evidence of increased vascular permeability and plasma leakage which includes: ( a ) hemoconcentration with a hematocrit ≥20% above the U . S . population mean for age and sex , [21] , [22] ( b ) ≥20% decline in hematocrit following volume-replacement treatment compared to baseline , ( c ) presence of pleural effusion or ascites detected by any imaging method , or ( d ) a serum protein or albumin <2 . 5 percentile for age and sex [23]–[25] . Dengue shock syndrome ( DSS ) is any case that meets the four criteria for DHF and has evidence of circulatory failure manifested by ( 1 ) rapid and weak pulse and narrow pulse pressure ( ≤20 mmHg [2 . 7 kPa] ) or ( 2 ) hypotension for age and cold , clammy skin and restlessness . A primary DENV infection ( first DENV infection ) is a laboratory-positive case in which the anti-DENV IgG titer was <1∶160 in an acute serum specimen collected ≤5 days after the onset of symptoms [14] . A secondary DENV infection ( ≥second DENV infection ) is a laboratory-positive case in which the anti-DENV IgG titer was ≥1∶160 in an acute serum specimen [14] .
Of the 11 laboratory-positive case-patients who died , eight were DENV RT-PCR positive in tissue , serum or both , and three were anti-DENV IgM positive in a single serum specimen ( Table 1 ) . Of the five DENV RT-PCR positive in serum , three were DENV-3 , one was DENV-2 , and one was DENV-1 . Among the six case-patients with an acute serum specimen , four had secondary infections and two had primary infections . In the four laboratory-positive case-patients with tissue specimens , DENV was identified by IHC or RT-PCR in lung , liver and kidney specimens , and the most common histopathologic findings were intraalveolar edema and hemorrhage; congestion in the spleen , liver , and/or kidney; and fatty metamorphosis of the liver . The median age of laboratory-positive case-patients was 26 years ( range: 5 months to 78 years ) . Five were aged <15 years , four were 20–45 years , and two were >70 years . Seven were male . Five of six adults had at least one co-morbidity: two had asthma; one had an autoimmune hypothyroid disease; one had Type 2 diabetes mellitus ( DM II ) and hypertension; and one had DM II , chronic anemia , congestive heart failure , chronic obstructive pulmonary disease , and hypertension . In addition , four adults were overweight ( i . e . , body mass index [BMI] of 25 . 0–29 . 9 ) , and one adult and one child were obese ( i . e . , BMI ≥30 . 0 or a BMI for age >95% ) . Seven of the 11 case-patients sought medical care at least once prior to first hospital admission or presenting dead on arrival ( DOA ) to an emergency department ( ED ) ; three were seen by a clinician more than once but only 1 was diagnosed with a “dengue-like-syndrome” . Instead , the most common diagnoses given at these outpatient visits were upper respiratory infection with pharyngitis and/or cough , followed by acute gastroenteritis and viral syndrome . None of the seven case-patients had specimens submitted for dengue diagnostic testing until hospitalization ( median 5 days post fever onset; range: 3–9 days ) even though they saw clinicians early in the clinical course ( median 2 days post fever onset; range 1–5 days ) . In addition , three case-patients had one or more warning signs for severe dengue at the time of fever defervescence , including persistent vomiting , severe abdominal pain , and narrow pulse pressure , and were sent home . A fourth case-patient , diagnosed with an upper respiratory tract infection as an outpatient , had a seizure at home the day after first being seen as an outpatient and died on the way to the hospital . Upon final presentation to an ED , the 11 laboratory-positive case-patients had been sick a median of 4 days ( range: 1–7 days ) . Two case-patients presented DOA and four were afebrile , three of which , had signs of shock . A seventh case-patient became afebrile while in ED and developed tachycardia , delayed capillary refill , and a narrow pulse pressure . Six case-patients had warning signs for severe dengue upon arrival to the ED including persistent vomiting ( 5/9 ) and abdominal pain ( 4/9 ) . However , five of the nine case-patients were given a low ( least severe ) or intermediate severity triage score . Nine case-patients were admitted to a hospital after a mean ED stay of 15 hours ( median 12 hours , range: 3–48 hours ) ( Table 2 ) . Initial complete blood count done in ED found that six case-patients had platelet counts <100 , 000/mm3 ( median 78 , 000; range 8 , 000–410 , 000 ) , five were leukopenic , two were hemoconcentrated ( hematocrit 20% above mean for age/sex ) and two had a hematocrit <2 . 5% for age and sex . Six of the nine case-patients met criteria for DF and three met criteria for DHF/DSS . Five case-patients had “dengue” listed in the admission differential diagnosis . During their hospital stay , several case-patients developed warning signs for severe dengue including persistent vomiting ( 1/9 ) , abdominal pain ( 1/9 ) , restlessness ( 4/9 ) , and mental status changes ( 4/9 ) . In six cases , warning signs were not recognized as such as there were no new orders or change in the clinical management . In the end , six of the 11 laboratory-confirmed fatal cases met criteria for DHF or DSS as determined throughout their clinical course and at autopsy ( Table 3 ) . Ten of the 11 case-patients had at least 1 hemorrhagic manifestation and nine case-patients had evidence of plasma leakage . Nine of 11 had thrombocytopenia documented . Of the nine laboratory-positive case-patients admitted to a hospital , only three had capillary refill time assessed in the ED or at admission . Vital signs were measured at a median of every 3 hours in the ED but most case-patients had less frequent measurements after admission ( Table 2 ) . Two case-patients were admitted from the ED to the ICU and had vital signs measured every 1 or 2 hours . Eight of nine hospitalized case-patients died during hospitalization , and the other case-patient was found dead at home within 18 hours of hospital discharge ( Table 3 ) . Three of the eight case-patients who died in the hospital had no recorded blood pressure measurements during the 8 hours before their terminal event . Five case-patients had their terminal event on the inpatient ward and another case-patient was transferred to ICU and had a terminal event within minutes of the transfer . Of those who died in hospital , six of eight case-patients died during a weekend ( between 1701 on Friday and 0759 Monday ) , and five had a terminal event between 2300 and 0759 . All nine hospitalized case-patients received intravenous fluids , most commonly 0 . 9% normal saline ( Table 2 ) . Four received 0 . 45% normal saline while in shock . Four received intravenous albumin ( 5% or 25% solution ) during their hospitalization but only one received colloids prior to the terminal event . Three of the nine hospitalized case-patients had signs of fluid overload prior to death including periorbital edema , dyspnea , and abdominal distention documented in their medical record . There was frequent use of corticosteroids in laboratory-positive case-patients ( Table 2 ) . Five of the nine hospitalized case-patients received intravenous methylprednisolone as inpatients and one received dexamethasone in the ED before hospital admission . One case-patient who was DOA received oral prednisone during an outpatient visit . Hematocrit levels were assessed every 17 hours on average ( range 10–42 hours ) for the six hospitalized case-patients with clinically significant hemorrhage ( Table 2 and 3 ) . Three of these six case-patients were given packed red blood cells; two in response to frank blood per nasogastric tube or rectum and one during the final code . One of these case-patients also received fresh frozen plasma in response to clinically significant bleeding . No case-patients were given whole blood . Two of the case-patients who received packed red blood cells also received platelets; one in response to clinically significant bleeding and one during the final code . Four additional case-patients had platelets ordered but they did not receive them prior to death . Many of the nine hospitalized case-patients developed complications including metabolic acidosis ( 6/9 ) , prolonged shock ( 6/9 ) , acute respiratory failure ( 6/9 ) , fluid overload ( 3/9 ) , and seizures ( 3/9 ) ( Table 2 ) . Only 1 of the 11 case-patients had evidence of a secondary bacterial infection even though most patients had blood and urine cultures taken . Dengue , DHF , DSS , or status-post dengue syndrome was listed as the cause of death or a contributing factor in only five case-patients . The remaining six death certificates listed causes or contributing factors including hypovolemic shock , hypotension , metabolic acidosis , septicemia unspecified , bronchopneumonia unspecified , viral infection unspecified , brain death , and ischemic cerebral infarct .
This case review showed that although there was a relatively low case-fatality rate among hospitalized patients with dengue during the 2007 epidemic in Puerto Rico , the clinical management of all fatal dengue cases deviated from the current WHO guidelines . Although chronic disease and bacteremia have been associated with poor outcomes , including death among adults with severe dengue [26]–[30] , only two adult case-patients had co-morbidities that may have contributed to their deaths . In short , the majority of laboratory-positive fatalities appeared to be due to dengue , and none of our case-patients were managed according to the 1997 WHO Guidelines . This review illustrates various levels of delay in receipt of appropriate level of care . Delay in receipt of the appropriate level of care and prolonged shock has been associated with poor outcomes among patients with severe dengue [31]–[33] . Most of our case-patients were seen by a clinician at least once before being hospitalized or presenting DOA , and only one case-patient was identified as having dengue . In fact , four of the seven case-patients seen as outpatients could have benefited from timelier referral to an inpatient facility including the three that sought care multiple times but were sent home even though they had warning signs of severe dengue and another that presented DOA the day after being seen . These findings indicate the need for educating patients and clinicians in identifying dengue and recognizing warning signs for severe dengue so that anticipatory guidance can be given to minimize delay and appropriate care can be initiated in a timely manner . Poor disease recognition and failure to detect increased disease severity in the ED appeared to contribute to the delay in receipt of appropriate inpatient care . Contributing factors included patients being given low triage scores and infrequent monitoring of vital signs . Lack of inpatient beds was only documented in two cases and probably did not contribute to poor outcomes . We are not aware of any published studies that indicate that use of triage scores/systems may be a factor in treatment delay for dengue as has been shown for severe sepsis [34] . Automated triage systems detect patients with high body temperature and low systolic blood pressure; whereas , it would be useful in dengue endemic countries for these systems to also identify patients with hypothermia , narrow pulse pressure and age-specific tachycardia in the absence of hyperthermia . In countries with a sizeable number of adult patients with severe dengue , it would be important for triage systems to identify patients with chronic hypertension as low- normal systolic blood pressure may be abnormal for these patients . The early markers for severe disease and mortality among dengue patients needs to be better defined [28] , [35] and used to develop rules for triage of patients in dengue endemic areas , especially during epidemics . Monitoring patients closely for warning signs of severe disease and early signs of shock until at least 24 hours after fever defervescence is important as patients may rapidly deteriorate at this time . Most of our case-patients were admitted to the general inpatient ward , had infrequent monitoring of vital signs , and hematocrit levels were not ordered at a frequency necessary to monitor plasma leakage with its attendant hemoconcentration and response to fluid resuscitation . In addition , we found that warning signs and early signs of shock were not acted upon in a timely manner even when clearly documented by the nursing staff . Many hospital deaths occurred during the night or weekend , and a few had no blood pressure measurement recorded for several hours before death . For patients with severe dengue , the mainstay of successful management is early and judicious replacement of plasma leakage with isotonic crystalloid solutions , including normal saline , Ringer's lactate , Ringer's acetate , or 5% glucose in normal saline , followed by colloid solutions in the event that shock is refractory [36]–[38] . Our review found that some case-patients were given intravenous non-isotonic crystalloid solutions during shock , and only one patient was given intravenous colloid solution before the terminal event even though six of the eight case-patients who died in hospital had refractory shock . Most case-patients received corticosteroids even though their use has been shown to be no more effective than placebo or no treatment in reducing the number of deaths and the need for blood transfusion [39] . Two recent reviews found insufficient evidence to justify the use of corticosteroids in managing dengue shock syndrome and recommended that corticosteroids not be used to treat dengue [39] , [40] . In addition , there is no convincing physiological rationale for their use and there are multiple potential side effects including stress ulceration and upper gastrointestinal bleeding in critically ill patients , hyperglycemia , and immunosuppression with an increased risk for infection . Although most case-patients had clinically significant bleeding , few received packed red blood cells and none received whole blood . Infrequent monitoring of hematocrit and vital signs may have contributed to the late detection of hemorrhage . Instead , complete blood counts may have been ordered to monitor platelet counts . Platelet transfusions were ordered for most of the hospitalized case-patients but many died before receiving them . Guidelines generally recommend platelet transfusions be given to patients who have clinically significant bleeding; however , use of prophylactic platelet transfusions for dengue is a subject of debate [41] . Recent studies have found that in children , platelet counts are not predictive of bleeding [32] , [42] , [43] nor do they correlate with bleeding severity [44] . Instead , prolonged shock was found to be a risk factor for severe hemorrhage [32] . In a study of 106 pediatric patients with DSS and coagulopathy , prophylactic platelet transfusions for platelet counts <30×109 L−1 and fresh frozen plasma did not prevent hemorrhage and may have contributed to the development of pulmonary edema resulting in increased hospital stays [45] . Moreover , prophylactic platelet transfusions do not seem to expedite platelet recovery [45] , [46] . Studies among adults have conflicting results; some found no association between a platelet count and bleeding [46] , while others found an association between platelet counts of 50×109 L−1 or less than 20×109 L−1 and bleeding [42] , [47] . Lastly , our review illustrates the difficulties in making a laboratory diagnosis of dengue , especially late in the course of the disease . Even though we tested acute and convalescent specimens by all available diagnostic tests , 37 . 5% ( 15 ) had an indeterminate diagnosis . This occurred because the available specimen ( s ) were incorrectly collected with respect to the course of dengue . Seven of the 15 case-patients with an indeterminate dengue diagnosis had specimens obtained at day 4 or 5 post onset of symptoms , a period when viremia or anti-DENV IgM may be undetectable . Physicians practicing in dengue endemic areas need to be aware of including dengue in their differential diagnosis of acute febrile illness and that they must obtain serum samples for diagnostic testing early ( days 1 to 3 ) after symptom onset . To improve diagnostic accuracy for patients who present late , samples should be collected immediately and a second collected 5 to 7 days later . Whether more sensitive diagnostic assays can be developed to increase diagnostic accuracy for specimens collected only during the critical period of dengue remains to be determined . There are limitations to our study . This was a small case-series study which only generates descriptive data and does not identify risk factors associated with dengue deaths . The identification of risk factors must await case-control studies in a larger number of dengue fatalities . However , the high frequency of certain findings ( e . g . , use of non-isotonic fluids for refractory shock , infrequent monitoring vital signs , not identifying warning signs for severe dengue ) suggests that they may be risk factors for poor outcomes . The other limitation is that our catchment systems may not have detected all fatal dengue cases , which may have introduced biases due to incomplete case ascertainment . However , our present surveillance approach again confirmed that in Puerto Rico , dengue continues to not be listed as the cause or underlying cause of death among laboratory-confirmed fatal cases [4] , [8] . Better estimates of the degree of under-recognition and reporting of dengue deaths must await additional assessment of reporting studies . However , the consistency of issues in clinical management in our case-series suggests that biases due to underreporting are probably minimal . Clearly there is need to improve surveillance of severe and fatal cases , and to evaluate clinicians' diagnosis and clinical management of dengue in Puerto Rico . Our presumption is that , even in a dengue endemic area where the disease should be well known , sustained health care provider education and training are necessary to improve detection , diagnosis , and management of dengue and lower dengue morbidity and mortality . CDC Dengue Branch and PRDH in collaboration with a number of medical organizations used findings from this fatal case review and a physician survey that we conducted in 2007–08 ( CDC data , not published ) to develop a post graduate course on the clinical management of dengue for physicians in Puerto Rico which was implemented in 2009–10 . | Dengue is a major public health problem in the tropics and subtropics; an estimated 50 million cases occur annually and 40 percent of the world's population lives in areas with dengue virus ( DENV ) transmission . Dengue has a wide range of clinical presentations from an undifferentiated acute febrile illness , classic dengue fever , to severe dengue ( i . e . , dengue hemorrhagic fever or dengue shock syndrome ) . About 5% of patients develop severe dengue , which is more common with second or subsequent infections . No vaccines are available to prevent dengue , and there are no specific antiviral treatments for patients with dengue . However , early recognition of shock and intensive supportive therapy can reduce risk of death from ∼10% to less than 1% among severe dengue cases . Reviewing dengue deaths is one means to identify issues in clinical management . These findings can be used to develop healthcare provider education to minimize dengue morbidity and mortality . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"medicine",
"infectious",
"diseases",
"dengue",
"fever",
"neglected",
"tropical",
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] | 2012 | Dengue Deaths in Puerto Rico: Lessons Learned from the 2007 Epidemic |
Neuropilin-1 ( Nrp1 ) guides the development of the nervous and vascular systems , but its role in the mature brain remains to be explored . Here we report that the expression of the 65 kDa isoform of Sema3A , the ligand of Nrp1 , by adult vascular endothelial cells , is regulated during the ovarian cycle and promotes axonal sprouting in hypothalamic neurons secreting gonadotropin-releasing hormone ( GnRH ) , the neuropeptide controlling reproduction . Both the inhibition of Sema3A/Nrp1 signaling and the conditional deletion of Nrp1 in GnRH neurons counteract Sema3A-induced axonal sprouting . Furthermore , the localized intracerebral infusion of Nrp1- or Sema3A-neutralizing antibodies in vivo disrupts the ovarian cycle . Finally , the selective neutralization of endothelial-cell Sema3A signaling in adult Sema3aloxP/loxP mice by the intravenous injection of the recombinant TAT-Cre protein alters the amplitude of the preovulatory luteinizing hormone surge , likely by perturbing GnRH release into the hypothalamo-hypophyseal portal system . Our results identify a previously unknown function for 65 kDa Sema3A-Nrp1 signaling in the induction of axonal growth , and raise the possibility that endothelial cells actively participate in synaptic plasticity in specific functional domains of the adult central nervous system , thus controlling key physiological functions such as reproduction .
Blood vessels and axons employ similar mechanisms and follow common guidance cues to grow and navigate tissues during embryonic development [1] , [2] . Blood vessels influence the trajectories taken by axons to reach their appropriate end organs [3] . In the adult brain , they communicate with neurons and glia in order to meet physiological demands [4] , [5] . Endothelial cells are well positioned to sense peripheral inputs and ideally suited to convey signals that could influence neuronal structure and synaptic plasticity . However , whether they are capable of influencing axonal plasticity in the mature central nervous system remains to be elucidated . Recent evidence suggests that the semaphorins , members of a family of secreted guidance molecules , continue to be expressed in the postnatal brain and may have important implications for neuronal plasticity and nervous system physiology [6] . Of these , Sema3A , which exerts both repulsive and attractive effects on growing axons [7]–[9] , is also expressed in endothelial cells during vascular development [10] , [11] . Interestingly , Sema3A acts as a guidance factor during the migration of a particular population of neuroendocrine neurons that secrete the fertility-regulating neuropeptide gonadotropin-releasing hormone ( GnRH ) [12] , [13] , and that moreover retain a high degree of plasticity in the mature brain [14] . In particular , GnRH neurons , which project to the hypothalamic median eminence ( ME ) and release their neurohormone into a specialized capillary network for delivery to the anterior pituitary ( Figure 1A ) , are known to undergo extensive axonal growth towards the vascular wall during critical time windows in adulthood , such as at the onset of the preovulatory surge , when massive GnRH release has to occur to trigger ovulation [14] , and are thus an ideal system in which to analyze endothelial-axonal interactions during adult nervous system homeostasis . In this study , we examined whether this periodic sprouting of GnRH axon terminals in the ME of the adult hypothalamus was regulated by endothelial cells , through the release of Sema3A and the activation of its cognate receptor , neuropilin-1 ( Nrp1 ) [15]–[17] . We report that endothelial cells of the ME do indeed release the 65 kDa isoform of Sema3A ( p65-Sema3A ) at key stages of the ovarian cycle , that Nrp1 is expressed in GnRH axons , and that Sema3A-Nrp1 signaling is required for the extension of GnRH axon terminals towards the vascular plexus on the day of the preovulatory surge . We also demonstrate that the selective inhibition of Sema3A expression in endothelial cells of the ME and the transient local manipulation of Sema3A signaling in vivo alter the preovulatory release of GnRH , suggesting that the endothelium-to-neuron communication mediated by 65 kDa Sema3A-Nrp1 signaling is of functional relevance in the adult brain . Our results thus indicate a hitherto unidentified role for brain vascular endothelial cells in mediating the cyclic plasticity of GnRH axons in the adult hypothalamus and , consequently , in reproductive physiology .
Sema3A is mainly known as a developmental signal regulating axon guidance . In order to assess the potential role of Sema3A as a guidance cue for hypothalamic GnRH neurons controlling the ovarian cycle , we first investigated its expression in the ME of adult animals . In situ hybridization of adult female rat brain sections revealed that the mRNA for Sema3A was selectively expressed in endothelial cells of the vascular compartment of the ME ( Figure 1B ) . Only a weak hybridization signal was seen in the ependymal layer and in the internal and external axon layers . Brain sections hybridized with the sense probe ( negative control ) did not exhibit any detectable labeling in the ME ( unpublished data ) . Further analysis by cell sorting , using an affinity-purified antibody to plasmalemmal vesicle-associated protein 1 ( PV1 ) [18] , a component of the fenestral diaphragms [19] , selectively expressed by endothelial cells of the ME ( Figure 1B , C; PV1 mRNA expression in fluorescent versus nonfluorescent cells , t ( 6 ) = 4 . 080 , p = 0 . 007 , n = 4 ) revealed that Sema3A expression was restricted to PV1-positive cells ( Figure S1 and Figure 1C; t ( 6 ) = 2 . 636 , p = 0 . 039 , n = 4 ) , unlike β3-tubulin , DARPP-32 , and thyroid-stimulating hormone ( TSH ) , markers for neurons , tanycytes , and endocrine cells , respectively , which were expressed only by non-PV1-positive cells and not found in the same fraction as Sema3A ( Figure S1 ) . Immunofluorescence analysis in adult female mice using a Sema3A-specific antibody [13] revealed bright Sema3A immunoreactivity in the capillary zone of the ME that extended into the nervous tissue , where it progressively vanished ( Figure 1D ) . Together , these findings indicate that Sema3A is expressed in vivo in the ME of the mature brain , and is localized in vascular endothelial cells of the pituitary portal system , onto which GnRH neurons abut . To further investigate the site of origin of Sema3A in portal blood vessels and to determine whether fenestrated endothelial cells from the rat ME can release Sema3A , we used a sequential panning method for their purification , as described previously [20] , [21] . Consistent with our findings in vivo [18] , [21] , purified ME endothelial cells in culture expressed PV-1 and were labeled by Bandeiraea simplicifolia lectin ( Figure S2 ) . We confirmed Sema3A mRNA expression in purified ME endothelial cells by RT-PCR expression analysis ( Figure 1E ) . Next , we used immunoblotting to analyze the conditioned medium of purified ME endothelial cells and compared it with total protein extracts from ME explants , revealing several bands corresponding to the different known isoforms ( the secreted 65 kDa and 95 kDa forms , and the 125 kDa precursor; see Figure 1F ) of Sema3A [22] . Notably , the conditioned medium of purified ME endothelial cells only contained a smear at 125 kDa and a discrete band for p65-Sema3A ( Figure 1F ) , which appears to be the furin cleavage product of the 95 kDa isoform ( Figure S2B ) . This confirms that fenestrated endothelial cells of the ME express Sema3A and release its 65 kDa isoform into the extracellular space . To determine whether Sema3A expression in the ME varies during the ovarian cycle , we performed Western blotting experiments during the onset of the preovulatory surge at proestrus ( when GnRH nerve terminals are close to portal plexus vessels ) and during diestrus ( when GnRH nerve terminals are distant from the endothelial wall ) [23] . Remarkably , we found that p65-Sema3A expression was significantly increased on the day of proestrus as compared to diestrus ( Figure 1G; p65-Sema3A , Di16h versus Pro16h; n = 5 independent experiments , p<0 . 01 , t test ) . This regulation appeared to be selective for p65-Sema3A , as the expression of other Sema3A isoforms did not change significantly during the ovarian cycle ( Figure 1G; p95-Sema3A , 0 . 226±0 . 0449 arbitrary units at Di16h versus 0 . 133±0 . 0411 arbitrary units at Pro16h , n = 5 , t ( 8 ) = 1 . 522 , p = 0 . 167; p125-Sema3A , 0 . 427±0 . 0455 arbitrary units at Di16h versus 0 . 379±0 . 0519 arbitrary units at Pro16h , n = 5 , t ( 8 ) = 0 . 698 , p = 0 . 505 ) . These data indicate that the levels of p65-Sema3A in the ME are maximal on the day of proestrus , when circulating levels of estradiol are also high and are known to exert their positive feedback effect on the hypothalamo-pituitary-gonadal axis [24] , [25] . To determine whether these changes are sex-steroid-dependent , we ovariectomized ( OVX ) adult cycling female rats and subsequently treated them with subcutaneous injections of sesame oil , alone or containing 17β-estradiol 3-benzoate ( E2 ) , progesterone ( P ) , or E2+P . As shown in Figure 1H , estradiol induced a significant increase in p65-Sema3A expression in the ME of OVX rats when compared with the other treatment groups , whereas progesterone inhibited this increase ( n = 5 rats per treatment , p<0 . 05 , one-way ANOVA ) . Interestingly , additional RT-PCR analyses revealed that PV1-positive endothelial cells expressed mRNA for the estrogen receptor ERα ( Figure 1C ) and that this expression was particularly enriched in the ME of adult female rats ( PV1-positive versus PV1-negative cells , t ( 6 ) = 2 . 793 , p = 0 . 031 , n = 4 ) . Altogether , these results provide direct evidence that the release of p65-Sema3A by fenestrated endothelial cells of the ME is strictly regulated during the ovarian cycle , being maximal during proestrus , under the action of circulating estradiol . We next investigated whether adult GnRH neurons express Nrp1 , the obligate receptor of Sema3A , by performing double in situ hybridization experiments using 33P-labeled Nrp1 and Dig-UTP-labeled GnRH antisense cRNA probes ( Figure 2A ) . High levels of Nrp1 mRNA were observed in cells of the diagonal band of Broca ( DBB ) and in several nuclei of the rostral and medial preoptic regions—for example , the anteroventral periventricular nucleus , the median preoptic nucleus , and the medial preoptic nucleus ( unpublished data ) . The distribution of neurons expressing GnRH mRNA was similar to that described in previous in situ hybridization studies [26]–[28]—that is , the highest density was seen in the rostral preoptic region , followed in decreasing order by the medial preoptic area and the DBB . Nrp1 mRNA was expressed at detectable levels in 38 . 2±2 . 5% of GnRH neurons in diestrus ( n = 4 animals , more than 200 GnRH neurons were considered per animal ) and in 50 . 0±1 . 5% of GnRH neurons in proestrus ( n = 4 animals , t ( 6 ) = 4 . 039 , p = 0 . 007 ) with no preferential anatomical distribution . Nrp1 mRNA was also expressed in the ME , the projection field of GnRH neurons ( Figure 2B ) . However , the hybridization signal was not seen in the neural layers , which contain various types of glial cells associated with neuroendocrine axons [14] , but instead was detected in the capillary zone ( Figure 2B , inset ) . To determine whether Nrp1 protein was present in GnRH axon terminals abutting onto the vascular plexus , we performed double immunolabeling studies with antibodies to Nrp1 and GnRH in the ME of the adult brain . Strong Nrp1 immunoreactivity was seen in the external zone of the ME ( Figure 2C ) at every anteroposterior level examined . Nrp1 labeling was distributed in the same regions as the majority of GnRH axon terminals , and consistent with Nrp1 mRNA expression by GnRH neuronal cell bodies , GnRH-containing fibers were also found to contain Nrp1 protein ( Figure 2C , arrows ) . However , many Nrp1-positive axon-like fibers did not contain GnRH ( Figure 2C ) , suggesting that additional neuroendocrine systems express this receptor . In agreement with in situ hybridization data , endothelial cells of the pituitary portal blood vessels were also found to express Nrp1 immunoreactivity ( Figure S3 ) . Thus , the spatial and temporal pattern of expression of Sema3A in the ME together with that of its receptor , Nrp1 , in GnRH neurons is consistent with a functional role for Sema3A signaling in promoting GnRH axonal growth towards the vascular plexus at proestrous , when a massive release of the neurohormone into the pituitary portal circulation is required to trigger the preovulatory surge of gonadotropins . In order to assess whether Sema3A can promote the outgrowth of GnRH axons in situ , we analyzed hypothalamic explants containing the ME , maintained ex vivo in artificial cerebrospinal fluid . Explants obtained from either diestrous or preovulatory proestrous rats were exposed to 1 µg/ml Sema3A for 30 min , then fixed and processed for electron microscopy . Using 15 nm gold-particle labeling , we revealed a striking transformation of GnRH nerve terminals as a function of the presence or absence of Sema3A in diestrous rats . Indeed , the distance between GnRH nerve terminals ( green ) and the pericapillary space of pituitary portal blood vessels ( p . s . , pink ) appeared to be significantly shorter in Sema3A-treated explants versus controls ( Figure 3A ) . Quantitative morphometric analysis showed that while the total number of GnRH nerve terminals at a distance of 10 µm or less from the parenchymatous basal lamina ( which delineates the pericapillary space ) did not vary significantly among treatments ( n = 4 animals per condition; more than 100 GnRH-immunoreactive axon terminals were considered per explant , one-way ANOVA , F ( 2 , 11 ) = 0 . 224 , p = 0 . 803 ) , their distribution was markedly changed ( Figure 3B ) . In fact , the fraction of GnRH nerve terminals found at a distance of less than 1 µm from the pericapillary space increased by 400% in diestrous ME explants exposed to 1 µg/ml Sema3A for 30 min when compared to controls ( Figure 3B , left panel; n = 4 hypothalamic explants per condition; p<0 . 001 , one-way ANOVA ) . Importantly , Sema3A-mediated effects on GnRH axonal growth in diestrous explants were abolished upon pretreatment with an Nrp1-neutralizing antibody ( Figure 3B , left panel; n = 4 hypothalamic explants per condition; p<0 . 01 , one-way ANOVA ) . In contrast , in ME explants obtained from animals in proestrus , when Sema3A is heavily released , GnRH axons naturally extend up to the pericapillary space . In this context , exogenous Sema3A treatment did not further affect the elongation of GnRH nerve terminals towards the pericapillary space ( Figure 3B , right panel; n = 4 hypothalamic explants per condition; p>0 . 05 , one-way ANOVA ) . However , exposing proestrous ME explants to neutralizing antibodies to either the Nrp1 or Sema3A caused GnRH nerve endings to retract from the pericapillary space ( Figure 3B , right panel; n = 4 hypothalamic explants per condition; p<0 . 01 , one-way ANOVA ) , suggesting that GnRH axon extension towards the endothelial wall at the transition between diestrus and proestrus is attributable to Nrp1 activation by Sema3A . To assess whether the structural changes promoted by Sema3A at the GnRH neurovascular junction require neuronal expression of Nrp1 , we generated mice in which Nrp1 expression was selectively knocked out in GnRH neurons . Animals harboring the conditional Nrp1 allele [29] were crossed with a mouse line expressing Cre recombinase under the control of the endogenous GnRH gene promoter [30] ( Figure 3C ) . To verify the efficacy of our genetic targeting strategy , we analyzed Nrp1 expression in GnRH neurons of wild-type ( Nrp1loxP/loxP ) and mutant ( GnRH::Cre; Nrp1loxP/loxP ) littermates by immunofluorescence . In wild-type mice , the expression patterns of GnRH and Nrp1 partially overlapped within the ME ( Figure 3D , arrow ) , as seen in rats ( Figure 2C ) . In contrast , upon Cre-mediated deletion of Nrp1 in GnRH-positive neurons , Nrp1 expression was abolished in the external zone of the ME where GnRH axons are found ( Figure 3D , asterisk ) , while it was maintained in other neuroendocrine axonal populations ( Figure 3D ) . Electron microscopic analyses of hypothalamic explants from diestrous mice treated with Sema3A ( as in Figure 3A , B ) confirmed the extension of GnRH nerve terminals towards the pericapillary space in the ME of Nrp1loxP/loxP mice ( Figure 3E; p<0 . 05 , two-way ANOVA , control versus Sema3A; n = 3–4 hypothalamic explants per condition; 150 GnRH-immunoreactive nerve terminals were considered per explant ) , while this was not observed in GnRH::Cre; Nrp1loxP/loxP littermates ( Figure 3E; p = 0 . 23 , control versus Sema3A ) , indicating that Nrp1 expression in GnRH neurons is required to mediate this functional regulation by Sema3A in vivo . In order to evaluate the role of Sema3A on neurite elongation in GnRH-expressing neurons , we took advantage of our ability to obtain primary cultures of GnRH neurons from the nose of 12 . 5-d-old GnRH-GFP embryos ( E12 . 5 ) ( Figure 4A ) . As expected , primary GFP-positive neurons were seen to be surrounded by numerous Sema3A-positive cells ( Figure 4B ) , which have been co-isolated with GnRH neurons from the nasal compartment [31] . While 24-h treatment with Sema3A had no effect on GnRH neurite elongation ( unpublished data ) , the addition of a Sema3A-neutralizing antibody to the culture medium for the same time period caused significant shortening of GnRH neuronal processes ( Figure 4C ) . These data strongly suggest that the production of Sema3A by the surrounding cells was responsible for neurite elongation in these GnRH neurons . To further explore the role of Sema3A on neurite outgrowth in mature GnRH-expressing neurons , we took advantage of the GnV-3 cell line , one of eleven clones of GnRH-expressing cells obtained by the conditional immortalization of cultured adult rat hypothalamic cells . GnV-3 cells grow in culture in the presence of doxycycline , but stop proliferating and undergo differentiation upon drug removal , exhibiting many of the features of mature adult GnRH neurons , including neurite growth [32] . Rat ME explants were cultured in proximity to aggregates of GnV-3 neuronal cells . After 72 h of co-culture , neurites grew to the same extent on both the proximal and distal sides of GnV-3 cell aggregates ( Figure 5A ) . To test whether Nrp1 was involved in GnRH neurite growth in response to factors released by the ME , Nrp1-neutralizing antibodies were added to the medium . These antibodies significantly attenuated the growth-promoting effect of the ME on GnV-3 neurites ( Figure 5B ) . Data shown in Figure 1 indicate that endothelial cells of the ME are a major source of p65-Sema3A , the expression of which is induced by estradiol during proestrus . To date , no biological function has been attributed to this 65 kDa isoform of Sema3A . In order to determine whether it is involved in the GnRH axonal-growth-promoting effect described above , we performed a second set of experiments using three-dimensional matrix co-cultures . Briefly , aggregates of GnV-3 cells were cultured for 72 h along with aggregates of mock-transfected COS-7 cells or COS-7 cells secreting the 95 kDa full-length ( Sema3A-FL ) or recombinant 65 kDa Sema3A , in the presence or absence of the Nrp1-neutralizing antibody ( Figure 5C ) . Remarkably , Sema3A-FL and p65-Sema3A were equally effective at promoting neurite elongation in GnV-3 cells , whereas the Nrp1 antibody stunted this Sema3A-dependent outgrowth ( Figure 5D ) . Altogether these findings suggest that p65-Sema3A , which is highly expressed in the ME during proestrus , unlike the relatively scarce 95 kDa or 125 kDa forms , acts on GnRH neuroendocrine axons through Nrp1 to promote their elongation . Consistent with the fact that Nrp1 is also expressed in GnRH neurons during embryogenesis [12] , [13] , [33] , we have observed that GnRH::Cre; Nrp1loxP/loxP mice exhibit some alterations in the development of the GnRH system , although they display a comparable number of GnRH terminals in the ME as Nrp-expressing mice ( Figure 3D , E ) . To study the physiological relevance of Sema3A-Nrp1 signaling in the mature brain independent of any potential developmental effects , however , we treated adult female rats with a regular 4-d estrous cycle with the Nrp1- or Sema3A-neutralizing antibodies found to inhibit the Sema3A-induced outgrowth of GnRH axon terminals in situ ( see Figure 3 ) . The antibodies were locally infused into the ME ( Figure 6A ) at a rate of 0 . 1 µg/h for 7 d , via a cannula connected to a subcutaneously implanted osmotic minipump . Estrous cycle monitoring by daily inspection of vaginal smears for 1 wk following the initiation of treatment revealed a clear disruption of the cyclic pattern ( Figure 6A ) . In fact , both Nrp1- and Sema3A-antibody-infused animals showed a preponderance of days in the diestrous phase , which is associated with reduced release of GnRH [34] and increased distance of GnRH axon terminals from the pericapillary space [23] , and a concomitant reduction of days in proestrus ( Figure 6B; n = 5–6 per group , p<0 . 05 , one-way repeated measures ANOVA , during versus before infusion ) . In contrast , animals infused with the vehicle alone ( PBS ) displayed normal 4-d estrous cycles ( n = 6 ) ( Figure 6A , B ) . One week after the initiation of treatment , the animals were sacrificed and subjected to control immunoprecipitation or immunofluorescent experiments to verify that the infused Nrp1- and Sema3A- antibodies had successfully targeted receptors and ligands in the ME , respectively ( Figure S4 and Figure S5 ) . Immunoprecipitation and immunoblot analyses indicated that the infused Nrp1 antibodies did bind to , and could therefore effectively block , about 50% of the endogenous pool of Nrp1 contained in the ME ( Figure S3A ) . Immunofluorescence analysis of the binding of the Sema3A-neutralizing antibody showed that it selectively targeted the external zone of the ME , where pituitary portal blood vessels and neuroendocrine terminals are localized ( Figure S4B ) . Together with our ex vivo results , these data suggest that Sema3A-Nrp1 signaling is required for the neuroendocrine control of the ovarian cycle in the adult rat brain . To further study the physiological relevance of Sema3A-Nrp1 endothelial-cell-to-neuron signaling in the mature brain , we used an intravenous injection of the TAT-Cre fusion protein , whose cellular uptake is enhanced compared to Cre recombinase [35] particularly in the ME of living animals [36] , to target endothelial cells in Sema3aloxP/loxP mice . Control experiments with tdTomatoloxP/+ reporter mice showed that a single injection of TAT-Cre into the tail vein caused gene recombination in tanycytes , which do not express Sema3A ( see Figure 1C ) , and in the capillary zone harboring Sema3A mRNA-expressing endothelial cells in adult females ( Figure 1B , Figure 7A ) . Quantitative RT-PCR analyses showed that Sema3A mRNA expression was decreased by 50% in the ME of virgin female Sema3aloxP/loxP mice treated with TAT-Cre and subjected to a male-pheromone-induced preovulatory GnRH/LH surge protocol [37] , when compared to vehicle-treated mice ( Figure 7C; n = 7–8 per group , t test , p<0 . 01 ) , while it remained unchanged in the adjacent mediobasal hypothalamus ( Figure 7C; n = 5–7 per group , t test , p>0 . 05 ) , where Sema3A mRNA is abundantly expressed ( Figure 1B ) and is known to play a key role in the control of GnRH release [38] . This selective attenuation of Sema3A expression in endothelial cells of the ME led to a significant decrease in preovulatory luteinizing hormone ( LH ) serum levels ( Figure 7B; n = 7–8 per group , t test , p<0 . 05 ) , used as an index of GnRH release [39] . Finally , real-time PCR analyses of Sema3A expression in the ME of wild-type mice across the estrous cycle revealed that Sema3A mRNA levels were significantly higher in proestrus than in diestrus ( 1±0 . 11 arbitrary units at proestrus versus 0 . 54±0 . 09 arbitrary units at diestrus , n = 7 and 3 , respectively , t ( 8 ) = 2 . 602 , p = 0 . 032 ) . Together , these data suggest that the ovarian cycle modulates Sema3A expression in endothelial cells of the ME , which in turn promotes the elongation of GnRH neuroendocrine axons on the day of proestrus to control the amplitude of the preovulatory GnRH/LH surge .
The reproductive cycle of mammals is critically regulated by hypothalamic GnRH neurons [25] , which periodically extend their axons in the ME towards the pericapillary space , into which they release the GnRH neuroendocrine signal during a specific time window [23] , [40] . The potential role of vascular endothelial cells in controlling this cyclic growth of axon terminals has not been investigated . Our in vivo and in vitro findings collectively indicate that Sema3A is a vascular factor promoting GnRH axonal growth in the adult brain and playing a pivotal role in orchestrating the central control of reproduction . We propose that Sema3A released by fenestrated endothelial cells of the hypothalamo-hypophyseal portal blood vessels cyclically induces GnRH neurons to extend their terminals towards the pericapillary space , this directionality being controlled by the glial scaffold along which GnRH axonal fibers travel within the ME ( see for review [14] ) . In turn , this mechanism regulates neuropeptide release at key stages of the ovarian cycle , such as at proestrus , when the preovulatory surge of GnRH occurs . Our ultrastructural analyses in GnRH::cre; Nrp1loxP/loxP mice , which do not exhibit any defect in GnRH axonal targeting when compared to Nrp1loxP/loxP mice ( Figure 3D , E ) , as well as the effect of locally restricted Sema3A infusion on GnRH axonal growth ( Figure 3B ) , strongly indicate that the effects of Sema3A on axonal plasticity within the ME depend on direct Sema3A-Nrp1 signaling in postdevelopmental GnRH terminals . The functional consequence of endothelial Sema3A secretion on GnRH axonal plasticity has , in addition , been demonstrated by the selective invalidation of Sema3A expression in the ME of adult Sema3aloxP/loxP mice by the intravenous injection of the recombinant TAT-Cre protein . Indeed , this approach , which further circumvents any putative developmental effect that might occur with the use of classic promoter-driven Cre expression technology , confirms that the endothelial-Sema3A-promoted elongation of GnRH axons modulates the amplitude of the preovulatory GnRH/LH surge on the day of proestrus . The molecular pathways that underlie this cyclic Sema3A-Nrp1-mediated GnRH axonal sprouting are unknown , although they appear to be intrinsic to GnRH neurons since Sema3A promotes GnRH neurite outgrowth both in tissue explants and in isolated cell cultures . A recent study has intriguingly suggested that Sema3A could promote axonal growth by inducing protein kinase G activity [41] . Notably , Sema3A receptors are broadly expressed in the axon terminals of other neuroendocrine systems and this signal has been proposed to serve as a coordinator of structural and functional synaptic plasticity in various neuronal circuits [6] . In our study , only about 50% of the Nrp1 expressed in the median emincence was neutralized by antibody infusion . It would be of interest to investigate the effects of more complete Nrp1 invalidation in adult animals , as well as the potential role of endothelial Sema3A in the growth of hypothalamic neuronal projections controlling other anterior pituitary functions , such as the growth- , stress- , and thyroid-hormone axes . An intriguing finding of this study is that endothelial cells of the ME appear to selectively release the 65 kDa isoform of Sema3A . Interestingly , we show that estradiol mimics ovarian-cycle effects on p65-Sema3A production in ovariectomized rats and that , in agreement with a previous in vitro study [42] , endothelial cells of the ME express the estrogen receptor ERα . The mechanisms underlying these changes in protein levels within the ME are unknown but likely involve changes in Sema3a transcription , rather than its translation or posttranslational processing such as furin cleavage . In line with this idea , analysis of the Sema3a gene using the ALGEN PROMO 3 . 0 software ( http://alggen . lsi . upc . es/cgi-bin/promo_v3/promo/promoinit . cgi ? dirDB=TF_8 . 3 ) predicts the presence of a putative estrogen-receptor-binding element at 100 bp upstream of the transcription initiation site; further experiments will be required to determine whether this presumptive binding site is actually functional . Even though the biological activity of p65-Sema3A has been validated in heterologous systems mimicking growth-cone collapse [43] and in co-culture systems using sympathetic ganglion explants [22] , this isoform was originally described as a proteolytic by-product of p95-Sema3A , with reduced functional activity [22] . Similarly , it has been reported recently that the anti-angiogenic activity of the 61 kDa proteolytic fragment of Sema3B is dramatically reduced compared to the full-length 83 kDa isoform [44] . In contrast , here we demonstrate that 65 kDa and 95 kDa Sema3A isoforms are equally effective at promoting GnRH neurite elongation ex vivo ( Figure 5C , D ) , indicating that the proteolytic cleavage of Sema3A does not interfere with its axonal-growth-promoting activity . In conjunction with the fact that the expression of the 65 kDa isoform of Sema3A , unlike the 125 kDa precursor and the best known 95 kDa secreted isoform , is subject to cyclic changes , being maximal on the day of proestrus , these results uncover for the first time a physiological role for p65-Sema3A in the adult brain . In conclusion , we show that in the ME of the hypothalamus , p65-Sema3A is an endothelial-cell-derived protein that acts on Nrp1 receptors in GnRH neuroendocrine processes , which have previously been seen to express axonal markers such as GAP-43 [27] , to promote their growth towards the target vascular wall during a time window of the reproductive cycle that is critical to ovulation . Because ovarian-cycle-regulated GnRH axonal elongation in the adult brain is likely to depend on the coordinated action of many extracellular factors , endothelial p65-Sema3A may work in concert or in competition with other secreted molecules including VEGF , nitric oxide , TGF-β1 , and BDNF , which are particularly enriched in the capillary zone of the ME [21] , [36] , [45] , [46] and may influence axonal plasticity by modulating the endothelial expression of or responsiveness to semaphorins [8] , [47]–[49] . These findings have implications for the possible roles of p65-Sema3A in adult brain function . Finally , our results raise the intriguing possibility that vascular semaphorins may play important and unexpected roles in the adult neural plasticity underlying several other key physiological processes such as learning , stress , and the control of energy homeostasis [50]–[53] .
All experiments were performed in accordance with the European Communities Council Directive of November 24 , 1986 ( 86/609/EEC ) regarding mammalian research and were approved by the Institutional Animal Care and Use Committee of Lille and the animal experimentation committee of the Royal Netherlands Academy of Arts and Sciences in Amsterdam . The purification of endothelial cells of the ME was realized by sequential immunopanning . Endothelial cells of the ME were isolated from 10-d-old rats using a procedure adapted from a protocol kindly provided by Dr . Ben Barres ( Stanford , CA ) [20] , as described previously [21] . In brief , ME explants were enzymatically dissociated at 37°C for 90 min using a solution of papain ( 33 U/ml ) ( Worthington/Cooper , Lakewood , NJ ) in MEM/HEPES ( Invitrogen ) containing L-cysteine ( 0 . 4 mg/ml ) ( Sigma ) and DNase ( 125 U/ml ) ( Sigma ) . Tissues were then triturated in a solution containing ovomucoid trypsin inhibitor solution ( 2 mg/ml ) ( Boehringer Mannheim , Mannheim , Germany ) , DNase ( 125 U/ml ) , and BSA ( 1 mg/ml ) ( Sigma ) , to obtain a suspension of single cells . The suspension was filtered through a 20 µm nylon mesh . After centrifugation at 550×g , single cells were successively panned on a Petri dish coated with an anti-CD90 mouse monoclonal antibody , which recognizes the rat Thy1 . 1 antigenic determinant ( MRC-OX7; Serotec , Oxford , UK ) to deplete macrophages and fibroblasts , and on a second Petri dish coated with rat neural antigen ( RAN ) -2 ascites ( LGC Promochem , Molsheim , France ) to deplete meningeal cells and type-1 astrocytes; the remaining cells were incubated in a Petri dish coated with an affinity-purified rabbit antibody raised against PV1 , which selectively recognizes fenestrated vascular endothelial cells of the ME [18] . Purified endothelial cells were cultured in DMEM supplemented with 10% fetal bovine serum , 1% L-glutamine , and 2% penicillin/streptomycin until they reached confluency . They were then recovered by trypsin digestion and plated in 10 cm dishes or on poly-D-lysine ( Sigma ) coated coverslips . Three primary cultures from three independent litters were used in the present study . To produce endothelial cell-conditioned medium ( EC-CM ) , cell monolayers were cultured in 10-cm dishes for 48 h in DMEM ( devoid of phenol red; Invitrogen ) supplemented with 1% L-glutamine , 1% penicillin/streptomycin , 5 µg/ml insulin ( Sigma ) , and 100 µM putrescine ( Sigma ) . For Western blot analysis , 10 ml of EC-CM were concentrated using a Centriplus centrifugal filter device ( size cutoff of 10 kDa , Cat . 4411 , YM10; Millipore , Bedford , MA ) to obtain a final volume of 30–40 µl . The concentrated medium was mixed with NuPAGE LDS sample buffer 4× ( Invitrogen ) to obtain a final concentration of 1× , boiled for 5 min , and stored at −80°C until loading . Confluent cultures of mouse endothelial cells SVEC4-10 were incubated in serum-free DMEM medium for 48 h , either in presence or absence of the furin protease selective inhibitor Dec-RVKR-CMK ( 100 µM; Bachem ) . Cell-conditioned media were concentrated with a size cutoff of 50 kDa ( Vivaspin , Sartorius ) , and a sample size equivalent to the medium collected from a 2 cm2 cell monolayer was separated by SDS-PAGE and eventually analyzed by Western blotting . Complementary DNA fragments derived from mRNAs encoding Sema3A were generated by reverse transcription ( RT ) -PCR of total RNA extracted from the neonatal rat brain , adult female rat ME , or primary cultures of ME endothelial cells . One µg of Trizol ( Life Technologies , Grand Island , NY ) -extracted RNA was reverse transcribed to cDNA in a final volume of 10 µl containing 200 U of SuperScript II reverse transcriptase ( Invitrogen ) , 20 U of RNase inhibitor ( Promega , Madison , WI ) , and 0 . 5 µg of oligo-dT primer . After a 1 h incubation at 42°C , the reaction was stopped by heating at 94°C for 5 min . PCR was performed by using 1 µl of each reverse transcription reaction and Hotstart Taq DNA polymerase ( Qiagen , France ) in a volume of 50 µl . The thermocycling conditions were 15 min at 95°C for enzyme activation , followed by 35 cycles at 94°C for 1 min , annealing at 53°C for 1 min , 72°C for 1 min , followed by a final extension period of 10 min at 72°C . A 364 bp DNA fragment ( sense 5′-TCATCCTGAGGACAACAT-3′ , antisense 5′-GCATATCTGACCTATTCT-3′ ) corresponding to nucleotides 444–807 ( NM017310 ) was amplified . PCR with the substitution of cDNA with RNA served as a control . All cDNAs generated by RT-PCR were verified by sequencing . β-actin cDNA was amplified with primers 5′-AACTGACAGACTACCTCA-3′ and 5′-GCTCATAGCTCTTCTCCA-3′ to verify the quality of samples ( not shown ) . MEs from female P90 rats ( n = 3 ) were microdissected and enzymatically dissociated using a Papain Dissociation System ( Worthington , Lakewood , NJ ) to obtain single-cell suspensions . Subsequently , dissociated cells were resuspended in Hanks' balanced salt solution ( HBSS; Invitrogen ) containing 1% BSA . Endothelial cells were labeled for 30 min at 4°C using an affinity-purified rabbit antibody raised against PV1 ( 1∶100 ) , which selectively recognizes fenestrated vascular endothelial cells of the ME [18] , followed by a 15-min incubation at 37°C with an AlexaFluor 488 anti-rabbit secondary antibody ( 1∶100 , Invitrogen ) . FACS was performed using an EPICS ALTRA Cytometer device ( Beckman Coulter , Inc . ) . Sorted GFP-positive cells and GFP-negative cells ( yield: 30 , 000 cells isolated from each animal ) were collected into two separate tubes containing 500 µl of sterile HBSS ( Invitrogen ) and subsequently centrifuged for 1 min at 7 , 500 g ( maximum ) to pellet the cells . HBSS was then aspired and 8 µl of a solution containing 1 µl of 0 . 1% Triton X-100 and 7 µl of Prime RNase inhibitor ( diluted 1∶100 in diethylpyrocarbonate-treated water; Invitrogen ) was added . Captured cells were used to synthesize first-strand cDNA using the SuperScript III First-Strand Synthesis System for RT-PCR ( Invitrogen ) following the manufacturer's instructions . Controls without reverse transcriptase were performed to demonstrate the absence of contaminating genomic DNA . RNA isolated from the adult rat brain was also reverse transcribed and used as a positive control . PCR was performed at 35 cycles on a thermocycler ( 30 s denaturation at 94°C , 30 s annealing at 55–65°C , and 2 min elongation at 72°C ) . PCR primer pairs were as follows: Sema3A forward primer , 5′-ATGAATGCAAGTGGGCTGGA-3′; Sema3A reverse primer , 5′-CGGTCCTGATGGGATGATGG-3′; PV1 forward primer , 5′- TGAAGGAGGGCAACAAGACC-3′ PV1 reverse primer , 5′-AACGGTAGACCAGCGAATCC-3′; β3-tubulin forward primer , 5′-CGTCTCTAGCCGAGTGAAGTC-3′; β3-tubulin reverse primer , 5′-TCCGAGTCCCCCACATAGTT-3′; DARP32 forward primer , 5′-CCTCATAGAGCGCGGGATTT-3′; DARP32 reverse primer , 5′-CGGATCATCTCCACCTGTCG-3′; TSH forward primer , 5′-GAGAGTGTGCCTACTGCCTG-3′; TSH reverse primer , 5′-CATCCCGGTATTTCCACCGT-3′; GAPDH forward primer , 5′-GGACCAGGTTGTCTCCTGTG-3′; GAPDH reverse primer , 5′-ATTCGAGAGAAGGGAGGGCT-3′ . Qualitative RT-PCR experiments were run three times on sorted cells from three different animals . Real-time PCR was carried out on Applied Biosystems 7900HT Fast Real-Time PCR System using exon-boundary-specific TaqMan Gene Expression Assays ( Applied Biosystems ) : PV1 ( PV1_Rn00571706_m1 ) , Sema3A ( Sema3A_Rn00436469_m1 ) , ERα ( Esr1_Rn01640372_m1 ) , ERβ ( Esr2_Rn00562610_m1 ) , and housekeeping ribosomal RNA ( Rn45_Rn03928990_g1 ) . Two-month-old female rats and monogenic and bigenic mouse littermates were perfused transcardially with 4% paraformaldehyde in 0 . 1 M PBS , pH 7 . 4 . Rat and mouse brains were postfixed in the same fixative containing 20% sucrose for 2 h at 4°C , immersed in 20% sucrose in 0 . 1 M phosphate buffered saline overnight at 4°C , embedded in Tissue-Tek , and frozen in liquid nitrogen . Coronal sections ( 14 µm ) were cut on a cryostat and mounted onto chrome-alum-gelatin–coated slides and subjected to fluorescent labeling . Briefly , the sections were washed in 0 . 1 M PBS , then incubated for 10 min at room temperature in blocking solution containing 2% normal donkey serum ( D9663; Sigma ) and 0 . 3% Triton X-100 in 0 . 1 M PBS . Sections were then incubated overnight at 4°C with the primary antibodies diluted in the same solution . Primary antibodies used were rabbit polyclonal antibodies diluted 1∶3 , 000 for GnRH [56] and PV-1 [18] and a goat polyclonal antibody to the extracellular domain of rat Nrp1 ( AF 566; R&D Systems ) diluted 1∶400 . Sections were washed in 0 . 1 M PBS , and labeling revealed by incubation for 1 h at room temperature with AlexaFluor 568–conjugated anti-rabbit or AlexaFluor 488–conjugated anti-rabbit antibodies ( 1∶400; Molecular Probes ) or biotin-conjugated donkey anti-goat IgGs ( 1∶400; Jackson Immunoresearch , West Grove , PA ) followed by AlexaFluor 488– or AlexaFluor 568–conjugated streptavidin ( 1∶500 ) for 1 h . Vascular endothelial cells were visualized with tetramethylrhodamine isothiocyanate ( TRITC ) -conjugated Bandeiraea simplicifolia lectin ( 1∶600; Sigma ) . After washes , slices were coverslipped with Permafluor medium ( 434990; Immunon , Pittsburgh , PA ) . For the detection of Sema3A , female mouse brains were quickly harvested , embedded in ice-cold Tissue Tek , frozen in isopentane ( −55°C ) , and stored at −80°C until use . Brains were cut into 20-µm-thick coronal sections and processed for immunohistochemistry as follows . Slide-mounted sections were ( 1 ) fixed by immersion for 1 min in methanol/acetone ( vol/vol ) at −20°C; ( 2 ) blocked for 30 min using a solution containing 4% normal goat serum and 0 . 3% Triton X-100; ( 3 ) incubated overnight at 4°C with a rabbit polyclonal anti-Sema3A ( 1∶50 , sc-10720; Santa Cruz Biotechnology , Santa Cruz , CA ) , which selectively recognizes Sema3A in Western blots ( Figure 1E , F , G and Figure S4 ) , and a rat anti-mouse PV1 ( MECA32 clone , 1∶200; gift from Professor Britta Engelhardt , Switzerland ) followed by 1 h at room temperature with a cocktail of secondary AlexaFluor-conjugated antibodies ( 1∶500 , Molecular Probes , Invitrogen , San Diego , CA ) ; and ( 4 ) counterstained with Hoechst ( 1∶10 , 000 , Molecular Probes , Invitrogen ) and coverslipped using Mowiol ( Calbiochem , USA ) . MEs were obtained from cycling diestrous and proestrous rats killed at 16 h . After dissection , each fragment was placed in a microcentrifuge tube , snap frozen in dry ice , and stored at −80°C . Protein extracts of a set of two MEs were prepared by trituration of the fragments through 22 and 26 gauge needles in succession in 200 µl of lysis buffer ( 25 mM Tris , pH 7 . 4 , β-glycerophosphate , 1 . 5 mM EGTA , 0 . 5 mM EDTA , 1 mM sodium pyrophosphate , 1 mM sodium orthovanadate , 10 µg/ml leupeptin and pepstatin A , 10 µg/ml apoprotinin , 100 µg/ml PMSF , and 1% Triton X-100 ) for straight analysis , or in 750 µl for immunoprecipitation . After 30 min of gentle rocking at 4°C , the tissue lysates were cleared by centrifugation at 14 , 000 rpm for 15 min . For straight analysis , the protein content of supernatants was determined using BCA protein assays ( Pierce Chemical , Rockford , IL ) , and equal amounts of proteins were mixed with SB4X to obtain a final volume of 50 microliters in 1× NuPAGE LDS sample buffer ( Invitrogen ) . For immunoprecipitation , 60 µl of protein A-sepharose ( 1∶1 slurry in lysis buffer , P3391; Sigma ) were added to the supernatants in order to remove endogenous IgGs ( preclearing ) . The samples were then rocked for 30 min at 4°C , the beads were centrifuged for 15 s at 14 , 000 rpm , and the supernatants collected . Equal amounts of protein ( 350 µg ) in 750 µl of lysis buffer were incubated with 2 µg of anti-Nrp1 ( AF566 , R&D Systems ) with gentle rocking overnight at 4°C . Thereafter , 60 µl of protein A-sepharose beads were added to the antibody-antigen complex and incubated for 3 h at 4°C . The sepharose beads were collected by centrifugation . Beads were then washed twice with ice-cold lysis buffer , and boiled for 5 min in 50 µl of 2× NuPAGE LDS sample buffer ( Invitrogen ) . Samples were stored at −80°C until use . Samples were boiled again for 5 min after thawing and electrophoresed for 1 h at 150 V in precast 3%–8% Tris-acetate gels or for 35 min at 200 V in precast 4%–12% MES polyacrylamide-SDS gels ( Invitrogen ) . Then , the proteins were transferred onto 0 . 2 µm pore-size polyvinylidene difluoride ( PVDF ) membranes ( Invitrogen ) for 1 h at room temperature ( RT ) . Blots were incubated for 1 h in Tris-buffered saline ( TBS; 0 . 05 M Tris , pH 7 . 4 , 0 . 15 M NaCl ) with 0 . 05% Tween 20 ( TBS-T ) and 5% nonfat milk at RT , or in TBS with 1% Tween 20 for 1 h at RT . The membranes were exposed to the primary antibody ( goat polyclonal anti-Nrp1 , 1∶100 , AF566 , R&D Systems , or rabbit polyclonal anti-Sema3A , 1∶100 , sc-10720 , Santa Cruz Biotechnology ) diluted in TBS-T with 5% nonfat milk overnight at 4°C with gentle rocking . Immunoreactions were detected with horseradish peroxidase-conjugated secondary antibodies ( Sigma ) in TBS-T with 5% nonfat milk for 1 h at room temperature , and developed using enhanced chemiluminescence ( NEL101; PerkinElmer , Boston , MA ) . When necessary , the membranes were stripped ( PBS; 5 min at 100°C ) and incubated with a goat polyclonal antibody against actin ( 1∶1 , 000; Santa Cruz Biotechnology ) . Protein expression was densitometrically analyzed using Scion Image software ( Scion Corporation , MA ) . To determine whether Sema3A promotes GnRH nerve terminal plasticity , ex vivo experiments were carried out according to previously described protocols [21] . Female rats weighing 250–300 g were killed on diestrus 2 ( n = 12 ) or proestrus ( n = 12 ) by decapitation . Four animals were used per condition . After rapid removal of the brain , hypothalamic explants were microdissected without damaging the MEs . Explants were placed in 12-well plates and preincubated for 30 min at 37°C in 1 mL of Krebs-Ringer bicarbonate buffer , pH 7 . 4 , containing 4 . 5 mg/ml D-dextrose and 5 µM tetrodotoxin , with or without Nrp1- or Sema3A-neutralizing antibodies ( 15 µg/ml , AF566 and MAB-1250 , respectively , R&D Systems ) , under an atmosphere of air containing 5% CO2 . The Nrp1-neutralizing antibody has been shown to selectively target the semaphorin-binding domain of Nrp1 [13] . In addition , we confirmed the specificity of the Sema3A-neutralizing antibody , which specifically detects Sema3A in the conditioned media from transfected COS-7 cells ( Figure S4C ) , using immunohistochemistry ( Figure S4B ) . After this preincubation , tissues were placed in fresh medium with or without a recombinant human Semaphorin-3A/Fc chimera ( 1 , 000 ng/ml; 1250-S3 , R&D Systems ) for an additional 30-min incubation period . Explants were then processed for electron microscopy as described previously [23] . Briefly , tissues were fixed by immersion in a solution of 2% paraformaldehyde , 0 . 2% picric acid , and 0 . 1% glutaraldehyde in 0 . 1 M phosphate buffer , pH 7 . 4 , for 2 h at 4°C . Tissues were postfixed with 1% OsO4 in phosphate buffer for 1 h at room temperature . After dehydration , tissues were embedded in Araldite . Semithin sections ( 1–2 µm thick ) were used to progressively approach and identify the portion of the ME targeted for ultrastructural studies—that is , the area where the pituitary stalk becomes distinct from the base of the hypothalamus but still remains attached to it by the hypophyseal portal vasculature [23] . This area , which does not extend beyond 20 µm , contains high numbers of GnRH fibers . To detect GnRH immunoreactivity , ultrathin sections ( 80–90 nm thick ) collected on Parlodion 0 . 8%/isoamyl acetate-coated 100 mesh grids ( EMS , Fort Washington , PA ) were treated using an immunogold procedure described previously [23] . Briefly , after a preliminary treatment with H2O2 ( 10%; 8 min ) and a blocking step in TBS ( 0 . 1 M Tris , pH 7 . 4 , 0 . 15 M NaCl ) containing 1% normal goat serum and 1% bovine albumin serum ( TBSB ) ( 10 min at room temperature ) , the grids were floated on a drop of the following reagents and washing solutions: ( 1 ) rabbit anti-GnRH ( 1∶5 , 000 ) in TBSB for 60 h at 4°C , ( 2 ) TBS to remove excess antibodies ( three times for 10 min ) , ( 3 ) colloidal gold ( 18 nm ) -labeled goat anti-rabbit immunoglobulins ( Jackson ImmunoResearch ) 1∶20 in TBS for 90 min at room temperature , ( 4 ) TBS ( three times for 10 min ) , and ( 5 ) distilled water ( three times for 10 min ) . The sections were then counterstained with uranyl acetate and lead citrate before observation . The specificity of the GnRH antisera used has been discussed previously [56] . Ultrathin immunolabeled sections were examined with a Zeiss transmission electron microscope 902 ( Leo , Rueil-Malmaison , France ) , and images were acquired using a Gatan Orius SC1000 CCD camera ( Gatan France , Grandchamp , France ) . Morphometric analysis was performed by an investigator blind to hypothalamic explant treatment on digitalized images taken at an original magnification of 12 , 000× from 10–15 ultrathin sections per animal , with a space of 25 sections between them , to avoid taking the same GnRH nerve terminal into consideration twice ( the diameter of a GnRH nerve terminal rarely exceeds 2 µm ) . All GnRH-immunoreactive nerve terminals located at less than 10 µm from the parenchymatous basal lamina ( i . e . , the pial surface of the brain ) were taken into consideration—that is , more than 100 distinct axon terminals per animals ( i . e . , almost all GnRH nerve terminals abutting onto the pituitary portal blood vessels in the aforementioned 20-µm-thick region of the ME ) . Immunolabeled terminals confined to a distance of 10 µm or less from the basal lamina were imaged and the distance from the nerve terminal to the pericapillary space recorded . Similar electron microscopic analyses were performed in 60-d-old diestrous Nrp1loxP/loxP ( n = 8 ) and GnRH::Cre; Nrp1loxP/loxP ( n = 8 ) mice . Three to four animals were used per condition . Timed-pregnant GnRH-GFP mice were anesthetized with an intraperitoneal injection of 200 mg/kg ketamine and killed by cervical dislocation . E12 . 5 embryos were harvested , and nasal regions were dissected from each embryo and dissociated using the Papain Dissociation System ( Worthington , Lakewood , NJ ) to obtain single-cell suspensions . Dissociated nasal tissue containing GnRH-GFP cells , mesenchymal cells , and olfactory/vomeronasal cells were cultured in DMEM/F12 ( Invitrogen ) supplemented 1% L-glutamine ( Invitrogen ) and D- ( + ) -glucose ( final concentration 1% ) at 37°C with 5% CO2 for 24 h in the presence or absence of mouse monoclonal anti-Sema3A ( 15 µg/ml ) [57] neutralizing antibody ( R&D Systems , MAB-1250 ) before processing for immunocytochemistry ( control , N = 3 independent experiments , n = 146 cells; treated , N = 3 independent experiments , n = 143 cells ) . Anti-Sema3A binding in living cells was visualized using an AlexaFluor 568–conjugated anti-mouse antibody ( 1∶400; Invitrogen ) , and GFP using an anti-GFP chicken primary antibody ( 1∶1 , 000 , ab13970 , Abcam ) and an AlexaFluor 488–conjugated anti-chicken secondary antibody ( 1∶400; Jackson Immunoresearch , West Grove , PA ) in 4% paraformaldehyde fixed cultures . Quantification of GnRH fiber length was performed on digitized photomicrographs using the NeuronJ plugin of ImageJ software ( National Institutes of Health ) ; 10–20 pictures were taken for each culture well , and a total of 200 cells for each treatment condition were analyzed . Twelve embryos were used for the control group and 13 for the treatment group . All experiments used primary cultures generated from different individuals on multiple culture dates . Data are presented as means ± SEM . For comparison between the two groups , a two-tailed unpaired Student's t test was used . Normality of the data was tested with the Shapiro-Wilk test . COS-7 and SVEC4-10 cells ( ATCC ) were grown in a monolayer at 37°C in a 5% CO2 atmosphere , in DMEM ( Life Technologies , Inc . ) containing 1 mM sodium pyruvate , 2 mM glutamine ( Life Technologies , Inc . ) , 100 µg/ml streptomycin , 100 U/ml penicillin , and 4 , 500 mg glucose ( ICN Biomedicals , Inc . ) , supplemented with 10% FBS ( Invitrogen ) . The medium was replaced at 2-d intervals . Subconfluent cells were routinely harvested by trypsinization and seeded onto 58 cm2 dishes ( 100 , 000 cells ) . For all experiments , only cells within six passages were used . GnV-3 cells are one of 11 clones of GnRH-expressing cells obtained by the conditional immortalization of cultured adult rat hypothalamic cells [58] . GnV-3 cells express markers of well-differentiated neurons and do not express markers of glial cells [59] . Cells were grown in Proliferation medium consisting of Neurobasal A medium with B27 supplement ( 20 µl/ml , Invitrogen-Gibco ) , PSN ( 1× , Invitrogen ) , Glutamax I ( Invitrogen ) , doxycycline hydrochloride ( 0 . 5 µg/ml , Sigma ) , FBS ( 10 µl/ml , Biological Industries ) , and βFGF ( 5 ng/ml , Invitrogen ) . Doxycycline promotes the proliferation of these conditionally immortalized cells . To induce the differentiation of GnV-3 cells , the culture medium was replaced by differentiation medium ( containing Neurobasal A , B27 supplement , PSN , Glutamax I , and βFGF ) . COS-7 cells were transiently transfected using the fast-forward protocol . Briefly , a 58 cm2 subconfluent dish was split into four dishes in OptiMEM medium ( Invitrogen ) about 1 h before high-efficiency liposome transfection ( Lipofectamine 2000 , Invitrogen ) . Each dish was transfected with 2–4 µg of DNA construct ( full-length human Semaphorin 3A-myc cDNA plasmid , 65 kDa truncated human Semaphorin 3A-myc cDNA plasmid , and empty vector for control ) . The latter construct was generated by site-directed mutagenesis using a QuickChange II XL site-directed mutagenesis kit ( Agilent Technologies ) to introduce a Stop codon after the conserved Arginine residue 555 , corresponding to the furin cleavage site . Three-dimensional matrix assays were performed by co-culturing GnV-3 cell aggregates with ME explants dissected from adult female rats as described above . In another set of experiments , GnV-3 cell aggregates were co-cultured with COS-7 cells transfected either with full-length Sema3A ( Sema3A-FL ) or 65 kDa Sema3A , or with the control vector , as previously described [60] . For the aggregates , cells were collected by trypsinization , resuspended in 5 µl of growth-factor-free Matrigel ( BD Biosciences , San Jose , CA ) ( 106 cells/ml for both GnV-3 and COS-7 ) , and placed on the lid of a culture dish . As cell aggregates were formed in the droplets , they were plated onto Millicell inserts coated with growth-factor free Matrigel ( Millipore ) and maintained in culture for 72 h . Cultures were grown for 2–3 d in Neurobasal medium containing B27 and gentamycin before staining . To test whether Sema3A acts on GnRH-1 processes in an Nrp1-dependent fashion , a rat-Nrp1-neutralizing antibody ( 1 µg/ml , R&D Systems , AF566 ) was added to the growth medium of the co-cultures . The following day , the cultures were fixed with 4% paraformaldehyde in 0 . 01 M phosphate buffer , pH 7 . 4 , and permeabilized with 0 . 3% Triton X-100 ( Sigma ) for 1 h at room temperature . Finally , they were stained with Alexa 568–X phalloidin ( Molecular Probes , Eugene , OR ) for 45 min at 37°C before image analysis . Quantification of GnV3 fiber growth was performed on digitized photomicrographs using the NeuronJ plugin of ImageJ software ( National Institutes of Health ) . For confocal observations and analyses , an inverted laser scanning Axio observer microscope ( LSM 710 , Zeiss ) with EC Plan NeoFluor 10×/0 . 3 NA , 20×/0 . 5 NA , and 40×/1 . 3 NA ( Zeiss ) objectives was used ( Imaging Core Facility of IFR114 of the University of Lille 2 , France ) . ImageJ ( National Institutes of Health , Bethesda , MD ) and Photoshop CS5 ( Adobe Systems , San Jose , CA ) were used to process , adjust , and merge the photomontages . To determine the importance of Nrp1 in the central control of reproductive function , in vivo experiments were performed to neutralize Nrp1 receptor-ligand interactions within the ME . Anti- Nrp1 or -Sema3A IgGs ( R&D Systems ) were chronically infused into the ME ( bregma −3 . 6 mm , 9 . 5 mm depth from the skull surface ) [61] through a stereotaxically implanted infusion cannula ( Plastics One , Roanoke , VA ) connected to subcutaneously implanted osmotic minipumps ( model 1007D; Alzet , Palo Alto , CA ) . The pump had a flow rate of 0 . 5 µl/h and a capacity of 100 µl , resulting in a delivery period of 7 d . Each pump was loaded with sterile DPBS ( Invitrogen ) containing the Nrp1-neutralizing antibody ( 0 . 25 µg/µl final ) or no antibody . After connection to the infusion device and overnight priming in 0 . 9% NaCl at 37°C , the assembly was implanted into cycling 190–200 g female rats with regular estrous cycles . Estrous cycles were monitored before and after surgery . Following infusion for 7 d , animals were killed to assess the implantation site of the cannula and check for exhaustion of the infused solution . MEs from Nrp1 and PBS-infused animals were collected , snap frozen in dry ice , and stored at −80°C . To determine whether the infused Nrp1 antibodies actually targeted the ME and bound endogenous Nrp1 , protein extracts from MEs were prepared and subjected to immunoprecipitation using Nrp1 antibodies as described above . The sepharose beads from the preclearing step allowed IgGs to be collected from the MEs . The beads were then separated by centrifugation , washed twice with ice-cold lysis buffer , and boiled for 5 min in 50 µl of 2× NuPAGE LDS sample buffer ( Invitrogen ) . After centrifugation , the supernatants were analyzed by Western blotting for Nrp1 . Anti-Sema3A-treated animals and their PBS-treated controls were perfused transcardially with fixative and processed for immunohistochemistry as described above . AlexaFluor 488–conjugated anti-mouse antibodies ( 1∶400 ) were used to detect the binding of the intracranially infused Sema3A antibodies in situ; vascular endothelial cells were visualized using TRITC-conjugated Bandeiraea simplicifolia lectin . A TAT-Cre fusion protein produced as detailed previously [35] was injected into the tail vein ( 40 µl at 2 . 1 mg/ml ) of mice 1 wk before they were placed for 62 h in a cage that had previously held a sexually experienced male to induce , a protocol used to induce a preovulatory surge in adult virgin mice [37] . For Sema3a gene expression analysis , mRNAs obtained from microdissected ME and mediobasal hypothalamus explants were reverse transcribed using SuperScript III Reverse Transcriptase ( Life Technologies ) . Real-time PCR was carried out on Applied Biosystems 7900HT Fast Real-Time PCR System using the SEMA3A ( Sema3a_Mm00436469_m1 ) exon-boundary-specific TaqMan Gene Expression Assays ( Applied Biosystems ) . Plasma LH was measured using a Rodent LH ELISA kit ( Endocrine Technologies , Newark , CA ) with a sensitivity of 0 . 03 ng/ml and 7% intra-assay and 10% inter-assay coefficients of variance . All analyses were performed using Prism 5 ( GraphPad Software ) and assessed for normality ( Shapiro-Wilk test ) and variance , when appropriate . Sample sizes were chosen according to the standard practice in the field . Before statistical analysis , percentages were subjected to arc-sine transformation to convert them from a binomial to a normal distribution [62] . Data were compared by a two-tailed unpaired Student's t test , one-way ANOVA for multiple comparisons , or two-way repeated-measures ANOVA . A Tukey's post hoc test was performed when appropriate . The significance level was set at p<0 . 05 . Data groups are indicated as mean ± SEM . The number of biologically independent experiments , p values , and degrees of freedom are indicated in the figure legends . | In the developing embryo , endothelial cells release chemotropic signals such as Semaphorin 3A ( Sema3A ) that , upon activation of its receptor Neuropilin-1 ( Nrp1 ) , regulate neuronal migration and axon guidance . However , whether endothelial cells in the adult brain retain the ability to secrete molecules that influence neuronal function is unknown . Here we show in the adult brain of rodents that vascular endothelial cells release Sema3A and that the amount released is regulated by the ovulatory cycle . Sema3A , in turn , promotes the outgrowth of axons of hypothalamic neurons that express Neuropilin-1 towards the endothelial wall of portal blood vessels . These neurons release there the neuropeptide that controls reproduction: gonadotropin-releasing hormone ( GnRH ) . Notably , this endothelial-cell-mediated sprouting of GnRH axons regulates neuropeptide release at a key stage of the estrous cycle , the proestrus , when the surge of GnRH triggers ovulation . Thus , by promoting GnRH axonal growth in the adult brain , Sema3A/Neuropilin-1 plays a pivotal role in orchestrating the central control of reproduction . Our results suggest a model in which vascular endothelial cells are dynamic signaling components that relay peripheral information to the brain to control key physiological functions , including species survival . | [
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] | 2014 | Brain Endothelial Cells Control Fertility through Ovarian-Steroid–Dependent Release of Semaphorin 3A |
Caenorhabditis elegans male copulation requires coordinated temporal-spatial execution of different motor outputs . During mating , a cloacal circuit consisting of cholinergic sensory-motor neurons and sex muscles maintains the male's position and executes copulatory spicule thrusts at his mate's vulva . However , distinct signaling mechanisms that delimit these behaviors to their proper context are unclear . We found that dopamine ( DA ) signaling directs copulatory spicule insertion attempts to the hermaphrodite vulva by dampening spurious stimulus-independent sex muscle contractions . From pharmacology and genetic analyses , DA antagonizes stimulatory ACh signaling via the D2-like receptors , DOP-2 and DOP-3 , and Gαo/i proteins , GOA-1 and GPA-7 . Calcium imaging and optogenetics suggest that heightened DA-expressing ray neuron activities coincide with the cholinergic cloacal ganglia function during spicule insertion attempts . D2-like receptor signaling also attenuates the excitability of additional mating circuits to reduce the duration of mating attempts with unproductive and/or inappropriate partners . This suggests that , during wild-type mating , simultaneous DA-ACh signaling modulates the activity threshold of repetitive motor programs , thus confining the behavior to the proper situational context .
Context-dependent motor patterns are the outcome of unique interplay amongst neuromodulators in the central nervous system ( CNS ) . The neurotransmitter dopamine ( DA ) modulates gamma-aminobutyric acid ( GABA ) , glutamate and acetylcholine ( ACh ) activity in cognitive and motor behaviors [1]–[6] . In vertebrates , DA adjusts motor outputs by selective synergy/antagonism of tiered neuronal population's activities [2] , [7] . In the brain this regulation is initiated by DA secretion from the substantia nigra , which antagonizes post-synaptic cholinergic striatal interneurons [8] . The result is a context-dependent voluntary motion modulated by DA [9]–[11] . Disturbing the DA-ACh balance causes impulsive motor disorders as described in Parkinson's disease and choreas [12]–[15] . However , vertebrate and invertebrate models that fully encompass the in vivo cellular and molecular components of the DA-ACh interaction , which refine motor outputs , remain elusive . With 383 neurons in males , the genetically tractable nematode Caenorhabditis elegans is a model for dissecting the cellular and molecular machinery involved in motor programs . DA secretion from sensory neurons mediates transitions between locomotor patterns to directly or indirectly regulate muscle contractile events [16] . Activation of D1-like Gαq-coupled receptors has been shown to regulate forward-to-backward locomotor switches and swimming-to-crawling gaits [17]–[20] . The opposing signaling cascade , activating Gαo-coupled D2-like receptors , reduces locomotion velocity upon finding novel food sources [21]–[23] . While these studies provide insight into general principles underlying DA neurotransmission , a more complex , goal-oriented and decision-based behavior such as male mating could better model subtle DA-ACh motor circuit regulation . The C . elegans male mating circuit integrates sensory-motor cues that result in successful insertion of the copulatory spicules into the hermaphrodite vulva ( Figure S1A ) [24] , [25] . The positioning of the male tail over the vulva is a stepwise process , redundantly executed by a bilateral set of nine sensory rays located at the male tail [26] , [27] . When the male contacts a mate , putative mechano- and chemosensory neurons within each ray projection initiate backward scanning locomotion . Scanning behavior facilitates additional male-specific sensilla , located anteriorly and posteriorly of the cloacal region , to sense the hermaphrodite's vulva . Upon vulval contact , scanning behavior ceases and a subset of post-cloacal sensilla sensory-motor neurons , PCB and PCC , release ACh to promote spicule insertion attempts . Ionotropic and metabotropic ACh receptors located on multiple genital muscles induce the male to press his tail against the vulva and stimulate rhythmic movements of the attached copulatory spicules , so that they repetitively thrust against the vulval slit . Full insertion is achieved by additional ACh secretions from the putative proprioceptive SPC motor neurons . Simultaneous stimulation from the post-cloacal sensilla and SPC neurons mediates tonic muscle contraction , resulting in complete spicule protrusion from the tail [24] , [25] , [28] . Intrinsic and extrinsic factors likely modify the cholinergic circuit's activity prior to and during mating [29]–[33] . Male-specific dopaminergic motor-sensory neurons suggest that DA might modulate aspects of mating behavior . In this study , we use pharmacology , genetics , behavioral observation , calcium imaging and optogenetics to determine that DA signaling , partly through D2-like receptors down-modulates ACh signaling to restrict mating attempts to the vulva and from inappropriate mates .
Male copulation requires monitoring mechanisms to initiate and terminate multiple sub-steps under the proper context . Mating begins when the male presses his tail against the hermaphrodite and moves backwards , scanning for the vulva [25] , [34] . After he locates the vulva , he initiates repetitive 7-11 Hz spicule thrusts to breach the vulval slit . During this sub-behavior , the male progressively adopts an arched body posture , which persists throughout spicule insertion and sperm transfer ( Figure S1A ) . In rare events , this arched posture is adopted during scanning . Successful ejaculation occurs after repeated attempts of these motor sub-behaviors [27] , [28] . Molecules that promote mating execution have been identified , but few modulators that regulate and refine the behavior have been described [24] , [26] , [30] , [35]–[41] . DA signaling is known to modulate general C . elegans locomotor behaviors . Since 3 pairs of sex-specific sensory ray neurons secrete the neurotransmitter , DA is a candidate for modulating mating [16] , [22] . Tyrosine hydroxylase is a key enzyme in the biosynthesis of DA . We first asked how well tyrosine hydroxylase deficient cat-2 ( lf ) males mate [16] , [22] . Initially we noticed that in cat-2 male populations , a higher percentage displayed spontaneously protracted spicules ( 44%; n = 67 ) relative to wild type ( 10%; n = 62 ) ( p = 0 . 0018 , Fisher's exact test ) . This suggested that DA might down-modulate the spicule protraction circuit . When we paired a non-protracted virgin mutant or wild-type 1-day-old adult male with a 1-day-old moving hermaphrodite for 24 hrs , we found that 56% of cat-2 males could sire progeny compared to the 88% of wild type ( Figure 1A ) . To confirm that cat-2 mating deficits were caused by DA depletion and not due to unknown background mutations , we attempted to phenocopy the cat-2 behavioral defect in a different manner . Dopaminergic neurons were artificially hyperpolarized by expressing a hyperpolarizing UNC-103 ERG-like K+ channel ( unc-103 ( gf ) ) [42] from the dat-1 dopamine transporter promoter [43] . The dat-1 promoter drives expression of this potassium channel exclusively in all DA neurons: CEP , ADE , PDE and rays 5 , 7 , 9A [26] , [43] . Similar to cat-2 mutants , males containing the unc-103 ( gf ) transgene had increased number of spontaneously protracted spicules ( 28%; n = 46 vs . 4%; n = 27 ) ( p-value = 0 . 04 , Fisher's exact test ) , and had decreased ability to sire progeny ( 40%; n = 49 vs . 69%; n = 50 ) when compared to the transgenic control strain ( Figure 1A ) . Since the behavior of unc-103 ( gf ) transgenic males mimics the cat-2 mating phenotype , this suggest that secretions from dat-1 and cat-2 expressing cells , likely DA , is necessary for efficient mating . To ask how the cat-2 mutation compromised mating , for 2 min we observed copulations between cat-2 males and 2-day-old paralyzed hermaphrodites . We assayed mating initiation time , vulva contact duration and the number of vulva contacts ( Figure S2 ) , and found no difference between wild type and cat-2 males . However , when we measured the average duration a male spent between vulval insertions attempts , we found that cat-2 males had longer intervals than wild type ( Figure 1B ) . This was because cat-2 males displayed abnormal arched postures and precocious spicule thrusts at random areas on the hermaphrodite ( Figure 1C ) . This defect also accounted for the mutant's reduced spicule penetration ability compared to wild type ( 36% vs . 73% , respectively ) . To quantify the variability of spicule insertion behavior , we calculated the efficiency of spicule insertion ( ESI ) in both groups . This metric combines how fast males initiate , sustain , re-attempt and complete spicule insertion . We found that cat-2 males had a lower ESI than wild type ( 0 . 19 vs . 0 . 075 , Figure 1D ) . Thus , DA signaling promotes spicule insertion by lowering the probability of displaying non-productive ectopic spicule thrusts . We used pharmacology to address whether DA modulates the cholinergic spicule circuit by pre- or co-regulating the ACh response . ACh agonists artificially stimulate receptors on the spicule neurons and muscles to induce spicule protraction . These agonists include levamisole ( LEV ) and nicotine ( NIC ) , which activate ionotropic ACh receptors ( AChR ) , and oxotremorine-M ( OXOM ) , which activates Gαq -coupled muscarinic AChRs ( mAChR ) [25] , [28] . Arecoline ( ARE ) has been reported to stimulate mAChRs in the pharynx [44]; however , we found that in the spicule circuit , ARE is a non-selective agonist . For the spicule protraction circuit to be ARE-insensitive , a male must contain mutations in the NIC receptor ( acr-16 ( ok789 ) ) , LEV receptor ( unc-29 ( e193 ) ) and the OXOM receptor ( gar-3 ( gk305 ) ) ( Table S1 ) . To test whether DA can attenuate the spicule protraction circuit , we exposed males to DA and ACh agonists simultaneously for 5 min and assayed males with protracted spicules . The effective concentrations inducing spicule protraction for 90% of the males ( EC90 ) were 5 µM for LEV , 1 mM for both NIC and ARE and 50 mM for OXOM . The EC90 concentration for DA , inducing paralysis in 90% of the animals , was previously reported to be between 20–30 mM [21] . Therefore , we exposed one-day-old virgin males to 30 mM of DA combined with individual AChR agonists at their respective EC90 concentration ( Figure 2A–2C ) . We found that DA reduced ACh-agonist induced protraction ( Figure 2A ) , supporting our hypothesis that DA antagonizes ACh signaling . To address if DA also preconditions the spicule protraction circuit to be less responsive to ACh stimulation , we bathed males in 30 mM of DA or water for 1 min , followed by ( f . b ) exposure to the EC90 ACh-agonist concentration . Exposure to DA or water did not induced spicule protraction in any male during a 2 min observation ( Figure S1B ) . We found that DA pre-application still inhibited LEV- and NIC- and to a lesser extent OXOM-induced protraction ( Figure 2B ) . Interestingly , DA pre-exposure didn't inhibit ARE-induced spicule protraction . To rule out the possibility that after DA exposure , ARE induces protraction independently of AChR stimulation , males were exposed to DA followed by an ACh-agonists mixture ( MIX ) . This MIX contained LEV , NIC and OXOM at the EC90 concentrations . Similar to the ARE responses , we found that pre-exposure to DA did not inhibit the MIX-induced protraction , and when treating males with the MIX and DA simultaneously , spicule protraction was down-regulated ( Figure 2A and 2B ) . These results suggest that DA down-modulation occurs simultaneously with ionotropic and muscarinic ACh signaling . Since 1 min DA pre-exposure didn't antagonize ARE-induced protraction , we tested if the inhibition , which occurred with simultaneous exposure to both compounds , would also dissipate rapidly . Instead , we found that antagonism of sex muscle contractions dissipated by 30 min with DA+ARE treatment; for other agonist combinations , the inhibition remained up to 1 hr ( Figure 2C ) . These results suggest that simultaneous DA and ACh secretion down-regulates spicule circuit excitability for a limited period . Since ARE's non- selectivity approximates more native ACh signaling ( Table S1 ) , DA+ARE co-treatment was used to characterize the mechanism of DA down-modulation . First , we tested males containing the G-protein coupled receptor loss-of-function ( lf ) mutations dop-1 ( vs100 ) , dop-2 ( vs105 ) , dop-3 ( vs106 ) or dop-4 ( tm1392 ) [17] , [21] . In the dop-1 and dop-4 mutants DA suppressed ARE-induced protraction to wild type levels , in accordance with previous studies where these receptors are found to enhance cellular excitability via Gαq pathways [21] , [45] . However , in the dop-2 ( lf ) or dop-3 ( lf ) single mutants and dop-2; dop-3 double mutants DA did not decrease ARE-induced protraction to wild type levels ( Table 1 ) . An additional candidate that could mediate DA signaling is the chloride ligand gated channel LGC-53 [46] . We measured the DA+ARE response of a loss-of-function mutant for this channel , lgc-53 ( n4330 ) and found that these mutants had wild type DA+ARE sensitivity ( Table 1 ) . Although our results indicate that the DOP-2 and DOP-3 receptors partially mediate the DA modulatory response , we cannot rule out that DOP-1 , DOP-4 , LGC-53 and other yet-to-be identified DA receptors might act in specific combinations and in other cells to further attenuate ACh-induced spicule protraction . Since these D2-like receptors signal via Gαo/i –pathways , the alleles goa-1 ( n363 ) , gpa-7 ( pk610 ) , gpa-14 ( pk347 ) and gpa-16 ( it143 ) , which impair Gαo/i- like molecules were also tested [47] . In all the single mutant males treated with DA and ARE , spicule protraction was still inhibited . Thus suggesting that these molecules acted in a redundant manner as published in other pathways [48] . We therefore tested the DA/ARE sensitivity in different combinations of loss-of-function mutations in these Gαo/i- like molecules ( Table 2 ) . We found that the double mutant goa-1 ( lf ) ; gpa-7 ( lf ) males were insensitive to the DA inhibition ( Table 2 ) . This is consistent with DOP-2 , DOP-3 , GOA-1 and GPA-7 expression in the spicule associated muscles and the neurons that innervate them ( Figure S3 ) [47] . In contrast , DOP-1 and DOP-4 are not expressed in these cells , but only in a few ray neurons ( data not shown and in [20] ) . Additionally , when DOP-2 and DOP-3 were transgenically expressed pan-neuronally from the aex-3 promoter or in sex-muscles from the unc-103E promoter , restored DA down-modulation in dop-2 and dop-3 males was observed with DOP-2 and DOP-3 sex muscles expression ( Table 1 ) . These data suggest that DA antagonizes ACh signaling via DOP-2 and DOP-3 coupled to GOA-1 and GPA-7 . In the hermaphrodite , broad D2-like receptor expression indicates that humoral DA secretions might activate these receptors [20]–[22] , [49] . The 3 sex-specific sensory dopaminergic ray neurons ( left/right Rn5A , Rn7A and Rn9A ) located in the male tail might provide humoral or synaptic DA necessary to antagonize ACh signaling [26] . These ray neurons synapse to other ray neurons , inter- and motor neurons , post-cloacal sensilla neurons ( p . c . s . ) and sex muscles ( Male Wiring Project , http://worms . aecom . yu . edu/pages/male_wiring_project . htm , [50] ) . To measure the Ca+2 transients in DA ray neurons during mating , we compared the changes in fluorescence emissions of the G-CaMP Ca+2 sensor to a mDSred internal standard , both co-expressed from the DA reuptake transporter promoter ( Pdat-1 ) . The G-CaMP transgene slightly reduces the mating potency of the males , but not statistically significant from wild-type males ( Figure 1A ) . This indicates that the calcium binding property of the sensor does not interfere too greatly with dopaminergic cell function . To distinguish fluorescent changes caused by focusing artifacts when the male is performing scanning behavior , from fluorescent changes caused by neural activity , we imaged males in which Rn5 , 7 , 9A were additionally hyper-polarized via a dat-1 promoter-expressing unc-103 ( gf ) transgene . The mutant K+ channel should attenuate the ability of neurons to depolarize , and thus allow one to determine the fluorescence ranges that can confidently be attributed to cell activity . We found that throughout matings of unc-103 ( gf ) -containing males , measurements in Rn5 , 7 , 9A fluorescence can range between 0 to a approximately 20% change ( Figure 3D , Figure S4 ) . These results suggest that focusing/motion artifacts can affect G-CaMP fluorescence measurements within this range . In contrast , we sometimes observed 30–80% Ca+2 transient changes in the DA neurons when the male located the vulva ( Figure 3A , Figure S4 , Video S1 ) . Occasionally , we also noticed up to 30% Ca+2 transient changes in DA ray neurons during arch scanning postures ( Figure 3 and Figure S4 ) . This observation led us to hypothesize that posture was correlated with DA ray neuron activity . To further correlate neuronal dynamics with copulatory postures , we measured In Contact Length percent ( %ICL ) between the male's body and the hermaphrodite , as a proxy for the adopted posture ( arch vs . non-arch ) . The %ICL was higher when a male was engaged in non-arched vs . arched postures either during scanning or at the vulva ( Figure 3 ) . Lower %ICLs , indicating progressive arched postures , coincided with higher Ca+2 transient dynamics in DA ray neurons occurring while the male pressed his tail sternly against the vulva and with milder Ca+2 transient dynamics during scanning . However , if the male reached the vulva in a non-arched posture and proceeded to attempt spicule insertion in this posture , the observed Ca+2 changes were within baseline levels ( 10–20% ) ( Figure 3B , Figure 3S ) . This suggests that during arched postures , DA ray neurons might be more active to down-modulate possible spicule circuit cholinergic activity . To confirm that DA and ACh systems were active simultaneously when the male's cloacal region contacted the vulva , we additionally measured Ca+2 transients in the male sex muscles: the gubernacular erector , the protractor and the anal depressor muscles ( Figure S5 ) . The contractile activities of these muscle cells are responsive to the ACh secretions of the PCB/PCC post-cloacal sensory neurons and the SPC motor neuron . In all of these muscles , Ca+2 transients increased when the male contacted the vulva ( Figure S5 ) [24] , [25] . Thus DA ray neurons likely down-modulate the simultaneously active cholinergic spicule protraction circuit during insertion attempts . DA sensory ray neuron activity might be increased at the vulva because of direct vulval chemosensory stimulation , mechanical stimulation from pressing against the vulva or from humoral or synaptic stimulation from other cells . Since the increased activities of DA ray neurons and the spicule protraction neurons coincide during the insertion step of mating , we asked if ray neuronal activity could change as a direct or indirect response to PCB and SPC stimulation ( Figure 4A ) . Therefore , we photo-stimulated PCB and SPC using channelrhodopsin-2 ( ChR2 ) , a light sensitive cation channel expressed from the gar-3 mAChR promoter , while simultaneously recording DA ray neurons G-CaMP fluorescence ( Figure 4B ) . The immobilized males were grown with or without all-trans retinol ( ATR ) , a cofactor for ChR2 . A microscope fitted with the mosaic imaging and illumination targeting system localized the blue light to the area of the G-CaMP-expressing ray neurons and then concurrently to the ChR2-expressing PCB , SPC neurons . We noticed that in ATR-grown males , the Ca+2 transients in Rn7A exclusively increased after PCB , SPC stimulation ( n = 14 males ) ( Figure 4B and 4C , Figure S6 , Video S2 ) . However , no obvious dynamic Ca+2 changes were observed in Rn5A and Rn9A . These data suggest that Rn7A can respond , directly or indirectly to spicule circuit activity , whereas Rn5A and Rn9A likely respond to other signals . Although the activity of the dopaminergic ray neurons is more dynamic when the male contacts the vulva , and D2-like receptors are expressed in the PCB neurons and sex-muscles ( Figure S3 ) , these ray neurons might attenuate the excitability of the spicule protraction circuits , not through endogenous DA and D2-like receptors , but through circuitous electrical signaling or other secreted neuropeptides . To address this , we photo-stimulated DA neurons by expressing ChR2 from the Pdat-1 promoter , while simultaneously exposing the males to agar pads soaked with a concentration of ARE that causes ∼80% of males to protract their spicules within 5 minutes ( Figure S1C ) . In a heterozygous dop-2; dop-3/+; Pdat-1:ChR2 background , 32% of the males protracted their spicules when photo-stimulated; however , in the homozygous dop-2;dop-3 ( lf ) background , 71% of the photo-stimulated males protracted their spicules ( Figure 4D ) . This suggests that endogenously evoked DA can attenuate ACh signaling in the spicule circuit via D2-like receptors . To ask how DOP-2 and DOP-3 regulate mating , we determined the mating potency of dop-2; dop-3 mutant males with moving hermaphrodites . The mutant and wild type potencies were similar , 92% ( n = 38 ) vs . 88% ( n = 40 ) , respectively . Thus , the functions of DOP-2 and DOP-3 are subtle . We then quantified dop-2; dop-3 males' mating performance with paralyzed hermaphrodites and found that wild type , the double and single mutants behaved similarly during various mating steps ( Figure S7 ) . However , the double mutants can insert their spicules faster , into the paralyzed and easy-to-penetrate hermaphrodites , than wild type ( Figure 5A ) . This paradoxical result suggests that having a wild-type version of D2-like receptors reduces reproductive fitness . However , males that lack D2-like receptors might not be at a behavioral advantage when paired with a more challenging mate . Therefore , we coupled dop-2; dop-3 or a wild-type male with a moving hermaphrodite and directly measured the first vulval contact duration . We found that dop-2; dop-3 males are displaced from the vulva faster than wild type ( Figure 5B ) . Unlike wild type mating events , most hermaphrodites coupled with mutant males would abruptly move during spicule insertion attempts , causing the males to move off the vulva and thrust their spicules at areas adjacent to the vulva . We previously showed that a K+ channel mutation disrupts the frequency and amplitude of sex muscle contractions during spicule insertion attempts; the arrhythmic spicule thrusts will startle the hermaphrodite and increase the probability of the male losing contact [29] . We reasoned that a similar phenomenon is occurring with dop-2; dop-3 males . Thus , we measured spicule movement frequency when a male attempted insertion at a paralyzed hermaphrodite vulval slit . We found that relative to wild type , dop-2;dop-3 spicule insertion attempts were less rhythmic . Among the assayed dop-2; dop-3 males , the variability of durations between spicule thrusts was greater than compared to wild-type males , indicating that the mutants displayed more random sustained thrusts in runs of rapid shallow thrusts ( Figure 5C , Figure S8 ) . Since dop-2; dop-3 and wild-type males behave differently during copulation with moving mates , we identified conditions where that difference would result in reduced mating fitness for the mutants . We paired either one wild type or one mutant male for 4 hrs with increasing numbers of moving fog-2 ( lf ) virgin females ( which contain a mutation disrupting self-sperm generation ) , and counted the number of impregnated females . Since mates with variable mating receptiveness exist in a population , the subtle defects of dop-2; dop-3 males might be more evident in a competition to impregnate the most partners . Although wild type and mutant males' refractory periods between ejaculations are similar ( Figure S9A ) , we found that when the female numbers increased , wild type impregnated more females than dop-2; dop-3 males ( Figure 6A ) . This difference was obvious when dop-2; dop-3 males were exposed to 20 females . The lower serial mating potency is likely attributed to behavioral differences; however it could also be due to subtle germ line variations between the wild type and mutant males . To address if behavioral differences caused the double mutants to impregnate fewer females , we simultaneously paired a mutant and a wild-type male with a single 1-day-old fog-2 female and asked which male mated first . We found that dop-2; dop-3 and wild type males impregnated a similar percentage of females ( 56% vs . 44% ) ( Figure 6B ) . Similar to Figure 6A data , this indicates that with a single mate , mutant males are similar to wild type in copulation . However , when we challenged the double mutant and a wild type male with a single fog-2 female and 10 paralyzed males , as unproductive mating distractions , we found that wild type impregnated 80% of the females ( Figure 6B ) . We observed that wild type and the mutant males contacted both sexes with equivalent frequency in this assay , and in a male-female mate choice assay , we did not find any indication that the mutant males had a greater chemotaxic preference to males ( Figure S9B , S9C , and S9D ) . However wild-type males immediately terminated mating attempts with paralyzed males , whereas mutant males would abnormally scan and attempt spicule insertion into these inappropriate mates ( Figure S9C ) . To rule out the possibility that dop-2; dop-3 males displayed general locomotor hyper-exploratory behaviors , which could lead the mutant males to contact another animal before wild-type males , we compared the velocity and distance travelled of mutant and wild-type males during crawling . We found that there were no gross differences in these parameters between wild type and mutant males ( Figure S10 ) . Therefore , this indicates that during mating , D2-like signaling dampens ACh-induced behaviors with uncooperative/inappropriate mates . Next , we addressed , in a more natural scenario , the importance of D2-like receptors in decreasing fruitless mating attempts with nematodes of other species . We paired one fog-2 female and 10 C . briggsae hermaphrodites or 10 C . remanei females , with one wild type or a dop-2; dop-3 male and counted how efficiently the fog-2 female became impregnated . We found that after 4 hrs , wild type impregnated 65% more females than mutant males , when challenged with 10 C . briggsae hermaphrodites ( Figure 6B and Figure S9E ) . In contrast , we found both wild type and mutant males' ability to impregnate a C . elegans mate is severely reduced when challenged with C . remanei females ( Figure S9E ) , consistent with the published report that C . remanei females are more attractive than C . elegans hermaphrodites [51] . This indicates that D2-like signaling might limit unproductive mating attempts with other hermaphroditic nematode species . Finally we addressed whether D2-like signals specifically dampened spicule circuit excitability and/or other mating circuits , to restrict aberrant mating attempts . The male's response to contacting a mate is primarily facilitated by the ray sensilla . However , published reports have demonstrated that other male sensilla , such as the post-cloacal sensilla ( p . c . s ) , spicule tips and possibly the hook sensillum can feebly substitute for ray function; therefore the activity of these sensilla might be increased in the dop-2; dop-3 males [26] , [27] . Since driving expression of DOP-2/DOP-3 exclusively in cells of the spicule circuit is not technically possible , we opted for an alternative approach of laser ablating the dop-2-expressing PCB neuron or all of the p . c . s neurons ( PCA , PCB and PCC ) , and asking if mating with a non-hermaphrodite is reduced . First we quantified in wild-type males lacking PCB or all 3 p . c . s . neurons , the cumulative and average duration in contact with another male during a 10 min assay period , when surrounded by 40–50 paralyzed males . We found that the cumulative time that operated males spent with other males was reduced ( Figure 6C ) . This result is consistent with the idea that the post-cloacal sensilla play a minor role in contact response and scanning behavior . However if an operated male does initiate scanning with another male , the average time was not significantly different amongst these groups ( Figure S9F ) . We then tested if PCB or p . c . s ablations in dop-2; dop-3 males would reduce abnormal mating attempts . We found that dop-2; dop-3 males on average spent longer amount of times scanning other males than wild type ( Figure S9F ) ; however , neither PCB nor p . c . s ablations reduced this phenotype . In addition , the cumulative time in contact with another male was similar between operated and intact males ( Figure 6C ) . This indicates that D2-like signaling must be modulating other circuits in addition to the post-cloacal sensilla to attenuate contact response and scanning behavior .
Although dopamine ( DA ) modulation in vertebrate models is known to regulate motor patterns [2] , [7] , there are few in-depth analysis for how DA fine-tunes context-dependent behaviors . To address this , we analyzed how DA signaling constrains specific neuromuscular outputs during C . elegans mating . As the male positions his tail over the vulva , he repetitively thrusts his spicules against the vulval slit while adopting an arched posture . This behavior is terminated after spicule penetration or loss of vulval contact . In contrast , in DA-deficient cat-2 males arched postures and rhythmic spicule insertion attempts were no longer confined to the vulval region , and sometimes even initiated randomly on the hermaphrodite . Thus , spicule motor behaviors coupled with appropriate postures and vulva sensing are partially coordinated by DA down-modulatory pathways . Consistent with the cat-2 male's ectopic display of motor behaviors , simultaneous application of exogenous DA with receptor-selective or nonselective acetylcholine ( ACh ) agonists constrains cholinergic-mediated sex-muscle contraction . Interestingly , the inhibitory effect of exogenous DA is less potent with a muscarinic ( G-protein coupled receptor ) agonist or a muscarinic and ionotropic ( ACh-gated ion channel ) nonselective ACh agonist , if DA is applied first . This suggests that during mating , context-relevant DA signaling occur coincidently with ACh-mediated signaling; additionally , mAChR signaling might make the spicule circuit refractory to non-coincident humoral DA secretions that occur elsewhere in the male . DA-dependent negative signals are partly transduced through the D2-like G-protein-coupled receptors DOP-2 and DOP-3 . Even though dop-2; dop-3 double mutant phenotypes are less severe than cat-2 animals , likely because every DA receptor is affected by the cat-2 mutation , we found that these receptors mediate restriction of spicule protraction behavior to the precise vulval slit area , and maintain rhythmic spicule thrusts during penetration attempts . Although previous reports demonstrate DOP-3 and GOA-1 signaling for hermaphrodite locomotion [21] , [23] and in vitro DOP-2 and GPA-14 interactions [52] , we provide genetic evidence that Gαo/i proteins , GOA-1 and GPA-7 , redundantly transduce DA inhibitory signals during vulva sensing/spicule insertion behavior ( Figure 6C ) . These Gαo/i proteins , and their βγ partners might regulate molecules such as adenylyl cyclase , L-type- voltage-gated Ca++ channels and K+ channels to decrease neuromuscular excitability [53]–[55] . ACh secreted from the cloacal ganglia sustains vulval contact [24] , while concurrently , the 9 pairs of sensory rays likely provide feedback to adjust the male's movement and posture according to the hermaphrodite's position and locomotion . Three of the 9 ray pairs contain DA sensory neurons; when optogenetically stimulated , they induce a shallow ventral tail flexure [26] , and when stimulated in the presence of non-selective ACh agonists endogenous DA secretion antagonizes spicule protraction . The 3 pairs of RnA neurons gap junction to their RnB counterparts , which express neuropeptides flp-5 , flp , -6 , and flp -17 [56] , raising the possibility that stimulation of RnA neurons indirectly leads to neuropeptide-dependent modulation of the spicule circuit . However in the dop-2; dop-3 double mutants , ChR2-stimulation of dat-1 expressing cells , and possibly including the RnB neurons via gap junctions , failed to reduce simultaneous ACh agonist-evoked contractions . This suggests that DA secretions , possibly from Rn5A , Rn7A and Rn9A , can attenuate the output of cholinergic signaling . Of these neurons , Rn7A and 9A make chemical synapses to cloacal-associated components ( Figure 4A ) , suggesting that DA-ACh signals might be involved during the vulva location/spicule insertion steps . In fact , the DA ray neurons and the spicule circuit components are more dynamic during vulval contact . However , heightened ray neuronal activity is sometimes detected when the male is at non-vulval regions , suggesting that DA secretion is not tightly coupled to an explicit sensory signal . Our optogenetic experiments indicate that cholinergic cloacal neuron activation stimulated Rn7A activity This DA-secreting cell is not post synaptic to the cholinergic cloacal neurons , indicating that DA secretion might be an indirect response to cholinergic circuit activity via humoral signaling or interneurons . Additionally , the Rn5A and Rn9A did not responded to PCB and SPC stimulation , further suggesting implications of additional internal signals from the locomotor circuit or other ray neurons . Throughout mating , ray neurons respond with an array of different dynamics correlated with the gradual arched body posture changes , which are perhaps a read-out of DA not only modulating the PCS , but also providing feedback onto a locomotor circuit . This is a possibility since DOP-2 and DOP-3 are expressed in ventral cord neurons and body wall muscles [21] , which facilitate locomotion . During non-arched postures , either scanning or at the vulva , DA ray neurons display stable activity . However; if a male develops an arched posture at the vulva , or during scanning , then the DA ray neurons display dynamic changes in their activity , maybe to modulate the transition in overall motor response . During male behaviors , we have observed a similar spicule circuit independent modulatory role for DA and D2- receptors , where reduction in D2-like signaling results in mutants engaging in prolonged backward scanning locomotion with other males and hermaphrodites of different species . Therefore , the DA-signaling mutant phenotypes , together with the expression of DA ray neuronal activity , suggest that DA refines motor outputs at the vulva and delimits them at other areas via interactions with neural-muscular networks that include the spicule protraction circuit . This ACh/DA interplay might share analogy with how the vertebrate CNS fine-tunes locomotor control . In the vertebrate CNS , DA secretions from the substantia nigra ( SN ) inhibit striatal ACh interneurons , and ACh-induced DA release in these networks coordinate voluntary movements [10] , [57] . Although this suggests bidirectional DA/ACh signaling in the CNS , direct evidence for how these neurons shape motor outputs at the animal behavioral level is scarce , due to complex CNS connectivities [54] , [58] . In the C . elegans male , the DA ray neurons and cholinergic cloacal ganglia interact bi-directionally to regulate sex-muscle behaviors . The optogenetic experiments suggest that cloacal ganglia neurons promote DA- ray activity and the pharmaco-genetic experiments indicate that DA attenuates the ACh spicule circuit output partly via DOP-2 and DOP-3 on PCB neurons and sex muscles . Decrease of PCB output could subsequently result in reduced DA secretion and dampening of DA and ACh circuits' interactions . During mating , how can a male insert his spicules while attenuating DA signaling occurs ? Possibly during the repetitive vulval penetration attempts , potent ACh secretions can override DA-negative signaling , due to acute vulval stimulation of the cloacal sensory-motor neurons . However , these cloacal neurons make reciprocal ( recurrent ) synapses with one another ( Figure 4A ) . If the wild-type male moves off the vulva or if these neurons are inappropriately stimulated ( on non-vulval regions , on another male , or by a mate from a different species ) , then a negative mechanism , such as D2-like signaling , must dampen the circuit's residual self-amplifying property . Indeed , the ectopic mating behaviors displayed by cat-2 and dop-2; dop-3 males give the illusion that they compulsively maintain motor behaviors ( spicule prodding ) in the absence or withdrawal of the appropriate stimuli . The ACh and D2-like signaling interactions in C . elegans are reminiscent of D2 receptor-regulated synaptic plasticity in vertebrate SN-striatal networks . In these networks , D2 receptors regulate long term synaptic depression . This form of plasticity reduces pre-existing motor memory storage and maintains a balance between old and newly encoded motor information . In DA-deficient Parkinson's disease models , dyskenisia ( voluntary movement disorder ) is caused by plasticity abolishment in these networks [12] , [59]–[62] .
Strains were maintained at 20°C on NGM agar plates and fed with E . coli OP50 . All C . elegans males contain the allele him-5 ( e1490 ) . Additional alleles used were: cat-2 ( e1112 ) , dop-1 ( vs100 ) , dop-2 ( vs105 ) , dop-3 ( vs106 ) , dop-4 ( tm1392 ) , goa-1 ( n363 ) , gpa-7 ( pk610 ) , gpa-14 ( pk347 ) , gpa-16 ( it143 ) , pha-1 ( e2123 ) , unc-64 ( e246 ) , fog-2 ( q71 ) , unc-29 ( e193 ) , gar-3 ( gk305 ) and acr-16 ( ok789 ) . Transgenic strains include: pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx197[ Punc-103E:G-CaMP1 . 3 , Punc-103E:mDsRed , pha-1 ( + ) ] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx317[Pdop-2:ChR2::YFP; pha-1 ( + ) ] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx326[Ptph-1:CFP; pha-1 ( + ) ] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx431[Phsp-16:egl-2 ( n693gf ) cDNA; Punc-103E:mDsRed; pha-1 ( + ) ] , dop-2 ( lf ) ; pha-1 ( lf ) ; lite-1 ( lf ) rgEx462 [Paex-3:dop-2::CFP] , dop-2; pha-1 ( lf ) ; lite-1 ( lf ) ; rgEX467 [Punc103E:dop-2::CFP] , dop-3; pha-1 ( lf ) ; rgEx482 [Punc103E:dop-3::YFP]; dop-3; pha-1 ( lf ) ; rgEx490 [Paex3:dop-3::YFP] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx491[Pgpa-7:YFP; pha-1 ( + ) ] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx512[Pgar-3B:GCaMP3::SL2:::mDsRed; pha-1 ( + ) ] , dop-2 ( lf ) ; dop-3 ( lf ) ; rgEx515[Ptph-1:YFP] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx517[Pdat-1:GCaMP3::SL2:::mDsRed; pha-1] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx519[Pgpa-16: gpa-16 exon1::YFP; pha-1 ( + ) ] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx523[Pdat-1:G-CaMP3::SL2:::mDsRed , Pgar-3B:ChR2::YFP , pha-1 ( + ) ] , pha-1 ( lf ) ; lite-1 ( lf ) ; rgEx549[Pdat-1:G-CaMP3::SL2:::mDsRed , Pdat-1:unc-103 ( gf ) , pha-1 ( + ) ] , dop-2 ( lf ) ; dop-3 ( lf ) ; pha-1 ( lf ) ; rgEx550[Pdat-1:ChR2::YFP , pha-1 ( + ) ] Plasmids were co-injected with pBX1 ( 50 ng/µl ) [65] into strains that contained the pha-1 ( e2123 ) allele . Transgenic lines that could stably propagate at 20°C were kept for further analysis . For strains that did not have the pha-1 allele , CFP or YFP expressed from one of the injected plasmids was used to identify transgenic animals . For all injections , pUC18 was used to make the final DNA concentration 200 ng/µl . The expression constructs pPC2 and pPC39 were injected at 100 ng/µL into pha-1 him-5 lite-1 hermaphrodites . To rescue the dop-2 ( lf ) and dop-3 ( lf ) DA+ARE sensitivity , pPC21 , pPC19 , pPC18 ( 25 ng/µL ) and pPC36 , pPC37 ( 50 ng/µL ) were injected into dop-2; pha-1 and dop-3; pha-1 hermaphrodites , respectively . To fluorescently label males for mating competition tests , pTG10 [Ptph-1:CFP] ( 100 ng/µL ) and pTG11[Ptph-1:YFP ] ( 100 ng/µL ) [30] were injected into him-5 and dop-2 ( lf ) ; dop-3 ( lf ) hermaphrodites , respectively . The goa-1 ( lf ) ; gpa-7 ( lf ) strain was injected with pPC41 [Pgpa-16:gpa-16::YFP] ( 100 ng/µL ) to test for RNAi effectiveness . To label separately the dopamine-expressing cells , the male cloacal neurons and the male sex muscles with G-CaMP3::SL2:::mDsRed , pha-1; him-5; lite-1 hermaphrodites were injected with pLR286[Pdat-1:G-CaMP3::SL2:::mDsRed] ( 30 ng/µl ) , pLR283[Pgar-3B:G-CaMP3::SL2:::mDsRed] ( 30 ng/µl ) or pLR289[Punc-103E:G-CaMP3::SL2:::mDsRed] ( 30 ng/µl ) , respectively . To co-express G-CaMP3 in dopamine-expressing cells and Channel Rhodopsin in the male cloacal cells , pha-1; him-5; lite-1 hermaphrodites were injected with a mixture of pLR286[Pdat-1:G-CaMP3::SL2:::mDsRed] ( 30 ng/µl ) , and pLR183[Pgar-3B:ChR2::YFP] ( 100 ng/µl ) [24] . To co-express G-CaMP3 and unc-103 ( gf ) in dopamine-expressing cells , pha-1; him-5; lite-1 hermaphrodites were injected with a mixture of pLR286[Pdat-1:G-CaMP3::SL2:::mDsRed] ( 30 ng/µl ) , and pPC47[Pdat-1:unc-103 ( gf ) ] ( 70 ng/µl ) . The dop-2; dop-3; pha-1 strain was injected with pPC48 [Pdat-1:ChR2::YFP] ( 70 ng/µl ) and then crossed into pha-1; him-5; lite-1 to obtained the heterozygous strain carrying the same transgene for optogenetic experiments . To obtain a strain for behavioral comparisons with transgenic males [Pdat-1:G-CaMP3::SL2:::mDsRed] , the pha-1; him-5; lite-1 hermaphrodites were injected with pPC46 [Pdat-1:YFP] . For behavioral and pharmacology assays , virgin males were isolated from non-crowded plates , either at the late L4 stage ( when cells in the male tail spike have completely migrated anteriorly ) or after they newly crawled out of their L4 cuticle . They were kept solitary or in groups of 10–20 . All drug tests scored the number of males that protracted their spicules by directly observing spicule protraction for at least 10 seconds in a 5 min observation window; these behavioral assays were not videotaped . If multiple mating parameters were measured for individual males , we videotaped the mating event from the time a male contacted a hermaphrodite until spicule insertion . Because all of the sensory-motor metrics were objective ( % fluorescent changes , motor duration , contact duration , number of contacts , locomoter velocity , sex muscle contraction frequency ) , and not subjectively defined , it was not necessary to collect data blinded to the genotype of the animals . For populations of objective metrics that were statistically different , but less than twofold between the experimental and control animals , two observers , Paola Correa and L . Rene Garcia , re-analyzed the movies independently to re-verify or amend the results . Graphpad Prism 5 software was used to perform all statistics . Fisher's exact test was used when comparing categorical variables ( protracted vs . non-protracted , potent vs . non-potent ) . The Mann-Whitney nonparametric test was used to compare the metrics of an experimental group with a control group , when the data did not fall under a normal Gaussian distribution . When the data fitted a Gaussian distribution , 1-way ANOVA and Tukey's post-test were used to compare the means and standard deviations of more than two groups . To assay agonist-induced spicule protraction , we dissolved levamisole ( LEV ) ( ICN Biomedicals , OH ) , arecoline ( ARE ) ( Acrose organics , NJ ) , nicotine ( NIC ) ( EM , NJ ) , oxotremorine M ( OXOM ) ( Sigma , MO ) and dopamine ( DA ) ( Sigma ) in water to make a stock solution of 10 mM , 10 mM , 100 mM , 50 mM and 30 mM , respectively . We added between 400–1000 µL of the drug to a three well round-bottom Pyrex titer dish . Five to ten males were then transferred to the drug bath and observed for five minutes at 20°C . Males were considered drug responsive if their spicules remained protracted for ≥5 sec . For simultaneous exposure , DA and ACh-agonists were pre-mixed . For sequential exposure , worms were bathed in DA for 1 min and then ACh-agonists were added at a concentration , such that the final DA concentration was not significantly changed and the ACh-agonists were at the EC90 concentration . For mating potency tests , 10 µl of a saturated E . coli culture was spotted onto a NGM agar plate , to make a 3 . 5 mm lawn . ∼20 hr later , a single male and a single adult virgin pha-1 ( lf ) hermaphrodite were put onto the mating lawn and incubated at 20°C for 4 days . A male was considered potent if the plate contained cross-progeny . For mating behavioral assays , we spaced ten 48 hr-old unc-64 ( lf ) adult hermaphrodites on a 5 mm diameter bacterial lawn and placed a male in the lawn's center . Movies were recorded using a stereomicroscope mounted with a Hamamatsu ImagEM CCD camera ( Hamamatsu , USA ) ; recordings were taken from the time a male contacted a hermaphrodite until spicule insertion or 5 min . Different mating performances were scored from observations of these recordings to address: the number of times a male contacted the vulva , total duration of vulval contact and the time a male spent scanning a hermaphrodite . The same population of males was used to obtain these data sets for each behavioral metric . Wild type and mutant males were tested in parallel for statistical comparisons . Through direct observation and using a hand-held timer , we measured the time it took wild type and cat-2 ( lf ) males to contact and start scanning a hermaphrodite . Moving hermaphrodites were used to measure the duration over the vulval slit after the 1st contact for wild type and dop-2 ( lf ) ; dop-3 ( lf ) males . To determine if dop-2; dop-3 ( lf ) males differ from wild type in their chemotactic or locomoter behaviors toward paralyzed pha-1; lite-1;him-5; rgEx431[Phsp-16:egl-2 ( n693gf ) cDNA; Punc-103E:mDsRed; pha-1 ( + ) ] hermaphrodites or males , 6 paralyzed hermaphrodites and 6 paralyzed males were alternately and equally positioned at the periphery of a 1 . 5 cm diameter OP50 lawn . One wild type or dop-2; dop-3 ( lf ) male was placed at the center of the lawn and allowed to crawl around for up to 5 minutes . The males were timed when they first placed the ventral side of their tail against the cuticle of a paralyzed worm ( either male or hermaphrodite ) for greater than 1 second . To determine the male's movement velocity , an 18–24 hr adult virgin male was placed in the center of a thin 3 mm OP50 lawn . The forward crawling animal was digitally recorded for 1 minute at 30 frames per second using a stereomicroscope mounted with a Hamamatsu ImagEM CCD camera ( Hamamatsu , USA ) . The lighting of the sample was adjusted to maximize the contrast of the male against the bacterial lawn . Recordings were then analyzed using the Hamamatsu SimplePCI ( version 6 . 6 . 0 . 0 ) software to determine the centroid of the male in each frame , and track the changes in the X and Y coordinates of the centroid as the male crawls forward . Microsoft Excel was then used to convert changes in the X and Y coordinates into the velocity and distance traveled during the 1 minute recordings . For mating assays with multiple mates , a one-day-old wild type or dop-2; dop-3 ( lf ) male was paired with 1 , 5 , or 20 two-day-old fog-2 ( lf ) females in a plate containing a small bacterial lawn . After 4 hrs , the male was removed . The number of females that laid eggs were determined 4 hours later and then subsequently monitored for an additional 18 hrs . In experiments where wild type or dop-2; dop-3 males must discriminate between C . elegans and either C . remanei or C . briggsae , L4 fog-2 ( lf ) females were grown to adulthood on OP50-seeded NGM agar plates containing 50 µM red fluorescent dye SYTO-17 ( Invitrogen , Eugene OR ) ; the dye allowed the fog-2 ( lf ) females to be identified from C . remanei or C . briggsae animals . One stained virgin 18–24 hrs adult fog-2 ( lf ) female was placed with ten 18–24 hrs adult C . briggsae hermaphrodites or 10 virgin C . remanei females on a 3 . 5 mm diameter lawn of OP50 . The animals were allowed to acclimate to the lawn for one to two hours before a single virgin wild type or dop-2; dop-3 male was introduced . Males were kept continuously with their mates for 18 hrs . Four and 18 hours later after the male was first introduce with his mates , using an epi-fluorescence-equipped stereomicroscope , we determined if SYTO-17 stained fog-2 ( lf ) females contained eggs in the uterus or sperm in the spermatheca . To observed how post-cloacal sensilla-ablated wild type and dop-2; dop-3 ( lf ) males behave with paralyzed males , the cells PCA , PCB and PCC were laser ablated ( using a Spectra-Physics VSL-337ND-S nitrogen laser attached to an Olympus BX51 microscope via the MicroPoint laser focusing system ) in L4 males prior to tail spike retraction . During the operation , the laser-ablated and mock-ablated males were immobilized between a microscope coverslip and an 8% noble agar pad ( a higher % pad caused the males to rupture through their anus ) containing Polybead polystyrene 0 . 1 µm microspheres ( Polysciences , Inc . , WA ) . Eighteen to 24 hrs later , 3–4 laser-ablated or mock-ablated adult males were added to a 3 mm diameter OP50 lawn that contained 40–50 paralyzed pha-1; lite-1; him-5; rgEx431[Phsp-16:egl-2 ( n693gf ) cDNA; Punc-103E:mDsRed; pha-1 ( + ) ] males . The animals were digitally recorded at 1 frame per second for 10 minutes . The recordings were then reviewed , and time and duration that the moving male placed the ventral side of his tail against the cuticle of a paralyzed worm or another moving worm for greater than 1 second was determined for the whole 10 min recording . Cumulative time was calculated by adding up the total time a male was in ventral contact with other males . The average time was calculated by dividing the total time in contact by the number of mating contacts . Because the mating behavioral steps that lead up to sperm transfer can be highly variable , we required a metric to score/rank a spectrum of behavioral responses that result in successful spicule insertion . We wanted that metric to differentiate a male that instantly found the vulva and inserted within a second or two of contact , from a male that meandered around the hermaphrodite for 110 secs , but eventually contacts the vulva , and inserts . However , this metric must be able to rank the spectrum of males that immediately undergo spicule insertion attempts and are persistent , but are unsuccessful in penetration , with males that were erratic in prodding behavior ( and other steps of mating behavior ) , but fully inserted their spicules . To achieve this , the metric had to measure the period between vulval contact and full insertion , but it also had to incorporate a penalty for not being diligent at immediately initiating vulval spicule insertion behavior after contacting with a mate , and a bonus if successful penetration occurred , even after erratic performance of other mating behavioral steps . The efficiency of spicule insertion , ESI , was calculated from recordings made during the first 120 seconds of contact between the male and a paralyzed 2-day-old hermaphrodite . The metrics recorded were: ( 1 ) duration of prodding at the vulva; ( 2 ) duration in contact with the hermaphrodite at areas outside the vulva . If the male successfully inserted his spicules before the 120 seconds were over , then the observation was stopped . ESI = ( time ( sec ) spent at spicule insertion attempts/total time ( sec ) in contact with hermaphrodite , up to 120 sec ) X ( 1/time ( sec ) in contact with the hermaphrodite , such scanning , but not attempting insertion ( penalty ) ) X ( 1+ ( 0 if no penetration , otherwise time ( sec ) remaining after a successful penetration , /120 sec ) ( bonus ) ) . A hypothetical ESI of 1 . 99 would mean that the male located the vulva and inserted his spicules approximately 1 sec after contact with the hermaphrodite; whereas a hypothetical ESI of 0 . 0 meant that the males spent their first 120 seconds of contact not attempting spicule insertion at the vulva [66] . For mating competition tests , transgenic males contained expressed YFP or CFP from the tph-1 promoter [30] . Mid to late L4 fog-2 females were separated from males; 48 hrs later , a single female was added to 5 mm diameter lawns of bacteria . One 18–20 hrs virgin CFP-expressing wild type and one YFP-expressing dop-2 ( lf ) ;dop-3 ( lf ) male were added simultaneously to the lawn . The plates were incubated at 20°C for one hour . If the female had sperm in the spermatheca ( determined via standard bright field microscopy ) , then both males were removed , otherwise the animals were allowed to mate for another hour . Majority of the females were mated within 1 hour; by 2 or 3 hours , all females were impregnated . The next day , the fluorescence color of serotonergic neurons in the L1 cross-progeny was determined . For the mating competition test with paralyzed males , rgEx431 males containing a heat shock promoter-driven egl-2 ( gf ) construct were incubated for 30 min at 30°C . After 3 hrs , the heat shocked-expressed EGL-2 ( gf ) K+ channels caused complete paralysis . Ten paralyzed males were placed onto a mating lawn with a single fog-2 ( lf ) female . One CFP-expressing wild type and one YFP-expressing mutant male were simultaneously placed in the middle of the plate . The first male to insert was determined via observations and subsequently identified using fluorescent microscopy . For mating assays with multiple hermaphrodites , a one-day-old wild-type or a dop-2; dop-3 ( lf ) male was paired with 1 , 5 , or 20 two-day–old fog-2 ( lf ) females in a plate containing a small bacterial lawn . After 4 hrs , the male was removed . The number of egg-gravid females were determined 4 hours later and then subsequently monitored for an additional 18 hrs . The genetically encoded Ca2+ indicator G-CaMP1 . 3 was used to visualize calcium transients in the sex muscles , and G-CaMP3 was used to visualize Ca2+ transients in neurons . A 2 cm square chunk from an NGM plate containing a 3 mm diameter OP50 lawn was placed on a microscope slide . 10–15 heat shocked paralyzed pha-1; lite-1; him-5; rgEx431[Phsp-16:egl-2 ( n693gf ) cDNA; Punc-103E:mDsRed; pha-1 ( + ) ] hermaphrodites were then evenly spaced on the lawn . The hermaphrodites were allowed to condition the lawn for ∼20 min before a male was added . One 18–20 hrs adult virgin transgenic male was placed on the lawn without a microscope coverslip and immediately placed on an epifluorescence–equipped Olympus BX51 microscope ( Olympus , USA ) . Matings were visualized using a 10× , or 20× long working distance objective . Males were not exposed to high intensity filtered blue and green light until they initiated mating behavior . Exposure to the high intensity blue light , even though the males contain the lite-1 mutation , interferes with the contact response step of mating . As the males were being recorded , the stage position and focusing were actively manipulated to keep the fluorescent cells in focus and in the center of the viewing field . New mating lawns were used after every two observations; long exposures to high intensity light affect the E . coli lawn in an unknown way that reduces the males' mating response . The G-CaMP and DsRed fluorescence signals at the male tail were recorded simultaneously using a Dual View Simultaneous Image splitter ( Photometrics , AZ ) and a Hamamatsu ImagEM Electron multiplier ( EM ) CCD camera , at the speed of ∼30 frames per second . The Ca2+ data was analyzed using the Hamamatsu SimplePCI ( version 6 . 6 . 0 . 0 ) software and Microsoft Excel , as described previously [24] , [30] . The recordings were reviewed to find the first instance of an uninterrupted behavioral step ( either moving forwards or backwards along the hermaphrodite cuticle , or attempting spicule insertion at the vulva ) with a duration of 6 seconds or greater . Region-of-interests ( ROIs ) , of equal areas , were generated in the Simple PCI software . The individual ray 5 , 7 , 9A neurons were too close to one another to separate with different ROIs , and thus their composite fluorescence was measured with a single ROI . The male gubernacular erector muscle , anal depressor and ventral protractor muscles were far enough so that separate ROIs could be drawn for each muscle . ROIs were used to measure the background and cellular fluorescence signals in both the green and red emission channels . The positions of the ROIs were manually adjusted for every frame in the movies . The mean pixel intensity ( MPI ) was measured for every ROI in every frame , in each recording ( Figure S11B and S11C ) . The data was then transferred from Simple PCI to Microsoft Excel . For each recording frame , background ROIs values were then subtracted from their respective ROIs that quantified neuronal or muscular fluorescence ( Figure S11D ) . Focusing/gross movement/muscle contraction/mercury arc lamp flicking and photobleaching artifacts caused non-interesting fluorescence changes in both channels and in every frame . In some cases , a higher rate of mDsRed photobleaching , relative to the minimal G-CaMP photobleaching , made a simple green-to-red fluorescence ratio-metric analysis not appropriate to use . To correct this , the red channel was used as a reference to analyze the green channel . In each frame , the red channel background-subtracted MPI for each ROI was plotted with respect to time , and an average line ( Figure S11E ) or a one-phase decay curve ( to correct for mDsRed specific photobleaching ) was fitted over the data points using GraphPad Prism ( version 4 . 03 ) . The fitted curve serves as an arbitrary reference to quantify the magnitude of non-interesting fluorescence changes that occurred in each frame . For each frame , the measured background subtracted red channel MPI value was divided by the average or fitted red value to give a correction value . The corrected inverse value for each frame was then multiplied to the subtracted green channel MPI of the respective frame ( Figure S11F ) . This corrects the values for the green channel , so that the fluorescence changes reflect calcium transients rather than gross experimental artifacts . The values for each recorded frame was then calculated as ΔF/F0 = ( ( ( corrected MPI ( frame n ) -corrected MPI ( frame 0 ( initial frame ) ) ) /corrected MPI ( frame 0 ( initial frame ) ) ) ×100 ) ( Figure S11G ) . The arbitrary F0 value was determined as the fluorescence value in the first frame of the recordings . The values were then plotted with respect to time . To determine whether the G-CaMP3 transgene might severely interfere with the behaviors displayed by the males , we quantified the mating behavior of the Pdat-1:G-CaMP3 strain . In a mating potency test , these males sire progeny similar to wild type ( Figure 1A ) . During mating , the vulval contact duration , number of vulval contacts and time in contact between insertion attempts in these males were also similar to males carrying the Pdat-1:YFP transgene ( Figure S2D–S2F ) . This indicates that any observed changes occurring in ray neurons of the Pdat-1:G-CaMP3 strain portray a biological relevant phenomenon possibly occurring during wild type mating . For the optogenetic analyses , rgEx523[Pdat-1:G-CaMP3::SL2:::mDsRed , Pgar-3B:ChR2::YFP , pha-1 ( + ) ] males , incubated +/− with all-trans-retinal , were immobilized between a microscope coverslip and a 10% noble agar pad containing Polybead polystyrene 0 . 1 µm microspheres ( Polysciences , Inc . , WA ) . In the cloacal region , we previously reported that the gar-3B promoter is expressed in PCA , PCB , SPC and the spicule muscles; however , in rgEx523 , expression in PCA and the spicule muscles were extremely variable , but expression in PCB and SPC were consistent . The mosaic nature of rgEx523 does not affect the experiments , since PCA , PCB and SPC are highly wired together . Stimulation of any one would result in increased activity of the set . The males were then put on an Olympus IX81microscope scope fitted with the Mosaic illumination targeting system ( Andor Technology , USA ) . Using the Metamorph microscopy automation and imaging analysis software ( Molecular Devices , PA ) , illumination regions were specified over the areas of the cloacal ganglia and dopaminergic ray neurons . The software then controlled the Mosaic targeted illumination system mirrors to reflect the filtered blue and green excitation light to the G-CaMP3/mDsRed expressing dopaminergic rays for ∼4 . 2 sec , followed by directing the illumination to both the ChR2-expressing cloacal ganglia and G-CaMP3-expressing dopaminergic rays for ∼5 . 7 sec , and then redirect the illumination to only the dopaminergic rays for ∼4 . 2 sec . The time between illumination protocols varied from 0 . 1 to 0 . 5 sec . The G-CaMP and mDsRed fluorescence signals were recorded simultaneously using an Optosplit II simultaneous image splitter ( Cairn Research , UK ) and an Andor iXon EM CCD camera , at the speed of ∼35 frames per second . After the males were recorded , the coverslip of the immobilized male was removed . If the male did not immediately crawl around the slide , the data was discarded . The fluorescence data was analyzed using the SimplePCI software and Microsoft Excel , as described earlier . For ChR2 activation of ray neurons , L4 dop-2; dop-3; rgEx550[Pdat-1:Chr2::YFP] and heterozygous males were incubated overnight with +/− all-trans-retinal . The adult males were then placed in a 2% noble agar pad containing 5 mM ARE on a slide and covered with a cover slip , while simultaneously being exposed to 4 . 2 mW/mm2 blue light illuminated through a 10× objective fitted to a Zeiss Stemi SV 11 stereomicroscope . To determine the working ARE concentration in 2% agar pads , a dose response curved was done with wild-type males ( Figure S1C ) . The spicule movements of wild-type or dop-2 ( lf ) ; dop-3 ( lf ) males copulating with heat-shocked paralyzed pha-1; lite-1; him-5; rgEx431[Phsp-16:egl-2 ( n693gf ) cDNA; Punc-103E:mDsRed; pha-1 ( + ) ] hermaphrodites were digitally recorded with Hamamatsu ImagEM camera at a rate of ∼35 frames per second . The grey-scale recordings were analyzed using the SimplePCI software . The recordings were reviewed to find the first instance where the male repetitively prods the vulva with his spicules for an uninterrupted duration of 6 to 10 seconds . In those frames of the recording , a rectangular ROI was drawn over the region of the male spicule . In the ROI of each frame , the standard deviation of the mean pixel intensity was calculated . The data was transferred to Microsoft Excel and plotted against time . Oscillation amplitudes greater than 5% were considered to be due to a spicule deflection . The durations between oscillations were graphed in Figure 5C . The In-Contact Length ( ICL ) was calculated by using the SimplePCI imaging software skeletonized tool to measure the length of the male outline that contacted the hermaphrodite cuticle of a representative frame . This measurement was then divided by the total male body length and converted to percentage values . To monitor gpa-16 RNAi effectiveness , pPC41[Pgpa-16:gpa-16::YFP] was injected into goa-1 ( lf ) ; gpa-7 ( lf ) strain . RNAi was induced by feeding worms bacteria producing double stranded RNA ( dsRNA ) to the target gpa-16 ORF . Bacteria with the L4440 double-T7 vector including gpa-16 fourth and fifth exons were grown and induced by IPTG using a standard protocol [67] . L4 males expressing the pPC41 transgene were transferred to plates spotted with the dsRNA bacteria or OP50 , as a control , and incubated for 20 hours . In a subset of these males , fluorescence of pharyngeal muscles and PDE neurons was checked . We found a similar percentage of males glowing in both tissues when fed with OP50 ( 80% vs . 75% , n = 20 ) ; however when males were fed with dsRNA there was a reduction in fluorescence of pharyngeal muscles when compared to PDE ( 78% vs . 4% , n = 23 ) . The adult males then were assayed for their response to DA+ARE drug baths ( Table 2 ) . | An animal's behavior is a complex output displayed in response to diverse external cues , which are sensed and processed by the nervous system . Nerve cells translate sensory information into chemical secretions ( neurotransmitters ) . These chemical signals allow neurons and muscles to communicate and coordinate motor responses . However , it is complicated how these signals are interpreted in neuronal circuits to start , continue , modify , and end specific behaviors , under the appropriate conditions . The neurotransmitter dopamine ( DA ) is involved in adjusting animal movements , thus DA neurotransmission is a candidate for coupling behaviors to the proper situational context . Here , we used C . elegans copulation to understand the DA-regulated neuronal mechanisms that promote when and where motor responses should be executed . During mating , DA is used as a feedback mechanism to adjust the activity of multiple sensory-motor neurons and muscles that promote the rhythmic thrusting of the male copulatory organs against his partner's vulval genitalia . If vulval signals are withdrawn when the male loses contact with his mate's genitalia , the DA-adjusted motor neurons' activities dampen to cease cue-independent genital penetration attempts . Therefore , DA secretions fine-tune these motor outputs to be exclusively displayed at the vulva and thus confine a behavior to its corresponding context . | [
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] | 2012 | C. elegans Dopaminergic D2-Like Receptors Delimit Recurrent Cholinergic-Mediated Motor Programs during a Goal-Oriented Behavior |
The phylogeographic population structure of Mycobacterium tuberculosis suggests local adaptation to sympatric human populations . We hypothesized that HIV infection , which induces immunodeficiency , will alter the sympatric relationship between M . tuberculosis and its human host . To test this hypothesis , we performed a nine-year nation-wide molecular-epidemiological study of HIV–infected and HIV–negative patients with tuberculosis ( TB ) between 2000 and 2008 in Switzerland . We analyzed 518 TB patients of whom 112 ( 21 . 6% ) were HIV–infected and 233 ( 45 . 0% ) were born in Europe . We found that among European-born TB patients , recent transmission was more likely to occur in sympatric compared to allopatric host–pathogen combinations ( adjusted odds ratio [OR] 7 . 5 , 95% confidence interval [95% CI] 1 . 21–infinity , p = 0 . 03 ) . HIV infection was significantly associated with TB caused by an allopatric ( as opposed to sympatric ) M . tuberculosis lineage ( OR 7 . 0 , 95% CI 2 . 5–19 . 1 , p<0 . 0001 ) . This association remained when adjusting for frequent travelling , contact with foreigners , age , sex , and country of birth ( adjusted OR 5 . 6 , 95% CI 1 . 5–20 . 8 , p = 0 . 01 ) . Moreover , it became stronger with greater immunosuppression as defined by CD4 T-cell depletion and was not the result of increased social mixing in HIV–infected patients . Our observation was replicated in a second independent panel of 440 M . tuberculosis strains collected during a population-based study in the Canton of Bern between 1991 and 2011 . In summary , these findings support a model for TB in which the stable relationship between the human host and its locally adapted M . tuberculosis is disrupted by HIV infection .
Host–pathogen co-evolution plays an important role in the biology of infectious diseases [1] . Coevolution between interacting host and pathogen species is difficult to demonstrate formally , but indirect evidence can be obtained by studying geographical patterns , which can indicate local adaptation of particular pathogen variants to geographically matched host variants [2]–[4] . Local adaptation is often studied using so-called reciprocal transplant experiments , in which the fitness of locally adapted ( sympatric ) pathogen variants is compared to the performance of allopatric pathogen variants [2] . Studies in several invertebrate systems have shown that sympatric pathogens ( infection with a phylogeographically concordant strain ) tend to outperform allopatric pathogens ( infection with a phylogeographically discordant strain ) in the corresponding host variants [1] , [5]–[7] . Mycobacterium tuberculosis , the agent causing human tuberculosis ( TB ) is an obligate human pathogen , which has been affecting humankind for millennia [8]–[13] . Contrary to previous beliefs linking the origin of TB to animal domestication ∼10 , 000 years ago [14] , more recent data suggest that M . tuberculosis evolved as a human pathogen in Africa , and might have co-existed with anatomically modern humans since their origins ∼200 , 000 years ago [8] , [10] , [12] , [13] , [15] . Analyses of multiple global strain collections have shown that M . tuberculosis exhibits a phylogeographic population structure consisting of six main phylogenetic lineages associated with different geographic regions and sympatric human populations [9] , [11]–[13] , [16]–[20] . Studies in San Francisco , London , and Montreal have shown that these sympatric host–pathogen associations persist in cosmopolitan settings , even under a presumed degree of host and pathogen intermingling [11] , [18] , [19] . Moreover , transmission of M . tuberculosis has been shown to occur more frequently in sympatric host–pathogen combinations compared to allopatric host–pathogen combinations [9] . Taken together , these observations are consistent with the notion that the different phylogeographic lineages of M . tuberculosis have adapted to specific sympatric human populations [21] . Based on the assumption that M . tuberculosis has been coevolving with humans , and that M . tuberculosis has locally adapted to sympatric human populations [9] , we hypothesized that HIV co-infection will alter this relationship [22] . Specifically , we postulated that because HIV induces immune suppression in humans , and because variation in host immunity will likely play a role in local adaptation , M . tuberculosis strains will cause disease in HIV–infected patients , irrespective of their usual sympatric host–pathogen relationship . To test this hypothesis , we performed a population-based molecular-epidemiological study of HIV–infected and HIV–negative TB patients in Switzerland between 2000 and 2008 , a country with a long history of immigration [23] .
A total of 518 patients were included in the study , of whom 112 ( 21 . 6% ) were HIV–infected . Of these 518 patients , 233 ( 45 . 0% ) were born in Europe ( 117 in Switzerland ) , 131 ( 25 . 3% ) were born in sub-Saharan Africa , 48 ( 9 . 3% ) in South-East Asia , 36 ( 7 . 0% ) in the Indian subcontinent , and 24 ( 4 . 6% ) in Central and South America . Similar to previous studies [9] , [18] , [19] , we found an association between the patient's place of birth and the particular M . tuberculosis lineages ( Figure 1 ) . Lineage 4 ( Euro-American lineage ) was present in all regions , but particularly common in patients born in Europe and South America . Lineages 5 and 6 ( West-African lineages also known as M . africanum [24] ) were exclusively found in patients originating from West Africa , whereas Lineage 2 ( which includes Beijing ) and Lineage 1 were mainly seen in patients originating from the Western Pacific and East Asian regions . Patient characteristics are summarized in Table 1 . Because in European-born patients the host–pathogen combinations defined as sympatric ( i . e . Lineage 4/Euro-American lineage in European-born patients ) or allopatric ( i . e . all other lineages in European-born patients ) have been well established [9] , [18] , [19] , [25] , we focused on this patient group ( n = 233 ) for the remaining of our analyses . M . tuberculosis transmission was more likely among patients in a sympatric host–pathogen relationship compared to patients in an allopatric host–pathogen relationship ( adjusted odds ratio [OR] 7 . 5 , 95% confidence interval [95% CI] 1 . 2-infinity , p = 0 . 03 , Table 2 ) . Of note , there was no molecular clustering among European-born TB patients infected with an allopatric M . tuberculosis strain . Moreover , we found that only the sympatric Lineage 4 ( Euro-American lineage ) was detected in European-born clusters as well as in mixed clusters ( Table S1 ) , suggesting that sympatric host–pathogen combinations in TB favor transmission . Overall , we found that HIV infection was strongly associated with allopatric M . tuberculosis lineages among European-born TB patients ( unadjusted OR 7 . 0 , 95% CI 2 . 5–19 . 1 , p<0 . 0001; Table 3 ) . Among the allopatric lineages , we found that Lineages 1 , 2 and 3 were more likely to be found in HIV–infected compared to HIV–negative patients when taking the sympatric Lineage 4 ( Euro-American lineage ) as the reference ( Table S2 ) . When investigating the ancestry of the nine HIV–infected patients with an allopatric M . tuberculosis strain , seven patients were confirmed to be of Swiss ancestry over the last three generations , and two patients had Swiss and Italian ancestors in the previous generations ( Italian father in the previous generation , or emigrating from Italy in the previous generation ) . Several factors could contribute to the association between HIV infection and allopatric lineages . We found that patients with an allopatric M . tuberculosis lineage were younger ( median age 41 . 5 versus 50 years ) , and had more often a history of frequent travelling ( 38 . 9% versus 4 . 2% , p<0 . 0001 ) . Therefore , we developed a model ( Figure 2 ) to take these and other putative confounding variables into account . These variables included age , sex , country of birth , frequent travelling , contact with the foreign-born population , and non–HIV associated immunosuppression . We considered “TB with an allopatric strain” as the outcome because disease is the only measurable outcome with a sympatric or allopatric M . tuberculosis strain ( only diseased individuals can yield a positive mycobacterial culture ) . Our multivariate analyses revealed that the association between HIV infection and allopatry remained statistically significant after adjustment for all social and patient factors included in our model ( OR 5 . 5 , 95% CI 1 . 5–20 . 6 , p = 0 . 01 , Table 3 ) . Age , sex , being Swiss-born , and non–HIV associated immunosuppression had only a minor effect on the association between HIV infection and TB with an allopatric strain ( Table 3 ) . In contrast , a history of repeated travelling to low-income countries had a stronger effect , decreasing the OR to 4 . 50 ( 95% CI 1 . 5–13 . 6 , p = 0 . 008 , Table 3 ) when adjusting for this variable . We also tested if the degree of immunodeficiency as measured by the nadir CD4 T cell count ( defined as the lowest CD4 T cell count ever measured in a patient ) would have an impact on the association between host population and M . tuberculosis lineage . Among Europeans , the strength of the association between HIV infection and allopatric lineages increased with a decreasing nadir CD4 T cell count in a dose-dependent manner: from an OR of 4 . 6 ( 95% CI 0 . 9–24 . 7 ) in patients with a nadir CD4 T cell count of >200 cells/µL to an OR of 12 . 5 ( 95% CI 2 . 6–60 . 8 ) in patients with nadir CD4 T cell counts <50 cells/µL ( test for trend p<0 . 0001; HIV–negative patients as reference ) . This trend remained statistically significant when adjusting for age , sex , being born in Switzerland , frequent travelling , contact with the foreign-born population , and non–HIV associated immunosuppression ( Table 4 ) . Increased contact with foreigners originating from high TB burden countries , who have a higher risk of TB [26] and are more likely to have TB caused by an allopatric M . tuberculosis strain , could also lead to an allopatric host–pathogen relationship in European-born patients . However , the association between HIV and allopatry remained statistically significant when adjusting for this variable ( Table 3 ) . Furthermore , we examined molecular clusters defined by standard bacterial genotyping [27] , [28] , to test the hypothesis that HIV–infected patients were more frequently seen among ethnically mixed clusters where transmission occurred between non-European and European-born cases [29] . We found that the prevalence of HIV infection was similar among TB cases in mixed clusters ( 5 HIV–infected cases out of 26 cases , 19 . 2% ) and among cases in clusters involving only European-born cases ( 4 out of 26 cases , 15 . 4% , see Table S1 ) . When restricting the main analysis ( n = 233 ) to European-born patients without a history of frequent travelling , we found that the association between HIV infection and allopatric TB remained statistically significant ( adjusted OR 6 . 96 , 95% CI 1 . 25–38 . 88 , p = 0 . 027 ) . Furthermore , we explored associations of socio-demographic and clinical factors with TB with an allopatric M . tuberculosis strain in a model focusing on HIV–infected European patients only ( Figure S1 , Table S3 ) : frequent travelling was confirmed to be an important factor , and patients with a low nadir CD4 T cell count tended to be associated with an allopatric TB although the associations did not reach statistical significance ( Table S3 ) . Finally , we obtained very similar results for the association between HIV infection and allopatric TB ( Table S4 ) , and for the association between the degree of immunodeficiency and allopatric TB ( Table S5 ) when repeating analyses using a Bayesian approach [30] , which is more robust when numbers are small . The birth origin of HIV–infected and non-infected patients is shown on a map in Figure S2 . The main phylogenetic M . tuberculosis lineages stratified by place of birth and HIV status are presented in Table S6 . To replicate our main finding , we investigated a second panel of M . tuberculosis strains from an ongoing population-based TB study in the Canton of Bern , Switzerland , between 1991 and 2011 . Of the 1 , 642 M . tuberculosis isolates analyzed , 1 , 350 ( 82 . 2% ) belonged to Lineage 4 ( Euro-American lineage ) , and 292 ( 17 . 8% ) to non-Euro-American lineages ( Lineages 1 , 2 , 3 , 5 or 6 ) . We compared all 40 European-born patients with an allopatric strain ( non-Lineage 4 ) with 400 randomly selected European-born patients with a sympatric strain ( Lineage 4 ) . We found that the proportion of HIV infection was 4 . 5 ( 95% CI 1 . 6–11 . 9 ) times higher in patients with an allopatric strain compared to patients with a sympatric strain ( 12 . 5% versus 2 . 8% , p = 0 . 010 , Table 5 ) .
The phylogeographic distribution of M . tuberculosis lineages observed here suggests local adaptation to sympatric human populations . In contrast , we found that allopatric host–pathogen relationships in European-born TB patients were strongly associated with HIV co-infection . The association with HIV infection became stronger in a ‘dose-dependent’ manner in patients with a history of more pronounced immunodeficiency , and was not explained only by frequent travelling to high TB-incidence countries or increased social mixing with the foreign-born population . The association of M . tuberculosis lineages with sympatric patient populations reported here is in agreement with previous findings [9] , [11]–[13] , [16]–[19] . Similarly , our finding that recent TB transmission was more likely to occur in sympatric compared to allopatric host–pathogen combinations supports previous work [9] . Taken together , these data are consistent with local adaptation of M . tuberculosis to different human populations , which in turn can be viewed as indirect evidence for coevolution between M . tuberculosis and its human host [1]–[4] , [9]–[13] . We found that TB allopatric host–pathogen combinations were strongly associated with HIV infection in a nation-wide study and a second panel of strains from one Canton of Switzerland . This supports the notion that M . tuberculosis lineages have evolved subtle differences in their interaction with different human immune systems . However , in the presence of HIV–induced immunodeficiency , any M . tuberculosis lineage seems to cause disease in a given human host . M . tuberculosis is an obligate human pathogen which lives in constant interaction with the host immune system [31] . Human populations , however , are known to differ genetically and immunologically [15] . The clinical disease reflects host-dependent immune-pathological processes [31] . In other words , while initially triggered by the pathogen , it is the host immune response which is ultimately responsible for the chronic inflammation and associated tissue destructions . These processes contribute to the successful transmission of M . tuberculosis [22] , [32] . On the other hand , only 5–10% of the 2 billion individuals estimated to be latently infected with M . tuberculosis globally will develop active TB during their lifetime [33]–[35] . Hence most of the time , humans are able to control the infection . In our study , we chose culture-confirmed TB cases as the main endpoint which reflects successful transmission and progression from infection to active disease . Our study on the association between allopatric TB and HIV was able to control for important cofactors [36] , [37] . These cofactors included frequent travelling abroad and increased contact to foreign-born populations . A particularly important cofactor for allopatric TB was frequent travelling to high TB burden countries with potential exposure to foreign M . tuberculosis strains; HIV–infected individuals may be at a higher risk for travel-related infectious diseases [38] . However , the association between HIV infection and allopatric TB remained even when adjusting for these behavioral and other patient characteristics . A previous study reporting on allopatric TB and HIV was not able to control for these factors [9] . Furthermore , we found no evidence for increased social mixing among HIV–infected individuals , which argues against mere social factors leading to the association between allopatric TB and HIV . A biological basis for this association is further supported by the striking dose-dependency we observed with increasing immunosuppression as defined by lower nadir CD4 T cell counts . Of note , this trend was also independent of other variables . Low nadir CD4 T cell counts are associated with incomplete immune recovery after starting combination antiretroviral therapy [39] , [40] and impaired functional immune restoration despite normalization of CD4 T cells [41] . More generally , infection with HIV and M . tuberculosis interferes with the immune system in many ways [42] , [43] . HIV infection disrupts the function of M . tuberculosis-infected macrophages [44] , [45] , but also seems to reduce the number and functionality of M . tuberculosis-specific T cells over time [46] . On the other hand , M . tuberculosis strains have been shown to induce variable immune responses [47] . Based on these observations , it is reasonable to hypothesize that HIV/TB co-infection might impact immune cell functions , intracellular signaling and immune regulation , perhaps leading to an immune response less capable of discriminating between M . tuberculosis variants . Besides M . tuberculosis , several other human pathogenic bacteria exhibit phylogeographic population structures , possibly reflecting local adaptation to different human populations . These include Haemophilus influenzae [48] , Streptococcus mutans [49] , M . leprae [50] and Helicobacter pylori [51] , [52] . Interestingly , like M . tuberculosis , all of these microbes are obligate human pathogens . In the case of H . pylori , functional studies have shown that strains associated with South America have adapted their adhesins to the human blood group O , which is particularly frequent in native populations of this region [53] . Similarly , a study of the bacterial genome evolution of an asymptomatic Escherichia coli bacteriuria strain showed adaptation at the genomic level in distinct human hosts [54] . No similar experimental work has yet been carried out in TB . However , several studies have reported associations between human genetic polymorphisms and particular M . tuberculosis lineages [55]–[59] , indicating possible interaction between human and M . tuberculosis variation . Whether such variation in pathogen and host genetics can be attributed to co-evolution will be difficult to demonstrate conclusively , but the data presented here support this possibility . The strength of our study was that we used a nation-wide sample to specifically look at the impact of HIV infection on the host–pathogen relationship in human TB . Yet , our study is limited by the relatively small sample size , and the difficulty to quantify the complex social context through which the host–pathogen relationship is influenced in human TB . In addition , we looked at European-born patients only , because sympatric and allopatric host–pathogen combinations are more easily defined for this patient population [9] , [18] , [19] , [25] . Additional studies in large cosmopolitan cities of Asia and Africa would be required to test whether the association between allopatric TB and HIV holds true in these settings . Ultimately , detailed experimental work is needed to establish the biological basis of the host–pathogen association in human TB . In conclusion , our data suggest that the phylogeographical host–pathogen relationship in TB influences transmission patterns . Among the studied European-born TB patients , we showed that HIV infection disrupts the sympatric host–pathogen relationship in human TB , and that this effect increased as a function of immunodeficiency . Various interactions between HIV and M . tuberculosis at the cellular level make an association biologically plausible [42] , [43] . Further studies are needed to investigate the impact of HIV on the genetic population structure of M . tuberculosis with its consequences for transmission and clinical manifestations in high TB burden countries [36] . This will lead to a better understanding of biological factors that shape the current HIV/TB syndemic [60] .
The Swiss Molecular Epidemiology of Tuberculosis ( SMET ) study is a collaborative project between the Swiss HIV Cohort Study ( SHCS ) , the National Center for Mycobacteria , diagnostic microbiology laboratories , departments of respiratory medicine and public health , and the Federal Office of Public Health ( FOPH ) [29] , [61] , [62] . The overarching aims were to examine the genetic population structure of M . tuberculosis and the associations between strain variation , patient origin , and clinical characteristics in HIV–infected and HIV–negative TB patients in Switzerland . Further information on the SMET project is available at www . tb-network . ch . All participating sites are listed in the Acknowledgements . The SHCS is a prospective observational study of HIV–infected individuals followed up in HIV outpatient clinics in Switzerland [63] . All HIV–infected patients diagnosed with TB between 2000 and 2008 whose M . tuberculosis complex ( MTBC ) isolate was available were included in the SMET study [29] . Furthermore , we randomly selected 288 from the 4 , 221 culture-confirmed TB cases reported to the National TB Surveillance Registry during the same period ( approximately three cases for one HIV–infected TB case within the SHCS ) . Finally , all reported drug-resistant TB cases were included . Two M . bovis isolates were excluded from this study as they are animal-adapted species within the MTBC and therefore represent a different host–pathogen relationship . We obtained clinical data by standardized questionnaires sent to the treating physicians and extracted relevant data from the SHCS database . We collected socio-demographic data ( age , sex , origin of birth , citizenship , legal status , immunosuppressive therapy , risk factors for TB such as recent TB within family or immediate social surroundings in the last two years ) , laboratory parameters ( CD4 cell count and plasma HIV RNA in HIV–infected cases ) and clinical information ( site of disease , radiography findings ) . Chest radiography parameters were consolidation , cavitations , enlarged intrathoracic lymph nodes and pleural thickening . Any drug resistance was defined as any resistance to isoniazid , rifampicin or ethambutol as reported to the FOPH . Most TB cases in Switzerland are treated under the guidance of experienced infectious and respiratory disease specialists , and the clinical data were of high quality . Geographic origin of patients was defined as the country of birth , and countries were grouped in seven geographic regions ( see Figure 1 ) according to the current understanding of the phylogeography of M . tuberculosis [25] . Birth country was used as a proxy of the ancestry of the study population . Immunosuppression due to other causes than HIV infection was defined as use of TNF-alpha inhibitors , malignancy , solid organ transplantation , use of steroids or methotrexate . Nadir CD4 T cell count was defined as the lowest CD4 T cell count ( cells/µL ) ever measured in a patient . Nadir CD4 T cell count is a predictor of poor immune recovery after ART [39] . Travel history was extracted from the free text field “Risk factor for TB” and defined as repeated travelling of longer duration ( >30 days ) to low-income countries with a high TB burden and a relevant exposure to M . tuberculosis according to the physician's judgment . Belonging to a molecular cluster involving Swiss-born and foreign-born TB cases was used as a proxy for contact with the foreign-born population . Mycobacterial isolates were cultured and DNA extracted according to standard laboratory procedures . We used spacer oligonucleotide typing ( spoligotyping ) and 24-loci mycobacterial interspersed repetitive units ( MIRU-VNTR ) which are based on repetitive DNA sequences as genotyping tools with high discriminatory power to identify recent TB transmission [29] , [64]–[66] . Data were analyzed with the MIRU-VNTRplus online tool ( http://www . miru-vntrplus . org ) . Molecular clusters were defined as a group of completely identical isolates in the spoligotyping and MIRU-VNTR pattern indicating a chain of TB transmission . In addition , we used single nucleotide polymorphisms ( SNPs ) as stable genetic markers to define the main phylogenetic M . tuberculosis lineages [67] . Lineages were determined by SNPs using multiplex real-time PCR with fluorescence-labeled probes ( Taqman , Applied Biosystems , USA ) adopted from previous studies [9] , [12] , [67] , [68] . The SNP used to define Lineage 4 was originally described by Sreevatsan et al . [69] and shown to be specific to this lineage [9] . Graphical models were built using the principles of directed acyclic graphs [70] . Our model considered infection and disease as a combined outcome ( “TB with an allopatric strain” ) . Our hypothesis that HIV infection causes TB with an allopatric strain is shown as a potentially causal direct effect , and risk factors potentially influencing this effect are shown in the hypothetical direction . Mediators represent variables that are caused by the independent variable and , in turn , have a direct effect on the outcome variable . We included age and sex in our model as risk factors for infection and disease [37] . We also considered contact with the foreign-born population who have a higher risk for TB compared to the native Swiss population [26] and who have a higher risk of exposure to “foreign” M . tuberculosis strains . Finally , we included frequent traveling to countries with a high TB burden , which increases exposure risk and thus potentially infection risk with “foreign” M . tuberculosis strains ( Figure 2 ) . We used χ2 tests or Fisher's exact tests to assess differences between groups in binary variables , and the Wilcoxon rank sum test for continuous variables ( Table 1 , Table 2 ) . Univariate and multivariate exact logistic regression models were fitted to estimate the association between transmission as defined by molecular clustering and patients with sympatric M . tuberculosis lineages ( patients with allopatric lineages were used as the reference , Table 2 ) . Results were presented as ORs unadjusted and adjusted for age group , being born in Switzerland and recent TB in families or social surroundings . To assess the association of HIV infection with allopatric TB , we fitted univariate and multivariate logistic models ( Table 3 ) , and presented ORs unadjusted and adjusted for age , sex , Swiss-born , frequent travelling , contact with foreign born populations , and/or immunosuppression . We used univariate and multivariate logistic models to estimate the association between the degree of immunodeficiency and allopatric TB ( Table 4 ) , and presented ORs unadjusted and adjusted for age , sex , Swiss-born , frequent travelling , contact with foreign-born populations , and immunosuppression other than HIV infection . Finally , we determined statistical significance of HIV prevalence in patients with allopatric M . tuberculosis lineages compared to patients with sympatric lineages using Fisher's exact tests ( Table 5 ) . All analyses were performed in Stata version 11 . 1 ( Stata Corporation , College Station , TX , USA ) . In sensitivity analyses , we excluded patients with a history of frequent travelling to remove its influence on the association between HIV infection and allopatric lineages . In addition , we repeated the analyses using fully probabilistic Bayesian methods using weakly informative prior distributions [71] . The CIs reported from these analyses are 95% credible intervals and correspond to tail probabilities of the coefficient's posterior distributions . Bayesian statistics are less sensitive to errors when calculating estimators and CIs in small datasets . We obtained 1 , 642 M . tuberculosis isolates from all TB cases ( n = 1 , 940 , 84 . 6% ) notified in the Canton of Bern , Switzerland , between 1991 and 2011 . For all patient isolates , we determined the main phylogenetic M . tuberculosis lineages . Of these , we included all patient isolates belonging to a non-Euro-American lineage ( Lineage 1 , 2 , 3 , 5 or 6 ) from European-born TB patients ( 40 of a total of 292 isolates belonging to lineages other than Lineage 4 ) . Furthermore , we randomly selected control strains belonging to the Euro-American lineage ( Lineage 4 ) from European-born TB patients ( 400 of a total of 1 , 350 isolates belonging to Lineage 4 ) . European ancestry was confirmed in HIV–infected patients with an allopatric M . tuberculosis strain . Finally , we determined the HIV status in these patients using the same procedures as in the main sample . The study was approved by the ethics committee of the Canton of Bern , Switzerland . Written informed consent was obtained from all patients enrolled in the SHCS . For patients outside the SHCS , written informed consent was obtained by the treating physicians . In some cases informed consent could not be obtained from the patient because he or she could not be located or was known to have died . For these cases we obtained permission from the Federal expert commission on confidentiality in medical research to use the data provided by the treating physician . | Human tuberculosis ( TB ) caused by Mycobacterium tuberculosis kills 1 . 5 million people each year . M . tuberculosis has been affecting humans for millennia , suggesting that different strain lineages may be adapted to specific human populations . The combination of a particular strain lineage and its corresponding patient population can be classified as sympatric ( e . g . Euro-American lineage in Europeans ) or allopatric ( e . g . East-Asian lineage in Europeans ) . We hypothesized that infection with the human immunodeficiency virus ( HIV ) , which impairs the human immune system , will interfere with this host–pathogen relationship . We performed a nation-wide molecular-epidemiological study of HIV–infected and HIV–negative TB patients between 2000 and 2008 in Switzerland . We found that HIV infection was associated with the less adapted allopatric lineages among patients born in Europe , and this was not explained by social or other patient factors such as increased social mixing in HIV–infected individuals . Strikingly , the association between HIV infection and less adapted M . tuberculosis lineages was stronger in patients with more pronounced immunodeficiency . Our observation was replicated in a second independent panel of M . tuberculosis strains collected during a population-based study in the Canton of Bern . In summary , our study provides evidence that the sympatric host–pathogen relationship in TB is disrupted by HIV infection . | [
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] | 2013 | HIV Infection Disrupts the Sympatric Host–Pathogen Relationship in Human Tuberculosis |
Recent genome-wide association studies ( GWAS ) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels . A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported . However , a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified . The identification of these “unknown metabolites” is still a demanding and intricate task , limiting their usability as functional markers of metabolic processes . As a consequence , previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis . Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites . We apply our method to original data of 517 metabolic traits , of which 225 are unknowns , and genotyping information on 655 , 658 genetic variants , measured in 1 , 768 human blood samples . We report previously undescribed genotype–metabotype associations for six distinct gene loci ( SLC22A2 , COMT , CYP3A5 , CYP2C18 , GBA3 , UGT3A1 ) and one locus not related to any known gene ( rs12413935 ) . Overlaying the inferred genetic associations , metabolic networks , and knowledge-based pathway information , we derive testable hypotheses on the biochemical identities of 106 unknown metabolites . As a proof of principle , we experimentally confirm nine concrete predictions . We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification , hypertension , and insulin resistance . Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms .
Recently , genome-wide association studies ( GWAS ) on metabolic quantitative traits have proven valuable tools to uncover the genetically determined metabolic individuality in the general population [1]–[5] . Interestingly , a great portion of the genetic loci that were found to significantly associate with levels of specific metabolites are within or in close proximity to metabolic enzymes or transporters with known disease or pharmaceutical relevance . Moreover , compared to GWAS with clinical endpoints the effect sizes of the genotypes are exceptionally high . The number and type of the metabolic features that went into these GWAS was mainly defined by the metabolomics techniques used: Gieger et al . [1] and Illig et al . [2] used a targeted mass spectrometry ( MS ) -based approach giving access to the concentrations of 363 and 163 metabolites , respectively . Suhre et al . [3] and Nicholson et al . [4] applied untargeted nuclear magnetic resonance ( NMR ) based metabolomics techniques , yielding 59 metabolites that had been identified in the spectra prior to the GWAS and 579 manually selected peaks from the spectra , respectively . In Suhre et al . [5] , 276 metabolites from an untargeted MS-based approach were analyzed . While these previous GWAS focused on metabolic features with known identity , untargeted metabolomics approaches additionally provide quantifications of so-called “unknown metabolites” . An unknown metabolite is a small molecule that can reproducibly be detected and quantified in a metabolomics experiment , but whose chemical identity has not been elucidated yet . In an experiment using liquid chromatography ( LC ) coupled to MS , such an unknown would be defined by a specific retention time , one or multiple masses ( e . g . from adducts ) , and a characteristic fragmentation pattern of the primary ion ( s ) . An unknown observed by NMR spectroscopy would correspond to a pattern in the chemical shifts . Unknowns may constitute previously undocumented small molecules , such as rare xenobiotics or secondary products of metabolism , or they may represent molecules from established pathways which could not be assigned using current libraries of MS fragmentation patterns [6] , [7] or NMR reference spectra [8] . The impact of unknown metabolites for biomedical research has been shown in recent metabolomics-based discovery studies of novel biomarkers for diseases and various disease-causing conditions . This includes studies investigating altered metabolite levels in blood for insulin resistance [9] , type 2 diabetes [10] , and heart disorders [11] . A considerable number of high-ranking hits reported in these biomarker studies represent unknown metabolites . As long as their chemical identities are not clarified the usability of unknown metabolites as functional biomarkers for further investigations and clinical applications is rather limited . In mass-spectrometry-based metabolomics approaches , the assignment of chemical identity usually involves the interpretation and comparison of experiment-specific parameters , such as accurate masses , isotope distributions , fragmentation patterns , and chromatography retention times [12]–[14] . Various computer-based methods have been developed to automate this process . For example , Rasche and colleagues [15] elucidated structural information of unknown metabolites in a mass-spectrometry setup using a graph-theoretical approach . Their approach attempts to reconstruct the underlying fragmentation tree based on mass-spectra at varying collision energies . Other authors excluded false candidates for a given unknown by comparing observed and predicted chromatography retention times [16] , [17] , or by the automatic determination of sum formulas from isotope distributions [18] . Furthermore , Gipson et al . [19] and Weber et al . [20] integrated public metabolic pathway information with correlating peak pairs in order to facilitate metabolite identification . However , these methods might not be applicable for high-throughput metabolomics datasets that have been produced in a fee-for-service manner , since the mass spectra as such might not be readily available . Approaching the problem from a conceptually different perspective , we here present a novel functional metabolomics method to predict the identities of unknown metabolites using a systems biological framework . By combining high-throughput genotyping data , metabolomics data , and literature-derived metabolic pathway information , we generate testable hypotheses on the metabolite identities based solely on the obtained metabolite quantifications ( Figure 1 ) . No further experiment-specific data such as retention times , isotope patterns and fragmentation patterns are required for this analysis . The concept of our approach is based on the following observations from our previous work on genome-wide association studies and Gaussian graphical modeling ( GGM ) with metabolomics: We showed that GWAS with metabolic traits can reveal functional relationships between genetic loci encoding metabolic enzymes and metabolite concentration levels in the blood [1]–[3] , [5] . A genetic variant can alter , for instance , the expression levels of mRNAs or affect the properties of the respective enzymes through changes of the protein sequence ( e . g . enzyme activity , substrate specificity ) . Moreover , we found that GGMs , which are based on partial correlation coefficients , can identify biochemically related metabolites from high-throughput metabolomics data alone [21] , [22] . These observations suggest that if an unknown compound displays a similar statistical association with a genetic locus in a GWAS or a known metabolite in a GGM , then this may provide specific information of where it is located in the metabolic network . Based on this information we can then derive testable hypotheses on the biochemical identity of the unknown metabolite . This annotation idea parallels classical concepts from functional genomics , where , for instance , co-expression between RNA transcripts is used to predict the function of poorly characterized genes [23] , [24] . The manuscript is organized as follows: We first conduct a full genome-wide association study on 655 , 658 genotyped SNPs with concentrations of 225 unknown metabolites using fasting blood serum samples from a large German population cohort ( n = 1768 ) [25] . We thereby extend our previous work on known metabolites [5] to a GWAS with hitherto unpublished unknown metabolic traits . We then compute a Gaussian graphical model including both known and unknown metabolites . In a third step , we integrate the results of the GWAS and GGM computations and combine them with metabolic pathway information from public databases to derive predictions for a total of 106 unknown metabolites . In order to validate the approach , we investigate six distinct cases , in which we derive specific identity predictions for a total of nine unknown metabolites , which we then confirm experimentally . Finally , we discuss the relevance of newly discovered genetic loci and unknown identity predictions in the context of existing disease biomarker discovery and pharmacogenomics studies . All GWAS and GGM results , unknown metabolite classifications and pathway annotations are available as spreadsheets and in . graphml format in Dataset S1 or from our study website at http://cmb . helmholtz-muenchen . de/unknowns .
In the first step of our analysis , we conducted a GWAS with the concentrations of known and unknown metabolites , testing a total of 655 , 658 genotyped SNPs from the KORA cohort for association . Thus , in addition to the unknown metabolite data , we included the association data for known metabolites from our previous study [5] into the present analysis . Unknown metabolites are uniquely labeled in the format “X-12345” , which are identical throughout all published studies that use the Metabolon platform . In total , we observe 34 distinct loci that display metabolite associations at a genome-wide significance level ( Figure 2 and Dataset S1 ) . Out of these 34 loci , 15 associate with at least one unknown compound . For 12 loci , an unknown compound constitutes the strongest association of all tested compounds . From the 213 unknown metabolites analyzed ( see Methods for the determination of this metabolite subset ) , 28 show at least one genome-wide significant hit . These 28 associations at the 15 loci are presented in Table 1 along with all previously described GWAS hits to metabolic traits or other endpoints . Associating traits were determined from the GWAS catalog [26] for SNPs in LD ( r2≥0 . 5 ) with the respective lead SNP . Seven of the 15 loci ( SLC22A2 , COMT , CYP3A5 , CYP2C18 , GBA3 , UGT3A1 , rs12413935 ) have not been described in GWAS with metabolic traits before and thus represent new genetic loci of metabolic individuality . Interestingly , genetic variants in strong LD with CYP2C18 have been reported to associate with warfarin maintenance dose [27] . In our previous GWAS with metabolic traits , we observed that metabolites associating with genetic variants in or near enzymes are likely to be functionally linked to these proteins . A SNP with detectable effects on the metabolome will , for instance , alter expression levels of mRNAs , or affect the properties of the respective enzymes ( e . g . enzyme activity , substrate specificity ) through modifications of the protein sequence . As an example of the latter case , the SNP rs4343 in the angiotensin converting enzyme ( ACE ) encoding gene was found to be associated with the activity of the enzyme [28] ( See Table 1 ) . To estimate the contribution of the first case , we compared our significant SNPs with expression quantitative trait loci ( eQTLs ) from published GWAS with expression levels . To this end , we queried the Genotype-Tissue Expression ( GTEx ) browser , an online eQTL database of the NIH GTEx roadmap project , which stores eQTL results for multiple human tissues ( liver , lymphoblastoid , brain ) [29] . For seven SNPs in three distinct loci ( PYROXD2 , CYP3A5 , SPPL3 ) , we found significant cis-eQTLs ( p-value<2 . 7×10−9 , see Methods ) in GTEx . All identified eQTLs with p-values below 10−5 are listed in Dataset S1 . Based on the observation that SNPs in or in the vicinity of enzymes are mostly associating with functionally related metabolites in case of the knowns , we used the GWAS data to derive hypotheses on the potential identity of the respective unknowns . For instance , the SNP rs296391 in close proximity to the SULT2A1 gene ( sulfotransferase family , cytosolic , 2A , dehydroepiandrosterone DHEA-preferring ) strongly associates with the concentrations of the unknown metabolites X-11440 and X-11244 ( p = 1 . 7×10−43 and p = 2 . 1×10−26 , respectively ) . The enzyme encoded by SULT2A1 , a bile salt sulfotransferase , converts steroids and bile acids into water-soluble sulfate conjugates for excretion [30] . Thus , we may speculate that X-11440 and X-11244 are biochemically related to steroids , bile acids , or water-soluble sulfate conjugates . Additional insights can be gained from genetic associations that involve both known and unknown metabolites . For instance , X-12510 , X-11787 , X-12093 and N-acetylornithine strongly associate with genetic variation at the NAT8 locus . NAT8 encodes the protein N-acetyltransferase 8 . In this case , we may speculate that the unknowns represent similar substrates or products of the N-acetylation processes linked to this enzyme . Finally , we can link the results obtained here with results from other GWAS on metabolic traits . For example , the unknown metabolite X-13431 associates with a genetic variant in the ACADL ( acyl-CoA dehydrogenase , long-chain ) gene . This locus does not associate with any other metabolite in the present study , but was previously reported to associate with the medium-chain length carnitines C9 and C10:1 [1] , [2] . Proteins from the ACAD family catalyze rate-limiting reactions in the β-oxidation pathway which generally associate with carnitines . This observation suggests that X-13431 may be a member of this medium-chain length carnitine family . These examples demonstrate that concrete information on the biochemical identity of unknown metabolites can be derived from our experimental dataset by using the GWAS approach . In the second step of our analysis we focused solely on intrinsic relations between the measured metabolites and , in particular , on associations between known and unknown compounds . To this end , we applied Gaussian graphical models ( GGMs ) , which we have previously shown to be able to reconstruct pathways involving directly related metabolites from cross-sectional blood serum metabolomics data [21] , [22] . GGMs are based on partial correlation coefficients , that is , correlations between pairs of metabolites corrected for the effects of all remaining metabolites . Each known metabolite is annotated with a “super-pathway” corresponding to its general metabolic class , and a “sub-pathway” representing more specific metabolic pathways ( see Dataset S1 ) . In order to obtain a dataset that is independent of our genetic analysis , and to avoid circular arguments , co-variations in metabolite concentrations that are due to association with genetic variants ( SNPs ) were specifically removed from the data ( see GGM methods for further details ) . A partial correlation was included in the model if it was significantly different from zero with α = 0 . 05 after Bonferroni correction , yielding a corrected significance level of = 7 . 9×10−7 and an absolute partial correlation cutoff of ζ = 0 . 178 . The resulting GGM consists of a total of 399 out of 62 , 835 theoretically possible edges ( 0 . 64% connectivity , Figure 3A ) . In line with our previous observations [21] , metabolites tend to be strongly connected within their respective metabolic class , while links between different classes are rare ( see Text S1 ) . Inspecting the GGM in detail , we observe that the unknowns are tightly integrated within the network and connected to known compounds of various metabolic classes . This is reflected both in the overall network ( Figure 3A , Text S1 ) and in the top list of high-scoring GGM edges ( Table 2 ) , where 18 of the 30 strongest partial correlations comprise at least one unknown metabolite . The highest partial correlation in the dataset actually involves a known-unknown metabolite pair , namely 3-indoxylsulfate and the unknown metabolite X-12405 ( ζ = 0 . 840 ) . For pairs of known metabolites , we consistently observe associations of biochemically related metabolites from various metabolic pathways , such as the metabolites inosine and guanosine ( ζ = 0 . 798 ) , which are involved in nucleotide metabolism , or androsterone sulfate and epiandrosterone sulfate ( ζ = 0 . 755 ) , which represent related steroid hormone metabolites . Other pathways with related metabolite pairs include amino acid metabolism , lipid metabolism , bile acid metabolism , and xanthine metabolism . Following our line of reasoning , correlating pairs of a known and an unknown metabolite then directly point to specific pathways of cellular metabolism on which the unknown metabolite may lie . The investigation of the sub-network structure around the unknown compounds provides additional biochemical context for that compound . We selected four high-scoring sub-networks in the GGM to show that this concept is indeed applicable to real data . The first two of these sub-networks consist of a series of intermediate compounds from purine metabolism , including guanosine , inosine , xanthine derivatives and urate ( Figure 3B and 3C ) . In these cases , one can actually follow the addition and removal of chemical groups by following the edges in the GGM network: Most edges in these sub-networks correspond to the change of either a single methyl group at the purine double-ring structure or to the removal of a ribose residue in the reaction from nucleosides to xanthine variants . While the compounds in both sub-networks appear structurally similar , the distinction into two groups by the GGM is indeed biochemically sound . The metabolites in Figure 3B correspond to endogenous substances in the nucleoside pathway , whereas the molecules in Figure 3C relate to signals from xenobiotic metabolism of drugs and caffeine . Here , the unknown metabolites X-11422 and X-10810 , as well as X-14473 and X-14374 are prominently placed in the networks , making them direct targets for closer inspection with respect to endogenous xanthines and xenobiotics , respectively . The third sub-network comprises three androsterone sulfate variants , which belong to the class of steroid hormones ( Figure 3D ) . We observe direct GGM links between the unknowns X-11450 , X-11244 and X-11443 with both dehydroepiandrosterone sulfate ( DHEAS ) and epiandrosterone sulfate , suggesting androsterone derivatives as likely candidates for these three metabolites . The fourth sub-network involves different stereoisomers of bilirubin , which is the degradation product of the oxygen transporter hemoglobin [31] ( Figure S1 ) . In this sub-network , we observe high partial correlations between the bilirubin variants and a series of unknown metabolites ( X-11441 , X-11530 , X-11442 , X-11793 , X-11809 , X-14056 , and X-14057 ) . The seven unknown compounds in this GGM sub-network are thus likely to be involved in hemoglobin degradation processes . Taken together , the examples confirm that further information on the biochemical identity of unknown metabolites can be extracted from GGM networks . The next step in our analysis was the integration of the GGM and GWAS approaches with general pathway information from external databases , in order to generate concrete predictions for the unknowns' metabolic pathway memberships . As a feasibility test , we first asked whether the local neighborhood of a metabolite in the GGM can be used to correctly predict its metabolic class . Using a majority-voting based classifier and subsequent permutation testing , we detected significant classification abilities ( mean sensitivity 0 . 674 , mean specificity 0 . 84 , macro-averaged F1 score 0 . 72 ) far beyond random ( p<10−8 , ) . Detailed results can be found in Text S2 . Note that we performed this approach only to demonstrate the systematic possibility to derive functional information from the GGM . The actual classification of the unknowns in the following will not be based on majority voting , but rather on the collection of all available functional information from GGM neighbors and GWAS hits . We combined functional annotations for both GGM neighbors and GWAS hits for each unknown in order to derive specific pathway classifications . For unknowns that did not have a known metabolite neighbor in the GGM , we also investigated the 2- and 3-neighborhoods . Since these hits certainly represent weaker evidence than a direct GGM neighbor , we distinguish between ‘GGM hit’ and ‘direct GGM hit’ in the following . Functional annotations were obtained from three sources: ( 1 ) The sub-pathway assignment provided for each known metabolite in the GGM neighborhood , ( 2 ) the GO functional terms for the associated gene of all genome-wide significant GWAS hits , and ( 3 ) the KEGG pathways on which the associated genes lie . To the best of our knowledge , there is presently no consistent mapping between annotations from the different data sources available for both metabolites and genes , so we here had to perform the only non-automatic step in the analysis: By manual interpretation of different functional classes ( Figure 4A ) , we derive a single consensus pathway annotation for a total of 106 of the unknown metabolites ( Figure 4B ) . For 98 unknowns , we obtained annotations from the GGM network , with 74 of these hits representing direct GGM hits . From the 28 genetic hits introduced above , 27 were in a genetic region with gene annotation . Overlaying the direct edge GGM set and the GWAS set , we obtained 16 unknowns with both biochemical and genetic evidence ( Figure 4C ) . A list of all functional evidence along with the respective predictions can be found in Table S1 . In the following , we selected several unknowns that were forwarded to detailed analysis and experimental validation . Five cases were obtained from the set of 16 high-confidence predictions in the previous section , since the combined evidence from GWAS and GGMs provides rich functional annotations that allow to derive possible compound candidates . Moreover , in order to demonstrate the power of GGMs in the absence of genetic associations , we selected one further case ( HETE ) where publically available pathway information was systematically exploited . Experimental validations were performed by running pure candidate compounds on the LC-MS/MS platform . For cases where no pure compound was available , we determined exact molecule masses and revisited the retention times and fragmentation spectra . We investigated six metabolic scenarios in-depth and attempted experimental confirmation of the respective predictions ( Table 3 ) . In the following , we discuss three example cases , termed DIPEPTIDE , STEROID , and HETE ( Figure 5 ) . Three further examples , named CARNITINE , BILIRUBIN , and ASCORBATE , are presented as Text S3 . In the discussion of these scenarios we now use all available evidence , the metabolite correlations , genetic associations , biochemical data , and in addition the molecular masses reported with the known and unknown compounds ( which do not represent exact masses at this point ) . Note that the presented scenarios represent the only cases where a detailed investigation has been attempted . Moreover , the candidate compounds mentioned in the following paragraphs and the supplementary material are the only compounds that have been experimentally tested ( there are no negative results not reported in this text ) .
The first example is a recent biomarker study , where Milburn et al . [34] reported an association of X-11593 with hepatic detoxification . In our GWAS , we find a strong association of X-11593 with the COMT locus , which encodes the catechol-O-methyltransferase enzyme . COMT is responsible for the inactivation of catecholamines such as L-dopa and various neuroactive drugs by O-methylation [35] . Following our identification approach , we experimentally confirmed the identity of X-11593 as O-methylascorbate . Notably , O-methylascorbate is a known product of ascorbate ( vitamin C ) O-methylation by COMT [36] , [37] . Thus , our observations establish a link between O-methylascorbate blood levels , common genetic variation in the COMT locus and COMT-mediated liver detoxification processes . The second example relates to the ACE gene locus , which is a known risk locus for cardiovascular disease , hypertension and kidney failure . The protein encoded by the ACE locus , angiotensin-converting enzyme , is an exopeptidase which cleaves dipeptides from vasoactive oligopeptides , and plays a central role in the blood pressure-controlling renin-angiotensin system [38] . Moreover , the ACE protein is a target for various pharmaceuticals ( ACE inhibitors ) , especially in the treatment of hypertension [39] . In our study , we identified three unknowns as dipeptides ( X-14205 , X-14208 and X-14478 ) , two of which also associated with the ACE locus . These dipeptides could thus represent novel , interesting biomarkers for the activity of ACE . Moreover , Steffens et al . [11] reported a connection between heart failure and X-11805 , which is in close proximity to angiontensin-related peptides in the GGM . This connection might be revisited after a successful identification of X-11805 in a future study . The third example is an explorative study to detect biomarkers for insulin sensitivity . Gall et al . [9] reported several known metabolites ( most prominently α-hydroxybutyrate ) as biomarkers for insulin resistance . They also reported a series of unknown metabolites among their top hits . In the present study , we investigated three of these unknowns: X-11793 associates with UGT1A ( UDP glucuronosyltransferase 1 ) and represents a bilirubin-related substance . Moreover , we experimentally validated X-11421 and X-13431 , which display a strong association with ACADM ( acyl-Coenzyme A dehydrogenase , C-4 to C-12 straight chain ) , as acylcarnitines containing 10 and 9 carbon atoms , respectively . The identification of these latter two unknown metabolites as medium-chain length acylcarnitines is coherent with reports by Adams et al . [40] . The authors found elevated blood plasma acylcarnitine levels in women with type 2 diabetes . Functionally , they attributed this finding to incomplete β-oxidation . Thus , our identification of X-11421 and X-13431 now suggests incomplete β-oxidation as an explanation for the associations found by Gall et al . and implies that acylcarnitines containing 10 and 9 carbon atoms are potential biomarkers for insulin resistance . In summary , we integrated high-throughput metabolomics and genotyping data from a large population cohort for elucidating the biochemical identities of unknown metabolites . To this end , we applied metabolomics genome-wide association studies and Gaussian graphical modeling in order to link these unknown metabolites with known metabolic classes and biological processes . For six specific scenarios , we went from systematic hypothesis generation over detailed investigation and identity prediction to direct experimental confirmation . Similar validations may now be undertaken for the remaining predictions that we report in Table S1 . Finally , we demonstrated the benefit of our method by discussing several of these newly identified metabolites in the context of existing biomarker discovery studies on liver detoxification , hypertension and insulin resistance . It is to be noted that our method does not specifically require genotyping data . Even metabolomics measurements alone , analyzed through the GGMs , may provide sufficient information for the classification and even precise identity prediction . The unknowns with GGM evidence but without GWAS hits in Figure 4 as well as the HETE scenario represent examples for this approach . One limitation of our approach is the requirement for associations with functionally described loci or known metabolites . Certain metabolite groups might thus systematically not be identifiable . For instance , if the identity of a whole class of biochemically related molecules is unknown ( which might be due to experimental reasons ) , then the GGM associations between those compounds will not aid in identity elucidation . The 118 unknown compounds for which we could not derive any classification might represent such cases . Thus , our functionally oriented method should be regarded as a complementary extension to the existing identity determination methods . Accordingly , our approach can be extended in several directions . It can be combined with method-specific , automated techniques that further exclude sets of metabolites . Previously mentioned methods relying on mass-spectra [15] or chromatographic properties [17] are suitable candidates here . Moreover , the method can be directly transferred to other types of metabolomics datasets not specifically originating from MS experiments , such as NMR-based metabolomics . Beyond the application to metabolite identification , our study demonstrates the general potential of functional metabolomics in the context of genome-wide association studies . The comprehensive metabolic picture provided by GGMs in combination with GWAS allows for the detailed analysis of metabolic functions , chemical classes , enzyme-metabolite relationships and metabolic pathways .
We used data from n = 1768 fasting serum samples used in a previously published genome-wide association study on a German population cohort . Details of the sample acquisition and experimental procedures can be found in [5] . Briefly , metabolic profiling was done using ultrahigh-performance liquid-phase chromatography and gas-chromatography separation , coupled with tandem mass spectrometry . The dataset contains a total of 292 known compounds and , in addition to the GWAS study in [5] , 225 unknown compounds . Metabolite concentrations were log-transformed since a test of normality showed that in most cases the log-transformed concentrations were closer to a normal distribution than the untransformed values [5] . Genotyping was carried out using the Affymetrix GeneChip array 6 . 0 . For our analyses , we only considered autosomal SNPs passing the following criteria: call rate >95% , Hardy-Weinberg-Equilibrium p-value p ( HWE ) >10−6 , minor allele frequency MAF>1% . In total , 655 , 658 SNPs were left after filtering . In order to avoid spurious false positive associations due to small sample sizes , only metabolic traits with at least 300 non-missing values were included and data-points of metabolic traits that lay more than 3 standard deviations off the mean were excluded by setting them to ‘missing’ in the analysis ( leaving 273 known and 213 unknown metabolites ) . Genotypes are represented by 0 , 1 , and 2 for major allele homozygous , heterozygous , and minor allele homozygous , individuals respectively . We employed a linear model to test for associations between a SNP and a metabolite assuming an additive mode of inheritance . Statistical tests were carried out using the PLINK software ( version 1 . 06 ) [41] with age and gender as covariates . Based on a conservative Bonferroni correction , associations with p-values<1 . 6×10−10 meet genome-wide significance , corresponding to a significance level of α = 0 . 05 . SNP-to-gene assignments were derived via linkage disequilibrium ( LD ) from HAPMAP [42] . A SNP was associated with a gene whenever there was at least one other SNP lying in the transcribed region of this gene ( that is from 5′UTR to 3′UTR ) that displays an r2≥0 . 8 with the query SNP . A detailed description of the GWAS procedure can be found in [5] . Lookups of previously known associations between phenotypes and genetic variants were performed using the GWAS catalog [26] . We list a phenotype with one of our GWAS hits , if the phenotype was reported with at least one SNP that displays an LD r2≥0 . 5 with the respective “lead SNP” . Lookups of eQTLs were performed for all significant SNPs ( 474 , see Dataset S1 ) using the GTEx database [29] . We applied a p-value cutoff of 2 . 7×10−9 , corresponding to a significance level of 0 . 05 and correction for 474×40 , 000 tests ( the number of SNPs times number of transcripts , conservative estimate ) . Detailed results up to a p-value of 10−5 can be found in Dataset S1 . For the GGM calculation , we require a full data matrix without missing values . From the original data matrix containing n = 1768 samples and 517 metabolites ( thereof 292 knowns and 225 unknowns ) , we first excluded metabolites with more than 20% missing values ( column direction ) , and then samples with more than 10% missing values ( row direction ) . The filtered data matrix still contained n = 1764 samples with 355 metabolites ( 217 knowns and 138 unknowns ) . Remaining missing values were imputed with the ‘mice’ R package [43] . Note that the numbers of metabolites used in the GWAS and in the GGM analysis differ due to specific constraints for the treatment of missing values in the two methods . Gaussian graphical models are induced by full-order partial correlation coefficients , i . e . pairwise correlations corrected against all remaining ( n-2 ) variables . GGMs are based on linear regressions with multiple predictor variables . When regressing two random variables X and Y on the remaining variables in the data set , the partial correlation coefficient between X and Y is given by the Pearson correlation of the residuals from both regressions . Since our dataset contains more samples than variables , full-order partial correlations can be conveniently calculated by a matrix inversion operation . A significance cutoff of α = 0 . 05 with Bonferroni correction was applied . A detailed description of the GGM calculation procedure can be found in [21] . Age , gender and SNP effects were removed by adding the respective variables and SNPs states to the data matrix . For each pair of variables under investigation , Gaussian graphical models remove the effects of all remaining variables on this correlation ( due to the above-mentioned linear regression approach ) . That is , adding a variable to the data matrix will automatically result in the removal of confounding effects of this variable on the correlations of all other variables . Note that age , gender and SNPs were not investigated as an actual node in the network but merely used for the correction procedure . For the later analysis steps , we then only considered metabolite-metabolite edges in the network . SNP states were coded as numerical values of 0 , 1 and 2 ( see previous section ) , such that the linear regressions that underlie the GGM correspond to an additive genetic model ( cf . [5] ) . Gender represents a “dummy variable” [44] in the linear regression model which only takes values of 1 ( male ) and 0 ( female ) . Metabolic reactions were imported from three independent human metabolic reconstruction projects: ( 1 ) H . sapiens Recon 1 from the BiGG databases [45] , ( 2 ) the Edinburgh Human Metabolic Network ( EHMN ) reconstruction [46] and ( 3 ) the KEGG PATHWAY database [47] as of January 2012 . We attempted to create a highly accurate mapping between the different metabolite identifiers of the respective databases , in order to ensure the identity of each compound in our list . Entries referring to whole groups of metabolites , such as “phospholipid” , “fatty acid residue” or “proton acceptor” were excluded from our study . Furthermore , we did not consider metabolic cofactors such as “ATP” , “CO2” , and “SO4” etc . in our analysis , since such metabolites unspecifically participate in a plethora of metabolic reactions . For each enzyme catalyzing one or more reactions in our pathway model , we retrieved functional annotations from two independent sources: ( i ) GO-Terms from the Gene Ontology [48] and ( ii ) enzyme pathway annotations from the KEGG PATHWAY database [47] . All imported metabolic pathways along with metabolite database identifiers , excluded compounds and pathway annotations can be found in Dataset S1 . | Genome-wide association studies on metabolomics data have demonstrated that genetic variation in metabolic enzymes and transporters leads to concentration changes in the respective metabolite levels . The conventional goal of these studies is the detection of novel interactions between the genome and the metabolic system , providing valuable insights for both basic research as well as clinical applications . In this study , we borrow the metabolomics GWAS concept for a novel , entirely different purpose . Metabolite measurements frequently produce signals where a certain substance can be reliably detected in the sample , but it has not yet been elucidated which specific metabolite this signal actually represents . The concept is comparable to a fingerprint: each one is uniquely identifiable , but as long as it is not registered in a database one cannot tell to whom this fingerprint belongs . Obviously , this issue tremendously reduces the usability of a metabolomics analyses . The genetic associations of such an “unknown , ” however , give us concrete evidence of the metabolic pathway this substance is most probably involved in . Moreover , we complement the approach with a specific measure of correlation between metabolites , providing further evidence of the metabolic processes of the unknown . For a number of cases , this even allows for a concrete identity prediction , which we then experimentally validate in the lab . | [
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] | 2012 | Mining the Unknown: A Systems Approach to Metabolite Identification Combining Genetic and Metabolic Information |
Varicella zoster virus ( VZV ) latency in sensory and autonomic neurons has remained enigmatic and difficult to study , and experimental reactivation has not yet been achieved . We have previously shown that human embryonic stem cell ( hESC ) -derived neurons are permissive to a productive and spreading VZV infection . We now demonstrate that hESC-derived neurons can also host a persistent non-productive infection lasting for weeks which can subsequently be reactivated by multiple experimental stimuli . Quiescent infections were established by exposing neurons to low titer cell-free VZV either by using acyclovir or by infection of axons in compartmented microfluidic chambers without acyclovir . VZV DNA and low levels of viral transcription were detectable by qPCR for up to seven weeks . Quiescently-infected human neuronal cultures were induced to undergo renewed viral gene and protein expression by growth factor removal or by inhibition of PI3-Kinase activity . Strikingly , incubation of cultures induced to reactivate at a lower temperature ( 34°C ) resulted in enhanced VZV reactivation , resulting in spreading , productive infections . Comparison of VZV genome transcription in quiescently-infected to productively-infected neurons using RNASeq revealed preferential transcription from specific genome regions , especially the duplicated regions . These experiments establish a powerful new system for modeling the VZV latent state , and reveal a potential role for temperature in VZV reactivation and disease .
Herpes Zoster , which results from reactivation of latent varicella zoster virus ( VZV ) is a common and debilitating disease that is frequently complicated by acute pain , diverse neurological sequelae , vision problems and difficult-to-treat chronic pain known as post-herpetic neuralgia . The VZV latent state is established in human sensory neurons of ganglia along the entire neuraxis during primary infection and disease , chickenpox . We know little of this state and how VZV reactivates from it to cause herpes zoster . Studies exploring VZV transcription in human dorsal root ganglia ( DRG ) removed post-mortem by methods such as in situ hybridization , northern blotting and RT PCR quantification , have suggested a limited VZV transcriptome ( reviewed in [1] , [2] and detection of VZV protein expression ( i . e . [3] , [4] ) in latently-infected ganglia . However , the recent recognition that latent VZV genomes undergo viral transcription in ganglia following post mortem removal raised doubt as to what transcriptional events occur in the latent state [2] . Furthermore , reports of immunohistochemical detection of VZV proteins in sections from latently-infected ganglia has been confounded by non-specific staining , lipofuschin granules and antibody cross-reactivity with blood group antigens [5] . While no transcripts analogous to the non-protein coding latency associated transcripts ( LATs ) of the closely related herpes simplex viruses ( HSV ) have been found , VZV transcripts from other genomic regions has been reported [6] , [7] . The most commonly reported transcript in human ganglia is that for ORF63 [7] ( which has also been observed in rodent neurons in a model for VZV latency , i . e . [8] , that encodes a transcriptional regulatory protein during lytic infection that may influence apoptosis and host cell survival [9] , [10] . The events underlying the VZV latent state and reactivation from it have been difficult to decipher because of the lack of model systems of VZV latency and reactivation . In contrast to HSV , for which there are both small animal and in vitro models for latent infection that can be reactivated , there is no widely-used in vivo small animal model of latency or any in vitro system of persistent infection in which reactivation can be experimentally induced . Indeed , VZV has proven to be difficult to induce to reactivate , even from post-mortem human ganglia harboring latent VZV genomes . The strict human specificity of VZV has precluded the use of most rodents as models of latency because no animal model reproduces human disease and most rodents do not even support VZV replication . A possible exception is the guinea pig , and VZV infection of enteric neurons in vitro [11] and a new in vivo model of enteric neuron infection [12] have been proposed as potential models for VZV latency . However , it is possible that data obtained from it may not extend to human ganglionic latency due to species differences . Human dorsal root ganglia tissue transplanted to SCID mice have been pioneered for study of VZV neuronal infection by Arvin and colleagues ( reviewed in [13] , [14] ) . Human DRG obtained from 2nd trimester fetuses can be infected with VZV administered either directly into the fetal DRG graft , or following venous administration of VZV infected human T-cells . VZV in the graft initiates a productive infection in neurons and satellite glial cells for several weeks , but then enters a state in which viral genomes are retained up to 56 days after infection without apparent productive replication . Low levels of transcripts from the ORF63 genomic region were detected in this model system , but reactivation of VZV in the model has not yet been documented . As a closed system of a complex tissue , it is not amenable to real time evaluation of VZV infection and reactivation . It is also hampered by the limited supply of fetal tissues , expense and being a technically demanding system . For these reasons , the modeling of latency in vitro with human neurons would be highly beneficial to the field . Several in vitro models of using cells of human origin have therefore been proposed for studying for VZV neuronal replication and persistence . These include the use of SH-SY5Y neuroblastoma derived neuron-like cells [15] , and human neurons obtained from fetal DRG or differentiated from stem cells ( hESC , iPSC and neural stem cells , i . e . [16] , [17] , [18] , [19] . Some have been shown to host a productive VZV infection , while others host an apparently non-productive infection in which the genome is maintained for prolonged periods [19] , [20] . We recently presented evidence that the different outcomes of VZV infection is partly influenced by the quantity of virus used to infect the cultures [21] , although lack of spread of infection observed in some models complicates the interpretation of what kind of infection the persistence actually reflects . One report in which persistent non-productive VZV infection of iPSC-derived neurons occurred , presented evidence for widespread transcription suggestive of an abortive type of infection , rather than a persistent quiescent state [6] . We argue that a compelling experimental model of neuronal VZV latency requires the ability of the cells to be able to fully support VZV infection and spread upon experimental reactivation . This has , to our knowledge , not yet been achieved . We present here an extension of our previously described hESC-derived neuron model system , for which we have established conditions that lead to a prolonged , non-productive neuronal VZV infection that can be experimentally reactivated . Persistent VZV infections can be established either using acyclovir to block lytic infections , or by infection of axons without use of antiviral drugs . Persistently-infected human neuronal cultures maintain viral genomes for up to two months , with minimal transcription and undetectable translation of several VZV proteins . Importantly , renewed replication of VZV genomes and virus protein production can be initiated experimentally by interfering with growth factor-PI3 Kinase signaling cascades . We further show that stimulation combined with incubation at a reduced temperature ( 34°C ) results in a productive , spreading infection . Comparison of the transcriptomes of quiescently vs productively-infection human neurons by RNAseq analysis reveals a preferential RNA transcription of select genomic regions during latency . In particular , the short repeated regions of the VZV genome encoding the regulatory proteins IE62 and IE63 are enriched for transcription in persistently-infected neurons . This model system should permit the elucidation of biology of VZV reactivation in detail that has not been possible until now .
We have previously reported that hESC-derived neurons exposed to high MOI cell-free recombinant pOKA-derived VZV with fluorescent protein reporters of viral protein expression , results in a spreading , productive infection [21] . In order to obtain non-productive persistent VZV infections in hESC-derived neurons , we exposed them to low PFU ( 0 . 001 MOI ) of cell-free VZV in the presence of acyclovir ( ACV ) for 6 days . To detect productive infection , we used VZV66GFP [22] where GFP is fused to the N terminus of ORF66 . GFP-tagged ORF66 has been shown to be functional , and as a presumed early gene , it should report productive ( lytic ) infection events , including those associated with reactivation . Using this approach ( shown schematically in S1 Fig ) , in approximately half of neuron-containing wells exposed to VZV individual ORF66GFP+ neurons appeared in 1–5 small clusters , while the remainder did not contain any GFP+ cells ( Fig 1 ) . By comparison , infection of parallel cultures with higher levels of cell free VZV ( MOI 0 . 02 ) resulted in GFP expression in many neurons by 3 dpi and an obvious productive , spreading infections . After 6 days we removed the ACV from the low MOI exposed cultures , and maintained them in its absence up to 7 weeks . All wells that were GFP-negative at the time of ACV withdrawal remained GFP-negative , strongly suggesting a lack of spontaneous reactivation . We conjecture that the combination of ACV treatment and low MOI conditions are at the threshold for generating a productive infection , resulting in ½ the wells containing GFP+ cells that overrode the ACV inhibition , while the other half were effectively prevented from expressing ORF66 . No visible cytopathic effect was observed in these GFP- wells throughout the experimental period . S1A Table indicates the numbers of GFP+ and GFP- negative wells we observed in our initial 7 experiments . Since our interest was only in studying events in the wells not containing GFP+ neurons , we eliminated GFP+ wells from further analysis . While the establishment of these GFP-negative cultures used a low level of virus , sufficient virus per well was added that would be expected to infect at least a fraction of neurons [18] . To address if VZV genomes were present in ACV treated neurons , wells devoid of GFP+ neurons were assayed for VZV DNA and transcripts using Taqman digital qPCR for ORF63 and ORF31 and compared to productively infected ( GFP+ ) wells . Viral DNA was detected in all GFP-negative wells , with DNA copy numbers at 2 and 4 wk pi calculated to be approximately 2 and 3 copies per cell , respectively . By contrast , wells containing GFP+ neurons infected productively with high MOI cell-free VZV contained more than 1000x more copies of the VZV genome ( Fig 2A ) . We conclude from these data that hESC-derived neurons exposed to VZV under these conditions maintain VZV genomes without productive infection , similar to the state of latency . Simultaneous digital qRT-PCR analyses of viral transcript levels for ORF63 ( IE63 ) and ORF31 ( gB ) in GFP-negative wells detected low levels ( Fig 2B ) , whereas wells containing GFP+ neurons contained transcripts for ORF63 and ORF31 at levels greater than three orders of magnitude higher , consistent with the higher levels of genomes . These results suggest that transcription of not only IE , but also late genes continues from persistent VZV genomes without viral protein expression ( as represented by the essential kinase ORF66 detected by GFP fluorescence ) in these neurons ( see RNASeq results below ) . This contrast to what has been reported in PCR studies of cadaver ganglia , where transcription appears to be limited to ORF63 [7] , While qPCR results suggested approximately 2–3 genomes per cell in the cultures , this technique does not permit the differentiation of a large number of neurons containing few VZV genomes from a few neurons containing higher copies of genomes . We therefore performed fluorescent in situ hybridization for VZV DNA ( DNA-FISH ) to estimate the number of neurons harboring quiescent VZV genomes . In nuclei of productively infected neurons , the hybridization signal for viral DNA fills most of the nucleus , consistent with viral replication and nuclear modifications induced by VZV infection ( Fig 3A ) . In contrast , VZV hybridization signal at 2 wk pi ( one week after removal of ACV ) was present in approximately 4% of neuronal nuclei from GFP negative cultures , with the signal appearing as small fluorescent puncta ( Fig 3B ) , reminiscent of puncta observed in neurons hosting latent HSV genomes [23] . Most labeled nuclei contained only a single hybridization punctum ( Table 1 ) . Given that DNA levels are at a few copies per cell in the population , each in situ hybridization punctum represents multiple VZV genomes in a single neuron . Similar analyses were carried out on longer term cultures maintained without the appearance of GFP expression or cytopathic effects for 7 weeks . Viral DNA content in these cultures , measured both by qPCR and FISH , showed that there was a continued presence of low levels VZV genomes and a similar proportion of neuronal nuclei harboring them , although the levels were more variable ( Fig 2A and Table 1 ) . Analysis of viral transcripts in quiescently infected neurons for the 10 experiments maintained for 7 weeks after ACV withdrawal revealed a great deal of variation between repetitions . These long-term experiments could be divided into two groups of 5 . In one group , very few viral transcripts were detected by PCR for the two VZV genes ( 0 . 003 copies/cell of ORF63 , 0 . 001 copies per cell of ORF31 ) , while the in the second group of 5 , there were transcripts at three orders of magnitude higher levels higher . Since all the longer-term neuronal cultures remained GFP-negative at the time of harvest , we hypothesize that the increase in VZV transcripts in one group of these extended cultures reflects partial release of repression of viral gene expression without progression to lytic replication and detectable GFP-ORF66 protein expression . After careful and repeated attempts using several different antibodies , we did not detect ORF63 , ORF62 , gI or GFP ( reflecting the ORF66 protein ) protein expression by immunocytochemistry in quiescently infected hESC-derived neurons . However , our cultured human stem-cell-derived neurons , like neurons in peripheral ganglia of humans and rodents , contain lipofuschin granules that are autofluorescent , and the presence of this autofluorescence may prevent unequivocal detection of very low levels of VZV proteins . It was argued above that a valid model system for the VZV latent state should be experimentally reactivatable . We therefore evaluated stimuli for their ability to drive the re-initiation of a productive infection in hESC-derived neurons quiescently infected ( GFP-negative ) with VZV , as measured by ORF66GFP expression . NGF signaling has been shown to be required to maintain the quiescent state of HSV in the dissociated rodent sympathetic neuron model [24] [25] , and can facilitate the maintenance of HSV latency in the murine ganglionic explant model [26] [27] . We therefore incubated persistently infected cultures in media lacking the three neurotrophin growth factors added to our neuronal cultures: NGF , BDNF and NT3 ( 18 ) . In approximately 30% of GFP-negative wells ( representing 50% of the original number of wells exposed to virus ) , individual GFP+ neurons were observed by 4 days after GF withdrawal , indicating ORF66kinase protein expression ( S1B Table and Fig 4 ) . Both single isolated and small foci of GFP+ neurons appeared , but GFP+ neurons did not increase in number with further incubation time , and most neurons in the culture had died by 5 days after GF removal . Similar results were obtained for 2 , 4 and 7 wk quiescently-infected neurons ( n = 3 each time point ) . The levels of VZV nucleic acids in wells undergoing GF withdrawal treatment were then determined and compared to those in parallel GFP-negative wells that continued to receive the three growth factors . At 2 wk post infection , GF withdrawal wells showed a modest increase in viral DNA and transcripts from ORF63 and ORF31 ( Fig 5A and 5B ) . Cultures undergoing GF withdrawal at 4 weeks pi also showed elevated levels of viral DNA and both viral transcripts as compared to unstimulated controls ( Fig 5A and 5B ) ( n = 2 at each time point ) . These data strongly suggest that GF withdrawal results in the initiation of reactivation events in hESC-neurons persistently infected with VZV . The relatively low numbers of GFP+ neurons could indicate a re-entry into a quiescent state , a failure of cells to support amplification of infectious virions due to cell changes resulting from growth factor withdrawal . The expression of GFP in a few neurons may also reflect a release of transcriptional repression in a non-productive “animation” event [28] . NGF binding to its receptor TrkA results in cellular signaling through PI3-K , and this has been shown to be important in the maintenance of the quiescent state of HSV1 , since treatment with the PI3-K inhibitor LY294002 ( LY ) results in HSV-1 reactivation [29] . Since GF withdrawal induced increases in VZV genomes and transcripts in persistently infected hESC derived neurons , we suspected that similar pathways might govern VZV latency and reactivation . Wells containing hESC-neurons quiescently infected ( with VZV i . e . not containing GFP+ cells ) for 2 , 4 and 7 wk ( n = 5 for each time point ) were therefore treated with LY , and observed for expression of GFP . Similar to the results obtained from GF withdrawal , LY treatment was observed to result in a few GFP+ cells or small foci appearing in about 1/3 of treated wells ( S1C Table ) , consistent with a role for PI3K signaling contributing to VZV latency , The clusters of GFP+ neurons did not increase in size , but we were only able to follow the cultures for 3–4 days after LY treatment , due to its toxicity at this temperature ( see below ) resulting in the death of the neurons . qPCR analysis revealed that viral DNA and transcripts greatly increased with LY treatment , more so than observed after GF withdrawal ( Fig 5A and 5B ) . Two week quiescently-infected , LY treated neurons contained more viral DNA , and increased transcription of ORF63 and ORF31 compared to wells with quiescently-infected neurons that were not treated . Similar studies of 4 wk and 7 wk quiescently infected neurons also revealed increases in viral DNA and transcripts . This indicates that PI-3K inhibition results in relaxation of VZV repression in hESC-derived neurons , viral gene transcription and the expression of viral protein . The presence of small foci of ORF66GFP positive cells in some wells strongly suggests full genome replication , viral transcription and virus production . We were also able to trigger reactivation using the type 1 histone deacetylases-inhibitor sodium butyrate ( increases in VZV DNA and transcripts induced by sodium butyrate with and without LY are shown in S2A Fig ) . FISH analyses of LY treated cultures were consistent with replication of VZV in the neurons . After 4 days of treatment with LY , 2 . 88% of the nuclei from treated wells were positive for viral DNA , but the number of FISH positive puncta in nuclei with a positive hybridization signal increased significantly . While 74% of the FISH positive nuclei of LY treated neurons contained one punctum , 6% two puncta , 8% 3 puncta , more than 12% of the cells contained over 3 puncta ( Table 1 ) . Although the hybridization events may not correspond to individual genomes , the increase in the number of hybridization signals is consistent with viral DNA replication . In the preceding experiments , application of reactivation stimuli resulted in viral genome amplification and increased transcription in a fraction of wells of quiescently infected neurons , but the ORF66GFP expression events were limited to single cells or small foci . This contrasts the productive , spreading infection seen in hESC-derived neurons exposed to high MOI VZV ( Fig 1 ) . We and others ( see discussion ) have observed that VZV replication in non-neural cells is facilitated at temperatures 2–4°C lower than 37°C . Therefore , we combined LY treatment of persistently infected neurons with incubation at reduced temperature ( 34°C ) . Multiwell plates containing neurons persistently-infected with VZV-66GFP one week after ACV withdrawal were transferred to 34°C , with half of the GFP- wells receiving LY , and half of the GFP- wells receiving only culture medium . Parallel cultures were maintained at 37°C . Persistently-infected cultures induced to reactivate at 34°C by LY ( n = 3 independent experiments ) , showed a more rapid appearance of GFP+ neurons than observed at 37°C , with fluorescent neurons present by two days after induction . This is at least one day earlier than GFP+ neurons were observed with induction of reactivation at 37°C by LY or other stimuli . Furthermore , combined treatment of LY and low temperature resulted in the population of GFP+ neurons expanding for up to 14d ( Fig 6A–6F ) . Enhanced VZV spread after induced reactivation at 34°C was observed in all ( 4/4 ) induced reactivation cultures . We further established that infectious progeny virus was produced from the reactivation by trypsinizing neurons reactivated by LY at 34°C and re-seeding them onto ARPE19 cells . The ARPE cells were infected within one day as visualized by GFP expression , and rapidly developed syncytia and viral plaques . However , even following incubation at 34°C with LY , not all individual GFP+ neurons produced a spreading infection , and some initially GFP+ neurons lost GFP expression ( Fig 6H–6M ) . Lower temperature by itself was not an effective stimulus for VZV reactivation from persistent infection induced with ACV: one of four wells of neurons persistently-infected with VZV and then incubated at 34°C without LY treatment contained neurons expressing ORF66GFP . This suggests that the lower temperature primarily enhances virus spread rather than acting as a direct stimulus of reactivation . The ability to reactivate VZV genomes in hESC-derived neurons establishes the persistence observed in this in vitro model as a latency-like state . We therefore interrogated the VZV transcriptome in persistently-infected hESC-derived neurons and compared it to the VZV transcriptome of productively-infected neurons using RNA-seq ( S2 Table contains a summary of RNASeq reads obtained ) . Alignment of transcript sequences with that of an annotated VARIVAX genome ( that differs from the pOka genome used in these experiments by ~42bp [30] ) ( Fig 7A ) revealed that transcripts from all genomic regions were expressed in both productively and quiescently infected hESC neurons , with levels of the viral transcripts between 20–50x higher in the productively infected cells , note the difference in Y-axis scale between the quiescent and productive alignments ) , as observed in the rt-PCR assays ( Fig 2B ) . However , the level of transcription from different portions of the genome varied considerably within quiescent and lytic infected samples , and these differences were relatively consistent between the biological replicates . The S3 Table lists FKPM counts for the ORFs of the vOka annotated genome in descending order of expression . Notably , transcripts from the ORF57 gene , which is non-essential in MeWo cells [31] and has a PRV but not a HSV1 homolog , were expressed at the highest levels in both productively and quiescently infected neurons . Striking differences in the relative levels of VZV transcripts was observed between quiescently and productively infected neurons for several genomic regions ( Fig 7B ) . Specifically , transcripts for the duplicated regions of the genomes bounding the short unique genomic region—containing genes ( 62/71 , 64/69 , 63/70 ) were significantly enriched in quiescently compared to productively-infected neurons . Conversely , transcripts for ORF31 , ORF36 , ORF39 , ORF8 , and ORF15/16 were expressed at relatively lower levels in quiescently infected neurons . In the human host , alphaherpesvirus latency is established in neurons without anti-viral treatment . In the course of a natural VZV infection , the latent state is established either via infection of axons in the skin and transport of the virus to the ganglia , or directly by T-cells that migrate to the ganglia [32] , [33] . Studies of HSV-1 have demonstrated that latency is preferentially established when neurons are exposed to virus only at their axons [34] . We therefore investigated whether a latency-like state could be established after axonal infection with cell-free VZV in hESC-derived neurons using compartmented microfluidic chambers . At two wk pi , no axonally-infected cultures contained GFP+ neurons in the cell body compartments of the chambers , even though GFP+ virus from the infection clearly coated axonal processes in the axonal compartments ( Fig 8 ) . Despite the absence of GFP fluorescence reporting productive infection in the somal compartment , qPCR for VZV DNA and RNA revealed that the presence of both VZV genomes and transcripts ( Fig 8 ) . We conclude from these data that VZV entered the distal axons , was transported to the cell bodies and initiated a quiescent infection without expression of detectable levels of the ORF66 GFP reporter . Parallel axonally-infected neuronal cultures induced for reactivation of VZV by LY treatment in the soma compartments showed increases in both VZV genomes and transcripts without the appearance of GFP+ neurons ( Fig 8 , n = 4 ) . Strikingly , when such axonally-infected cultures were incubated 34°C in the presence of LY , GFP+ neurons appeared that in some cases subsequently formed foci of multiple infected cells ( n = 2 independent experiments , total of 5 chambers , ) . These results demonstrate that a VZV persistent infection of hESC-derived neurons can be established following infection via their axons , and that this infection can be experimentally reactivated using the same stimuli used for reactivating persistent infections established using ACV .
Our operative definition of experimental latency is the maintenance of viral genomes without virus production for extended periods that can be reactivated into a state of productive virus infection that spreads to other cells . This definition distinguishes between latency and abortive , incomplete or partial infections , which may apply to latency models in non-permissive hosts or cell types unable to support a full , productive infection . The factors maintaining the latent state and the drivers of reactivation are of high importance , since by understanding them , we may eventually be able to target such processes for prevention of reactivation disease . Latency involves a program in which the normal lytic viral gene expression program leading to virion production is largely suppressed . However , recent studies suggest that latency may not only involve expression of specific transcripts or proteins that promote and maintain the latent state , but may also involve dynamic repression and de-repression of lytic genes without virus production . Some aspects of the lytic-latent-lytic switch have been elucidated from several rodent in vitro and in vivo models systems of HSV1 ( reviewed in [35] , [28] , [24] , [36] ) . By contrast , little has been learned concerning the VZV latent state and factors leading to reactivation . Herpes zoster and the sequellae of post herpetic neuralgia and a host of neurological complications that may follow , remain serious worldwide health issues . By the definition of latency just outlined , a reactivatable model of the VZV latent state in human neurons has , until the present study , not been developed . The proposed models for VZV latency in mice [37] rats [38] and guinea pigs [12] have not been demonstrated to be reactivatable . In the more VZV-susceptible in vitro guinea pig enteric ganglion model , reactivation has only been shown by the overexpression of a viral transcriptional regulatory protein ( ORF61 ) , a stimulus that greatly influences the host cell transcriptional environment [39] . Long term quiescent infection of human fetal DRG transplanted to SCID mice [40] and neural precursors in suspension has been achieved [19] , but experimental reactivation has not been reported . Thus , our hESC-derived neuron system , which has the ability to host a reactivatable VZV persistent state is novel and unique . We established persistent , silent VZV infections in hESC-derived neurons using two methods . The first is similar to an established in vitro model for HSV1 latency , in which productive infection of rat cervical ganglia neurons is repressed using the DNA replication inhibitor ACV . Initially developed by Wilcox and Johnson [25] , [41] , this model has been recently refined using GFP-expressing HSV1 to elucidate factors affecting the lytic/latent switch [24] . The second approach uses axonal infection with cell-free VZV in compartmented cultures without the use of ACV . By contrast , axonal infection of hESC-derived neurons performed with cell associated VZV results in a lytic , spreading infection [16] , [42] , [43] . Axonal infection with ( cell free ) HSV1 has also been observed to lead primarily to a silent infection [34] . It has been proposed that axonal infection establishes quiescence due to the reduced delivery of tegument proteins to the nucleus that act to promote the lytic cycle [28] . We speculate that cell-free VZV infections lead to minimal tegument protein delivery , whereas infection with cell associated virus [16] , [42] leads to more efficient virus entry into neurons and higher delivery of lytic-infection promoting tegument proteins . These may also be delivered via fusion of infecting cells and neurons in cell associated infections [44] . Since VZV replication can be reactivated in neurons persistently infected by either of two different methods , it seems unlikely that the use of ACV generates an artifactual model of persistence . Interestingly , after removal of ACV , HSV1 spontaneously reactivates from a fraction of ACV-established persistently infected cultures [29] , while we have never observed VZV spontaneous reactivation after ACV removal in dozens of experiments over the course of years . This suggests that in our model for VZV latency , repression is maintained more tightly . This could reflect the situation in humans: while HSV may reactivate many times , VZV in most people does not show signs of reactivation and in those who do develop zoster , it usually only occurs once . A central unanswered question is how the different reactivation patterns of HSV and VZV are regulated . Now that a model of VZV persistence that can be reactivated has been established , this issue may be addressable experimentally . The quiescent state of VZV infection of hESC-derived neurons shows several hallmarks of a latent state . First , VZV genomes are detectable in neuronal cultures for up to 7 weeks as shown by both qPCR and DNA in situ hybridization . Second , DNA FISH reveals small puncta of hybridization in 4–5% of the neurons , rather than large hybridization signals filling the nuclei seen in lytically VZV-infected neurons . These small foci are similar to those reported in FISH studies of quiescent HSV1 [23] . Third , low level transcription from two VZV genes was consistently detected in quiescently-infected neurons by qRT-PCR . The copy number of transcripts from ORF63 ( the transcript most often detected in VZV latency studies in other systems [35] [1] was consistently higher than that from ORF31 ( gB ) , but both were far below than those in observed in lytic infections of the same cells . This observation was confirmed in RNASeq analyses ( Fig 8 and S3 Table ) . It is not yet clear if these low levels of transcripts represent continuous transcription that occurs without detectable expression of the ORF66 GFP fluorescence , or represent sporadic transcriptional de-repression events reflecting VZV “animation . ” VZV in a fraction of the persistently infected neurons responded to stimuli by being reactivated in terms of expression of ORF66GFP reporter protein after prolonged periods of undetectable expression . ORF66GFP fluorescence was accompanied by increases of viral genomes and transcripts in the cultures , as well as the production of virus capable of infecting susceptible cells . However , most quiescently-infected neurons do not reactivate VZV in our cultures . About 4% of neurons contain viral genomes as detected by FISH , yet induced expression of ORF66 protein was largely restricted to few events in a culture of several thousand neurons . The lack of reactivation for some persistently infected neurons in response to experimental stimuli has also been observed for HSV1 [45] [46] . The observation that most quiescent genomes are not reactivatable is also seen in both in vivo and in vitro models for HSV1 and may apply to the other members of the herpesvirus family . It is possible that quiescently infected neurons consists of a mixed population where some are capable of responding to different reactivation stimuli , while others may not be able to support the complete reactivation process . Consequently , the low levels of reactivation seen here may reflect the number of quiescently-infected neurons able to support reactivation . Alternatively others may require different , unknown reactivation stimuli . This system may provide a platform to identify other VZV reactivation triggers . We induced reactivation using stimuli that act through the PI3K pathway , growth factor removal and PI3K inhibition . This strongly suggests that the latency/reactivation switch of VZV is controlled to some extent by the same cellular pathways that affect this switch in HSV1 [29] . Reactivation of quiescent VZV was also achieved using the type 1 histone deacetylases inhibitor sodium butyrate , which favors a more permissive environment for transcription . The PI3K pathway also interacts with downstream events in the cell that can alter the state of chromatin , so it is possible that all three stimuli act through the same downstream effectors . The dramatic contribution of reduced temperature to the outcome of in vitro reactivation we observed was not expected . This experimental condition was evaluated because of the long standing observation that VZV replicates more efficiently at lower temperature [47] and growth at 34°C to permits larger quantities of virus to be generated for virion purification studies [48] . While at 37°C , LY- induced reactivation events were mostly detected as single fluorescent cells or small foci , neurons reactivated at 34°C showed spread of infection to neighboring neurons , and the ability to infect non-neuronal cells when transferred to fresh cultures . However , it is not yet clear why reduced temperature enhances the spread of infection and the generation of infectious virus in our model . We speculate that higher temperature may favor the quiescent state for VZV , and the small plaques seen with reactivation events may represent re-entry of VZV into a quiescent state in secondarily infected neurons . Alternatively , this could reflect an aspect of in vivo VZV infection . In both varicella and zoster , the main site of lytic infection is the skin , which is at a slightly reduced temperature compared to the environment of the peripheral ganglia containing the reservoir of latent virus . The high core temperature may limit virus spread at the onset of latency as well as limit reactivation events . This is obviously a highly efficient process in humans , since the majority of people never suffer from a reactivation event leading to zoster . Such temperature related effects could be due to activity of viral enzymes , initiation of pathways at lower temperature or less efficient activity of innate antiviral responses . Regardless of the mechanism , this observation helps shed light on recent studies that reported the reactivation of VZV gene expression post-mortem [2] , with the longer the period from death to assay , the higher the levels of VZV transcripts measured . The reactivation we observe in vitro at 34°C suggests that the reduced body temperature after death may have participated in a partial reactivation event of the virus and some transcription . A recent study called for caution when interpreting experiments where herpesvirus reactivation is studied in vitro . It was found that stimuli causing apoptosis could activate a pathway in which herpesviruses from all three families began to replicate and transcribe their genomes and make proteins [49] . We do not believe that our experimental reactivation was due to apoptosis for two reasons . First , we obtained much stronger reactivation at low temperature , but there were much lower levels of cell death caused by the pharmacological agents: at 37°C LY or growth factor withdrawal-treated neurons died within 5 days , while at 34°C , they survived for two weeks . In addition , when we treated quiescently infected neuronal cultures with inducers of apoptosis TRAIL , doxorubicin and nocodazole , it did not result in the appearance GFP+ neurons or an increase in VZV transcripts or genomes using qPCR ( S2B Fig ) . This new model now permits detailed examination of gene expression during persistent infection . HSV1 encodes several species of non-translated RNA during latency both in vivo and in vitro , collectively termed LATs , that encode for multiple miRNAs and longer non-coding RNAs that may participate in the maintenance of latency [50] [51] . Non-coding RNAs , long or miRNAs , have not yet been found in latently-infected post-mortem ganglia or other models of VZV latency so far [52] . Investigations of transcripts and proteins expressed in VZV latent infection have been performed on cadaver and experimentally infected fetal ganglia in SCID mice which are potentially complicated by the effects of post-mortem changes [2] , and/or presence of multiple cells types in intact ganglia [53] . Low levels of VZV transcripts have been detected in human ganglia using both of these models , but whether these are translated remains an open question . The transcripts most often associated with VZV latency in most models are those from the genomic region coding for ORF63 . More recently guinea pig enteric ganglia [12] and human neural precursors in suspension [19] have been used to model VZV quiescence , but it is not yet clear how well these system parallels human primary neuronal latency . The VZV transcriptome in a 95% pure population of human neurons reveals that transcripts from all of the VZV genomic regions are expressed in both quiescent and productive infection , with transcription in quiescently infected neurons at a level several orders of magnitude lower . A similar finding was reported in an RNASeq study of iPSC-derived neurons infected with vOKA at low MOI [6] . Interestingly , the ORFs with the highest levels of transcription in VZV infected neurons ( the present study and [6] ) , fibroblasts [6] and keratinocytes [54] are ORFs 57 , 49 and 9 . Strikingly , transcription of RNAs from the VZV genome mapping to the internal and terminal short repeats ( IRS and TRS ) were significantly enriched in quiescently as compared to productively-infected neurons . This suggests that the repeat regions are more transcriptionally permissive when the genome is maintained in a repressed state . Transcription of ORF63 , which lies in these genomic repeat regions , has been frequently reported in models of experimental VZV latency , but our results suggest that enhanced transcription during latency may occur from a larger region than just this ORF . Interestingly , the repeat regions of HSV1 were also reported to be preferentially transcribed in a fibroblast model of persistent non-productive HSV1 infection , where the expression of IE proteins is eliminated [55] . They postulated that one reason for the preferred transcription of the repeat regions was their higher G+C content , which may permit a more permissive transcriptional environment . We note that the repeat regions of VZV are also higher in G+C content as compared to the unique genomic regions . Elucidation of the mechanism and physiological implications of the favored transcription of the repeat regions requires further investigation using this model . An intriguing possibility is that this region of the genome transcribes as yet undiscovered non-coding RNAs involved in maintaining the latency state . In conclusion , our development of a robust in vitro model for VZV latency that can be experimentally reactivated and be dynamically monitored will now permit the mechanistic and transcriptional events underlying VZV persistence and reactivation to be studied in greater detail .
The H9 ( US National Stem Cell Bank ( WA09 ) human embryonic stem cell line , human neonatal foreskin fibroblasts ( HFF ) , PA6 ( Riken cell Bank , Japan ) and ARPE19 ( human retinal pigment epithelium ( ATCC#CRL-2302 ) were maintained as previously described [56] . Parent Oka-based VZV expressing GFP as fusion proteins to ORF66 [22] was described previously and propagated in ARPE19 cells . Cell-free virus ( titers 2000–10 , 000 PFU/ml ) and infected debris ( titers approximately 105 PFU/ml ) preparation and concentration were performed as described previously [56] . Neurons were differentiated from hESC-derived neural precursor-containing aggregates ( neurospheres , NSP ) generated by co-culturing hESC with the PA6 mouse stromal cell line as previously described [42] . Cultures were performed in 24-well culture plates , with 5–10 neurospheres seeded in each well . We estimate that each well was seed with between 50–100 , 000 neural precursors , based on performing digital PCR for GAPDH DNA on 10 uninfected neurospheres . After a minimum of 10 days of terminal differentiation of neurons into cultures that contained extensive axonal outgrowth , cells were pretreated with acyclovir ( ACV , 50 μM ) for 24 h , and then incubated with recombinant cell-free VZV expressing GFP fused to the ORF66 protein kinase . Cultures were exposed to low multiplicity of infection VZV ( MOI: approximately 0 . 001 based on an estimate of 50–100 , 000 cells/well ) for two hours in the presence of ACV . After removal of virus , the neurons were maintained in the presence of ACV for 6 days . Media without ACV was then used to maintain the cultures up to 7 weeks post infection ( pi ) , with changes twice/wk . Cultures were examined regularly over the incubation period for GFP expression microscopically . At 2 , 4 and 7 weeks pi , wells that did not contain GFP+ neurons received one of two treatments to induce reactivation . Growth factor withdrawal was achieved by incubation in media lacking the three neurotrophic factors NGF , BDNF and NT3 . Reactivation using PI3 kinase inhibition was performed using LY294002 hydrochloride ( LY , 10 μM , Tocris , cat . # 1130 ) . Where indicated in the text , LY-treated cultures were incubated at 34°C . The strategy for silent infection and reactivation is shown schematically in S1 Fig VZV reactivation was assessed by monitoring for GFP expression for 3–4 ( 37°C ) or 7–14 ( 34°C ) days and then photographic documentation . Total RNA and DNA were extracted for digital Taqman qPCR analysis or RNA for RNA-Seq analysis ( see below ) . Microfluidic chambers with two compartments connected by microchannels ( length , 450 μm; height , 3 μm; width , 10 μm ) were prepared as previously described [42] . Briefly , hESC-derived neurospheres were plated adjacent to microchannels in one compartment ( cell body compartment ) , and axonal extension induced with a growth factor gradient . Axons reaching the axonal compartment were infected with a VZV cell-free lysate we term the “debris” fraction [56] containing ~100 , 000 PFU/ml of infectious virus . A volume gradient of medium was established between the two compartments to prevent cell free VZV from accessing the soma compartment by diffusion [57] [16] . A protocol obtained from the clinical cytogenetics laboratory of Meir Hospital , Kfar Sabba , Israel was used for DNA in situ hybridization on nuclei isolated from the neurons . A VZV DNA probe labeled with DIG was generated and hybrization detected by indirect immunofluorescence for DIG . 1684 nuclei were examined , pooled from 3 independent experiments Details of the method are in S1 Methods . DNA and total RNA were extracted simultaneously using Tri-Reagent ( Sigma ) . Total RNA was reverse-transcribed using an oligo dT primer and M-MLV reverse transcriptase . Gene specific DNA probes for viral genes ORF31 and ORF 63 and human GAPDH were used for quantification with a digital PCR in duplicate samples . Copy number of viral DNA and transcripts were normalized using human GAPDH . Others have reported GAPDH expression is not affected significantly by VZV infection and is therefore a good standard for normalization of rt-PCR results [58] . Sequences of primers and probes and additional details are provided in the S1 Methods and in the S4 Table . Cultures of human neurons were infected , and 1 week after ACV withdrawal RNA was extracted from cultures not containing any GFP+ cells or from neurons productively infected with high-MOI cell-free VZV . Total RNA ( less ribosomal RNA ) was labeled with the TruSeq Stranded Total RNA LT Sample Prep Kit ( with Ribo-Zero Gold ) ( #RS-122-2301 ) and run on an Illumina HiSeq 2500 sequencer at the Crown Institute for Genomics at the Weizmann Institute , Rehovot , Israel . Analysis was performed using bioinformatics tools as described in the S1 Methods . Live cultures were monitored with Olympus IX70 or IX81 microscopes and photographed with digital cameras . Images were enhanced using ImageJ and Paint Shop Pro software with all changes in the images ( i . e . , contrast , brightness , gamma , and sharpening ) made evenly across the entire field . No features were removed or added digitally . | Most adults worldwide harbor latent VZV in their ganglia , and reactivation from it causes herpes zoster . This painful disease is frequently complicated by long-term pain , neurological sequelae , or vision loss that require improved prevention and treatment strategies . Study of VZV latency and reactivation has been severely hampered by the inability to reproduce a persistent state in vitro or in vivo that can be experimentally reactivated . Our study establishes a system using human neurons derived from embryonic stem cells where multiple stimuli can induce reactivation from long term experimental latency . A potential role for temperature in VZV reactivation has been revealed with this system , which can now be used to study the latent/lytic switch of VZV for the first time . | [
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] | [] | 2015 | An In Vitro Model of Latency and Reactivation of Varicella Zoster Virus in Human Stem Cell-Derived Neurons |
Rapid typing of Leptospira is currently impaired by requiring time consuming culture of leptospires . The objective of this study was to develop an assay that provides multilocus sequence typing ( MLST ) data direct from patient specimens while minimising costs for subsequent sequencing . An existing PCR based MLST scheme was modified by designing nested primers including anchors for facilitated subsequent sequencing . The assay was applied to various specimen types from patients diagnosed with leptospirosis between 2014 and 2015 in the United Kingdom ( UK ) and the Lao Peoples Democratic Republic ( Lao PDR ) . Of 44 clinical samples ( 23 serum , 6 whole blood , 3 buffy coat , 12 urine ) PCR positive for pathogenic Leptospira spp . at least one allele was amplified in 22 samples ( 50% ) and used for phylogenetic inference . Full allelic profiles were obtained from ten specimens , representing all sample types ( 23% ) . No nonspecific amplicons were observed in any of the samples . Of twelve PCR positive urine specimens three gave full allelic profiles ( 25% ) and two a partial profile . Phylogenetic analysis allowed for species assignment . The predominant species detected was L . interrogans ( 10/14 and 7/8 from UK and Lao PDR , respectively ) . All other species were detected in samples from only one country ( Lao PDR: L . borgpetersenii [1/8]; UK: L . kirschneri [1/14] , L . santarosai [1/14] , L . weilii [2/14] ) . Typing information of pathogenic Leptospira spp . was obtained directly from a variety of clinical samples using a modified MLST assay . This assay negates the need for time-consuming culture of Leptospira prior to typing and will be of use both in surveillance , as single alleles enable species determination , and outbreaks for the rapid identification of clusters .
Leptospirosis is a zoonotic disease caused by pathogenic species of Leptospira that can be carried naturally by most mammalian species [1–3] . Transmission to humans most commonly occurs via direct animal contact or via water contaminated with animal urine [2 , 4] . Symptoms range from a mild febrile illness to severe disease with pulmonary haemorrhage or central nervous system involvement [3 , 5] . In its early stages leptospirosis resembles many other febrile illnesses , hampering clinical diagnosis . The highest disease burden is in tropical low and middle income countries , driven by high humidity , close human-animal contact , and inadequate sewage disposal and water treatment [3] . Annual worldwide case number was estimated at around 1 million with the majority of cases and death occurring in tropical regions [6] . Despite these relatively high numbers the epidemiology of leptospirosis is not well understood . Epidemics in humans and animals are increasingly reported and are often related to natural events like floods [3 , 7] . In these settings rapid typing is essential to identify potential clusters and transmission pathways . The gold standards for laboratory diagnosis of leptospirosis are culture or a four-fold rise in antibody titre between admission and convalescent samples by the microscopic agglutination test ( MAT ) . Culture of Leptospira spp . is time consuming and diagnosis by MAT is retrospective by nature , hence both methods have disadvantages as diagnostic tools . To enable early detection several quantitative real-time PCR assays have been developed , some of which allow for species distinction [8–20] . Three MLST schemes are currently hosted by the public MLST database [21–23] , two of which have been tested directly on clinical samples from humans [24–26] . Only two studies tried to amplify all seven loci and showed that MLST is possible directly from serum and whole blood . However the bacterial load required was high ( ~5x104 leptospira/mL ) with only 21% and 5% or 10% success rates for partial and full profiles , respectively [24 , 26] . The objective of this study was to develop an assay based on a published MLST scheme that lowers the limit of detection ( LoD ) to enable rapid provision of typing data directly from patient specimens whilst minimising costs for subsequent sequencing [22] .
Specimens included in the study were not collated specifically for this study . Specimens included those within a collection of specimens submitted to the Public Health England Leptospira Reference Laboratory received routinely for Leptospira testing , identification of infecting species , confirmation of infection and for epidemiological investigation . Specimens were anonymised prior to testing . IRB board approval was not required as this involved routine specimens submitted for Leptospira testing by MLST as a secondary test for confirmation of infection and species identification and for the provision of epidemiological information . The protocol was validated on 25 isolates from the WHO recommended Serovar panel ( data in S1 Table ) which is currently used for serological diagnostic and serovar identification . The assay was tested using 104 clinical specimens ( 45 serum , 6 whole blood , 13 buffy coat , 40 urine ) from the UK ( n = 35 ) and the Lao PDR ( n = 69 ) , ( Mahosot Hospital Microbiology Laboratory , Vientiane ) . For initial laboratory diagnosis samples were tested with a triplex qPCR assay targeting the 16S rRNA gene ( rrs ) containing three different probes to distinguish between pathogenic , intermediate and environmental strains [27] . Using this assay , 44 samples ( 23 serum , 6 whole blood , 3 buffy coat , 12 urine ) tested positive for pathogenic Leptospira spp . and 15 were negative . In addition , 16 environmental and 29 intermediate Leptospira spp . positive samples were included in the panel as negative controls as they should not be detected by the MLST scheme . Testing was performed blinded . A detailed list of pathogenic Leptospira spp . positive samples and origin can be found in the table in S2 Table . For each sample , 200 μl sample material was used for extraction . For urine samples from Lao PDR 1 . 5 mL was spun down at 14000 rpm for 15 minutes before it was used for extraction . DNA from bacterial isolates and Lao PDR samples was extracted using the QIAmp DNA Mini Kit ( Qiagen , Germany ) according to manufacturer‘s instructions . DNA from UK samples ( C1-C10 ) was extracted on the MagNA Pure Compact ( Roche , Germany ) using the DNA_Bacteria Protocol . These samples and bacterial isolates were eluted once in 50 μL nuclease-free water . Samples from Lao PDR were eluted twice in 50 μl nuclease-free water to reach a final volume of 100 μL . UK samples P1-P25 were extracted on the EZ1 investigator platform ( Qiagen , Germany ) and eluted in 120 μL . MLST was performed based on a published scheme targeting seven loci ( glmU , pntA , sucA , tpiA , pfkB , mreA , caiB ) of seven pathogenic Leptospira species ( L . alexanderi , L . borgpetersenii , L . interrogans , L . kirschneri , L . noguchii , L . santarosai , L . weilii ) [22] . The protocol was adapted by using the HotStar Taq Master Mix ( Qiagen , Germany ) in a 20 μl reaction including additional 100 nmol MgCl2 for locus 4 ( tpiA ) only , 5 pmol of each primer , and 40–60 ng DNA . For clinical samples , 5 μl DNA extract was used . Cycling conditions remained unchanged , except for additional initial 15 minutes incubation at 95°C to activate the enzyme . Further to the published protocol , nested primers were designed for all loci in the original MLST scheme ( Table 1 ) to improve the LoD . Primer sequences were based on multi-sequence alignments of all serovars available in this study . To facilitate downstream sequencing primers were extended with M13 anchor primers . The nested PCR was performed in 20 μl reaction using 5 pmol of each primer and 2 μl of the first-round PCR product . Cycling conditions were as follows: 10 min at 95°C , 5 cycles of 30 sec at 95°C , 30 sec at 46°C , 30 sec at 72°C . This was followed by 10 cycles with the annealing temperature increasing by 1°C per cycle and 20 cycles with an annealing temperature of 56°C . The final extension period was 7 min at 72°C . To avoid contamination different processes were performed in physically separated rooms . For detection of possible cross-contamination between samples that could occur during transfer of the amplicon from first to second round PCR non-template controls were included in all experiments and handled last . Further , only one sample was opened at a time and stringent cleaning measures were applied after each experiment . To compare the detection limits serial dilutions of six DNA extracts from Leptospira isolates ( Serovars Canicola , Grippotyphosa , Copenhageni , Hardjo , Mini , Pyrogenes ) were tested using the original typing scheme and the second round PCR of the modified assay . Initial DNA concentration was 4 ng/μl , corresponding to 800 , 000 copies of genomic DNA ( gDNA ) or 8 x 105 organisms ( calculations based on the size of the genome of L . interrogans strain Fiocruz L1130 ( 4 . 6 Mb ) ; 1 genome is ~5 fg ) . Serial dilutions were tested from 10−2 to 10−5 and PCR products were visualised on 2% agarose E-gels ( Thermo Fisher Scientific , USA ) . In addition , 15 patient specimens ( P1-P15 ) were tested with the modified assay first and second round PCRs . PCR products were purified on an automated liquid handling robot ( Biomek NXP ) using Ampure XP paramagnetic beads ( Beckman Coulter , USA ) . Sanger sequencing was carried out on the Applied Biosystems 3730XL Genetic Analyser ( Thermo Fisher Scientific , USA ) . Sequences were assembled , edited , and trimmed using BioNumerics version 6 . 1 ( Applied Maths NV ) . Sequence types ( ST ) were assigned by BioNumerics using allelic profiles in the order glmU-pntA-sucA-tpiA-pfkB-mreA-caiB . The same order was used to concatenate sequences for phylogenetic analysis . All new sequences have been submitted to the leptospira MLST database ( http://pubmlst . org/leptospira/ ) . For species assignment sequences from all patient samples were included in phylogenetic analyses along with isolates from the WHO panel for which the species are known . Sequences were aligned in seaview4 [28] and used to construct maximum likelihood trees in MEGA version 6 [29] using the best suitable and available model for each alignment as determined by jModeltest [30] .
Fifteen clinical specimens ( P1-P15 ) were tested using the first-round MLST assay and none gave a positive result . Applying the improved nested MLST assay five of these yielded at least one amplified locus; two samples gave full allelic profiles ( P1 and P12 ) . In total , using the improved nested assay on 44 clinical samples PCR positive for pathogenic Leptospira species , 22 yielded a result in at least one allele detected that could be sequenced ( 50% ) . Full allelic profiles were obtained from 10 ( 23% ) specimens , and partial allelic profiles from 12 specimens ( 27% , Table 2 ) . No nonspecific amplicons were observed in any of the clinical samples . All negative control samples ( including those positive for environmental and intermediate Leptospira species ) were negative by MLST . Out of the twelve positive urine specimens , three gave full allelic profiles ( 25% ) , and two a partial profile ( 4 and 5 loci ) . In total , eleven new alleles were detected and five of the specimens revealed allelic profiles representing new ST . Despite several attempts three samples resulted in ambiguous nucleotides in sequences of two ( L29 , sucA and caiB ) and one ( C4 and P8 , pfkB ) loci . No numbers could be assigned to those alleles . The locus that was amplified most often from clinical samples was caiB ( 19/44 , 43 . 2% ) , followed by glmU ( 18/44 , 40 . 9% ) ( data in S3 Table ) . Using the nested approach it was possible to lower the LoD of the assay . The minimum DNA concentration for simultaneous detection of all loci ( 42 PCRs ) using the nested MLST scheme was 4x10-4 ng , corresponding to 80 copies of genomic DNA ( gDNA; S1 Fig ) . In contrast , after the first round of amplification weak bands were visible for only eight loci ( 8/42 , 19% ) . When using eight gDNA copies per reaction in the nested assay only two PCRs did not yield a detectable product ( strain Hardjoprajitno /pntA and Salinem /pfkB ) while no product was detectable using the first round PCR only . For species assignment sequences from all patient samples were included in the phylogenetic tree along with isolates from the WHO panel for which species are known ( S1 Table ) . A maximum likelihood tree showing all samples for which a full allelic profile could be obtained is shown in Fig 1 . Trees based on separate alleles , are in concordance with the full-profile tree ( S2 Fig ) . L . interrogans was the most frequently detected species in 17 samples ( 17/22 , 77% ) . Table 3 shows the different species detected in each country .
Using the developed nested amplification approach presented in this study it was possible to increase the MLST assay’s analytical sensitivity and obtain typing information of pathogenic Leptospira species directly from a variety of clinical samples . The developed assay is based on an established MLST scheme supported by a public website ( http://leptospira . mlst . net/ ) and it will therefore not negatively impact comparability of already typed leptospires . The simplified PCR setup along with the anchor primers incorporated in the nested assay enables sequencing using two primers for all loci which will reduce costs . No nonspecific amplification was observed in any of the clinical samples . Consequently , in resource-limited settings where quantitative real-time PCR facilities are not available , the assay ( or defined loci only ) may be a useful diagnostic tool when applied with all necessary precautions to avoid cross-contamination between samples . Sample numbers in the presented study are too low to make any inferences as to which specimen type is most promising for molecular typing . Success rates between different samples varied between 40–100% . The highest proportion of full allelic profiles was obtained from buffy coat ( 2/3 ) and whole blood ( 3/6 ) , followed by urine ( 3/12 ) . Due to the dynamics of the disease Leptospira may be found in blood or urine at different time points [8 , 31] . Consequently , choice of specimen type and sampling time post symptom onset may prove critical for molecular MLST determination direct from specimens . In addition , as for any PCR based assay , detection is influenced by the genomic sequence of the strain present . Most primers used in the modified typing scheme were degenerated to account for sequence differences between the different strains , leading to variable specificity . Samples used for the present study were extracted using different platforms and elution volumes . However , all extracts were tested using the same diagnostic qPCR method and there does not appear to be a correlation between the original CT values and whether full or partial profiles were obtained ( data in S3 Table ) . Similarly , there was no correlation between sample type or Leptospira species and successfully amplified locus . Interestingly , the locus that performed best in the nested assay ( caiB ) was the least reliable in a study from Argentina using the unmodified MLST scheme [26] . Overall , using the nested approach the success rate of detecting full or partial profiles could be improved by more than two fold when compared to previous studies applying the original MLST scheme directly on clinical specimens [24 , 26] . Typing results of samples from the WHO serovar panel are 100% concordant with previously published results . Of note , the panel does not include an isolate of L . alexanderi and none of the clinical samples turned out as such . Boonsilp et al . ( 2013 ) characterized 325 isolates that resolved into 190 different ST and showed that L . alexanderi is detected by the original MLST scheme [22] . All loci represent conserved genes and the nested primers fit a representative sequence of L . alexanderi . It hence can be assumed that the nested assay would detect L . alexanderi , enabling it to detect all pathogenic Leptospira species , as well as ST that could not be tested for in the present study . Single alleles amplified from clinical specimens allow for species determination when used in phylogeny , opening up the possibility for the assay to support surveillance . Currently , most human leptospirosis cases are not identified to species level , so it is difficult at this point to draw any further conclusions from the presented results . A recent survey conducted in Southeast Asia identified four pathogenic species in native rodents: L . weilii , L . kirschneri , L . interrogans and L borgpetersenii , the latter being the most prevalent [32] . This is consistent with the findings of our study . Similarly , in the UK and Europe , L . interrogans was identified in indigenous rodents [33 , 34] . The variety of species found in the UK patients might be attributable to the fact that many cases in the UK are diagnosed in returning travellers . Of the 34 cases diagnosed in the UK , 15 reported a travel history ( 44% ) . Of these , 9 ( 26% ) had travelled to South East Asia ( Malaysia , Thailand and Indonesia ) . One case found to be infected with L . weilii had travelled to Thailand and one case infected with L . santarosai reported travel to Central America . The ability to obtain typing data directly from clinical specimens is ideal for pathogens that are difficult and slow to isolate in culture . The use of direct typing on urine specimens allows for non-invasive sampling and in some cases the provision of typing information in the absence of data from blood samples . One patient was positive for pathogenic Leptospira spp . in both serum and buffy coat by qPCR . MLST in this patient yielded a full profile from buffy coat , but only a partial profile ( 5 loci ) from serum . While this is consistent with our finding that success rates for amplifying MLST loci were higher in buffy coat than in serum it has to be interpreted with caution due to low sample numbers . Despite several attempts one sample resulted in ambiguous nucleotides in two loci ( L29 ) and two samples in one locus ( C4 and P8 ) . This could indicate active infection with more than one strain . Another possibility is that more than one copy of the gene is present in the genome , as has been shown for the mompS gene of several Legionella strains [35] . In summary , the reported improved MLST assay represents a fast and specific tool for typing of Leptospira direct from clinical specimens , including non-invasive samples such as urine . It may be of use during epidemics and outbreaks by enabling rapid identification of Leptospira species and MLST types without the inherent delay involved in Leptospira culture . | Leptospirosis is a zoonotic disease with more than 1 million cases per year globally and epidemics are increasingly reported . In this setting rapid typing is essential to identify potential clusters and transmission pathways . Typing of bacteria commonly requires bacterial isolates but culturing Leptospira is difficult and time consuming and requires invasive samples , such as blood or cerebrospinal fluid . We modified an existing typing scheme to lower the limit of detection and were able to amplify and sequence alleles directly from clinical specimens . Samples included blood ( whole blood , serum , or buffy coat ) and urine from patients diagnosed by PCR with leptospirosis between 2014 and 2015 in the United Kingdom and the Lao Peoples Democratic Republic . Using the sequences in phylogenetic analysis we identified the predominant Leptospira species in both countries as L . interrogans . With its increased sensitivity the modified assay allows for typing and species determination of Leptospira directly from blood or urine . It will be of use during epidemics and outbreaks for rapid identification of clusters and can support surveillance without the need to culture fastidious isolates . | [
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... | 2016 | An Extended Multilocus Sequence Typing (MLST) Scheme for Rapid Direct Typing of Leptospira from Clinical Samples |
The ratio of forced expiratory volume in one second to forced vital capacity ( FEV1/FVC ) is a measure used to diagnose airflow obstruction and is highly heritable . We performed a genome-wide association study in 7 , 691 Framingham Heart Study participants to identify single-nucleotide polymorphisms ( SNPs ) associated with the FEV1/FVC ratio , analyzed as a percent of the predicted value . Identified SNPs were examined in an independent set of 835 Family Heart Study participants enriched for airflow obstruction . Four SNPs in tight linkage disequilibrium on chromosome 4q31 were associated with the percent predicted FEV1/FVC ratio with p-values of genome-wide significance in the Framingham sample ( best p-value = 3 . 6e-09 ) . One of the four chromosome 4q31 SNPs ( rs13147758; p-value 2 . 3e-08 in Framingham ) was genotyped in the Family Heart Study and produced evidence of association with the same phenotype , percent predicted FEV1/FVC ( p-value = 2 . 0e-04 ) . The effect estimates for association in the Framingham and Family Heart studies were in the same direction , with the minor allele ( G ) associated with higher FEV1/FVC ratio levels . Results from the Family Heart Study demonstrated that the association extended to FEV1 and dichotomous airflow obstruction phenotypes , particularly among smokers . The SNP rs13147758 was associated with the percent predicted FEV1/FVC ratio in independent samples from the Framingham and Family Heart Studies producing a combined p-value of 8 . 3e-11 , and this region of chromosome 4 around 145 . 68 megabases was associated with COPD in three additional populations reported in the accompanying manuscript . The associated SNPs do not lie within a gene transcript but are near the hedgehog-interacting protein ( HHIP ) gene and several expressed sequence tags cloned from fetal lung . Though it is unclear what gene or regulatory effect explains the association , the region warrants further investigation .
Chronic obstructive pulmonary disease ( COPD ) is the fourth leading cause of death in the US and one of the most prevalent disabling diseases of adults . One of the defining features of COPD is airflow obstruction that is not fully reversible when contrasting measured spirometry before and after administration of a bronchodilator medication . A diagnosis of airflow obstruction is made utilizing the ratio of the spirometric measures forced expiratory volume in the first second ( FEV1 ) to forced vital capacity ( FVC ) . The FEV1/FVC ratio is reduced in obstructive lung diseases , most notably COPD and asthma . FEV1 , a measure of airflow , is commonly used to predict clinical outcomes , grade severity and follow the natural history of the disease in staging systems such as the one employed by The Global Initiative for Chronic Obstructive Lung Disease [1] . Low FEV1 can be due to restrictive or obstructive lung disease , whereas low FEV1/FVC is a more specific indicator of airflow obstruction . Tobacco smoking is the major environmental cause of COPD , but genetic factors also influence the risk of developing this condition . COPD aggregates in families [2] , and spirometric measurements of pulmonary function are heritable [3] , [4] . A severe deficiency of alpha-1-antitrypsin , caused by mutations of the SERPINA1 ( AAT ) gene , causes premature and severe emphysema ( OMIM 107400 ) ; however , SERPINA1 mutations explain only a small proportion of cases of COPD . The association of SERPINE2 SNPs with risk of COPD has been successfully replicated in independent samples [5] , but the functional role of SERPINE2 in the development of COPD remains to be characterized [6] . A number of other candidate gene associations reported in the literature have failed to replicate convincingly [7] . At present , the genetic factors other than SERPINA1 mutations that increase susceptibility to COPD remain uncertain . One approach to identifying genes as potential risk factors for COPD is by studying quantitative measures of lung function in population based samples using genome-wide linkage and association methods . Genome-wide linkage to quantitative spirometry traits such as FEV1 , FVC , and the FEV1/FVC ratio [8] has led to the identification of positional candidate genes like SMOC2 [9] , [10] . Quantitative spirometry traits were examined in a previous genome-wide association study using 70 , 987 single nucleotide polymorphisms ( SNPs ) in about 1220 related individuals in the Framingham Heart Study [11] . Though no results met the strict criteria for genome-wide significance , GSTO2 emerged as a candidate gene that warranted replication studies . The Framingham Heart Study ( FHS ) has collected spirometry and smoking history data on three generations of adults , and these research participants provided DNA samples that have recently been genotyped for 550 , 000 SNPs using microarray technology . These genotype and phenotype data , which have been made publicly available through the NHLBI's SNP Health Association Resource ( SHARe ) initiative ( http://public . nhlbi . nih . gov/GeneticsGenomics/home/share . aspx ) , provide a powerful resource to conduct genome-wide association studies with the goal of discovering novel genetic risk factors for airflow obstruction . Since the prevalence of moderate to severe COPD in this population based sample is low and genetic studies of quantitative traits are thought to have higher statistical power than dichotomous traits , we report here the findings from a genome-wide association ( GWA ) study for the quantitative pulmonary function measure FEV1/FVC , analyzed as a percent of predicted . The Family Heart Study was developed from existing epidemiologic studies in Forsyth County , NC , Framingham , MA , Minneapolis , MN , and Salt Lake City , UT . Participants were invited to provide a family health history and of those responding , a subset of families was enrolled for a clinical examination that included cross-sectional spirometry and smoking history data as well as DNA for genetic studies [12] . Genome-wide linkage analyses were previously reported for FEV1 , FVC , and FEV1/FVC [13] , and many of the DNA samples were available for further study . We now report the results of SNP association with the percent predicted FEV1/FVC ratio in the Family Heart Study sample that was undertaken to evaluate independent replication of findings arising in the Framingham GWA .
All participants provided written informed consent and local institutional review boards approved the study protocols . The Framingham Heart Study has measured spirometry on three generations of families from clinical examinations that began in 1948 . Original Cohort participants , their Offspring and offspring spouses , and the Third Generation ( GEN3 ) of participants were recruited and examined in Framingham , Massachusetts [14] . A total of 7691 white participants had both genotyping and spirometry available . A sample of 835 non-asthmatic white Family Heart Study participants , including 225 cases of spirometrically defined airflow obstruction and 610 controls , was used to screen Framingham GWA analysis results . The sample excluded Framingham field center participants in order to define an independent replication sample that included 91 families ( 545 individuals ) with 81 cases of spirometrically defined obstructive pulmonary disease and a case/control sample with an additional 144 cases and 146 controls frequency-matched to cases on age and smoking status . Case and control criteria for airflow obstruction were defined by spirometry as described below . Controls included both family members of cases and unrelated controls . Spirometry from each Framingham participant's latest examination with acceptable pulmonary function data was used; eligible examinations included Cohort exams 19 , 17 , 16 and 13 , Offspring exams 7 , 6 , 5 , and 3 , and GEN3 exam 1 . Predicted values for FEV1 and FEV1/FVC were calculated using cohort and gender-specific regression models predicting spirometry measurements on the basis of age , age squared , and height squared among Framingham subjects who were lifetime nonsmokers and had no history of chronic bronchitis , pulmonary disease , COPD/emphysema , asthma , or wheezing . The percent predicted value was calculated by dividing the observed by the predicted value . Standardized residuals were then created by regressing the percent predicted on smoking status ( never , former , current ) coded using dummy variables to indicate current smoking ( yes/no ) and former smoking ( yes/no ) , pack-years , and body mass index ( BMI: kg/m2 ) , in cohort and gender-specific models . The standardized residuals for percent predicted FEV1 and FEV1/FVC ratio were correlated at 0 . 52 ( p-value<0 . 0001 ) in the Framingham participants . The percent predicted FEV1/FVC ratio standardized residual was examined as the primary quantitative trait in the GWA . Spirometry in the Family Heart Study was performed at only one point in time , thus the data are cross-sectional . The observed spirometry was compared to expected values in order to define a percent predicted measure that was further adjusted for smoking status , pack-years , and BMI in gender-specific models using all participants with spirometry . The subset of participants genotyped was analyzed with a FEV1/FVC ratio standardized residual generated from the larger sample , paralleling the analysis of Framingham data . In both Framingham and Family Heart studies , a dichotomous trait for airflow obstruction was defined by a percent predicted FEV1/FVC ratio less than 90 and a percent predicted FEV1 less than 80 . These values were selected to standardize a definition for mild airflow obstruction across cohorts and gender using the percent predicted FEV1/FVC ratio , which accounts for the normal reduction in FEV1/FVC with increasing age [15] . Controls used in these analyses were required to have neither a percent predicted FEV1/FVC ratio less than 90 nor a percent predicted FEV1 less than 80 , which results in a smaller sample size for studies of the dichotomous trait compared to studies of percent predicted FEV1/FVC ratio . In Framingham , asthma was classified based on self-report at Cohort exam 1 , 3 , or 5 , Offspring exam 7 , and GEN3 exam 1 . In the Family Heart Study , asthma was also classified based on self-report . Framingham participants were genotyped using the Affymetrix ( Santa Clara , CA ) GeneChip Human Mapping 500K Array Set , which was comprised of two arrays generating approximately 262 , 000 SNPs with Nsp arrays and 238 , 000 SNPs with Sty arrays . An additional Affymetrix 50K Array ( HuGeneFocused50K ) with gene-centric and coding SNPs was also genotyped for a total of approximately 550K SNPs . For population-based analyses of quantitative traits , we examined standardized residuals using linear-mixed effects ( LME ) models with fixed effects for SNP genotypes and random effects for individuals correlated within families due to polygenic/familial shared effects [16] . Logistic regression via generalized estimating equations ( GEE ) to account for correlated observations in pedigrees and adjust for covariates was used for dichotomous outcomes . Family-based association tests were implemented in the FBAT software [17] . To assess population stratification in the Framingham sample , principal components were generated using Eigenstrat [18] . None of the first ten components was significantly ( p<0 . 05 ) related to the percent predicted FEV1/FVC ratio , suggesting that population substructure was unlikely to confound the population-based association analysis . Analyses implemented additive genetic models using individuals with ≥97% genotyping call-rate . SNP results were filtered on Hardy-Weinberg Equilibrium p-value of 1×10−6 , SNP call-rate of 95% , and Minor Allele Frequency of 0 . 01 . Imputation implemented in MACH [19] ( http://www . sph . umich . edu/csg/abecasis/MACH/ ) was applied to the GWA data to produce imputed genotypes on 2 , 540 , 223 HapMap SNPs . An additive model was used for association analysis of the dosage data for imputed genotypes , which provided information on new SNPs and new genotypes for SNPs with previously missing data . The ratio of the empirically observed dosage variance to the expected ( binomial ) dosage variance was computed as a quality control metric for imputed SNPs [20] , and only those with a ratio above 0 . 9 were considered of high quality . LME model results were used to rank the regions identified in the GWA of percent predicted FEV1/FVC in Framingham data . A SNP from each of the ten regions with the lowest LME p-values was genotyped in the set of 835 Family Heart Study participants . As only one region met genome-wide statistical significance , selecting ten regions for follow-up was arbitrary . Genotyping was performed using TaqMan technology implemented on the ABI PRISM 7900HT Sequence Detection system at Boston University School of Medicine . The ten SNPs were analyzed for association with the percent predicted FEV1/FVC phenotype in the Family Heart Study sample . Based on testing association to ten SNPs representing the top regions of association , a Bonferroni p-value of 0 . 005 was considered evidence of association in the replication study . Results from screening these top ten regions led to testing additional SNPs and phenotypes on chromosome 4 .
Descriptive statistics of the Framingham and Family Heart Study samples are provided in Table 1 . Though the proportion of current smokers is similar across Framingham cohorts , GEN3 had more never-smoking participants than did the older generations . The proportion of participants reporting asthma was greater in the younger generations , and the proportion of participants meeting criteria for COPD was higher in the older generations . The higher proportion of current smokers and airflow obstruction in the Family Heart Study reflects the selection of the sample . The GWA results for percent predicted FEV1/FVC based on LME analysis are reported in Table 2 and a Q-Q plot of the results is presented in Figure 1 . Ten top regions of interest are described based on 18 SNPs with p-values less than 1 . 5E-05 . The genomic inflation factor value of 1 . 043 suggested the results were appropriately distributed . Only one region ( four SNPs ) reached the threshold for genome-wide statistical significance ( p<5E-08 ) , which was located on chromosome 4 in an intergenic region near the gene HHIP . Also of note among the top regions identified was the MMP15 gene , which belongs to the matrix metalloproteinase ( MMP ) family . MMPs have been previously implicated in pulmonary disease , although this particular gene has not been related to COPD development . Table 2 also presents corresponding p-values from analysis of adjusted standardized residuals with FBAT , which is a more conservative test than LME but protects against population stratification . The FBAT results in the HHIP region include the lowest FBAT p-values observed genome-wide and support the strong association observed using the LME model . The results of the GWA for percent predicted FEV1/FVC using imputed SNP genotypes were evaluated to determine whether new regions were implicated by the improved SNP density and whether the associations improved by imputing missing values for genotyped SNPs . The region with the best p-values continued to be the chromosome 4 region near HHIP , and the results from imputed genotypes in this region are depicted in Figure 2 . The p-value for rs13147758 was slightly better in the results from imputed genotypes , and a total of 27 SNPs in the region ( including the four reported in Table 2 ) met the criterion for genome-wide statistical significance ( p<5E-08 ) . The colors plotted in Figure 2 reflect the linkage disequilibrium ( LD ) of each SNP with rs13147758 and demonstrate the strong LD among the SNPs with the smallest p-values . All SNP results for association with percent predicted FEV1/FVC with a p-value<0 . 001 for LME analysis of high quality imputed genotypes have been included in Table S1 in the Online Data Supplement . Table 3 reports the association results for the top GWA regions screened in 835 Family Heart Study participants . The SNP with the best overall p-value ( rs11100860 on chromosome 4 ) was ordered for genotyping , but the assay did not meet Applied Biosystems' internal quality control criteria and therefore was not genotyped . SNP rs13147758 , with the second best overall p-value , was therefore studied to represent the chromosome 4 region . The SNP on chromosome 5 , rs7707619 , had a minor allele frequency of 1% in Framingham , and was not observed in the Family Heart Study samples . Of the nine SNPs studied for association with the percent predicted FEV1/FVC ratio in the Family Heart Study , only one p-value was observed that met a Bonferroni corrected cutoff for statistical significance ( p<0 . 005 ) , which was located in the chromosome 4 GWA significant region ( rs13147758; p-value = 2 . 0E-04 ) . To further evaluate the region of chromosome 4 that demonstrated replication with SNP rs13147758 , we examined association with additional SNPs and additional phenotypes in the Family Heart Study . Table 4 presents association to FEV1 , the FEV1/FVC ratio , and a dichotomous airflow obstruction trait in the ever-smoking participants . When the analysis was restricted to subjects with a history of ever smoking cigarettes , the association between rs13147758 genotype and the FEV1/FVC ratio had a smaller p-value ( 2 . 2E-06 ) and larger estimate of effect . The SNP identified with association to the FEV1/FVC ratio phenotype was also statistically significant for FEV1 ( p = 0 . 0001 ) and airflow obstruction ( p = 6 . 18E-06 ) in smokers . Furthermore , minor alleles at neighboring SNPs exhibited association to lower lung function and increased risk of airflow obstruction ( e . g . rs17019336: OR = 1 . 76 , p = 0 . 004; rs2353397: OR = 1 . 65 , p = 0 . 003 ) . Table 5 presents the results of the Family Heart Study sample alongside Framingham results for rs13147758 and includes analyses of FEV1 , a dichotomous airflow obstruction trait , and analyses restricted to smokers . The results demonstrate a modest association with FEV1 in the Framingham sample , and a significant association with FEV1 in the Family Heart Study . When restricted to ever smokers , the effect size and p-values for FEV1 and the ratio improve in the Family Heart sample , but not in Framingham . For analysis of the dichotomous airflow obstruction phenotype , restricting the analysis to ever smokers improves the effect size and p-value in both samples . The minor allele of rs13147758 was protective for airflow obstruction among ever smokers in both samples , though only a trend toward statistical significance was seen in the Framingham sample .
The GWA results for percent predicted FEV1/FVC in the Framingham Heart Study identified a region on chromosome 4q31 around 145 . 68 Mb with LME p-values that achieved genome-wide statistical significance ( rs13147758 p = 2 . 31E-08 for genotyped SNP; p = 1 . 95E-08 for the same SNP imputed ) . FBAT analyses , which protect against false positive results arising from population stratification , also provided strong support for an association ( p = 6 . 9E-06 ) . Screening the top ten identified regions in 835 Family Heart Study participants further implicated the chromosome 4 region , and did not support the SNP association in the other nine regions . Though the replication sample size may have had limited power to detect SNPs with smaller MAFs or effect sizes , the identification of replication on chromosome 4 is not likely to be a false positive . The minor allele of rs13147758 with a frequency of 39% was associated with higher levels of the FEV1/FVC ratio and a 15–55% reduced risk of airflow obstruction among smokers . The reported beta estimates were on the scale of the standardized residual and reflect an approximately 1% increase in the percent of predicted FEV1/FVC ratio . For example , among Framingham smokers , the unadjusted mean percent predicted FEV1/FVC ratio was 94 . 4% , 95 . 5% , and 96 . 3% for the homozygous major allele , heterozygous , and homozygous minor allele genotypes , respectively . In a related publication describing results for a GWA of COPD , the chromosome 4q31 region was identified among the top 100 SNP results and replicated in two independent populations [21] . The two SNPs reported with combined p-values of 1 . 5E-07 and 1 . 7E-07 both exhibit strong LD with rs13147758 ( r2 = 0 . 97 in HapMap CEPH ) . The independent finding for this region based on samples ascertained for moderate to severe COPD provides compelling evidence for the association on chromosome 4q31 . The genomic region on chromosome 4 does not lend itself to easy interpretation of the SNP association results . The closest characterized gene , hedgehog-interacting protein ( HHIP ) , which is located distal to the associated SNPs , is an intriguing candidate because the hedgehog signaling pathway is known to influence lung development [22] and is activated in the airways during repair of injury [23] . However , the association does not explicitly implicate the gene . The specific SNP ( rs13147758 ) that was replicated lies 107 kb 5′ of the start site of HHIP and does not exhibit LD with SNPs located within the HHIP gene transcript . Another gene , CR620567 , is transcribed on the opposite strand , and rs13147758 is 104 kb 3′ from the end of that gene . The colored arrows in Figure 2 show the genes′ orientation . Also shown is the presence of an expressed sequence tag ( EST ) in the same region . Three overlapping ESTs transcribed on the opposite strand , including W05107 , BX104541 and AI822050 ( labeled in Figure 2 only as BX104541 ) , extend between 225–300 kb in length and were cloned from a Soares fetal lung library ( NbHL19W ) [24] , [25] . The location of the association leaves open the possibility for regulatory effects on HHIP , the uncharacterized ESTs from fetal lung , or members of the GYP gene cluster . Though located nearly 400 kilobase pairs from the associated SNPs , the gene glycophorin A ( GYPA ) is the nearest known proximal gene and lies adjacent to its homolog GYPB . Glycophorins are proteins of the red blood cell membrane , and glycophorin expression has been shown to be lower among COPD patients than controls [26] . The red blood cell is susceptible to oxidative alterations , which have been proposed as a useful marker for cell damage in COPD [27] . Review of the imputed data results for a missense SNP in GYPA showed a modest association ( rs7658293; p = 0 . 037 ) , with a common minor allele associated with a decreased FEV1/FVC ratio , and additional SNPs in LD exhibited similar p-values . However , the GYPA missense SNP does not exhibit LD with the replicated SNP rs13147758 ( HapMap CEPH r2 = 0 . 006 ) . In contrast to SNP results within GYPA , none of the SNPs within HHIP had p-values less than 0 . 05 for association with percent predicted FEV1/FVC . The GWA results provide only limited insight into the functional role of the most strongly associated region . It is not clear if the chromosome 4 region is associated with pulmonary disease or variability in pulmonary function in normal populations . The definitions of airflow obstruction and asthma in these observational studies have limitations . Both studies use spirometry to define airflow obstruction , but post-bronchodilator spirometry is not available , which may misclassify some individuals with reversible airflow obstruction as seen in asthma . However , results excluding diagnosed asthmatic participants ( data not shown ) suggest that the association is not driven by an asthma phenotype . GWA analyses for the FEV1/FVC ratio after exclusion of self-reported asthma identified the same chromosome 4 region with the best p-value at genome-wide significance , and all the results in the Family Heart Study represent a non-asthmatic sample . Though we have not identified a functional variant in the region , the replication of genome-wide significant results suggests that this intergenic region of chromosome 4 may influence pulmonary function and risk of obstructive pulmonary disease . The related manuscript describing a GWA for COPD identified association to a region of chromosome 15 that includes the nicotinic acetylcholine receptor ( CHRNA3/5 ) [21] . Two SNPs were identified to be associated with COPD in the discovery cohort and replicated in two independent populations . We evaluated whether these SNPs ( rs8034191 and rs1051730 ) provided evidence of association using the Framingham imputed genotypes , as they were not directly genotyped , but neither SNP was significantly associated with percent predicted FEV1/FVC or dichotomous airflow obstruction . Reviewing results across the region , we noted that SNPs in the nearby IREB2 gene provided evidence for association to percent predicted FEV1/FVC ( best p-value = 0 . 006 ) . This lack of a specific replication to CHRNA3/5 may reflect differences in the severity of airflow obstruction present in the population based Framingham study in contrast to a clinically ascertained COPD case sample . Further , as this locus has been reported to be associated with nicotine dependence [28] , [29] , differences in smoking behavior may influence the power to detect an association . As the Framingham study represents three generations ascertained at different time points , secular trends in smoking behavior may confound the association despite adjustment for pack-years within cohort . We have provided , in the online supplement , a resource of all SNP association results with nominal statistical significance ( p<0 . 001 ) for the percent predicted FEV1/FVC to facilitate future efforts to replicate SNP association with pulmonary phenotypes . The region of association on chromosome 4 warrants follow-up to explore the expressed sequence in fetal and adult lung , and to evaluate the relationship of the associated SNPs to gene expression of the nearby genes HHIP and GYPA and the ESTs . Our results demonstrate the utility of genome-wide association analysis in the identification of genetic determinants of obstructive lung diseases . | Cigarette smoking is the primary risk factor for impaired lung function , yet only 20% of smokers develop chronic obstructive pulmonary disease ( COPD ) . This observation , along with family studies of lung function and COPD , suggests that genetic factors influence susceptibility to cigarette smoke . We examined the relationship between common genetic variants and measures of lung function in a sample of 7 , 691 participants from the Framingham Heart Study and confirmed our observations in 835 participants from the Family Heart Study selected to include cases of airflow obstruction . We identified a variant on chromosome 4 that was strongly associated with FEV1/FVC in the Framingham Study and confirmed the association in the Family Heart Study . The accompanying manuscript identified the same region to be associated with COPD . Several interesting genes are present in the region that we identified , including a gene ( HHIP ) interacting with a biological pathway involved in lung development , but it is not yet clear which gene in the region explains the association . Our results identified a region of chromosome 4 that warrants further study to understand the genetic effects influencing lung function . | [
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] | 2009 | A Genome-Wide Association Study of Pulmonary Function Measures in the Framingham Heart Study |
Malaria vaccine developers are concerned that antigenic escape will erode vaccine efficacy . Evolutionary theorists have raised the possibility that some types of vaccine could also create conditions favoring the evolution of more virulent pathogens . Such evolution would put unvaccinated people at greater risk of severe disease . Here we test the impact of vaccination with a single highly purified antigen on the malaria parasite Plasmodium chabaudi evolving in laboratory mice . The antigen we used , AMA-1 , is a component of several candidate malaria vaccines currently in various stages of trials in humans . We first found that a more virulent clone was less readily controlled by AMA-1-induced immunity than its less virulent progenitor . Replicated parasites were then serially passaged through control or AMA-1 vaccinated mice and evaluated after 10 and 21 rounds of selection . We found no evidence of evolution at the ama-1 locus . Instead , virulence evolved; AMA-1-selected parasites induced greater anemia in naïve mice than both control and ancestral parasites . Our data suggest that recombinant blood stage malaria vaccines can drive the evolution of more virulent malaria parasites .
Evolution is a significant challenge to malaria control . Malaria parasites have repeatedly evolved resistance to frontline drugs [1] , [2] , and mosquitoes have evolved resistance to all classes of approved insecticides [3] , [4] . Here we report experimental studies investigating how malaria parasites might evolve in response to the “natural” selection imposed by a blood stage malaria vaccine . There is currently no licensed malaria vaccine , but a number of candidates are in human trials [5]–[9] , and a vaccine targeting the pre-erythrocytic stages of Plasmodium falciparum has provided partial protection to young children in a large phase 3 trial in Africa [10] . There are two ways parasites could evolve in vaccinated populations . Vaccine developers have traditionally been concerned with epitope evolution ( antigenic escape ) [5] , [8] , [9] , [11] , [12] . This is where pre-existing or de novo variants of target antigens emerge and spread because they enable parasites to evade vaccine-induced immunity . Epitope evolution in response to vaccination occurs in a range of infectious agents , including hepatitis B virus [13] , [14] , Bordetella pertussis [15]–[18] , and Streptococcus pneumoniae [19] , [20] . Epitope evolution has been of particular concern for those developing blood stage malaria vaccines because target antigens are often highly polymorphic , presumably because of natural immune selection . Considerable ingenuity is currently going towards inducing variant-independent immunity against these targets [7] , [21]–[27] . Epitope evolution is not the only type of evolution that can occur in response to vaccination . Immunization can also promote the emergence of variants at loci other than those targeted by vaccine-induced immunity [28] . Of particular interest are virulence determinants because , in theory , immunization can under some circumstances promote the emergence and spread of strains causing more severe disease ( morbidity and mortality ) [28]–[37] . The idea that vaccines could prompt the evolution of more virulent pathogens is controversial , but it has been described as one of the key unexpected insights to arise from the nascent field of evolutionary medicine [38] . Several veterinary vaccines have failed in the face of more virulent strains , apparently in the absence of epitope evolution [39]–[43] . Vaccination could favor virulent malaria parasites in two ways . First , if the primary force preventing the evolution of more virulent strains is that they kill their hosts and therefore truncate their infectious periods , keeping hosts alive with vaccination will allow more virulent strains to circulate [28]–[37] , [44] . Second , immunity might be less effective against virulent strains [36] . For instance , a given antibody titer or a proliferating immune response might better control slower replicating strains than more aggressive strains [45] . Virulence factors that reduce the efficacy of primed immune responses might also have a selective advantage in vaccinated hosts [46] . Epitope evolution and virulence evolution are not necessarily mutually exclusive ( some antigens can be virulence determinants ) , but they will have different consequences for public and animal health . Epitope evolution will erode vaccine efficacy but need not lead to more severe disease in unvaccinated individuals . Virulence evolution on the other hand would both erode vaccine efficacy and cause more severe disease outcomes in unvaccinated individuals [28] , [35] , [36] . Note that virulence evolution will not occur for vaccines that induce sterilizing immunity: evolution can proceed only where vaccines are leaky so that wild-type pathogens can transmit from vaccinated hosts . Because natural immunity against malaria is neither life-long nor sterilizing [47] , [48] , it seems likely that malaria vaccines will be leaky . To investigate the consequences of blood stage malaria vaccination for epitope and virulence evolution , we performed serial passage experiments with the rodent malaria Plasmodium chabaudi in laboratory mice immunized with a candidate blood stage vaccine . In this system , virulence , which we measure as weight loss and particularly anemia , is positively related to transmission and competitive ability [35] , [36] . Anaemia is due to direct red cell destruction by parasites and bystander killing by host responses [35] , [36] , [49] . As with many pathogens [50] , [51] , serial passage of P . chabaudi creates more virulent parasites [52] . Serial passage through mice immunized with live parasites augments this effect [30] , consistent with the idea that parasites evolving in vaccinated populations could become more virulent . However , most probably , actual blood stage vaccines will consist of recombinant antigens [53]–[69] . Here we specifically test the evolutionary impact of vaccination with Apical Membrane Antigen-1 ( AMA-1 ) , a component of at least 10 vaccines in human trials [6] , [66]–[68] . Antibodies elicited by this antigen are believed to confer protection by inhibiting the invasion of merozoites into red blood cells ( RBCs ) [55] , [65] , [69] . In nature , the ama-1 gene is highly polymorphic , and this antigenic diversity is thought likely to compromise vaccine efficacy in the long term [7] , [70]–[72] . By immunizing with a highly defined single recombinant blood stage antigen , we could specifically determine whether antibodies raised against AMA-1 select for parasites with altered ama-1 sequence ( epitope evolution ) and/or for parasites that cause more severe disease ( virulence evolution ) . We found no evidence of epitope evolution in response to vaccination , but virulence increased .
Before beginning experimental evolution in vaccinated animals , we wanted to test whether AMA-1 vaccine-induced immunity would be less effective against virulent parasites . In order to generate virulent parasites , we serially passaged a single clonal lineage of P . c . adami ( clone DK ) through 30 successive naïve mice ( “serial passage A” ) . We then tested the performance and virulence of these virulent parasites and their less virulent ancestral precursors in sham- and AMA-1-vaccinated mice ( “evaluation experiment 1” ) . As expected , serial passage produced parasites that were more virulent in naïve mice than were the ancestral parasites ( Figure 1A–B; anemia F1 , 6 = 6 . 5 , p = 0 . 04 ) . Vaccination with recombinant AMA-1 reduced anemia ( Figure 1A–B ) . It also suppressed parasite densities ( Figure 1C–D ) . Importantly , vaccine-induced immunity was disproportionately effective at containing the avirulent ( ancestral ) parasites , even though they shared complete sequence identity at ama-1 with the more virulent ( derived ) parasites ( Figure 1C–D; total parasite density×vaccination: F1 , 12 = 5 . 4 , p = 0 . 03 ) . This suggests that AMA-1 vaccination has the potential to selectively favor more virulent P . chabaudi parasites . Serial passage did not affect the nucleotide sequence of ama-1 ( Figure S1 ) . To test the evolutionary impact of vaccination with AMA-1 , we contemporaneously passaged P . c . adami DK parasites every week for 20 wk through either sham-vaccinated mice or through mice vaccinated with recombinant AMA-1 ( “serial passage B” ) . We refer to the parasite lines evolved under these contrasting conditions as C-lines and V-lines , respectively . We set out to evolve five independent replicate lines of each type , but particularly in vaccinated groups , lineage loss occurred when parasites failed to reach high enough densities to allow onward syringe passage . Failure to achieve transmissible densities in vaccinated hosts is likely to be an important evolutionary force . When lines were lost , sub-lines were derived from surviving lines . The full evolutionary history of the lines is shown in Figure S2 . Throughout the 20 passages , parasite densities on the day of passage were lower in AMA-1 vaccinated mice ( Figure S3 ) . However , the densities of those V-lines increased steadily over the successive passages , presumably because of parasite adaptation to vaccine-induced immunity . To test whether parasite virulence had evolved during the passages , we evaluated the virulence of the parasite lines in naïve mice at two time points during the evolution of the lines: once after 10 rounds of serial passage ( “evaluation experiment 2” ) and again after 21 rounds ( “evaluation experiment 3” ) . In that latter experiment , we also assayed the virulence of the ancestral parasites ( passage 0 ) . We used naïve mice in these experiments because the hypothesis under test is that evolution through AMA-1 vaccinated mice will produce parasites that do more harm to unvaccinated hosts . Parasites passaged through AMA-1 vaccinated mice ( V-lines ) became more virulent than parasites passaged through sham-vaccinated mice ( C-lines ) ( Figures 2 and 3 ) . This difference had already arisen by the 10th passage and was still apparent after 21 passages . Thus , in naïve mice , V-line parasites from both the 10th and 21st passage “generations” caused more anemia than their comparator C-lines ( Figure 2A–B; Figure 3A–B; F1 , 28 = 8 . 4 , p = 0 . 007 , and F1 , 27 = 6 . 2 , p = 0 . 02 , respectively ) . The V-lines also induced more anemia than the parasites from which they were derived ( passage 21 versus passage 0: F1 , 22 = 8 . 2 , p = 0 . 008 ) . After 20 passages , no changes in ama-1 nucleotide sequence were detected in any of the lines ( Figure S1 ) . Thus , over the course of the experiment , parasites evolved in AMA-1 immunized mice became more virulent to naïve animals , and there was no evidence of nucleotide evolution at the ama-1 target sequence . The virulence differences apparent at the 10th round of selection were associated with differences in parasite densities ( Figure 2C–D ) . V-line parasites produced more parasites in total ( Figure 2D; F1 , 28 = 11 . 5 , p = 0 . 002 ) , and had higher densities on the day of serial passage ( F1 , 28 = 4 . 3 , p = 0 . 04 ) than did C-line parasites . This is consistent with the hypothesis that selection by AMA-1 vaccination results in faster growing parasites , and that was why vaccine-evolved lines were more virulent . However , vaccine-adapted parasites from 21 passages , while still more virulent , did not achieve higher densities than C-line parasites ( Figure 3D; V-lines versus C-lines: F1 , 27 = 1 . 6 , p = 0 . 2 ) , even though they did achieve higher densities than ancestral parasites ( Figure 3D; passage 21 versus passage 0: F1 , 22 = 12 . 3 , p = 0 . 002 ) . We performed another evaluation experiment , this time to compare the virulence and performance of V- and C-lines from passage 21 in AMA-1 vaccinated and sham-vaccinated mice ( “evaluation experiment 4” ) . This allowed us to ask whether V-lines and C-lines were better adapted to the immune environment in which they evolved . Note that the half of this experiment conducted in sham-vaccinated mice closely replicates our previous evaluation of the virulence of the lines in naïve mice ( “evaluation experiment 3” ) . Again , we found that the V-lines were more virulent than the C-lines in control mice ( Figure 4A–B; anemia F1 , 38 = 4 . 0 , p = 0 . 05 ) . This virulence difference was also apparent in vaccinated mice ( Figure 4A–B; anemia F1 , 38 = 4 . 0 , p = 0 . 05 ) . The magnitude of the virulence difference was unaltered by the vaccine status of the host ( Figure 4A–B; anemia , parasite×vaccination: F1 , 76 = 1 . 0 , p = 0 . 3 ) . Thus , vaccine-line parasites were more virulent in both sham- and AMA-1-vaccinated hosts . If parasites had become adapted to the immune environment in which they evolved , we would expect V-lines to perform best in AMA-1-vaccinated hosts and C-lines to do better than V-lines in sham-vaccinated hosts . In fact , C- and V-lines did equally well in sham-vaccinated hosts ( Figure 4C–D: F1 , 38 = 1 . 9 , p = 0 . 1 ) , just as they did in naïve mice in evaluation experiment 3 ( Figure 3 ) . The V-lines did achieve higher densities in AMA-1-vaccinated hosts ( Figure 4C–D; F1 , 38 = 3 . 9 , p = 0 . 05 ) , as expected if indeed the V-lines were better adapted to vaccinated hosts , but this difference was itself not significantly different from that observed in sham-vaccinated hosts ( Figure 4C–D; parasite×vaccination: F1 , 76 = 2 . 8 , p = 0 . 09 ) .
Our data show that immunization with a recombinant malaria vaccine can create ecological conditions that favor parasites that cause greater disease severity in unvaccinated individuals . But we are a long way from being able to assess the likelihood of this occurring in human malaria populations , were a malaria vaccine to go into widespread use . Most obviously , generalizing from animal models is notoriously difficult in malaria ( reviewed in this context by [76] , [83] ) , so extreme caution is warranted . But in addition to this generic issue , many potentially important considerations remain to be evaluated . Some of these are the following . First , in human populations there will be variation in levels of immunity due to prior infection . Whether existing natural immunity will act to enhance or suppress vaccine-imposed selection for more virulent parasite variants remains to be determined . In mice , live parasite-induced immunity [30] and AMA-1-induced immunity ( this study ) both promote the evolution of virulence . Further experiments are needed to determine whether both occurring together in the same host would further promote virulence or whether the effects might be less than additive . It could be argued that semi-immune individuals will already naturally be imposing selection for greater virulence in the field , and the effects of vaccination will be no worse . However , the aim of vaccination programs is to increase the number of immune people in a population , and if that is achieved , a greater proportion of the parasite population will be evolving in immune hosts . Second , our data show that virulence rises with serial passage , as it does in many systems [51] . In nature , something must counter within-host selection for virulence ( or all pathogens would be extremely virulent ) . It has been hypothesized that syringe passage , which by-passes natural transmission , eliminates this counter-selection against excessive virulence that arises through host death [51] . This must be true in the limit , but the virulence increases we observed here as a consequence of immunity are likely to be far from this limit because mouse death played no role in the selection process in our serial passages ( Figure S2 ) . In the P . chabaudi-mouse model , more virulent infections are more infectious to mosquitoes [35] , [36] , and serial passage enhances virulence and transmission stage production [30] , [52] . Virulence differences generated by experimental evolution using protocols identical to ours , but using whole-parasite immunized mice rather than a recombinant antigen , were not eliminated by mosquito transmission [30] , [84] . If within- and between-host selection on virulence are somehow antagonistic , an important question is how they play out in the field now , and how vaccination might affect that . Our data show that the within-host selection for virulence is strengthened by vaccine-induced immunity . Third , our experimental design involved passaging parasites every 7 d . We chose that timing because that is after a period of rapid parasite population expansion ( selection ) but before naïve mice begin mounting a strong acquired response against malaria [49] , [85]–[90] . This meant that , in contrast to parasites in our vaccinated mice , our control-selected lines were under only modest antibody-mediated immunity . Without further experimentation , it is unclear whether onward transmission on any other days would lead to more or less potent selection on virulent variants . Later passage could select for parasite variants that are even more resilient against the mounting immune response; earlier passage may relax selection against competitively less able variants . How that would play out in terms of transmission to mosquitoes summed over the whole infectious period remains to be determined . Our data demonstrate that immunity induced by a recombinant antigen that is a candidate for human malaria vaccines can increase the potency of within-host selection for more virulent malaria parasites . In contrast , we found no evolution of the parasite locus controlling production of the target antigen . This does not exclude antigenic polymorphism as a challenge for vaccine efficacy , nor does it mean that virulence evolution is inevitable in populations immunized with a leaky ( non-sterilizing ) vaccine . But it does argue that a range of evolutionary trajectories are possible in response to vaccination [36] , [44] , and that epitope evolution is not the only evolution that can occur . We suggest that investigation of the impact on blood stage parasite densities and transmission should be a standard component of all Phase 3 malaria vaccine trials [10] , and that whole genome analyses of parasites that survive and are transmitted from individuals in vaccinated and control arms in clinical trials should be a priority . Until there is a better understanding of the selection processes set up by imperfect vaccination , there is no reason to think that vaccine-driven evolution will occur only in genes encoding target antigens . Evaluating the medium term effects of widespread vaccination ( evolutionary risk ) is a substantial challenge , not least because evolutionary change is likely to occur long after clinical trials have concluded ( Box 1 ) . More generally , there is little reason to think the vaccine-driven virulence evolution we have seen will be limited to malaria parasites . Analysis of virulence evolution in range of infectious diseases for which leaky vaccines are in widespread use would be of substantial interest .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Animal Care and Use Committee of the Pennsylvania State University ( Permit Number: 27452 ) . We used the DK clone of P . chabaudi adami , which was originally collected from thicket rats ( Thamnomys rutilans ) in the Congo Brazzaville [91]–[93] , and subsequently cloned by limiting dilution . Laboratory genotypes are stored as stable isolates in liquid nitrogen with subscript codes used to identify their position in clonal history [52] . Mice in our experiments were female C57Bl/6 , at least 6–8 wk old . Parasite densities were estimated from day 4 from samples of tail blood using Giemsa-stained thin smears and red blood cell density was estimated from day 0 by flow cytometry ( Beckman Coulter ) , or by genotype-specific real-time quantitative real-time PCR ( qPCR ) assays as described previously [74] . For amplification of the DK genotype , we used the forward primer previously used to amplify AS/AJ genotypes [74] and the DK genotpe-specific reverse primer 5′ GATTGTAGAGAAGTAGAAAATACA GATACAACTAA 3′ . All mice were in one of the following three immune classes: naïve ( never vaccinated with the adjuvant or the AMA-1 antigen ) , sham-vaccinated ( which were immunized with adjuvant alone ) , or vaccinated ( which were immunized with AMA-1 antigen plus adjuvant ) . We use that terminology consistently throughout . Immunization protocols were similar to those described by Anders and others [53] , [94] , [95] . Briefly , vaccination was with the ectodomian of the AMA-1 protein derived from P . c . adami genotype DK [53] . AMA-1 was emulsified with Montanide ISA 720 adjuvant ( Seppic ) . Each mouse was injected intra-peritoneally with a total of 10 µg of protein on two occasions with a 4-wk interval . Sham-vaccinated mice were injected with Montanide ISA720 plus PBS . During serial passage , and during the evaluation experiments , mice were infected with parasites 14 d after the second immunization . We conducted two separate serial passage experiments ( denoted A and B ) . All passages involved the syringe transfer of 0 . 1 ml of diluted blood containing 5×105 parasites between mice every 7 d . We first used serial passage simply to derive a more virulent parasite lineage from the ancestral DK ( “serial passage experiment A” ) . This allowed us to test whether AMA-1-induced immunity controlled the derived ( virulent ) line less successfully than the ancestral ( less virulent ) line . P . c . adami genotype DK294 was derived via serial passage of ancestral P . c . adami genotype DK122 after a total of 30 passages though immunologically naïve mice . The second serial passage ( “B” ) was the experimental evolution phase of our study ( Figure S2 ) . This was aimed at comparing the evolutionary consequences of passaging parasites through two contrasting selection treatments: sham- and AMA-1-vaccinated mice . We used sham-vaccinated mice so as to ensure that any evolved differences could be attributed to AMA-1 antigen , and not the adjuvant . We initially aimed to derive five independent parasite lines per selection treatment . At the start ( generation 1 ) , five mice that had been previously immunized with the AMA-1 vaccine ( V- lines ) or a sham vaccine ( C-lines ) were infected with P . c . adami genotype DK247 ( generation 0 ) ( Figure S2 ) . Parasites from each one of the five mice at generation 1 were then used to infect at least two mice at generation 2 ( forming a total of 10 sublines per treatment ) . Duplicate infections helped reduce the possibility of losing lines during the selection phase . Thus , from generation 2 to 21 , parasites from each mouse within a selection treatment were used to infect a fresh mouse in the next generation . Some lines were lost ( notably where AMA-1 vaccination induced a strongly protective anti-parasitic response ) ( Figure S2 ) . When lines were lost , blood from a mouse in another line within that treatment group was used to infect at least two other mice in the next generation . This protocol ensured that at each generation 10 mice were infected with parasites within each selection treatment . A total of 410 mice were used during this experimental evolution phase . Virulence and clone performance were assessed in four separate “evaluation” experiments conducted after the serial passages . In all cases frozen lines ( P . c . adami-infected erythrocytes ( IRBC ) ) were first introduced into naïve donor mice and then into naïve or sham-immunized experimental mice . Naïve donors are used because exact doses to initiate experiment infections cannot be obtained from frozen stock . Note that this single passage in naïve mice would , if it does anything , act to narrow the virulence differences observed in our experiments . Experimental mice were intra-peritoneally injected with 1×106 IRBCs . Evaluation experiment 1 compared the performance of parasites derived from serial passage A with their pre-passage progenitors in vaccinated and naïve hosts ( Table S1 ) . Two mice died ( one control immunized and one AMA-1 immunized both infected with derived parasites ) . These were included in the calculation of daily densities until death as death always occurred after the peak of infection ( days 17 and 15 , respectively ) . Three further evaluation experiments were used to compare the virulence and parasites dynamics of the C-lines and V-lines from serial passage B ( Table S1 ) : evaluation experiment 2 , parasites from passage 10 in naïve mice; evaluation experiment 3 , parasites from passage 21 in naïve mice; and evaluation experiment 4 , parasites from passage 21 in sham- and AMA-1-vaccinated mice . In these three evaluation experiments , we compared five surviving C-lines with five surviving V-lines , with each line used to infect three mice . The lines used and their history are as shown in Figure S2 . In evaluation experiment 3 , nine naïve mice were also infected with the ancestral lineage ( P . chabaudi genotype DK247 ) . During evaluation experiment 2 , one mouse infected with C-line parasites died on day five and was thus excluded from all analyses To test selected parasites for epitope evolution , ama-1 nucleotide sequences of the ancestral and derived parasites from experiment one and the ancestral , C- and V-line parasites from experiments 3 and 4 ( passage 21 parasites ) were established using a series of overlapping oligonucleotide primers designed by reference to the published sequences of P . c . adami DK [94] , [95] . Parasite DNA was extracted as previously described [74] . AMA-1 was amplified as two gene fragments: Outer Forward 5′ CTTGGGTAATTGTTCCGA 3′ and Inner Reverse 5′ GCACTTCTAACCCTTTGGT 3′; Inner Forward 5′ GGGTCCAAGATATTGTAG 3′ and Outer Reverse 5′ GGGTTTCGTCTTTTCTAC 3′ . PCR was performed using Nova Taq ( Novagen ) , with the thermocycle profile; 95°C for 12 min , then 95°C for 1 min , 57°C for 1 min , and 72°C for 1 min ( ×30 cycles ) ending at 72°C for 10 min . Amplified DNA was visualized on a 1% agarose gel and positive amplifications were cleaned with QIAquick Gel extraction kit ( Qiagen ) and sequenced in both directions with the same primers that were used for amplification . Sequencing was performed by Penn State DNA sequencing core facility and sequences were aligned and analyzed using ClustalW . All analyses were conducted in R 2 . 10 . 1 [96] . All parasite density data were log transformed to meet normality assumptions of the models . For the analysis of evaluation experiments 2–4 , which determined the consequences of evolution through sham- and AMA-1-vaccinated hosts ( serial passage B ) , differences among sub-line variances ( C-lines and V-lines ) were first analyzed using mixed effect linear models with sub-line as a random effect [97] . In all experiments there were no sub-line variances with selection treatments so we only report the between-selection effects . For completeness , we report the more conservative analysis , based only on line means , in Table S2 . | Vaccination can drive the evolution of pathogens . Most obviously , molecules targeted by vaccine-induced immunity can change . Such evolution makes vaccines less effective . A different possibility is that more virulent pathogens are favored in vaccinated hosts . In that case , vaccination would create pathogens that cause more harm to unvaccinated individuals . To test this idea , we studied a rodent malaria parasite in laboratory mice immunized with a component of malaria vaccines currently in human trials . We found that a more virulent parasite clone was less well controlled by vaccine-induced immunity than was its less virulent ancestor . We then passaged parasites through sham- or vaccinated mice to study how the parasites might evolve after multiple rounds of infection of mouse hosts . The parasite molecule targeted by the vaccine did not change during this process . Instead , the parasites became more virulent if they evolved in vaccinated hosts . Our data suggest that some vaccines can drive the evolution of more virulent parasites . | [
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"biology"
] | 2012 | The Evolutionary Consequences of Blood-Stage Vaccination on the Rodent Malaria Plasmodium chabaudi |
Epstein-Barr virus ( EBV ) nuclear antigen 2 ( EBNA2 ) plays an important role in driving immortalization of EBV-infected B cells through regulating the expression of many viral and cellular genes . We report a structural study of the tumor suppressor BS69/ZMYND11 C-terminal region , comprised of tandem coiled-coil-MYND domains ( BS69CC-MYND ) , in complex with an EBNA2 peptide containing a PXLXP motif . The coiled-coil domain of BS69 self-associates to bring two separate MYND domains in close proximity , thereby enhancing the BS69 MYND-EBNA2 interaction . ITC analysis of BS69CC-MYND with a C-terminal fragment of EBNA2 further suggests that the BS69CC-MYND homodimer synergistically binds to the two EBNA2 PXLXP motifs that are respectively located in the conserved regions CR7 and CR8 . Furthermore , we showed that EBNA2 interacts with BS69 and down-regulates its expression at both mRNA and protein levels in EBV-infected B cells . Ectopic BS69CC-MYND is recruited to viral target promoters through interactions with EBNA2 , inhibits EBNA2-mediated transcription activation , and impairs proliferation of lymphoblastoid cell lines ( LCLs ) . Substitution of critical residues in the MYND domain impairs the BS69-EBNA2 interaction and abolishes the BS69 inhibition of the EBNA2-mediated transactivation and LCL proliferation . This study identifies the BS69 C-terminal domains as an inhibitor of EBNA2 , which may have important implications in development of novel therapeutic strategies against EBV infection .
Epstein-Barr virus ( EBV ) is a widespread herpes virus that transforms resting B cells into permanent lymphoblastoid cell lines [1 , 2] . Under some circumstances , this may further lead to several malignancies , including Burkitt’s lymphoma , Hodgkin lymphoma and nasopharyngeal carcinoma [3] . One of the key EBV proteins that drive immortalization of B cells is Epstein-Barr virus nuclear antigen 2 ( EBNA2 ) . It is , together with another EBV protein , EBNA-LP , the first protein to be expressed upon infection [4 , 5] . These two proteins then cooperate to promote the G0–G1 phase transition of the cell cycle [6] . EBNA2 plays a critical role in controlling the expression of many viral and cellular genes [7] . For instance , it recruits a variety of cellular proteins , including histone acetyltransferases ( e . g . P300 ) [8] and basal transcription factors [9–11] , to regulate chromatin structure and gene expression . Sequence comparison of EBNA2 across serotypes of EBV , combined with mutational studies , has identified nine evolutionarily conserved regions ( CR1-CR9 ) ( Fig 1A ) that define the functional domains of EBNA2 [12] . Most notably , CR8 ( residues 437–477 ) is the transactivation domain ( TAD ) [12] , which interacts with both acetyltransferases and EBNA-LP to mediate transcriptional activation [8 , 13 , 14]; CR5 and CR6 mediate indirect contact of EBNA2 with DNA [15 , 16] . In addition , several other domains , including CR7 , are important for EBNA2-LP coactivation [17 , 18] . BS69 ( ZMYND11 ) is an emerging tumor suppressor [19–21] that was originally identified as the Adenovirus protein E1A and EBNA2-interacting protein [22 , 23] . It has been shown that low expression of BS69 correlates with poor prognosis in breast cancer patients , whereas its overexpression suppresses cancer cell growth both in vitro and in vivo [21] . BS69 contains , in addition to a C-terminal MYND ( MYeloid translocation protein 8 , Nervy and DEAF-1 ) domain , an N-terminal Plant Homo Domain ( PHD ) zinc finger , a Bromo domain and a PWWP domain . Recent studies have demonstrated that the tandem Bromo-PWWP domains of BS69 specifically recognize histone H3 . 3 trimethylated at lysine 36 ( H3 . 3K36me3 ) , thereby linking BS69 to transcriptional elongation , tumor suppression and pre-mRNA splicing [19 , 21] . The structure and function of the BS69 C-terminal region is less clear . Nevertheless , it has been reported that BS69 interacts with E1A , EBNA2 and a variety of cellular transcriptional regulators through its MYND domain [20 , 22–25] , which recognizes a common PXLXP ( X denotes any amino acid ) sequence motif present in many of these proteins [22] . In this study , we identified a coiled-coil domain that precedes the MYND domain of BS69 . The crystal structure of the coiled-coil domain in tandem with the MYND domain of BS69 , bound to an EBNA2 peptide ( residues 381–389 , EBNA2381–389 ) , reveals that BS69 forms a homodimer through the self-association of its coiled-coil domain , permitting the two MYND domains of the BS69 dimer to cooperatively bind to the EBNA2381–389 peptide . Through ITC assays , we also demonstrated that the BS69 homodimer binds to the two PXLXP motifs within CR7 and CR8 of EBNA2 in a synergistic manner . Interestingly , analysis of the EBV-infected B cells indicates that BS69 interacts with EBNA2 at the early stage of EBV infection , but is subsequently suppressed by EBNA2 at both mRNA and protein levels . We confirmed that the ectopically expressed BS69 coiled-coil-MYND domains can interact with EBNA2 in cells through a co-immunoprecipitation ( co-IP ) assay . Furthermore , through in vivo transcription reporter and cell viability assays , we showed that ectopic expression of the BS69 coiled-coil-MYND domains leads to inhibition of the EBNA2-mediated transcriptional activation in the EBNA2-transfected BJAB B lymphoma cell line [26] , and decreased viability of lymphoblastoid cell lines ( LCLs ) . By contrast , substitution of critical residues in the MYND domain disrupts the BS69-EBNA2 interaction and abolishes the BS69 inhibition of both the EBNA2-mediated transactivation and LCL cell growth . Taken together , this study identifies the BS69 C-terminal domains as a potential inhibitor of EBNA2 , thereby providing a mechanism for development of novel therapeutic strategies against EBV infection .
A previous study suggested that the interaction between BS69 and EBNA2 is mediated by the MYND domain of BS69 ( residues 521–562 ) and the PXLXP motif from EBNA2 [22] . However , sequence analysis of BS69 using the program Paircoil2 [27] suggests that this protein may also contain a coiled-coil domain ( residues 429–520 ) , immediately preceding its C-terminal MYND domain ( Fig 1A ) . Therefore , we have set out to investigate the interaction of a C-terminal fragment of BS69 , comprised of both the predicted coiled-coil domain and the MYND domain ( BS69CC-MYND ) , with a PXLXP motif-containing peptide derived from region CR7 of EBNA2 ( residues 381–389 , EBNA2381–389 ) ( Fig 1A ) . The crystal structure of BS69CC-MYND ( residues 440–562 ) in complex with EBNA2381–389 was determined at 2 . 4 Å resolution ( Fig 1B and 1C ) . The structure of the BS69MYND domain reveals a ββα fold that was observed for other MYND domains [28 , 29] . Two zinc finger clusters , formed by a Cys4 motif and a Cys3His motif , respectively , are arranged in a cross brace topology . In addition , a short 310-helical turn ( α2 ) immediately follows the major α-helix ( α1 ) , participating in formation of one of the zinc clusters . Structural analysis of the BS69CC-MYND-EBNA2381–389 complex also confirmed the presence of a coiled-coil domain upstream to the MYND domain ( Fig 1B ) . The coiled-coil domains from two BS69CC-MYND molecules further associate to form a homodimeric fold . Consequently , the two MYND domains within the same BS69CC-MYND dimer are brought in close proximity ( Fig 1B and 1C ) . Association of BS69CC-MYND with EBNA2381–389 is mainly mediated through hydrogen bonds and van der Waals contacts , involving a surface area formed by the α1-helix and β1-strand of BS69CC-MYND ( Fig 1C and 1D ) . Notably , the side chain of BS69 Q546 forms direct and water-mediated hydrogen bonds with the backbone atoms of EBNA2 P383 and L385 , respectively; and BS69 M531 and H533 form direct and water-mediated backbone hydrogen bonds with EBNA2 V388 and S386 , respectively . The three conserved residues in the PXLXP motif of EBNA2381–389: P383 , L385 and P387 , all make contacts with BS69CC-MYND ( residues Y532 , C534 , Y540 , Q546 , W550 , C558 and R560 ) through van der Waals contacts ( Fig 1D ) . Additional intermolecular contacts involve the residues flanking the PXLXP motif: EBNA2 M382 makes contacts with BS69 Q546 , Q547 and W550 , and EBNA2 V388 is in close proximity with W536 from a second MYND domain within the same BS69 dimer ( Fig 1D ) . The engagement of W536 from the neighboring MYND domain in the BS69MYND-EBNA2381–389 interaction implies that the BS69 coiled-coil domain plays a role in enhancing the interaction between the BS69MYND domain and EBNA2 . To test our structural observation , we performed mutational studies of BS69 and Isothermal Calorimetry Titration ( ITC ) assays to evaluate the BS69-EBNA2 binding ( Fig 2 and S1 Fig ) . We observed that BS69CC-MYND and BS69MYND bind to the EBNA2381–389 peptide with a dissociation constant ( Kd ) of 7 . 4 μM and 25 . 6 μM , respectively ( Fig 2A and 2B and S1A Fig ) , confirming that the coiled-coil domain of BS69 contributes to its binding to EBNA2 . In addition , we showed that mutation of Y532 and R560 of BS69MYND each to alanine decreased the BS69MYND-EBNA2381–389 binding by about 3- and 10-fold , respectively ( Fig 2C and 2F and S1A Fig ) . Even more dramatically , mutation of Q546 and W550 of BS69MYND largely abolished the interaction between BS69MYND and EBNA2381–389 ( Fig 2D and 2E ) . Together , these data lend a strong support for our structural observation of the BS69CC-MYND-EBNA2381–389 complex . A previous study based on GST pull-down assay suggests that the BS69MYND domain may also interact with another PXLXP motif from region CR8 of EBNA2 [22] . To gain further insight , we characterized the interactions between BS69 and an EBNA2 peptide encompassing this PXLXP motif ( residues 435–445 , EBNA2435–445 ) . Our results showed that BS69CC-MYND and BS69MYND both bind to the EBNA2435–445 peptide , with a Kd of 34 . 9 μM and 93 . 4 μM , respectively ( Fig 2G and S1B Fig ) . To determine whether the two PXLXP motifs in EBNA2 cooperate in BS69 association , we also measured the binding affinity of BS69CC-MYND for an EBNA2 fragment ( residues 381–445 , EBNA2381–445 ) encompassing both PXLXP motifs . As shown in Fig 2H , BS69CC-MYND binds to EBNA2381–445 with a Kd of 0 . 24 μM and a monophasic binding curve , which is 30–150 fold stronger than the binding of BS69CC-MYND to either motif alone . Therefore , these data suggest that the two monomers of the BS69CC-MYND homodimer synergistically bind to the two sequential PXLXP motifs of EBNA2 , resulting in enhanced BS69-EBNA2 recognition ( S2 Fig ) . On the other hand , the Q546A mutation substantially decreases the respective binding affinities of the BS69MYND-EBNA2435–445 and BS69CC-MYND-EBNA2381–445 complexes ( S1C and S1D Fig ) , similar to what was observed for the BS69MYND-EBNA2381–389 interaction . Furthermore , a Q546A/W550A double mutation largely abolishes the binding between BS69CC-MYND and the EBNA2381–445 peptide ( S1E Fig ) . In addition to BS69 , the MYND domain exists in a variety of chromatin-related proteins [30] . Currently , structural information on the interactions between this class of domains and their binding partners has only been limited to leukaemogenic protein AML1-ETO [29] and transcriptional regulator DEAF-1 [28] . An NMR structural study of the AML1-ETO MYND domain ( ETOMYND ) revealed that it recognizes a PPPLI sequence motif , present in nuclear co-repressors SMRT and N-CoR , through antiparallel β-pairing interactions [29] . Using NMR titrations , a similar binding model was later revealed for the interactions of the DEAF-1 MYND domain with SMRT and N-CoR [28] . Our structure-based sequence alignment of BS69MYND with these reported MYND domains , as well as its closely related RACK7 MYND domain ( RACK7MYND ) , reveals that the overall sequence identity between these MYND domains is only around 30% , the majority of which are the cysteine and histidine residues that coordinate the zinc ions ( Fig 3A ) . Nevertheless , the protein interaction sites of BS69MYND , AML1-ETO MYND domain ( ETOMYND ) and DEAF-1 MYND domain ( DEAF-1MYND ) appear to be highly conserved , with each formed by the α1-helix and β1-strand of the respective proteins ( Fig 3B–3D ) . Of particular note , three strictly conserved , non-zinc binding residues ( Y540 , Q546 and W550 in BS69MYND ) are important for mediating the protein interactions of all three proteins [28 , 29] . For instance , the BS69MYND Q546 and W550 equivalent residues in ETOMYND ( Q688 and W692 , respectively ) interact with SMRT1101–1113 through hydrogen bond formation and packing against a proline residue from SMRT1101–1113 , respectively ( Fig 3C ) [29] , in a similar fashion to what is observed for BS69 Q546 and W550 in the BS69CC-MYND-EBNA2381–389 complex ( Fig 1D ) . The equivalent residues in DEAF-1MYND ( Q529 and W533 , respectively ) also underwent large chemical shift perturbations in DEAF-1MYND when peptides from nuclear co-repressor N-CoR or SMRT are present ( Fig 3D ) [28] . Together , these observations demonstrate that MYND domains , despite having large sequence variation , adopt evolutionarily conserved surface sites for protein interaction . On the other hand , we also observed a number of BS69MYND-unique protein interaction sites ( e . g . Y532 , Q547 , R560 ) ( Fig 3A and 3B ) , which help define the surface complementarity between BS69MYND and EBNA2381–389 , and provide the molecular basis for the binding specificity of the BS69MYND domain , as suggested by a previous molecular modeling analysis [28] . A previous study has indicated that EBNA2 is recruited to the BS69 promoter upon EBV infection of B cells [31] . Along this line , we ask whether EBNA2 influences the expression of BS69 in EBV-infected cells . For this , we performed quantitative real time PCR to monitor the changes of the endogenous BS69 mRNA levels in B cells with EBV infection from 0 to seven days of post-infection . Prominently , we observed that the relative mRNA level of BS69 over Glyceraldehyde 3-phosphate dehydrogenase ( GAPDH ) in primary B cells was significantly decreased following EBV infection , with a >20-fold reduction at day 5 post-infection ( Fig 4A ) . Among a number of EBV-infected cell lines , a large down-regulation of the BS69 mRNA level was also observed for three independent immortalized LCLs ( Fig 4A ) . On the other hand , the BS69 mRNA remained expressed abundantly in the EBV latency I AKATA cells [32] , regardless of the presence of EBV genome ( Fig 4A ) . Given the fact that EBNA2 is not expressed in EBV-infected AKATA cells [32] , we postulate that the presence of EBNA2 leads to down-regulation of the BS69 expression in the EBV-infected primary B cells and LCLs . We therefore analyzed the BS69 mRNA from EBV-negative B lymphoma cell line , BJAB , and two of BJAB derivative stable clones , BJAB-E1 and BJAB-E2 , with constitutive expression of Epstein-Barr virus Nuclear Antigen 1 ( EBNA1 ) and EBNA2 , respectively . As shown in Fig 4A , the BS69 mRNA is expressed in parental BJAB and BJAB-E1 , but downregulated in BJAB-E2 , indicating that its expression is abrogated by the presence of EBNA2 . Consistently , Western blotting analysis of the BS69 protein in EBV-infected B cells , B lymphoma or lymphoblastoid cells revealed that the protein level of BS69 became barely detectable in those B cells with EBV infection at 4 or 7 dpi and the BJAB-E2 or LCL cells , but persisted in the primary B cells , parental BJAB , BJAB-E1 and AKATA cells ( Fig 4B ) . Together , these data suggest that EBNA2 down-regulates the expression of BS69 in EBV-infected B cells . To address whether the BS69 C-terminal domain , if expressed , interacts with EBNA2 in vivo , we ectopically co-expressed flag-tagged wild-type , Q546A , or Q546A/W550A BS69CC-MYND with EBNA2 in BJAB cells , followed by immunoprecipitation using anti-flag M2-conjugated sepharose and Western blotting with antibodies against EBNA2 and M2 , respectively . As shown in Fig 4C , EBNA2 co-precipitates with wild-type flag-tagged BS69CC-MYND , indicating that BS69CC-MYND and EBNA2 indeed can form a complex in cells . By contrast , the Q546A and Q546A/W550A BS69CC-MYND mutants exhibits a ~50% and ~85% reduction of EBNA2 binding , confirming our structural and ITC analysis that BS69 Q546 and W550 are important for the BS69-EBNA2 interaction . Of importance , we showed that the colocalization of EBNA2 and BS69 appeared in EBV infected B cells at 1 and 3 dpi ( Fig 4D ) , implicating the interaction of two proteins could take place in the host cells . In addition , the immunofluorescence ( IF ) image pointed to a reduction of large amount of BS69 expression in EBV infected B cells at 5 dpi , confirming the barely low expression levels of BS69 shown in Fig 4B . Next , we ask whether the BS69 C-terminal domains , when expressed , influence the EBNA2-mediated transcriptional regulation . Toward this , we performed luciferase reporter assays on the EBNA2 target promoters , including LMP1 [33 , 34] and BamHI C promoter ( Cp ) [35 , 36] . In the absence of BS69 , the EBNA2 protein activated a co-transfected LMP1-luciferase ( LMP1-Luc ) or Cp-luciferase ( Cp-Luc ) reporter genes both by around 10-fold ( Fig 5A ) , confirming the strong transcriptional activation potential of EBNA2 [37] . The presence of wild-type BS69CC-MYND reduced the relative transcriptional activation to 2-fold or less ( Fig 5A ) , suggesting that BS69 down-regulates the EBNA2-mediated transcriptional activation . On the other hand , the presence of BS69 failed to suppress the EBNA1-mediated transcriptional activation of OriP ( origin of replication ) -Luc reporter , suggesting that BS69 specifically inhibits EBNA2 . The BS69CC-MYND Q546A mutation partially reduced its inhibition of the EBNA2-mediated transcriptional activation ( Fig 5A ) , presumably due to the residual binding affinity of this mutant toward EBNA2 . By contrast , the BS69CC-MYND Q546A/W550A double mutation lost the inhibitory effect on EBNA2-mediated transcriptional activation . To further delineate the functional consequence of the BS69 Q546A mutation , we next transfected various amounts of wild-type or Q546A BS69CC-MYND vector into HeLa cells and tested the effects on activation by a co-transfected C-terminal fragment of EBNA2 ( residues 375–465 , containing both CR7 and CR8 ) fused to the Gal4 DNA-binding domain ( Gal4-EBNA2 ) using the G5-TK-luciferase ( G5-TK-Luc ) as a reporter gene [38] . Our choice of the EBNA2 ( 375–465 ) construct was based on a previous study [18] showing that this fragment exhibits a stronger transcriptional activation potential than the full-length protein . Indeed , we observed that the GAL4-EBNA2 fusion protein activated the G5-TK-Luc reporter gene by over 100-fold in the absence of BS69 ( S3A and S3B Fig ) . However , in the presence of wild-type BS69CC-MYND the transcriptional activation was reduced to less than 4-fold ( S3B Fig ) , supporting the inhibitory role of BS69 in the EBNA2-mediated transcriptional activation . By contrast , a 60-fold activation by Gal4-EBNA2 was observed in the presence of the Q546A mutant ( S3B Fig ) , and this mutant remained less repressive than wild type even at 5-fold excess ( S3C Fig ) . To decipher the importance of the PXLXP motifs in BS69 inhibition of EBNA2 dependent transcription , we next introduced the EBNA2 PXLXP mutations , in which the proline residues within either of the two PXLXP motifs were replaced by alanine residues . Although mutations on either PXLXP motif did not affect the expression levels and transactivation function of EBNA2 ( S4A Fig ) , mutations on the second PXLXP motif relieved ~50% of the BS69 inhibitory effect , while mutations on the first PXLXP motif had no effect ( S3B–S3D Fig ) . These observations suggest that the BS69-EBNA2 interaction is required for BS69 inhibition of EBNA2-mediated transcriptional activation . To determine whether BS69CC-MYND is able to associate with EBNA2 target promoters through protein-protein interactions , we performed Chromatin Immunoprecipitation ( ChIP ) assays using BJAB cells , in which flag-tagged wild-type , Q546A , or Q546A/Q550A BS69CC-MYND was co-transfected with or without EBNA2 expression vector and a reporter plasmid containing the LMP1 promoter , LMP1-Luc . In the absence of EBNA2 , the amount of LMP1-DNA brought down by wild-type , Q546A or Q546A/Q550A BS69CC-MYND ChIP is similar to that of IgG negative control ( 7–8% over input DNA ) ( Fig 5B ) , indicating no appreciable BS69CC-MYND-LMP1 DNA binding . On the other hand , an EBNA2 ChIP brought down ~13% of LMP1-DNA , consistent with the fact that LMP1 is one of the target sites of EBNA2 [35 , 36] . Notably , the presence of EBNA2 also increases the amount of wild-type and Q546A BS69CC-MYND ChIPed LMP1-DNA to ~17% and ~12% , respectively ( Fig 5B ) , indicating that BS69CC-MYND binding to LMP1-DNA is EBNA2 dependent . By contrast , the BS69 Q546A/Q550A mutation reduces the LMP1-DNA brought down by BS69CC-MYND to a level similar to that of IgG negative control ( Fig 5B ) . These data suggest that EBNA2 can recruit BS69CC-MYND to its target sites through direct protein interactions , which therefore establishes BS69 C-terminal domains as a potential inhibitor of EBNA2 . It has been established that EBNA2 is essential for EBV-mediated B cell immortalization [39] . To determine whether introduction of the BS69-EBNA2 interaction could affect the proliferative activity of LCL , we generated an inducible retroviral vector harboring a destabilization domain ( DD ) N-terminally fused to wild type or Q546A flag-tagged BS69CC-MYND . The expression of the DD-BS69CC-MYND fusion protein , wild-type or Q536A , can therefore be regulated by the ligand Shield 1 , which binds to DD to keep the fusion protein from degradation [40] . Cell viability assays were performed on two independent LCLs transduced with the retrovirus vector encoding DD-BS69 , wild-type , Q546A or Q546A/W550A , DD-GFP fusion protein or GFP alone ( Fig 6A ) . We observed that the Shield 1-induction led to a complete loss in viability of both LCLs transduced with the DD-BS69CC-MYND wild-type vector . The Q546A single mutation partially impaired the cell viability . By contrast , no appreciable change in viability occurred for the LCLs transduced with the DD-Q546A/W550A , DD-GFP or GFP vectors . On the other hand , the viability of the EBV-negative BJAB cells transduced with any of these vectors was not affected by Shield 1 induction , suggesting that the BS69 inhibition of cell viability is EBNA2-dependent ( Fig 6B ) , either through direct inhibition of EBNA2 function or interference with its downstream signaling events ( e . g . LMP1 signaling ) . Together , these data suggest that introduction of the interaction between BS69CC-MYND and EBNA2 can lead to inhibition of the EBNA2-mediated cell proliferation .
In this study , we determined the crystal structure of the BS69CC-MYND-EBNA2381–389 complex , which provides important insights into how BS69 recognizes the common PXLXP motif , present in EBNA2 , E1A and a variety of cellular transcriptional regulators . Importantly , dimerization of the BS69 coiled-coil domain results in an enhanced and synergistic interaction between BS69 and the two sequentially proximate EBNA2 PXLXP motifs , located in CR7 and CR8 , respectively . Note that both CR7 and CR8 of EBNA2 play a role in its oncogenic function: CR7 is important for coactivation of EBNA2 by EBNALP protein [17 , 18] , while CR8 , the transcriptional activation domain of EBNA2 , mediates gene transcriptional activation through its recruitment of cellular basal transcription machinery and histone acetyltransferases [8–12] . Interactions of BS69 with CR8 may affect the binding of EBNA2 with basal transcriptional machineries or histone acetyltransferases , thereby inhibiting the transactivation potential of EBNA2 . This study reveals that , whereas endogenous BS69 interacts with EBNA2 in EBV-infected B cells , the expression of BS69 , at both mRNA and protein levels , is gradually down-regulated by EBNA2 , which may be one of the strategies used by EBV to evade host defense . Given such a situation , it is impractical to determine the functional consequence of the BS69-EBNA2 interaction in cells through knockdown of endogenous BS69 . Nevertheless , we show that ectopically expressed BS69CC-MYND binds to EBNA2 , leading to its enrichment at the EBNA2-targeted LMP1 promoter and inhibition of the EBNA2-mediated transcriptional activations . Through an inducible expression system , we further show that expression of BS69CC-MYND inhibits the EBNA2-mediated proliferation of LCL cells . Therefore , this study suggests that restoration of BS69 expression in EBV-infected B cells may provide a mechanism to inhibit the proliferation of EBV-infected cells , which may have important implications in development of novel therapeutic strategies against EBV infection . The coiled-coil domain of BS69 may have important biological functions other than EBNA2 binding . For instance , recent studies reveal that this region is responsible for mediating the interaction between BS69 and RNA splicing factor EFTUD2 [41] , and that aggregation of BS69 is required for LMP1-mediated JNK signaling [42] . In addition to BS69 , tandem arranged coiled-coil and MYND domains have been identified in AML1-ETO [43 , 44] and DEAF-1 [45] . In AML1-ETO , oligomerization of the coiled-coil ( also known as NHR2 ) domain is required for the interactions of AML1-ETO with ETO , MTGR1 , MTG16 and E protein in the transcription factor complex ( AETFC ) [43 , 44] , and essential for AML1-ETO’s ability in inducing haematopoietic stem/progenitor cell self-renewal and leukaemogenesis [44] . In DEAF-1 , the coiled-coil domain stabilizes the interaction between an unstructured region of DEAF1-1 and LMO4 protein [45] . In this context , this study adds a new example on how the coiled-coil domain of the MYND domain-containing proteins regulates their target recognition . Genetic mutations within the BS69MYND domain have been implicated in neuropsychiatric diseases [46 , 47] . Our structural and biochemical studies therefore shed new light onto the pathological roles of the BS69 mutations in these diseases . For instance , the syndromic intellectual disability-associated mutations , Q547Δ and R560W [46 , 47] , are both located in the protein interaction sites of BS69MYND ( Fig 1D ) . Therefore , these two mutations may affect neural development through impairing the interaction between BS69 and its cellular partner ( s ) . It would be interesting to identify the affected interacting partners in future studies .
The DNA sequence encoding residues 440–562 ( BS69CC-MYND ) of mouse BS69/ZMYND11 was inserted into a modified PRSF-duet vector ( Novagen ) , in which the BS69 fragments are separated from the preceding His6-SUMO tag by a ubiquitin-like protein ( ULP1 ) cleavage site; and the DNA sequence encoding BS69 516–562 ( BS69MYND ) was inserted into a pGEX-6P-1 vector ( GE Healthcare ) , preceded by a GST tag and a PreScission protease cleavage site . Note that the amino acid sequences of both fragments are identical to the corresponding regions of human BS69 . The BS69MYND mutants were constructed by site-directed mutagenesis and confirmed by DNA sequencing . The plasmid was then transformed into BL21 ( DE3 ) RIL cell strain for overexpression . The bacterial cells were grown at 37°C and induced by 0 . 4 mM isopropyl β-d-1-thiogalactopyranoside ( IPTG ) when the cells density reached an optical density ( OD600 ) of 0 . 6 . After induction , the cells continued to grow at 20°C for overnight . The cells were then harvested and lysed , and the His6-SUMO- or GST-tagged BS69 fusion proteins were purified through a Ni-NTA column or a Glutathione Sepharose fast flow column ( GE Healthcare ) , followed by removal of His6-SUMO tag and GST tag by ULP1 and PreScission protease cleavages , respectively . The tag-free BS69MYND or BS69CC-MYND was finally purified through size-exclusion ( Superdex 200 16/60 , GE Healthcare ) chromatography in a buffer containing 20 mM Tris-HCl ( pH 7 . 5 ) , 50 mM Arginine hydrochloride , 50 mM Sodium Glutamate , 200 mM NaCl and 5 mM DTT . The protein samples used for crystallization were concentrated to ~10 mg/ml and stored at -80°C , and the samples for ITC analysis were concentrated to 2–4 mg/ml . The EBNA2381–389 ( NH2-SMPSLEPVL-CONH2 ) and EBNA2435–445 ( NH2-EAPILFPDDWY-CONH2 ) peptides were synthesized from the proteomic facility of Tufts University . The EBNA2381–445 fragment was inserted into the PRSF-duet vector for expression , and sequentially purified through a Ni-NTA column , ULP1 proteolytic cleavage , ion exchange chromatography ( HiTrap Q XL column , GE Healthcare ) and size exclusion chromatography . The protein sample was concentrated to ~10 mg/ml in a buffer containing 20 mM Tris-HCl , 200 mM NaCl and 5 mM DTT for storage . The numbering systems used for EBNA2381–389 , EBNA2435–445 and EBNA2381–445 peptides are based on full-length EBNA2 from EBV strain B95-8 [48] . For in vivo transcription assay , full-length or partial fragment of EBNA2 from EBV W91 strain [49] were used . The BS69CC-MYND-EBNA2381–389 complex was prepared by direct mixing BS69CC-MYND with the EBNA2381–389 peptide in a 1:2 molar ratio . Initial crystallization condition was identified using sparse-matrix screens ( Hampton Research inc ) . The crystals were subsequently generated by hanging-drop vapor-diffusion method at 20°C , with the drops mixed from 0 . 5 μl of BS69CC-MYND-EBNA2381–389 solution and 0 . 5 μl of precipitant solution ( 19–23% PEGMME 350 , 6% methanol , 0 . 1 M Tris-HCl , pH 8 . 0 ) . The crystals were further improved by the microseeding method , and flash frozen with the cryoprotectant ( 17% PEGMME 350 and 20% glycerol ) in liquid nitrogen before X-ray data collection . Anomalous and native diffraction data sets for the BS69CC-MYND-EBNA2381–389 complex were collected on the BL 8 . 2 . 2 and 5 . 0 . 1 beamlines , respectively , at the Advanced Light Source ( ALS ) , Lawrence Berkeley National Laboratory . The diffraction data were indexed , integrated and scaled using the HKL 2000 program . The structure of the BS69CC-MYND-EBNA2381–389 complex was solved using the AutoSol module embedded in the PHENIX software package [50] . The structural model was manually built using the program COOT [51] and was improved with iterations of manual model building and refinement using PHENIX . The final model was refined to 2 . 4 Å resolution using a native data set . The B-factors were refined with individual B values . The statistics for data collection and structural refinement for the BS69CC-MYND-EBNA2381–389 complex is summarized in S1 Table . The EBNA2381–389 peptide with an additional Gly-Tyr dipeptide at the C-terminus ( NH2-SMPSLEPVLGY-CONH2 ) , the EBNA2435–445 peptide , and the EBNA2381–445 peptide were used for ITC assays . The BS69MYND or BS69CC-MYND domain ( ~0 . 1 mM each ) and the peptides ( ~1 mM ) were dialyzed against a buffer containing 20 mM Tris-HCl pH 7 . 5 , 100 mM NaCl , 2 mM DTT . The peptide was titrated into the BS69MYND or BS69CC-MYND sample at 5°C using the microCal ITC200 instrument ( GE healthcare ) . The ITC data was analyzed with the Origin 7 . 0 software using a one-site fitting model . 5x104/per 100 μl primary B cells were cultured in RPMI 1640 supplemented with 15% fetal calf serum ( FCS ) , 2 mM L-glutamine , and penicillin/streptomycin , and aliquoted into a 96-well plate . 100 μL previously purified EBV [52] or PBS ( mock infection ) was utilized as the inoculum . Cells were collected at 0 , 1 , 3 , 5 , and 7 days of post-infection ( dpi ) and BS69 mRNA expression in virus or mock -infected B cells were determined as the relative expression levels versus GAPDH by quantitative reverse transcription real time PCR ( qRT-PCR ) . For Western blot analysis , a total of 5x105 B cells with EBV infection were collected at 0 , 1 , 4 , and 7 dpi , respectively . The primers for BS69 are GTCCACGGTATGCACCCTAAAGAG ( F ) and AACACCTCTCCAGGCAAATGG ( R ) , whereas the primers for GAPDH internal control are AAGGTGAAGGTCGGAGTCAA ( F ) and AATGAAGGGGTCATTGATGG ( R ) . The primers for chromatin immunoprecipitati on assays of the LMP1 promoter and GAPDH promoter control have been described previously [52] . The amount of ChIPed DNA was quantified by real time PCR and represented as % of input DNA . For Immunofluorescence analysis ( IFA ) , cells were fixed in 2% paraformaldehyde ( Sigma ) and subjected to an immunostaining protocol using antibodies for BS69 ( Santa Cruz ) , and EBNA2 ( Santa Cruz ) . In co-immunostainings , Rhodamine-conjugated goat anti-mouse ( for BS69 ) and FITC-conjugated donkey anti-goat ( for EBNA2 ) ( Kirkegaard & Perry Laboratories , Inc . ) were used as fluorochromes , and DNA was counterstained with DAPI ( Sigma ) . The transfection-mediated co-immunoprecipitation ( Co-IP ) assay was employed to identify the physical interaction of EBNA2 with BS69CC-MYND using EBV-negative B lymphoma cells ( BJAB ) . Cells were co-transfected with the expression plasmids of EBNA2 ( E2 ) and flag- BS69CC-MYND wild type , Q546A mutant ( Q546A ) , or Q546A/W550A mutant ( Q546A/W550A ) and subjected to IP analysis using M2-conjugated sepharose ( Sigma ) . The immunoprecipitated samples were subjected to SDS-PAGE , followed by Western blotting with antibodies for EBNA2 ( Millipore ) and M2 ( Sigma ) , respectively . The antibody for actin internal control was purchased from Santa Cruz . The proteins were detected and visualized using an ECL detection kit ( Millipore ) . ChIP assays were performed using BJAB cells that have been transfected with LMP1-promoter reporter plasmid ( LMP1-Luc ) and flag-BS69CC-MYND wild type or Q546A , with or without E2 using M2-conjugated sepharose and IgG negative control ( Millipore ) following the previously described protocol [53 ) . The protein levels of endogenous BS69 in the obtained cell lines were determined by Western blot using BS69 antibody ( Santa Cruz biotechnology ) . The antibodies for immune blots are 6F9/60 ( Novas Biologicals ) for EBNA1 , MABE8 ( Millipore ) for EBNA2 , and C4 ( Santa cruz ) for actin internal control . The proteins were detected and visualized using an ECL detection kit ( Millipore ) . BJAB is an EBV negative B lymphoma cell line [26] . BJAB-E1 is an EBNA1 stably expressed BJAB cell line and BJAB-E2 is an EBNA2 stably expressed cell line . LCL#1–3 are three independent lymphoblastoid cell lines . EBV latently infected type I AKATA , AKATA ( EBV+ ) , and EBV-negative AKATA ( EBV- ) are two Burkitt’s Lymphoma ( BL ) cell lines [26 , 32] . All the above cell lines were cultured in RPMI-1640 ( Life Technology ) supplemented with 10% fetal calf serum ( FCS ) ( Life Technology ) . HeLa cells were maintained in Dulbecco's Modified Eagle Medium supplemented with 10% fetal bovine serum at 37°C under 5%CO2 . The BS69CC-MYND dual domain , wild-type or the Q546A mutant , were inserted into a pCBS/FLAG mammalian expression vector [54] , in which the BS69 fragments are preceded by a FLAG-tag . In the case of the Q546A/W550A BS69CC-MYND expression plasmid , the flanking DNA fragment was subcloned into the pSG5-Flag vector [55] . The EBNA2 expression vector harboring PXLXP1 or PXLXP2 mutations was generated by carrying out the PCR-based mutagenesis protocol provided by the manufactory ( Stratagene ) . In each case , the codons of two prolines were replaced with the codons of alanine simultaneously . For EBNA2-dependent transcription reporter assays , 10 μg of the expression vector of EBNA2 ( E2 ) , 5 μg flag-BS69CC- MYND wild type , or Q546A plasmid , and 5 μg LMP1-Luc or Cp-Luc reporter plasmid , and 1 μg CMV-βGal control plasmid were co-transfected into BJAB cells following the protocol as described previously [52] . For the parallel control , 10 μg EBNA1 expression plasmid and 5 μg OriP-Luc reporter plasmid were used instead [53] . Luciferase and β-Gal activities were assayed by Orion L ( Berthold ) . A DNA fragment encoding residues 375–465 of EBNA2 from EBV W91 strain [49] containing CR7 and CR8 of EBNA2 were inserted into pCD-Gal4 ( 1–147 ) vector to generate pCD-Gal4-EBNA2 ( 375–465 ) , which encodes a GAL4-EBNA2 fusion protein . Hela cells in 6-well plates were transfected using Lipofectamine 2000 ( Life Technologies ) with 500 ng pG5-TK-Luc ( a gift from Dr . Yang Shi , Harvard Medical School ) , 200 ng of pCMV-β-Galactosidase , 500 ng pCD-Gal4-EBNA2 ( 375–465 ) , and the indicated amounts of pCbS/Flag-BS69CC-MYND wild-type or Q546A mutant . In control transfection assays 500ng of the pCDNA 3 . 1 ( + ) empty vector , or the derivatives pCD-Gal4 ( 1–147 ) vector expressing the Gal4 DNA-binding domain ( DBD ) , or the pCD-Gal4-VP16 vector expressing the Gal4-VP16 fusion activator , were used instead of pCD-Gal4-EBNA2 , as indicated . Cell extracts were prepared after 48h and luciferase , beta-galactosidase assays , and Western blotting were performed essentially as previously described [54] . Luciferase activities were normalized to beta-galactosidase activities and results are the means ± S . D . of a minimum of 3 independent experiments each in triplicates . The flanking fragments of flag-BS69CC-MYND wild type , Q546A , and Q546A/W550A were subcloned inframe into the downstream of the destabilizing domain ( DD ) in the context of the modified pQCXIP expression vector ( Clontech ) , respectively . Retrovirus vectors harboring DD-flag BS69CC-MYND wild type , Q546A , or Q546A/W550A , DD-GFP , or GFP alone were produced and harvested by performing the protocol provided by the manufacture . 1 mL of virus suspension was used to transduce 1 mL LCL ( 106 per mL ) or control BJAB ( 106 per mL ) cells following a cell culture procedure with selection of 5 ng/mL puromycin . In addition , the culture medium was supplemented with 8 μg/mL polybrene ( Sigma ) in order to enhance virus transduction efficiency . For the cell viability assays , 105 LCL or BJAB cells per mL were aliquoted into 6-well plate . The DD-fusion was induced by treatment of 0 . 1 mM Shield 1 and viable cells were counted by cellometer K2 ( Nexcelom ) using the trypan blue exclusion method every 24 hours ( hrs ) for five consecutive days . | Since the discovery of Epstein-Barr virus ( EBV ) 50 years ago , the etiologic links between EBV and a variety of human cancers have gained wide recognition . It is estimated that >90% of the worldwide population carry this virus , which causes over 200 , 000 cancers across the world every year . One of the key proteins in driving immortalization of EBV-infected B cells is Epstein-Barr virus nuclear antigen 2 ( EBNA2 ) , which regulates the expression of many cellular and viral genes . However , the molecular mechanism underlying the interactions between EBNA2 and cellular transcriptional regulators remains enigmatic . Here , we determined the crystal structure of the coiled-coil and MYND tandem domains of BS69/ZMYND11 , a candidate tumor suppressor , in complex with an EBNA2 peptide containing a PXLXP motif . We found that the coiled-coil and MYND domains of BS69 cooperate in binding to EBNA2 . We also showed that EBNA2 interacts with BS69 and down-regulates its expression at both mRNA and protein levels in EBV-associated B cells . Ectopic BS69 coiled-coil-MYND dual domain is recruited to viral target promoters through interaction with EBNA2 , inhibits EBNA2-mediated transcription activation , and impairs proliferation of lymphoblastoid cell lines ( LCLs ) . Together , this study identifies the BS69 C-terminal domains as an inhibitor of EBNA2 . | [
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"vi... | 2016 | BS69/ZMYND11 C-Terminal Domains Bind and Inhibit EBNA2 |
NOD-like receptor ( NLR ) proteins ( Nlrps ) are cytosolic sensors responsible for detection of pathogen and danger-associated molecular patterns through unknown mechanisms . Their activation in response to a wide range of intracellular danger signals leads to formation of the inflammasome , caspase-1 activation , rapid programmed cell death ( pyroptosis ) and maturation of IL-1β and IL-18 . Anthrax lethal toxin ( LT ) induces the caspase-1-dependent pyroptosis of mouse and rat macrophages isolated from certain inbred rodent strains through activation of the NOD-like receptor ( NLR ) Nlrp1 inflammasome . Here we show that LT cleaves rat Nlrp1 and this cleavage is required for toxin-induced inflammasome activation , IL-1 β release , and macrophage pyroptosis . These results identify both a previously unrecognized mechanism of activation of an NLR and a new , physiologically relevant protein substrate of LT .
Anthrax lethal toxin ( LT ) is a key virulence determinant of Bacillus anthracis . This bipartite toxin consists of the receptor-binding protein protective antigen ( PA ) and the metalloprotease lethal factor ( LF ) [1] . LT injection into experimental animals induces a vascular collapse similar to that occurring during anthrax infections . LT also induces rapid lysis of macrophages from certain inbred rodent strains , but macrophage lysis in vivo is not essential for toxin-induced death of mice . LF's only known substrates are the mitogen-activated protein kinase kinases ( MEKs 1 , 2 , 3 , 4 , 6 and 7 ) . To date , no link between their cleavage and macrophage lysis or animal death has been found [1] . LT induces death in certain inbred and outbred rat strains in as little as 37 minutes [2] , [3] . We recently used recombinant inbred rats to map the gene controlling LT sensitivity of both the rats and their macrophages to a region of rat chromosome 10 encoding the NOD-like receptor ( NLR ) Nlrp1 [3] . The Nlrp proteins are pattern recognition receptors ( PRRs ) that act as cytoplasmic sensors of danger signals ranging from dividing bacteria to crystalline materials [4] . The activated sensors oligomerize and recruit caspase-1 to a multiprotein complex ( the inflammasome ) . Inflammasome-mediated activation of caspase-1 allows it to cleave the precursors of the inflammatory cytokines IL-1β and IL-18 , as well as currently unidentified substrates that cause the rapid lysis of macrophages and dendritic cells ( pyroptosis ) . Unlike other Nlrp sensors that have been shown to be activated by a diverse set of stimuli [4] , Nlrp1's only identified activator is LF . The mechanism for activation has not yet been identified for any inflammasome sensor . In this study we report that LF cleaves Nlrp1 and that susceptibility of Nlrp1 to this cleavage dictates sensitivity of macrophages to the pyroptosis induced by this toxin .
Our earlier studies found that polymorphisms within the N-terminus of the rat Nlrp1 ( rNlrp1 ) protein correlate perfectly with LT sensitivity in twelve inbred rat strains [3] . The top two sequences in Fig . 1 are rNlrp1 sequences which represent the previously identified alleles associated with resistance or sensitivity . The CDF Fischer rat ( CDF ) sequence represents Nlrp1 from LT-sensitive rats ( Nlrp1S , abbreviated as S in Fig . 1 ) . The Lewis rat ( LEW ) sequence represents Nlrp1 from LT-resistant rats ( Nlrp1R , abbreviated as R in Fig . 1 ) . Residues 41–48 of the Nlrp1 from LT-sensitive rats include 3 Arg and 2 Pro residues and are entirely different from the largely hydrophobic residues in the corresponding positions of the Nlrp1 of LT-resistant rats . The other 8 aa differences between the resistant and sensitive Nlrp1 proteins from 12 inbred rat strains are distributed throughout the N-terminal 100 aa [3] . Notably , residues 41–48 of the Nlrp1 from LT-sensitive rats fit consensus cleavage motifs of its MEK substrates [5]–[7] ( Fig . 1 , top row shows one previously identified consensus motif where the locations of basic ( positively-charged ) residues ( B ) and hydrophobic residues ( h ) relative to the cleavage site are shown ) . We hypothesized that LF cleaves Nlrp1 proteins at this site . Nlrp1 proteins are expressed at low levels and are difficult to detect . Therefore we expressed full-length ( 1218 aa ) N-terminally hemagglutinin ( HA ) epitope-tagged rNlrp1S ( CDF ) , rNlrp1R ( LEW ) , as well as chimeric protein constructs consisting of aa 1–53 of rNlrp1S and aa 54–1218 of rNlrp1R ( CDF53-LEW ) and the reciprocal construct ( LEW53-CDF ) ( Fig . 1 , lines 3 , 4 ) in HT1080 human fibroblasts . The proteins were expressed both transiently and in stable lines selected for maximal expression . Western blots and immunoprecipitation ( IP ) with anti-HA antibodies showed expression of a full length HA-tagged rNlrp1 ( 140 kDa ) in addition to a shorter protein of about 110 kDa ( Fig . 2a , 2b , 2c ) . This smaller protein , which is often present in equal ( data not shown ) or larger quantities ( Fig . 2a , 2b , 2c ) than the full-length rNlrp1 , probably results from autoproteolysis within the ZU5-UPA/FIND domain that lies upstream of the CARD ( caspase-1 recruitment ) domain [8] . LT treatment of fibroblasts expressing the HA-tagged rNlrp1 proteins showed a small quantity of a 6-kDa HA-antibody reactive fragment present in LT-treated CDF ( Fig . 2a ) and CDF-LEW53 cells ( data not shown ) , but not in LEW lines ( Fig . 2a ) , suggesting a possible cleavage event in proteins with rNlrp1S sequences at their N-termini . HT1080 cells expressing any of the three Nlrp1 variants were all equally viable after the LF treatment ( data not shown ) . Cleavage of MEK3 confirmed that LF was active within the cells under the conditions used ( Fig . 2a ) . Direct LF treatment of sucrose lysates of these cells showed loss of the HA-tag from the full length and 110-kDa CDF and CDF53-LEW proteins , but not from the LEW and LEW53-CDF proteins having N-terminal rNlrp1R sequences ( Fig . 2b ) . Loss of HA western blot reactivity from the 110-kDa proteins was consistent with cleavage occurring within aa 1–53 , and pointed to the 8-aa polymorphic sequence as being the cleavage site . Anti-HA IP of the toxin-treated lysates showed a single HA-tagged 6-kDa fragment appearing coincidentally with loss of HA-epitope reactivity from both the 140-kDa and 110-kDa rNlrp1 proteins , further supporting the 8-aa sequence as the site of LF action ( Fig . 2c ) . HA-tagged rNlrp1R ( LEW ) was not cleaved in response to LT ( Fig . 2c ) . An inactive LF variant ( LF E687C ) was unable to cleave any of the proteins ( data not shown ) . To precisely identify the cleavage site , we expressed and purified aa 3–100 of the rNlrp1S ( designated CDF100 ) as a fusion containing an N-terminal 6His-GST tag ( Fig . 1 , line 7 ) . This CDF100 protein was cleaved efficiently even when using an enzyme ( LF ) : substrate ( Nlrp1 ) molar ratio of 1∶1000 ( Fig . 3a ) . Mass spectrometry analyses of the intact protein and the two cleavage fragments yielded protein sizes of 39720 , 33260 , and 6478 Da ( Fig . S1 ) , showing that LF cleaved the Pro44-Leu45 bond in the sequence RPRP∧LPRV of rNlrp1S . Variants of the tagged CDF100 protein in which the 2 or 4 aa immediately preceding the LF cleavage site were changed to the corresponding rNlrp1R sequence ( CDF100 ( EQ ) and CDF100 ( QVEQ ) ) ( Fig . 1 , lines 8 , 9 ) were also tested as substrates for LF or the enzymatically inactive LF variant ( LF E687C ) . LF E687C did not cleave any proteins ( Fig . 3b ) . CDF100 ( EQ ) was cleaved by LF at a lower efficiency than was CDF100 and CDF100 ( QVEQ ) was not cleaved ( Fig . 3b ) . Thus , LF cleaves rNlrp1S at a single site and the specificity of LF for rNlrp1 depends at least in part on the aa sequence immediately preceding the bond cleaved . We next asked whether rNlrp1S cleavage is required for LT-mediated macrophage death . Functional inflammasomes containing various NLR proteins have previously been reconstituted in non-macrophage cell lines such as HEK293 cells by expressing the NLR proteins in conjunction with caspase-1 and using IL-1β as a reporter for activation in response to stimuli . Expression of the mouse paralog Nlrp1b and caspase-1 in HT1080 fibroblasts was also shown to be sufficient for induction of approximately 30% cell death following LT treatment [9] . However , we found that robust transient or stable expression of rNlrp1S in conjunction with rat pro-caspase-1 in HT1080 ( or CHO WTP4 ) cells did not sensitize these cells to LT-mediated cell death ( data not shown ) . Because macrophages may express additional components needed for induction of Nlrp1 inflammasome-mediated cell death , we retrovirally expressed various rNlrp1 proteins in the LT-resistant BMAJ mouse macrophage cell line . This cell line expresses the mouse Nlrp1bR protein . Expression of CDF53-LEW in the BMAJ macrophages sensitized them to LT-induced cell death ( Fig . 4a ) . Expression of the CDF53-LEW protein containing the 2 aa substitutions preceding the cleavage site ( CDF53 ( EQ ) -LEW ) resulted in partial sensitization , while substitution of 4 aa ( CDF53 ( QVEQ ) -LEW abrogated sensitization ( Fig . 4a ) . These data strongly implicate the proteolytic cleavage of rNlrp1 by LF as a key step in inflammasome activation and induction of pyroptosis in macrophages . Coincident with cell death , there were equally robust releases of IL-1β from the cells expressing either CDF53-LEW or CDF53 ( EQ ) -LEW ( Fig . 4b ) , in spite of the weaker sensitization of the latter to LT-induced cell death ( Fig . 4a ) . This indicates that there may be different efficiency and timing requirements for the LT-induced caspase-1 activation events that lead to cell death and to IL-1β processing . Supporting this hypothesis , a recent report showed that a single stimulus can be sensed by a single NLR , but activate distinctly different inflammasomes , which activate caspase-1 through different mechanisms [10] . Both mouse and rat Nlrp1 inflammasome activation and the ensuing macrophage death in response to LT can be prevented by a variety of treatments , including thermal shock [11] , or inhibition of caspase-1 [12]–[14] , cathepsin B [14] , [15] , and proteasome activity [12] , [14] , [16] . These treatments all act downstream of LF endocytosis and delivery to the cytosol , as evidenced by their failure to prevent cleavage of the MEK substrates . We tested a panel of these inhibitors for their abilities to protect BMAJ cells expressing CDF53-LEW against LT-induced cell death and to prevent IL-1β release . Protection was provided by nearly all the inhibitors ( Fig . 4c , d ) , the exceptions being the proteasome inhibitors , which are usually highly protective against LT toxicity toward mouse macrophage cell lines [12] , [14] , [16] . This poor protection may be linked to the longer times ( 9–10 h ) required to achieve complete death of CDF53-LEW-expressing BMAJ cells compared to CDF rat bone marrow-derived macrophages ( which succumb in 3 h ) . The presence of endogenous mouse Nlrp1bR in BMAJ cells likely contributes to the slower death of the transformed cells .
We present here data indicating that cleavage of rNlrp1 by anthrax LT , in an N-terminal domain having no known function , leads to inflammasome activation and macrophage death . This represents the first report on biochemical modification of an inflammasome sensor by a bacterial protein as a mechanism of inflammasome activation . Recent reports demonstrate inflammasome activation mediated by binding of flagellin to NAIP sensor proteins as a simple receptor-ligand activation mechanism that allows for interaction of NAIP proteins with Nlrc4 and caspase-1 in a large oligomer [17] , [18] . It remains to be determined how cleavage activates the Nlrp1 inflammasome . Cleavage may induce conformational changes , further proteolytic/autoproteolytic events , release from inhibitory proteins , or alteration in cytosolic localization . Furthermore , it remains to be seen whether cleavage of Nlrp1 directly activates the Nlrp1 inflammasome , or simply allows for subsequent biochemical or cellular events that allow for this sensor or another NLR to recruit and activate caspase-1 . Rodent Nlrp1 proteins differ from most NLRs in that they do not contain a pyrin domain , a domain that is found at the N-terminus of human Nlrp1 . This pyrin domain is required for association of NLR proteins with the scaffold protein , ASC , which is required for caspase-1 autoproteolysis and IL-1β processing , but not for caspase-1 mediated cell death [10] . The data presented here raises the question whether mouse or human Nlrp1 , or other NLRs , can also be activated by LF through similar cleavage-based mechanisms . Although mouse and human Nlrp1 proteins do not contain the same sequence found in Nlrp1 of sensitive rats , it is possible that LF can also cleave these proteins at a different site in the N-terminal domain or elsewhere . Furthermore , one can speculate that these and other NLR may be activated through cleavage-based mechanisms by other cellular proteases responding to the stimuli associated with inflammasome activation , or even by a conformational change-mediated autoproteolytic event , in a manner similar to caspase-1 . An autoproteolytic capability of Nlrp1 was recently reported [8] , although a link between this cleavage and inflammasome activation was not demonstrated . It would not be surprising , however , if cleavage of Nlrp1 allowing release of the CARD domain leads to constitutive activation of caspase-1 . Expression of the CARD domain of murine Nlrp1 has been shown to result in constitutive caspase-1 activation [9] , and deletion of the leucine-rich repeat of Nlrc4 also leads to constitutive caspase-1 activation [17] . This report also identifies a new LF substrate , distinct from the only previously-identified physiologically relevant LF substrates , the MEKs . Although the identification of a substrate with a role in a cell death ( i . e . , pyroptotic ) pathway now explains how the toxin induces rapid macrophage lysis , a role for this pyroptosis in the rapid rat death induced by LT remains to be elucidated . An increasing number of bacterial virulence factors have been identified which act catalytically to perturb normal cellular processes . In some cases , the catalytic activity mimics activities already present in the cell , as in the case of anthrax edema factor adenylate cyclase , which produces an endogenous signaling molecule , cAMP [19] . In other cases , the bacterial enzyme catalyzes a reaction that inactivates a host process . In the case of the LF-induced cleavage of NLR protein demonstrated here , it is not yet clear whether this cleavage mimics a natural process of inflammasome activation or whether it is an event with no parallel in a normal process . Resolution of this question will come only after there is a better understanding of the mechanisms of inflammasome activation . As has occurred through the study of other bacterial toxins , the further analysis of LF's effects may aid in deciphering the biochemical basis of a key cellular process - in this case , inflammasome activation .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . All bone marrow harvests were from euthanized rats and were performed in accordance to protocols approved by the NIAID Animal Care and Use Committee ( approved protocols OSD1 , 3 ) . PA , LF , and LF E687C were purified from B . anthracis as described previously [20] . Concentrations of LT correspond to the concentration of each toxin component ( i . e . , 1 µg/ml LT has 1 µg/ml PA and 1 µg/ml LF ) . 6His-GST-fused rNlrp1 proteins were expressed in plasmid pEX-N-His-GST ( cat# PS100028 , Origene , Rockville , MD ) and purified on glutathione-Sepharose columns by standard methods . High affinity anti-HA ( cat# 11867423001 , Roche Diagnostics , Indianapolis , IN ) , anti-Mek3 ( cat# sc-959 , Santa Cruz BT , Santa Cruz , CA ) , anti-IL-1β ( cat# AF-401-NA , R&D Systems , Minneapolis , MN ) and various IR-dye conjugated secondary antibodies ( Licor Biosciences , Lincoln , NE and Rockland Immunochemicals , Gilbertsville , PA ) were purchased . BMAJ mouse macrophages ( gift of D . Radzioch ) , Phoenix 293T ( gift from Iain Fraser , NIAID , NIH ) , HT1080 , and L929 cells were cultured in Dulbecco's Modified Eagle Medium ( DMEM ) with 10% fetal bovine serum , 10 mM HEPES and 100 µg/ml gentamicin ( all obtained from Invitrogen , Carlsbad , CA ) at 37°C in 5% CO2 . CHO WTP4 cells were grown in AMEM with the same supplements . For certain experiments , cells were heat shocked ( 42°C ) for 1 h prior to toxin treatments . For all transfections , endotoxin-free plasmid DNA was prepared ( Endo-Free Maxiprep , Qiagen , Valencia , CA ) and cells were transfected with TurboFect reagent ( Fermentas , Glen Burnie , MD ) according to the manufacturer's instructions . For transient transfections , cells were treated with LT 24 h after transfection [3] . Transfected cells were also lysed at the same time point in RIPA buffer and subjected to Western blot ( WB ) analyses to verify expression of rNlrp1 , caspase-1 or IL-1β proteins , as previously described [12] . For stable transfections , HA-rNlrp1 overexpressing cell lines were derived by selection with hygromycin B ( 500 µg/ml; Invitrogen ) for 12 days . High expressing clones were identified by Western blot with anti-HA antibody . For retroviral transduction of macrophages , Phoenix 293T packaging cells were transfected first , as above and at 24 h , culture media were replaced with DMEM . At 48 h , virus-containing media was removed , filtered ( 0 . 45 µm membrane , Millipore , Billerica , MA ) and supplemented with polybrene at 10 µg/ml ( Millipore ) prior to incubation with BMAJ macrophages . Virus-containing media were removed 24 h later and replaced with DMEM . Stably-transfected cells were selected over 2–3 weeks in the continuous presence of G418 sulfate ( 500 µg/ml , Invitrogen ) . Bone marrow-derived macrophages ( BMDMs ) used as a source of cDNA were obtained from rats purchased from Charles River Laboratories ( Wilmington , MA ) and cultured as previously described [12] . cDNA was obtained from different rat strains by isolating RNA from BMDMs [11] , [12] , [14] . To construct N-terminally hemagglutinin ( HA ) tagged rNlrp1 , an N-terminal HA tag was incorporated into forward primers along with two restriction sites ( 5′ NheI and 3′ EcoRV- for primer table , see Table S1 ) . For rNlrp1 from LT-sensitive Fischer ( CDF ) and LT-resistant Lewis ( LEW ) rats , full length rNlrp1 was amplified from CDF cDNA with Ex Taq polymerase ( TAKARA Bio Inc . , Otsu , Japan ) and TOPO TA cloned into the pCR2 . 1-TOPO vector ( Invitrogen ) . Full length rNlrp1 was amplified from LEW cDNA with Phusion polymerase ( Finnzymes , Woburn , MA ) and blunt-end TOPO cloned into the pCR-Blunt II-TOPO vector ( Invitrogen ) . NheI/EcoRV digested fragments were ligated into the pIREShyg3 mammalian expression vector ( Clontech , Mountain View , CA ) . CDF53-LEW- and LEW53-CDF chimeric constructs were made by digestion of CDF-pIREShygV3 and LEW-pIREShygV3 with XcmI/XbaI ( New England Biolabs , Ipswich , MA ) , gel purification of fragments , followed by ligation to yield the appropriate fused constructs . For retroviral expression , 5′ BamHI and 3′ NotI sites were introduced into full length CDF53-LEW and LEW rNlrp1 amplified from CDF53-LEW-pIREShygV3 and LEW-pIREShygV3 using Phusion polymerase ( New England Biolabs ) . PCR products were cloned into the CloneJET vector ( Fermentas ) prior to BamHI/NotI digestion and ligation into the pFB-NEO retroviral vector ( Agilent Technologies , Santa Clara , CA ) . QuikChange ( Agilent Technologies ) was used to introduce 4 and 7 nucleotide changes into CDF53-LEW-pFB-NEO to create CDF53 ( EQ ) -LEW-pFB-NEO and CDF53 ( QVEQ ) -LEW-pFB-NEO . CDF100 was synthesized by Blue Heron Biotechnologies ( Bothell , WA ) and cloned into the pEX-N-6HIS-GST vector using AsiSI and AscI sites . The sequence of this construct differs from the originally reported CDF rNlrp1sequence at one residue , K61N , resulting in the rNlrp1S allele 1 sequence [3] . The QuikChange system ( Agilent Technologies ) was used to introduce 4 and 8 nucleotide changes . All clones were identified by restriction digests and verified by sequencing ( Macrogen , Rockville , MD ) . WB were performed using either anti-HA ( 1∶1000 ) , anti-MEK3NT ( 1∶500 ) or anti-IL-1β ( 1∶2 , 500 ) and proteins were detected using the Odyssey Infrared Imaging System ( Licor Biosciences ) [11] , [12] , [14] . For immunoprecipitation ( IP ) experiments , anti-HA antibody ( Roche Diagnostics ) was added to each lysate sample ( 5–15 µg/ml ) and samples were continuously mixed by rotation at 4°C for 1 h . Protein A/G agarose ( Santa Cruz Biotechnology ) was added , and samples were incubated at 4°C overnight with rotation . Beads were centrifuged at 4 , 000 rpm for 2 min and washed with 10 mM HEPES three times prior to elution of proteins using SDS loading buffer ( 10% SDS , 0 . 6 M DTT , 30% glycerol , 0 . 012% bromophenol blue , at 90°C , 5 min ) . To assess Nlrp1 cleavage in cell lysates , cells were grown to confluence in 10-cm tissue culture dishes . For canonical cleavage , cells in two confluent plates were treated with LT , detached by trypsinization , washed 2 times ( PBS , 1000 rpm for 5 min ) and lysed in 130 µl of sucrose buffer ( 250 mM sucrose , 10 mM HEPES , 0 . 05 M EDTA , 0 . 2% Nonidet-P40 ) containing 5 ng/ml LF inhibitor PT-168541-1 ( gift of Alan Johnson , Panthera Biopharma ) followed by addition of 60 µl SDS loading buffer ( 90°C , 5 min ) . For cleavage by LF treatment of pre-lysed cells , 5 confluent dishes were detached by trypsinization and washed 3 times ( PBS , 1000 rpm for 5 min ) and resuspended in 2 . 5 ml of sucrose buffer ( 250 mM sucrose , 10 mM HEPES ) . Cells were centrifuged ( 2500 rpm , 10 min ) , resuspended in 350 µl sucrose buffer containing 0 . 2% Nonidet-P40 and incubated on ice 30 min . All samples were extensively syringed by passage through a 29 gauge hub-less syringe ( Terumo , Somerset , NJ ) until >99% of all cells were lysed . ZnCl2 and NaCl were added to final concentrations of 1 µM and 5 mM , respectively . Lysates were treated with LF for 3 . 5 h at 37°C . Cleavage was analyzed by WB or IP/WB . For in vitro cleavage assays with purified proteins , CDF100 , CDF100 ( EQ ) and CDF100 ( QVEQ ) ( at final concentrations of 1 mg/ml were incubated in cleavage buffer ( 1 µM ZnCl2 , 5 mM NaCl , 10 mM HEPES for 3 h at 37°C ) with purified LF at varying concentrations . Samples were separated on an 8–25% SDS-PAGE gel using the PhastSystem ( GE Life Sciences , Piscataway , NJ ) and stained with Coomassie blue . All toxicity and protection assays were modified from methods previously described [11] , [12] , [14] with minor modifications . Drugs were tested over concentrations ranging from 0 . 1–100 µM and were added 1 h prior to toxin ( cathepsin B and caspase-1 inhibitors ) or 5 h post-toxin ( proteasome inhibitors ) treatment . LT treatment was performed with a concentration of 5 µg/ml ( 9 h ) . Cell viability was assessed by staining with MTT [3- ( 4 , 5-dimethyl-2-thiazolyl ) -2 , 5-diphenyl tetrazolium bromide] as previously described . Cells were pretreated with 0 . 5–1 µg/ml LPS ( Calbiochem , San Diego , CA ) for 2 h prior to toxin treatment . LT was added for 7 h at varying concentrations . Cytokine in culture supernatants was measured using an IL-1β ELISA kit ( R&D Systems ) according to the manufacturer's instructions . The molecular masses of the H6-GST-LEW and CDF proteins and their cleavage products were determined by liquid chromatography-electrospray mass spectrometry using an HP/Agilent 1100 MSD instrument ( Hewlett Packard , Palo Alto , CA ) at the NIDDK core facility , Bethesda , MD . | Anthrax lethal toxin ( LT ) is a protease which can induce rapid death of macrophages accompanied by activation and release of pro-inflammatory cytokines . The previously identified cellular substrates for this toxin have not been shown to play a role in this rapid cell death . This report identifies a new substrate for LT , and demonstrates that its cleavage by the toxin is required for macrophage death . The substrate , Nlrp1 , is a member of a large family of intracellular sensors of danger . These sensors , once activated , form a multiprotein complex called the inflammasome and are essential to the host innate immune response . The mechanism of activation for these sensors is not known . The demonstration of cleavage-mediated activation of Nlrp1 in this study represents the first report on a direct biochemical mechanism for inflammasome activation . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
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"and",
"Methods"
] | [
"immunity",
"biology",
"microbiology"
] | 2012 | Anthrax Lethal Factor Cleavage of Nlrp1 Is Required for Activation of the Inflammasome |
Meiotic recombination is initiated by DNA double-strand breaks ( DSBs ) made by Spo11 ( Rec12 in fission yeast ) , which becomes covalently linked to the DSB ends . Like recombination events , DSBs occur at hotspots in the genome , but the genetic factors responsible for most hotspots have remained elusive . Here we describe in fission yeast the genome-wide distribution of meiosis-specific Rec12-DNA linkages , which closely parallel DSBs measured by conventional Southern blot hybridization . Prominent DSB hotspots are located ∼65 kb apart , separated by intervals with little or no detectable breakage . Most hotspots lie within exceptionally large intergenic regions . Thus , the chromosomal architecture responsible for hotspots in fission yeast is markedly different from that of budding yeast , in which DSB hotspots are much more closely spaced and , in many regions of the genome , occur at each promoter . Our analysis in fission yeast reveals a clearly identifiable chromosomal feature that can predict the majority of recombination hotspots across a whole genome and provides a basis for searching for the chromosomal features that dictate hotspots of meiotic recombination in other organisms , including humans .
Genome-wide analysis of a molecular event can provide insights , such as identification of patterns within the data at multiple scales , which are unavailable through study of single events . Using the fission yeast Schizosaccharomyces pombe , we analyze an event , the formation of DNA double-strand breaks ( DSBs ) , which is essential for meiotic recombination . We find that hotspots of DSB formation occur primarily in unusually large intergenic regions ( IGRs ) and are widely separated by apparently break-free regions . Our results show that a clearly identifiable subset of noncoding DNA has a special function that is critical for meiosis . These results contrast with those obtained in the distantly related budding yeast Saccharomyces cerevisiae . Meiosis consists of two special nuclear divisions during which chromosome number is reduced from two in somatic cells to one in gametes . Uniquely , in the first meiotic division homologs , rather than sister chromatids , segregate from each other . In most organisms homolog segregation is aided by crossover recombination . Meiotic recombination is initiated by DSBs in both budding and fission yeasts and perhaps in all species [1–3] . Repair of the DSBs by interaction with a homolog produces the crossovers ( reciprocal recombination events ) important for homolog segregation and for the generation of genetic diversity . DSBs are formed by a meiosis-specific protein Spo11 ( Rec12 in S . pombe ) through a topoisomerase-like mechanism , in which a tyrosine residue of the protein forms a phosphodiester covalent linkage with each 5′ end at the DSB [3 , 4] . DSBs can be detected by extraction of DNA from meiotic cells and analyzing it by Southern blot hybridizations , which reveal distinct hotspots of break formation . DSBs are transient in wild-type cells but accumulate to high levels in mutants , such as rad50S , in which the Spo11 or Rec12 protein is not removed from the ends of breaks [2 , 5] . Analysis of DSBs is thus more sensitive in rad50S cells . DSB hotspots were first detected at recombination hotspots , sites at which gene conversion ( nonreciprocal recombination ) occurs at especially high frequency . Gene conversion hotspots have been identified in the wild-type chromosomes of many species , including humans , and may be a universal aspect of meiotic recombination . A hotspot created by the ade6-M26 mutation in S . pombe has been especially informative . The single bp mutation M26 creates a binding site for the transcription factor Atf1-Pcr1 , which is essential for hotspot activity and DSB formation at M26 [6–9] . Although a few meiotic hotspots in S . pombe are binding sites for this transcription factor [9 , 24] , most hotspots are not , since only a few DSBs depend on Atf1-Pcr1 . Similarly , in S . cerevisiae a few hotspots depend on the Bas1 transcription factor [10] . The determinants for the majority of the hotspots have been unclear . To determine the global distribution of DSBs in S . pombe , we have used two methods—an extensive analysis of DSBs by Southern blot hybridizations and a genome-wide microarray analysis of Rec12-DNA linkages . We find that these two methods agree remarkably well and show , surprisingly , that the small class of exceptionally large IGRs is highly predictive and contains the majority of meiotic DSB hotspots .
Previous evidence suggested that Rec12 , like S . cerevisiae Spo11 , becomes covalently linked to DNA during meiosis . Rec12 Tyr98 corresponds to a Tyr residue present in all analyzed Spo11 proteins [4] , and the alteration Tyr98 → Phe98 abolishes meiotic recombination [3] . The corresponding Tyr135 of S . cerevisiae Spo11 is essential for recombination and DSB formation and is thought to be the active site residue linked to DNA [4] . To confirm Rec12-DNA covalent linkage , we analyzed , by locus-specific PCR , DNA in immunoprecipitates ( IPs ) of chromatin from rec12-FLAG rad50S cells without an exogenous crosslinking agent; the Rec12-FLAG protein is fully active in meiotic recombination ( Table S1 ) . We analyzed DNA at two known hotspots—the M26-related ade6–3049 mutational hyper-hotspot [11] and the natural mbs1 hotspot [12] . We also analyzed the ura1 locus , at which DSBs are not detectable by Southern blot hybridization [5] . At both hotspots , but not at ura1 , strong PCR signals were observed at 4 , 5 , and 6 h after meiotic induction ( Figure S1 ) , in parallel with the accumulation of DSBs at these times in rad50S cells as measured by Southern blot hybridizations [5] . This meiosis-specific signal was dependent on the FLAG antibody ( unpublished data ) , the FLAG tag on Rec12 , and intracellular Rec10 protein , which is also essential for recombination and DSB formation [13] . Approximately 30 times more signal was observed with primers specific to ade6 , which contains a DSB hotspot , than with primers specific to ura1 , which lacks detectable DSBs . As expected , approximately equal yields of product using the whole cell extract ( WCE ) ( nonimmunoprecipitated ) DNA were obtained at the ade6–3049 , mbs1 , and ura1 sites . Since no crosslinking agent such as formaldehyde was used , we infer that the observed PCR signals using the IPs reflect covalent linkage or very tight binding of Rec12 specifically to DNA at meiotic hotspots . To extend this analysis to the entire genome , we analyzed DNA in IPs of chromatin prepared from cells harvested before and 5 h after meiotic induction , using tiling microarrays of oligonucleotides ( 60-mers; “probes” ) spaced on average every 300 bp across the S . pombe genome [14] . These arrays have been used to analyze the genome-wide distribution of heterochromatin proteins and histone modifications [14] . We have analyzed three meiotic inductions , two with a haploid strain ( Dataset S1 ) and one with a diploid ( Dataset S2 ) . These strains are pat1–114 ( Ts ) mutants , which initiate meiosis synchronously when the temperature is raised , even from the haploid state [15]; where tested , haploids and diploids give DSB patterns indistinguishable by Southern blot analysis [5] . Using the tiling microarrays , we compared the Rec12-DNA signals obtained using the IPs to those obtained using WCEs . The ratio of the IP:WCE signals at each probe reflects the enrichment of DNA linked to Rec12 . This ratio was normalized using the median values of the IP and WCE signals for all of the ∼41 , 000 probes [14] . Because the chromatin DNA was sheared to ∼0 . 5–1 kb before immunoprecipitation , and because Spo11 ( and likely Rec12 ) becomes linked to both 5′ ends at a DSB [4] , we expect genuine Rec12-DNA signals to hybridize to at least three adjacent probes on the array . For our subsequent analysis we thus excluded isolated single or double probes ( “spikes” ) with high values ( IP:WCE ratios >2 ) , as done similarly by Cam et al . [14] . High points in the induced ( 5 h ) data corresponding to spuriously high uninduced ( 0 h ) points were also removed . The effect of this filtering and its comparison with other noise-reducing methods are shown in Figure S2 . High Rec12 enrichment ratios in the 0-h data are restricted to isolated spikes , whereas in the 5-h data high enrichment ratios are also seen in contiguous groups of high ratio probes , indicative of genuine Rec12 enrichment ( Figure S10 ) . Filtering removed the spikes ( Figure S11 ) . As expected [16] , the filtered data from the 0-h samples were approximately normally distributed , whereas the data from the 5-h samples were approximately normally distributed but with an additional , substantial non-normal distribution of high values ( i . e . , sites of Rec12-DNA linkage ) ( Figures S3 and S4 ) . Greater variance in the diploid 0-h background distribution meant that filtering of spikes was somewhat less effective in that dataset than in the others . We first focused our attention on a 0 . 5-Mb region near the left end of Chromosome I , defined by NotI restriction fragment J , which we have previously analyzed extensively for meiotic recombination and DSBs [5] . Our microarray analysis reveals a consistent distribution of Rec12-DNA linkages that closely parallels the distribution of DSBs determined by Southern blot hybridization ( see next section ) . Strong Rec12-DNA signals ( IP:WCE ratios >2 and up to ∼40 ) were observed at ∼20 consecutive probes at the position of the previously mapped mbs1 and mbs2 DSB hotspots of NotI fragment J in each of the three inductions ( 5-h data; Figures 1 and S5 ) . Additional strong hotspots of Rec12-DNA linkage were observed at six other sites to the right of the mbs2–mbs1 pair . This pattern was consistent for all three inductions , and the hotspots were essentially absent in the uninduced ( 0-h ) samples ( Figure S5 ) . Between the obvious hotspots in the 5-h DNA the signals varied in a small range around a ratio of 1 , similar to the 0-h data and consistent with the expected background ( non-Rec12 enriched ) distribution [16] . Thus , the microarray analysis reveals strong hotspots of Rec12-DNA linkage separated by large regions in which linkage is undetectable ( see additional discussion below ) . We directly compared the distribution of Rec12-DNA linkages to that of DSBs determined by Southern blot analysis . A close correlation of both positions and intensities was seen across NotI fragment J ( Figure 2A ) . At this scale ( 50–500-kb DNA fragments ) , the microarray data provide higher resolution and a higher signal-to-noise ratio than Southern blot analysis . Comparison of the microarray data with direct DSB analysis of shorter DNA fragments gave a slightly different picture . The positions of the peaks at two well-studied DSB hotspots , mbs1 on Chromosome I and ade6–3049 on Chromosome III , again were coincident , but the Southern blot analysis of shorter DNA fragments now provides higher resolution than the microarray data ( Figure 2B and 2C ) , as expected from the length of DNA linked to Rec12 ( see above ) . We extended this dual analysis beyond the DSB hotspots on NotI fragment J noted by Young et al . [5] . We first analyzed DSBs by Southern blot analysis of the NotI fragments adjacent to fragment J ( Figure S6 ) . This analysis encompassed ∼1 . 8 Mb of the left arm of Chromosome I , including NotI fragment J , and represented ∼15% of the S . pombe genome . We found 25 discrete DSB hotspots , or tight clusters of hotspots , scattered across the region . These hotspots were then mapped more precisely by analysis of smaller restriction fragments , ∼10–100 kb long , covering much but not all of the 1 . 8 Mb region . Every DSB hotspot was coincident with a hotspot of Rec12-DNA linkage independently obtained by microarray analysis ( Figure 3 ) . We did not survey at higher resolution all of the weak Rec12-DNA linkage peaks or stronger DSB sites whose diagnostic DNA fragments were poorly separated from those of nearby DSB sites . Except for these few Rec12-DNA linkage sites that have not been tested by Southern blot analysis , there is a strict correspondence of Rec12-DNA linkage and DSB hotspots . We noted above that the relative signal strengths of the peaks on the 0 . 5 Mb NotI fragment J were similar by Rec12-DNA and direct DSB analyses ( Figure 2A ) . We extended this comparison for the 25 peaks seen by both measures in the 1 . 8 Mb region shown in Figure 3 . We observed a strong trend between the strength of the DSB signals ( percent of DNA broken at the site ) and the strength of the microarray signals ( IP:WCE ratios integrated over the peak ) ( Figure 4 ) . The Pearson correlation coefficient r was 0 . 89 ( p < 0 . 001 by t-test; r2 = 0 . 79 ) ; the Spearman rank correlation coefficient ρ was 0 . 90 ( p < 0 . 001 by t-test ) . The data in Figures 2–4 show that the microarray data faithfully reflect direct assay of DSBs by Southern blot analysis , with respect to both the position of the DSBs and the amount of DNA breakage ( strength of the hotspot ) . This outcome allows us to use the microarray data as a reliable substitute assay for DSBs across the entire genome . Hereafter , we use “DSB hotspots” and “Rec12 peaks” interchangeably . Because the limit of detection on our Southern blots is ∼0 . 5% ( Figure S6 ) , we consider sites with ≥0 . 5% estimated DSBs , based on the relation in Figure 4 , to be “prominent” DSB hotspots or Rec12 peaks and those with <0 . 5% to be “weak . ” From the peak integrals we estimate that the prominent Rec12 peaks represent ∼90% of all detectable Rec12-DNA linkages ( see below ) . In the Discussion , we describe additional differences between the prominent and weak hotspots , which suggest that they are distinct . Young et al . [5] noted that the DSB hotspot mbs1 is located in an IGR that is especially large—7 . 2 kb . The mode of S . pombe IGRs is 0 . 4 kb , and the median is 0 . 7 kb [17] . mbs2 is also in a large IGR—6 . 3 kb . We therefore examined the microarray data for the location of hotspots and found a strong correlation between DSB hotspots and large IGRs . ( For this analysis we classed as intergenic the putative coding sequences annotated as “dubious” or “very hypothetical . ” ) For example , in the 150-kb interval that includes mbs1 and mbs2 there are no other prominent hotspots and no other IGRs larger than 1 . 9 kb ( Figure 5B ) . A nearby 150-kb interval on the left arm of Chromosome I has four prominent hotspots , which fall in IGRs of size 5 . 2 , 4 . 3 , 4 . 1 , and 2 . 3 kb ( Figure 5A ) . In these 300 kb all IGRs >2 . 8 kb have prominent hotspots , and five out of six such hotspots occur in IGRs >4 kb . In the 1 . 8-Mb region depicted in Figure 3 , 16 of the 25 prominent Rec12 peaks confirmed by Southern blot analysis fall in IGRs >2 . 8 kb , five fall in IGRs of 0 . 6–2 . 3 kb , and the remaining four fall in coding sequences . We extended this analysis genome-wide , except for the telomeres and centromeres , which are considered separately in the Discussion . Rec12-DNA linkage positions were identified as the maxima of Rec12 peaks using the PeakFinder program [18] . Using the mean of the haploid 5-h data , this program identified 353 such positions genome-wide . The 149 , 124 , and 80 positions seen on Chromosomes I , II , and III respectively ( Figure S7 ) are in good agreement with the relative sizes of the chromosomes ( 5 . 6 , 4 . 5 , and 2 . 5 Mb , not including the rDNA at the ends of Chromosome III ) . Of the 353 Rec12 peak positions from the haploid data , 340 ( 96% ) were also identified in the diploid data . Using a cutoff of p < 10−8 , a cutoff that excludes all but 18 ( 12% ) of the false-positive peaks identified in the 0-h data , the ChIPOTle program [19] independently identified peak regions in the haploid data that included all of the positions identified by PeakFinder , plus an additional 16 ( 4% ) . We used the peak positions identified by PeakFinder from the haploid data for the analysis below . As mentioned above , we estimated the relative frequency of Rec12-DNA linkage at each hotspot by integrating the IP:WCE ratios across each peak and classified the peaks as “prominent” ( ≥0 . 5% estimated DSBs ) or “weak” ( <0 . 5% ) based on the relation in Figure 4 . Among the 353 peaks identified by PeakFinder , 194 are prominent hotspots . Based on the sum of the integrals , these prominent peaks account for 88% of all detectable Rec12-DNA linkages ( DSBs ) . Our analysis shows that DSB hotspots are over-represented in large IGRs and under-represented in coding DNA . Figure 6A shows the proportion of all genomic DNA and the proportion of all prominent and weak Rec12 peaks found in coding sequences and in IGRs of different sizes . The distributions of all DNA and Rec12 peaks are clearly distinct ( for prominent peaks p < 0 . 0001 and for weak peaks p < 0 . 005; two-tailed contingency chi-squared test ) . IGRs >2 kb comprise only 13% of the genome , but they contain 62% of all prominent Rec12 peaks and 37% of the weak Rec12 peaks . Similarly , IGRs >3 kb comprise only 8% of the genome but contain 47% of the prominent Rec12 peaks and 19% of the weak Rec12 peaks . Thus , prominent peaks are more strongly enriched in large IGRs than are weak peaks . In fact , among prominent peaks , the probability of a peak lying in an IGR >3 kb rises with peak integral so that the very strongest peaks are even more highly associated with large IGRs ( Figure S8 ) . The Rec12 peak integrals , a measure of Rec12-DNA linkages and hence DSBs , are similarly skewed toward large IGRs: 65% of all Rec12-DNA linkages are in IGRs >2 kb , and 52% are in IGRs >3 kb . In addition , fewer prominent hotspots than weak hotspots ( 19% versus 38% ) occur in coding DNA , which accounts for 61% of the genome . Prominent DSB hotspots , as indicated by sites of Rec12-DNA linkage , are therefore particularly strongly associated with large IGRs . In addition , large IGRs are highly predictive of DSB hotspots , since 44% of the IGRs >3 kb long have prominent Rec12 peaks ( Figure 6B ) and an additional 12% have weak Rec12 peaks . Furthermore , prominent Rec12 peaks are about 20 times more dense ( peaks per kb ) in IGRs >3 kb than in coding sequences or short IGRs ( Figure 6C ) . The density of DSBs ( peak integrals per kb ) is also 20–40 times greater in IGRs >3 kb than in coding sequences or in short IGRs ( Figure 6D ) . We discuss below the significance of these observations .
The most prominent feature associated with DSB hotspots that we have noted is exceptionally large IGRs ( Figures 5 and 6 ) . Approximately 50% of the IGRs >3 kb in length have Rec12 peaks ( Figure 6A ) , which collectively account for approximately 50% of all detectable Rec12-DNA linkages ( DSBs ) . Thus , this genomic feature is highly predictive . We presume that one or more nucleotide sequences , currently unknown but restricted to the IGRs with DSBs , are essential for DSB formation . These sequences might be collections of transcription factor binding sites , exemplified by M26 , the binding site for Atf1-Pcr1 . The S . cerevisiae transcription factor Bas1 and its binding site also account for a small fraction of the meiotic DSB hotspots observed in that distantly related yeast [10] . We think it likely that transcription factors other than Atf1-Pcr1 also influence DSB formation in S . pombe . Transcription factor binding sites are concentrated in IGRs and collectively may account for a significant fraction of meiotic DSBs . Other sequences , such as those affecting nucleosome positioning , may determine additional DSB hotspots . The data reported here provide a framework for identifying such determinants . Ludin and colleagues also noted an enrichment of Rec12 in IGRs in their genome-wide analysis of Rec12 crosslinked with formaldehyde to meiotic DNA ( K . Ludin , J . Mata , J . Bahler , and J . Kohli , personal communication ) . In their analysis Rec12 was more uniformly distributed across the genome than in our analysis , perhaps a reflection of only a fraction of chromatin-bound Rec12 being active in making DSBs and becoming crosslinked without an exogenous agent . We observed that DSB hotspots are over-represented in IGRs whose flanking genes are divergently transcribed , compared to their frequency in all IGRs ( p < 0 . 001; contingency chi-squared test ) . However , the orientation of transcription of adjacent genes is itself correlated with the size of the IGR [17] , and no enrichment for DSB hotspots was seen in divergent IGRs when the size of the IGR was controlled for ( unpublished data ) . We know of no clear evidence that transcription per se influences meiotic DSB formation ( e . g . , [8] ) . We searched for other features of the S . pombe genome that might correlate with DSB hotspots and thus be important factors for their determination . The meiosis-specific cohesin subunit Rec8 is required for most DSB formation in S . pombe [13] . Rec8 is thought to be part of the chromosomal core that forms the basis of the axial elements ( linear elements in S . pombe ) and from which chromatin loops emanate [27] . Studies in S . cerevisiae have led to the conclusion that DSB hotspots preferentially occur in the loops , where Rec8 density is low [18] . S . pombe Rec8 is distributed nonuniformly , at least over the portion of the genome surveyed ( Chromosome II and part of Chromosome III ) [28] . We observed a weak negative correlation between Rec8 binding and Rec12-DNA linkage . For example , 83% of the 69 prominent Rec12 peaks on Chromosome II are centered on an 11-probe window whose Rec8-binding ratio is below the median for Chromosome II ( ignoring the centromere ) , significantly different from the 50% expected ( p < 0 . 001; contingency chi-squared test ) . Among the 55 weak Rec12 peaks , this bias falls to 64% , not significantly different from 50% . Conversely , the quarter of probes on Chromosome II with the highest Rec8 enrichment ratios contain only 9% of the prominent Rec12 peaks , less than the 25% expected , and the quarter with the lowest Rec8 enrichment ratios contain 55% of the prominent Rec12 peaks , more than the 25% expected . Therefore , in S . pombe as in S . cerevisiae , DSBs are associated with regions of low Rec8 localization , but neither distribution is very useful for predicting the other . As noted above , Atf1-Pcr1 and perhaps other transcription factors are required for some DSB hotspots . We therefore looked for a correlation between DSB hotspot positions and the promoters of genes induced during meiosis . Among such genes [29] , we found no significant enrichment of Rec12 peaks 5′ of the corresponding open reading frame . We then looked among all genes with Rec12-peaks in their presumptive promoters for enrichment of related functions , as indicated by related gene ontology ( GO ) terms . Using the GOstat Web server ( http://gostat . wehi . edu . au ) [30] with Benjamini correction for multiple hypothesis testing , we found a significant enrichment for terms , such as GO:0051704 ( Interaction between organisms ) , GO:0000746 and GO:0019953 ( Conjugation and Sexual reproduction , subsets of GO:0051704 ) , and GO:0003700 ( Transcription factor activity ) , listed in Table S2 . The causal basis of these correlations is not obvious . However , the enrichment of Rec12 peaks in the promoter regions of genes associated with conditions and processes related to meiosis and meiotic induction suggests that meiosis-specific chromatin remodeling may occur at some promoters , allowing access or activation of Rec12 for DSB formation . A particular group of transcription factors and transcription factor-binding sequences may be involved . As noted above , this is the case for the M26 hotspot and Atf1-Pcr1 , factors involved in the meiosis-specific remodeling of chromatin and Rec12-dependent DSB formation [31–33] . In S . pombe mitotic replication origins are spaced about 25–30 kb apart , and , where tested , the same origins are used during both mitotic growth and meiosis [34] . We found , however , no obvious relationship between origins of replication and meiotic DSB hotspots . For example , 14% of all replication origins correspond to haploid 5-h probe positions with a Rec12 IP:WCE ratio >2 ( i . e . , all regions of Rec12-DNA linkages ) , not significantly different from the number expected based on the proportion ( 11% ) of all probes with a Rec12 enrichment value >2 ( p > 0 . 05; contingency chi-squared test ) . Therefore , the frequency of origins of replication is neither enriched nor depleted significantly in regions of Rec12-DNA linkage . We looked for an unusual nucleotide sequence composition surrounding DSB hotspots and found only a weak correlation with higher than average guanine-cytosine ( GC ) content . For example , 5-kb windows centered on the 194 prominent Rec12 peak positions have a slightly enriched GC content compared to 5-kb windows , moved in 100-bp steps , covering the whole genome ( median of 36 . 6% versus 36 . 0%; p < 0 . 0001 by two-tailed t-test ) . However , base composition is a very poor predictor of prominent Rec12 peak position , as 37% of prominent Rec12 peaks are centered on windows with GC content below the global median and only 0 . 2% of windows with GC content above the global median are centered on a prominent Rec12 peak . No significant GC content enrichment was seen at weak Rec12 peak positions . Sliding windows of 0 . 5 kb , 1 kb , or 10 kb showed even less difference between peak-centered and whole-genome window compositions ( unpublished data ) . In S . cerevisiae , DSB hotspots are also weakly correlated with higher than average GC content when a 5-kb sliding window is used [20] . As in S . pombe , DSBs have been analyzed in S . cerevisiae by both direct Southern blot analysis and microarray analysis of DNA covalently linked to Spo11 , the Rec12 homolog [e . g . , 20 , 21 , 35] . These analyses show that DSB hotspots in S . cerevisiae are in general slightly less prominent and often more closely spaced than those in S . pombe . For example , on the most thoroughly analyzed S . cerevisiae Chromosome III , the maximal fraction of DNA broken at a hotspot is 9% [35] , and the maximal enrichment of Spo11-DNA linkage is 8-fold [20] or 15-fold [21] . In many regions of the genome , most of the open reading frames have a detectable DSB at their 5′ end . In S . pombe many DSB hotspots have 10%–15% of the DNA broken ( Figures 4 and S6 ) [5] , and Rec12-enrichment factors ( IP:WCE ratio ) are often >25 and occasionally up to 50 ( Figures 3 and S7 ) . Between the prominent Rec12 peaks , there are frequent intervals of 50–100 kb with few if any DSBs detectable by either Southern blot or microarray analysis ( Figures 3 and S7 ) [5] . We have not noted continuous stretches of genes with DSB hotspots at their 5′ ends . Thus , DSB hotspots ( or small groups of hotspots ) are usually isolated from others in S . pombe . In spite of hotspots being far apart , crossovers in S . pombe are nearly uniformly distributed [5] . Crossing-over far from detectable DSBs may involve migration of DNA intermediates far from a DSB , or they may be initiated by DNA lesions not detectable by current technology [12 , 36] . In these two yeasts meiotic DSBs are distributed differently in the centromeric and telomeric regions than in other regions of the genome . In S . cerevisiae centromeres are small ( ∼0 . 1 kb ) , and DSBs are infrequent to one or both sides of many of the centromeres [10 , 20 , 21 , 35] . In S . pombe centromeres are large ( 35–100 kb ) , and DSBs are undetectable within them by microarray analysis ( Figure S7 ) or by Southern blot analysis ( C . Ellermeier and G . Smith , unpublished data ) . In the adjacent intervals , however , DSBs appear as frequent as in other regions of the genome . In both yeasts ChIP-on-chip analysis suggests that Rec12 ( Spo11 ) -DNA linkages are under-represented in the terminal 40 kb by ∼20% ( unpublished data ) [10 , 20 , 21] . In S . pombe , however , there is a prominent DSB ∼18 kb from the left end of Chromosome I , including ∼10 kb of unsequenced DNA ( Figure 3 ) , although prominent Rec12 peaks occur only 70–150 kb from the other three telomeres . DSB-free intervals of similar size occur throughout the chromosomes . Thus , with respect to meiotic DSBs S . pombe telomeres do not appear substantially different from other regions of the genome . Meiotic DSBs have not been directly detected in human cells , but hotspots of recombination , presumably reflecting hotspots of DSB formation , have been detected using both linkage disequilibrium analysis and sperm typing . These methods have detected small regions of DNA , often only 1–2 kb wide by sperm typing , where meiotic recombination rates are greatly elevated compared to neighboring intervals or the genome average [37–40] . Like S . pombe and S . cerevisiae , human DSB formation and recombination appear to be dominated by hotspots accounting for only a small proportion of the total genome and located most frequently in IGRs of the genome [40 , 41] . Human hotspots are located 50–100 kb apart [39 , 42] , similar to the distribution of hotspots in S . pombe ( Figure 3 ) . The strongest human hotspot has a crossover frequency of ∼1% [43] , lower than that at mbs1 ( 5% ) [12] and presumably than at several even stronger DSB hotspots in S . pombe ( Figures 3 and S7 ) . Human DSB hotspots are expected to be weaker than those of S . pombe , given the similar hotspot spacing and map sizes of the two organisms but greatly larger physical genome size of humans . Like S . pombe and S . cerevisiae , human hotpots are positively , but only weakly , correlated with GC content; this correlation has low predictive power [44] . Therefore , several characteristics of meiotic DSB hotspots are common to S . pombe , S . cerevisiae , and humans , but there are also distinct differences in any pair-wise comparison of the organisms . Further comparisons of meiotic DSB patterns in these yeasts and additional species will help understand how this process , crucial to sexual reproduction , is controlled . The analysis reported here provides a foundation for identifying the genomic features that regulate meiotic DSB formation , a problem so far unsolved for any organism .
The genotypes of strains and the sources of published alleles are in Table S3 . The rec12–201::6His-2FLAG allele , called rec12-FLAG in the text , was constructed as plasmid pJF32 ( Text S1 ) . The 1 . 6-kb BamHI fragment of pJF32 was used to transform strain GP5622 ( rec12–171::ura4+ ) to fluoro-orotic acid-resistance to generate strain GP5960; the chromosomal allele was confirmed by automated nucleotide sequence analysis . The rec12–202::6His-2HA allele , called rec12-HA in the text , was similarly constructed as plasmid pJF41 and transferred to the chromosome to generate strain GP5961 . Strains with these alleles are recombination proficient ( Table S1 ) . Other strains were constructed by standard meiotic crosses . Cells were grown and induced for meiosis as described [5] and then harvested , washed , and stored at −20 °C . Chromatin was prepared , sonicated to yield DNA 0 . 5–1 kb long as determined by agarose gel electrophoresis ( unpublished data ) , and subjected to immunoprecipitation with anti-FLAG antibody ( Sigma , http://www . sigmaaldrich . com ) as described [45] . Precipitates ( IP ) were washed , treated with proteinase K , extracted with phenol-CHCl3 , and treated with RNase; the residual DNA was purified on a QIAquick column ( Qiagen , http://www1 . qiagen . com ) , amplified in the presence of amino-allyl dUTP , and labeled with Cy5 as described [14] . DNA from unfractionated , sonicated chromatin ( WCE ) was prepared and labeled with Cy3 in parallel . The samples were mixed and hybridized to an Agilent 44-K S . pombe oligonucleotide microarray and analyzed as described [14] . The IP:WCE ratio for each probe on the array ( ∼41 , 000 ) was then calculated after adjustment of the IP values to equalize the IP and WCE medians . Data were analyzed with Excel ( Microsoft , http://www . microsoft . com ) , PeakFinder [18] , and ChIPOTle [19] software . Peakfinder was run using the haploid and diploid datasets after noise reduction as in Figure 1 , with linear plotting , Gaussian weighting , a single round using a 13-probe window , right and left minimum smoothed delta = 100 , and a threshold of 2 , 000 . ChIPOTle was run on the haploid dataset with noise reduction as in Figure S3 . The Gaussian background setting was used with a window size of 1 kb , a step size of 150 bp , and a cutoff of p < 0 . 001 . | Homologous genetic recombination has two immediate benefits for cells—faithfully repairing broken DNA and aiding chromosome segregation during the first division of meiosis . Meiosis comprises a pair of special nuclear divisions that convert diploid somatic cells into haploid sex cells; in humans , meiosis leads to formation of eggs and sperm . By introducing double-strand breaks ( DSBs ) into their own DNA during meiosis , organisms promote recombination and hence production of viable sex cells . Although meiotic DSBs , and therefore recombination , occur throughout genomes , they arise at high frequency in certain genomic regions called hotspots , whose molecular bases are rarely understood . In this article we determine the locations of DSBs across the entire genome of the fission yeast Schizosaccharomyces pombe by taking advantage of physical linkages between DNA and the protein Rec12 that makes DSBs . This analysis shows that most of the DSB hotspots are in exceptionally large intergenic ( gene-free ) regions spaced on average about 65 kb apart and making up only a small fraction of the genome . Between the hotspots we see very little evidence of DSBs . The concentration of hotspots in large intergenic regions suggests that DSBs may be determined by special nucleotide sequences buried in these regions . Determining these special sequences will allow predictions of hotspots and , perhaps , the proteins and features of genome architecture that lead to DSBs being made at these special sites . | [
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] | 2007 | A Discrete Class of Intergenic DNA Dictates Meiotic DNA Break Hotspots in Fission Yeast |
Dengue virus ( DENV ) is a mosquito-borne pathogen for which no vaccine or specific therapeutic is available . Although it is well established that dendritic cells and macrophages are primary sites of DENV replication , it remains unclear whether non-hematopoietic cellular compartments serve as virus reservoirs . Here , we exploited hematopoietic-specific microRNA-142 ( miR-142 ) to control virus tropism by inserting tandem target sites into the virus to restrict replication exclusively in this cell population . In vivo use of this virus restricted infection of CD11b+ , CD11c+ , and CD45+ cells , resulting in a loss of virus spread , regardless of the route of administration . Furthermore , sequencing of the targeted virus population that persisted at low levels , demonstrated total excision of the inserted miR-142 target sites . The complete conversion of the virus population under these selective conditions suggests that these immune cells are the predominant sources of virus amplification . Taken together , this work highlights the importance of hematopoietic cells for DENV replication and showcases an invaluable tool for the study of virus pathogenesis .
Dengue virus ( DENV ) is a mosquito-borne flavivirus and the etiological agent of dengue fever and the more severe clinical presentations known as dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) [1] . The virus is endemic in tropical and subtropical regions around the world , and poses a major global health concern [2] . The four serotypes of DENV ( DENV-1 , -2 , -3 , and -4 ) are transmitted by Aedes mosquitoes which , given their global distribution , renders at least 2 . 5 billion people at risk of contracting the virus , resulting in as many as 10 million annual infections worldwide [2] , [3] . Like other flaviviruses , the DENV genome is translated into a single polyprotein precursor that is co-translationally processed by cell- and virus-specific proteases [4] . Virus infection results in the generation of three structural proteins , C , prM and E , and seven non-structural ( NS ) proteins , NS1 , NS2a , NS2b , NS3 , NS4a , NS4b , and NS5 [1] , [4] . Primary infection of DENV results in mild febrile illness and confers only partial protection from the remaining serotypes [5] , [6] . Consequently , a heterotypic secondary infection with a different serotype can lead to more severe or fatal clinical manifestations such as DHF/DSS [7] . This pathogenesis feature is referred to as antibody-dependent enhancement , a phenomenon mediated by Fc receptors found on cells such as macrophages , neutrophils , and DCs [5] , [6] , [8] . Non-neutralizing antibodies against DENV during a heterotypic secondary infection allows for infectious immune complexes to enter Fc-receptor expressing cells and initiate replication , thereby enhancing virus growth within these cells [6] . DENV replication originates at the site of mosquito inoculation in resident cutaneous Langerhans dendritic cells ( DCs ) , whose migration through the lymphatic system results in the induction of cytokines and the chemokine-mediated recruitment of immune cells [9] , [10] . These include monocytes and macrophages , which are also known to be primary targets of DENV infection [11] . Although it is understood that these cells of hematopoietic lineage are major sites of DENV replication , it remains uncertain whether other cells of non-hematopoietic origin are permissive to virus replication during natural infection . In vitro studies describe a variety of cell lines of different type and host lineage ( human , murine , monkey , hamster , mosquito ) that are permissive to DENV replication [12] . Furthermore , the virus engages a number of different receptors including heparan sulfate , DC-specific intercellular adhesion molecule 3-grabbing nonintegrin , CD14 , and heat shock proteins 70 and 90 [13]–[17] . This broad range of receptors has led some to propose endothelial cells may be targets of DENV replication during natural infection [18]–[20] . However , the interpretation of these studies has been questioned , as the presence of viral antigen in endothelial cells could be a result of virus entry rather than active replication . A counter argument in support of viral entry , in the absence of replication , is supported by results that found undetectable levels of viral RNA in endothelial cells in response to infection [21] . In this study , we sought to determine the effects of excluding DENV infection from hematopoietic cells through exploitation of the host microRNA ( miRNA ) machinery , and determine whether DENV replication could still be harbored in the absence of its primary cell targets . miRNAs are small ( 18–22 nucleotides ( nts ) ) , trans-acting RNAs that modulate post-transcriptional silencing ( PTS ) of target genes by binding to miRNA response elements present in the open reading frame and/or 3′ untranslated regions ( UTRs ) of target transcripts [22] , [23] . This activity is mediated by members of the Argonaute family , most notably , Argonaute 2 ( Ago2 ) , the catalytic member of the RNA-induced silencing complex ( RISC ) . The Ago2-containing , miRNA-loaded RISC binds to target mRNA via the “seed” region of the miRNA ( bases 2–7 ) , or through extensive centered complementarity , resulting in translational repression or RNA degradation [24]–[26] . In contrast to miRNAs , short-interfering RNAs ( siRNAs ) bind with perfect complementarity and disrupt target expression levels more effectively through Ago2-mediated cleavage of the messenger RNA transcript [22] , [27]–[30] . It therefore follows that incorporation of fully complementary miRNA target sites into a virus can effectively convert an endogenous miRNA into a virus-specific “siRNA” . This concept has been successfully demonstrated for a number of viruses , including influenza A virus , poliovirus , vesicular stomatitis virus , and measles virus [31]–[39] . In addition , DENV itself has been rendered susceptible to a miRNA , but only in the context of a replication-incompetent replicon or as a chimera with tick-borne encephalitis virus structural proteins [37] , [40] . In an effort to exclude DENV selectively in immune cells , we incorporated perfect hematopoietic-specific miRNA target sites into the 3′ UTR of the virus to serve as a tool to study DENV replication in non-hematopoietic cells in vivo . We demonstrate that miR-142 targets can confer cell-specific attenuation in both in vitro and in vivo assays , and that hematopoietic cell types , such as monocytes , DCs , and macrophages , are required for DENV growth and dissemination during natural infection .
Before designing a method of exploiting miRNA machinery to suppress DENV replication , we sought to confirm DENV infection does not block miRNA biogenesis or function . To assess effective knockdown of a miRNA target , human fibroblasts expressing a green fluorescent protein ( GFP ) targeted by miR-142 ( pEGFP-142t ) were transfected with either vector or a plasmid expressing miR-142 ( p142 ) , a miRNA normally absent in these cells . In uninfected cells , expression of miR-142 resulted in a dramatic reduction of visible GFP expression ( Figure 1A ) , equating to a 75% loss of fluorescence ( Figure 1B ) . Furthermore , despite a multiplicity of infection ( MOI ) that would ensure high levels of virus replication , verified by quantitative RT-PCR ( qRT-PCR ) ( Figure 1C ) , the presence of DENV-2 did not alter the efficiency of miR-142-mediated PTS . In accordance with the lack of DENV-2-mediated disruption of miR-142-targeting , northern blots for the expression of other exogenous ( miR-124 ) and endogenous ( miR-93 ) miRNAs also demonstrated no discernable differences during virus replication ( Figure S1 ) . Taken together , these results suggest DENV-2 does not interfere with miRNA-mediated PTS or miRNA biogenesis , and that cellular miRNA machinery may be exploited for DENV targeting . During natural infection , DENV displays tropism for cells of hematopoietic lineage , specifically , monocytes , macrophages and DCs [9] , [11] , [36] , [41] , [42] . As miR-142 is one of the most abundant hematopoietic-specific miRNAs [43] , [44] , it served as an ideal candidate for mediating cell-specific attenuation . To generate a DENV-2 strain that demonstrated miR-142-susceptibility , we incorporated four target sites into the 3′ UTR of the virus ( Figure 2A ) . Given the highly structured secondary conformations important for both transcription and translation in the 3′ UTR [45] , [46] , we incorporated the target sites downstream of the NS5 coding frame , in the variable region believed to be more amenable to nt insertions [46] ( Figure S2 ) . Following construction of both a miR-142-targeted ( 142t ) virus and a parental control ( ctrl ) virus , encoding reverse target sites , we next sought to determine their replicative properties in cells lacking miR-142 . To this end , we performed multi-cycle growth curves in mosquito Aedes Albopictus larvae cells ( C6/36 ) and baby hamster kidney ( BHK ) cells , neither of which express miR-142 ( Figure 2B ) . By qRT-PCR and western blot , both ctrl and 142t strains grew to similar levels in C6/36 cells ( Figure 2C ) reaching equal growth at 84 hrs post infection ( hpi ) , but demonstrating an approximate one-log reduction as compared to wild type , unmodified virus ( wt ) . This data was further validated by plaque assay , demonstrating peak titers of ∼1×103 PFU/mL for both ctrl and 142t viruses as compared to ∼1×104 PFU/mL for wt virus ( Figure S3 ) . Furthermore , experiments in BHK cells , another cell devoid of miR-142 , demonstrated peak virus loads at 48 hpi , a time at which cytopathic effects reduced overall titers and NS5 expression ( Figure 2D ) . Consistent with the data from C6/36 cells , wt virus exceeded both ctrl and 142t strains , suggesting that the 157 nt insertion lowered the overall fitness of the virus , independent of sequence . To determine whether DENV-2 transcripts could be targeted by the miRNA machinery , we first attempted to see whether the presence of miR-142 could prevent virus production from the in vitro transcription ( IVT ) of the 142t cDNA clone . BHKs , expressing vector or p142 , were electroporated with IVT products from wt , ctrl , or 142t DENV-2 cDNA clones . Twenty-four hrs post transfection ( hpt ) , miR-142 production was evident by northern blot , albeit lower than observed in bone-marrow derived primary macrophages ( BMMs ) ( Figure 3A ) . Given the production of miR-142 , we evaluated NS5 protein levels from the IVT product to determine the degree of transcript targeting ( Figure 3B ) . Consistent with previous work demonstrating miRNA-mediated virus attenuation [31] , NS5 expression derived only from 142t IVT product was abrogated in a miRNA-specific manner ( Figure 3B ) . This result would suggest that virus synthesis from the 142t IVT product was blocked in the presence of miR-142 . Next , we sought to determine whether miR-142 could attenuate 142t in the context of infection . To this end , fibroblasts expressing vector or p142 were infected with wt , ctrl , or 142t strains , and assessed for virus products . As evident by NS5 expression , miR-142 abolished replication of the 142t strain , while exuding no impact on wt or ctrl strains ( Figure 3C ) . It is noteworthy that residual levels of NS5 for the 142t strain likely reflect replication in cells not successfully transfected with p142 , a constraint that would not be in place during an endogenous infection of a hematopoietic cell . Taken together , these data clearly demonstrate that insertion of miRNA target sites into the 3′UTR of DENV-2 renders the virus susceptible to miRNA expression . In an attempt to ascertain the mechanism underlying miR-142-mediated attenuation , we modified the miR-142 target sites to render the DENV genome resistant to canonical miRNA binding and Ago2 cleavage . To this end , the nucleotides complementary to positions 3 and 10 of miR-142 were mismatched in the virus targets to destroy the seed sequences and cleavage sites , respectively [47] . To assess whether this mutated 142t strain , herein referred to as 142tm , was targeted by miR-142 , we compared NS5 synthesis in the presence and absence of the target miRNA ( Figure 3D ) . Surprisingly , virus production from the 142tm strain was still abrogated in a miR-142-specific manner , despite harboring two critical mismatches in the miRNA targeting sites . As loss of NS5 in the 142tm strain maintained miR-142-specificity , these results would suggest that RISC was still engaging on the mutated targets in a non-canonical manner , perhaps as a result of the remaining extensive complementarity . To determine the underlying mechanism responsible for attenuation of the 142tm strain , we examined the levels of genomic viral RNA to discern between RNA degradation and translational inhibition . Quantitative RT-PCR analysis demonstrated that , at the level of RNA , the amount of miR-142-mediated repression of the 142tm virus was significantly ( p = 0 . 0178 ) lower than that of 142t , suggesting the mode of attenuation may no longer be RNA cleavage , but may involve a form of translational repression ( Figure 3E ) . This translational repression could be the result of extensive 3′ miRNA complementarity acting in a canonical manner or could be the result of miR-142/RISC sterically blocking the association of the 5′ and 3′ ends , preventing cyclization and amplification of the virus genome . While future studies will be required to ascertain the exact mechanism of 142tm attenuation , these data strongly demonstrate that inhibition of virus replication occurs in a miR-142-dependent manner . As the 142tm virus showed decreased silencing activity , subsequent characterization focused only on the comparison between ctrl and 142t strains . As cells of hematopoietic lineage are the natural sites of DENV infection and express high levels of miR-142 ( Figure 3A ) [1] , [44] , we aimed to determine whether we could observe differential cell-specific attenuation between the ctrl and 142t strains . To this end , we compared infections of the recombinant DENV-2 strains in two hematopoietic lineages , B cells and BMMs , as well as a non-hematopoietic fibroblast lineage , HEK293s ( Figure 3F ) . Consistent with exogenous targeting by miR-142 , infection of hematopoietic cell types demonstrated selective attenuation of the 142t strain , while infection of the non-hematopoietic cells demonstrated no change in NS5 levels between ctrl- and 142t-virus infected samples ( Figure 3F ) . Taken together , these data demonstrate that the incorporation of miR-142 target sites into the 3′UTR of DENV-2 confers endogenous attenuation of the virus in a cell-specific manner . As human DENV isolates currently lack an adequate animal model that recapitulates human pathogenesis during infection , we chose Ifnar1−/−/Il28r−/− mice to investigate our recombinant viruses in vivo . These mice lack the ability to respond to type I and III interferon and are , thus , more susceptible to virus infection [48] , [49] . To ensure that the in vitro and ex vivo data reflected in vivo attenuation , we first infected mice with ctrl and 142t viruses and isolated CD11b+ and CD11c+ macrophages and DCs , respectively ( Figure 4A ) . qRT-PCR analysis of RNA derived from these cells demonstrated minimal detection of NS5 transcripts derived from the 142t strain and a significant decrease compared to ctrl virus . These data support our in vitro data , where 142t virus replication is excluded in hematopoietic cell types . To examine in vivo replication of the 142t virus in non-hematopoietic compartments , we sorted splenocytes from infected mice for CD45-expressing cells , and assessed viral gene expression in each population ( Figure S4A ) . The nature of this experiment however is complicated by the fact that , should hematopoietic cells be required for virus dissemination , we would anticipate lower virus titers in non-hematopoietic fractions as well . As anticipated , 142t virus growth was attenuated in the CD45+ hematopoietic fraction . In addition , the CD45− , non-hematopoietic fraction demonstrated a decrease in 142t virus growth as compared to ctrl , supporting that dissemination of the virus is dependent on its ability to replicate in hematopoietic cell types . To account for this lack of dissemination , we compared the relative level of virus growth between hematopoietic and non-hematopoietic populations , and determined that the 142t virus displayed enhanced replication in CD45− , non-hematopoietic cells ( Figure 4B ) . Taken together , these data suggest that the 142t virus is not attenuated in non-hematopoietic cells , but that titers are impaired as a result of decreased hematopoietic replication . Following verification of in vivo targeting , we further assessed dissemination of the ctrl and 142t strains in Ifnar1−/−/Il28r−/− mice . To this end , three different routes of inoculation were administered to assess virus replication in both liver and spleen . Different cohorts of mice were administered virus by either intraperitoneal ( IP ) , intraveneous ( IV ) , or subcutaneous ( SC ) injection ( Figure 4C , Figure S4B ) . For IP administration , qRT-PCR analysis of the spleen revealed an ∼1 . 5-log reduction in viral transcript levels of the 142t strain as compared to those given ctrl virus ( Figure 4C ) . This attenuation was further enhanced by IV and SC injection , routes that better simulate natural inoculation . Interestingly , decreased amounts of NS5 transcript was also evident in the liver ( Figure S4B ) , where miR-142 expression is reduced compared to the spleen [50] . Taken together , these in vivo results suggest that virus levels in the liver reside predominantly in resident macrophages and DCs , and that perhaps these cells are needed to maintain basal levels of virus replication in non-hematopoietic cells . To further validate the reduction of viral growth in vivo , viral titers from infected mice corroborated qRT-PCR data by demonstrating an approximate three-log reduction in titers from spleen and liver ( Figure 4D , Figure S4C ) . Viral titers were undetectable in heart , lung , and brain from these mice , demonstrating that the spleen and liver were the primary sites of virus replication in the context of this animal model . As low levels of 142t virus were evident in both the spleen and the liver , we next sought to determine whether this reflected a non-hematopoietic reservoir for the virus or whether this was evidence of virus escape . In an effort to characterize the genotype of the recombinant DENV viruses over the course of infection , we amplified the 3′UTR of the ctrl and 142t viruses derived from in vitro and in vivo infections ( Figure S5 and Figure 5A ) . To this end , non-hematopoietic fibroblasts expressing varying levels of miR-142 were infected for 3′UTR sequence analysis . In vitro , 142t-specific transcripts were only detectable in conditions where miR-142 expression was decreased , a result that could reflect lower transfection efficiency or insufficient miRNA levels . To assess the 3′UTR sequence in vivo , splenocytes from infected mice were analyzed , demonstrating abundant transcript levels of the ctrl virus and significantly lower levels of the 142t virus ( Figure 5A ) . Furthermore , the only product derived from the 142t virus migrated faster during gel electrophoresis , suggesting it had been truncated . Sequence analyses of transcripts from these two infections demonstrated an unbiased mutation frequency in vitro that was comparable between the two viral cohorts , suggesting the mutations were a reflection of low polymerase fidelity , rather than a result of evolutionary constraint . In contrast , sequence analyses of the transcripts found in vivo demonstrated a complete absence of 142t virus . Rather , virus species remaining displayed loss of all four miRNA target sites either by complete excision or by replacement with a small host RNA fragment ( Figure 5B ) . The sequence analysis performed here explains why low levels of 142t virus was detected by qRT-PCR in Figure 4 , as this assay measured levels of NS5 , which encompasses any escape mutants present during infection . Our data in Figure 5 reveals that the percentage of 142t virus represented is , in fact , much lower . Furthermore , this is unlikely attributable to the presence of quasi-species in our virus stocks , as these mutations were not observed in vitro ( Figure S5B ) . Previous efforts aimed at defining the tropism of DENV infection have focused on the detection of virus components in various tissues of the host . Unfortunately , distinguishing between active DENV replication within a cell versus the presence of virus due to cellular engulfment has been difficult . Here we take an innovative approach to addressing this question with the generation of a virus that is selectively attenuated in a cell-type specific manner . Through the exploitation of hematopoietic-specific miR-142 , we were able to exclude the replication of DENV at its major sites of replication in vivo , and demonstrate that these cell populations are critical for dissemination of the virus to other tissues . Our initial in vitro work clearly demonstrated the specificity of the system and provided a foundation for the subsequent studies performed in mice . The technology enabled a precise mechanism to eliminate primary target cells of DENV replication and study the effects in the context of a dynamic in vivo infection . An additional noteworthy finding that resulted from this study centers on the mechanism of miRNA-mediated attenuation . While attenuation of the 142t strain is presumably the result of target RNA cleavage by Ago2 , the repression of 142tm is an enigma . The data supports that 142tm attenuation occurs less at the level of RNA than it does at the level of protein , making the underlying molecular biology responsible for this repression unclear . While the extensive complementarity may be responsible for some level of post-transcriptional silencing , the complete loss of the seed sequence makes this unlikely . An alternative hypothesis would be that the level of complementarity is sufficient to recruit miR-142-containing RISC , but not sufficient to confer post- transcriptional silencing . In this model , RISC-binding may do nothing more but provide a steric hinderance to the virus . To undergo replication , DENV , as well as other flavivirus genomes , cyclize through complementarity at conserved elements in the 5′ and 3′ ends of the viral RNA [51] . As such , RISC function may be at the level of physically disrupting proper folding of the virus genome . This latter model is supported by the isolation of escape mutants . Rather than identifying single point mutations in the seed or center sites of each target , the only escape mutants isolated were viruses in which the complete targeting cassette was excised . Taken together , this suggests that miRNA-mediated attenuation of viruses may be occurring both through transcriptional silencing , and through the steric interference of RNA folding . Future work to address this possibility is ongoing . In closing , this technology has become a unique and effective tool to study cell populations involved in the DENV life cycle , and can be easily applied to other viruses to examine the relevance of cellular subsets involved in virus replication . In this regard , it is interesting that restricting hematopoietic replication of DENV-2 prevents overall dissemination of the virus suggesting that non-hematopoietic primary cells may not be productively infected in vivo or that macrophages and DCs are essential for viral spread . Altogether , the successful attenuation of DENV in a cell-specific manner suggests this technology may be exploited for studying the relative contributions of cell subsets to virus pathogenesis in vivo .
All animal studies were approved by the Animal Care and Use Committee of Mount Sinai School of Medicine and were performed in compliance with relevant institutional policies , the Association for the Accreditation of Laboratory Animal Care guidelines , the National Institutes of Health regulations , and local , state , and federal laws . A full-length infectious cDNA clone of DENV-2 ( 16681 ) , pD2/IC-30P-A , was used as a template for site-directed mutagenesis ( SDM ) to introduce a unique AflII restriction enzyme site for subsequent insertion of miR-142 target sites into the 3′ NCR [52] . The pD2/IC-30P-A construct was a kind gift from Richard Kinney ( Arbovirus Disease Branch , CDC , Fort Collins , CO ) . The AflII site was generated with complementary SDM primers resulting in the following sequence: 5′-TAGAAAGCTTAAGTAACATGAAA-3′ ( AflII site is underlined ) . Target sites for miR-142 have been described elsewhere [53] and were cloned into the AflII sites in both the forward ( 142t DENV-2 ) and reverse ( ctrl DENV-2 ) orientations . For the 142m strain , the following complementary oligonucleotides were annealed and cloned into the AflII site: Forward 5′-GGCTTAAGTCCATAAAGTAGGCAACACTCCAAGGCGATCCATAAAGTAGGCAACACTCCAGCGGCCGCTCC-3′ , Reverse 5′- CCCTTAAGTGGAGTGTTGCCTACTTTATGGAGAGCCCTGGAGTGTTGCCTACTTTATGGAGCGGCCGCTGG -3′ . Recombinant pD2/IC-30P-A clones were linearized with XbaI , in vitro transcribed with T7 , and electroporated into BHK cells as previously described [52] . Viral titers were determined by 1% agarose-based plaque assays performed in Vero cells . Transfected and infected HEK293s were harvested 48 hpi and analyzed for GFP fluorescence on the Becton Dickinson FACS Caliber and mean fluorescence intensity was calculated using the Flowjo analysis software . Experiments were performed in triplicate and mean fluorescence intensity for GFP was calculated as a percentage of the vector control . Unless otherwise specified , all mammalian cell lines were cultured in DMEM/FBS . C6/36 cells were cultured in RPMI 1640/FBS and maintained at 33°C . BHK and C6/36 cells were infected with either wt , ctrl , or 142t DENV-2 at an MOI of 1 in serum-free DMEM . The inoculum was allowed to adsorb for two hrs at 37°C , washed and replaced with DMEM ( 5% FBS ) . Infection for plaque assays were performed as described above in BHKs in duplicate with 1∶10 serial dilutions of virus stocks . Infected mouse organs were harvested and homogenized in 1× PBS , and frozen at −80°C prior to plaque assay . After 2 hrs of adsorption , an overlay of 1% agarose/DMEM was added to the cells and allowed to incubate for 6 days at 33°C . Cells were stained using 1% crystal violet . Cells were transfected in suspension using Lipofectamine 2000 for HEK293s ( Invitrogen ) and Lipofectamine LTX ( Invitrogen ) for BHKs as per manufacturer's instructions . For studies involving knockdown of DENV IVT products , BHKs were transfected with vector or p142 for 24 hrs , followed by electroporation of DENV-2 IVT products for 12 ( Figure 3E ) , 24 or 48 hrs with the Amaxa nucleofector ( Lonza ) as per manufacturer's instructions . For virus rescue , 5×106 BHKs were electroporated with DENV-2 IVT products and seeded into 10 cm plates . Six days post electroporation , supernatant was harvested and stored at −80°C . Ifnar1−/−/Il28r−/− mice have been described elsewhere [48] . Mice were given 2×106 PFU for IP , 4×105 PFU for IV , and 2×105 PFU for SC injections . Indicated organs were removed from animals 48 hpi . All experiments involving animals were performed in accordance with the Mount Sinai School of Medicine Institutional Animal Care and Use Committee . Spleens from infected Ifnar1−/−/Il28r−/− mice were processed into single cell suspensions and stained with anti- CD11b ( Miltenyi Biotech ) , CD11c ( Miltenyi Biotech ) , or CD45 microbeads ( Miltenyi Biotech ) . Positive and negative cell fractions were isolated using autoMACS Pro Separator ( Miltenyi Biotech ) followed by RNA isolation . Protein and RNA cellular extract were harvested as previously described ( Perez et al , 2009 ) . NS5 antibody ( anti-NS5 , rabbit polyclonal , a kind gift from A . Garcia-Sastre ) and actin ( anti- actin , Abcam , ) were used at a concentration of 1 microgram per milliliter in 5% milk . Secondary rabbit or mouse antibodies ( GE Healthcare ) were used at 1∶5000 dilutions for 1 hr at room temperature . Immunobilon Western Chemiluminescent HRP Substrate ( Millipore ) was used as per manufacturers instructions . Quantitative RT-PCR ( qRT-PCR ) analysis was performed with random hexamers using Superscript II ( Invitrogen ) and cDNA samples was performed using KAPA SYBR FAST qPRC Master Mix ( KAPA Biosystems ) . PCR reactions were performed on a Mastercycler ep realplex ( Eppendorf ) . α-Tubulin and actin primers were used as endogenous housekeeping genes for mammalian and mosquito cultures , respectively . Delta delta cycle threshold ( ΔΔCT ) values were calculated with replicates over α-tubulin or actin . Values represent the fold change over mock-infected samples . Sequences for PCR primers used: NS5: 5′- ACAAGTCGAACAACCTGGTCCAT-3′ , 5′-GCCGCACCATTGGTCTTCTC-3′ , α-tubulin: 5′-TGCCTTTGTGCACTGGTATG-3′ , 5′- CTGGAGCAGTTTGACGACAC-3′ , actin: 5′-GCACTGGACTTTGAACAGGAAATG-3′ , 5′-AGGAACGATGGCTGGAAGAGAG-3′ , capsid: 5′-TGGTGGCGTTCCTTCGTTTC-3′ , 5′ GCATCCTTCCAATCTCTTTCCTG-′3 , and DENV 3′UTR: 5′- TCCCTTATAGGCAATGAAGAATACA-3′ , 5′-TTATGATGGCCTGACTTCTTTTAAC-3′ . cDNA from BHKs transfected with 1 . 5 micrograms of p142 and infected with either ctrl or 142t viruses for 24 hrs was reverse transcribed with random hexamers using Superscript II ( Invitrogen ) . Virus transcript was PCR-amplified with Econotaq PLUS green ( Lucigen ) using primers specific for the 3′UTR of DENV: forward 5′-TTTGGGGAAGTCTTACGC-3′ , reverse 5′-GTTGCTGCGATTTGTAAGG-3′ . Hamster actin primers were included as a loading control: forward 5′-TCTACAACGAGCTGCG-3′ , reverse 5′-CAATTTCCCTCTCGGC-3′ . Twenty PCR cycles were performed with the following conditions: 94°C for 10 sec , 58°C for 30 sec , 72 for 1 . 5 min . Bands were gel purified using the Qiagen gel extraction kit and cloned into pCR-TOPO 2 . 1 ( Invitrogen ) following manufacturer's protocols . Fifteen random clones were submitted for sequencing with the M13R primer . Sequences were then aligned to miR-142 complementary sites in the targeted and untargeted orientation . Mutations identified in these regions were noted . For escape mutants found in vivo , reverse transcription was performed on cDNA from spleens of Ifnar1−/−/Il28r−/− mice infected with ctrl or 142t viruses for 48 hrs . PCR was performed as described above for 40 cycles . Bands were gel purified and cloned as described above . Small RNA northern blotting and probing were performed on total RNA samples as described previously ( Perez et al , 2009 ) . Probes include: miR-142: 5′- TCCATAAAGTAGGAAACACTACA-3′ , U6: 5′-GCCATGCTAATCTTCTCTGTATC -3′ . Statistical significance was calculated using a two-tailed , unpaired T test . Data considered significant demonstrated p values less than 0 . 05 . | Dengue virus ( DENV ) is becoming a global threat as anthropogenic factors are increasing the prevalence of vector species capable of transmitting the pathogen . There are currently no vaccines or therapeutics against DENV , and the study of virus pathogenesis and dissemination has been largely limited to artificial mouse models . As DENV is capable of infecting many cell types including dendritic cells ( DCs ) , macrophages , and fibroblasts , it remains unclear which cells permit DENV replication in vivo and are responsible for virus spread . To this end , we inserted microRNA ( miRNA ) target sites into the virus genome to render it incapable of replicating in DCs and macrophages , while having no direct effect on replication in other cell types . The purpose of this study was to exclude virus growth in a defined cellular population and assess the effects of this restriction on viral dissemination and replication in vivo . With this technology , we demonstrate that restricting replication in hematopoietic cells results in a complete loss of miRNA-targeted virus , indicating that these cells are the predominant reservoirs for virus replication . | [
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] | 2012 | Replication in Cells of Hematopoietic Origin Is Necessary for Dengue Virus Dissemination |
As human population density and antibiotic exposure increase , specialised bacterial subtypes have begun to emerge . Arising among species that are common commensals and infrequent pathogens , antibiotic-resistant ‘high-risk clones’ have evolved to better survive in the modern human . Here , we show that the major matrix porin ( OmpK35 ) of Klebsiella pneumoniae is not required in the mammalian host for colonisation , pathogenesis , nor for antibiotic resistance , and that it is commonly absent in pathogenic isolates . This is found in association with , but apparently independent of , a highly specific change in the co-regulated partner porin , the osmoporin ( OmpK36 ) , which provides enhanced antibiotic resistance without significant loss of fitness in the mammalian host . These features are common in well-described ‘high-risk clones’ of K . pneumoniae , as well as in unrelated members of this species and similar adaptations are found in other members of the Enterobacteriaceae that share this lifestyle . Available sequence data indicate evolutionary convergence , with implications for the spread of lethal antibiotic-resistant pathogens in humans .
Host adaptation and niche specialisation are well described in bacteria . As human population density rises , commensals and pathogens among the Enterobacteriaceae are transmitted directly from human to human and increasingly exposed to antibiotics . K . pneumoniae is now a common cause of healthcare-associated infections and is one of the most important agents of human sepsis [1] . High morbidity and mortality are associated with acquired antibiotic resistance , most importantly by horizontal transfer of genes encoding extended-spectrum β-lactamases ( ESBL ) [2] and plasmid-mediated AmpC β-lactamases ( pAmpC ) [3] . Carbapenem antibiotics have been effective against such isolates for decades , but resistance to these antibiotics is increasingly common in turn [4] and in February 2017 , carbapenem resistant Enterobacteriaceae were listed among the highest ( ‘critical’ ) research priorities by the World Health Organisation . Acquired genes encoding efficient carbapenem hydrolysing enzymes [5] typically require phenotypic augmentation by permeability reduction to be clinically relevant in the Enterobacteriaceae . Indeed , clinically significant carbapenem resistance may even be seen with the less specialised AmpC or ESBL enzymes in strains with sufficiently reduced outer membrane permeability [6 , 7] . K . pneumoniae expresses two major nonspecific porins ( OmpK35 and OmpK36 ) through which nutrients and other hydrophilic molecules such as β-lactams diffuse into the cell [8 , 9] . The expression of these two major porins in K . pneumoniae is strongly linked with β-lactam susceptibility [6 , 7] and strains lacking both porins exhibit high levels of resistance [10] . K . pneumoniae is commonly present in the human gut [1] but also grows in low-nutrient and low-osmolarity conditions , with decreased expression of the ‘osmoporin’ , OmpK36 , and increased expression of the ‘matrix porin’ , OmpK35 , which has greater general permeability . In the mammalian host in vivo , and in nutritious media in vitro , OmpK36 is the principal general porin and the gateway for β-lactam antibiotics , which are the most frequently prescribed antibiotic class in humans and the cornerstone of therapy for serious infections . The fitness cost of certain antibiotic resistance mutations is well described [11 , 12 , 13 , 14] . Significantly reduced expression of porins provides some protection from β-lactam antibiotics but may incur a considerable metabolic cost as vital nutrients are simultaneously excluded [15] . Outer membrane permeability is thus a balance between self-defence and competitive fitness [16 , 17] . Global antibiotic restriction policies are founded on the premise of an inverse relationship between competitive fitness and resistance to antibiotics [18] and the expectation that antibiotic-resistant mutants will fail to successfully compete with their antibiotic-susceptible ancestors [19] . However , analysis of the principal porin relevant to infection in the mammalian host , OmpK36 , revealed a key role for a transmembrane β-strand loop ( loop3 , L3 ) in the porin inner channel ( ‘eyelet’ ) , which is electronegative at physiological pH . Minor changes in this region have been observed that are expected to be relatively permissive of small nutrient molecule diffusion but which may exclude more bulky anionic carbapenem and cephalosporin antibiotics [20] . Highly antibiotic-resistant K . pneumoniae is both a critical threat pathogen and a model of adaptation in a world with increasing human density and antibiotic exposure . The aim of this study was therefore to understand the pathogenesis and antimicrobial resistance implications of common changes in major porins that diminish membrane permeability .
The bacterial strains , plasmids and primers used in this study are listed in Table 1 and S1 Table . Porin mutants were constructed in three antibiotic-susceptible K . pneumoniae strains ( ATCC 13883 , and clinical isolates 10 . 85 and 11 . 76 from our laboratory ) . Bacterial isolates were stored at -80°C in Nutrient broth ( NB ) with 20% glycerol and recovered on LB agar plates . Unless otherwise indicated , strains were routinely grown in Mueller-Hinton broth ( MHB , BD Diagnostics , Franklin lakes , NJ , USA ) or Luria-Bertani ( LB , Life Technologies , Carlsbad , CA , USA ) . E . coli and K . pneumoniae strains carrying the chloramphenicol-resistant plasmids pKM200 and pCACtus were grown at 30°C on LB agar or in LB broth supplemented with 20 μg/ml chloramphenicol ( Sigma-Aldrich , St . Louis , MO , USA ) . The growth of bacterial cells was determined by measuring the optical density at 600 nm ( OD600 ) in an Eppendorf Biophotometer ( Eppendorf AG , Hamburg , Germany ) . Chemical transformation , conjugation and electroporation were carried out using standard protocols . Platinum pfx DNA polymerase ( Invitrogen , USA ) was used to amplify blunt-ended PCR products . All PCR products were purified ( PureLink Quick PCR Purification Kit; Invitrogen , USA ) . PCR and Sanger sequencing were used to confirm all constructs . Genomic DNA extractions were performed using a DNeasy Blood and Tissue kit ( Qiagen , Valencia , CA , USA ) and plasmid DNA using a PureLink Quick Plasmid Miniprep kit ( Life Technologies , Carlsbad , CA , USA ) or a HiSpeed Plasmid Midi Kit ( Qiagen , Valencia , CA , USA ) . Porin deletions mutants of K . pneumoniae ATCC 13883 , 10 . 85 and 11 . 76 were created by introduction of tetA ( tetracycline-resistance ) or aphA-3 ( kanamycin-resistance ) into unique sites in ompK35 and ompK36 ( HincII and StuI , respectively ) which had been previously cloned into pGEM-T easy ( Promega , Madison , WI , USA ) . The disrupted porin genes were then cloned into the pCACtus temperature-sensitive suicide vector ( pJIAF-7 to pJIAF-12 ) to replace the respective chromosomal genes by homologous recombination [25] . Confirmation of correct single-copy chromosomal mutations were finally verified by PCR ( S1 Table ) . OmpK36GD mutants were obtained by amplification of OmpK36 from each parental strain using K36GD1 / K36GD2 and K36GD3 / K36GD4 primers ( S1 Table ) . The amplicon , containing a GD duplication in L3 , was cloned first in pGEM-T easy and after digestion with SphI and SacI ( New England Biolabs , MA , USA ) was introduced into pCACtus . The pCACtus-based constructs ( pJIAF-13 to pJIAF-18 ) were transformed into S17λpir and conjugated into K . pneumoniae ΔOmpK36 ( kanamycin-resistance mutant ) in which the interrupted gene was replaced by OmpK36 porin with GD duplication in L3 by homologous recombination . Mutants were selected by loss of kanamycin resistance and confirmed by PCR and sequencing . Double mutants ( ΔOmpK35ΔOmpK36 and ΔOmpK35OmpK36GD ) were constructed using Lambda Red-mediated recombineering as described previously [26 , 27] , with some modifications . A tetracycline cassette flanked by OmpK35 deletion ( ~2 . 5 kb in size ) was PCR amplified from an OmpK35 deletion mutant ( ΔOmpK35; tetracycline resistant-previously obtained ) using primers ompK35X-F and ompK35X-R ( S1 Table ) , and the PCR products were purified . The Red helper plasmid pKM200 was electroporated into ΔOmpK36 or OmpK36GD single mutants . ompK35:tetA fragments were electroporated into ΔOmpK36 or OmpK36GD clones carrying pKM200 . Bacteria were grown at 30°C for 2 h with agitation ( 225 rpm ) followed by overnight incubation at 37°C . Different dilutions of the electroporated cells were spread on LB agar plates containing 10 μg/ml tetracycline to select for transformants at 37°C . The correct structure was confirmed by sequencing of PCR amplicons ( primers ompK35F1 and ompK35R2 , S1 Table ) . All the engineered strains were verified by whole genome sequencing . The ompK36 gene with its predicted ribosomal binding site and transcriptional terminator was PCR amplified using 5’-GACAAGCTTTAAAAGGCATATAACAAACAG-3’ ( forward ) and 5’-CTGGGATCCAGCGAGGTTAAACCGG-3’ ( reverse , S1 Table ) . Genomic DNA from K . pneumoniae ATCC 13883 wild-type strain ( Table 1 ) was used as template . To generate ompK36 PCR product with the L3 GD mutation , the ATCC OmpK36GD strain was used as template DNA ( Table 1 ) . DNA inserts containing ompK36 and ompK36GD were cloned into the low-copy number pACYC184 vector [28] at the HindIII/BamHI restriction sites to generate pJIQQ-1 ( pACYC184-OmpK36 ) and pJIQQ-2 ( pACYC184-OmpK36GD ) plasmids ( S1 Table ) , respectively . Two K . pneumoniae strains , 10 . 85ΔOmpK35ΔOmpK36 and JIE2771 ( a clinical strain with naturally-occurring lesions in ompK35 and ompK36 ) , were grown overnight in LB broth . On the next day , the strains were centrifuged and washed three times with ice-cold 10% glycerol . pACYC184 , pJIQQ-1 and pJIQQ-2 were electroporated into the electrocompetent cells to generate the strains shown in Table 1 . Sanger sequencing was performed to verify the absence of unintended non-synonymous mutations in the coding regions of ompK36 and ompK36GD . Susceptibilities to cefazolin ( CFZ , Sigma-Aldrich , St . Louis , MO , USA ) , cephalothin ( CEF , Sigma-Aldrich , St . Louis , MO , USA ) , cefoxitin ( FOX , Sigma-Aldrich , St . Louis , MO , USA ) , cefuroxime ( CXM , Sigma-Aldrich , St . Louis , MO , USA ) , cefotaxime ( CTX , A . G . Scientific , Inc . , San Diego , CA , USA ) , ceftazidime ( CAZ , Sigma-Aldrich , St . Louis , MO , USA ) , ertapenem ( ETP , Sigma-Aldrich , St . Louis , MO , USA ) , imipenem ( IPM , Sigma-Aldrich , St . Louis , MO , USA ) , meropenem ( MEM , A . G Scientific , Inc , San Diego , CA , USA ) and ampicillin ( MEM , A . G Scientific , Inc , San Diego , CA , USA ) were performed by broth microdilution in cation-adjusted Mueller-Hinton ( MH ) broth ( Becton Dickinson ) with inocula of 5 x 105 CFU/ml in accordance with CLSI MO7-A9 recommendations [29] . All MICs were determined in triplicate at least on three separate occasions to obtain at least 9 discrete data points and compared with EUCAST and CLSI clinical breakpoints for all antibiotics [30 , 31] . E . coli ( ATCC 25922 ) and Pseudomonas aeruginosa ( ATCC 27853 ) were included in each experiment as quality controls . For the in trans complemented strains , the susceptibilities of plasmid-bearing mutants of 10 . 85ΔOmpK35ΔOmpK36 to CEF , CFZ and FOX , as well as the susceptibilities of the plasmid-bearing mutants of JIE2771 to ETP , IPM and MEM , were performed in cation-adjusted Mueller-Hinton broth with chloramphenicol at a concentration of 25 μg/ml . Subsequent procedures follow those used for all other bacterial strains in this study . The filter mating method [32] was used to transfer plasmids from clinical isolates carrying blaCTX-M-15 , ( pJIE143 ) [33] blaIMP-4 ( pEl1573 ) [34] and blaKPC-2 ( pJIE2543-1 ) [22] to K . pneumoniae ATCC 13883 and porin mutants ( ΔOmpK35 , ΔOmpK36 , OmpK36GD , ΔOmpK35ΔOmpK36 and ΔOmpK35OmpK36GD ) . The presence of resistance genes in transconjugants was confirmed by PCR [22 , 35 , 36] and the presence of plasmids of the expected size confirmed by S1 nuclease pulsed-field gel electrophoresis ( S1 Fig ) ( Promega , Madison , WI , USA ) [37 , 38] . Isolates were grown overnight under different temperatures ( 37°C , 30°C and 25°C ) and different nutrient concentrations ( MH and MH 1:10 ) . Bacteria were disrupted by sonication and outer membrane porins ( OMPs ) isolated with sarcosyl ( Sigma-Aldrich , St . Louis , MO , USA ) , as previously described [21 , 39] . Samples were boiled , analyzed by sodium dodecyl sulfate-polyacrylamide gel electrophoresis ( SDS-PAGE ) ( 12% separating gels ) , and stained with Imperial Protein Stain ( Thermo Scientific , Rockford , IL , USA ) , following the manufacturer’s instructions . K . pneumoniae ATCC 13883 , which produces both porins ( OmpK35 and OmpK36 ) was used as a control [40] . Colour prestained protein standard , broad range ( 11–245 kDA ) ( New England Biolabs , MA , USA ) was used as size marker . The expression levels of the different porins were measured by real-time RT-PCR . Cells were harvested in logarithmic phase at an OD600 of 0 . 5–0 . 6 . Total RNA was isolated using RNeasy system ( Qiagen ) . RNA was treated with DNase ( TURBO DNA-free Kit , Ambion ) . cDNA was synthesized by high-capacity cDNA reverse transcriptase kit ( Applied Biosystems ) . One microgram of the initially isolated RNA was used in each reverse transcription reaction . cDNA was diluted 1:10 and 2 μl were used for the real-time reaction . Three biological replicates , each with three technical replicates , were used in each of the assays . The relative levels of expression were calculated using the threshold cycle ( 2−ΔΔCT ) method [41] . The expression of rpoD was used to normalize the results . The primers used are listed in S1 Table . Growth rates were determined as previously described [42] . Overnight broth cultures were diluted 1:1000 . Six aliquots of 200 μl per dilution were transferred into 96-well microtiter plates ( Corning Incorporated , Durham , NC , USA ) . Samples were incubated at 37°C and shaken before measurement of OD600 in a Vmax Kinetic microplate reader ( Molecular Devices , Sunnyvale , CA , USA ) . Growth rates and generation times were calculated on OD600 values between 0 . 02–0 . 09 . The relative growth rate was calculated by dividing the generation time of each mutant by the generation time of the parental strain ( K . pneumoniae ATCC 13883 , 10 . 85 or 11 . 76 ) , which was included in every experiment . Experiments were performed in six technical replicates in three independent cultures on three different occasions . Results are expressed as means ± standard errors of the means . For complemented strains , K . pneumoniae plasmid-bearing mutants of 10 . 85 ΔOmpK35ΔOmpK36 and JIE 2771 were streaked on Mueller-Hinton agar with 25 μg/ml chloramphenicol and incubated overnight at 37°C . On the next day , bacterial cells were resuspended in 0 . 85% saline to a turbidity of 0 . 5 McFarland . The inoculum was diluted 1:400 in cation-adjusted Mueller-Hinton broth , which were then transferred in six aliquots of 200 μl into 96-well microtiter plates ( Corning Incorporated , Durham , NC , USA ) and incubated with continuous gentle orbital shaking at 37°C in a SpectraMax iD5 Hybrid Multi-Mode Microplate Reader ( Molecular Devices , San Jose , CA , USA ) . Measurements of OD600 were obtained every 5 minutes . Subsequent procedures follow those used for all other bacterial strains in this study . Competition experiments were carried out as described previously [43] . Viable cell counts were obtained by plating every 24 h on antibiotic-free LB agar and on LB agar supplemented with antibiotic ( kanamycin 20 μg/ml or tetracycline 10 μg/ml ) to distinguish between mutants and wild-type cells . PCR ( with primer pair K36GD4 / K36GD11 or K36GD12 / K36GD13 primers , S1 Table ) was performed for the calculation of the competition results between the parental strain and OmpK36GD mutant ( in this particular experiments , bacteria were diluted in fresh media every 24 h and PCR on 100 viable colonies of each replicate was performed every 48 h ) . All experiments were carried out in triplicate with three independent cultures . Mean values of three independent experiments ± standard deviation were plotted . Five to six week-old female BALB/c mice ( Animal Resources Centre ( ARC ) , Sydney , Australia ) were used for GI colonization [44 , 45 , 46] and competition experiments . Mice were caged in groups of three and had unrestricted access to food and drinking water . Faecal samples were collected and screened for the presence of indigenous K . pneumoniae before inoculation . For the colonization study , three mice were inoculated with the parental strain or a porin mutant ( 1 x 1010 CFU / mouse ) , suspended in 20% ( w/v ) sucrose . For individual colonization , ampicillin was added to drinking water on day 4 ( 0 . 5 g / L ) after an inoculation [47] . For the competition experiment , equal volumes of the parental strain and each mutant or equal volume of different mutants ( 1x 1010 CFU / mouse ) were mixed and suspended in 20% ( w/v ) sucrose . Colonization was maintained with ampicillin 0 . 5 g / L throughout the experiment [48 , 49 , 50] . Faeces samples were collected every second day , emulsified in 0 . 9% NaCl and appropriate serial dilutions plated on MacConkey-inositol-carbenicillin agar , which selectively recovers K . pneumoniae [51] . Animal experiments were approved by the Western Sydney Local Health District Animal Ethics Committee ( AEC Protocol no . 4205 . 06 . 13 ) . Five-six week-old female BALB/c mice [Animal Resources Centre ( ARC ) , Sydney , Australia] used in the inhalation ( pneumonia ) model [52 , 53 , 54] were exposed to ATCC 13883 and 10 . 85 and their isogenic ΔOmpK35OmpK36GD mutants . Overnight bacterial cultures were harvested , washed and resuspended at 109 CFU in 20 μl of saline and inoculated into the nasal passages . A control group of mice was inoculated with saline . Following infection , survival studies were performed ( 10 mice per strain ) . At the same time and using the same inoculum , organ ( lung and spleen ) and blood infection burdens were also assessed at various points throughout the infection period , by plating out blood and homogenised tissue onto LB agar , and counting CFU ( 5 mice per strain , per time point ) . Animal experiments were approved by the Western Sydney Local Health District Animal Ethics Committee ( AEC Protocol no . 4275 . 06 . 17 ) . Tri-dimensional structural models of ATCC 13883 OmpK36 and its mutated variant OmpK36GD were computed with ProMod3 Version 1 . 1 . 0 on the SWISS-MODEL online server [55] using the target–template alignment method . The best scoring model used as a template was 5nupA ( 93 . 84% sequence identity , with a QMEAN equal to -2 . 29 and -2 . 14 , respectively for both sequences ) . For comparison purposes , models were also computed using the second best OmpK36 structure available in PDB ( 1osmA ) . All predicted models were evaluated using MolProbity [56 , 57] and Verify3D [58 , 59] , with Ramachandran plots generated by MolProbity indicating for all computed models that at least >98% of residues were in allowed regions . Predicted structures were displayed by PyMol software ( version 2 . 1 . 1 ) [60] . Additionally , the specific impact of two amino-acids insertions was also investigated by altering the OmpK36 structure under PDB accession 5nupA , adding either the amino-acids GD- , TD- or SD- , after position G113 and modeling the resulting variant sequences in the same manner as mentioned above . All isolates used in final experiments were subjected to whole genome sequencing to verify their altered sequence and ensure that no additional mutations had arisen . Genomic DNA was extracted from 2 ml overnight cultures using the DNeasy Blood and Tissue kit ( Qiagen ) . Paired-end multiplex libraries were prepared using the Illumina Nextera kit in accordance with the manufacturer’s instructions . Whole genome sequencing was performed on Illumina NextSeq 500 ( 150bp paired-end ) at the Australian Genome Research Facility ( AGRF ) and at Professor Vitali Sintchenko’s laboratory ( Translational Public Health Bacterial Genomics Group , Centre for Infectious Diseases and Microbiology ( CIDM ) Public Health , Westmead Hospital , NSW , Australia ) . Raw sequence reads are available on NCBI under Bioproject accession number PRJNA430457 . Reads were quality-checked , trimmed and assembled using the Nullarbor pipeline v . 1 . 20 ( available at: https://github . com/tseemann/nullarbor ) , as previously described [61] , but with the exception of the assembly step which was performed using Shovill ( available at: https://github . com/tseemann/shovill ) , a genome assembler pipeline wrapped around SPAdes v . 3 . 9 . 0 [62] which includes post-assembly correction . Assemblies were also reordered against reference strain K . pneumoniae 30660/NJST258_1 ( accession number CP006923 ) using progressive Mauve v . 2 . 4 . 0 [63] prior to annotation with Prokka [64] and screened for antibiotic resistance genes using Abricate v . 0 . 6 ( available at: https://github . com/tseemann/abricate ) . To investigate the significance of OmpK35 and OmpK36 mutations in a wider population , we collected a total of 1 , 557 draft and complete K . pneumoniae genomes publicly available in Genbank ( Feb 2017 , S2 Table ) . Sequences were typed using Kleborate v0 . 1 . 0 [65] to identify MLST ( S3 Table ) and minimum spanning trees were generated using Bionumerics v . 7 . 60 . Presence and absence of porins were assessed in the pangenome using Roary v3 . 6 . 0 [66] with default parameters , and mutations in loop 3 ( L3 ) identified using BLAST . The 2 , 253 , 033 bp core genes alignment predicted by Roary was used to build a maximum-likelihood tree using IQ-TREE v1 . 6 . 1 [67] , with a GTR+G+I nucleotide substitution model and branch supports assessed with ultrafast bootstrap approximation ( 1 , 000 replicates ) . Trees were visualized alongside contextual information with Phandango [68] . Statistical analysis was performed using Chi-squared test and Wilcoxon test , to determine associations between ST , porin defects and antibiotic resistance genes . Extended mosaic plots were used to assess the distribution of OmpK35 and OmpK36 with or without GD/TD insertion across i ) ST , ii ) country of origin and iii ) year of isolation . Extended mosaic plots offer a convenient way to visualize the relative frequencies of a set of categorical data using proportional areas , as well as the fit of a log-linear model ( assuming independence ) . Areas are thus colored according to the direction and magnitude of standardized deviation from the expected frequency ( Pearson residual ) . Cut-offs of +/- 2 and 4 are defined heuristically on the assumption that the Pearson residuals approximate a standard normal distribution , and can be approximated to the statistical significance alpha = 0 . 05 and and alpha = 0 . 001 levels , respectively [69] . All statistical analyses were performed in R version 3 . 5 . 1 and “vcd” package . Relevant R scripts were also made available at https://github . com/nbenzakour/Klebsiella_antibiotics_paper . For doubling time and Real Time RT-PCR , the results were analysed using the Student t test to determine their significance . For survival studies the results were analysed using Long-rank ( Mantel-Cox ) test and Gehan-Breslow-Wilcoxon test . To compare bacterial load in organs during lung infection , results were compared using Mann-Whitney unpaired t test . The analyses were performed using Prism7 ( GraphPad Software ) .
Minimal inhibitory concentrations for commonly used carbapenems ( ertapenem and meropenem ) , third-generation cephalosporins ( ceftazidime , cefotaxime and ceftriaxone ) , cephamycins ( also called ‘second generation cephalosporins’ , cefoxitin and cefuroxime ) , first generation cephalosporins ( cephalothin and cefazolin ) , and the semi-synthetic penicillin ampicillin were determined in three K . pneumoniae strains and their isogenic porin mutants , with representative results in Table 2 ( for complete results , see S4 Table ) . The SHV enzyme characteristically expressed by K . pneumoniae hydrolyses ampicillin very effectively , providing high MICs to ampicillin [70 , 71 , 72 , 73] , but does not provide clinically important resistance to cephalosporins or carbapenems in the setting of normal membrane permeability . Loss of OmpK36 ( ΔK36 in Table 2 and S4 Table ) is associated with a minor increase in MIC for carbapenems and cephalosporins ( Table 2 and S4 Table ) , with a lesser impact from OmpK36GD mutations , consistent with an important role for OmpK36 in the nutritious growth media ( MHB ) normally used for standardised MIC determinations ( Table 2 and S4 Table ) . OmpK35 loss ( ΔK35 in Table 2 and S4 Table ) has little impact alone but further increases MICs for most antibiotics in the presence of OmpK36 lesions ( e . g . ΔK35ΔK36 and ΔK35ΔK36GD ) . In addition to ertapenem non-susceptibility , ΔOmpK35ΔOmpK36 and ΔOmpK35OmpK36GD strains are clinically resistant to first ( e . g . cephalothin , CEF ) and second generation cephalosporins/ cephamycins ( e . g . cefoxitin , FOX ) ( Table 2 and S4 Table ) . Naturally occurring plasmids from other K . pneumoniae strains encoding a common ESBL ( blaCTX-M-15 ) [33] , a metallo-carbapenemase ( blaIMP-4 ) [34] and a serine-carbapenemase ( blaKPC-2 ) [22] were transferred into ATCC 13883 and its isogenic mutants by conjugation , with transfer verified by PCR ( S1 Table ) and S1/PFGE ( S1 Fig ) . Even the common ESBL CTX-M-15 confers reduced susceptibility to ETP in the presence of an OmpK36 deletion or inner channel mutation ( GD duplication ) , especially if accompanied by an OmpK35 defect ( Table 3 ) . Expression of the specialised carbapenemases IMP and KPC from their naturally occurring plasmids resulted in greatly increased carbapenem MICs ( Table 3 ) , with the double porin mutants being highly resistant to all carbapenems tested . K pneumoniae JIE2771 is a clinical isolate of K pneumoniae carrying blaKPC and a natural double mutant of ompK35 and ompK36 [22] . As expected , attenuation of the resistance phenotype was evident in this wild-type double mutant and susceptibility restored to the constructed 10 . 85 double mutant by in trans complementation with ompK36 but not ompK36GD ( S5 Table ) . Other porins may compensate for the loss of major outer membrane porins in K . pneumoniae [76 , 77 , 78 , 79] . Expression of ompK35 , ompK36 , ompK37 , phoE , ompK26 and lamB was measured in isogenic porin mutants of ATCC 13883 and 10 . 85 K . pneumoniae strains ( Fig 1 , S6 Table ) . Neither the introduction of a GD duplication into the OmpK36 inner channel ( OmpK36GD ) nor the loss of OmpK35 ( ΔOmpK35 and ΔOmpK35OmpK36GD ) affected expression of OmpK36 in MH broth . Loss of OmpK36 , however , was associated with increased OmpK35 expression in MH broth , in which OmpK36 , but not OmpK35 , is ordinarily expressed ( S2 Fig ) . Restitution of OmpK36 by replacing the interrupted gene with ompK36GD directly in the chromosome restored normal porin regulation ( Fig 1 , S6 Table ) . Loss of both of these major porins ( ΔOmpK35ΔOmpK36 ) resulted in increased expression of phoE and lamB . ( Fig 1 , S6 Table ) . Exponential phase growth in MH broth was only affected when both major porins were absent ( ΔOmpK35ΔOmpK36 , S7A Table ) . In trans complementation with either ompK36 or ompK36GD resulted in amelioration of the growth defect in JIE2771 wild-type double mutant and the constructed 10 . 85ΔOmpK35ΔOmpK36 ( S7B Table ) . The ability of ΔOmpK35 strains to directly compete against their intact isogenic parents in MH broth was little affected over seven-day growth ( Fig 2A1 and 2A2 and S3 Fig ) . However , any ΔOmpK35 mutant was rapidly outcompeted by its isogenic parent in nutrient-limited conditions ( S4A1 and S4B1 Fig ) . Furthermore , competition experiments clearly illustrate the importance of OmpK36 in high osmolarity highly nutritious media ( Fig 2B1 and 2B2 and S3 Fig ) but not in low nutrient conditions ( S4B1 and S4B2 Fig ) . OmpK36GD strains are clearly much more able than ΔOmpK36 strains to compete with their isogenic parent strains ( Fig 2C1 vs 2B1 and 2C2 vs 2B2 ) . For ATCC 13883 , at day 3 , the OmpK36GD population was still 40% of the total combined population ( Fig 2C1 ) , while ΔOmpK36 fell to 20% in the same period ( Fig 2B1 ) . This difference was more marked in the presence of an OmpK35 lesion but ΔOmpK35OmpK36GD populations were still clearly more able than ΔOmpK35ΔOmpK36 to compete with the intact parent strain ( Fig 2F1 vs 2E1 ) . In fact , the introduction of an OmpK36GD mutation had no detectable cost at all in K . pneumoniae 10 . 85 ( Fig 2C2 vs 2B2 and 2F2 vs 2E2 ) , with ΔOmpK35OmpK36GD competing very successfully against the isogenic parent 10 . 85 ( Fig 2F2: 37±4% and 26±15% of the total population represented by ΔOmpK35OmpK36GD on days 6 and 7 respectively ) . Finally , as expected , directly competing OmpK36GD with ΔOmpK36 ( and ΔOmpK35OmpK36GD with ΔOmpK35ΔOmpK36 ) further illustrates the competitive advantage , with OmpK36GD strains quickly displacing isogenic ΔOmpK36 strains in MH broth ( Fig 2D1 and 2G1 ) . Mouse gut colonizing studies yielded similar results ( Fig 3 ) . Mice were confirmed not to harbor indigenous K . pneumoniae on arrival [51] , and stable colonisation at ∼109 CFU/g faeces was achieved ( S5 Fig ) . OmpK35 deficient mutants ( ΔOmpK35 ) were not disadvantaged ( Fig 3A3 ) and OmpK36GD strains strongly outperformed OmpK36 strains in competition with their isogenic parents ( Fig 3A1 vs 3B1 and 3A2 vs 3B2 ) . Similarly , direct in vivo competition confirmed a clear fitness advantage of OmpK36GD over ΔOmpK36 ( Fig 3C1 and 3C2 ) . In a mouse pneumonia model [52 , 53 , 54] , we showed no difference in lethality between a wild type strain and its isogenic mutant ΔOmpK35/OmpK36GD ( Fig 4 ) . Intranasal inoculation of mice with 10 . 85 and ATCC 13883 ΔOmpK35/OmpK36GD strains showed that these mutations had no significant impact on virulence , with equivalent mortality curves ( Fig 4A1 and 4A2 ) and similar viable counts developing in lung , blood and spleen over the course of infection compared with their isogenic wild-type strains 10 . 85 and ATCC 13883 , respectively ( Fig 4B to 4D ) . As OmpK36 deletion mutants have been clearly shown by other studies to be attenuated in vivo [80 , 81] , and we also demonstrate this completely predictable virulence cost by in vitro and in vivo competition assays , experiments in the acute pneumonia model were confined to these two isolates and their key isogenic ompK36 variants in order to minimize the use of animals in experimentation . Two crystal structures of native OmpK36 available in the Protein Data Bank under accession number 5nup ( 2 . 9 Å , Xray ) and 1osm ( 3 . 2 Å , Xray ) were evaluated as templates for structural modelling of OmpK36 and OmpK36GD from ATCC 13883 , with targets and templates sharing around 93% nucleotide sequence identity . While Ramachandran plots analysis for all predicted models show at least 98% of residues in allowed regions , other metrics such as QMEAN and Molprobity score were marginally better for ATCC 13883 OmpK36 and OmpK36GD models based on the 5nup structure ( Fig 5 , S8 Table ) . Although several differences can be observed in the final alignment ( Fig 5D ) , the most prominent differences between the original structure ( Fig 5A ) and the ATCC 13883 OmpK36 model lie within the loop L6 , which can be seen in yellow , slightly obstructing the outmost channel of the porin ( Fig 5B ) . Much more striking is the impact of single two amino-acid -GD insertion within loop L3 , which is expected to further constrict the porin channel ( Fig 5C ) and is likely responsible for the difference in phenotype between the two variants . The successful antibiotic resistance , colonisation and pathogenicity phenotypes of ΔOmpK35OmpK36GD strains should be reflected in their representation among strains causing human infection . Of 165 unique K . pneumoniae ompK36 sequences in GenBank , 16% varied from the consensus L3 inner channel motif ( PEFGGD ) . The most common was the GD duplication ( PEFGGDGD , in 14 of 26 OmpK36 L3 variants identified ) , along with 6 additional variants: PEFGGDD , PEFGGDSD , PEFGGDTD , PEFGGDTYD , PEFGGDTYG and PEFGGDTYGSD ( S6 Fig , showing modeled secondary structures based on previous studies [82 , 83] , including of the pore eyelet region [84] ) . Using the native OmpK36 structure 5nup as a template , modelled structures of mutants , namely GD- , TD- and SD- insertions in L3 , were computed as previously described , and showed similar restriction of the porin channel , slightly greater in the case of a bulkier amino-acid such as Threonine ( S7 Fig ) . Inspection of their corresponding nucleotide sequences suggests that these variants originated from various combinations of short in-frame duplications , combined with additional point mutations in rare cases ( S8 Fig ) . Similar variations in L3 of OmpK36 homologues were found when analysing ompK36 sequences in other Enterobacteriaceae ( S9 Fig ) . To investigate OmpK36 among clinical isolates without specialised carbapenemases , we specifically analysed L3 variation in all such K . pneumoniae isolates with an Ertapenem MIC > 1 in our local clinical collection ( Table 1 ) by PCR and sequencing ( S1 Table ) . Of ( n = 51 ) , 17 strains ( 33% ) were identified: all revealed either the previously described GD or TD mutation in the L3 loop of ompK36 on sequencing and these encoded up to 6 distinct beta-lactamases . These isolates were genetically diverse but belonged to major epidemic clones found elsewhere in the world: i . e . ST14 , ST16 , ST101 , ST147 , with as many as 6 distinct ompK35 mutations , all of which introduced disrupting frame-shifts and all of which were relatively lineage-specific ( S10 Fig ) . Finally , all K . pneumoniae ( complete and draft ) genomes available from Genbank , i . e . 1 , 557 entries ( as of February 2017 ) were examined: the two common ( GD and TD ) variants are shown in a minimum spanning tree built using MLST profiles ( Fig 6 ) to be distributed across the whole spectrum of diversity of K . pneumoniae , including in most major epidemic clones , e . g . ST258 and its derivative ST512 , ST11 , ST101 , ST147 , ST14 and ST37 . A maximum likelihood phylogeny using a 2 , 253 , 033 bp core genome alignment of all 1 , 557 genomes was computed to contextualize variations in ompK36 and ompK35 , with metadata relative to the population ( year , source , geographical region of isolation , as well as major beta-lactamases genes ) ( Fig 7 ) . Those genes most relevant to a carbapenem resistance phenotype are shown , and the expected clustering of some of these is as expected ( e . g . blaCTX-M-15 with OXA-1 and TEM-1b ) . Major associations with other genes not affected by porin changes are not shown ( e . g . aminoglycoside resistance due to 16S methylase genes that are common companions of blaNDM , other class I integron cassettes from the array in which blaIMP-4 is found , etc ) . The predominance of ompK36 variations in L3 compared to its loss or disruption is evident at a glance , as is the common loss or disruption of ompK35 in unrelated strains . There is no obvious relationship between ompK36 L3 variations and the presence of blaKPC but there is strong clustering of these variations in certain types ( ST258 , 512 etc ) . As expected for a gene so clearly linked to fitness and virulence , ompK36 is highly conserved across the dataset ( present in 1 , 499 out of 1 , 577 ) , and we found no statistical evidence of ST-dependence ( Chisq = 207 . 51 , df = 227 , p-value = 0 . 8188 ) . Conversely , ompK35 ( evidently dispensable in the host ) is disrupted in nearly a third of all strains ( Fig 6 ) , with statistically significant association with ST ( Chisq = 603 . 7 , df = 227 , p-value = 5 . 748e-36 ) . Three-way comparison of the distributions of frequencies of presence/absence of ompK35 , mutations in ompK36 , and ST ( considering only those STs harbouring ompK36 GD/TD variants ) was performed using an extended mosaic plot ( S11 Fig ) . Standard Pearson’s residuals were calculated and displayed on the mosaic plot to identify over-represented categories ( residuals [2 , 4] and >4 ) and under-represented categories ( residuals [–2 , –4] and <-4 ) . We found statistically significant evidence ( residual cut-off 2 and 4 equivalent to p < 0 . 05 and p < 0 . 001 ) that i ) some STs prevalently have both ompK36 and ompK35 intact ( mainly ST15 , ST16 , ST17 ) ; ii ) others prevalently have intact ompK36 with ompK35 disrupted ( ST129 , ST258 ) ; and iii ) some STs prevalently have ompK36 ( GD/TD ) variants combined with ompK35 disrupted ( ST11 , ST14 , ST147 , ST258 and ST37 ) . Furthermore , we found statistically significant evidence for more disruptions of ompK35 in i ) strains from the USA compared to other countries ( S12A Fig ) and ii ) strains from 2011 and 2014 ( S13A Fig ) . We also observed over-representation of ompK36 ( GD/TD ) variants in i ) China , Greece , Germany , Italy and India ( S12B Fig ) and ii ) in 2011 ( S13B Fig ) . Finally , we looked at associations between the number of resistance genes and porin defects in major STs , and found that the presence of ompK36 GD/TD variants did not correlate with a higher number of resistance genes ( with the exception of OmpK36GD in ST14 ) . In fact , successful clones such as ST258 and ST11 harbouring OmpK36GD encoded significantly less resistance genes ( p<0 . 001 , Wilcoxon test ) ( S14 Fig ) . It should be noted that due to the inherent opportunistic nature of the sampling present in Genbank ( e . g . USA ) , our conclusions are only applicable to this dataset . More sampling would be required to assess the significance of porin mutations in an unbiased K . pneumoniae population .
β-lactam antibiotics are among the most commonly prescribed for severe infections [85 , 86] and the emergence of β-lactam resistance in K . pneumoniae has become a global health threat [87 , 88] . In general , E . coli and K . pneumoniae carrying transmissible β-lactam resistance genes have predictable and normally distributed β-lactam MICs [21] but carbapenem MICs in K . pneumoniae are bimodally distributed with higher MICs correlating with OmpK36 defects [21] . OmpK36 loss or mutation is not uncommonly reported in highly resistant clinical isolates producing KPC , ESBL and AmpC β-lactamases [20 , 89 , 90] . Diffusion of β-lactam antibiotics through non-specific porins such as OmpK35 and OmpK36 is dependent on size , charge and hydrophobicity [91 , 92] , with bulky negatively charged compounds diffusing at a lower rate than small zwitterions of the same molecular weight [93] . OmpK35 is much less expressed in high osmolarity nutrient-rich conditions than OmpK36 , which has the narrower porin channel of the two ( S2 Fig ) [9] and large negatively charged β-lactams such as third-generation cephalosporins and carbapenems diffuse more efficiently through OmpK35 than OmpK36 [80 , 94] . Here we confirm the significantly increased MICs , commonly attributed to mutations in these two major porins [10 , 95 , 96] in three K . pneumoniae strains ( the widely-published ATCC strain 13883 and two locally isolated clinical strains ( Table 2 and S4 Table ) and unequivocally identify the primary role of OmpK36 in carbapenem resistance . Comparable MIC changes in single ( OmpK36GD and ΔOmpK36 ) and double ( ΔOmpK35OmpK36GD and ΔOmpK35ΔOmpK36 ) mutants indicate that duplication of a glycine aspartate ( GD ) pair in a critical position in the porin eyelet region ( loop 3 ) is almost as effective as a complete deletion of the porin in excluding large anionic antibiotics . Both single and double porin mutants were susceptible to extended-spectrum cephalosporins ( cefotaxime and ceftazidime ) in the absence of acquired hydrolysing enzymes , demonstrating the impotence of the naturally occurring chromosomal SHV enzymes [70 , 71 , 72] against these compounds [95] . Differences relating to porin permeability in K pneumoniae are most striking and important in the presence of acquired carbapenemases and it is clear that these permeability changes greatly enhance the associated resistance phenotypes . The common Ambler Class A serine protease KPC-2 and Class B metalloenzyme IMP-4 expressed from their natural plasmids produce only borderline resistance against meropenem and the smaller zwitterionic imipenem in the presence of the ‘wild type’ OmpK36 osmoporin ( Table 3 ) but MICs that exceed therapeutic tissue levels [97 , 98] are the rule in strains of the commonly occurring ΔOmpK35OmpK36GD genotype . We also show here that the OmpK35 matrix porin has little or no relevance in vivo or in vitro conditions that reliably predict antibiotic efficacy in the clinic ( MICs and competitive fitness in Mueller-Hinton broth ) . Consistent with this , a high percentage of clinical isolates whose genomes have been lodged with GenBank appear to have lost their ability to express OmpK35 altogether ( Fig 7 ) . Increased production of the larger channel OmpK35 is expected under low-temperature , low-osmolarity and low nutrient conditions ( S2 Fig ) . These favour survival outside the mammalian host and we show that ΔOmpK35 strains fail to compete successfully with their isogenic parents in nutrient-limited conditions ( S4 Fig ) . We confirm that OmpK35 is not naturally expressed at significant levels in optimal growth conditions nor in the mammalian host , as previously described [78 , 80] . As expected , competition experiments , the most sensitive and direct measures of comparative fitness , evince no discernible disadvantage from the loss of OmpK35 in vivo [19 , 99] . Loss of OmpK36 trades off nutrient influx for antibiotic resistance [42 , 80] , and we show that these more resistant bacteria cannot compete successfully with the antibiotic-susceptible populations from which they arise once antibiotic selection ceases to operate ( Fig 2 ) . Double porin mutants ( ΔOmpK35ΔOmpK36 ) are the most antibiotic-resistant ( Table 2 and S4 Table ) but this resistance comes at the cost of a 10% relative growth reduction in nutritious media ( S7 Table ) . OmpK36 , the main porin normally expressed in vivo , is responsible for most of this fitness cost ( Figs 2 and 3 and S3 Fig ) . The less permeable phosphoporin PhoE and maltodextrin channel LamB , most important in the usual compensatory response when OmpK35 is not available , are not efficient substitutes ( Fig 1 and S6 Table ) . Defects in these porins have been implicated in carbapenem resistance in association with only an AmpC-type enzyme [42 , 76 , 79 , 100] , but other defects are ill-defined and the fitness cost may be high as such strains are rarely described . By contrast , ΔOmpK35OmpK36GD mutants are little disadvantaged in vivo or in optimal growth conditions in vitro ( Figs 2 and 3 and S7 Table ) . Expression of OmpK36 is unaffected ( Fig 1 and S6 Table ) as is that of other porins such as OmpK35 ( Fig 1 and S6 Table ) , presumably because OmpK36 ‘rescue’ is not required . The precise loop 3 variation in OmpK36 is best explained by a convergent evolutionary process , as a range of different variants occur within genetically distant K . pneumoniae populations , all with an extra negatively charged aspartate ( D ) residue that significantly constricts the inner channel ( Fig 5 ) . The most common solution is the extra glycine and aspartate ( PEFGGD to PEFGGDGD in the critical region ) which we recreated in isogenic mutants for our experiments . The next most frequent , an extra TD ( rather than GD ) , is similarly likely to spontaneously arise ( S8 Fig ) but is much less common , including in STs in which both GD and TD are found ( Figs 6 and 7 ) , implying a less optimal conformation . A recent survey of nearly 500 ertapenem-resistant Klebsiellae lacking specialised carbapenemases [101] supports our own finding of the extra aspartate in that position , most commonly as a GD pair , with TD and SD much less often , and other variants being rare . We found no examples of similarly acidic ( glutamate ) residues occurring in this position , perhaps reflecting the fact that even simple sequence changes ( here , GAY to GAR ) add an additional step to a simple duplication event , or the fact that glutamate’s extra carbon makes it slightly less compact than an aspartate in this position . Other Enterobacteria face the same challenge of excluding bulky anionic carbapenem antibiotics in order to survive high concentrations , even in the presence of a specialist carbapenemase . High level antimicrobial resistance has been ascribed to similar variations in L3 of OmpK36 homologues in Enterobacter aerogenes , Escherichia coli ( S9 Fig ) and Neisseria gonorrhoeae [102 , 103 , 104 , 105 , 106] . In comparison with their E . coli homologues ( OmpF and OmpC ) , OmpK35 and OmpK36 permit greater diffusion of β-lactams [107] . Specifically , OmpK35 appears to be highly permeable to third-generation cephalosporins such as cefotaxime due to its particular L3 domain , which is also seen in Omp35 in E . aerogenes but not in other species , and has been proposed as an explanation for the high proportion of K . pneumoniae clinical isolates that lack this porin [84 , 107] . Our findings of increased MICs in OmpK35 mutants are consistent with those of others [107] but we show here that the more permeable OmpK35 is not important in the mammalian host . Rather , the much less permeable OmpK36 ( equivalent to E coli OmpC ) [107] is the bottleneck for large anionic antibiotics . The term ‘high risk clone’ [108 , 109] is given to host-adapted/pathogenic strains that dominate the epidemiology of ( antibiotic resistant ) infections , presumably because they are more transmissible , more pathogenic and/or more tolerant of host-associated stresses ( including antibiotics ) . Here , we see a range of unrelated clonal groups already identifiable as high-risk clones that are dispensing with the OmpK35 porin ( Fig 7 ) . The minimal antibiotic resistance advantage in nutritious media is only evident with carbapenems and is unlikely to arise in the presence of an existing OmpK36 loss mutation because the fitness cost is substantial . The loss of OmpK35 through low-level carbapenem exposure in environmental conditions is possible [110] but has a marked fitness cost and the exposure to carbapenems in the environment is expected to be limited , as they are a still a minority class of prescribed antibiotics and are not yet as common in environmental waters as the sulfonamides , quinolones , macrolides , tetracyclines and other beta-lactams [111] . A recent review of antibiotic resistance in Klebsiella pointed out that “The exact role of porins in antimicrobial resistance is difficult to determine because other mechanisms…are commonly present …” [112] . We suggest that host-adaptation in K . pneumoniae is widespread and that many K . pneumoniae have dispensed with the OmpK35 matrix porin required for an environmental life cycle . Bacteria are expected to adapt effectively to major stress such as antibiotic pressure or high concentrations of bile salts in the intestinal lumen [113] . Our hypothesis of adaptive loss of OmpK35 is based on results presented in this study and on strong evidence from others: i ) toxic agents as antibiotics and bile salts diffuse better through the larger OmpF channel ( homolog of OmpK35 ) than the narrower OmpC ( equivalent to OmpK36 in K . pneumoniae ) [114]; ii ) high osmolarity , high temperature , low pH and anaerobiosis ( typical conditions in gut environment ) induce the production of OmpK36 but inhibit the expression of ompK35 [115] [116] [117] and iii ) E . coli mutants with reduced permeability ( decreased ompF and increased ompC mRNA and protein levels compared with parental strain ) can be easily recovered from intestinal gut of germ-free mice after few days of colonization [118] . In addition , we have shown that the highly specific variation in the inner channel of OmpK36 provides carbapenem resistance at no cost to colonising ability , competitiveness or pathogenicity and can be expected to be an increasingly common feature of host-adapted ‘high-risk clones’ . There are three direct and immediate implications . Firstly , efforts to control the spread of such strains will be facilitated to some extent by the loss of environmental hardiness resulting from OmpK35 deletion , and should shift slightly more toward managing interpersonal transmission . Secondly , K . pneumoniae can be expected to become more antibiotic resistant overall , and organisms expressing currently circulating plasmid-borne carbapenemases will more commonly be untreatable with carbapenem antibiotics ( e . g . ST258 strains with blaKPC ) ; the second ( higher MIC ) peak in the bimodal distribution of carbapenem MICs in K . pneumoniae populations will become more prominent . Finally , the mobile carbapenemase gene pool can be expected to flourish in the protected niche provided by host-adapted K . pneumoniae populations under strong carbapenem selection pressure in human hosts , thereby increasing the general availability of highly transmissible carbapenem resistance plasmids among host-adapted pathogens in the Enterobacteriaceae . | Klebsiella pneumoniae is a Gram-negative enteric bacterium and a significant cause of human disease . It is a frequent agent of pneumonia , and systemic infections can have high mortality rates ( 60% ) . OmpK35 and OmpK36 are the major co-regulated outer membrane porins of K . pneumoniae . OmpK36 absence has been related to antibiotic resistance but also decreased bacterial fitness and diminished virulence . A mutation that constricts the porin channel ( Gly134Asp135 duplication in loop 3 of the porin , OmpK36GD ) has been previously observed and suggested as a solution to the fitness cost imposed by loss of OmpK36 . In the present study we constructed isogenic mutants to verify this and test the impact of these porin changes on antimicrobial resistance , fitness and virulence . Our results show that loss of OmpK35 has no significant impact on bacterial survival in both nutrient-rich environments and in the mammalian host , which is consistent with a predicted role outside that niche . When directly compared with the complete loss of the partner osmoporin OmpK36 , we found that isogenic OmpK36GD strains maintain high levels of antibiotic resistance and that the GD duplication significantly reduces neither gut colonisation nor pathogenicity in a pneumonia mouse model . These changes are widespread in unrelated genomes . Our data provide evidence of specific variations in the loop 3 of OmpK36 and the absence of OmpK35 in K . pneumoniae clinical isolates that are examples of successful adaptation to human colonization/infection and antibiotic pressure , and are features of a fundamental evolutionary shift in this important human pathogen . | [
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"phar... | 2019 | Host adaptation and convergent evolution increases antibiotic resistance without loss of virulence in a major human pathogen |
Local translation at the synapse plays key roles in neuron development and activity-dependent synaptic plasticity . mRNAs are translocated from the neuronal soma to the distant synapses as compacted ribonucleoparticles referred to as RNA granules . These contain many RNA-binding proteins , including the Fragile X Mental Retardation Protein ( FMRP ) , the absence of which results in Fragile X Syndrome , the most common inherited form of intellectual disability and the leading genetic cause of autism . Using FMRP as a tracer , we purified a specific population of RNA granules from mouse brain homogenates . Protein composition analyses revealed a strong relationship between polyribosomes and RNA granules . However , the latter have distinct architectural and structural properties , since they are detected as close compact structures as observed by electron microscopy , and converging evidence point to the possibility that these structures emerge from stalled polyribosomes . Time-lapse video microscopy indicated that single granules merge to form cargoes that are transported from the soma to distal locations . Transcriptomic analyses showed that a subset of mRNAs involved in cytoskeleton remodelling and neural development is selectively enriched in RNA granules . One third of the putative mRNA targets described for FMRP appear to be transported in granules and FMRP is more abundant in granules than in polyribosomes . This observation supports a primary role for FMRP in granules biology . Our findings open new avenues for the study of RNA granule dysfunctions in animal models of nervous system disorders , such as Fragile X syndrome .
Neurons are remarkably diverse in shape [1] . They vary from simple unipolar to highly complex multipolar cells , decorated with complex projections of up several centimeters and even one meter in certain cases . Through billions of synaptic connections , these cell-to-cell interactions are the basis for neural circuits that are highly adaptable and functionally autonomous . Their remodelling and adaptation properties contribute to synaptic plasticity [2–5] . These changes rely on rapid local modulation of protein synthesis that is dependent on the presence of the translational machinery and mRNA at the synapse [6] . At the time , the discovery of ribosomal RNA in the axoplasm of the squid giant axon was considered odd and specific to this species [7] . Later , the observation of polyribosome aggregates beneath postsynaptic sites at the base of dendritic spines [8] and in the postsynaptic area of the squid giant synapse [9] convinced scientists that translation could also occur outside of the soma in an autonomous and rapid response to synaptic activity . Supplying and maintaining subcellular compartments far away from the neuronal soma brings up complex conceptual and biological questions in terms of logistics . Most of protein synthesis takes place in the soma , as the bulk of mRNAs is translated into the cell body . However , a subset of mRNAs is delivered either at presynaptic axonal terminals or at postsynaptic dendritic spines [10 , 11] where they are further translated into proteins , the synthesis of which is specifically required for adaptation to the local needs of the synapse [12 , 13] . This extrasomatic targeting of mRNAs allows to rapidly control the synthesis and distribution of the corresponding protein , regulating its level at individual axonal terminals or dendritic spines , in response to external stimuli . The mechanisms of transport , targeting , and release of neurospecific mRNAs at the synapse are gradually being unveiled . While PeriAxoplasmic Ribosomal Plaques ( PARPs ) corresponding to the translation apparatus have been detected in squid axon [14] , RNA granules were observed in the arborisation of neurons in culture [15] . When isolated using sucrose gradients , these granules exhibited sedimentation properties with S values higher than those of polyribosomes [16] . However , while these granules can be isolated from neurons in primary culture , attempts to purify them from total brain have not been conclusive . Preparations of granules were contaminated by co-sedimentation of other structures such as clathrin-coated vesicles [17] or polyribosomes [18] . Consequently , little is known about the protein and RNA species present in these structures . While mRNAs present in dendrites [11] may possibly be transported in travelling modules , there is no formal biochemical evidence yet for isolated granules . In the present study , we describe a method to isolate and purify RNA granules from mouse brain homogenates , using FMRP as a tracer . Furthermore , we provide a comprehensive proteomic and transcriptomic profiling of mouse brain RNA granules . Finally , we propose a definition of what we consider to be a single granule unit and what we conceive as a cargo of granules .
To study the diversity and complexity of neuronal RNA granules , we sought to obtain substantial amounts of these structures . Using mouse brain , we first applied the procedure described by Aschrafi et al . [18] which is based on different sedimentation rates of RNA granules and of polyribosomes [16 , 19] . Therefore , these two populations can be separated using isokinetic centrifugation through linear sucrose density gradients . Total brain cytoplasmic extracts were prepared without detergent from 10 days-old mice brain , loaded on a 15–30% w/v sucrose gradient over a 70% sucrose pad and centrifuged at 34 000 rpm for 2 hours . Continuous UV monitoring during the course of gradient unloading showed the presence of distinct peaks corresponding to two UV-absorbing populations with different sedimentation properties . A minor peak was observed in the middle of the gradient while a prominent second peak was at the 30–70% sucrose interphase ( Fig 1A ) . According to Ashrafi et al . [18] , the latter retains the granules fraction . To determine the position of polyribosomes in such a shallow sucrose gradient ( 15–30% w/v ) , we fractioned the gradient and analysed the distribution of both the ribosomal protein L7 and FMRP , a well-known RNA-binding protein associated to polyribosomes [20 , 21] . This showed that the minor peak corresponded to monosomes sedimenting at 80S , while most of L7 and FMRP was recovered at the sucrose interface ( Fig 1A ) . These observations strongly suggested that the fraction studied by Ashrafi et al . was unlikely to correspond to granules , as it contained also a high proportion of polyribosomes . Moreover , staining the gel with Coomassie blue showed that the great majority of the sedimenting material was concentrated at the 30–70% sucrose interface ( Fig 1A ) . Finally , electron microscopy of the interface fraction revealed the coexistence of dense amorphous-looking granules and polyribosomes , assembled as beads on a string ( Fig 1A ) . Altogether , this suggested that the method described by Ashrafi et al . was inadequate to separate RNA granules from polyribosomes in mouse brain . Since discriminating polyribosomes from granules with a 15–30% w/v sucrose density gradient was not possible , we opted for a more standard 15–60% w/v linear gradient over a 70% w/v sucrose cushion . We used brains from P10 mice , since preparing a cytoplasmic fraction enriched in polyribosomes and heavy sedimenting structures is easier at this age [20] . The cytoplasmic sap was ultracentrifuged over a 60% w/v sucrose cushion . The resulting opalescent pellet was resuspended and further analysed by isokinetic centrifugation . Under these conditions , the ribosomal L7 protein and FMRP were detected at the level of polyribosomes ( Fig 1B ) , as previously documented [20 , 21] , indicating that polyribosomes could be separated from the 60–70% sucrose interface . Also , both proteins were present at the 60–70% w/v interface , but penetrated the sucrose cushion as they also appeared in lower fractions . To confirm that the material present at the interface did correspond to granules , samples were examined by electron microscopy following negative staining . Electron dense round shaped particles ranging from 100 to 800 nm were observed ( note the extended scale in Fig 1B as compared to Fig 1A ) . Altogether , these data demonstrate that a fraction enriched in granules is obtained at the interface under the conditions described here . However , contamination by large sedimenting polyribosomes could not be ruled out because of the close vicinity of the collected fractions . We therefore modified three parameters: 1 ) we eliminated the sucrose cushion to allow rapidly sedimenting material to concentrate at the bottom of the centrifuge tube; 2 ) we reduced the centrifugation time down to 45 minutes to separate the polyribosome fractions from heavy sedimenting structures , and 3 ) we unloaded the sucrose gradient from the top using the Auto Densi-Flow ( Buchler ) rather than piercing the tube , thus protecting the integrity of the pellet . These adjustments , allowed the separation of polyribosomes from heavy sedimenting structures containing L7 and FMRP , which were recovered at the bottom of the tube ( Fig 2A ) . The presence of structures sedimenting faster than polyribosomes , suggested that this pellet fraction corresponded either to granules or polyribosomes aggregates . To discriminate between these two structures , polyribosome-enriched extracts were submitted to different treatments prior to velocity sedimentation through sucrose density gradients . An EDTA treatment , which dissociates polyribosomes into their ribosomal subunits ( RSU ) , and an RNAse treatment , that completely destroys polyribosomes had no effect on the presence of FMRP and L7 in the pellet ( Fig 2B and 2C ) . This suggested that these heavy-sedimenting structures did not correspond to classical bone fide polyribosomes . We then tested the effect of high-salt conditions ( 0 . 4 M NaCl ) on the material contained in the pellet . We observed that approximately 50 to 70% of FMRP was removed from polyribosomes and was recovered at the top of the gradient , while the rest of FMRP remained associated with polyribosomes ( Fig 2D ) , as previously reported [22] . Under these high-salt conditions , we neither detected a visible pellet , nor recovered UV absorbing material or FMRP and L7 at the bottom of the tubes ( Fig 2D ) . Similarly , while treatment with the anionic detergent deoxycholate ( DOC ) did not affect the polyribosomal UV profile , it eliminated UV absorbing material from the pellet ( Fig 2E ) . This is in agreement with previous studies showing that FMRP together with other proteins , is stripped off polyribosomes by DOC [20 , 21 , 23] . These observations strongly suggested that the material recovered at the bottom of the tubes present a tertiary structure different from classical polyribosomes . We then performed electron-microscopy analyses to visualize the components present in the pellet and in the polyribosomal fractions ( Fig 3A ) . Electron micrographs of polyribosomal fractions revealed the typical appearance of polyribosomes with ribosomes assembled on mRNA as beads on a string , while clumps of amorphous structures were visible in the resuspended pellet fractions . We hypothesized that these aggregates were due to the high g forces generated during ultracentrifugation , compacting the particles against the tube bottom wall . We therefore added a step in which the pellet was dispersed by two short bursts of ultra-sonication . Following that treatment , the structures present in the pellet fraction appeared as a heterogeneous set of small granules with size ranging from 100 to 300 nm ( Fig 3B ) . Higher magnification revealed a morula-like structure of granules , each formed of round units of 25 nm in diameter similar to ribosomes [24] ( Fig 3C ) . Quantification in an array of 350 granules showed that the number of units present in a single granule range from 5 to 20 ( Fig 3C and 3D ) . Because EM preparations tend to flatten structures , we hypothesized that the number of ribosomal units was underestimated . Indeed , 3D reconstruction models revealed the hidden face of the preparations . Thus , granules estimated to contain 7 ribosomes , might in fact accommodate 12 to 13 units ( Fig 3E ) . Immunogold labelling on ultra-thin sections of LR-White resin embedded granules with antibodies against the large L7 and the small S6 ribosomal subunit proteins , confirmed that the units composing the granules corresponded to ribosomes ( Fig 3F and 3F’ ) . While we expected that all ribosomes contained in a granule would react to the antibodies , these granules were not uniformly labelled . This was probably due to the fact that the protein epitopes were localized above or below the levels of the ultrathin sections . Immunogold labelling of FMRP showed that the protein was not present in all granules ( Fig 3G ) , as it was the case for its two homologues , FXR1P and FXR2P ( S1 Fig ) . The average number of FMRP-gold signals detected in each granule was 3 as determined in n = 100 FMRP positive granules ( S2 Fig ) . However , this number might be underestimated , because the FMRP epitopes might be missing , as is the case for L7 ( see above ) . On the other hand , only 30% of the granules carried FMRP-gold signals . A more detailed quantitative study of the distribution of FMRP in granules , using immunofluorescence approach , is presented below . We observed the presence of granules in the pellets recovered after RNase and EDTA treatments of the cytoplasmic sap ( see Fig 2B and 2C ) . However , we systematically noted that their morphology was slightly altered as less defined images were obtained ( Fig 3H and 3H’ ) . Finally , to serve as negative controls , sections were incubated in the presence of the sole secondary gold-labelled antibodies . Occasionally , single gold-particle was observed outside of the granules in different regions of the sections ( Fig 3I ) . These results collectively suggest that although granules are highly diverse in terms of size and composition , their basic unit remains the ribosome . While the procedures described above were appropriate to obtain fractions highly enriched with granules , we wondered whether they were purified sufficiently for further biochemical studies . Granule fractions obtained as pellets ( see Fig 3A ) were analysed by SDS-PAGE and their protein composition compared to that of purified polyribosomes . Coomassie blue staining revealed that granules contained a majority of ribosomal proteins . However additional bands were observed , in particular at around 230–100 and 55–40 kDa ( Fig 4A , highlighted with stars and in grey area ) accounting for 36% of the total protein content as determined after scanning of the stained gels . We hypothesized that these bands would correspond to components of the cytoskeleton framework that might have contaminated the granule preparations . Therefore , we tested for the presence of three main cytoskeletal proteins: the neurofilaments ( NF ) , β-actin and β-tubulin . Immunoblot analyses using antibodies to these proteins confirmed their presence ( Fig 4A ) . This evidenced that cytoskeleton components had contaminated the granule fraction during differential centrifugations . An additional purification step was thus necessary . Granules and polyribosomes recovered after isokinetic ultracentrifugation in sucrose density gradients ( S3A Fig ) were subjected to equilibrium ( isopycnic ) ultracentrifugation in a 10 to 60% w/v Metrizamide linear gradient [25] ( S3B Fig ) . Both structures were detected in fractions with a calculated density of 1 . 295 g/cm3 , corresponding to the buoyant density of polyribosomes [26] . Centrifugation prolonged for 48 hours did not change the particle density indicating that they have reached their equilibrium density already by 18 hours . FMRP and L7 were systematically detected in fractions corresponding to this density . Coomassie blue staining highlighted that polyribosomes and granules shared common protein profile ( Fig 4B ) , suggesting similar protein content . More importantly , the contaminant peaks highlighted in gray in Fig 4A were no longer detected after this additional purification step . To assess the level of purification , we also performed immunoblot analyses and observed a 8-fold decrease in the signals corresponding to neurofilament , actin and tubulin , indicating that the majority of cytoskeletal contaminants were removed by the Metrizamide step . Altogether , these data showed that the procedure described here enables the isolation of granules from mouse brain with a minimum of contaminants . To reveal the protein content of polyribosomes and granules purified by isopynic centrifugation in Metrizamide gradients , fractions were analysed by Mass Spectrometry ( MS ) . A total of 128 proteins sharing at least 1 peptide with known proteins registered in databases were found in granules ( Fig 5 and S1 Table ) , while 155 proteins were present in polyribosomes . Gene ontology-based pathway enrichment analyses revealed that the most significantly enriched biological processes in granules were notably ‘translational elongation’ ( adjusted p-val = 2 , 67 . 10−128 ) , ‘RNA processing’ ( adjusted p-val = 1 , 38 . 10−12 ) , ‘ribonucleoprotein complex biogenesis’ ( adjusted p-val = 1 , 41 . 10−12 ) and ‘cytoskeleton-dependent intracellular transport’ ( adjusted p-val = 3 , 40 . 10−3 ) ( S2 Table ) . In addition , the most significantly enriched molecular functions in granules were ‘structural constituent of ribosome’ ( adjusted p-val = 2 , 36 . 10−98 ) , ‘RNA binding’ ( adjusted p-val = 2 , 65 . 10−64 ) , ‘translation regulator activity’ ( adjusted p-val = 4 , 11 . 10−2 ) or ‘structural constituent of cytoskeleton’ ( adjusted p-val = 1 , 42 . 10−2 ) . The same analyses performed on the 155 proteins identified in polyribosomes highlights essentially the same classes of biological processes linked to ‘translation’ , ‘ribosome’ or ‘RNA binding’ ( S3 Table ) . However , cytoskeleton-related processes were not significantly enriched in the polyribosomal protein pool , suggesting that the main divergences observed with polyribosome and granule preparations expressed in terms of protein content , belong to this class of processes . Based on the results of the gene ontology analysis results , we sorted the proteins detected in granules according to the following functional classes: ribosomal proteins , RNA-binding proteins and cytoskeleton-linked proteins ( Fig 5 ) . Fifty five percent of the identified proteins were core ribosomal proteins ( Fig 5 ) . RNA-binding proteins constituted the second major class of proteins in granules ( 26 . 5% ) . Some of them were already described as part of RNA granules , such as polyA Binding Protein 1 and 4 ( PABP1 ) , members of the ELAV family ( HuB , HuC ) , Staufen1 , Staufen2 , Pur-α/β , series of heterogeneous ribonucleoproteins ( hnRNPC1/C2; hnRNPR; hnRNPQ/SYNCRIP ) and the Fragile X protein FMRP [16 , 17 , 19] ( Fig 5 ) . Also , several proteins known to interact directly with FMRP were detected , such as Caprin-1 [22] or the Fragile X-related proteins FXR1P and FXR2P [27] . Proteins , not previously described in RNA granules were also detected , such as the ATP-dependent helicase of the DEAD box family ( DHX30 ) required for unwinding of mRNA during translation , and the translation initiation factors eIF3b and eIF3c . Other RNA-binding proteins were also present , for instance the splicing factors PRPF19 , SRPK1 or UPF1 . Finally , the presence of the axonal RNA-binding protein La suggested the presence of axonal granules in our preparations . Indeed , FMRP positive granules have been detected in axons [28 , 29] . Cytoskeleton-linked proteins represent 12 . 5% of the proteins identified in granules with a number of motor proteins ( mostly myosins ) and structural constituents of the cytoskeleton: actin , tubulin and neurofilaments ( Fig 5 ) . Using immunoblot analyses , we further tested the presence of a series of proteins detected in granules or polyribosomes namely: ribosomal proteins , translation factors and RNA-binding proteins . Equal amounts of proteins from granules or polyribosomes were analysed; we then compared the intensities of immunoblot signals . Levels of the core ribosomal proteins L7 and S6 in preparations of polyribosomes and of granules were similar . Reports of the presence of translation factors in granules are contradictory . Krichevsky and Kosik [16] observed that the granule fraction contained trace amounts of the initiation factors eIF4E and eIF4G1 . On the other hand , Kanai et al . [19] detected eIF2A , eIF2B and eIF2G while Elvira et al . [17] reported the presence of eIF4A . In view of the absence of a consensus , we tested the presence of eIF4E , eIF4EBP1/e4BP1 , eIF4G1 and eIF2A . All these factors were detected with equal signal intensities in both polyribosome and granule preparations ( Fig 6A ) . We further tested a series of RNA-binding proteins . We confirmed that PABP1 and Ago2 were equally represented in granules and polyribosomes ( Fig 6A ) . In addition , we quantified signal intensities for the FXR proteins , FMRP , FXR1P and FXR2P . Contrary to the other RNA-binding proteins tested , FXR proteins signals were higher in granules when compared to polyribosomes ( Fig 6B ) . Since quantification of chemiluminescent signals is not linear [30] , we first determined the optimum conditions to ascertain the increased signals of FMRP in granules . We therefore checked , using a titration assay for FMRP , that our analyses were performed in the linear signal range ( S4 Fig ) . In the case of FMRP , we quantified by densitometry analyses an increase of 1 . 83 fold in granules . These results are supported by our proteomics data , as the normalized spectral counts for the Fragile X Proteins appear higher in granules than polyribosomes ( S1 Table ) . Finally , as controls , the post-synaptic protein PSD-95 and the mitochondria encoded cytochrome C oxidase MTCO1 were not detected in either of the preparations ( Fig 6A ) in agreement with the proteomic analyses . Having studied the protein content of granules , we then wondered about their RNA content as compared to polyribosomes . To identify the RNA species present in granules , we compared RNA extracted from granules to polyribosomal RNA using whole transcriptome mouse microarrays . We focused on mRNA with signal intensities equal or higher than those from polyribosomes ( Fold-of-Change ( FC ) >1 , p-val<0 . 002 ) . This corresponded to 1 , 806 annotated mRNAs ( S4 Table ) that can be assimilated to the mRNA species encountered in granules , corresponding to 7% of the total transcriptome . We performed gene ontology analysis to gain insights into the functional categories selectively over-represented in the granules ( S5 Table ) . The biological processes enriched in granules mRNA were notably ‘actin cytoskeleton organization’ ( adjusted pval = 6 , 16 . 10−8 ) , ‘cytoskeleton-dependent intracellular transport’ ( adjusted pval = 5 , 41 . 10−4 ) , ‘neuron projection development’ ( adjusted pval = 1 , 03 . 10−5 ) , ‘synapse organization’ ( adjusted pval = 7 , 54 . 10−4 ) , ‘axonogenesis’ ( adjusted pval = 5 , 5 . 10−4 ) or ‘ubiquitin-dependent protein process’ ( adjusted pval = 2 , 16 . 10−3 ) . In addition , the most enriched molecular functions concerned notably signal transduction pathways involving ‘GTPase regulator activity’ ( adjusted pval = 7 , 47 . 10−8 ) or ‘calcium ion binding’ ( adjusted pval = 8 , 62 . 10−8 ) or cytoskeleton-remodelling processes involving ‘cytoskeletal protein binding’ ( adjusted pval = 1 , 81 . 10−15 ) or ‘motor activity’ ( adjusted pval = 6 , 34 . 10−4 ) . Finally , the most enriched cellular components included ‘cytoskeleton’ ( adjusted pval = 7 , 21 . 10−19 ) , ‘axon’ ( adjusted pval = 1 , 87 . 10−12 ) , ‘growth cone’ ( adjusted pval = 3 , 48 . 10−6 ) ‘synapse’ ( adjusted pval = 4 , 14 . 10−9 ) or ‘dendrite’ ( adjusted pval = 7 , 26 . 10−9 ) . All these processes are in line with the presumed functions of granules that are thought to transport mRNA dedicated to the regulation of synaptic development and plasticity [31 , 32] . Considering the crucial role of FMRP in all these processes , we sought for overlap between the list of mRNA granules , and the previously published list of putative FMRP mRNA targets [33] . Interestingly , 15% of the mRNA listed in granules has been described as putative mRNA targets of FMRP ( 270 out of 1806 , Fig 7A and S3 Table ) . Also , 32% of FMRP mRNA targets ( 270 out of 842 , Fig 7A and S4 Table ) are detected in RNA granules . These data support the important role played by FMRP in these structures . To identify the specific subset of RNA preferentially transported in RNA granules , we focused on highly enriched mRNA ( FC>4 ) as compared to polyribosomes . Interestingly , the mRNA Map1b encoding the microtubule-associated protein MAP1b , a transcript known to be targeted to the dendritic arborization [32] is enriched by a factor of 4 in granules ( Fig 7B ) . The mRNA encoding αCaMKII , a known dendritic mRNA was not detected in our analyses since it is not yet expressed in brain of young mice [34] . A series of mRNA is enriched above 10 folds in granules in particular mRNAs encoding the cytoskeleton regulator Drebrin1 , the myosin motor proteins Myo6 and Myh9 , and a LIM-domain containing protein Limch1 ( Fig 6 and S4 Table ) . While it has been reported that the granule size varies between 300 and 1000 nm [16] , our electron microscopy results suggest that large granules might be composed of independent smaller granules of 100 to 300 nm in size ( see Fig 3B to 3D ) . A plausible scenario would be that these independent granules fuse to form a “cargo of granules” . In the case of FMRP , transfection studies of neurons in culture have shown the presence of GFP-tagged FMRP in travelling granules [35–38] . However , vectors used previously to express RNA-binding proteins in neuron primary cultures contain strong promoters , either from the cytomegalo- ( CMV ) or SV40-viruses , which result in high expression levels of the GFP-tagged RNA-binding proteins . Excess expression of RNA-binding proteins , in particular of FMRP , leads to the formation of cytoplasmic foci corresponding to stress granules ( SG ) [39] . We wish to point that , morphologically speaking , SG cannot a priori be discriminated from neuronal granules . To ensure a neurospecific and more physiological expression of FMRP , we used the pShuttle-GFP-FMR1 vector under the synapsin promoter . To investigate in living neurons the dynamics and kinetics of granules , we performed time-lapse video-microscopy experiments to follow the movements of GFP-FMRP in transfected cultured rat hippocampal neurons . We observed high levels of GFP-FMRP in the soma and proximal dendritic compartments , while smaller GFP-FMRP containing puncta were distributed throughout the distal dendritic arborisation ( Fig 8A ) . In neurons grown for 7 days in culture , we noted a high proportion ( 42 . 8% , see below ) of granules showing dynamic movements . The mean speed of these moving puncta was 0 . 123 ± 0 . 005 μm/s ( calculated on n = 100 granules disseminated along 4 dendritic segments from 5 neurons ) . Interestingly , we regularly observed coalescing of GFP-FMRP puncta into larger ones , a phenomenon that has not been reported yet . Fig 8A and 8B ( and S5 Fig ) illustrate this phenomenon observed in two dendritic segments . In that neuron , we followed the fate of 70 GFP-FMRP puncta , 30 of which were moving distally from the soma , while the others had oscillatory movements . Among these moving puncta , 14 merged to form larger cargo-like structures . As shown in Fig 8A and 8B , four puncta fused to form a single larger one ( an additional sequence of events is shown in S5 Fig ) . Distances travelled by each puncta along the X and Y axes are presented in Fig 8C . We quantified the moving speed of the individual puncta over a period of 20 min . In this example ( detailed in Fig 8B to 8D ) , we observed 3 slow puncta ( number 1 , 2 and 3 ) and a faster one . Of high interest , when the slow puncta merged , they kept a slow speed ( puncta 2 and 3 ) , while when the fast puncta fused with slow one , it appeared that the former imposed its faster momentum ( Fig 8D ) . These observations were reproduced when following a set of 5 other puncta in a more distal area of the same neuron ( see dashed box in Fig 8A and details in S5 Fig ) . Interestingly , the intensity of the fluorescent puncta increased as they merged , although not linearly , likely because of fluorescence quenching [40] . Altogether , these results are consistent with the existence of dynamic dendritic granules that can coalesce into cargoes as they move through the dendritic arborization . How dendritic mRNAs are delivered to the synapse remains poorly understood . For instance , how could the narrow aperture of the spine neck , less than 200 nm in the case of hippocampal neurons [41 , 42] , allows for the passage of the large granules described by Krichevsky and Kosik [16] . A plausible scenario would be that small granules are released from cargoes enabling their passage through the spine neck to reach the synaptic compartment . Interestingly , while tracking the movements of 221 puncta in different dendrites , we found 24 that moved out of the directional flow ( see examples in Fig 9 arrow heads ) . We noticed small puncta budding from large fluorescent clusters , translocating through the neck into the spine head . Whether these structures corresponded to FMRP associated with the whole translation apparatus could not be determined and needs further analyses that are beyond the scope of the present study . However , since Antar et al . [36] have reported that only approximately 50% of FMRP colocalizes with ribosomal RNA and as we observed that not all granules contain FMRP ( Fig 3G ) , we decided to determine to which extent FMRP colocalizes with the ribosomal marker L7 in dendrites . We performed immunofluorescence analyses on rat hippocampal neurons in primary cultures using IgY#C10 anti-FMRP and anti-L7 antibodies . Deconvolution and binary merging analyses of images using the MetaMorph Software enabled us to study L7 and FMRP colocalization in 4534 granules spread over 15 dendritic segments ( Fig 10A ) . Several observations could be made from these colocalization studies . Firstly , the population of puncta positive for L7 is 2 . 5 fold larger than the FMRP one . Secondly , L7 and FMRP co-localize in 30% of the puncta . Thirdly , 60% of L7 positive granules were free of FMRP . Fourthly , FMRP positive puncta free of L7 represent 10% of the total puncta ( Fig 10B ) . This last population that contains no ribosomes may serve as supply cargoes required to replenish and feed the translation machinery at the synapse , such as the translationally silent mRNPs containing CYFIP and FMRP [43] . Immunofluorescence studies have shown the huge heterogeneity of RNA-binding proteins contained in granules . Since we did not aim at establishing an exhaustive list of the many RNA-binding protein combinations present in the granules , we limited our studies to the FXR family members . In vitro , all three members of this small family interact with each other to form homomers or heteromers [27] and have been detected in dendritic RNA granules after transfection of neurons with the respective expression vectors [44] . But so far , the coexistence of endogenous FMRP , FXR1P and FXR2P in the RNA granules has not been investigated . Therefore , we performed triple immunofluorescence labelling on hippocampal neurons in primary cultures ( 10 DIV ) and studied the relative co-localization of the three endogenous FXR proteins ( Fig 10C ) . We observed that 48% of FMRP-positive dendritic granules contain only FMRP , 43% of FXR2P-positive dendritic granules exhibit only FXR2P , while 33% of FXR1P-positive granules contain only FXR1P . Therefore , the FXR proteins are represented in various combinations , ranging from 12% to 23% of the granules that are double-labelled for two of the FXR proteins . Some granules exhibit only two members of the FXR family: 23% contain both FMRP and FXR2P or both FMRP and FXR1P and 12% show dual labelling for FXR1P and FXR2P . Interestingly , the three members of the family co-localize in only 22% of the granules ( Fig 10D ) . These data are suggestive of the high level of diversity and heterogeneity in the composition of neuronal RNA granules .
Our high-resolution electron micrographs reveal an impressive and orderly morula-like architecture of granules resulting from densely packed polyribosomes . The proteomic analysis shows that 85 . 4% of the proteins identified in granules are also found in polyribosomes . While granules and polyribosomes share enriched pathways linked to ‘translation’ , ‘ribosome’ and ‘RNA-binding’ , cytoskeleton-linked terms appear only in RNA granule preparations . The latter data can be interpreted in two ways . On one hand , these proteins may represent trace contaminants from the cytoskeleton , which could remain in the fractions even after the Metrizamide purification step . On the other hand , they may be related to granule motility , as several myosin motors are represented . In particular , MyosinVa was previously described to transport major synaptic scaffolding proteins to dendritic spines [47] . Concerning the translation initiation and elongation factors in granules , our study documents the presence of eIF4E and eIF2a , in agreement with others [16 , 19] . We also report the identification a number of translation initiation factors IF3b , eIF3c , eIF4G as well as 4EBP1a and the RNA helicase DHX30 . The presence in granules of the splicing regulators PRPF and SRPB1 seems a priory puzzling . However , the splicing factors and regulators , Nova-1 and Nova-2 , are also present in the neuronal cytoplasm [48] , where they control mRNA localization , stability and translation [49] . It is therefore plausible that PRPF and SRPB1 contribute to the localization and homeostasis of mature mRNA in neuronal cytoplasm and dendrites . Also intriguing is the presence of the argonaute family member Ago2 , involved both in mRNA degradation and translation [50] . The diversity of the RNA-binding proteins identified in granules support the hypothesis that RNA granules might constitute a platform of local degradation or translation of mRNA transported at the synapse . Using immunoblot analyses , we showed the presence of a series of proteins that were not detected by MS such as Ago2 , eIF4E , e4EBP1 , eIF4G , and eIF2A . The reason for this is not known . Interestingly , using MS , Elvira et al . [17] did not detect FMRP , or Pur α/β in granules while they were able to show the presence of these proteins by immunoblotting . It is possible that overrepresentation of ribosomal proteins may preclude adequate quantification or detection of other classes of proteins by MS . This might explain why some proteins are not detected . Alternatively , the relative abundances of some proteins may not be adequately reflected by normalized spectrum counts in proteomics versus immunoblotting analyses . Proteomic analyses show that RNA granules contain both axonal and dendritic proteins . We believe that discrimination between dendritic and axonal granules is not possible when using total brain extract . The use of primary neurons grown in Campenot compartmentalized chambers [51] may allow such separation . It is not possible to conceive that all proteins described in the present study interact with each other in the same granule unit , neither it is envisioned that several mRNAs are targeted in the same granule . This is consistent with the fact that co-localization of different RNA-binding proteins in a single granule is not a general rule . Fig 10 shows that the FXRs only rarely colocalize in vivo , while the three members of the FXR family interact with each other in vitro . The same conclusions can be drawn for FMRP and Caprin1 as they interact physically in vitro , while little co-localization is observed in dendritic granules [22] . Therefore we propose that each granule contains a single mRNA species with its dedicated RNA-binding protein ( s ) ensuring translation repression . Pathway enrichment analyses show that the transcripts present in granules are mainly associated with cytoskeleton-linked biological processes . In particular , the Map1b transcript , an identified FMRP target [33 , 52 , 53] , is enriched at least 4-fold . Also , Ppp1r9a mRNA that codes the negative regulatory subunit of protein phosphatase-1 ( PP1 ) is enriched over 16-fold in granules . The activation of PP1 is important for the induction of long-term depression ( LTD ) , while its down regulation is required for the normal induction of long-term potentiation ( LTP ) of synaptic transmission [54] . Therefore , the fine-tuning of localized synthesis of PP1 regulatory subunit seems to modulate LTP and LTD locally at the synapse . LTP and LTD result from activity-dependent long-term adaptations of synaptic protein repertoire , in particular proteins involved in cytoskeleton remodelling . A strong enrichment ( above 21-fold ) of the F-actin and the microtubules modulator Drebrin1 mRNAs suggests a crucial role for localized synthesis of the cognate protein . Indeed , Drebrin1 acts as a positive regulator of microtubule entry into spines [55] , which plays a crucial role in synaptic function and plasticity . Also , mRNAs encoding LIM domain-containing proteins such as Limch1 , Lmo7 , Lima1 , Mcal1 and Mcal3 are selectively enriched in granules preparations . LIM-domain containing proteins have been shown to play roles in cytoskeletal organisation , particularly at the synapse where their local synthesis would contribute to cytoskeleton remodelling , as described for LIM-domain containing kinase 1 [56] . In our granule preparations , we recovered approximately one third of the mRNA targets of FMRP described by Darnell et al . [33] . In addition , there is almost twice as much FMRP in granules than in polyribosomes . This speaks for a crucial role played by FMPR in these structures . Twenty putative mRNA targets of FMRP are enriched at least 4-fold in granules when compared to polyribosomes , indicating that these mRNAs are mainly targeted towards the dendritic compartment where they undergo localized synaptic translation regulated by FMRP , as is the case for Map1b [33 , 52 , 53] . The enrichment in granules of mRNA encoding motor proteins ( Dync1h1 , Myo18a , Myh10 , Myo5a ) described as targets of FMRP , raises the possibility that FMRP could address and modulate their local translation in dendrites and synapses , thereby controlling locally the movement of granules . In addition , the mRNA Sptbn1 , Sptbn2 and Ank2 , the encoding members of the spectrin and ankyrin family that form a sub-membranous network involved in the regulation of synaptic stability and maintenance , are highly concentrated in granules and are putative mRNA targets of FMRP [33 , 57] . It is tempting to speculate that these mRNAs are addressed to the synapse and that dysregulation of their transport and local translation in the absence of FMRP contribute to the spine dysgenesis observed in Fragile X patients . We postulate that a class of granules emerge from stalled somatic polyribosomes [33 , 58] to form independent small RNA granules that are transported on microtubules , as is the case for granules carrying FMRP [36 , 59] . These small granules merge to form larger granules through a mechanism that has yet to be uncovered . Therefore , we propose the concept of RNA cargoes in which individual granules are recruited en route ( Fig 11 ) . Using time-lapse video microscopy , we observed small puncta coalescing into larger ones , suggesting that the so-called RNA granules can fuse into large cargoes of RNA granule entities . Forward movements display an average velocity of 0 . 123 ± 0 . 005 μm/sec , compatible with the reported speeds for granules labelled for GFP-Staufen ( 0 . 1 μm/sec [60] ) and GFP-Pur-α ( 0 . 10–0 . 12 μm/sec [19] ) . Our tracking observation suggest that fast granules merging with slower granules , can impose their kinetics to the latter . This implies that each granule is under the control of a motor that determines its speed , and that once granules have merged , one specific motor determines the speed of the cargo . Importantly , FMRP interacts physically with members of the kinesin family [38 , 61] and immunoprecipitation studies have shown that it is present in complex brain structures containing the actin-based motor protein myosin Va and dynein [62] as well as KIF5A [19] . Furthermore , we detected in granules enriched mRNAs encoding motor proteins: myosins ( Myo1e , 5a , 6 , 18a and Myh9 , 10 , 14 ) and dynein Dync1h1 . Myo5a is required for the transport of FMRP mRNP [63] and associates with mRNP present in peripheral axons [64] . Myosin 10 is a motor involved in the formation of filopodia and development of dendritic spines and synapses in hippocampal neurons [65] . These data suggest that individual granules may be carried by different motors cooperating for their transport in the neuronal arborization [66 , 67] . It is also possible that granules switch from microtubule to actin tracks to join other travelling granules , the nature of the motor of that switch being determined by the motor load [68] . The resistance of granules to EDTA and RNase treatments strongly argues for a role of protein-protein interactions , rather than by protein-RNA links in maintaining the granule structure . This network of protein-protein interactions is dissociated by high-salt conditions , that remove certain RNA-binding proteins from brain polyribosomes [22] . Also , the association of these RNA-binding proteins with polyribosomes is sensitive to the anionic detergent deoxycholate that preserves only core polyribosomal proteins [20 , 21 , 23] . These observations support the hypothesis that dissociation of granules is conditioned by the removal of RNA-binding proteins , such as FMRP that is engaged into protein-protein interactions . This leads to the unfolding of the densely packed granule structure , which in turn can evolve towards the polyribosomes open structure ( see below ) . Importantly , the close and compact structures described in the present study , have not been described in isolated polyribosomes that appear either as ring-shaped forms collapsed into double row structures or as linear polyribosomes densely packed into 3D helices [69 , 70] . Also , they have not been observed in organelles containing polyribosomes such as the Vault particles [71] . The present study provides converging evidence that mRNA present in granules is blocked once the translation initiation complex is formed . Firstly , the EM micrographs of granule preparations present striking similarities with those illustrating complexes of stalled polyribosomes [33] . Secondly , the compact-close structure of stalled polyribosomes is maintained even after EDTA or micrococal nuclease treatments [33] , as is the case for the granules described in the present study . Thirdly , we confirmed by proteomic and immunoblotting analyses the presence in granules of several members of the translation initiation complex . These results are in line with a recent study performed in primary neuronal cultures revealing the presence of ribosome-bound nascent polypeptide chains budding from neuronal RNA granules , together with the RNA-binding proteins Staufen 2 and FMRP [58] . This suggests that neuronal mRNAs are transported in granules in the form of packed polyribosomes stalled after the first round of translation elongation . A number of studies have shown that high levels of exogenous FMRP induce translation repression of reporter transcripts [39 , 72 , 73] . In addition , for the wide majority of well-characterized FMRP mRNA targets , the levels of the cognate protein are increased in the absence of FMRP . Finally , a landmark study from Darnell et al . [33] unveiled ribosomal stalling as an unexpected mode for translation repression by FMRP . We envision that FMRP functions as a translational repressor in RNA granules to prevent ectopic translation of its target mRNA during transport . Similarly , the orthologs FXR1P and FXR2P are enriched in granules in which they can be detected by immuno-electron microscopy . However their specific roles in translation regulation have been neglected so far . Because we showed that ribosomes are the basic unit of the granules , we postulate that the granule size is function of the number of ribosomes , presumably in relation itself to the length of the transported mRNA . Indeed , it has been hypothesized by Schuman et al . [74] that neuronal RNA granules exist as single entities and that each granule contains and transports a single mRNA . It results that , the longer the mRNA , the larger is the granule . The average size of a dendritic spine neck ( 200 nm [41 , 42] ) represents a spatial constraint such that large RNA granules or cargoes cannot enter the synapse . In fact , as shown by our time-lapse video microscopy , small granules might bud from large cargoes and pass through the neck into the spine . These observations lead us to speculate that once located in the spine , the translation apparatus is reactivated following adequate stimulations; in turn , the RNA-binding proteins or other repressors are released from the complex and are degraded . In the case of FMRP , it has been reported that mGluR activation leads to FMRP loss at the synapse [36] due to rapid degradation by the ubiquitin-proteasome pathway [75 , 76] . While our model predicts that a specific class of granules emerges from stalled polyribosomes , it does not rule out that other granular structures are formed in distinct cellular compartments . Indeed , the non-coding BC1 RNA , a translation repressor [13] , is predominantly detected in dendritic granules and does not transit in polyribosomes [77] . Also , some granules might correspond to travelling repressed mRNPs , such as the ZBP1-associated mRNA complexes , that are formed in the nucleus [78 , 79] . The study of neuronal RNA granules have driven considerable attention since the discovery that the RNA-binding proteins FMRP , TDP-43 , and SMN , respectively associated with Fragile X Syndrome [80] , amyotrophic lateral sclerosis [81 , 82] and spinal muscular atrophy [83 , 84] , are components of RNA granules . The primary morphological abnormality observed in the brain of Fragile X patients , is the presence of immature-looking dendritic spines [85] most likely resulting from alteration in the cytoskeleton architecture , as a consequence of defects in transport and translation of specific mRNA at the synapse . In the present study , we present evidence that travelling RNA granules are as heterogeneous as perhaps the whole extrasomatic transcriptome , and we hope that our approach will enable the in-depth study of dysregulations of RNA granules transport in neuropathologies . In the case of the Fragile X syndrome , we believe that the CLIP-RNAseq approach on granules preparations will enable to determine the precise nature of FMRP RNA targets addressed to the synapse , contributing to precisely reveal the defective mRNA involved in the syndrome .
C57BL/6J mice were bred in our animal facility and treated following the guidelines of the Canadian Council on Animal Care . The ethics committee of Université Laval has approved all procedures used in this study . Hippocampal neuron cultures were prepared from neonatal rats as described [86] . Briefly , hippocampi were dissected out of postnatal day 1 rats . After dissociation , cells were washed , centrifuged and plated on poly-D-lysine-coated Aclar coverslips . Growth media consisted of Neurobasal supplemented with B27 , penicillin/streptomycin ( 50 U/ml; 50 μg/ml ) , and 0 . 5 mM Glutamax ( Thermo Fischer Scientific ) . Cytosine arabinofuranoside ( Ara-C , 5 μM , Sigma ) was added 2 days after plating to reduce the number of glial cells . After 4 days invitro , half of the growth medium was replaced with medium without Ara-C . Neurons were cultured between 7 and 13 DIV before use . Total brain cytoplasmic extracts were prepared from 10 days old C57BL/6J mice , using two different methods . Protein concentration was determined using the Bradford method after TCA precipitation of the extracted proteins and resolubilization in 0 . 2 N NaOH followed by neutralization with 0 . 2 N HCl . To adjust with accuracy the quantities of polyribosomal and granules protein loaded on SDS-PAGE , gels were stained with Coomassie Brilliant blue , scanned and total stained peaks integrated using the ImageJ program , and the loaded volumes consequently adjusted . Protein were analysed by SDS-PAGE ( 10% acrylamide ) and the resolved proteins stained with Coomassie brilliant blue . Resolved proteins were also transferred onto 0 . 45 μm nitrocellulose membranes ( BioRad ) and processed for immune-detection after blocking in 5% non-fat dry milk in PBS . The following primary antibodies were used: chicken anti-FMRP #C10 ( dil 1:2000; [22] ) , mouse anti-FMRP mAb1C3 ( 1:2000; [89] ) , rabbit anti-FXR1P #ML13 ( 1:25000 [39] ) . Mouse anti-FXR2P mAb42 ( 1:2000 ) , mouse anti-MTCO1 ( 1:2000 , ab7291 ) , rabbit anti-PSD95 ( 1:5000 ) , mouse anti-α–Tubulin ( 1:5000 , ab7291 ) were purchased from Abcam . Rabbit anti-L7 ribosomal protein ( 1:10000 ) was from Novus Biological; rabbit anti-S6 ribosomal protein ( 1:2000 ) , rabbit anti-PABP1 ( 1:1000 ) , rabbit anti-eIF4G ( 1:1000 ) , rabbit anti-eIF2A ( 1:1000 ) from Cell Signaling . Rabbit polyclonal anti-NeuroFilaments ( 1:1000 , NF18934-1-AP ) was from Proteintech . Mouse anti-Ago ( 1:500 ) from Upstate , rabbit anti-eIF4E ( 1:1000 ) , and rabbit anti-e4EBP1 ( 1:1000 ) from Assay BioTech , and mouse anti-actin JLA-20 ( 1:1000 ) was obtained from Developmental Studies Hybridoma Bank ( Iowa City , IA ) . Detection of bound antibodies was performed with HRP-coupled goat secondary antibodies to mouse or chicken or rabbit ( Immunoresearch ) followed by ECL reaction ( Perkin Elmer ) and exposure to Fuji X-ray films . Quantitation of signals was performed after scanning the films and analyses using the ImageJ software . Hippocampal neurons grown on coverslips for 10-12DIV were processed for immunofluorescence . Rabbit anti-FXR1P #ML13 , chicken anti-FMRP #C10 , mouse anti-FXR2 and rabbit polyclonal anti-L7 primary antibodies were used at 10 times less than the dilutions used for immunoblot analyses ( see above ) , followed by Alexa secondary antibodies ( green , red , blue respectively ) . Samples were mounted in Prolong Gold medium ( Invitrogen ) . Images were captured using a Zeiss LSM 510 confocal microscope and a 63x ( 1 . 4 NA ) objective and analysed using the MetaMorph Software . For time-lapse videomicroscopy experiments , hippocampal cultures were transfected using Lipofectamine as described [86] with the pShuttle/GFP-FMR1 under the synapsin promotor , ensuring a neurospecific expression of FMRP . The vector was engineered by subcloning GFP-FMR1 cDNA from pGFP-C2/FMRP construct [38] into the pShuttle ( Stratagene ) downstream of the synapsin promotor . Cells were imaged at 36°C–37°C in an open perfusion ( 0 . 2–0 . 5 ml/min ) Qe-1 RC-41LP chamber ( Warner Instruments ) mounted onto a Zeiss Axiovert inverted microscope equipped with a 63x ( 1 . 4 NA ) or 100x ( 1 . 3 NA ) objectives . Images were captured with a cooled CCD camera ( Cool Snap HQ , Roper Scientific ) every 5 sec for 20 min . The intensities of fluorescence along the processes of each neuron were measured with a user-defined threshold with the MetaMorph software ( Universal Imaging ) . The mean movements of granules were measured on a 20 minutes scale using the SpotTracker plugging of the ImageJ software ( NIH , Bethesda ) . Analyses were performed at the McGill University-Genome Québec Innovation Centre facility ( Montréal , Canada ) . Fourty μg of proteins from granules or polyribosomes were run on an 11% acrylamide SDS-PAGE . Gels were stained with Coomassie blue and twenty gel slices per lane were excised . Proteins were digested in situ with trypsin , and the resulting tryptic peptides analysed by tandem mass spectrometry . All MS/MS spectra were analysed using Mascot [90] and X ! Tandem [91] . Mascot was set up to search mouse proteome ( Mus musculus released 2009/11/24 ) assuming non-specific digestion by trypsin . Mascot and X ! Tandem were searched with a tolerance of 0 . 50 Da for both fragment and parent ion mass . Iodoacetamide derivatives of cysteine were specified in Mascot and X ! Tandem as fixed modifications , while deamidation of asparagine and glutamine , methyl ester of aspartic acid and glutamic acid , methylation of cysteine and oxidation of methionine were specified in X ! Tandem and Mascot as variable modifications . Scaffold software was used to validate MS/MS based peptide and protein identifications [92] . Peptide Prophet algorithm was used for peptide identification with a 95 , 0% confidence [93] . Protein identifications were accepted on the basis of at least 1 identified peptides . Protein probabilities were assigned by the Protein Prophet algorithm [94] . Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony . To provide a semi-quantitative appreciation of protein abundance , the dedicated function of Scaffold software was used to normalize individual protein spectral counts data to the total spectral counts for each MS samples . The data presented in S1 Table are scaled accordingly . Total RNA from polyribosomes and granules from wild-type mice ( n = 3 biological replicates ) were extracted using Trizol LS ( Invitrogen ) according to the manufacturer's instructions . After DNase I digestion ( Qiagen ) , RNA was further purified using RNeasy Mini kit ( Qiagen ) . Quality and concentration of extracted RNA was measured using the 2100-Bioanalyzer ( Agilent Technologies , Palo Alto , CA , USA ) with the RNA PicoLab Chip ( Agilent Technologies ) . Only high-quality RNA ( RIN over 8 ) was used for RNA amplification . Thereafter , RNA was subjected to two rounds by T7 amplification using the RiboAmp HSPlus RNA Amplification Kit ( Life Science , Foster City , CA , USA ) , purified and quantified using NanoDrop ND-1000 spectrophotometer ( NanoDrop , Wilmington , DE , USA ) . Antisense-RNA ( aRNA ) samples were labelled with Cy3 or Cy5 using the Universal Linkage System ( ULS ) kit ( Kreatech Diagnostic , Amsterdam , Netherlands ) and 825 ng of labelled aRNA were hybridized on the SurePrint G3 Mouse GE 8x60K Microarray kit ( Agilent ) in a two-color dye-swap design in a hybridization oven for 17 h at 65°C . A simple direct comparison between treatments was done in full dye swap . Microarrays slides were then washed and scanned with the PowerScanner ( Tecan , Männedorf , Switzerland ) and analysed with the Array-Pro Analyzer software ( MediaCybernetics , Bethesda , MD , USA ) . Microarray data were pre-processed and analysed using the FlexArray 1 . 6 . 1 ( http://genomequebec . mcgill . ca/FlexArray ) . Raw data correction consisted of a Lowess intra-array normalization and Quantile inter-array normalization . Statistically significant variations were detected using Limma ( Bioconductor ) . Multiple hypotheses testing correction was done using the Benjamini-Hochberg procedure [95] . Differences in gene expression were evaluated by calculating the fold of change ( FC ) of signal intensity in granules preparations versus polyribosomal preparations . FC were considered significant when: i ) net signal intensity is significantly over background in all arrays ( technical and biological replicates ) ; ii ) the cut-off adjusted p-value <0 . 002 and iii ) fold change reaches at least 1 ( log2FC>0 ) . Positive signal threshold was determined for each array from the average background value plus two standard deviations . Gene ontology-based pathway analyses and downstream exploitation of protein and gene lists were performed using the freely available DAVID bioinformatics resources [96 , 97] . | Fragile X syndrome is the most common form of inherited mental retardation affecting approximately 1 female out of 7000 and 1 male out of 4000 worldwide . The syndrome is due to the silencing of a single gene , the Fragile Mental Retardation 1 ( FMR1 ) , that codes for the Fragile X mental retardation protein ( FMRP ) . This protein is highly expressed in brain and controls local protein synthesis essential for neuronal development and maturation as well as the formation of neural circuits . Several studies suggest a role for FMRP in the regulation of mRNA transport along axons and dendrites to distant synaptic locations in structures called RNA granules . Here we report the isolation of a particular subpopulation of these structures and the analysis of their architecture and composition in terms of RNA and protein . Also , using time-lapse video microscopy , we monitored granule transport and fusion throughout neuronal processes . These findings open new avenues for the study of RNA transport dysfunctions in animal models of nervous system disorders . | [
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"biochemistry"... | 2016 | Tracking the Fragile X Mental Retardation Protein in a Highly Ordered Neuronal RiboNucleoParticles Population: A Link between Stalled Polyribosomes and RNA Granules |
Infections are a common cause of infant mortality worldwide , especially due to Streptococcus pneumoniae . Colonization is the prerequisite to invasive pneumococcal disease , and is particularly frequent and prolonged in children , though the mechanisms underlying this susceptibility are unknown . We find that infant mice exhibit prolonged pneumococcal carriage , and are delayed in recruiting macrophages , the effector cells of clearance , into the nasopharyngeal lumen . This lack of macrophage recruitment is paralleled by a failure to upregulate chemokine ( C-C ) motif ligand 2 ( Ccl2 or Mcp-1 ) , a macrophage chemoattractant that is required in adult mice to promote clearance . Baseline expression of Ccl2 and the related chemokine Ccl7 is higher in the infant compared to the adult upper respiratory tract , and this effect requires the infant microbiota . These results demonstrate that signals governing macrophage recruitment are altered at baseline in infant mice , which prevents the development of appropriate innate cell infiltration in response to pneumococcal colonization , delaying clearance of pneumococcal carriage .
Many infectious diseases target infants , although our understanding of the host factors that contribute to the increased susceptibility of early childhood remains incomplete [1] . Prominent among the causes of infection of the infant period is Streptococcus pneumoniae , the pneumococcus . Despite effective antibiotics and vaccines , pneumococci are responsible for more than 1 million deaths annually , predominantly in the developing world [2] . Worldwide , this gram-positive bacterium is a common cause of pneumonia at all ages . The spectrum of pneumococcal disease ranges from local infections such as acute otitis media , acute rhinosinusitis and pneumonia , to invasive infections including meningitis and sepsis . In all these diseases , the pathogenic pneumococci initially disseminate from the nasopharynx , a single common site of colonization and carriage [3 , 4] . Disease , however , represents an evolutionary dead-end for the pneumococcus , since transmission to a new host occurs only via respiratory secretions from the reservoir of bacteria colonizing the nasopharynx [5 , 6] . Clinical studies and experimental colonization in humans have revealed that different pneumococcal serotypes can colonize repeatedly and concurrently . Each carriage event is maintained for weeks to months before being cleared [3 , 7 , 8] . Pneumococcal colonization is particularly common in young children , with a peak prevalence of 55 percent in children 3 years old , declining to 8 percent of 10 year olds and an even smaller proportion of adults [4 , 9] . Carriage is not only more frequent in children , but is also prolonged . Multiple studies across 3 continents demonstrate a consistent 2-fold increase in the duration of a given pneumococcal colonization event in children compared to adults [10–12] . The mechanism for delayed pneumococcal clearance by infants is not clear , however . One proposed explanation for more efficient clearance with increasing age is the development of antipneumococcal antibodies following clearance of pneumococcal carriage . These anticapsular antibodies cannot be the sole mediator of acquired protection against pneumococci , however , as pneumococcal disease decreases in childhood for all serotypes at a similar rate , a finding that would not be expected if each serotype would need to be carried to generate type-specific anticapsular antibodies [13] . This analysis implies that non-serotype specific mechanisms are responsible for the faster clearance of pneumococcal colonization that occurs with increasing age . The molecular mechanisms underpinning pneumococcal clearance have been studied in an adult mouse model that faithfully recapitulates multiple aspects of human carriage , including the duration of carriage [14] . Clearance of colonization is independent of the acute inflammatory response and neutrophil influx into the nasopharynx , and furthermore does not require the development of anticapsular antibodies [15 , 16] . Rather , clearance depends on the recruitment of macrophages into the airway lumen , a process that requires Th17 cell immunity and the expression and sensing of the monocyte chemoattractant chemokine ( C-C motif ) ligand 2 , or CCL2 ( MCP-1 ) [17–19] . Chemokine production and macrophage recruitment occur in response to sensing by pattern recognition receptors TLR2 and Nod2 , [18 , 19] as well as the macrophage scavenger receptor MARCO [20] . It is unclear how these pathways that normally lead to clearance of colonization from an adult host are absent or altered in infant mice . Here , we show that infant mice are delayed in clearing pneumococcal colonization , and that this prolonged carriage is accompanied by slower macrophage recruitment . We demonstrate that increased macrophage chemoattractant expression due to acquisition of the infant microbiota prevents the formation of a chemokine gradient , and that this lack of chemokine gradient delays macrophage recruitment and pneumococcal clearance .
To determine whether pneumococcal carriage is prolonged in infant mice , adult ( 6 week old ) and infant ( 7 day old ) mice were intranasally inoculated with a pneumococcal isolate that does not cause systemic infection in mice ( S1 Table ) using a small volume and without anesthesia to prevent aspiration into the lower respiratory tract . At different timepoints , bacterial density was measured by plating nasal lavages . Adult mice started to clear colonization within a week after bacterial challenge , and had fully cleared pneumococci from the nasopharynx by 21 days postinoculation . By contrast , mice inoculated as infants maintained pneumococcal carriage for >6 weeks , and only started to clear colonization at 21 days postinoculation ( Fig 1A ) . Delayed clearance in infant mice was not a strain-specific effect , as it was also seen with a clinical isolate of a different pneumococcal serotype ( Fig 1B ) . The effect of age at inoculation waned over time , as seen by the gradual increase in clearance by 21 days postinoculation in mice inoculated at 7 , 14 , 24 or 42 days old ( Fig 1C ) . Half of all mice inoculated as infants had cleared colonization by 45 days postinoculation , while half of all mice inoculated as adults cleared colonization completely at approximately 18 days postinoculation . Since pneumococcal clearance in adults is dependent on the sustained presence of macrophages into the nasopharynx , [18] we examined whether macrophages infiltrated the airway lumen of infant mice by using flow cytometry to quantify different cell populations in nasal lavages . Macrophage influx into the nasopharynx was delayed in infant mice compared to adults ( Fig 2A ) . Inflammatory responses were not completely absent in infants , however , as the neutrophil influx into the nasopharynx in the first week post-inoculation was equivalent between adult and infant mice ( Fig 2B ) . Myeloid cell maturation was not impaired in infant mice , as myeloid cells present in the nasal lavages from adult and infant mice had the same level of CD11b surface expression . ( Fig 2C ) . Further evidence for a lack of age-related difference in macrophage maturation came from analysis of macrophages isolated from the peritoneal cavity of adult and infant mice . These had equal expression of surface receptors MHC class II , CD36 and MARCO , as well as the alternatively-activated macrophage polarization transcript Rtnla ( S1 Fig ) . Furthermore , infant and adult mice were equally capable of mounting a humoral immune response to colonization , as measured by serum titers of antibodies specific to the colonizing strain of pneumococci ( Fig 2D ) . In adult mice , the pattern recognition receptors Nod2 and TLR2 play redundant roles in macrophage recruitment and eventual clearance following pneumococcal colonization [19] . The infant clearance defect was epistatic with these pathways , as there was no additional delay in clearance of colonization in infant mice deficient in these pattern recognition receptors , implying that the infant clearance defect was redundant with these pathways ( Fig 2E ) . Previous work in adult mice demonstrated that induction of the monocyte/macrophage chemoattractant protein CCL2 ( MCP-1 ) during colonization temporally correlated with clearance and occurs in macrophages in culture following exposure to pneumococci [19] . Colonized infants , however , did not upregulate Ccl2 expression in the upper respiratory tract relative to expression in mock infants , as measured by qRT-PCR on RNA isolated from nasal lavages with RLT lysis buffer ( Fig 3A ) . Due to the small volumes and dilution , it was not possible to reliably measure chemokine concentrations directly in lavage fluid . When directly comparing Ccl2 expression in mock-infected adult and infant mice , baseline levels were significantly higher in the infant URT than the adult ( Fig 3A ) . As with other chemokines , a concentration gradient of CCL2 is required to attract macrophages [21] . The lack of induction of Ccl2 expression in infant mice during colonization suggested the concentration gradient of this macrophage-attracting chemokine was insufficient . Serum CCL2 levels were also elevated in infant mice compared to adults , potentially contributing to the failure to induce a concentration gradient from low CCL2 levels systemically to high levels at the site of colonization ( Fig 3B ) . We next wanted to identify the source of increased baseline CCL2 in infant mice . Since macrophages were not abundant in the nasal cavity , we turned to a distal site , the peritoneal cavity . We examined macrophage-intrinsic CCL2 signaling by eliciting macrophages from the peritoneal cavity with thioglycollate injection followed by peritoneal lavage 3 days later . Macrophages were purified by adherence , RNA was harvested from cells , and qRT-PCR performed . Ccl2 expression was higher in infant macrophages than adults at baseline ( Fig 3C ) . In the adult nasopharynx , stimulation by pneumococcal colonization led to increased Ccl2 expression , while Ccl2 expression in the infant nasopharynx did not increase above an already elevated baseline ( Fig 3A ) . Macrophage-intrinsic Ccl2 expression followed the same pattern . When stimulated with bacterial lysates , adult peritoneal macrophages increased Ccl2 production , while infant peritoneal macrophages maintained the same elevated level of Ccl2 , without further upregulation ( Fig 3C ) . Therefore , cultured macrophages in isolation were sufficient to recapitulate the same pattern of CCL2 expression and upregulation as found in the nasopharynx . This tonic increase in Ccl2 production by infant systemic macrophages was accompanied by a decrease in infant Ccr2 production ( Fig 3D ) . Baseline expression of the related macrophage chemoattractant Ccl7 ( Fig 3E ) and proinflammatory cytokine Il6 ( Fig 3F ) were also elevated in infants compared to adults . In contrast , expression of the neutrophil chemoattractants Cxcl1 ( Kc ) and Cxcl2 ( MIP2 ) was not elevated in the infant nasopharynx ( Fig 3G and 3H ) . Together these findings suggested an inflamed state in the infant mucosa and that elevated serum and mucosal levels of macrophage chemoattractants in infants compromise the generation of a concentration gradient leading to the nasopharynx . To induce a concentration gradient of CCL2 in infant mice , we infected 4 day-old infant mice with adeno-associated viral ( AAV ) vectors expressing GFP ( mock/vector control ) or murine CCL2 . Three days later , mice were colonized with pneumococci . At 7 and 21 days postinoculation , mice were sacrificed , nasal lavages obtained and flow cytometry performed to assess the macrophage influx into the nasopharyngeal lumen . CCL2 overexpression in the URT increased the local gradient in CCL2 concentration , as mice infected with the CCL2-expressing vector exhibited increased Ccl2 transcription ( Fig 4A ) and CCL2 levels ( Fig 4B ) , while CCL2 concentration in serum was unchanged ( Fig 4C ) . CCL2 protein measurements in nasal lavage fluid are an underestimate , since lavage fluid is at least a 67-fold dilution of the fluid lining the nasal airway surface [22] . The impairment in macrophage recruitment in infant mice was partially recovered by ectopic CCL2 overexpression ( Fig 4D ) . We assessed whether partial macrophage recruitment was sufficient to accelerate pneumococcal clearance by measuring colonization density at 21 days postinoculation , and found a small but significant recovery of the infant defect in clearance ( Fig 4E ) . We next sought to explain the elevated CCL2 expression in infants that was associated with higher baseline Ccl2 expression in the URT , delayed macrophage recruitment and persistent pneumococcal colonization . Among the changes infants experience during normal development is the acquisition of a stable microbiota [23] . We examined whether the microbiota contributed to C-C motif chemokine expression by treating the drinking water of mice with antibiotics to deplete the flora . Adult mice were directly exposed to antibiotic-treated drinking water , while infant mice were exposed indirectly by treating the water of the dams from which the infants nursed . This indirect exposure was sufficient to decrease the commensal flora of the infant upper respiratory tract ( Fig 5A ) . The magnitude of the depletion of the URT flora was consistent with the decrease in gut microbiota previously found in indirectly exposed infants [24] . Antibiotic treatment had no effect on URT expression of Ccl2 in adults , but decreased baseline infant Ccl2 expression to adult levels ( Fig 5B ) . The microbiota was also responsible for the elevated infant baseline levels of Ccl7 ( Fig 5C ) . Limiting baseline C-C motif chemokine expression in the URT of infants allowed for normal responses to pneumococcal colonization , as macrophages were recruited into the nasopharynx of antibiotic-treated infant mice following 7 days of pneumococcal colonization , unlike tap-water treated mice ( Fig 5D ) . Prior antibiotic treatment accelerated pneumococcal clearance , even 15 days after antibiotics were discontinued ( Fig 5E ) . Even though antibiotics were removed from the drinking water starting 24 hrs before pneumococcal challenge in these experiments , it was still possible that any residual antibiotics could have direct effects on pneumococcal density in the nasopharynx . To exclude this possibility , we measured bacterial load at 7 days postinoculation , before the onset of clearance , and found no effect of antibiotics ( Fig 5F ) . Nasopharyngeal expression of Ccl2 was suppressed in germ-free infants compared to tap-water treated infants , confirming the microbiota was responsible for tonically elevated Ccl2 expression in infants ( Fig 5G ) .
Pneumococcal colonization and disease are more common in children than adults , but the mechanism underlying this predisposition has not been clear . Here , we demonstrated that an infant mouse model of carriage recapitulates the human delay in pneumococcal clearance . Using this model , we found that infant mice have delayed macrophage responses during colonization , which correlated with a failure to upregulate CCL2 signaling . Infant mice had tonic CCL2 production in the URT , indicating a compromised chemokine gradient . Reestablishing a gradient by ectopic overexpression of CCL2 partially restored macrophage recruitment and contributed to pneumococcal clearance . We found that the microbiota contributed to tonic macrophage chemoattractant expression , as depleting the commensal flora lowered expression of CCL2 and CCL7 , restored normal macrophage responses and accelerated clearance of pneumococcal colonization . This effect was apparent even 14 days after stopping antibiotic treatment . Higher pneumococcal loads in the infant nasopharynx have been previously reported , [25] but prior work did not examine innate immune responses in vivo that could explain delayed bacterial clearance , such as macrophage recruitment or expression of a CCL2 concentration gradient . Another study found delayed pneumococcal clearance in elderly mice , which correlated with decreased monocytic phagocyte recruitment and an increased inflammatory state at baseline in elderly mice [26] . This study did not find a role for elevated CCL2 expression in aged mice , however [26] . There is a growing understanding that overly exuberant inflammatory responses can be found both early and late in life , both in humans and mice [27] . Our observation of increased IL-6 expression in the infant URT is consistent with a more pro-inflammatory milieu early in life . It would be important to determine whether alterations in macrophage chemoattractant signaling are a consequence of this more generalized inflammatory state . We found tonically elevated CCL2 expression in the URT of infant mice , which was dependent on the presence of the microbiota in infant mice . It was not clear whether the effect was restricted to infants due to the recent acquisition , size or composition of the flora in infant mice , or a unique response to the flora of the infant host . The mechanism by which the infant URT responds to the presence or acquisition of the microbiota by increasing production of CCL2 , CCL7 and other inflammatory mediators remains unknown . Signaling events in the URT could reflect sensing of the local airway flora , or of commensals at distal sites such as the gut . The infant gut in mice is porous until weaning , [28] which could promote leakage of microbial products outside the containment of the gut lumen into otherwise sterile sites . Constitutive intestinal epithelial NF-κB activity is present in infant mice , and may be associated with endotoxin tolerance [29] . The flora has been shown to systemically prime innate immune responses in both adults [30] and newborns [24] . Our finding that CCL2 levels were elevated in infant serum and in infant macrophages isolated from a sterile site without a local commensal flora of its own , the peritoneal cavity , suggested a systemic effect of the flora on the proinflammatory environment of infants . Alternatively or additionally , sensing of microbial products that stimulate inflammation in infants may occur locally at the site of commensal colonization . We also found macrophage recruitment to the infant murine nasopharynx was delayed during pneumococcal colonization . There is precedent for this pattern in humans as well , as the number of macrophages recruited to the nasal lumen during URT infections increased with age [31] . This effect , moreover , was independent of the number of prior infections [31] . CCL2 signaling in the human infant airway has not been studied , but there is some evidence for altered CCL2 production . One study found that serum CCL2 levels in normal children were higher than those found in normal adults , [32] consistent with our findings of elevated serum CCL2 in infant mice . Inducing a gradient of CCL2 by local overexpression in the infant URT partially rescued the defect in macrophage recruitment . This partial recovery was associated with an increase in clearance of pneumococcal colonization . The incomplete recovery may have been due to continued tonically elevated expression of a related macrophage chemoattractant , CCL7 ( MCP-3 ) . This chemokine can also bind CCR2 , the receptor for CCL2 , and both it and CCL2 have additive functions in monocyte homeostasis and recruitment during infection [33 , 34] . In our study , the microbiota stimulated tonically high expression of both CCL2 and CCL7 in the infant nasopharynx . Together , these data suggest that simultaneous overexpression of CCL7 in addition to CCL2 and possibly other signals could lead to adult-like levels of macrophage recruitment , potentially fully accelerating pneumococcal clearance . CCR2 expression was appropriately suppressed considering the elevated CCL2 levels in infant macrophages , which indicated that the infant defect in CCL2 signaling was not a failure to respond to the ligand . Altered monocyte/macrophage trafficking and CCL2 signals could be particularly important in mediating infant susceptibility to other infections , such as those with Listeria monocytogenes , which require both CCL2 and recruited monocyte-derived cells for clearance [35] . Infections in infancy are commonly caused by encapsulated bacteria , including opportunistic pathogens that colonize the URT , like the pneumococcus [36] . The delayed clearance of colonization in infant mice resembles tolerance , the failure to respond to an antigen . Elevated inflammatory pathways that cannot be further upregulated could be a mechanism for such tolerance . As a result , the mechanisms described here may reflect a general defect in infant innate immune responses and extend beyond pneumococcal carriage to clearance of other mucosal agents .
This study was conducted according to the guidelines outlined by the Public Health Service Policy on the Humane Care and Use of Laboratory Animals . The protocol was approved by the Institutional Animal Care and Use Committee , University of Pennsylvania Animal Welfare Assurance Number A3079-01 , protocol number 803231 . C57Bl/6 mice were obtained from Jackson Laboratory . Germ-free mice were bred and raised in the Penn Gnotobiotic Mouse Facility at the University of Pennsylvania . Procedures were carried out according to an animal protocol approved by the University of Pennsylvania IACUC . For antibiotic treatment , tap water was supplemented with 0 . 5 g/L ampicillin ( Sigma ) , neomycin ( Calbiochem ) , gentamicin ( Invitrogen ) and metronidazole ( Sigma ) , as well as 0 . 25 g/L vancomycin ( Santa Cruz Biotechnology ) , then sterile-filtered . Water was changed every 4–5 days . Mice were sacrificed by CO2 inhalation and cardiac puncture . Pneumococcal strains used were the clinical isolates TIGR4 ( capsule type 4 ) , [37] P1547 ( capsule type 6A ) and P1121 ( capsule type 23F , which is avirulent when injected into the murine bloodstream ) [7] . For mouse colonization , pneumococci were grown in tryptic soy broth at 37°C until mid-log phase , then resuspended in sterile PBS . Mice were colonized with doses shown to be sufficient to establish high density colonization , 2x103 CFU for infants and 1x107 CFU for adults [38] . Pilot experiments using the adult dose in both infants and adults showed similar effects on clearance , macrophage recruitment and Ccl2 expression . Mice were sacrificed at indicated timepoints , and nasal lavages obtained with 200 μL sterile PBS , as previously described [19] . Lavages were diluted onto TS agar with catalase ( 5 , 000 U/plate ) ( Worthington Biochemical ) and 5 μg/mL neomycin added for quantitative culture overnight at 5% CO2 . Nasal lavages were fixed in 2% paraformaldehyde , and then stained with antibodies to identify macrophages and neutrophils: anti-Ly6G ( clone 1A8 ) , anti-CD11b and anti-F4/80 . Samples were run on a FACS Calibur instrument ( Becton Dickinson ) and analysis performed using FlowJo software ( Tree Star ) . For measurements of anti-pneumococcal antibody titers , pneumococcal strain P1121 was grown and resuspended to an OD620 of 0 . 1 in coating buffer ( 0 . 015 M Na2CO3 , 0 . 035 M NaHCO3 ) , then plated onto Immulon 2HB 96-well plates ( Thermo ) at 4°C overnight . Plates were washed with 0 . 05% Brij in PBS , and blocked for 1 hr at 37°C in 1% BSA in PBS . After additional washes , serum samples were added in serial 2-fold dilutions ( made in 1% BSA in PBS ) and incubated overnight at 4°C . Anti-pneumococcal antibodies were detected by incubating for 1 . 5 hrs at room temperature with an alkaline phosphatase-conjugated goat anti-mouse IgG antibody , followed by developing for 1 hr at 37°C with p-nitrophenyl phosphatase . Absorbance was measured at 415 nm . The sample dilution at which the absorbance equaled 0 . 1 was used to calculate the geometric mean titer . For measurements of CCL2 protein levels in serum and nasal lavages , an ELISA kit was used according to the manufacturer’s protocol ( eBioscience ) . RNA was obtained from URT epithelium by lavage with RLT buffer ( Qiagen ) with 1% β-mercaptoethanol , or from cultured peritoneal macrophages by lysing cells in RLT buffer with 1% β-mercaptoethanol and frozen at -80°C until used . An RNeasy kit ( Qiagen ) was used to isolate RNA , and cDNA reverse transcribed by the High-Capacity cDNA Reverse Transcription kit ( Applied Biosystems ) . qRT-PCR reactions were performed with Sybr Green ( Applied Biosystems ) with 10 ng cDNA and 0 . 5 μM primers . The ΔΔCT method was used to compare conditions . Primer sequences were as follows: Gapdh-F 5’-AGG-TCG-GTG-TGA-ACG-GAT-TTG-3’; Gapdh-R 5’-TGT-AGA-CCA-TGT-AGT-TGA-GGT-CA-3’; [39] Ccl2-F 5′-AGC-TCT-CTC-TTC-CTC-CAC-CAC-3′; Ccl2-R: 5′-CGT-TAA-CTG-CAT-CTG-GCT-GA-3′; [19] Ccl7-F 5’-GCT-GCT-TTC-AGC-ATC-CAA-GTG-3’; Ccl7-R 5’-CCA-GGG-ACA-CCG-ACT-ACT-G-3’; Il6-F 5’-AGT-TGC-CTT-CTT-GGG-ACT-GA-3’; Il6-R 5’-TCC-ACG-ATT-TCC-CAG-AGA-AC-3’; [40]; Cxcl1-F 5’-CTG-GGA-TTC-ACC-TCA-AGA-ACA-TC-3’; Cxcl1-R 5’-CAG-GGT-CAA-GGC-AAG-CCT-C-3’; [41] Cxcl2-F 5’-CCA-CCA-ACC-ACC-AGG-CTA-C-3’; Cxcl2-R 5’-GCT-TCA-GGG-TCA-AGG-GCA-AA-3’; Ccl2ORF-F 5’-TTA-AAA-ACC-TGG-ATC-GGA-ACC-AA-3’; Ccl2ORF-R 5’-GCA-TTA-GCT-TCA-GAT-TTA-CGG-GT-3’; Ccr2-F 5’-GGT-CAT-GAT-CCC-TAT-GTG-G-3’; Ccr2-R 5’-CTG-GGC-ACC-TGA-TTT-AAA-GG-3’ [42] Macrophages were obtained by injecting adult and infant mice with thioglycollate , followed 3 days later by peritoneal lavage with cold sterile PBS . Cells were spun down and resuspended in DMEM + 10% FBS . Cells were counted and adjusted to equal concentrations , then plated on 24-well non-tissue culture treated plates . After 2 hrs to allow macrophages to adhere , wells were washed 3 times and then media added back . Cells were used for RNA isolation after an overnight incubation , with or without stimulation with heat-killed bacterial lysates ( 107 CFU pneumococci in 100 microliters heated to 65°C for 30 min , with an aliquot plated to verify complete killing ) . For overexpression , an AAV vector with the capsid from serotype AAV5 was used that expressed the open reading frame of murine CCL2 , or GFP for the vector control , under the control of the chicken-beta actin promoter ( Vector BioLabs , catalog # AAV-254826 for CCL2 , 7006 for GFP ) . Vectors were concentrated to ~1013 GC/mL , and each mouse was inoculated with 1011 GC of vector . DNA was extracted from 100 μL nasal lavage samples using the ZR Soil Microbe DNA Miniprep kit according to manufacturer’s instructions ( Zymo Research ) . 16S rDNA copy number was measured using qPCR with a standard curve with a Topo vector containing Escherichia coli 16S rDNA ( courtesy of Dr . Frederic Bushman ) . Reactions were performed using primers , probe and conditions as previously described [43] . Comparisons were made using Prism software ( Graphpad ) . Comparisons between groups for colonization data were made by Mann-Whitney U-test or Kruskal-Wallis test with Dunn’s posttest for two and three or more groups , respectively . All other comparisons were made by unpaired t-test or 1-way ANOVA with Newman-Keuls posttest for two and three or more groups , respectively . | Infants are particularly susceptible to infections , though why is not well understood . One important cause of infant mortality worldwide is infection with Streptococcus pneumoniae , the pneumococcus . All pneumococcal disease begins with asymptomatic colonization of the upper respiratory tract . Infants are also more likely to carry pneumococci , and on average each carriage event has a longer duration . Here , we used an infant mouse model of pneumococcal colonization to study the mechanisms underlying delayed clearance of carriage . We found that infant mice were unable to recruit the effector cells of clearance , macrophages , into the lumen of the upper airway , and that this delay was accompanied by an inability to produce a macrophage chemoattractant in the nasopharynx . We attribute this defect to a dysregulation in the expression of these chemokines and show this effect results from the commensal bacterial flora of infants . Our findings provide an explanation for why infants are more susceptible to being colonized with and infected by pneumococci . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Clearance of Pneumococcal Colonization in Infants Is Delayed through Altered Macrophage Trafficking |
Automated docking of drug-like molecules into receptors is an essential tool in structure-based drug design . While modeling receptor flexibility is important for correctly predicting ligand binding , it still remains challenging . This work focuses on an approach in which receptor flexibility is modeled by explicitly specifying a set of receptor side-chains a-priori . The challenges of this approach include the: 1 ) exponential growth of the search space , demanding more efficient search methods; and 2 ) increased number of false positives , calling for scoring functions tailored for flexible receptor docking . We present AutoDockFR–AutoDock for Flexible Receptors ( ADFR ) , a new docking engine based on the AutoDock4 scoring function , which addresses the aforementioned challenges with a new Genetic Algorithm ( GA ) and customized scoring function . We validate ADFR using the Astex Diverse Set , demonstrating an increase in efficiency and reliability of its GA over the one implemented in AutoDock4 . We demonstrate greatly increased success rates when cross-docking ligands into apo receptors that require side-chain conformational changes for ligand binding . These cross-docking experiments are based on two datasets: 1 ) SEQ17 –a receptor diversity set containing 17 pairs of apo-holo structures; and 2 ) CDK2 –a ligand diversity set composed of one CDK2 apo structure and 52 known bound inhibitors . We show that , when cross-docking ligands into the apo conformation of the receptors with up to 14 flexible side-chains , ADFR reports more correctly cross-docked ligands than AutoDock Vina on both datasets with solutions found for 70 . 6% vs . 35 . 3% systems on SEQ17 , and 76 . 9% vs . 61 . 5% on CDK2 . ADFR also outperforms AutoDock Vina in number of top ranking solutions on both datasets . Furthermore , we show that correctly docked CDK2 complexes re-create on average 79 . 8% of all pairwise atomic interactions between the ligand and moving receptor atoms in the holo complexes . Finally , we show that down-weighting the receptor internal energy improves the ranking of correctly docked poses and that runtime for AutoDockFR scales linearly when side-chain flexibility is added .
Structure-based computational drug design is an essential tool in computational medicinal chemistry [1–3] . Docking is used for optimizing known drugs and for identifying novel binders by predicting their binding mode and affinity [4 , 5] . While the exploration of the ligand conformational space during the docking procedure is common , modeling receptor flexibility upon ligand binding still remains a major challenge because of the computational resources required [6] . Recent reviews provide an excellent and detailed analysis of state-of-the-art techniques for modeling receptor flexibility in structure-based drug design [7 , 8] . In summary , the motions induced in receptors upon ligand binding range from small local adjustments to large re-arrangements [9] . Modeling the receptor as fully flexible during the docking calculation is too expensive computationally because of the large number of degrees of freedom to explore during the search [10] . Instead a number of computationally feasible approximations have been proposed that can broadly be classified into the following three categories: 1 ) methods altering interaction potentials , where repulsive potentials between ligand and receptor atoms are attenuated [11] or grids representing these potentials are deformed [12] , or a consensus potential is created to represent various conformations of the receptor [13]; 2 ) ensemble docking methods [13 , 14] , using a discrete set of receptor conformations; and 3 ) induced fit methods , where changes in receptor conformation are explored during the docking [15–24] . Some approaches may fall into multiple categories depending on the classification criteria . Potential altering approaches are computationally inexpensive; however the range of motions they can account for is rather limited . The elastic deformation of affinity grids has been shown to be a computationally effective way to increase the accuracy of cross-docking ligands into non-native structure . However , the authors observed that this approach failed on a case where a large receptor side-chain conformational change is needed for the ligand to bind . The ensemble docking approach does not require any modification to existing docking codes , is embarrassingly parallel , and has been used successfully in the design of an HIV reverse transcriptase inhibitor [25] . The success rate of this method depends on the presence of a suitable receptor conformation for the ligand being docked . This limitation is somewhat attenuated in approaches that use receptor conformations to define receptor fragments that are combined during the docking procedure , thus exploring a larger subset of the receptor conformational space [15 , 16] . Induced fit methods vary in their strategies for accounting for receptor and ligand flexibility . Some methods rely on pre-computed , low energy ligand conformers which are placed into the receptor structure , and either re-pack the receptor side-chains around the docked ligand , or adjust the receptor and the ligand conformations to resolve clashes . These techniques do not require the a-priori specification of the receptor side-chains to be made flexible and can potentially modify the conformation of a large number of binding-site side-chains . SLIDE [17] resolves the clashes by minimal rotations [18] and mean-field optimization of a simplified scoring function , making it efficient for virtual screening studies . In addition to modeling the motions of receptor side-chains , Rosetta Ligand can also induce changes in the backbone conformation [26] , but this approach is computationally expensive . Other methods [15 , 16 , 20–24 , 27] rely on the explicit and a-priori specification of the parts of the receptor to be made flexible . These methods explore a solution space spanning all possible ligand rotations and translations , and all possible conformations of both the ligand and the flexible parts the receptor . ADFR falls into this category , which we refer to “explicit methods” as they require the explicit specification of the flexible parts of the receptor prior to docking . While these approaches have mostly focused on receptor side-chain motions , some of them also include limited backbone motion [15 , 16 , 23 , 27] . The main challenges of explicit methods include: 1 ) the difficulty of finding the global minimum in solution-spaces that grow exponentially with the number of degrees of freedom added by the receptor; and 2 ) the increased number of false positives arising from the evaluation of more potential solutions , using scoring functions with inherent approximations and defects as underlined in [28] . Because of these limitations , reports of successful usage of these programs have been limited to docking studies with relatively small numbers of flexible receptor side-chains , typically 2–5 , putting the burden of selecting the side-chains that will move on the user . AutoDock is a widely used docking program that allows the specification of flexible side-chains . However , its hardcoded limit of 32 rotatable bonds is easily exceeded when receptor side-chains are made flexible . Moreover , the Genetic Algorithm ( GA ) implemented in AutoDock does not perform well for docking complexes with more than ~20 rotatable bonds . Here , we present a new docking engine–AutoDockFR: AutoDock for Flexible Receptors ( ADFR ) - implementing a new genetic algorithm . We demonstrate its application to the high-dimensional solution spaces corresponding to docking a fully flexible ligand into a receptor with up to 14 explicitly specified , flexible side-chains . While ADFR is designed to allow the inclusion of a wide variety of receptor motions , this work focuses on receptor motion occurring in receptor side-chains with minor backbone motion . The previously developed Flexibility Tree ( FT ) data structure supports the encoding of a wide variety of hierarchically nested molecular motions [29] and was first used in our earlier docking software FLIPDock [23] . AutoDockFR supersedes FLIPDock and introduces a new and more efficient Genetic Algorithm ( GA ) , as well as a new motion descriptor for the Flexibility Tree optimized for representing flexible receptor side-chains . The new GA developed for ADFR introduces the concept of clustering of the ensemble of solutions optimized by the GA ( i . e . the population ) . Clustering enables maintaining diversity in the population and the implementation of an efficient termination criterion . This new GA also implements a new strategy for minimizing solutions during the GA optimization . In this paper , we provide an overview of the algorithm and describe the key concepts supporting the efficiency of the new GA . We validate the implementation of the AutoDock4 scoring function [30] and quantify its improvement in efficiency over the one implemented in AutoDock by re-docking ligands of the Astex Diverse set into their native rigid receptors . Next , we demonstrate the ability of ADFR to cross-dock flexible ligands into flexible apo receptors using two datasets , one emphasizing receptor diversity and the other on ligand diversity . The first dataset ( SEQ17 ) comprises 17 diverse apo-holo receptor pairs . These 17 systems were selected to represent a wide range of receptors and present at least one severe clash between a ligand atom and a receptor side-chain in its apo conformation . We show that ADFR significantly increases the docking success rate over AutoDock Vina when cross-docking each ligand into the apo conformation of its receptor , with up to 14 flexible receptor side-chains . The second dataset comprises an apo conformation of the Cyclin Dependent Kinase receptor ( CDK2 ) and 52 ligands from holo complexes of this receptor . The 52 ligands are docked into the apo conformation of the receptor with a number of flexible receptor side-chains varying from 0 to 12 . We show that increasing the number of flexible side-chains increases the docking success rate , and that ADFR achieves better success rates than AutoDock Vina with a linear scaling in run time when increasing the number of flexible receptor side-chains . For the CDK2 cross-docking experiment we also provide a detailed analysis of conformational changes induced by the ligands in the twelve side-chains made flexible in the apo conformation . We show that , in the docked complexes , the receptor side-chains move to re-create on average 79 . 8% of the atomic pairwise interactions observed in the holo complex . Finally , we show that in both cross-docking experiments , down weighting the contribution of the receptor internal energy in the score increases the ranking of correctly docked solutions .
The three main components of docking programs are: the representation ( i . e . the encoding of the docking problem as a set of variables to be optimized ) , the scoring function for which these variable are optimized , and the search method . ADFR encodes the docking problem into a list of variables describing a docking solution and optimizes it for the AutoDock4 force field using a Genetic Algorithm ( GA ) combined with a Solis-Wets local search [31] . The source code of the program is available online along with binaries and all input files for reproducing calculations reported in this paper [http://adfr . scripps . edu/] . In ADFR , the problem of docking a flexible ligand into a receptor with flexible side-chains is encoded as a set of variables called a genome , and representing the degrees of freedom associated with: ( i ) the ligand orientation ( rotation and translation ) ; ( ii ) the ligand conformation; and ( iii ) the receptor conformation; ( Fig 1 ) . In our approach , the ligand translation adds three variables to the genome . The rotation of the ligand is described by a quaternion[32] , which adds four variables corresponding to a quaternion . Quaternion representation is used over Euler angles to avoid gimbal lock singularities and for stable interpolations of rotations . The ligand conformation is encoded as torsion-angle values for rotatable bonds in the ligand . Hence , a ligand with two rotatable bonds will add two variables to the genome for its conformation . The receptor conformational changes are currently limited to side-chain motions . Each flexible side-chain adds its list of χ angles to the genome . For instance , a lysine will add four variables to the genome when made flexible . Fig 1 shows an example of a genome for a ligand with two rotatable bonds and a receptor with two flexible side-chains . Related variables in the genome are called genes and are implemented as programmatic objects . For instance the three variables corresponding to the translation of the ligand are grouped in a translation gene object . This object-oriented approach enables the implementation of gene-specific operations for the initialization , randomization , perturbation , and mutation of the gene values . For instance the initialization operator of the ligand translation gene object randomly picks a translation from a pre-defined set , while the initialization operator of flexible receptor side-chains gene object initializes the χ angles with the angles obtained from the input conformation of the receptor . Likewise , the mutation of the translation genes modifies the x , y , z values of the gene using a Gaussian distribution centered on its current values , while the mutation operator of a flexible receptor side-chain object randomly selects a set of rotameric angles ( with deviations ) from the rotamer library . This object-oriented architecture is instrumental for the focused sampling of various dimensions of the search space ( see below ) , which is one of the key features for successfully searching large solutions spaces . A given set of values for the variables in the genome ( i . e . the genotype ) corresponds to a docking solution for which the coordinates of the receptor and ligand atoms ( i . e . phenotype ) can be calculated and used to compute the value of the scoring function for this solution . In ADFR the genome is assembled dynamically at runtime from the description of molecular flexibility provided in the input files . The ligand is specified using the AutoDock file format ( i . e . PDBQT ) , which describes ligand rotatable bonds . The receptor side-chains to be made flexible are specified in the docking settings file using residue names ( i . e . residue type and number ) . The ligand translation is limited to a set of possible values called translational points ( see below ) , which are stored in a file specified in the docking settings file . Below we describe the scoring function and the GA implemented in AutoDockFR , followed by a description of focused sampling techniques that support the GA performance . The AutoDock energy function [30] ( Eq 1 ) is a weighted sum of terms representing van der Waals , hydrogen bond , electrostatic , and desolvation contributions , which are calculated between pairs of atoms . The ADFR score ( Eq 2 ) uses this energy function to independently score the interactions between the following three groups of atoms: Ligand atoms ( L ) , Rigid Receptor atoms ( RR ) and Flexible Receptor atoms ( FR ) . The total score is the sum of these interaction terms: SADFR=EL−L+EL−RR+EL−FR+EFR−FR+EFR−RR ( Eq . 2 ) In the case of a rigid receptor , only the first two terms ( i . e . , EL-L or ligand intra-molecular and EL-RR or ligand-rigid receptor inter-molecular interactions ) are considered . The additional terms ( EL-FR , EFR-FR , EFR-RR ) are automatically included in the scoring functions when receptor atoms are made flexible . A weight can be assigned to each term of the scoring function . Similarly to AutoDock , ADFR uses affinity maps to represent interactions between ligand or flexible receptor atoms and rigid receptor atoms; hence the EL-RR and EFR-RR terms are efficiently obtained by interpolating values in affinity maps . The remaining terms ( EL-L , EL-FR , EFR-FR ) are computed using explicit atom pairs for every non-bonded pair of atoms excluding 1–3 interactions , and 1–4 interactions not mediated by a rotatable bond . Affinity maps are regular 3D grids defined on a box aligned with the Cartesian axis . This box defines the space that ligand atoms can occupy . Affinity maps are computed prior to docking using AutoGrid from the AutoDockTools suite [20] with a default grid map spacing of 0 . 375Å . Affinity values calculated for grid points inside the protein present dramatic fluctuations with the highest values centered on receptor atoms , and the potential falling off rapidly around the atom centers ( Fig 2A ) . We designed a map post-processing protocol , which replaces the potential on grid points inside the receptor with a repulsive potential that provides a gradient pointing toward ligand binding surface ( Fig 2B ) . While this figure shows an example of an open pocket , the same protocol works for a buried cavity . This protocol produces maps that facilitate the search by providing a gradient for resolving clashes and by removing buried favorable cavities too small to accommodate a ligand e . g . , trapped water cavities . The overall workflow of ADFR is depicted in Fig 3 . The ligand and receptor flexibility description is first used to assemble a list of variables ( genome ) encoding the flexible ligand—flexible receptor docking problem . The initial population is then generated by creating a list of initial solutions in which each solution ( i . e . an individual ) is a list of values , one for each variable in the genome . Initial ligand translations are randomly selected from translational points ( see below ) ; rotations are initialized using random quaternions; ligand torsions are set to random angles; and finally , flexible side chains are initialized with χ angles from the input conformation of the receptor . The size of the population can be specified by the user or can be inferred by ADFR . Once the initial population has been generated , the GA will optimize it by creating successive generations as follows . First , the population is sorted and the top-ranking individuals ( i . e . within 2kcal/mol of the lowest energy solution ) are clustered . Clustering of solutions is used to remove duplicate solutions from the population , thus ensuring diversity . It also supports the implementation of adaptive elitism by automatically adding to the next generation the best solution from each cluster . The use of clustering in our implementation leads to the simultaneous exploration and optimization of multiple minima during the search . Clustering also enables the implementation of an efficient termination criterion described below . The clustering is performed by using the lowest energy , not yet clustered solution as a cluster seed , and adding to the cluster all solutions with RMSD less than 2Å ( for ligand atoms ) with respect to the cluster seed . The procedure is repeated until all solutions to be clustered belong to a cluster . Next a mating population , containing the best individual of each cluster along with all un-clustered individuals is created . The best individual from each cluster is automatically copied into the next generation ( adaptive elitism ) . The GA then selects parents to crossover , mutate , and minimize to generate offspring , which compete with their parents to be added to the next generation population . The probability of an individual to have offspring is proportional to its score . A pair of parents selected for mating is crossed-over 80% of the time and the resulting offspring are mutated and minimized . In the case , where no crossover takes place ( 20% of the time ) , the two parents are mutated and minimized to obtain offspring . Details of the implementation of crossover , mutation , and minimization are provided in Supporting Information [S1 Text] . All created individuals undergo a quick minimization step . If the minimized individual has a score that is better than the reference score ( best score seen so far ) , it undergoes a more aggressive minimization and its score becomes the reference score . The best two individuals identified during this mating procedure are added to the next generation if they are not already present in that population . Once the population for the next generation is complete ( i . e . its size reaches the size of the incoming population ) it becomes the incoming population for the next generation in the GA optimization loop . If the clusters remain unchanged ( i . e . the same number of clusters and the energy of the best solution of each cluster remains the same ) for three consecutive generations , the entire population is submitted to an aggressive minimization . If the clusters remain unchanged for five consecutive generations the search is considered to have converged and the optimizations stops . The optimization also stops if user-specified limits such as the maximum number of generations or maximum number of scoring function evaluations are reached . After the optimization stops , the solutions within 1kcal/mol of the best solution are clustered and the best solution from each cluster is written to a file . By default , an ADFR docking experiment performs 50 independent GA evolutions , each producing one solution . These solutions are then clustered to remove duplicated solutions and the best scoring individual from each cluster is reported , resulting in a ranked list of solutions for the docking . The solution space explored during an ADFR calculation is very large and reducing the extent of any of the variables in the genome facilitates the search . In ADFR we apply this principle by reducing the sampling of the ligand’s translation to a sub-space of translations more likely to yield good docking poses , thus eliminating ligand translations known to place it either inside the receptor , or too close or too far from the receptor . Likewise , so called “soft-rotamers” ( see below ) allow ADFR to sample receptor side-chain conformations resembling the ones observed in crystal structures more frequently . We performed docking experiments on three different datasets . The Astex Diverse Set is used to validate the implementation in ADFR of the AutoDock4 scoring function , and quantify the increase in performance of ADFR’s GA over the one used by AutoDock . While the performance of AutoDock has been benchmarked [38] using other data sets such as the Astex Clean Set , we chose to the use the Astex Diverse Set in our study , as it is more recent and has been developed specifically to address the shortcomings of the Astex Clean Set [33] . The Astex Diverse Set contains 85 well-curated protein-ligand complexes , has no overlap with the Astex Clean Set , and is best suited for testing docking programs . We define two additional datasets for assessing cross-docking success rate when docking flexible ligands into apo conformations of receptors with explicitly specified flexible side-chains , specifically for cases where severe clashes need to be resolved in order to properly dock the ligand . Rotatable ligand bonds are obtained by ADFR from the PDBQT files used by both AutoDock and AutoDock Vina . Flexible receptor side-chains are specified in ADFR calculations by listing the corresponding amino acids in the input configuration file . All RMSD values reported in this paper are computed using the Hungarian matching algorithm [41] . The open source Python implementation of the algorithm ( http://software . clapper . org/munkres/ ) was used to find the optimal pairing between atoms of the same type in the two binding poses for which RMSD is being computed . Details of the implementation are described in Supporting Information ( S2 Text ) . Input ligand structures were randomized in their position , orientation and torsions prior to running dockings , using the AutoDock Vina randomization function . This prevents possible biases toward the initial conformation in the search algorithm . Flexible side-chains are not randomized in the initial population in order to start from a reasonable initial receptor conformation . This choice does not create a favorable bias in receptor conformation when cross-docking in the apo protein conformation . The population size used for AutoDock and ADFR was based on the following heuristic: 50 + 10 × Lv , where Lv is the number of variables pertaining to the ligand in the genome , i . e . 4 ( rotation ) + 3 ( translation ) + NLRB ( number of ligand rotatable bonds ) . Details on the structure preparation for the 3 datasets are provided in Supporting Information ( S3 Text ) .
ADFR and AutoDock4 re-dock the ligands of the Astex Diverse Set into their rigid receptors with the following success rates: ADFR: 74% , AD2 . 5M: 77 . 65% and AD25M: 73% using an RMSD cutoff of 2Å . We used these docking runs to verify our implementation of the AutoDock4 scoring function and compare the performances of the search engines of these two programs . Scoring AutoDock solutions with ADFR and vice versa yielded identical results . Moreover , 76 out of 85 solutions ( 89 . 4% ) have an energetic difference of less than 0 . 5 kcal/mol between the lowest energy solutions identified by ADFR and either AD2 . 5M or AD25M . For 76 and 80 systems ( 89 . 4% and 94 . 1% ) both programs identified the same docking pose ( RMSD< 2 . 0 Å between the ADFR solution and the AD2 . 5M and AD25M solutions respectively ) . These results are strong evidence for the fact both programs explore the same energy landscape , thus validating our implementation of the AutoDock4 scoring function and enabling a direct comparison of the GAs implemented in AutoDock4 and ADFR . We compared the performance of the GA implementations using the following three properties: 1 ) the best score ( i . e . lowest energy ) found by the programs indicate the power of the search method . 2 ) The efficiency of the search engine pertains to the speed at which it finds the solution . For GA algorithms , this corresponds to the number of evaluations of the objective function ( i . e . the scoring function ) . Finally , 3 ) since GAs are stochastic algorithms , multiple runs are carried out with different random seed numbers . The number of times a GA run identifies the same best solution , measures the algorithm’s reliability . The comparison of solutions used to verify that both programs explore the same energy landscape demonstrates that both search techniques have the same power . The fact that no significant energy improvements have been found by increasing the number of evaluations for AutoDock from 2 . 5 to 25 million confirms that AutoDock identified the global minimum after 2 . 5 million evaluations and both programs reached convergence identifying virtually the same solutions . Fig 5A shows the energy differences for the 9 systems with a difference of more than 0 . 5 kcal/mol . Only one system has a difference larger than 2 . 0 kcal/mol . Fig 5B shows that the GA implemented in ADFR is more efficient as it identifies the same solutions as AutoDock , but only using on average 810 thousand energy evaluations per GA evolution . Only three complexes required more than 2 . 5 million evaluations . The number of evaluations required by each system shows no correlation with either the number of variables in the genome , or the energy differences between scores obtained by ADFR and AutoDock . Fig 5C compares the reliability of the 2 GAs . In this figure , the 85 complexes are binned based on the fraction of the 50 runs for which the final pose is within 2 . 0Å RMSD from the pose with the best score . AutoDock shows an increased reliability in runs with 25 million evaluations . However , ADFR found the solutions more reliably with 59 complexes found with high reliability ( i . e . blue and green bars ) , versus 54 for AD25M and 40 for AD2 . 5M . Conversely , the number of complexes found with low reliability ( i . e . , red bars ) is smaller for ADFR ( 6 complexes ) than in AD25M ( 12 complexes ) and in AD2 . 5M ( 15 complexes ) . Overall , the GA implementation in ADFR is more efficient and reliable than the one in AutoDock , and its termination criterion is able to limit effectively the number of energy evaluations used during docking , while allocating more evaluations when needed . AutoDock has a hard-coded upper limit of 32 rotatable bonds that prevents a direct comparison with ADFR on the two datasets used for flexible cross-docking . Moreover , the GA implemented in AutoDock is known to lose efficiency for problems with more than ~20 rotatable bonds . AutoDock Vina has no implementation limit on the number of rotatable bonds it can search , and it uses the same explicit representation of flexible side-chains as ADFR , and finally it is known to have better performance than AutoDock for high dimensional searches . Hence it provides a good reference for comparing success rates when docking flexible ligands into receptors with explicitly specified flexible side-chains . For holo re-docking , an RMSD value of 2 . 0Å between experimental and docked structures for ligand atoms is widely accepted for identifying correctly docked poses . When docking a ligand into an apo structure , the reference position of the ligand is obtained by superimposing the holo and apo receptor structures . The alignment is influenced by the differences between the two receptor conformations , and by the subset of atoms used for the superposition . In order to mitigate these approximations , we relax the RMSD cutoff value to 2 . 5Å in our apo cross-docking experiments and analyses . Therefore , a rank of 1 for a solution indicates that the lowest energy solution has an RMSD less than 2 . 5Å RMSD , while a rank of N ( N>1 ) indicates that N-1 false positive solutions were reported . When the moving receptor atoms greatly outnumber the ligand atoms , the receptor internal energy component ( EREC = EFR-FR + EFR-RR ) dominates the score . Without correction , this leads the GA to primarily optimize the conformation of the flexible receptor rather than the ligand-receptor interactions ( ELIG = EL-FR + EL-RR ) . Our results show that down weighting the receptor contribution EREC by the inverse of the number of flexible side-chains ( e . g . , EREC/12 , for the FS12 set ) results in an overall improvement in ranks of correct solutions . Fig 10A and 10B show the sorted ranks of the first correct solution without ( blue ) and with ( green ) weighted EREC values for the SEQ17 and CDK2 FS12 cross-dockings respectively . We observe an overall improvement in ranks and , with an increased success rate from 61 . 5% to 76 . 9% in the CDK2 dataset and 35 . 2% to 52 . 9% in SEQ17 when considering the top 10 solutions . At the same time , the interaction energy improved significantly in docked solutions obtained by down-weighting the receptor internal energy . The improvement ranges from 1 to 7 kcal/mol ( Fig 10C and 10D ) with most complexes gaining 3 to 4 kcal/mol . This result supports the idea that without attenuating the receptor internal energy component , the GA fails to optimize the ligand-receptor interactions increasing the rate of false positives . It also indicates that applying separate weights for the different energy terms ( i . e . EREC vs . ELIG vs . EREC-LIG ) allow shifting the focus of the search engine . In preliminary results on docking very large ligands such as peptides , we observe a reversed situation with the ligand internal energy dominating the score . Hence more sophisticated approaches for balancing energetic contributions of the various atom sets are needed . We are currently working on a more elaborate scheme for normalizing the various terms of the scoring function and deriving appropriate weight to apply to these normalized terms of the scoring function .
The RMSD of moving receptor atoms is not a suitable metric for gaining insight into receptor side-chain motions for the following reasons . First , in many cases , including the SEQ17 and CDK2 datasets used here , only a small subset of receptor side-chains interacting with the ligand undergo a substantial change in their conformation . The contributions of these side-chains to the RMSD of moving receptor atoms is outweighed by the contributions of a larger number of side-chain staying close to their initial conformation . Second , computing RMSD requires a reference conformation , which is the target conformation to be achieved for success . Ideally the holo conformation should be induced when docking a ligand into an apo conformation . However , this would require the receptor to be fully flexible . In our experiments only side-chains interacting with the ligand can move . These side-chains exist in the context of an apo conformation; hence they are not always expected to achieve the holo conformation . For example , side-chains not interacting with a particular ligand have no reason to deviate much from their apo conformation . Moreover , even small backbone perturbations can change the Cα-Cβ vector potentially forcing a side-chain to adopt an alternate conformation to interact with the ligand . For these reasons , we used pairwise atomic interactions between ligand and moving receptor atoms in the holo complex to assess receptor side-chains motions . Results show that an average of 79 . 8% of these atomic pairwise interactions are recovered in docked solutions , showing that the flexible receptor side-chains move to re-create the interaction pattern observed in the holo complex . Fig 11 shows the success rates ( i . e . percentages of CDK2 complexes for which the top ranking solutions have an RMSD from the crystallographic structure of less than 2Å ( holo ) and 2 . 5Å ( apo ) ) achieved by ADFR when docking into both the apo and holo structures , rigidly and with 12 flexible side-chains . An expected decrease in performance is observed between docking a ligand into its rigid native holo receptor ( 69 . 2% ) versus docking it into the same receptor with flexible side-chains ( 50% ) . This can be attributed to shortcomings of the model ( e . g . , implicit solvent , scoring function limitations ) that result in false positives out-scoring the correct solution . However , adding flexible side-chains improves the results considerably when cross-docking ligands into the apo structure , increasing the success rate from 23% to 44% . Cross-docking into the apo structure is a more challenging task compared to docking a ligand into a native or non-native holo structure [43] , and represents a realistic scenario where candidate molecules are evaluated for their capability to bind into a given structure . Both cross-docking tests were performed using apo conformations . Further , the ability to handle as many as 12 side-chains reduces the burden of having to choose which side-chains should be considered flexible before running the docking calculation . The SEQ17 and CDK2 datasets are representative of specific , but relevant type of receptor conformational change . They have shown to be challenging for AutoDock Vina and ADFR . Comparing the merits of the various other approaches described in the introduction for dealing with receptor flexibility on these datasets will be interesting but is beyond the scope of this paper . | Docking programs are widely used to identify drug-like molecules interacting with a given receptor to inhibit its function . Although receptors are known to change conformation upon ligand binding , most docking programs model small molecules as flexible while modeling receptors as rigid , thus limiting the range of therapeutic targets for which docking can be applied . Here we introduce a new docking program , AutoDockFR , which simulates partial receptor flexibility by allowing a large number of explicitly specified receptor side-chains to explore their conformational space , while searching for energetically favorable binding poses for a given ligand . We show that we achieve higher docking success rates by including receptor flexibility in the binding site of receptor conformations that are experimentally determined without the ligand present ( i . e . apo conformations ) . Previous approaches based on the a-priori and explicit specification of the part of the receptor to be considered flexible , have so far been limited to a small number of flexible protein side-chains ( 2–5 ) , thus requiring prior knowledge of receptor side-chains undergoing conformational change upon binding of a given ligand . The demonstrated ability of AutoDockFR in identifying correct solutions for problems with up to 14 flexible receptor side-chains lessens this requirement . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [] | 2015 | AutoDockFR: Advances in Protein-Ligand Docking with Explicitly Specified Binding Site Flexibility |
Evolutionary conservation of protein interaction properties has been shown to be a valuable indication for functional importance . Here we use homology interface modeling of 10 Ras-effector complexes by selecting ortholog proteins from 12 organisms representing the major eukaryotic branches , except plants . We find that with increasing divergence time the sequence similarity decreases with respect to the human protein , but the affinities and association rate constants are conserved as predicted by the protein design algorithm , FoldX . In parallel we have done computer simulations on a minimal network based on Ras-effector interactions , and our results indicate that in the absence of negative feedback , changes in kinetics that result in similar binding constants have strong consequences on network behavior . This , together with the previous results , suggests an important biological role , not only for equilibrium binding constants but also for kinetics in signaling processes involving Ras-effector interactions . Our findings are important to take into consideration in system biology approaches and simulations of biological networks .
Protein-protein interactions are the central elements in all signal transduction processes . The life times of protein complexes as well as regulatory processes need to be tightly controlled for proper systems functioning . Affinities are used to characterize the strength of protein interactions and the affinities between proteins involved in signaling processes have been shown to correlate with the activities ( output/response ) in such signal transduction processes [1] , [2] . In the majority of the cases , affinities between proteins and protein-ligands are determined using equilibrium binding methods , like isothermal titration calorimetry and fluorescence based methods , while rate constants of association and dissociation are only rarely determined . However , correlations of either association or dissociation rate constants with in vivo activity suggest that kinetic properties play a role in the cellular context [3]–[7] . As the affinity ( Kd ) can be described as the ratio between the dissociation ( koff ) and association ( kon ) rate constants , different ratios of kon and koff values can give rise to similar affinities . Kinetic rate constants have been shown to be important for signal transduction , however to which extent kinetics influence signaling might depend on the actual network and network topology . We could speculate that fast kon and koff values could result in rapid activation and deactivation upon short pulses of a stimulus , while slow ones could filter noise and result in prolonged signaling . If this is true it might open new aspects of cellular signal transduction regulation and could probably lead to conceptually new strategies in drug design . It is likely that the answer will depend on the network topology: rate constants might be important in some signaling branches , in others not . Evolutionary conservation of protein composition and biochemical properties is usually a valuable indication for the cellular importance of a specific protein complex . In this study we have selected the Ras-effector complex formation , in order to analyze whether kinetic rate constants are evolutionary conserved . Ras proteins belong to the Ras superfamily of small GTPases and they have key roles in various signal transduction pathways , like proliferation and differentiation [8] . They act as molecular switches by cycling between an active GTP-bound and an inactive GDP-bound state [9] , [10] . Active Ras ( Ras·GTP ) can interact with effector molecules such as the Ser/Thr kinase Raf . The resulting Raf activation triggers the MAP kinase pathway , which leads to the transcription of target genes in the nucleus [11] , [12] . Other Ras·GTP binding effector proteins that have been identified are the PI3-kinase , members of the RalGDS family , and AF6 [13]–[16] . Effector proteins bind to Ras·GTP via a common domain with a ubiquitin-like topology [17]–[22] , and various structures of effector domains in complex with Ras proteins have revealed a similar binding mode that involves mainly two antiparallel ß-sheets of the RBD and Ras , respectively [23]–[29] . As Ras-effector protein interactions play a key role in cells , pathways involving Ras-effector interactions can be assumed to be at least partially conserved during evolution . In this study we analyzed whether the affinities and the association rate constants are conserved for 10 Ras-effector complexes in 12 different species , including worms , flies , fishes , and mammalian organisms . We used homology interface modeling and energy calculations , using FoldX 2 . 8 ( http://foldx . crg . es/ ) [30] , [31] in order to model Ras-effector interactions of proteins from different organisms . FoldX uses an algorithm based on the original work of Schreiber and co-workers to calculate relative changes in kon which has been validated experimentally numerous times [32] . Homology modeling was performed in a similar way as done in a previous study , on a genome-wide level for all human Ras-effecter complexes [33] , [34] .
Binding of effector proteins to Ras proteins is mediated via a domain with an ubiquitin-like topology [35] . Members of the ubiquitin domain superfamily are the RA , the RBD , the PI3Krbd , the UBQ and the B41/ERM domain families [36] . However , the binding of Ras to effector domains does not depend on the fold itself , but rather on certain amino acid residues on the surface that are crucial for binding . An important observation found in Ras-effector complex structures is the high charge complementarity between the proteins of the complex , where Ras is mainly negatively charged and the effector RBDs are mainly positively charged [23]–[29] . Various studies have shown that a strong electrostatic surface complementarity in a protein complex enhances the association rate constant by forming of a low affinity encounter complex before the final high affinity complex is formed [37]–[42] . The complex formation itself is promoted by electrostatic steering which stabilizes the transition state by decreasing the energy barrier for association [42] , [43] . In agreement with this concept of electrostatic steering and encounter complex formation , the association rate constants between Ras and effector domains were found to be fast ( reviewed in [44] ) . Interestingly , the variance in binding energies when comparing different Ras-effector complexes is mainly the consequence of different association rate constants , while the dissociation rate constants are in a similar range [45]–[47] . For example , RafRBD is highly positively charged in its Ras binding region , and here the association rate constant was found to be very high in complex with the mainly negatively charged Ras proteins . In contrast , RalGDS has a mixed charged distribution ( Figure 1A ) , and the kon in complex with Ras is much lower . Interestingly , introducing positively charged residues at the edge of the interface of RalGDS can change binding kinetics and these RalGDS mutants were shown to bind “Raf-like” to Ras [43] . In Figure 1B–F we show the electrostatic surface potentials of several other RA/RB domains , which can bind to Ras ( Rgl1 , Rgl2 , Grb7 , AF6_RA1 , PLCe_RA2 ) and for which structures have been solved , either by NMR or X-Ray , and we orient them similar as the RA domain of RalGDS in complex with Ras . In all cases the interface surface areas have a strong positively charged electrostatic potential , which suggests that association kinetics are important for these RA/RBD domains as well . Although the algorithm developed by Schreiber and co-workers implemented in FoldX ( http://foldx . crg . es/ ) [30] , [31] , has been validated experimentally on many different proteins , still it is a prediction method and as such needs some validation on the particular system under study . For this , we have selected the Ras-Raf complex and calculated kon values ( ΔG kon ) at different salt concentrations , ranging from 0 to 800 mM NaCl ( corresponding to an ionic strength of ∼50 to 850 mM in 50 mM Tris-buffer ) , and compared these results with experimental kon values measured at different ionic strength using stopped-flow ( Table S1; [48] ) . The experimental kon values range from 7 . 4 to 60 µM−1 s−1 and an excellent correlation with calculated association rate constants was observed ( R = 0 . 99 ) ( Figure 2A ) . Further , we used FoldX in order to generate in silico a series of mutations of charged residue in RalGDS , located either in the binding site , or at the edge of the binding site , and we calculated binding energies as well as association rate constants using the Ras-Ral complex . When comparing these results with experiments [43] , we find again a very good correlation between experimental and calculated kon values ( Figure 2B ) ( R = 0 . 89 ) , with the slopes of the two correlations ( ionic strength and mutants ) been similar . This indicates that absolute values of association rate constants can be reliably calculated over a wide range for different ionic strengths and mutations of Ras-effector complexes . We have selected proteins containing RA , RBD , PI3Krbd , and B41 domains , similar as in our previous genome-wide Ras-effector homology interface modeling study [34] ( Figure 3A ) , for which binding to Ras has been shown experimentally ( Table 1 ) . These include the different isoforms of the Raf kinases , RalGDS , and the related proteins , Rgl1 , and Rgl2 . Other Ras binding domains are the PI3K-p110 gamma subunit , and Krit . In the following we will often refer to members of the ubiquitin superfamily as UBDs , without differentiating between RA , RBD , PI3Krbd , or B41 . In order to derive Ras and effector protein “interactions” from organisms representing the major eukaryotic branches , we have selected the following species ( Figure 3B ) : Homo sapiens ( hs ) and Mus musculus ( mm ) were chosen for mammals , Gallus gallus ( gg ) for birds , Xenopus tropicalis ( xt ) for amphibians , Fugu rubripes ( fr ) , and Dario rerio ( dr ) for fishes , Drosophila melanogaster ( dm ) , Drosophila pseudoobscura ( dp ) , Anopheles gambiae ( ag ) and Apis mellifera ( am ) for arthropods , and Caenorhabditis elegans ( ce ) and Caenorhabditis briggsae ( cb ) for nematodes . The orthologs were predicted by using the ENSEMBL ( http://www . ensembl . org ) [49] and the IMPARANOID databases ( http://inparanoid . cgb . ki . se/ ) [50] , [51] . Domains were predicted using SMART [52] , [53] and the sequences were aligned automatically and by manual curation taking structural information into account [35] ( Figure S1 ) . Depending on the organism , between 22% and 78% of all human proteins orthologs were identified . When taking into account that certain proteins in lower organisms are orthologs of more than one human protein , e . g . , RalGDS of C . elegans is also an ortholog of Rgl1 and Rgl2 , the number of orthologs in different organisms ranges from 33% to 100% . The alignments of the UBDs of Ras effector proteins show a high similarity within orthologs and often also between different proteins of the same domain family . Furthermore , the similarity within the secondary structures of the RBD is higher than within the loops , indicating a conservation of the binding mode . The sequence identity of ortholog proteins ( for detailed description see method ) ranges between 100 and ∼20% ( Table S2 ) . However , in the majority of the cases the sequence identity decreases to ∼30/40% . The only exceptions are the different PI3kinase p110 isoforms , where a drastic drop in sequence identity is observed for the corresponding othologs/isoforms in C . elegans/C . briggsae . The sequences of proteins that have a key role in cells are usually highly conserved among all organisms . In accordance with this , the sequences of Ras proteins were found to be nearly identical , especially in the effector binding region ( Figure S2 ) . The three Ras proteins , H-Ras , N-Ras and K-Ras could only be found in vertebrates , for arthropods and nematodes there is only one Ras protein which is most likely to be an ortholog of H-Ras . Due to the similarity in the effector binding region , only HRas was modeled ( here termed as Ras ) . The first three secondary structure elements ( β1 , β2 , and α1 ) of the ubiquitin-like domain determine the interaction surface towards Ras and they have the largest impact on binding energy of the complex [33] . In those cases in which a crystal structure of Ras in complex with a RBD domain was available we use the structure to model the ortholog sequences ( Ras-Raf , Ras-Ral , Ras-PI3 Kinase , Ras-Byr ) . For the rest we used the templates modeled in our previous study [34] ( Table S3 ) that were validated experimentally by pull-down experiments ( for details see methods ) . Only those UB domains that could be reliably modeled were selected ( e . g . , no van der waals’ clashes above a fixed threshold of 2 kcal/mol ) . The species were then grouped into human ( hs ) , mouse ( mm ) , birds ( gg ) , amphibians ( xt ) , insects ( dm , dp , ag , am ) and nematodes ( ce , cb ) . The mean of ΔG and ΔGkon within each group was calculated and taken as value for the complete group . By grouping the different organisms , the problem of missing sequences can be solved for many proteins and mean values as well as standard deviation of ΔG and ΔGkon can be calculated ( the results do not change if we consider individual organisms , data not shown ) . The results for all interaction energies ( ΔGint FoldX ) and contribution of association rate constants ( ΔGkon FoldX ) were plotted against the divergence time ( Table S4 and Figure S3 ) . While the sequence similarity decreases with increasing divergence time , the interaction energies as well as the association rate constants are conserved . A selection of representative results is shown in Figure 4 . A comparison of the mean values for all interaction energies and kon values calculated for a particular Ras-effector complex in different organisms shows that the standard deviations are in the majority of the complexes small ( Table S5 and Figure 5A ) . Interestingly , the interaction energies correlate with the association rate constant contribution ( Figure 5B ) ( R = 0 . 71 ) . This indicates that also for the so far kinetically uncharacterized UBD domains , the changes in ΔG are mainly a consequence of changing kon . Thus , this could be underlying binding principle for the complete Ras-effector family . In order to demonstrate that large changes in association rate constants would have been possible theoretically , we have selected the human Ras-RalGDS complex as an example for an in silico mutagenesis using FoldX . By either introducing positively or negatively charged residues at all positions at the surface of RalGDS , the FoldX-kon contribution could be increased from −3 . 65 kcal/mol to −7 , 6 kcal/mol or decreased to −0 . 47 kcal/mol , respectively ( data not shown ) . In order to analyze whether compensating changes in kon and koff can influence signal transduction , we used in silico simulations of a sub-network within the EGF signal transduction pathway . Activation of proteins following EGF stimulation is one of the most studied signaling systems , which involves the Ras-CRaf interaction as central elements , and numerous simulation models exist , which are able to correctly predict different aspects of EFG signaling found experimentally [54]–[59] . Based on these earlier models we have constructed a minimal network involving Ras and Raf kinase ( Figure 6A and Table S6 ) . This minimal model involves activation of GEF upon stimulation ( A ) , which results in activation of Ras ( RasT = RasGTP ) . Subsequent binding of Raf to RasT activates Raf ( Raf_act ) , which in turn leads to activation of a downstream target ( X ) . Negative regulation was introduced by the GAP catalyzed hydrolysis reaction of RasGTP to RasGDP ( RasD ) . We simulated this network by first applying a constant stimulus of “A” for 500 seconds using the wild type kon and koff values for the Ras-Raf interaction . Then we simulated the network with either 10-fold higher kon and koff rate constants , or 10 fold lower kon and koff ( Figure 6B ) . Minor changes are observed when following X over time ( activation peak ) for the simulation with 10-fold higher kon and koff compared to the wild type situation . Only the simulation of 10-fold lower kon and koff resulted in a slightly smaller activation peak . However , when simulating the network by applying a pulse of stimulation , of 10 s of “A” ( and then removing the stimulus ) , large changes in the activation peak are observed , with a higher maximum for the simulation of 10-fold higher kon and koff values for the Ras-Raf interaction ( Figure 6C ) . This shows , that under certain cellular conditions , like short pulse of activation , large changes in activation are expected for mutants with similar affinity , but changed and compensating effects on kon and koff . Thus , kinetic properties can be crucial , and in the case of Ras-effector interactions , association kinetics will be important to result in sufficient activation , when the system is activated by applying a pulse .
The complex formation of Ras and effector proteins is driven by high association rate constants and only moderate dissociation rate constants [45]–[47] . Further , changes in affinity are mainly the consequence of changed association rate constants . Association rate constants can be influenced by mutating charged residues at the edge of the interface [32] , [43] . If electrostatic interactions and association rate constants are important for the biological function of the cell , they should be conserved during the course of evolution . Using homology modeling and energy calculation covering a wide-range of sequences , and relating the output to the sequence conservation , we found that interaction energies as well as the electrostatic contributions and the association rate constants are conserved as well . While the sequence identity decreases with divergence time between the selected organisms , no trend could be found for the interaction energy and energies related to the electrostatics and kon , although theoretically it should be possible , when sampling the possible contributions of kon at different amino acid positions ( Figure S4 ) . Biologically , electrostatic interactions within Ras-effector complex interfaces could be functionally important , because they are the basis for the observed dynamic behavior , as observed in the case of Ras binding to the Raf kinase effector protein: The Ras-RafRBD complex formation is characterized by both high association and dissociation rate constants ( kon and koff ) , leading to affinities ( Kd = koff/kon ) in the range of 1 to 0 . 05 µM , under physiological conditions ( this relatively low affinity seems to be functionally sufficient , since Ras is attached to the membrane via a lipid modification ) . The high kon values provide the possibility to have a fast dissociation of the complex , while still having a reasonable tight binding complex ( the lifetime of the complex between Ras and RalGDS , for example , is 0 . 1 s-1; see reference [47] ) . As Ras signaling depends very crucially on a strict control through regulating proteins like GAPs ( GTPase activating proteins ) and GEFs ( guanine nucleotide exchange factors ) , this fast dissociation allows regulatory proteins to access and act . We assume that electrostatics contributions and binding kinetics could be important in other Ras signaling pathways , since association rate constants were found to be conserved during evolution , as demonstrated in this study for 10 effector domains . Further in vivo analysis will be needed to prove this hypothesis . These experiments could be performed by designing mutant variants , which are expected to have similar affinities , but changed association and dissociation rate constants . These protein variants could be expressed in cells and the effect on signal transduction monitored , e . g . , after different pulses of stimulation . It is expected that the effect of changing rate constants depends also on the network topology ( negative feedback , feed forward inhibition , etc ) . This knowledge will be important for systems biology and simulation approaches , in order to know , at which positions in the network affinities will be sufficient , while for other accurate rate constants will be crucial for correct prediction . Further , it could open conceptually new aspects in drug design .
Proteins from the following species were used in order to get a good representation of all branches: Homo sapiens and Mus musculus ( mammals ) , Gallus gallus ( birds ) , Xenopus tropicalis ( amphibians ) , Fugu rubripes and Danio rerio ( fishes ) , Drosophila melanogaster , Drosophila pseudoobscura , Anopheles gambiae and Apis mellifera ( arthropods ) , and Caenorhabditis elegans and Caenorhabditis briggsae ( nematodes ) . Only two RA domain containing were retrieved from Saccharomyces cerevisiae ( sc ) , because these proteins are involved in a different pathway . For each protein , the human ENSEMBL protein ID was retrieved from ENSEMBL ( http://www . ensembl . org ) [49] . The orthologs were predicted by using the ENSEMBL database for Xenopus tropicalis , or the INPARANOID database ( http://www . inparanoid . cgb . ki . se ) [50] , [51] . ENSEMBL [49] classifies the prediction based on the BLAST results . Only those orthologs were chosen that were a unique best reciprocal hit in both directions . As the INPARANOID database [50] , [51] provides more information about orthologs of members of protein families , e . g . , PI3K p110 , the prediction was preferentially used . The sequences retrieved from ENSEMBL or INPARANOID were analyzed using SMART ( http://smart . embl-heidelberg . de ) [52] , [53] , in order to determine the domain architecture of the protein and the domain sequences . For modeling of Ras-binding domains in complex with Ras proteins , we have taken the pdb-files of the following Ras effector complexes: Ras-RalGDS ( pdb-entry: 1LFD ) , Ras-PI3Kinase ( pdb-entry: 1HE8 ) , and Raps-Raf ( pdb-entry: 1GUA ) . Different template structures have been generated by deleting certain parts in the complex; the decision was mainly based on the alignment used to model the different binding domains . The ortholog sequences for one protein were aligned using standard automatic alignment tools , since sequence homology is high . However , the alignment of different effector domains from different families ( RA , RBD , PI3Krbd , B41 ) , was done based on manual curated structural-based sequence alignments as discussed in detail in a previous publication [35] . Basically two kinds of template structures have been generated ( Table S3 ) : a short version , where all secondary structure elements and loops ( apart from ß1 , ß1 , ß2 , a1 ) were deleted , as this is the part mainly contributing to the binding energy ( similar as done in our previous study [33] , [34] . In addition ‘long template’ structures have been generated . We could not model loop regions in those cases where the loops where not of the same length . For having a proline at the beginning of ß-strand 1 ( position 26 in RalGDS , position 229 in PI3K , position 66 in Raf and position 81 in spByr2 ) , we prepared special template structures by moving the backbone slightly , after introducing the proline at these positions ( we checked that the proline was in acceptable dihedral angles and that the main chain CO group was still H-bonded to Ras ) . These template structures were then used to model the complex structures for AF6_RA2 . The homology modelling was done as described before [33] , [34] . The homology modeling was done based on the sequence alignment ( Figure S1 and Figure S2 ) , using different template structures using the design option in a new version of FoldX 2 . 8 [30] , [31] . During this design procedure , FoldX is testing different rotamers and allows neighbor side chains to move . After reconstruction , all models have been passed through an additional optimization step by using the repair function of FoldX ( detailed description in [33] , [34] ) . Energy calculations of Ras-effector complexes have been done using FoldX as described before ( http://fold-x . crg . es ) [30] , [31] . A model was generated based on previous models of EGF signal transduction ( see Table S6 ) . Simulations were performed using the SmartCell software ( http://www . smartcell-crg . es ) [60] using ordinary differential equations . | Cellular signal transductions processes are based on protein interactions . Proteins can either associate transiently with each other or form stable complexes , and the strength of the interaction is described by the affinity ( the affinity is the ratio between the rate of dissociation and association ) . Protein complexes with similar affinities can bind and dissociate with different rates , and these rates describe the kinetic properties of protein binding . These kinetic rates are important for signaling; however , to what extent individual changes in such rate constants are biologically important or whether the affinity is more crucial might be different in different signaling processes . In this study we analyze whether association rates are conserved during evolution , because evolutionary conservation of protein biochemical properties is usually a valuable indication of its importance . We analyzed the binding of Ras proteins to effector domains , which are central proteins in many signal transduction pathways , in different organisms . On the basis of homology modeling and energy calculations we find that association rates are conserved , although the sequence similarity decreases compared to the human protein . Our finding should encourage further analysis of the importance of kinetics for cellular signal transduction . | [
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] | 2008 | Association Rate Constants of Ras-Effector Interactions Are Evolutionarily Conserved |
Despite being entirely preventable , canine rabies still kills 55 , 000 people/year in developing countries . Information about local beliefs and practices can identify knowledge gaps that may affect prevention practices and lead to unnecessary deaths . We investigated knowledge , attitudes and practices related to rabies and its prevention and control amongst a cross-section of households ( n = 5 , 141 ) in urban and rural areas of central , southern and northern Tanzania . Over 17% of respondents owned domestic dogs ( average of 2 . 3 dogs/household ) , >95% had heard about rabies , and>80% knew that rabies is transmitted through dog bites . People who ( 1 ) had greater education , ( 2 ) originated from areas with a history of rabies interventions , ( 3 ) had experienced exposure by a suspect rabid animal , ( 4 ) were male and ( 5 ) owned dogs were more likely to have greater knowledge about the disease . Around 80% of respondents would seek hospital treatment after a suspect bite , but only 5% were aware of the need for prompt wound cleansing after a bite . Although>65% of respondents knew of dog vaccination as a means to control rabies , only 51% vaccinated their dogs . Determinants of dog vaccination included ( 1 ) being a male-headed household , ( 2 ) presence of children , ( 3 ) low economic status , ( 4 ) residing in urban areas , ( 5 ) owning livestock , ( 6 ) originating from areas with rabies interventions and ( 7 ) having purchased a dog . The majority of dog-owning respondents were willing to contribute no more than US$0 . 31 towards veterinary services . We identified important knowledge gaps related to , and factors influencing the prevention and control of rabies in Tanzania . Increasing knowledge regarding wound washing , seeking post-exposure prophylaxis and the need to vaccinate dogs are likely to result in more effective prevention of rabies; however , greater engagement of the veterinary and medical sectors is also needed to ensure the availability of preventative services .
Rabies is one of the oldest recognized infectious diseases , and affects all mammals [1] . The disease is caused by a rhabdovirus [2] and is most usually transmitted to humans by domestic dog bites [3] . Canine rabies remains a major socioeconomic and public health problem in developing countries , claiming the lives of an estimated 55 , 000 people each year [4] , [5] . Annual incidence of human rabies deaths typically fall between 1 and 6 cases/100 , 000 people in canine rabies-endemic areas , with an incidence of 4 . 9 cases/100 , 000 reported in Tanzania , 2 . 5 cases/100 , 000 in Kenya ( Machakos District ) , 2–3 cases/100 , 000 in India , 5 . 8 cases/100 , 000 in Cambodia , and 1 . 4 cases/100 , 000 in Bangladesh [6]–[10] . These reported estimates are from active surveillance studies and not from official records , which typically underestimate the disease burden [4] , [6] , [11] . The high burden of rabies mortality in most developing countries suggests that , despite the existence of effective human and animal rabies vaccines , rabies prevention and control efforts in these settings are inadequate . Knowledge , attitudes and practices ( KAP ) studies have been widely used around the world for different applications in public health based on the principle that increasing knowledge will result in changing attitudes and practices to minimize disease burden [12] . For example , in Thailand a KAP study demonstrated the influence that increasing community knowledge on the control and prevention of dengue had on improving practices for its prevention [13] . Other applications of KAP surveys include identifying knowledge gaps , cultural beliefs and behaviour patterns that may pose barriers to controlling infectious diseases [14]–[17] , designing relevant public health awareness campaigns [18] , [19] , and provision of baseline data for planning , implementation and evaluation of national control programmes . In Swaziland , for instance , KAP surveys were used to investigate local communities' understanding of malaria transmission , recognition of symptoms , perceptions of causes , treatment-seeking patterns , and preventive measures and practices in order to provide baseline data for a national malaria control programme [20] . KAP surveys have also been applied to the study of rabies [17] , [21]–[24] . However , before the research described herewith , no such studies had been conducted in Tanzania . The motivation behind this study was the need to provide baseline data that would allow the identification of knowledge gaps that may be affecting rabies control and prevention practices in affected communities in Tanzania . Human rabies deaths are almost entirely preventable through prompt delivery of post-exposure prophylaxis ( PEP ) to victims of bites by rabid animals [25] and through successive annual mass dog vaccination campaigns that achieve 70% vaccination coverage to bring rabies under control in reservoir populations [26]–[29] . Thus , for effective rabies control and prevention , veterinary services must coordinate mass dog vaccinations and medical services must provide rabies-exposed individuals with access to PEP ( Figure 1 , Boxes C1 and C2 ) . However , individuals also need to know the risks associated with rabies and the actions required to prevent human infection , such as seeking PEP when a bite occurs and bringing their dogs to rabies vaccination campaigns . We hypothesized that knowledge about rabies translates into better practices for control and prevention . To address this , we developed a systematic framework , which summarises our hypothesized relationships between rabies knowledge , attitudes and practices , and disease outcomes ( Figure 1 ) , for understanding how knowledge may influence rabies control and prevention . Within this framework we investigated existing levels of knowledge , as well as determinants of knowledge and attitudes towards rabies prevention and control in endemic settings of rural and urban Tanzania to determine their influence on practices ( Figure 1 , Boxes A , B and D ) . We used data generated from detailed KAP surveys covering over 5 , 000 respondents in a range of settings across Tanzania , which is the largest KAP survey for rabies that we are aware of in Africa .
KAP surveys were conducted across Tanzania in seven districts covering approximately 74 , 748 km2 , representing 8% of the country's land mass . These areas are inhabited by about 1 . 8 million people ( 5 . 4% of the Tanzanian population according to the 2002 national census [30] ) . The seven districts were selected to cover three zones ( Figure 2 ) representative of areas with different levels of rabies research and control efforts . The first zone included two districts in northern Tanzania ( Musoma Urban and Serengeti ) that have been subject to long-term interventions involving mass dog vaccination campaigns and research since the 1990s [6] , [26] , [27] , [31] , [32] . The second zone comprised Ulanga and Kilombero districts in southern Tanzania where rabies research and control activities began in 2008 . Finally , the third zone included three districts in central-southern Tanzania ( Kilosa , Dodoma Urban and Mpwapwa ) where no rabies interventions have ever been conducted . The study area represented both urban ( Musoma and Dodoma ) and rural ( other districts ) settings . KAP surveys were conducted during two field seasons , August–September 2009 ( Ulanga , Kilombero and Kilosa ) and April–June 2010 ( remaining districts ) , by nine enumerators who were trained in survey techniques during a pilot study . In each study district , 25% of villages were selected randomly . Assuming an average household size of 4 . 9 persons , based on the Tanzania population and housing census [30] , we estimated the number of households necessary to survey 5% of the population in the selected villages . Questionnaires were then administered to approximately 5% of households in each surveyed village ( a total of 5 , 141 respondents ) after being randomly selected from village households lists . A questionnaire was designed based on consultation with researchers who had conducted KAP surveys elsewhere . The questionnaire was pretested in one village of the survey area and was revised accordingly . The questionnaire was semi-structured with both open and closed-ended questions , and captured details of individual and household characteristics that were used to assess socioeconomic status and education levels . The choice of variables for assessing socioeconomic status followed methodology used by other studies [33] , [34] . Additional questions covered ( a ) knowledge of rabies , including a description of the disease , mode of transmission , outcome , range of species affected and means of prevention and control; and ( b ) attitudes and practices in relation to rabies prevention strategies and actions towards suspect rabid animals . Further questions were administered to respondents who owned dogs to assess attitudes and practices relevant to rabies control , including willingness to travel to vaccination points , and willingness to pay for dog vaccination . Research personnel were accompanied by sub-village leaders to identify household heads . Questions were asked to household heads or other household members of at least 18 years of age in the absence of the household head . The questionnaire was conducted in Swahili . Scores were given according to the completeness and accuracy of respondents' answers , ranging from zero to three depending on the nature of the question [35]–[37] ( Appendix S1 ) . For example , regarding the respondent's ability to describe rabies , a score of 2 was assigned if the participant described rabies as a disease , a score of 1 if rabies was described as a change of behaviour and a score of zero if the answer was inaccurate or not provided . If all answers were complete and accurate , a respondent would obtain overall scores of 11 and 10 for ( 1 ) rabies knowledge , and ( 2 ) attitudes and practices , respectively . For a respondent to be classified as knowledgeable about rabies , a score of 7 or more out of eleven ( for knowledge ) and 6 or more out of 10 ( for attitudes and practices ) had to be obtained , which is equal to or more than 60% according to the cut-off point of the Likert-type scale [37] . We also explored other cut-off points ( 50% to 70% ) . Binary outcomes were assigned to participants who were knowledgeable and not knowledgeable about rabies , and its prevention and control . Respondents who had dogs were asked if they would be willing to pay for veterinary services such as vaccination and sterilization . If the respondent replied that he/she would , an additional question was asked as to how much he/she would be willing to pay . This question was formulated in the format of a bidding game whereby respondents were asked to bid the maximum amount they would be willing to pay and the answers ( maximum/final bid ) were recorded [38] . For the purpose of logistic regression analyses , the respondents were classified into two groups: those that were willing or unwilling to pay more than 500 Tanzanian Shillings ( TZS ) ( ∼US$ 0 . 31 ) to vaccinate each of their dogs . No logistic regression was performed for willingness to sterilize dogs . Principal component analysis ( PCA ) was used to estimate the socioeconomic status of respondents ( STATA version 10 , Stata Corporation; College Station , TX , USA ) . The possession of household assets was used to calculate mean socio-economic scores for each quintile ( least poor , less poor , poor , poorer and poorest [39] . Since the differences in the average scores were small between quintiles we decided to adjoin quintiles into three quintiles ( 40% corresponding to low economic status , 40% corresponding to medium economic status and 20% corresponding to high economic status ) as applied by Filmer and Pritchett [40] and previously used in Tanzania [41] , [42] . All other analyses were performed using the R statistical programming language [43] . For categorical explanatory variables ( gender , socioeconomic status , education level , residence ( i . e . urban/rural dweller ) and previous history of bite exposure in the household ) , frequencies of respondents' answers were compared using Pearson's chi-square test . Pearson's chi-square test was also used to investigate the reported travel time for vaccinations that were charged for versus those that were free-of-charge and in relation to willingness to pay for veterinary services . To determine the influence of all the explanatory variables on each dependent variable ( i . e . the level of the respondents' knowledge regarding specific questions ) , a series of univariate regression analyses were carried out . Variables that were significant in the univariate analysis ( p≤0 . 25 ) were included in a multivariate analysis . Interaction terms were tested using multiple regression with backward stepwise deletion of non-significant terms ( p>0 . 05 ) . The models were tested for correlations between explanatory variables . Correlations between variables were quantified using corvif function of the AED package in R to obtain the variance inflation factors ( VIFs ) ( Table S1 ) [44] . Logistic regression was used to analyse binary outcome data on whether participants were knowledgeable or lacked knowledge about rabies and whether they reported effective or ineffective practices for rabies prevention and control . Odds ratios ( ORs ) and 95% confidence intervals ( CIs ) of binary outcomes about factors that influenced: ( a ) knowledge about rabies and practices for its prevention and control , ( b ) dog ownership , ( c ) vaccination status of dogs and ( d ) willingness to pay for vaccinating a dog were obtained from the final model . Goodness of fit was examined using the Hosmer-Lemeshow goodness-of-fit test , using the R package ResourceSelection . The study protocol was approved by the Medical Research Coordinating Committee of the National Institute for Medical Research of Tanzania ( with approval number NIMR/HQ/R . 8a/vol . IX/994 ) and the Institutional Review Board of the Ifakara Health Institute , including the use of oral consent for the collection of data . Written consent was not obtained as the study followed established procedures in Tanzania related to the collection of interview data without collecting biological samples from humans . The study was cleared by the District Executive Director in every study district and the village executive officers were asked for permission prior to the initiation of the research in each village . Before administering questionnaires , participants were orally informed about the purpose of the study , emphasizing that participation was voluntary , and that their answers would be kept confidential . Only participants who verbally agreed were interviewed .
Of the 5 , 141 respondents ( from households covering a population of 27 , 412 ) , 55% were female with ages ranging from 18–90 ( median 35 ) . Most were from rural areas ( 68% ) and the majority ( 85% ) were pastoralists ( dependent on grazing livestock ) , subsistence farmers or engaged in mixed farming practices systems ( agro-pastoralists ) . Most respondents were household heads ( 61% ) and were from areas with no rabies interventions ( 61% ) or areas with recent interventions only ( 26% ) . The majority ( 74% ) had only attended primary school , with just 9% having secondary or higher education , whilst 17% had no formal education . Among the respondents without formal education , the majority ( 73% ) were from rural areas ( p<0 . 001 ) and more were female ( 69% ) than male ( p<0 . 001 ) . Around 8% of households had family members who had previously been bitten by a suspect rabid animal . Almost one-sixth of households in the survey ( 17% ) owned domestic dogs , with 1–11 dogs per dog-owning household ( average 2 . 3 , standard deviation 1 . 7 ) . The overall human∶dog ratio was 14 . 3∶1 . Dogs were kept for security ( 78% ) , for multiple purposes ( 18% ) , hunting ( 4% ) and herding livestock ( 0 . 5% ) . About 99% of dog owners fed their dog at least one meal per day , while 78% of households allowed their dogs to roam freely , 6% tied their dogs with a rope during daytime and 15% kept their dogs caged at all times . Levels of knowledge about rabies transmission , disease outcome , and prevention in humans and control in animal populations are detailed in the supporting information ( Table S2 ) . In brief , the majority ( 96% ) of respondents had heard about rabies . Twenty-seven percent were able to describe rabies as a disease , 41% described it only as a change of behaviour in dogs , and 32% were unable to provide any description . Eighty-one percent knew that rabies was transmitted through bites by suspect rabid animals . While 70% knew that domestic dogs and humans can suffer from rabies , only 7% could name three or more types of animals capable of transmitting rabies . Although the majority of respondents ( 63% ) knew that rabies is fatal following the onset of symptoms , a large percentage was unaware of the fatal nature of the disease . When knowledge of rabies prevention was investigated , 35% of respondents reported that they would expect anti-rabies vaccine at a hospital , 14% reported that they would expect other treatments ( e . g . antibiotics , tetanus and pain relief ) , whereas the rest of the respondents ( 51% ) reported that they would depend on physicians' advice . When asked about methods to control rabies in animals , the majority knew of dog vaccination ( mentioned by 67% of respondents ) , but only 4% knew additional methods such as restraining dogs , and killing suspect animals . Of 5 , 141 respondents , 1 , 907 ( 37% ) were classified as knowledgeable about rabies . Results of logistic regression ( Table 1 ) indicated that rabies knowledge was greater among respondents ( 1 ) with more education , ( 2 ) in areas with long-term research interventions , ( 3 ) originating from households that had experienced suspect rabid bites , ( 4 ) that were male and ( 5 ) that owned dogs . There were no significant correlations between any of the variables . Secondary education ( and above ) was associated with better practices for rabies ( Table 1 ) . We explored different cut-offs ( 50% , 70% ) to determine whether respondents where knowledgeable about rabies and its control , but their differences in our findings were negligible . Following a suspect bite , only 5% of respondents reported that they would apply first aid measures before going to hospital ( Table S3 ) . About 95% were not aware of wound cleaning: they claimed that they would report to hospital or to the village leader/police without cleaning the wound or would do nothing . Of the ∼90% of respondents that would seek hospital treatment , 83% claimed that they would seek medical care immediately after a bite , 3% within 2 weeks of being bitten , and 12% after 2 weeks . When asked about actions to be taken with regards to a suspect rabid animal , most respondents ( 79% ) reported that they would kill the animal , whereas only 7% would report the incident to a livestock office for further investigation ( Table S3 ) . Moreover , only 15 respondents ( <1% ) were aware that the head of a suspected rabid animal should be submitted to a laboratory for diagnostic confirmation . Most respondents ( 75% ) reported that they would bury or burn the carcass , whereas a minority ( 25% ) stated they would throw away the carcass . Dog ownership varied significantly across districts in the study and was significantly associated with home ownership , presence of children in households , livestock ownership , and pastoralism , and was negatively associated with farming ( Table 2 ) . Determinants of dog vaccination included being a male-headed household , presence of children , low economic status , residing in urban areas , owning livestock , originating from areas with rabies interventions ( recent or long-term ) and having purchased the dog as opposed to having obtained it for free ( Table 2 ) . Of the 1 , 907 respondents who were classified as knowledgeable of rabies , 494 ( 26% ) were dog owners . Fifty-one percent of dog owners who were classified as knowledgeable of rabies reported to have vaccinated their dogs against rabies ( χ2 = 3 . 09 , p = 0 . 07 ) . The majority of dog-owning respondents stated their willingness to pay for rabies control including dog sterilization ( by surgical or chemical means ) . However , over half reported that they would not pay more than 500 TZS ( Table 3 ) and very few indicated that they would pay more than ∼US$ 0 . 31 . Instead , if required to incur greater costs , most respondents stated they would opt to vaccinate fewer of their animals . Imposing charges slightly affected reported travel distances to utilise the offered veterinary service with significantly greater willingness to travel further if vaccination was provided for free ( Figure 3 ) . The most common source of information about rabies was through personal contacts ( neighbours , parents and friends , 70% ) , while 15% of respondents received information from the media ( television , radio and newspapers ) and 12% from professionals such as health workers ( for bite patients ) , researchers ( during their research activities ) , or teachers at school . The remaining respondents ( 3% ) knew about rabies from other sources ( e . g . leaflets ) . Multivariate analysis showed that residing in areas with interventions and being of high socioeconomic status influenced the sources of information through which respondents knew about rabies ( p<0 . 001 and p<0 . 007 , respectively ) .
Most respondents showed low levels of knowledge about key aspects of rabies and its control and prevention , which should be addressed by key stakeholders . Awareness-raising campaigns focusing on information about the risks associated with rabies and correct behaviour to prevent these risks could prevent unnecessary deaths . Simple messages such as “vaccinate your dogs and cats against rabies” , “immediately wash your wound with water and soap and seek anti-rabies vaccination after a bite from a rabid animal” , “all mammals suffer from rabies” , and “bury or burn carcasses of dead rabid animals” , channelled through government and community networks , could go a long way toward improving community practices . Our recommendations based on experiences in Tanzania could be applied in other developing countries where rabies is endemic . | Rabies remains a major public health problem in Africa and Asia , although means to control and prevent the disease are available through mass dog vaccination and provision of post-exposure prophylaxis to people exposed to bites by suspect rabid animals . Here we report the results of an extensive community survey on knowledge , attitudes and practices related to rabies control and prevention , covering rural and urban settings in central , northern and southern Tanzania . Our results showed that the majority of people across Tanzania had heard about rabies and knew that it is transmitted by dog bites , but most lacked comprehensive knowledge about key practices , such as the need for wound cleansing , which could prevent unnecessary deaths from the disease . In other circumstances , knowledge ( for example , about the need to vaccinate dogs to control rabies ) did not reflect good practice . In order to address the knowledge gaps identified by this study , there is a need for interventions aimed at increasing awareness , focusing on simple messages and targeting the community as a whole . This information could be channelled through media , community meetings and professionals including community leaders , health workers , teachers , livestock officers and clinicians . | [
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"hea... | 2014 | Knowledge, Attitudes and Practices (KAP) about Rabies Prevention and Control: A Community Survey in Tanzania |
Previous studies suggest that humans can acquire immunity to reinfection with schistosomes , most probably due to immunologic mechanisms acquired after exposure to dying schistosome worms . We followed longitudinally two cohorts of adult males occupationally exposed to Schistosoma mansoni by washing cars ( 120 men ) or harvesting sand ( 53 men ) in Lake Victoria . Men were treated with praziquantel each time S . mansoni infection was detected . In car washers , a significant increase in resistance to reinfection , as measured by the number of cars washed between cure and reinfection , was observed after the car washers had experienced , on average , seven cures . In the car washers who developed resistance , the level of schistosome-specific IgE increased between baseline and the time at which development of resistance was first evidenced . In the sand harvesters , a significant increase in resistance , as measured by the number of days worked in the lake between cure and reinfection , was observed after only two cures . History of exposure to S . mansoni differed between the two cohorts , with the majority of sand harvesters being lifelong residents of a village endemic for S . mansoni and the majority of car washers having little exposure to the lake before they began washing cars . Immune responses at study entry were indicative of more recent infections in car washers and more chronic infections in sand harvesters . Resistance to reinfection with S . mansoni can be acquired or augmented by adults after multiple rounds of reinfection and cure , but the rate at which resistance is acquired by this means depends on immunologic status and history of exposure to S . mansoni infection .
Schistosoma mansoni age-infection curves in endemic human populations characteristically show a peak prevalence in children and early adolescence and then a decline beginning in the late teenage years to lower levels of prevalence among adults [1] . This has led many researchers to hypothesize that humans can acquire immunity to S . mansoni , leading to partial resistance against reinfection [2] . Since the natural lifespan of S . mansoni worms is approximately 5–10 years [3] , [4] , the decline in prevalence coincides with the time at which worms acquired in early childhood would naturally begin to die in persons living in endemic areas . One theory holds that upon worm death , either naturally or as a result of treatment , critical schistosome antigens not normally or appropriately encountered by the host during chronic infection are released . The release of these antigens alters the immune response patterns that result from exposure to intact worms [5] , [6] , and it is hypothesized that these changes in immune responses are responsible for the increased resistance to reinfection [2] . We previously reported the age-independent development of immunological resistance to reinfection with S . mansoni in a cohort of adult males occupationally exposed , by washing cars in Lake Victoria , undergoing repeated cycles of reinfection and praziquantel-induced cure [7] . Resistance to reinfection by all three of the schistosome spcies that cause most human disease has been associated with both cellular [8] , [9] , [10] and humoral immune responses , most notably IgE in response to parasite-specific antigens [11]–[16] . In turn , variations in these immune responses have been related to factors such as age , stage of disease , and duration of infection [17]–[24] . More recently , we have expanded our studies to include a second cohort of men who are also exposed to infectious water through their occupation of harvesting sand in Lake Victoria . Upon discovering differences in the two cohorts in the number of treatments and cures needed before increased resistance to reinfection was demonstrated , we explored demographic and immunologic factors that may explain the discrepancies .
All participants in this study were adult males occupationally exposed to S . mansoni by washing cars or harvesting sand on the shores of Lake Victoria near Kisumu , Kenya . The car washers stand ankle- to knee-deep in the lake to wash cars that have been driven into the shallow water at the edge of the lake . Enrollment of car washers began in June 1995 , and follow-up continued until January 2009 . With the exception of the period between January 2000 and September 2003 , enrollment of new car washers was continuous throughout the duration of the study , so follow-up time varies for each individual . The sand harvesters stand waist- to chest-deep in the water to shovel sand off the bottom of the lake . After filling their boats with sand , they then transport the sand to shore and stand in the water at the edge of the lake while they unload the sand onto the shore . Recruitment of sand harvesters began in March 2005 , and follow-up continued until January 2009 . Both groups of men are ethnically homogeneous , with 90% of the car washers and 98% of the sand harvesters belonging to the Luo tribe . The car washing and sand harvesting sites are shown in Figure 1 . The carwash is adjacent to the city of Kisumu , and the site is a busy area also populated with fishermen , fish merchants , and various other vendors . Although located only 5 . 2 km around the lakeshore and 3 km across the lake , the sand harvesting site differs considerably as it is located off the shores of the small fishing village of Usoma , a rural community separated and distinct from the city of Kisumu . The presence of S . mansoni-infected Biomphalaria sudanica snails has been confirmed at both exposure sites [25] , [26] . All study participants gave written informed consent prior to enrollment . Study procedures were approved by the institutional review boards of the University of Georgia and the Centers for Disease Control and Prevention , the Scientific Steering Committee of the Kenya Medical Research Institute ( KEMRI ) , and the KEMRI/National Ethics Review Committee of Kenya . Upon enrollment , men were tested for S . mansoni eggs by the modified Kato-Katz method using two slides from each of three consecutive stool samples . Individuals positive for S . mansoni were treated with 40 mg/kg praziquantel ( PZQ ) , and follow-up stool samples were taken 4–6 weeks later to assess for cure . If necessary , men were re-treated with PZQ until cure was demonstrated by three consecutive stool samples that were negative for schistosome eggs . Upon becoming stool negative , men were continually followed and retested for S . mansoni eggs at 4-week intervals . Each time a new infection was found , the study participant was treated with PZQ until he demonstrated cure . Blood samples were taken every six months for subjects enrolled in 2003 or later and approximately yearly for car washers enrolled prior to 2003 . Blood was tested for schistosome-specific antibodies , HIV-1 specific antibodies , and the ability of their peripheral blood mononuclear cells ( PBMCs ) to produce cytokines [7] , [27] . The prevalence of malaria and soil-transmitted helminths in these populations was low . In the rare event malaria or soil-transmitted helminths were found the subjects were offered appropriate treatment . Water exposure was measured by the number of cars washed or the number of days worked in the lake harvesting sand . Daily records of the number of cars washed by each car washer or the number of hours worked each day harvesting sand for each sand harvester were kept by on-site members of the carwash and sand harvester consortia who were employed as field workers for the present study . Since the number of hours spent in the water each day for sand harvesters was highly consistent ( mean 5 . 3±0 . 9 hours ) , and sand harvesters likely receive most of their exposure to schistosomes as they are standing near the edge of the lake unloading the sand from their boats rather than when they are harvesting sand in waist- to chest-deep water away from the shore , we have chosen to use days worked rather than hours worked in water exposure calculations for the sand harvesters . Sand harvesters were given credit for one day of work for each day that they worked for at least one hour . It is important to note that one car washed is not equivalent to one day of work harvesting sand , thus direct comparisons between the two groups of men are not appropriate , Isolation of PBMCs and cell cultures were performed as previously described [28] . Briefly , PBMCs were separated from venous blood using the ficoll-hypaque technique . PBMCs were washed and resuspended in RPMI containing 5% AB+ normal human sera , antibiotics and L-glutamine . The cells were incubated with 10 µg/ml soluble worm antigen preparation ( SWAP ) or 5 µg/ml soluble egg antigens ( SEA ) for five days at 37C in 5% CO2 and the supernatant fluids collected . PBMC production of the cytokines interleukin ( IL ) -5 , IL-10 , IL-13 , and IFN-γ in response to SWAP and SEA was measured by capture ELISA using commercially-available kits ( R&D Systems , Minneapolis , MN ) according to manufacturer's instructions . Cytokine production was only performed on blood samples obtained after October 2003 . Anti-SWAP IgE isotype ELISAs were performed on plasma from the venous blood samples as previously described [29] , [30] . External positive and negative controls ( EC ) comprised of pooled samples of high responders and normal human serum ( NHS ) from non-endemic volunteers , respectively , were run on each plate . Anti-SWAP IgE values for each sample were standardized according to the following formula: IgE-specific ELISAs against the recombinant antigens ‘tegument allergy like’ ( TAL ) -1 ( formerly Sm22 . 6 ) and TAL-2 ( formerly Sm21 . 7 ) [31] were performed on a subset of baseline samples from 23 car washers and 20 sand harvesters . TAL-1 and TAL-2 were cloned and purified as previously described [32] , [33] . ELISA plates were coated with recombinant antigen at 2 µg/ml . Following incubation with plasma samples ( 1∶20 dilution ) , antigen-specific IgE binding was measured using directly conjugated mouse anti-human IgE ( Southern Biotech , Birmingham , AL ) . Since almost all measurements were non-normally distributed , the Wilcoxon rank sum test was used for group comparisons , and the Wilcoxon sign rank test was used for paired comparisons of the same subjects at different time points . An alpha level of 0 . 05 was considered statistically significant for all comparisons . All analyses were performed with GraphPad Prism 5 or SAS version 9 . 1 . The number of cars washed or days worked harvesting sand between each cure and reinfection was estimated in an accelerated failure time model with the LIFEREG procedure in SAS [34] . Each infection interval was defined as the time between the documentation of cure and subsequent reinfection . Thus , “interval 1” is the interval between the first cure after study entry and the first reinfection following the first cure , “interval 2” is the interval between the time of the second cure and second reinfection , and so forth . Interval number was entered into the model as a categorical variable with interval 1 as the reference category . Thus the length of each cure-to-reinfection interval was statistically compared to that of the first interval . The LIFEREG procedure can accommodate failure time data that is right- or left-censored . The first interval was considered left-censored for subjects negative at study entry . Intervals during which the subject left the study or follow-up ended before reinfection occurred were considered right-censored . Censored observations accounted for 59 of 570 total intervals ( 10 . 4% ) among the car washers and 30 of 144 total intervals ( 20 . 8% ) among the sand harvesters . Intervals during which more than three months elapsed between the last negative stool and a subsequent positive stool were excluded from the analyses , though other intervals from that same subject could be included . Entire subjects were excluded from the analysis if they did not have at least one complete infection interval—i . e . left the study without ever becoming egg-negative or after the initial cure but before the first reinfection . Because daily records of car washing activities are incomplete prior to 1999 , subjects whose entire follow-up occurred before February 1999 are not included in this analysis . For subjects enrolled before February 1999 and followed further , the cure-to-reinfection intervals occurring after February 1999 are included , beginning with the numbered interval that the subject had reached at that point . The final study population consisted of 120 car washers with a mean follow-up time of 74 . 4 months ( range: 9 . 1–165 . 5 ) and 53 sand harvesters with a mean follow-up time of 37 . 9 months ( range: 12 . 6–61 . 1 ) . The mean number of cure-to-reinfection intervals was 6 . 5 ( range: 1–18 ) and 3 . 0 ( range: 1–8 ) for the car washers and sand harvesters , respectively . For each car washer , the number of reinfections per 100 cars washed ( RCW ) during the at-risk time over the course of follow-up was calculated as an indication of relative resistance to S . mansoni reinfection . For the sand harvesters , this measure was calculated as the number of reinfections per 100 days worked harvesting sand ( RDW ) during the at-risk time over the course of follow-up . At-risk time is the time between cure and reinfection . Cars washed ( or days worked ) in the time between infection and cure are not included in the RCW or RDW calculations . As the RCW or RDW is averaged over the entire duration of follow-up , in theory those men who enter the study with a higher level of resistance or develop resistance over the course of the study will have a lower RCW or RDW than men who retain a high degree of susceptibility over the course of follow-up . For some analyses , car washers and sand harvesters are dichotomized based on the mean RCW or RDW of each respective group . For ease of discussion , men with a below-mean number of reinfections are referred to as “more resistant phenotype , ” and men with an above-mean number of reinfections are referred to as “more susceptible phenotype . ” Factors associated with having a more resistant phenotype were evaluated in a logistic regression model .
The RCW or RDW for each car washer and sand harvester is plotted in Figure 6 . The mean RCW for the car washers was 0 . 29 infections per 100 cars washed , with the individual RCWs uniformly distributed around the mean . Conversely , the sand harvesters exhibited a skewed pattern of resistance indexes , with the majority of the individual RDWs concentrated below the mean of 0 . 79 infections per 100 days worked , and only a few men with higher outlying RDWs . Figure 7 shows the median number of cars washed in the intervals between each successive cure and reinfection . The figure depicts all car washers together ( Figure 7a ) , and also stratified into more resistant ( Figure 7b ) and more susceptible ( Figure 7c ) phenotypes based on being below or above the mean RCW , respectively . For the entire cohort of car washers , the number of cars washed before reinfection was relatively stable until the seventh cure , at which point the number of cars washed between cure and reinfection begins to progressively increase with each successive cure . In the seventh cure-to-reinfection interval , and each interval thereafter , the number of cars is significantly greater than the number of cars washed in the interval between the initial cure and the first reinfection . When the car washers were stratified based on the RCW , those with the more resistant phenotype ( Figure 7b ) showed a pattern of increasing cure-to-reinfection intervals similar to that seen in the overall cohort . With the exception of interval ten ( p = 0 . 0922 ) , the median number of cars washed in each cure-to-reinfection interval after the eighth cure in the more resistant phenotype group was significantly greater than the initial interval ( p-value range: 0 . 0005–0 . 0195 ) . However , a pattern of increasing number of cars per cure-to-reinfection interval was not seen in the group of men with the more susceptible phenotype ( Figure 7c ) . While some later intervals were significantly greater than the initial interval , overall these men did not , by the end of the study , exhibit a consistent pattern of increased resistance to reinfection upon repeated cures . The median number of days worked in Lake Victoria between each cure and reinfection for all sand harvesters are shown in Figure 8a . The number of days in the interval between the second cure and second reinfection was significantly increased relative to the initial interval ( p = 0 . 0118 ) . Thus , as opposed to the car washers , the increase in resistance occurred in the sand harvesters after having experienced only two previous cures . This pattern was true for men with both more resistant and more susceptible phenotypes ( Figures 8b–8c ) , though the more susceptible men started with a lower initial days worked to reinfection , and days to reinfection remained lower throughout follow-up . Although the graph appears to show a trend towards increased susceptibility after three previous cures , intervals 3–5 do not have significantly fewer days worked than interval two , and the apparent decrease is likely due to low numbers of subjects and high numbers of censored observations in intervals three and above .
Previous research by our group has shown that among men similarly exposed to S . mansoni by virtue of their occupation as car washers in infectious waters of Lake Victoria , a portion of the men developed resistance to reinfection after multiple rounds of cures and reinfections , while others remained susceptible despite equal or greater numbers of cures and reinfections [7] . We now show that the same observation holds true with a modified definition of resistance , based on exposure rather than time-to-reinfection , and after the addition of another cohort of men at the same carwash and additional follow-up of the original cohort . If car washers were maximally immune or non-immune at study entry , we would not have observed a progressively increasing number of cars before each reinfection as we did in many of the cohort , suggesting that these men are actively developing resistance . Those car washers who developed resistance began to do so after experiencing an average of seven previous cures . However , a different pattern of the development of resistance emerged in a different cohort of men who receive daily exposure to schistosomes by harvesting sand in the lake just three km across the lake from the car washing site . In these men , the interval between cure and reinfection significantly increased after only two previous cures , after which point there were no further increases in the number of days worked between cure and reinfection , suggesting that no further increases in resistance occurred . The numerical values of reinfections per 100 cars washed and reinfections per 100 days worked harvesting sand are not directly comparable as the S . mansoni transmission situation is different for each cohort . The sand harvesters spend on average 5 . 3 hours in the lake each work day , while the car washers wash an average of 3 . 2 cars per work day . However , the water at the car washer site is probably more heavily contaminated with S . mansoni cercariae , as the prevalence of infection in B . sudanica snails collected at the car wash site is higher than prevalence in snails collected at the sand harvesting site in Usoma [26] . Also , much of the sand harvesters' time is spent in deeper water , away from the shore where snails are not as likely to be present , so the majority of their cercarial exposure likely occurs during the time they are unloading sand onto the shore . Despite the differences in exposure between cohorts , the definition of resistance for each cohort is valid for comparisons within that cohort , and overall patterns of resistance should be comparable between the two cohorts . The car washers exhibited a wide range of overall resistance levels according to the distribution of number of reinfections per 100 cars washed , which were fairly symmetrically distributed from the low to the high end of the spectrum . Although some car washers in the more susceptible group appeared to develop resistance after multiple cures and some might have eventually become resistant with longer follow-up , the general pattern observed was a gradual increase in resistance among those men who experienced a below-mean number of reinfections and no apparent consistent increase in resistance among men who experienced an above-mean number of reinfections . In contrast , the distribution of number of reinfections per 100 days worked for the sand harvesters was much less uniform , with the majority of the sand harvesters clustering towards fewer reinfections , indicating that a majority of the cohort entered the study with similar relatively high levels of resistance . Albeit to a much less degree than those in the more resistant group , even those relatively more susceptible sand harvesters exhibited an increase in time-to-reinfection after two previous cures . The different histories of S . mansoni exposure in these two groups of men prior to study enrollment likely explain the differences in development of resistance upon multiple rounds of treatment and reinfection . The car washers reported working in the lake a mean of 5 . 7 years , while the sand harvesters had worked in the lake for a mean of 11 . 1 years . Moreover , while the majority of car washers were lifelong residents of the city of Kisumu or immigrants from other areas of Kenya , almost all of the sand harvesters were born in Usoma , the lakeside village where they now harvest sand . S . mansoni infection has been seen in children in Usoma as early as one year of age , with >90% becoming positive for antibodies to schistosomes by age 10 ( J . Verani , unpublished data ) . A similar situation has been reported among children in fishing villages along the Ugandan shoreline of Lake Victoria , where Odogwu and colleagues found S . mansoni infection in 25% and 86% of children aged <3 years in two endemic villages [35] . Thus , men from Usoma likely had exposure to the lake as children long before they began working as sand harvesters , were probably initially infected with S . mansoni at an early age , and had likely experienced the natural death of worms multiple times prior to being treated as part of this study . In contrast , S . mansoni infections present at study entry in car washers likely represent more recent infections , and they had likely experienced the death of no or few worms prior to treatment with praziquantel . These two groups of occupationally exposed adult males also differed considerably in their immune response patterns to schistosome antigens , and these differences are also likely explained by their different histories of exposure to S . mansoni . The baseline immune responses are suggestive of more recent infections in car washers . PBMC cytokine production in response to SEA at the time of enrollment was higher in car washers than amongst sand harvesters by all four measured cytokines . High responses to SEA have been associated with early S . mansoni infection , and these responses then decrease as infection becomes more chronic and exposure to constantly released egg antigens leads to development of immunoregulatory mechanisms [19] , [20] , [22] , [23] . Similar to other researchers who have shown no difference in humoral responses to crude worm antigens in patients with early and chronic schistosomiasis [19] , baseline anti-SWAP IgE responses did not differ between our cohorts . However , in both car washers and sand harvesters , older men had significantly higher levels of anti-SWAP IgE than did younger men , independent of exposure history . While increases in parasite-specific IgE with increased age are usually attributed to longer exposure to infection , Naus et al also reported increased IgE responses against schistosome worm antigens in older age groups in an immunologically naïve immigrant population recently arrived to an S . mansoni-endemic area of Kenya , suggesting that the increase may be innately age-related and not dependent on duration of schistosome infection [18] . Although baseline differences between car washers and sand harvesters were not seen in IgE responses to the heterogeneous worm antigens present in SWAP , differential IgE responses to the recombinant S . mansoni antigens TAL-1 and TAL-2 were observed between the two groups . Fitzsimmons et al have shown that TAL-1 expression is concentrated primarily in the adult worm , while TAL-2 is expressed on all life cycle stages , including miracidia , cercariae , and eggs [33] . Levels of anti-TAL-1 IgE antibodies were increased after treatment of S . mansoni infected individuals in the Fitzsimmons et al study , while anti-TAL-2 IgE antibodies were unchanged by treatment . The authors hypothesized that TAL-1 worm antigens are sequestered during active infection and are only released upon worm death . Conversely , the immune system is continuously exposed to TAL-2 due to the constant release of eggs during S . mansoni infection [32] , thus leading to down regulation of responses to TAL-2 . The current finding of higher pretreatment levels of anti-TAL-1 IgE in sand harvesters than in car washers and similarly low anti-TAL-2 responses in both groups fits this hypothesis . As the natural lifespan of an adult S . mansoni worm is approximately 5–10 years [3] , [4] , the car washers had likely not been exposed to any or many dying worms before receiving PZQ treatment as part of the current study , while the sand harvesters had likely already experienced multiple episodes of naturally dying worms , based on exposure since early childhood . Car washers who were HIV positive at study entry were less likely to develop resistance over the course of follow-up than were men who were HIV negative . We previously reported that patients with schistosomiasis and HIV coinfection had significantly lower production of the cytokines IL-4 and IL-10 than schistosome-infected persons who were HIV negative [28] . Other researchers have reported an association between IL-4 production in response to schistosome antigens and increased resistance to reinfection with S . mansoni [8] , Schistosoma haematobium [9] , and Schistosoma japonicum [10] . HIV infection was not related to the ability to develop resistance in the sand harvesters , most probably because they had already been infected with and developed protective immune mechanisms against schistosomes prior to becoming infected with HIV as adults . While neither age nor number of years worked in Lake Victoria prior to study entry were associated with resistance among the car washers , only age was independently predictive of a resistant phenotype among the sand harvesters . As most sand harvesters likely had lake exposure since childhood before they began working harvesting sand , length of time worked in the lake became insignificant in the analysis after adjustment for age , as age is a better predictor for duration of water exposure in this group . Many previous studies have shown various immune responses to be correlated with resistance to reinfection with all three species of schistosomes , most commonly the production of parasite-specific IgE [11] , [14]–[16] . While we did not find any baseline antibody or cytokine responses to be predictive of the ability to develop resistance among the car washers , this was not unexpected given that a change in resistance did not become apparent until the men had experienced on average seven previous cures . However , among those car washers that did eventually demonstrate an increase in resistance against reinfection , we have documented increases in anti-SWAP IgE production that parallel the development of resistance . Increases in anti-SWAP IgE production did not occur in those who remained susceptible . We did not see a similar increase in anti-SWAP IgE as the interval between cure and reinfection increased in sand harvesters . In conclusion , we have again demonstrated that resistance to reinfection with S . mansoni can be acquired or augmented by adults after multiple rounds of reinfection and PZQ-induced cure . However , we now also show that the ability to acquire this resistance and the rate at which resistance is acquired is markedly different in two populations within close geographic proximity to one another that share high levels of occupational exposure to S . mansoni infested water . These differences are likely attributable to differences in history of exposure to S . mansoni infection and their resulting immunologic status at baseline . As many conflicting results have been reported in the literature regarding immunologic parameters associated with the development of resistance to schistosome infection , these factors should be considered in the design of future immuno-epidemiologic studies and eventual vaccine study design . | Schistosomiasis is a parasitic blood fluke infection of 200 million people worldwide . We have shown that humans can acquire immunity to reinfection after repeated exposures and cures with the drug praziquantel . The increase in resistance to reinfection was associated with an increase in schistosome-specific IgE . The ability to develop resistance and the rate at which resistance was acquired varied greatly in two cohorts of men within close geographic proximity and with similar occupational exposures to schistosomes . These differences are likely attributable to differences in history of exposure to Schistosoma mansoni infection and immunologic status at baseline , with those acquiring immunity faster having lifelong S . mansoni exposure and immunologic evidence of chronic S . mansoni infection . As many conflicting results have been reported in the literature regarding immunologic parameters associated with the development of resistance to schistosome infection , exposure history and prior immune status should be considered in the design of future immuno-epidemiologic studies . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases/helminth",
"infections",
"immunology/immunity",
"to",
"infections"
] | 2010 | Influence of Exposure History on the Immunology and Development of Resistance to Human Schistosomiasis Mansoni |
We show , for the first time , that in cortical areas , for example the insular , orbitofrontal , and lateral prefrontal cortex , there is signal-dependent noise in the fMRI blood-oxygen level dependent ( BOLD ) time series , with the variance of the noise increasing approximately linearly with the square of the signal . Classical Granger causal models are based on autoregressive models with time invariant covariance structure , and thus do not take this signal-dependent noise into account . To address this limitation , here we describe a Granger causal model with signal-dependent noise , and a novel , likelihood ratio test for causal inferences . We apply this approach to the data from an fMRI study to investigate the source of the top-down attentional control of taste intensity and taste pleasantness processing . The Granger causality with signal-dependent noise analysis reveals effects not identified by classical Granger causal analysis . In particular , there is a top-down effect from the posterior lateral prefrontal cortex to the insular taste cortex during attention to intensity but not to pleasantness , and there is a top-down effect from the anterior and posterior lateral prefrontal cortex to the orbitofrontal cortex during attention to pleasantness but not to intensity . In addition , there is stronger forward effective connectivity from the insular taste cortex to the orbitofrontal cortex during attention to pleasantness than during attention to intensity . These findings indicate the importance of explicitly modeling signal-dependent noise in functional neuroimaging , and reveal some of the processes involved in a biased activation theory of selective attention .
In the past decade , Granger causality ( GC ) has emerged as a widely used method for causal inferences , and has been applied to biological time series obtained from many different types of investigation , for example , to the fMRI blood-oxygen level dependent ( BOLD ) signals to detect effective connectivity between brain areas and thus to shed light on how the brain works [1]–[4] . The basic idea of GC can be traced back to Wiener [5] , who conceived the notion that if the prediction of one time series can be improved by incorporating the past history of a second one , then the second time series has a causal influence on the first . Granger later formulated this idea in the context of linear autoregressive ( AR ) models [6] . GC is completely data-driven and based on time precedence . The interactions discovered by GC may be unidirectional or reciprocal . GC is easy to implement , relies on a small set of straightforward assumptions , and does not need any knowledge about how the data are generated . Therefore , it can be applied directly to almost any time series data [7] . However , over-simplification of the model may result in an incorrect use or interpretation of GC and even spurious causal inferences in some situations [8]–[10] . Care is therefore needed in the use of GC . One possible over-simplification in some scenarios is that the covariance matrix of the noise , conditional on the past history of the time series and the noise process , is assumed to be time invariant . For example , spike trains of neurons are typically close to Poisson processes in their timing , and the variance thus increases linearly with the signal [11] , [12] . Similar conditionally heteroskedastic data have been observed in many physiological recordings , such as the data collected from patients with epilepsy and Parkinson's disease [13] . Therefore , it is natural to conjecture that changes in the volatility of one time series may have an impact on the mean activity or volatility of another time series , which indicates that causal influences may be evident in the second order statistics . Clearly , these causal relationships cannot be captured by classical GC based on a simple AR model , which does not deal with time series data with changing volatility ( variance ) . Moreover , although it has been widely observed and investigated that the signal-dependent noise plays important roles in neuronal activities [14]–[16] , it is still unclear whether this property carries through to fMRI BOLD signals , after the neuronal signals are delayed and smoothed by the haemodynamic response function . In this paper , we provide empirical evidence that the variance of the noise in the fMRI BOLD time series increases linearly with the square of the signal in a number of cortical areas , such as the insular taste , orbitofrontal , and lateral prefrontal cortical areas . In this context we present a Granger causal model with signal-dependent noise to detect GC in both the mean and variance of data with time varying volatility . We also propose a likelihood ratio test to infer GC with signal-dependent noise accurately and efficiently . We show , by simulation studies , that this novel method substantially outperforms classical GC when signal-dependent noise is present . The new method is evaluated with an fMRI investigation [17] to identify the source of the top-down selective attentional control that differentially biases brain systems involved in affective vs sensory analysis [17]–[19] . Instructions to pay attention to and later rate the pleasantness of a taste increased the activations to taste stimuli measured with fMRI in the orbitofrontal and pregenual cingulate cortices [17] , where the subjective pleasantness of taste is represented [20]–[24] , but not the primary taste cortex in the anterior insula [17] , where the subjective intensity and identity of taste are represented [20]–[22] , [24]–[26] . Instructions to pay attention to and later rate the intensity of a taste increased the activations to taste in the insular taste cortex but not in the orbitofrontal and pregenual cingulate cortices [17] . Our new method reveals how the effective top-down connectivity changes when attention is paid to the pleasantness vs the intensity of a taste , and helps in the interpretation of the source of the signals that implement top-down attention .
The fMRI dataset is the same as that obtained and used in previous investigations [17] , [47] , [48] . We describe key imaging acquisition , preprocessing and psychophysiological interaction ( PPI ) analyses for completeness . We refer the readers to previous publications for the full details .
Figure 2 shows a comparison of performance for the classical Granger causal model and the Granger causal model with signal-dependent noise by ROC ( receiver operating characteristic ) analysis . Clearly , classical GC cannot capture the causal influences well in the presence of signal-dependent noise , while the signal-dependent noise Granger causal model substantially outperforms the classical GC model , and shows a good sensitivity and specificity . Figure 3A shows the mean BOLD signals calculated across the trials for the three firing rates before downsampling . As expected , higher firing rates evoked larger mean modelled haemodynamic responses . However , the variability of the modelled BOLD response was considerable , as illustrated for the mean firing rate of 40 Hz in Figure 3A for 10 randomly selected trials . Figure 3B shows the relation between the empirically estimated variance of the noise and the squared empirically estimated signal at different time points within a trial , using the projection space spanned by the second-order polynomial basis , i . e . , . This shows that the variance of the noise at any point in the time course of a trial is approximately linearly related to the square of the signal . We obtained consistent results using different projection spaces . Consistent results with those just described can also be obtained with a simpler model in which the spike trains are convolved with the canonical haemodynamic response function to generate the BOLD signal , as described previously [73] , [74] . This simple generative model of BOLD signals thus confirms that Poisson spike trains could produce fMRI BOLD time series in which the variance of the noise across the time course of a trial would be linearly related to the squared signal . We show below that this is also exactly what was found empirically in the fMRI data . Figure 4 shows the empirically estimated variance of the noise in the fMRI BOLD time series obtained in this investigation as a function of the squared empirically estimated signal at each time point within a trial , using the projection space spanned by the second-order polynomial basis , i . e . , . Significant correlations are observed for both experimental conditions by pooling data from the four brain regions ( attention to intensity , , , attention to pleasant , , ) , which clearly indicates the presence of signal-dependent noise in the fMRI BOLD time series . In particular , the results shown in Figure 4 show that the variance of the noise in BOLD time series is approximately linearly related to the squared signal . A similar effect was also found for each brain region when analyzed separately . The results were consistent using different projection spaces . In particular , we observed significant correlations when the project space was spanned by ( 1 ) linear bases up to 9 time lags; ( 2 ) second-order polynomial bases up to 6 time lags; and ( 3 ) sixth-order Fourier bases up to 2 time lags . These results provide strong evidence for the presence of signal-dependent noise in fMRI BOLD time series . Moreover , when fitting our signal-dependent noise model to the real data , we observed excellent concordance and significant correlation between the empirically estimated signal , , and the model estimate of the signal , ( attention to intensity , , , attention to pleasantness , , ) , and between the empirically estimated variance of the noise , , and the variance of the noise estimated by the model , ( attention to intensity , , , attention to pleasantness , , ) . The results were also consistent using different projection spaces . This indicates that the AR-BEKK model is a good description of the data and captures a large portion of the variance in the empirical signal and noise . Table 1 shows the causal influences between the four brain areas ( OFC , AntINS , AntLPFC , PostLPFC ) detected by the Granger causality with signal-dependent noise analysis . First , we consider attention to intensity . There are significant ( top-down ) causal influences from both the AntLPFC and PostLPFC to the insular taste cortex ( AntINS ) . Second , we consider attention to pleasantness . There are significant ( top-down ) causal influences from both the AntLPFC and PostLPFC to the OFC , and a significant effect from the OFC to the antLPFC . There is also a ( top-down ) effect of the PostLPFC on the taste insula ( AntINS ) . Very interestingly too , during attention to pleasantness , there is increased effective connectivity from the insular taste cortex to the OFC , suggesting that information is routed especially to the OFC during attention to pleasantness . For comparison , Table 2 shows the causal influences between the four brain areas detected by the classical Granger causal model . Only one effective connectivity influence ( PostLPFC to AntLPFC , when paying attention to intensity ) was identified as significant . The greater power of the signal-dependent noise model can be clearly observed . Table 3 shows the difference of the causalities in opposite directions by the Granger causality with signal-dependent noise analysis . In the pleasantness condition , consistent with the hypothesis that the lateral prefrontal cortex is the source of the top-down modulation of activations in the OFC , there are significantly stronger effects from both the AntLPFC and the PostLPFC to the OFC than vice versa . It is also of interest that in the pleasantness condition , a significantly stronger forward influence was detected from the antINS to the OFC . Only one significant difference was detected for the intensity condition , that is the effect from the PostLPFC to AntINS is greater than in the reverse direction . This is consistent with the hypothesis that the major top-down effect on the taste insula during attention to intensity is from the PostLPFC . The bi-directional interaction in the pleasantness condition between the AntLPFC and OFC ( Table 1 ) may be interpreted in the context that there is a significant difference of the causality with AntLPFC to OFC greater than OFC to AntLPFC , thus indicating a stronger influence of AntLPFC on OFC than vice versa ( Table 3 ) . These analyses provide evidence for the effective connectivities in the attention to intensity and pleasantness conditions that are summarized in Figure 5 .
Conditionally heteroskedastic data often show volatility clustering and outliers . In particular , the unconditional distribution of the data is leptokurtic , which means that it has more mass around zero and in the tails than the normal distribution and , hence , it can produce occasional outliers [27] . Therefore , models with time varying volatility can better capture the nature of the data , and it is expected that more reliable causal inferences can be made . Comparing to the earlier approaches of causal inferences in data with time varying volatility [75]–[78] , including [13] , the model presented in this paper that takes into account signal-dependent noise provides an accurate , efficient and unified method to detect causality in both the mean and variance . The model has a corresponding frequency domain representation [13] , which may further shed light on frequency-specific interactions . The model described here applies when the variance of the noise is proportional to the square of the signal , which is what we observed from real fMRI BOLD time series , but could in principle be extended to deal with other cases . There are alternative measures of Granger-type causality such as partial directed coherence ( PDC ) [79] , relative power contribution ( RPC ) [80] and directed transfer function ( DTF ) [81] that do not explicitly use the noise covariance function to define causality but are based on the transfer function or model coefficients . However , because they are typically formulated under the simple AR model , all these methods are unable to capture causal influences in the second order statistics such as signal-dependent noise . It is not easy to tease apart ‘signal’ and ‘noise’ from an observed time series . In this paper , we define ‘signal’ as the part of the observations that can be well predicted from the past history of the time series , and ‘noise’ as what is completely unpredictable and produces the variation across realizations . We therefore empirically estimate the signal by projecting the current state of the time series onto the subspace spanned by its past history . In practice , if the projection space is not constructed appropriately , part of the signal that does not lie in the projection space may migrate to the residual process and produce artificial signal-dependent noise phenomena . In this case , expanding the projection space , i . e . , using a more complex model to describe the mean activity of the time series , may mitigate the issues of signal-dependent noise . However , if the variance of the noise is indeed dependent on the signal , simply increasing the complexity of the model in the mean structure will not remove this dependence . In the present paper , we investigated a number of projection spaces , spanned by linear or nonlinear basis functions with different time lags , and always observed signal-dependent noise . Therefore , there is strong evidence for the presence of signal-dependent noise in fMRI BOLD time series . In particular , the variance of the noise is approximately linearly related to the square of the signal . When constructing our signal-dependent noise model , we made use of this relationship and specified a linear function to describe the mean activity of the observations , and a quadratic function for the variance of the noise . Although the model appears to be simple , we have shown that it captures a large portion of the variance in the signal and noise in the empirical BOLD time series . Future studies will be of interest to provide more evidence on signal-dependent noise in different brain areas and different data sets , to further examine the relationship between the variance of the noise and the signal , and to develop more complex models , e . g . , using nonlinear functions or kernels , accordingly . Although we only applied our model to fMRI time series , it is clear that the model can be applied to very many types of data that might exhibit signal-dependent noise , including neurophysiological data such as single or multi-neuron recordings , magnetoencephalography , local field potentials , and beyond neuroscience also to any possibly causal system where there are time series of data from two or many sources . Indeed , the significance of detecting causality from data with time varying volatility might be partly demonstrated in the 2003 Nobel Prize in Economics shared by Granger , who set up the foundation of Granger causal analysis [6] , and Engle , who invented the first changing volatility model [31] . Although our initial implementation of the signal-dependent noise model appears to be successful , due to the highly nonlinear form of the log-likelihood function and optimization problem , fast and robust optimization algorithms deserve future investigation . Also , although a low-order low-dimensional AR-BEKK model is a relatively parsimonious representation of the conditional covariance structure of a process , the number of parameters still grows quickly with the dimension of the underlying system . This impedes the application of the model to a modest number of time series . Future studies are needed to find more restricted models that ensure uniqueness of the parameterization , guarantee the positive definiteness of the conditional covariance , while at the same time still produce rich dynamics . In spite of the wide and successful applications in neurophysiological data , there is still an ongoing debate on applying GC to fMRI data [10] , [82]–[87] . Inferring causality from fMRI time series — an indirect measure of neuronal activities – imposes many more challenges than direct electrophysiological recordings . Granger causal models use the observed fMRI data as a surrogate for the underlying neuronal activity , which is a potential flaw of the method and the main controversy against the application of GC to fMRI data , since the BOLD signal is a blurred and delayed representation of the original neuronal signal , and it is now widely recognized that there is intra- and inter-subject variability of haemodynamic responses [88]–[91] . However , there have been a series of numerical and theoretical works showing that GC is quite robust to the difference in haemodynamic delays [92]–[94] . Moreover , as in [4] , we calculated the cross-correlation function for each pair of time series used in our Granger causal analysis , and most of the cross-correlation peaks appeared at zero lag , indicating that differences in the regional haemodynamic responses may not be a significant factor in this study . We therefore feel that the application of Granger type causal inferences in the analysis of this particular fMRI data set is justified . However , given the complexity of the brain , much work remains to do to provide reliable and accurate causal analyses for neuroscience . The interpretation of the effective connectivity revealed with our signal-dependent noise model is that during attention to pleasantness , the AntLPFC and PostLPFC regions identified by PPI analysis exert a top-down control of the responsiveness of the OFC to its taste-related inputs , and indeed to how strongly information is routed to the OFC from its preceding area , the AntINS taste cortex . In contrast , during attention to intensity , the PostLPFC identified by PPI analysis exerts a top-down control of the responsiveness of the insular taste cortex to its taste-related inputs . This interpretation is strengthened by the findings with our componential Granger causal analysis [48] , which provides evidence that the top-down effects depend on the level of activity in the areas on which there is a top-down effect . The way that we think of top-down biased competition as operating normally in , for example , visual selective attention [95] is that within an area , e . g . a cortical region , some neurons receive a weak top-down input that increases their response to the bottom-up stimuli [95] , potentially supra-linearly if the bottom-up stimuli are weak [50] , [51] , [63] . The enhanced firing of the biased neurons then , via the local inhibitory neurons , inhibits the other neurons in the local area from responding to the bottom-up stimuli . This is a local mechanism , in that the inhibition in the neocortex is primarily local , being implemented by cortical inhibitory neurons that typically have inputs and outputs over no more than a few mm [50] , [51] , [96] . This model of biased competition is illustrated in [47] . That locally implemented biased competition situation may not apply in the present case , where we have facilitation of processing in a whole cortical area ( e . g . orbitofrontal cortex ) or even cortical processing stream ( e . g . the linked orbitofrontal and pregenual cingulate cortex [47] ) in which the activity of taste neurons may reflect pleasantness and not intensity . So the attentional effect might more accurately be described in the present case as biased activation , without local competition being part of the effect . This biased activation theory and model of attention , illustrated in Figure 6 , is a rather different way to implement attention in the brain than biased competition , and each mechanism may apply in different cases , or both mechanisms in some cases [19] , [47] , [97] . | We show that in cortical areas such as the insular , orbitofrontal , and lateral prefrontal cortex , the variation of the blood-oxygen level dependent ( BOLD ) time series across trials measured with functional magnetic resonance imaging ( fMRI ) increases with the magnitude of the signal . We describe a new method of measuring causal effects with Granger causality that takes into account this signal-dependent noise . We show in a functional neuroimaging investigation with the new method that there is a causal influence from the anterior lateral prefrontal cortex that during attention to the pleasantness of taste stimuli increases the response of the orbitofrontal cortex to the taste; and there is a causal influence from the posterior lateral prefrontal cortex to the insular taste cortex during attention to the intensity of taste stimuli . This shows how part of the circuitry involved in the effects of selective attention on the pleasantness and intensity of stimuli operates in the brain . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [] | 2013 | Attention-Dependent Modulation of Cortical Taste Circuits Revealed by Granger Causality with Signal-Dependent Noise |
The salamander has the remarkable ability to regenerate its limb after amputation . Cells at the site of amputation form a blastema and then proliferate and differentiate to regrow the limb . To better understand this process , we performed deep RNA sequencing of the blastema over a time course in the axolotl , a species whose genome has not been sequenced . Using a novel comparative approach to analyzing RNA-seq data , we characterized the transcriptional dynamics of the regenerating axolotl limb with respect to the human gene set . This approach involved de novo assembly of axolotl transcripts , RNA-seq transcript quantification without a reference genome , and transformation of abundances from axolotl contigs to human genes . We found a prominent burst in oncogene expression during the first day and blastemal/limb bud genes peaking at 7 to 14 days . In addition , we found that limb patterning genes , SALL genes , and genes involved in angiogenesis , wound healing , defense/immunity , and bone development are enriched during blastema formation and development . Finally , we identified a category of genes with no prior literature support for limb regeneration that are candidates for further evaluation based on their expression pattern during the regenerative process .
The salamander's capability to regenerate various body parts , including limbs , has been the focus of study for almost 200 years [1] . Regeneration of the limb of the axolotl ( Ambystoma mexicanum ) , in particular , has been widely studied , both in classical studies , and more recently in studies using modern molecular tools [2]–[10] . Recent publications have incorporated microarray , sequencing , and mass spectrometric technology into the study of limb regeneration . For example , a study focusing on the nerve dependence of limb generation utilized microarrays and 454 sequencing to identify genes expressed in the blastema with and without the presence of nerves [3] . A more recent study utilized a microarray with 20 , 000 probe sets to identify genes expressed during the regenerative process [11] . However , there are limitations to this approach , as microarrays can only identify the expression of genes for which probes are designed , and the shallow depth of 454 sequencing in the first study limits the ability to detect low abundance transcripts . Another study in Xenopus identified several genes likely to be important to hind limb regeneration [12] , and a proteomic analysis of the blastema has also been published [2] . More recently , a study employed microarrays to identify a list of genes specific to the regenerating epithelium [13] . In addition to the above axolotl studies , Expressed Sequence Tag ( EST ) and other sequencing efforts have made inroads in defining the transcriptome of the newt [14]–[16] . In our study , we examine the axolotl transcriptome using RNA-seq technology , which can provide accurate expression level estimates for genes across a wide range of abundances [17] . To the best of our knowledge , a deep sequencing of the developing axolotl blastema using RNA-seq has not yet been reported . Despite its advantages , RNA-seq data analysis for the axolotl is challenging as the species' genome has not yet been sequenced , probably due to its size ( ∼30 GB ) [18] , and its transcriptome is largely uncharacterized . To address these challenges , we have developed a novel computational approach to the analysis of axolotl RNA-seq data that allows for characterization of axolotl transcriptional dynamics in terms of the human gene set . Our approach uses statistical tools for transcript quantification when no reference genome is available [19] and comparative transcriptomic analysis with the human transcript set . Given the critical role played by the blastema in the regenerative process [20] , we used RNA-seq and our novel comparative analysis approach to uncover the sets of genes regulated in the blastema over the first 28 days after amputation . We established a website that contains all of the RNA-seq read and assembly information from this work and other relevant “omic” information on the axolotl ( www . axolomics . org ) . This paper provides information on genes and gene products likely important for the maintenance , growth and proliferation of stem cells , and for the regulation of growth and tumor formation . It supplies critical resources for a deeper understanding of the blastema's contribution to limb regeneration and lays the groundwork for important advances in the field of regenerative biology .
We amputated juvenile ( 4 . 5–10 cm ) axolotl right forelimbs at the mid-stylopod level and harvested tissue at 0 hours , 3 hours , 6 hours , 12 hours , 1 day , 3 days , 5 days , 7 days , 10 days , 14 days , 21 days , and 28 days ( Figure 1 ) . We focused on forelimbs because gene expression patterns can differ between forelimb and hindlimb during regeneration [21] and used only right limbs because left-right expression asymmetries may also exist . Tissue from three animals at each time point were pooled to reduce the impact of individual variation . We then prepared mRNA and performed sequencing using the Illumina GAII platform ( see Materials and Methods ) . Full details of our comparative RNA-seq analysis approach are provided in Materials and Methods . Briefly , RNA-seq reads were first mapped to an axolotl mRNA contig set containing 113 , 925 contigs . These contigs were then matched to human transcripts to allow for differential expression and functional analyses using the human gene set . Read counts and measures of Transcripts Per Million ( TPM ) were calculated for each human gene matched by at least one contig [22] . The numbers of reads sequenced and mapped to axolotl contigs and human genes for each sample are provided in Table S1 . The validity of this approach can be seen through analogy with standard methods for gene-level RNA-seq analyses , which typically sum the counts of reads mapping to each exon or isoform of a gene to arrive at a gene-level count . In our case , the axolotl contigs are simply treated as “isoforms” of the human genes to which they are matched . Other quality axolotl transcriptome assemblies exist . A recent study producing axolotl ESTs resulted in 15 , 384 contigs , but these contigs are derived from brain sampling and thus may not give a good representation of genes active in limb regeneration [23] . Sal-Site ( www . ambystoma . org ) also provides a web-searchable assembly of 17 , 000 contigs that map to human genes . We chose to use our assembly because many of the tissues used to construct the assembly are derived from the limb or blastema . Our samples have , on average , 10 , 242 human genes with at least one read associated with them and , on average , samples have 9 , 285 human genes with a TPM>1 ( Figure S1 ) . Analyzing all samples , we identified 11 , 927 genes with reads ( 47% of the 25 , 484 protein-coding gene symbols in the human gene set ) . Given that approximately 60–70% of genes in the genome are expected to be expressed in any particular cell type , the 47% detected in the present study likely represents a substantial percentage of the genes actually expressed in the axolotl blastema [24] . We further characterized the completeness of our axolotl transcriptome through an analysis of the high-quality RNA-seq reads without an alignment to an axolotl contig . A fraction of these unalignable reads are true axolotl sequences that could not be mapped due to an incomplete assembly while the remainder of these reads are probably RNA-seq artifacts , such as adapter sequences . To estimate these fractions we ran BLASTX on both high-quality alignable ( AL ) and unalignable ( UN ) RNA-seq reads against the human protein set . We found that 17% of AL reads had a significant BLASTX hit , while only 4% of UN reads had such a hit . We thus estimate that the fraction of unalignable reads that are truly from axolotl is 23% = 4%/17% . Given that 49% of all reads were unalignable , we estimate that 11% = 49%×23% of all reads were from axolotl but were unalignable either due to an incomplete assembly or sequencing error . Examining the sets of human proteins hit by the AL and UN reads , we found that 73% of the human proteins hit by either the AL or UN reads are hit by the AL reads . Thus we estimate that our axolotl assembly represents 73% of the transcriptome of our sampled tissues . A key assumption used by these analyses is that the transcripts in our axolotl assembly are not biased with respect to their sequence similarity to the human protein set . Although this assumption is not likely to be true across the board , we have no reason to believe that our assembly would be heavily biased in this respect . For additional details on the logic behind these calculations , see Materials and Methods . Our approach is dependent on comparative techniques between axolotl and human transcriptomes , and thus it excludes salamander clade-specific genes . To address this , we mapped contigs to salamander genes available in NCBI ( as of 5-23-2011 ) , and recovered 677 salamander genes through this analysis . 614 of these 677 genes have reads associated with them . However , 88% of the contigs that mapped to a salamander gene were also mapped to a human gene . We also identify ∼80 , 000 contigs with BLAST e values > = 10−5 ( to both salamander and human gene sets ) , which are generally shorter ( median length = 480 ) than contigs with significant BLAST hits ( median length = 602 ) . While these contigs lack ties to human and salamander annotations , the expression patterns of many of them indicate that they likely are involved in the regenerative response . ( Contig expression data is available on www . axolomics . org . ) We performed differential expression ( DE ) analysis through the time course comparing each time point after the zero hour control to the zero hour control using EdgeR ( see Materials and Methods for details ) [25] . For all downstream analyses , we consider only those genes with a false discovery rate ( FDR ) <0 . 05 . To assess RNA-seq data quality , we chose 19 genes from the DE set , and performed real-time quantitative PCR ( qPCR ) on biological replicates in triplicate ( and triplicate technical qPCR replicates ) as an orthogonal analysis . We found that the expression pattern ( relative to the zero hour control ) matches closely in all 19 genes ( average Pearson coefficient for all 19 genes = 0 . 741 ) ( see Figure 2 , Figure S2 and Table S2 ) . Four of these genes are shown in Figure 2 . These data indicate that our RNA-seq data accurately represent expression . The log2 ratios relative to the zero hour control for all 19 genes for both the RNA-seq data and qPCR data is shown in Table S2 . Our comparative approach to DE analysis of RNA-seq data may be contrasted with a simpler approach that performs the analysis directly at the level of individual contigs . In this contig-level approach , read counts are estimated for individual contigs in each sample and used to predict contigs that are DE ( see Materials and Methods ) . Contigs predicted to be DE may then be functionally analyzed through annotations obtained by BLAST hits to a well-characterized gene set , such as that of the human . We implemented the contig-level DE approach and found that it predicted 3 , 671 contigs as DE at any time point . These 3 , 671 DE contigs mapped to 1 , 040 human genes , a significantly smaller set ( p<2 . 2e-16 , Fisher's exact test ) than the 1 , 656 genes predicted as DE by the comparative approach . The smaller number of DE genes predicted by the contig-level approach is probably due to the fact that read counts had a significantly higher variance ( p<0 . 002 , Wilcoxon signed rank test ) at the contig-level ( median edgeR common dispersion = 0 . 11 ) than at the human gene level ( median edgeR common dispersion = 0 . 05 ) . Read count estimates have higher variance at the contig level because contigs are typically shorter than full-length transcripts and often have high sequence similarity to other contigs that may represent alternative isoforms of the same gene . Thus , the comparative approach gains more statistical power for DE tests through its use of less variable counts at the human gene level . Of the 19 genes validated by qPCR that were called DE by the comparative gene-level approach , only 13 were called as DE by the contig-level method , and of the 19 contigs used for qPCR primer design , only 10 were called as DE . Given that the qPCR data support that these 19 genes and contigs were all DE across the time course , these results indicate that the comparative approach has superior statistical power . Examining the two DE gene sets , 709 genes are called as DE by both approaches . Of the 331 DE genes uniquely called by the contig-level method , two were identified as limb development genes ( based on the 101 limb genes in Figure S3 ) , whereas the 947 DE genes uniquely called by the comparative method included 19 of these 101 limb development genes . This result also indicates that the comparative approach had more power on our data , although the difference between the numbers of limb development genes uniquely called by the two methods is not statistically significant ( p = 0 . 13 , Fisher's exact test ) . Interestingly , a large fraction ( 53% ) of the DE contigs did not have significant BLAST hits with the human gene set . These contigs were somewhat shorter ( 640 bp median length ) than DE contigs with a significant BLAST hit ( 727 bp median length , p<2 . 2e-16 , Wilcoxon rank sum test ) . More revealingly , through an analysis of the lengths of the longest open reading frames ( ORFs ) within the DE contigs , we found that the DE contigs without a significant BLAST hit generally had a much smaller fraction of their length contained within their longest ORF ( Figure S4 ) . In addition , the distribution of the fractional lengths of the ORFs in shuffled contig sequences was relatively similar to that in the DE contigs without significant BLAST hits . These results suggest that these contigs are largely non-coding and may be derived from non-coding genes or the UTRs of protein-coding genes . Thus , the DE results obtained from our comparative method are likely capturing the vast majority of the protein-coding genes involved in axolotl limb regeneration . We evaluated the entire list of DE genes and a subset of that list containing only transcription factors ( TFs ) . Global DE statistics are available in . ( And lists of all significantly differentially expressed genes and the TF list are available at www . axolomics . org . ) We performed two-dimensional clustering of all DE TFs along the time course ( Figure 3 ) , and found that oncogenes dominate during the first day , while TFs associated with limb development or regeneration , such as HOXD10 and HOXD11 , peak at days 10 to 14 ( Figure 3 ) . We assessed the confidence of our sample clustering by performing cluster bootstrapping analysis ( 10 , 000 iterations ) with the R package PVClust . PVClust gives both a bootstrap proportion ( BP ) measure as well as an approximately unbiased ( AU ) measure ( see Materials and Methods ) [26] . This bootstrap analysis provides statistical support for the existence of an early cluster ( day 1 and earlier samples ) and a later cluster ( day 3 and later samples ) ( Figure 4 ) . A heat map showing the pairwise Pearson coefficient correlations between each of the time points is shown in Figure S6 ( and all of the correlation values can be found in Table 3 ) . The cluster of TFs upregulated during the first day include many known oncogenes ( e . g . ATF3 , EGR1 , ETS2 , FOS , FOXO1 , JUN , JUND , KLF4 , KLF6 , MYC , ZFP36 ) ( Figure 3 , upper right cluster ) [27] , [28] . The observation of a burst of oncogenes early in the formation of the blastema is novel . We evaluated the statistical significance of oncogene upregulation throughout the entire time course by determining the number of upregulated oncogenes ( based on the list of oncogenes available from the Memorial Sloan Kettering Cancer Center at http://cbio . mskcc . org/CancerGenes/Select . action ) and by performing a Fisher's exact test to measure significance ( Figure 5 ) . Note that oncogenes are very significantly enriched in the upregulated DE set ( p<10−5 ) during the first day , and continue to show some enrichment ( p<0 . 05 ) through day 10 . Later in the time course , oncogenes are not significantly enriched in the upregulated DE gene sets . We also find significant enrichment of oncogenes in upregulated DE gene sets of the time points of the first day as a group ( 0 hr , 3 hr , 6 hr , 12 hr , 1 d ) compared to later time points as a group ( 3 d , 5 d , 7 d , 10 d , 14 d , 21 d , 28 d ) ( p = 0 . 018 ) . In addition to TFs , we evaluate all DE genes . Among the most significantly upregulated genes during the first day are matrix metalloproteinases ( MMP1 , MMP2 , MMP3 , MMP8 , MMP9 , MMP10 , MMP12 , MMP13 , MMP12 , MMP19 ) , many of which were observed previously [3] , [29] . ( See axolomics . org for lists of upregulated and downregulated genes and the subset of TFs . Files are provided for up and down regulation at each time point , and consolidated files showing all genes upregulated or downregulated at any point along the time course ( prefix = wholeTC ) ) . Another category of genes expressed during this time period are the dual specificity phosphatases , DUSP1 , DUSP5 , and DUSP7 , which are involved in MAP/ERK signaling and can play roles in development or cancer [30] . Later in the time course ( between 3 d–14 d ) there is a prominent cluster of genes ( Figure 3 , lower right cluster ) , many of which are involved in limb development or patterning . Expression of oncogenes early in the time course and limb genes later corresponds to the two well-recognized phases of limb regeneration , the early “preparatory” and later “redevelopment” phases respectively [31] . Upon examination of the DE lists , significantly upregulated genes include HMG group genes ( HMGA1 , SOX11 , SOX4 , HMGA2 ) and genes known to be expressed in blastemas or limb buds , or to be involved in limb development and patterning ( PRRX1 , HOXD10 , PRDM1 , SALL1 , TBX18 , SALL4 , HOXD11 , GLI3 , SALL3 , TGFB1 , TNC ) [31]–[40] . Also upregulated during this time include genes upregulated in embryonic or adult stem cells ( JARID2 , SALL1 , ZIC2 , HMGA2 , SALL4 ) [41]–[45]; SHH , a gene crucial to limb regeneration , and SUFU , which regulates SHH [46]; tumor suppressor genes ( APC , SMAD4 ) [47]; and a component of the polycomb complex ( EED ) [48] . It is also noteworthy that many limb genes peak at day 10 or 14 ( Figure S3 ) . At the end of the time course ( 21 d–28 d ) , expression of the HMG genes , limb genes , tumor suppressor , polycomb , and stem cell genes decreases so that , of the 24 genes listed above for the 3 d–14 d time points , only one gene ( SALL1 ) remains upregulated by days 21–28 . During the last two time points , upregulated genes include keratins , collagens , and genes associated with collagen or cartilage formation ( EPYC , MATN4 ) ( genecards . org ) . We used DAVID ( Database for Annotation , Visualization and Integrated Discovery ) to perform gene ontology ( GO ) analysis for biological process ( BP ) and molecular function ( MF ) GO terms at level 5 ( see Materials and Methods ) on all DE genes [49] . The results are shown in the heat map in Figure 6 . The GO data for the figure is available in Table S4 . During the first day enriched GO terms include GO terms representing immune response , chemotaxis , regulation of leukocytes , blood vessel development , and angiogenesis . In the middle of the time course ( 3 d–14 d ) , GO terms for tissue development , limb morphogenesis , bone development , and forebrain development are enriched . An ectoderm development GO term is enriched at 28 days . ( Detailed GO enrichment information for upregulated axolotl genes is available at www . axolomics . org ) . We performed RNA-seq on human embryonic stem ( ES ) cells , human induced pluripotent ( iPS ) stem cells , and human foreskin ( FS ) cells , and compared the expression of ES and stem cell genes in these cells and blastemas ( see axolomics . org for reads , alignments and expression measures of ES , FS , and iPS cells ) . Whereas blastemal cells do express some embryonic stem ( ES ) cell genes , they are distinct from pluripotent cells and bear more in common with adult stem cells . We found upregulation of induced pluripotent stem ( iPS ) cell reprogramming factors KLF4 and c-MYC in blastemas in agreement with prior work [50] , [51] . SALL4 , which is thought to be involved in the maintenance of embryonic and/or adult stem cells [52] , [53] , is significantly upregulated during the time course . However , the key embryonic stem cell markers POU5F1 ( OCT4 ) , SOX2 , and NANOG are not highly upregulated in blastemas ( Figure 7 ) . PAX7 is upregulated and HMGA2 is significantly upregulated in the blastemas ( Figure 7 ) , and both are markers of adult stem cells [42] , [54] .
The SALL genes ( SALL4 , SALL3 , SALL1 ) are among the most highly upregulated TFs . SALL4 has been shown to play a role in Xenopus limb development , and thus is a strong candidate for being involved in the regenerative process [40] . The concordance of axolotl blastemal expression patterns of SALL1 , SALL3 , and SALL4 with those in Xenopus supports the suggestion that SALL4 plays a role in initiating blastema cell formation while SALL1 and SALL3 are involved in patterning events [37] , [40] ( Figure 8 ) . The continued expression of SALL4 is consistent with data indicating that SALL4 is also involved in limb patterning [55] . SALL4 is one of the few TFs thought to play a role in maintenance of the ES state and in the putative maintenance of adult stem cells [56] , [57] . It is possible that SALL4 is important for establishment or maintenance of the multiple adult stem cell types thought to be present within the blastema . In support of its regeneration-specific role , SALL4 is upregulated during Xenopus limb regeneration , but a wound without amputation in Xenopus does not result in SALL4 upregulation [40] . Similarly , SALL4 is upregulated in innervated axolotl blastemas when compared to a non-regenerative flank wound [11] . Important homeobox ( HOX ) genes that are markers of , and perhaps specifiers of , proximal/distal pattern ( HOXD10 , HOXD11 ) are upregulated during the juvenile time course . Many other genes ( see Figure 3 ) share expression patterns similar to HOXD10/11 . Some of these genes have known roles in limb development ( such as GLI3 ) , however most have no known role in pattern formation or limb development . The genes in this expression cluster thus become candidate genes for playing a role in pattern formation or other aspects of limb redevelopment during limb regeneration . All nine genes chosen from this cluster for further validation by qPCR ( CDH6 , GLI3 , HMGA2 , HOXD10 , LMO1 , MECOM , PRRX1 , SALL3 and SALL1 ) have corroborating qPCR evidence for this expression pattern ( Figure 2 and Figure S2 ) . Analysis of genes expressed in the blastema over the time course indicates that oncogenes are activated early in the process and then downregulated . Oncogenes play multiple roles during development and may be crucial for opening chromatin to reactivate developmental programs . During the early time periods of blastema formation , both dedifferentiation and proliferation are thought to occur [7] , and oncogenes may be playing roles in both of these processes . RAS and JUN family oncogenes are upregulated during newt lens regeneration [16] . Tumor suppressors are also upregulated in the juvenile blastema . It is possible that tumor suppressors and oncogenes maintain a critical balance in the blastema . HMGA2 , which is highly upregulated in the blastema , inactivates the tumor suppressor ARF in neural stem cells [42] . ARF inactivation has been shown to be important for activating regenerative muscle cells in the mouse [58] . The interplay between oncogenes and tumor suppressors may be crucial for the maintenance of multiple adult stem cell types within the blastema . The fact that salamanders are resistant to carcinogens [9] , [59] may be a result of the ability to control oncogenes during the regenerative response . HMGA1 and HMGA2 are highly over-represented in the blastema . HMG genes play a role in adult stem cells and in opening chromatin , preventing differentiation , and encouraging self-renewal of stem cells [42] , [60] . HMGA2 has been identified as a promoter of self-renewal and is more highly expressed in young adult stem cells than older adult stem cells [42] . Our observed upregulation of HMGA2 in the blastema might be an indicator of the presence of actively renewing adult stem cells in the blastema . The axolotl blastema is likely composed of a variety of adult stem cells each with its own limited differentiation capacity [4] . Likewise , the zebrafish jaw blastema appears composed of distinct progenitors for muscle and skeleton [61] . In the present study , no attempt is made to separate out the various progenitor cell types within the blastema . The upregulated genes identified here provide a springboard for the development of markers and affinity reagents for dissecting out the various cell types within the blastema . For instance , further studies using in situ hybridization for highly upregulated factors could potentially identify progenitor cell-specific transcripts based on position within the developing blastema , time of expression , and/or co-staining with mature cell-specific markers ( e . g . muscle markers , cartilage markers , nerve markers ) , which would be expressed later in the time course or during limb regeneration . Finally , the upregulation of HMGA2 , with its association with young stem cells , is consistent with the suggestion that the blastema is a “young” tissue where it is recapitulating developmental patterns of gene expression resulting in a possibly rejuvenated limb after regeneration [62] . Verification of this possibility will require investigation of telomere length and other markers of aging before and after regeneration . Reprogramming differentiated cells back to the appropriate adult stem cell state likely involves chromatin remodeling and suppression of parental gene expression programs via the polycomb group ( PCG ) complex . Blastema-enriched genes include several involved in chromatin remodeling , the PCG complex , gene silencing , and DNA methylation ( UHRF1 , EED , SETDB1 ) [63]–[65] . In addition to limb development or patterning genes , HMG genes , and oncogenes , we found several other interesting genes upregulated over the time course including MYF5 and FOXO1 ( likely involved in regulation of myogenesis ) , RUNX1 , SPI1 ( PU . 1 ) , and CBFB ( all involved in blood development ) , and PRDM1 [66]–[69] . PRDM1 ( BLIMP1 ) is involved in limb development [35] , [70] and is also important for germ cell determination in mice [71] . Recent papers suggest that the regenerating blastema may acquire a germline-like state [72] , [73] . Many TFs known to be involved in WNT signaling are upregulated during the middle of the time course ( 3 d–14 d ) ( ZIC2 , ZIC5 , MSX2 , SALL1 , SALL4 , GLI3 , NFATC1 , SOX4 ) [74]–[79] . WNT5A is also upregulated during this time , and WNT5A has been shown to be required for the outgrowth of the limb and other structures [80] . WNT signaling has been shown to be necessary for limb and fin regeneration in zebrafish and Xenopus , and promotes regeneration under typically non-regenerative conditions [81] . Beta-catenin signaling has also been shown to be required for apical ectodermal ridge ( AER ) maintenance and for proper expression of patterning genes in the mouse [82] . Our results indicate that it is likely that WNT signaling is playing a crucial role in axolotl limb regeneration . Keratins and collagens are upregulated during the last two time points of the time course ( 21 to 28 days ) . Keratins and collagens are also found to be enriched in regenerating Xenopus hind limbs when compared to non-regenerating Xenopus hind limbs expressing the BMP inhibitor noggin [12] . Keratins and collagens are also upregulated in the blastema of newt species [83] , [84] . In addition , a gene involved in regulating size control in the hippo pathway ( MST1 ) , is upregulated [85] . Size regulation of the limb is probably a crucial characteristic of this phase . Patterns of gene expression in the blastema are more similar to adult stem cells than to ES and iPS cells , in agreement with prior results investigating expression levels of embryonic stem cell genes during limb regeneration [50] , [51] . This is also consistent with a recent study suggesting that the blastema is composed of a variety of progenitor cell types rather than a homogenous collection of pluripotent cells [4] . Finally , some genes highly expressed in ES/iPS cells are blastema-enriched . These genes , though , are also expressed in other cell types and are not necessarily specific to the pluripotent state . Genes in common between ES/iPS cells and blastemas , such as KLF4 and c-MYC , are likely to be important for opening chromatin to facilitate reprogramming , but may not have specific functions related to establishing the ES cell state , as neural stem cells can be reprogrammed to the iPS state with OCT4 alone [86] . Thus the blastema bears some similarities to ES and iPS cells , but lacks the hallmarks of pluripotency ( POU5F1 , NANOG , SOX2 ) . The observed expression patterns are more consistent with the blastema being composed of multiple adult progenitor cells , and of other mesenchymal cells [4] , [10] , [50] , [51] , [87] , [88] . In summary , the genes identified in this study as blastema-enriched , and active during different phases of blastema progression greatly expand the list of potential regulators of the regeneration process , providing clues as to how the axolotl regenerates its limbs and , by extension , how to potentially improve the regenerative response in mammals . To elicit a more effective regenerative response in mammals , it will be necessary to activate endogenous and exogenous genes in an appropriate , time- and pattern-controlled manner . It is feasible , however , that activation of appropriate networks would engage latent mechanisms that might simplify portions of this process . Although enhancing limb or tissue regeneration in mammals may be complicated by salamander-specific innovations [10] , even partial regeneration of limb tissue or enhanced regeneration of tissues and organs has significant clinical implications . The fact that we identify many genes previously known to be involved in the regeneration process validates our comparative RNA-seq analysis methods as does the close concordance of expression patterns between our RNA-seq data and our qPCR results . Our methods draw strength from aligning reads directly to axolotl transcript contigs while performing differential expression analysis with respect to the better characterized human gene set . This strategy will likely be beneficial in studies of transcriptomes from other non-model organisms . Although we are unable to identify the functional roles of axolotl-specific genes that play a part in regeneration with this strategy , our analyses provide lists of axolotl-specific contigs that should be studied in more depth experimentally . We provide the data from this paper as well as other axolotl-related “omics” information at www . axolomics . org . Information available includes all the data from this manuscript including normalized gene expression measures , differential expression information , GO enrichment files , the sequencing reads , alignments , and the axolotl transcriptome assembly . In addition , several published datasets have been uploaded .
All surgical procedures and animal care were carried out in accordance with the Association for Assessment and Accreditation of Laboratory Animal Care ( AAALAC ) guidelines at the University of Wisconsin-Madison . We established an axolotl colony ( Ambystoma mexicanum ) from seven axolotls obtained from Dr . Gerald Eagleson ( Loras College , Dubuque , IA ) . The animals were housed in 40% Holtfreter's salts , kept at 16–18 . 9°C with a pH range of 7 . 0–7 . 4 . For all surgical procedures , the animals were anesthetized with 0 . 5–1 g/L Tricaine ( MS-222 , Sigma ) until they were unresponsive to a tail pinch stimulus . We amputated juvenile axolotl right forelimbs at the mid-stylopod level . For the RNA-seq experiment , the animals were 4 . 5–8 cm in length . For the qPCR validation experiments , the animals were 7–10 cm in length . Tissue was harvested as described in Figure 1 at 0 hours , 3 hours , 6 hours , 12 hours , 1 day , 3 days , 5 days , 7 days , 10 days , 14 days , 21 days , and 28 days . Note that for the early time points , some tissue was harvested proximal to the original amputation plane owing to the small amount of regenerative tissue distal to the amputation plane at the time of harvesting . In all cases the harvested tissues were stored in RNAlater ( Qiagen , Valencia , CA ) at 4°C . In all experiments , samples are placed in denaturing lysis buffer provided in the RNA isolation kit from the manufacturer and homogenized by passing through a sterile 20-gauge needle attached to a sterile plastic syringe at least 5–10 times until a homogeneous lysate is achieved . For the RNA-seq experiments , the RNA was purified using the RNeasy purification kit from Qiagen . For the qPCR experiments total RNA was isolated with the mirVana miRNA isolation kit ( Life Technologies ) according to the manufacturer's protocol . We cultured human FS cells ( newborn foreskin fibroblasts ( CRL-2097; American Type Culture Collection ( ATCC ) ) according to ATCC recommendations . We maintained cells in 10% ( v/v ) FBS ( Hyclone Laboratories ) , 1 mM L-glutamine ( Invitrogen ) , 0 . 1 mM β-mercaptoethanol ( Sigma-Aldrich ) and 0 . 1 mM nonessential amino acids in DMEM ( both from Invitrogen ) . We passaged cells at roughly 70% confluency at a 1∶3 splitting ratio , using Tryp-LE ( Invitrogen ) . ES cells were H1 ES passage 28 cells cultured in E8 [89] , harvested by direct lysis on plate with RLT lysis buffer . RLT lysis buffer is a component of the Qiagen RNeasy kit . iPS cells were DF19 . 7 [90] passage 27 cells cultured in E8 , harvested by direct lysis on plate with RLT lysis buffer . For all three cell types , total RNA was isolated using the RNeasy kit ( Qiagen ) . We linearly amplified axolotl polyA+ RNAs using a modified T7 amplification method [91] resulting in cDNA . Subsequent steps followed the Illumina Paired End ( PE ) preparation kit ( PE-102-1001 , Illumina , San Diego , CA ) . After the Illumina PE adapters were ligated , 150–250 bp DNA fragments were isolated via gel electrophoresis . Then ten cycles of polymerase chain reaction ( PCR ) were performed to amplify the selected fragments using the Illumina supplied PCR primers and protocol . The sample was quantitated with the Invitrogen Qubit fluorometer ( Q32857 ) . Samples were loaded on the flowcell cluster station at a concentration of 8 pM , and sequenced on the Illumina GAII . The Genome Analyzer II Paired End recipe was used . However , for the samples in this study , only single-end data were obtained . After the sequencing was complete , the data were processed by Illumina Pipeline software for quality analysis and read filtering . For FS cells , RNA was amplified as it was for the axolotl samples . Subsequent steps followed the Illumina Single Read ( SR ) preparation kit ( Illumina , San Diego , CA ) . After the Illumina SR adapters were ligated and a NotI digestion performed for directionality , 200–300 bp DNA fragments were isolated via gel electrophoresis . Then 10 cycles of polymerase chain reaction ( PCR ) were performed to amplify the selected fragments using the Illumina supplied PCR primers and protocol . The sample was quantitated with the Invitrogen Qubit fluorometer ( Q32857 ) . Samples were loaded on the flowcell cluster station at a concentration of 8 pM , and sequenced on the Illumina GAII . The Genome Analyzer II SR recipe was used . After the sequencing was complete , the data were processed by Illumina Pipeline software for quality analysis and read filtering . For ES and iPS cells , we prepared samples for sequencing using the Illumina TruSeq RNA Sample Preparation Kit v2 ( RS-122-2001 ) . The samples were quantitated with Life Technologies Qubit fluorometer ( Q32857 ) . Samples were pooled six per lane and loaded on the Illumina cbot at a final concentration of 6 pM , and sequenced on the Illumina HiSeq . The HiSeq 2000 SR multiplex recipe was used . After the sequencing was complete , the data were processed by Illumina Pipeline software for quality analysis and read filtering . For axolotl transcript quantification and differential analyses , we used an axolotl contig set assembled with MIRA [92] from a combination of Sanger and 454 EST sequences . Details of the assembly process are provided in Text S1 . For improved read mapping , ambiguous characters in the contigs ( e . g . , “N” ) were replaced with one of the standard bases uniformly at random . The Illumina GA ( Genome Analyzer ) Pipeline v . 1 . 4 software system was used to produce the set of sequencing reads . RSEM v1 . 1 . 6 [19] , an RNA-seq quantification tool that does not require a reference genome , was used to estimate the relative abundances and expected read counts for the contigs . The default options for RSEM were used except where we specified the –no-polyA option for rsem-prepare-reference ( as is appropriate for de novo transcriptome assemblies ) and the –phred64-quals option for rsem-calculate-expression ( to indicate the correct quality score encoding of the read data ) . By default , RSEM uses the Bowtie aligner [93] to map the reads against the contigs and we had Bowtie v0 . 12 . 1 installed for this purpose . Contigs mapping to rRNA transcripts ( as determined through BLAST analyses described below ) were removed and abundances were renormalized . Because the axolotl transcriptome is largely uncharacterized , we analyzed the dynamics and functions of transcripts in the regenerating limb with respect to the human gene set . To this end , we first used BLAST ( NCBI BLAST v2 . 2 . 18 ) to align the contigs against human RefSeq ( dated 12-07-2009 ) [94] RNA ( via BLASTN ) and protein sequences ( via BLASTX ) , taking the best BLAST hit with e-value less than 10−5 as the most closely related human homolog for each contig . When there was a tie for the best BLAST hit , the hit listed first was used arbitrarily . The expected read counts for contigs mapping to the same homologous human transcript were summed to give abundances . Read counts for human transcripts belonging to the same gene were summed to give human gene-level abundances . Abundances in terms of TPM were calculated by normalizing the read counts by the sums of the effective lengths ( contig length – read length ) of the axolotl contigs mapping to each gene and subsequently normalizing these values so that they summed to one million . Expected read counts were used as input to differential expression analysis by EdgeR ( version 3 . 0 . 0 , R version 2 . 1 . 5 ) [25] . Because we only had one biological replicate per time point , we compared pairs of samples from consecutive time points to obtain estimates of biological variation for DE analyses . EdgeR was used to estimate the common dispersion factor for each pair of samples from consecutive time points and the median of these values was used as the common dispersion factor for our DE analyses . For our comparative method , which uses counts at the human gene level , the common dispersion was 0 . 05 , whereas for the contig-level approach , this value was 0 . 11 . With the appropriate common dispersion factor , EdgeR was used to predict the set of DE genes or contigs at each time point with respect to the zero hour control sample . Unless otherwise specified , genes were determined to be DE by application of one criterion: a Benjamini-Hochberg [95] adjusted p-value of less than 0 . 05 . For the FS samples , RNA-seq results were calculated using Illumina's Casava 1 . 7 pipeline and RSEM version 1 . 1 . 7 , aligned to the hg18 genome build [19] . For the ES and iPS samples , Casava 1 . 8 . 2 and RSEM 1 . 1 . 21 were used , aligned to the hg19 genome build . By default , RSEM uses the bowtie short-read aligner . For FS , ES , and iPS cells , RSEM was run with Bowtie pass-through parameters m = 200 , n = 2 , and seed-length = 28 , using Bowtie version 0 . 12 . 7 [93] . From each of the juvenile time course samples ( average total number of reads ∼17 . 5 million ) , we extracted all very high quality reads ( average phred score > = 38 ) and partitioned them into two sets: those that had an alignment against the axolotl assembly ( AL = aligned ) and those that did not ( UN = unaligned ) . Only the highest quality reads were selected to reduce bias in later steps because lower quality reads are less likely to be alignable . This step resulted in ∼5 million reads per sample on average ( ∼2 . 8 million aligned and ∼2 . 2 million unaligned on average ) . We ran BLASTX with these reads against the human RefSeq protein set with an e-value cutoff of 1e-5 and a single top hit for each read was reported . For each read , we consider the following possible events: it has an alignment to the axolotl assembly ( AL ) ; it does not have an alignment to the axolotl assembly ( UN ) ; it is truly derived from an axolotl transcript ( A ) ; it is not derived from an axolotl transcript ( NA ) ; it has a BLASTX hit ( B ) ; and it has average phred score > = 38 ( HQ ) . For the BLASTX analysis of the UN reads , we havewhere in the second line we assume that NA reads will not have any BLASTX hits , as these are presumably RNA-seq protocol artifacts , and in the third line , we assume that the quality of a read is independent of whether it is derived from axolotl and that having a BLASTX hit is independent of whether the read is aligned ( AL ) , given that it is truly from axolotl . Then , considering the BLASTX analysis of the AL reads , we havewhere in the second line we assume that all AL reads are from axolotl and in the third line we again assume that having a BLASTX hit is independent of whether the read as aligned ( AL ) , given that it is truly from axolotl . Putting these two equations together , we haveand thus we can estimate the fraction of UN reads that are truly from axolotl from the fractions of the UN HQ and AL HQ reads that have BLASTX hits . To estimate the fraction of reads that are from axolotl but were unalignable , we simply compute . In the case of false positive BLASTX hits from NA reads , we will overestimate because such hits will cause our estimate of to be higher . Therefore our estimates of the completeness of the assembly are conservative . We used DAVID ( version 6 . 7 ) to perform Gene Ontology ( GO ) analysis [49] . The statistically significant ( FDR<0 . 05 ) upregulated genes compared to the zero hour control were used as input to DAVID . Each time point's upregulated genes were submitted to DAVID functional analysis chart GO analysis , with the background set being the set of human genes to which axolotl contigs had significant ( e-value<1e-5 ) best BLAST hits and had at least one matching read in our RNA-seq data . ( The background set is available at axolomics . org , as are all of the DAVID result files . ) For DAVID analysis , the following settings were used: categories selected are GOTERM_BP_5 , GOTERM_MF_5; threshold options are Counts = 2 , EASE = 0 . 1 ) . The GO heat map ( Figure 6 ) was constructed by creating a table of the DAVID FDR scores for each GO term with an FDR<0 . 01 at any time point . The negative log base 10 of these FDR values was used in the heat map , and these values are shown in Table S4 . MeV 4 . 8 . 1 ( http://www . tm4 . org/mev/ ) was used to cluster the GO terms ( hierarchical clustering with distance measure = Pearson Correlation , linkage = average ) . We performed sample and gene clustering using MeV 4 . 8 . 1 . Hierarchical cluster analyses were carried out with Pearson uncentered correlation ( Figure 3 , Figure 4 ) or Pearson correlation ( Figure 6 ) as the distance measurement with average linkage . Pearson uncentered correlation was used for Figures 3 and 4 because log fold changes are expected to be centered around zero ( no fold change ) , whereas Pearson correlation was used for Figure 6 because −log ( FDR ) values are all non-negative . Clusters and heat maps were visualized via MeV 4 . 8 . 1 . To assess the uncertainty in hierarchical cluster analysis over samples , we applied bootstrap resampling ( 10 , 000 iterations ) via the R package Pvclust [26] . The uncentered Pearson correlation is used as the distance metric with average linkage . Pvclust provides the Bootstrap Probability ( BP ) value from the ordinary bootstrap resampling [96] and the Approximately Unbiased ( AU ) probability value from multiscale bootstrap resampling [26] , [97] . The ordinary bootstrap resampling method has been shown to be biased especially when genes are correlated . The multiscale bootstrap resampling method was introduced to develop an approximately unbiased test , and therefore it provides better estimations of the probability values [26] , [97] . Let N denote the original number of genes . In multiscale resampling , instead of resampling N genes in each of 10 , 000 bootstraps , we resampled the genes with 10 different data sizes ( as is the default setting of the package ) . The resampled data sizes vary from 0 . 6*N to 1 . 4*N . The numbers above each edge show the probability of nodes below that edge occurring as a cluster in resampled trees , via ordinary bootstrap resampling ( BP , green ) or multiscale bootstrap resampling ( AU , red ) . We generated a list of human TFs by querying genes for GO terms that match: “RNA polymerase I transcription factor activity” , “RNA polymerase II transcription factor activity” , “RNA polymerase II transcription factor activity , enhancer binding” , “RNA polymerase III transcription factor activity” , “transcription factor activity” , “transcription activator activity” . We also added genes with “transcription factor” in their refseq gene functional description , giving a total of 2260 TFs . The TF file , DE gene lists , and all the RNA-seq data are available at axolomics . org . We isolated total RNA with the mirVana miRNA isolation kit ( Life Technologies ) according to the manufacturer's protocol . Total RNA ( an input range between 88 to 808 ng/µl ) was treated with DNaseI ( Life Technologies ) for 10 min at 37°C followed by heat inactivation for 5 min at 65°C . DNaseI-treated total RNA was combined with ( oligo-dT ) 15 primer and denatured by heating to 70°C for 5 min and chilling to −4°C for 5 minutes . The denatured ( oligo-dT ) 15 primer and RNA was reverse transcribed by adding the ImPromII reverse transcription system ( Promega ) to generate cDNA using the following program: 5 min at 25°C , 60 min at 42°C , and 15 min at 70°C to inactivate the reverse transcriptase . Total cDNA was diluted five-fold and used at 1 µl per 10 µl of qPCR reaction with TaqMan Universal PCR Master Mix ( Life Technologies ) . Axolotl gene specific qPCR assays were designed using PrimerQuest for PrimeTime qPCR assays ( Integrative DNA Technologies ) . Sequences for gene specific oligos are listed below . The qPCR reactions were performed with ViiA 7 Real-Time PCR System ( Life Technologies ) under the following cycle conditions: 2 min at 50°C , 10 min at 95°C followed by 40 cycles of 15 sec at 95°C , 1 min at 60°C . To quantify the relative expression level of a particular gene , three independent qPCR reactions ( technical replicates ) were performed on biological triplicate samples for each time point analyzed , unless otherwise stated . All data points were normalized to GAPDH and relative to samples collected at the 0 hour time point using the comparative CT ( ΔΔCT ) method . Primer sequences for qPCR are in Table S5 . For comparing RNA-seq data to qPCR data , the data were analyzed as follows: For RNA-seq data , the log2 ratio = log2 ( TPM at time point x+1 ) / ( TPM at time zero+1 ) . One was added to TPMs to avoid divide by zero errors , log ( 0 ) errors , and to avoid obtaining a high ratio when both TPMs are low . For qPCR , ΔCT is defined as the difference in the cycle threshold ( CT ) between the gene of interest and the GAPDH control . For qPCR data , the log2 ratio = log2 ( ΔCT at time point x ) / ( ΔCT at time zero ) . Any ΔCT ratios of 0 were changed to 0 . 1 before log transformation to avoid error . For genes with undetectable levels from samples collected at 0 hour , CT values were assigned at 40 to avoid error . Figure 2 contains plots of the log2 ratios of RNA-seq and qPCR for four genes . The plots for the additional 15 genes validated by qPCR are shown in Figure S2 . Upon analysis of this data , we noticed that for genes with a late peak ( limb genes such as HOXD10 ) , the qPCR peak is often delayed compared to the RNA-seq peak . The animals used for the qPCR experiment were slightly larger than the animals used for the RNA-seq experiment ( 7–10 cm for qPCR , vs . 4 . 5–8 cm for RNA-seq ) . It is well known that larger animals regenerate more slowly . Because of this lag in the later qPCR time points , when we calculate the Pearson correlation , we remove the 3 d time point from the qPCR data and then “shift left” the remaining qPCR time points . Thus we compare the 3 hr–21 d RNA-seq samples to the 3 hr–1 d plus 5 d–28 d qPCR samples . This comparison was used for the Pearson correlation calculation . | Salamanders such as the axolotl can fully regenerate a limb upon amputation , making them the vertebrate champions of regeneration . On the other hand , humans and other mammals possess a very limited ability to regenerate limb structures . Learning about the genes , gene networks , and pathways activated in the salamander during limb regeneration will provide cues to improving the regenerative response in mammals . Elucidating these genes , networks , and pathways is difficult , however , because the axolotl does not yet have its genome sequenced and because it has diverged evolutionarily from species with a sequenced genome . Here , we produce a set of gene transcripts via RNA sequencing ( RNA-seq ) for the axolotl and provide information on the nature of the genes activated during regeneration . To determine the identity of these axolotl genes , we use comparative transcriptomics techniques to match the axolotl transcript data to that of the well-annotated human gene set . Supporting previous studies , we find upregulation of many genes previously found to be involved in limb development and regeneration . In addition , we find a burst of cancer-related genes during the first phase of regeneration and identify a set of genes previously not associated with the regeneration process . | [
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] | 2013 | Comparative RNA-seq Analysis in the Unsequenced Axolotl: The Oncogene Burst Highlights Early Gene Expression in the Blastema |
Hantaviruses are among the most important zoonotic pathogens of humans and the subject of heightened global attention . Despite the importance of hantaviruses for public health , there is no consensus on their evolutionary history and especially the frequency of virus-host co-divergence versus cross-species virus transmission . Documenting the extent of hantavirus biodiversity , and particularly their range of mammalian hosts , is critical to resolving this issue . Here , we describe four novel hantaviruses ( Huangpi virus , Lianghe virus , Longquan virus , and Yakeshi virus ) sampled from bats and shrews in China , and which are distinct from other known hantaviruses . Huangpi virus was found in Pipistrellus abramus , Lianghe virus in Anourosorex squamipes , Longquan virus in Rhinolophus affinis , Rhinolophus sinicus , and Rhinolophus monoceros , and Yakeshi virus in Sorex isodon , respectively . A phylogenetic analysis of the available diversity of hantaviruses reveals the existence of four phylogroups that infect a range of mammalian hosts , as well as the occurrence of ancient reassortment events between the phylogroups . Notably , the phylogenetic histories of the viruses are not always congruent with those of their hosts , suggesting that cross-species transmission has played a major role during hantavirus evolution and at all taxonomic levels , although we also noted some evidence for virus-host co-divergence . Our phylogenetic analysis also suggests that hantaviruses might have first appeared in Chiroptera ( bats ) or Soricomorpha ( moles and shrews ) , before emerging in rodent species . Overall , these data indicate that bats are likely to be important natural reservoir hosts of hantaviruses .
Emerging infectious diseases have a substantial and ongoing impact on public health and agricultural production [1]–[3] . Over half of the currently recognized pathogens are zoonotic , and nearly all of the most important human pathogens are either zoonotic or originated as zoonoses before adapting to human transmission [4] , [5] . Hence , wildlife species play a key role in disease emergence by providing a “zoonotic pool” from which previously unknown pathogens may emerge [1] . A major goal of infectious disease research is therefore to characterize those unknown pathogens circulating in animal host reservoirs before they emerge in human populations [6] , [7] . Hantaviruses ( genus Hantavirus , family Bunyaviridae ) are the etiological agent ( s ) of hemorrhagic fever with renal syndrome ( HFRS ) and hantavirus pulmonary syndrome ( HPS ) in humans [8] . Unlike the other genera of Bunyaviridae , hantaviruses are not known to be transmitted by arthropods , and instead are harbored by small mammals , particularly rodents [9] . The first hantavirus ( Thottapalayam virus ( TPMV ) ) , was isolated from the Asian house shrew ( Suncus murinus ) in India in 1964 [10] , but it had not been classified as a Bunyavirus until 1989 [11] . All hantaviruses found subsequently and until 2006 were from Muroidea ( i . e . ‘mouse-like’ ) rodents . To date , only rodent-borne viruses have been shown to cause human diseases , namely HFRS in Eurasia and HPS in the Americas [8] . As the phylogeny of the rodent-borne hantaviruses appears to be largely congruent with that of subfamily Muridae and family Cricetidae of Muroidea , hantaviruses are often considered to have co-diverged with their rodents hosts over time-scales of millions of years [12]–[15] . Since 2006 , at least 22 new species of hantaviruses have been identified in Soricomorpha insectivores ( shrews and moles ) worldwide [9] , [16] . Recently , TPMV was also found in China , Nepal , and Vietnam [17]–[19] , and is thought to have had an early evolutionary divergence from rodent-borne hantaviruses [20] , [21] . More recently , hantavirus RNA sequences have been detected in bats from western Africa [22] , [23] . The presence of newly described hantaviruses in insectivores and bats has challenged the conventional view that hantaviruses originated from rodents , and suggests there may be additional unrecognized hantaviruses circulating in a wide range of animal hosts . Furthermore , that the viruses sampled from rodents and insectivores ( Soricomorpha ) do not form strict monophyletic groups [22] , [24] , [25] indicates that host jumping has also occurred during the evolutionary history of these viruses . As a consequence , the respective roles of virus-host co-divergence and cross-species virus transmission are more complex than previously envisioned , although determining the relative frequency of these two processes is critical for understanding the evolutionary and biogeographic processes that have produced the current diversity of hantaviruses and their potential for future emergence . In this study , we describe four novel hantavirus sequences detected in bats and shrews collected in China . With these data we then explore key aspects of hantavirus evolution , particularly the frequency of cross-species virus transmission .
A total of 450 bats of eight different species were captured in Longquan city and Wenzhou city , Zhejiang Province in the spring of 2011 ( Figure 1 and Table 1 ) . Similarly , 155 bats representing eight species were captured in Hubei Province in the spring of 2012 . A total of 81 insectivores ( representing two species – Anourosorex squamipes and Suncus murinus ) were captured in Lianghe county , Yunnan Province in the spring of 2010 and autumn of 2011 . In 2006 , two shrews ( from the species Sorex isodon and Suncus murinus ) were collected from Yakeshi city , Inner Mongolia Autonomous Region . RT-PCR was performed to detect hantaviral RNA based on the L segment sequences . In bats , PCR products of the expected size were amplified from six Rhinolophus affinis , three Rhinolophus sinicus , one Rhinolophus monoceros collected from Longquan , and one Pipistrellus abramus from Huangpi . In insectivores , expected size products were generated from one Sorex isodon from Yakeshi and nine Anourosorex squamipes from Lianghe . These sequences most closely resembled those of other hantaviruses ( Table S1 ) ( see below ) . To characterize the novel hantaviruses found in this study , sequences of the complete S and M segments were recovered from the RNA positive bat and shrew samples described above . Key features of these sequences are described in detail in Table 2 and Figure S1 . Clearly , the viruses from bats and shrews are distinct from each other and from other known hantaviruses , representing four novel species of hantavirus ( see below ) . We therefore named these new viruses as Huangpi virus ( HUPV ) , Longquan virus ( LQUV ) , Lianghe virus ( LHEV ) , and Yakeshi virus ( YKSV ) , and which were found in P . abramus , Rhinolophus spp . ( R . affinis , R . sinicus , and R . monoceros ) , A . squamipes , and in S . isodon , respectively . HUPV , LQUV , and YKSV exhibit ≤89 . 6% sequence similarity in the N , GPC and L proteins from all known hantaviruses ( Tables S2 , S3 ) . In contrast , LHEV is clearly related to Cao Bang virus ( CBNV ) also identified in Anourosorex squamipes in Vietnam [26] in sequences of the N ( ≤95 . 6% similarity ) , GPC ( ≤92 . 7% ) and L ( ≤94 . 3% ) proteins . However , LHEV is different from CBNV in the GPC protein , exhibiting more than the 7% amino acid difference required for hantavirus species demarcation [9] . To determine the phylogenetic relationships among the novel hantaviruses described here and those described previously , phylogenetic trees based on 103 S and 71 M segment sequences were inferred using three methods . The Bayesian and Maximum Likelihood ( ML ) trees were rooted in the way suggested by the ( molecular clock-rooted ) MCC tree . The ML trees based on the M or S segment sequences produced very similar topologies ( Figure 2 ) . In the S segment tree ( Figure 2A ) , all known hantaviruses including the viruses identified in bats and insectivores could be placed into four well supported ‘phylogroups’ . The first phylogroup only comprised viruses from insectivore ( Soricidae ) species and included the Asian viruses TPMV and Imjin virus ( MJNV ) sampled from the Ussuri white-toothed shrew ( Crocidura lasiura ) in South Korea [27] . Notably , this phylogroup occupied a basal position with respect to the remaining viruses . The second phylogroup comprised HUPV and LQUV found in bats in this study and which were closely related each other , along with the more divergent Nova virus ( NVAV ) identified in the European common mole ( Talpa europaea ) in Hungary [24] . Phylogroup III contained all other known Soricomorpha-associated viruses , including LHEV and YKSV found in this study , as well as a distinct clade of Murinae-borne ( i . e . rodent ) viruses . Finally , the fourth phylogroup included viruses sampled from the Arvicolinae , Neotominae , and Sigmodontinae subfamilies of rodents , although these did not form three clearly distinct monophyletic groups in the S segment , along with the reassortant RKPV sampled from an insectivore ( see below ) . Importantly , the topologies of ML and Bayesian trees estimated using amino acid sequences of N ( encoded by the S segment ) and GPC ( encoded by the M segment ) proteins were consistent with those of the trees based on the nucleotide sequences , indicating that site saturation has not adversely affected our phylogenetic inference ( Figure S2A–F ) . Although a closer phylogenetic relationship between the first and second phylogroups were observed in the Bayesian tree ( Figures S2B and S2E ) , these two phylogroups still occupied basal positions . The most striking difference between the S and M segment trees was that phylogroup II ( i . e . LQUV and NVAV ) were basal in the M segment tree with relatively strong statistical support ( Figure 2B ) ( although it is important to note that we were unable to amplify the M segment sequence from HUPV ) , while the Soricidae-associated viruses of phylogroup I occupied the basal position in the S segment tree and with much stronger support ( Figure 2A ) . This different phylogenetic pattern was also apparent in the relevant amino acid trees of the N and GPC proteins ( Figure S2A–F ) . Such phylogenetic incongruence is strongly suggestive of reassortment among hantaviruses of phylogroups I and II , and which might have occurred during the evolution of hantaviruses carried by bats and insectivores as these phylogroups are currently only associated with these mammalian species . Irrespective of this history of reassortment it is clear that there have been multiple cross-species transmission events in the evolutionary history of the hantaviruses with , for example , those viruses sampled Soricomorpha forming a paraphyletic group , as do those from bats shown in the L tree . In both the M and S segment trees YKSV ( a member of the Soricomorpha clade of phylogroup III ) showed a close phylogenetic relationship with Qiandao lake virus ( QDLV ) sampled from Sorex cylindricauda in China ( GU566023 ) , Kenkeme virus ( KKMV ) collected from the Flat-Skulled Shrew ( Sorex roboratus ) in the far eastern Asian region of Russia [28] , Seewis virus ( SWSV ) from the Eurasian common shrew ( Sorex araneus ) in Switzerland [29] , and Asama virus ( ASAV ) from the Japanese shrew mole in Japan ( Urotrichus talpoides ) [30] . Hence , this well-supported subgroup contained four viruses from Asia and one from Europe . Also of note was that all LHEV sequences exhibited a close relationship with CBNV and Jeju virus ( JJUV ) sampled from the Asian lesser white-toothed shrew ( Crocidura shantungensis ) in South Korea [16] , Jemez Springs virus ( JMSV ) from the dusky shrew ( Sorex monticolus ) [31] and Oxbow virus ( OXBV ) from the American shrew mole ( Neurotrichus gibbsii ) [32] , with the latter two viruses both found in the USA . Kang et al . [33] found that RKPV sampled from a S . aquaticus mole in the USA shared a closer relationship with viruses harbored by Cricetidae rodents than with Soricomorpha-borne hantaviruses , a topology confirmed by our analysis . Interestingly , in the S segment tree RKPV was most closely related to another novel hantavirus ( LUXV ) identified in the Yunnan red-backed vole ( E . miletus ) in China [34] . More notable was that both viruses were more closely related to Sigmodontinae/Neotominae-borne hantaviruses in the S segment tree but with Arvicolinae-borne hantaviruses in the M segment tree , suggesting that both LUXV and RKPV were generated by a common reassortant event ( Figures 2A–2B ) . A rather different picture of the evolutionary history of hantaviruses was observed in the phylogenies of 62 L segment sequences . In particular , these trees provided evidence for five phylogroups , as viruses from phylogroup II could be subdivided into a subgroup containing HPUV , Mouyassué virus ( MOUV ) detected in bat from Cote d'Ivoire [22] , NVAV , and Altai virus ( EU424341 ) sampled from a Soricidae shrew in the neighboring area of Russia with China , and a subgroup containing the LQUV and MGB virus sampled from bats in Sierra Leone [23] ( phylogroup V , Figure 2C ) . However , this novel subdivision of phylogroups was not supported strongly . The clustering patterns of other viruses were similar to those in the S and M segment trees ( Figure S3A–B ) , although LQUV and MGB virus grouped with TPMV and MJNV in the Bayesian tree ( Figure S3B ) . Finally , and in contrast what is seen in the L nucleotide sequence phylogenies , MGB virus shared a closer relationship with TPMV and MJNV than HUPV and LQUV in the L amino acid tree ( Figure S2G–I ) . Our phylogenetic analysis also provided insights into the geographic distribution of these viruses . All those S segment phylogroup I viruses identified so far are from Asia ( Soricidae , Figure 3 ) , while phylogroup II viruses have been recovered from both Asia and Europe ( Talpidae and Chiroptera ) . In the L gene tree the two viruses found in African bats were closely related to HPUV and LQUV found in bats from China , respectively ( Figure 2C ) . With respect to phylogroup III , viruses of Soricomorpha clade have been mainly found in Asia , with a few from Europe , North America , and Africa . With the exception of Sangassou virus ( SANGV ) found in the wood mice from Guinea [35] , almost all viruses of the Murinae clade are from Asia and Europe . Finally , for phylogroup IV , most of the Arvicolinae clade viruses have been identified in Asia and Europe , with a few sampled from North American animals . In contrast , almost all viruses of the Sigmodontinae clade are from the New World , and the lineage comprising LUXV from China and RPKV found in USA occupied a basal position in this clade . Overall , those hantaviruses sampled from Asian mammalian species exhibit the greatest genetic diversity and tend to fall at basal positions on the phylogenetic trees . This tentatively suggests that hantaviruses may have an Asian origin , although this will need to be confirmed on a far larger sample of taxa . We inferred ML and MCC trees of mitochondrial cytochrome b ( mt-cyt b ) gene sequences among the known mammalian hosts ( Chiroptera , Soricomorpha , and Rodentia ) of the hantaviruses . Both trees had very similar topologies . Specifically , using Ornithorhynchus anatinus as an outgroup , the rooted phylogenetic trees based on mt-cyt b gene sequences including the sequences obtained in this study ( Table S4 ) resulted in a clear phylogenetic division between those viruses sampled from Rodentia , Chiroptera and Soricomorpha , with each forming a monophyletic group as expected ( Figure 4 ) . In agreement with previous studies [36] , Soricomorpha showed a closer relationship with Chiroptera than with Rodentia . Within Rodentia , the Murinae subfamily and Cricetidae family formed two monophyletic groups . The Cricetidae were further subdivided into the subfamilies Neotominae , Arvicolinae and Sigmodontinae . Based on this single-locus study , Neotominae , which was once considered an exclusively North American subset of the South American Sigmodontinae , was more closely related to Arvicolinae than Sigmodontinae . However , studies based on multiple nuclear loci place the Neotiminae as a distinct sister subfamily with the Sigmodontinae [37] . We used TreeMap 2 . 0 to test the strength of congruence between the viral S , M , and S+M segment trees with that of the host mt-cyt b gene ( Figure 5 , Figure S4; Table S5 ) . Notably , the viral phylogenies inferred using the S segment sequences were not always consistent with their hosts' phylogeny as measured by both CE ( P = 0 . 098±0 . 009 ) and NCE ( P = 0 . 1±0 . 009 ) frequencies , with multiple deep and more recent topological differences , and hence an indication of relatively frequent host jumping ( Figure 5 ) . This analysis also indicated that cross-species transmission events had occurred at four levels during hantavirus evolution ( Figure 5 , Table 3 ) : inter-species within a genus ( e . g . HTNV and ASV , DOBV and SAAV ) , inter-genus within a family ( e . g . DBSV and HTNV ) , inter-family within an order ( e . g . OXBV and JMSV , ASAV and SWSV ) , and even inter-order ( e . g . NVAV and LQUV; LUXV and RKPV ) . In addition , some viruses exhibited a phylogenetic pattern that reflected their geographic origins rather than the phylogeny of their hosts – such as viruses OXBV and JMSV , DOBV and SAAV within the Soricomorpha and Murinae clades of phylogroup III ( Table S5 ) – such that the likelihood of host jumping in part reflects geographic proximity . However , in other instances there were clear matches between the virus and host phylogenies . Most notably , there was significant congruence between phylogenies of the two clades of phylogroup IV and their rodent hosts - Arvicolinae ( CE ( P = 0 . 006±0 . 002 ) and NCE ( P = 0 . 005±0 . 002 ) ) and Sigmodontinae ( CE ( P = 0 . 041±0 . 006 ) and NCE ( P = 0 . 01±0 . 003 ) ) – indicating that these rodent hantaviruses may have a long history in their primary hosts , likely co-diverging with their hosts in some cases .
We describe four novel hantavirus sequences from bats and insectivores captured in China . The hantavirus harbored by three Rhinolophus bats and one carried by the Sorex isodon shrew exhibited ≤89 . 6% amino acid similarity in the N , GPC and L protein sequences with any recognized hantaviruses , while the hantavirus carried by one Pipistrellus bat shared ≤81 . 9% amino acid similarity in both the N and L protein sequences with known hantaviruses . The hantavirus found in Anourosorex squamipes ( shrew ) from Lianghe ( Yunnan Province ) was most closely related to CBNV also identified in Anourosorex squamipes in Vietnam , but with quite different N ( >4 . 4% amino acid ) , L ( >5 . 7% ) , and GPC ( >7 . 3% ) amino acid sequences . Interestingly , the mt-cyt b gene differences between Anourosorex squamipes in Yunnan of China and Vietnam are 1 . 7% , compatible with the existence of the two subspecies of Anourosorex squamipes . According to the criteria for species demarcation in the genus Hantavirus proposed by the International Committee on Taxonomy of Viruses [9] , these four hantaviruses are sufficiently genetically distinct that they should be recognized as distinct species . Accordingly , we propose naming these four novel hantaviruses as Huangpi virus ( HUPV ) , Lianghe virus ( LHEV ) Longquan virus ( LQUV ) , and Yakeshi virus ( YKSV ) , reflecting their geographic origins . In addition , as LHEV has not been isolated , such that two-way cross neutralization tests cannot be performed , further studies are needed to clarify whether LHEV is a novel species or simply a variant of CBNV . Finally , the identification of LQUV in three Rhinolophus bats also means that hantaviruses may spread relatively easily among different species of bats . Although rodents are considered the primary hosts of hantaviruses [13] , the increasing number of viruses found in insectivore species ( shrews and moles ) over the past five years has raised an important question mark over the host range and origin of hantaviruses . Indeed , the first hantavirus ( TPMV ) was isolated from shrews in India in 1964 [10] . Our work further suggests that bats are likely to be important hosts for hantaviruses . Bats ( order Chiroptera ) have been shown to be sources of a broad variety of emerging pathogens , including coronaviruses , filoviruses , henipaviruses , and lyssaviruses [38] . Recently , partial hantaviral sequences were found in one slit-faced bat ( Nycteris hispida ) and two banana pipistrelles ( Neoromicia nanus ) in West Africa [22] , [23] . We document two novel hantaviruses in Rhinolophus bats ( R . affinis , R . sinicus , R . monoceros ) and P . abramus . Consequently , these data , together with other recent studies [22] , [23] , demonstrate that bats in China and Africa are hosts of hantaviruses and thereby constitute a potential sylvatic mammalian reservoir of hantaviruses . As their global distribution , abundance , ability to fly long distances , often large population densities , and sociality favor the efficient maintenance , evolution , and spread of viruses , it is clear that further study is needed to elucidate the potential importance of bats as hantavirus hosts . Indeed , it seems likely that additional hantaviruses will be isolated from bats , and especially from insectivorous bats as all four bat species in which hantavirus sequences were detected in this study are insectivorous . Moreover , because they consistently occupy basal phylogenetic positions , these phylogenetic data suggest that the ancestor of the extant hantaviruses might have first appeared in Chiroptera and/or Soricomorpha , although this will need to be confirmed on a larger sample of mammalian taxa . One notable feature of our phylogenetic analysis was the basal position of phylogroup I viruses in the S segment tree but of phylogroup II viruses in the M segment . Such deep phylogenetic incongruence is strongly suggestive of an ancient reassortment event . In the S segment tree , HUPV and LQUV share a closer relationship with NVAV identified in Talpa europaea [24] , as does LQUV in the M segment tree and HPUV in the S segment tree , suggesting that these viruses share common ancestry . In addition , hantaviruses identified in bats in Africa are closely related to NVAV or TPMV [22] , [23] . Within phylogroup IV , RKPV identified in a mole is closely related to LUXV from the Yunnan red-backed vole in China . These viruses also share a history of reassortment since they occupy the basal positions within the Arvicolinae clade in the M segment tree but with the Sigmodontinae clade in the S segment tree . The current geographic distribution of hantaviruses in large part reflects that of their host species [13] , [39] . If hantaviruses have indeed been associated with mammalian species for millions of years , then it is possible that these geographic distributions are long established . The oldest eutherian is Juramaia sinensis ( an insectivore ) found in China , at an estimated 160 million years ago [40] . It was recently suggested that both Euarchontoglires and Laurasiatheria , excluding Chiroptera , originated in Eurasia [41] . Geographic reconstructions further suggest that bats originated in the Laurasian land masses , with an Asian origin for the suborder Yinpterochiroptera and a most likely Asian/European origin for the suborder Yangochiroptera [42] . Within the insectivores , Talpidae occupies the basal position within the Soricomorpha [36] , [43] , and both molecular clock dating and the fossil record suggest a Eurasian origin of the Soricidae [43]–[45] . As the hantaviruses sampled from Asian mammals are genetically very diverse and tend to occupy basal positions in the phylogenetic trees , these data tentatively support an Asian origin for hantaviruses , although this will need to be assessed on a more geographically diverse sample . Hantaviruses have traditionally been considered to have co-diverged ( including co-speciation ) with their rodent hosts on time-scales of millions of years [12]–[15] , and some evidence for such co-divergence was apparent here . In particular , rodent hantaviruses clustered according to whether their hosts were members of the Murinae subfamily and Cricetidae family . Indeed , the close phylogenetic relationships among some hantavirus taxa across large geographic areas , and which infect related hosts , supports the occurrence of long-term virus-host co-divergence [46] . Hence , rodent hantaviruses might have a long history in their primary hosts , and which in part explains their biodiversity [12] . Despite this , more examples of incongruence between the gene trees of hantaviruses and their hosts are being identified , suggesting that some of the congruence between the two might have arisen from preferential host switching and local adaptation [25] . Indeed , it was recently shown that cross-species transmission has even played a role in shaping the genetic diversity of the currently known Murinae-associated hantaviruses [46] . In accord with this , the current study provides evidence for cross-species transmission events at the family , genus , and species levels . In particular , that viruses from both the Chiroptera and Soricomorpha form paraphyletic groups in all our analyses strongly suggests that ancestral hantaviruses jumped between mammalian orders . As a consequence , it is clear that cross-species virus transmission as well as the geographic dispersal of Chiroptera , Soricomorpha and Rodentia , has also contributed to the high biodiversity and near global distribution of those hantaviruses known today , although the time-scale of these host jumping events remains uncertain .
This study was reviewed and approved by the ethics committee of the National Institute for Communicable Disease Control and Prevention of the Chinese CDC . All animals were treated in strict according to the guidelines for the Laboratory Animal Use and Care from the Chinese CDC and the Rules for the Implementation of Laboratory Animal Medicine ( 1998 ) from the Ministry of Health , China , under the protocols approved by the National Institute for Communicable Disease Control and Prevention . All surgery was performed under ether anesthesia , and all efforts were made to minimize suffering . Bats were captured with mist nets or harp traps in caves of natural roosts in Zhejiang Province in the spring of 2011 , or in villages or caves in Hubei Province in the spring of 2012 ( Figure 1 ) . According to protocols described previously [47] , insectivore animals were trapped in cages using fried foods as bait in the Inner Mongolia Autonomous Region in 2006 or in Yunnan Province in the autumns of 2010 and 2011 . All animals kept were alive after capture . They were initially identified by morphological examination according to the criteria for bats described by Wang [48] and for insectivores by Chen [49] , and further confirmed by sequence analysis of the mt-cyt b gene . All animals were anesthetized with ether before surgery , and all efforts were made to minimize suffering . Tissue samples of heart , liver , spleen , lung , kidney and brain were collected from bats and insectivores for detecting hantaviruses . Total DNA was extracted using the DNeasy Blood & Tissue kit ( QIAGEN ) from tissue samples of bats or insectivores according to the manufacturer's protocol . The mitochondrial ( mt ) -cyt b gene ( 1140 bp ) was amplified by PCR with the primer pair for bats [50] and one for insectivores [51] . Total RNA was extracted from tissue samples using TRIzol reagent ( Invitrogen , Carlsbad , CA ) according to the manufacturer's instructions . cDNA was prepared with AMV reverse transcriptase ( Promega , Beijing ) with the primer P14 [52] . Hantaviral RNA was detected by RT-PCR as described previously [17] , [35] . Primers designed based on the conserved regions of known complete S and M segment sequences from hantaviruses were used to amplify the entire S and M segments . In the amplification of the 5′ terminus of unknown hantaviruses , an adaptor plus P14 was used as a primer in the synthesis of cDNA . Semi-PCR was used to amplify the 5′ terminus with the adaptor as the forward primer and two specific reverse primers . Semi-PCR was also used to amplify the 3′ terminus with two specific forward primers and an adaptor plus modified P14 ( 5′-TAGTAGTRGACWCC-3′ ) [52] as the reverse primer . Primer sequences used in this study are provided in Table S6 . The RT-PCR products were separated by agarose gel and further purified using the Agarose Gel DNA Purification kit ( TaKaRa , Dalian , China ) . Amplicons less than 700 bp were sequenced from both directions . Amplicons greater than 700 bp were cloned into pMD18-T vector ( TaKaRa , Dalian , China ) . Sequencing was performed using the ABI-PRISM Dye Termination Sequencing kit and ABI 373-A genetic analyzer . At least three clones were sequenced . One to three sequences of the entire open reading frame ( ORF ) were randomly chosen from each hantavirus species for phylogenetic analysis . The RDP3 program [53] was used to examine potential intra-segment recombination in the viral sequences , although no recombinant sequences were identified ( although we do find evidence for segment reassortment – see below ) . Identical sequences were excluded from this study . Both animal mt-cyt b gene and viral genome sequences were aligned using the ClustalW method implemented in the Lasergene program , version 5 ( DNASTAR , Inc . , Madison , WI ) . Poorly aligned positions and divergent regions of the alignment , and which could negatively affect phylogenetic analysis , were removed using Gblocks [54] . The following data set sizes were used in the final analysis: hantavirus S segment = 103 sequences , 1201 bp; M segment = 71 sequences , 3024 bp; L segment = 30 sequences , 6519 bp , partial L segment = 32 , 330 bp; mt-cyt b gene = 97 sequences , 1140 bp . Phylogenetic trees were estimated using the Maximum Likelihood ( ML ) method available at the RAxML Blackbox web-server [55] . The best-fit evolutionary model was determined using jModelTest version 0 . 1 [56] , and found to be the General Time Reversible ( GTR ) with a gamma-distribution model of among site rate heterogeneity and a proportion of invariant sites ( GTR+Γ+I ) . Phylogenetic trees were also inferred using the Bayesian method implemented in MrBayes v3 . 1 . 2 [57] . The same evolutionary model was employed as described above . For this analysis , three hot and one cold Markov chain Monte Carlo ( MCMC ) chains were used , sampling every 100 generations and with a 25% burn-in . The Effective Sample Size ( ESS ) of all parameters was larger than 200 indicating that parameter convergence had occurred . A ( molecular clock ) rooted tree of these sequences was inferred using the Bayesian MCMC method available in the BEAST v1 . 6 . 0 package [58] . The same evolutionary model was employed as described above . We also incorporated a relaxed ( uncorrelated lognormal ) molecular clock , with an extended Bayesian Skyline tree prior . Two independent runs were undertaken sampling every 1 , 000 generations . Each run was continued until ESS >200 was achieved , with the output analyzed in Tracer v1 . 5 . TreeAnnotator was used to generate a Maximum Cade Credibility ( MCC ) tree with a burn-in of 10% of the sampled trees . Because the MCC tree is automatically rooted on the assumption of a molecular clock it enables determination of which viral lineages are most likely to be basal . Accordingly , the basal lineage estimated by the MCC tree was used as an outgroup to root the phylogenetic trees inferred under the ML and Bayesian phylogenetic analyses . In addition , because the high levels of sequence divergence across the hantaviruses , we also inferred phylogenetic trees based on the amino acid sequences of the L protein , N protein ( encoded by the S segment ) , and GPC protein ( encoded by the M segment ) using the ML approach available in the phyML program [59] . The LG amino acid substitution model was used for both the L and GPC proteins , and while the FLU model was used for the N protein . Finally , a phylogenetic tree for the host mt-cyt b sequences tree was estimated using the ML and BEAST ( MCC tree ) methods , again employing the GTR+Γ+I substitution model as estimated by jModelTest . In the case of the BEAST analysis a relaxed ( uncorrelated lognormal ) molecular clock was used along with the Yule model as a coalescent prior . To determine the degree of congruence between the phylogenies of hantaviruses and their hosts we used Tree-Map ( 2 . 0b ) [60] , although such analyses are complicated by uncertainties in the virus or host trees . A tanglegram was generated by matching each hantavirus species to their associated host ( s ) . Specifically , nodes of the viral MCC tree were mapped onto the related nodes of the host ( MCC ) tree . Significance testing was undertaken by generating 1000 viral trees with randomized branches and mapping these random trees onto the fixed host tree . We then evaluated the proportion of these reconciliations with the same or fewer non-co-divergence events ( NCEs ) , or the same or more co-divergence events ( CEs ) , compared to the “real” viral tree . If the p value is greater than 0 . 05 , we can reject the null hypothesis that the level of congruence is no more than that expected between randomly generated trees . Due to computational limitations in TreeMap [61] , we reduced the complexity of the host and virus phylogenies as much as possible before performing the full reconciliation analysis . Thus , for viruses-host matches , we divided all hantaviruses and their hosts into four groups: ( i ) bats and insectivores and their viruses ( Rockport virus ( RKPV ) was removed because it was a reassortant virus ) , ( ii ) Murinae and their viruses , ( iii ) Arvicolinae and their viruses ( Luxi virus ( LUXV ) was removed because it was a reassortant virus ) , and ( iv ) Sigmodontinae and their viruses . For the full reconciliation analysis , three species ( virus or host ) representing each of four groups ( bats and insectivores , Murinae , Arvicolinae , and Sigmodontinae ) were used to compare the host and the virus phylogenies ( and including Scalopus aquaticus and RKPV , Eothenomys miletus and LUXV ) . Cross-species transmission events were then inferred by comparing the topologies of the virus and host phylogenies . Specifically , we considered the degree of congruence between the viral S , M , and S+M segment trees with that of the host mt-cyt b gene tree ( Figure 5 , Figure S4; Table S5 ) . Importantly , any viruses or hosts exhibiting phylogenetic uncertainty were excluded from the analysis . | Hantaviruses are important human pathogens , occasionally emerging from animal reservoirs . However , both the biodiversity of hantaviruses in nature , as well as the frequency with which they have jumped species barriers in the past , are unclear . Here , we describe four novel hantaviruses ( Huangpi virus , Lianghe virus , Longquan virus , and Yakeshi virus ) that were sampled from bats and shrews in China . These viruses are different from known hantaviruses , with each representing a novel species . An evolutionary analysis of all known hantaviruses including the novel viruses described here reveals the existence of four distinct phylogenetic groups of viruses that infect a range of mammalian hosts , and which have sometimes exchanged genes through segment reassortment . Our analysis also suggests that hantaviruses might have first appeared in bats or insectivores , before spreading to rodents , even though rodents are currently the best documented hosts of hantaviruses . Because the phylogenetic trees of the hantaviruses do not always match those of their mammalian hosts , we conclude that both host-jumping and co-divergence have played important roles in hantavirus evolution . Overall , our study shows that bats are likely to be important natural reservoir hosts of hantaviruses from which novel hantaviruses may emerge in the future . | [
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] | 2013 | Phylogeny and Origins of Hantaviruses Harbored by Bats, Insectivores, and Rodents |
Regulatory T cells represent a specialized subpopulation of T lymphocytes that may modulate spontaneous HIV-1 disease progression by suppressing immune activation or inhibiting antiviral T cell immune responses . While the effects of classical CD25hi FoxP3+ Treg during HIV-1 infection have been analyzed in a series of recent investigations , very little is known about the role of non-classical regulatory T cells that can be phenotypically identified by surface expression of HLA-G or the TGF-β latency-associated peptide ( LAP ) . Here , we show that non-classical HLA-G-expressing CD4 Treg are highly susceptible to HIV-1 infection and significantly reduced in persons with progressive HIV-1 disease courses . Moreover , the proportion of HLA-G+ CD4 and CD8 T cells was inversely correlated to markers of HIV-1 associated immune activation . Mechanistically , this corresponded to an increased ability of HLA-G+ Treg to reduce bystander immune activation , while only minimally inhibiting the functional properties of HIV-1-specific T cells . Frequencies of LAP+ CD4 Treg were not significantly reduced in HIV-1 infection , and unrelated to immune activation . These data indicate an important role of HLA-G+ Treg for balancing bystander immune activation and anti-viral immune activity in HIV-1 infection and suggest that the loss of these cells during advanced HIV-1 infection may contribute to immune dysregulation and HIV-1 disease progression .
The hallmark of HIV-1 infection is a progressive reduction of CD4 T cells . The main function of these cells is to provide antigen-specific helper cell activity against a wide panel of microbial antigens , however , some of these cells also have regulatory immunosuppressive activities . Classical regulatory T cells ( Treg ) are immunophenotypically defined as being CD25hi and CD127lo , and they intracellularly express the Forkhead Box P3 protein ( FoxP3 ) [1] . The importance of classical Treg for maintaining immune homeostasis has been highlighted by signs of autoimmune pathology that occur in the setting of deficient Treg activity [2] , [3] . During progressive HIV-1 infection , the relative frequency of classical Treg is increased , while their absolute counts are reduced as a consequence of lower total CD4 T cell counts [4] . This indicates that classical Treg decline at a slower rate than conventional CD4 T cells during progressive HIV-1 infection , and suggests that these cells may play an important role in the immune pathogenesis of HIV-1 infection . Functional data from previous studies indeed demonstrated that classical Treg can potently suppress HIV-1-specific T cell responses [5] , [6] , and in this way may contribute to the failure of achieving T cell-mediated immune control of HIV-1 replication . However , classical Treg may also have beneficial effects on HIV-1 disease progression by reducing the deleterious consequences of HIV-1 associated immune activation [7] , [8] . Recently , several alternative Treg populations have been identified that differ from classical Treg by the lack of intracellular FoxP3 expression . One group of such non-classical Tregs is defined by surface expression of HLA-G [9] , an HLA class Ib molecule that is mainly expressed on placental trophoblasts . However , ectopic expression of HLA-G can also be observed on small populations of peripheral blood CD4 and CD8 T cells , which seem to be enriched at sites of inflammation [9] . These cells have the ability to suppress proliferation of T lymphocytes in a cell-contact independent manner , and their regulatory effects are reversible following neutralization with HLA-G blocking antibodies [10] . Previous reports suggested that the proportion of HLA-G-expressing CD8 T lymphocytes is increased during HIV-1 infection [11] , however , such investigations were conducted in unselected populations of HIV-1 positive persons , and did not address the functional role of HLA-G+ T cells during different stages of HIV-1 disease progression . A second group of non-classical Tregs is characterized by surface expression of the latency-associated peptide ( LAP ) , a membrane bound form of TGF-β [12] . These LAP+ CD4 T cells lack FoxP3 expression but can inhibit proliferative activities of T lymphocytes in vitro and in vivo . Under physiologic conditions , a small proportion of LAP-expressing CD4 T cells can be detected in human peripheral blood [12] . The numeric distribution and functional role of LAP+ CD4 Treg during HIV-1 infection is not known . In the present study , we systematically analyzed the expression and function of HLA-G-and LAP-expressing Tregs in patients with different stages of HIV-1 disease infection . Our results indicate a profound reduction of HLA-G+ CD4 Treg in individuals with progressive HIV-1 disease that may stem from a higher susceptibility of these cells to HIV-1 infection , and functionally contribute to HIV-1-associated immune overactivation .
HIV-infected patients and HIV-1 seronegative control persons were recruited according to protocols approved by the Institutional Review Board of the Massachusetts General Hospital in Boston . Samples of mononuclear cells extracted from lymph nodes and peripheral blood were obtained from HIV-1 infected study patients recruited at the University of Hamburg ( Germany ) according to a protocol approved by the local Ethics Committee . All subjects gave written informed consent and the study was approved by the Institutional Review Board of Massachusetts General Hospital/Partners Healthcare . Peripheral blood mononuclear cells ( PBMC ) were isolated from whole blood using Ficoll density centrifugation . Lymph node mononuclear cells ( LNMC ) were extracted from freshly-excised lymph node samples according to routine procedures . PBMC or LNMC were stained with LIVE/DEAD cell viability dye ( Invitrogen , Carlsbad , CA ) and monoclonal antibodies directed against CD4 , CD25 , CD127 , CD45RA , CCR7 ( BD Biosciences , San Jose , CA ) , CD57 and PD-1 ( Biolegend , San Diego , CA ) , CD8 ( Invitrogen ) , HLA-G ( clone MEM-G/9 , Abcam , Cambridge , MA ) , LAP ( clone 27232 , R&D systems , Minneapolis , MN ) and , when indicated , LILRB1 ( clone HP-F1 , ebioscience , San Diego , CA ) . After incubation for 20 minutes at room temperature , cells were fixed with PBS containing 0 . 5% fetal calf serum and 1% formaldehyde . Anti-FoxP3 antibodies ( ebioscience ) were used with a dedicated staining buffer ( ebioscience ) per the manufacturer's instruction . Subsequently , cells were acquired on an LSR II flow cytometer ( BD Biosciences , San Jose , CA ) using FACSDiva software . Data were analyzed using FlowJo software ( Tree Star , Ashland , OR ) . Indicated total CD4 or CD8 T cell populations were isolated using a negative cell purification kit ( StemCell Technologies , BC , Canada ) , according to the manufacturer's instructions . Cell purity was >90% in all cases . Classical LAP− HLA-G− CD25hi CD4 T cells , HLA-G+ CD4 T cells , LAP+ CD4 T cells and a control cell population of LAP− HLA-G− CD25− CD4 T cells were sorted on a FACSAria instrument ( BD Biosciences ) at 70 pounds per square inch . For isolation of CD8 Treg subsets , purified bulk CD8 T cells were sorted into three T cell subsets: HLA-G+ CD8 T cells , CD25hi CD28− CD8 T cells and a control cell population of HLA-G− CD25− CD8 T cells , using similar sorting conditions . PBMC from HIV-1 infected individuals were stained with 0 . 25 µM carboxyfluorescein succinimidyl ester ( CFSE; Invitrogen ) and mixed with sorted autologous Treg populations or control T cells without regulatory activity at a ratio of 4∶1 . Afterwards , cells were stimulated with a pool of overlapping peptides spanning the clade B consensus sequence of HIV-1 gag , a pool of overlapping peptides spanning the entire sequence of human CMV pp65 ( concentration of 2 µg/ml per peptide ) , or PHA . After incubation for 6 days , cells were washed , stained with viability dye and surface antibodies , fixed and acquired on an LSR II flow cytometer . Suppression of T cell proliferation by Tregs was calculated as: ( T cell proliferation ( % ) in the non-Treg co-culture – T cell proliferation ( % ) in the Treg co-culture ) /T cell proliferation ( % ) in the non-Treg co-culture . CFSE-stained responder T cells from HIV-1-infected patients were mixed with sorted autologous Treg populations or control CD4 T cells at a ratio of 2∶1 . Cells were then stimulated with a pool of overlapping peptides spanning HIV-1 gag ( concentration of 2 µg/ml per peptide ) in the presence of antibodies directed against CD28 and CD49d ( 2 µg/ml ) . Cells were incubated for 6 h at 37°C , and Brefeldin A was added at 5 µg/ml after the first hour of incubation . Afterwards , cells were stained with viability dye and surface antibodies , fixed , permeabilized using a commercial kit ( Caltag , Burlingame , CA ) , and subjected to intracellular cytokine staining with monoclonal antibodies against interferon-γ and IL-2 ( BD Biosciences ) . Following final washes , cells were acquired on an LSR II instrument . Responder T cells from healthy individuals were mixed with sorted autologous Treg populations or autologous control T cells without regulatory activities at a ratio of 2∶1 . Following stimulation of cells with Staphylococcal Enterotoxin B ( SEB , 5 µg/ml , kindly provided by Dr . Eric J . Sundberg , University of Maryland ) , cells were incubated at 37°C for 4 days . Afterwards , cells were stained with antibodies against CD4 , CD8 , CD38 , HLA-DR , CD69 and Vβ13 . 1 and viability dye before being subjected to flow cytometric acquisition on an LSR II instrument . The surface expression of activation markers in responder T cells was analyzed after gating on T cells . Treg-dependent suppression of bystander activation was calculated as: ( CD38/HLA-DR/CD69-expressing T cells ( % ) in the non-Treg co-culture – CD38/HLA-DR/CD69-expressing T cells ( % ) in the Treg co-culture ) /CD38/HLA-DR/CD69-expressing T cells ( % ) in the non-Treg co-culture . HLA-G+ and HLA-G− CD3 T cells were isolated by immunomagnetic enrichment and cultured in IL-2 supplemented medium for 4 days . Equal amounts of culture supernatants and cell lysates were then subjected to SDS-PAGE ( 8 to 16% Tris-glycine gels , Invitrogen ) , electroblotted and incubated with HLA-G antibodies ( clone 4H84 , Abcam ) , followed by visualization with horseradish peroxidase ( HRP ) -labeled secondary antibodies and enhanced chemiluminescence ( ECL ) detection reactions ( GE Healthcare , Little Chalfont , UK ) according to standard protocols [13] . CD4 T cells were activated with recombinant IL-2 ( 50 U/ml ) and an anti-CD3/CD8 bi-specific antibody ( 0 . 5 µg/ml ) . On day 5 , cells were infected with GFP-encoding X4- ( NL4-3 , MOI = 0 . 02 ) or R5- ( Ba-L , MOI = 0 . 07 ) tropic viral strains [14] ( kindly provided by Dr . Dan Littman , New York University ) for 4 h , or with a YFP-encoding VSV-G-pseudotyped HIV-1 vector ( MOI = 0 . 02 ) ( kindly provided by Dr . Abraham Brass , University of Massachusetts ) for 2 h at 37°C . After two washes , cells were plated at 5×105 cells per well in a 24-well plate . On day 2 ( VSV-G-pseudotyped virus ) or day 4 ( X4-/R5-tropic viruses ) , cells were stained with surface antibodies and viability dye and analyzed on an LSR II instrument . For infection of quiescent cells , negatively-selected CD4 T cells with a purity of >95% were directly infected with the described HIV-1 constructs . After in vitro culture for 96 h in the absence of exogenous IL-2 , cells were analyzed by flow cytometry . Data are expressed as mean and standard deviation/standard error , or as box and whisker plots indicating the median , the 25% and 75% percentile and the minimum and maximum of all data . Differences between different cohorts or different experimental conditions were tested for statistical significance using Mann-Whitney U test , paired T test or one-way ANOVA , followed by post-hoc analysis using Tukey's multiple comparison test , as appropriate . Spearman correlation was used to assess the association between two variables . A p-value of 0 . 05 was considered significant . The level of significance was labeled as: *:p<0 . 05; **:p<0 . 01; ***:p<0 . 001 .
Investigations of T cells with regulatory properties in HIV-1 infection have so far been mostly limited to classical , CD25hi and/or FoxP3 expressing Treg . To analyze the role of alternative , non-classical Treg populations in patients infected with HIV-1 , we initially focused on the recently described population of Treg defined by surface expression of HLA-G [9] . These cells do not express FoxP3 or CD25 ( Figure S1 ) , and are phenotypically and functionally distinct from classical Treg [9] , [10] . To analyze these cells in HIV-1 infection , we used flow cytometry to determine the relative and absolute numbers of HLA-G+ CD4 and CD8 T cells in treatment-naïve HIV-1 infected individuals with chronic progressive infection ( n = 28 , median viral load: 48 , 215 copies/ml [IQR 20 , 187–685 , 000]; median CD4 cell count: 396/µl [IQR 204–652] ) , spontaneous control of HIV-1 replication ( n = 24 , viral load <1000 copies/ml; median CD4 cell count: 924/µl [IQR 347–1879] ) , or patients with primary HIV-1 infection and seroconversion within 3 months prior to recruitment ( n = 22 , median viral load: 99 , 900 copies/ml [IQR 36 , 600–2 , 790 , 000]; median CD4 cell count: 475/µl [IQR 265–1047] ) . HIV-1 infected persons successfully treated with Highly Active Antiretroviral Therapy ( HAART ) ( n = 26 , viral load <50 copies/ml; median CD4 cell count: 402/µl [IQR 242–1493] ) , as well as a cohort of HIV-1 negative persons ( n = 21 ) , were recruited for control purposes . Consistent with prior reports [15] , we observed that relative proportions of classical CD25hi CD127lo CD4 Treg were increased in progressive HIV-1 infection , while absolute Treg numbers were decreased ( Figure S2 ) ; no correlation was found between relative proportions of classical Treg and levels of immune activation ( Figure S2 ) . In contrast , we observed that the relative and absolute numbers of HLA-G-expressing CD4 T cells were lowest in HIV-1 progressors , while no significant difference was found between the numbers of HLA-G+ CD4 T cells in any of the other HIV-1 patient cohorts and HIV-1 negative persons ( Figure 1 A/B ) . The relative frequencies of HLA-G+ CD8 T cells were lower in all HIV-1 infected patient populations compared to HIV-1 negative persons; this reduction was again most pronounced in persons with untreated progressive disease . Notably , the numbers of HLA-G-expressing CD4 and CD8 T cells were positively correlated to total CD4 T cell counts ( Figure 1C ) , and proportions of HLA-G+ T cells were inversely associated with corresponding levels of immune activation on T cells , as determined by surface expression of HLA-DR and CD38 ( Figure 1D ) . These data indicate a selective numerical decrease of HLA-G-expressing T cells in chronic progressive HIV-1 infection , and suggest that a reduction of HLA-G+ Treg may contribute to higher levels of immune activation during progressive HIV-1 infection . Since HLA-G+ Treg express multiple tissue homing factors [16] , a redistribution of these cells to lymphoid tissues may be responsible for the apparent reduction of HLA-G-expressing Treg in the peripheral blood during progressive HIV-1 infection . To investigate this , we analyzed the proportion of HLA-G+ T cells in lymph node and peripheral blood samples collected from patients treated with antiretroviral therapy ( HIV-1 viral load<75 copies/ml , median CD4 count: 762/µl [IQR 528–1 , 152] ) or with untreated progressive HIV-1 infection ( median HIV-1 viral load: 73 , 500 copies/ml [IQR 1 , 300–252 , 000] , median CD4 count: 430/µl [IQR 254–1 , 267] ) . Within these patients , proportions of HLA-G+ CD4 and CD8 Treg in lymph nodes and peripheral blood were not significantly different , suggesting that compartmentalization of HLA-G+ Treg to lymph nodes does not represent the major reason explaining the decreased number of circulating HLA-G+ Treg in progressive HIV-1 infection ( Figure 2A ) . In contrast , classical CD25hi CD127lo Treg were significantly enriched in lymph nodes compared to peripheral blood in patients on and off HAART , consistent with previous results [17] ( Figure 2B ) . We next investigated whether the reduced frequencies of circulating HLA-G+ Treg during progressive HIV-1 infection are associated with an altered phenotypic differentiation or maturation status . We found that in all study cohorts , the T cell subset distribution of HLA-G+ CD4 T cells into naïve , central-memory , effector-memory and terminally-differentiated CD4 T cells was not substantially different from corresponding bulk CD4 T cells ( Figure S3 ) . Moreover , the expression of CD57 and PD-1 , two surface markers associated with senescence and exhaustion of T cells , was not markedly different between HLA-G+ CD4 T cells and the respective bulk CD4 T cells ( Figure S4 ) . In contrast , we noted that in all study cohorts , HLA-G+ CD8 T cells tended to have a more immature naïve or central-memory phenotype when compared to reference bulk CD8 cell populations ( Figure S3 ) . There was also a trend for reduced surface expression of CD57 surface expression on HLA-G+ CD8 T cells in comparison to corresponding bulk CD8 T cells ( Figure S4 ) . Overall , these data indicate that during HIV-1 infection , HLA-G-expressing CD8 , but not CD4 T cells , are skewed to a more immature differentiation status , but this difference is not correlated to the rates of spontaneous HIV-1 disease progression . T cells expressing LAP , a membrane-bound form of TGF-β , have recently been characterized as an alternative , FoxP3-negative population of lymphocytes with immunosuppressive properties [12] [18] . To determine whether this non-classical population of regulatory cells is involved in HIV-1 disease pathogenesis , we analyzed the frequency of LAP+ T cells in our study cohorts . We did not observe significant differences in the proportions of LAP+ T cells between our study groups ( Figure S5 ) . Absolute numbers of LAP+ CD4 Treg were positively associated with total CD4 T cell counts ( Figure S5 ) , and were lowest in progressors , likely reflecting the decline of total CD4 T cells in this patient population ( Figure S5 ) . Proportions of neither LAP+ CD4 nor LAP+ CD8 T cells were significantly associated with corresponding levels of immune activation ( Figure S5 ) . LAP+ T cells did not substantially differ from bulk T cells in terms of T cell subset distribution , although LAP+ CD8 T cells appeared to be slightly overrepresented in central-memory cells during HIV-1 infection ( Figure S6 ) . No difference was found between the surface expression of PD-1 and CD57 on LAP+ T cells and bulk T cells ( Figure S4 ) . Taken together , these results do not suggest that LAP+ T cells play a major role in HIV-1 immune protection or restriction of HIV-1 associated immune activation . A functional hallmark of classical Treg is their ability to inhibit antigen-specific T cell responses [19] . Prior work has shown that non-classical Tregs can also inhibit proliferative properties of T cells , but their functional effects on HIV-1-specific T cells remain unclear [9] . To investigate this , CFSE-labeled PBMC from HIV-1 controllers were stimulated with viral peptides or PHA and individually mixed with sorted autologous HLA-G+ CD4 Treg , HLA-G+ CD8 Treg or classical CD25hi CD4 Treg; HLA-G− CD25− CD4 or CD8 T cells were added as negative controls . Subsequently , proliferation of HIV- and CMV-specific T cells was monitored after six days of culture . These experiments demonstrated suppressive effects of classical CD25hi Treg on the proliferative activities of HIV-1- and CMV-specific CD4 and CD8 T cells , consistent with prior reports showing potent Treg-mediated inhibition of T cell proliferation [5] . In contrast , HLA-G+ Treg did not effectively suppress the proliferative activity of autologous virus-specific CD4 ( Figure 3A/C ) or CD8 ( Figure 3B/D ) T cells in these study patients . LAP-expressing CD4 Treg had a moderate suppressive effect on proliferative activities of HIV-1-specific T cells ( Figure S7 ) . None of the tested classical or non-classical Treg populations had a measurable impact on interferon-γ or IL-2 secretion in HIV-1-specific CD8 T cells ( Figure S8 ) . Taken together , these data show that HLA-G+ Treg have minimal effects on the functional activities of virus-specific T cell responses in controllers . To further explore the role of non-classical Tregs in HIV-1 disease pathogenesis , we focused on how these cells influence T cell activation . Activation of T lymphocytes can either occur through direct antigenic triggering of the TCR , or by mechanisms involving a TCR-independent mode of T cell stimulation , commonly referred to as “bystander activation” [20] , [21] . Both of these pathways seem to contribute to the pathological immune activation observed during progressive HIV-1 infection [22] , [23] , and may be influenced by the non-classical Treg populations described in this manuscript . As a functional assay to investigate and quantify the effects of non-classical Tregs on TCR-dependent and bystander immune activation , we stimulated T cells with Staphylococcal Enterotoxin B ( SEB ) , an antigen that elicits T cell responses by a broad panel of different TCR clonotypes , but cannot be recognized by T cells using TCR Vβ13 . 1 [24] , [25] . Immune activation in Vβ13 . 1-expressing T cells following exposure to SEB can therefore only be attributed to bystander activation , while immune activation in Vβ13 . 1-negative T cells after SEB exposure reflects classical TCR-dependent activation . To analyze the effects of non-classical Tregs on immune activation , SEB-stimulated responder T cells were individually co-cultured with autologous populations of sorted LAP+ CD4 Treg , HLA-G+ CD4 Treg , or classical LAP− HLA-G− CD25hi CD4 Treg; LAP− HLA-G− CD25− CD4 T cells were added for control purposes . Alternatively , HLA-G+ CD8 T cells , CD25hi CD28− CD8 T cells or HLA-G− CD25− CD8 control cells were added to autologous SEB-stimulated responder T cells . On day 4 of culture , immune activation was measured by flow cytometric analysis of CD38 , HLA-DR and CD69 surface expression in Vβ13 . 1-expressing and Vβ13 . 1-negative T cells . As demonstrated in Figure 4 , we observed that classical CD25hi Treg potently suppressed CD38/HLA-DR expression in Vβ13 . 1-negative T cells , consistent with prior reports about the immunosuppressive properties these cells [5] . In contrast , HLA-G-expressing Treg led to a significantly reduced surface expression of CD38 on Vβ13 . 1-expressing T cells , but had limited effects on immune activation of Vβ13 . 1-negative cells . This selective inhibitory effect on bystander activation was seen both for HLA-G+ CD4 ( Figure 4A/C ) and CD8 ( Figure 4B/D ) T cells and substantially exceeded regulatory effects on bystander activation of classical CD25hi Treg or LAP+ Treg . None of the tested Treg populations significantly affected CD69 expression on responder cells over the 4-day incubation period , likely because in comparison to CD38 , CD69 is only transiently upregulated for a short period after immune activation [26] , and therefore could not be properly evaluated in our 4-day co-culture experiment . To explore reasons for the differential susceptibility of Vβ13 . 1-positive and Vβ13 . 1-negative responder T cells to classical and non-classical Tregs , we analyzed the dynamics of LILRB1 surface expression on responder T cells over a 4-day incubation period . LILRB1 can effectively inhibit functional properties of T cells [27] and represents one of the highest-affinity receptors for HLA-G [28] , which is secreted by HLA-G+ Treg ( Figure S9 ) and responsible for the immunomodulatory effects of HLA-G+ Treg [9] , [10] . Interestingly , we observed that following TCR-dependent T cell activation , LILRB1 surface expression on responder T cells declined , while stable or slightly increased LILRB1 surface expression was observed on Vβ13 . 1-negative T cell after “bystander activation” ( Figure 5 ) . Overall , these data indicate that HLA-G+ Treg differ from alternative Treg populations by their ability to reduce bystander activation of T cells , and suggest that TCR-dependent and TCR-independent mechanisms of immune activation are associated with altered susceptibilities to inhibitory effects of classical and non-classical Tregs . Conventional CD25hi CD4 Treg express HIV-1 co-receptors and are targets for HIV-1 infection [29] , [30] . Direct HIV-1 infection of HLA-G+ CD4 Treg may contribute to the reduction of these cells in progressive HIV-1 infection . To investigate this , we analyzed the susceptibility of HLA-G+ CD4 Treg to X4- or R5-tropic HIV-1 viruses , or to a VSV-G-pseudotyped HIV-1 construct causing single-round HIV-1 infection . We observed that HLA-G+ CD4 Treg were significantly more susceptible to HIV-1 infection than autologous HLA-G− CD4 T cells; this was true both for in vitro activated cells and for cells directly infected ex-vivo ( Figure 6A–C , E ) . This enhanced susceptibility was in line with higher expression of the HIV-1 co-receptors , CXCR4 and CCR5 , on HLA-G+ CD4 Treg , in comparison to HLA-G− CD4 T cells ( Figure 6D/F ) . These data suggest that reduction of circulating HLA-G+ CD4 Treg in progressive HIV-1 infection may , at least in part , be due to their enhanced susceptibility to HIV-1 infection .
Regulatory T lymphocytes can influence immune homeostasis by suppressing innate and adaptive effector cell activity , and in this way may importantly modulate immune defense mechanisms against HIV-1 [31] . The majority of currently available data indicate that classical CD25hi CD127lo Treg are expanded during chronic progressive HIV-1 infection [32] , [33] , [34] , [35] , [36] , [37] and may worsen spontaneous HIV-1 disease progression by potently suppressing functional activities of HIV-1-specific T cell responses [5] , [17] , [38] . Here , we demonstrate several numerical and functional aspects of non-classical HLA-G-expressing Treg in HIV-1 infection that clearly distinguish them from these recognized characteristics of classical Treg . We found that absolute numbers and relative proportions of HLA-G-expressing Treg are diminished in progressive HIV-1 infection , that they are inversely correlated to phenotypic markers of immune activation , and that they may have a functional role for reducing bystander immune activation , while only minimally suppressing proliferative activities of HIV-1-specific T cells . In contrast , an alternative population of non-classical Treg expressing the TGF-β latency-associated antigen ( LAP ) was not correlated to immune activation during HIV-1 infection and weakly affected immune activation in functional assays . Overall , these data suggest that HLA-G-expressing Treg may contribute to balancing and fine-tuning anti-viral immune activity and bystander immune activation during HIV-1 infection . HLA-G+ Treg represent a relatively recently discovered group of suppressive T cells that can inhibit the activation and proliferation of T cells after TCR triggering with CD3/CD28 antibodies . However , how HLA-G+ Treg functionally compare to classical Treg in terms of their ability to suppress virus-specific T cells or TCR-independent bystander activation of lymphocytes remained unclear . Our data show that HLA-G+ Treg do not effectively inhibit proliferation of HIV-1- and CMV-specific T cells , compared to the effects of classical Treg in HIV controllers . In contrast , we observed a seemingly stronger ability of HLA-G+ Treg to reduce TCR-independent bystander activation of T cells , using an assay that excludes TCR cross-reactivity as a possible source of activation in heterologous T cells . Yet , due to the numeric reduction of HLA-G+ Treg in progressive HIV-1 infection , all functional effects of these cells could not be evaluated using cells from this particular patient population . Whether functional properties of HLA-G+ Treg from HIV-1 progressors or HAART-treated patients resemble those of HIV-1 negative persons , or exhibit an altered or dysfunctional profile , remains to be investigated . Nevertheless , our results suggest that HLA-G+ Treg differ from alternative Treg populations by a unique profile of suppressive functions that may allow for reducing bystander immune activation while simultaneously minimizing inhibitory effects on virus-specific T cell immune responses . The preservation of this HLA-G-expressing Treg population in HIV-1 controllers may represent an additional immunological feature of this specific patient population . This work demonstrates that in contrast to classical Treg , HLA-G-expressing Treg progressively decline during advanced HIV-1 infection . This selective loss of HLA-G+ Treg during advanced HIV-1 infection may , in conjunction with other mechanisms , contribute to immune overactivation during progressive HIV-1 infection . The reduction of HLA-G+ CD4 Treg during progressive HIV-1 infection may be related to their increased susceptibility to HIV-1 infection , which is likely due to enhanced expression of the viral co-receptors CCR5 and CXCR4 demonstrated in this study . An upregulation of these chemokine receptors may also lead to elevated sequestration of HLA-G+ Treg into inflamed tissues , where these cells were indeed preferentially observed in previous investigations [9] , [39] . However , in our study , we did not find any positive evidence for a selective enrichment of HLA-G+ CD4 and CD8 Treg in lymphoid tissues , either in HAART-treated or in untreated HIV-1 patients; but this observation in a limited number of patients does not exclude the possibility of tissue compartmentalization of HLA-G+ Treg in HIV-1 infection . In addition , the specific reason for the loss of HLA-G+ CD8 Treg in untreated progressive HIV-1 infection remains unclear and warrants further investigation . Over the recent years , HIV-1 infection has increasingly been recognized as a chronic inflammatory condition characterized by elevated T cell immune activation [40] . The mechanisms leading to this abnormal immune activation are most likely multifactorial and include direct stimulation of T cells by HIV-1 antigens , as well as direct TCR-mediated activation of T cells by alternative viral and bacterial antigens that challenge the host during conditions of HIV-1 associated immune deficiency . TCR-independent bystander immune activation does not seem to play a significant role under physiologic conditions , however , increasing data suggest that bystander activation represents a major driving factor for pathological immune activation during progressive HIV-1 infection . For instance , bystander activation occurs mainly through cytokines , including interferon-α/β , IL-2 and IL-15 [41] , which are all increased in HIV-1 infection and represent independent and accurate predictors of disease progression [42] . Moreover , the majority of activated T cells in HIV-1 infected patients typically do not exhibit phenotypic markers of recent TCR stimulation [43] , suggesting that their activation occurred by TCR-independent processes . In addition , activation of T cells specific for Influenza virus has been documented during HIV-1 infection in the absence of serological evidence of Influenza co-infection , or detectable TCR cross-reactivity between HIV-1 and Influenza antigens [44] . Interestingly , our data suggest that T cells activated by bystander mechanisms may have a higher susceptibility to inhibitory effects of HLA-G+ Treg , likely because they do not downregulate the HLA-G receptor LILRB1 in a similar way as T cells activated by TCR triggering . These observations indicate that TCR-dependent and TCR-independent mechanisms of immune activation are associated with altered susceptibilities to classical and non-classical Tregs , and shed new light on target cell characteristics that influence inhibitory effects of Tregs . By selectively reducing the deleterious effects of TCR-independent bystander activation , HLA-G+ Treg may provide a previously unrecognized form of immune protection against HIV-1 associated disease manifestations . | HIV-1 causes disease by inducing a chronic inflammatory state that leads to progressive CD4 T cell losses and clinical signs of immune deficiency . Regulatory T cells ( Treg ) represent a subgroup of T lymphocytes with immunosuppressive activities that can reduce HIV-1 associated immune activation , but may also worsen HIV-1 disease progression by inhibiting T cell responses directed against HIV-1 itself . Here , we describe a non-classical population of regulatory T cells that differ from conventional Treg by the expression of HLA-G , a molecule that contributes to maternal tolerance against semiallogeneic fetal tissue during pregnancy . We show that HLA-G-expressing Treg have a unique functional ability to reduce harmful bystander immune activation , while minimally inhibiting potentially beneficial T cell-mediated immune responses against HIV-1 . In this way , HLA-G-expressing Treg may represent a previously unrecognized barrier against HIV-1 associated immune activation and a possible target for future immunotherapeutic interventions in HIV-1 infection . | [
"Abstract",
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] | 2013 | Functional Characterization of HLA-G+ Regulatory T Cells in HIV-1 Infection |
Post-translational modification by the Small Ubiquitin-like Modifier ( SUMO ) regulates a variety of cellular functions , and is hijacked by viruses to remodel the host cell during latent and productive infection . Here we have monitored the activity of the SUMO conjugation machinery in cells productively infected with Epstein-Barr virus ( EBV ) . We found that SUMO2/3 conjugates accumulate during the late phase of the productive virus cycle , and identified several viral proteins as bone fide SUMOylation substrates . Analysis of the mechanism involved in the accumulation of SUMOylated proteins revealed upregulation of several components of the SUMO-conjugation machinery and post-transcriptional downregulation of the SUMO-targeted ubiquitin ligase RNF4 . The latter effect was mediated by selective inhibition of RNF4 protein expression by the viral miR-BHRF1-1 . Reconstitution of RNF4 in cells expressing an inducible miR-BHRF1-1 sponge or a miR-BHRF1-1 resistant RNF4 was associated with reduced levels of early and late viral proteins and impaired virus release . These findings illustrate a novel strategy for viral interference with the SUMO pathway , and identify the EBV miR-BHRF1-1 and the cellular RNF4 as regulators of the productive virus cycle .
Increasing evidence implicates post-translational modification by the small ubiquitin-like modifiers SUMO1 , SUMO2 and SUMO3 in the regulation of a broad variety of cellular functions [1] . Conjugation of the SUMO paralogs , SUMOylation , is a highly dynamic process , with dramatic changes occurring in response to different types of intracellular or exogenous stress , including oxidation , heat shock and hypoxia [2–4] . Similar to ubiquitination , SUMOylation is a multistep process involving an activating enzyme , the SAE1/SAE2 heterodimer [5] , a conjugating enzyme , UBC9 [6] , and one of several SUMO ligases , including the PIAS ( protein inhibitor of activated STATS ) family [7–9] . SUMO2 and SUMO3 can polymerize to form polySUMO chains whereas SUMO1 prevalently forms mono conjugates and may serve as terminator of mixed chains [10] . SUMO-specific peptidases , such as the six members of the SENP family , mediate the maturation of SUMO pro-peptides , remove SUMO from conjugates , and depolymerize SUMO chains [11] . In addition , the proteasome-dependent turnover of poly-SUMOylated proteins is modulated by SUMO-targeted ubiquitin ligases ( STUbLs ) , such as the human RNF4 and RNF111 ligases , that recognize their SUMOylated substrates via multiple SUMO interacting motifs ( SIMs ) [12] . Pathogenic viruses and intracellular bacteria adopt a variety of different strategies to interfere with SUMOylation in order to establish a cellular environment that is favorable to their survival and replication [13] . Bacteria examples include the Yersinia pestis virulence protein YopJ that mimics the activity of SENPs to inhibit the MAPK signaling pathway [14] , and the listeriolysin-O of Listeria monocytogenes that promotes bacterial infection by inducing the proteasomal degradation of UBC9 through a yet unknown mechanism [15] . Different types of viruses exploit or inhibit SUMOylation during different phase of the infection . For example , SUMOylation regulates the function of many immediate-early ( IE ) and early ( E ) products of DNA viruses . These are often transcriptional factors , such as the IE1 and IE2 of cytomegalovirus ( CMV ) [16 , 17] , E1 and E2 of human papillomavirus ( HPV ) [18 , 19] , BZLF1 and BRLF1 of Epstein-Barr virus ( EBV ) [20 , 21] , and the K-pZIP of Kaposi's sarcoma associated herpesvirus ( KSHV ) that also serves as a specific SUMO2/-3 ligase [22] . Some virus structural proteins were shown to be SUMOylated [23] . The early proteins ICP0 of herpes simplex virus ( HSV ) -1 [24] and K-Rta of KHSV [25] are STUbLs that inhibit antiviral responses by promoting the degradation of the promyelocytic leukemia protein ( PML ) . Viral proteins may modulate the SUMOylation of specific cellular proteins , including the tumor suppressor Rb [26] and the transcriptional co-repressor KAP1 [27 , 28] , while the avian adenovirus Gaml protein promotes a global impairment of SUMOylation by interfering with the activity of the SAE1/SAE2 heterodimer and by reducing the expression of UBC9 [29 , 30] . EBV is a gamma-herpesvirus that establishes latent infection in B-lymphocytes and is associated with lymphoid and epithelial cell malignancies [31] . The switch from latent to productive infection is mediated by products of the immediate early genes BZLF1 and BRLF1 [32] . SUMO1 , -2 and -3 modify BZLF1 on Lys12 , which regulates its transcriptional activity and is at least partially responsible for the destruction of PML bodies during productive infection [33] . BRLF1 interacts with UBC9 , RanBP2 and PIAS1 and is modified by the SUMO paralogs at multiple Lys residues [21 , 34 , 35] . Interaction with the EBV L2 protein promotes SUMOylation while inhibiting the transcriptional activity of BRLF1 [35] . In addition , the early protein BGLF4 binds SUMO2 through N- and C-terminal SIM motifs . Mutation of the SIMs changes the intracellular localization of BGLF4 and inhibits its capacity to: regulate the SUMOylation of BZLF1 , trigger the DNA damage response , and promote virus release [36] . The latent membrane protein ( LMP ) -1 interacts with UBC9 via its C-terminal activating region ( CTAR ) -3 [37] , and promotes SUMOylation of the interferon regulatory factor IRF7 , which limits the transcriptional activity of IRF7 and the activation of innate immune responses [38] . Collectively , these findings point to a pivotal role of SUMOylation in the regulation of both viral and cellular functions during different phases of EBV infection . In this study , we have monitored the abundance of SUMO conjugates in EBV infected cells entering the productive virus cycle . We found that virus replication is accompanied by a robust accumulation of poly-SUMOylated proteins , which is mediated by transcriptional upregulation of several components of the SUMOylation machinery , and post-transcriptional downregulation of RNF4 due to selective blockade of RNF4 expression by the EBV encoded BHRF1-1 miRNA . Reversal of RNF4 downregulation in productively infected cells expressing a miR-BHRF1-1 sponge or a miR-BHRF1-1 resistant RNF4 was accompanied by reduced accumulation of SUMO conjugates , proteasome-dependent degradation of viral proteins and impaired release of infectious viral particles . Thus , miR-BHRF1-1 promotes virus production by regulating a RNF4-mediated cellular defense against infection .
The abundance of SUMO conjugates was monitored in Akata-Bx1 cells following induction of the productive virus cycle by cross-linking of surface IgG . Western blots of cell lysates prepared at different times post-induction in the presence of cysteine protease inhibitors that block the activity of SUMO deconjugases were probed with antibodies specific for the EBV transactivator BZLF1 , SUMO1 and SUMO2/3 ( Fig 1 ) . The activation of productive infection was confirmed by detection of a strong BZLF1 specific band starting from 8 hrs post-induction , followed by the expression of early and late viral gene products ( Fig 1A and S1 Fig ) . Several distinct bands and a weak smear of high molecular weight species that are likely to correspond to mono-SUMOylated proteins and SUMO1 terminated poly-SUMO chains were detected by the SUMO1 specific antibody in untreated Akata-Bx1 ( Fig 1B ) . In agreement with the notion that poly-SUMO conjugates rapidly accumulate in stressed cells [39] , a significantly increased intensity of the high molecular weight species was reproducibly observed after treatment for 1 h with the anti-IgG antibody , which corresponds to time 0 of the monitoring kinetics . However , while the increase of SUMO conjugates induced by acute stress is usually transient , in productively EBV infected cells the high molecular weight species progressively accumulated , reaching a maximum 72 hrs post-induction when monitoring was ended due to increasing cell death . Re-probing the blots with antibodies to SUMO2/3 revealed a similar increase in the intensity of high molecular weight species ( Fig 1C ) , suggesting that productive EBV infection is specifically associated with the accumulation of poly-SUMO conjugates . The specificity of the effect was confirmed by the failure to accumulate poly-SUMO conjugates in anti-IgG treated EBV negative Akata cells and TPA/Bu treated AGS cells , whereas accumulation of SUMO2/3-reactive high molecular weight species was observed in EBV positive AGS-Bx1 upon induction of the productive virus cycle by treatment with TPA/Bu ( Fig 2A and 2B ) . A similar virus reactivation-dependent accumulation of polySUMO conjugates was observed in TPA/Bu treated B95 . 8 cells ( S2 Fig ) . It is noteworthy that the virus induced effect was clearly appreciated in all EBV positive cell lines tested in spite of widely different baseline abundance of SUMO conjugates . The accumulation of poly-SUMO conjugates reflects a substantial remodeling of the SUMOylation machinery that may feature either enhanced conjugation or decreased turnover of the conjugates , due to impaired deconjugation or slowdown of ubiquitin-dependent degradation . To discriminate between these possibilities , the mRNA and protein levels of members of the SUMO conjugation cascade were monitored over time by specific qPCR and western blot ( Fig 3 and S1 Table ) . A progressive upregulation of SUMO2 mRNA was detected starting from 8 h post-induction ( Fig 3A ) . The SAE1 subunit of the SUMO activating enzyme , the SUMO ligase PIAS1 and PIAS3 and the SUMO specific proteases SENP1 , SENP3 , SENP6 and SENP7 that preferentially recognizes SUMO2/3 [40] were also significant upregulated with approximately the same kinetics , whereas the mRNA levels of SUMO1 , SAE2 , UBC9 , SENP2 , SENP5 and RNF4 were not significantly affected . The increase of SUMO2 , SENP6 and to a minor extent PIAS1 mRNAs were paralleled by corresponding changes in the abundance of the encoded proteins ( Fig 3B and 3C ) . In contrast , a progressive decrease of RNF4 was observed starting from 24–48 hrs post induction ( Fig 3B and 3C ) . RNF4 was downregulated upon induction of the productive virus cycle in the EBV positive cell lines , Akata-Bx1 and AGS-Bx1 , while this effect was not observed in the anti-IgG treated of EBV negative Akata and TPA/Bu treated AGS , suggesting that the loss of RNF4 is dependent on virus reactivation ( Fig 4A and 4B ) . A comparable downregulation of RNF4 and accumulation of poly-SUMOylated proteins was observed upon TPA/Bu treatment in the EBV positive B95 . 8 cell line ( S2 Fig ) . Furthermore , in agreement with the possibility that the downregulation may occur post-transcriptionally , the decrease of the RNF4 specific band was not reversed by treatment with the proteasome inhibitor MG132 ( Fig 4C and 4D ) . MicroRNAs have emerged as an important means of viral interference with a variety of cellular functions and signaling pathways [41] . EBV infected cells express miRNAs encoded in two clusters located in the BHRF1 and BART regions of the viral genome [42] . The BHRF1 cluster is selectively upregulated during productive infection [43] and S3 Fig , suggesting that one or more of the encoded miRNAs may target RNF4 . This possibility was substantiated by scanning the 3’UTR of RNF4 with six open source miRNA target prediction programs , which consistently identified high confidence target sites for the BHRF1-1 miRNA ( henceforth indicated as miR-BHRF1-1 , S4 Fig ) . The prediction was tested by measuring the effect of the miRNAs on the expression of an RNF4-3’UTR-LUC reporter . A significant reduction of luciferase activity was reproducibly observed when the reporter was co-transfected in HEK293T cells together with a plasmid expressing miR-BHRF1-1 , whereas expression of miR-BHRF1-2 or miR-BHRF1-3 had only slight effects ( Fig 5A ) . The specificity of the inhibition was confirmed by mutation of the predicted miR-BHRF1-1 seed site in the RNF4-3’UTR . A highly significant reduction of the inhibitory effect of miR-BHRF1-1 was observed when the RNF4-3’UTR seed sequence TCAGGTT was mutated to TACAGTT whereas a weaker but still significant reduction was observed with the TCAATCT mutant ( Fig 5B ) , which may be due to different effects of the mutations on the affinity of binding to the RNF4 mRNA . The inhibitory effect of miR-BHRF1-1 was further substantiated by a dose-dependent decrease of RNF4 in Akata-Bx1 cells transfected with a corresponding synthetic oligonucleotide ( Fig 5C ) . To assess whether the capacity of miR-BHRF1-1 to inhibit RNF4 is also observed in a relevant cell type and under physiological levels of expression , Akata-Bx1 was stably transduced with recombinant lentiviruses expressing miR-BHRF1-1 under the control of a doxycycline-regulated promoter . Treatment of the transduced cells with doxycycline resulted in more than 10-fold increase in the levels of the miRNAs relative to untreated cells ( Fig 5D ) , which is similar to the expression of the endogenous miRNA during productive infection ( S3 Fig ) . Expression of miR-BHRF1-1 alone , in the absence of other lytic viral products , was sufficient for significant reduction of the RNF4 protein levels , while expression of a control scrambled miRNA had no effect ( Fig 5E ) . We next asked whether the upregulation of miR-BHRF1-1 is responsible for the decrease of RNF4 and accumulation of polySUMO conjugates in productively infected cells . To this end , Akata-Bx1 cells were stably transduced with a recombinant lentivirus expressing a doxycycline-regulated miR-BHRF1-1 sponge . As shown in Fig 6 , expression of the sponge had no significant effect on the expression of either miR-BHRF1-1 ( Fig 6A ) or RNF4 ( Fig 6B and 6C ) in untreated Akata-Bx1 , whereas it significantly reduced the upregulation of miR-BHRF1-1 and the downregulation of RNF4 in anti-IgG treated cells . In line with the possibility that the miR-BHFR1-1 mediated downregulation of RNF4 may be responsible for the accumulation of polySUMO conjugates , expression of the miR-BHRF1-1 sponge was accompanied by a significant reduction in the accumulation of polySUMO conjugates ( Fig 7A and 7C ) . The involvement of RNF4 downregulation in the induction of this phenotype was further substantiated using a stable recombinant lentivirus transduced subline of Akata-Bx1 expressing an inducible RNF4 that lacks the 3’UTR ( Fig 7B and 7C ) . Sustained expression of the ectopic miRNA-resistant RNF4 during productive infection was accompanied by failure to accumulate high molecular weight SUMO2/3 conjugates . In line with the capacity of the ligase to promote the proteasome-dependent degradation of poly-SUMOylated protein , the effect of RNF4 reconstitution was reversed by treatment with the proteasome inhibitor MG132 ( Fig 8A and 8B ) . This finding supports the conclusion that the miR-BHRF1-1-mediated downregulation of RNF4 during productive EBV infection protects poly-SUMOylated proteins from proteasomal degradation .
Compelling evidence supports a pivotal role of SUMO in regulating key facets of the interaction of viruses with their host cells . Here we discovered a novel function of the EBV miR-BHRF1-1 whereby , through inhibition of the SUMO-targeted ubiquitin ligase RNF4 , the virus counteracts a cellular defense that operates during the late phase of productive infection . Changes in the global levels of SUMOylation were shown to correlate with either enhanced or reduced virus yields in cells infected with different viruses [48 , 49] , suggesting that SUMOylation may play different roles depending on the type of virus and stage of the virus cycle that is affected . We have found that a robust accumulation of SUMO conjugates is required for efficient virus production in EBV infected cells . At least two events act synergistically in promoting the accumulation of SUMOylated proteins . First , we observed transcriptional upregulation and increased protein levels of several components of the SUMOylation machinery , which , in light of the general shutoff of cellular transcription that accompanies the productive virus cycle [50] , suggests that SUMOylation is actively promoted . Second , we found that the accumulation of SUMO conjugates correlates with a progressive decrease of RNF4 , resulting in very low to undetectable levels of the protein 72 hrs post-induction . Our finding that the decrease of RNF4 is dependent on the activity of a viral microRNA illustrates a new strategy by which the virus interferes with SUMO-regulated events , and highlights a previously unrecognized function of EBV miRNAs during productive infection . The capacity of EBV miR-BHRF1-1 to specifically target RNF4 was confirmed using luciferase reporters expressing wild type and mutant RNF4 3’UTR sequences , by expression of a synthetic miR-BHRF1-1 oligonucleotide or the inducible miRNA at levels comparable to those observed during productive infection , and , most importantly , by reversal of the phenotype upon selective inhibition of the endogenous miRNA in cells expressing an inducible miR-BHFR1-1 sponge . The effect was also reproduced in different cell types , which excludes cell-specific artifacts . Since miR-BHRF1-1 is expressed at relatively low levels in latently EBV infected cells where RNF4 is clearly detected , including in the Akata-Bx1 cell line used in our experiments , the inhibition observed upon induction of the productive infection highlights the importance of the relative amounts of the miRNA in determining its functional target repertoire . Previous work using recombinant EBV lacking the entire or individual members of the BHRF1 cluster suggested that these miRNAs contribute to B-cell transformation and may play a role in the expansion of the latent viral reservoir [51] , possibly due to their capacity to promote cell proliferation and inhibit apoptosis [52] . However , although several putative targets have been identified by bioinformatics predictions and in RISC-IP studies [42] , evidence for the capacity of miR-BHRF1-1 to regulate specific viral or cellular genes during latent infection is still lacking . Furthermore , cells infected with viruses lacking the BHRF1 cluster do not spontaneously enter the productive cycle as measured by the expression of immediate early and late viral genes [51] , suggesting that the miRNAs are not essential for the maintenance of latency . The upregulation of the BHRF1 cluster during productive infection clearly point to a distinct role of the miRNAs in the lytic cycle . It is noteworthy that , although all the BHRF1 miRNAs are induced upon entry into lytic cycle [14 , 43 , 53] , miR-BHRF1-1 is upregulated with a slower kinetics ( S3 Fig and [43] ) , suggesting that it may have a specific function in the late phase of the replicative cycle when infectious virus particles are assembled . This scenario is consistent with the findings of Wahl et al . [54] who demonstrated a significant delay in the appearance of circulating viral DNA in humanized mice infected with a mutant EBV lacking the BHRF1 cluster without concomitant impairment of the oncogenic potential in vivo , suggesting a predominant effect on virus production and spread rather than on the proliferation of the infected cells . Our finding that the miR-BHRF1-1 mediated inhibition of RNF4 promotes the accumulation of SUMO conjugates during the late phase of productive EBV infection suggests that the cellular ligase could have different roles in the virus cycle . RNF4 regulates the activity of PML bodies by promoting the proteasome-dependent degradation of its poly-SUMOylated components , including SP100 , DAXX and PML itself [55] . PML bodies participate in the intrinsic cellular defense against virus infection and are rapidly dispersed upon induction of the productive cycle [56] . Several members of the herpesvirus family express functional homologs of RNF4 , such as the immediate early proteins ICP0 of HSV1 [57] and K-Rat of KSHV [25] , that promote virus production by targeting the PML bodies . EBV does not encode a functional homolog of RNF4 and it is therefore tempting to speculate that the virus may hijack the cellular protein to overcome cellular constraints that prevent virus reactivation . This putative early virus promoting effect stands in sharp contrast to the capacity of RNF4 to inhibit events that occur later during infection , as illustrated by the ubiquitination of SUMOylated BRLF1 in vitro and its proteasome-dependent degradation in productively infected cells , which hampers the efficiency of virus reactivation [58] . Collectively , these findings highlight the complexity of the relationship between the virus and the host cell where the same cellular function may be either harnessed or suppressed in different phases of the infection . The concomitant downregulation of RNF4 and upregulation of several components of the SUMO conjugation machinery , together with the inhibitory effect of RNF4 on the yield of virus particles , emphasizes the importance of SUMOylation for the production of infectious virus . Given the involvement of SUMO in the regulation of nuclear transport [59 , 60] , SUMOylation may be required for the accumulation of viral proteins produced in the cytoplasm to the site of virus replication and assembly in the nucleus . SUMOylation may also guide the gathering of protein complexes that control the late phases of virus production , as shown for several multistep cellular processes , such as DNA repair [61] . In addition , SUMOylation may play an important role in the building of virus particles . While the assembly of virions is dependent on the local accumulation of structural proteins , high protein concentrations are likely to favor protein misfolding , with consequent aggregation and ubiquitin-dependent degradation . SUMOylation could counteract this process by promoting the solubility of aggregation-prone proteins , as illustrated by the finding that SUMOylation delays aggregation of the natively unfolded neuronal protein α-synuclein in vitro , whereas mutation of two lysines required for SUMOylation promotes aggregation and toxicity in vivo [62] . Thus , SUMOylation may be required to maintain a pool of soluble viral proteins available for incorporation into the nascent virus particles . In this attractive scenario , the increased SUMOylation capacity achieved through upregulation of the SUMO conjugation machinery , and the concomitant downregulation of RNF4 , could work in concert towards the generation of infectious virus . While a comprehensive list of the viral and cellular SUMOylation substrates in productively infected cells is beyond the scope of this report , it is remarkable that all the early and late viral proteins that were included in the study were shown to be bona fide SUMOylation substrates , suggesting that the SUMOylation of viral proteins may be more common than previously appreciated . It is also noteworthy that although putative SUMOylation sites are predicted in several EBV proteins , in particular tegument proteins and other virion components , only one of the putative substrates identified in our study , BVRF2 , contains a canonical SUMO-conjugation motif . SUMOylation at non-canonical sites may be a distinctive characteristic of the viral protein or , alternatively , it may reflect the particular environment in which the modification occurs . Indeed , SUMOylation at non canonical sites has been observed also in cellular proteins , and adherence to the consensus motif was shown to drop significantly in cells exposed to heat-shock or other type of stress [63] . Collectively , our findings highlight a key role of SUMOylation in the regulation of productive EBV infection and point to the SUMO machinery as a possible target for the development of new antiviral drugs .
N-Ethylmaleimide ( NEM , E1271 ) , Iodoacetamide ( I1149 ) , IGEPAL CA-630 ( NP40 , I3021 ) , Sodium deoxycholate monohydrate ( DOC , D5670 ) , Triton X-100 ( T9284 ) , Bovine serum albumin ( BSA , A7906 ) , Sodium dodecyl sulphate ( SDS , L3771 ) , Tween-20 ( P9416 ) , Ethylenediaminetetraacetic acid disodium salt dehydrate ( EDTA-E4884 ) , Trizma base ( Tris , 93349 ) , Sodium butyrate ( Bu , B5887 ) and 12-O-tetradecanoylhporbol-13-acetate ( TPA , 4174 ) were purchased from Sigma-Aldrich ( St . Louis , MO , USA ) . Complete protease inhibitors cocktail tablets were from Roche Diagnostic ( Mannheim , Germany ) . The following antibodies were used in immunoblot: mouse anti-βactin ( AC-15 , 1:20000 ) and mouse anti-FLAG ( F-3165 , 1:5000 ) from Sigma-Aldrich; mouse anti-EBV BZLF1 ( sc-53904 , 1:1000 ) ; mouse anti-EBV-EaR p85 ( BORF2 , sc-56979 , 1:1000 ) from Santa Cruz Biotechnology ( Santa Cruz , CA ) ; mouse anti-SUMO2+3 ( 8A2 , 1:3000 ) ; rabbit anti-PIAS1 ( EPR2581Y , 1:10000 ) , mouse anti-SENP6 ( ab57239 , 1:2000 ) from AbCam ( Cambridge , MA , USA ) ; mouse anti-EBV-gp220/350 ( 1:1000 ) , rabbit anti-BGLF5 ( 1:5000 ) , mouse anti-BMRF1 ( 1:15000 ) , rabbit anti-BdRF1 ( 1:1000 ) and rabbit anti-BVRF2 ( 1:1000 ) ; rabbit anti-EBNA1 ( 1:200 , purified rabbit antibody K67 . 3 ) are a gift of Dr . Jaap M . Middeldorp ( CCA-VUMC , Amsterdam , The Netherlands ) ; mouse anti-SUMO1 ( 21C7 , 1:1000 ) from Invitrogen ( Invitrogen , Carlsbad , CA ) ; chicken anti-RNF4 ( 1:3000 ) was a gift of Dr . Ron T . Hay , University of Dundee , Dundee , Scotland , UK . The coding sequencing of EBV BdRF1 , BGLF5 , BMRF1 , and BVRF2 were amplified using primers: BdRF1: 5´-TGACAAGCTTATGCTATCAGGTAACGCAGGAG-3´ , 5´-TCGATGAATTCTCA AGCCACGCGTTTATTCAG-3´; BGLF5 5´-TGACAAGCTTATGGCCGACGTG GATGAG-3´ , 5´-TCGATGAATTCCTATGGAGTTGACTCGTCGTCG-3´; BMRF1: 5´-TGACAAGCTTATGGAAACCACTCAGACTCTCC-3´ , 5´-TCGATGAATTCTT AAATGAGGGGGTTAAAGGCC-3´; BVRF2: 5´-TGACAAGCTTATGGTGCAG GCACCGTCTG-3´ , 5´-TCGATGAATTCTCAAGCCACGCGTTTATTCAG-3´ and cloned in the HindIII and EcoRI sites of the eukaryotic expression plasmid p3xFLAG-CMV-10 ( E7658 , Sigma-Aldrich ) . In order to construct recombinant lentiviruses encoding the BHRF1-1 miRNA BHRF1-1 sponge and FLAG-tagged RNF4 , the coding sequences of the mature miRNAs was amplified using the primers 5´-AGAGACTCGAGGCTGCCTTTGGGATGCATCACTTT-3´ , 5´-AGAGAACGCGT ACGTGACATCTCGTACTGCC-3´; the BHRF1-1 sponge was annealed using the primers: 5´-TCGAGAACTCCGGGTGCGATCAGGTTAAAAAAAAACTCCGGG CGCGATCAGGTTAATATATAACTCCGGGTTGGATCAG-3´ , 5´-CGCGTTAAC CTGATCCAACCCGGAGTTATATATTAACCTGATC-GCGCCCGGAGTTTTTTT TTAACCTGATCGCACCCGGAGTTC-3; the coding sequences of human RNF4 was amplified by the primers 5´-CGACCGGTATGGATTACAAGGATGACGACG ATAAGATGAGTAC AAGAAAGCGTCGT-3´ , 5´-AGAGAACGCGTTCATATAT AAATGGGGTGGTA-3´ . The PCR fragments were cloned in the XhoI and MluI sites of the pTRIPZ lentiviral vector for doxycycline-inducible expression ( Thermo Fisher Scientific , USA ) . For lentivirus production , HEK293FT cells were co-transfected with the plasmids psPAX , pMD2G ( Addgene , Cambridge , MA ) and scrambled control/miRNA constructs using JetPEI ( Polyplus , Illkirch , France ) and cultured in complete medium . The medium was refreshed after one day and harvested after 2 days . After brief centrifugation , the supernatants were stored at -80°C . A reporter plasmid ( pLightSwitch-3UTR ) containing the RNF4 3’UTR cloned downstream of an optimized firefly luciferase gene was purchased from SwitchGear genomics ( Menlo Park , CA ) . The Quickchange Site-Directed Mutagenesis kit ( Agilent Technologies , Santa Clara , CA ) was used to make RNF4-3’UTR mutants according to the manufacturer’s protocol using primers: RNF4m1: 5´-TACGCGGGAGCCTACAGTTCTCTCAGGGGCAGCAAAG-3´; 5´-CTTTGCTGCCCCTGAGAGAACTGTAGGCTCCCGCGTA-3´; RNF4m2: 5’-AGG TACGCGGGAGCCTCAATCTCTCTCAGGGGCAGCAAAG3´; 5´-CTTTGCTGC CCCTGAGAGAGATTGAGGCTCCCGCGTACCT-3´ . The mutations were confirmed by sequencing . The EBV-producing marmoset B-cell line B95 . 8 [64] and the EBV-negative B lymphoma line Akata [65] were cultured in RPMI-1640 medium ( R8758 Sigma-Aldrich ) , supplemented with 10% Fetal Bovine Serum ( 10270 , GIBCO-Invitrogen ) , and 10 μg/ml Ciprofloxacin ( Sigma-Aldrich ) ( complete medium ) . The Akata-Bx1 cell line [66] that carries a recombinant EBV where the thymidine kinase gene was replaced by a CMV immediate-early promoter-driven green fluorescent protein was cultured in complete medium supplemented with 500 μg/ml Geneticin ( GIBCO-Invitrogen ) . The AGS-Bx1 cell line ( kindly provided by Alan Chiang , Hong Kong University , Hong Kong ) , and the EBV negative parental AGS were cultured in Dulbecco's Modified Eagle Medium: Nutrient Mixture F-12 ( DMEM/F12 ) ( GIBCO-Invitrogen , Carlsbad , USA ) supplemented with 10% Fetal Bovine Serum . The HEK293T ( ATCC CRL3216 ) , HEK293FT ( Thermo Fisher Scientific , Waltham , MA , USA ) and HeLa ( ATCC RR-B51S ) cell lines were cultured in DMEM ( Sigma-Aldrich ) supplemented with 10% Fetal Bovine Serum and 2 mM L-glutamine ( GIBCO-Invitrogen ) . For the HEK293FT cell line , the medium was also supplemented with 0 . 1 mM Non-Essential Amino Acids and 1 mM Sodium Pyruvate ( Sigma-Aldrich ) . Stable lentivirus transduced Akata-Bx1 cells were selected in medium containing 1 μg/ml puromycin ( Sigma-Aldrich ) for more than 14 days before use in the assays . Doxycycline-regulated genes were induced by culture for 48 h or 72 h in medium containing 1 μg/ml of doxycycline ( Sigma-Aldrich ) . Akata-Bx1 cells were induced by incubating cells for 90 min at 37°C with rabbit polyclonal anti-human IgG ( A0423 , 1:100 , DAKO , Glostrup Denmark ) as described [67] . Induction of the productive virus cycle was monitored by GFP fluorescence or by probing western blots with antibodies to the viral transactivator BZLF1 . B95 . 8 cells were induced by culture in medium supplemented with 10 ng/ml 12-O-tetradecanoylphobol-13-acetate and 0 . 2 mM sodium butyrate ( TPA/Bu ) for 72 hours . AGS-Bx1 cells were induced by culture in medium supplemented with 30 ng/ml TPA and 0 . 5 mM Bu for 48 hours . The release of infectious virus was monitored in spent culture supernatants by quantitative PCR . Briefly , supernatants collected after 3 and 7 days were cleared of cell debris by centrifugation of 5 min at 14000 rpm and treated with 20 U/ml DNase I ( Promega , Madison , WI , USA ) to remove free viral DNA according to the manufacturer’s protocol . DNA was isolated from the culture supernatant with DNeasy Blood & Tissue Kit ( Qiagen , Hilden , Germany ) and quantified by qPCR using Power SYBR Green PCR Master Mix ( Applied Biosystems by Life Technologies , Woolston Warrington , UK ) and primers specific for unique sequences in BZLF1: 5´-AAATTTAAGAGATCCTCGTGTAAAACAT-3´; 5´-CG CCTCCTGTTGAAGCAGAT-3´ with cycling conditions: initial 50°C 2 min , denaturation 95°C 10 min , followed by 40 cycles of 95°C for 15 sec , 60°C for 1 min . Melting curve was run by incubating the reaction mixtures at 95°C for 15 sec , 60°C for 20 sec , 95°C for 15 sec , ramping from 60°C to 95°C at a rate of 1°C/sec . The plasmid pcDNA3-3xFlag-BZLF1 was used to prepare standard curve as described [68] . The EBV copy number was calculated relative to the standard curve using the Ct value . The cells were lysed on ice for 30 min ( 50 mM Tris-HCl pH 7 . 4 , 150 mM NaCl , 1% Triton X-100 , 1% sodium dodecyl sulfate , 0 . 5% sodium deoxycholate , 20 mM NEM , 20 mM Iodoacetamide , protease inhibitors cocktail ) and protein concentration was measured with a Protein Assay kit ( Bio-Rad Laboratories , Sundbyberg , Sweden ) . The lysate was denatured at 100°C for 10 min in NuPage loading buffer and fractionated in acrylamide Bis-Tris 4–12% gradient gel ( Life Technologies Corporation , Carlsbad , USA ) . After transfer to PVDF membrane ( Millipore Corporation , Billerica , MA , USA ) , the membrane was blocked in TBS containing 0 . 1% Tween-20 and 5% non-fat milk . Incubation with primary antibodies was carried out for 1 h at room temperature followed by incubation for 1 h with the appropriate horseradish peroxidase-conjugated secondary antibodies . The immunocomplexes were visualized by enhanced chemiluminescence ( GE Healthcare Limited , UK ) . For affinity purification of SUMO conjugates , expression plasmids encoding FLAG-tagged version of the viral proteins were co-transfected in HEK293T cells together with a plasmid expressing His-tagged SUMO2 . After 48 h , the cells were lysed in denaturing buffer containing 7 M urea , 0 . 1 M Na2HPO4/NaH2PO4 , 0 . 5 M NaCl , 20 mM Imidazole . The lysates were sonicated and then mixed with 50 μl of Ni2+-NTA-agarose beads and incubated for 4 hrs at 4°C . After 3x washing in buffer containing 7 M urea , 20 mM Na2HPO4/NaH2PO4 , 0 . 5 M NaCl , 20 mM Imidazole the bound SUMO2 conjugates were eluted in buffer containing 200 mM Imidazole , 5% SDS , 150 mM Tris-HCl pH 6 . 7 , 30% glycerol , 720 mM β-mercaptoethanol and 0 . 0025% bromophenol blue as describe [69] . The eluted factions were loaded into SDS-PAGE and immunoblotted with the anti-FLAG antibody . HEK293T cells were seeded in a 48 well plates ( 2x105 cells/well ) , after 24 hours , cells were co-transfected with 50 ng pLightSwitch report vector containing RNF4 or RNF4 mutant 3’UTR fragments , 100 ng EBV miRNA-expressing plasmid pRRLSIN . PGK-PuroR ( Addgene , Cambridge , MA , USA ) , and 1 . 2 ng Renilla luciferase expressing plasmid pRL-TK ( Promega , Madison , WI , USA ) . Luciferase activity was measured 24 h post transfection using the Dual-Glo luciferase reporter assay system ( Promega , Madison , WI , USA ) according to the manufacturer’s instruction . The results are presented as the fold change of normalized luciferase ( Firefly/Renilla ) . Messenger RNA expression of components of the SUMOylation machinery was measured by qRT-PCR . Briefly , total RNA was isolated using the RNeasy Mini Kit ( Qiagen , Hilden , Germany ) and reverse transcribed using a high capacity reverse transcription kit ( Applied Biosystems , USA ) . Quantitative RT-PCR assays were setup using the Power SYBR Green PCR Master Mix ( Applied Biosystems by Life Technologies , Woolston Warrington , UK ) with a final primer concentration of 100 nM . The sequences of the qPCR primers are listed in ST1 Table . Stem-loop RT-qPCR assays was applied to quantify EBV encoded miRNAs based on the stem-loop reverse transcription primer method described previously [43 , 70] . RNAs were extracted using the miRNeasy Mini Kit ( Qiagen , Hilden , Germany ) and reverse transcribed using specific reverse primers for miRNAs by a high capacity reverse transcription kit ( Applied Biosystems , Foster City , CA , USA ) with condition: 16°C for 30 min followed by 42°C for 30 min and 80°C for 5 min . Real-time quantitative PCR was performed using Power SYBR Green PCR Master Mix with cycling conditions: initial 50°C 2 min , denaturation 95°C 10 min , followed by 40 cycles of 95°C for 15 sec , 60°C for 1 min . Melting curves was run by incubating the reaction mixtures at 95°C for 15 sec , 60°C for 20 sec , 95°C for 15 sec , ramping from 60°C to 95°C at 1°C/sec . The values were normalized to an endogenous small nucleolar RNA control RNU48 . Fold change was calculated as: Fold Change = 2-Δ ( ΔCt ) where ΔCt = Ct target−Ct housekeeping and Δ ( ΔCT ) = ΔCt treated− ΔCt untreated , according to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments ( MIQE ) guidelines . | We have investigated the activity of the SUMOylation machinery in cells infected with Epstein-Barr virus ( EBV ) , a human herpesvirus that infects B-lymphocytes and is associated with malignancies . We found that activation of the productive virus cycle is accompanied by accumulation of SUMO conjugates , upregulation of components of the SUMO conjugation machinery , and downregulation of the SUMO-targeted ubiquitin ligase RNF4 . The decrease of RNF4 is due to post-transcriptional downregulation by miR-BHRF1-1 , a member of the BHRF1 microRNA cluster that is upregulated during productive infection . The effect of miR-BHRF1-1 was confirmed in luciferase reported assays , by mutation of the RNF4 3’UTR seed site , by transfection of a synthetic miR-BHRF1-1 mimic , by ectopic expression of miR-BHRF1-1 and by the reversal of RNF4 downregulation in cells expressing a miR-BHRF1-1 sponge . We also found that several early and late viral proteins are bona fide SUMOylation substrates . Reconstitution of RNF4 in productively infected cells was accompanied by proteasome-dependent degradation of the SUMOylated viral protein and by a significantly reduced virus yield . These findings illustrate a new strategy for viral interference with the SUMO pathway , an unexpected contribution of miR-BHRF1-1 to the productive cycle of EBV and a previously unrecognized role of the RNF4 ligase in the regulation of virus production . | [
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"sumoy... | 2017 | The Epstein-Barr virus miR-BHRF1-1 targets RNF4 during productive infection to promote the accumulation of SUMO conjugates and the release of infectious virus |
Chagas cardiomyopathy is a serious and common complication of Chagas disease . Through bibliometric and Social Network Analysis , we examined patterns of research on Chagas cardiomyopathy , identifying the main countries , authors , research clusters , and topics addressed; and measuring the contribution of different countries . We found 1932 documents on Chagas cardiomyopathy in the MEDLINE database . The most common document type was ‘journal article’ , accounting for 79 . 6% of the total ( n = 1538 ) , followed by ‘review’ ( n = 217 , 11 . 2% ) . The number of published records increased from 156 in 1980–1984 to 311 in 2010–2014 . Only 2 . 5% were clinical trials . Brazil and the USA dominated the research , participating in 53 . 1% and 25 . 7% , respectively , of the documents . Other Latin American countries where Chagas is endemic contributed less , with Bolivia , where Chagas disease is most prevalent , producing only 1 . 8% of the papers . We observed a high rate of domestic collaboration ( 83 . 1% of the documents published in 2010–2016 ) and a lower but significant rate of international collaboration ( 32 . 5% in the same time period ) . Although clinical research dominated overall , the USA , Mexico and several countries in Europe produced a considerable body of basic research on animal models . We identified four main research clusters , focused on heart failure and dysfunction ( physical symptoms , imaging techniques , treatment ) , and on myocarditis and parasitemia in animal models . Research on Chagas cardiomyopathy increased over the study period . There were more clinical than basic studies , though very few of the documents were clinical trials . Brazil and the USA are currently leading the research on this subject , while some highly endemic countries , such as Bolivia , have contributed very little . Different approaches could help to redress this imbalance: encouraging researchers to conduct more clinical trials , launching international collaborations to help endemic countries contribute more , and strengthening links between basic and clinical research .
Chagas disease , or American trypanosomiasis , is a systemic chronic infection caused by the parasite Trypanosoma cruzi and mainly transmitted to humans by reduviid insects . It occurs primarily in Central and South America , affecting an estimated 7 . 7 million people [1–4] . However , globalization has entailed large-scale population movements , including migratory flows from Latin America to Europe , North America , and elsewhere , and there are now cases reported worldwide [5–6] , making the disease a global public health problem . Chagas disease has two phases: acute and chronic . The acute infection normally manifests as a self-limiting fever . In the chronic phase , around one third of sufferers will develop cardiac or digestive complications within three decades of the initial infection . Some 20% to 30% of people infected with Trypanosoma cruzi develop Chagas cardiomyopathy [7–10] , a complication with no characteristic signs or symptoms to distinguish it from general heart disease [11] . According to the estimates based on 2010 data , the number of cases of Chagas cardiomyopathy in countries of Latin America stands at 1 . 17 million people ( 1 , 171 , 193 ) . Estimated numbers were highest in Argentina ( 376 , 309 ) , Brazil ( 231 , 364 ) , Colombia ( 131 , 388 ) and Bolivia ( 121 , 437 ) , followed by Mexico ( 70 , 117 ) [12] . Currently , doctors still rely on nonspecific criteria for its diagnosis , namely a cardiothoracic ratio greater than 50% or abnormalities detected by electrocardiography or echocardiography [11 , 13] . While not exclusive to Chagas cardiomyopathy , right bundle branch block and arrhythmias might be considered distinguishing features more commonly observed than in non-Chagas cardiomyopathy . For diagnosis of Chagas cardiomyopathy , the key points are that infection would first be diagnosed via serology , and there are currently no known biomarkers to reliably predict which T . cruzi positive patients will progress to Chagas cardiomyopathy , although research is underway [7 , 14] . A new screening method based on brain natriuretic peptide levels and diastolic function could detect early cardiac involvement in Chagas disease [15] , potentially overcoming the limitations of traditional diagnostic methods that may misclassify patients . Such advances are highly significant , as the condition is the most common non-ischemic cardiomyopathy and a leading cause of complications and death in Latin America [16] . It carries a risk of malignant arrhythmias , conduction disturbances , heart failure , and pulmonary and systemic embolism , killing around 4% of patients treated for the condition on an outpatient basis every year . Given the disease burden associated with Chagas cardiomyopathy , a specific analysis of research publications and collaboration networks in this area is warranted to build on the more general bibliometric studies of Chagas disease [17–18] . Increased knowledge on research in this pathology can help to foster North-South collaborations and other research initiatives with and among endemic countries that nevertheless may have relatively little scientific development on the topic . Moreover , this type of study is useful for the research community , clarifying the main lines of research that are being developed with regard to the diagnostic methods and treatments for the disease . In this study , by analyzing scientific papers on Chagas cardiomyopathy published in the main international scientific journals , we aimed to identify the leading researchers , the contribution of different countries to the overall research effort , the degree and nature of scientific collaboration , and the topics addressed .
We selected the body of documents for our study from the MEDLINE and Science Citation Index Expanded ( SCI-Expanded ) databases . MEDLINE is the main international database for health sciences , and the terminology included in its Medical Subject Headings ( MeSH ) thesaurus can be used to search for published documents on specific aspects of a given topic . For our search , we identified all the documents indexed in the MEDLINE database with the MeSH descriptor of “Chagas cardiomyopathy” . We then restricted the results to the “article” and “review” document types and to the 1980–2016 period for the calculation of all indicators and analyses , as articles and reviews are the main document types of reference that are subjected to peer review with regard to the dissemination of research activities . The use of the MeSH thesaurus ensures that all the documents recovered focus on the topic analyzed , as this detailed instrument for controlling terminology combines the use of a team of specially trained indexers who analyze each article and assign medical subject headings to it with automated functions to improve the indexing process [19–21] . To identify the documents with a primary focus on Chagas cardiomyopathy , we screened search results by hand , analyzing titles , abstracts and key words . Moreover , we classified all documents that used “humans” or “humans” and “animals” as clinical , epidemiological , or basic studies . One limitation of MEDLINE is that until 2013 , journal articles included only the address of the first author . To depict more precisely the geographical distribution of research activities and collaboration , we identified the documents from MEDLINE that were also included in SCI-Expanded ( 61 . 64% of the MEDLINE documents ) , as this database shows the addresses of all authors . Moreover , as SCI-Expanded is a multidisciplinary database containing the publications with the greatest visibility and impact , it can reveal the patterns of collaboration in the most internationally relevant journals and enables the analysis of the impact of the publications , based on the citations they generate . We performed the electronic searches on 25 March 2017 using the platform Web of Science , which contains both databases . Although we did not limit our results by date of publication , it is worth noting that the term “Chagas cardiomyopathy” was not in the MeSH thesaurus until 1981 .
We retrieved 1932 papers from the MEDLINE database for the whole study period . The most common document type was ‘journal article’ , accounting for 79 . 6% of the total ( n = 1538 ) , followed by ‘review’ ( n = 217 , 11 . 2% ) . Other prominent document types that we identified were ‘letter’ ( n = 127 , 6 . 6% ) and ‘editorial’ ( n = 43 , 2 . 2% ) . We also identified document types by clinical interest . In that sense , only 2 . 5% of the published documents were clinical trials , and 9 . 7% were case reports; other document types appear only occasionally ( Table 1 ) . Fig 1 shows the number of articles and reviews ( n = 1755 ) in MEDLINE on Chagas cardiomyopathy by five-year periods . From 1980–1984 to 1985–1989 there was a 45 . 5% increase in the number of publications . The number of documents remained stable for the next three five-year periods , before increasing again by 32 . 9% from 2000–2004 to 2005–2009 , then by 2 . 6% from 2005–2009 to 2010–2014 . When the number of publications was plotted over time , the best fit to the data was a straight line ( coefficient of determination for linear model , r2 = 0 . 803 ) . The records were published in 382 scientific journals . Four journals contained 25 . 2% of the Chagas cardiomyopathy literature . About half the literature was concentrated in 20 journals , while the remaining half was scattered throughout the other 362; 221 journals featured only one paper on the topic . Table 2 lists the 24 journals containing the most records , showing their impact factor , subject category and ranking in the 2015 Journal Citation Reports ( JCR ) as well as their country and language of publication . Two of these journals were not included in the JCR because they had no impact factor . The most common subject categories among the core journals ( >14 papers ) were “Cardiac & Cardiovascular systems” ( n = 7 ) , “Tropical Medicine” ( n = 7 ) , “Parasitology” ( n = 4 ) , and “Medicine , general & internal” ( n = 3 ) . Nine journals were published in the USA , nine in Latin America ( six in Brazil plus one each in Argentina , Mexico , and Chile ) and six in Europe ( one each in France , Germany , Ireland , the Netherlands , Spain , and the UK ) . The 1070 documents recovered from SCI-Expanded were published by authors from 35 countries . Table 3 presents data concerning the production of papers from the most productive institutions ( >19 documents ) and Table 4 , the scientific production in each country . Authors from Latin America and the Caribbean produced by far the most reports on Chagas cardiomyopathy ( 82 . 9% ) . North America ranked second ( 26 . 8% ) and Europe , third ( 13 . 6% ) . Brazil was the most productive country ( 53 . 1% ) , followed by the USA ( 25 . 7% ) —where Chagas is not endemic—and Argentina ( 16 . 2% ) . The next most productive endemic countries were Venezuela ( 7 . 2% ) , Colombia ( 4 . 1% ) , Chile ( 2 . 6% ) , Mexico ( 2 . 5% ) , and Bolivia ( 1 . 8% ) ; while the next most productive non-endemic countries were France ( 4 . 1% ) , Spain ( 3 . 1% ) , Italy ( 1 . 9% ) , the UK ( 1 . 8% ) , Germany ( 1 . 6% ) , and Switzerland ( 0 . 9% ) ( Table 4 ) . Of the 1070 documents found in SCI-Expanded , 736 ( 68 . 8% ) involved domestic collaborations between different departments or institutions in a single country . International collaborations produced 278 ( 26% ) of the documents . Table 5 shows the evolution of domestic and international collaboration from 1980 to 2016 . Domestic collaborations were almost three times as prevalent as international collaborations , though both types increased progressively over the study period . Most countries in Latin America and the United States show a high degree of domestic collaboration ( 54%–66% of the documents in which they participated ) , with the exception of Bolivia ( 26 . 3% ) and Mexico ( 44 . 4% ) . More significant are the differences observed in relation to international collaboration , with countries like Chile ( 21 . 4% ) , Brazil ( 29 . 7% ) and Argentina ( 36 . 4% ) showing values that are far below those observed in the United States and especially European countries ( 61%–100% ) . On the other hand , Bolivia ( 94 . 7% ) and Colombia ( 75% ) do present high degrees of international collaboration ( Table 6 ) . Brazil is the country that stands out the most in terms of leading collaborative papers , as authors of this country occupy the position of first author in 59 . 8% of the collaborative publications in which Brazilian authors participated , and of corresponding author in 77 . 8% . These values are markedly higher than those seen in the United States ( 44% as first authors and 58 . 9% as corresponding authors ) . Germany and Spain also hold positions of leadership on these indicators , while Bolivia is at the bottom of the ranking on both ( Table 6 ) . Fig 2 shows the collaboration network between countries . By far the strongest cooperative link was between Brazil and the USA , with 102 documents published in collaboration . The USA also had notable links with other Latin American countries such as Argentina ( n = 24 ) and Venezuela ( n = 14 ) ; Brazil had important links with other Latin American countries such as Colombia ( n = 19 ) and Argentina ( n = 17 ) , and European countries such as France ( n = 17 ) and Italy ( n = 12 ) . The 1755 papers ( articles and reviews ) in MEDLINE were produced by 4686 authors responsible for 9858 signatures . The average number of authors per paper over the whole study period was 5 . 6 . This number was less than 2 . 5 in the first period analysed ( 1949 to 1979 ) , 4 . 6 in 1980–1989 , 4 . 8 in 1990–1999 , 5 . 9 in 2000–2009 and 7 . 4 in 2010–2016 . Fig 3 depicts the main research foci of the co-authorship network , comprising 207 authors who are directly or indirectly interlinked and who have co-authored four or more documents together . We identified four research clusters: cluster I contained the largest number of authors ( n = 72 ) , of whom B . M . Ianni and E . Cunha-Neto had established the most links; in cluster II ( n = 69 ) the most prominent author was H . B . Tanowitz; in cluster III ( n = 40 ) , A . L . P . Ribeiro; and in cluster IV ( n = 26 ) , J . A . Marin-Neto . Table 7 shows the ranking of the 30 most productive authors ( ≥ 24 papers ) and the authors with the highest values of betweenness centrality in the co-authorship network . The most productive author was C . Mady ( n = 65 ) , followed by M . O . C . Rocha ( n = 58 ) , and F . Pileggi ( n = 57 ) . The most influential cutpoints in the networks were J . A . Marin-Neto , followed by A . C . C . Carvalho and A . L . P . Ribeiro . Thirteen of the 30 authors appear in both lists ( Fig 3 ) . The manual revision of the studies we retrieved showed that 1225 documents ( 69 . 8% ) mentioned Chagas cardiomyopathy in the title or abstract , indicating that this was the central focus of the publications . There were also 336 documents ( 19 . 1% ) that referred to Chagas disease and 176 ( 10% ) to Trypanosoma cruzi in the title or abstract . While these studies focused on those subject areas , the abstracts also mentioned terms like cardiomyopathy , myositis , heart , or another word that justified their inclusion under the MeSH descriptor of Chagas cardiomyopathy . Finally , 18 documents ( 1% ) include a generic description of cardiovascular disease in Latin America . Table 8 lists the 100 most frequently used MeSH in documents on Chagas cardiomyopathy . The term “humans” ( n = 1412 ) appeared twice more often than “animals” ( n = 722 ) . We assigned the “humans” descriptor to 58 . 9% ( n = 1033 ) of the documents and the “animals” descriptor to 19 . 4% ( n = 341 ) , while we used both descriptors for 21 . 7% ( n = 381 ) . Of the documents reporting studies on humans , 63 . 9% ( n = 660 ) were clinical studies that addressed aspects related to diagnosis or therapeutic strategies; 16 . 1% ( n = 166 ) were epidemiological studies , and 20% ( n = 207 ) reported basic research on immunological , biochemical or molecular aspects of the disease . The documents classified under the joint “human-animal” descriptor were also most commonly rooted in basic research approaches ( 43 . 3% , n = 165 ) , while the rest were on animal models ( 17 . 6% , n = 67 ) , clinical research studies ( 24 . 9% , n = 95 ) or epidemiological studies ( 14 . 2% n = 54 ) . Overall , 43% of the documents were clinical ( n = 755 ) , 12 . 5% ( n = 220 ) epidemiological , and 44 . 4% either animal ( n = 408 ) or basic ( n = 372 ) studies . The most commonly used MeSH related to human research were “Chronic Disease” ( n = 420 ) , “Electrocardiography” ( n = 341 ) , “Heart Failure” ( n = 168 ) , “Arrhythmias , cardiac” ( n = 115 ) , “Echocardiography” ( n = 114 ) , and other descriptors related to patient follow-up and prognosis assessment . Some descriptors , such as “Myocarditis” ( n = 136 ) and “Parasitemia” ( n = 69 ) were more common in animal-based research . Brazil ( n = 121 ) is the main geographic MeSH term assigned to the documents , followed at considerable distance by the United States ( n = 40 ) , Mexico ( n = 30 ) , Chile ( n = 23 ) , Argentina ( n = 19 ) and Bolivia ( n = 16 ) . Fig 4 maps the most common MeSH , showing how they are linked to the four research clusters . There are two main areas of research: heart failure and dysfunction ( MeSH describing physical symptoms , graphic representation techniques , treatments and outcomes ) , shown on the left of the graph; and animal models on the right . The figure reveals the thematic focus of each of the four research clusters . Cluster I has a strong clinical focus , whereas cluster II is more closely associated with research on animal models . Cluster III is the most heterogeneous of the four , with ties to both approaches . For its part , cluster IV is associated with the study of genetic aspects of the disease . When we analysed the documents by country of publication , we found some striking differences in research foci ( Table 4 ) . Human-based research is most prominent in Brazil , with 55 . 3% of records containing the MeSH “humans” , versus 24 . 3% for “animals” . This trend was even more pronounced in Bolivia ( 73 . 7% of records containing “humans” ) , Chile ( 67 . 9% ) , Colombia ( 68 . 2% ) and Venezuela ( 57 . 1% ) . In the USA , although human-based research predominated , a considerable proportion of records were related to animals ( 39 . 3% vs 33 . 8% , respectively ) . We found a similar trend in France ( 31 . 8% vs 29 . 5% ) and Mexico ( 29 . 6% vs 18 . 5% ) . The distribution of the documents by study type confirms the predominance of the clinical approach in countries like Bolivia ( 57 . 9% of the documents ) or Chile ( 39 . 3% , compared to 21 . 4% of documents reporting basic research ) . It also reflects the hegemony of the basic approach in the United States ( 61 . 1% ) , European countries like France ( 77 . 3% ) and Spain ( 57 . 9% ) , and in Argentina ( 62 . 4% ) and Mexico ( 48 . 1% ) . The most productive institutions generally present a higher degree of citation , with the Universidade de São Paulo the main reference center with regard to all citation indicators . The Fundação Oswaldo Cruz ( FIOCRUZ ) and the Universidade Federal de Minas Gerais also stand out in terms of the absolute number of citations received and the H index . Other prominent institutions include the Universidade Federal do Triângulo Mineiro , the Consejo Nacional de Investigaciones Científicas y Técnicas ( CONICET ) and the University of Texas Medical Branch , which all present a high average number of citations per document despite showing a more moderate absolute production ( Table 3 ) . With regard to the clusters of authors and their citations , cluster II shows the highest absolute degree of citation ( n = 5336 ) and the highest H index ( n = 37 ) , with an average number of citations per document of 26 . 8 ( SD ±34 ) . Cluster IV , despite its lower degree of citation ( n = 3167 ) and H index ( n = 28 ) , the average number of citations per document is greater ( 41 . 7 ±90 . 4 ) . Documents published by authors of cluster I yielded a total of 3932 citations ( mean 33 . 6 SD ±43 . 8; H index = 35 ) , while publications by authors from cluster III generated 2937 ( mean 23 . 3 SD ±25 . 8; H index = 32 ) . All of the clusters present mean degrees of citation above the average observed for the whole set of documents analyzed ( 23 SD ±36 . 3 ) , with cluster III showing the lowest levels of citation among the research groups , despite its relevance as a bridge between the other clusters . This relatively moderate impact may be a reflection of the lower number of authors and their disperse relationships , which show a lower density of research ties .
Scientific production on Chagas cardiomyopathy has grown considerably since the turn of the 21st century , probably reflecting the increased incidence of Chagas disease in non-endemic areas like the USA and Europe , and the possibility of providing new knowledge of the disease , with the introduction of treatment projects and new diagnostic tests . Another relevant factor is the increase in domestic and international collaboration , which stimulates further research [15 , 22–24] . Our findings revealed a high degree of domestic collaboration , especially in recent years , in response to the multidisciplinary nature of this clinical entity , which is relevant to tropical medicine , parasitology , cardiology , pathology , biochemistry and immunology , infectious diseases , physiology , microbiology and public health [25–26] . We found that most research on Chagas myocardiopathy was concentrated in journals related to cardiovascular systems , tropical medicine and parasitology , which is logical because the disease affects the heart , is caused by a parasite , and is most prevalent in tropical and subtropical regions . Brazil and other South American countries , together with the USA , produced the most papers on Chagas cardiomyopathy , for two main reasons: firstly , Chagas disease mainly affects Latin American populations; and secondly , the USA , a leader in the research of this subject , has established many collaborative ties with these countries , especially with Brazil [17–18] . Among the published documents , 9 . 7% were case studies and only 2 . 5% clinical trials , probably because measuring the efficacy of the few existing therapeutic tools is highly problematic [25 , 27] . Thus , pharmacological treatments and antiarrhythmic therapies for Chagas myocardiopathy are often based on results for other etiologies . Specific clinical trials are crucial for helping health professionals to better understand and manage the complication [1] . Our findings showed Brazil at the center of the collaboration network , with strong links to other countries in Central and South America ( particularly the bordering Argentina , Venezuela and Colombia ) and also to non-endemic countries—primarily the USA , but also some countries in Europe . The fact that Brazil and the United States are the two primary geographic MeSH terms assigned to the documents confirms the research leadership exercised by these countries , but it also reflects the high prevalence of the disease in Brazil and the fact that the USA also has vectors and documented transmission [28] . Brazil plays a central role in research on Chagas myocardiopathy because it has the largest population of all Latin American countries and also a strong research sponsorship scheme that has driven international collaboration [29] . Bolivia , on the other hand , has the highest prevalence of Chagas disease and therefore of Chagas myocardiopathy , but it produced only 1 . 8% of the records in our study , a reflection of its low overall health system expenditure . Strengthening cooperative networks could help to address this deficit . Accordingly , in recent years Bolivia has increased collaborative research with other countries in Latin America , North America , Europe , and even Japan [23 , 30] . We found that the average number of authors included in the published documents increased over the study period , consistent with all health sciences disciplines . This increase in scientific cooperation will doubtless have several positive consequences , and may help to integrate less developed countries into research activities , but the trend may also be associated with negative aspects that should be avoided wherever possible: processes of neocolonialism or scientific dependence , whereby the more developed countries decide on the lines and topics of research without taking into account the expectations and interests of the countries where the disease has the largest impact [31–32] . The medium-term goal of establishing collaborative links between countries with more and less economic and scientific development should be to empower these latter countries with mutually beneficial and balanced partnerships [33–34] . Among the authors who have established strong collaborative links , we found a few key players who occupy a prominent position , collaborating with several other investigators and acting as intermediaries between different research clusters . These investigators play a crucial role in driving research in the field , since they help to integrate new researchers and generate resources . Moreover , they produce a high volume of work , disseminate information and new ideas , and foster the application of research methodologies and exchange of resources [35–36] . The fact that MEDLINE indexers use the most specific term available in the MeSH branched hierarchy [19] explains the high percentage ( 70% ) of documents tagged as focusing on Chagas cardiomyopathy . This is a specific descriptor for Chagas disease that is only assigned to documents when the title , abstract or key words make explicit reference to it , or when the thematic focus can be deduced by statements linking Chagas or Trypanosoma cruzi with a cardiovascular concept . The proportion of human-based research varied from 55% in Brazil to 74% in Bolivia . The predominance of human-based research in Latin American countries like Bolivia , Brazil , Chile , Colombia and Venezuela reflects the importance of applied clinical practice in Chagas disease in these settings . In contrast , higher income countries without vector-borne transmission of Trypanosoma cruzi conduct a higher proportion of animal research . The USA and France are the leaders in this respect , with 34% and 29 . 5% of papers in these countries containing the MeSH term “animals” , which is roughly balanced with the 39% and 32% of reports related to “humans” . Among the countries in Latin America with the highest scientific production , Brazil ( 24 . 3% ) , Argentina ( 23 . 1% ) and Mexico ( 18 . 5% ) conducted the highest proportion of research on animal models . Since the physiopathology of myocardial damage is poorly understood in Chagas disease , animal models are crucial for improving scientific knowledge , and high-income countries are largely the ones generating this research . Thus , their collaboration with countries where the disease is endemic in humans is crucial . Initiatives like the Special Programme for Research and Training in Tropical Diseases ( TDR ) , an independent global program of scientific collaboration cosponsored by the United Nations Children's Fund , the United Nations Development Program , the World Bank , and the World Health Organization , could serve as a model in this respect , as it enables researchers from different countries and institutions to work together on projects related to tropical diseases [25 , 37] . The manual revision also showed the relevance of animal research and experimentation in the field of Chagas cardiomyopathy due to the gaps in knowledge around the etiopathogenesis of the Trypanosoma cruzi infection and the resulting myocardial damage . Indeed , analysis of the kind of animal experimentation described in the documents that also report basic research in humans shows that nearly half of the research papers on Chagas cardiomyopathy are focused on clarifying the underlying mechanisms of myocardial damage and the factors involved in this pathology . The most commonly used MeSH related to research in humans described diagnostic imaging tests . This reflects the fact that Chagas cardiomyopathy is a chronic disease that can present as arrhythmia and even heart failure , two complications where traditional and simple diagnostic techniques such as electrocardiography are being displaced by other methods like echocardiography [38–39] . We also found MeSH terms detailing the follow-up of patients with cardiomyopathy , and the evolution of Chagas disease towards cardiomyopathy , which shows that these topics are still poorly understood [25 , 40–42] . With regard to animal models , the related MeSH terms reflect an interest in understanding the immunoallergic and microbiological implications of Chagas disease and Chagas myocardiopathy [26 , 43] . The main limitation of our study is that the databases used ( MEDLINE and SCI-Expanded ) do not index some of the journals published in Central and South America , particularly in some countries where the disease is endemic , such as Bolivia , Paraguay and El Salvador . This may have limited the visibility of the papers produced in these countries . We performed the analysis based on searches of MEDLINE and WoS because these are the databases of reference at a bibliometric level and with regard to the most widely disseminated literature at an international level . They also enable far more exact analyses because bibliographic data are standardized , citations of publications are recorded , and search terminology is controlled very precisely through the MeSH . Furthermore , it is possible that our search strategy , which was based on a clinical manifestation of the disease , could have resulted in a relatively modest presence of basic studies , which may be indexed under “Chagas disease” rather than “Chagas cardiomyopathy” . Finally , it is worth noting that the use of addresses may not reflect the actual nationality of authors and the geographic setting where the studies took place , due to researcher mobility , the existence of research stays in other countries , and the development of research projects in endemic , low- and middle-income countries that are nevertheless funded and directed by institutions and researchers from high-income countries . Thus , the indicators obtained from authors’ institutional affiliations should be interpreted only as an approximation to the institutions that are responsible for driving the performance of the studies and publishing their results . In our study we identified the authors who comprise the main international research foci on Chagas cardiomyopathy , measured the extent of collaboration between authors , and identified the topics covered . It is noteworthy that despite the important degree of research development described , less than 1% of patients are able to access treatment . This wide gap points to the need to translate research results into comprehensive public health policy that extends access to clinical services for the disease . Related to this point , the low number of studies with Bolivian authors and researchers from other countries with a high prevalence of the disease , such as Mexico or Colombia , reflects the concentration of resources and higher education institutions in larger , wealthier countries . There is thus an acute need for capacity building in research infrastructure in endemic countries , especially the development of clinical trials with funding from states , industry and civil society , among other initiatives [44] . Domestic and international collaboration plays an important role in the research on this area . Further international cooperation could help to reduce the concentration of research activities in countries with more developed scientific systems such as Brazil or the USA , and reduce the polarity observed between endemic and non-endemic countries , where clinical research and basic research predominate , respectively . It is crucial to strengthen the link between basic research , which is focused on understanding the physiology of the disease , and clinical research , which concerns diagnosis and treatment . | Scientific production on Chagas cardiomyopathy has grown considerably since the turn of the 21st century , probably reflecting the increased incidence of Chagas disease in non-endemic areas like the USA and Europe . Brazil and the USA dominate the research , but we found a very small proportion of clinical trials on Chagas cardiomyopathy and a low scientific production in several endemic countries with a high prevalence of the disease such as Colombia , Chile , Mexico and Bolivia . We observed a polarity between endemic and non-endemic countries where clinical research and basic research predominate , respectively . Different approaches could help to redress the observed imbalance of research on Chagas cardiomyopathy: encouraging researchers to conduct more clinical trials , launching international collaborations to help endemic countries contribute more , and strengthening links between basic and clinical research . It is crucial to foster translational research in order to link basic knowledge on the physiology of the disease with clinical applications in diagnosis and treatment . | [
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"geographical",... | 2018 | Scientometrics analysis of research activity and collaboration patterns in Chagas cardiomyopathy |
Reliance on just one drug to treat the prevalent tropical disease , schistosomiasis , spurs the search for new drugs and drug targets . Inhibitors of human cyclic nucleotide phosphodiesterases ( huPDEs ) , including PDE4 , are under development as novel drugs to treat a range of chronic indications including asthma , chronic obstructive pulmonary disease and Alzheimer’s disease . One class of huPDE4 inhibitors that has yielded marketed drugs is the benzoxaboroles ( Anacor Pharmaceuticals ) . A phenotypic screen involving Schistosoma mansoni and 1 , 085 benzoxaboroles identified a subset of huPDE4 inhibitors that induced parasite hypermotility and degeneration . To uncover the putative schistosome PDE4 target , we characterized four PDE4 sequences ( SmPDE4A-D ) in the parasite’s genome and transcriptome , and cloned and recombinantly expressed the catalytic domain of SmPDE4A . Among a set of benzoxaboroles and catechol inhibitors that differentially inhibit huPDE4 , a relationship between the inhibition of SmPDE4A , and parasite hypermotility and degeneration , was measured . To validate SmPDE4A as the benzoxaborole molecular target , we first generated Caenorhabditis elegans lines that express a cDNA for smpde4a on a pde4 ( ce268 ) mutant ( hypermotile ) background: the smpde4a transgene restored mutant worm motility to that of the wild type . We then showed that benzoxaborole inhibitors of SmPDE4A that induce hypermotility in the schistosome also elicit a hypermotile response in the C . elegans lines that express the smpde4a transgene , thereby confirming SmPDE4A as the relevant target . The orthogonal chemical , biological and genetic strategies employed identify SmPDE4A’s contribution to parasite motility and degeneration , and its potential as a drug target . Transgenic C . elegans is highlighted as a potential screening tool to optimize small molecule chemistries to flatworm molecular drug targets .
Schistosomiasis , also known as bilharzia , is a ‘neglected’ tropical disease caused by the Schistosoma flatworm parasite that resides in the bloodstream . The disease is chronic and morbid , and affects more than 240 million people worldwide [1–3] . For over 35 years , treatment and control has relied on just one drug , praziquantel ( PZQ ) [4–6] . There are a number of ongoing international efforts that aim to increase the distribution of PZQ for mass drug administration [7 , 8] . Consequently , there is concern regarding the possible emergence and establishment of drug resistance [5 , 9–11] . Furthermore , PZQ has a number of pharmacological problems that encourage the search for alternate anti-schistosome therapies . The drug has diminished or no efficacy against developing schistosomes [12–15] and is rarely curative at the single 40 mg/kg dose employed [4 , 16–18] , in part due to its rapid metabolism [19 , 20] . Also , the recommended dose used is high relative to other oral anthelmintics and medications in general , especially given its unpalatable taste [21] and that the primary target patient population is children . Cyclic nucleotide phosphodiesterases ( PDEs ) [22–24] hydrolyse the second-messenger signalling molecules , cyclic adenosine monophosphate ( cAMP ) and cyclic guanosine monophosphate ( cGMP ) to produce 5’-AMP and 5’-GMP , respectively [24 , 25] . Their activities contribute to the control of the intracellular concentrations of these ubiquitous cyclic nucleotides and influence signalling pathways in health and disease [23 , 25–27] . In mammals , the PDE superfamily is divided into 11 families ( PDE1–11 ) based of their sequence identity , biochemical and pharmacological properties , regulation and substrate specificity [23 , 24 , 28–30] . PDEs share a conserved C-terminal catalytic domain and have various N-terminal regulatory domains . Some PDEs hydrolyse cAMP or cGMP exclusively , whereas others hydrolyse both molecules [27–29 , 31] . Among those PDEs that exclusively hydrolyse cAMP , the most extensively studied is the PDE4 multi-gene family with over 20 isoforms , each with a unique non-redundant role [32–35] . Due to their importance in angiogenesis , neuronal function , and immune and inflammatory stress responses , PDE4s have attracted considerable attention over the past decade as drug targets and selective inhibitors have shown promise in in vitro and in vivo models of asthma , depression , and Parkinson’s and Alzheimer’s diseases [23 , 35–40] . Currently , first generation PDE4-selective inhibitors , such as rolipram , and second generation inhibitors , such as roflumilast and cilomilast , are used to treat chronic obstructive pulmonary disease [41–43] . For cognitive decline and Alzheimer’s disease , PDE4 inhibitors are under investigation in animal models and in the clinic [44–48] . In relation to parasitic ( protozoal ) diseases , PDEs , including PDE4 and their inhibitors , have been investigated for their therapeutic potential [49–51] . For example , Trypanosoma brucei expresses five PDEs [52] of which TbrPDEB1 and TbrPDEB2 are confirmed druggable targets [53–56] . The chemical validation of TbrPDEB1 and TbrPDEB2 as targets has been performed using phenylpyridazinones [57 , 58] and a series of catechol pyrazolinones [59] . In Leishmania , PDEs are also considered valuable therapeutic targets [60 , 61] given their contributions to the regulation and compartmentalization of cAMP signaling , processes that are essential for parasite transformation , differentiation and proliferation . In Trypanosoma cruzi , an ortholog of huPDE4 , TcPDE4 ( TcPDEB1 ) displayed an inhibition profile characteristic of the PDE4 subfamily , including a specificity for cAMP over cGMP [62] . Lastly , PDE inhibitors block the in vitro proliferation of Plasmodium falciparum and Toxoplasma gondii [63 , 64] . For the schistosome parasite , the function ( s ) and potential of PDE4 as a drug target are unknown . G-protein-coupled receptors , adenylyl cyclases ( AC ) and protein kinase A ( PKA ) have been characterized in S . mansoni suggesting that the parasite possesses a functional cAMP signaling pathway [65 , 66] . For the snail-infective miracidial stage of S . mansoni , both cAMP and a cAMP-dependent protein kinase have been chemically shown to control ciliary motion [67] and treatment of miracidia with AC modulators [68 , 69] inhibited transformation of miracidia to mother sporocysts . Furthermore , exposure of miracidia to the general PDE inhibitor , IBMX , delayed transformation [69] . From a phenotypic screen involving Schistosoma mansoni and 1 , 085 benzoxaboroles ( Anacor Pharmaceuticals ) , we identified a subset of human ( hu ) PDE4 inhibitors that induced hypermotility and degeneration . Benzoxaboroles , which incorporate a boron atom into the 5-membered ring of a six-five bicyclic molecule , are a new class of versatile drugs and drug candidates that interact with a variety of enzymes . Thus , the drug , tavaborole , which inhibits aminoacyl-tRNA synthetase [70] , has been approved for treatment of onychomycosis [71]; also , tavaborole derivatives have been developed as anti-bacterial candidates [72] . In addition , crisaborole , which inhibits human ( hu ) PDE4 [73] , has completed clinical trials for treatment of atopic dermatitis [74 , 75] . Finally , SCYX-7158 has completed Phase I clinical testing as an oral , single dose cure of Human African Trypanosomiasis [76] . A phase II/III trial is now underway with recruitment of patients in the Democratic Republic of Congo [77] . Based on data from the phenotypic screen , we followed up by recombinantly expressing the orthologous S . mansoni PDE4 enzyme , SmPDE4A , and uncovered an association between enzyme inhibition and anti-parasite activity . Given the challenges of genetically manipulating the schistosome [78] , we employed Caenorhabditis elegans to understand whether the parasite gene could functionally replace the nematode’s endogenous pde4 gene , and whether that transgene is the target of the anti-schistosomal benzoxaborole inhibitors .
A 5 μM single concentration screen of S . mansoni somules ( schistosomula ) over 6 days with a collection of 1 , 085 benzoxaboroles identified three phenotype response groups as judged by microscopical observation ( Fig 1 ) : ( i ) 104 compounds eliciting an early and sustained hypermotile phenotype , of which , 30% was associated with a progressive degeneration of the parasite; ( ii ) 94 compounds that yielded a range of phenotypic responses ( e . g . , rounding , darkening ) , including hypermotility , which was either transient ( noted at 24 h only ) or appeared later in the incubation period ( on or after day 3 ) , and ( iii ) 887 compounds that yielded no phenotype . Of the 1 , 085 benzoxaboroles phenotypically screened , 174 also had associated IC50 data for inhibition of huPDE4B2 ( Fig 1 ) that were distributed as 77 , 82 and 15 compounds across the sustained hypermotile , no phenotype and transient hypermotile groups , respectively . Of the 77 compounds in the sustained hypermotile group , 65 had IC50 values for inhibition of huPDE4B2 of < 1 μM . In contrast , for the 82 compounds with no phenotype , only 16 had IC50 values of < 1 μM . The association between the sustained hypermotile phenotype and sub-micromolar inhibition of huPDE4 was highly significant with a Fishers exact p-value of <0 . 0001 . Also , the 5- ( 3-cyanopyridyl-6-oxy ) benzoxaborole scaffold , which is known to preferentially inhibit huPDE4 over other PDEs [73] , was enriched in compounds that caused the sustained hypermotile phenotype ( Fig 1 ) . In sum , therefore , the phenotypic and associated biochemical data focused our attention on identifying a schistosome PDE4 and understanding whether engagement of that enzyme was associated with the hypermotility ( and degeneracy ) recorded . To identify orthologs of huPDE4 in S . mansoni , we employed the huPDE4B2 ( NP_001032416 . 1 ) in a BlastP analysis constrained to taxid ID: 6183 ( Schistosoma mansoni ) . We retrieved four PDE4-like protein sequences , namely Smp_134140 ( CCD81292 . 1; 626 amino acids in length ) , Smp_141980 ( CCD80549 . 1; 1 , 022 amino acids ) , Smp_129270 ( 454 amino acids ) and Smp_044060 ( CCD77807 . 1; 482 amino acids ) . We term these sequences SmPDE4A through SmPDE4D , respectively . A protein sequence alignment of the S . mansoni sequences against huPDE4B2 ( NP_001032416 . 1 ) , the recently published sequence used to generate a crystal structure of huPDE4B1 ( PDB ID: 4X0F ) [79] and the C . elegans ortholog ( NP_495601 . 1 ) confirmed their homology with the PDE4 family ( Fig 2 ) . Among huPDEs , PDE4 has unique sequence features upstream of the catalytic domain , namely Upstream Conserved Regions ( UCR ) 1 and 2 , each of which is succeeded by a Linker Region ( LR1 and 2; Fig 2 ) . The presence or part absence of these UCRs characterizes the three principal huPDE4 variants . Thus , PDE4 ‘long isoforms’ contain both UCRs; ‘short isoforms’ lack the UCR1 and ‘super short isoforms’ contain an N-terminally-truncated UCR2 [34 , 79–81] . As a general rule , long isoforms act as dimers whereas short forms are monomers [82] . Dimerization is facilitated via both UCRs [28 , 83] and in an engineered construct of huPDE4B2 , the dimerization domain comprises the C-terminus of UCR1 and the N-terminus of UCR2 which form an antiparallel helix pair [79] . Based on the sequence alignment ( Fig 2 ) , huPDE4B2 , SmPDE4A-C and the C . elegans ortholog share obvious homology with the human enzyme in the last one-third of UCR1: SmPDE4D has no UCR1 . Downstream of LR1 , UCR2 is better conserved across all of the sequences except for SmPDE4C which is missing approximately the C-terminal half of the region as well as LR2 and approximately 60 amino acids at the N-terminus of the catalytic domain . The catalytic domain itself is well-conserved across all of the sequences , except , as just indicated for SmPDE4C; also , SmPDE4D has a 16 amino acid insert at position 892 ( Fig 2 ) . SmPDE4B stands out in possessing the longest N-terminal sequence ( ~315 amino acids ) upstream of UCR1 and a large insert ( ~156 amino acids ) between UCR2 and the catalytic domain . The catalytic domain of the PDE4 sequences examined possesses the conserved PDE signature motif HNX2HNXNE/D/QX10HDX2HX25E [84] and the four key residues that coordinate with the catalytic zinc cation ( H765 , H801 , D802 and D935 ) [28 , 33 , 85] ( Fig 2 ) . For huPDE4 , UCR1 regulates phosphohydrolase activity via a R-R-E-S variant of the R-X-X-S/T phosphorylation consensus motif for protein kinase A ( PKA; Fig 2 ) which increases PDE4 activity and results in enhanced cAMP degradation [86–88] . This site is present in SmPDE4A and B but absent in the other helminth orthologs ( Fig 2 ) . Absent in all of the helminth sequences is a P-X-S/T-P consensus motif for ERK phosphorylation near the C-terminus of the catalytic domain [89–91] . The functional consequences of ERK phosphorylation are dependent on the presence of UCR1 and UCR2 such that long isoforms are catalytically inhibited whereas short isoforms have increased activity , and super-short isoforms are again weakly inhibited [33 , 91] . The absence of PKA and ERK phosphorylation sites in some or all the helminth orthologs suggest differences in how the respective proteins are regulated relative to mammalian PDE4s . SmPDE4A was chosen for recombinant expression and subsequent enzyme activity/inhibition studies as it was the least divergent in its protein sequence and domain organization from huPDE4 , which has been successfully expressed by Anacor for its own drug development programs [92] . Querying the GeneDB database reveals that all four SmPDE4 enzymes are expressed in a number of different developmental stages of S . mansoni relevant to infection in humans ( cercariae , somules , and adult male and/or female worms; S1 Table ) . NCBI BLAST analysis of the genomes of S . haematobium [94] and S . japonicum [95] , indicates that orthologs of SmPDE4A , B and D ( not C ) are present in adult S . haematobium , and that orthologs of SmPDE4A and B ( not C or D ) are found in the adult male and somules of S . japonicum , respectively ( S1 Table and S1–S3 Figs for alignments ) . Each of the SmPDE4 genes shares greatest homology with its respective ortholog in S . haematobium over the full sequence ( 83–90%; S2 Table ) or the catalytic domain ( 87–98%; S3 Table ) . For SmPDE4A and B , the corresponding ortholog identities in S . japonicum are generally lower , 63 and 34% for the full sequence , and 89 and 35% for the catalytic domain . For either the full length sequence or that of the catalytic domain , the percentage identities with huPDE4B2 are approximately 60% for SmPDE4A and SmPDE4B , 50% for SmPDE4C and 34% for SmPDE4D . Similar data were obtained for the same comparisons between the SmPDE4 and the C . elegans sequences . The three-step purification scheme involving metal-ion affinity chromatography , hydrophobic interaction chromatography and Mono Q ion-exchange chromatography yielded a single purified protein with the expected molecular mass of 44 . 2 kDa ( Fig 3A ) . Starting with approximately 55 g bacterial paste from a 6 L culture , 8 . 2 mg of purified His6-tagged SmPDE4A was obtained . Phosphohydrolase activity was measured using [3H]-cAMP as described [96] . The recombinant enzyme displayed Michaelis-Menten kinetics with a Michaelis constant ( Km ) of 3 . 0 μM and a maximum velocity ( Vmax ) of 32 . 6 pmol/min ( Fig 3B ) . The Km value is similar to values of 0 . 98 , 2 . 25 and 7 . 81 μM reported previously for huPDE4A , B and D , respectively [96] . The phenotypic screen of 1 , 085 benzoxaboroles with S . mansoni somules had identified a cluster of compounds that induced sustained hypermotility and , in 30% of those cases , degeneracy . For 77 of these compounds for which huPDE4B2 inhibition data were also available to Anacor , the majority ( 65 ) had sub-micromolar IC50 values . The underlying implication was , therefore , that a schistosome PDE4 may be associated with the phenotypes observed . Accordingly , we selected benzoxaboroles ( compounds 1–7 ) with various peripheral substitutions to understand whether an association between enzyme inhibition and anti-parasite activity could be measured ( Fig 4 ) . Compounds were selected on the bases of ( i ) availability , ( ii ) existence of IC50 values for huPDE4 and ( iii ) absence of IP-constraints to reveal structures . The analysis also included two catechol drugs that inhibit huPDE4 , namely , rolipram and roflumilast [41] . IC50 values for the selected benzoxaboroles and catechols were determined for the recombinant SmPDE4A and compared to Anacor’s in-house data for huPDE4B2 ( Fig 4 ) ; see Fig 5 for representative IC50 curves ) . For SmPDE4A , the most potent benzoxaborole inhibitors ( 1–5 ) containing p-cyano and 2-oxy substitutions yielded IC50 values of < 50nM . Compounds 6 and 7 with 3-sulfone and 2-hydroxy substitutions were less effective ( 415 and 123 nM , respectively ) . The catechol , roflumilast , was an effective inhibitor ( IC50 = 18 and 11 nM ) , whereas rolipram was ineffective ( IC50 >10 μM ) . For huPDE4B2 , a similar trend was noted: compounds 1–4 yielded IC50 values of ≤ 30nM , whereas the values for 5–7 ranged between 19 and 69 nM . The catechol , roflumilast , was a potent inhibitor ( IC50 = 0 . 65 nM ) and rolipram much less so ( IC50 = 540 nM ) but still at least 19-fold more effective than against SmPDE4A ( Fig 4 ) . Inhibition values obtained for these two catechols against huPDE4 are consistent with those previously reported [79 , 97 , 98] . For the nine compounds , bioactivity as a function of time against both somules ( at 5 μM ) and adult S . mansoni ( at 10 μM ) was recorded observationally using our constrained nomenclature [99–101]: for adults , we also used Wormassay [102] to measure motility ( Fig 4 ) . Those most potent inhibitors of SmPDE4A were also the most bioactive against the parasite irrespective of developmental stage . Thus , by the first time point of 24 h for somules and 1 h for adults , compounds 1–4 induced intense hypermotility , which by day 6 for somules and day 3 for adults had progressed to include severe degenerative changes ( Fig 4 ) . For both somules and adults , degeneration appeared to occur throughout the worm body ( not localized to a particular region or feature ) and was irreversible upon removal of the inhibitors after the respective incubation periods employed ( see S4 Fig for examples of adult parasites after incubation with compound 2 ) . In support of the intense hypermotility observed for adults after 1 h in the presence of compounds 1–4 , motility as measured by Wormassay was 6-10-fold greater than that of the DMSO control . For the less potent inhibitors ( 5–7 ) of SmPDE4A , bioactivity , if observed at all , was restricted to mild and/or transient increase in motility ( for adults a maximum 3 . 9-fold over DMSO controls by Wormassay ) without any associated degeneration . Finally , the catechols were inactive against somules and only induced transient hypermotility in adults ( maximum 3 . 2-fold by Wormassay for roflumilast ) without major degenerative changes . Overall , therefore , there appears to be a reasonable association between the potency of inhibition of SmPDE4A and the degree of hypermotility of the parasite , which at its most extreme , is associated with degenerative changes . In the model nematode , C . elegans , a single PDE4 gene is responsible for maintaining normal motility such that disruption of that gene ( ce268 mutation ) results in hypermotility [103] . Hypermotility of this C . elegans mutant is thought to be due to excessive cAMP accumulation and consequent hyper-activation of signaling pathways that promote motility [103] . To investigate whether the SmPDE4A can functionally substitute for the C . elegans pde4 , we generated transgenic C . elegans that express full-length SmPDE4A under a pan-neuronally expressed promoter . In two independently generated transgenic lines of C . elegans that express smpde4a in the C . elegans pde4 ( ce268 ) mutant background , smpde4a ( a ) and smpde4a ( b ) , we found that the S . mansoni transgene restored normal motility rates ( Fig 6 ) . This was not simply due to non-specific motility-reducing effects of these transgenes as the same two transgenes did not affect the motility rates of otherwise wild type ( WT; N2 Bristol strain ) animals ( Fig 6 ) . Thus , the function of the endogenous C . elegans pde4 gene can be complemented by the smpde4a transgene . We first asked whether exemplar PDE4 inhibitors ( compounds 2 and 4 ) could induce hyper-motility in WT C . elegans . Exposure of WT C . elegans to rolipram , roflumilast and compound 2 increased motility relative to the DMSO control whereas compound 4 did not ( Fig 7A ) . Consistent with the notion that these compounds cause hypermotility through inhibition of PDE4 , the already elevated motility of pde4 mutant C . elegans was not further modulated by exposure to any of the PDE4 inhibitors ( Fig 7B ) . Exposure of the two mutant C . elegans lines carrying the smpde4a transgene to each of the PDE4 inhibitors induced hypermotility ( Fig 7C and 7D ) indicating that the compounds act via the schistosome transgene . The differential effects of compound 4 on WT and transgenic C . elegans may indicate differences between the susceptibilities of the C . elegans and S . mansoni PDE4 target enzymes to inhibition by this compound . Interestingly , rolipram , which was a weak inhibitor of recombinant SmPDE4A ( Fig 4 ) , increased motility significantly in both mutant C . elegans lines , albeit less so than the hypermotility induced by compounds 2 and 4 ( Fig 7C and 7D ) . Fig 4 shows that the inhibition by the PDE4 catechol inhibitors , rolipram and roflumilast , is approximately 20-fold weaker for the schistosome enzyme compared to the human ortholog . To interpret these data , molecular models of each enzyme in complex with rolipram and roflumilast were built using ICM-pro and huPDE4B1 as a template ( PDB ID: 4X0F ) [79] . The ligand-protein interaction diagrams of rolipram and roflumilast are shown in Fig 8 . The ligand-binding residues are highly conserved between both enzymes with the exception of two and three differences for binding to rolipram and roflumilast , respectively . Specifically , for rolipram-binding , I953 and M954 in huPDE4 are replaced by L and I in SmPDE4A , respectively ( Fig 8 , left panel ) . For binding to roflumilast , the same residues are changed in the same manner with the addition of a S→T809 substitution ( Fig 8 , right panel ) . Notably , and common for both inhibitors , the switch from I953 to L953 would make ligand binding unfavorable as shown by the high positive changes of binding free energies ( 14 . 06 kcal/mol and 15 . 77 kcal/mol for rolipram and roflumilast , respectively ) . This change helps explain the weaker inhibition measured for SmPDE4A with rolipram and roflumilast compared to the human enzyme .
A number of benzoxaboroles have been successfully brought through to the clinic and/or market for a variety of molecular drug targets , including aminoacyl-tRNA synthetase [70] and huPDE4 [73] , and disease conditions [74 , 75] , such as Human African Trypanosomiasis [76] . Accordingly , we leveraged a benzoxaborole library from Anacor Pharmaceuticals to identify new drug development opportunities for schistosomiasis , a disease for which treatment currently relies on just one drug , praziquantel . We first employed a phenotypic screen of 1 , 085 benzoxaboroles using S . mansoni somules to identify phenotypes of interest . We resolved three phenotype response groups: ( i ) 104 compounds eliciting an early and sustained hypermotile phenotype , including 30% that also induced degenerative changes; ( ii ) 94 compounds that yielded a range of phenotypic responses and ( iii ) 887 compounds that yielded no phenotype . The possibility that a PDE4 may be a target of interest arose via a statistically significant association for compounds that induced sustained hypermotility in the parasite and were sub-micromolar inhibitors of huPDE4 . This notion was strengthened by a previous report that a mutation ( ce268 ) of the single pde4 gene in C . elegans causes hypermotility [103] . Given the circumstantial evidence , therefore , we searched for and identified four PDE4-like gene sequences in the S . mansoni genome , which we termed SmPDE4A through SmPDE4D . Of the four putative PDE4 proteins identified in the S . mansoni genome , SmPDE4A is the most similar in protein sequence and inferred domain architecture to huPDE4 whereas the other three are more divergent in various respects such as possessing an extended N-terminal domain ( SmPDE4B ) , sequence inserts ( SmPDE4B and D ) or sequence truncations ( SmPDE4C ) . At this time , the possible functional significance of the sequence variations is unclear , however , based on the conservation of key amino acids directly involved in catalysis , all four gene products may be active . For each of the four protein sequences , corresponding expression products were identified in various developmental stages of S . mansoni relevant to infection in humans and orthologs of three of the SmPDE4 sequences ( A , B and D ) were identified in adults and/or somules of S . haematobium and S . japonicum . Because of SmPDE4A’s greater similarity in sequence and domain organization to huPDE4 , which had already been functionally expressed in bacteria during a campaign to develop benzoxaborole inhibitors [74] , we chose to recombinantly express this enzyme and determine whether , for exemplar benzoxaboroles , an association between enzyme inhibition and phenotypic effects on the parasite existed . After expression of the SmPDE4A catalytic domain in E . coli and chromatographic purification , an enzyme that was catalytically active against the relevant cAMP substrate was obtained . For seven exemplar benzoxaboroles , the potency of inhibition of SmPDE4A trended with the severity of parasite hypermotility , either recorded observationally in somules and adults , or using Wormassay , an image-based method to measure motility in adults [102] . For the most potent benzoxaborole inhibitors ( 1–4 ) of SmPDE4A , somules and adults underwent degenerative changes in addition to , and perhaps , as a consequence of , the extreme hypermotility recorded . Neither the sustained hypermotility nor degeneracy was noted for the weaker inhibitors of SmPDE4A . Although we cannot discount the possibility that the inhibitors tested interact with one or more of the other three putative PDE4s identified in S . mansoni , or indeed , other phosphodiesterases , the trends uncovered for the benzoxaboroles tested would indicate that the induction of parasite hypermotility and degeneracy is , at the least , mediated via inhibition of SmPDE4A , an interpretation that is consistent with the data for C . elegans transfected with a cDNA for SmPDE4A ( discussed below ) . Relevant in this context is that the 5- ( 3-cyanopyridyl-6-oxy ) benzoxaborole scaffold represented in the most potent SmPDE4A inhibitors ( Fig 4 ) provides between 4 and >2 , 000-fold better potency ( IC50 values ) for huPDE4 over other huPDE enzymes [73] . The catechol , roflumilast , although a potent inhibitor of SmPDE4A , produced only marginal and transient phenotypic effects on the parasite . The reason ( s ) for this is unclear , but may be due to a lack of penetrance or rapid metabolism of the catechol by the parasite . The second catechol tested , rolipram , was inactive against SmPDE4 and , again , marginally bioactive . Both catechols are considerably weaker inhibitors of SmPDE4A than huPDE4 ( approximately 20-fold ) . Molecular modeling revealed that there are up to three amino acid residue differences in the ligand-binding sites between SmPDE4A and huPDE4 , but that the change at one position in particular , namely I→L953 , would generate an unfavorable binding free energy value that would contribute to the weaker inhibition values measured for SmPDE4A with the catechols . Given how similar the ligand binding sites are otherwise , the differences noted could be important in a future campaign to derive more parasite-specific inhibitors and decrease potential off-target interactions with host PDE4 . To determine whether SmPDE4A can operate as a bona fide PDE4 in a heterologous biological system , we generated two C . elegans transgenic lines for full-length smpde4a on the ce268 background , which lacks a functional pde4 gene and is , consequently , hypermotile relative to WT worms [103] . The decision to use C . elegans as a functional read-out was motivated by the fact that genetic manipulation of S . mansoni , in spite of progress in this area [78] , is still not a standardized undertaking . Both smpde4a transgene lines depressed hypermotility in ce268 worms to the levels measured for WT–an original demonstration that a platyhelminth gene can compensate for gene functionality in this nematode model . The finding opens the possibility of using the smpde4a transgenic C . elegans as a research tool to perform mutational/mechanistic studies on enzyme function . To understand whether the transgenic C . elegans system responded to PDE4 inhibitors , we tested the two catechols , rolipram and roflumilast , and two benzoxaboroles , compounds 2 and 4 , with the pde4 ( ce268 ) ;smpde4 lines . Encouragingly , all of the inhibitors tested increased worm motility demonstrating that they engage and inhibit the SmPDE4A transgene . This raises the interesting possibility that the current transgenic model could be a useful tool in the further development of more specific inhibitors ( see below ) , especially considering that the bacterial expression of ‘long isoform’ huPDE4 , i . e . , including both UCR1 and UCR2 , is associated with difficulties relating to activity and the aggregation of different molar forms [79] . The finding that rolipram produced a modest , yet statistically significant , increase in the motility of transgenic C . elegans was initially surprising given the compound’s apparent lack of inhibition of the recombinant catalytic domain of SmPDE4A ( IC50 >10 μM ) . One possible explanation may lie in the recent demonstration ( consistent with earlier reports [82 , 83 , 104 , 105] ) that huPDE4B dimerizes via certain residues in UCR1 and UCR2 , and that the UCR2 from one monomer contributes to the topography of the active site of the other monomer [79] ( Fig 2 ) . This UCR2-mediated alteration of the active site increases the rolipram-binding contacts and accounts for the existence of a high-affinity inhibition of huPDE4 by rolipram versus a low-affinity inhibition that involves the catalytic domain only [97 , 98 , 106] . In support of this explanation , the residues in UCR1 and UCR2 that contribute to the dimerization interface in huPDE4 are strongly conserved in SmPDE4A ( and isoforms B and C , but not D; Fig 2 ) . Also , the rolipram-facing residue in the UCR2 of huPDE4B ( Y471 in Fig 2 designated Y274 in [79] ) that enhances the binding potential of rolipram is conserved in SmPDE4A ( and isoform B , but not C and D ) . Thus , it is conceivable that the UCR2 region present in the full length SmPDE4A cDNA that was transfected into the C . elegans pde4 ( ce268 ) mutant provides the additional necessary contacts for rolipram’s enhanced binding and induction of hypermotility . This interaction would , however , imply adjustments in the pose and contacts made by rolipram in the SmPDE4A binding site given the unfavorable binding presence of L953 compared to I953 in huPDE4 . Unfortunately , our attempts to perform the corollary experiment of transfecting C . elegans with a truncated form of SmPDE4 , i . e . , minus the UCR2 domain , and provide support for UCR2’s contribution to rolipram’s enhanced binding were unsuccessful . If confirmed , however , then the extra specificity determinants present in the UCR2-augmented binding site ( in addition to other control elements in the full-length enzyme [79 , 107] ) could be exploited in a program to improve inhibitor specificity especially given the strong similarities between the ligand binding sites of SmPDE4A and huPDE4 noted above . To conclude , a phenotypic screen of a benzoxaborole collection with S . mansoni identified a particular phenotype-chemotype association that suggested an underlying PDE4 molecular target . An association between inhibition of the recombinant SmPDE4A , and parasite hypermotility and degeneration was noted . Employing C . elegans as a transgene expression system , we confirmed SmPDE4A’s contribution to modulating worm motility and its relevance as the molecular target for benzoxaborole inhibitors . The applicability of C . elegans as screening platform for small molecules to flatworm ( schistosome ) molecular targets , coupled with the differences noted between the human and schistosome PDE4s could support a structure-based approach to optimize inhibitor specificity , bioavailability and safety .
Maintenance and handling of vertebrate animals were carried out in accordance with a protocol ( AN107779 ) approved by the Institutional Animal Care and Use Committee at the University of California San Francisco . UCSF-IACUC derives its authority from the United States Public Health Service Policy on Humane Care and Use of Laboratory Animals , and the Animal Welfare Act and Regulations . We employ a Puerto Rican isolate of S . mansoni that is cycled between Biomphalaria glabrata snails and female Golden Syrian hamsters ( infected at 4–6 weeks of age ) as intermediate and definitive hosts , respectively . The acquisition , preparation and in vitro maintenance of mechanically transformed somules ( derived from infective stage cercariae ) and adults have been described [99 , 108] . The catalytic domain of SmPDE4A ( Smp_134140; XM_002573613; residues 668–1 , 060 in Fig 2 ) was synthesized ( Genscript ) with codons optimized for Escherichia coli expression ( including a translation-start methionine codon ) and cloned into the pET15b vector to yield an N-terminally His6-tagged protein . The protein was produced in E . coli BL21 ( DE3 ) cells grown in Terrific Broth medium supplemented with 0 . 1 mM zinc acetate and 50 μg/ml carbenicillin . For the large scale expression of recombinant SmPDE4A , cells were grown at 37°C to an OD600 approaching 0 . 5 , the temperature was then dropped to 15°C , and the cells induced for 24 h with 0 . 1 mM isopropyl β-D-1-thiogalactopyranoside . Cells were collected by centrifugation at 4°C , flash frozen in liquid nitrogen and stored at -80°C . For purification , frozen cells were suspended ( 1 g/ 5 ml ) in 20 mM TRIS-HCl , pH 7 . 2 , 250 mM NaCl , 10 mM imidazole , 1 mM phenyl methane sulfonyl fluoride ( PMSF ) , and once fully homogenous , were lysed by microfluidization . Cellular debris was centrifuged at 4°C for 1 h at 12 , 500 g . The resulting lysate was then purified by metal-ion affinity chromatography using a His-TRAP FF column ( GE Healthcare ) . Prior to purification , the column was washed with 10 column volumes of elution buffer ( 20 mM TRIS-HCl , pH 7 . 2 , 250 mM NaCl , 500 mM imidazole ) and equilibrated with 10 column volumes of binding buffer ( 20 mM TRIS-HCl , pH 7 . 2 , 250 mM NaCl , 10 mM imidazole ) . The protein eluted at ~27 . 5% elution buffer . Major fractions containing the protein of interest were combined , concentrated and treated with an equal volume of 20 mM TRIS-HCl , pH 8 . 0 , 2 M ( NH4 ) 2SO4 . This was done in order to prepare the protein sample for hydrophobic interaction chromatography using a HiTRAP Butyl HP column ( GE Healthcare ) . The column was equilibrated with binding buffer ( 20 mM TRIS-HCl , pH 8 . 0 , 1 M ( NH4 ) 2SO4 ) , followed by loading of SmPDE4A and elution with a linear gradient of 20 mM TRIS-HCl , pH 8 . 0 . The protein eluted at ~50% elution buffer . Fractions containing the protein of interest were pooled , concentrated and buffer exchanged to decrease the concentration of ( NH4 ) 2SO4 to below 5 mM . As a final step , the protein was purified by ion-exchange chromatography using a Mono Q column ( GE Healthcare ) . The column was equilibrated with binding buffer ( 20 mM TRIS-HCl , pH 8 . 0 ) followed by loading of SmPDE4A and elution with a linear gradient of 20 mM TRIS-HCl , pH 8 . 0 , 1 M NaCl . The protein eluted at ~65% elution buffer . Fractions containing the protein of interest were pooled and concentrated , and the purity assessed by SDS-PAGE . The concentration of recombinant SmPDE4A was estimated by the Bradford Assay ( BioRad ) using bovine serum albumin ( BSA ) as a standard . Assay of PDE4 enzymatic activity was as described [96]: huPDE4B2 was purchased from Proteros Biostructures , GmbH , Martinsried , Germany . The reaction contained 0 . 15 μM [3H]-cAMP ( 10 uCi/ml; Perkin Elmer , Waltham , MA ) and activity was measured by ZnSO4/Ba ( OH ) 2 precipitation of the AMP product after reaction quenching . The precipitate was collected by filtration onto Multi-Screen HTS FB plates ( Millipore , Billerica , MA ) , washed and then dried for quantitation of radioactivity . For tests with PDE4 inhibitors , fifty percent inhibitory concentration ( IC50 ) values were calculated based on a four-parameter logistic equation: the means and number of replicates are reported in Fig 4 . Racemate rolipram was purchased from Sigma ( Cat . no . R6520 ) and roflumilast was from Selleckchem ( cat . no . S2131 ) . Phenotypic screens involving somules and adults were carried out as described [99–101 , 109] . For somules , approximately 300 parasites , newly transformed from cercariae [110] , were manually dispensed into flat-bottomed 96-well plates ( Corning Inc . , cat . # 3599 ) containing 100 μl Basch medium and 4% FBS [99 , 111] . Compound was then added in a volume of 1 μl DMSO and the final volume brought up to 200 μl with medium . The final compound concentration was 5 μM; somules were incubated for 6 days at 37°C under 5% CO2 . Adult schistosome screens were performed in 24-well plates ( Corning Inc . , cat . # 3544 ) using five worm pairs per well in a final volume of 2 ml of the above Basch medium . Compound was added in a volume of 1 μl DMSO such that the final concentration was 10 μM . Incubations were maintained for 3 days at 37°C under 5% CO2 . Parasite responses to chemical insult were adjudicated visually every 24 h ( also at the 1 h time point for adults ) using an inverted microscope and employed a constrained nomenclature of phenotype descriptors ( e . g . , rounding , degeneration , overactivity and slowed motility ) as described [99–101] . For adult parasites , in addition to observation-based annotations , we employed Wormassay [102] to measure worm motility . Briefly , Wormassay comprises a commodity digital movie camera connected to an Apple personal computer that operates an open source software application to automatically process multiple wells ( in 6- , 12- or 24-well plate formats ) . The application detects worm-induced changes in the occupation and vacancy of pixels between frames ( outputted as an average ± S . D . ) . Worm motion was quantified using the “Consensus Voting Luminance Difference” option . To determine in which developmental stages the SmPDE4 genes are expressed , the GeneDB ( http://www . genedb . org/Homepage ) Gene IDs for SmPDE4A ( Smp_134140 ) , SmPDE4B ( Smp_141980 ) , SmPDE4C ( Smp_129270 ) and SmPDE4D ( Smp_044060 ) were each used as key words to search for the respective sequences . The “Transcript Expression” file was selected for each sequence to view transcriptomic expression data [65 , 66] . To determine whether the SmPDE4 genes are expressed differentially in adult male and female parasites , the amino acid sequences were queried via tBLASTn in NCBI ( http://ncbi . nlm . nih . gov/ ) against the EST ( Expressed Sequence Tag ) database and constraining the organism ID to “Schistosoma” ( taxid: 6181 ) . Only the information associated with returned sequences that shared ≥ 97% identity with the query sequence was scrutinized . To identify orthologous sequences in the genomes of the human , C . elegans , S . haematobium [94] and S . japonicum [95] , each SmPDE4 protein sequence was analyzed via tBLASTn in NCBI , again constraining for the taxid ID of 6181 . The returned sequences that shared an identity of 30% or more were subsequently analyzed via BLASTp ( at NCBI and GeneDB ) to ( i ) obtain the full length sequence ( ii ) confirm the accession ( gene ID ) numbers and ( iii ) determine the sequence identity . Then , a sequence alignment was generated using the PRABI ( Pôle Rhône-Alpes de Bioinformatique ) MULTALIN tool ( https://npsa-prabi . ibcp . fr/ ) to define the relative positions of the various PDE4 domains ( UCR1 , UCR2 and the catalytic domain ) . The catalytic domains were then used as queries via BLASTp in NCBI to determine sequence identities with the other orthologs . Modeling was performed with the internal coordinate mechanics ( ICM-pro ) package for structure prediction , homology modeling and docking [112] . HuPDE4B1 in complex with ( R ) - ( - ) -rolipram ( PDB code 4X0F; [79] ) was used to build models incorporating the rolipram binding site . The residues surrounding the binding site of rolipram were mutated into the corresponding residues in SmPDE4A to build a SmPDE4A-rolipram model . The residue side chains around the binding site of rolipram in the human and parasite enzymes were then globally optimized using Biased Probability Monte Carlo ( BPMS ) sampling [112] with ICM energy functions in the context of rolipram . Similarly , the model of huPDE4B-roflumilast was built based on the PDB structures 4X0F [79] and 1XOQ [113] . A model of SmPDE4A-roflumilast was built by the mutating residues around the binding site of roflumilast and globally optimized with BPMS sampling in the context of roflumilast . With the models for huPDE4B1 and SmPDE4A in complex with rolipram and roflumilast , 2D diagrams of the ligand-residue interactions were built using the requisite tool in ICM-pro . The hydrophobic interaction cutoff was 5 . 0 Å . Then , with the model of huPDE4B1 in complex with rolipram and roflumilast , the residues around the binding site of rolipram and roflumilast that distinguish SmPDE4A from the human ortholog were mutated . The differences in ligand binding free energies were calculated using following equations: ΔΔGbind=ΔGbindmut−ΔGbindwt ΔGbind= ( Eintracomp−Eintraparts ) + ( ΔGsolvcomp−ΔGsolvparts ) where ΔGbindwt represents the binding free energy of protein and ligand in WT huPDE4B1; ΔGbindmut represents the binding free energy of protein and ligand in those residues mutated in huPDE4B1; Eintracomp represents the internal energy of the protein-ligand complex and Eintraparts represents the sum of the internal energy of protein and ligand . Similarly , ΔGsolvcomp represents the solvation energy of the protein-ligand complex and ΔGsolvparts represents the sum of solvation energies of protein and ligand [114] All strains were cultured at 20°C on nematode growth medium ( NGM ) plates seeded with E . coli strain OP50 [115] . N2 Bristol was used as the WT reference strain . The mutant strain used in this study , pde4 ( ce268 ) , carries a D448N mutation relative to WT pde4 and is encoded by the C . elegans gene denoted as R153 . 1 . The D448N change disrupts the catalytic domain by changing one of the four active residues that together chelate an active-site zinc atom . The consequence is a strong decrease in gene function [103] . Plasmids were constructed using Gateway Technology ( Invitrogen ) reagents as described [116] . The entire S . mansoni SmPDE4A sequence ( Smp_134140; XM_002573613 ) was PCR amplified from the start codon to immediately preceding the stop codon ( 2–1880 bp ) using mixed sex , adult S . mansoni cDNA . PCR primers contained the gateway attB recombining sequences ( in lower case ) : SmPDE4Fw , 5’- ggggacaagtttgtacaaaaaagcaggctTGGAGTTACGAACCGATAAAGTGATTTCATC-3’ and SmPDE4Rv , 5’- ggggaccactttgtacaagaaagctgggTATGTGTTTCCTGAAGTTGTAGA . The PCR fragment was cloned into a donor vector pKA5 ( pDONR-221 ) and the correct sequence confirmed . Then , the entry vector pKA5-SmPDE4A was recombined with the 2 kb of sequence upstream of the start site of unc-119 promoter [117–119] into a Gateway destination vector containing GFP ( pKA453 ) to obtain promoter::SmPDE4A::intercistronic::gfp polycistronic fusions as previously described [120] . This also allows for co-expression of GFP and SmPDE4A from the same transcript without modifying SmPDE4A and facilitates the selection of transgenic animals via the GFP tag . To generate C . elegans that carry the SmPDE4A transgene , the plasmids described above were purified and microinjected into the gonads of both WT ( N2 ) and pde4 ( ce268 ) mutant strains at a concentration of 50 ng/μl . The injected worms ( P0 ) were transferred to individual freshly seeded bacteria plates and allowed to reproduce . F1 progeny were screened for evidence of transgene expression based on the GFP marker . Each transgenic F1 was then singled onto a new plate . This process was repeated until lines that stably transmit the transgene were established in WT or pde ( ce268 ) animal backgrounds . To verify the consistency of GFP expression in the transgenic lines , 10–20 transgenic animals from each line were examined using a Zeiss Axioplan II stereoscope equipped with a FITC/GFP filter ( emission 500–515 nm ) . Compounds were directly added onto the OP50 food source at a 100 μM final concentration or the equivalent 0 . 2% DMSO as control . Previously synchronized early L4 stage larvae were cultivated on NGM plates with OP50 and compounds at 20°C for 16 h . For each experimental condition and transgenic line , motility was measured for 10–20 animals . The animals were washed twice in S basal buffer , transferred onto a new NGM plate in the absence of bacteria . After a brief period of recovery from this manipulation , locomotion was measured by counting the number of body bends in 30 s intervals under a stereoscope . | Just one drug , praziquantel ( PZQ ) , is available to treat schistosomiasis , a flatworm disease that infects over 240 million people , mainly in Africa . With the expanding distribution of PZQ , and the associated threat of drug resistance , new drugs and drug targets are needed . We screened Schistosoma mansoni worms with over 1 , 000 benzoxaborole chemical molecules from Anacor Pharmaceuticals to identify a subset of human cyclic nucleotide phosphodiesterase 4 ( huPDE4 ) inhibitors that cause parasite hypermotility and degeneration . We identified four PDE4 genes in the genome of the parasite and recombinantly expressed one of them ( SmPDE4A ) in bacteria . This enzyme was then used to uncover a relationship between the degree of enzyme inhibition , and the generation of parasite hypermotility and degeneration . To understand whether the SmPDE4A enzyme is the target of the benzoxaboroles in the parasite , we incorporated the coding DNA for SmPDE4A into the model nematode Caenorhabditis elegans that lacked its own functional PDE4 and , as a consequence , was hypermotile . These ‘transgenic’ worms displayed normal motility which could be increased by applying the most potent huPDE4 benzoxaborole inhibitors . In summary , the chemical , biological and genetic approaches taken identify SmPDE4A as a potential drug target for treating schistosomiasis . The potential value of C . elegans as a tool to test and optimize therapeutic chemistries for a flatworm disease is also highlighted . | [
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"inhibi... | 2017 | Phenotypic, chemical and functional characterization of cyclic nucleotide phosphodiesterase 4 (PDE4) as a potential anthelmintic drug target |
Accumulated evidence indicates that rare variants exert a vital role on predisposition and progression of human diseases , which provides neoteric insights into disease etiology . In the current study , based on three independently retrospective studies of 5 , 016 lung cancer patients and 5 , 181 controls , we analyzed the associations between five rare polymorphisms ( i . e . , p . Glu116Lys , p . Asn118Ser , p . Arg138Cys , p . Ala195Thr and p . Leu259Phe ) in MKK7 and lung cancer risk and prognosis . To decipher the precise mechanisms of MKK7 rare variants on lung cancer , a series of biological experiments was further performed . We found that the MKK7 p . Glu116Lys rare polymorphism was significantly associated with lung cancer risk , progression and prognosis . Compared with Glu/Glu common genotype , the 116Lys rare variants ( Lys/Glu/+ Lys/Lys ) presented an adverse effect on lung cancer susceptibility ( odds ratio [OR] = 3 . 29 , 95% confidence interval [CI] = 2 . 70–4 . 01 ) . These rare variants strengthened patients’ clinical progression that patients with 116Lys variants had a significantly higher metastasis rate and advanced N , M stages at diagnosis . In addition , the patients with 116Lys variants also contributed to worse cancer prognosis than those carriers with Glu/Glu genotype ( hazard ratio [HR] = 1 . 53 , 95% CI = 1 . 32–1 . 78 ) . Functional experiments further verified that the MKK7 p . 116Lys variants altered the expression of several cancer-related genes and thus affected lung cancer cells proliferation , tumor growth and metastasis in vivo and in vitro . Taken together , our findings proposed that the MKK7 p . Glu116Lys rare polymorphism incurred a pernicious impact on lung cancer risk and prognosis through modulating expressions of a serial of cancer-related genes .
Ever-increasing epidemiological studies , especially the genome-wide association studies ( GWAS ) , have extensively identified numerous genetic variants , including single-nucleotide polymorphisms ( SNPs ) , to be associated with risk and progression of various human malignancies[1–3] . Despite these discoveries , much of the genetic contributions to complex diseases remains unclearly illuminated because of the fact that only a small proportion of cancer heritability can be explained by those common SNPs , typically with minor allele frequency ( MAF ) >5% , reflecting that some ‘missing heritability’ existed [4 , 5] . Recently , accumulating evidence revealed that rare variants ( MAF<1% ) could decipher accessional disease risk or trait variability [6–8] . An example is that the rare variants located in proto-oncogenes or tumor suppressor genes may contribute to phenotypic variations through modifying their biological functions or genes expression , and thus play an important role in cancer initiations and progressions[9 , 10] . These findings provide novel approaches for the exploration of cancer mechanism . Human mitogen-activated protein kinase kinase 7 ( MKK7 , also known as MAP2K7 , MIM: 603014 ) belongs to the MAP kinase kinase family , and is identified as a tumor suppressor gene [11] . Evidence has demonstrated that MKK7 serves as a critical signal transducer involved in several cancer-related signaling pathways and genes , and thus participates in regulating cells proliferation , differentiation and apoptosis [12–14] . MKK7 deletion in mice caused distinct phenotypic abnormalities[15] , whereas expression of MKK7 could inhibit lung cancer cells development[16] . In addition , several studies also indicated that MKK7 acts as a suppressor in tumors migration , invasion and metastasis [17–19] . Human MKK7 gene is located at chromosome 19p13 . 3-p13 . 2 , a region spanning over a fragile site associated with various human diseases [20 , 21] . A study reported that the somatic mutations and loss of heterozygosity at 19p13 . 2 commonly existed in lung cancer [22] . Furthermore , another study showed that several non-synonymous somatic mutations of the MKK7 gene also occurred and were associated with colorectal cancer predisposition [23] . Nevertheless , it is still molecularly unexplained how these rare variants implicated in cancer initiation and development . Therefore , in the current study , we test the hypothesis that the rare variants in MKK7 might be associated with lung cancer risk and prognosis by disturbing the biological functions of MKK7 . Based on three independent case-control studies , we genotyped five rare SNPs in MKK7 ( i . e . , rs28395770G>A: p . Glu116Lys , rs56316660A>G: p . Asn118Ser , rs56106612C>T: p . Arg138Cys , rs55800262G>A: p . Ala195Thr and rs1053566 C>T: p . Leu259Phe ) and investigated their associations with lung cancer risk , metastasis and prognosis . The biological effects of those promising rare variants on lung cancer were further assessed by a series of functional experiments .
The demographic distributions of the three study populations are described in Table 1 . Consistently , no significant deviations were observed in distributions of age , sex , drinking and family cancer history from the cases to controls in all the studied sets ( P >0 . 05 for all ) , except for smoking status ( P < 0 . 05 ) . These variables were further adjusted in the multivariate logistic regression model to control possible confounding on the main effects of the studied polymorphisms . The histological types and clinical stages of the cases were also enumerated in Table 1 . In addition , we recalculated the samples size based on population sources . There were 3005 cases and 3013 healthy controls in Guangzhou area , 2011 cases and 2168 cancer-free controls in Suzhou area . Table 2 summarized the genotype distributions of the studied MKK7 rare SNPs and their associations with lung cancer risk . In the discovery set , we found a significant frequency deviation between the cases and controls ( exact P = 4 . 12×10−12 ) in p . Glu116Lys rare polymorphism . Compared to individuals with 116Glu/Glu genotype , the carriers with Lys/Glu heterozygote harbored a 3 . 33-fold increased risk of lung cancer ( odd ratio [OR] = 3 . 33 , 95% confidence interval [CI] = 2 . 29–4 . 86 ) , and carriers with Lys/Lys variant genotype exerted a much higher cancer risk ( OR = 3 . 94 , 95% CI = 1 . 09–14 . 3 ) . When combined with variant genotypes , they ( Lys/Glu+Lys/Lys ) also contributed a pernicious impact on lung cancer risk ( OR = 3 . 38 , 95% CI = 2 . 35–4 . 85 ) , conforming to the fitted genetic model with the smallest akaike information criterion ( AIC = 4415 . 3 ) . However , we did not receive any association between other rare SNPs and lung cancer risk . We further confirmed the above associations in another two validation sets , and obtained consistent results . The p . 116Lys variants genotypes ( Lys/Glu+Lys/Lys ) exerted a 3 . 52-fold increased risk of lung cancer ( OR = 3 . 52 , 95% CI = 2 . 54–4 . 89 ) in validation set I , and a 2 . 87-fold increased risk of lung cancer ( OR = 2 . 87 , 95% CI = 2 . 04–4 . 04 ) in validation set II . Because the homogeneity test showed that the association in the above three sets was homogeneous ( P = 0 . 711 ) , we then merged the three populations to increase the study power , and found that the compared with Glu/Glu common genotype , the carrier with Lys/Glu or Lys/Lys had a remarkably adverse effects on lung cancer risk ( OR = 3 . 23 , 95% CI = 2 . 62–3 . 98; OR = 3 . 75 , 95% CI = 2 . 09–6 . 71; respectively ) . Similarly , the Lys ( Lys/Glu+Lys/Lys ) variants also had a 3 . 29-fold increased risk of lung cancer under the dominant model ( OR = 3 . 29 , 95% CI = 2 . 70–4 . 01 ) . The heritability test indicated that the p . Glu116Lys rare variant could explain about 2 . 16% of lung cancer heritability . In stratification analysis , as is presented in Table 3 , no deviation of p . 116Lys variants on cancer risk was observed in most subgroups except for the strata of clinical stages ( P = 0 . 016 ) . We further evaluated the relationships between MKK7 p . Glu116Lys and lung cancer progression , and found that p . Glu116Lys was significantly associated with pejorative clinical stages ( P <0 . 001 , shown in S1 Table ) . As is revealed in S1 Table , the patients with 116Lys variants had increased probability of progressing to IV stage ( OR = 1 . 69 , 95% CI = 1 . 28–2 . 24 ) . Likewise , the frequency of p . 116Lys adverse genotypes elevated continuously along with the risk of lymphatic metastasis extent at diagnosis ( 5 . 8% for 0 , 8 . 5% for 1 , 8 . 7% for 2 , and 10 . 8% for 3 ) , and with the distal metastasis extent at diagnosis ( 7 . 0% for 0 , 9 . 9% for 1 ) . In brief , patients with 116Lys variants were more likely to have metastasis ( either nodal or distal metastasis ) than those with Glu/Glu genotype ( OR = 1 . 84 , 95% CI = 1 . 34–2 . 53 ) . We further evaluated the associations between the combined types of those selected rare SNPs and lung cancer risk . As is shown in S2 Table , the individuals with only p . Glu116Lys variant was associated with lung cancer susceptibility ( exact P = 1 . 18×10−33 ) , accompanying by a 3 . 24-fold increased cancer risk ( OR = 3 . 24 , 95% CI = 2 . 64–3 . 97 ) , which was best fitted for the heredity model ( AIC value = 13840 . 2 ) . It achieved 100% study power and yielded a value of 0 . 000 with a 0 . 001 prior probability lower than the preset FPRP-level criterion 0 . 20 , suggesting that this finding is noteworthy . Individuals with a combination of p . Glu116Lys and p . Asn118Ser variant genotypes also had an increased risk of lung cancer ( OR = 3 . 16 , 95% CI = 1 . 02–9 . 76 ) , but it achieved only 66 . 7% moderate power and a 0 . 985 FPRP value at a 0 . 001 prior probability , which is higher than the preset criterion 0 . 20 . Furthermore , we also used the SKAT method to test combined genotypes associated with lung cancer risk , and found that only those combinations containing the p . Glu116Lys rare variation had prominent relevancies with lung cancer risk ( P <0 . 01 for all ) . All these results indicated that among all of the MKK7 five rare polymorphisms , the p . Glu116Lys contributed the main effect on lung cancer risk . A serial of experiments was further conducted to decipher the biological mechanisms of p . Glu116Lys on lung cancer . The distributions of demographic and clinical characteristics in the three datasets are presented in S3 Table . The Log-rank test and univariate Cox analysis revealed that patients with characteristics including ≥60 , smoking or advanced stage had a significantly shorter median survival time ( MST ) and an increased death risk ( P <0 . 05 for all ) . In contrast , the female patients , and those patients suffering from surgical operations , chemotherapy or radiotherapy prolonged survival time and had a more benignant prognosis ( shown in S3 Table ) . The relevancies between the MKK7 rare SNPs and lung cancer outcomes are shown in Table 4 . In the discovery set , compared with Glu/Glu genotype , the patients with p . 116Glu/Lys heterozygote had a significantly shorter MST ( 7 months vs . 13 months; Log-rank test P = 6 . 19×10−5 ) and a higher death risk ( hazard ratio [HR] = 1 . 69 , 95% CI = 1 . 31–2 . 19 ) . Multivariate proportional hazards regression analysis indicated that this rare variant appeared an undesirable survival of lung cancer under the additive genetic model ( HR = 1 . 63 , 95% CI = 1 . 31–2 . 03 ) . Congruously , the 116Lys ( Lys/Glu+Lys/Lys ) variants exerted a poor prognosis ( HR = 1 . 73 , 95% CI = 1 . 35–2 . 21 ) and a shorter MST ( 7 months vs . 13 months; Log-rank test P = 9 . 61×10−5; Fig 1A ) , while compared to the Glu/Glu wild-genotype . However , for other rare SNPs , no significant associations with lung cancer survival were found . The associations between MKK7 rare SNPs and prognosis of lung cancer were further verified in other two validation sets . In those two datasets , when compared with the Glu/Glu genotype , patients with Lys/Glu genotype had a decreased MST ( validation set I: 9 months vs . 15 months , Log-rank test P = 0 . 033; validation set II: 12 months vs . 16 months , Log-rank test P = 0 . 031 ) and had shown an increased death risk ( validation set I: HR = 1 . 43 , 95% CI = 1 . 10–1 . 85; validation set II: HR = 1 . 41 , 95% CI = 1 . 04–1 . 92 ) ; those patients carrying Lys/Lys homozygote also exerted a pernicious cancer prognosis ( validation set I: HR = 1 . 99 , 95% CI = 1 . 09–3 . 64; validation set II: HR = 2 . 34 , 95% CI = 1 . 15–4 . 75 ) , along with a shorter MST ( validation set I: 7 months vs . 15 months , Log-rank test P = 0 . 048; validation set II: 9 months vs . 16 months , Log-rank test P = 0 . 026 ) . Similarly , the patients with p . 116Lys ( Lys/Lys+Lys/Glu ) variants presented a shorter MST ( validation set I: 9 months vs . 15 months , Log-rank test P = 0 . 013 , Fig 1B; validation set II: 12 months vs . 16 months , Log-rank test P = 0 . 006 , Fig 1C ) and worse survival outcomes ( validation set I: HR = 1 . 49 , 95% CI = 1 . 17–1 . 90; validation set II: HR = 1 . 50 , 95% CI = 1 . 13–2 . 00 ) . Pooled analysis of the three cohorts indicated that patients with Lys/Glu or Lys/Lys variant genotype harbored reduced 4 and 7 MST months ( P = 2 . 61×10−7 ) , coupling with a 149% ( HR = 1 . 49 , 95% CI = 1 . 27–1 . 74 ) and a 194% ( HR = 1 . 94 , 95% CI = 1 . 31–2 . 89 ) cancer death risk , respectively , while compared to patients with Glu/Glu genotype . Also , the p . 116Lys ( Lys/Lys+Lys/Glu ) detrimental genotypes conferred a 5-months decreased in MST compared with that of Glu/Glu genotype ( 9 months vs . 14 months , Log-rank test P = 1 . 03×10−6 ) and had a 53% higher death risk ( HR = 1 . 53 , 95% CI = 1 . 32–1 . 78 ) . As is revealed in Table 5 , although the strength of relevance represented by the HR values between the p . 116Lys variants and lung cancer prognosis were different across a plurality of stratums , the homogeneity test showed that the difference was only significant in subgroups of clinical stage and distant metastasis ( P values equal to 0 . 019 and 0 . 010 , respectively ) . The unfavorable influence of the p . 116Lys variants on cancer prognosis was more conspicuous in advanced stages ( HR = 1 . 88 , 95% CI = 1 . 53–2 . 30 ) . The patients in the distant metastasis stage had 62% higher death risk than those without metastasis ( HR: 1 . 88 vs . 1 . 26 ) . We also found a remarkable modification effect between the clinical stage and the p . 116Lys variants on lung cancer prognosis ( P = 0 . 039 ) . We further analyzed the associations between the combinational genotypes of MKK7 rare SNPs and lung cancer prognosis . As is presented in S4 Table , combined-type of only p . Glu116Lys was significantly associated with cancer outcome . Patients only with Lys variants genotypes showed a shorter MST ( 9 months vs . 14 months , Log-rank test P = 4 . 01×10−8 ) and a higher death risk ( HR = 1 . 58 , 95% CI = 1 . 35–1 . 85 ) when compared with patients without those genotypes . This noticeable result achieved 100% study power and yielded a value of 0 . 000 at a 0 . 001 prior probability , which is lower than the preset FPRP-level criterion 0 . 20 . Although the combination of the p . Glu116Lys and p . Asn118Ser was significantly associated with survival time using Log-rank test ( P = 0 . 025 ) , but it obliterated the relevance in the Cox regression analysis ( HR = 1 . 74 , 95% CI = 0 . 99–3 . 08 ) and obtained a FPRP value of 0 . 991 at a 0 . 001 prior probability higher than the preset criterion 0 . 20 , suggesting that this result was likely to be untrustworthy . To explore the effects of the MKK7 p . Glu116Lys rare variant on cell biological behaviors , multitudinously functional experiments were further executed . The proliferation test showed that cells with over-expressing MKK7-116Lys displayed a higher proliferation potential than cells with over-expressing MKK7-116Glu ( Fig 2A , ANOVA test P<0 . 001 ) . Cells highly expressing MKK7-116Lys also had strikingly promoted abilities of colony formation in common plate , as well as in soft-agar , compared to the cells with MKK7-116Glu ( Fig 2B and 2C ) . In addition , we further performed flow cytometry to evaluate the influence of p . Glu116Lys variants on cells cycle and apoptosis . We found that the over-expressing MKK7-116Lys in A549 cells induced a significantly reduction in the G0/G1 phase ( 12 . 9% decreased , P = 0 . 015 ) and a corresponding increase in the G2/M phase ( 7 . 4% increased , P = 0 . 038 ) , while compared with the cells stably expressing MKK7-116Glu ( Fig 2D ) . Notably , the A549 cells with MKK7-116Lys also had decreased apoptosis rate than cells with MKK7-116Glu ( Fig 2E , P = 0 . 043 ) . Furthermore , cells with highly expression of MKK7-116Lys showed remarkably promoted migration and invasion capabilities in comparison to cells with over-expressing MKK7-116Glu ( Fig 2F and 2G ) . These arresting results also occurred in the L78 cells with stably over-expressing MKK7-116Lys . All these findings suggested that the MKK7-116Lys variant had a detrimental impact on promoting cell proliferation , invasion and immigration . To further determine the effect of p . Glu116Lys on tumor growth and metastasis in vivo , cells with stably over-expressing MKK7-116Glu or MKK7-116Lys were injected into nude mice subcutaneously ( both for A549 and L78 cell lines ) , and intravenously ( for A549 cell line only ) , respectively . As is shown in Fig 3A , the injection of MKK7-116Lys cells resulted in tumor formation began 4 days earlier compared to the results from injection of MKK7-116Glu cells . The tumor grew faster , and after 4 weeks , the tumor size in the former group was larger than the latter group ( For A549: 1246 . 3±102 . 3 mm3 vs . 846 . 3±78 . 5 mm3 , P <0 . 001 , Fig 3A; for L78: 1474 . 5±99 . 4 mm3 vs . 921 . 1±88 . 4 mm3 , P <0 . 001 , Fig 3B ) . Moreover , we used the MRI and histology examination to determine whether the MKK7 p . Glu116Lys could cause tumor metastases , and found that all the mice injected with A549 cells over-expressing MKK7-116Lys suffered from pulmonary metastasis , while the mice group injected with MKK7-116Glu A549 cells did not ( Fig 3C , 3D and 3E ) . These findings demonstrated that MKK7-116Lys variant enhanced lung tumor growth and metastasis in vivo . To decipher the potential mechanisms behind the MKK7 with p . Glu116Lys rare variant induced lung cancer risk and progression , we further performed DGE sequencing to compare gene profiles between A549-MKK7-116Glu cells and A549-MKK7-116Lys cells . We found that compared to cells with stably over-expressing MKK7-116Glu , cells with MKK7-116Lys had 192 genes differentially expression with a q value of <0 . 001 ( S1 File ) . Among these genes , 128 genes were up-expressed , and 64 genes were down-expressed . We further validated the DGE results using qRT-PCR assay , with detecting differently expressed genes including 5 up-expression genes ( STC2 , SLC1A3 , MSMO1 , BCL10 and HMGCR ) and 5 down-expression genes ( SAA1 , SBK2 , CDH5 , COL4A2 and BCL9L ) . The results were in concordance with those findings through DGE sequencing . Furthermore , we conducted Gene Ontology ( GO ) analysis using these differentially expressed genes . The GO results indicated that these 192 differentially expression genes were annotated to be associated with cell cycle process , cell proliferation , apoptosis , tissue development , tumor invasion , and metastasis et al . Above results suggests that alteration from 116Glu to Lys in MKK7 might influence its downstream targets expression and thus facilitate lung cancer initiation and development .
In the current study conducted among southern and eastern Chinese with a total of 5 , 016 lung cancer patients and 5 , 181 controls , we estimated the relationships between rare variants in MKK7 gene and lung cancer risk and prognosis , and found that the p . Glu116Lys rare variant was significantly associated with an increased lung cancer risk , progression and prognosis . The individuals with 116Lys variants had promotional cancer risk and higher probability of metastasis at diagnosis . The harmful role of the 116Lys variants also resulted in a poorer lung cancer prognosis in the patients than in the patients with Glu/Glu genotype . Further functional assays demonstrated that lung cancer cells with p . 116Lys variant accelerated cell growth , proliferation , colony formation , migration and invasion . They also promoted the xenograft growth and metastasis of nude mice in vivo through regulating a serial of cancer-related genes . However , no conspicuous evidence was obtained to prove any significant association between other rare SNPs and lung cancer risk and prognosis . To the best of our knowledge , this is the first study to investigate the associations between the genetic rare variants in MKK7 and lung cancer risk , as well as metastasis and prognosis . Accumulating evidence indicated that ‘missing heritability’ in complex human diseases caused increasing attention over the past a few years because the findings from the GWAS and other epidemiological studies did not completely explained the genetic heritability [4 , 5 , 24] . With rapid advances in high-throughput sequencing technologies , unnoticed genetic components such as low-frequency ( 1%≤MAF< 5% ) and rare genetic variants ( MAF< 1% ) are being thoroughly assessed and investigated for their associations with complex human diseases [6 , 7 , 25] . These approaches highlight an unparalleled opportunity to decipher unexplained genetic contributions in forming complex traits [26 , 27] , especially in human malignancies [10] . MKK7 has been identified to be a tumor suppressor gene constitutive activation of JNK signaling pathway to induce cell apoptosis [28 , 29] . Recently , a study reported that tissue-specific inactivation of the stress signaling kinase MKK7 in ras-driven lung carcinomas and NeuT-driven mammary tumors markedly accelerates tumor onset and reduces overall survival through directly coupling oncogenic and genotoxic stress to the p53 stability[11] . Lin HJ et al . identified that MKK7 could negatively regulate the expressions of MMP-2 and MMP-9 and thus inhibited cancer cell migration and invasion [17] . In addition , another report showed that ectopic expression of the MKK7 suppresses the formation of overt metastases by inhibiting the ability of disseminated cells to colonize the lung[14] . Furthermore , several studies display an intimate linkage with germline mutations in MKK7 and cancer onset and progression [30–32] . In the present study , we found that p . Glu116Lys rare variant in MKK7 contributed a pernicious impact on lung cancer risk and prognosis . We also observed a remarkable interaction between clinical stage and the rare variant on cancer survival . The p . Glu116Lys variant located at kinase activity domain of the MKK7 gene , which might influence the structure and functions of the MKK7 based on the bioinformatics analysis ( http://snpinfo . niehs . nih . gov/ ) . Our biological assays demonstrated that the 116 locus alteration from Glu to Lys in MKK7 could promote cells proliferation , migration , invasion , and reduce cells apoptosis in vitro; the adverse role of 116Lys variant was also found to facilitate the xenograft growth and metastasis in vivo . The 116Lys variant further altered the expression of downstream genes modulated by MKK7 as the DGE results showed , which might be closely related with lung cancer initiation and development . Among these differentially expressed genes , there were ones annotated as cell-regulated genes , cell apoptosis , cancer-related genes , tumor invasion and metastasis . For example , as the DGE results had indicated , STC2 , YEATS4 and SLC1A3 genes were up-expressed in the cells with stably over-expressing MKK7-116Lys compared with the cells with MKK7-116Glu . A study had reported higher mRNA and protein expressions of STC2 in lung cancer tissues compared to the adjacent normal tissue . Knockdown of STC2 slowed down lung cancer cell growth progression , colony formation and metastasis [33] . Another article showed the proof that overexpression of YEATS4 abrogated senescence in human bronchial epithelial cells , while RNAi-mediated attenuation of YEATS4 could conversely reduce lung cancer cells proliferation and tumor growth , impair colony formation , and induce cellular senescence[34] . In addition , several genes such as CDH5 and UBA7 ( also known as UBE1L ) were significantly down-regulated in the cells with MKK7-116Lys . A previous study had reported a downregulation of CDH5 in Bulgarian patients with early-stage non-small cell lung cancer [35] . Loss of UBE1L is a common event in lung carcinogenesis , and the UBE1L gene suppressed lung cancer growth by preferentially inhibiting cyclin D1 [36 , 37] . All the above public evidence was in accordance with our findings in the current study , which convincingly supported our ultimatums that MKK7 p . Glu116Lys rare variant exerted adverse effects on lung cancer risk , progression and prognosis by modulating a number of cancer-related genes . Our study has several strengths and limitations . Based on three independent case-control studies , we have obtained consistent results of the association between the MKK7 p . Glu116Lys rare variant and lung cancer risk and prognosis , with a compellingly strong study power of 100% ( two-sided test , α = 0 . 05 ) to detect an OR of 3 . 29 for the 116Lys variant genotypes ( which occurred at a frequency of 2 . 7% in the controls ) , and with a 100% statistical power for HR with a value to 1 . 53 , while compared with the 116Glu wild-genotype . A serial of functional experiments further sustained the results that the p . 116Lys variants conferred noxious effects on lung cancer risk , progression and prognosis . However , there are also some limitations . The selection bias is unavoidable on account of the hospital-based retrospective studies . Also , with restriction to a Chinese Han population , it is uncertain whether our findings could be generalized to other populations . Furthermore , due to the technological limitations , we did not promulgate any direct target genes of MKK7 with respect to the p . Glu116Lys rare polymorphism , which might help us to understand the precisely molecular mechanism of this rare SNP on influencing cancer risk and progression . In conclusion , our findings indicated that the p . Glu116Lys rare variant of MKK7 was associated with an increased lung cancer risk and worsened prognosis in Chinese , which was likely to be related to modulation of a serial of cancer-related genes . These results suggested that the MKK7 p . Glu116Lys may be a useful predictive biomarker for lung cancer susceptibility and prognosis . Validations through larger population-based studies in different ethnic groups , and functional assay to reveal target gene of the p . Glu116Lys rare SNP in MKK7 are warranted .
Each participant was scheduled for an interview to collect individual information on smoking status , alcohol use , and other selected factors , and to obtain a donated 5 mL of peripheral venous blood under his or her informed consent . The study was approved by the institutional review boards of Guangzhou Medical University ( Ethics Committee of Guangzhou Medical University: GZMC2007-07-0676 ) and Soochow University ( Ethics Committee of Soochow University: SZUM2008031233 ) . All experiments and procedures involving animals were conducted in accordance with guidelines approved by the Laboratory Animal Center of Guangzhou Medical University . In this study , three two-stage independently retrospective studies with a total of 5 , 015 lung cancer patients and 5 , 181 healthy controls were performed in southern and eastern Chinese populations . In brief , 1 , 559 lung cancer cases and 1 , 679 cancer-free controls as the discovery set , which included southern Chinese with 1 , 056 primary lung cancer cases and 1 , 056 healthy subjects recruited from the Guangzhou city , and eastern Chinese with 503 patients and 623 controls enrolled from Suzhou city , have been previously described[1 , 38 , 39] . In the validation set I , 1 , 949 lung cancer patients that were continuously recruited from Guangzhou between April 2009 and June 2014 with a 90% response rate and 1 , 957 sex and age ( ± 5 years ) frequency matched cancer-free controls who were randomly selected from about 3 , 000 individuals participating in health community programs with an 83% response rate were used . Moreover , the other population from Suzhou city was used as validation set II , in which 1 , 508 lung cancer cases were enrolled between December 2009 and March 2014 with an 85% response rate and 1 , 545 controls were randomly selected from 8000 participators in the annual healthy checkup programs with a response rate of 91% . All the participants were genetically unrelated ethnic Han Chinese and none had blood transfusion in the last six months . Definitions of smoking status , pack-years smoked , drink status , family history of cancer and family history of lung cancer have been previously described[38 , 39] . As was previously reported , clinical information and characteristics of patients were also collected [40] . Patient follow-ups were performed through telephone calls every three months from time of enrollment to the last scheduled follow-up or death . Survival time was calculated starting from the day the patients first received confirmed diagnoses to the date of the last follow-up or death , and dates of death were acquired from medical records or information provided by family members through telephone follow-ups . Patients that were lost to follow-ups or had no accurate data on clinical information were excluded . In the finalized study , 908 patients from the discovery set , 1027 patients from validation set I and 971 patients from validation set II that have completed the follow-up and had intact survival data were included in this study . In addition , to eliminate the bias in patient selection , we analyzed the differences in clinical features , as well as in survival data , between the included and excluded groups , and no deviated results were observed . Because no published data reveal potentially functional variants in MKK7 , we only selected those exon variants in gene coding region causing amino acid change that are supposed to be with most functional potential . Through the strategy of searching for the rare polymorphisms located in the MKK7 gene exons region based on the public dbSNP database ( http://www . ncbi . nlm . nih . gov/snp/ , access to 1/1/2014 ) , we found that 5 SNPs of MKK7 gene ( i . e . , rs28395770G>A: p . Glu116Lys , rs56316660A>G: p . Asn118Ser , rs56106612C>T: p . Arg138Cys , rs55800262G>A: p . Ala195Thr and rs1053566C>T: p . Leu259Phe ) were rare with MAF<1% in Chinese population . We then re-sequenced the whole cDNA of MKK7 in 100 normal Chinese Hans randomly picked from the controls , and no newfound rare variants outside of those 5 SNPs were obtained . Therefore , we chose these above rare SNPs in the current study . Genomic DNA was extracted from 2 mL peripheral blood using the routine method . Genotypes of all the selected SNPs were determined by direct DNA sequencing . A fragment of a total of 1 , 102 bp from the whole genomic DNA templates with the forward primer 5′-CCCAGCATTGAGATTGACCAGA-3′ and reverse primer 5′- TGCCATGTAGGCGGCACA-3′ , which comprises the 5 studied SNPs was amplified . The PCR program for the amplification was as follows: 95°C for 5 minutes and then 40 cycles of denaturation ( 95°C for 45 seconds ) , annealing ( 61°C for 1 minute ) , and extension ( 72°C for 1 minute and 30 seconds ) , and a final polymerization step at 72°C for 7 minutes . The products were then separated by a 1% agarose gel and extracted . Finally , the PCR products were sequenced by an automated sequencing system ( ABI Prism 3730 Genetic Analyzer; Applied Biosystems , Foster City , USA ) operating according to the manufactures’ protocols ( S1 Fig ) . The cDNA sequence of human MKK7 gene with a wild-type ( p . 116Glu ) was synthesized by the Sangon Biotech Company ( Shanghai , China ) and cloned into pLVX-IRES-neo expression vector ( Clontech Laboratories Inc . , San Francisco , CA , USA ) . The mutated pLV-MKK7-116Lys plasmid was induced by site-directed mutagenesis using the Quick Change XL site-directed mutagenesis kit ( Stratagene , La Jolla , CA , USA ) . The resulting constructs were verified by direct sequencing . The lentiviral production and transduction were performed abiding by protocol described elsewhere [40] . In brief , replication-defective VSV-G pseudotyped viral particles were packaged using a 3-plasmid transient cotransfection method ( Lenti-T HT packaging mix , Clonetech , San Francisco , CA , USA ) . Viruses were then harvested and concentrated . For transfection , two human lung cancer cell lines , A549 ( a human lung adenocarcinoma cell line ) and L78 ( a human lung squamous carcinoma cell line ) were infected with control lentivirus ( an “empty” vector without the MKK7 fragment inserted ) , pLV-MKK7-116Glu lentivirus and the pLV-MKK7-116Lys lentivirus , respectively . The cells were stably selected with G418 at 100 μg/ml ( Gibco , Lyon , France ) , and the drug-resistant cells were confirmed by qRT-PCR and western blotting assays ( S2 Fig ) . Cells infected with different allele lentivirus ( pLV-MKK7-116Glu and pLV-MKK7-116 Lys ) were seeded into 96-well flat-bottomed plates . 1 , 000 cells per 100 μl of cell suspension were used to add in each well . After a certain time of cultivation , cell viability was measured by MTT assay as is previously described [40] . In brief , 20 μl MTT solutions ( 5 mg/mL , Sigma , USA ) per well were added for 4 h before the end of the experiment . After that , the supernatant fluid was removed and 150 μl of DMSO was added to each well . The absorbance was then measured at 490 nm wavelength using a Plate Reader ( Bio-Tec Instruments , Inc . ) after shaking the plate for 15 min at room temperature . For cell cycle analysis , cells with stably expressing MKK7-116Glu or MKK7-116Lys were collected , washed with PBS and fixed by 70% ethanol for at least 1 h . Subsequently , the cells were stained with 0 . 5 mL propidium iodide ( PI ) staining solution , and cellular DNA content was analyzed using a flow cytometry ( BD Biosciences , CA , USA ) . For cell apoptosis , an annexin v-fluoresce-in isothiocyanate ( V-FITC ) /PI double staining assay was conducted according to the manufacturer instructions . In brief , the cells were harvested and stained with annexin V-FITC and PI for 20 min at room temperature in the dark . The cells were then washed twice with PBS , and the fluorescence of the cells was measured by flow cytometry . Cells with stably over-expressing MKK7-116Glu or MKK7-116Lys were seeded into a 6-well plate ( 100 cell/well ) with RPMI 1640 medium supplemented with 10% fetal bovine serum ( FBS ) , and allowed to grow until visible colonies formed ( approximately 2 weeks ) . After washing with PBS , the cell colonies were fixed with 4% paraformaldehyde and stained with crystal violet ( Invitrogen ) for 30 min , then washed , air dried , photographed and counted . Furthermore , colony formation assay in soft-agar was also executed to detect the effect of MKK7 Glu116Lys rare variant on cell malignant transformation . The detailed procedures were previously described [40] . Briefly , cells suspended with DMEM medium containing a concentration of 0 . 35% soft agar were poured onto 6-cm tissue culture dishes coated with 5 ml of 0 . 75% bottom agar . At the end of the experiment , the colonies were then stained , photographed and counted . Cell migration and invasion abilities were appraised by Corning transwell insert chambers ( 8-uM pore size; Costar , USA ) and BD BioCoat Matrigel Invasion Chamber ( Becton Dickinson Biosciences , USA ) , respectively . 2×104 ( migration assay ) or 2×105 ( invasion assay ) transfected cells in 200μl serum-free RPMI 1640 medium were seeded in the upper chamber , and 800 μl medium with 10% FBS were added to the lower compartment . After 24 h for migration assays or 48h for invasion assays at 37°C in a 5% CO2 humidified atmosphere , cells in the upper chamber were carefully scraped off using a cotton swab , and the cells that had migrated to or invaded the lower surfaces of the membrane were fixed with 4% paraformaldehyde solution and stained with crystal violet ( Invitrogen ) , imaged and counted . Assays were independently conducted for three times . Female BALB/c nude mice that were 4–5 weeks of age were purchased from the Laboratory Animal Center of Guangdong province ( Guangzhou , China ) . Cells with MKK7-116Glu or MKK7-116Lys were diluted to a concentration of 5×107/ml in physiological saline . 0 . 1 ml of the cells suspension was injected subcutaneously into the dorsal flank of mice to construct tumor growth model ( both for A549 and L78 cell lines ) , or injected intravenously into the caudal vein of mice to construct tumor metastasis model ( for A549 cell line only ) . Six nude mice were used for each group . When a tumor was palpable in the growth model , tumor size was measured every other day using a caliper along two perpendicular axes and calculated according to the following formula: Volume = 1/2×length×width2 . The tumor metastases were evaluated by magnetic resonance imaging ( MRI ) and histology examination . MRI was performed proximately 10 weeks post-injection using Philips Gyroscan Intera 1 . 5T ultraconducted MRI scanner ( Netherlands ) and incorporating a removable gradient coil insert . The details of MRI imaging were conducted as suggested by the public literature [41] . In brief , mice were placed prone on an MR-compatible sled within a carrier tube and positioned in the magnet . Induction and maintenance of anesthesia during imaging was achieved through inhalation of 10% chloral hydrate . MRI examination of coronal T2-weighted ( T2WI ) scanning was conducted with the following variables: Repetition time ( TR ) = 4000ms , echo time ( TE ) = 111ms , field-of-view ( FOV ) = 3 cm , number of slices = 20 , slice thickness = 1 . 0 mm , matrix = 256×256 . Following image acquisition , raw image sets were transferred to a processing workstation and processed using the medical imaging software . Tumor metastatic burden were calculated from manually traced regions-of-interest ( ROI ) . The animals were euthanized and their tumor masses were harvested and fixed with 10% neutral formalin solution , embedded in paraffin , and sectioned at 5 μm . The sections were then stained with hematoxylin-eosin ( HE ) staining and examined by light microscopy at 20× magnification . The total RNAs from different A549 transfectant cells were extracted using the TRIzol reagent ( Invitrogen ) in accordance with the manufacturer instructions . RNA quantity and quality were assessed using a NanoDrop 2000 spectrophotometer ( Thermo Scientific , MA , USA ) . The gene expression profiling both in A549-MKK7-116Glu cells and A549-MKK7-116Lys cells were conducted using Illumina NlaIII digital gene expression ( DGE ) sequencing . Analyses were performed according to the manufacturer recommendations [42] . Briefly , DGE sequence libraries were sequenced using Illumina HiSeq 2000 platform . Differentially expressed genes between the two groups of cells were identified using the reads per kilobase of transcript per million mapped reads ( RPKM ) method . The q value ≤ 0 . 001 and the absolute value of log2 ratio ≥ 1 were as the threshold to judge the significance of gene expression differences . On the basis of genes profiling sequencing results , the expression levels of 10 selected genes ( includes 5 up-expression genes and 5 down-expression genes ) in the A549-MKK7-116Glu cells and A549-MKK7-116Lys cells were verified by the quantitative real time PCR ( qRT-PCR ) assay described elsewhere [39] . The relative levels of RNA were detected using the ABI Prism 7900HT sequence detection system ( Applied Biosystems ) and with the SYBRPremix Ex Taq ( Perfect Real Time , TaKaRa , China ) and β-actin as the internal reference . Each assay was performed in triplicate and independently repeated three times . All the primers used for PCR amplification are listed in S5 Table . The chi-square test was used to assess differences in the distributions of demographic characteristics between cases and controls . The distributions of genotypes between cases and controls were analyzed with Fisher’s exact test . Unconditional logistic regression model with or without adjustment for surrounding factors was used to evaluate the associations between the MKK7 rare SNPs and lung cancer risk and metastasis . The correlations between MKK7 rare genotypes and lung cancer clinical features were tested using Spearman rank correlation . The sequence kernel association test ( SKAT ) was used to estimate the combined effect of multiple variants in MKK7 and lung cancer risk using R software ( version 3 . 0 . 2; The R Foundation for Statistical Computing ) with the SKAT package[43] . The REML model was used to assess the heritability explained by the genetic variants [44] . Breslow-Day test was used to test the homogeneity between the subgroups . The statistical power was calculated using the PS Software . The false-positive report probability ( FPRP ) test was applied to detect false-positive association findings [45] . The associations between clinical variables , as well as genotypes , and overall survival time were estimated using the Kaplan-Meier method and Log-rank test . The Cox proportional hazards regression model with or without adjustment for confounders was used to evaluate the effect of rare polymorphisms on lung cancer prognosis . Multiplicative interactions were assessed by logistic regression or Cox regression [38] . The differences in gene expression , colonies number levels , and cells’ ability to invade and migrate were analyzed using the student’s t-test . Repeated measure ANOVA test was performed to analyze the deviation of cell proliferation and tumor growth in different groups . All tests were two-sided using the SAS software ( version 9 . 3; SAS Institute ) and P <0 . 05 was considered statistically significant . | Rare variants have been identified to be associated with a variety of human malignancies , which account for a considerable fraction of heredity for complex diseases . To date , however , the precise molecular mechanism of rare variants involved in tumors initiation and progression largely remains unclear . We tested the associations between rare variants in MKK7 and lung cancer risk and prognosis in two-stage retrospective studies with a total of 5 , 016 lung cancer patients and 5 , 181 controls in Chinese . We found that the rare variant from Glu to Lys in MKK7 p . 116 locus exerted a detrimental effect on lung cancer risk , progression and prognosis . Further functional experiments demonstrated that lung cancer cells with p . 116Lys variant accelerated the potentials of cell growth , proliferation , colony formation , migration and invasion than the cells with p . 116Glu . This rare variant also promoted the xenograft growth and metastasis of nude mice in vivo through regulating a serial of cancer-related genes . Our data indicated that p . Glu116Lys rare variant in MKK7 might be a novel biomarker for lung cancer risk and prognosis . | [
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"histo... | 2016 | The MKK7 p.Glu116Lys Rare Variant Serves as a Predictor for Lung Cancer Risk and Prognosis in Chinese |
In many brain areas , sensory responses are heavily modulated by factors including attentional state , context , reward history , motor preparation , learned associations , and other cognitive variables . Modelling the effect of these modulatory factors on sensory responses has proven challenging , mostly due to the time-varying and nonlinear nature of the underlying computations . Here we present a computational model capable of capturing and dissociating multiple time-varying modulatory effects on neuronal responses on the order of milliseconds . The model’s performance is tested on extrastriate perisaccadic visual responses in nonhuman primates . Visual neurons respond to stimuli presented around the time of saccades differently than during fixation . These perisaccadic changes include sensitivity to the stimuli presented at locations outside the neuron’s receptive field , which suggests a contribution of multiple sources to perisaccadic response generation . Current computational approaches cannot quantitatively characterize the contribution of each modulatory source in response generation , mainly due to the very short timescale on which the saccade takes place . In this study , we use a high spatiotemporal resolution experimental paradigm along with a novel extension of the generalized linear model framework ( GLM ) , termed the sparse-variable GLM , to allow for time-varying model parameters representing the temporal evolution of the system with a resolution on the order of milliseconds . We used this model framework to precisely map the temporal evolution of the spatiotemporal receptive field of visual neurons in the middle temporal area during the execution of a saccade . Moreover , an extended model based on a factorization of the sparse-variable GLM allowed us to disassociate and quantify the contribution of individual sources to the perisaccadic response . Our results show that our novel framework can precisely capture the changes in sensitivity of neurons around the time of saccades , and provide a general framework to quantitatively track the role of multiple modulatory sources over time .
In many brain areas , particularly ‘associative’ regions including parietal and prefrontal cortex , sensory processing is affected by various intrinsic or extrinsic nonsensory covariates such as task or context variables , attention , learned associations , motor preparation , or cognition-related control signals . The fact that multiple such variables may simultaneously modulate sensory activity , and that their influence can change rapidly over the course of a task , poses challenges for precise experimental or computational quantification of their relative contributions to neuronal responses . In this paper , we develop a data-driven computational framework , which provides a rich statistical description of encoding time-varying sensory information by capturing and dissociating multiple time-varying modulations on the order of milliseconds . We develop and test our model in the context of changes in visual sensitivity around the time of eye movements , which is an exemplar of such time-varying modulatory computations . There is a considerable literature demonstrating that visual neurons’ responses are modulated during rapid eye movements , known as saccades; these neurophysiological changes presumably underlie the biases in perception which also occur around the time of eye movements . Even during fixation , extrastriate cortical responses can show complex spatiotemporal dynamics which encode information about the stimulus [1] . Various types of perisaccadic response modulations have been reported . For example , many studies have shown that visual neurons lose their sensitivity to stimuli appearing in their receptive fields ( RFs ) shortly before a saccade ( saccadic suppression ) [2–7] . Other studies have demonstrated that visual neurons may preemptively shift their RF to the post-saccadic RF ( future field remapping , or FF-remapping ) [8–15] , or to the saccade target ( saccade target remapping , or ST-remapping ) [16–18] , even before a saccade is initiated . Moreover , there are several reports suggesting that the spatial distribution of the population of visual neurons’ RFs changes during or just prior to a saccade [19–21] . Taken together , these findings indicate that the perisaccadic responses evoked in visual neurons are modulated by several sources , i . e . stimuli perisaccadically presented at multiple locations in the visual field contribute to driving neurons’ responses . Although existing experimental data identify those sources contributing to perisaccadic response modulation , they do not quantitatively characterize the contribution of each modulatory source to the response , alone or in combination with the other sources . A full understanding of visual perception during saccades may require the ability to reconstruct the visual scene across an eye movement based on neural activity , and this in turn necessitates a comprehensive understanding of how each modulatory source individually contributes to the perisaccadic representation , how the contributions of multiple sources are combined , and more importantly , how knocking out one of the sources may impact the reconstruction of the scene based on neural activity . Some of the limitations of the experimental data are practical: the short timescale on which the perisaccadic changes take place , combined with the limited number of trials that can be recorded in a single recording session , do not permit a full test of the contribution of different sources at each time point and in all combinations . This is where computational models come into play with two important roles , ( 1 ) making predictions of the neural responses to a wide variety of stimuli , and , ( 2 ) providing a quantitative description of how the modulatory sources contribute to response generation at different times relative to a saccade . The fast changes in sensitivity around the time of saccades pose challenges for computational as well as experimental approaches . These changes make the stimulus-response relationship time-variant and create nonstationary responses and computations . This property makes many existing computational approaches , which are often based on time-invariant assumptions about the neural system , not applicable for modeling the nonstationary responses observed during a saccade . The approaches that have commonly been used to characterize nonstationary responses can be divided into three main categories . In the first approach , separate models are applied to several ( overlapping or nonoverlapping ) time intervals assuming that the stimulus-response relationship remains constant within each of those intervals [22–26] . This approach is more suitable for providing coarse snapshots of the neural states rather than analyzing how the states evolve over time . To address this limitation , the second approach provides methods for estimating the temporal variations of the model parameters to keep track of the temporal evolution of the underlying system . Among the methods using this approach , adaptive filtering solutions have widely been used in neural data analysis , especially in studying the temporal evolution of the spatiotemporal and spectrotemporal receptive field of neurons [27–32]; however , depending on the size of their parameter space , these models require a large amount of data for their parameter estimation , and as a result they fall short in the cases where the evolution of the underlying system happens on a very short timescale , which is the case in perisaccadic studies . The third approach uses state-space methods such as linear dynamical systems [33–35] or hidden Markov models [36–41] , in which the next state of the system is determined based on its current state and its input . Although these methods have been successful in modeling dynamic neural data and especially for neural decoding applications , similar to adaptive filters , they require a large amount of data to work , which makes them insufficient for the resolution or precision required for modeling the perisaccadic responses . To address the need for a quantitative means to study nonstationary responses across a saccade , we recently developed an extension of the widely-used generalized linear models ( GLMs ) [42–47] , termed the nonstationary generalized linear model ( NSGLM , referred to as the N-model in this article ) to describe response dynamics in visual neurons across a saccade [48] . The multiplicative spatiotemporal gain kernels introduced in the N-model recovered the rapid eye displacement signal and the resulting nonstationarity in responses solely based on the statistical relationship between the stimulus and response across a saccade . However , the N-model structure remained limited to the types of nonstationarity that could be described by gain factors modulating the filtered input stimuli , and could not be generalized to explain the range of response nonstationarities and their underlying modulatory computations observed during saccades . None of the existing models are capable of tracking the dynamic changes in sensitivity which accompany saccades on millisecond timescales . In this paper , we present a new nonstationary approach to develop a sparse-variable generalized linear model ( referred to as the S-model ) , which is capable of tracking the rapid changes occurring in the stimulus-response relationship across a saccade in the middle temporal ( MT ) cortex of nonhuman primates . The S-model is composed of a set of time-varying stimulus kernels which represent the time-varying spatiotemporal sensitivity of a neuron . Building on the success of the S-model in predicting perisaccadic responses , a circuit-inspired factorized version of the S-model ( referred to as the F-model ) was developed in order to dissociate and to emphasize the role of individual sources contributing to perisaccadic response modulation . In the F-model , each stimulus kernel is decomposed into a parsimonious set of multiple modulatory sources , combined to describe the spatiotemporal receptive field of the neuron . The F-model not only accurately captures the perisaccadic changes in neural sensitivity , but also provides a tractable computational model by which the contribution of various modulatory sources can be dissociated . This temporally precise , quantitative decomposition of a neuron’s perisaccadic responses offers unique opportunities for quantifying perisaccadic modulations and testing the perceptual effects of various perisaccadic modulatory sources . Furthermore , the computational framework can be applied to quantify and dissociate the effects of multiple modulatory factors on neuronal responses in a variety of brain areas and behavioral tasks .
The principal objective for the new computational framework developed in this study is to provide a statistical framework that will capture the encoding of time-varying information in higher brain areas . The desire for such a model was motivated by our findings about the perisaccadic response properties of neurons in area MT of macaque monkeys . The activity of 41 single neurons in MT cortex was recorded while animals performed a visually-guided saccade task with probe stimuli ( Fig 1A ) . Each stimulus appeared on the screen for only 7 milliseconds ( ms ) , allowing a high spatiotemporal resolution mapping of the neurons’ visual sensitivity . Neurons exhibited several types of changes in perisaccadic sensitivity , including saccadic suppression , FF-remapping , and ST-remapping . Fig 1B shows the saccadic suppression effect in an example neuron and the subpopulation of significantly modulated neurons ( n = 8 ) . Perisaccadic visual responses to a stimulus in the original RF are reduced compared to responses during fixation . For the example neuron , the average response to an RF stimulus dropped from 60 . 89 ± 2 . 82 ( mean ± SE ) spk/s during fixation to 40 . 49 ± 12 . 64 ( mean ± SE ) spk/s when the stimulus appeared just prior to saccade onset ( example neuron , p < 0 . 001 ) . For the subpopulation of neurons with significant saccadic suppression ( see Methods ) , the average normalized response dropped from 2 . 53 ± 0 . 37 to 1 . 51 ± 0 . 29 ( mean ± SE ) . Fig 1C shows the FF-remapping effect in an example neuron and the subpopulation of significantly modulated neurons ( n = 23 ) . During fixation there is no response to stimuli appearing in the FF; however , during the perisaccadic period neurons respond to stimuli in the FF . Note that the perisaccadic FF response occurs at longer latency than RF responses ( neurons responding 50–75 ms after stimulus onset for the RF , vs . 80–150 ms after stimulus onset for the FF ) . For the example neuron , the average late response to an FF stimulus increased from 7 . 90 ± 1 . 02 ( mean ± SE ) spk/s during fixation to 18 . 42 ± 3 . 56 ( mean ± SE ) spk/s perisaccadically ( example neuron , p < 0 . 001 ) . For the subpopulation of neurons with significant FF-remapping , the average normalized response to FF stimuli increased from 0 . 90 ± 0 . 03 to 1 . 30 ± 0 . 08 ( mean ± SE ) . Fig 1D shows the ST-remapping effect in an example neuron and the subpopulation of significantly modulated neurons ( n = 37 ) . During fixation there is no response to stimuli appearing around the ST; however , during the perisaccadic period neurons respond to stimuli around the ST . Like the FF-remapping effect , the perisaccadic ST response occurs at longer latency than RF responses . For the example neuron , the average late response to a stimulus near the ST increased from 23 . 80 ± 1 . 77 ( mean ± SE ) spk/s during fixation to 64 . 64 ± 7 . 91 ( mean ± SE ) spk/s perisaccadically ( example neuron , p < 0 . 001 ) . For the subpopulation of neurons with significant ST-remapping , the average normalized ST response increased from 0 . 83 ± 0 . 03 to 1 . 35 ± 0 . 06 ( mean ± SE ) . The RF , FF , and ST effects could occur in different combinations or relative strengths across neurons . Fig 1E shows the FF- and ST-remapping effects for an example neuron and the subpopulation of neurons exhibiting both effects ( n = 22 ) . For the example neuron , the average late response to a stimulus in the FF increased from 15 . 83 ± 0 . 91 ( mean ± SE ) spk/s during fixation to 39 . 10 ± 5 . 14 ( mean ± SE ) spk/s perisaccadically ( example neuron , p < 0 . 001 ) , and the average response to a stimulus near the ST increased from 16 . 15 ± 0 . 94 ( mean ± SE ) spk/s during fixation to 36 . 17 ± 4 . 49 ( mean ± SE ) spk/s perisaccadically ( example neuron , p < 0 . 001 ) . For the subpopulation of neurons with significant FF- and ST-remapping , the average normalized FF response increased from 0 . 90 ± 0 . 03 to 1 . 32 ± 0 . 08 ( mean ± SE ) , and the average normalized ST response increased from 0 . 80 ± 0 . 03 to 1 . 46 ± 0 . 08 ( mean ± SE ) . A set of novel variants on a classical GLM framework were developed with the goal of capturing perisaccadic modulations in visual sensitivity . The principal idea for using a GLM framework as a base structure in this study has several folds: ( i ) providing a rich , statistical description of stimulus-response relationship , ( ii ) computational tractability of the estimation procedure , ( iii ) providing biologically plausible response generation and modulation components , and finally , ( iv ) its flexibility to incorporate a variety of external and internal covariates depending on the experimental design and the estimation or prediction tasks at hand , which is crucial in this study . The structure of the models is largely similar ( see Methods ) , with the exception of the stimulus kernels which are used to represent changes in spatiotemporal sensitivity . In the sparse-variable generalized linear model ( S-model ) , the stimulus-response relationship in a neuron is characterized by time-varying stimulus kernels representing the time-dependent spatiotemporal receptive field profile of the neuron . The kernels of the S-model were estimated by maximizing the likelihood of the observed spikes under the instantaneous firing rate predicted by the model over the training set , and validating over the validation set . We next produced a parsimoniously factorized version of the S-model ( F-model ) , in which the fitted kernels of the S-model were represented as a mixture of three time- and delay-dependent spatial skewed Gaussian kernels , where each captures the modulation arising from one of the RF , FF , or ST sources ( corresponding to the modulations observed in perisaccadic responses as detailed in the last subsection ) . Finally , an aggregate model ( A-model ) was constructed by fitting the S-model components ten times ( over subsets each including a randomly selected 65% of the data ) , creating an F-model from each of these S-models , and then taking the average of the F-model components , in order to obtain a model with kernels reflecting the entire dataset . Fig 2 shows the full structure of the S- and F-models . Both models specify the probabilistic relationship between a sequence of input stimuli , defined in time and space , and the measured neural spike trains on the scale of single trials . Inputs to the models ( Fig 2A ) are convolved with a set of time-varying stimulus kernels ( Fig 2B and 2C ) . For the S-model , each probe location has its own time-varying kernel ( S-kernels , Fig 2B ) . In the F-model , each S-kernel is optimally approximated by a combination of the RF , FF , and ST modulatory sources added to a fixation kernel ( F-kernels , Fig 2C ) . The output ( Fig 2D ) is then summed with an offset kernel ( Fig 2E ) and the feedback signal ( Fig 2F ) generated by the post-spike kernel ( Fig 2G ) . The resulting generator signal ( Fig 2H ) is then passed through a nonlinearity ( Fig 2I ) to generate the instantaneous firing rate ( Fig 2J ) . A Poisson spike generator ( Fig 2K ) is used to generate the spiking response ( Fig 2L ) . Fig 3 illustrates the ability of the S- and F-models to reproduce the time course of the neural activity across trials . Two sample trials are shown for three neurons indicating how well the models captured the instantaneous firing rate of the neurons on the unseen data . The trials on the left show examples of high prediction accuracy ( from top to bottom , ΔLL/spk = 1 . 12 , 0 . 80 , and 0 . 63 bits/spk for the S-model; and ΔLL/spk = 0 . 81 , 0 . 47 , and 0 . 55 bits/spk for the F-model ) , and the trials on the right show examples of median prediction accuracy ( from top to bottom , ΔLL/spk = 0 . 46 , 0 . 41 , and 0 . 19 bits/spk for the S-model; and ΔLL/spk = 0 . 41 , 0 . 34 , and 0 . 05 bits/spk for the F-model ) for each neuron and model . ( The A-model was omitted from this analysis as it uses all data when aggregating over the multiple F-model fits , and so , there is no unseen data for the A-model . ) A summary of the models’ structures and key properties is presented in Fig 4 . The N-model was described in a previous publication [48] and is presented here for the purpose of comparison . The N- , S- , and F-models are all variations on the well-known GLM structure , using different approaches to capture the nonstationarities existing in the perisaccadic responses . In terms of model components ( Fig 4 , first row ) , the N-model has a gain kernel and time-invariant stimulus kernel at each probe location , as well as post-spike and offset kernels , while the S-model has time-variant stimulus kernels at each probe location , a post-spike kernel , and an offset kernel . The components of the F-model were three skewed Gaussian kernels , and post-spike and offset kernels taken from the S-model . In the N-model , the nonstationarity is modeled by multiplying a space- and time-varying gain by the result of the convolution of input stimuli and time-invariant stimulus kernels ( Fig 4 , second row ) . Although successful in capturing the shift of spatial sensitivity following an eye movement , the N-model fails to describe the perisaccadic responses arising from a range of modulatory computations beyond an instantaneous gain mechanism , e . g . changes in response latency . The S-model deals with this issue by employing time-varying stimulus kernels , providing more degrees of freedom for the model; in effect , the S-model can be considered as a set of GLMs , each one corresponding to a single time point and all fitted simultaneously . The S-model accurately characterizes the observed perisaccadic modulations , but does not lend itself easily to a mechanistic level interpretation . To find a way to interpret the S-kernels , and identify and dissociate possible sources that give rise to the spatiotemporal sensitivities revealed by the S-kernels , the fitted stimulus kernels of the S-model were factorized into sources of modulation in the F-model , such that stimulus kernels were reconstructed by a combination of three modulatory sources added to a fixation kernel representing the neuron’s behavior during fixation . This allowed the interpretation of changes in responses ( especially perisaccadic responses ) as resulting from changes in the characteristics of a few modulatory sources . Due to the structure of the N-model , the model components responsible for response generation ( stimulus kernels ) and response modulation ( gain kernels ) are fitted independently , while these components are interwoven in the S- ( two-dimensional stimulus kernels ) and F-models ( skewed Gaussian kernels ) ( Fig 4 , third row ) . While in the N- and S-models there are separate stimulus kernels corresponding to separate probe locations , the modulatory sources in the F-model have a spatial profile , and so , the spatiotemporal receptive field structure of the neuron in the F-model is described by just three sources at each time point relative to the saccade instead of a set of stimulus kernels ( Fig 4 , fourth row ) . Moreover , while the response latency cannot vary across time in the N-model , and can vary at each time and probe location in the S-model , the F-model determines the response latencies based on three modulatory sources ( Fig 4 , fifth row ) . The ability of the N- , S- , and F-models to predict single spike trains ( over test data ) during the fixation vs . perisaccadic period is compared in Fig 5A ( again the A-model was omitted from this analysis for the same reason described in Fig 3 ) . The N-model is no better at predicting the spike times during the perisaccadic period compared to the fixation period ( ΔLL/spk = 0 . 12 ± 0 . 02 ( mean ± SE ) bits/spk during fixation vs . 0 . 13 ± 0 . 02 ( mean ± SE ) bits/spk during saccade , p-value = 0 . 26 ) . For the S-model , the prediction accuracy increases in the perisaccadic period ( ΔLL/spk = 0 . 24 ± 0 . 02 ( mean ± SE ) bits/spk during fixation vs . 0 . 30 ± 0 . 02 ( mean ± SE ) bits/spk during saccade , p-value < 0 . 001 ) . The F-model , despite being based on an approximation of the S-model , also shows greater prediction accuracy during the perisaccadic period compared to fixation ( ΔLL/spk = 0 . 14 ± 0 . 01 ( mean ± SE ) bits/spk during fixation vs . 0 . 18 ± 0 . 02 ( mean ± SE ) bits/spk during saccade , p-value = 0 . 003 ) . These improvements reflect the efficiency of the fitting strategy tailored to capture dynamic aspects of the response . The richer architectures of the S- and F- models allowed them to capture dynamic changes in the neuron’s spatiotemporal response as a function of time to the saccade , and as a result , both better predict the responses compared to the N-model . Both the S- , and F-models outperform the N-model during both the fixation ( S-model vs . N-model: p-value < 0 . 001; F-model vs . N-model: p-value = 0 . 001 ) and perisaccadic ( S-model vs . N-model: p-value < 0 . 001; F-model vs . N-model: p-value < 0 . 001 ) periods . In all comparisons , the statistical significance was determined by the Wilcoxon signed-rank test ( n = 41 neurons ) . As seen in Fig 5A , the F-model trails the S-model in terms of spiking time prediction , which is not surprising given the greater number of variables in the S-model; however , when it comes to the previously reported perisaccadic modulations ( i . e . , saccadic suppression , FF-remapping , and ST-remapping ) , the F-model performs as well as the S-model . In order to demonstrate this point , Fig 5B examines the ability of all four models to reproduce the saccadic suppression , FF-remapping , and ST-remapping effects ( over all recorded trials , not only test trials , to avoid a bias toward a subset of data ) . For this analysis , neurons were categorized as displaying or not displaying each of three perisaccadic effects using their recorded responses . Then , the models’ predictions were used to re-classify neurons into the same categories , and the response-based and model-based classifications were compared using four different measures of classification performance ( sensitivity , accuracy , the geometric mean of sensitivity and precision , and F-measure ) . The predictions of each model were compared using the one-tailed mid P-value McNemar test [49] , with the null hypotheses that the S-/F-/A- and N-models have equal performance in terms of reproducing each of three perisaccadic effects ( P-values less than 0 . 05 were considered a rejection of the null hypothesis ) . For the FF-remapping effect , the A-model significantly outperforms the N-model ( p = 0 . 048 ) ; the S- and F- models did not significantly outperform the N-model ( S-model vs . N-model: p = 0 . 17 , and F-model vs . N-model: p = 0 . 08 ) . The S- , F- , and A-models all significantly outperform the N-model in terms of reproducing the ST-remapping effect ( S-model vs . N-model: p = 0 . 004 , F-model vs . N-model: p = 0 . 006 , and F-model vs . N-model: p = 0 . 006 ) . Finally , we did not find any significant difference between the S- , F- , and A-models and N-model in terms of reproducing the saccadic suppression effect ( S-model vs . N-model: p = 0 . 38 , F-model vs . N-model: p = 0 . 17 , and F-model vs . N-model: p = 0 . 11 ) , indicating that a global change in gain is sufficient to reproduce the saccadic suppression effect . Additionally , in order to compare the ability of the S- and F-models to accurately predict responses specifically during the perisaccadic period , we calculated the ratio of perisaccadic performance to fixation performance ( in terms of ΔLL/spk over test data ) for the S- and F-models . Since adding additional variables is generally expected to improve model performance , during both the fixation and perisaccadic periods , this ratio of perisaccadic to fixation performance was used to quantify the added perisaccadic predictive value of the model regardless of overall changes due to the degrees of freedom . The S-model has a lower perisaccadic to fixation ratio compared to the F-model ( 1 . 33 ± 0 . 99 ( median ± SE ) for the S-model vs . 1 . 51 ± 0 . 25 ( median ± SE ) for the F-model , Wilcoxon signed‐rank p‐value = 0 . 003 ) , indicating that although overall performance is higher due to the S-model’s greater degrees of freedom , the F-model does better at specifically reproducing perisaccadic changes in the response . In order to assess the contribution of individual sources , represented by time- and delay-dependent spatial skewed Gaussian kernels , to the F-model’s performance , different variants of the F-model were created ( as detailed in the Methods section ) . These variants included models in which only one of the three sources was included ( +RF , +FF , and +ST ) , models in which one of the sources was eliminated ( -RF , -FF , or -ST ) , and one in which all three modulations were eliminated ( no-source ) . A linear regression was conducted in which the slope of a line fitted to the perisaccadic vs . fixation performance for the population of 41 neurons was considered as an estimate for the perisaccadic to fixation ratio of each model . In order to evaluate the role of each source in the model’s perisaccadic performance , the ratios of perisaccadic to fixation performance were compared across the model variants described above . As seen in Fig 5C , top panel , the no-source model has the lowest perisaccadic to fixation performance ratio ( 0 . 28 ± 0 . 08 ) . The perisaccadic to fixation performance ratio increases when any individual source is added to the model ( +RF: 0 . 53 ± 0 . 09 , +FF: 0 . 95 ± 0 . 14 , +ST: 0 . 48 ± 0 . 09 ) . The F-model which incorporates all three sources has the highest perisaccadic to fixation performance ratio ( 1 . 35 ± 0 . 09 ) , and eliminating any individual source decreases the perisaccadic to fixation performance ratio ( -RF: 1 . 20 ± 0 . 11 , -FF: 0 . 72 ± 0 . 08 , -ST: 1 . 27 ± 0 . 11 ) . Fig 5C , bottom panel , summarizes these results in terms of what percent of the total improvement ( the difference between the F-model and the no-source model ) is present after adding or eliminating each of the sources . As seen , adding the RF , FF , or ST source improves the perisaccadic to fixation ratio by 23 . 38% , 62 . 65% , and 18 . 89% , respectively , and eliminating the RF , FF , or ST source decreases the ratio by 14 . 79% , 59 . 38% , and 7 . 87% , respectively . From now on , “model” refers to the A-model whenever not otherwise specified . As seen in Fig 6 , the model mimics the perisaccadic responses at both the level of individual neurons and the subpopulation of neurons which display each perisaccadic modulation . The model predictions are shown for the same sample neurons depicted in Fig 1B–1E . Fig 6A , top panel , shows the saccadic suppression effect observed in Fig 1B ( left ) as predicted by the model . For this neuron , the average model-predicted firing rate over the early response window ( 50–75 ms after stimulus onset ) in response to an RF stimulus dropped from 22 . 69 ± 0 . 78 ( mean ± SE ) spk/s during fixation to 15 . 72 ± 1 . 72 ( mean ± SE ) spk/s when the stimulus appeared just prior to saccade onset ( example neuron , p < 0 . 001 ) . The average of the perisaccadic normalized responses for the subpopulation of neurons with a significant saccadic suppression effect in both the experimental data and model prediction are shown in Fig 6A , middle and bottom panels respectively ( n = 7 out of 8 ) . For this subpopulation , the average normalized response over the early response window dropped from 2 . 48 ± 0 . 42 to 1 . 38 ± 0 . 30 ( mean ± SE ) , and the average predicted firing rate over the same response window decreased from 1 . 59 ± 0 . 16 to 1 . 06 ± 0 . 09 ( mean ± SE ) . Fig 6B , top panel , shows the FF-remapping effect observed in Fig 1C ( left ) as predicted by the model . For this neuron , the average model-predicted firing rate over the late response window ( 80–150 ms after stimulus onset ) in response to an FF stimulus increased from 8 . 01 ± 0 . 06 ( mean ± SE ) spk/s during fixation to 11 . 64 ± 0 . 36 ( mean ± SE ) spk/s when the stimulus appeared just prior to saccade onset ( example neuron , p < 0 . 001 ) . The average of the perisaccadic normalized responses for the subpopulation of neurons with a significant FF-remapping effect in both the experimental data and model prediction are shown in Fig 6B , middle and bottom panels respectively ( n = 20 out of 23 ) . For this subpopulation , the average normalized response over the late response window increased from 0 . 89 ± 0 . 03 to 1 . 36 ± 0 . 08 ( mean ± SE ) , and the average normalized predicted firing rate over the same response window increased from 0 . 93 ± 0 . 02 to 1 . 19 ± 0 . 04 ( mean ± SE ) . Fig 6C , top panel , shows the ST-remapping effect observed in Fig 1D ( left ) as predicted by the model . For this neuron , the average model-predicted firing rate over the late response window in response to an ST stimulus increased from 24 . 53 ± 0 . 39 ( mean ± SE ) spk/s during fixation to 47 . 24 ± 2 . 25 ( mean ± SE ) spk/s when the stimulus appeared just prior to saccade onset ( example neuron , p < 0 . 001 ) . The average of the perisaccadic normalized responses for the subpopulation of neurons with a significant ST-remapping effect in both the experimental data and model prediction are shown in Fig 6C , middle and bottom panels respectively ( n = 24 out of 37 ) . For this subpopulation , the average normalized response over the late response window increased from 0 . 79 ± 0 . 03 to 1 . 48 ± 0 . 07 ( mean ± SE ) , and the average normalized predicted firing rate over the same response window increased from 0 . 92 ± 0 . 02 to 1 . 19 ± 0 . 04 ( mean ± SE ) . Finally , Fig 6D , top row , shows the FF- and ST-remapping effects observed in Fig 1E ( left column ) as predicted by the model . For this neuron , the average model-predicted firing rate over late response window to FF and ST stimuli increased from 17 . 39 ± 0 . 38 ( mean ± SE ) spk/s and 15 . 85 ± 0 . 36 ( mean ± SE ) spk/s during fixation to 32 . 12 ± 2 . 50 ( mean ± SE ) spk/s and 33 . 11 ± 1 . 98 ( mean ± SE ) spk/s when the stimuli appeared just prior to saccade onset respectively ( example neuron , p < 0 . 001 for both ) . The average of the perisaccadic normalized responses for the subpopulation of neurons with significant FF- and ST-remapping effects in both the response and model prediction are shown in Fig 6D , middle and bottom rows respectively ( n = 17 out of 22 ) . For this subpopulation , the average normalized response to FF and ST stimuli over the late response window increased from 0 . 88 ± 0 . 03 to 1 . 43 ± 0 . 09 ( mean ± SE ) and from 0 . 78 ± 0 . 04 to 1 . 56 ± 0 . 09 ( mean ± SE ) respectively , and the average predicted firing rate in response to FF and ST stimuli over the same response window increased from 0 . 92 ± 0 . 02 to 1 . 22 ± 0 . 05 ( mean ± SE ) and from 0 . 91 ± 0 . 02 to 1 . 23 ± 0 . 06 ( mean ± SE ) respectively . Next , we compared the actual and predicted prevalence maps of the saccadic suppression , FF-remapping , and ST-remapping effects as a function of time from saccade and from stimulus onset . Fig 7A shows the experimental prevalence maps of saccadic suppression , FF-remapping , and ST-remapping ( left to right , respectively ) ; each plot shows the percent of neurons displaying the corresponding effect at each time point relative to saccade and stimulus onset . Fig 7B displays the same prevalence maps based on the model predictions . The frequency of occurrence and the timing of effects across the population are similar for the experimental data and the model prediction . This fact confirms that not only can the model replicate the perisaccadic effects well , but also it follows the dynamics ( timing ) of those perisaccadic modulations very closely . Strong correlations between the experimental and model-predicted values confirmed the model ability to replicate the timing and frequency of occurrence of effects across the population ( saccadic suppression , r = 0 . 65 , p < 0 . 001; FF-remapping , r = 0 . 60 , p < 0 . 001; and ST-remapping = 0 . 62 , p < 0 . 001; Pearson product-moment correlation ) . As detailed in the Methods section , the experimental prevalence maps are drawn based on the empirical firing rate values obtained by smoothing the observed spike trains , and accordingly have higher variability than the model-predicted firing rate values . This high variability means that fewer effects reach statistical significance , as reflected in the low values of the experimental maps in comparison with the model-predicted ones . The model allows us to disassociate the contributions of multiple modulatory sources in generating the instantaneous firing rate of a neuron at each point in time relative to a saccade , which was not possible with any previous computational approach . The contributions of the ST , FF , and RF sources to the neurons’ response are illustrated schematically in Fig 8A and 8B . Fig 8A shows how the three sources contribute differently to the neuron’s firing activity at different times relative to saccade onset . As seen , the evoked response in the neuron at each time point is due to stimuli presented in the ST , FF , and/or RF probe locations at different latencies relative to the response onset . In addition , each source contributes with a specific gain . The latency and gain corresponding to each contributing source vary over time . Fig 8B simplifies this idea in a schematic: multiple modulation sources ( here: the ST , FF , and RF sources ) contribute to the response generation at each instant of time with time-varying gains and latencies . Fig 8C illustrates one’s neurons changing ability to detect stimuli presented at different locations over time relative to saccade . To quantify a neuron’s ability to detect stimuli , the receiver operating characteristic ( ROC ) curve analysis was used: for each location and latency , the detectability of the neuron was defined as the ROC between the model-predicted responses and the trial-shuffled responses ( see Methods ) . The neuron’s maximum detectability at a specific location and the latency at which that maximum occurs correspond , respectively , to the gain and latency values shown in Fig 8A and 8B . Fig 8C shows the detectability values of an example neuron for visual stimuli appearing in the RF , FF , and ST locations as a function of time relative to saccade and stimulus onset . The neuron can detect stimuli presented in the RF location with a latency of ~60 ms; it loses its ability to detect stimuli in the original RF location after the saccade ( ~60 ms after saccade onset ) . At almost the same time that the neuron loses the ability to detect stimuli at the RF location , the neuron becomes able to detect stimuli presented at the FF location with a latency of ~100 ms , and stimuli presented at the ST location with a latency of ~100 ms . Finally , at ~100 ms after a saccade , the neuron can detect stimuli presented in its new RF location ( former FF location ) with a latency of ~60 ms ( the normal latency of MT neurons ) . In brief , Fig 8C shows that a visual neuron can detect stimuli presented at different locations at different latencies during the perisaccadic period , while it is only sensitive to the stimuli presented at the current RF location at normal latency during the fixation period . In order to make this point clear , Fig 8D examines the stimulus detectability and latency for different locations at a single time point relative to the saccade ( responses measured at 55 ms after saccade ) for the example neuron . As seen , peak detectability at the FF and ST locations occurs at longer latencies than peak detectability for the RF location; however , peak detectability values for the FF and ST locations are nearly equal to the original RF . So , the stimuli which appeared in the RF ~60 ms earlier and stimuli which appeared in the FF or ST ~100 earlier are equally detectable based on the neuron’s activity at that time point , demonstrating how multiple sources can simultaneously drive the neuron’s response with different latencies and gains . The temporal evolution of the example neuron’s ability to detect stimuli at different locations , and the latency at which the neural response reflects the presence of a stimulus , are shown in Fig 8E , left panel . In this figure , the maximum detectability and the corresponding latency value for a single neuron for a stimulus presented at the RF , FF , or ST probe location are displayed . The stimulus detectability at the RF , FF , and ST probe locations is represented by the red , blue , and green circles , respectively . The x-axis represents time from saccade onset , and the y-axis represents the latency at which peak detectability occurs . The value of peak detectability at each time point is indicated by the size of circles . As seen , at first , the example neuron can only detect stimuli presented at the RF location ~60 ms earlier ( red circles ) . Next , ~65 ms before the saccade , the neuron can also detect stimuli presented at the FF location ~90 ms earlier ( blue circles ) . This ability to detect FF stimuli disappears 25 ms later , then reemerges ~13 ms after the saccade ( with a latency of ~80–100 ms ) and persists for ~60 ms . At that point the former FF becomes the RF , and the neuron responds to stimuli in this location with the normal latency of MT neurons , i . e . ~60 ms . Beginning ~80 ms after the saccade , the neuron can no longer detect stimuli presented at the RF location ( no more red circles ) . Meanwhile , from 66 to 130 ms after the saccade , the example neuron detects ST stimuli which were presented ~130 ms earlier ( green circles ) . Taken together , Fig 8E ( left panel ) confirms that while the neuron can only see the stimuli presented at the ( former or new ) RF location at normal latency during the fixation period , it can detect stimuli presented at different locations at different latencies during the saccade . To provide a sense of how Fig 8E , left panel , was generated , an animated 3D movie depicting the same information was created ( S1 Movie: see Supporting Information ) . This movie shows the temporal evolution of the maximum detectability and the corresponding latency value for the same neuron shown in Fig 8E , left panel , in response to stimuli presented at the RF , FF , and ST probe locations as a saccade is prepared and executed . The detectability to the RF , FF , and ST locations is represented by the red , blue , and green discs , respectively . In this 3D space , the x and y values represent horizontal and vertical coordinates within the visual field , and the z-axis represents the latency at which peak detectability occurs . Time relative to the saccade changes over time ( at a much slower speed ) , shown by the time label values on top of the plot . The color intensity of the discs represents the peak detectability value . To understand how the neural detectability of FF and ST stimuli evolves over time in a population of neurons , synthetic neurons were constructed corresponding to the subpopulation average FF- and ST-remapping effects . These synthetic neurons were constructed by averaging the Gaussians of the neurons displaying each effect in both the response and model prediction ( as detailed in the Methods section ) . These synthetic neurons illustrate the population time course of the changes in detectability across the saccade . Fig 8E , middle panel , illustrates how the stimulus detectability at the FF location changes over time relative to a saccade . From ~50 ms before to ~60 ms after a saccade , the population can detect FF stimuli with latencies of ~60–90 ms , indicating that the neural population is detecting stimuli appearing in the FF ~140 to ~0 ms before the saccade . This figure also shows that at around 60 ms after a saccade , the latency of the stimulus detectability at the FF location returns to ~60 ms ( i . e . , the RF latency value ) , indicating that the FF has become the new RF . Fig 8E , right panel , illustrates how the population’s ability to detect stimuli presented at the ST location changes over time . Similar to the FF-remapping effect , the population can detect ST stimuli at longer latencies ( ~100–120 ms ) from 45 before to 15 ms after a saccade and from 74 to 114 ms after a saccade . 3D depictions of Fig 8E , middle and right panels , are presented as animated S2 & S3 Movies ( see Supporting Information ) respectively . These figures ( and movies ) reveal that FF and ST stimuli contribute to response generation well before saccade initiation , influencing neurons’ responses both during the saccade and after the eyes have landed .
We developed a time-varying model framework capable of precisely capturing fast changes in neuronal responses–induced by dynamic task and behavioral variables–at the resolution of individual neurons , spikes , and trials , as well as dissociating and quantifying the modulatory sources contributing independently to these changes . This new framework yielded several new insights into the coding of time-varying sensory information when applied to the spike trains in the visual cortex measured across rapid eye movements . We tested our model’s ability to capture and dissociate multiple time-varying sources of modulation in the context of perisaccadic changes in visual sensitivity . Neurons’ visual sensitivity changes dramatically around the time of saccades [3–6 , 11–16] . The fast timescale on which these changes occur creates challenges both for experimental approaches and for computational models aiming to quantitatively characterize the relationship between various extrinsic or intrinsic system covariates and the modulations in sensitivity to visual input . Our approach combines high spatiotemporal resolution visual stimulation across many locations within the visual field of a neuron with a novel GLM-based model structure in order to accurately capture these fine timescale modulations . The model successfully predicted the responses of visual neurons in area MT , including during the perisaccadic period , at the level of single trials and with a temporal resolution beyond that of existing approaches . Moreover , the model could accurately reproduce the perisaccadic modulations observed in perisaccadic responses , including saccadic suppression , FF-remapping , and ST-remapping , at the level of both single neurons and the population; a goal which , to our knowledge , was unattainable using any previously reported computational framework . These modulations are consistent with previous neurophysiological studies; for example , the FF-remapping effect seen here is consistent with previous reports of memory remapping , but no predictive/anticipatory remapping , in MT [50–52] . The combination of the computational framework designed to capture dynamic changes in sensitivity and a high spatiotemporal resolution stimulus presentation paradigm allowed us to investigate how the spatiotemporal receptive field of a visual neuron evolves over time with a resolution on the order of <10 ms . Importantly , the stimulus presentation paradigm makes no assumptions about the time windows or spatial locations to which neurons will respond , and therefore in comparison with conventional experimental techniques , which mostly rely on a-priori selection of relevant stimulus locations and comparatively large temporal windows , offers both a more complete and higher spatiotemporal resolution picture of dynamic changes in neural sensitivity across saccades . The fitted kernels in the models represent the time-varying spatiotemporal receptive field structure of the neuron as captured by the models . Beyond merely tracking the changes in neurons’ RFs , this research provides a means to quantitatively study how multiple modulatory sources interact in generating the response of a neuron at each moment in time , and accordingly how the visual scene is encoded by neurons . This in turn can provide insight into the problem of how the pre- and post-saccadic scenes are integrated across saccades . The computational framework developed in this paper opens up a plethora of opportunities for future research and applications; here we discuss some of these possible future uses of the model . First of all , the model can be used to examine the neural basis of psychophysical phenomena observed during eye movements . There are several reports indicating that saccades alter the perception of space and time [53–64] . Several research groups have tried to find explanations for these perceptual changes using abstract models designed to simulate the spiking behavior of visual neurons [21 , 65–68] . The present computational framework provides a data-driven , more complete , and quantified description of perisaccadic response modulations , solely based on the statistical features of real spiking data and their relationship with a variety of covariates , allowing examination of the contribution of independent components to various perisaccadic perceptual phenomena . Secondly , the model can provide a read-out of visual information based on neural responses over the time course of a saccade . The choice of pseudorandom visual probe patterns across space and time generated an unbiased estimation of the encoding model , which is crucial for decoding arbitrary temporal and spatial information , which in turn has not been provided to the model during training . This model-based decoding of the spiking activity allows us to directly and quantitatively link neural activity to perception . Thirdly , the decomposition of modulatory effects provides tools for identifying the sources and consequences of their associated modulations . Sources can be selectively eliminated from the model to test their effects on the perceptual read-out , and the effect of source elimination on neural responses can be compared to the results of inactivation experiments . An important question in visual neuroscience is which area or areas in the brain are responsible for the perisaccadic changes in visual neurons . Our model provides an opportunity to explore this question by dissociating sources of modulation , quantifying their strength over time , and correlating them with the activity of various areas of the brain with the aim of finding which area might control each of the modulatory sources , and how inactivation of that area may alter perception across saccades . The current version of the F-model makes some assumptions about the different types of perisaccadic modulatory signals , based on previously reported neurophysiological effects; future work , however , will screen the S-models’ response functions to identify perisaccadic modulations without the assumptions built into the F-model . Finally , the proposed computational framework can serve to generate artificial populations of neurons and test their responses to a larger number of perisaccadic stimuli than would be experimentally feasible , allowing investigation of population-level visual representations . In total , the model presented here provides a blueprint for how sensory stimuli and internal covariates evoke different neural responses due to changes in the neural state . This framework can be applied not only to perisaccadic visual responses , but to a wide variety of brain areas and behavioral contexts in which sensory responses are modulated by combinations of factors such as attentional state , context , reward history , motor preparation , learned associations , and other cognitive variables .
Two adult male rhesus monkeys ( Macaca mulatta ) were used in this study . All experimental procedures were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and the Society for Neuroscience Guidelines and Policies . The protocols for all experimental , surgical , and behavioral procedures were approved by the Montana State University Institutional Animal Care and Use Committee . Animals were pair-housed when possible and had daily access to enrichment activities . During the recording days , they had controlled access to fluids , but food was available ad libitum . All surgical procedures were carried out under Isoflurane anesthesia and strict aseptic conditions . Prior to undergoing behavioral training , each animal was implanted with a stainless steel headpost , attached to the skull using orthopedic titanium screws and dental acrylic . All surgical procedures were carried out under Isoflourane anesthesia and strict aseptic conditions . Following behavioral training , custom-made PEEK recording chambers ( interior 22 × 22 mm ) were mounted on the skull and affixed with dental acrylic . Within the chamber a 22 × 22 mm craniotomy was performed above the extrastriate visual areas including areas V4 and MT ( extrastriate craniotomies were centered at −6 mm A/P , 23 mm M/L and −13 mm A/P , 23 mm M/L ) . Monkeys performed a visually guided saccade task during which task-irrelevant random dot stimuli flashed on screen . Two adult male rhesus monkeys were trained to fixate on a fixation point ( FP; a red dot ) located in the center of the screen . After they fixated , a saccade target ( ST; a red dot ) appeared 10 degrees away horizontally . Then , after a randomized time interval between 600 and 750 ms ( drawn from a uniform distribution ) , the fixation point disappeared , cuing the monkeys to make a saccade to the ST . After remaining fixated on the ST for 600 ms monkeys received a reward . During this procedure , a series of randomly located probe stimuli were presented on the screen in a 9 by 9 grid of possible locations . Each stimulus was a white square ( full contrast ) , 0 . 5 by 0 . 5 degree of visual angle ( dva ) , against a black background . Each stimulus lasted for 7 ms and stimuli were presented consecutively without any overlap , such that at each time point there was exactly one stimulus on the screen . The locations of consecutive probe stimuli followed a pseudorandom order , called a condition . In each condition , a complete sequence of 81 probe stimuli was presented throughout the length of a trial . Conditions were designed to ensure that each probe location occurred at each time in the sequence with equal frequency across trials . The pseudorandom presentation of the probe stimuli made it possible to track the temporal evolution of the neurons’ spatiotemporal receptive field ( RF ) using an unbiased set of stimuli [69] and independent of their relative timing to the saccade events . For each recording session , the grid of the possible locations of the probes was positioned such that it covered the estimated pre- and post-saccadic receptive fields of the neurons under study , as well as the fixation point and saccade target . The spatial extent of the probe grids varied from 24 to 44 . 8 ( mean ± SD = 30 . 25 ± 6 . 13 ) dva horizontally , and from 16 to 28 ( mean ± SD = 18 . 11 ± 3 . 97 ) dva vertically . The ( center-to-center ) distance between two adjacent probe locations varied from 3 to 5 . 6 ( mean ± SD = 3 . 78 ± 0 . 77 ) dva horizontally , and from 2 to 3 . 5 ( mean ± SD = 2 . 26 ± 0 . 50 ) dva vertically . Each trial lasted between 2100 to 2300 ms . Throughout the entire course of the experiment , the spiking activity of the neurons in area MT was recorded using a 16-channel linear array electrode ( V-probe , Plexon Inc . , Dallas , TX ) at a sampling rate of 32 kHz , and sorted offline using the Plexon offline spike sorter . The spike sorter program was employed to perform a principal component analysis , clusters of spikes with similar waveform properties were manually classified as belonging to a single neuron ( single unit ) . The sorted spikes were then read into Matlab to verify the presence of a visually-sensitive receptive field . From a population of 49 well-isolated neurons , 8 neurons were discarded because they did not respond to any probe stimuli before and/or after the saccade , and the rest were used for analyses . The eye position of the monkeys was monitored with an infrared optical eye tracking system ( EyeLink 1000 Plus Eye Tracker , SR Research Ltd . , Ottawa , CA ) with a resolution of < 0 . 01 dva , and a sampling frequency of 2 kHz . Stimuli presentation in the experiment was controlled using the MonkeyLogic toolbox [70] . Visual stimuli were presented on a 24-inch ASUS VG248QE LED monitor with a resolution of 1920X1080 pixels with a refresh rate of 144 Hz , positioned 28 . 5 cm in front of the animal’s eyes . A photodiode ( OSRAM Opto Semiconductors , Sunnyvale CA ) , mounted on the lower left corner of the monitor , was used to record the actual onset and offset times of stimuli appearing on the screen with a continuous signal sampled and stored at 32 kHz . In total , data were recorded from 41 MT neurons during 11 recording sessions . In 9 recording sessions , the saccades were made to the left ( 27 of 41 neurons ) , and in 2 recording sessions , the saccades were made to the right ( 14 of 41 neurons ) . The positioning of the probe grids , the spatial distribution of the receptive field of the neurons , and the average of the photodiode signal for an example session is provided in the Supporting Information ( S1 Fig ) . The experimental data are available at http://dx . doi . org/10 . 6080/K0FB514J and https://github . com/nnategh/SFA-Models . The RF location refers to the probe location which generated the maximum firing rate during the fixation period ( -500 to -100 ms from saccade onset ) in the early response window , i . e . 50 to 75 ms from probe onset . The future field ( FF ) location was then set to the probe location shifted away from the RF probe in the direction and ( rounded ) size of the saccade vector . Finally , the saccade target ( ST ) location was defined as the probe location , out of the 4 × 4 probe locations centered around the ST , which generated the maximum firing rate during the perisaccadic period ( -50 to 0 ms from saccade ) in the late response window , i . e . 80 to 150 ms after probe onset , compared to the fixation period . To avoid overlap between the ST and FF locations , the FF probe location and all probe locations surrounding it were excluded from the potential ST probe locations ( if they fell within the 4 × 4 probe locations centered around the ST ) . In this paper , three perisaccadic response modulations were studied: saccadic suppression , FF-remapping , and ST-remapping . Saccadic suppression was defined as a significant decrease in the spike count of the neuron over the early response window ( 50–75 ms after stimulus onset ) in response to a stimulus presented in the RF probe location shortly before a saccade ( -30 to 0 ms from saccade onset ) , compared to the same stimulus presented during fixation ( -500 to -100 ms from saccade onset ) . In the same way , the FF- and ST-remapping in a neuron were defined as increases in the spike count of the neuron in the late response window ( 80–150 ms after stimulus onset ) to a stimulus presented in the FF or ST probe location shortly before a saccade ( -50 to 0 ms from saccade onset ) compared to the same stimulus presented during fixation ( -500 to -100 ms from saccade onset ) . In all three cases , the statistical significance was tested by comparing the perisaccadic and fixation spike counts ( during the same stimulus-aligned window ) using the Wilcoxon one-sided signed-rank test , and a p-value of less than 0 . 05 ( with no multiple comparison adjustment ) was considered statistically significant . Note that all statistical analyses were performed on spike counts between windows of the same duration ( not estimates of firing rate ) . For graphical purposes , however , the spike trains were smoothed by convolving with a Gaussian with full width at half-max ( FWHM ) 13 ms . The response of a neuron to a stimulus presented in a given time interval was estimated by averaging the stimulus-aligned spike trains from 0 to 150 ms after stimulus onset . Due to differences in the mean firing activity of MT neurons , their perisaccadic and fixation responses were normalized by dividing by the grand mean of their firing rates ( from 0–150 ms after onset of a stimulus , across both conditions ) before averaging over a subpopulation of neurons . The grand mean was defined as the mean of the means of the perisaccadic and fixation responses . We developed a sparse-variable generalized linear model framework , termed the S-model , which is able to track the saccade-induced rapid changes in the spatiotemporal sensitivity of the neurons on the order of 7 milliseconds ( which is the resolution of stimuli presentation ) . The principal idea of the S-model is that the stimulus-response relationship in a neuron is characterized by a set of two-dimensional stimulus kernels ( k ( t , τ ) ) , which represent the spatiotemporal receptive field of the neuron as varying along the time dimension ( t ) . Fixing the stimulus kernels along the time dimension results in the conventional one-dimensional stimulus kernels ( k ( τ ) ) used in ordinary generalized linear models ( GLMs ) [43 , 71] . More specifically , the conditional intensity function ( CIF ) of the S-model , representing the instantaneous firing rate of an MT neuron , i . e . , λ ( l ) ( t ) , under our experimental paradigm is described by , λ ( l ) ( t ) =f ( ∑x , y , τkx , y ( t , τ ) . sx , y ( l ) ( t−τ ) +∑τh ( τ ) . r ( l ) ( t−τ ) +b ( t ) +b0 ) , ( 1 ) where sx , y ( l ) ( t ) ∈{0 , 1} denotes a temporal sequence of probe stimuli presented at probe location ( x , y ) on trial l where 0 and 1 represent , respectively , an off and on probe condition , r ( l ) ( t ) ∈{0 , 1} indicates the spiking response of the neuron on that trial , kx , y ( t , τ ) represents the two-dimensional stimulus kernel corresponding to the stimulus sequence being presented at probe location ( x , y ) , h ( τ ) is the post-spike kernel applied to the spike history , b ( t ) is the offset kernel which captures the saccade-induced changes in the baseline activity ( activity in the absence of a visual stimulus and feedback from spiking responses ) of the neuron over the time course of the experiment , b0 = f−1 ( r0 ) where r0 is defined as the measured mean firing rate ( spikes per second ) across all trials in the experimental session , and finally , f ( u ) =rmax1+e−u , ( 2 ) is a static sigmoidal function representing the response nonlinear properties where rmax indicates the maximum firing rate of the neuron obtained empirically from the experimental data . Compared with the empirical nonlinearity estimated nonparametrically from data , this choice of model nonlinearity adequately captured the nonlinear properties of the neurons’ response . All trials were saccade aligned , i . e . , t = 0 refers to the time when a saccade was initiated . In order to reduce the high dimensionality of the problem , all the kernels were parameterized as a linear combination of a set of basis functions defined across time , delay , or both variables as follows , kx , y ( t , τ ) =∑i , jκx , y , i , j . Bi , j ( t , τ ) , ( 3 ) h ( τ ) =−∑iηi2 . Hi ( τ ) , ( 4 ) b ( t ) =∑jβj . Oj ( t ) , ( 5 ) where {κx , y , i , j} , {ηi} , and {βj} are the basis parameters of the stimulus kernels , post-spike kernel , and offset kernel respectively . Since the basis functions {Hi ( τ ) } were set to be positive and the post-spike kernel was set to reflect only the response refractory effects [72] , the negative square of the basis parameters {ηi} were used such that the resulting h ( τ ) produced a non-positive kernel . The basis functions representing the two-dimensional stimulus kernels were set as follows , Bi , j ( t , τ ) =Ui ( τ ) Vj ( t ) , ( 6 ) where Ui ( τ ) and Vj ( t ) were chosen to be B-spline functions of order two . {Ui ( τ ) } span over the delay variable τ , representing a 150 ms-long kernel using a set of 26 knots uniformly spaced at {-13 , -6 , … , 155 , 162} ms ( in total , 23 basis functions ) , and {Vj ( t ) } span over the time variable t , representing a 1081 ms-long kernel centered at the saccade onset using a set of 159 knots uniformly spaced at {-554 , -547 , … , 545 , 552} ms ( in total , 156 basis functions ) . The basis functions {Hi ( τ ) } representing the post-spike kernel were chosen to be B-spline functions of order two with non-uniformly distributed 23 knots over the delay variable τ; the spacing of the knots around zero , which indicates the spike time , was smaller and increased further away from the spike time and its associated refractory period ( the knots were spaced at {1 , 2 , 3 , 4 , 6 , 8 , 15 , 22 , 29 , 36 , 43 , 50 , 57 , 64 , 71 , 78 , 92 , 106 , 120 , 134 , 148 , 162 , 176} ms , in total 20 basis functions ) . Finally , {Oj ( t ) } span over the time variable t , representing a 1081 ms-long kernel centered at the saccade onset using a set of 77 knots uniformly spaced at {-570 , -555 , … , 555 , 570} ms ( in total , 74 basis functions ) . A visualization of basis functions is presented in the Supporting Information ( S2 Fig ) . From ( Eqs 1 and 3–5 ) together , λ ( l ) ( t ) =f ( ∑x , y , τ , i , jκx , y , i , j . Bi , j ( t , τ ) . sx , y ( l ) ( t−τ ) −∑τ , iηi2 . Hi ( τ ) . r ( l ) ( t−τ ) +∑jβj . Oj ( t ) +b0 ) . ( 7 ) Eq ( 7 ) denotes the CIF of the spiking process described by the S-model . The probability of a spike train associated with this Poisson process is thus given by , p ( r ( l ) |s ) =∏tp ( r ( l ) ( t ) |s ) ∝∏t ( λ ( l ) ( t ) . Δ ) r ( l ) ( t ) e−λ ( l ) ( t ) . Δ , ( 8 ) where s is the sequence of input stimuli , and r ( l ) = {r ( l ) ( t ) } represents the sequence of binned spike counts with bins of size Δ ms on trial l . Here , the bin size was chosen equal to 1 ms which ensures that at most one spike can fall in each time bin . The point process log-likelihood ( LL ) [44] of the observed spike trains given the model is , LL ( {κx , y , i , j} , {ηi} , {βj} ) =∑l , t ( r ( l ) ( t ) . log ( λ ( l ) ( t ) . Δ ) −λ ( l ) ( t ) . Δ ) . ( 9 ) {κx , y , i , j} , {ηi} , and {βj} are estimated by maximizing the log-likelihood function given in Eq ( 9 ) . The choice of nonlinear function in the S-model’s CIF made the problem of log-likelihood maximization non-convex; the block coordinate ascent method was used to solve the optimization problem ( detailed later in this subsection ) . To avoid overfitting despite the high dimensionality of the S-model , multiple computational approaches were adopted . First , representing each model kernel using a linear combination of smooth basis functions resulted in an optimization process in a lower dimensional space and with well-behaved search paths . Second , a parameter selection strategy was used to identify the subset of parameters most important for mediating the stimulus-response relationship; only those parameters were included in the model fitting procedure , in order to reduce the high dimensionality of the problem . In this strategy , the basis parameters {κx , y , i , j} , which comprise the majority of the model parameters , were ranked according to their significance in response prediction , and the less significant ones were eliminated by the following procedure: first , the model’s CIF was assumed to be obtained by a single κx , y , i , j and with no dependency on the spike train history or the fluctuations in the baseline activity ( i . e . , without post-spike and offset kernels ) . To fit this simplified model , an MLE procedure was performed over 100 subsets of the data , each one obtained by randomly selecting 65% of the data ( 35% as training and 30% as validation ) to generate a distribution of the estimated values for κx , y , i , j . To evaluate the significance of κx , y , i , j , a control distribution of this parameter was constructed using the same strategy but with a set of shuffled responses . A κx , y , i , j parameter was assessed as a significant parameter for response prediction if the mean of its distribution ( μx , y , i , j ) satisfied the following condition: |μx , y , i , j−μ¯x , y , i , j|≥1 . 5σ¯x , y , i , j , ( 10 ) where μ¯x , y , i , j , and σ¯x , y , i , j are the mean and standard deviation of the control distribution . Those κx , y , i , j parameters that were detected as significant were included in the model fitting and all others were set to zero . Lastly , to help prevent overfitting of the S-model to the training dataset , a cross-validation approach was used to regularize the model parameters . In this approach , the data were randomly split into a training set ( 35% ) , a validation set ( 30% ) , and a test set ( 35% ) . Then , in order to estimate the model parameters , the likelihood function in Eq ( 9 ) was maximized using the block coordinate ascent method as follows: ( initialization ) The model parameters were set to a very small non-zero value ( here: 10−6 , in order to have a non-zero gradient ) ; ( step 1 ) The likelihood function was maximized with respect to the selected parameters ( chosen based on the parameter selection strategy detailed above ) describing the stimulus kernel corresponding to the probe location ( x , y ) = ( 1 , 1 ) while the rest of model parameters were held fixed at their current estimates . The selected parameters were iteratively updated ( using only the training data ) to maximize the log-likelihood function over both the training and validation data , until the relative change in the root mean of squares of the selected parameters was less than 1% . Then the same procedure was repeated for the parameters describing the stimulus kernel corresponding to the next probe location , until all probe locations were completed . ( step 2 ) the same as step 1 , but for the parameters describing the post-spike kernel . ( step 3 ) the same as step 1 , but for the parameters describing the offset kernel . ( step 4 ) steps 1–3 were repeated until no update in the model parameters was observed during these steps . Then , the estimation process was terminated , and fitting was considered complete . The test data were withheld from the model fitting procedure and were used to measure the model goodness-of-fit , ensuring the generalizability of the fitted model to unseen data . The block coordinate ascent method gave a stable solution for the data and with regard to different initializations of the parameters , and benefited the convergence time of the estimation procedure . Employing basis functions as well as the parameter selection strategy reduced the dimensionality of the parameter space; this reduced dimensionality , along with the cross-validation approach , provided a detailed map of the neuron’s time-variant spatiotemporal sensitivity with less concern of overfitting . A visualization of a few sample stimulus kernels , post-spike kernels , and offset kernels obtained from the S-model fitting are provided in the Supporting Information ( S3 Fig ) . Although the S-model can capture the dynamics of the spatiotemporal receptive field , and thus can characterize the response modulation on the timescale of a saccade , it does not identify explicitly what sources contribute to the response modulation . The idea of identifying modulatory sources was inspired by the perisaccadic modulations observed in our experimental data and in many previous studies ( see Introduction ) , i . e . saccadic suppression , FF-remapping , and ST-remapping . In fact , the stimulus kernels fitted using the S-model , S-kernels , can be approximated by time- and delay-dependent mixtures of spatial skewed Gaussians where each Gaussian captures the response modulation arising from one of the RF , FF , or ST sources at a given time and delay ( for more details , see S4 Fig in the Supporting Information ) . To quantitatively dissociate the effects of the RF , FF , and ST sources at different times and delays , a factorized sparse-variable generalized linear model , called the F-model , was developed which approximates the fitted S-kernels kx , y ( t , τ ) by k^x , y ( t , τ ) as: k^x , y ( t , τ ) =k˜x , y ( τ ) +∑srG ( x , y;φsr ( t , τ ) ) +c ( t , τ ) , ( 11 ) where k˜x , y ( τ ) , termed the fixation kernel , represents the average spatiotemporal receptive field of the neuron over the fixation period , defined as k˜x , y ( τ ) =1t2−t1∑t=t1t2kx , y ( t , τ ) ; ( 12 ) where t1 = −400 ms , and t2 = −300 ms from saccade; c ( t , τ ) represents the time- and delay-dependent baseline profile which is uniform across spatial dimensions; and finally , each G ( x , y;φsr ( t , τ ) ) indicates a time- and delay-dependent spatial skewed Gaussian representing the modulation source sr∈{RF , FF , ST} , which is parameterized by φsr ( t , τ ) ={asr ( t , τ ) , μxsr ( t , τ ) , μysr ( t , τ ) , σxsr ( t , τ ) , σysr ( t , τ ) , ρsr ( t , τ ) , γxsr ( t , τ ) , γysr ( t , τ ) } as follows , G ( x , y;φsr ( t , τ ) ) =asr ( t , τ ) . e−12 ( 1−ρsr ( t , τ ) 2 ) { ( x−μxsr ( t , τ ) ) 2σxsr ( t , τ ) 2+ ( y−μysr ( t , τ ) ) 2σysr ( t , τ ) 2−2ρsr ( t , τ ) . ( x−μxsr ( t , τ ) ) ( y−μysr ( t , τ ) ) σxsr ( t , τ ) . σysr ( t , τ ) } . Φ ( γxsr ( t , τ ) ( x−μxsr ( t , τ ) ) ) . Φ ( γysr ( t , τ ) ( y−μysr ( t , τ ) ) ) , ( 13 ) where asr ( t , τ ) , ( μxsr ( t , τ ) , μysr ( t , τ ) ) , ( σxsr ( t , τ ) , σysr ( t , τ ) ) , ρsr ( t , τ ) , and ( γxsr ( t , τ ) , γysr ( t , τ ) ) represent the amplitude , the x – and y-coordinate of the center , the horizontal and vertical spread , the orientation , and the horizontal and vertical skewness of the Gaussian kernel G ( x , y;φsr ( t , τ ) ) corresponding to the modulation source sr at time t and delay τ , and Φ ( ∙ ) indicates the standard normal cumulative distribution function . The time- and delay-dependent parameters φsr ( t , τ ) and c ( t , τ ) were estimated by minimizing the sum square difference between the F-kernel k^x , y ( t , τ ) , as specified in Eq ( 11 ) , and the S-kernel kx , y ( t , τ ) obtained from the S-model for each time t and delay τ . In order to eliminate noise included in the S-kernels as well as to alleviate overfitting , the Gaussian parameters were estimated over non-overlapping bins across the delay dimension ( instead of single values of delay ) , i . e . φsr and c corresponding to time t from saccade and delays τb≤τ<τb+1 were estimated by minimizing ∑x , y∑τb≤τ<τb+1 ( k˜x , y ( τ ) +∑srG ( x , y;φsr ) +c−kx , y ( t , τ ) ) 2 ( 14 ) where {τb} = {1 , 20 , 40 , 50 , 53 , 56 , 59 , 62 , 65 , 68 , 71 , 74 , 77 , 80 , 85 , 90 , 95 , 100 , 105 , 110 , 115 , 120 , 125 , 130 , 135 , 140 , 145 , 151} ms . In order to constrain each Gaussian kernel within an area surrounding the modulation source which the Gaussian kernel represented ( i . e . surrounding the RF , FF , or ST probe location as defined before ) , and to avoid overlap between Gaussian kernels , the Gaussian parameters were subject to a set of bounded limits during the estimation process , such that ( 1 ) the center of Gaussian kernels could only move one probe location away from the probe location corresponding to the effect they were intended to represent ( i . e . , one probe location away from the RF probe location for the center of the Gaussian kernel for the RF , etc . ) ; ( 2 ) the horizontal and vertical spread of the Gaussian kernels could not exceed 2 probe locations; ( 3 ) their orientations were limited to between -1 and +1; and finally , ( 4 ) the absolute values of the horizontal and vertical skewness of the Gaussian kernels were bounded to 5 . In order to remove the discontinuities created by estimating the F-kernels over delay bins , the reconstructed F-kernels were smoothed in the delay dimension using a moving average filter with a span of 10 ms . As a result , the F-kernels had lower values in comparison with the corresponding S-kernels , and so , the firing rates predicted by the F-model were lower in magnitude compared to the corresponding S-model . The models’ performance was evaluated over test data , which was used neither for training the model parameters nor for validating the fitted ones , in terms of the log-likelihood of the observed spike trains given the estimated instantaneous firing rate . In order to estimate the instantaneous firing rates , the sequences of stimuli presented to the neuron were given to the model according to Eq ( 1 ) . To simulate the effect of spiking history , the recorded spike trains were used . Using the true spike history always raises the concern of getting an unfairly good estimate for the model performance due to the presence of a strong refractory period or self-excitatory component in the post-spike kernel . However , neither of these was an issue here: the post-spike kernels were defined to reflect only the refractory effects ( as detailed in the “S-model framework” subsection ) , and in this dataset only a small number of MT neurons fired at rates in which the absolute refractory period came into play . As a result , including the post-spike kernels had no significant impact on the model performance , as demonstrated by comparing the model performance using the true history , the simulated history ( as detailed in [73]: algorithm 2 ) , and no history . The results indicated that there is no significant difference in the model performance measures between these three scenarios ( S5 Fig in the Supporting Information ) . Throughout this paper , the true history was used whenever the model was employed to simulate the recorded spiking responses ( Figs 3 and 5–7 , except Fig 5B ) . The log-likelihood evaluates how well the spike times are predicted by the model , and can do so at the level of individual trials . In the log-likelihood formula ( see Eq ( 9 ) ) , the first term is larger when the spikes are observed at high values of the estimated firing rate , and the second term is larger when there are fewer spikes at low values of the predicted firing rate . The log-likelihood was normalized by spike counts , as reported in Williamson et al . [74] and Cui et al . [72] , to indicate the amount of information being conveyed by individual spikes . One problem with the log-likelihood is understanding the meaning of a given log-likelihood value in isolation . To address this issue , the log-likelihood of the models were compared with the log-likelihood of a NULL model in which the instantaneous firing rate of the neuron was set to its average firing rate; so , the amount of improvement compared to the NULL model normalized by spike counts , i . e . the log-likelihood per spike ( ΔLL/spk ) , was reported instead of the raw likelihood value . To prove that the S- and F-models better predict the perisaccadic responses compared to alternative models , the quality of perisaccadic response prediction by the S- and F-models was compared with the N-model -which is the state-of-the-art model in the perisaccadic response modeling as detailed in [48]- in terms of the log-likelihood per spike . For each model , the log-likelihood of the perisaccadic spikes was scatter plotted vs . the log-likelihood of the fixation spikes for the population of 41 MT neurons ( Fig 5A ) . Because the stimuli presented during the perisaccadic period ( -50 to 0 ms from saccade onset ) evoke responses after 50–150 ms from stimulus onset , the perisaccadic spikes were defined as those spikes observed from 0 to 150 ms after saccade onset . By the same reasoning , the fixation spikes were defined as those spikes observed from 450 to 0 ms before saccade onset . In order to robustly capture the fast perisaccadic modulations ( saccadic suppression , FF-remapping , and ST-remapping ) , it is critical to reduce the variability of the model prediction over the limited perisaccadic data; for that purpose , an aggregate version of the F-model , termed the A-model , was developed . The A-model was constructed by ( 1 ) fitting the S-model ten times as explained earlier , on different randomly selected subsets of the data , ( 2 ) fitting ten F-models corresponding to each fitted S-model , ( 3 ) averaging the model variables ( φsrs , and c ) obtained from each F-model in order to gain a set of aggregate variables , and finally , ( 4 ) constructing the A-model kernels using the aggregate variables through Eq ( 11 ) . Our code for estimating the S- , F- , and A-model parameters and performing model evaluation analysis is provided at https://github . com/nnategh/SFA-Models . After confirming the goodness-of-fit of the S- and F-models over test data , all trials ( and not only those withheld for model testing ) for each cell were employed for the rest of the analyses in this study to provide more accurate empirical measures from the experimental data . In order to evaluate the models’ ability to capture the perisaccadic effects observed in the experimental data , i . e . saccadic suppression , FF-remapping , and ST-remapping , sequences of stimuli ( n = 1000 ) were presented to the fitted ( N- , S- , F- , and A- ) models and the corresponding sequences of instantaneous firing rates were generated ( based on Eq ( 1 ) ) . The spatial frequency and timing of the stimulus sequences were the same as those for the experimental paradigm . To model the effect of spiking history , the algorithm developed by Chen et al . ( algorithm 2 , [73] ) was employed to simulate the spike history ( self-history ) . Then , the saccadic suppression effect was defined as a significant decrease in the mean firing rate predicted by the model over the early response window ( 50–75 ms after stimulus onset ) in response to a stimulus being presented in the RF probe location shortly before a saccade ( -30 to 0 ms from saccade onset ) compared to the same stimulus presented during fixation ( -500 to -100 ms from saccade onset ) . The FF- and ST-remapping were defined as significant increases in the mean firing rate predicted by the model in the late response window ( 80–150 ms after stimulus onset ) in response to a stimulus being presented in the FF or ST probe location shortly before a saccade ( -50 to 0 ms from saccade onset ) compared to the same stimulus presented during fixation ( -500 to -100 ms from saccade onset ) . In all cases , the statistical significance was tested by comparing the perisaccadic and fixation predicted firing rates using the one-sided Wilcoxon rank-sum test . A p-value less than 0 . 05 was considered statistically significant . The model-predicted firing rate in response to a stimulus presented in a given time interval was estimated by averaging the stimulus-aligned sequences of firing rates predicted by the model from 0 to 150 ms after stimulus onset . The perisaccadic and fixation firing rates predicted by the model were normalized by dividing by the grand mean of the model-predicted firing rates ( from 0–150 ms after onset of a stimulus , across both conditions ) before averaging over a population of neurons . The grand mean was defined as the mean of the means of the perisaccadic and fixation firing rates predicted by the model . A dichotomous analysis was performed for each model and for each perisaccadic effect to assess in how many neurons the effect is significantly observed/not observed in both the experimental data and the model prediction ( as detailed in the “Statistical analysis of model predictions” subsection ) . To this purpose , for each model ( N- , F- , S- , and A- ) and each perisaccadic effect ( saccadic suppression , FF-remapping , ST-remapping ) , a confusion matrix was constructed which reported the presence or absence of the statistical significance of an effect measured from the experimental data versus from the model prediction . The confusion matrix contained the following values: the number of neurons in which the corresponding effect was statistically significant in both the response and the model ( true positive , TP ) , statistically significant in the response but not the model ( false negative , FN ) , statistically significant in the model but not the response ( false positive , FP ) , and statistically significant in neither the response nor in the model ( true negative , TN ) . There are multiple measures to evaluate the overall classification accuracy of a model using a confusion matrix , including sensitivity , accuracy , and precision . The agreement between the experimental data and the model prediction in terms of displaying/not displaying each of the perisaccadic effects was assessed using sensitivity , accuracy , the geometric mean of the sensitivity and precision ( GSP ) , and F-measure ( with α = 1/2 ) , defined as follows: sensitivity=TPTP+FN ( 15 ) accuracy=TP+TNTP+FN+FP+TN ( 16 ) precision=TPTP+FP ( 17 ) GSP=sensitivity . precision ( 18 ) F‐measure= ( 1+α2 ) . sensitivity . precisionα2 . precision+sensitivity ( 19 ) Note that the dichotomous analysis was performed over the entire data set ( including the training and validation trials , as mentioned before ) in order to use sufficiently large number of trials recorded for each cell for measuring firing rates . The McNemar test [49] was employed to evaluate the statistical significance of different models’ abilities to classify neurons as displaying each of the perisaccadic effects . In order to evaluate a model’s ability to capture perisaccadic modulations specifically , the perisaccadic performance to fixation performance ratios of the models were calculated and compared . The performance was quantified in terms of ΔLL/spk ( as detailed in the “Model evaluation” subsection ) , resulting in a unitless performance ratio value . Since the perisaccadic modulations are only measurable if a stimulus appears in one of the RF , FF , or ST probe locations during the perisaccadic period , the perisaccadic and fixation performance values were calculated over those trials from the test data which met this criterion . The perisaccadic to fixation ratio was employed to compare the S- and F-models . In addition to the S- and F-models , different variants of the F-model were created to assess the contribution of individual sources , represented by time- and delay-dependent spatial skewed Gaussian kernels , to the F-model’s performance . For this purpose , one , two , or all of the sources were eliminated by nulling the parameters ( φsr ) of the relevant Gaussian kernel . To null the parameters of a Gaussian kernel , its parameters were replaced by parameters randomly selected from the fixation period ( 400 to 300 ms before saccade ) . By eliminating the RF , FF , or ST Gaussian kernel , the F-model without an RF , FF , or ST source was constructed ( -RF , -FF , -ST respectively ) . By preserving only the RF , FF , or ST Gaussian kernel , the F-model with only the RF , FF , or ST source was constructed ( +RF , +FF , +ST respectively ) . By eliminating all of the RF , FF , and ST Gaussian kernels , the F-model with no source was constructed ( no-source ) . In order to handle outliers created when taking the ratio , the perisaccadic performance values of the population of neurons were regressed as a linear function ( with a fixed intercept of zero ) of the fixation performance values for a model . The slope of the fitted line ( using a robust-fit algorithm to remove outliers ) was considered as an estimate for the perisaccadic to fixation ratio for the corresponding model . S6 Fig shows the regression fits and values . For each of the partial models as well as the F-model , the perisaccadic to fixation performance ratio obtained by this way are plotted in Fig 5C , top panel . The error bars indicate the standard errors of the estimated slopes . Finally , in order to quantify how much adding one source contributes to the performance of the F-model , the increase percentage was defined for the +RF , +FF , and +ST models as the difference between the performance of the corresponding model and the no-source model divided by the difference between the performance of the F-model and the no-source model , stated as a percentage . Also , to quantify how much eliminating one source reduces the performance of the F-model , the decrease percentage was defined for the -RF , -FF , and -ST models as the difference between the performance of the corresponding model and the F-model divided by the difference between the performance of the no-source model and the F-model , stated as a percentage . All instances of the ‘model performance’ here refer to the perisaccadic to fixation performance ratio . The prevalence of the perisaccadic effects at different times relative to a saccade as well as stimulus onset was assessed for both the experimental data and the model prediction ( Fig 7 ) . For the saccadic suppression effect , the prevalence map using the experimental data was constructed as follows: for each neuron , the set of measured firing rate values generated by the neuron in response to a stimulus presented at the RF probe location at time t relative to the saccade and latency τ from stimulus onset was collected , r^t , τ . In the same manner , the set of measured firing rate values generated by the neuron in response to the same stimulus presented during fixation ( -500 to -100 from saccade ) was collected for the latency of τ , called r¯τ . Then , mt , τ , a map of 0’s and 1’s , was built for the corresponding neuron: mt , τ={1 , ifr^t , τissignificantlylessthanr¯τ0 , otherwise ( 20 ) By averaging all mt , τs obtained from those neurons displaying saccadic suppression in both the response and the model prediction , the prevalence map of the saccadic suppression effect was generated . This map illustrates the percentage of the neurons displaying suppression at each time point relative to the saccade as well as to the stimulus onset . The same approach was employed to draw the prevalence map for the FF- and ST-remapping effects using the experimental data , except that mt , τ was built as: mt , τ={1 , ifr^t , τissignificantlygreaterthanr¯τ0 , otherwise ( 21 ) Similarly , the prevalence maps were drawn for each of the perisaccadic effects using the model predictions . In the model-based prevalence maps , the instantaneous firing rate predicted by the model was used instead of the measured firing rate employed in the data-based maps . The instantaneous firing rate of a neuron was measured by smoothing spike trains through convolving with a Gaussian with FWHM 33 ms . The statistical significance was tested by the one-sided Wilcoxon rank-sum test . A p-value less than 0 . 05 was considered statistically significant . To quantify the similarity between the data-based and model-based prevalence maps for each perisaccadic effect , a Pearson product-moment correlation was used . The receiver operating characteristic ( ROC ) curve analysis [75] was used in this paper to investigate how the spatiotemporal detectability of a neuron changes over time relative to a saccade . For this purpose , sequences of stimuli ( n = 1000 ) were presented to a fitted A-model , and the corresponding sequences of instantaneous firing rates were generated ( based on Eq ( 1 ) ) . The spatial frequency and timing of the stimulus sequences were as the same as those for the experimental paradigm . To model the effect of spiking history , the algorithm developed by Chen et al . ( algorithm 2 , [73] ) was employed to simulate the spike history . Then , for each probe location ( x , y ) , each time point t relative to saccade , and each latency τ , the set of predicted firing rate values at time point t was collected if a stimulus was presented at probe location ( x , y ) at time t−τ; this distribution of firing rates was called rx , y , t , τ . This procedure was repeated after pairing the same sequences of firing rate with shuffled sequences of stimuli , in order to have an estimate of the firing rate distribution when there is no causal relationship between the stimuli and responses ( NULL hypothesis ) ; this shuffled distribution of firing rates was called r˜x , y , t , τ . Afterwards , an ROC curve was constructed based on the rx , y , t , τ and r˜x , y , t , τ values , and the area under the curve was considered as the detectability of the neuron , roc ( x , y , t , τ ) , to probe location ( x , y ) at time t when a stimulus was presented there τ ms earlier ( or equivalently , with a latency of τ ms ) . The detectability of a neuron indicates how reliably a stimulus presented at a given probe location at a given time and latency can be detected by the neuron . To assess the significance of the detectability value , the ROC between two distinct shuffled firing rate distributions was calculated 100 times to obtain a null detectability distribution . The model-predicted detectability value and null detectability distribution were compared ( two-sample t-test ) , and considered statistically significant if p was less than 10−9 . To better visualize the changes in neurons’ detectability over time relative to the saccade , a plot showing the maximum detectability and the corresponding latency over time is shown for an example neuron ( see Fig 8E , left panel ) . For this purpose , at each time point t relative to saccade , the maximum detectability of the neuron to each probe location ( x , y ) , Ix , y , t , and the corresponding latency at which the maximum detectability occurred , Tx , y , t , were determined as follows: Ix , y , t=maxτ{roc ( x , y , t , τ ) } , ( 22 ) Tx , y , t=argmaxτ{roc ( x , y , t , τ ) } , ( 23 ) if Ix , y , t was a statistically significant detectability value . Then , Tx , y , t for the RF probe location ( red ) , FF probe location ( blue ) , and one of the closest probe locations to the ST ( green ) were plotted vs . time to saccade , and the maximum detectability reached at each time point , Ix , y , t , was indicated by the size of the markers . Tx , y , t and Ix , y , t were smoothed over time with a moving average filter spanning 7 ms . Note that values of Ix , y , t were included only for corresponding Tx , y , t values greater than 50 ms , i . e . the maximum detectability had to occur at or after the normal latency of MT neurons . In addition to this plot , an animated movie was created ( S1 Movie: see Supporting Information ) for the same neuron . In this movie , for each time point t , the corresponding Tx , y , t was visualized by the height of a disc centered at probe location ( x , y ) in a 3D space . In this 3D space , the x- and y-axis represent the horizontal and vertical position in the visual field , and the z-axis represents the latency at which the maximum detectability to a probe location occurs . The amount of the maximum detectability , Ix , y , t , is represented by the intensity of the discs’ color . Only the detectability to the three main probe locations displayed in the plot ( indicated by color ) is shown here . Finally , in order to have a sense of how the perisaccadic effects were displayed by the population of MT neurons , similar plots and movies were generated for artificially generated neurons representing the population statistics ( see Fig 8E , middle and right panels; and S2 and S3 Movies in the Supporting Information ) . A population analysis was done as follows: the variables ( φsrs , and c ) obtained from the A-models of the neurons in which a perisaccadic effect was significantly observed in both the response and model prediction were normalized by the distance between the adjacent probes , and averaged . Then , a synthetic neuron was built for each perisaccadic effect using the variables corresponding to the relevant Gaussian kernel ( for example , the average of the normalized φFFs was used to construct the kernels corresponding to a synthetic neuron representing the FF-remapping effect , while φRFs and φSTs were set to zeros for that neuron ) . Then , the same procedure used for the sample neuron was employed to produce a 2D plot as well as an animated movie for that effect ( p-value less than 10−7 was considered significant ) . Due to the low number of neurons displaying the saccadic suppression effect in both the response and model prediction ( n = 7 ) , this effect was excluded from the analysis . | The sensory responses of neurons in many brain areas , particularly those in higher prefrontal or parietal areas , are strongly influenced by factors including task rules , attentional state , context , reward history , motor preparation , learned associations , and other cognitive variables . These modulations often occur in combination , or on fast timescales which present a challenge for both experimental and modelling approaches aiming to describe the underlying mechanisms or computations . Here we present a computational model capable of capturing and dissociating multiple time-varying modulatory effects on spiking responses on the order of milliseconds . The model’s performance is evaluated by testing its ability to reproduce and dissociate multiple changes in visual sensitivity occurring in extrastriate visual cortex around the time of rapid eye movements . No previous model is capable of capturing these changes with as fine a resolution as that presented here . Our model both provides specific insight into the nature and time course of changes in visual sensitivity around the time of eye movements , and offers a general framework applicable to a wide variety of contexts in which sensory processing is modulated dynamically by multiple time-varying cognitive or behavioral factors , to understand the neuronal computations underpinning these modulations and make predictions about the underlying mechanisms . | [
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... | 2019 | Characterizing and dissociating multiple time-varying modulatory computations influencing neuronal activity |
Type IV pili are long , protein filaments built from a repeating subunit that protrudes from the surface of a wide variety of infectious bacteria . They are implicated in a vast array of functions , ranging from bacterial motility to microcolony formation to infection . One of the most well-studied type IV filaments is the gonococcal type IV pilus ( GC-T4P ) from Neisseria gonorrhoeae , the causative agent of gonorrhea . Cryo-electron microscopy has been used to construct a model of this filament , offering insights into the structure of type IV pili . In addition , experiments have demonstrated that GC-T4P can withstand very large tension forces , and transition to a force-induced conformation . However , the details of force-generation , and the atomic-level characteristics of the force-induced conformation , are unknown . Here , steered molecular dynamics ( SMD ) simulation was used to exert a force in silico on an 18 subunit segment of GC-T4P to address questions regarding the nature of the interactions that lead to the extraordinary strength of bacterial pili . SMD simulations revealed that the buried pilin α1 domains maintain hydrophobic contacts with one another within the core of the filament , leading to GC-T4P's structural stability . At the filament surface , gaps between pilin globular head domains in both the native and pulled states provide water accessible routes between the external environment and the interior of the filament , allowing water to access the pilin α1 domains as reported for VC-T4P in deuterium exchange experiments . Results were also compared to the experimentally observed force-induced conformation . In particular , an exposed amino acid sequence in the experimentally stretched filament was also found to become exposed during the SMD simulations , suggesting that initial stages of the force induced transition are well captured . Furthermore , a second sequence was shown to be initially hidden in the native filament and became exposed upon stretching .
Type IV pili ( T4P ) , long ( lengths at the micron scale ) filamentous proteins composed of pilin subunits , are associated with a variety of bacteria , and emanate from the surface of the bacterial cell [1] , [2] . T4P have been known as virulence factors for a long time as they are borne by many pathogens [1] , [3] . They are of paramount importance in mediating attachment between bacteria and other surfaces , and perform a wide variety of functions for the bacterial cell including adhesion , motility , micro-colony formation , infection , and are implicated in immune escape [1] , [3] . While other pili such as Type 1 or Type P pili provide function such as adhesion by their presence on the cell surface , T4P are also dynamic [4] , [5] . T4P undergo cycles of elongation and retraction as pilin subunits are either added to or removed from the filament in a mechanism that is still poorly understood [1] , [2] . When retracting , a single gonococcal ( GC ) -T4P filament can exert a force greater than 100 pN [6] , [7] . The ability of GC-T4P to form bundles of 8–10 individual filaments has been observed , and these bundles can exert forces in the nanonewton range [8] . These are the highest recorded forces generated by bacteria ( equivalent of 100 , 000 times the bacterial bodyweight ) . Because of their involvement in surface attachment , GC-T4P filaments often find themselves under tension . The biological role of force in the interaction with host cells has been demonstrated to activate various mechanical signaling pathways in epithelial cells [9] . In addition , the physical forces exerted by the bacteria elicited dramatic rearrangements of the cell cortex [10] , [11] . However , the mechanisms at play to go from force generation to biological function have yet to be established . Recent experimental evidence points to the impact of tension on the structure of T4P filaments . Specifically , experiments have shown conformational rearrangements of GC-T4P filaments expose buried amino acid sequences to the environment [12] . It is of interest to determine all of the regions exposed to the environment under tension for understanding the extraordinary plasticity of GC-T4P filaments . In addition , by uncovering what regions of the pilus filament become exposed under strain , more effective drugs , acting as inhibitors to T4P binding , could potentially be engineered [13] , [14] . Among the many type IV pilins , the GC-pilin subunit , PilE [15] , and the Pseudomonas aeruginosa subunit , PilA [16] have received the most attention , and their structures exemplify the canonical shape of type IV pilin: a globular head attached to a hydrophobic extended α-helix . In these two cases , the full-length subunits were crystallized . The N-terminal half of the helix ( α1-N domain ) protrudes from the protein , while the other half ( α1-C domain ) interacts with an anti-parallel four to five stranded β-sheet globular head domain . α1-N is almost completely hydrophobic except for a single charged residue , Glu5 , which is conserved in nearly all type IV pilins , with only one exception: an aspartate is found at position 4 in the subunit PilS of S . enterica [1] . It has been speculated that Glu5 ( and Asp4 in PilS ) , may serve to neutralize the electrostatic nature of the core of the filament by compensating for the positively charged N-terminus of α1-N [15] , [17] . The globular head domain of the subunits lines the surface of the filament , and is therefore thought to be involved in its interactions with the environment [1] . The globular heads exhibit features relevant to pilus function . The αβ-loop possesses two post-translational modifications in GC-pilin , glycosylation of Ser63 and phosphorylation of Ser68 [1] , [18] , [19] , which may protect epitopes from immune response and change the surface chemistry of the pilus and have been recently shown to play a role in the dispersal of the bacteria [18] , [20] , [21] . The D-region includes a hyper-variable loop , named as such because of the high variability of its amino acid sequence from one bacterial strain to another , which has been suggested to contribute to immune system evasion and persistent infection [22]–[24] . Additionally , along the filament surface , grooves between the globular heads of adjacent pilins are lined with positively charged residues in some locations , which may help to facilitate GC-T4P binding to DNA [15] , [17] . A model for the GC-T4P filament assembly has been constructed by fitting the x-ray structure of GC-pilin ( 158 residues ) into a cryo-EM map of a segment of GC-T4P filament at 12 . 5 Å resolution [17] . The cryo-EM reconstruction helped to shed light on its structural characteristics . Pilin subunits wind along the central filament axis , with approximately 3 . 6 pilin subunits per turn [17] . Subunits are arranged following symmetries along the filament axis ( right-handed 1-start , left-handed 3-start and right-handed 4-start helices ) [1] , [2] , [17] . These symmetries represent the various ways to divide the filament into progressions of pilin subunits that wind helically around the central filament axis . The right-handed 1-start helix symmetry describes positions of all pilin subunits in the filament using the smallest axial rise . The three left-handed 3-start helices of pilin subunits connect subunits n , n+3 , n+6 , etc , while the four right-handed 4-start helices of pilin subunits connect subunits n , n+4 , n+8 , etc , ( see Figure 1A ) . The cryo-EM model also exhibited the presence of a channel of variable width ( 6–11 Å ) through the filament core [17] . The α1-N domains of the subunits are thought to contribute to the strength of the filament due to their extensive hydrophobic interaction network in the core of the structure . Interaction between the N-terminal helices consists of about 75% of the total hydrophobic buried surface area of every pilin subunit [17] . While providing strength , the α1 helices are also expected to be flexible , since they possess glycine and proline that induce kinks and flexibility in α-helices . For example , Pro22 and Gly42 contribute to the S-like shape of α1 and are conserved amongst the Type IVa pilin [1] . In PilA from Pseudomonas aeruginosa , the conserved Pro22 residue leads to a kink in α1-N [16] . When over-expressed , PulG can form pilus filaments that have similar function to T4P [25] . If Pro22 is mutated in the pseudopilus PulG , pilus formation in K . oxytoca is significantly decreased , implying that the flexibility induced by Pro22 may be critically important for filament assembly [26] . The kink in α1-N has also been observed in recent crystal structures for the D . nodosus and F . tularensis pilins , with different crystallization procedures for the F . tularensis subunit implying that kinking in α1-N is natural [27] . Given the wealth of molecular data available on T4P and the importance of force in their biological roles , the powers of in silico methods were used to better understand the role of tension on T4P structure . MD simulations have been previously used to study filaments such as in the case of the actin filaments or microtubules [28] , [29] . Complementing biophysical single protein pulling experiments , the effects of the application of external forces to proteins can also be studied using steered molecular dynamics ( SMD ) simulations [30] , where external forces are applied to specific atoms in the biological system . Such SMD studies have been carried out on the adhesion protein FimH , a component of the related Type 1 pili [31] , [32] . As T4P are known to sustain a considerable amount of force , understanding at the molecular level how T4P filaments respond under strain can provide insights into their function . Therefore SMD simulations of GC-T4P using the 18 subunits long cryo-EM reconstruction were carried out to probe the dynamics of GC-T4P under tension , and to gain insights about the response of GC-T4P to external force at an atomistic level of detail . Even though the simulations are based on a 3-bundle model obtained from low-resolution cryo-EM experiments , predictions from simulation in agreement with experimental data would prove the possibility of such of model for the GC-T4P . The current study represents the first SMD simulation of a full pilus filament model , which would help contribute to the growing understanding of the wide variety of biological filaments found in nature . The aim of this computational study is to capture only the initial rearrangements of the filament coming under tension , as a full extension would be computationally prohibitive , in order to identify the strongest and weakest points of the filament structure . Structural changes in the GC-T4P filament , interactions between inter-subunit interfaces and residues that become exposed to the filament's external environment under tension , are discussed .
The pdb coordinates for GC-T4P were obtained from the Protein Data Bank entry 2HIL [17] . It consists of 18 individual pilin subunits , each subunit being 158 residues in length ( Figure 1B , C ) . This system was placed in a water box using the VMD [33] plug-in , Solvate ( using the TIP3P force field for the water model ) , and waters within 2 . 4 Å of the protein were removed . The water box dimensions were ∼100 Å×100 Å×350 Å . The system was brought to electrostatic neutrality using the VMD Autoionize plug-in to add 47 Na+ ions and 29 Cl− ions . Finally , additional water molecules within 2 . 7 Å of the alpha-helical core of the T4P system were removed to reduce the number of waters initially present in the filament core which is not expected to be filled with water [17] . This led to 287 , 272 atoms in the final system . The package NAMD [34] was used with the CHARMM27 force fields [35] to carry out all simulations . Minimization was accomplished in two segments . First , 500 steps of minimization were carried out in which only the protein atoms were harmonically constrained , followed by 500 steps with all atoms in the system unconstrained . Subsequently to minimization , the system was equilibrated at a constant temperature of 310 K and a constant pressure of 1 atm with all atoms unconstrained for 500 ps . Constant temperature and pressure were maintained through the use of Langevin dynamics [36] , with a Langevin damping coefficient of 5 ps−1 and a Langevin piston period of 0 . 1 ps , and periodic boundary conditions were used . The minimized and equilibrated structure served as the starting point for all simulations . A free simulation with no applied forces was carried out for an additional 20 ns with constant pressure and constant temperature maintained using the same parameters as in the 500 ps equilibration . SMD simulations were carried out at constant velocity using the approach implemented in NAMD [34] . The spring constant was 500 pN/Å , and pulling velocities of 10 Å/ns , 5 Å/ns , 2 . 5 Å/ns , and 1 Å/ns were used . The four SMD simulations are referred to as T4P-v10 , T4P-v5 , T4P-v2 . 5 , and T4P-v1 according to their pulling velocities . The SMD force was applied directly to the atoms in the top four subunits of T4P ( Figure 1B ) and the atoms in the first 30 residues of the four bottom-most subunits ( Figure 1B ) were fixed . Subunits that were not directly pulled on during the simulations are referred as the ‘bulk’ subunits . In all SMD simulations , pulling was stopped when the filament was stretched to the edge of its periodic box , which maintained a separation of approximately 30 Å between the filament and its periodic image along the z-dimension . The length of a pilin subunit was defined as the distance between the center of mass of the alpha-carbon atoms for residues 1–3 and residues 51–53 for that subunit . The length of the complete GC-T4P filament was defined as the distance between the center of mass of the alpha-carbon atoms of residues 1–30 in each of the first four subunits ( fixed selection ) to the center of mass of the alpha-carbon atoms of residues 51 to 53 in the last four subunits ( Figure 1B , subunits p7 , p8 , p9 , p10 ) of the ‘bulk’ ( i . e . , excluding the four pulled subunits ) . Length extensions were defined as the difference between the GC-T4P length ( or pilin subunit length ) and its initial value . The separations between globular heads were calculated as the distance between the center of mass of two subunit head domains . The distance between pilin subunits along the left-handed 3-start helices ( subunit n , n+3 , … ) and the right-handed 4-start helices ( subunit n , n+4 , … ) was calculated by finding the change in the z-coordinate of the center of mass of pairs of subunits ( Figure 1A ) . The z-coordinate was used as the z-axis is approximately the central GC-T4P filament axis . To measure bending angles in the N-terminal half of α1 , each α1 helix was divided into three segments: residues 1–13 , residues 15–21 and residues 23–53 . The angle with Gly14 at its vertex was measured by defining a line of best fit for backbone atoms of residues 1–13 and residues 15–21 . The angle made by these lines will be called θG . Similarly for Pro22 , the angle , θP , was measured by calculating the angle made by the line of best fit for backbone atoms of residues 15–21 and residues 23–53 , which have Pro22 as their vertex . 0 degree corresponds to a straight angle . A schematic of these angles can be viewed in Figure S1 . Contacts were identified as existing between any two residues , which had any atoms coming within 3 . 3 Å of one another . The number of contacts was then monitored over time . Only the number of contacts based on proximity were tracked , and not their type . The contacts were monitored separately between the α1-domain interfaces , and for globular head interfaces . For quantities that can be measured for each of the pilin subunits ( for example , the tail extension , or the angles θG and θP ) , data is presented as an average over the “bulk” subunits as defined above and also pictured in Figure 1B . For quantities which are measured for pairs of subunits ( such as the separation between p3–p7 , which is analogous by symmetry to the separation between p2–p6 , p6–p10 , etc . ) , an average over the value for all of the similar pairs is shown . For contacts between interfaces , a representative subunit from the filament , subunit p5 ( Figure 1B ) was chosen , with data from the T4P-v1 simulation mainly presented . The average number of contacts with all neighboring subunits whose interface involved the α1-domain were calculated . Similar calculations were performed for the contacts involving the globular head interfaces . Additional data for subunits p3 , p4 and p6 are presented in the Supplemental Data , as well as data from simulations carried out at different pulling velocities . The SASA for 5 amino acid long patches ( 5-mers ) was calculated for each of ten frames over a period of 0 . 75 ns at the end of the T4P-v1 simulation that corresponds to an overall extension of the filament of 5% . The same criteria of 5% extension was also used to choose the frames over which the SASA calculation was performed for the other three SMD simulations . Similarly , SASA were calculated for the cryo-EM structure and over a period of 0 . 1 ns at the end of the free simulation . The final reported SASA values and their standard deviations were calculated by averaging over all ten frames , and then over the 10 “bulk” subunits ( Figure 1B ) . SASA calculations were performed in VMD [33] . Polyclonal rabbit antibodies were raised against two regions of the pilin primary sequence around the regions that were thought to behave like SM1 in the molecular simulations ( Genscript , Inc ) . Antibody #1 was raised against residues 94–108 ( SSGVNNEIKGKKLSL ) and antibody #2 was raised against residues 109–120 ( WARRENGSVKWF ) . Those antibodies were further purified against bands of denatured pilins [37] . Pili were purified as previously published [8] . 50 µL of purified pili in 50 mM CHES buffer ( ∼100 µg/mL ) were either added to 50 µL of 50 mM CHES buffer or to 50 µL of 2X Laemmli buffer . The first solution was a solution of T4P filament , after 5 minutes boiling the second was a solution of pilin subunits ( denatured pili ) . Dot blots of 2 µL of either solution were blotted twice on nitrocellulose membranes . The membranes were blocked with 5% dry milk in TBS for one hour , then incubated overnight with either antibody #1 or antibody #2 ( 1/1 , 000 dilution ) , washed 3 times with TBST , incubated for one hour with goat anti-rabbit HRP secondary antibodies ( 1/5 , 000 dilution ) and revealed using ECL reagents . Either unstretched or stretched ( transitioned ) T4P purified from Neisseria gonorrhoeae were obtain in a modified molecular combing technique [12] . Briefly pili sheared from Neisseria gonorrhoeae MS11 were first unspecifically labeled with carboxytetramethylrhodamine ( TAMRA ) , a red fluorophore . They were then let to interact with clean coverslips for 15 minutes at the bottom of a 6 well plate ( 2 ml of the solution per well/coverslip ) . They were then either dried by removing excess liquid with a lint free Kimwipes tissue while maintaining the coverslip to obtain stretched samples or let as is to obtain unstretched samples . All wells were then fixed with 4% formaldehyde and subsequently processed for immunostaining .
To further characterize changes in the GC-T4P filament , subunit-subunit interfaces for four pilin subunits in the ‘bulk’ of the filament ( subunits p3 , p4 , p5 and p6 , see Figure 1B ) were studied . Changes seen for ‘bulk’ subunits during the SMD simulations are expected to be more representative of what would occur in the GC-T4P filament in in vitro pulling experiments . Results for contacts for subunit p5 are presented here , while representative results for p3 , p4 and p6 and for p5 at the pulling simulation T4P-v2 . 5 are presented in the Supplemental Data . Subunits p5 and p6 share 1-start , 3-start and 4-start interfaces only with other ‘bulk’ subunits; they do not share any interface with either fixed or pulled subunits . Subunits p3 and p4 share interfaces with fixed subunits as well as with ‘bulk’ subunits .
The interactions in the core of the GC-T4P filament originate from the packing of the α1 domains against one another , and are thought to contribute to the incredible strength of bacterial pili . Each subunit's α1 domain has been proposed to make contact with the α1 domain of six other subunits by participating in three sets of ‘three-helix bundles’ based on the filament model describe by Craig et al . [17] . The SMD simulations demonstrate that such a model could allow for filament extension and underscore the strength of these non-covalent , and in many cases hydrophobic contacts between the α1 domains ( Figure S7 ) . Even as the filament and individual subunits extended in length ( Figure 2 and 5 ) , the contacts between α1 interfaces remained well conserved ( Figure 7 , S2 and S4 ) , which have been suggested to provide stability to the filament [17] , though such contacts might not be required for assembly , as it has been demonstrated that globular domains of the type IV pilins can assemble into fibers in vitro under certain sets of conditions [39] . These SMD simulations were performed to capture the initial steps of the elongation process , as elongations observed in experiments would be computationally prohibitive . Experimentally observed elongations would require an entirely new packing of the α1 domains to be realized . Coarse-grained simulations could provide insights to the nature of the packing in this extended conformation by probing numbers of contacts and residues in contact in the extended conformation . One specific interaction , an inter-subunit Glu5 oxygen-Phe1 nitrogen hydrogen bond , is thought to be formed in order to neutralize charge in the filament core and increase hydrophobicity [17] . Additionally , in the crystal structure for the full PAK pilin filament a close contact in between the Phe1 backbone nitrogen and the Glu5 side-chain oxygen within a single subunit ( intra-subunit interaction ) was observed [16] . For the ‘bulk’ subunits inter-subunit hydrogen bonding between Glu5 and Phe1 was found to occur more frequently in the T4P-v1 simulation compared to the free simulation ( Table 1 ) , which could imply that the interaction also plays a role in maintaining stability in the core as the filament comes under tension . Mutation of Pro22 in the pseudopilin PulG leads to a significant decrease in pilus formation in K . oxytoca , suggesting that the flexibility of α1-N around Pro22 may be critically important for pilus assembly [26] . Kinking of α1-N has also been observed in two more recent pilin crystal structures , suggesting that this bend is natural [27] . Additionally , it has been proposed that flexibility of the α1-N domain could lead to more efficient packing of pilin α1 helices within the filament core [16] . Fluctuations of angles θG and θP observed in the free simulation demonstrate the natural flexibility of α1 , which could account for the effects observed upon assembly . The observed elongations of the ‘bulk’ subunits ( Figure 5 ) may represent initial stages of the transition to the force-induced conformation of GC-T4P that was recently observed experimentally [12] . The more extensive straightening of angles θG and θP in the T4P-v1 simulation may imply that the filament becomes less flexible as it is stretched , and that eventually all subunits in the pulled conformation become straightened . In the experimentally determined stretched structure [12] , the diameter of the filament decreases by 40% and its overall length increases by a factor of 3 . In order to reproduce experimental data of this nature , the filament would need to be simulated for a much longer time and in a much larger water box along the z dimension ( the filament axis ) , which would require simulations beyond the allowed time scale of all-atom MD . This computational study was designed to capture the initial rearrangements of the filament coming under tension , in order to identify the strongest and weakest point of the filament structure . While the actual extension of the filament involves a large increase in the axial rise per subunit , neither this feature nor a decrease in filament diameter ( Figure S3 ) was captured by our simulations . However , elongations of α1 upon straightening of θG and θP could represent features in the initial stage of the transition towards the elongated GC-T4P conformation . Furthermore , the thinning of the filament observed experimentally may be the result of significant rearrangements of the pilin subunits that occur at timescales that these simulations cannot access . The longer extensions of the filament observed experimentally would also require more extensive rearrangements of the pilin subunits than seen in the simulations . Conformational rearrangements between the globular head and the α1 domains of individual subunits are unlikely to produce longer extension; rather the slipping of α1 of one subunit along the α1 of adjacent subunits could produce such extension . In this case , neutralization of the charge of the Glu5 side-chain by hydrogen bonding to a residue on another subunit might become a concern , since of the first 23 residues of α1 , only Glu5 is hydrophilic . However , residues 24 to 53 on α1 include side-chains available for hydrogen bonding or salt-bridge formation [17] . The α1 domain of a subunit could potentially slip out of its proposed 3-bundle interactions and translate up along the filament axis until Glu5 is able to interact with one of the adjacent subunit hydrophilic residues . To test this hypothesis , coarse-grained simulations would need to be carried out to study the filament at longer timescales . To further verify the models that would be obtained from such a computational approach , successful modeling of stretched pili based on cryo-EM would be useful , but such results are difficult to obtain . In contrast to α1 , the globular heads of the pilin subunits are considerably more free to move . Water exchange between the external environment and the GC-T4P core can occur in spaces between the globular heads in the free simulation ( Figure 6A ) , as well as through the larger gaps formed between globular heads due to the application of pulling forces . Waters that enter through the surface gaps can proceed to interact with the buried pilin α1-domains , which are water accessible even in the free simulation ( Figure 6B ) . In the functionally related , but structurally different Vibrio cholerae type IVb pilus ( VC-T4P ) , deuterium exchange experiments demonstrated that the D-region ( in the globular head domain ) was significantly exposed , and hence could not be buried by interaction with the αβ-loop ( connecting the α1 domain to the β-sheets ) , suggesting that the αβ-loop had to interact with another region of an adjacent subunit [40] . A recent cryo-EM study of VC-T4P shed further light onto the differences between GC-T4P and VC-T4P , including that VC-T4P packing is not as tight as the packing of subunits in GC-T4P , and that in VC-T4P a segment of the pilin α1 domains are exposed through gaps along the filament surface [41] . In the GC-T4P model , both the D-region and the αβ-loop are already well-exposed to the environment [17] , [38] , though some polar interactions are present [17] . Fluctuations observed in the free simulation can further diminish contacts between globular heads , which include the contacts between the D-region and the αβ-loop in GC-T4P , even when the filament is not under tension ( see Figure 7 , S2 and S4 ) as observed experimentally for VC-T4P . Reduction of contacts at these interfaces supports that the globular heads are not packed too tightly against one another , which would potentially limit filament flexibility [2] , [17] . Experimentally , it has been observed that T4P can bundle , creating larger filaments able to exert greater force [8] . Pilus bundle formation might be occurring by initial binding of one T4P filament to a surface , which would result in its extension under tension , followed by the association of additional filaments to the initial one [8] . However , the mechanism by which subsequent filaments associate to the first filament is unknown . The increased space between globular heads observed in the SMD simulations , demonstrated both by increases in head-head distances ( Figure 2 ) and changes in the 3-start and 4-start inter-subunit axial distances ( Figure 3 and 4 ) , potentially provide locations along the filament surface that adjacent filaments could ‘dock’ into , in turn promoting the creation of the experimentally observed T4P bundles . Finally , the increased spacing between globular heads produced along the filament surface in the SMD simulations ( Figure 2B , C for T4P-v1 ) also leads to the exposure of the EYYLN sequence ( Figure 8 , 9 , S5 and Movie S1 ) and S1 ( SSGVNNEIKG ) . The interest of predicting these regions of exposure lies in the possibility of understanding the plasticity of GC-T4P filaments and to potentially developing drugs that target T4P functions during infection . As these sequences were exposed further in the SMD simulations compared to the free simulation , it is most likely a direct consequence of the forces applied to the system . Exposure of EYYLN is consistent with the experimental result in [12] which showed EYYLN could bind with an antibody in its force-transitioned conformation , but not in the absence of tension forces . Exposure of SSGVN under force was demonstrated experimentally following prediction from our simulation . Exposure of EYYLN and SSGVN in the SMD simulations suggests that a model based on the 3-helix bundle can capture conformational changes in the T4P filament that have been previously observed in vitro [12] or demonstrated in this study . Because simulations of the experimentally observed elongation would be computationally prohibitive , here only the initial changes were probed . In vivo , filaments are dynamic , constantly alternating between retraction and elongation phases while releasing some of the force they are subjected to . Therefore , our simulations also suggest that EYYLN and SSGVN might become accessible early on under physiological conditions .
Conformational rearrangements of the GC-T4P filament under tension were studied utilizing MD simulations starting from the GC-T4P structure determined from a cryo-EM map and the crystal structure of a single GC pilin subunit . These studies were carried out in an effort to better understand the dynamics of the GC-T4P filament , its response to application of external forces and to probe initial stages of the transition between the relaxed and the tension-induced conformation . Even though SMD simulations are based on 3-helix bundle model derived from low-resolution cryo-EM experiments , exposure of the sequences EYYLN and SSGVN , consistent with in-vitro experiments [12 , present study] , were observed . Therefore , such 3-helix bundle model could represent the actual structure of the filament . Simulations based on such a model reveal that the strength of the GC-T4P filament comes from the interactions between the α1 domains [17] , as during elongation the contacts between these domains were well maintained . Contacts between subunit head domains decreased , creating additional gaps along the surface that could be related to filament bundling . These gaps lead to exposure of regions , which are hidden when not stretched , for potential drug targeting . This work shows that SMD simulations can be used to narrow down the range of potential binding sites for drug therapy targeting bacterial filaments as the SSGVN was predicted as a possible site and confirmed experimentally . Finally , GC-T4P shares with the T4P from Neisseria meningitidis the presence of multiple post-translational modifications . As the functional importance of certain of these modifications is being discovered [21] , simulations including the known modifications could shed more light on the function of the biological systems . | There are a large number of infectious bacteria that can be harmful to humans . Some bacterial infections are facilitated by long , tether-like filaments called type IV pili which extend from the surface of bacterial cells and attach to the surface of host cells . Type IV pilus filaments can grow to be many micrometers in length ( bacterial cells themselves , on average , are only a couple of micrometers in length and half a micrometer in diameter ) , and can exert very large forces ( up to 100 , 000 times the bodyweight of the bacteria ) . Because they extend from the surface of the cell , type IV pili are very good candidates for drug targeting . Computer simulation was used to exert forces on a segment of one of these filaments , in an effort to mimic the effects of tension that would be experienced by the pilus upon binding during infection . Regions of the filament that become exposed to the external environment in the pulled state were determined , in an attempt to identify amino acid sequences that could act as targets for drug design . | [
"Abstract",
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] | 2013 | Steered Molecular Dynamics Simulations of a Type IV Pilus Probe Initial Stages of a Force-Induced Conformational Transition |
Severe Acute Respiratory Syndrome caused substantial morbidity and mortality during the 2002–2003 epidemic . Many of the features of the human disease are duplicated in BALB/c mice infected with a mouse-adapted version of the virus ( MA15 ) , which develop respiratory disease with high morbidity and mortality . Here , we show that severe disease is correlated with slow kinetics of virus clearance and delayed activation and transit of respiratory dendritic cells ( rDC ) to the draining lymph nodes ( DLN ) with a consequent deficient virus-specific T cell response . All of these defects are corrected when mice are treated with liposomes containing clodronate , which deplete alveolar macrophages ( AM ) . Inhibitory AMs are believed to prevent the development of immune responses to environmental antigens and allergic responses by interacting with lung dendritic cells and T cells . The inhibitory effects of AM can also be nullified if mice or AMs are pretreated with poly I:C , which directly activate AMs and rDCs through toll-like receptors 3 ( TLR3 ) . Further , adoptive transfer of activated but not resting bone marrow–derived dendritic cells ( BMDC ) protect mice from lethal MA15 infection . These results may be relevant for SARS in humans , which is also characterized by prolonged virus persistence and delayed development of a SARS-CoV-specific immune response in individuals with severe disease .
The lung is exposed to many challenges , both environmental and pathogenic . Defense of this portal must be tightly regulated so that appropriate immune responses to pathogens are mounted but responses to innocuous antigens are minimized . Alveolar macrophages ( AM ) play a central role in maintaining this immunological homeostasis [1] , [2] , [3] . In the lung , resident AMs are continuously encountering inhaled substances due to their exposed position in the alveolar lumen , but they are kept in a quiescent state . They function poorly as accessory cells for in vitro T cell activation [4] , [5] and in many situations actively suppress the induction of adaptive immunity through their effects on alveolar and interstitial DCs and T cells [6] , [7] , [8] . In vivo elimination of alveolar macrophages using clodronate-filled liposomes ( CL ) leads to overt inflammatory reactions to otherwise harmless particulate and soluble antigens [9] . Alveolar macrophages adhere closely to alveolar epithelial cells ( AECs ) at the alveolar wall and are separated by a distance of only 0 . 2–0 . 5 µm from rDCs [6] . In macrophage-depleted mice , DCs have enhanced antigen-presenting function [6] . It has been estimated that the pool of murine alveolar macrophages can process up to 109 intratracheally injected bacteria before there is “spillover” of bacteria to DCs and before adaptive immunity is induced [10] . Although the importance of such mechanisms to control undesirable responses to inert environmental antigens is self-evident , it is also axiomatic that countermeasures must be available to allow reversal of this inhibition after challenge with inhaled pathogenic ( notably microbial ) antigens . During infection with respiratory pathogens , such as influenza virus , antigen is acquired by respiratory dendritic cells ( rDCs ) and these cells must be sufficiently activated to overcome anti-inflammatory factors in the lungs . These rDCs then migrate to the lung draining lymph nodes ( DLN ) to initiate an antiviral CD8 T cell response [11] , [12] . After the interaction of naive T cells with such antigen-bearing DCs , CD8 and likely CD4 T cells undergo activation and division in the DLNs and migrate into the lungs to eliminate virus-infected cells , leading to resolution of the infection [13] , [14] , [15] . Recently , a secondary peripheral interaction of CD8 T cells with antigen-bearing rDCs in the lung was found important for effective antiviral immunity [16] . Overall rDC activation is a prerequisite for initiation and maintenance of the immune response . Patients with the Severe Acute Respiratory Syndrome ( SARS ) , caused by a novel coronavirus ( SARS-CoV ) , developed mild to fatal pulmonary disease , with a mortality incidence of 10% [17] . Patients with worse outcomes generally exhibited a more protracted clinical course , characterized by the development of Adult Respiratory Distress Syndrome ( ARDS ) , as well as lymphopenia , neutrophilia and prolonged cytokine production [17] , [18] , [19] , [20] . Virus could be detected in nasopharyngeal aspirate and feces for as long as 21 days after disease onset [19] , [21] . Delayed virus clearance may have resulted from suboptimal T and B cell responses; suboptimal neutralizing antibody responses are detected in patients with severe disease [17] , [18] , [19] , [20] . Numerous studies demonstrated that SARS-CoV infection fails to activate macrophages and dendritic cells . Although these cells can be infected , they are functionally impaired: antiviral cytokines such as type I interferon were not expressed and endocytic capacity ( antigen capture ) was compromised ( [22] , [23] , [24] , [25] , [26] , [27] , [28] and reviewed in [29] ) . These unusual findings raised the possibility that initial infection with the virus resulted in delayed or suboptimal activation of the innate immune system . Inefficient activation of rDCs might be unable to counter the potent anti-inflammatory factors that are normally present in the lung , resulting in both a deficient T cell response and delayed kinetics of virus clearance . Recently , rodent-adapted strains of SARS-CoV , which cause mild to fatal respiratory disease , were developed in several laboratories [30] , [31] . Here , we demonstrate that lethal disease in mice infected with a mouse-adapted strain of SARS-CoV ( MA15 ) can be prevented if AMs with anti-inflammatory properties are depleted from the lung prior to infection . Treatment with toll-like receptor ( TLR ) agonists to activate rDCs or transfer of activated bone marrow-derived dendritic cells ( BMDC ) also prevents a lethal outcome . Together , these results demonstrate that SARS-CoV , by inefficiently activating the immune system , uses a novel mechanism to evade immune recognition .
SARS-CoV infection results in inefficient activation of macrophages and DCs in vitro [22] , [23] , [24] , [25] , [26] , [27] , [28] and slow virus clearance and a prolonged clinical course in humans [17] , [18] , [19] . Similarly , MA15 infection in vitro did not result in upregulation of CD86 on AM ( Fig . S2 , Gating shown in Fig . S1 A ) . To determine whether inhibitory AMs play a role in MA15-mediated severe lung disease , we depleted these cells by intranasal administration of clodronate liposomes ( CL ) . CL are useful for depletion of AM , and to a lesser extent , alveolar/airway DCs [9] , but intranasal administration does not affect the level of circulating macrophages [32] . As a control , we treated mice with PBS as described previously [33] . BALB/c mice were treated with 75 µl of CL or PBS intranasally ( i . n . ) and total lung cells were harvested after enzymatic digestion . After 24 h , there was a decrease of AMs ( CD11c+CD11b−siglec F+ [34] ) in the lung , both in frequency ( >70% ) and absolute number ( from 5–6×104 to 1–2×104 cells/lung ) , in CL , but not PBS-treated mice ( Fig . S3 A and B ) . By 48 h , approximately 90% of AMs in the lung were depleted ( Fig . S3 A and B ) . To determine whether there was a change in clinical disease after AM depletion , BALB/c mice were treated with 75 µl of CL and infected i . n . with 3×104 PFU of MA15 virus . Mice were monitored daily for weight loss and mortality . At this virus dosage , control mice lost more than 20% of their body weight and 60%–70% of them died ( Fig . 1 A ) , generally from day 6 to day 8 post infection ( p . i . ) . Depletion of AM before inoculation ( at day −1 and day −2 ) completely protected mice from this lethal infection and animals rapidly regained their body weight ( Fig . 1 A ) . AM depletion at day 2 p . i . was not protective and may have resulted in more severe disease , as observed also in influenza A virus-infected mice [16] . Of note , 6 week old C57Bl/6 mice are resistant to MA15 infection and treatment at day −1 or 2 with clodronate had no effect on the clinical course in these mice ( data not shown ) . Clodronate treatment resulted in enhanced kinetics of virus clearance , with virus cleared from all treated but not control BALB/c mice by day 7 p . i . ( Fig . 1 B ) . We next examined lung sections for changes in histology . There were no histological differences in the lungs between CL-treated and control mice at day 0 , indicating that depletion of AMs did not result in significant inflammatory cell recruitment to the lung . From day 2 p . i . , PBS-treated mice developed a rapidly progressive interstitial pneumonia with extensive edema and damage to bronchiolar and alveolar epithelial cells ( Fig . 1 C ) . Inflammatory infiltrates were consistently identified from days 2-to 6 p . i . CL-treated mice had a much better outcome with less destruction of the pulmonary architecture , but extensive alveolar , interstitial and perivascular inflammatory cell infiltration ( Fig . 1 C , day 4 and day 6 ) . Total lung cell numbers are shown in Fig . 2 A . Clodronate treatment , by removing AM , also altered the inflammatory milieu of the lungs . As a consequence , levels of pro-inflammatory cytokines and chemokines , such as IL-1β , Il-6 , IL-12 , CCL2 and CCL3 increased within 24 hours of CL , but not PBS treatment , prior to virus infection . By day 2 p . i . , levels of these cytokines were generally similar in CL and PBS-treated mice , consistent with the notion that a delayed , and possibly dysregulated , immune response contributed to severe disease in control mice ( Table S1 ) . Infection with respiratory viruses such as influenza A virus and respiratory syncytial virus ( RSV ) results in recruitment of CD11c+MHC II+ DCs to the lung [12] , [35] , [36] , [37] . Unlike these infections , recruitment of inflammatory cells , including DCs , to the lung is impaired in MA15-infected mice ( Fig . 2 A ) . The total lung cell number increased slightly , but there was no appreciable change in numbers of the respiratory dendritic cells ( rDC ) in control mice . Clodronate treatment resulted in enhancement of inflammatory cell recruitment to the lung ( Fig . 1 C and 2 A ) , with a nearly tenfold increase in numbers of rDCs within 6 days ( Fig . 2 A ) . For these experiments , we distinguished two populations of rDCs: alveolar/airway dendritic cells ( aDC: CD11c+CC11b−MHC II+ ) and interstitial dendritic cells ( iDC: CD11c+CD11b+MHC II+ ) using the gating strategy shown in Fig . S1 . By day 4 p . i . , the frequencies of MHC IIhigh/CD86+ and MHC IIhigh/CD40+ aDC and iDC increased significantly in drug-treated mice but only modestly on iDC and not at all on aDC in PBS-treated mice Over the next few days aDCs and iDCs remain activated in CL-treated mice but mostly returned to a baseline state in control mice ( Fig . 2 B and C ) . Concomitant with this recruitment and activation of rDCs , we also observed enhanced rDC migration to draining lymph nodes ( DLN ) , using a tracking method in which rDCs are labeled in the lung by i . n . inoculation of carboxyfluorescein diacetate succinimidyl ester ( CFSE ) ( see Materials and Methods and Fig . S1 B for gating ) [12] . In all mice , rDC migration to the DLNs peaked at 18 h p . i . , but migration was accelerated by treatment with clodronate . After 48 hours the frequency and number of CFSE+ rDCs in the DLNs decreased suggesting that the first 48 h p . i . were most important period for rDC migration . There was also a two-three fold increase in total cell numbers in the DLNs ( Fig . 2 D ) . Collectively , these results show that DCs remained activated for longer times in the lung and exhibited enhanced migration to DLNs after CL treatment . A consequence of the increase in both numbers of rDCs and the frequency that was activated was a 30–50 fold increase in total activated DCs in the lung . Since enhanced rDC migration to DLNs is predicted to result in enhanced virus-specific T cell responses , we next examined the magnitude of total and MA15-specific T cell responses in the lungs of CL treated and control infected mice . Clodronate treatment resulted in greater numbers of activated CD8 and CD4 T cells in the MA15-infected lung ( Fig . S4 A and B ) , compared to PBS treatment , as determined by CD43 ( clone 1B11 ) expression . The latter is upregulated on activated effector T cells [38] , [39] . To assess effects on MA15-specific T cell responses , we initially identified a set of H-2d-restricted virus-specific CD4 and CD8 T cells epitopes using lung derived cells harvested from infected mice and a peptide library covering all four structural proteins ( S , N , M , E ) of SARS-CoV . Several IFN-γ inducing CD8 and CD4 epitopes in the spike ( S ) and nucleocapsid ( N ) proteins ( S366–374 , S521–529 , S1061–1071 and N353–370 ) were identified ( manuscript in preparation ) . Some of these epitopes were described previously , but S521 and S1061 epitopes were newly discovered . Of note , all other previously described H-2d-restricted T cell epitopes were not recognized by lung-derived T cells in our assays [40] , [41] , [42] . These previous reports identified T cell epitopes using adenovirus vectors or DNA constructs expressing single SARS-CoV proteins , or isolated peptides . We speculate that the numbers of T cells recognizing these previously described epitopes are present at very low levels in infected mice compared to the immunodominant epitopes that we identify , possibly because of differences in antigen presentation between infected and immunized mice . Using these epitopes , we found that AM-depleted mice exhibited earlier and more robust virus-specific T cell responses , as measured by intracellular cytokine staining ( ICS ) for IFN-γ , whereas control mice had almost no virus-specific T cell responses at days 6 and 7 p . i . ( Fig . 3 A and B ) . PBS-treated mice that survived until day 8 p . i . mounted virus-specific T cell responses in the lung , but at a level that was much less than observed in CL-treated mice . We confirmed that these cells were functional using in vivo cytotoxicity assays . Naïve splenocytes were costained with PKH26 and CFSE , pulsed with MA15-specific CD8 T cell peptides and adoptively transferred i . n . into mice 12 h before harvest of total lung cells . Robust CD8 T cell cytotoxic responses were observed in AM-depleted mice , with 40%–50% killing of virus-specific targets . By comparison , only about 5% of target cells were lysed in control mice ( Fig . 3 C ) . Results thus far suggest that inhibitory macrophages are dominant in MA15-infected lungs . In support of this , AM were only transiently and slightly activated , as measured by CD86 and CD40 expression , after infection with MA15 ( Fig . 4 A ) . F4/80 , considered a marker for macrophage maturation and phagocytosis [43] , was present at lower levels on AMs harvested from uninfected mice compared to macrophages isolated from other sites ( e . g . , peritoneal macrophages [44] , Fig . S5 ) and was not upregulated after MA15 infection ( Fig . 4 A ) . Further , surface levels of CD200R , important in maintaining lung homeostasis , were higher on AM than peritoneal macrophages [44] ( Fig . S5 ) and were not significantly downregulated after infection ( Fig . 4 A ) , indicating that AMs continued to be inhibitory even after the onset of the infection . The number and frequency of AMs increased at day 2 before returning to baseline by day 6 p . i . in control mice but , as expected , remained low throughout the infection after clodronate treatment , ( Fig . 4 B ) . Mature “resting” AMs are able to suppress in vitro proliferation of homologous T-cells , and freshly isolated rDCs are poor antigen-presenting cells , consistent with a suppressive state [6] , [45] . To confirm the inhibitory properties of AMs , we isolated aDCs from total lung cells and cultured them in vitro for 24 h in the presence and absence of AMs . When cultured in the absence of AMs , aDC upregulated expression of CD86 , MHC II and CD40 . Co-culture with AMs prevented CD86 and MHC class II , and to a lesser extent , CD40 upregulation ( Fig . 4 C ) . The prolonged presence of AMs in MA15-infected lungs suggested that AMs not only inhibited rDCs activation , and thereby delayed DC migration from lung to lymph nodes , but also inhibited the function of anti-virus T cells in the lung . To examine this possibility , we co-cultured AMs and T cells in vitro . Concanavalin A ( Con A ) and soluble anti-CD3 ( sCD3 ) antibody treatment of lung cells resulted in proliferation of both CD4 and CD8 T cells as measured by CFSE dilution . This proliferation was almost completely inhibited by co-culture with purified AMs at a ratio of 10∶1 ( 10 T cells∶1 AM ) ( Fig . 4 D ) . Of note , endogeous AMs were removed from the lung cell preparations by incubation in a tissue culture plate for 2 h ( 90% depletion , measured by flow cytometry ) . In the absence of this prior incubation , no robust proliferation was observed . To assess the effect of AM on virus-specific T cells , we isolated CD8 T cells from MA15-infected , CL-treated mouse lungs at day 8 p . i . using microbeads and stained them with CFSE . Cells were then stimulated for 72 hours with lung cells or splenocytes that were pulsed with three MA15-specific CD8 T cell peptides ( S366/S521/S1061 ) with or without AMs . Although only about 30% of CD8 T cells were MA15-specific , proliferation of CD8 T cells was clearly detected . When co-cultured with AMs , CD8 T cell proliferation was totally inhibited ( Fig . 4 E ) . Thus , AMs inhibited both nonspecific and specific CD8 T cell proliferation . However , AM co-culture in vitro did not inhibit IFN-γ expression after stimulation with MA15-specific peptides ( Fig . S6 A ) , consistent with previous data , showing that AMs did not inhibit IL-2 secretion by Con A-stimulated T cells [45] . Further , when AMs and T cells were separated by a transwell during co-culture , no significant decrease of proliferation was observed as measured by CFSE dilution ( Fig . S6 B ) suggesting that AM inhibition of T cell proliferation required direct cell contact . The results described above raised the possibility that direct activation of rDCs in the lung or adoptive transfer of activated DCs to the lung would bypass AM inhibitory function . Signaling through Toll-like receptors ( TLR ) results in a series of signaling events that leads to the induction of an acute inflammatory response . Ligand binding to TLRs also results in dendritic cell maturation , which is necessary for the initiation of adaptive immune responses [46] , [47] , [48] . Previous reports showed that Poly I:C or CpG treatment protected animal from lethal virus infection , but the mechanism of protection was not investigated in those studies [49] , [50] . In preliminary experiments , we treated mice with ligands for several TLRs , including poly I:C ( TLR3 ) , LPS ( TLR4 ) , CpG ( TLR9 ) , R837 ( TLR7 ) , R848 ( TLR7/8 ) , Pam3CSK4 ( TLR1/2 ) , and Pam2CSK4 ( TLR2/6 ) . We observed that treatment with poly I:C ( Fig . 5 A ) and , to a lesser extent , CpG ( data not shown ) , but not the other TLR ligands , protected mice from lethal disease . Consequently , additional analyses were performed after treatment with poly I:C and as a control , LPS since both are widely used to stimulate macrophages and DCs [51] . Poly I:C ( 20 µg/mouse ) -treated mice lost about 10% of their original weight but quickly recovered within 7 days . The LPS-treated group ( 5 µg/mouse ) , lost more than 20% of their weight with death occurring in all mice within 6–7 days . Virus titers were higher at day 5 in the lungs of these mice compared to mice treated with poly I:C ( Fig . 5 B ) . Poly I:C , and to a much less extent LPS treatment resulted in enhanced CD86 and CD40 upregulation on AMs ( Fig . 5 C ) and rDCs ( Fig . S7 ) . Treatment with both TLR agonists resulted in a modest increase in F4/80 and a small decrease in CD200R expression ( Fig . 5 C ) . Consistent with the results obtained after clodronate treatment ( Fig . 3 A ) , poly I:C treatment resulted in an earlier and more robust antigen-specific T cell responses than observed in PBS ( Fig . 3 A ) or LPS-treated mice ( Fig . 5 D , E ) . Nearly twenty fold more MA15-specific T cells were detected in the lungs of poly I:C treated mice compared to LPS recipients at day 7 p . i . and these cells were functional in in vivo killing assays ( Fig . 5 E and F ) . To determine whether poly I:C or LPS directly activated AMs , AMs were isolated and stimulated in vitro with both agonists . After 24 h stimulation , poly I:C but not LPS treatment resulted in a pronounced upregulation of CD86 ( Fig . 6 A ) . Further , treatment with poly I:C but not LPS partially reversed the ability of AM to inhibit CD8 T cell proliferation after stimulation with Con A or sCD3 ( Fig . 6 B ) . These results indicate that poly I:C can abrogate AM inhibitory function both in vivo and in vitro , by directly activating AMs and rDCs . Given these results , direct delivery of activated DCs to the lungs might overcome AM-mediated inhibition . Activated DCs exhibit an enhanced ability to migrate to DLNs and to stimulate CD8 T cell proliferation and IFN-γ expression [52] , [53] , [54] , [55] . Since AMs were unable to inhibit costimulatory molecule expression on previously activated DCs ( Fig . 7 A ) , we next assessed whether adoptively transferred activated DCs could bypass AM inhibition and protect mice from a lethal MA15 infection . For this purpose , bone marrow cells were harvested from naïve mice , and DCs selectively cultured by treatment with GM-CSF plus IL-4 for 6 days [56] . BMDCs were then activated with either LPS or poly I:C , which resulted in enhanced CD86 and MHC class II expression on BMDCs ( Fig . 7 B ) . As expected , MA15 was unable to activate these cells . 3×105 activated or resting BMDCs were transferred to mice i . n . 18 h prior to infection . Mice that received BMDCs activated with either poly I:C or LPS were protected from a fatal outcome , although they still lost about 15% of their body weight . In marked contrast , recipients of resting BMDC were not protected ( Fig . 7 C ) . Further , higher virus titers were detected in the lungs at day 5 mice that received resting BMDC as opposed to activated BMDC ( Fig . 7 D ) . BMDC migration from the lungs to DLNs was accelerated by prior activation . More CFSE+ activated BMDC than resting BMDC accumulated in the DLNs of recipient mice ( Fig . 8 A and B ) and additionally , the total number of cells in the DLNs was increased dramatically by activated BMDC transfer ( Fig . 8 B ) . Consistent with enhanced rDC migration to the DLNs , recipients of activated BMDCs developed more robust CD4 and CD8 T cell responses in the lungs when compared to those that received resting BMDC ( Fig . 8 C and D ) . Nearly tenfold more MA15-specific T cells were detected in the lungs of activated BMDC compared to resting BMDC recipients at day 7 p . i . and these cells were functional in in vivo killing assays ( Fig . 8 E ) . Collectively , these results indicate that adoptive transfer of activated BMDCs to the lung amplified virus specific T cell responses , cleared virus earlier and protected mice from lethal MA15 infection .
The pathogenesis of SARS in patients that exhibit more severe disease is not well understood but includes slow virus clearance and a prolonged clinical course [19] , [21] , [57] , [58] . The results presented herein suggest that this severe disease may occur in part because infected individuals do not mount an appropriate anti-virus T cell response . Anti-virus CD8 T cells are critical for virus clearance in mice infected with other pathogens , such as influenza A virus and LCMV [15] , [59] , so it is not unexpected that they are necessary for resolution of infection with SARS-CoV . While lymphopenia is associated with a worse prognosis in SARS patients [17] , [18] , [19] , no prior studies , to our knowledge , has shown that this poor prognosis results , in part , from a sub-optimal CD8 T cell response . This defect in development of a protective T cell response occurs because the virus does not reverse the anti-inflammatory state that is naturally present in the uninfected lung . These results are consistent with in vitro studies in which the SARS-CoV is able to infect but can not activate human DCs or macrophages [22] , [23] , [24] , [25] , [26] , [27] , [28] . This may occur , in part , because coronaviruses , including SARS-CoV , are “invisible” to cellular sensors in some cell types [29] . Alveolar macrophages play a central role in maintaining immunological homeostasis [1] , [2] and actively suppress the induction of adaptive immunity through their effects on alveolar and interstitial DCs and T cells [6] , [7] , [8] . Several molecules , including nitric oxide , TGF-β and CD200R have been implicated in AM suppressive function . These molecules have either short half lives or require cell-to-cell contact [1] , [44] , [60] , [61] . Consistent with this , AMs are separated by a distance of only 0 . 2–0 . 5 µm from rDCs in the lung [10] . Our results also suggest that cell contact or close proximity to target cells is required , because AMs were unable to suppress T cell proliferation if separated from responders by a transwell membrane ( Fig . S6 B ) . In another mechanism that maintains an anti-inflammatory state in the lungs , AMs ingest and process innocuous antigen and bacteria before they can reach and activate rDCs [10] . AM depletion results in enhanced antigen-presenting function by rDCs [6] and in increased ability to lyse influenza A virus-infected cells [62] . These reports indicate that countering the quiescent , anti-inflammatory state of AM is critical for developing a protective immune response; our results indicate that infection with SARS-CoV reverses this quiescent state inefficiently . We used three approaches to support this conclusion . First , pre-treatment of MA15-infected mice with clodronate depleted AM , resulting in enhanced activation and migration of rDCs , which in turn led to the development of a vigorous and protective virus-specific T cell response in the lung ( Fig . 2 and 3 ) . The activation and migration of rDCs at early times p . i . are critical for the timely initiation of anti-SARS-CoV T cell responses . Consistent with this , treatment with clodronate at day 2 p . i . was not protective ( Fig . 1 ) , because rDC migration to the DLNs is largely complete by 48 hours p . i . ( ( Fig . 2 D ) and [12] ) . Depletion at day 2 p . i . resulted in more severe disease , suggesting that in SARS-CoV-infected mice , virus-specific T cells require additional DC stimulation in the lungs , as occurs in influenza A-infected animals [16] . Second , activation of AMs and rDCs in situ via treatment with TLR agonists also circumvented the anti-inflammatory state of the lung . Our results showed that only poly I:C , a TLR3 agonist , and to a lesser extent CpG , a TLR9 agonist , were able to perform this function . TLR7 is primarily located on plasmacytoid DCs and the inability of R848 to protect mice indicates that activation of these cells was insufficient to induce a protective immune response . Poly I:C , which activated AMs and rDC in vivo ( Fig . 5 C and S7 ) and in vitro ( Fig . 6 A ) , protected animals from lethal MA15 infection . The ability of poly I:C to stimulate rDC activation and migration has been described previously [12] , and is likely to explain its protective ability . It should be noted that poly I:C treatment also induced type 1 IFN expression in the lung . This may also have contributed to the protective effect of poly I:C , but this is not likely to be the major effect because SARS-CoV is only modestly sensitive to IFN treatment of cultured cells or of mice [63] , [64] . In addition , CL treatment did not induce type 1 IFN in the lungs , showing that IFN induction is not required for protection ( data not shown ) . LPS , which is a TLR4 agonist , was unable to protect mice from lethal disease . We considered the possibility that LPS might have toxic effects unrelated to TLR4 binding , but treatment with monophosphoryl lipid A ( MPLA ) , a derivative of LPS that is a TLR4 agonist but is less toxic [65] , [66] , was also not protective ( data not shown ) . Our results are consistent with a recent study that showed that TLR4 ligation contributed to worse outcomes in several models of lung injury [67] . TLR4 ligation , in the absence of treatment with specific agonists , did not contribute to worsened disease in MA15-infected BALB/c mice since infection of TLR4−/− BALB/c mice did not result in significant differences in clinical disease when compared to wild type BALB/c mice ( data not shown ) . Third , we showed that adoptive transfer of activated but not resting BMDCs bypassed AM-mediated suppression and protected mice from lethal disease ( Fig . 7 ) . While DC maturation makes these cells the most potent in antigen presentation in an animal , it also results in the loss of ability to take up antigen . However , antigen macropinocytosis is transiently stimulated after activation [52] , possibly explaining how transferred BMDC could acquire SARS-CoV antigen for presentation to T cells in the DLNs . Alternatively , mature DCs are able to uptake antigen for cross-presentation [68] . Activated BMDCs preferentially migrated to the DLNs ( Fig . 8 A and B ) and initiated a protective T cell response in the lungs ( Fig . 8 C–E ) . This transfer was successful because inhibitory AMs cannot reverse prior rDC activation ( Fig . 7 A ) . All of these three experimental interventions resulted in enhanced rDC migration to the DLNs , enhanced MA15-specific T cell responses at the site of infection , the lungs , and improved outcomes . It is notable that virus-specific T cells are also critical for virus clearance in C57BL/6 mice , which are resistant to MA15 infection . Six week old mice deficient in recombination activating enzyme activity 1 ( RAG1−/− ) on a C57Bl/6 background do not clear virus when measured at 9 days [69] or even 21 days p . i . ( data not shown ) , yet remain completely asymptomatic . On the other hand , mice with Severe Combined Immunodeficiency Syndrome ( SCID ) on a BALB/c background , which , like RAG1−/− mice , are genetically unable to mount a T cell response , develop clinical disease that is more severe than that observed in wild type BALB/c mice . All SCID mice succumb to the infection ( data not shown ) , compared to a 60–70% mortality rate in BALB/c mice that are infected with the same dosage of virus ( Fig . 1 A ) . Collectively , these results show that an optimal T cell response is required for virus clearance but that strain-specific components of the initial immune response , not yet defined , are critical for preventing clinical disease in resistant strains . An outstanding question is why SARS-CoV does not activate AMs and rDCs in BALB/c mice . As described above , SARS-CoV does not efficiently activate human DCs or macrophages . We have also shown that MA15 does not efficiently induce costimulatory molecule upregulation on murine rDCs or AM in vivo and/or in vitro ( Fig . 2 C , 4 A and S2 ) . However , while most viruses have mechanisms to evade host recognition sensors , they still efficiently induce an immune response . For example , successful resolution of influenza A virus infections requires activation of immune responses via TLR7 , RIG-I and NLR ( NOD-like receptors ) inflammasome pathways [70] , even though influenza A virus encodes an immune-evading protein , nsp1 [71] . HSV , lymphocytic choriomeningitis virus , hepatitis C virus , RSV and human cytomegalovirus are recognized via TLR2-dependent mechanisms while the RSV F protein activates cells via a TLR4-dependent mechanism [37] . Some viruses , such as vaccinia virus , directly inhibit TLR expression , confirming the importance of these molecules in virus recognition by the host [72] . TLR signaling is also important for SARS-CoV recognition by the innate immune system , since C57BL/6 mice , which are very resistant to the virus , become susceptible when MyD88 is genetically deleted [69] . The precise TLR or other receptor required for protection in C57BL/6 mice is not known at present . Why this same pathway is not efficiently induced in BALB/c mice after MA15 infection will be an area of future investigation . In conclusion , we have shown that lethal disease in mice infected with a mouse-adapted strain of SARS-CoV ( MA15 ) is correlated with a lack of activation of AMs and rDCs . Further , lethal disease can be prevented if AMs with anti-inflammatory properties are depleted from lungs prior to infection . Depletion results in enhanced DC recruitment to the lung and accelerated migration to DLN , and a more vigorous anti-SARS-CoV T cell response . Treatment with TLR agonists to activate AMs and rDCs or transfer of activated BMDCs also prevents a lethal outcome . Together , these results demonstrate that SARS-CoV , by “hiding” from the immune system , uses a novel mechanism to evade immune recognition in mice . The pathogenesis of SARS in humans may involve similar stealth mechanisms .
Pathogen-free BALB/c mice were purchased from the National Cancer Institute ( Frederick , MD ) . Mice were maintained in the animal care facility at the University of Iowa . Animal studies were approved by the University of Iowa Animal Care and Use Committee . African Green monkey kidney-derived Vero E6 cells were grown in Dulbecco's modified Eagle's medium ( DMEM , GIBCO , Grand Island , NY ) supplemented with 25 mM HEPES and 10% fetal bovine serum ( FBS ) ( Atlas Biologicals , Fort Collins , CO ) . Mouse-adapted SARS-CoV ( MA15 ) was a kind gift from Dr . Kanta Subbarao ( N . I . H . , Bethesda , Maryland ) [30] . Virus was passaged once on Vero E6 cells . Mice were lightly anesthetized with isoflurane and infected intranasally ( i . n . ) with 3×104 PFU of MA15 virus in 25 µl of DMEM medium . Mice were monitored for weight loss and mortality daily . All work with MA15 virus was conducted in the University of Iowa Biosafety level 3 ( BSL3 ) Laboratory Core Facility . To obtain lungs for virus titers , animals were sacrificed at the indicated time points post-infection ( p . i . ) and lungs were removed into phosphate buffered saline ( PBS ) . Tissues were homogenized using a manual homogenizer , and titered on Vero E6 cells . For plaque assays , cells were fixed with 10% formaldehyde and stained with crystal violet three days post-infection . Viral titers are expressed as PFU/g tissue . A peptide library , covering all 4 structural proteins of SARS-CoV was provided by BEI Resources ( Manassas , VA ) . Virus-specific peptides were synthesized by BioSynthesis Inc . ( Lewisville , TX ) . TLR agonists poly I:C , Monophosphoryl Lipid A ( MPLA ) , CpG , Imidazoquinoline compound ( R837 and R848 ) , Pam3CSK4 and Pam2CSK4 were purchased from Invivogen ( San Diego , CA ) . LPS was purchased from Alexis Biochemicals ( Farmingdale , NY ) . Alveolar macrophage depletion was performed by treatment with liposomes containing dichloromethylene bisphosphonate ( clodronate ) . Clodronate was a gift from Roche Diagnostics GmbH ( Mannheim , Germany ) , and it was encapsulated in liposomes as described earlier [9] , [33] . At the indicated times , mice were anesthetized by intraperitoneal injection of 2% avertin and administered 75 µl of clodronate liposomes , or PBS i . n . Animals were anesthetized and transcardially perfused with PBS followed by zinc formalin . Lungs were removed , fixed in zinc formalin , and paraffin embedded . Sections were stained with hematoxylin and eosin . Mice were anaesthetized with 100 µl pentobarbital ( 50 mg/ml , Lundbeck Inc . , Deerfield , IL ) at the indicated time points . The lung vascular bed was flushed via the right ventricle with 5 ml PBS to eliminate any blood and lungs and draining lymph nodes were then removed . Lungs were cut into small pieces and digested in HBSS buffer containing 2% FCS , 25 mM HEPES , 1 mg/ml Collagenase D ( Roche , Indianapolis , IN ) and 0 . 1 mg/ml DNase ( Roche ) for 30 min at RT . Lymph nodes were minced and pressed though a wire screen . Particulate matter was removed with a 70 µm nylon filter to obtain single-cell suspensions . Cells were enumerated by 0 . 2% trypan blue exclusion . CFSE ( Molecular Probes , Eugene , OR ) was dissolved at 25 mM in DMSO stored at −20°C until use . The CFSE stock solution was diluted in DMEM media to a concentration of 8 mM and then administered i . n . ( 50 µl/mouse ) following anesthesia with isoflurane [12] . The following monoclonal antibodies were used for these studies: rat anti-mouse CD3 ( 145-2C11 ) , rat anti-mouse CD4 ( RM4-5 ) , rat anti-mouse CD8β ( 53-6 . 7 ) , rat anti-mouse CD11b ( M1/70 ) , hamster anti-mouse CD11c ( HL3 ) , rat anti-mouse CD16/32 ( 2 . 4G2 ) , rat anti-mouse Siglec F ( E50-2440 ) , mouse anti-mouse I-Ad ( AMS-32 . 1 ) , all from BD Bioscience ( San Diego , CA ) ; rat anti-mouse IFN-γ ( XMG1 . 2 ) , anti-mouse F4/80 ( BM8 ) , rat anti-mouse CD40 ( 1C10 ) , all from eBioscience ( San Diego , CA ) ; rat anti-mouse CD43 ( 1B11 , Biolegend , San Diego , CA ) ; rat anti-mouse CD200R ( OX-110 , Serotec , Raleigh , NC ) . For surface staining , 106 cells were blocked with 1 µg anti-CD16/32 antibody and 1% rat serum , stained with the indicated antibodies , and then fixed using Cytofix Solution ( BD Biosciences ) . For intracellular cytokine staining ( ICS ) , cells were cultured at 1×106 per 96-well at 37°C for 6 h or the indicated time period in the presence of brefeldin A ( BD Biosciences ) . Cells were then labeled with surface antibodies , fixed/permeabilized with Cytofix/Cytoperm Solution ( BD Biosciences ) and labeled with anti-IFN-γ antibody . All flow cytometry data were acquired on a BD FACSCalibur or an LSR II ( BD Biosciences ) flow cytometer with CellQuest ( BD Biosciences ) and were analyzed using FlowJo software ( Tree Star , Inc . Ash , OR ) . In vivo cytotoxicity assays were performed on day 6 after MA15 infection , as previously described [73] . Briefly , splenocytes from naive mice were costained with PKH26 ( Sigma-Aldrich , St . Louis , MO ) and either 1 µM or 100 nM CFSE ( Molecular Probes , Eugene , OR ) . Labeled cells were then pulsed with the indicated peptides ( 3 µM ) at 37°C for 1 h and 5×105 cells from each group were mixed together ( 1×106 cells in total ) . Cells were transferred i . n . into mice and at 12 h after transfer , total lung cells were isolated . Target cells were distinguished from host cells on the basis of PKH26 staining and from each other on CFSE staining . After gating on PKH26+ cells , the percentage killing was calculated as previously described [73] . AMs were obtained from uninfected lungs as previously described [74] . Briefly , lungs were inflated with warm PBS containing 0 . 2% BSA and 12 mM lignocaine ( Sigma-Aldrich , St . Louis , MO ) via cannulation of the trachea , and were lavaged at least 6 times . Cells were collected by centrifugation , resuspended in RPMI 1640 ( Gibco , Grand Island , NY ) containing 10% FCS ( Atlanta , Lawrenceville , GA ) and cultured at 4×104 in each 96-well for 48 h before use in the presence or absence of stimulators [75] . To demonstrate inhibition of polyclonal T cell proliferation , 4×105 splenocytes or lung cells ( after AM-depletion by attachment to plates for 2 h at 37°C ) were labeled with 1 µM CFSE and added to wells , stimulated with 2 . 5 µg/ml Con A ( Sigma ) or 1 µg/ml soluble CD3 ( eBioscience ) and cultured with AMs at a ratio of 10∶1 for 72 h . For inhibition of virus-specific CD8 T cell proliferation , lung CD8 T cells were purified from AM-depleted , MA15-infected animals at day 8 p . i . using CD8 Microbeads ( Miltenyi Biotec , Cologne , Germany ) at day 8 . Splenocytes pulsed with 1 µM peptides or CD8 T cell-depleted lung cells were added as APCs and cultured with AMs at a ratio of 10∶1 for 72 h . Cells were then harvested , stained with antibodies and subjected to flow cytometric analysis . aDC population were purified from the lungs of naïve BALB/c mice by FACS sorting based on their expression of CD11c+MHC II+CD11b− ( Fig . S1 ) and enriched to about 80% purity . Bone marrow-derived DCs ( BMDC ) were generated as previously described [56] . Briefly , red blood cell-depleted BM cells were plated at a density of 1×106/ml in RP10 ( RPMI with 10% fetal calf serum , 1 . 0 mM HEPES , 0 . 2 mM L-glutamine , 0 . 05 mM gentamicin sulfate , 1% penicillin- streptomycin , 1 mM sodium pyruvate , and 0 . 02 mM 2-mercaptoethanol ) supplemented with 1 , 000 U/ml recombinant granulocyte-macrophage colony stimulating factor ( BD Pharmingen ) and 50 U/ml recombinant interleukin-4 ( eBioscience ) . Cells were incubated for 6 days , with 75% medium replacement every 2 days . At day 6 , BMDCs were stimulated with or without 20 µg/ml Poly I:C or 1 µg/ml LPS for 18–24 h . CD11c microbeads and a Miltenyi autoMACS magnetic cell sorter ( Miltenyi Biotec , Cologne , Germany ) were used to purify CD11c+ DCs . Purity was confirmed by flow cytometry . BALB/c mice were lightly anesthetized with isoflurane and 3×105 BMDCs in 75 µl PBS were adoptively transfer i . n . 18 h before MA15 infection . A Student's t test was used to analyze differences in mean values between groups . All results are expressed as means±standard errors of the means ( SEM ) . P values of <0 . 05 were considered statistically significant . | Severe Acute Respiratory Syndrome ( SARS ) occurred in human populations in 2002–2003 and was caused by a novel coronavirus ( CoV ) . Human SARS was characterized by prolonged virus excretion , lymphopenia and delayed adaptive immune responses in patients with severe disease . Recently , small animal models have been developed that mimic some of the features of the human disease . Specifically , BALB/c mice infected with mouse-adapted SARS-CoV develop severe respiratory disease . Here , we show that the T cell response is defective in these mice and that this results from inefficient activation of the initial immune response to the virus . This defect can be corrected by several treatments , including depletion of inhibitory macrophages from the lungs and direct activation of respiratory dendritic cells , important in initiating the immune response or transfer of activated dendritic cells prior to infection . All of these modalities result in improved initiation of the immune response and an enhanced anti-virus T cell response . Inefficient activation of the immune response may play a role in human SARS , and our results suggest possible strategies that might be used to develop novel anti-viral therapies . | [
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] | 2009 | Evasion by Stealth: Inefficient Immune Activation Underlies Poor T Cell Response and Severe Disease in SARS-CoV-Infected Mice |
Many new gene copies emerged by gene duplication in hominoids , but little is known with respect to their functional evolution . Glutamate dehydrogenase ( GLUD ) is an enzyme central to the glutamate and energy metabolism of the cell . In addition to the single , GLUD-encoding gene present in all mammals ( GLUD1 ) , humans and apes acquired a second GLUD gene ( GLUD2 ) through retroduplication of GLUD1 , which codes for an enzyme with unique , potentially brain-adapted properties . Here we show that whereas the GLUD1 parental protein localizes to mitochondria and the cytoplasm , GLUD2 is specifically targeted to mitochondria . Using evolutionary analysis and resurrected ancestral protein variants , we demonstrate that the enhanced mitochondrial targeting specificity of GLUD2 is due to a single positively selected glutamic acid-to-lysine substitution , which was fixed in the N-terminal mitochondrial targeting sequence ( MTS ) of GLUD2 soon after the duplication event in the hominoid ancestor ∼18–25 million years ago . This MTS substitution arose in parallel with two crucial adaptive amino acid changes in the enzyme and likely contributed to the functional adaptation of GLUD2 to the glutamate metabolism of the hominoid brain and other tissues . We suggest that rapid , selectively driven subcellular adaptation , as exemplified by GLUD2 , represents a common route underlying the emergence of new gene functions .
The process of gene duplication is the major mechanism underlying the origin of new gene functions and has thus significantly contributed to the emergence of adaptive evolutionary novelties during evolution [1] , [2] . DNA-based gene duplication—the duplication of chromosomal segments containing genes—has been prevalent during hominoid evolution [3] . Similarly , the process of retroposition ( or “retroduplication” ) , a mechanism generating intronless gene copies ( retrocopies ) via the LINE retrotransposon-mediated reverse transcription of mRNAs from “parental” sources genes [4] , [5] , has resulted in a large number of gene copies in apes [6] . A small number of functional ape-specific duplicates created by these mechanisms have been identified ( e . g . refs . [6]–[11] ) . However , although several of these genes revealed signatures of positive Darwinian selection ( e . g . [9] ) , suggestive of adaptive protein sequence evolution , the evolutionary fate and functional protein evolution of new ape genes remains poorly understood . GLUD2 is one of the few hominoid-specific proteins for which positively selected amino acid substitutions could be related to functional change and adaptation . It is encoded by the intronless GLUD2 gene , which emerged via the reverse transcription of a messenger RNA from its parental gene GLUD1 in the hominoid ancestor 18–25 million years ago ( Mya ) [7] . The GLUD genes encode two distinct isoforms of glutamate dehydrogenase ( GLUD , also termed GDH ) , an enzyme catalyzing the oxidative deamination of glutamate to α-ketoglutarate ( generating ATP through the Krebs cycle ) and ammonia , a reversible reaction that takes place in mitochondria [12] . Previous work showed that the GLUD2-encoded enzyme evolved unique , potentially brain-specific functional properties soon after the duplication event by virtue of two key amino acid substitutions that were fixed as a result of positive selection [7] , [13] . Due to these substitutions , GLUD2 shows higher activity at neutral pH than GLUD1 , is less sensitive to GTP inhibition , and—unlike GLUD1—requires high ADP levels for its allosteric activation [14] . It was suggested that these properties reflect the functional adaptation of GLUD2 to the metabolism of neurotransmitter glutamate in the brain [13] , [15] . Here we have further investigated the functional adaptation of the ape-specific glutamate dehydrogenase . We show that whereas GLUD1 localizes to the mitochondria as well as the cytoplasm , GLUD2 is specifically targeted to mitochondria , due to a single key amino acid substitution in its signal peptide , which emerged in the common hominoid ancestor and appears to have been fixed under the influence of positive selection . The enhanced mitochondrial targeting capacity of GLUD2 probably reflects further selectively driven optimization of this enzyme to the glutamate/energy metabolism of the brain and other tissues . More generally , the evolution of GLUD2 represents a remarkable example of rapid , selectively driven subcellular adaptation and thus reveals a novel mode for the functional adaptation of new duplicate genes .
We previously identified selectively driven substitutions in the mature GLUD2 protein that led to altered enzymatic properties [7] . When further investigating the evolution of the GLUD coding sequences in apes , we noticed an overall significantly higher nonsynonymous to synonymous substitution rate on the GLUD2 branches ( dN/dS∼2 ) compared to those of GLUD1 ( dN/dS∼0 . 2; P<10−3 ) after the duplication event , when restricting the analysis to the 5′-end ( first 159 nucleotides ) of the sequences ( Figure 1A , see also legend and Materials and Methods for details ) . Notably , this region codes for a mitochondrial targeting sequence ( MTS ) of 53 amino acids ( Figure 2A ) , which is required to direct the GLUD1 enzyme to mitochondria [16] . Prompted by this observation , we sought to assess whether the accelerated change of the MTS of GLUD2 reflects the action of positive selection ( rather than relaxation of selective constraint ) and might therefore be of functional relevance . To this end , we traced the evolutionary history of the full-length GLUD2 coding sequence ( including the MTS-encoding sequence ) , focusing on the first two internal branches after the duplication event ( Figure 1A ) . These branches were previously shown to represent an adaptive phase during the evolution of the mature GLUD2 protein [7] . A maximum likelihood procedure that tests for selection at certain sites ( see Materials and Methods for details ) revealed two amino acid substitutions ( position 7 and 25 ) under positive selection in the MTS region ( P>0 . 95 , Figure 2A ) . We thus hypothesized that GLUD2 might have functionally adapted by evolving altered subcellular targeting . To explore this hypothesis , we first used an in silico method ( [17] , Materials and Methods ) to predict subcellular localization of reconstructed ancestral GLUD2 MTS variants , representing sequences before ( sequence upon duplication event , node A ) and after ( great ape ancestor , node C ) the period of positive selection ( Figure 1A ) . Interestingly , this analysis suggested a substantially higher mitochondrial targeting probability for the node C targeting sequence ( 0 . 92 ) than for that of node A ( 0 . 28; Figure 1A ) . To experimentally test these predictions , we synthesized the reconstructed MTSs for the node A and node C variants and fused them to a fluorescent ( GFP ) reporter . As GLUD2 is thought to have particularly adapted to a function in degrading neurotransmitter glutamate in astrocytes [13] , [14] , we transfected a human astrocyte-derived cell line ( LN229 , glioblastoma ) with a vector encoding these recombinant proteins . These experiments revealed a striking pattern . We found that the node A MTS-GFP fusion protein localized to mitochondria , as expected , but that it could also be detected in the cytoplasm in most cells ( Figure 1B , 1D , and Figure S1 ) . In contrast , the node C MTS protein localized specifically to mitochondria in the vast majority of cells ( Figure 1C , 1D , and Figure S1 ) . Thus , consistent with the in silico predictions , our experimental analysis strongly suggests that the MTS of GLUD2 evolved the capacity to more specifically target the GLUD2 enzyme to mitochondria during the period of positive selection . Further localization experiments showed that—similarly to the node A protein ( Figure 1B , 1D , and Figure S1 ) —the human GLUD1 MTS-GFP fusion protein generally localizes to both mitochondria and the cytoplasm ( Figure 1D and Figure S1 ) . This suggests that GLUD1 preserved the ancestral localization pattern since the time of the duplication event ( node A ) , which is consistent with the paucity of amino acid substitutions during GLUD1 evolution and its low mitochondrial targeting prediction value ( 0 . 30 , Figure 1A ) . These experiments also showed that the human GLUD2 MTS retained the increased mitochondrial targeting capacity ( Figure 1D and Figure S1 ) , in agreement with the high mitochondrial localization probability ( 0 . 92 ) estimated in silico ( Figure 1A ) . Thus , the enhanced mitochondrial targeting specificity of the GLUD2 MTS was preserved after the period of positive selection on the lineage leading to humans . We obtained similar results for two other cell lines ( human HeLa cells and COS7 from African green monkeys ) , further supporting the notion of a subcellular targeting shift of GLUD2 during its early evolution ( Figure S2 ) . To more precisely date the shift of the GLUD2 targeting specificity , we assessed the subcellular localization of GLUD2 from the last common hominoid ancestor ( node B ) . We found that the resurrected node B protein localized specifically to mitochondria in the majority of cells ( Figure 1D and Figure S1 ) , consistent with the high mitochondrial prediction value ( 0 . 91 , Figure 1A ) . This suggests that GLUD2 had already evolved an increased mitochondrial localization specificity in the common hominoid ancestor ∼18–25 million years ago . To assess the subcellular targeting capacities of the GLUD MTS sequences in the context of their physiologically targeted proteins , we performed similar experiments using full-length GLUD-fluorescent protein fusions . These experiments confirm the results obtained using the MTS-GFP fusions for the human GLUD1 and GLUD2 proteins ( Figure 3A–C and Figure S3 ) . We also analyzed the subcellular localization of extant GLUD2 proteins from the other apes . Indeed , GLUD2 from all apes localizes predominantly to mitochondria ( Figure 3A and Figure S3 ) . Thus , the enhanced mitochondrial targeting specificity of GLUD2 was conserved throughout hominoid evolution . Which substitutions in the GLUD2 MTS contributed to the increased mitochondrial targeting capacity ? A typical MTS contains several positively charged residues , such as lysines or arginines , and hydrophobic residues , generating an amphipathic helix [18] , [19] . Due to the electrical potential across the inner membrane of mitochondria ( the mitochondrial matrix being negatively charged ) , positive charges within the MTS are assumed to electrophoretically promote transfer of proteins across this membrane [18] , [19] . One of the two positively selected amino acid changes in the GLUD2 MTS involves a glutamic acid ( E ) to lysine ( K ) substitution at position 7 of the sequence ( Figure 2A ) . Notably , this substitution–which occurred in the common hominoid ancestor during the time of the switch in targeting specificity–introduces a positive charge to the MTS by replacing a negatively charged residue ( Figure 2A ) . The second amino acid substitution under positive selection ( D25H ) replaces a negatively charged residue ( aspartate , D ) and at the same time introduces a partially positively charged amino acid ( histidine , H ) at position 25 of the MTS . A helical wheel representation of the secondary structure of the helix formed by the GLUD2 MTS illustrates its modified properties ( Figure 2B ) . The E7K and D25H substitutions introduce additional positively charged amino acids at one side of the α-helix within a previously weakly positively charged patch . Opposite to this charged patch , an ancestral patch with hydrophobic amino acids is found , which has remained largely unchanged during the evolution of GLUD2 . Thus , the two positively selected substitutions are predicted to promote the formation of an amphipathic helix , which may function as an optimized MTS . Consistently , changing these residues in the different GLUD sequences alters the in silico predictions of the GLUD mitochondrial targeting capacities . In particular , the E7K substitution dramatically alters these predictions . For example , introducing this substitution into the human GLUD1 sequence leads to an increase of the mitochondrial targeting probability from 30% to 90% , whereas the D25H substitution increases the GLUD1 wild type value to only 35% . The predominant effect of E7K is expected , given that this substitution represents the more radical substitution , as it replaces a negatively charged residue with a fully positively charged amino acid ( see above , Figure 2B ) . Thus , we hypothesized that the E7K substitution was the key contributor to the evolution of optimized mitochondrial targeting of GLUD2 . To test this hypothesis , we introduced the E7K substitution into the MTS of human GLUD1 using site-directed mutagenesis . Remarkably , the mutant GLUD1E7K MTS shows a dramatic increase in mitochondrial localization capacity relative to the wild type variant ( Figure 4 ) , which is indistinguishable from those observed for extant or ancestral GLUD2 variants from node B and C ( Figure 1 and Figure S1 ) . We obtained similar results when introducing the E7K substitution into the full-length GLUD1 protein ( Figure 5 ) . Thus , in accord with our prediction , the subcellular adaptation of GLUD2 appears to have been mainly driven by this one key substitution that occurred soon after the retroduplication event in the common hominoid ancestor . In support of the notion that E7K substitution was key to the increased mitochondrial targeting capacity of GLUD2 , we find that reverting this substitution back to the ancestral glutamic acid in the GLUD2 sequence reduces its mitochondrial targeting specificity to a level that is indistinguishable from that of the parental GLUD1 protein ( Figure 5 ) . In conclusion , while the D25H substitution and potentially other substitutions that occurred during the period of positive selection might have contributed to enhanced or altered mitochondrial targeting in vivo , the key substitution rendering GLUD2 specific to mitochondria was E7K . Notably , this residue is conserved ( as glutamic acid ) in GLUD targeting sequences from other mammals , including mouse ( Figure 2A ) and opossum ( not shown ) , a marsupial that diverged from primates around 180 Mya [20] . Generally , our results lend striking experimental support to a hypothesis suggesting that subcellular localization changes of duplicate proteins could occur by key substitutions in protein targeting sequences [21] . We finally note that the GLUD2 MTS seems to have lost some of the enhanced mitochondrial targeting specificity on the gibbon lineage ( Figure 3 ) , consistent with the lower in silico prediction value for the gibbon GLUD2 MTS ( 0 . 74 , Figure 1A ) . This is presumably mainly due to a substitution at the third position of the gibbon GLUD2 MTS–a change from the ancestral arginine residue in the positively charged patch of the MTS helix to a non-charged cysteine residue ( Figure 2A ) –which reduces the net positive charge of the protein and leads to a reduction of the in silico-predicted mitochondrial localization probability ( from 0 . 92 to 0 . 74 ) . Based on previous work , it was suggested that the emergence of GLUD2 in hominoids may have permitted an increased astrocyte metabolism of glutamate [7] , [13] . GLUD2 evolved its unique enzymatic properties soon after the duplication event in the common hominoid ancestor ( ∼18–25 Mya ) , on the basis of two positively selected amino acid substitutions in the mature protein ( see introductory paragraph and refs . [7] , [13] , [14] ) . Here we have identified an additional mechanism through which GLUD2 appears to have functionally adapted . We show that GLUD2 evolved an enhanced mitochondrial targeting specificity , mainly by virtue of a single amino acid change in its MTS , which also appeared during the period of positive selection in the common hominoid ancestor . Thus , while its parental protein GLUD1 localizes to mitochondria ( as previously reported , ref . [16] ) but also to the cytoplasm , the subcellular localization of GLUD2 is largely restricted to mitochondria . What was the selective benefit of the enhanced mitochondrial targeting capacity of GLUD2 ? We propose two—not mutually exclusive—scenarios that may explain this observation . First , in addition to its mitochondrial function , GLUD1-encoded GDH may have an–as yet–unknown function in the cytoplasm , akin to other mitochondrial enzymes ( e . g . , fumarase , ref . [22] ) . Due to the changes in its targeting sequence upon the retroduplication event , the ape-specific GLUD2 protein evolved a specific function in one of these ancestral compartments–the mitochondrion . This subcellular adaptation might have been particularly relevant with respect to the presumed function of GLUD2 in astrocytes ( see above ) , where it is thought to be involved in the degradation/metabolism of neurotransmitter glutamate—a process taking place in mitochondria . We note , however , that recent work revealed that GLUD2 ( similarly to GLUD1 ) is transcribed in many or most human tissues ( Bryk et al . , unpublished ) . This finding is in contrast to a previous study , which suggested that GLUD2 is rather specifically expressed in brain , retina , and testis [23] . Consequently , the subcellular adaptation of GLUD2 is likely of functional significance for hominoid tissues in general . A second possibility that might explain the more specific mitochondrial targeting of GLUD2 involves the rather large variability of mitochondrial membrane potentials , which depend on the tissue and cell type [24] , [25] . While mitochondria in tissues such as heart and muscle have high membrane potentials ( i . e . , they are more negatively charged inside the mitochondrial matrix than mitochondria in cells from other tissues ) , glial cells—such as astrocytes—have lower membrane potentials . Thus , GLUD2 may have evolved a more positively charged targeting sequence to compensate for the low membrane potential of mitochondria in astrocytes , thus ensuring efficient import of GLUD2-encoded GDH into mitochondria in these glial brain cells . Further work is required to more precisely understand the physiological implications of the enhanced mitochondrial localization specificity of this recently emerged hominoid protein . In any event , our results suggest that the shift in subcellular targeting specificity of GLUD2 was beneficial to the evolution of the glutamate/energy metabolism of the hominoid brain and/or other tissues , as it appears to have been driven by positive selection . More generally , our study provides a remarkable example of a novel mode for the origin of new gene functions [21] , [26]–[28] . It has long been known that paralogous protein family members may localize differently in the cell ( e . g . , ref . [29] ) . Indeed , recent work using yeast as a model system suggests that subcellular adaptation represents a rather common mechanism through which duplicate genes may functionally diversify [30] . Interestingly , a hominoid-specific protein was recently shown to have completely changed its subcellular localization during its evolution due to positive selection [31] , thus representing a case of “neolocalization” [30] . Here we have shown that newly emerged proteins such as GLUD2 may rapidly adapt to specific ancestral compartments ( a process termed “sublocalization”; ref . [30] ) under the influence of positive selection at key sites . We thus suggest that in addition to changes in gene expression and/or the biochemical function of the protein , rapid and selectively driven subcellular adaptation ( by either neo- or sublocalization ) is likely to represent a common mechanism underlying the emergence of new gene function .
The phylogenetic tree of the GLUD1/GLUD2 sequences coding for the mitochondrial targeting peptide was based on the known GLUD topology ( ref . [7] , which also corresponds the commonly accepted hominoid species phylogeny ) . dN/dS ratios and the number of synonymous and nonsynonymous changes in the phylogenetic tree were estimated using the codeml free-ratio model as implemented in the PAML4 package [32] . To assess whether the dN/dS ratio of the GLUD2 MTS is significantly elevated relative to that of GLUD1 , we first compared a one-ratio codeml model ( which assumes an equal dN/dS ratio for all the branches in the phylogeny ) to a two-ratio model , where an additional dN/dS ratio is allowed on the GLUD2 lineages . Differences between these two models as well as the null and alternative models described in the following were compared using a likelihood-ratio test [33] . We note that the dN/dS rate of GLUD1 after the duplication event is not significantly different from that in the remaining GLUD1 branches in the tree ( P<0 . 49 ) , which suggests that the selective constraint on the coding sequence of GLUD1 has not changed after the emergence of GLUD2 . To assess whether the GLUD2 coding sequence ( including its MTS ) has evolved under the influence of positive selection , we used a conservative branch-site test [34] . We compared the likelihood of a model , which allows for dN/dS>1 at a subset of sites ( i . e . , dN/dS is estimated from the data ) on the two internal branches after the duplication event , to that of a null model where dN/dS of this site class was fixed to 1 . The dN/dS ratio was found to be significantly larger than 1 ( P<0 . 02 ) , consistent with a previous analysis focusing on the sequence encoding the mature protein [7] . Specific sites under positive selection were predicted using a Bayesian approach [35] as implemented in codeml . The ancestral sequences for nodes A , B , and C , were reconstructed using a one-ratio model ( M0 ) as implemented in codeml . The posterior probabilities for reconstructed codons at all nodes were high ( >0 . 95 ) . Only the ancestral sequences for the two codons at positions 24 and 25 could not be unambiguously determined at nodes B and C , as these positions overlap with the deletion of 9 nucleotides in gibbon and two substitutions occurred at these positions on the branches between nodes A and C . The substitutions were assigned to branch A–B ( Figure 1A and 2A ) , as determined by codeml , but could equally be assigned to branch B–C . To analyze the mitochondrial targeting sequences of GLUD1 and GLUD2 and to assess subcellular localization , we used the PREDOTAR software ( [17] , http://urgi . versailles . inra . fr/predotar/french . html ) . We note that other target sequence analysis/subcellular prediction tools provided similar results ( not shown ) . To analyze the structure and property changes of the GLUD1/GLUD2 mitochondrial targeting sequences , we used a helical wheel prediction tool ( http://rzlab . ucr . edu/scripts/wheel/wheel . cgi ) . GLUD1 and GLUD2 coding sequences were obtained by PCR ( primers sequences available upon request ) using the following primate genomic DNA samples from the ECACC repository ( Wiltshire , UK ) : Human “Caucasian” , chimpanzee ( Pan troglodytes ) , gorilla ( Gorilla gorilla ) , orangutan ( Pongo pygmaeus ) , gibbon/siamang ( Symphalangus syndactylus ) , and African green monkey ( Cercopithecus aethiops sabaeus ) . The reconstructed GLUD sequences ( see above , section Evolutionary Analysis ) were synthesized by GenScript and cloned . GLUD targeting sequence mutants were obtained through site-directed mutagenesis by introducing the substitutions E7K and K7E in the GLUD1 and the GLUD2 sequences , respectively ( all primers and restriction enzymes used are available upon request ) . All sequences were cloned into pEGFP-N1 ( Clontech ) vectors using standard procedures . GLUD sequences that were not already available ( GLUD1 MTS coding sequences from apes and African green monkey ) were determined using standard sequencing procedures ( sequences were run on an ABI 3730 automated sequencer ) and the samples described above . These sequences were deposited in Genbank ( see below for accession numbers ) . HeLa , LN229 and COS7 cells were cultivated under standard conditions . Cells grown on MatTek Glass Bottom Culture Dishes ( MatTek ) for 24 hours were transiently transfected with the different GLUD constructs using Lipofectamine Plus ( Invitrogen ) according to the protocol of the supplier . 23 . 5 hours after transfection , mitochondria were stained with MitoTracker Red CMXRos ( Invitrogen ) . Living cells were analyzed using a Confocal Microscope Zeiss LSM 510 Meta INVERTED by using a 63-fold oil objective . We used LSM for image analysis . In order to quantify the number of transfected cells that express GLUD proteins specifically in mitochondria , or in both the cytoplasm and mitochondria , we assigned a code to each dish with the respect to the construct used for transfection . We then proceeded with blind counts of the cellular phenotypes for each experiment . Specifically , the percentage of cells with GFP signals only in mitochondria was assessed by examining 10–50 transfected cells at 40-fold magnification over ten arbitrarily chosen areas on the dish . Each experiment was repeated five times . Differences between treatment groups were evaluated using ANOVA followed by a Post Hoc ( Tukey HSD Test ) , with significance set at P<0 . 01 . The Genbank ( http://www . ncbi . nlm . nih . gov/Genbank/ ) accession numbers for the previously unpublished GLUD1 MTS coding sequences are: EU828516 ( chimpanzee , Pan troglodytes ) , EU828520 ( gorilla , Gorilla gorilla ) , EU828517 ( orang-utan , Pongo Pygmaeus ) , EU828518 ( Siamang , Symphalangus syndactylus ) , and EU828519 ( African green monkey , Cercopithecus aethiops sabaeus ) . | Little is known about the functional evolution of new hominoid genes . In this study , we utilized a combination of evolutionary analyses and cell biology experiments to unveil a novel mode by which the human- and ape-specific glutamate dehydrogenase enzyme ( GLUD2 ) functionally adapted . We find that whereas the GLUD1 parental protein ( present in all mammals ) localizes to mitochondria and also to the cytoplasm , GLUD2 is specifically targeted to mitochondria . Using resurrected ancestral proteins and site-directed mutagenesis , we show that the optimized mitochondrial targeting capacity of GLUD2 is due to a single positively selected amino acid substitution in its N-terminal targeting sequence , which occurred soon after the duplication event in the ape ancestor 18–25 million years ago . The specialization in mitochondrial localization is probably linked to the function of GLUD2 in the glutamate metabolism of the brain ( recycling of glutamate in astrocytes ) , but is likely also of functional relevance in other tissues in which GLUD2 is expressed . We suggest that in addition to the traditionally considered modes of functional adaptation ( changes in gene expression and/or the biochemical function of the protein ) , rapid and selectively driven subcellular adaptation to specific ancestral compartments may represent a common yet previously little-considered mechanism for the origin of new gene functions . | [
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] | 2008 | Mitochondrial Targeting Adaptation of the Hominoid-Specific Glutamate Dehydrogenase Driven by Positive Darwinian Selection |
Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces , but are sensitive to noise processes that degrade memory retention . Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention , while short-term depression possibly increases continuous attractor volatility . Here , we present a comprehensive description of the combined effect of both short-term facilitation and depression on noise-induced memory degradation in one-dimensional continuous attractor models . Our theoretical description , applicable to rate models as well as spiking networks close to a stationary state , accurately describes the slow dynamics of stored memory positions as a combination of two processes: ( i ) diffusion due to variability caused by spikes; and ( ii ) drift due to random connectivity and neuronal heterogeneity . We find that facilitation decreases both diffusion and directed drifts , while short-term depression tends to increase both . Using mutual information , we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory . Finally , our theory predicts the sensitivity of continuous working memory to distractor inputs and provides conditions for stability of memory .
Information about past environmental stimuli can be stored and retrieved seconds later from working memory [1 , 2] . Strikingly , this transient storage is achieved for timescales of seconds with neurons and synapse transmission operating mostly on time scales of tens of milliseconds and shorter [3] . An influential hypothesis of neuroscience is that working memory emerges from recurrently connected cortical neuronal networks: memories are retained by self-generating cortical activity through positive feedback [4–7] , thereby bridging the time scales from milliseconds ( neuronal dynamics ) to seconds ( behavior ) . Sensory stimuli are often embedded in a physical continuum: for example , positions of objects in the visual field are continuous , as are frequencies of auditory stimuli , or the position of somatosensory stimuli on the body . Ideally , the organization of cortical working memory circuits should reflect the continuous nature of sensory information [3] . A class of cortical working memory models able to store continuously structured information is that of continuous attractors , characterized by a continuum of meta-stable states , which can be used to retain memories over delay periods much longer than those of the single network constituents [8] . Continuous attractors were proposed as theoretical models for cortical working memory [9–11] , path integration [12–14] , and other cortical functions [15–17] ( see e . g . [3 , 18–21] for recent reviews ) , well before experimental evidence was found in cortical networks [22] and the limbic system [18 , 23] . The one-dimensional ring-attractor in the fly responsible for self-orientation [24 , 25] is a particularly intriguing example . Continuous attractor models have been successfully employed in the context of visuospatial working memory to explain behavioral performance [26–29] , to predict the effects of neuromodulation [30 , 31] , or the implications of cognitive impairment [32 , 33] . However , in networks with heterogeneities , the continuum of memory states quickly breaks down , since noise and heterogeneities break , transiently or permanently , the crucial symmetry necessary for continuous attractors [10 , 11 , 13 , 16 , 34–40] . For example , the stochasticity of neuronal spiking ( “fast noise” ) leads to transient asymmetries that randomly displace encoded memories along the continuum of states [10 , 11 , 35 , 37 , 39 , 40] , leading , averaged over many trials , to diffusion of encoded information . More drastically , introducing fixed asymmetries ( “frozen noise” ) due to network heterogeneities causes a directed drift of memories and a collapse of the continuum of attractive states to a set of discrete states . Examples of heterogeneities in biological scenarios include the sparsity of recurrent connections [13 , 36] , or randomness in neuronal parameters [36] and values of recurrent weights [16 , 34 , 38] . Since both ( fast ) noise and heterogeneities are expected in cortical settings , the feasibility of continuous attractors as computational systems of the brain has been called into question [3 , 6 , 41] . The question then arises , whether short-term plasticity of recurrent synaptic connections can rescue the feasibility of continuous attractor models . In particular , short-term depression has a strong effects on the directed drift of attractor states in rate models [42 , 43] , but no strong conclusions were drawn in a spiking network implementation [44] . Short-term facilitation , on the other hand , increases the retention time of memories in continuous attractor networks with noise-free [38] and , as shown parallel to this study , noisy [45] rate neurons . In simulations of continuous attractors implemented with spiking neurons for a fixed set of parameters , facilitation was reported to cause slow drift [46 , 47] and a reduced amount of diffusion [47] . However , despite the large number of existing studies , several fundamental questions remain unanswered . What are the quantitative effects of short-term facilitation in more complex neuronal models and across facilitation parameters ? How does short-term depression influence the strength of diffusion and drift , and how does it interplay with facilitation ? Do phenomena reported in rate networks persist in spiking networks ? Finally , can a single theory be used to predict all of the effects observed in simulations ? Here , we present a comprehensive description of the effects of short-term facilitation and depression on noise-induced displacement of one-dimensional continuous attractor models . Extending earlier theories for diffusion [39 , 40 , 45] and drift [38] , we derive predictions of the amount of diffusion and drift in ring-attractor models of randomly firing neurons with short-term plasticity , providing , for the first time , a general description of bump displacement in the presence of both short-term facilitation and depression . Our theory is formulated as a rate model with noise , but since the gain-function of the rate model can be chosen to match that of integrate-and-fire models , our theory is also a good approximation for a large class of heterogeneous networks of integrate-and-fire models as long as the network as a whole is close to a stationary state . The theoretical predictions of the noisy rate model are validated against simulations of ring-attractor networks realized with spiking integrate-and-fire neurons . In both theory and simulation , we find that facilitation and depression play antagonistic roles: facilitation tends to decrease both diffusion and drift while depression increases both . We show that these combined effects can still yield reduced diffusion and drift , which increases the retention time of memories . Importantly , since our theory is , to a large degree , independent of the microscopic network configurations , it can be related to experimentally observable quantities . In particular , our theory predicts the sensitivity of networks with short-term plasticity to distractor stimuli .
To untangle the observed interplay between diffusion and drift and investigate the effects of short-term plasticity , we derived a theory that reduces the microscopic network dynamics to a simple one-dimensional stochastic differential equation for the bump state . The theory yields analytical expressions for diffusion coefficients and drift fields , that depend on short-term plasticity parameters , the shape of the firing rate profile of the bump , as well as the neuron model chosen to implement the attractor . First , we assume that the system of Eq ( 3 ) together with the network Eqs ( 1 ) and ( 2 ) has a 1-dimensional manifold of meta-stable states , i . e . the network is a ring-attractor network as described in the introduction . This entails , that the network dynamics permit the existence of a family of solutions that can be described as a self-sustained and symmetric bump of firing rates ϕ0 , i ( φ ) = F ( J0 , i ( φ ) ) with corresponding inputs J0 , i ( φ ) ( for 0 ≤ i < N ) . Importantly , the center φ of the bump can be located at any arbitrary position φ ∈ { j N 2 π − π | 0 ≤ j < N } . For example , if ϕ0 , i ( 0 ) is a solution with input J0 , i ( 0 ) , then ϕ 0 , i + 1 ( 2 π N ) is also a solution with input J 0 , i + 1 ( 2 π N ) . This solution is illustrated in Fig 1C for a bump centered at φ = 0 . Second , we assume that the number N of excitatory neurons is large ( N → ∞ ) , such that we can think of the possible positions φ as a continuum . Third , we assume that network heterogeneities are small enough to capture their effect as a linear ( first order ) perturbation to the stable bump state . Our final assumption is that neuronal firing is noisy , with spike counts distributed as Poisson processes , and that we are able to replace the shot-noise of Poisson spiking by white Gaussian noise with the same mean and autocorrelation , similar to earlier work [39 , 53]; see Diffusion in Materials and methods , and Discussion . Under these assumptions , we are able to reduce the network dynamics to a one-dimensional Langevin equation , describing the dynamics of the center φ ( t ) of the firing rate profile ( see Analysis of drift and diffusion with STP in Materials and methods ) : φ ˙= B η ( t ) + A ( φ ) . ( 4 ) Here , η ( t ) is white Gaussian noise with zero mean and correlation function 〈η ( t ) , η ( t′ ) 〉 = δ ( t − t′ ) . The first term is diffusion characterized by a diffusion strength B1 , which describes the random displacement of bump center positions due to fluctuations in neuronal firing . For A ( φ ) = 0 this term causes diffusive displacement of the center φ ( t ) from its initial position φ ( t0 ) , with a mean ( over realizations ) squared displacement of positions 〈[φ ( t ) − φ ( t0 ) ]2〉 = B ⋅ ( t − t0 ) that , during an initial phase , increases linearly with time [14 , 54 , 55] , before saturating due to the circular domain of possible center positions [39] . Our theory shows ( see Diffusion in Materials and methods ) that the coefficient B can be calculated as a weighted sum over the neuronal firing rates ( Fig 1D ) B = ∑ i ( C i S ) 2 ( d J 0 , i d φ ) 2 ϕ 0 , i , ( 5 ) where d J 0 , i d φ is the change of the input to neuron i under shifts of the center position ( Fig 1C , orange line ) , and S is a normalizing constant that tends to increase additionally with the synaptic time constant τs . The analytical factors Ci express the spatial dependence of the diffusion coefficient on the short-term plasticity parameters through C i = U ( 1 + 2 τ u ϕ 0 , i + U τ u 2 ϕ 0 , i 2 ) ( 1 + U ϕ 0 , i ( τ u + τ x ) + U τ u τ x ϕ 0 , i 2 ) 2 . ( 6 ) 1In Brownian motion , the diffusion constant is usually defined as D = B/2 . The dependence of the single summands in Eq ( 5 ) on short-term plasticity parameters is visualized in Fig 1D , where we see that: a ) due to the squared spatial derivative d J 0 , i d φ of the bump shape and the squared factors Ci/S , the important contributions to the sum arise primarily from the flanks of the bump; b ) for a fixed bump shape , summands increase with stronger short-term depression ( larger τx ) and decrease with stronger short-term facilitation ( smaller U , larger τu ) . The second term in Eq ( 4 ) is the drift field A ( φ ) , which describes deterministic drifts due to the inclusion of heterogeneities . For heterogeneity caused by variations in neuronal reversal potentials and random network connectivity , we calculate ( see Frozen noise in Materials and methods ) systematic deviations Δϕi ( φ ) of the single neuronal firing rates from the steady-state bump shape that depend on the current position φ of the bump center . In Drift in Materials and Methods , we show that the drift field is then given by a weighted sum over the firing rate deviations: A ( φ ) = ∑ i C i S d J 0 , i d φ Δ ϕ i ( φ ) , ( 7 ) with weighing factors depending on the spatial derivative of the bump shape d J 0 , i d φ and the parameters of the synaptic dynamics through the same factors Ci/S . This is illustrated in Fig 1E: in contrast to Eq ( 5 ) summands are now asymmetric with respect to the bump center , since the spatial derivative is not squared . To calculate the diffusion and drift terms of the last section , we assume the number of neurons N to be large enough to treat the center position φ as continuous: this allows us ( similar to [39] ) to derive projection vectors ( see Projection of dynamics onto the attractor manifold in Materials and methods ) that yield the dynamics of the center position . However , the actual projection yields sums over the system size N , whose scaling we made explicit ( see System size scaling in Materials and methods ) . For the diffusion strength B ( cf . Eq ( 5 ) ) we find a scaling as as 1/N , in agreement with earlier work [11 , 14 , 36 , 39 , 46] . For drift fields caused by random connectivity , we find a scaling with the connectivity parameter p and the system size N to leading order as 1/ ( p N ) , whereas drift fields due to heterogeneity of leak potentials ( and other heterogeneous single-neuron parameters ) will scale as 1/N , both in accordance with earlier results [16 , 36 , 38 , 46] . In addition to reproducing the previously known scaling with the system size N , our theory exposes the scaling of both drift and diffusion with the parameters τx , τu , and U of short-term depression and facilitation via the analytical pre-factors Ci/S appearing in Eqs ( 5 ) and ( 7 ) . Our result extends the calculation of the diffusion constant [39] to synaptic dynamics with short-term plasticity: In the limiting case of no facilitation and depression ( U → 1 , τx → 0ms ) , the pre-factor reduces to Ci = 1 and the normalization factor simplifies to S static = τ s ∑ i ( d J 0 , i d φ ) 2 ϕ 0 , i ′ , where ϕ 0 , i ′ =d ϕ i d J i |J 0 , i is the derivative of the firing rate of neuron i at its steady-state input J0 , i . For static synapses we thereby recover the known result for diffusion [39 , Eq . S18] , but also add an analogous relation for the drift A static ( φ ) = ( ∑ i d J 0 , i d φ Δ ϕ i ( φ ) ) / ( τ s ∑ i d J 0 , i d φ 2 ϕ 0 , i ′ ) . Our approach relies on the existence of a stationary bump state ( which is stable for large noise-free homogeneous networks ) , around which we calculate drift and diffusion as perturbations . Following earlier work [11 , 50 , 52] , we use in our simulations with spiking integrate-and-fire neurons a slow synaptic time constant ( τs = 100ms ) as an approximation of recurrent ( NMDA mediated ) excitation . While our theory captures the effects of changing this time constant τs in the pre-factors Ci/S , we did not check in simulations whether the bump state remains stable and whether our theory remains valid for very short time constants for τs . Finally , two limiting cases are worth highlighting . First , for strong facilitation ( U → 0 ) we obtain pre-factors C i/S = ( 1 + 2 τ u ϕ 0 , i ) ( ∑ i ( d J 0 , i d φ ) 2 ϕ 0 , i ′ [ τ s ( 1 + 2 τ u ϕ 0 , i ) + τ u 2 ϕ 0 , i ] ) − 1 , indicating that ( i ) this limit will leave residual drift and diffusion which ( ii ) will both be controlled by the time constants for facilitation ( τu ) and synaptic transmission ( τs ) , with no dependence upon depression . Second , for vanishing facilitation ( U → 1 and τu → 0 ) we find that the normalization factor S will tend to zero if the depression time constant τx is increased to a finite value τx , c . Through the pre-factors Ci/S this , in turn , yields exploding diffusion and drift terms ( see S8 Fig ) . While this is a general feature of bump systems with short-term depression , the exact value of the critical time constant τx , c depends on the firing rates and neural implementation of the bump state ( see section 6 in S1 Text ) : for the spiking network investigated here , we find a critical time constant τx , c = 223 . 9ms ( see S8 Fig ) . In networks with both facilitation and depression , the critical τx , c increases as facilitation becomes stronger ( see S8 Fig ) . To demonstrate the accuracy of our theory , we chose random connectivity as a first source of frozen variability . Random connectivity was realized in simulations by retaining only a random fraction 0 < p ≤ 1 ( connection probability ) of excitatory-to-excitatory ( EE ) connections . The uniform connections from and to inhibitory neurons are taken as all-to-all , since the effects of making these random or sparse would have only indirect effects on the dynamics of the bump center positions . Our theory accurately predicts the drift-fields A ( φ ) ( see Eq ( 7 ) ) induced by frozen variability in networks with short-term plasticity ( Fig 2 ) . Briefly , for each neuron 0 ≤ i < N , we treat each realization of frozen variability as a perturbation Δi around the perfectly symmetric system and use an expansion to first order of the input-output relation F to calculate the resulting changes in firing rates ( see Frozen noise for details ) : Δ ϕ i ( φ ) = d F d Δ i Δ i . ( 8 ) The resulting terms are then used in Eq ( 7 ) to predict the magnitude of the drift field A ( φ ) for any center position φ , which will , importantly , depend on STP parameters . The same approach can be used to predict drift fields induced by heterogeneous single neuron parameters [36] ( see next sections ) and additive noise on the E-E connection weights [16 , 38] . We first simulated spiking networks with only short-term depression and without facilitation ( Fig 2A , left , same network as in Fig 1B1 ) , for one instantiation of random ( p = 0 . 5 ) connectivity . Numerical estimates of the drift in spiking simulations ( by measuring the displacement of bumps over time as a function of their position , see Spiking simulations in Materials and methods for details ) yielded drift-fields in good agreement with the theoretical prediction ( Fig 2B , left ) . At points where the drift field prediction crosses from positive to negative values ( e . g . Fig 2B , left , φ = π 2 ) , we expect stable fixed points of the center position dynamics in agreement with simulation results , which show trajectories converging to these points . Similarly , unstable fixed points ( negative-to-positive crossings ) can be seen to lead to a separation of trajectories ( e . g . Fig 2A , left , φ = − π 2 ) . In regions where the positional drifts are predicted to lie close to zero ( e . g . Fig 2A , left φ = 0 ) the effects of diffusive dynamics are more pronounced . Finally , numerical integration of the full 1-dimensional Langevin equation Eq ( 4 ) with coefficients predicted by Eqs ( 5 ) – ( 7 ) , produces trajectories with dynamics very similar to the full spiking network ( Fig 2C , left ) . When comparing the center positions after 13 . 5s of delay activity between the full spiking simulation and the simple 1-dimensional Langevin system , we found very similar distributions of final positions ( Fig 2D , left , compare to Fig 1B1 , histogram ) . Our theory thus produces an accurate approximation of the dynamics of center positions in networks of spiking neurons with STP , thereby reducing the complex dynamics of the whole network to a simple equation . It should be noted that , in regions with strong drift or steep negative-to-positive crossings , the numerically estimated drift-fields deviate from the theory due to under-sampling of these regions as trajectories move quickly through them , yielding fewer data points . In Short-term plasticity controls drift we additionally show that the theory , as it relies on a linear expansion of the effects of heterogeneities on the neuronal firing rates , tends to generally over-predict drift-fields as heterogeneities become stronger . Introducing strong short-term facilitation ( U = 0 . 1 ) reduces the predicted drift fields ( Fig 2B , left , dashed line ) , which resemble a scaled-down version of the drift-field for the unfacilitated case . We confirmed this theoretical prediction by simulations including facilitation ( Fig 2A , right ) : the resulting drift fields show significant reduction of speeds ( Fig 2B , right ) while zero crossings remained similar to the unfacilitated network , similar to the results in [38] . Theoretical predictions of the drift fields with bump shapes extracted from these simulations again show an accurate prediction of the dynamics ( Fig 2B , right ) . Thus , as before , forward integrating the simple 1-dimensional Langevin-dynamics yields trajectories ( Fig 2C , right ) highly similar to those of the full spiking network , with closely matching distributions of final positions ( Fig 2D , right ) , indicative of a matching strength of diffusion . In summary , our theory predicts the effects of STP on the joint dynamics of diffusion and drift due to network heterogeneities , which we will show in detail in the next sections . To isolate the effects of STP on diffusion , we simulated networks without frozen noise for various STP parameters . For each combination of parameters , we simulated 1000 repetitions of 13 . 5s delay activity ( after cue offset ) distributed across 20 uniformly spaced initial cue positions ( see Fig 3A for an example ) . From these simulations , the strength of diffusion was estimated by measuring the growth of variance ( over repetitions ) of the distance of the center position from its initial position as a function of time ( see Spiking simulations in Materials and methods for details ) . For all parameters considered , this growth was well fit by a linear function ( e . g . Fig 3A , inset ) , the slope of which we compared to the theoretical prediction obtained from the diffusion strength B ( Eq ( 5 ) ) . We find that facilitation and depression control the amount of diffusion along the attractor manifold in an antagonistic fashion ( Fig 3B and 3C ) . First , increasing facilitation by lowering the facilitation parameter U from its baseline U = 1 ( no facilitation ) towards U = 0 , while keeping the depression time constant τx = 150ms fixed , decreases the measured diffusion strength over an order of magnitude ( Fig 3B , dots ) . On the other hand , increasing the facilitation time constant τu from τu = 650ms to τu = 1000ms ( Fig 3B , orange and blue dots , respectively ) only slightly reduces diffusion . Our theory further predicts that increasing the facilitation time constants above τu = 1s will not lead to large reductions in the magnitude of diffusion ( see S2 Fig ) . Second , we find that increasing the depression time constant τx for fixed U , thereby slowing down recovery from depression , leads to an increase of the measured diffusion ( Fig 3C ) . More precisely , increasing the depression time constant from τx = 120ms to τx = 200ms leads only to slight increases in diffusion for strong facilitation ( U = 0 . 1 ) , but to a much larger increase for weak facilitation ( U = 0 . 8 ) . For a comparison of these simulations with our theory , we used two different approaches . First , we estimated the diffusion strength by using the precise shape of the stable firing rate profile extracted separately for each network with different sets of parameters . This first comparison with simulations confirms that the theory closely describes the dependence of diffusion on short-term plasticity for each parameter set ( Fig 3B , crosses ) . The observed effects could arise directly from changes in STP parameters for a fixed bump shape , or indirectly since STP parameters also influence the shape of the bump . To separate such direct and indirect effects , we used for a second comparison a theory with fixed bump shape , i . e . the bump shape measured in a “reference network” ( U = 1 , τx = 150ms ) and extrapolated curves by changing only STP parameters in Eq ( 5 ) . This leads to very similar predictions ( Fig 3B , dashed lines ) and supports the following conclusions: a ) the diffusion to be expected in attractor networks with similar observable quantities ( mainly , the bump shape ) depends only on the short-term plasticity parameters; b ) the bump shapes in the family of networks we have investigated are sufficiently similar to be approximated by measurement in a single reference network . It should be noted that the theory tends to slightly over-estimate the amount of diffusion , especially for small facilitation U ( see Fig 3B and 3C left ) . This may be because slower bump movement decreases the firing irregularity of flank neurons , which deviates from the Poisson firing assumption of our theory ( see Discussion ) . However , given the simplifying assumptions needed to derive the theory , the match to the spiking network is surprisingly accurate . Having established that our theory is able to predict the effect of STP on diffusion , as well as drift for a single instantiation of random connectivity , we wondered how different sources of heterogeneity ( frozen noise ) would influence the drift of the bump . We considered two sources of heterogeneity: First , random connectivity as introduced above , and second , heterogeneity of the leak reversal potential parameters of excitatory neurons: leak reversal potentials of excitatory neurons are given by VL + ΔL , where ΔL is normally distributed with zero mean and standard deviation σL [36] . The resulting fields can be calculated by calculating the resulting perturbations to the firing rates of neurons by Eq ( 8 ) ( see Frozen noise in Materials and methods for details ) . The theory developed so far allowed us to predict drift-fields for a given realization of frozen noise , controlled by the noise parameters p ( for random connectivity ) and σL ( for heterogeneous leak reversal-potentials ) ( see S3 Fig for a comparison of predicted drift fields to those measured in simulations for varying STP parameters and varying strengths of frozen noises ) . We wondered , whether we could take the level of abstraction of our theory one step further , by predicting the magnitude of drift fields from the frozen noise parameters only , independently of a specific realization . First , the expectation of drift fields under the distributions of the frozen noises vanishes for any given position: 〈A ( φ ) 〉frozen = 0 , where the expectation 〈 . 〉frozen is taken over both noise parameters . We thus turned to the expected squared magnitude of drift fields under the distributions of these parameters ( see Squared field magnitude in Materials and methods for the derivation ) : 〈 A2 〉frozen=1S2∑iCi2 ( ( ϕ0 , i′ ) 2NE2 ( 1p−1 ) ∑j ( s0 , j ) 2 ( wijEE ) 2+ ( dϕ0 , idΔiL ) 2σL2 ) , ( 9 ) where s0 , j is the steady-state synaptic activation . Here , we introduced the derivatives of the input-output relation with respect to the noise sources that appear in Eq ( 8 ) : ϕ 0 , i ′ = d F d J ( J 0 , i ( φ ) ) is the derivative with respect to the steady state synaptic input , and d ϕ 0 , i d Δ i L is the derivative with respect to the perturbation in the leak potential . In Squared field magnitude in Materials and Methods , we show that Eq ( 9 ) is independent of the center position φ , and can be estimated from simulations as the variance of the drift field across positions , averaged over an ensemble of network instantiations . We defined the root of the expected squared magnitude of Eq ( 9 ) as the expected field magnitude: ⟨ A 2 ⟩ frozen . ( 10 ) This quantity predicts the magnitude of the deviations of drift-fields from zero that are expected from the parameters that control the frozen noise—in analogy to the standard deviation for random variables , it predicts the standard deviation of the fields . To compare this quantity to simulations , we varied both heterogeneity parameters . First , the connectivity parameter p was varied between 0 . 25 and 1 . Second , for heterogeneities in leak reversal-potentials , we chose values for the standard deviation σL of leak-reversal potentials between 0mV and 1 . 5mV , which lead to a similar range of drift magnitudes as those of randomly connected networks . For each combination of heterogeneities and STP parameters ( networks had either random connections or heterogeneous leaks ) we then realized 18–20 networks , for which we simulated 400 repetitions of 6 . 5s of delay activity each ( 20 uniformly spaced positions of the initial cue ) . We then estimated the drift-field numerically by recording displacements of bump centers along their trajectories ( as in Fig 2A and 2B ) and measured the standard deviation of the resulting fields across all positions . Similar to the analysis of diffusion above , we find that facilitation and depression elicit antagonistic control over the magnitude of drift fields . In both simulations and theory , we find ( Fig 4A and 4B ) that the expected field magnitude decreases as the effect of facilitation is increased from unfacilitated networks ( U = 1 ) through intermediate levels of facilitation ( U = 0 . 4 ) to strongly facilitating networks ( U = 0 . 1 ) . Our theory predicts this effect surprisingly well , which we validated twofold ( as for the diffusion magnitude ) . First , we used Eq ( 10 ) with all parameters and coefficients estimated from each spiking simulation separately ( Fig 4A and 4B , plus-signs and crosses ) . Second , we extrapolated the theoretical prediction by using coefficients in Eq ( 9 ) from the unfacilitated reference network only ( U = 1 , τx = 150ms ) but changed the facilitation and heterogeneity parameters ( Fig 4A and 4B , dashed lines ) . The largest differences between the extrapolated and full theory are seen for U < 1 and randomly connected networks ( p < 1 ) , which we found to result from the fact that bump shapes for these networks tended to be slightly reduced under random and sparse connectivity ( e . g . the top firing rate is reduced to ∼ 35Hz for U = 0 . 1 , p = 0 . 25 ) . Generally , as noise levels increase , our theory tends to over-estimate the squared magnitude of fields , since we rely on a linear expansion of perturbations to the firing rates to calculate fields ( Eq ( 8 ) ) . Such deviations are expected as the magnitude of firing rate perturbations increases , and could be counter-acted by including higher-order terms . Since in the theory facilitation ( and depression ) only scales the firing rate perturbations ( Eq ( 7 ) ) , these deviations can also be observed across facilitation parameters . Finally , we performed a similar analysis to investigate the effect of short-term depression on drift fields . Here , we varied the depression time constant τx for randomly connected networks with p = 0 . 6 , by simulating networks with combinations of short-term plasticity parameters from U ∈ {0 . 1 , 0 . 4 , 0 . 8} and τx ∈ {120ms , 160ms , 200ms} ( Fig 4C ) . We find that an increase of the depression time constant leads to increased magnitude of drift fields , which again is well predicted by our theory . The theory developed in previous sections shows that diffusion and drift of the bump center φ are controlled antagonistically by short-term depression and facilitation . In a working memory setup , we can view the attractor dynamics as a noisy communication channel [56] that maps a set of initial positions φ ( t = 0s ) ( time of the cue offset in the attractor network ) to associated final positions φ ( t = 6 . 5s ) , after a memory retention delay of 6 . 5s . We used the distributions of initial and ( associated ) final positions to investigate the combined impact of diffusion and drift on the retention of memories ( Fig 5A ) . Because of diffusion , distributions of positions will widen over time , which degrades the ability to distinguish different initial positions of the bump center ( Fig 5A , top ) . Additionally , directed drift of the dynamics will contract distributions of different initial positions around the same fixed points , making them essentially indistinguishable when read out ( Fig 5A , bottom ) . As a numerical measure of this ability of such systems to retain memories over the delay period , we turned to mutual information ( MI ) , which provides a measure of the amount of information contained in the readout position about the initially encoded position [57 , 58] . To measure MI from simulations ( see Mutual information measure in Materials and methods ) , we analyzed network simulations for varying short-term facilitation parameters ( U ) and magnitudes of frozen noises ( p and σL ) ( same data set as Fig 4A and 4B ) . We recorded the center positions encoded in the network at the time of cue-offset ( t = 0 ) and after 6 . 5s of delay activity , and used binned histograms ( 100 bins ) to calculate discrete probability distributions of initial ( t = 0 ) and final positions ( t = 6 . 5 ) . For each trajectory simulated in networks of spiking integrate-and-fire neurons , we then generated a trajectory starting at the same initial position by using the Langevin equation Eq ( 4 ) that describes the drift and diffusion dynamics of center positions . The MI calculated from the resulting distributions of final positions ( again at t = 6 . 5 ) for each network serve as the theoretical prediction for each network . As a reference , we used the spiking network without facilitation ( U = 1 , τu = 650ms , τx = 150ms ) and no frozen noises ( p = 1 , σL = 0mV ) and normalized the MI of all other networks ( both for spiking simulations and theoretical predictions ) with respect to the reference , yielding the measure of relative MI presented in Fig 5B–5E . We found that the relative MI decreased compared to the reference network as network heterogeneities were introduced ( Fig 5B , green ) . This was expected , since directed drift caused by heterogeneities leads to a loss of information about initial positions . There were two effects of increased short-term facilitation ( by decreasing the parameter U ) . First , diffusion was reduced , which was visible in a vertical shift of the relative MI for facilitated networks ( Fig 5A , orange and blue , at 0 heterogeneity ) . Second , the effects of frozen noise decreased with increasing facilitation , which was visible in the slopes of the MI decrease ( see also S4 Fig ) . The MI obtained by integration of the Langevin equations ( see above ) matched those of the simulations well ( Fig 5A , lines ) . From earlier results , we expected the drift-fields to be slightly over-estimated by the theory as the heterogeneity parameters increase ( Fig 4 ) , which would lead to an under-estimation of MI . We did observe this here , although for U = 1 the effect was slightly counter-balanced by the under-estimated level of diffusion ( cf . Fig 3A , right ) , which we expected to increase the MI . For networks with stronger facilitation ( U = 0 . 1 ) , we systematically over-estimated diffusion ( cf . Fig 3 , left ) , and therefore under-estimated MI . Using our theory , we were able to simplify the functional dependence between MI , short-term plasticity , and frozen noise . Combining the effects of both diffusion and drift into a single quantity for each network , we replaced the field A ( φ ) by our theoretical prediction 〈 A 2 〉 frozen in Eq ( 4 ) and forward integrated the differential equation for a time interval Δt = 1s , to arrive at the expected displacement in 1s: | Δ φ | ( 1 s ) = ⟨ A 2 ⟩ frozen · 1 s + B · 1 s . ( 11 ) This quantity describes the expected absolute value of displacement of center positions during 1s: it increases as a function of the frozen noise distribution parameters ( Fig 5C ) , but even in the absence of frozen noise it is nonzero due to diffusion . Plotting the MI data in dependence of the first term only ( 〈 A 2 〉 frozen ) , shows that the MI curves collapse onto a single curve for each facilitation parameter ( Fig 5D ) . Finally , plotting the MI data against |Δφ| ( 1s ) we find that all data collapse on to nearly a single curve ( Fig 5E ) . Thus , the effects of the two sources of frozen noise ( corresponding to 〈A2〉frozen ) and diffusion ( corresponding to B ) are unified into a single quantity |Δφ| ( 1s ) . We performed the same analyses on a large set of network simulations with fixed random connectivity ( p = 0 . 6 ) and varying STP parameters for both depression ( τx ) and facilitation ( U ) ( same data set as in Fig 4C ) . Increasing the short-term depression time constant τx leads to decreased relative MI with a positive offset induced through stronger facilitation ( Fig 6A , blue line ) . Calculating the expected displacement for these network configurations collapsed the data points mostly onto the same curve as earlier ( Fig 6B ) . For strong depression combined with weak facilitation ( τx = 200ms , U = 0 . 8 ) , the drop-off of the relative MI saturates earlier , indicating that for these strongly diffusive networks the effect on MI may not be sufficiently captured by its relationship to |Δφ| ( 1s ) . The abstraction of our theory condenses the complex dynamics of bump attractors in spiking integrate-and-fire networks into a high-level description of a few macroscopic features , which in turn allows matching the theory to behavioral experiments . Here , we demonstrate how such quantitative links could be established using two different features: 1 ) the sensitivity of the working memory circuit to distractors , and 2 ) the stability of working memory expressed by the expected displacement . We stress that our model is a simplified description of biological circuits , in which several further sources of variability and also dynamical processes influencing displacement should be expected ( see Discussion ) . Thus , at the current level of simplification , the results presented in this section should be seen as proofs of principle rather than quantitative predictions for a cortical setting .
Similar to an earlier theoretical approach using a simplified rate model [38] , we find that the slowing of drift by facilitation depends mainly on the facilitation parameter U , while the time constant τu has a less pronounced effect . While the approach of [38] relied on the projection of frozen noise onto the derivative of the first spatial Fourier mode of the bump shape along the ring , here we reproduce and extend this result ( 1 ) for arbitrary neuronal input-output relations and ( 2 ) a more detailed spatial projection that involves the full synaptic dynamics and the bump shape . While , our theory can also accommodate noisy recurrent connection weights as frozen noise , as used in in [38] ( see Frozen noise in Materials and methods for derivations ) , the drifts generated by these heterogeneities were generally small compared to diffusion and the other sources of heterogeneity . A second study investigated short-term facilitation and showed that it reduces drift and diffusion in a spiking network , for a fixed setting of U ( although the model of short-term facilitation differs slightly from the one employed here ) [47] . Contrary to what we find here , these authors find that an increase in τu leads to increased diffusion , while we find that an increase over the range they investigated ( ∼ 0 . 5s − 4s ) would decrease the diffusion by a factor of nearly two . More precisely , for our shape of the bump state ( which we keep fixed ) we predict a reduction from ∼ 26 to ∼ 16 deg2/s for a similar setting of facilitation U . These differences might arise from an increasing width of the bump attractor profile for growing facilitation time constants in [47] , which would then lead to increased diffusion in our model . Whether this effect persists under the two-equation model of saturating NMDA synapses used there remains to be investigated . Finally , increasing the time constant of recurrent NMDA conductances has been shown to also reduce diffusion [47] , in agreement with our theory , according to which the normalization constant S increases with τs [39] . A study performed in parallel to ours [45] used a similar theoretical approach to calculate diffusion with short-term facilitation in a rate-based model with external additive noise , but did not compare the results for varying facilitation parameters . The authors report a short initial transient of stronger diffusion as synapses facilitate , followed by weaker diffusion that is dictated by the fully facilitated synapses . Our theory , by assuming all synaptic variables to be at steady-state , disregards the initial strong phase of diffusion . We also disregarded such initial transients when comparing to simulations ( see Numerical methods ) . In a study that investigated only a single parameter value for depression ( τx = 160ms , no facilitation ) in a network of spiking integrate-and-fire neurons similar to the one investigated here , the authors observed no apparent effect of short-term depression on the stability of the bump [44] . In contrast , we find that stronger short-term depression will indeed increase both diffusion and directed drift along the attractor . Our result agrees qualitatively with earlier studies in rate models , which showed that synaptic depression , similar to neuronal adaptation [10 , 85] , can induce movement of bump attractors [42 , 43 , 86 , 87] . In particular , simple rate models exhibit a regime where the bump state moves with constant speed along the attractor manifold [42] . We did not find any such directed movement in our networks , which could be due to fast spiking noise which is able to cancel directed bump movement [85] . The coefficients of Eq ( 4 ) give clear predictions as to how drift and diffusion will depend on the shape of the bump state and the neural transfer function F . The relation is not trivial , since the pre-factors Ci and the normalization constant S also depend on the bump shape . For the diffusion strength Eq ( 5 ) , we explored this relation numerically , by artificially varying the shape of the firing rate profile ( while extrapolating other quantities ) . Although a more thorough analysis remains to be performed , a preliminary analysis shows ( see S6 Fig ) that diffusion increases both with bump width and top firing rate , consistent with earlier findings [11 , 32] . Our theory can be used to predict the shape and effect of drift fields that are generated by localized external inputs due to distractor inputs; see Section Linking theory to experiments: Distractors and network size . Any localized external input ( excitatory or inhibitory ) will cause a deviation Δϕi from the steady-state firing rates , which , in turn , generates a drift field by Eq ( 7 ) . This could predict the strength and location of external inputs that are needed to induce continuous shifts of the bump center at given speeds , for example when these attractor networks are designed to track external inputs ( see e . g . [10 , 88] ) . It should be noted that in our simple approximation of this distractor scheme , we assume the system to remain at approximately steady-state , i . e . that the bump shape is unaffected by the additional external input , except for a shift of the center position . For example , we expect additional feedback inhibition ( through the increased firing of excitatory neurons caused by the distractor input ) to decrease bump firing rates . A more in depth study and comparison to simulations will be left for further work . Our networks of spiking integrate-and-fire neurons are tuned to display balanced inhibition and excitation in the inhibition dominated uniform state [53 , 89] , while the bump state relies on positive currents , mediated through strong recurrent excitatory connections ( cf . [44] for an analysis ) . Similar to other spiking network models of this class , this mean–driven bump state shows relatively low variability of neuronal inter-spike-intervals of neurons in the bump center [90 , 91] ( see also next paragraph ) . Nevertheless , neurons at the flanks of the bump still display variable firing , with statistics close to that expected of spike trains with Poisson statistics ( see S7 Fig ) , which may be because the flank’s position slightly jitters . Since the non-zero contributions to the diffusion strength are constrained to these flanks ( cf . Fig 1D ) , the simple theoretical assumption of Poisson statistics of neuronal firing still matches the spiking network quite well . As discussed in Short-term plasticity controls diffusion , we find that our theory over-estimates the diffusion as bump movement slows down for small values of U—this may be due to a decrease in firing irregularity in stable bumps in particular in the flank neurons , at which the Poisson assumption becomes inaccurate . More recent bump attractor approaches allow networks to perform working memory function with a high firing variability also during the delay period [3] , in better agreement with experimental evidence [92] . These networks show bi-stability , where both stable states show balanced excitation and inhibition [90] and the higher self-sustained activity in the delay activity is evoked by an increase in fluctuations of the input currents ( noise-driven ) rather than an increase in the mean input [93] . This was also reported for a ring-attractor network ( with distance-dependent connections between all populations ) , where facilitation and depression are crucial for irregularity of neuronal activity in the self-sustained state [46] . Application of our approach to these setups is left for future work .
For the following , we define a concatenated 3 ⋅ N dimensional column vector of state variables y = ( sT , uT , xT ) T of the system Eq ( 3 ) . Given a ( numerical ) solution of the stable firing rate profile ϕ → 0 we can calculate the stable fixed point of this system by setting the l . h . s . of Eq ( 3 ) to zero . This yields steady-state solutions for the synaptic activations , facilitation and depression variables y0 = ( s0 , u0 , x0 ) : s 0 , i = τ s u 0 , i x 0 , i ϕ 0 , i , u 0 , i = U 1 + τ u ϕ 0 , i 1 + U τ u ϕ 0 , i , x 0 , i = 1 + U τ u ϕ 0 , i 1 + U ( τ u ϕ 0 , i + τ u τ x ϕ 0 , i 2 + τ x ϕ 0 , i ) . ( 12 ) We then linearize the system Eq ( 3 ) at the fixed point y0 , introducing a change of variables consisting of perturbations around the fixed point: y = y0 + δ y = y0 + ( δ sT , δ uT , δ xT ) and ϕi = ϕ0 , i + δϕi . To reach a self-consistent linear system , we further assume a separation of time scales between the neuronal dynamics and the synaptic variables , in that the neuronal firing rate changes as an immediate function of the ( slow ) input . This allows replacing δϕi=dϕidJi|J0 , i∑jdJidsjδsj=ϕ0 , i′∑jwijδsj , where we introduce the shorthand ϕ0 , i′≡dϕidJi|J0 , i . Finally , keeping only linear orders in all perturbations , we arrive at the linearized system equivalent of Eq ( 3 ) : δ y ˙ = ( − 1 τ s I + D ( u 0 · x 0 · ϕ → 0 ′ ) W D ( ϕ → 0 · x 0 ) D ( ϕ → 0 · u 0 ) U D ( ( 1 − u 0 ) · ϕ → 0 ′ ) W − 1 τ u I − U D ( ϕ → 0 ) 0 − D ( u 0 · x 0 · ϕ → 0 ′ ) W − D ( x 0 · ϕ → 0 ) − 1 τ x I − D ( ϕ → 0 · u 0 ) ) δ y ≡K δ y ( 13 ) Here , dots between vectors indicate element-wise multiplication , the operator D : R n → R n × n creates diagonal matrices from vectors , and W = ( wij ) is the synaptic weight matrix of the network . Spiking simulations are based on a variation of a popular ring-attractor model of visuospatial working memory of [11] ( and used with variations in [27 , 29 , 32 , 36 , 47] ) . The recurrent excitatory connections of the original network model have been simplified , to allow for faster simulation as well as analytical derivations of the recurrent synaptic activation . The implementation details are given below , however the major changes are: 1 ) all recurrent excitatory conductances are voltage independent; 2 ) a model of synaptic short-term plasticity via facilitation and depression [49 , 94 , 95] is used to dynamically regulate the weights of the incoming spike-trains 3 ) recurrent excitatory conductances are computed as linear filters of the weighted incoming spike trains instead of the second-order kinetics for NMDA saturation used in [11] . | The ability to transiently memorize positions in the visual field is crucial for behavior . Models and experiments have shown that such memories can be maintained in networks of cortical neurons with a continuum of possible activity states , that reflects the continuum of positions in the environment . However , the accuracy of positions stored in such networks will degrade over time due to the noisiness of neuronal signaling and imperfections of the biological substrate . Previous work in simplified models has shown that synaptic short-term plasticity could stabilize this degradation by dynamically up- or down-regulating the strength of synaptic connections , thereby “pinning down” memorized positions . Here , we present a general theory that accurately predicts the extent of this “pinning down” by short-term plasticity in a broad class of biologically plausible network models , thereby untangling the interplay of varying biological sources of noise with short-term plasticity . Importantly , our work provides a novel theoretical link from the microscopic substrate of working memory—neurons and synaptic connections—to observable behavioral correlates , for example the susceptibility to distracting stimuli . | [
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"me... | 2019 | Stability of working memory in continuous attractor networks under the control of short-term plasticity |
Acute Encephalitis Syndrome ( AES ) is a major seasonal public health problem in Bihar , India . Despite efforts of the Bihar health department and the Government of India , burden and mortality of AES cases have not decreased , and definitive etiologies for the illness have yet to be identified . The present study was undertaken to study the specific etiology of AES in Bihar . Cerebrospinal fluid and/or serum samples from AES patients were collected and tested for various pathogens , including viruses and bacteria by ELISA and/or Real Time PCR . Of 540 enrolled patients , 33 . 3% ( 180 ) tested positive for at least one pathogen of which 23 . 3% were co-positive for more than one pathogen . Most samples were positive for scrub typhus IgM or PCR ( 25% ) , followed by IgM positivity for JEV ( 8 . 1% ) , WNV ( 6 . 8% ) , DV ( 6 . 1% ) , and ChikV ( 4 . 5% ) . M . tuberculosis and S . pneumoniae each was detected in ~ 1% cases . H . influenzae , adenovirus , Herpes Simplex Virus -1 , enterovirus , and measles virus , each was detected occasionally . The presence of Scrub typhus was confirmed by PCR and sequencing . Bihar strains resembled Gilliam-like strains from Thailand , Combodia and Vietnam . The highlights of this pilot AES study were detection of an infectious etiology in one third of the AES cases , multiple etiologies , and emergence of O . tsutsugamushi infection as an important causative agent of AES in India .
Acute Encephalitis Syndrome ( AES ) is a major seasonal public health problem in many states of India including Bihar . Muzaffarpur and adjacent districts , including Sitamarhi , Sheohar and East Champaran districts of Bihar consistently experience a great burden of the disease [1] . The mean numbers of reported AES cases and deaths per year from Bihar during 2011 to 2014 were 835 and 243 respectively . During these years , the state of Bihar contributed 9 . 5% and 18% of the mean number of AES cases and deaths respectively reported from all over India ( data from NVBDCP website ) [2] . Japanese Encephalitis virus ( JEV ) was hitherto considered as the most important cause of AES , however , it contributed to only 20 ( 1 . 5% ) of AES cases in 2014[2] . Other etiologies , including enteroviruses and Nipah Virus have also been implicated [1] . The National Centre for Disease Control ( NCDC ) , New Delhi and the Global Disease Detection , Regional Centre , India , Center for Disease Control and prevention ( CDC ) , US , have jointly classified Muzaffarpur , Bihar mystery to a noninfectious toxic encephalopathy associated with consumption of litchi fruit after ruling out pesticides , heavy metal poisoning and infectious diseases [3] . However , despite the efforts of the Bihar health department and the Government of India , the burden and mortality of AES cases have not decreased , and definitive etiologies for these illnesses have yet to be identified . Identification of a specific agent is important for patient management and for understanding the epidemiology . Therefore , the present study was undertaken to study the specific etiology of AES in Bihar , India .
The patient enrollment and sample collection center was Patna Medical College and Hospital ( PMCH ) Patna ( Coordinates: 25 . 5941° N , 85 . 1376° E ) , a tertiary care referral center catering to the population of Patna and the surrounding districts . The testing centers were ( 1 ) the Virus Research and Diagnostic Laboratory ( VRDL ) at King George’s Medical University ( KGMU ) , Lucknow , Uttar Pradesh , and ( 2 ) VRDL at PMCH , Patna . Cases presenting with clinical diagnosis of AES as per WHO [4] and admitted at PMCH were enrolled in the study during June 2015 to September 2016 . Depending on the feasibility , samples of serum , CSF or both were collected from each case after obtaining written informed consent from the patient . In case of unconscious patient or children written informed consent was obtained from guardian of the patient . The study was approved by the institutional ethics committee ( Reference code: 83rd ECM IIA/P8 ) . At the VRDL of PMCH , Patna , the CSF ( preferred ) / serum ( in the absence of CSF ) sample was tested the same day for anti- JEV IgM antibodies ( MAC ELISA kits manufactured by the National Institute of Virology , Pune , India ) and the remaining samples were transported to the VRDL at KGMU Lucknow in dry ice for further testing ( Fig 1 ) . At KGMU , the serum samples were tested by ELISA for anti-Dengue Virus ( DV ) IgM and anti- Chikungunya Virus ( Chik V ) IgM , using kits by the National Institute of Virology , Pune , India . Anti-West Nile Virus ( WNV ) IgM , and anti-Scrub typhus IgM ( ST ) antibodies were tested using Inbios International , USA kits . For anti-Scrub typhus IgM , samples with an optical density ( OD ) >0 . 5 were considered positive . For scrub typhus IgM the baseline titres need to be established for each region; for India this value has been calculated as 0 . 5 [5] . For all the other ELISAs the cut off values were calculated based upon the manufacturer’s instructions . All ELISAs were done in serum samples except anti-JEV IgM ELISA , which was preferably done in CSF samples ( as per the manufacturer’s recommendations ) . External Quality Assessment for PMCH , Patna anti-JEV IgM ELISAwas done atVRDL , KGMU . Total 40% and 15% of the anti- JEV IgM antibody positive and negative samples respectively , were retested by ELISA using the same kit and protocol . All results from both the laboratories were concordant . All Real time PCRs were done in CSF samples , except bacterial PCRs which were done in serum samples in case CSF was not available ( Fig 1 ) . RNA was extracted from 140 μl processed clinical samples using the QIAamp Viral RNA mini kit ( Qiagen , Hilden , Germany ) following the manufacturer’s protocol . For DNA extraction , QIAamp Viral DNA mini kit ( Qiagen , Hilden , Germany ) was used . All the PCRs were done using Taqman chemistry . The target gene used for selecting the primer and probes of various pathogens , their product size and their references are described in Table 1 . For Enterovirus , JEV and VZV the primers and probe were self-designed ( Table 1 ) . The properties of the primers were analyzed by IDT oligoanalyzer software . Amplification was done with Real Time PCR machine ( ABI 7500 , Applied Biosystems , USA ) . The Ct value of 35 was taken as the cutoff . For samples testing positive for scrub typhus DNA , sequencing was done for the 56 kDa TSA gene region with a nested PCR [6] using a high fidelity Taq polymerase ( Thermo Fisher Scientific , Waltham , MA ) . The gene sequence thus obtained spanned three of the four major variable regions . Sequencing was performed by utilizing two sets of primers as described by Ruang-areerate T . The outer primers were JG-OtF584 ( 5’-CAA TGT CTG CGT TGT CGT TGC ) and RTS9 ( 5’-ACAGAT GCA CTA TTA GGC AA ) , and the inner primers were F ( 5’-AGC GCTAGG TTT ATT AGC AT ) and RTS8 ( 5’-AGG ATT AGA GTG TGG TCCTT ) [6] . The PCR product was sequenced bidirectionally using the Big Dye Terminator cycle sequencing kit ( Applied Biosystems , Foster City , CA ) and ABI Prism Genetic Analyzer 3130 ( Applied Biosystems ) . The GenBank accession numbers obtained for the sequences from this study are MG940993 to MG940998 . Phylogenetic analysis was performed and a tree was constructed using the Maximum Likelihood Method ( Tamura-Nei model ) and the MEGA version 6 program . The number of bootstrap replications was set to default . Phylogenetic tree was constructed using the sequences obtained and the reference sequences retrieved from the GenBank database ( Karp , AY956315 . 1; Kato , AY836148 . 1; Gilliam , HQ718429 . 1 ( Cambodia ) , HQ718460 . 1 ( Vietnam ) , EF213099 . 1 ( Thailand ) and DQ485289 . 1 ) . The study was approved by the institutional ( King George’s Medical University ) ethics committee . Samples were collected after obtaining written informed consent from the patient or guardian in unconscious patients/ children . All statistical analyses were done using GraphPad Prism software version 5 . Intergroup comparisons of categorical and continuous variables were done using Fischer’s exact test and Chi square tests respectively .
Total 540 patients were enrolled . Both serum and CSF samples were obtained from 280 cases and only CSF and only serum were obtained from 139 and 121 cases respectively . Due to limited availability and quantity of sample , all the tests could not be performed in all the cases . The testing details are given in Fig 1 . Total 521/540 ( 96 . 5% ) cases were children ( aged< 180 months old , Mean age: 84 . 4 months , Range: 2 months to 78 years ) and 312 ( 57 . 8% ) were males ( Male to female ratio; 1 . 4:1 ) . Total 33 . 3% ( 180 of 540 ) patients tested positive for at least one pathogen . The total positivity of all the etiological agents combined together was not significantly different between age or sex groups ( Table 2 ) . Most samples were positive for scrub typhus IgM or PCR ( 25% ) , followed by IgM positivity for JEV ( 8 . 1% ) , WNV ( 6 . 8% ) , DV ( 6 . 1% ) , and ChikV ( 4 . 5% ) . ( Table 3 ) . Since many samples were co-positive for 2 or more antibodies , the exact proportion of each agent could not be known . M . tuberculosis and S . pneumoniae each was detected in approximately 1% cases . H . influenzae , adenovirus , HSV-1 , enterovirus , and measles virus , each were detected in less than 1% cases . N . meningitidis , HSV-2 and VZV were not detected in any case ( Table 3 ) . Of the cases testing positive , co-detection of more than one pathogen was seen in 23 . 3% ( 42/180 ) cases; the co-detection of antibodies against more than one arboviruses was more common . The frequency varied from 50% for anti JEV IgM to 83 . 3% for anti- DV IgM ( p value = 0 . 04 , Chi square = 8 . 14 ) . Difference between co-detection among flaviviruses ( JEV , DV , WNV ) and alphavirus ( ChikV ) was not statistically significant ( p value = 0 . 59 , 95% CI: 0 . 79–1 . 09 ) . Anti- scrub typhus IgM antibodies showed a significantly lower co-detection than the arboviruses antibodies ( p value<0 . 0001 , 95% CI: 1 . 93–3 . 32 ) ( Table 3 ) . The different combinations of co-positives are shown in Table 4 . Simultaneous detection of nucleic acid of more than one pathogen was found in only one case ( scrub typhus and HSV1 DNA ) . The clinical features were available for 124 patients out of 180 patients positive for any pathogen . The most common clinical features were fever ( 100% , n = 124 ) , altered sensorium ( 79 . 8% , n = 99 ) , headache ( 71 . 8% , n = 89 ) , nausea/ vomiting ( n = 53 . 2% , n = 66 ) , seizures ( 50 . 8% , n = 63 ) , and neck rigidity ( 32 . 3% , n = 40 ) . No significant difference in clinical features was seen in cases with different etiologies . Since scrub typhus was the most common etiology detected and AES due to scrub typhus has not been reported from Bihar till date , scrub typhus real time PCR was done in cases where samples were available . CSF was preferred over the serum sample ( Fig 2 ) . The PCR detected total eight cases of scrub typhus of which five also had anti-scrub typhus IgM antibodies . Six of eight Real Time PCR positive samples could be sequenced , which on BLAST analysis showed a maximum similarity with the Thailand , Cambodia and Vietnam scrub typhus strains . On conducting a molecular phylogenetic analysis by Maximum Likelihood method based on the Tamura-Nei model in MEGA6 software , the scrub typhus sequences obtained clustered with Gilliam like strains ( Fig 3 ) . Most of the patients were referred from Patna and its surrounding districts . Nepal and Jharkhand ( shared boundaries ) referred 13 and 6 cases respectively . Geographic location of 23 patients could not be traced ( missing data ) . Analysis was done only for 15 districts referring more than 10 cases , of which , eleven showed overall high positivity ( >30% positives ) , three districts showed moderate positivity ( >20–30% ) and one ( Muzaffarpur ) showed low positivity ( 10% ) ( p value = 0 . 0014 , Chi square = 13 . 14 ) ( Fig 4A ) . District wise total number of samples referred , total number of tests positive , the names of tests positive and the co-positives are mentioned in Fig 4B . A month wise analysis was done on the total AES cases referred to the virology laboratory and of arbovirus positivity ( Fig 5 ) . AES cases were reported throughout the year with a dip in the number of cases during February and March . Similarly , anti-DV IgM and anti-WNV IgM were positive throughout the year , but with a small peak during August through October . Anti-JEV IgM and anti ChikV IgM showed a distinct seasonality with maximum number of cases being observed during August to October and during June through July respectively . Scrub typhus peaks were also seen during September and October .
For over 20 years , the state of Bihar has witnessed periodic outbreaks of Acute Encephalitis Syndrome . The victims are usually malnourished children , with the median age been reported from 4–5 years . In the present study most of the AES cases were children less than 15 years of age . The disease has no remarkable sex preference as was also observed over the years during the Bihar AES outbreaks from 2011 to 2014 [7] . We screened all the patients for DV , WNV and ChikV because all these flaviviruses are closely related to JEV and are known to cause AES . We screened for scrub typhus since the organism Orientia tsutsugamushi has increasingly been recognised as a cause of AES [8 , 9] . Enteroviruses , HSV , VZV , and Adenovirus are already established causes of AES . We also wanted to know the percentage of bacterial infections presenting as AES as these are easily treatable conditions , provided patient is diagnosed and treated in a timely manner . An infectious etiology could be determined in about one-third of the AES cases , which was a mixed pot showing the simultaneous presence of several pathogens . This observation is similar to that obtained from AES cases from Uttar Pradesh [10] . Since few samples were referred from each district , we could not draw any conclusion from the geographical distribution of the cases . JEV , ChikV and scrub typhus showed a definite seasonality with an increase in the number of cases in the monsoon and the post monsoon season as per the previous studies [11 , 12] . Seasonality of DV cannot be commented upon as most of the anti DV IgM positives were co-positives with antibodies against other pathogens . JEV was found only in about 8% cases . In the year 2012–13 the Government of India initiated JE vaccination and other AES/JE control activities in following districts of Bihar i . e . Araria , East Champaran , West Champaran , Darbhangha , Gaya , Muzaffarpur , Gopalganj , Jehanabad , Nawada , Nalanda , Patna , Samastipur , Vaishali , and Saran [7] . We could not know the vaccination coverage in these districts . However , in the neighbouring state of Uttar Pradesh the JE vaccination coverage in 7 districts of the Lucknow region viz . Raebareli , Hardoi , Sitapur , Unnao , Lakhimpur Khiri , Lucknow ranged from 66 . 80% in the year 2014–15 to 76 . 54% in the year 2016–17 ( personal communication with UP Vector Borne Disease Control Program ) . In north India , the protective efficacy of a single dose of SA-14-14-2 JE vaccine has been reported to be varying from 94 . 5% [13] to 84% [14] . JE vaccination program might have brought down the incidence of JE in Bihar . Surprisingly , most of the data available from Bihar are only from Muzaffarpur district with limited data from other parts of the state . Several theories have been put forward by different researchers to explain the etiology of AES cases in Muzaffarpur district . These hypotheses include non-infectious , toxic encephalopathy due to the toxin methylenecyclopropyl-glycine present in lychee fruit , which causes hypoglycemia and encephalopathy on the background of malnourishment [3 , 15] . Among infectious etiologies besides JEV , the usual cause of AES in India , Nipah virus was also thought as a possible etiology [1] because of a large number of bats being usually present in lychee orchards feeding on the lychee fruit , which were later consumed by the children . In 2014 , the National Institute of Virology , Pune and the National Communicable Disease Center , New Delhi found that samples from AES patients like CSF , serum , urine , nasal swabs , throat swabs , rectal swabs , postmortem brain and liver biopsy were negative for JEV , Nipah virus , WNV and Chandipura virus [3] . The present study focuses on cases from Patna , which drains cases from all over Bihar . An infectious etiology could be determined in one third of the total cases studied , which comprised of scrub typhus , JEV , DV , WNV , ChikV in a good number of cases and M . tuberculosis , S . pneumoniae , H . influenzae , adenovirus , HSV-1 , enterovirus , and measles virus occassionally . For the first time , we detected scrub typhus in AES cases from Bihar , though O . tsutsugamushi is known to exist in this region [5] . O . tsutsugamushi has already been established to invade the central nervous system [8 , 9 , 16] . In fact in a recent prospective study from Laos , O . tsutsugamushi was detected in 12% patients with CNS infection and having evidence of bacterial or fungal infection [17] . Scrub typhus is easily treatable when diagnosed correctly , though untreated cases have a case fatality rate of 30–35% [5] . The differential diagnosis of scrub typhus is a long list , because of its nonspecific clinical and laboratory features , combined with limited diagnostic facilities in developing countries like India . Therefore , the clinicians need a high index of suspicion for detecting this neglected and treatable disease in cases with AES at least in endemic areas [8 , 9 , 12] . The clinicians may start specific treatment with doxycycline or azithromycin when scrub typhus is considered likely [5 , 16] . In the present study , we established the diagnosis of O . tsutsugamushi infection based upon an ELISA technique since IgM capture assays are the most sensitive tests for diagnosing recent rickettsial infections , as significant titers of IgM antibody appear by the end of first week [5 , 18] . Real time PCR could detect three extra cases , which we would have missed if we relied only on the serological test; samples from these cases were collected within the first week of illness thereby increasing the rickettsemia [5] detection . Studies recommend using whole blood/ buffy coat [19] for scrub typhus PCR and not serum/ CSF , which may account for its low positivity in the present study . We confirmed O . tsutsugamushi Gilliam-like strains presence , similar to those isolated from Vietnam and Thailand , by sequence analysis . In 2015 , studies reported equal proportions of Karp-like and Kato-like strains from Northern India [20] and Gilliam-like strains from Vellore and Shillong but not from Northern India . Though , in 2007 studies reported Gilliam- like strains from Northern India [21] . This highlights the need for a comprehensive genotype study from this region , which will help in vaccine development as well as in understanding implications of strain variations and pathogenesis [22] . We tested the cases for M . tuberculosis since India is a country with a high burden of the disease and about 1% tuberculosis cases develop CNS complications . Moreover , the AES case definition given by the World Health Organization is very broad and includes viral encephalitis , bacterial meningitis , tubercular meningitis , cerebral malaria and acute disseminated encephalomyelitis [23] . Since CNS tuberculosis usually presents as acute to subacute meningitis with symptoms of less than two weeks duration , we can label these cases as having AES as per the clinical case definition . Identifying M . tuberculosis is important for initiating specific treatment in these patients , who would otherwise have higher chances of mortality and poor outcome . Some cases showed co-positivity between the arboviruses . In a hyperendemic region , if a case is positive for more than one arbovirus antibodies , three possibilities exist a . cross-reactivity , the arboviruses share antigenic epitopes in the major envelope ( E ) protein due to which cross-reacting antibodies are produced [24]; b . pre-existing immunity due to previous flavivirus infection or vaccination [25]; c . co-infection , already reported in areas with high transmission rates varying from 2% in Gabon to 34% in Nigeria[26 , 27] . The limitation of the present study is that we could not accurately determine the etiological agent/ agents in these cases as we could not perform the gold standard plaque reduction and neutralization test ( PRNT ) due to logistic reasons . Similarly , cross-reactivity or co-infection in hyperendemic regions [28] may explain the co-positivity of scrub typhus and viruses , as was also indicated by the PCR results . Obtaining both CSF and serum from an ailing child is not always possible . At times CSF tap cannot be performed owing to the low general condition of the patient or the amount of CSF tapped is low and just enough to do the cell counts and biochemistry for immediate patient management . At times venepuncture becomes difficult or in a few cases , the amount of blood obtained becomes a limiting factor . In the present study in about fifty percent cases , the samples obtained were either CSF or serum , hence all the tests could not be performed in each case . The highlights of this pilot AES study were the detection of an infectious etiology in one-third of the AES cases , multiple etiologies , and the emergence of O . tsutsugamushi infection as an important causative agent of AES in Bihar , India . We need more comprehensive studies to confirm the findings of this study . | Acute encephalitis syndrome ( AES ) is a dreaded disease in India including the state of Bihar . Every year several people specially children , succumb to this disease and often the survivors are left with permanent residual disorders . The present research throws light on specific etiological agents that may cause AES and have found scrub typhus to be an important etiology . Knowing the specific etiology would help in definitive management of the patients that may improve the outcome both in terms of morbidity and mortality , as well as help the policy makers to take specific action for prevention and control of the disease . | [
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"viruses",... | 2018 | Emergence of Orientia tsutsugamushi as an important cause of Acute Encephalitis Syndrome in India |
The early and correct diagnosis of human leishmaniasis is essential for disease treatment . Another important step in the control of visceral leishmaniasis is the identification of infected dogs , which are the main domestic reservoir of L . infantum . Recombinant proteins and synthetic peptides based on Leishmania genes have emerged as valuable targets for serodiagnosis due to their increased sensitivity , specificity and potential for standardization . Cathepsin L-like genes are surface antigens that are secreted by amastigotes and have little similarity to host proteins , factors that enable this protein as a good target for serodiagnosis of the leishmaniasis . We mapped a linear B-cell epitope within the Cathepsin L-like protein from L . braziliensis . A synthetic peptide containing the epitope and the recombinant protein was evaluated for serodiagnosis of human tegumentary and visceral leishmaniasis , as well as canine visceral leishmaniasis . The recombinant protein performed best for human tegumentary and canine visceral leishmaniasis , with 96 . 30% and 89 . 33% accuracy , respectively . The synthetic peptide was the best to discriminate human visceral leishmaniasis , with 97 . 14% specificity , 94 . 55% sensitivity and 96 . 00% accuracy . Comparison with T . cruzi-infected humans and dogs suggests that the identified epitope is specific to Leishmania parasites , which minimizes the likelihood of cross-reactions .
Leishmaniasis is a complex disease with cutaneous , mucocutaneous and visceral forms , and it is caused by protozoan parasites of the genus Leishmania . The disease is present in more than 98 countries , with approximately 0 . 2 to 0 . 4 million and 0 . 7 to 1 . 2 million cases of visceral ( VL ) and tegumentary ( TL ) leishmaniasis , respectively , occurring each year [1] , [2] . Currently , the disease is expanding to non-endemic areas such as Canada , the United States , Italy and Germany [3] , [4] . The early and correct diagnosis of VL is important for reducing mortality , as the disease is lethal if left untreated . Another important step in the control of VL is the identification of infected dogs , which are the main domestic reservoir of L . infantum [5] . Although the tegumentary diseases are often non-lethal , there are grave consequences for the patient [6] that can be prevented with rapid and accurate diagnosis and treatment . Currently , the diagnosis of TL is based on Montenegro skin test [7] , [8] that identifies a delayed-type hypersensitivity response to parasite antigens in infected individuals and directly detects parasites in lesions [9] , [10] . Canine and human visceral leishmaniasis diagnosis requires the identification of clinical symptoms and serological tests . Crude Leishmania antigen preparations including soluble antigens are the most common parasite proteins employed in immunodiagnosis of leishmaniasis . Serological techniques performed with this antigen have high sensitivity , but they lack specificity [11] . False positive results are frequently observed in sera from humans and dogs infected with T . cruzi [12]–[14] . Additionally , the different parasite strains and protocols used for crude antigen preparations cause variations that may affect the sensitivity of this method . Improving serological tests for Leishmaniasis diagnosis is important because they are rapid , easy to perform and can easily be implemented under the conditions commonly encountered in developing countries [15] , [16] . Furthermore , serological tests can potentially diagnose infections before lesions are formed by tegumentary disease [17] . Recombinant Leishmania proteins have emerged as valuable targets for serodiagnosis due to their increased sensitivity , specificity and potential for standardization [17] . In this context , various recombinant proteins , among them k39 , KMPII , Peroxidoxins , LACK , nucleosomal histones ( H2A , H2B , H3 and H4 ) and heat shock proteins ( families 60 , 70 and 83 ) have been tested in the diagnosis of visceral and tegumentary leishmaniasis and obtained promising results for development of diagnosis kits using these antigens [17]–[24] . Moreover , in silico and experimental methods for epitope mapping and peptide synthesis have great potential for the discovery of new potential pathogen antigens [25] , [26] . Chemically synthesized peptides have low costs and high specificity and are also free of contaminants from bacteria or other host cells that are frequently used to produce recombinant proteins [27] , [28] . Cysteine proteases have been implicated in several processes during parasite life cycles , including interaction with host cells and immune evasion . In Leishmania parasites , Cathepsin L-like ( CatL ) genes are more abundant in stationary promastigotes and amastigotes [29] , and the mature protein is both surface-associated [30] and secreted [31] . Knockout studies of this protein in L . mexicana and L . infantum demonstrate its importance for parasite survival inside macrophages; for example , it modulates host immune responses [32]–[34] . Beyond expression in the intracellular stage , CatL proteins have less than 40% identity with human proteins and more than 60% identity with other Leishmania species . Hence , this protein is a good target for serodiagnosis . We evaluated the potential use of L . braziliensis CatL protein for the serodiagnosis of human tegumentary and visceral leishmaniasis as well as of canine visceral leishmaniasis ( CVL ) . Furthermore , we mapped a linear B-cell epitope in the CatL protein sequence and compared its performance with the recombinant protein using current serology methodologies . Finally , we evaluated the reactivity of the CatL epitope with human and canine sera .
Experiments involving dog samples were performed in compliance with the guidelines of COBEA ( Brazilian College of Animal Experimentation ) , strictly followed the Brazilian law for “Procedures for the Scientific Use of Animals” ( 11 . 794/2008 ) and were approved by the Institutional Animal Care and Committee on Ethics of Animal Experimentation ( Comitê de Ética em Experimentação Animal – CETEA ) from the Federal University of Minas Gerais ( protocol number 44/2012 ) . The use of human samples was approved by the Ethics Committee of the Federal University of Minas Gerais ( protocol CAAE – 00842112 . 2 . 0000 . 5149 ) . All subjects provided written informed consent before blood collection . A total of 65 sera samples were obtained from TL patients from the Centro de Referência em Leishmaniose ( Januária , Minas Gerais , Brazil ) , of which 45 and 20 patients presented cutaneous ( CL ) and mucosal ( ML ) clinical disease , respectively . Sera samples from 55 patients with VL were also obtained from the University Hospital ( Montes Claros , Minas Gerais state , Brazil ) . Parasitological confirmation of Leishmania infection was performed by microscopic analysis of biopsies from cutaneous lesions ( TL ) or bone marrow aspirates ( VL ) , and molecular detection of the parasite was performed by PCR using specific primers for Leishmania kDNA [35] . All Leishmania-infected patients are known to be uninfected with T . cruzi . Chagasic human sera was collected from 20 patients with T . cruzi infections , which were confirmed by hemoculture or the Chagatest recombinant ELISA v . 3 . 0 kit in combination with the Chagatest hemagglutination inhibition ( HAI ) tests; the absence of Leishmania infection was also confirmed in these patients . Sera samples from 50 healthy humans from non-endemic Leishmania or Trypanosoma areas were used as negative controls . Dog sera samples were obtained from the endemic area for CVL in Minas Gerais , Brazil . The infection was confirmed in 30 animals by the presence of amastigotes in bone marrow aspirates , as observed by microscopic analysis . Samples from 15 dogs experimentally infected with T . cruzi ( CD ) and negative for Leishmania were used to evaluate cross-reactivity . A total of 30 dogs from areas without endemic visceral leishmaniasis and negative for Leishmania and T . cruzi were included as a control group . To facilitate the visualization of various human and dog sera samples used in this study a flowchart was prepared and is shown in supplementary figure 1 ( S1 Fig . ) . The sequences of L . braziliensis CatL protein ( Gene ID: LbrM . 08 . 0830 ) and its orthologs in L . infantum ( Gene ID: LinJ . 08 . 0960 ) and T . cruzi ( Gene ID: TcCLB . 509429 . 320 ) were obtained from TritrypDB [36] . The Homo sapiens ( RefSeq ID: NP_003784 . 2 ) and Canis familiaris ( XP_005631485 . 1 ) proteins most similar to the L . braziliensis sequence were identified using BLASTp [37] against the NCBI non-redundant protein database [38] . Multiple alignments of sequences were performed with ClustalX 2 . 0 [39] with the default parameters . To predict linear B-cell epitopes in the L . braziliensis CatL protein , the Bepipred program [40] was used with a cut-off of 1 . 3; at least 9 contiguous amino acids with individual prediction scores above the cutoff were considered as candidate epitopes . Intrinsically unstructured/disordered regions ( IURs ) were predicted using the IUPred program [41] . IURs were classified as at least 9 continuous amino acids with individual prediction scores above 0 . 5 . To analyze for epitope specificity , BLAST search , with parameters adjusted for short input sequence , using the epitope sequence as query was performed against all reported human and dog sequences from NCBI non-redundant protein database . The specific parameters were set to 20 . 000 expect threshold , the word size length was 2 , PAM30 without compositional adjustment was used as substitution matrix and low complexity regions filter was switched off ( parameters available at http://www . ncbi . nlm . nih . gov/blast/Why . shtml ) [42]–[44] . L . braziliensis strain MHOM/BR/75/M2904 promastigotes were grown to stationary phase at 24°C in Schneider's insect medium ( Sigma-Aldrich ) supplemented with 10% inactivated fetal bovine serum , 100 U/mL penicillin and 100 µg/mL streptomycin and pH adjusted to 7 . 2 . A total of 1×1010 parasites were washed three times with cold phosphate buffered saline , followed by three cycles of freezing ( liquid nitrogen ) and thawing ( 42°C ) . After ultrasonication with 10 alternating cycles of 30 s at 35 MHz , the lysate was centrifuged at 6 , 000×g at 4°C for 15 min . The supernatant containing SLbA was collected , and the protein concentration was estimated using the Pierce BCA Protein Assay ( Thermo Scientific ) . To assess the potential use of L . braziliensis CatL protein in the diagnosis of human and canine leishmaniasis , we expressed this protein as a His-tagged recombinant protein . Initially , the CatL gene was PCR-amplified from L . braziliensis genomic DNA using forward ( 5′GCTAGCATGACGGTGCCGAGGGTC ) and reverse ( 5′GGATCCCTACTTGAACGTGCAGATGCTCT ) primers with NheI and BamHI restriction sites , respectively ( underlined letters ) . The 1 . 33-kb fragment was excised from the gel , purified , digested with the restriction enzymes and ligated to a similarly digested pET28a-TEV vector [45] . The recombinant plasmid was introduced to electrocompetent E . coli BL21 Arctic Express ( DE3 ) cells ( Agilent Technologies , USA ) by electroporation using a MicroPulser Electroporation Apparatus ( Bio-Rad Laboratories , USA ) . Gene insertion was confirmed by colony PCR and sequencing using T7 primers ( Macrogen , South Korea ) . The recombinant CatL ( rCATL ) expression was performed by adding 1 . 0 mM IPTG ( Isopropyl-β-D-thiogalactopyranoside , Promega , Canada ) for 24 h at 12°C with shaking at 200 rev min−1 . The cells were then lysed by sonication and centrifuged at 10 , 000×g for 30 min at 4°C . The recombinant CatL protein was purified using a HisTrap HP affinity column connected to an ÄKTAprime chromatography system ( GE Healthcare , USA ) . The eluted fractions containing rCatL were concentrated using Amicon Ultra 15 Centrifugal Filters , 10 , 000 NMWL ( Millipore , Germany ) , and further purified on a SuperdexTM 200 gel filtration column ( GE Healthcare Life Sciences , USA ) . Soluble peptide was manually synthesized in the solid phase on a 30-µmol scale using 9-florenyl-methoxy-carbonyl ( Fmoc ) chemistry [46] . First , Fmoc-amino acids were activated with a 1∶2 solution of Oxyme and DIC . The activated amino acids were incorporated into a Rink amide resin with a substitution degree of 0 . 61 . Fmoc deprotection was then performed using 25% 4-methylpiperidine . These steps were repeated until peptide synthesis was complete . The side-chain was deprotected and released from the resin by a solution of 9 . 4% trifluoroacetic acid , 2 . 4% water , and 0 . 1% triisopropylsilane . The peptide was precipitated with cold diisopropyl ether and purified by high-performance liquid chromatography ( HPLC ) on a C18 reverse-phase column using a gradient program of 0 to 25% acetonitrile . The peptides were obtained with 90% purity , as confirmed by mass spectrometry using Autoflex Speed MALDI/TOF equipment [25] . First , rCatL and SLbA were coated onto 96-well microplates ( Nalge Nunc Intl . , USA ) overnight at 2–8°C at a concentration of 2 . 5 µg/mL for rCatL and 0 . 5 µg/mL for SLbA . For the peptide , flat-bottom plates ( Costar , USA ) were coated with 10 µg/well of soluble peptide overnight at 37°C . After blocking with BSA ( 0 . 05 g/mL ) in PBS ( pH adjusted to 7 . 2 ) for 1 hour at 37°C , the plates were washed three times with PBS containing Tween 20 ( PBS-T; 0 . 5 µL/mL ) and incubated with human or dog serum ( 1∶100 dilution ) . The plates were washed three times with PBS-T , and a secondary HRP-conjugated anti-human or anti-dog IgG antibody ( 1∶5 , 000 ) was added for 1 hour at 37°C , followed by four washes . The 3 , 3′ , 5 , 5′-Tetramethylbenzidine ( TMB ) substrate ( Sigma-Aldrich , USA ) in citrate buffer containing hydrogen peroxide was used for detection . The reaction was stopped after 30 min with 4 N H2SO4 , and the absorbance was measured at 450 nm . For the depletion ELISA , the sera was incubated in peptide-coated and blocked plates at a 1∶100 dilution overnight at 2–8°C [23] , [24] . Depleted and undepleted samples were transferred to plates coated overnight with rCatL ( 50 ng/well ) and blocked . ELISAs were performed as described above . For ELISA assays , each serum sample was evaluated in duplicate . All of the statistical analyses were performed using GraphPad Prism release 5 . 0 . The cut-off values for rCatL , SLbA and the synthetic peptide were established using the receiver-operator curve ( ROC curve ) . The cut-off was chosen based on the point that provides the maximum sum of sensitivity and specificity [47] . The EIE-LVC cut-off was obtained according to the manufacturer's recommendation ( twice the average of the negative control ) . Each test was evaluated for sensitivity ( Se ) , specificity ( Sp ) , positive predictive value ( PPV ) , negative predictive value ( NPV ) , area under curve ( AUC ) and accuracy ( AC ) . The degree of agreement between the ELISA assays using rCatL , SLbA or the EIE-LVC Kit and the parasitological test ( biopsy , aspirate or PCR ) was determined by kappa index ( κ ) values with 95% confidence intervals and classified according to the Fleiss scale: 0 . 00–0 . 20 , poor; 0 . 21–0 . 40 , fair; 0 . 41–0 . 60 , moderate; 0 . 61–0 . 80 , good; 0 . 81–0 . 99 , very good and 1 . 00 , perfect . The normal distribution of data was evaluated by the Kolmogorov-Smirnov test . For depletion assays , significant differences were detected using a two-way ANOVA . The differences were considered to be statistically significant at p<0 . 05 .
The most similar human and canine Cathepsin detected in the NCBI database had 28 . 34% and 28 . 12% identity , respectively , with L . braziliensis CatL ( Table 1 ) . This protein shares 65 . 99% and 47 . 60% identity and 70 . 34% and 56 . 63% of similarity with its orthologs in L . infantum and T . cruzi , respectively ( Table 1 ) . In relation to host orthologs , L . braziliensis CatL has 28 . 12% and 28 . 34% identity and 35 . 98% and 36 . 96% of similarity with C . familiaris and H . sapiens , respectively ( Table 1 ) . Only one B-cell linear epitope peptide was predicted by Bepipred program as B-cell linear epitope in the carboxy terminal region of L . braziliensis CatL protein ( Fig . 1 ) . This peptide is not present in the human and dog orthologs , but it has 40 . 00% and 53 . 30% of identity and 73 . 33% and 86 . 67% of similarity to L . infantum and T . cruzi , respectively . We also evaluated if other proteins from human and dog genomes could have a similar sequence to linear B-cell epitope of L . braziliensis CatL ( S1–S2 Tables ) . No human or dog sequence is 100% similar to peptide-1 . The most related human and dog sequences display 60% similarity with peptide-1 ( 2 for H . sapiens and 3 for C . familiaris ) ( S1–S2 Tables ) . This data suggest low probability of cross-reactivity with host proteins . The identified B-cell linear epitope is present in a predicted non-structured region; this characteristic is important for the exposition of epitope and accessibility to antibodies [48] . The recombinant protein had a predicted molecular weight of 47 . 9 kDa , and it was successfully expressed and obtained at a high level of purity ( Fig . 2 ) . A peptide ( QTSGSTTPGPTTTT ) representing the predicted B-cell linear epitope was also chemically synthesized . rCatL , synthetic peptide and SLbA were evaluated for reactivity against sera from patients with tegumentary and visceral leishmaniasis ( Fig . 3 ) . Human tegumentary leishmaniasis included patients with cutaneous and mucosal clinical disease . Cross-reactivity with sera from chagasic patients was also evaluated . Samples from patients with mucosal disease had lower reactivity with the synthetic peptide than patients with cutaneous and visceral leishmaniasis , but this difference was not observed for the other antigens . The performance of each antigen ( sensitivity , specificity , positive and negative predictive value and accuracy ) is summarized in Table 2 . Considering all metrics , the recombinant protein showed the best diagnostic value for tegumentary leishmaniasis ( 96 . 30% accuracy ) , followed by synthetic peptide , which also had a similarly high performance ( above 90% for all parameters , including 94 . 07% accuracy ) . Analysis of area under curve ( AUC ) using ROC curves ( Fig . 4 ) confirmed the better performance of rCatL ( AUC = 0 . 992 ) and the synthetic peptide ( AUC = 0 . 971 ) compared with SLbA ( AUC = 0 . 753 ) . Furthermore , these two antigens showed very good agreement with parasitological tests ( Table 3 ) , the gold standard for diagnosis of tegumentary leishmaniasis . Moreover , the soluble antigen had low sensitivity ( 70 . 77% ) , specificity ( 68 . 57% ) and accuracy ( 69 . 63% ) in diagnosing patients with cutaneous and mucosal disease . Using samples from patients with visceral leishmaniasis , the synthetic peptide showed better performance values than rCatL and SLbA ( Table 2 ) ; their accuracy values were 96 . 00 , 84 . 00 and 51 . 20% , respectively . Of the metrics evaluated , sensitivity showed the highest difference between peptide ( 94 . 55% ) and recombinant protein ( 74 . 55% ) . Only the synthetic peptide showed very good agreement with the parasitological test ( Table 3 ) , and the AUC for each antigen ( 0 . 884 , rCatL; 0 . 964 , peptide; 0 . 510 , SLbA; Fig . 4 ) confirmed that the peptide could potentially be used for serodiagnosis of human visceral leishmaniasis . The recombinant protein and synthetic peptide were also evaluated for the serodiagnosis of visceral canine leishmaniasis , and both were compared with the commercial EIE-LVC kit ( Fig . 3 ) . Although rCatL and the synthetic peptide had higher specificity than EIC-LVC , the commercial kit showed the highest sensitivity ( Table 2 ) . The accuracy value for rCatL ( 89 . 33% ) was higher than synthetic peptide ( 85 . 33% ) and EIE-LVC antigen ( 72 . 00% ) . The area under the curve for recombinant protein ( 0 . 885 ) was also higher than the peptide ( 0 . 876 ) . Furthermore , these two antigens showed good agreement with parasitological tests , while the kit had only a moderate correlation ( Table 3 ) . Depletion ELISA was performed to confirm that the synthetic peptide represents a human and canine B-cell linear epitope in CatL . This technique is based on the reduction of serum reactivity via the depletion of peptide-specific antibodies; in this case , the sample is incubated with the synthetic peptide prior to ELISA with a known antigen [23] , [24] . The depleted sample was tested for reactivity against the recombinant protein , and the reduction was proportional to antibody levels that bind to the similar peptide within the protein sequence . IgG reactivity against rCatL after antibody depletion was reduced in all Leishmania-infected human and dog groups ( Fig . 5 ) . Furthermore , the reduction in reactivity for both types of human tegumentary leishmaniasis ( 41% , p<0 . 05 for cutaneous and 60% , p<0 . 001 for mucosal ) was higher than for human visceral disease ( 26% , p<0 . 01 ) and lower than the reduction in canine samples ( 9% , p<0 . 05 ) . For the control groups and chagasic humans and dogs , no significant reduction was observed ( Fig . 5 ) , suggesting that this epitope is specific to Leishmania parasites .
Serological tests have significant advantages for leishmaniasis diagnosis . These tests allow the early detection of infection before lesion formation , and they are non-invasive , quantitative and easily automated , allowing the concurrent analysis of a large number of samples [17] , [25] , [49] . However , the specificity and sensitivity of current methods using crude antigens vary depending on antigen composition , parasite species and strain , production protocol and experimental conditions [11] , [13] . For example , the soluble L . braziliensis antigen or the commercial EIE-LVC kit , which uses antigen prepared from Leishmania major-like promastigotes , both produce many false positive results . Thus , antigen selection is crucial for improving the specificity and sensitivity of the diagnostic technique . Recombinant protein antigens can be standardized and are safer than crude antigens because they do not require the maintenance and processing of live parasites . The recombinant K39 protein is the most promising protein; when used as a rapid test for visceral leishmaniasis , diagnosis reached 77–90% specificity and 87–93% sensitivity [50] , [51] . The WHO Special Program for Research and Training in Tropical Disease ( TDR ) has evaluated five different immunodiagnostic tests using recombinant K39 or recombinant protein derived from the kinesin gene of L . donovani from East Africa , Brazil and India [52] . The sensitivities ranged from 36 . 8–100% and specificities from 90 . 8–100% with no test winner across all regions and conditions , thus demonstrating the importance of antigen identification for leishmaniasis serodiagnosis . Because multiple Leishmania genomes have been sequenced , parasite protein sequences can be compared with those of their host and other pathogens to identify patterns associated to infection [53] , including new targets for diagnosis . Proteins associated with infection and the intracellular survival of the parasite are attractive targets because they are generally secreted or expressed on the parasite surface [54] . Of these genes , CatL is a potential diagnostic target because it is expressed on the surface or secreted by intracellular parasites [29] , [30] , [34] and is involved in the infection of mammals . Importantly , the L . braziliensis CatL protein sequence has less than 50% identity with the orthologous proteins in humans , dog and T . cruzi but approximately 66% identity with the L . infantum protein . Furthermore , the predicted linear B-cell epitope in L . braziliensis CatL is absent from the human and dog proteins , reducing the potential of antigen cross-reactivity with non-infected hosts . In context of the antigen-antibody or antigen-TCR ( T-cell receptor ) binding , the detection of specific amino acid residues that contribute to the specificity and strength of protein/peptide interactions is a problem of the utmost importance [25] , [55] . In this sense , previous studies employing alanine scanning method indicates that there are several structural factors determined by physical-chemical properties of the amino acid sequence of the peptide that determine its affinity with epitope binding site [55]–[58] . Among these factors , the hydrophobic and electrostatic interactions they establish , as well as the flexibility of the molecules involved , are very significant [55] . Through similarity analysis , CatL B-cell epitope demonstrated to be more similar to the sequence in T . cruzi ( 86 . 67% ) than in L . infantum ( 73 . 33% ) . However , experimental data obtained in this study for peptide-1 showed high sensitivity in the identification of infected-L . infantum individuals and high specificity in the discrimination of individuals infected with T . cruzi . These results together suggest that there are specific amino acids conserved only in Leishmania species , and the substitutions of some amino acids may imply a significant change in the affinity of antibody , as described in previous studies employing synthetic peptides [25] . In fact , only 1 of 20 chagasic patient sera and 1 of 15 T . cruzi-infected dog sera was reactive against the synthetic peptide above the cut-off , thus confirming the Leishmania specificity for this linear epitope . In agreement , the low cross-reactivity observed with the proteins of the host was important for obtaining high ability to discriminate infected individuals to controls . The high conservation of proteins among the various Leishmania species opens the possibility for identification of an antigen able to simultaneously diagnose the various clinical forms of the disease would represent an interesting strategy for the technological development and large-scale production of tests for diagnosis [17] . In this sense , the present study was designed to identify antigens for multiple diagnosis of leishmaniasis . Thus , due to low antibody titers observed in patients with TL in comparison to individuals with VL , we chose to select the protein present in the causative agent of TL ( L . braziliensis ) to ensure greater spectrum of diagnosis when employed in individuals infected by species that induce high production of antibodies , as L . infantum [14] , [59] . Interestingly , we observed slight reduction in performance of the recombinant L . braziliensis CatL for diagnosis of the human visceral leishmaniasis when compared to respective synthetic peptide ( accuracy value: 84 . 00% and 96 . 00% , respectively ) while in the case of TL , performance data were similar . Based on these information , we speculate that the few amino acid substitutions that occurs in CatL sequence and its epitope in the native protein of different Leishmania species causing TL and VL , can trigger different exposure of the epitope due to conformational structure , and these characteristics associate the possibility of inducing selection of different B-cell clones producing of specific antibodies , can induce small variations in the recognition of the same epitope by individuals affected by different clinical forms of leishmaniasis , especially at the protein level . Epitope prediction is a useful tool for screening and eliminating potential targets , which reduces research costs [27] . After epitope prediction , the experimental validation of peptide binding to antibodies specific to the original protein is important . For this purpose , depletion ELISA was performed in this study . After the depletion of antibodies that bound the peptide , both canine and human sera showed reactivity against the recombinant protein . This result confirmed the mapped epitope , as the reduced reactivity suggests that some antibodies in the sample reacted to the epitope in the protein and the synthetic peptide . However , the reactivity was not completely reduced , most likely because there are other non-predicted linear epitopes as well as conformational epitopes not accessed in this analysis . Both recombinant protein and synthetic peptide were evaluated for their potential in Leishmania serodiagnosis . rCatL showed the best performance for the immunodiagnosis of human tegumentary and visceral canine disease , with specificities of 95 . 71 and 95 . 56% and sensitivities of 96 . 92% and 80% , respectively . The synthetic peptide was the best antigen for discriminating visceral leishmaniasis in human samples ( 94 . 55% sensitivity and 97 . 14% specificity ) . Notably , the re-calculation of performance metrics employing a balanced data ( same number of individuals per group , randomly selected; S3–S4 Tables ) did not return values significantly different from the unbalanced data ( Tables 2 and 3 ) , and the conclusions remain that rCatL is the best antigen for the immunodiagnosis of TL and CVL , and Peptide-1 for VL . An alternative method for the production of antigens for immunoassays is peptide synthesis . Peptides are relatively simple to synthesize , and they are less expensive and have fewer contaminants than recombinant proteins . Moreover , chemical synthesis protocols do not require the manipulation of living organisms . In general , synthetic peptides increase the specificity of immunoassays compared with crude antigens . The increased sensitivity and specificity of synthetic peptides is associated with specific immunogenic regions in parasite proteins and potential immunodominant proteins that are absent in the host or other organisms frequently associated with cross-reactivity . Both the recombinant protein and synthetic peptide showed higher specificity and sensitivity than crude preparations commonly used for other antigens [17] , [52] , and thus , they are valuable targets to compose an antigen panel that could significantly improve leishmaniasis diagnosis . | Leishmaniasis is one of the major diseases of importance in public health and its precise diagnosis may represent one of the most relevant challenges for the control and possible eradication of the disease . In this context , recombinant proteins and synthetic peptides based on Leishmania genes have emerged as valuable targets for serodiagnosis due to their increased sensitivity , specificity and potential for standardization . Cathepsin L-like ( CatL ) genes are more abundant in stationary promastigotes and amastigotes , and have less than 40% identity with human proteins and more than 60% identity with other Leishmania species . We mapped a linear B-cell epitope in the CatL protein sequence and compared its performance with the recombinant protein and current serology methodologies for the diagnosis of human tegumentary and visceral leishmaniasis as well as of canine visceral leishmaniasis ( CVL ) . Both the recombinant protein and synthetic peptide showed higher specificity and sensitivity than crude preparations commonly used for other antigens , and thus , they are valuable targets to compose an antigen panel that could significantly improve leishmaniasis diagnosis . | [
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] | [
"parasitic",
"diseases",
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] | 2015 | Improving Serodiagnosis of Human and Canine Leishmaniasis with Recombinant Leishmania braziliensis Cathepsin L-like Protein and a Synthetic Peptide Containing Its Linear B-cell Epitope |
A total of 178 strains of V . parahaemolyticus isolated from 13 , 607 acute diarrheal patients admitted in the Infectious Diseases Hospital , Kolkata has been examined for serovar prevalence , antimicrobial susceptibility and genetic traits with reference to virulence , and clonal lineages . Clinical symptoms and stool characteristics of V . parahaemolyticus infected patients were analyzed for their specific traits . The frequency of pandemic strains was 68% , as confirmed by group-specific PCR ( GS-PCR ) . However , the prevalence of non-pandemic strains was comparatively low ( 32% ) . Serovars O3:K6 ( 19 . 7% ) , O1:K25 ( 18 . 5% ) , O1:KUT ( 11 . 2% ) were more commonly found and other serovars such as O3:KUT ( 6 . 7% ) , O4:K8 ( 6 . 7% ) , and O2:K3 ( 4 . 5% ) were newly detected in this region . The virulence gene tdh was most frequently detected in GS-PCR positive strains . There was no association between strain features and stool characteristics or clinical outcomes with reference to serovar , pandemic/non-pandemic or virulence profiles . Ampicillin and streptomycin resistance was constant throughout the study period and the MIC of ampicillin among selected strains ranged from 24 to >256 µg/ml . Susceptibility of these strains to ampicillin increased several fold in the presence of carbonyl cyanide-m-chlorophenyldrazone . The newly reported ESBL encoding gene from VPA0477 was found in all the strains , including the susceptible ones for ampicillin . However , none of the strains exhibited the β-lactamase as a phenotypic marker . In the analysis of pulsed-field gel electrophoresis ( PFGE ) , the pandemic strains formed two different clades , with one containing the newly emerged pandemic strains in this region .
Vibrio parahaemolyticus is a Gram-negative bacterium , which is normally found in several niches of the coastal environments . In humans , this pathogen causes three major clinical syndromes: gastroenteritis , wound infections and septicemia [1] . Intestinal infections caused by this pathogen are mainly associated with the consumption of raw or undercooked seafood with clinical symptoms such as moderate to severe diarrhea , abdominal cramps , nausea , vomiting , with or without fever and tenesmus [1] . In infected individuals , the frequency of diarrhea may vary from 3 to 10 times per day and in the case of persistent diarrhea; the duration may last for 4–7 days . V . parahaemolyticus infection has been reported all over the world , either as sporadic diarrhea or contaminated food-related outbreaks [2] , [3] . Generally , the isolation rate of this pathogen from diarrheal cases has been high in Asian countries [4]–[6] . A recent surveillance conducted during 1996–2010 in the US revealed an increase in the infection rate of V . parahaemolyticus [7] . To confirm their role in the diarrheal epidemiology , V . parahaemolyticus isolated from clinical , food and environmental sources are further tested for virulence and other genetic characteristics . The virulence of this pathogen has been attributed to the production two major factors: thermo-stable direct hemolysin ( TDH ) encoded by the tdh , and TDH-related hemolysin encoded by trh . Either or both of these genes have been commonly detected in clinical strains , but not always from food/environmental strains [8] . The emergence of the first pandemic strain of V . parahaemolyticus belonging to serovar O3:K6 has been reported from Kolkata during 1996 [9] . Since then , this pathogen has been associated with several large outbreaks of diarrhea in many countries [10] . In addition to virulence characteristics , V . parahaemolyticus strains have been tested for the prevalence of different serovars and pandemic marker genes encoded in the ToxRS region by using a group specific PCR ( GS-PCR ) [11] . This GS-PCR was developed based on the nucleotide sequence variations in the toxRS operon , which encode transmembrane proteins involved in the regulation of virulence-associated genes . This specific variation was found only in the pandemic strains of V . parahaemolyticus and hence used as a genetic marker for its detection . The toxRS gene sequence in the new pandemic strains has difference at 7 base positions compared with non-pandemic strains , of which 2 bases have been used to design primers in the GS-PCR . In an active surveillance of diarrheal infection , we monitor several enteric pathogens among acute diarrheal patients admitted at the Infectious Diseases Hospital ( IDH ) , Kolkata , India . Since multiple antimicrobial resistances have been reported in other enteric pathogens [12]–[15] , we examine the susceptibility patterns of V . parahaemolyticus strains . In this study , V . parahaemolyticus strains isolated during 2001–2012 from the hospitalized acute diarrheal patients were examined for serovar prevalence , virulence traits , antimicrobial resistance and genetic lineage of strains , along with the association of clinical symptoms of the cases .
Ethical approval has been obtained from the National Institute of Cholera and Enteric Diseases Ethics Committee ( Ref . C-4/2012-T&E ) , and the enrolled patients/parent in the case of children in this study provided written informed consent . Between January 2001 and December in 2012 , every fifth diarrheal patient admitted at the IDH was enrolled in the active surveillance . During enrollment , patients or primary caretakers of children undertook a standardized questionnaire to solicit demographic , epidemiologic , and clinical information . Stool specimens were collected before the administration of antibiotics using sterile catheters and transported to the laboratory with 2 hrs . In the event of any anticipated delay , soaked swabs in stool specimens were stored in Carry Blair transportation medium ( Difco , BD , Sparks , MD ) at ambient temperature for 6–8 hrs . FLC and RBC have been examined microscopically ( Olympus CX41 , Olympus Corporation , Tokyo , Japan ) by smearing a thin layer of fresh stool on a glass slide and counts were made the under high power in five or more fields . Microscopic presence of RBC was further confirmed by Hemaoccult 11 ( Smith Kline Diagnostics , San Jose , CA ) . The stool pH was determined using a portable pH meter ( Jenway , Staffordshire , UK ) . Stool specimens/swabs were processed for the detection of V . parahaemolyticus after enrichment in alkaline peptone water ( Difco ) with 1% NaCl and pH 8 . 5 . After 4–6 hrs of incubation at 37°C , a loop full of culture was plated onto thiosulphate citrate bile salts sucrose agar ( TCBS , Eiken , Tokyo , Japan ) , followed by incubation at 37°C overnight . Typical green colonies grown on the TCBS agar have been tested in triple-sugar iron agar , production of cytochrome oxidase , and tolerance to NaCl at various concentrations [16] . Somatic ( O ) and capsular antigen ( K ) of V . parahaemolyticus were detected using commercially available kits ( Denka Seiken , Tokyo , Japan ) that contained 9 pooled polyvalent K group antisera ( KI to KIX ) , 65 monovalent K type antisera ( K1 to K71; K2 , K14 , K16 , K27 , K35 , K62 are not included ) , and 11 O group antisera ( O1 to O11 ) . Freshly grown cultures on nutrient agar ( Difco ) supplemented with 1% NaCl and heat-killed cells suspended in normal saline were used for K and O serotyping , respectively . V . parahaemolyticus strains were tested for virulence traits such as tdh , trh genes and pandemic group specific ( GS ) toxRS gene using PCR assays as described previously [11] , [17] , [18] . Antimicrobial susceptibility test was performed by disc diffusion method in accordance with Clinical and Laboratory Standards Institute guidelines [19] using commercially available ampicillin ( AM ) ( 10 µg ) , azithromycin ( AZM ) ( 15 µg ) , ceftriaxone ( CRO ) ( 30 µg ) , chloramphenicol ( C ) ( 30 µg ) , ciprofloxacin ( CIP ) ( 5 µg ) , nalidixic acid ( NA ) ( 30 µg ) , norfloxacin ( NOR ) ( 10 µg ) , ofloxacin ( OFX ) ( 5 µg ) , streptomycin ( S ) ( 10 µg ) , tetracycline ( TE ) ( 30 µg ) , trimethoprim/sulfamethoxazole ( SXT ) ( 25 µg ) , discs ( BD , Sparks , MD ) in Mueller Hinton agar ( MHA ) ( Difco ) . These antimicrobials are generally used in the empirical treatment of acute diarrheal cases and hence included in the susceptibility testing . MICs of ampicillin streptomycin and nalidixic acid have been determined by using an E-test ( AB bioMèrieux , Solna , Sweden ) , following the manufacturer's instructions . Escherichia coli strain ATCC 25922 was used as the quality control strain for each batch of the assay . Since there is no published interpretive breakpoint to categorize susceptible/resistant V . parahaemolyticus strains , we have followed the interpretive breakpoint of E . coli strain ATCC 25922 in this study . Simplex PCR assays were used to detect antibiotic resistance genes such as strA , aadA1 ( encoding aminoglycoside [3′] adenylyltransferases ) , blaSHV , blaOXA and blaTEM ( encoding β-lactamases ) as described before [14] , [20] . New primers ( VP-bla F-CCTGTTGGTTGGGCTGATGGTT and VP-bla R-GAAGCGAAAGGTCTGTGT CTGTGA ) were designed to detect chromosomally encoded V . parahaemolyticus beta-lactamase gene ( VPA0477 ) and a qnr homologue VPA0095 ( QnrVPF- CGAATATCCAGCCCGTCCAGTT and QnrVPR- AATCCAAAGCGCTAGAAGGGTGTA ) using a DNA gene sequence of V . parahaemolyticus RIMD 2210633 ( accession No . BA000032 ) with the DNAStar software ( Madison , WI ) . Template DNA was prepared by boiling the cultures grown in Luria Bertani ( LB , Miller ) broth ( Difco ) for 10 min , rapidly cooled on ice followed by brief centrifugation at 10 , 000 rpm and the supernatant was used in the PCR . Synergy testing was performed using MHA supplemented with or without the efflux pump inhibitor carbonyl cyanide-m-chlorophenyldrazone ( CCCP , 1 . 5 µM ) and ampicillin E-test strips [21] . General log-linear model ( GLM ) has been used to analyze the association of clinical parameters and stool characteristics with V . parahaemolyticus infection . In this analysis , all the variables were treated equally as “response” variables whose mutual association was explored . Using Newton-Raphson with Poisson method , the maximum likelihood parameter estimation model was obtained using SPSS version 19 software [SPSS , Inc . , Chicago , IL] . In this analysis , age was grouped in four categories: 1 = up to 10 years , 2 = >10–20 years , 3 = >20–40 years and 4 = >40–≥60 years . The nature of diarrhea was categorized in three groups: 1 = watery , 2 = loose stool and 3 = bloody and mucoid stool . The duration of diarrhea was classified in two groups: 1 = up to 24 hrs and 2 = >24 hrs . Frequency of stool per day was considered in three groups: 1 = up to 5 times , 2 = >5–10 times and 3 = >10 times . Abdominal pain and vomiting were categorized in two groups , each with 1 = present and 2 = absent . Stool characteristics such as the stool consistency , pH , number of RBC , and number of pus cells were made in three categories , each with: 1 = liquid , 2 = mushy and 3 = formed; 1 = <7 , 2 = ≥7–8 and 3 = >8; 1 = 1–10 , 2 = 11–20 and 3 = absent; 1 = 1–10 , 2 = 11–20 and 3 = absent , respectively . The categorical data can highlight the interrelationship in a log linear analysis . PFGE has been made following the PulseNet International protocol [22] . About 40 V . parahaemolyticus pandemic strains belonging to diverse serovars have been selected in the PFGE , which includes all the newly identified pandemic serovars ( n = 11 ) , representative pandemic serovars ( n = 26 ) , along with 3 pandemic O3:K6 strains isolated before 2001 in Kolkata . Briefly , the chromosomal DNA of each strain was digested with NotI enzyme ( Fermentas , Germany ) at 37°C overnight . The XbaI ( Fermentas ) digested DNA of Salmonella Braenderup strain H9812 was used as a molecular weight marker . The restriction fragments were resolved in a CHEF Mapper system ( Bio-Rad , Hercules , CA ) . The PFGE patterns were analyzed using the BioNumerics version 4 . 0 software ( Applied Maths , Sint Martens Latem , Belgium ) after normalization of the TIFF images with the size standard of strain H9812 . Clustering was performed using the unweighted pair group method ( UPGMA ) and the Dice correlation coefficient with a position tolerance of 1 . 0% . The PFGE profiles of three O3:K6 pandemic strains isolated before 2001 ( VP101 , VP174 and VP232 isolated during 1996 , 1997 and 1998 , respectively ) were included in the clonal comparison .
In a span of 12 years from 2001 to 2012 , 178 ( 1 . 3% ) V . parahaemolyticus strains were isolated from 13 , 607 diarrheal patients . The prevalence of V . parahaemolyticus was maximum in 2009 ( Fig . 1 ) . Although the isolation rate was low , diverse serovars were identified in this study ( Table 1 ) . Overall , the serovars O3:K6 ( 19 . 6% ) , O1:K25 ( 18 . 5% ) , O1: KUT ( K-untypable , 11 . 2% ) , O3:KUT ( 6 . 7% ) , O4:K8 ( 6 . 7% ) , and O2:K3 ( 4 . 5% ) were comparatively higher than the others . In the GS-PCR , pandemic strains of V . parahaemolyticus were detected ( 68% ) more than non-pandemic counterparts ( 32% ) . Among the pandemic strain category , serovars O3:K6 ( 91 . 4%; 32/35 ) , O3:KUT ( 100%; 12/12 ) , O1:KUT ( 80%; 16/20 ) , O1:K25 ( 100%; 33/33 ) and O1:K36 ( 100%; 11/11 ) were predominantly detected . Though less in numbers , the other new serovars such as O2:K4 , O4:KUT , O4:K4 , O4:K13 , O8:K21 , and O10:K60 were identified as pandemic strains in the GS-PCR ( Table 1 ) . Based on the virulence gene PCR assay results , V . parahaemolyticus strains were categorized in four groups: tdh+trh+ , tdh+trh− , tdh−trh+ , and tdh−trh− . The most predominant virulence gene profile was tdh+trh− ( 94 . 9% , 169/178 ) . V . parahaemolyticus strains with other gene profiles remained were: tdh−trh− ( 2 . 2% , 4/178 ) , tdh−trh+ ( 1 . 7% , 3/178 ) and tdh+trh+ ( 1 . 1% , 2/178 ) . When correlating virulence gene profiles with GS-PCR results , 97 . 5% ( 118/121 ) of the strains harbored only the tdh gene . However , 3 trh positive strains ( 2 . 5% , 3/121 ) were identified as pandemic strains in the GS-PCR . Of these , two trh positive pandemic strains belonged to O1:KUT and the other was identified as O3:KUT . Among the non-pandemic serovars , the tdh+trh− ( 89 . 5% , 51/57 ) profile was predominantly detected . However , 4 ( 7% ) non-pandemic strains did not harbor any of these virulence markers , and 2 ( 3 . 5% ) had the tdh+trh+ profile . Ninety-eight percent ( 174/178 ) of the strains were resistant to ampicillin , 86% to streptomycin , 3 . 4% to nalidixic acid , and 1 . 7% to chloramphenicol . One non-pandemic strain with an unknown serovar ( OUT:KUT ) was resistant to trimethoprim-sulfamethoxazole , tetracycline , chloramphenicol , nalidixic acid ampicillin and streptomycin . Three strains were found to be susceptible to all the antimicrobials . Ampicillin resistance was common among pandemic and non-pandemic strains . The MIC of ampicillin against 10 randomly selected strains ranged from 24 to >256 µl/ml and 6 to 12 µl/ml for streptomycin . All the strains remained negative for β-lactamase-production . All the strains were screened for strA , aadA1 and blaTEM genes that encode resistance to aminoglycosides and extended-spectrum β-lactamase ( ESBL ) , respectively . Only two strains harbored strA , and one harbored with aadA1 . All the strains were negative for blaTEM , blaSHV and blaOXA genes . However , the newly reported ESBL encoding open reading frame ( ORF ) VPA0477 was found in all the strains , including the strains susceptible to ampicillin . Except for two , the chromosomally encoded qnr homologue was detected in all the strains , irrespective of the quinolone resistant/susceptible phenotype . The qnr homologue negative nalidixic acid susceptible strains had 1–3 folds lower MIC values compared to the strains harboring this gene . Synergy test results showed that the MIC of ampicillin was 1 . 5 to 16-folds less in the selected V . parahaemolyticus strains with CCCP as compared to the growth in the inhibitor-free medium ( Table 2 ) . The GLM showed a significant association between V . parahaemolyticus infection and some of the stool characteristics and clinical symptoms . Liquid and mushy stool consistency , presence of mucus , alkaline stool ( pH 8 . 0 ) , presence of RBC up to 10 and ≥20 FCL counts were significantly associated with the V . parahaemolyticus infection ( p<0 . 001 ) ( Table 3 ) . In the older than 30 years age group , short duration of diarrhea ( ≤24 hrs ) , frequency of stool more than 5 times/day , the presence of abdominal pain , and high frequency of vomiting were significantly associated with the V . parahaemolyticus infection ( p<0 . 001 ) ( Table 4 ) . It is worth to mentioning that in the majority ( 78 . 1%; 139/178 ) of V . parahaemolyticus positive cases , this organism was detected as a sole pathogen and in the rest ( 21 . 9%; 39/178 ) as a mixed infection ( data not shown ) . The other pathogens identified in 39 mixed infection cases included V . cholerae , V . fluvialis , Salmonella spp . , Shigella spp . , diarrhegenic E . coli , ( ETEC , EPEC , EAEC ) , Campylobacter spp . , Aeromonas spp . , Rota virus , Adeno virus , Naro virus , Sappo virus , Giardia spp . , Entamoeba histolytica , and Cryptosporidium spp . Cluster analysis based on the NotI-PFGE profiles revealed two distinct clades ( A and B ) in the dendrogram ( Fig . 2 ) . Clade A had 26 V . parahaemolyticus pandemic strains , of which 46% ( 12/26 ) of the strains belonged to O3:K6 , 27% ( 7/260 ) to O1:K25 , 11% to O4:K68 ( 3/26 ) and 8% to O1:KUT ( 2/26 ) . All these serovars have been previously reported and had an overall similarity of more than 75% , which includes three O3:K6 strains isolated during 1996–1998 . In clade B , the serovar O10:K60 isolated between 2011 and 2012 was more frequent compared to others ( 57% , 4/7 ) . One unusual O3:K6 serovar was also identified in this clade . From the dendrogram , it appears that the newly emerged pandemic servoars of V . parahaemolyticus are heterogeneous with about 50% genetic similarity with serovars placed in clade A ( Fig . 2 ) .
In this surveillance study , we found variation in the isolation rates of V . parahaemolyticus from hospitalized acute diarrheal patients . Combined genetic and molecular typing analysis verified emergence of newer pandemic serovars in this region . The tested V . parahaemolyticus strains reveled susceptibility towards a wide range of antimicrobials used in the treatment of diarrheal infection . | Vibrio parahaemolyticus has been associated with several epidemics of foodborne diarrheal infection . Recent observations in several counties have shown the emergence of pandemic strains of V . parahaemolyticus with unique genetic features and their role in diarrheal outbreaks . Unlike other enteric pathogens , the appearance of pandemic strains of V . parahaemolyticus has not been associated with the economic/hygiene status of the population . The pandemic strains of V . parahaemolyticus continue to prevail in Kolkata , India since its appearance during 1996 . The present communication describes not only the prevalence of pandemic serovars of V . parahaemolyticus , but also the appearance of novel serovars under the pandemic strain category . In addition , the trh gene was detected in some of the pandemic strains for the first time . In the newly emerged serovars genetic changes have occurred , as evidenced from the PFGE analysis . Overall , the antimicrobial susceptibility of pandemic strains remains unchanged for the past 20 years . The observations made in this study re-emphasize the importance of this pathogen and shows the recent genetic and serovar changes in the epidemiology of V . parahaemolyticus-mediated diarrhea . | [
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] | [
"infectious",
"diseases",
"biology",
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"epidemiology"
] | 2014 | Trends in the Epidemiology of Pandemic and Non-pandemic Strains of Vibrio parahaemolyticus Isolated from Diarrheal Patients in Kolkata, India |
Identification of candidate causal variants in regions associated with risk of common diseases is complicated by linkage disequilibrium ( LD ) and multiple association signals . Nonetheless , accurate maps of these variants are needed , both to fully exploit detailed cell specific chromatin annotation data to highlight disease causal mechanisms and cells , and for design of the functional studies that will ultimately be required to confirm causal mechanisms . We adapted a Bayesian evolutionary stochastic search algorithm to the fine mapping problem , and demonstrated its improved performance over conventional stepwise and regularised regression through simulation studies . We then applied it to fine map the established multiple sclerosis ( MS ) and type 1 diabetes ( T1D ) associations in the IL-2RA ( CD25 ) gene region . For T1D , both stepwise and stochastic search approaches identified four T1D association signals , with the major effect tagged by the single nucleotide polymorphism , rs12722496 . In contrast , for MS , the stochastic search found two distinct competing models: a single candidate causal variant , tagged by rs2104286 and reported previously using stepwise analysis; and a more complex model with two association signals , one of which was tagged by the major T1D associated rs12722496 and the other by rs56382813 . There is low to moderate LD between rs2104286 and both rs12722496 and rs56382813 ( r2 ≃ 0:3 ) and our two SNP model could not be recovered through a forward stepwise search after conditioning on rs2104286 . Both signals in the two variant model for MS affect CD25 expression on distinct subpopulations of CD4+ T cells , which are key cells in the autoimmune process . The results support a shared causal variant for T1D and MS . Our study illustrates the benefit of using a purposely designed model search strategy for fine mapping and the advantage of combining disease and protein expression data .
Genome-wide association studies have been very successful at identifying disease-associated variation by exploiting linkage disequilibrium ( LD ) , meaning that only a subset of markers need be surveyed to detect association . However , this same LD , particularly when combined with the presence of multiple disease causal variants in relative proximity , makes disentangling the specific causal variants difficult . Mapping the likely causal variants in regions associated with complex traits as precisely as possible is becoming increasingly important for two reasons . Firstly , as detailed chromatin annotations become available , more precise maps of probable causal variants will allow researchers to fully exploit these resources through integrative analyses targeted at identifying the specific genes , molecules and cells underlying disease association [1] . Secondly , the functional studies which are ultimately required to confirm causal mechanisms are laborious and need to focus , from the outset , on the smallest yet most complete set of plausible causal variants . It is widely recognised that the most associated single nucleotide polymorphism ( SNP ) in a region is not necessarily the causal variant [2] , yet attempts at fine mapping disease signals typically proceed by successive conditioning on the most associated SNPs , a form of stepwise regression that may produce incorrect results , particularly when causal variants are correlated , i . e . in linkage disequilibrium ( LD ) [3] . Bayesian methods have been used for fine mapping association signals in dense and imputed genotyping data and generating credible sets of variants that are likely to contain the causal variant , analogous to credible intervals for odds ratios [4] . However , these approaches typically make the simplifying assumption that exactly one causal variant exists at any individual region [4] because accounting for multiple causal variants leads to exponential increase in the number of models that need to be considered with the number of causal variants considered . Bayesian methods that summarise evidence across SNPs in a region to assess either enrichment of signals in chromatin states [5] or colocalisation between association signals for different traits [6] make a similar single causal variant assumption . As stepwise approaches often obtain evidence for additional , independent association signals in a region [7–10] , this assumption is unrealistic . One exception is BimBam [11] which can fit multi SNP models , but considers all possible models up to a specified maximum number of causal SNPs . As the number of potential models grows exponentially , BimBam is limited to regions with relatively few SNPs for computational reasons . Monte Carlo methods can avoid limitations on the number of causal variants by sampling the model space rather than visiting all possible models . Here we adapt a Bayesian evolutionary stochastic search algorithm , GUESS [12 , 13] , to the fine mapping problem . This method , and its fast computational implementation , is tailored to efficiently explore the multimodal space created by multiple SNP models . However , the very dense SNP map that is required for fine mapping leads to extreme LD , which presents two specific challenges for GUESS . The first is that SNPs in extremely tight LD can cause numerical instability in model fitting , so we use minimal tagging to explore the model space and then expand all the tag models initially selected by GUESS ( Fig 1 ) . Second , posterior support is diluted across SNPs in tight LD , potentially preventing direct inference on the importance of individual SNPs . We therefore use posterior model probabilities and patterns of LD to define sets of SNPs which have strong joint posterior support for the hypothesis that one member of the set is causal for the trait . These are analogous to the credible sets generated in the Bayesian fine mapping framework which assumes a single causal variant per region [4] , but allow for multiple causal variants . Our adaptions around GUESS are available in an R package , GUESSFM ( https://github . com/chr1swallace/GUESSFM ) . We used GUESSFM to fine map the association of multiple sclerosis ( MS ) and type 1 diabetes ( T1D ) to an established susceptibility region for immune-mediated diseases on chromosome 10p15 ( S1 Fig ) , which contains the candidate causal gene IL2RA . IL2RA encodes a subunit CD25 ( IL-2RA ) of the interleukin 2 ( IL-2 ) receptor that is essential for the high affinity binding of IL-2 [14] . The region has provided some prime examples of evolving genetic research . Initially associated with T1D susceptibility using a candidate gene and tag SNP approach [15] , further studies revealed association to other autoimmune diseases , including MS [16] and rheumatoid arthritis [17] , and demonstrated that multiple genetic markers were needed to explain the T1D association [18] . Genotype to phenotype studies have demonstrated that disease-associated variants in the region also associate with IL2RA mRNA expression and CD25 protein expression on the surface of naive and memory CD4+ T cells [19–21] and sensitivity of memory CD4+ T and activated T regulatory cells to IL-2 [22] .
The use of different genotyping panels for different diseases has presented a challenge for comparative analysis across diseases . Here , we refined the T1D and MS association signals in the associated region on chromosome 10p15 by taking advantage of a unified dense SNP map provided by the ImmunoChip , a custom Illumina 200K Infinium High-Density array covering 186 distinct autoimmune susceptibility risk regions [7 , 23] . We used genotypes on a total of 52 , 637 samples ( S1 Table ) , and supplemented the ImmunoChip variants by imputation from the 1000 Genomes Project data . This gave a total of 667 SNPs and small indels with minor allele frequency > 0 . 005 in controls for analysis after genotype quality control . To allow for the effects of LD , traditional conditional regression is often supplemented by identifying subsets of SNPs with some minimum LD ( e . g . r2 > 0 . 8 ) with individual SNPs selected by a forward stepwise procedure , within which it is suggested the causal variant may lie . We compared our proposed approach to traditional conditional regression by simulating phenotypes conditional on multiple “causal variants” selected randomly from within these genetic data , and confirmed that the proposed approach both recovered a higher proportion of the true effects and simultaneously detected fewer false positive results ( Fig 2 ) . GUESSFM requires specification of the a priori expected number of causal variants in a region , nexp . We considered either setting nexp = 3 or nexp = 5 , and found the performance decreased when nexp was less than the true value , but not when it was greater than the true value , suggesting that for recovery of the correct causal variants it is better to set nexp higher rather than lower . We also considered three regularised regression approaches: the lasso , the elastic net and the group lasso . The ROC curves of those methods ( for decreasing penalties ) are intermediate to the stepwise and stochastic GUESSFM search approaches ( Fig 2 ) . However , at traditional optimization thresholds ( minimising the ten fold cross validation ) , regularized regression approaches were extremely anti-conservative , with a very high false discovery rate , compared to stepwise ( stopping at p < 10−8 or p < 10−6 ) or GUESSFM for suitable thresholds on posterior probabilities ( S2 Table ) . Picking a regularisation parameter according to the number of predictors included ( three or five ) produced a more competitive false discovery rate , but discovery rates were still lower than with GUESSFM ( S2 Table ) . We applied our approach to the MS and T1D data sets . Posterior support for the number of SNPs required to model the association is strongly peaked , favouring a two-SNP model in MS and a four SNP model in T1D ( S2 Fig ) . We summarised inference for SNPs over multiple models by considering the support for a SNP group according to the sum of posterior probabilities ( PP ) over all models containing a SNP from that group . We term this the group marginal posterior probability of inclusion ( gMPPI ) , which can be understood as the probability that one SNP in the group is causal for the trait of interest . For T1D , there is high confidence ( gMPPI > 0 . 8 ) for four SNP groups that we denote according to their order in S3 Fig: A ( indexed by rs12722496 ) , C ( rs11594656 ) , E ( rs6602437 ) and F ( rs41295159 ) . As the T1D data have previously been analysed using stepwise regression [23] , we compared our results to those found by forward stepwise analysis of the same data ( Table 1 ) . We saw a direct correspondence in terms of LD ( r2 > 0 . 6 ) between the SNPs identified by the two approaches ( Table 2 ) . However , models found by GUESSFM had larger log likelihoods for a given number of SNPs , indicating that these models offered a better explanation of T1D-associated genetic variation in the region . In contrast , for MS , there were no SNP groups with high gMPPI . We instead saw two distinct models , which together accounted for > 80% of the posterior support amongst the 514 , 476 visited models: either model M1 , consisting of SNP groups A ( indexed by rs12722496 ) and D ( rs56382813 ) , or model M2 consisting only of SNP group B ( rs2104286 ) . SNPs from M1 and M2 were rarely selected together ( total PP = 0 . 039 across the visited models ) . rs2104286 was reported as the single associated SNP in the region in the original MS ImmunoChip analysis after forward stepwise regression [7] ( Table 1 ) . rs2104286 is in low to moderate LD with both of the M1 index SNPs ( r2 ≃ 0 . 3 , Table 3 ) . Haplotype analysis showed that whilst haplotypes carrying the rs2104286:C allele are protective for MS , so is a less common haplotype carrying the rs2104286:T allele and the protective allele at rs56382813 ( Table 4 ) . Indeed , the addition of rs2104286 to a model consisting of the haplotypes formed by M1 was non-significant ( p = 0 . 196 ) whilst the addition of the haplotypes formed by the M1 SNPs to a model consisting of only rs2104286 was significant ( p = 2 . 64 × 10−6 ) . However , the posterior support favours model M1 over M2 only by a factor of 1 . 7 and this is dependent on our prior expectation for the number of causal variants: had we expected only a single causal variant in the region , we would have favoured M2 by a factor of 1 . 7 , with M1 being favoured only with a prior expectation of 1 . 8 or more causal variants ( S4 Fig ) . Under any reasonable prior , the posterior support for the two models is so close that we cannot choose between them on statistical evidence alone: we must consider them as plausible alternative explanations for MS association in the region . Note that the M1 SNPs were not within the credible set created under the single causal variant assumption , which consists of rs2104286 alone [7] . We selected all high confidence SNP groups for more detailed exploration ( Fig 3 ) . The T1D signals are located in ( 1 ) intron 1 of IL2RA—SNP group A , ( 2 ) intergenic between IL2RA and RBM17—C , ( 3 ) 5’ of RBM17—E , and ( 4 ) 5’ of RBM17 to intron 2 of PFKFB3—F . Under the model M1 for MS , SNP group A was also associated with MS , with the same alleles protective for both ( Table 5 ) whilst the second M1 signal ( SNP group D ) physically overlapped , but was not in LD with , SNPs from group C . Under the model M2 , the sole-MS associated SNP ( B ) is located in intron 1 of IL2RA , neighbouring the T1D-associated SNP group A , but there was only weak LD between A and B ( r2 = 0 . 3 ) . Since we were unable to distinguish between the competing M1 and M2 models for MS using SNP-disease association data alone , we sought corroborating evidence to support SNPs in either model using CD25 flow cytometric expression data . The M2 SNP rs2104286 has been associated with the age-dependent proportion of naïve T cells that express CD25 on their cell surface and rs12722495 ( within SNP group A , located in intron 1 of IL2RA ) with the total expression of CD25 on memory T cells [19 , 20] . rs12722495 has also been associated with IL2RA mRNA expression in T cells , both resting [19] and activated by 48 hour culture with anti-CD3+CD28 [21] . We selected a single tag SNP from each of the credible sets and examined which could best explain CD25 protein expression phenotypes using previously published data from 179 samples [19] , plus an additional 30 samples . Both phenotypes were best explained by a single SNP: rs12722495 from group A again showed the strongest association with intensity of CD25 expression on memory T cells ( p = 5 . 50 × 10−10 , S5 Fig ) . For the proportion of naive CD4+ cells that express CD25 , the M1 SNP rs41295055 from group D , whose association with CD25 expression was not previously tested , was preferred to the M2 SNP rs2104286 ( p = 3 . 45 × 10−8 versus p = 2 . 56 × 10−6 , ΔBIC = 8 . 43 , which is interpreted as “strong” evidence in favour of rs41295055 [24]; Fig 4 ) , supporting the hypothesis that rs2104286 is not itself functional , but merely tags other functional variants which are causal for MS . The A and D SNP groups coincide with regions in IL2RA that contain DNase I sensitivity sites indicating open chromatin available to bind transcription factors under appropriate conditions ( Fig 3 ) . In addition , the existence of RNA-seq reads from resting and stimulated CD4+ T cells within intron 1 , where group A SNPs lie ( Fig 3 ) , support the regulatory nature of this region [25] .
The 10p15 region contains at least five apparently distinct associations to the immune-mediated diseases , T1D and MS . Our results are the first to support a four SNP model in T1D , which most likely reflects a combination of increased sample sizes and the increased variant density available due to ImmunoChip and imputation , as this model was supported by both stepwise regression and GUESSFM . A previous comparative study of MS and T1D susceptibility in this region using conditional analysis of tag SNPs identified three groups of SNPs [26]: their group I matches our group A , their group II our group C , and their group III our group B . The results showed that the minor allele of SNPs in all these groups was protective for T1D , but that group I ( A ) was not associated with MS and that group II ( C ) was susceptible for MS . Our results , based on larger sample sizes and more extensive variant coverage , suggest that the minor allele of SNPs in group A , shared between T1D and MS , are associated with the same protective effect for both diseases . This emphasises the need for surveying the most complete set of variants possible in order to make cross disease comparisons . Variants in this region have previously been linked to several aspects of IL-2R signalling in T1D and MS patients [27] and the MS-associated variants we identify ( whether under the M1 or M2 models ) all showed some evidence of association with expression of CD25 on the surface of T cells , linking IL2RA in a primary way into the etiology of this disease . For T1D , we were only able to link the IL2RA intron 1 signal , shared with MS under the M1 model , to CD25 expression on memory T cells . Neither the intergenic T1D SNP group ( C ) , despite physical overlap with the MS associated SNP group D , indexed by rs41295055 , nor the sets near RBM17 and PFKFB3 ( E and F ) have yet been linked to IL2RA expression . These signals could relate to CD25 expression on other cell subsets or under specific conditions . The cell-specific regulation of IL2RA expression and its role in modulating the immune system are both complex and only partially understood . For example , in addition to T cells , IL2RA is also expressed on many other cell types , especially under inflammatory conditions [28] . We have data on only a subset of IL-2R phenotypes that have been previously reported , and previous studies have genotyped only a subset of the disease associated SNPs described in this paper . Further genotyping of large samples with IL-2R related phenotypes is warranted to properly assess any other potential effects of the disease signals we identify on IL-2R signalling . Alternatively , it may be that other genes in the region , which have not been as well studied as IL2RA , are also causal for T1D , either directly or through interaction with IL2RA . For example , PFKFB3 , an inducible 6-phosphofructo-2-kinase/fructose-2 , 6-bisphosphatase isoform , allows rapid responses to energy requirements and insufficiency can lead to apoptosis and an inability to undergo autophagy in CD4+ T cells in RA [29] . PFKFB3 has also been implicated in regulating insulin secretion in pancreatic beta cells [30] , which could explain the T1D specific signals across this gene . All three genes , IL2RA , PFKFB3 and RBM17 are upregulated in CD4+ T cells upon ex vivo activation ( S3 Table ) and two candidate SNPs , from groups E and F , sit between two DNase I sites close to the RBM17 promoter . Furthermore , gene regulation can extend over 100 kb [31] and further studies will be needed to confirm the gene ( s ) through which these other SNPs influence T1D risk . Two or more independent signals are commonly observed in fine mapped autoimmune disease regions using stepwise regression [7–10] . Despite long established doubts regarding the validity of stepwise regression [3] , its use has continued to dominate in GWAS because of the number of SNPs measured simultaneously . Stepwise regression addresses the questions: ‘which is the single variant that can best explain the maximum trait variance ? ’ and , conditional on this single variant , ‘do any other variants explain additional trait variance ? ’ This is not equivalent to asking what set of variants best jointly explain trait variance . Often , as we have observed for T1D , stepwise regression may be expected to identify the same signals as a stochastic search approach . However , our analyses demonstrate that different results may be produced by stepwise and full multi-variant search approaches in some cases . In particular , we highlight and prefer a two SNP model for MS association to the IL2RA region , one of the SNPs shared with T1D ( group A , indexed by rs12722495 ) and associated with CD25 expression on memory T cells , the other ( group D , indexed by rs41295055 ) a better predictor for CD25 expression on the surface of naive CD4+ T cells than the SNP identified by stepwise regression ( group C , rs2104286 ) . Methods that search the multimodal model space formed by multiple SNPs without conditioning on univarate association signals can therefore reveal a more accurate picture of the likely disease causal variants in any region . Other Bayesian [32] , stochastic search [33] and penalised regression approaches such as lasso [34 , 35] have been proposed on a GWAS scale , but these aim primarily to detect association in data sets with relatively sparse genotype coverage . Our focus is the adaptation of a Bayesian variable selection method to the fine mapping problem , and it is likely that an alternative method , such as piMASS [36] , could have been substituted for GUESS in the model selection step . A detailed and direct comparison of piMASS with GUESS showed a similar recovery of models in simulated data , but indicated that GUESS was more computationally efficient [13] . We chose to use GUESS for its computation efficiency , but also because its g-prior formulation is specifically designed to deal with highly correlated predictors , a beneficial feature for fine mapping . Applying the regularised regression approaches to our simulated data showed that the ROC curve for lasso with decreasing penalty out-performed stepwise analysis , but optimising the regularization parameter by minimising the cross validation error led to very high false discovery rates , suggesting that in this context this criterion is highly anti-conservative . Previous studies have also indicated that while regularised regression has strengths in terms of prediction , it may have weaknesses in terms of model selection [37] , particularly for correlated predictors [38] . Elastic net [39] and the group lasso [40] have been proposed as regularised approaches tailored to correlated predictors , but in our hands did not outperform the simple lasso plus construction of a set of plausible SNPs according to r2 > 0 . 8 . We considered that the information supplied to GUESSFM as a prior expected number of causal variants could be included equivalently in a regularised approach by setting the regularisation parameter according to the number of predictors selected . Although this produced a more competitive false discovery rate , discovery rates remained lower than with GUESSFM ( S2 Table ) . Our multidimensional analysis strategy , tailored to the high genotyping coverage ( and , hence , high LD ) required for fine mapping causal variants can provide a more complete picture of the likely causal variants in a region . Any fine mapping study is limited by the set of variants included for study . While we attempted to survey the fullest possible set of SNPs and small indels by using dense genotyping data , plus imputation to the 1000 Genomes Project data , we cannot be sure we included all variants that might affect gene function without sequencing of cases and controls [41] . Further , larger indels , VNTRs and microsatellites remain particularly difficult to genotype with accuracy yet may contribute to disease . Therefore , it is important to bear in mind that all claims that a particular variant is causal are conditional on the true causal variants being accurately genotyped and included in the study . It is well known that functional biological interactions may exist between variants [42] , and therefore the possibility of statistical interactions needs to be considered in fine mapping studies . However , the need to fit interaction terms depends on the evolutionary history of the region . In the IL2RA region , there has been very little historical recombination ( S1 Fig ) and D′ between markers is nearly always 1 , also indicating a lack of recombination [43] . As a consequence , only three of the possible four haplotypes that may be formed from any pair of SNPs are observed , and statistical interaction parameters are inestimable . This also implies that there is a one to one transformation between a haplotype model and a model expressing the log odds of disease as a linear function of single SNP effects [44] , as we have employed . Thus , for the IL2RA region , we may neglect statistical interactions without making any assumptions about the existence of biological interactions . For our method to be applied to regions in which D′ < 1 , it would need extension to include statistical interaction terms . One simple approach , if the number of SNPs is not to large , would be to generate additional variables representing interactions between SNPs with D′ < 1 , but extending GUESS to fit interaction terms is another direction for further research . An alternative approach would be to attempt to study disease risk across haplotypes directly [45] , although these need to be inferred probabilistically when recombination has occurred . Haplotype-based analysis has become less common as marker density has increased . One reason for this is that haplotypes are particularly useful when marker density is low , because a haplotype may tag a causal variant better than any single variant , and haplotype studies have thus fallen out of fashion as denser genotype data have become available . This is exemplified by a study that found multiple statistical interactions underlying gene expression [46] ( statistical interactions may be thought of as haplotype models ) using SNPs on a genomewide array . Subsequent analysis found that all interactions which replicated in an external dataset with whole genome sequencing , and thus with more complete coverage of potential causal variants , could be better explained by a single SNP [47] . This suggests , perhaps , that statistical interactions between SNPs will prove rare . However , if biological interactions between variants separated by LD do exist , and the interaction depends on the phase of the variants , i . e . , the diplotype risk differs from the two locus genotype risk , haplotype methods will again be required to infer likely causal variants . One way to understand the means through which the effects of a disease-associated variant are mediated is to perform functional assays to examine its effects on a gene’s function [48] or to identify potentially intermediate phenotypes with which it is also associated [19] . Improved identification of likely causal variants should lead to more powerful and reliable follow-up studies , an important factor when many of these experiments require fresh primary cells and laborious wet lab protocols . Similarly , comparison with summary results from eQTL studies would be facilitated by the application of multi modal search strategies to the eQTL data set to ensure the effects of genetic variation are mapped as accurately as possible [49] . Even with large samples , and careful fine-mapping analyses , the causal candidacy of SNPs in high LD cannot be resolved through statistical association alone . Functional studies designed to directly address the confounding induced by linkage disequilibrium , such as allele-specific expression using rare haplotypes that distinguish SNPs in the same tag group , may be helpful in refining further the likely causal variants . Informative approaches to understanding disease etiology have recently been developed based on looking for enrichment of the cell-specific chromatin marks localising to likely causal variants , and these are being used to highlight disease relevant cells [23 , 50] . Here , again , more accurate identification of causal variants will lead to more powerful and precise comparisons , and our approach , which is associated to a more accurate estimation of posterior probabilities for the SNPs in the considered region , should be readily adaptable to the growing set of methods that aim to examine enrichment [5] or colocalisation [6] by model averaging over the posterior probabilities that a SNP is causal .
For fine mapping , we require dense coverage of genetic polymorphisms in the region . We targeted the ImmunoChip fine mapping region centred on IL2RA , namely chr10:6030000-6220000 ( hg19 ) . This region is bounded by recombination hot spots ( S1 Fig ) . Genotype data for this region comes from the MS [7] and the T1D [23] ImmunoChip studies . Quality control measures were applied to SNPs and samples as previously described [7] . As MS samples were derived from multiple international cohorts and we found allele frequencies for SNPs in the region varied between cohorts , we manually inspected plots of the first five principal components formed from ancestry informative markers [7] and additionally excluded samples lying outside the main cluster in each cohort . We used IMPUTE2 [51] to impute untyped SNPs using the 1000 Genomes Phase I reference panel . We included a 500 kb window either side of our target region to allow variants in the less densely genotyped neighbouring regions to contribute information on the untyped SNPs . Priors could conceivably be generated on the basis of individual SNP content , for example , to prioritise those overlapping genomic annotations of particular interest . Such an approach has been adopted in a hierarchical framework , albeit with the single causal variant assumption [5] . For simplicity , and because we were interested to discover likely causal variants without predefining their likely mechanism , our model priors were determined only by the number of SNPs contained in a model , Nm . A natural prior is then binomial or beta binomial . Given that , for a fixed expected value , a beta binomial puts larger weight on implausibly large models , we chose to use a binomial model , and , given published data on T1D , set the expected number of SNPs at three . For N SNPs in the target region , this means the prior for a model containing Nm SNPs is π m = ( N N m ) q n ( 1 - q ) N - N m where q is set so the expected value of the binomial distribution was 3 , ie q = 3 N . Priors and posteriors for the number of causal SNPs are shown in S2 Fig . We used simulated data to compare the performance of our proposed method , traditional stepwise regression , the lasso [53] and the elastic net [39] . For each simulation , we selected a random subset of 2 , 000 T1D control samples genotyped in the IL2RA region and a random set of between two and five “causal variants” from amongst the SNPs with MAF > 0 . 01 . We simulated a Gaussian phenotype for which the causal variants acted in an additive manner to jointly explain 10% of the phenotypic variance . We conducted a total of 1 , 000 simulations for each scenario . The data were analysed in parallel using the four different methods . We performed forward stepwise regression , and selected index SNPs at a given p value threshold , α , as those SNPs selected at any stage of the stepwise process with p < α . For each index SNP , we created pseudo “credible sets” as the set of SNPs with r2 > 0 . 8 with the selected index SNPs . Note , by this definition , simulated causal SNPs may appear in more than one SNP group . We calculated the false discovery rate as the proportion of SNP groups which did not contain a causal variant , and we calculated the discovery rate as the proportion of causal variants found in at least one selected SNP group . We used the snp . picker function from GUESSFM ( http://github . com/chr1swallace/GUESSFM ) with default settings to define credible sets for causal SNPs . By definition of the algorithm , SNPs may only be members of at most one SNP group . We selected SNP groups according to varying thresholds for the gMPPI , and calculated false discovery and discovery rates as above . For lasso and elastic net , we used the R package glmnet [54] , and optimised the regularisation parameter λ by minimising the ten fold cross validation error using the cv . glmnet ( ) function . For elastic net , we kept the folds constant across different values of α ∈ [0 . 1 , 1] and chose the pair of values ( α , λ ) which minimised the ten fold cross validation error overall . To examine the path of elastic net solutions , we fixed α at this value and varied λ . We used the R package gglasso [55] to implement the grouped lasso , predefining groups of variables as those with r2 > 0 . 8 with each other using heirarchical clustering . We also considered the discovery and false discovery rates with the first three or first five predictors selected , as something analagous to the prior information given to GUESSFM about our expectation of the number of causal variants . Naive CD4+ T cells from 209 donors were gated as previously described [19 , 56] . Association with index SNPs from disease-associated groups was assessed through linear regression . All possible one , two and three SNP models were considered , and the model with the minimum BIC reported . Expression was log transformed to reduce right skew of the phenotype . We downloaded DNase I hypersensitivity for CD4+ T cells from the Roadmap Consortium [57] from http://vizhub . wustl . edu/VizHub/hg19 , accessed 19 September . We measured gene expression in the studied region in two pooled samples ( n = 3 and n = 4 individuals / pool ) comparing unstimulated and stimulated CD4+ T cells . For each individual , 250 , 000 CD4+ T cells ( 93–99% pure , RosetteSep Human CD4+ T Cell Enrichment Cocktail , StemCell Technologies ) were stimulated with anti-CD3/CD28 T-activator beads ( Dynabeads Life Technologies ) at a ratio of 0 . 3 beads / cell for four hours at 37°C in X-VIVO-15 ( Lonza ) + 1% AB serum ( Lonza ) and penicillin/ streptomycin ( Life Technologies ) . Unstimulated CD4+ T cells were cultured in medium alone for four hours . Cells were harvested directly into Qiagen lysis buffer ( Qiagen ) and stored at -80°C until RNA isolation . RNA was isolated using RNeasy micro kit including gDNA depletion ( Qiagen ) . RNA integrity and concentration was evaluated using the Bioanalyzer platform ( Agilent ) , with all samples showing an RNA integrity score ( RIN ) > 9 . 8 . 750 ng of total RNA were used for the preparation of cDNA libraries using the Illumina TruSeq ( Illumina ) platform with a low-cycle-number PCR protocol , and was followed by transcriptome sequencing on an Illumina HiSeq 2000 . This yielded four libraries with ∼ 38 million 100 bp paired-end reads each . We trimmed raw reads to remove primer and adapter contamination , which affected 2% of our sequences , using HTSeq [58] . Reads were aligned to the reference genome Ensembl GRCh37 . p13 using STAR [59] . Removal of low quality and unpaired reads , indexing , IL2RA region extraction , and depth counting were performed using SAMtools [60] . We employed HTSeq and DESeq2 [61] to carry out a differential expression analysis between the two conditions based on normalised read counts . We only considered paired-end reads that featured a total and unambiguous overlap with genomic sequence assigned to genes , around 73% of the initial raw sequences . Mapped read counts at each position in each sample are in S4 Table . This study was approved by local Institutional Review Boards ( IRBs ) and written informed consents were obtained from all participants in the study . For JDRF/Wellcome Trust Diabetes and Inflammation Laboratory the IRBs are: NRES Committee East of England—Cambridge Central ( ref: 08/H0308/153 ) , statistical analysis; and NRES Committee East of England—Norfolk ( ref: 05/Q0106/20 ) , for gene expression work . This study was approved by the University of Virginia Institutional Review Board ( IRB number 17457 ) ; the Type 1 Diabetes Genetics Consortium collection sites obtained approvals for all subject collections and written consent and/or assent was obtained from all participants or their surrogates in the study . | Genetic association studies have identified many DNA sequence variants that associate with disease risk . By exploiting the known correlation that exists between neighbouring variants in the genome , inference can be extended beyond those individual variants tested to identify sets within which a causal variant is likely to reside . However , this correlation , particularly in the presence of multiple disease causing variants in relative proximity , makes disentangling the specific causal variants difficult . Statistical approaches to this fine mapping problem have traditionally taken a stepwise search approach , beginning with the most associated variant in a region , then iteratively attempting to find additional associated variants . We adapted a stochastic search approach that avoids this stepwise process and is explicitly designed for dealing with highly correlated predictors to the fine mapping problem . We showed in simulated data that it outperforms its stepwise counterpart and other variable selection strategies such as the lasso . We applied our approach to understand the association of two immune-mediated diseases to a region on chromosome 10p15 . We identified a model for multiple sclerosis containing two variants , neither of which was found through a stepwise search , and functionally linked both of these to the neighbouring candidate gene , IL2RA , in independent data . Our approach can be used to aid fine mapping of other disease-associated regions , which is critical for design of functional follow-up studies required to understand the mechanisms through which genetic variants influence disease . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2015 | Dissection of a Complex Disease Susceptibility Region Using a Bayesian Stochastic Search Approach to Fine Mapping |
Mitochondrial DNA ( mtDNA ) is useful to assist with identification of the source of a biological sample , or to confirm matrilineal relatedness . Although the autosomal genome is much larger , mtDNA has an advantage for forensic applications of multiple copy number per cell , allowing better recovery of sequence information from degraded samples . In addition , biological samples such as fingernails , old bones , teeth and hair have mtDNA but little or no autosomal DNA . The relatively low mutation rate of the mitochondrial genome ( mitogenome ) means that there can be large sets of matrilineal-related individuals sharing a common mitogenome . Here we present the mitolina simulation software that we use to describe the distribution of the number of mitogenomes in a population that match a given mitogenome , and investigate its dependence on population size and growth rate , and on a database count of the mitogenome . Further , we report on the distribution of the number of meioses separating pairs of individuals with matching mitogenome . Our results have important implications for assessing the weight of mtDNA profile evidence in forensic science , but mtDNA analysis has many non-human applications , for example in tracking the source of ivory . Our methods and software can also be used for simulations to help validate models of population history in human or non-human populations .
Human mitochondrial DNA ( mtDNA ) has long been a useful tool to identify war casualties and victims of mass disasters , the sources of biological samples derived from crime scenes or to confirm matrilineal relatedness [1–3] . The autosomal genome is much larger and has higher discriminatory power , but the mitochondrial genome ( mitogenome ) has multiple copies per cell , allowing better recovery of sequence information from degraded samples [1 , 3] , including ancient DNA [4 , 5] . Some biological samples such as fingernails , old bones , teeth and hair have mtDNA but little or heavily degraded autosomal DNA . In addition , because of the lack of recombination , mtDNA can be used to confirm relatedness over many more generations than is possible using autosomal DNA , though only in the female line . It has now become widely feasible to sequence all 16 , 568 mitogenome sites as part of a forensic investigation [6–8] . For autosomal short tandem repeat ( STR ) profiles , there are two alleles per locus and because of the effects of recombination , the alleles at distinct loci are treated as independent , after any adjustments for sample size , coancestry and direct relatedness [9] . In contrast , the maternally-inherited mitogenome is non-recombining , behaving like a single locus at which many alleles , or haplotypes , can arise . Due to relatedness and limited population size , the variation in mitogenomes in any extant population is greatly restricted compared with what is potentially available given the genome length . Whereas a match of two mitogenomes without recent shared ancestry is in effect impossible , there can be large sets of individuals sharing the same mitogenome due to matrilineal relatedness that is distant compared with known relatives but much closer than is typical for pairs of individuals in the population . This limited variation has important implications for the use of mtDNA to help identify individuals or establish relatedness . A match between the mtDNA obtained from bones found under a Leicester UK carpark and a living matrilineal relative of the former King of England , Richard III , played an important role in establishing the bones as those of the king . However , in contrast with popular reports of genetic evidence “proving” the identification , the mtDNA evidence was not decisive , contributing a likelihood ratio ( LR ) of 478 towards an overall LR of 6 . 7 million in favour of the identification [10] . Although that mitogenome was at the time unobserved in the available databases , its observation in both the skeleton and a contemporary individual meant that it was expected to exist in hundreds and perhaps thousands of others . The public interest in the story led to multiple matches being subsequently observed in contemporary individuals , raising the question of how many humans alive today share this “royal” mitogenome ? We recently addressed similar questions for paternally-inherited Y chromosome profiles [11] . Forensic Y profiles focus on a few tens of STR loci , but these can have a combined mutation rate as high as 1 per 7 generations [11 , 12] , much higher than the mutation rate for the entire mitogenome , for which estimates range up to around 1 per 70 generations ( see Methods ) . We showed that the high mutation rate of Y profiles has dramatic consequences for evaluating weight of evidence . For example , males with matching Y profiles are related through a lineage of up to a few tens of meioses . Further , the number of males with a matching Y profile varies only weakly with population size , and since the population size relevant to a forensic identification problem is typically unknown , it follows that the concept of a match probability that can be useful for autosomal DNA profiles is of little value for Y profiles . Because of the lower mutation rate for the mitogenome , the situation is less extreme for mtDNA profiles than for Y profiles . Here we describe the distribution of the number of individuals with the same mitogenome as a randomly-chosen individual under three demographic scenarios and two mitogenome mutation models , finding that the number is typically of the order of hundreds rather than the tens that share a Y profile . The number of mitogenome matches is consequently more sensitive to demographic factors than is the case for Y profiles , but it remains a small fraction of the population relevant to a typical crime scenario . As we did previously for Y profiles , we also describe the conditional distributions given database frequencies for the observed mitogenome , assuming that the database is randomly sampled in the population . We show for example that a mitogenome that is unobserved in a large database can nevertheless exist in hundreds of individuals in the population . We also show that individuals sharing a mitogenome are related , typically within up to a few hundred meioses , which is much more distant than recognised relationships but still much closer than the relatedness of random pairs of individuals in a large population . Therefore the matching individuals may not be well-mixed in the population so that database statistics can be an unreliable guide to the number of matching individuals in the population .
See Methods for details of our two mutation models , based on [13] and [14] , and three demographic scenarios which we denote 1 . 2M growth , 1 . 2M constant and 300K constant ( suffix M for 106 , i . e . millions , and suffix K for 103 , i . e . thousands ) . As for Y profiles , it is difficult to rigorously check our simulation models against empirical databases because real-world databases often result from informal sampling schemes that are far from random samples . They are often drawn from a much larger population than is relevant to a specific crime scenario , and sometimes from a number of different administrative regions such as states . However , broad-brush comparisons are useful , because while the databases are not scientific in their design , the resulting deviations from population values may not be very large . For this purpose we identified a US Caucasian database of 263 mitogenomes [15] , which includes 259 distinct haplotypes , a very high level of diversity ( 259/263 = 98% ) that reflects sampling from many US states . Most of our simulated databases of size 263 show less haplotype diversity than this database , but those under the 1 . 2M constant model come close ( Fig 1 and S1 Fig ) . We also considered an Iranian database [16] of size 352 with 315 distinct haplotypes ( 89% diversity ) . This total included several distinct ethnic identities: Persians ( 181 , 91% diversity ) , Qashqais ( 112 , 84% diversity ) and Azeris ( 22 , 100% diversity ) . The simulated databases of size 352 under the 1 . 2M growth and 300K constant models show mtDNA diversity close to that of the Iranian database . Low mitogenome diversity has been reported in three Philippines ethnic groups with 39 , 43 and 27 mitogenomes yielding a diversity of 51% , 58% and 81% [17] , which may reflect low population size and isolation . These lower levels of diversity may be appropriate in some forensic contexts , and can be analysed with our methods using a smaller population size than the examples presented here . For both mutation schemes , Fig 2 ( black curves , which are the same in each row ) shows the cumulative distribution of the number of mitogenomes in the live population matching that of the PoI ( person of interest ) . The distributions ( see Table 1 for quantiles ) are similar for the 1 . 2M and 300K constant models ( middle and right columns ) , with the number of sequence matches with the PoI almost always < 1 , 000 , but for 1 . 2M growth model some PoI have > 5 , 000 matches . These distributions are altered by conditioning on an observation of m matches in a randomly-sampled database of size n ( Fig 2 , coloured curves ) . For the largest database we now see a clear difference between the two constant-size populations . For example m = 10 represents 0 . 1% of the database , consistent with 300 matches in the smaller population , a value that is well supported by the unconditional distribution and so the conditional distribution is centred around 300 . However , 0 . 1% of the larger population is 1 , 200 , which is not supported by the unconditional distribution and so the conditional distribution is shifted towards lower values , with most support between about 600 and 1 , 200 . There is a similar effect for the m = 10 conditional distribution in the 1 . 2M growth population ( note the different x-axis scale ) . Estimated quantiles for the solid curves in the middle column of Fig 2 are given in Table 2 . For the other two demographic scenarios under the Översti mutation scheme [13] , see S1 Table ( 300K constant ) and S2 Table ( 1 . 2M growth ) . Corresponding quantiles for the Rieux mutation scheme [14] are given in S3 Table ( 1 . 2M constant ) , S4 Table ( 300K constant ) and S5 Table ( 1 . 2M growth ) . The number of meioses separating individuals with matching mitogenomes ranges up to a few hundred , and is almost never larger than 500 ( Fig 3 ) . This is close to unrelated for most practical purposes , but random pairs of individuals are very unlikely to be related within 1 , 000 meioses , and so pairs with matching mitogenomes are much more closely related than average pairs of individuals . Key quantiles for the distributions of matching pairs are given in Table 3 . As a guide for comparison , a coalescent theory approximation [18] for the mean numbers of meioses separating a random pair are 100K and 400K for our small and large constant-size populations , respectively .
Empirical mitogenome databases do not in practice represent random samples from a well-defined population , so that detailed comparisons with our simulation models are not meaningful . However , we have verified here that the haplotype diversity generated by our simulation models is broadly comparable with that observed in two real databases from large populations . In our related paper on Y profile matching [11] , we showed that because of the high mutation rates of contemporary Y profiles , the numbers of males with Y profile matching a PoI ( person of interest ) are low , typically up to a few tens , and that this number is little affected by population size or growth . Moreover the clusters of matching males are related within a few tens of meioses and so are unlikely to be randomly distributed in the population relevant to a typical crime scene . We argued that it was therefore not appropriate to report a match probability ( a special case of the likelihood ratio ) to measure the weight of evidence , even though likelihood ratios are central to the evaluation of autosomal DNA profiles . In the present paper we have shown that the situation for mtDNA evidence is intermediate between Y and autosomal profiles . Because the whole-mitogenome mutation rate is an order of magnitude smaller than the mutation rate for contemporary Y profiles , the number of individuals matching a PoI is correspondingly larger , and varies more with demography . The unconditional distribution ( Table 1 ) is very similar for the two constant-size populations that differ in size by a factor of four , but for the growing population the median number of matches is about twice as big . As for the case of Y profiles , our simulation-based approach can easily take into account information from a frequency database , although this requires the assumption that the database is a random sample from the population , which is rarely the case in practice . The mitolina software that we have presented here can be used to inform the evaluation of the weight of mtDNA evidence in forensic applications , similar to our recommended approach to presenting Y-profile evidence: simulation models are used to obtain an estimate of the number of individuals sharing the evidence sample mitogenome , with conditioning on a database frequency if available . Current methods for evaluating mtDNA evidence rely directly on a database count of the observed mitogenome [2 , 3] , and are affected by poor representativeness of the databases , and its limited informativeness when there are many rare mitotypes . Our approach can also make use of a database count of the haplotype , but this information is used to adjust an unconditional distribution and so is less sensitive to the database size and sampling scheme . Limitations of our analysis include the range of demographic scenarios that we can consider , and the difficulty in assessing which demographic scenario is appropriate for any specific crime . Our assumption of neutrality is unlikely to be strictly accurate [19] , nor our assumption of a generation time of 25 years , constant over generations . We used two mutation rate schemes [13 , 14] based on phylogenetic estimates , as no pedigree-based mutation rates were available for the entire mitogenome . Some discrepancy has been noted between the two estimation methods [20] , and the rate may have changed over time [21] . If contemporary pedigree-based mutation rates become available we could improve our mutation model , but that would not address mutation rate changes over time . We have not here addressed the case of mixed mtDNA samples or heteroplasmy ( multiple mitogenomes arising from the same individual ) . While we have focussed our examples on human populations because of the important role of the mitogenome in human identification and relatedness testing , with appropriate modifications of the demographic model , mitolina and the methods described here can be used for non-human applications of mtDNA . Examples include tracking the source of ivory [22] , other areas of wildlife forensics [23] and inferences about the demographic histories of natural populations [24] . Our software may be useful for generating simulation data in approximate Bayesian computation and related methods , and the number of matching sequences may also provide a useful summary statistic for such methods .
We simulated the mitogenome as a binary sequence subject to neutral mutations , using the rates estimated by both Rieux et al . ( 2014 ) [14] and Översti et al . ( 2017 ) [13] , shown in Table 4 . They both partitioned the mitogenome into four regions: hypervariable 1+2 ( HVS1 + HVS2 ) , protein coding codon 1+2 ( PC1 + PC2 ) , protein coding codon 3 ( PC3 ) , and ribosomal-RNA + transfer-RNA ( rRNA + tRNA ) . However , the HVS1 + HVS2 region of [14] consisted of 698 sites whereas that of [13] had 1 , 122 sites , although their total mutation rate estimates for the region are similar . We simulated populations of mitogenomes under three demographic scenarios . Two constant-size Wright-Fisher populations [25] , of 50K and 200K females per generation , were simulated for 1 , 200 generations . The third scenario started with a constant female population size of 10 , 257 for 1 , 000 generations , followed by growth at a rate at 2% per generation over 150 generations to reach a final generation with 200K females . Following [11] , individuals in the final three generations are considered to be “live” , and in those generations males were also simulated making total live population sizes of 300K , 1 . 2M and 1 . 2M . All the females in any generation had the same distribution of offspring number ( no between-female variation in reproductive success ) . We assigned mitogenomes to the founders randomly with replacement from a US Caucasian database of 263 mitogenomes ( 259 distinct haplotypes , see Fig 1 ) [15] , coding each site as 0 if it matched the rCRS reference sequence [8] , and 1 otherwise . Each mother-child transmission was subject to mutation , which changed a 0 to a 1 , and vice versa . The same mutation rate was assigned to each site within each region , sampled from a normal distribution with 95% interval from Table 4 . The mean whole-mitogenome mutation rate per generation was 0 . 0135 for [13] and 0 . 0110 for [14] , or about 1 mutation per 74 generations and 1 per 90 generations , respectively . Therefore , following one line of descent over 1 , 200 generations , the expected numbers of mutations to affect the mitogenome are 16 . 3 using [13] and 13 . 2 using [14] . The probabilities that there is any site affected by two mutations and so reverts to its original state during those 1 , 200 generations are 0 . 024 and 0 . 033 , respectively . We simulated five population under each of the three demographic scenarios . For each population simulation and both mutation models , we conducted five replicates of the sequence evolution process: assigning sequences to the founders and then mutations at each meiosis . Thus , for each mutation model and demographic scenario , 25 live populations of mitogenomes were created . In each live population , a PoI ( person of interest ) was randomly drawn 10 , 000 times , and we recorded how many live individuals had the same mitogenome as the PoI . Thus , a total of 5 × 5 × 10K = 250K PoIs were sampled for each mutation and demography combination . Further , for 10% of the PoI , the number of meioses between the PoI and each matching individual was recorded . Following the methodology of [11] , in addition to the unconditional distribution of the number of mitogenome matches between a PoI and another live individual , we use importance sampling reweighting to approximate the distribution conditional on observing the PoI mitogenome m times in a database of size n , assumed to have been chosen randomly in the population . Software to perform these simulations is implemented in the open-source R packages mitolina [26 , 27] , based on Rcpp [28] , and malan [29] , previously used for Y profile simulations [11] . | The maternally-inherited mitochondrial DNA ( mtDNA ) represents only a small fraction of the human genome , but mtDNA profiles are important in forensic science , for example when a biological evidence sample is degraded or when maternal relatedness is questioned . For forensic mtDNA analysis , it is important to know how many individuals share an mtDNA profile . We present a simulation model of mtDNA profile evolution , implemented in open-source software , and use it to describe the distribution of the number of individuals with matching mitogenomes , and their matrilineal relatedness . The latter is measured as the number of mother-child pairs in the lineage linking two matching individuals . We also describe how these distributions change when conditioning on a count of the profile in a frequency database . | [
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... | 2018 | How many individuals share a mitochondrial genome? |
G protein-coupled receptors ( GPCRs ) constitute the largest family of proteins that transmit signal to regulate an array of fundamental biological processes . Viruses deploy diverse tactics to hijack and harness intracellular signaling events induced by GPCR . Herpesviruses encode multiple GPCR homologues that are implicated in viral pathogenesis . Cellular GPCRs are primarily regulated by their cognate ligands , while herpesviral GPCRs constitutively activate downstream signaling cascades , including the nuclear factor of activated T cells ( NFAT ) pathway . However , the roles of NFAT activation and mechanism thereof in viral GPCR tumorigenesis remain unknown . Here we report that GPCRs of human Kaposi’s sarcoma-associated herpesvirus ( kGPCR ) and cytomegalovirus ( US28 ) shortcut NFAT activation by inhibiting the sarcoplasmic reticulum calcium ATPase ( SERCA ) , which is necessary for viral GPCR tumorigenesis . Biochemical approaches , entailing pharmacological inhibitors and protein purification , demonstrate that viral GPCRs target SERCA2 to increase cytosolic calcium concentration . As such , NFAT activation induced by vGPCRs was exceedingly sensitive to cyclosporine A that targets calcineurin , but resistant to inhibition upstream of ER calcium release . Gene expression profiling identified a signature of NFAT activation in endothelial cells expressing viral GPCRs . The expression of NFAT-dependent genes was up-regulated in tumors derived from tva-kGPCR mouse and human KS . Employing recombinant kGPCR-deficient KSHV , we showed that kGPCR was critical for NFAT-dependent gene expression in KSHV lytic replication . Finally , cyclosporine A treatment diminished NFAT-dependent gene expression and tumor formation induced by viral GPCRs . These findings reveal essential roles of NFAT activation in viral GPCR tumorigenesis and a mechanism of “constitutive” NFAT activation by viral GPCRs .
Herpesviruses are ubiquitous pathogens and their infections contribute to a number of malignancies in humans [1] . The lymphotropic gamma herpesviruses , including Kaposi’s sarcoma-associated herpesvirus ( KSHV , also known as HHV-8 ) and Epstein-Barr virus ( EBV or HHV-4 ) , are large DNA tumorigenic viruses [2] . Remarkably , these viruses have pirated a number of cellular genes to assist the completion of crucial steps of infection cycle consisting of lytic replication and latent infection . Under immuno-compromised conditions , uncontrolled replication of these viral pathogens results in aberrant cell proliferation that is associated with and underpinned by inflammation [3 , 4] . Discovered by Yuan Chang , Patrick Moore and their coworkers in 1994 , KSHV is the etiological agent of Kaposi’s sarcoma ( KS ) , primary effusion lymphoma ( PEL ) and multicentric Castleman’s disease ( MCD ) [5 , 6] . It is believed that KS is of endothelial origin , whereas PEL and MCD are malignancies of lymphoid cells . Among genes pirated by human herpesviruses , G protein-coupled receptor ( GPCR ) is a common target and implicated in viral pathogenesis [7] . All gamma herpesviruses express one GPCR homologue , while genomes of beta-herpesviruses contain up to four copies of GPCR [8 , 9] . Herpesviral GPCRs activate multiple cellular signaling cascades that collectively contribute to viral infection and pathogenesis[10] . The GPCR homologue of KSHV ( designated kGPCR ) is capable of activating diverse signaling pathways [11 , 12] . Prominent examples are the PI3K-AKT axis for cell proliferation [13 , 14] and pertinent signal pathways leading to the activation of key transcription factors , including NF-κB , NFAT and AP-1 [15 , 16] . When expressed in transgenic mouse , kGPCR is sufficient to induce KS-like tumors , implying its contribution to the development of human KS [17] . Importantly , kGPCR activates downstream signaling events independent of association with its cognate ligands , which is known as constitutive activity [12] . Previous structural studies pointed to the conformation adopted by the transmembrane helices that enable the constitutive signaling capacity of kGPCR [18] . However , the molecular detail of viral GPCRs in activating specific signaling cascade remains unclear , one of which is the NFAT signaling cascade . The NFAT family consists of five closely-related members , known as NFAT1-NFAT5 . In contrast to NFAT5 that is regulated by osmotic stress [19 , 20] , the other four NFAT proteins are activated by elevated cytosolic calcium concentration [21 , 22] . Structurally , NFAT proteins contain an amino-terminal transactivation domain , a regulatory domain , a DNA-binding domain and a carboxyl-terminal domain [22] . The DNA-binding domain belongs to the large family of Rel-homology domain ( RHD ) that was originally characterized in NF-κB members . The regulatory domain consists of multiple serine-rich sequences that are phosphorylated by several kinases , including casein kinase 1 ( CK1 ) , glycogen synthase kinase 3 ( GSK3 ) and dual-specificity tyrosine-phosphorylation-regulated kinase ( DYRK ) in resting cells [23–26] . When cells are activated by surface receptors that are coupled to calcium influxes , cytosolic calcium increase enables the activation of calmodulin and diverse calmodulin-dependent enzymes . Calcineurin , a phosphatase of the calmodulin-dependent enzymes , binds to its docking site within the amino-terminal region of NFAT and dephosphorylates serine/threonine residues of the NFAT regulatory domain , resulting in the nuclear translocation and activation of NFAT [21 , 27] . Nuclear dephosphorylated NFAT up-regulated the expression of diverse genes , including COX-2 , RCAN1 , IL-8 and ANGPT2 [28–34] . The sarco/endoplasmic reticulum calcium ATPase ( SERCA ) pumps calcium back to the SR/ER compartment , thereby restoring calcium gradient and cellular resting state [35] . Although viral GPCRs , e . g . , KSHV kGPCR and human cytomegalovirus ( HCMV ) US28 , are known to potently activate NFAT [15 , 16 , 36] , the mechanism of NFAT activation and the contribution thereof to the tumorigenesis of these viral GPCRs remain unclear . Moreover , it is not well understood how NFAT activation impacts KSHV infection and pathogenesis in particular and herpesvirus in general . We report here that viral GPCRs target the SERCA ATPase to elevate cytosolic calcium and promote NFAT activation . kGPCR expression in endothelial cells installed a gene expression profile of NFAT signature and NFAT-dependent genes were up-regulated in kGPCR-induced mouse lesions and human KS tumors . Uncoupling NFAT activation from kGPCR diminished tumor formation in a xenograft mouse model , indicating the critical roles of NFAT in kGPCR tumorigenesis . Similarly , HCMV US28 interacted with SERCA2 to activate NFAT and NFAT activation is necessary for US28-induced tumor formation . These results unveil a molecular mechanism by which viral GPCRs activate signaling events independent of ligand binding , underpinning the constitutive activity of viral GPCRs in signaling and tumorigenesis .
We and others have shown that herpesviral GPCRs induce NFAT activation [16 , 37 , 38] , despite the roles and mechanism of NFAT activation by these viral GPCRs are not well understood . Using a luciferase reporter , we found that kGPCR expression potently activated NFAT signaling cascades in a dose-dependent manner ( Fig . 1A ) . Moreover , kGPCR expression induced robust nuclear translocation and dephosphorylation of NFAT ( S1A–S1C Fig ) , comparable to ionomycin treatment , while demonstrated no effect on the expression of the catalytic subunit of calcineurin ( CnA ) ( S1D Fig ) . To examine signaling events downstream of kGPCR in endothelial cell , we performed a genome-wide microarray analysis with human umbilical vein endothelial cells ( HUVEC ) expressing kGPCR and searched for NFAT-related genes . This analysis uncovered a list of top candidate genes that centered on NFAT signal transduction ( Fig . 1B ) . With a top-down view , we classified these factors according to their link to GPCR , intracellular calcium , NFAT or genes of NFAT-dependent expression ( Fig . 1B ) . Specifically , these included GPCR ligands [CxCL12 , CCL2 , IL-8 and KIT ligand ( KITLG ) ] [39–42] , calcium-dependent effectors [phospholipase A2 ( PLA2G4A ) and S100 calcium-binding protein] [43 , 44] , NFAT co-activators ( EGR ) [45] , and finally a number of proteins whose expression is up-regulated by NFAT activation [COX-2 ( also known as PTGS2 ) , RCAN1 , CCL2 , IL-8 and angiopoietin 2 ( ANGPT2 ) ] [33 , 46] . Among them , microarray analysis indicated that the expression of COX-2 and RCAN1 were up-regulated by ~24 and 13-fold , respectively ( Fig . 1C ) . These proteins constitute a signaling network that is meshed by key components of the NFAT pathway . We then selected a few NFAT-dependent genes for quantitative real-time PCR ( qRT-PCR ) analysis . This analysis showed that , in comparison to control HUVEC , the expression of COX-2 and RCAN1 were up-regulated by ~18 and ~30-fold in kGPCR-expressing HUVEC , respectively ( Fig . 1D ) . The expression of IL-8 , ANGPT2 and ICAM1 was also increased by kGPCR ( Fig . 1D ) . Notably , we purposefully selected the NF-κB-dependent ICAM1 transcript for specificity comparison . To further validate the NFAT-dependent expression of these genes , we treated HUVEC/kGPCR cells with cyclosporine A ( CsA ) , a specific pharmacological inhibitor of calcineurin , and determined their expression by qRT-PCR . We found that CsA reduced COX-2 expression by ~50% ( Fig . 1D ) , whereas completely abolished the expression of RCAN1 , IL-8 and ANGPT2 ( Fig . 1D ) . The expression of ICAM1 induced by kGPCR was not affected by CsA treatment , consistent with its NF-κB-dependent expression . Immunoblot analysis further confirmed the elevated protein levels of COX-2 and RCAN1 that were reduced or abolished by CsA treatment , respectively ( Fig . 1E ) . One key step of NFAT activation is the elevation of intracellular calcium concentration . Thus , we assessed cytosolic calcium with a fluorescent probe in kGPCR-expressing HUVEC cells . This analysis showed that kGPCR expression increased cytosolic calcium by more than four-fold , compared to control HUVECs ( Fig . 1F ) . Together , these results show that kGPCR activates NFAT to influence host gene expression . To dissect the regulation of NFAT activation by kGPCR , we used pharmacological inhibitors that target key components of the GPCR-NFAT pathway ( Fig . 2A ) . These include inhibitors of phospolipase C [edelfosine] and IP3 receptor [2-aminoethoxydiphenyl borate ( 2-APB ) ] , chelators to deplete extracellular ( EGTA ) or intracellular ( BAPTA-AM ) calcium , and CsA to block calcineurin . Reporter assays indicate that kGPCR-induced NFAT activation was not impacted by edelfosine and 2-APB , inhibitors of PLC and IP3R , respectively ( Fig . 2B and C ) . By contrast , calcium chelators ( EGTA and BAPTA-AM ) and CsA significantly inhibited NFAT activation induced by kGPCR ( Fig . 2D-F ) . Under the same conditions , edelfosine and 2-APB impaired NFAT activation induced by K15 that did so in a PLC-dependent manner [47] ( Fig . 2B and C ) . Importantly , the treatment with these pharmacological inhibitors did not impact cell viability ( S2A–S2E Fig ) . These results , together with the increased cytosolic calcium by kGPCR , suggest that kGPCR increases intracellular calcium independent of PLC , pointing to the step of calcium release . To identify cellular target ( s ) that interacts with kGPCR , we performed one-step affinity purification and analyzed kGPCR-binding proteins by mass spectrometry . This approach identified SERCA2 as a major kGPCR-interacting partner ( Fig . 3A ) . Mammalian cells express three isoforms of SERCA , among which SERCA2 is ubiquitously expressed in many tissues . Indeed , SERCA2b and kGPCR were precipitated together from extract of transfected 293T cells ( Fig . 3B ) . Considering that kGPCR and SERCA2 are multi-transmembrane proteins and are not amenable for deletion or truncation analysis , we employed proximity ligation assay ( PLA ) to assess protein interaction in situ . PLA is devised to detect and localize interacting proteins with single molecule resolution and to be objectively quantified in unmodified cells or tissues [48] . In principle , each single spot of PLA is amplified from one pair of interacting molecules , providing quantitative measurement of physiological protein-protein interactions . As shown in Fig . 3C , fluorescence was barely detected in control HUVEC cells . However , bright fluorescent spots were readily detected in HUVEC cells stably expressing kGPCR . Fluorescent spots were scattered surrounding the nucleus , reminiscent of the intracellular distribution of ER/TGN organelles . Counting more than 100 cells of each group , we found that HUVEC/kGPCR cells yielded >10-fold fluorescent spots than control HUVEC cells ( Fig . 3D ) . These results indicate that kGPCR interacts with SERCA2 . SERCA2 transports calcium from the cytosol into the ER lumen , restoring a calcium gradient between the cytosol and the ER compartment . This active transfer of calcium against gradient by SERCA is powered by and coupled to ATP hydrolysis . To examine the effect of kGPCR on SERCA2 , we precipitated SERCA2 from transfected cells and determined the ATPase activity of SERCA2 without or with kGPCR . This assay showed that kGPCR expression reduced the ATP hydrolysis of SERCA2 by more than 60% , indicating that kGPCR inhibits the ATPase activity of SERCA2 ( Fig . 3E ) . Given the opposing activity of kGPCR and SERCA in regulating cytosolic calcium concentration , we then assessed the effect of SERCA2 over-expression on kGPCR-induced NFAT activation . As shown in Fig . 3F , we found that SERCA2 expression reduced kGPCR-induced NFAT activation in a dose-dependent manner , indicating that these two molecules antagonize each other in regulating NFAT activation . Thapsigargin is a pharmacological agent that specifically inhibits SERCA and elevates cytosolic calcium , thereby promoting NFAT activation . We exploited thapsigargin to probe the interaction between kGPCR and SERCA2 , given that both kGPCR and thapsigargin inhibit SERCA2 to increase cytosolic calcium . We first examined the effect of thapsigargin on kGPCR-induced NFAT activation . Reporter assays showed that treatment with thapsigargin had no enhancing effect on NFAT activation induced by kGPCR ( S3A Fig ) . Similar result was observed for ionomycin , an ionophore that raises intracellular calcium . These results suggest that kGPCR functions redundantly with thapsigargin and ionomycin in elevating cytosolic calcium and activating NFAT . We next examined kGPCR interaction with SERCA2 with and without thapsigargin . Remarkably , thapsigargin completely abolished kGPCR interaction with SERCA2 ( S3B Fig ) , suggesting that kGPCR and thapsigargin disrupt SERCA2 activity in a similar manner . These findings collectively support the conclusion that kGPCR inhibits SERCA to increase cytosolic calcium and enable NFAT activation . All gamma herpesviruses encode at least one GPCR , while beta herpesviruses express up to four GPCR homologues . We then examined NFAT activation by GPCR homologues of human EBV ( BILF1 ) and CMV ( US28 ) . Reporter assay indicated that , similar to kGPCR , US28 potently activated NFAT in a dose-dependent manner , whereas BILF1 failed to do so ( S4A Fig ) , despite that all three viral GPCRs were expressed at similar levels . To probe the mechanism of US28-mediated NFAT activation , we assessed the interaction between viral GPCRs and SERCA2 . When US28 was precipitated from transfected 293T cells , SERCA2 was readily detected , indicating that US28 associates with SERCA2 ( S4B Fig ) . However , BILF1 did not interact with SERCA2 by co-IP assay , agreeing with the observation that EBV BILF1 failed to activate NFAT . These results suggest that the interaction with SERCA2 is crucial for HCMV US28 to activate NFAT . Next , we measured the relative intracellular calcium concentration in control and US28-expressing HUVEC cells . Fluorescent microscopy and semi-quantitative analysis showed that US28 expression increased intracellular calcium concentration by ~3-fold in HUVEC cells ( Fig . 4A ) . Furthermore , qRT-PCR analysis demonstrated that US28 up-regulated the expression of RCAN1 , COX-2 , IL-8 and ANGPT2 , among which RCAN1 and COX-2 were also confirmed by immunoblotting analysis ( Fig . 4B and C ) . Treatment with CsA reduced the expression of all genes to various extents by qRT-PCR analysis , with completely diminished expression of RCAN1 ( Fig . 4B and C ) . Similar to what was observed for kGPCR , CsA treatment abolished US28-induced RCAN1 protein expression , while partly reduced that of COX-2 , as analyzed by immunoblotting ( Fig . 4D ) . Taken together , these findings indicate that HCMV US28 , like kGPCR , targets SERCA2 to activate NFAT . Despite being a homologue of KSHV kGPCR , EBV BILF1 failed to interact with SERCA2 and activate NFAT . Conventional mutagenesis entailing deletion and truncation is not applicable to the multi-transmembrane GPCR protein . Thus , we explored the chimera strategy to identify sequences that enable vGPCR’s ability to bind SERCA2 and activate NFAT . Previous reports have shown that the cytoplasmic tail is necessary for kGPCR to activate downstream signaling . However , replacing the BILF1 cytoplasmic tail with its counterpart of kGPCR did not confer BILF1 to activate NFAT . Remarkably , when all cytoplasmic loops and tail of BILF1 were replaced with kGPCR equivalents , the BILF1 chimera ( designated BILF1c ) activated NFAT in a dose-dependent manner ( S4C Fig ) . NFAT activation by BILF1c was not as robust as kGPCR , suggesting that other elements of kGPCR ( e . g . , transmembrane helices , extracellular N-terminus and loops ) also contribute to NFAT activation by kGPCR . In support of the ability of BILF1c to activate NFAT , co-IP assay showed that BILF1c interacted with SERCA2 in transfected 293T cells ( S5A Fig ) . Moreover , NFAT activation by BILF1c was inhibited by calcium chelators ( EGTA and BAPTA-AM ) and CsA ( S5B-D Fig ) , supporting the conclusion that BILF1c increases cytosolic calcium to activate NFAT . Collectively , these results show that the cytoplasmic loops and tail of kGPCR endow EBV BILF1 to target SERCA2 via physical interaction and that targeting SERCA2 by vGPCRs is sufficient to enable NFAT activation . kGPCR is expressed predominantly in the lytic phase . We reasoned that KSHV lytic replication up-regulates NFAT-dependent genes . In iSLK . 219 cells that KSHV lytic cycle was induced with doxycycline , mRNAs of COX-2 and RCAN1 were increased to ~3- and 4 . 5-fold at 48 hours post-induction ( Fig . 5A ) . The recently reported KSHV BAC16 system provided a tool to efficiently induce KSHV lytic replication [49] . To examine the roles of kGPCR in NFAT activation during KSHV lytic replication , we have employed the BAC16 genetic system and engineered a kGPCR-deficient recombinant KSHV ( S6A Fig ) . When iSLK cells harboring wild-type BAC16 were induced by RTA expression and sodium butyrate , a histone deacetylase inhibitor , we found that COX-2 and RCAN1 proteins greatly increased at 48 hours post-induction ( Fig . 5B ) . Importantly , treatment with the NFAT-specific CsA significantly reduced COX-2 induction , while completely abolished RCAN1 protein induction . In iSLK cells harboring kGPCR-deficient BAC16 , KSHV lytic replication marginally increased the protein of COX-2 and RCAN1 . Moreover , CsA treatment minimally reduced COX-2 and RCAN1 protein , agreeing with the minimal NFAT activation , if any , in the absence of kGPCR . Taken together , kGPCR expression and downstream NFAT activation are responsible for the induced expression of COX-2 and RCAN1 in KSHV lytic replicating cells . To corroborate the NFAT-dependent gene expression induced by kGPCR in KSHV lytic replication , we constructed recombinant KSHV that kGPCR deletion was restored with wild-type kGPCR or a kGPCR chimera in which the cytoplasmic tail was replaced with the EBV BILF1 counterpart ( S6A-C Fig ) . Consistent with that the cytoplasmic tail of kGPCR is critical for NFAT activation , the kGPCR chimera failed to activate NFAT by reporter assay ( Fig . 5C ) . Moreover , co-immuno-precipitation assay indicated that the kGPCR chimera did not interact with SERCA2 , but wild-type kGPCR did ( Fig . 5D ) . Recombinant KSHV BAC16 DNA carrying wild-type , kGPCR revertant and kGPCR chimera were obtained via homologous recombination . Gel electrophoresis confirmed that deletion of kGPCR gene reduced the size of the targeted fragment by ~1 kb analyzed by digestion with both KpnI and SbfI , and the size of the targeted fragment was restored in BAC16 revertant of kGPCR or kGPCRc ( S6A-C Fig ) . The kGPCR loci of BAC16 wild-type , kGPCR deletion , revertant with wild-type kGPCR or kGPCR chimera were further validated by PCR amplification and subsequent sequencing of the PCR products ( S6C Fig ) . We then transfected KSHV BAC16 DNA and its derivatives into iSLK cells and cells stably carrying KSHV were selected with hygromycin . KSHV lytic replication was reactivated with sodium butyrate and doxycycline to induce RTA expression . In cells that KSHV lytic replication was induced , we also inhibited NFAT activation with cyclosporine A . Using BAC16 wild-type as the positive reference and BAC16ΔkGPCR as the negative reference , we found that the kGPCR revertant demonstrated nearly identical expression of COX-2 and RCAN1 of wild-type BAC16 that were induced by KSHV lytic replication and inhibited by cyclosporine A . By contrast , BAC16 revertant with kGPCR chimera essentially replicated the phenotype in COX-2 and RCAN1 expression of BAC16ΔkGPCR ( Fig . 5E ) . When KSHV early lytic gene products , including RTA and thymidine kinase ( TK or ORF21 ) , were examined , no significant difference of these viral proteins were detected among these recombinant KSHV carrying various kGPCR mutants . Consistent with kGPCR-dependent NFAT activation , we also observed NFAT1 dephosphorylation in KSHV replicating iSLK cells in a kGPCR-dependent manner ( S6D Fig ) . Thus , kGPCR expression and consequent NFAT activation during KSHV lytic replication are important for the expression of COX-2 and RCAN1 , two NFAT-dependent cellular genes . Although the major constituent of KS tumors is the KSHV latently-infected spindle cell , lytic replicating cells were invariably observed in KS tumors . The TIE2-tva mouse model that kGPCR expression in endothelial cells is sufficient to induce KS-like tumors provides a useful tool to recapitulate the genesis of Kaposi’s sarcoma [17] . We examined NFAT-dependent gene expression in tumor tissues derived from the TIE2-tva mice infected with retrovirus carrying kGPCR . qRT-PCR analysis revealed that mRNAs of COX-2 and RCAN1 were up-regulated by ~4- and 3-fold in kGPCR-tumors compared to control tissues , respectively ( Fig . 6A ) . Immunohistochemistry staining further showed apparent induction of COX-2 and RCAN1 in kGPCR-tumor ( Fig . 6B ) . Based on the expression level of COX-2 , two types of cells were identified in kGPCR-tumors . A small subset of cells of high COX-2 expression had round shape and small cytoplasm , which likely represented infiltrated immune cells . The majority of tumor tissue consists of spindle-shaped or other endothelial cells that had lower COX-2 expression , displaying light brown color in the cytoplasm . By contrast , a uniformed RCAN1 staining pattern was observed in most tumor cells , specifically the spindle-shaped endothelial cells . This result indicates the differential expression of NFAT-dependent genes in cell type-specific manner , which signifies distinct roles of NFAT in corresponding tumor constituents . We further examined the expression of these NFAT-dependent genes in human KS tissue . The expression of COX-2 and RCAN1 was apparent in regions that were positive for LANA , the nuclear antigen and marker for KSHV infected cells ( Fig . 6C and S7A-C ) . Specifically , COX-2 was high in cells with relatively small cytoplasm , implying their immune cell identity . In the spindle-shaped cells , heterogeneous COX-2 expression was observed , with majority of these tumor cells demonstrating cytoplasm staining of COX-2 . Interestingly , cells with high RCAN1 protein , residing in region proximal to the slit-like structure , were reminiscent of immune cells , while spindle-shaped tumor cells were stained relatively low for RCAN1 . These results show that COX-2 and RCAN1 proteins are highly expressed in KS-like mouse lesion and human KS tumors . Given that kGPCR is expressed within approximately 5% of cells of tumors derived from the TIE2-tva mouse , we reasoned that kGPCR can induce NFAT via paracrine stimulation . In fact , a number of factors that were induced by kGPCR , including IL-8 , CCL-2 and KITLG , instigate NFAT activation when bound to their cognate receptors on the cell surface , constituting a feed-forward loop that fuels signaling amplification . To test this hypothesis , we collected conditioned medium from control or kGPCR-expressing HUVECs to stimulate fresh HUVECs . We found that conditioned medium from kGPCR-expressing HUVEC modestly up-regulated the expression of COX-2 and RCAN1 to ~3-fold , compared to conditioned medium from control HUVECs ( Fig . 6D ) . However , when anti-IL-8 antibody was added into medium to neutralize IL-8 , we observed minimal impact on the mRNA levels of COX-2 and RCAN1 ( S7D Fig ) . This is likely due to other secreted factors that activate NFAT , such as CCL2 and KITLG . Alternatively , exosome-mediated delivery of these factors or kGPCR may be resistant to neutralizing antibodies . Nevertheless , these results support the conclusion that kGPCR also activates NFAT via a paracrine mechanism , in addition to an autocrine mechanism . NFAT is crucial for GPCR-mediated gene expression and many of gene products downstream of NFAT are key players in tumorigenesis . Thus , we examined tumor formation in the xenograft mouse model using murine SVEC endothelial cells expressing kGPCR and US28 , under the condition that NFAT activation was inhibited with CsA . As expected , SVEC expressing kGPCR and US28 were sufficient to induce tumor formation in nude mice . Tumor volumes were detectable at two weeks post-inoculation and reached ~500–600 mm3 at 24 days post-inoculation ( Fig . 7A and B ) . Under the conditions that nude mice were treated with CsA , tumor volume was reduced by ~60 and ~50% for kGPCR and US28 groups , respectively ( Fig . 7A and B ) . When mice were euthanized and tumor weight was determined , we found that CsA treatment reduced tumor weight by ~50% for both groups derived from kGPCR- and US28-expressing cells ( Fig . 7C and D ) . We then quantified the expression of IL-8 and RCAN1 , two NFAT-dependent genes , by qRT-PCR using tumor tissues . We found that kGPCR and US28 had similar levels of induction on IL-8 and RCAN1 , with IL-8 being more robustly induced than RCAN1 ( Fig . 7E ) . Strikingly , CsA treatment abolished the induction of IL-8 and RCAN1 gene expression by both viral GPCRs . These results support the conclusion that NFAT activation is critical for tumor formation induced by KSHV kGPCR and HCMV US28 .
GPCRs constitute the largest family of signaling molecules that regulates nearly every fundamental biological process . Upon ligand binding , cellular GPCRs are coupled to a panel of heterotrimeric G proteins that are composed of a α subunit and a βγ dimer . These small G proteins are activated via guanidine nucleotide exchanging catalyzed by agonist-stimulated GPCRs and relayed to diverse effectors that influence cellular metabolism and proliferation chiefly through regulated gene expression [50] . Some herpesviral GPCRs , e . g . , KSHV kGPCR and HCMV US28 , demonstrate ligand-independent constitutive activity to instigate signaling cascades that culminate in regulated gene expression [12 , 51] . We report here that KSHV kGPCR and HCMV US28 bypass the upstream components of NFAT pathway by interacting with and inhibiting the SERCA calcium ATPase , a key negative regulator of NFAT activation . As such , kGPCR and US28 elevated cytosolic calcium and activated signaling events downstream of SERCA . Furthermore , kGPCR expression installed a gene expression profile signature of NFAT activation . Key effectors downstream of NFAT transcription factors were confirmed in human KS tumors and KS-like lesions derived from the tva mouse model . Importantly , pharmacological inhibitors targeting components upstream of ER calcium release had no detectable effect on NFAT activation induced by kGPCR and US28 , whereas calcium chelators and the calcineurin inhibitor CsA effectively diminished kGPCR- and US28-induced NFAT activation . Notably , these results do not exclude the possibility that viral GPCRs activate small G proteins and downstream signaling thereof , either with or without cognate agonists . Previous studies have identified structural elements that enable these viral GPCRs to efficiently couple with small G proteins , contributing to the constitutive signaling capacity [18 , 52] . Although EBV BILF1 is closely-related to kGPCR , BILF1 failed to interact with SERCA2 and activate NFAT . Replacing the cytoplasmic loops and tail of BILF1 with counterparts of kGPCR enabled BILF1 to interact with SERCA2 and activate NFAT . Conversely , replacing the cytoplasmic tail of kGPCR with that of EBV BILF1 resulted in a kGPCR chimera that failed to interact with SERCA2 and activate NFAT . These studies , entailing gain- and loss-of-function experiments , highlight the pivotal role of interaction with and likely inhibition of SERCA2 in NFAT activation and identifies additional structural elements underpinning the constitutive activation of viral GPCRs . Taken together , our study unravels a distinct action of viral GPCRs in activating NFAT independent of agonist association . NFAT activation is central for the gene expression of a large spectrum of effectors participating in key physiological events such as immune response , development and homeostasis . Dys-regulation of GPCR-NFAT signaling circuitry underpins diverse human diseased conditions ranging from mild inflammatory responses to life-threatening malignancies including cancer [22 , 53] . Despite that the roles of NFAT signaling cascades in cell differentiation and development are well established , the contribution of NFAT activation in the development and metastasis of various types of cancers is gradually emerging . Initial studies using clinical samples demonstrated that elevated NFAT activation was detected in tumor biopsies from patients with invasive breast carcinoma . Moreover , expression of constitutively active NFAT1 in breast cancer cells promoted migration and invasion [54 , 55] . Paradoxically , NFAT proteins were shown to serve as tumor suppressors under certain physiological conditions . For example , NFAT4-deficient mice were reported to be more susceptible to develop T cell lymphomas induced by murine leukemia virus SL3-3 than wild-type mice [56] . This is supported by the observation that NFAT inhibits the expression of cyclin-dependent kinase 4 and cyclin A2 [57 , 58] . NFAT activation was shown to up-regulate the expression of many effectors that have proliferative and transforming activity . Among NFAT downstream effectors that are involved in the development and maintenance of tumor microenvironment , ANGPT2 has been shown to promote tumor growth and angiogenesis [28 , 29 , 59] . Disrupting the interaction between ANGPT2 and its TIE2 receptor suppressed tumor growth and angiogenesis [60] . In support of the findings that intimately link IL-8 and COX-2 to tumor angiogenesis [61–63] , KSHV infection induces COX-2 expression that enables viral latent infection and angiogenesis thereof [64] . KSHV K15 and vFLIP , indeed , have been implicated in promoting COX-2 expression [65–67] . vFLIP has also been shown to induce cytokine production including IL-8 [68] . Furthermore , COX-2 is crucial for inflammatory cytokine production , angiogenesis and invasion of KSHV-infected cells [67] . Inhibition of COX-2 blocks HCMV replication [69] and leads to a significant reduction of tumor formation induce by HCMV US28 [70] . Similarly , inhibiting COX-2 also impaired cell survival of the KSHV latently-infected PEL cells [71] . Combining inhibitors of COX-2 and NFAT may synergistically ameliorate malignant conditions associated with HCMV and KSHV infection , given that NFAT inhibition can only partially reduce COX-2 expression . RCAN1 , a negative feedback regulator of NFAT activation , was implicated in rendering resistance to the development of various cancers in Down’s syndrome patients . Transgenic mice mimicking RCAN1 trisomy showed significant suppression of tumour growth [72] . Surprisingly , knockout of RCAN1 in mouse also inhibited tumor growth due to hyperactivated calcineurin and apoptosis of endothelial cells [73] . These findings suggest that , perhaps , a balanced NFAT activation is critical for tumorigenesis . We demonstrate here that viral GPCRs activate NFAT to install a unique gene expression profile consisting of effectors ( e . g . , COX-2 , RCAN1 , IL-8 and ANGPT2 ) that constitute the autocrine and paracrine circuitries in amplifying the igniting stimulation ( Fig . 7F ) . We noted that the induction of NFAT-dependent transcripts ( e . g . , COX-2 and RCAN1 ) varies in HUVEC cells expressing kGPCR and SLK cells infected with replicating KSHV . This likely reflects different viral factors and cellular proteins that impinge on NFAT activation , in addition to kGPCR . Nevertheless , the autocrine and paracrine mechanism of NFAT activation may be applicable to the tumor microenvironment wherein diverse inflammatory effectors and growth-promoting factors signal through GPCRs and NFAT activation . The central role of NFAT activation in these signaling cascades implies that inhibiting NFAT activation will thwart tumor formation induced by viral GPCRs and other oncogenic proteins . The findings that CsA inhibited kGPCR- and US28-mediated tumorigenesis provide a proof-of-principle to target NFAT for antitumor therapy . In an immune competent host , however , approaches that selectively attack tumor tissues while sparing functional immune system are prerequisite to enable the application of an anti-tumor strategy targeting NFAT activation .
If not specified , pcDNA5/FRT/TO ( Invitrogen ) and pCDH-CMV-EF-Puro ( System Bioscience ) were used for transient and stable expression of corresponding genes . For protein expression , the HA epitope was inserted upstream or downstream of protein coding sequence . pSF91-K15-IRESGFP was a gift from Dr . Thomas Schulz ( Medizinische Hochschule Hannover ) . pcDEF-HA-US28 was kindly provide by Dr . Liliana Soroceanu ( California Pacific Medical Center Research Institute ) . MSCV-BILF1 was purchased from addgene . pcDNA3 . 1-SERCA2b and pNFAT1 ( 1–460 ) -EGFP were kindly provided by Drs . Jonathan Lytton ( University of Calgary ) and Yousang Gwack ( UCLA ) . pcDNA5/FRT/TO-HA-BILF1c was constructed by replacing the three intracellular loops and C-terminal tail with the counterpart of KSHV GPCR . pcDNA/FRT/TO-HA-kGPCRc was constructed by replacing the C-terminal tail of kGPCR with the counterpart of EBV BILF1 . HEK293T and immortalized murine endothelial cells ( SVECs ) were maintaind in Dulbecco's modified Eagle's medium ( DMEM ) containing 10% fetal bovine serum supplemented with 100 U penicillin/streptomycin . iSLK . 219 cells were maintained with G418 ( 250 μg/ml ) , hygromycin ( 400 μg/ml ) and puromycin ( 10 μg/ml ) . iSLK cells were maintained with G418 ( 250 μg/ml ) and puromycin ( 1 μg/ml ) . BAC16 and all the mutants were introduced into iSLK cells by using Fugene HD ( Promega ) transfection and the stable cell lines were maintained with puromycin ( 1 μg/ml ) , G418 ( 250 μg/ml ) and hygromycin B ( 1 , 200 μg/ml ) . Human umbilical vein endothelial cells ( HUVEC ) were purchase from Lifeline Cell Technology and maintained in human endothelial culture medium according to the instructions . To establish stable cell lines , SVECs or HUVECs were infected with lentivirus containing indicated genes and selected with puromycin ( 1 μg/ml ) as described previously [38 , 74] . Chemicals used in the study include ionomycin ( Sigma ) , cyclosporin A ( Cell Signaling ) , edelfosine ( Sigma ) , 2-Aminoethyl diphenylborinate ( Sigma ) , BAPTA-AM ( Abcam ) , thapsigargin ( Sigma ) Commercial antibodies used in this study include mouse anti-HA monoclonal antibody and agarose ( Sigma ) , mouse anti- β-Actin monoclonal antibody ( Abcam ) , rabbit anti-COX-2 polyclonal antibody ( Abcam ) , rabbit anti-RCAN1 polyclonal antibody ( Sigma ) , mouse anti-SERCA2 ( IID8 ) monoclonal antibody ( Santa cruz ) , mouse anti-calcineurin Aα ( Santa cruz ) . Thymidine kinase ( TK ) anti-serum was generated by immunizing rabbit with GST fusion protein containing N-terminal ( aa1-330 ) of TK . RTA antibody were kindly provided by Dr . Yoshihiro Izumiya ( UC-Davis ) . Immunoprecipitation and immunoblotting were carried out as described previously [37] . Briefly , cells were harvested and lysed with NP40 buffer ( 50 mM Tris-HCl [pH 7 . 4] , 150 mM NaCl , 1% NP-40 , 5 mM EDTA ) supplemented with a protease inhibitor cocktail ( Roche ) . Centrifuged cell lysates were pre-cleared with Sepharose 4B beads and incubated with HA-agarose at 4°C for 4 h . The agarose beads were washed three times with lysis buffer and precipitated proteins were released by boiling with 1×SDS sample buffer at 95°C for 5 min . Immunoblotting analysis was performed with the indicated primary antibodies and proteins were visualized with IRDye800 conjugated secondary antibodies ( Licor ) using an Odyssey infrared imaging system ( Licor ) . HEK293T cells in 24-well plates were transiently transfected with a reporter cocktail as previously described [74 , 75] . The reporter cocktail contained 50 ng of the plasmid expressing firefly luciferase under the control of response elements of NFAT and 100 ng of the plasmid expressing β-galactosidase . The reporter cocktail contained 100 ng , 200 ng and 500 ng of plasmid when increasing dose was indicated and all transfections were balanced with empty vector . Cells were harvested at 24 h post-transfection , lysed and centrifuged supernatant was used to measure luciferase and β-galactosidase activity according to the manufacturer’s instruction ( Promega ) . For inhibitors treatment , cells were treated with the inhibitors and cell toxicity of the inhibitors was evaluated by trypan blue staining ( Amresco ) . HEK293T cells were transfected with plasmids containing EGFP-NFAT1 ( N ) and kGPCR . At 24 h post-transfection , cells were treated with 1 μM of ionomycin for 1 h , fixed with 4% paraformaldehyde and permeabilized with 1% Triton X-100 . After staining with primary antibody ( anti-HA antibody ) and secondary antibody ( Alexa 568-conjugated goat anti-mouse antibody ) , cells were analyzed with a Nikon E800M microscope . For immunohistochemistry staining [76] , mouse or human tissue samples were fixed with 10% ( vol/vol ) formalin solution ( Sigma ) overnight . Tissue specimens were dehydrated , embedded in paraffin , and cut into 3-μm sections . Tissue sections were analyzed by immunohistochemistry staining with antibodies against COX-2 , RCAN1 , HA or LANA and DAB substrate kit ( Vector Laboratories ) . Images were visualized with a Nikon E800M microscope equipped with a Nikon DXM1200 digital camera and the Nikon ACT-1 imaging software system . Proximity ligation assay ( PLA ) was performed by using the Duolink in situ starter kit ( Sigma-Aldrich ) according to previous reports [77 , 78] . Briefly , HUVEC-Vector or kGPCR stable cells were fixed with 4% PFA for 10 min at room temperature , and incubated with DuoLink blocking buffer for 30 min at 37°C . Cells were then reacted with primary antibodies diluted in Duolink antibody diluents for 1 h and then incubated for another 1 h at 37°C with species-specific PLA probes under hybridization conditions . The PLA probes can be hybridized only when they were in close proximity ( <40 nm ) . Ligation was then performed for 30 min at 37°C . After which , a detection solution containing fluorescently labeled oligonucleotides was used to amply the signal for 100 min at +37°C . The signal was detected as a distinct fluorescent dot under fluorescence microscope . Conditioned medium from vector or kGPCR-expressing HUVECs were used to stimulate primary HUVECs . Control IgG or IL-8 neutralization antibody ( R&D systems ) was included in the conditioned medium ( 0 . 5 μg/ml ) for 24 h . Then , cells were collected for RNA extraction , reverse transcription-PCR and quantitative real-time PCR analysis . HEK293T cells were transfected with a plasmid containing Flag-SERCA2b together with a vector or a plasmid containing kGPCR . SERCA2b was precipitated with anti-Flag antibody-conjugated agarose and used for in vitro ATPase assay . The ATPase activity of SERCA2b was determined by using ATPase assay kit according to the manufacturer's instructions ( Innova Biosciences ) . Briefly , the reaction was carried out in a mixture containing 0 . 5 M of assay buffer , 0 . 1 M of MgCl2 , 2 μM of CaCl2 and 10 mM of ATP for 30 min at 37°C . Then 50μl of Gold mix was added to stop reactions . After 2 min , 20 μl of stabilizer solution was added and the absorbance was read at 620 nm at 30 min later . HUVEC stable cells were loaded with Fluoro-4-AM ( Molecular Probes ) for 45 min at 37°C . Cells were then washed and further incubated with fresh medium for 20 min . Live cells were analyzed with a Nikon E800M microscope and the fluorescence signal was quantified by ImageJ ( NIH ) . To determine the relative levels of the NFAT downstream genes , reverse- transcription PCR and quantitative real-time PCR were performed as previously reported . Briefly , total RNA was extracted from cells using RNAeasy kit ( Qiagen ) . The RNA was digested with DNase I ( New England Biolabs ) to remove genomic DNA . One microgram of total RNA was used for reverse transcription with Superscript II reverse transcriptase ( Invitrogen ) according to the manufacturer’s instruction . The abundance of mRNAs was assessed by qRT-PCR using StepONEPlus Real-Time PCR system ( Applied Biosystems ) . Mouse or human β-actin was used as internal controls . The primers were listed in Table S2 . The kGPCR-deficient KSHV was generated by deleting kGPCR coding sequence in bacterial artificial chromosome 16 ( BAC16 ) as previously described [49] . To generate the revertant mutants , we performed the first around of PCR by amplifying a KanR/I-SceI cassette from the pEP-Kan-S plasmid with the following primers: forward primer GTAGATATCTTCAGTGTTGTGTGCGTCAGTCTAGTGAGGTACCTCCAGGATGACGACGATAAGTAGGG and reverse primer TGCGATATCAACCAATTA ACCAATTCTGATTAG . The PCR products were digested with EcoRV and inserted into pcDNA5/FRT/TO-kGPCR or kGPCRc . Then the second around of PCR was performed with the forward primers AAAGGCGTGGCTAAACAACACCTATACTACTTGTTATTG TAGGCCATGTATCCGTATGATGTTCCTGA or AGGCTAGATTAAATTAAGGGGGAAG GGCACGTAGACATCCGCGGGTCAGGTGGACTGGCTAGGCACCCT and reverse primer AGGCTAGATTAAATTAAGGGGGAAGGGCACGTAGACATCCGCGGGCTACG TGGTGGCGCCGGACATGA to get the revertant PCR segments . The recombination was performed in the GS1783 Escherichia coli strain as previously described [49] . KpnI and SbfI digestion of the BAC16 DNA followed by either conventional agarose gel electrophoresis or pulsed-field gel electrophoresis were used to verify the constructs . In addition , colony PCR and direct sequencing were performed to verify the correct insertion of the revertant mutants . All animal experiments were carried out according to the National Institutes of Health principles of laboratory animal care and approved by the University of Southern California Institutional Animal Care and Use Committee ( IACUC ) with permit number A0372 . Six to eight-week old athymic ( nu/nu ) nude mice ( Jackson Laboratory ) were used for xenograft experiment . KSHV GPCR or HCMV US28 stable SVEC cells ( 0 . 5x106 ) were harvested , washed , resuspended in PBS , mixed with 106 SVEC cells and injected subcutaneously into the flank of nude mice . Mice were treated with CsA ( 20 μg/g body weight ) every the other day via intrapretoneal injection , and monitored for tumor development twice every week . At 4–6 wk after inoculation , mice were euthanized and tumor weight was determined . | G protein-coupled receptors ( GPCRs ) constitute the largest family of proteins that transmit signal across plasma membrane . Herpesviral GPCRs ( vGPCRs ) activate diverse signaling cascades and are implicated in viral pathogenesis ( e . g . , tumor development ) . In contrast to cellular GPCRs that are chiefly regulated via cognate ligand-association , vGPCRs are constitutively active independent of ligand-binding . vGPCRs provide useful tools to dissect signal transduction from plasma membrane receptors to nuclear transcription factors . To probe the activation of nuclear factor of T cells ( NFAT ) , we demonstrate that vGPCRs target the ER calcium ATPase to increase cytosolic calcium concentration and activate NFAT . Inhibition of NFAT activation impairs tumor formation induced by vGPCRs , implying the antitumor therapeutic potential via disabling NFAT activation . | [
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] | [] | 2015 | Herpesviral G Protein-Coupled Receptors Activate NFAT to Induce Tumor Formation via Inhibiting the SERCA Calcium ATPase |
Many proteins consist of folded domains connected by regions with higher flexibility . The details of the resulting conformational ensemble play a central role in controlling interactions between domains and with binding partners . Small-Angle Scattering ( SAS ) is well-suited to study the conformational states adopted by proteins in solution . However , analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble models and fitting to noise in the experimental data . To address these challenges , we developed a method based on Bayesian statistics that infers conformational ensembles from a structural library generated by all-atom Monte Carlo simulations . The first stage of the method involves a fast model selection based on variational Bayesian inference that maximizes the model evidence of the selected ensemble . This is followed by a complete Bayesian inference of population weights in the selected ensemble . Experiments with simulated ensembles demonstrate that model evidence is capable of identifying the correct ensemble and that correct number of ensemble members can be recovered up to high level of noise . Using experimental data , we demonstrate how the method can be extended to include data from Nuclear Magnetic Resonance ( NMR ) and structural energies of conformers extracted from the all-atom energy functions . We show that the data from SAXS , NMR chemical shifts and energies calculated from conformers can work synergistically to improve the definition of the conformational ensemble .
Proteins are highly dynamic systems [1] often with large scale conformational dynamics facilitated by regions of flexible or disordered amino acid sequence linking stably folded structured domains [2] . Close to half to the proteins coded in the human genome contain significant disordered regions of greater than 30 residues [3] and there is a multitude of multi-domain proteins with shorter flexible linkers or hinges that are important for their biological function ( e . g . : in enzyme catalysis [4 , 5] , DNA damage signalling and repair [6] , DNA binding and allosteric signalling [7] , mechanical properties in the giant protein muscle protein titin [8 , 9] , target recognition by the intracellular regulatory Ca2+-receptor calmodulin [10] , and ubiquitin-mediated regulatory mechanisms [11 , 12] . These multi-domain proteins connected by flexible regions are difficult to characterize structurally as they tend to be resistant to crystallization , too large for NMR solution structure techniques and often present ambiguous results for microscopy techniques . The small-angle scattering ( SAS ) from proteins in solution samples the time and ensemble average of the randomly oriented structures present . For mono-dispersed macro-molecules of uniform size , one can reliably extract accurate structural parameters such as the radius of gyration ( Rg ) , molecular weight ( M ) , the probability distribution of inter-atomic distances ( P ( r ) vs . r ) , and an estimate of the molecular volume [13 , 14] . Advances with 3D structural modelling against SAS data have further provided more detailed structural interpretation and yielded important biological insights ( reviewed in Trewhella et al . [15] ) . This success has been achieved in spite of the fact that the SAS profile from a protein in solution represents the rotationally averaged 3D structure , hence directional information is lost leaving only 1D distance information that generally can fit multiple 3D solutions . Further , the SAS profile is a smooth function that decays rapidly and can be adequately defined by as few as 10–15 points [16] . When experimental errors are taken into account , the information content is further reduced and it is not uncommon that only 5–10 parameters can be extracted from a SAS profile [17] . Successful 3D modelling against SAS data thus depends upon restraining the conformational space to be sampled by a priori knowledge of protein structure and wherever possible by other experimental data . In the event that a structural ensemble is present , the values of the structural parameters determined and any optimized individual 3D model will represent a population weighted average . Given the abundance of multi-domain proteins with structurally undefined linking sequences , and the difficulty in characterizing them , ensemble or multi-state modelling against SAS data is an increasingly popular choice ( see reviews [18–20] . However , the problems arising from the limited information content of the SAS profile are many times amplified with the ensemble model . An ensemble model of 3D structures will have many more degrees of freedom than a single 3D model . As a result , ensemble modelling against a SAS profile is even more vulnerable to over-fitting and over-interpretation , even considering limits to the conformational space to be sampled via restraints such as knowledge of domain structures , specific flexible regions , contact information from NMR , cross-linking or FRET measurements , etc . The objective of ensemble modelling is to return a set of structural models and their corresponding population weights . Conceptually , we can divide this process into two steps: model selection and weights inference . In model selection we determine the size of the ensemble and which members of the structural library to include . In weight inference the population weights of the selected ensemble is determined . In practice , these steps are often done simultaneously , using minimization of the difference between observed and predicted experimental data as guiding principle ( often measured as χ or χ2 ) . A number of different approaches has been presented to limit ensemble sizes and overfitting . MultiFoXS [21] optimizes χ for a given number of conformers ( usually in the range 1–5 ) from which a minimal ensemble can be defined . The Sparse Ensemble Selection ( SES ) method [22] finds an optimal ensemble using linear least squares with a regularization term to obtain a sparse ensemble of conformations . Overfitting can also be combatted by using model comparison metrics like Aikake Information Criteria ( AIC ) , an approach used by Bowerman et al . [23] to select optimal ensembles in their Bayesian ensemble modelling method . For highly flexible systems such as intrinsically disordered proteins , a small number of conformers cannot realistically describe the ensemble . Methods like EOM [24] result in sizable shrinkage of the initial structural library but do not explicitly limit the ensemble size . The use of discrete protein conformations can also be avoided altogether in the modelling of flexible proteins by using a generative probabilistic model of protein structure in Bayesian modelling [25] . A more extensive discussion of approaches for model selection and weight inference is found in the review by Bonomi et al . [26] . Because SAS data does not contain enough information to infer the full ensemble as it is sampled in solution , we choose to find an ensemble that is “optimal” in the sense that it is the simplest model that explains the available experimental data while avoiding fitting to noise . In this study we use model evidence [27] or marginal likelihood , to select ensembles with optimal sets of members . Model evidence ( ME ) is widely used in Bayesian model comparison and provides an automatic Occam’s razor effect [28] by balancing between fit to data and model complexity , thereby providing a rigorous approach to combat overfitting . However , ME is a multidimensional integral that can be very difficult to evaluate , which is a significant barrier to its use in ensemble selection . Our ensemble selection method is based on an approximate , variational Bayesian inference ( VBI ) method for model selection pioneered by Fisher and colleagues who used the method to infer ensembles of intrinsically disordered protein from NMR chemical shifts and residual dipolar couplings [29] . The VBI approach has two major benefits . First , it is significantly faster than complete Bayesian inference , which enables the use of large structural libraries . Second , VBI implicitly leads to maximization of ME without the need for evaluation of a multidimensional integral . A downside of the VBI approach is that it involves a few approximations in the probabilistic model . Hence , after arriving at the optimal ensemble with VBI we carry out a complete Bayesian inference of weights which we use to quantify uncertainties in the ensemble model and population weights . Here , we first demonstrate the feasibility of Bayesian inference based on large structural libraries from detailed all-atom simulations . By inferring ensembles from synthetic data we show that the method is capable of accurate recovery of population weights and ensemble sizes . We then investigate how noise in the experimental data impacts the accuracy of ensemble inference , showing that information encoded in energy functions can compensate for noisy SAS data . The inference machinery is then applied to evaluate conformational ensembles of two well-characterised proteins , previously studied by SAXS and NMR , each having two domains connected by a flexible linker: calmodulin ( CaM ) and a two-domain construct , designated ΔmC2 , from the cardiac myosin binding protein C . A significant benefit of Bayesian methods is that multiple experimental observations along with simulations and force fields can be rigorously combined in both model selection and weight inference to gain insight into the underlying ensemble . This approach is exemplified in the study of our two example proteins where we demonstrate how data from SAXS , NMR and structural energy values of individual conformers can be combined into one probabilistic model for improved ensemble inference .
We seek to determine optimal structural ensembles from experimental data by selecting conformers from a structural library and inferring their population weights . The experimental measurements generated by a discrete ensemble of conformers can be modelled as a weighted sum of measurements expected from each conformer m→ ( x ) =∑i=1nwiM→ ( x ) ( 1 ) where M→ ( x ) is the expected measurement for a single conformer i over a sampling point x and wi is the population weight of conformer i . For SAXS measurements M→ ( x ) =I→ ( q ) where I→ ( q ) is intensity defined for scattering vector amplitude q . The objective of the Bayesian methodology is to infer the population weights wi on the basis of experimental measurements m→ and a set of structural models , which can be done by employing Bayes’ theorem f ( w→|m→ , S ) =f ( m→|w→ , S ) f ( w→|S ) f ( m→|S ) ( 2 ) where f ( w→|S ) is the prior probability of weights w→=[w1 , … , wn] , S = {S1 , … , Sn} is a structural library , f ( m→|w→ , S ) is the likelihood of observing the measurements given the weights and set of structures , and f ( w→|m→ , S ) is the posterior probability of the weights given the experimental measurements . The likelihood function measures how well a given model matches experimental data . In our modeling , we assume that the experimental errors are normally distributed with standard deviations that can be estimated from the data , and that the individual data points are independent . We primarily focus on experimental data from SAXS but also employ chemical shift data from NMR . SAXS and NMR data can easily be combined by multiplying their respective likelihood functions . Finally , we need to define a prior distribution over the weights w→ . It is convenient to use Dirichlet distribution , which guarantees that weights sum up to 1 g ( w→|α→ , S ) =Γ ( α0 ) ∑i=1nΓ ( αi ) ∏i=1nwiαi-1 ( 3 ) where αi are the parameters of the Dirchlet distribution and α0 is the sum of αi’s . At this stage we assume that all conformers are equally likely in the modeling and chose αi’s as the non-informative Jeffrey’s prior . However , if a more realistic energy function has been used to generate the structural library it is possible to bias the inference towards those conformers with favorable energies . In a scenario where several structurally different conformers have very similar scattering curves , such energy data can be used to select a more realistic ensemble . There are several different approaches that could be used to employ structural energy data in the ensemble inference . Our preference is to bias the prior probability distribution over weights by energy values from simulations . The structural energy values can be used to predict the population weights based on the Boltzmann distribution wi=e- ( Uref+Ui ) /kT∑ine- ( Uref+Ui ) /kT=e-Ui/kT∑ine-Ui/kT ( 4 ) where Ui is the energy of conformer i . Uref can be thought of as a variable that shifts the energy measured by the energy function onto the absolute energy scale but does not affect the populations . By using a Dirichlet distribution with concentration parameters αi=e- ( Uref+Ui ) /kT , the prior can be centered around the Boltzmann values , with Uref controlling the sharpness of the distribution . We assign a uniform prior to the hyperparameter Uref and treat it as sampling parameter . Once likelihood and prior distributions are defined it is possible to evaluate the posterior probability distribution by employing Markov Chain Monte Carlo sampling . However , when large structural libraries are used there can be thousands of parameters in such probabilistic models , which make complete Bayesian inference computationally intractable . We therefore use variational Bayesian inference to shrink the size of the ensemble to a more tractable size range , at which point a complete Bayesian inference is applied to infer population weights . The goal of model selection is to determine the size of the ensemble and which members of the structural library to include . In variational Bayes , the true posterior probability distribution is approximated by a distribution with a favorable mathematical form . The parameters of this approximate distribution are found by minimizing the difference to the true posterior . This can be achieved by minimizing the Kullback-Leibler ( KL ) divergence between the true and approximate distribution: two identical distributions have zero KL-divergence . The KL-divergence cannot be easily evaluated , but it turns out that minimizing the KL-divergence is equivalent to maximizing a lower bound on the value of the model evidence ( ELBO , denominator in Eq 2 ) : f ( m→|S ) =∫f ( m→|w→ , S ) f ( w→|S ) dw→ ( 5 ) We can find an analytical form for ELBO , which means that the inference problem can be turned into an optimization problem that is much more computationally tractable than sampling . Maximizing ELBO thus also leads to maximization of the model evidence function , which is a central property in Bayesian model selection . Consider two possible subsets of structures ( or , mathematical “models” ) S ( 1 ) and S ( 2 ) from a structural library . To compare the models , we can calculate the ratio of likelihoods of the competing models given experimental data ( the Bayes factor ) f ( S ( 1 ) |m→ ) f ( S ( 2 ) |m→ ) =f ( m→|S ( 1 ) ) f ( m→|S ( 2 ) ) f ( S ( 1 ) ) f ( S ( 2 ) ) =f ( m→|S ( 1 ) ) f ( m→|S ( 2 ) ) ( 6 ) where the second identity comes from assuming that each model is equally probable a priori . Thus , finding the most likely model given the experimental data is identical to selecting the ensemble with the highest model evidence . As demonstrated by Fisher and colleagues [29] , the variational approach can be used to build a straightforward model selection approach along these lines: with a given structural library the KL-divergence is minimized by maximizing the ELBO . Members of the ensemble with lowest population weights ( below preset wcut threshold ) are pruned and the calculation is repeated on the reduced ensemble until the ELBO no longer increases , at which point the optimal ensemble has been identified . To carry out the inference we need to approximate the posterior distribution over the weights w→ . In the variational approach we assume that the posterior probability distributions over the weights can be well described by a Dirichlet distribution ( Eq 3 ) and ELBO is maximized by optimizing the concentration parameters αi . The choice of the Dirichlet distribution to approximate the posterior results in a closed-form solution for ELBO [29] . Simulated annealing is then used to maximize with the respect to the concentration parameters αi . The population weights are then calculated as wi=αi∑iαi ( 7 ) These weight estimates are compared to the cutoff value in the model selection algorithm . Our method enables optimal ensemble selection from large structural libraries using variational Bayesian inference . Before we demonstrate the full potential of the model selection , we first demonstrate the power of model evidence to identify optimal ensemble sizes when it can be accurately calculated ( not approximated ) . To illustrate the concept , we generated synthetic data and a structural library of ten members from discrete structural models of the two-domain construct ΔmC2 from cardiac Myosin Binding Protein C ( which will be described in more detail below in the context of the applications with real experimental data ) . We created an ensemble of 3 arbitrarily selected models from the set of ten and simulated a combined scattering curve for these models . Using these simulated data and a structural library of 10 members , we calculated the model evidence for all possible ensembles with 2 , 3 and 4 members . Fig 1A shows the maximal model evidence as a function of model size . As expected , model evidence picks out 3 as the most optimal ensemble size . We then investigated the ability of VBI to accurately recover the correct ensemble and population weights using synthetic data based on a structural library of ΔmC2 . From a larger structural library of 1000 conformers a smaller library of 100 was generated by selecting structures that covered a similar distribution of radius of gyration ( Rg ) values to the larger library . From this subset a handful of structures ( 5 models ) was selected , each with an arbitrarily chosen population , to generate synthetic experimental data . Gaussian statistical errors were added to the data according to the method described by Karaca et al . [30] . A key challenge in ensemble inference is to identify the optimal set of members . This step can be very difficult because even with a relatively small structural library of 100 members the number of possible ensembles is staggering; e . g . there are 1010 unique ensembles available having 1–7 members . S1 Fig illustrates the process of ensemble inference by the algorithm on synthetic data generated with 5 members and added synthetic noise starting from the 100-member structural library . VBI recovers the correct members of the ensemble and their corresponding weights . Although the recovery of weights in this example is impressive , there are a couple of caveats . One is that ensemble members with small population weights may be prematurely pruned during iterations of the ensemble selection algorithm . This simple algorithmic issue could be corrected by optimizing the threshold used to cull members from the ensemble . But there is also a more fundamental issue with uniqueness of the ensemble . In a bigger structural library , there will be conformers with nearly identical scattering profiles . As the size of the structural library increases , the exact identity of members in the ensemble may not be recovered . When we expand the library from 100 to 1000 members this behavior is indeed observed . However , the alternative ensembles recovered in this case have similar model evidence to the simulated ensemble and are thus equally optimal . Synthetic ensembles allow us to characterize the effects of experimental noise on the ensemble selection , such as reduced accuracy of population weights inference or a reduction in information content in the data that leads to a smaller number of members of the ensembles that can be supported by the data . Information content in a SAS curve has traditionally been estimated using information theory by calculating the number of Shannon channels needed to completely recover the data [31] . However , this approach does not take into consideration the effect of noise . Such effects can be evaluated by calculating the “number of good parameters” , Ng , instead . Ng provides the number of parameters that can be determined from measurements and can be estimated from data using maximum entropy regularization [32] . Vestergaard and Hansen [33] have developed a Bayesian approach to evaluate Ng for SAXS data , an approach we employ here . Based on the synthetic ensemble with 5 members we increased the amount of synthetic noise applied to the data and calculated Ng . VBI was then applied to these data to recover optimal ensembles . Fig 1B shows the size of the ensemble as a function of added noise . Ng for the simulated data is around 6 and drops down to 4 at the highest levels of noise . At lower noise levels all 5 ensemble members are recovered . However , increasing noise leads to smaller inferred ensembles with only two members at the highest noise levels . A second effect of increasing noise is a change in the identity of the recovered ensemble members . As the noise increases and the size of the ensemble is reduced , the original ensemble members are not necessarily part of the optimal ensembles . To further investigate how noise affects the accuracy of inference we repeated the above model selection with synthetic data and signal-to-noise levels set with reference to the experimental data for ΔmC2 ( described below ) . In Fig 1C the accuracy of the inferred weights , characterized by the root mean square deviation ( rmsd ) between simulated and inferred weights , is plotted as a function of increasing noise in the data . The results demonstrate that the inference is still very accurate up to three times the experimentally observed noise in our example ΔmC2 . As the added noise increases beyond this value the number of inferred ensemble members decreases , which is the primary reason for the rapid increase in error in rmsd . So far , we have assumed that all conformers are equally likely in the modeling . However , we can also bias the inference with the energies generated for conformers from the structural library . In our simulations , Uref , which controls the strength of the prior , is selected by optimizing evidence using a variational Bayes approach . In this way , the uncertainty in the experimental data will automatically control the strength of the energy prior . This effect is demonstrated by carrying out inference with an energy prior that is centered around Boltzmann weights whose values differ from the simulated values . When the noise level is low and the information content high in the experimental data , the inference relies strongly on the experimental data with small rmsd differences between inferred and simulated weights . As the noise levels increase and the information content is reduced , the energy prior takes over and the weights move towards the values predicted by the Boltzmann distribution ( S2 Fig ) . By establishing the impact of inference with structural energies on the fixed set of models , we further investigate the power of using structural energies on model selection in the presence of experimental noise . In Fig 1D we show the result of the inference of a synthetic ensemble of 5 lowest energy conformers from a library of 100 members as a function of noise . In the absence of the energy prior , the number of recovered members from the simulated ensemble is reduced to 4 and 3 as the noise increases . With the energy prior turned on , the full ensemble is recovered at much higher levels of noise . This result is obtained even when the Boltzmann weights did not exactly match the simulated population weights . However , due to the different weights , the rmsd relative to the simulated weights is slightly higher with the energy prior turned on . In order to demonstrate that the introduction of energy priors does not steer the resulting ensembles excessively towards the lowest energy structures , we added an energy refined conformer with substantially improved energy to the library . With this addition , there was little effect on the identity of recovered models ( S3 Fig ) and the trend observed in Fig 1D is retained . Once a smaller subset of models has been selected using VBI , we subject the optimal ensemble to Complete Bayesian Inference ( CBI ) to determine the population weights and their distributions . In general , a strong benefit of Bayesian inference is that we can go beyond single values ( point estimates ) for population weights and characterize the complete posterior probability distributions of inferred parameters . This step provides probability distributions over the individual weights in the ensemble , together with credibility intervals if requested . It is also possible to characterize the uncertainty of the complete ensemble . Fisher and colleagues [29] developed a useful metric to measure the uncertainty of ensembles , the expectation value of the Jensen-Shannon divergence ( JSD ) relative to the optimal weights over the posterior distribution σw→B , S=∫JSD ( w , →w→B , S ) f ( w→|m , →S ) dw→ ( 8 ) where JSD ( w→ , w→B , S ) =12∑i=1nw→ilog2 ( 2w→iw→i+w→B , Si ) +12∑i=1nw→B , Silog2 ( 2w→B , Siw→i+w→B , Si ) and ranges between 0 and 1 for two maximally identical and different vectors , respectively , which means that also σw→B , S falls within this range . We carry out the complete Bayesian inference using the No-U-Turn sampler ( NUTS ) [34] implemented in the Stan software library [35] . NUTS is an extension of Hamiltonian Monte Carlo , an MCMC algorithm that avoids the random walk behavior and sensitivity to correlated parameters that often plague MCMC inference . To validate the inferred ensembles , it is useful to carry out posterior predictive checks [36] . This check can be achieved by repeatedly simulating scattering curves with the inferred ensemble model and then comparing these to the experimental data . As seen in S4 Fig , experimental curves simulated by our statistical model closely match experimental data . For example , when ensembles are inferred using an unsuitable error model , it is immediately obvious in these predictive checks . Having characterized the performance of Bayesian inference methods on synthetic data sets with relatively small structural libraries , we now apply the method to two experimental systems from our previous work: a two-domain protein calmodulin ( CaM ) [14] , and the two-domain construct , ΔmC2 , from the cardiac myosin binding protein C ( cMyBP-C ) [37] . CaM is the major intracellular Ca2+ receptor that binds to a diverse array of target proteins ( numbering in the 100s ) to regulate their activities in response to Ca2+ signals ( reviewed by Tidow et al . and Crivici et al . [10 , 38 , 39] ) . The crystal structure of CaM [40] shows a mostly α-helical structure with an unusual dumbbell shape formed by two globular , cup-shaped domains connected by an extended α-helix of 7–8 turns . Upon Ca2+-binding at the base of each cup-shaped domain a hydrophobic cleft , which is essential for target binding , opens via the concerted movements of pairs of helices . NMR studies showed the interconnecting helix is broken in solution by a short sequence of four highly mobile amino acids [41] that allow CaM to orient and position the hydrophobic clefts and additional contact regions to accommodate structurally diverse targets . Thus CaM’s structure encodes for both structural diversity and specificity for target binding . CaM was chosen as a test case because it is an extensively characterized protein and understanding the nature of the conformations present in solution for uncomplexed CaM and how that conformational equilibrium is influenced by the presence of binding partners is thus of considerable interest . It is also a popular target for molecular dynamics ( MD ) simulation , including studies aimed both to gain insight into CaM dynamics ( e . g . [42–47] and to test MD results against experiment ( e . g . [48] ) . To generate a library of structurally and energetically reasonable conformers of CaM ( which herein refers to the Ca2+-saturated form with the four Ca2+ sites fully occupied , and thus primed for target binding ) we developed a Monte Carlo based simulation of linker flexibility . A sampling protocol was developed in the Rosetta macromolecular modeling package where the torsion angles in the linker segment were sampled in a Monte Carlo simulation followed by an all atom energy refinement of the linker segment and the neighboring residues . In addition , the 3 N-terminal residues and the last C-terminal residue ( lysine 148 ) missing in the crystal were modelled de novo as well . Around 10000 models were generated by this procedure and a structural library was created by taking the lowest energy 1000 . The distribution of Rg-values in the structural library for all 10000 models and after applying energy filter is shown in S5A and S5B Fig . The Rg distribution for the lowest energy subset models covers the same Rg range as for the complete library but is slightly more peaked . Using a high quality SAXS data set of CaM obtained using in-line SEC ( size exclusion chromatography ) at the Australian Synchrotron [14] and NMR chemical shift data [49] , we performed model selection using VBI with the 1000 lowest energy conformers . We evaluated four inference scenarios using: 1 ) SAXS data only , 2 ) SAXS data + Rosetta energies , 3 ) SAXS data + chemical shifts and 4 ) SAXS data + chemical shifts + Rosetta energies . Once VBI converged and the ensemble consisting of a few members was selected , we used CBI to infer population weights and their distributions . While condensing the probability distributions into point estimates ( single values ) of parameters is undesirable in general , it is sometimes convenient in comparison with alternative methods to easily summarize error residual plots and evaluate other figures of merit . For this purpose , we calculate scattering curves for inferred ensembles using point estimates of parameter taken from the VBI inference . These point estimates are found as the parameters ( e . g . population weights ) that maximizes the ELBO metric . Each of the ensembles inferred with the prior distribution unbiased by the inclusion of energies ( scenarios 1 and 3 ) consists of 4 members ( Fig 2A and 2C ) , while the scenarios with the Rosetta energies included for the prior distribution ( 2 and 4 ) result in 3 members ( Fig 2B and 2D ) . The drop in the number of members upon inclusion of energy priors is due to the peaked energy landscape , which reduces the number of possible solutions and also results in faster convergence of selection algorithm ( S1 Table ) . Inferred weights for each scenario have relatively peaked distributions ( Fig 2E–2H ) and JSD ranges from 0 . 05 to 0 . 08 , which means that there is high certainty in the predicted parameters given the ensemble of models and the experimental errors . The predicted scattering profile from each of the ensembles for the different inference scenarios matches the SAXS data well , as illustrated in Fig 2 ( panels I-L ) and a number of statistical measures . The reduced χ2 value obtained for the predicted scattering profile for each ensemble is in the expected range for an excellent model fit to the data ( i . e . near 1; in this instance in the range 0 . 81–0 . 87 ) . The use of energy priors leads to a small increase in χ2 in the presence and absence of the CS data . The addition of CS data slightly improves the fit to the SAXS data compared to when SAXS data is used alone , indicating the data sources are at least not in conflict and potentially may be reinforce each other . The absolute value of χ2 depends critically on accurate counting statistics and error propagation . Further as a global parameter , χ2 will not identify significant regions in q-space of mis-fit . The predicted scattering profiles were therefore also assessed ( 1 ) using an error weighted difference plot over the measured q-range and ( 2 ) with the recently developed correlation map ( CorMap ) test [50] that is independent of the errors and identifies regions of misfit with a significance test . Simply put , CorMap identifies the longest stretch of data points that lie on one side of the model profile and provides a probability ( P ) for that occurrence given the number of points in the data set . Consistent with the observed flatness of the error weighted model versus experiment intensity difference plots ( Fig 2M–2P ) over the entire q-range , CorMap gives P-values indicating high confidence in the model fit ( 0 . 53–0 . 96 ) . Thus by all measures each of the inferred ensembles are in excellent agreement with the SAXS data , have high certainty in the predicted parameters . Arguably , one could conclude that the “best-fit” to the SAXS data is obtained for scenario 3 ( SAXS data + CS ) as assessed by the lowest χ2 value combined with the highest P-value and the fact that the longest stretch of points on one side of the model profile lies , uniquely among the four scenarios , in the high-q background scattering region . All parameters for the inferred ensembles are summarized in S2 Table . Examining the CaM conformers in each selected ensemble , with a single exception , the Rg values are all in the relatively narrow range 20 . 6–23 . 0 Å ( S2 Table ) . This range is consistent with the original SAXS study of CaM in solution [51] that concluded that the CaM lobes are on “average” reoriented and closer together in solution compared to the crystal structure ( PDB 1CLL ) with its fully extended helical inter-domain connector ( Rg = 22 . 7 Å ) . The main distinction among the inferred Rg distributions is that the inclusion of Rosetta energies results in a higher proportion of more compact structures within this range , although the SAXS + Rosetta energies inference also yields the most extended conformer with an Rg value 26 . 0 Å , albeit with a relatively low population weight ( 0 . 06 ± 0 . 1 ) . The conformers of the inferred CaM ensembles all show variable orientations of the N- and C-terminal target-binding hydrophobic clefts and variable degrees of extension in the flexible linker ( Fig 2A–2D ) . Inspection of known crystal or NMR solution structures of CaM complexed with target binding proteins or domains also reveals conformers with highly variable domain dispositions ( reviewed in Tidow et al . [10] ) . They also include CaM conformers that are significantly more compact or more extended than either the crystal structure or those present in the majority conformers from inferred ensembles; e . g . CaM with its binding domain in myosin light chain kinase has an Rg of 17 Å with its two globular lobes wrapped tightly around the helical binding domain ( PDB 2LV6 ) while the 20 lowest energy NMR structures for CaM complexed with its binding domain from Munc13 ( PDB 2KDU ) includes CaM conformers with Rg values as large as 26 . 4 Å . A systematic comparison of all CaM conformers represented in complexes with binding partners in the PDB identified 1 crystal structure ( 4DJC ) and 3 NMR solution structures ( 1CFF , 2KDU and 1L53 ) with similar dispositions of the CaM domains as assessed by rmsd values for Cα coordinates in the range 4 . 6–7 . 3 Å ( S3 Table ) . Of this set of structures , only the 2KDU structure has both CaM binding domains involved in the target domain interaction , the remaining three only involve C-terminal domain binding , and the 1CFF crystal structure has the fully extended helical inter-domain connector , similar to the Ca2+-CaM 1CLL structure . A library of CaM structures was generated from all the structures in the PDB of CaM complexed with a target involving interactions with both of CaM’s N-and C-terminal domains . When inference is carried out with this structural library , the resulting ensemble cannot describe the experimental data well . In sum , each of the inferred ensemble models show variable dispositions of the target-binding hydrophobic clefts and includes some conformers that have similar dispositions to conformers observed in crystal or NMR solution structures of CaM complexes . Further , the Rg values for the ensemble model conformers are all in a range that is within the range observed in these structures . However , each inference scenario results in distinct set of conformers in an ensemble that fits the available data more-or-less equally well . Thus , while the model evidence justifies an ensemble model of 3–4 models , the solution is not uniquely defined by the available experimental data . This ambiguity can be potentially removed by introducing additional experimental data that informs on inter-domain orientation . Such information is found in data from NMR Paramagnetic Contact Shifts ( PCS ) and Residual Dipolar Couplings ( RDCs ) measurements for example , and has proven to be useful in combination with SAXS [52 , 53] . Developing methods required to incorporate this type of data into our statistical framework is beyond the scope of this study . However , we can test how well the ensembles inferred in this study explain experimental PCS values from paramagnetic data . We compared predicted values from inferred ensembles with available paramagnetic data for Tb ( terbium ( III ) ) , Dy ( dysprosium ( III ) ) and Tm ( thulium ( III ) ) bound to the N-terminal domain of CaM derivatives [54] . The predicted ensembles do not fit particularly well with the PCS data for the C-terminal domain . This could be because PCS reports on orientational information not available in SAXS and chemical shift data . However , the conditions at which the PCS data is significantly different than used for SAXS ( pH ( 6 . 5 vs 7 . 5 ) and ionic strength ( 300 vs 400 mM ) ) . Since CaM is very negatively charged [55] , it cannot be ruled out that the ensembles are different at these two conditions . It is the hydrophobic cleft in the C-terminal lobe of CaM that is generally the initial recognition site for target binding in a two-step binding process whereby subsequent N-terminal lobe binding is necessary for full cooperative target binding . Further , it is not unusual for the CaM binding sequences to be anchored via other interactions within the target proteins; e . g . in myosin light chain kinase the CaM-binding domain has to be released and translocated away from the kinase’s catalytic cleft [56] , and in CaM’s interaction with the MA protein from HIV-1 the two-tryptophan’s that bind to the C- and N-terminal domains of CaM are deeply buried in the helical head domain of MA [57 , 58] . The ensemble models thus support the idea that the flexible linker in CaM primarily allows the hydrophobic clefts to reorient independently . This mobility enables target recognition and binding by the C-terminal hydrophobic cleft of CaM that in turn triggers the unfolding and folding events required to form the interaction surfaces . Such a process is consistent with the conclusions of Liu and colleagues from their molecular dynamics study of CaM binding to its binding domain in skeletal muscle myosin light chain kinase , that the binding process is “quite complex with the mixture of induced fit , conformational selection , and simultaneous binding–folding . ” [42] . Our second example of the application of VBI to experimental data considers ΔmC2 from cMyBP-C , which has never been crystallized but our NMR solution structure ( PDB:2KDU ) [37] reveals it to have a two-domain structure with a 7-residue flexible linker . The cMyBP-C is a modular protein with eleven predominantly β-structured immunoglobulin ( Ig ) or fibronectin ( Fn ) domains ( designated C0 through C10 ) and a 100-amino acid sequence between C1 and C2 that contains cardiac specific phosphorylation sites and is mostly unstructured ( referred to as the “motif” or m-domain ) [59 , 60] . Found in the cross-bridge bearing C zone of the A band of the muscle sarcomere , cMyBP-C interacts with both thick and thin filaments and has both structural and regulatory functions [61] . It exercises its regulatory function via alternate myosin/actin interactions with its N-terminal domains ( C0-C1-m-C2 ) , with phosphorylation of the motif implicated in the switching [62–64] . The ΔmC2 construct includes the loosely structured C-terminal region of the m-domain that is a tri-helix bundle [65] with a tightly structured C2 that has an Ig-type fold [66] . Our NMR structure showed the same folded tri-helix bundle as previously determined by NMR and the C2 domain connected by a 7-amino acid linker that is highly mobile , and yet there is a surprisingly high degree of sequence conservation in this linker sequence across all known chordates [37] . Further , the linker includes sites of severe disease-linked mutations and also forms part of the interface of a stable , Ca2+-dependent interaction with CaM . These observations , combined with evidence implicating ΔmC2 in actin binding , led us to postulate that , like CaM , the flexible linker region of ΔmC2 may facilitate its role as a polymorphic binding domain that interacts with multiple proteins to regulate muscle action in the sarcomere [37] . SAXS and NMR chemical shift data for highly purified ΔmC2 were from [37] . The SAXS data were of good quality , also from the Australian Synchrotron , but measured in a typical batch mode without the benefit of in-line SEC . A small concentration dependence was observed in the lowest-q data that , while corrected by a linear extrapolation to zero concentration , amplified the errors in this region . Following the procedure described for CaM , and assuming two stable folded domains connected by a 7-residue linker , we generated a structural library of 1000 lowest energy conformers using the Rosetta protocol and ran the same 4 inference scenarios: 1 ) SAXS data only , 2 ) SAXS data + Rosetta energies , 3 ) SAXS data + chemical shifts and 4 ) SAXS data + chemical shifts + Rosetta energies . The ensembles inferred in scenarios 1 and 3 consist of 5 members ( Fig 3A and 3C ) , while scenarios 2 and 4 ( Fig 3B and 3D ) yield 3 and 4 members , respectively . Similar to CaM , model selection when Rosetta energies are included in the prior leads to a smaller subset of inferred models . The Rg range in each of the inferred ensembles is similar ( ~17–27 Å ) . As was observed for CaM , inclusion of Rosetta energies distributions significantly alters the weighting of more compact structures to more extended ones ( 0 . 80–0 . 84 and 0 . 42–0 . 58 without and with energy priors , respectively ) . In contrast to the CaM , however , the change in weights with energy priors shifts the distribution to an increase in the proportion of more extended structures . The most highly extended conformer ( Rg = 27 . 0 Å ) appears in all four variants ( model 1 ( green ) in Fig 3A–3D ) though its population weight with the inclusion of both CS and energy priors ( inference 2 ) is significantly smaller than in the other ensembles . In all scenarios except 3 , which is the only one for which a conformer with the intermediate Rg value ( 24 Å ) is absent , inferred weights have a peaked distribution over the weights and JSD ranges from 0 . 04 to 0 . 07 . The JSD is slightly higher for variant 3 ( 0 . 11 ) , primarily due to the long tail of the lowest weight , even so it still corresponds to an ensemble that is well-defined . Similar to CaM we can assess the fit to data based on a point estimate of weights from VBI ( Fig 3I–3L ) . Compared to CaM χ2 are considerably higher ( ranges from 3 . 55 to 3 . 81 ) , although error weighted difference plots ( Fig 3M–3P ) and CorMap P-values values ( 0 . 19–0 . 81 ) indicate good fits to the data over the measured q-range with no statistically significant specific region of mis-fit . We can thus conclude that the errors propagated from counting statistics were on this occasion underestimated , which has been a common issue for SAS data . χ2 drops when simulations include Rosetta energies in the prior over weights ( variant 2 and 4 ) . In interpreting this result , it is important to highlight that the ensemble and population weights are not selected by minimizing χ2 . The drop in χ2 is the result in improved quality of the ensemble and highlights how multiple data sources can work together to provide a better-defined ensemble . Inference with chemical shift data leads to slightly increased χ2 for SAXS of 3 . 81 , suggesting that the ensemble observed by SAXS and NMR chemical shift may differ somewhat , potentially due to subtly different solution conditions . The detailed values of inferred parameters can be found in S2 Table . To further investigate this issue , we ran CBI with the ensemble only selected from SAXS data with the four different data scenarios as presented above ( results found in S4 Table , which also presents values for CaM ) . With the SAXS ensemble , inference of SAXS+NMR data is essentially identical to when only SAXS data is used . However , no improvements in the inference is observed when the Rosetta energy is used in this scenario . This highlights that differences with or without NMR and Rosetta energies is a consequence of identifying different conformers from the structural library with the additional data . The ensemble members in each of the scenarios 1 , 2 and 4 adopt 3 distinct conformations that upon aligning the tri-helix bundle form an approximate cross-like configuration , while those from scenario 3 form an approximate T-shaped configuration ( Fig 3A–3D ) . However , given that the inference with energy priors have better match to SAXS data as well as lower JSD values we can conclude that the ensemble with cross-like conformation is more likely . Much less is known about ΔmC2 and its putative binding partners . The measured binding affinities are moderate ( ~100 nM ) compared to CaM ( ~nM ) [37] and , to date , there is no evidence for a common recognition motif . The ensemble modelling indicates that the longer flexible linker in ΔmC2 compared to CaM allows for significantly greater flexibility and relative positioning of its two domains , and more highly extended conformers are favored . Such an ensemble may be optimized for binding targets with moderate affinity where there is not a common initial recognition motif , and the binding process will also involve a mixture of induced fit , conformational selection , and simultaneous binding–folding . Many methods have been proposed for building conformation ensembles from SAS data . Typically , ensembles have been optimized by minimizing χ2 . The fits are then characterized by visualization of fitting residuals . We compared the results from point estimates of weights from VBI with two popular methods for conformational ensembles modeling from SAS data: Ensemble Optimization Method or EOM [67] and MultiFoXS [21] . The results were summarized in terms of Rg distributions , number of ensemble members , χ2 and CorMap P-values ( S5 Table ) . Focusing on the CaM ensembles obtained with SAXS-only data , with and without energies for the VBI ensembles , we see a striking similarity between the Rg-values of conformers and weights between MultiFoXS and VBI for SAXS-only results . In contrast , the EOM and SAXS+Rosetta energies ensembles are more similar to each other , differing from the MultiFoXS results in the relative proportions of the more compact and more extended conformers . The inclusion of CS data does not significantly alter the VBI results in terms of Rg values and weights . For MultiFoXS , the minimal number of conformers required to minimize χ2 is selected and all structures that have correct stereochemistry , while for EOM a genetic algorithm is used to find an ensemble that minimizes χ2 and flexible regions are treated simply as a self-avoiding polyglycine chain . Thus , as might be expected , the number of ensemble members selected by VBI is much smaller than the number of representative structures selected by EOM but larger than for MultiFoXS . In the case of EOM the Rg distribution for the ensemble is a continuous double-peaked distribution that is represented by 13 conformers from this distribution , which is more than twice the number from the other methods . While we have compared the χ2 values for the ensemble model fits to the SAXS data here , it is important to keep in mind that in contrast to EOM and MultiFoXS , the Bayesian approach does not select ensembles and weights based on direct minimization of χ2/χ and uses chemical shift and energy data in addition to data from SAXS in the inference . Nonetheless , by this comparison we see that the resulting χ2 values for the SAXS data fits are similar those obtained using EOM and MultiFoXS .
Small angle scattering data can provide structural insights into conformationally heterogeneous biological samples . Due to its inherently low information content , SAS data typically must be complemented with structural modeling to draw biologically relevant conclusions . While we want to extract as much information from the data as possible , care must also be taken to avoid overfitting . In ensemble inference there are two areas where overfitting may become a problem . First , with structural libraries containing thousands of members the number of modeling degrees of freedom significantly exceeds the information content in the data and this can result in inferences of overly complex ensembles . Second , by optimizing model parameters directly with respect to χ2 there is a risk of fitting to noise rather than signal in the experimental data . Model evidence provides a principled approach to balance model complexity with fit to experimental data . We demonstrate that the approach can identify the optimal number of members using simulated ensembles with a known ensemble size . Model evidence also enables investigation of how experimental noise affects the inference of optimal ensembles . Our results show that although the ensemble inference is robust to high levels of noise , increasing noise eventually leads to the reduction of the information content in the data and smaller ensembles sizes that can be supported by data . Encouragingly , the analysis of the experimental data sets reports optimal ensemble sizes that are similar to the values obtained from the analysis of the number of good parameters ( Ng ) suggesting that a good balance between model complexity and fit to data is reached . Model evidence is only one of several approaches for model selection employed in Bayesian inference . We have also employed model selection using WAIC and PSIS-LOO [68] but found that they did not result in stable ensemble inference . In the simulation experiments with synthetic data , the exact identity of members in the optimal ensemble could be inferred from SAS data alone , except when the added noise became high . However , in scenarios with experimental data and large structural ensembles we do not necessarily expect there to be single optimal solution and many competing ensembles may equally well describe the experimental data . This result is not surprising as many different conformations can give rise to the same scattering profile . This is a fundamental consequence of the three-dimensional averaging of coordinates in SAS and not something that can be tackled with improved inference methods . Bayesian approaches have some inherent properties that provide protection against overfitting to noise by balancing the fit to experimental data with information encoded in prior distributions over model parameters . The protection from the prior is particularly important in situations where the amount of experimental data is limited . Another benefit of the Bayesian methodology is that it returns probability distributions over modeling parameters rather than point estimates . Point estimates of population weights are a convenient approach to summarize results but represents an unnecessary reduction of information . The posterior probability distributions provide information about uncertainty of individual population weights . This can be complemented by the JSD metric that summarize uncertainty over the complete ensemble . We find small JSD values overall , suggesting relatively well-defined ensembles . Altogether , the posterior probability distributions and the JSD metric gives a full picture of the uncertainties in the ensemble inference given the available data . Our approach for ensemble inference involves two separate stages . First , fast model selection is carried out using a variational approach that enables Bayesian inference with structural libraries consisting of thousands of members . This is followed by a complete inference the selected set using a full Bayesian inference . Comparison of the weight inference for CaM and ΔmC2 using the variational and complete suggests that the two approaches gives highly similar results , indicating that the approximations used in the variational method do not lead to any significant inaccuracies . A powerful approach to better define ensembles is to include additional data into the inference and thereby increasing the information content . An additional benefit is that different data sources can provide different types of structural information . SAS provides information about relative positions of atoms in a structure . NMR chemical shift data on the other hand provides information about local structure of the protein while energies calculated through a force field or energy function provides information about stereochemistry and intermolecular interactions in the protein . The Bayesian approach straightforwardly enables the use of several information sources simultaneously in the inference . Our study of the two-domain proteins CaM and ΔmC2 with data from SAXS and NMR chemical shifts as well as Rosetta structural energies shows that for ΔmC2 that had higher levels of noise in the low-q SAXS regime , the use of Rosetta energy information leads to a significant improvement of the inference . The resulting ensembles have more peaked population weights distributions , better fit to the SAXS data ( measured through χ2 ) , fewer members and the Monte Carlo simulations converge faster . For the more ideal CaM data , we also observe more peaked probability distributions , fewer member and faster simulation convergence but see no improvement with the inclusion of energy priors in model fit to SAXS data measured through χ2 . The inferred ensembles using SAXS only , SAXS+chemical shifts and SAXS+chemical shifts+structural energy have some conformers in common , but are different enough to present an alternative view of the conformational states of the proteins . Because the different inference scenarios are based on different data input , it is not straightforward to compare them statistically . Nonetheless , the ensemble inferred from the SAXS+chemical shifts+structural energies has the strong benefit that the conformers are consistent with the distance distributions measured through SAXS , the torsional preferences of the linker assessed by NMR and are energetically and stereochemically realistic through the use of the Rosetta energy values . When SAXS data is used alone , there are many ensembles with almost identical model evidence . Because of the lack of orientational information in the SAXS data , such ensemble can be quite different . The additional information from NMR and Rosetta can then tip the balance between these competing ensembles . In reality we do not expect proteins with flexible linkers to populate only a discrete number of conformational states . The inferred ensembles represent a simplified model for explaining the dominant conformational states adopted by the protein . The small ensemble sizes are a reflection of the limited information content in the data which is not sufficient to infer more detailed picture of the conformational landscape . A fuller picture of the conformational ensemble could emerge if discrete structural library is replaced by a continuous model for structure . Antonov et al . have developed a probabilistic model for protein structure that enables sampling of conformations of the protein during ensemble inference [25] , a method that does not rely on structural libraries . The challenge in employing such approaches is the development of probabilistic models over structure that samples energetically realistic protein conformations . For this reason , the use of structural libraries generated by atomistic force fields and energy functions still represent a useful strategy for inference of structural ensembles . Further research is necessary to develop approaches that combines the rigor of complete Bayesian inference with the structural and energetic realism encoded in force fields and energy functions .
In order to apply Bayes’ theorem ( Eq 2 ) to infer the population weights wi on the basis of experimental measurements m→ and a set of structural models S , we need to state the prior probability f ( w→|S ) , and the likelihood function f ( m→|w→ , S ) . We define a prior probability over the weights w→ as Dirichlet distribution ( Eq 3 ) . The αi parameter that defines Dirichlet distribution is either chosen to assume that all conformers are equally probable ( non-informative Jeffrey’s prior ) or to bias toward lower energy conformations from Rosetta simulations . For the non-informative prior the probability density function is defined as: f ( w→|S ) =Γ ( n/2 ) nΓ ( 1/2 ) ∏i=1nwi-1/2 ( 9 ) However , when Rosetta energies are used the prior probability equals to: g ( w→|S ) =Γ ( β0 ) ∑i=1nΓ ( βi ) ∏i=1nwiβi-1 ( 10 ) where βi=e- ( Uref+Ui ) /kBT , Uref is the Boltzmann reference energy , kB Boltzmann constant and β0=∑i=1nβi . The likelihood function describes uncertainty in experimental data . For SAXS data with normally distributed errors it is defined for each measurement mj as a Gaussian density function: f ( mj|w→ , λ ) =12πεSAXS2exp ( - ( mj-λ∑inwiIij ) 22εSAXS2 ) ( 11 ) where λ is a scaling factor , Iij is a SAXS intensity calculated from the ensemble and εSAXS is the experimental error . We assume that measurements are independent and the joint likelihood is the product of individual likelihood functions: fSAXS ( m→|w→ ) =∏j=1Nf ( mjw→ , λ ) ( 12 ) where N is the number of experimental measurements . The Bayesian framework provides an easy approach to add structural information from different experimental sources . In the case of NMR chemical shifts measurements , we also assume that measurements are normally distributed and uncertainty of theoretical prediction of chemical shifts εCS can be summed up with experimental errors εpre . fNMR ( mj|w→ ) =12πεCS2+εpre2exp ( - ( mj-∑inwiCij ) 22 ( εCS2+εpre2 ) ) ( 13 ) where Cij are chemical shifts calculated from the ensemble . Similar to SAXS data we assume that NMR chemical shift measurements are independent and joint probability fNMR ( m→|w→ ) is the product of individual likelihood functions . The overall goal of variational Bayesian inference is to maximize the model evidence f ( m→|S ) . This is typically intractable , but we can find a lower bound for model evidence ( ELBO ) by introducing an approximate posterior g ( w→|α→ , S ) and applying Jensen’s inequality to the model evidence and maximize that instead [29]: logf ( m→|S ) =log∫g ( w→|α→ , S ) f ( m→|w→ , S ) f ( w→|S ) g ( w→|α→ , S ) dw→≥∫g ( w→|α→ , S ) logf ( m→|w→ , S ) f ( w→|S ) dw→g ( w→|α→ , S ) dw→≡-L ( α→|S ) ( 15 ) ELBO is determined by maximization of -L ( α→|S ) or minimization of L ( α→|S ) ( Eq 16 ) through the choice of the parameters of the approximate distribution g ( w→|α→ , S ) . In this way the parameters of g ( w→|α→ , S ) are chosen to minimize the KL divergence to the true posterior f ( w→|α→ , S ) . The choice of g ( w→|α→ , S ) as a Dirichlet distribution enables a closed form solution for L ( α→|S ) . The derivation for NMR chemical shift data can be found in Fisher et al . [29] . We modified the method to accommodate SAXS data: L ( α , S ) =logΓ ( α0 ) Γ ( n2 ) +∑i=1nlogΓ ( 12 ) Γ ( αi ) +∑i=1n ( αi−1/2 ) {ψ ( αi ) −ψ ( α0 ) }+1/2∑j=1Nεi−2 ( mj−λ/α0∑i=1nIijαi ) 2+12∑i=1n∑j=1n ( ∑k=1NIikIjkεk2 ) αi ( α0−αi ) δij−αiαj ( 1−δij ) α02 ( α0+1 ) ( 16 ) where δij is Kronecker delta function , ψ ( · ) is digamma function and λ is a scaling factor between experimental and ensemble averaged inferred measurements calculated according to the formula described in Svergun et al . [69]: λ=∑j=1Nεj-2α0-1mj∑i=1nIijαi∑j=1Nεj-2 ( α0-1∑i=1nIijαi ) 2 ( 17 ) When the Rosetta energies are used in the inference , L function has the following form: L ( α , S , Uref ) =logΓ ( α0 ) Γ ( β0 ) +∑i=1nlogΓ ( βi ) Γ ( αi ) +∑i=1n ( αi−βi ) {ψ ( αi ) −ψ ( α0 ) }+1/2∑j=1Nεj−2 ( mj−λ/α0∑i=1nIijαi ) 2+12∑i=1n∑j=1n ( ∑k=1NIikIjkεk2 ) αi ( α0−αi ) δij−αiαj ( 1−δij ) α02 ( α0+1 ) ( 18 ) In each round of the model selection algorithm the L function is minimized for the current set of conformations S by identifying the optimal set of parameters αi and Uref ( when Rosetta energies are available ) using simulated annealing . After finding the optimal weights through the αi parameters , the conformers with lowest weights are removed from the ensemble by applying a cut , wcut ( fixed at the start of the simulation , explicit values are provided in S1 Table ) , so that conformers with wi < wcut are culled from the set . This procedure is repeated , and the simulation stops when the L function does not improve in 10 iterations . In the case of SAXS-only data and SAXS with NMR chemical shifts we restart optimization several times , starting from the set of structures from previous run until the L function did not improve ( see S1 Table ) . When running simulations with structural energies this was not necessary because the algorithm converged in a single run . Because of the stochastic nature of the algorithm the inferred ensemble may not always converge to the same set of structural models and population weights . We repeated entire procedure 2 to 4 times depending on the data type used in the inference to monitor convergence and selected solutions with the lowest L . We implemented VBI using openmp library allowing for parallel computation , which provides considerable speed up compare to original method by Fisher et al . [29] . Once the small subset of models has been selected using VBI , we determine corresponding population weights with complete Bayesian inference . We based CBI implementation on the Stan library—platform for statistical modeling and high-performance statistical computation [35] . The weights w→ , scaling factor λ and parameter defining the shape of Boltzmann distribution Uref are sampled using Markov Chain Monte Carlo ( MCMC ) simulations . In each run we performed 2000 simulations with No-U-Turn sampler [34] using 4 chains and 4 jobs . We monitored MCMC simulations by inspecting the effective sample size and split R^ parameter , which are diagnostics available directly from Stan . In addition to these metrics , we used a few statistics from stan_utility ( https://github . com/betanalpha/jupyter_case_studies/blob/master/pystan_workflow/ ) : trajectory tree depth , energy Bayesian fraction of missing information and posterior parameters divergence . VBI and CBI were implemented with python and C++ and are available from: https://andre-lab . github . io/bioce/ as well as through web-server: https://andre-lab . github . io/bioce/webserver . html . In the case when model evidence was explicitly evaluated and not approximated we performed numerical integration of the integral from Eq 5: ∫f ( m→|w→ , S ) f ( w→|S ) dw→ ( 19 ) This was calculated by determining the expectation value of the likelihood function f ( m→|w→ , S ) evaluated on the weight values sampled from prior distribution f ( w→|S ) ( Dirichlet distribution ) . We used the FoXS [21] program to calculate scattering profiles from atomic coordinates of conformers . In cases where experimental data was available scattering profiles were calculated for experimental q values , otherwise we used equally spaced q values ranging from 0 to 0 . 5 1/nm ( default in FoXS ) . Scattering profiles calculated on experimental q values were subsequently descaled by dividing intensities with the c1 scaling parameter ( returned by FoXS ) to have equally scaled intensities for the Bayesian inference . To predict NMR chemical shift data Cij and their uncertainties εCS from the set of structural models we used the SHIFTX2 program [70] . Python scripts for generating scattering profiles and chemical shift data and converting them to the required input format are available with the software . To generate a library of energetically reasonable conformers of ΔmC2 and CaM we developed a sampling protocol in Rosetta macromolecular modeling package [71] . The protocol samples torsion angles in the linker segment using Monte Carlo simulations ( 1000 iterations ) and subsequently repacks side chains . The linker modeling was followed by all atom energy refinement of the linker segment and the neighboring residues with fast relax protocol [72] . Around 10 000 models were generated by this procedure and the 1000 lowest energy conformers constituted the lowest energy structural library . In order to demonstrate that presence of low energy conformer does not considerably bias simulations towards Boltzmann weights , we used the Rosetta Relax protocol to optimize energy of one of the ΔmC2 models . Constraints on atomic coordinates were introduced to ensure that model did not substantially deviate from its starting conformation so that the scattering pattern of the energy-refined model is highly similar . NMR chemical shifts measurements for CaM were described in [73] and the data was obtained from Biological Magnetic Resonance Data Bank ( BMRB Entry 547 ) . This data was recorded for CaM from Drosophila , which differs from human CaM in three amino acid positions: Y99F , N143T , and T136S . We excluded these three substitutions in our simulations by omitting them in experimental and predicted chemical shift data . SEC-SAXS data for CaM are deposited in the SASBDB ( https://www . sasbdb . org/ ) , identifier SASDCQ2 , and fully described in [14] an open access article for which the CaM data are publicy available under the uniform resource identifier https://creativecommons . org/licenses/by/2 . 0/uk/legalcode . SAXS data for ΔmC2 are deposited in SASBDB ( identifier SASDDD9 ) , while NMR chemical shift data was taken from [37] . The web version of MultiFoXS ( https://modbase . compbio . ucsf . edu/multifoxs/ [21] ) and the ATSAS on-line version of EOM ( https://www . embl-hamburg . de/biosaxs/atsas-online/ [67] ) were used to obtain the multi-state and ensemble optimization modelling results , respectively , for CaM and ΔmC2 shown in S5 Table . The crystal structure coordinates of CaM ( PDB:1CLL ) and Model 1 from the NMR ensemble for ΔmC2 were the starting structures ( PDB:5K6P ) . In the case of CaM the 3 missing N terminal amino acids ( Ala1 , Gln2 , Asp3 ) from the crystal structure and the flexible linker ( Lys77 , Asp78 , Thr79 , Asp80 , Ser81 ) were assigned unknown structure . In the case of ΔmC2 the 7-amino acid flexible linker ( Arg356 , Arg357 , Asp358 , Glu359 , Lys360 , Lys361 , Ser362 ) was assigned unknown structure . MultiFoXS generates structures for the unknown regions that have correct stereochemistry , while for EOM the random coil option was chosen to model the missing amino acids . The SAXS data used for modelling were for CaM SASBDB ID SASDCQ2 , q = range 0 . 0066–0 . 3104 Å-1 , and for ΔmC2 SASBDB ID SASDDD9 . The amount of structural information covered by SAXS experimental data was assessed using the BayesApp program [33] . We included all data points in the analysis and used default input parameters . The radius of gyration for individual models was calculated using CRYSOL program from ATSAS package [74] . | Proteins are commonly built up by folded domains connected by regions with higher flexibility . The interdomain orientations encoded by such hinges or linkers can play central roles in controlling the function of multidomain proteins , which makes them important to characterize . Small Angle X-ray Scattering ( SAXS ) is uniquely suited to study the conformational ensembles adopted by these kinds of proteins . However , because of the limited information provided by SAXS , ensemble models must be built by combination with other information sources and care have to be taken to avoid constructing ensembles that are more complex than data can support . We developed a method based on Bayesian statistics that combine data from molecular simulation with experimental data from SAXS and Nuclear Magnetic Resonance while automatically balancing the complexity of ensemble model with information in the data . We demonstrate that this method is capable of accurate inference of ensembles even in the presence of high levels of experimental noise . The method represents a general approach to combine data and simulation in the modeling of protein ensembles and can be extended to employ additional sources of experimental information . | [
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"me... | 2018 | Bayesian inference of protein conformational ensembles from limited structural data |
Paternal repression of the imprinted H19 gene is mediated by a differentially methylated domain ( DMD ) that is essential to imprinting of both H19 and the linked and oppositely imprinted Igf2 gene . The mechanisms by which paternal-specific methylation of the DMD survive the period of genome-wide demethylation in the early embryo and are subsequently used to govern imprinted expression are not known . Methyl-CpG binding ( MBD ) proteins are likely candidates to explain how these DMDs are recognized to silence the locus , because they preferentially bind methylated DNA and recruit repression complexes with histone deacetylase activity . MBD RNA and protein are found in preimplantation embryos , and chromatin immunoprecipitation shows that MBD3 is bound to the H19 DMD . To test a role for MBDs in imprinting , two independent RNAi-based strategies were used to deplete MBD3 in early mouse embryos , with the same results . In RNAi-treated blastocysts , paternal H19 expression was activated , supporting the hypothesis that MBD3 , which is also a member of the Mi-2/NuRD complex , is required to repress the paternal H19 allele . RNAi-treated blastocysts also have reduced levels of the Mi-2/NuRD complex protein MTA-2 , which suggests a role for the Mi-2/NuRD repressive complex in paternal-specific silencing at the H19 locus . Furthermore , DNA methylation was reduced at the H19 DMD when MBD3 protein was depleted . In contrast , expression and DNA methylation were not disrupted in preimplantation embryos for other imprinted genes . These results demonstrate new roles for MBD3 in maintaining imprinting control region DNA methylation and silencing the paternal H19 allele . Finally , MBD3-depleted preimplantation embryos have reduced cell numbers , suggesting a role for MBD3 in cell division .
Genomic imprinting , an epigenetic process resulting in expression of one parental allele , is an important mechanism of transcriptional control in mammals [1 , 2] . Failure of transcriptional regulation defines the molecular basis for many human diseases , emphasizing the importance of this control for normal growth and development . Accordingly , loss of imprinting is implicated in a number of human diseases and cancers . For example , Beckwith-Wiedemann syndrome , a disorder characterized by overgrowth and tumor development , results from defects in gene expression from either of two linked but independently controlled imprinted gene clusters on 11p15 . 5 , the H19/IGF2 or KCNQ1OT1/CDKN1C clusters [3] . The mechanism by which imprinted gene expression is established and maintained has been extensively studied but still remains incompletely understood . DNA methylation represents one of the best candidates for conferring parental-specific expression patterns because DNA methylation is differentially acquired in the parental germlines , maintained following fertilization , and subsequently employed to silence the nonexpressed allele . One of the best examples of such regulation is observed at the mouse H19/Igf2 locus [4] . The H19 gene , which encodes a noncoding RNA , and the Igf2 gene , which encodes a fetal mitogen , are expressed from opposite parental alleles , but their imprinted expression is regulated by common DNA elements [5–7] . One such element is a 2-kb imprinting control region located 2 kb upstream from the H19 transcriptional start site , designated the differentially methylated domain ( DMD ) , which is required for imprinted expression of both H19 and Igf2 [8] . The DMD forms an active CTCF-dependent insulator on the maternal allele that governs expression of H19 and repression of Igf2 . In the male germline , the DMD acquires methylation that is necessary for repression of the paternal H19 allele [9–13] . Methylation of the DMD is essential for imprinted expression because H19 , which is normally maternally expressed throughout development , is biallelically expressed in DNA methyltransferase 1 ( Dnmt1 ) mutants [14] . Thus , while much is known about the sequences required for H19 imprinting and the necessity for differential DNA methylation at this locus , less is known about how DNA methylation leads to silencing of the paternal allele . Although CTCF has been shown to bind the unmethylated DMD on the maternal H19 allele , leading to formation of an insulator , cofactors that are required for recognition of the repressed paternal allele have not been identified . The methyl-CpG-binding proteins ( MBDs ) are possible candidates for the dual jobs of recognizing DNA methylation marks and silencing transcription from the locus [15] . Biochemical studies have demonstrated that these proteins can bind methylated DNA and associate with repressive complexes in vitro . These proteins may , therefore , provide the link between DNA methylation and transcriptional silencing at imprinted loci . To determine the role of MBDs in imprinted gene regulation , we have taken an RNAi approach to generate hypomorphic alleles . Although we and others have found that transcripts for all Mbd genes are present in early mouse embryos ( [16]; KJR , unpublished data ) , Mbd3 was chosen as the first target for RNAi because Mbd3 null embryos die early in embryogenesis , suggesting a critical role in development [17] . Additionally , we have determined that MBD3 is bound to the H19 DMD . Using both injections of one-cell embryos with double-stranded RNA ( dsRNA ) against Mbd3 ( dsMbd3 ) and a transgenic ( TG ) RNAi approach that reduces Mbd3 mRNA in oocytes , we demonstrate that H19 is activated on the normally methylated and repressed paternal allele in blastocysts with reduced amounts of MBD3 protein . This biallelic H19 expression in Mbd3 RNAi embryos is significantly different from control embryos , which retain monoallelic expression . Other imprinted genes analyzed in the Mbd3-depleted embryos are monoallelically expressed , suggesting each locus may require a separate MBD partner . Interestingly , Mbd3 RNAi embryos have reduced DNA methylation at the H19 locus , but not at the imprinted Snrpn locus , indicating a critical role for MBD3 in maintaining paternal methylation marks at the H19 locus during a critical window of genome wide demethylation . Finally , reducing MBD3 levels affects cell division and , consequently , the size of RNAi embryos . These findings support the hypothesis that MBD proteins are required for the interpretation and maintenance of allele specific methylation marks leading to repression of the paternal H19 allele .
RT-PCR experiments using Mbd3 specific primers demonstrated that Mbd3 RNA is present in oocytes and preimplantation mouse embryos ( Figure 1A ) . The RNA was quantified and normalized relative to known amounts of rabbit globin RNA added prior to RNA preparation . Mbd3 RNA levels are high in the oocyte , become reduced through early stages of preimplantation embryogenesis , but rise again by the blastocyst stage . This profile is consistent with expression of many other transcripts; degradation of maternal transcripts followed by replacement at the two-cell stage with zygotic transcripts . MBD3 protein follows a similar distribution , with levels highest in the oocyte and blastocyst ( Figure 1B ) . Interestingly , MBD3 protein is present on the DNA and spindle in eggs , and is nuclear at all other stages with punctate foci of stronger staining . As proposed for transcription factors that remain associated with chromosomes during mitosis and direct transcription following entry into interphase , MBD3′s association with chromatin in eggs may provide a similar molecular memory to ensure appropriate marking of the maternal allele . Thus , both Mbd3 RNA and protein are present at this critical period for imprinted gene expression when imprints are becoming established . To determine if MBD3 is associated with the H19 locus , chromatin immunoprecipitation ( ChIP ) experiments were performed with an MBD3 antibody on C57BL/6 X B6 ( CAST7P12X ) hybrid embryonic stem ( ES ) cells ( Joanne Thorvaldsen , MSB , unpublished data ) . After immunoprecipitation of chromatin , PCR analysis showed that MBD3 was associated with the H19 DMD , with a slight preferential association with the methylated paternal allele ( Figure 1C ) . An antibody against acetylated histone H3 ( AcH3 ) was used as a control because the 3′ end of the DMD was reported to associate with acetylated histone H3 on the maternal allele [18] . The region of the DMD reported here showed a similar preference for the maternal allele ( RIV and MSB , unpublished data ) . Furthermore , MBD3 associates with the Oct4 locus in undifferentiated ES cells [19] , a finding we also observed , therefore validating our immunoprecipitation results . Interestingly , MBD3 was not associated with either allele of the imprinted Snrpn gene ( Figure 1C ) , suggesting a specific role for MBD3 at the H19 locus . To confirm that these results were different from no-primary controls , quantitative real-time PCR was performed at both the H19 DMD and the Snrpn imprinting control region for three or more independent ChIP experiments ( Figure 1D ) . At the H19 locus , MBD3 was significantly associated with the locus when compared to the no-primary ChIP samples ( p < 0 . 05 ) . At the Snrpn locus , however , we did not see a significant association when compared to the control samples ( p = 0 . 12 ) . Although MBD3 was reported to be unable to bind directly to methylated DNA because of amino acid substitutions in its MBD domain , our results , along with MBD3 ChIP results at other loci [19 , 20] suggest that MBD3 is associated with DNA at repressed loci . Our data showing that MBD3 is expressed early during development , along with these ChIP experiments that demonstrate that MBD3 is bound at H19 , provide compelling evidence for a role for MBD3 at the H19 locus in preimplantation embryos . To determine if MBD3 was required for imprinted gene expression , we employed two RNAi approaches ( injection and TG RNAi ) that reduce maternal stores of RNA , but could also persist to reduce newly transcribed zygotic Mbd3 messages ( Figure 2A ) . In the first method ( injection RNAi ) , in vitro transcribed dsRNA ( dsMbd3 or control dsGfp ) was injected into one-cell embryos . Embryos were then cultured to the blastocyst stage and collected for analysis . An additional group of uninjected embryos was also cultured to control for culture conditions . The second RNAi approach ( TG RNAi ) utilized a transgene to reduce Mbd3 RNA levels . The transgene employs the zona pellucida 3 ( Zp3 ) promoter , which drives expression of linked sequences in growing oocytes [21] . The promoter is upstream of an Mbd3 inverted repeat that generates an approximately 510-bp dsRNA that is identical to that used for the injection RNAi experiments . Both RNAi strategies resulted in reduced Mbd3 RNA and protein levels . The RNA levels were examined by semi-quantitative reverse transcriptase PCR ( RT-PCR ) and real-time RT-PCR using Mbd3-specific primers and Gapdh primers for normalization ( Figure 2B and 2C ) . Injection RNAi embryos were assessed at the blastocyst stage for RNA and protein . On average , blastocysts used for these experiments had only 35% of Mbd3 RNA compared to control embryos ( Figure 2B ) . RNAi-treated embryos were assayed for Mbd2 RNA levels ( Figure 2D ) as Mbd2 encodes the family member that is most closely related to Mbd3 [22] . Mbd2 levels were unchanged in the dsMbd3-injected embryos , demonstrating that dsMbd3 is both effective and specific . RNAi-treated blastocysts were also assayed by immunocytochemistry with an anti-MBD3 antibody to determine protein levels at the blastocyst stage . dsMbd3-injected embryos had very little MBD3 protein compared to control embryos at the blastocyst stage , demonstrating that the MBD3 protein is labile enough to respond to dsRNA injection during this preimplantation period ( Figure 2E ) . For the TG RNAi experiments , 11 TG lines were generated . Each line was assessed for levels of Mbd3 RNA at the GV stage of oogenesis , a time following the initiation of transgene expression . The lines showed a range of RNAi efficacy from 14% of control levels in the best line , line 37 , ( normalized to Gapd and compared to nontransgenic ( NTG ) controls as described above ) to 100% of Mbd3 RNA in poor lines . The experiments described here were conducted using line 37 ( Figure 3C ) , the best line in terms of RNA and protein depletion . Similar to the injection RNAi embryos , TG RNAi embryos showed a severe reduction in MBD3 protein at the blastocyst stage ( Figure 2E ) . Together , these experiments revealed that the RNAi that targets Mbd3 is effective in reducing MBD3 protein levels when administered in the growing oocyte or at the one-cell stage . With RNAi effectively and specifically reducing Mbd3 RNA and protein during the preimplantation period , these embryos could be used to determine if Mbd3 is required for proper imprinted gene expression during this time . Only a small group of imprinted genes , including the maternally expressed H19 gene and the paternally expressed Snrpn gene , are first expressed and imprinted during the preimplantation period . As H19 transcription commences at the late blastocyst stage , blastocysts from both injection RNAi and TG RNAi were examined for changes in expression from the normally silent paternal allele . An allele-specific real-time RT-PCR assay enables simultaneous examination of both parental alleles , thereby allowing for an assessment of paternal allele activation in single blastocysts [23] . In blastocysts derived from injection of one-cell embryos with dsRNA , a quarter of the embryos showed biallelic H19 expression whereas only 4% of uninjected blastocysts and no dsGFP-injected blastocysts exhibited biallelic H19 expression ( Figure 3 ) . Blastocysts from line 37 showed a similar but more robust effect; 40% of blastocysts derived from TG mothers exhibited biallelic H19 expression whereas blastocysts from NTG mothers expressed only the maternal H19 allele ( Figure 3 ) . In both sets of RNAi experiments , the ratio of paternal expression to total H19 expression varied from 15%–55% . It should be noted that although the imprinting of H19 is closely coordinated with that of the linked and oppositely imprinted Igf2 gene , expression of Igf2 is not evident until after implantation . Thus , the role of MBD3 in the regulation of Igf2 could not be assessed using this RNAi system . Nevertheless , these experiments demonstrate that Mbd3 is required for proper imprinted expression of H19 at the blastocyst stage . RNAi-treated embryos were also assayed for the few other imprinted genes expressed at this stage . All embryos were assayed for Snrpn gene expression , but no change in Snrpn imprinting was observed , consistent with the absence of MBD3 binding in the Snrpn imprinting control region as determined by ChIP ( unpublished data ) . TG RNAi animals were also assayed for Peg3 , Gtl2 , and Atp7a . Peg3 is an imprinted gene on Chromosome 7 and is paternally expressed at the blastocyst stage ( M . Mann and MSB , unpublished data ) . Gtl2 is an imprinted gene on Chromosome 12 and is maternally expressed at the blastocyst stage [23] . Atp7a is located on the X chromosome and is silenced on the paternal allele due to imprinted X-inactivation in the preimplantation embryo [24] . No changes in expression of Peg3 and Gtl2 ( in all embryos ) and Atp7a ( in female embryos ) were observed in TG embryos ( unpublished data ) . These results suggest that MBD3 protein is not required for all imprinted gene expression at the blastocyst stage , and may also indicate that the silent alleles at these loci employ other MBD family members to confer silencing of the inactive allele . Biochemical experiments indicate that MBD3 is an integral member of the chromatin-remodeling complex Mi-2/NuRD , and recent data suggest that in the absence of MBD3 , this complex is unstable [25] . To determine whether this complex is intact in Mbd3 RNAi embryos , blastocysts from NTG ( Figure 4A ) and TG mothers ( Figure 4B ) were stained with an MTA-2-specific antibody . Consistent with the result seen with Western blotting of MTA2 in Mbd3 null ES cells [25] , Mbd3 RNAi TG blastocysts showed more than a 50% reduction in average MTA-2 fluorescence levels in blastocyst nuclei ( Figure 4C ) , a statistically significant difference ( p < 0 . 001 ) . The requirement for MBD3 for H19 imprinted expression and the accompanying reduction in MTA-2 levels strongly implicates a role for the Mi-2/NuRD complex in repression at this imprinted locus . The relaxation of paternal H19 expression in Mbd3 RNAi blastocysts suggested that there might be changes in DNA methylation at the paternal DMD . To address this question , several pools of five blastocysts from RNAi-treated animals and controls were collected and DNA methylation changes in the H19 DMD examined using bisulfite methylation assay . After the pools were subjected to bisulfite mutagenesis , PCR products were obtained from the 5′ portion of the DMD , cloned , and sequenced to determine the DNA methylation status of individual DNA strands ( Figure 5 ) . C57BL/6 ( maternal ) and Mus musculus castaneus ( paternal ) DNA polymorphisms were used to distinguish the two parental alleles . The 5′ DMD contains 17 CpGs over 426 bp , and strands that lacked nine or more methylated CpGs were considered hypomethylated . A reduced number of DNA methylated strands was seen in at the H19 DMD in RNAi-treated embryos when compared to controls . DsMbd3-injected embryos had 42% hypomethylated paternal strands compared to 13% and 17% for uninjected and dsGfp-injected controls , respectively ( Figure 5 ) . Similarly , pools from TG RNAi embryos had 42% hypomethylated paternal strands whereas NTG control pools contained only 16% hypomethylated paternal strands ( Figure 5 ) . A Snrpn PCR product was also obtained from these embryos and sequenced , but the normally methylated maternal alleles from dsMbd3-treated embryos did not differ from controls ( unpublished data ) . These results suggest that MBD3 is required to maintain the paternal methylation mark during the preimplantation period at the H19 locus . The absence of Mbd3 in preimplantation embryos leads to both biallelic expression of H19 and loss of methylation at the normally methylated paternal allele at the blastocyst stage . Since the RNAi effect is transient , we analyzed later stage embryos ( 7 . 5 days post coitus [dpc] ) to determine if H19 expression and DNA methylation defects are maintained . Both TG ( n = 7 ) and NTG ( n = 9 ) embryos expressed H19 exclusively from the maternal allele . Likewise , both TG and NTG 7 . 5-dpc embryos exhibited a hypermethylated DMD on the paternal allele . Presumably , Mbd3 RNA and protein levels rise soon after the blastocyst stage . These results with later embryos suggest that either reduction of Mbd3 mRNA and protein has only a transient effect that can be repaired or that embryos with serious imprinting problems fail to develop further . During the course of these experiments , we noted that some dsMbd3-injected blastocysts were smaller than both uninjected and dsGfp-injected control blastocysts . To investigate this observation further , control and experimental blastocysts were collected to determine if the Mbd3 RNAi embryos had fewer cells . These experiments revealed that RNAi embryos have significantly fewer cells than control embryos ( Table 1 ) . For injection RNAi , uninjected blastocysts have an average of 97 cells , dsGFP injected embryos have an average of 87 cells , and dsMbd3 RNAi blastocysts have an average of 74 cells per embryo ( p < 0 . 01 ) . TG RNAi blastocysts ( 77 . 5 cells ) also have fewer cells than NTG control blastocysts ( 93 . 8 cells; p < 0 . 05 ) . The dsMbd3 embryos did not appear grossly different from control embryos other than the noticeable difference in size , although the incidence of hatching of dsMbd3-treated blastocysts was reduced ( unpublished data ) . DAPI or propidium iodide staining revealed the nuclei to be normal in size and morphology and the blastocysts did not show any evidence of increased apoptosis as determined by TUNEL assay ( unpublished data ) . The DAPI staining revealed that Mbd3-injected RNAi embryos but not TG RNAi embryos have a significantly increased number of metaphase chromosome pairs ( Table 1 ) . RNAi-treated embryos were also assayed by immunocytochemistry for expression of markers of the inner cell mass ( OCT4 ) and trophoblasts ( TROMA-1 ) . DsMbd3-treated embryos showed expression of both of these markers at the blastocyst stage , suggesting that RNAi blastocysts form both of these tissue types at an appropriate time . Interestingly , Kaji et al . saw analogous results with smaller 5 . 5-dpc Mbd3 −/− null embryos even though blastocysts were normal size [26] . Given that the Mbd3 −/− embryos were derived from Mbd3 +/− parents , the null embryos had maternal stores of MBD3 protein and RNA , which is lacking in our RNAi embryos and which would support normal cell division longer than RNAi-treated embryos . Together , these data indicate that MBD3 is required for proper cell division timing during development , but that eventually these embryos become blastocysts with both inner cell mass and trophoblast cells .
There are two times in development when the mammalian genome undergoes demethylation [27 , 28] . The first is during primordial germ cell migration and colonization of the genital ridge , when the entire genome , including imprinted genes , is demethylated . The second time is during preimplantation development , when the majority of the genome undergoes demethylation . Whereas the paternal genome undergoes almost immediate , presumably active , demethylation after fertilization [29–32] , the maternal genome is demethylated gradually , due to a loss of maintenance DNA methylation , during cleavage divisions [33] . Most methylated sequences are demethylated during these two events , but imprinting control regions escape this demethylation . Specifically , for the paternally methylated H19 DMD , this region must survive the active demethylation and then stay methylated in subsequent stages during passive demethylation [34 , 35] . How these processes occur has remained completely unknown up to this point . We propose that MBD3 is intimately involved in these processes at the H19 locus during preimplantation development . We initiated our studies with MBD3 for several reasons: it is expressed during this critical period of preimplantation development , Mdb3 knockouts display an early embryonic lethal phenotype [17] , and MDB3 is associated with chromatin at the H19 DMD ( Figure 1C ) . Two RNAi approaches allowed us to reduce Mbd3 RNA and protein from oocytes and preimplantation mouse embryos . The RNAi strategy for depleting target RNA has several advantages over traditional gene targeting experiments: RNAi allows for ( 1 ) reduction in maternal RNA and protein that persists in traditional knockouts , ( 2 ) production of a hypomorphic allelic series , and ( 3 ) easy use of hybrid strains for allelic gene expression analysis . Using two types of RNAi approaches—injection of dsRNA and TG RNAi—we observe the same effects on the imprinted expression and DNA methylation of H19 . Both methods proved effective at reducing Mbd3 RNA and protein . In RNAi embryos , but not control embryos , H19 is biallelically expressed in a significant fraction of blastocysts examined ( 40% of TG RNAi and 26% of injection RNAi blastocysts ) . These results demonstrate that MBD3 is required for proper imprinted expression of H19 . Our initial hypothesis was that an MBD would bind to the methylated DMD and recruit repressive complexes to silence the paternal allele , but it is important to note that MBD3 fails to bind methylated substrates as assayed by in vitro DNA binding assays , likely due to a phenylalanine instead of a critical tyrosine in its MBD motif [22 , 36] . Whereas in vitro experiments suggest that MBD3 does not bind directly to the methylated DMD , our ChIP results indicate that MBD3 is directly associated with chromatin at the H19 DMD . Similarly , chromatin immunoprecipitation experiments demonstrate that MBD3 interacts with the methylated maternal allele of the imprinted gene Zrsr1 in adult mouse livers [20] . Furthermore , MBD3 is an integral member of the chromatin-remodeling enzyme Mi-2/NuRD , and recent data suggest that in the absence of MBD3 , this complex is unstable [25] . The reduction of MTA-2 levels in Mbd3 RNAi embryos suggests that the amount of repressive NuRD complexes is reduced in RNAi embryos , and that in the absence of these complexes , H19 can be expressed from the paternal allele . These data suggest an important role for the NuRD complex in imprinted gene expression . Given the previously described genetic and biochemical interactions between MBD2 and MBD3 [17 , 37] , MBD2 may also have a role in imprinted gene repression . However , Mbd2−/− embryos have been examined and shown to have proper expression of several imprinted genes including H19 [17] . Experiments to reduce simultaneously levels of MBD3 and other MBDs should uncover any such role for MBD2 that may have been compensated for by the presence of MBD3 . Although H19 is biallelically expressed in a significant number of Mbd3 RNAi blastocysts , not all blastocysts exhibit such a loss of imprinting . Likewise , the DMD is not completely demethylated on every strand , although there are strands that were completely demethylated ( Figure 5 ) . There are several possible explanations for this result . First , RNAi may not completely ablate MBD3 function . Quantification of RNA levels indicated a fairly robust reduction in Mbd3 RNA levels ( Figure 2 ) , and a nearly complete loss of protein , as determined by immunocytochemistry , but , with the technology currently available , it is difficult to determine the effective loss of MBD3 at the level of chromatin , where it presumably acts . Second , even if robust reduction of MBD3 is attained , MBD3 is one of several family members that are expressed in preimplantation embryos , and these other family members might provide compensatory activity in the absence of MBD3 . Finally , the timescale of this experiment may not provide adequate time for MBD3 protein to turn over completely , and for the results of the RNA reduction by RNAi to be completely realized . We examined expression of a handful of other imprinted genes in RNAi embryos , but none showed derepression of the silent allele , suggesting that MBD3 is not involved in their repression . In this case , other MBDs might be required for their imprinted expression . Alternatively , the presence of other MBDs might be sufficient to compensate for the reduction in MBD3 at these loci , but not at H19 . By reducing several MBDs in concert by RNAi , such compensatory mechanisms might be uncovered . Also , H19 may be especially sensitive to perturbation of imprinting cofactors and the paternal allele may be derepressed with the slightest insult to the repressive complex , whereas other imprinted genes may be able to make do with reduced MBD3 protein . Other types of experiments with longer-lived effects such as knockout alleles might be better suited to uncovering these effects as well as determining if MBD3 is required for the imprinting of genes such as Igf2 that are expressed later in development . Finally , the continued discovery of new proteins with an affinity for methylated DNA might eventually uncover different mechanisms for the recognition and silencing of methylated sequences . Our data suggest that MBD3 is required for maintenance of DNA methylation at the H19 DMD during preimplantation development . Similar to the result in which a portion of embryos exhibit biallelic expression , a significant fraction of DNA strands are demethylated . For the reasons outlined above , the loss of MBD3 at this locus may not be complete or its effects may not be complete at the blastocyst stage . The partial loss of DNA methylation and accompanying loss of repression at H19 suggests there may be a threshold for DNA methylation loss and concomitant paternal expression of H19 . Indeed , the percentage of hypomethylated strands is similar to the number of embryos with biallelic expression of H19 . Our data reporting loss of methylation at the H19 DMD in the absence of MBD3 uncovers a new role for MBD proteins: protection of methylated regulatory sequences from DNA demethylation during preimplantation development . This role may not have been predicted by the MBD3 protein sequence , but it is clear that methylated imprinted regions must both be recognized and maintain their parental marks during this developmental time period . It is an interesting finding that a repressive complex that recognizes a differentially methylated region would also be responsible for maintaining that difference during development . In conclusion , development of embryos that lack MBD3 is perturbed , whether MBD3 is removed transiently by RNAi ( this study ) or by a traditional deletion of the Mbd3 locus [17] . In the RNAi experiments , imprinted H19 expression is disturbed along with loss in DNA methylation on the paternal allele . In addition , the Mbd3 RNAi blastocysts are smaller and slower to hatch than controls , suggesting delays in development and perturbed cell cycles . Mbd3 −/− animals die during the peri-implantation period , but the phenotype had not been extensively described until recently [17 , 26] . Kaji et al . ( 2006 ) reported that Mbd3−/− ES cells did not exhibit any cell cycle delay when the ES cells were examined by flow cytometry [25] , but that 5 . 5-dpc embryos have reduced cell numbers [26] . These results suggest that the smaller Mbd3 RNAi embryos may be due to a developmental timing defect rather than a cell-cycle delay . TUNEL assays did not detect an increase in the number of cells undergoing apoptosis between RNAi embryos and controls , suggesting that RNAi embryos are not smaller due to increased cell death ( unpublished data ) . Furthermore , both inner cell mass and trophoblast cells are present , suggesting no defects in the first preimplantation lineage decisions . Consistent with delays in cell division that we observe , cells deficient for Hells , a member of the SNF2 family of chromatin remodelers that is also a global regulator of DNA methylation , have defects in DNA methylation and are slow in completing mitosis [38] . Thus , the hypomethylation observed in Mbd3 RNAi embryos might similarly lead to problems in cell division in embryos that may not be observed in cultured ES cells . Finally , in both our study and in the studies of Mbd3−/− ES cells , loss of Mbd3 negatively affects the Mi-2/NuRD complex [25] . Consequently , even a transient MBD3 loss could lead to a global disruption of silencing carried out by this complex beyond the perturbation we report at the H19 locus . A microarray analysis of global transcript profiles might uncover more genes regulated by this complex or genes specifically responsible for the other phenotypes we see beyond imprinting defects . Our results , thus , uncover an essential role for MBD3 , and the Mi-2/NuRD complex , in regulating the imprinted H19 locus , and provide tools to explore additional roles for MBD3 in genome-wide transcriptional regulation .
For dsRNA injections , oocytes and embryos were obtained from either CF1 ( Harlan , http://www . harlan . com/ ) females mated to CF1 males ( for embryo assays ) or from C57BL/6J ( The Jackson Laboratory , http://www . jax . org/ ) females mated to B6 ( CAST7 ) males ( for allelic assays ) [23] . Embryos from TG animals were obtained by mating TG females to B6 ( CAST7 ) or to B6 ( CAST7P12X ) males . This latter strain has Mus musculus castaneus Chromosomes 7 , 12 , and X in a C57BL/6 background ( M . Mann , J . Mager , C . Krapp , and MSB , unpublished data ) . All experiments were conducted with the approval of the Institutional Animal Care and Use Committee at the University of Pennsylvania . Primers KR1 ( 5′-CTATGGAGCGGAAGAGGTGGGA-3′ ) and KR3 ( 5′-CAGGCCCACTCCCTGCAGGC-3′ ) were used to amplify a 510-bp fragment corresponding to bp 1 to 510 of Mbd3 with Ready-to-Go PCR beads ( Amersham http://www . amersham . com/ ) from ES cell cDNA . PCR was performed for one cycle of 94 °C for 2 min followed by 35 cycles of 15 s at 94 °C , 10 s at 55 °C , and 15 s at 72 °C , and one cycle of 72 °C for 15 min . The PCR product was cloned using the Gateway BP reaction ( Invitrogen , http://www . invitrogen . com/ ) into the pDONR201 vector to give the pKR3 . 03 entry clone . This plasmid was combined in a Gateway LR reaction with the L4440 Gateway C plasmid that contains two T7 promoters flanking the recombination sites to give pKR3 . 04 . For in vitro transcription , pKR3 . 04 was used as a template for PCR with primers L4440F ( 5′-AGCCGAACGACCGAGCGC-3′ ) and L4440R ( 5′-TGCAAGGCGATTAAGTTG-3′ ) that flank the T7 promoters . The PCR product was used as a template for a single T7 reaction that produces both sense and antisense RNA . For in vitro transcription of Gfp , a NotI -HindIII fragment from pEGFPN-2 ( Clontech , http://www . clontech . com/ ) was also cloned into the L4440 Gateway C plasmid . Each 100-μl in vitro transcription reaction contained 5–10 μg of PCR template , 1× T7 transcription buffer , 30 mM rNTPs , and T7 enzyme mix ( RiboMAX T7; Promega , http://www . promega . com/ ) . Samples were incubated at 37 °C for 2 h , after which the template was digested with RQ1 RNase-free DNaseI ( Promega ) for 15 min at 37 °C . Enzymes were removed with two phenol:chloroform:isoamyl alcohol ( 25:24:1 ) and two chloroform:isoamyl alcohol ( 24:1 ) extractions and RNA was recovered from the aqueous phase by two ethanol precipitations with 1/10 volume of 10 M ammonium acetate . The dsRNA was resuspended in 100 μl of RNase-free H2O , and its concentration was estimated on a 1% agarose gel by comparing to a mass ladder standard ( Invitrogen ) . Oocytes and preimplantation embryos from CF1 or C57BL/6J mothers were microinjected with approximately 10 pl of 0 . 25 μg/μl dsRNA . Oocytes were cultured in CZB medium for 24 h at 37 °C in an atmosphere of 5% CO2 . One-cell embryos were isolated at 0 . 5 dpc and cultured to the blastocyst stage ( 96 h ) in KSOM with amino acids ( KSOM + AA ) at 37 °C in an atmosphere of 5% CO2 , 5% O2 , and 90% N2 [39] . DsGFP-injected and uninjected control embryos were cultured alongside dsMbd3-injected embryos to control for culture conditions . To generate the Zp3-dsMbd3 TG construct , an inverted repeat was cloned into pRNAi-Zp3–1 [40] . First , primers KR1 and KR3 were used to generate a 510-bp PCR fragment that was cloned into pCR2 . 1 using the TOPO TA kit ( pKR3 . 18 ) . The inverted repeat was generated by excising an XhoI–XbaI Mbd3 fragment from pKR3 . 03 , and ligating it into XhoI- and XbaI-digested pKR3 . 18 , creating the inverted Mbd3 repeat plasmid pKR3 . 19 . The inverted repeat was then excised with SpeI and ligated into pRNAi-Zp3–1 that had been digested with XbaI , to give pMoZp3-dsMbd3 . TG animals were generated as previously described [41] . Animals were genotyped by PCR assay for Gfp as previously described using DNA isolated from tail or ear clippings as previously described [21] . This was confirmed by Southern blot using a Gfp probe ( unpublished data ) . TG lines were maintained on a C57BL/6J background . Line 37 Mbd3 RNAi TG females described in this study are fertile . Germinal vesicle ( GV ) stage oocytes were isolated from females at least 6 wk of age , as previously described [21] . For later-stage embryos , females , at least 6 wk of age , were superovulated by injection of 7 . 5 IU of pregnant mare serum gonadotropin ( Calbiochem , http://www . emdbiosciences . com/ ) and 44–48 h after injection , injected with 5 . 0 IU of human chorionic gonadotropin ( hCG ) and set up for matings with stud males; females were checked for copulatory plugs the following morning ( 0 . 5 dpc ) . Fertilized zygotes were collected at 0 . 5 dpc , two-cell embryos at 1 . 5 dpc , and blastocysts at 3 . 5 dpc . Embryos were isolated in phosphate-buffered saline containing 3 mg/ml polyvinylpyrrolidone ( PBS/PVP , Calbiochem ) . For isolation of RNA , embryos were transferred to 100 μl of lysis buffer ( Dynal , http://www . invitrogen . com/ ) . Embryos used for bisulfite mutagenesis were transferred in several microliters of PBS/PVP to a 1 . 5-ml tube . One-cell embryos were cultured to the blastocyst stage in KSOM + AA as described above . Poly-A+ mRNA was isolated from oocytes and preimplantation embryos using the Dynabead RNA Isolation Kit ( Dynal ) according to manufacturer's instructions and reverse transcribed for 60 min at 42 °C followed by 10 min at 95 °C as described previously [23] . All PCRs were carried out using Ready-to-go-PCR beads in a final volume of 25 μl and included 0 . 1 μCi of [α-32P]-dCTP ( New England Nuclear [NEN] , http://las . perkinelmer . com/ ) . The reaction conditions were such that the amount of product was in the linear region of semi-log plots of the amount of product versus cycle number [42] . Mbd3 was amplified from 1 . 5 oocyte ( or blastocyst ) equivalents using a final concentration of 0 . 6 μM of each primer , KR15 ( 5′-TGTCAGCCATTGCGAGTGCTC-3′ ) and KR18 ( 5′-CTACACTCGCTCTGGCTCCGG3–3′ ) . These primers span an intron . Mbd3 PCR was carried out for one cycle of 94 °C for 2 min followed by 27 cycles for oocytes ( 25 cycles for blastocysts ) of 15 s at 94 °C , 10 s at 63 °C , and 20 s at 72 °C . Gapd was amplified from 0 . 5 oocyte ( or blastocyst ) equivalents as described previously [21] . Samples for developmental profiles were spiked with 0 . 125 pg/embryo equivalent of rabbit α-globin ( Invitrogen ) . Rabbit α-globin was amplified from 0 . 5 oocyte equivalents as described previously [43] . Mbd3 and Gapd were amplified from the same samples to determine efficacy of RNAi treatments using cycle numbers in the linear range for each as described above . The Mbd3 levels were then normalized to Gapd levels from the same samples . Quantification was performed using the ImageQuant 5 . 2 program ( Molecular Dynamics , http://www6 . gelifesciences . com/ ) . Results with TG RNAi samples were confirmed by real-time RT-PCR using the Mbd3 and H2afy3 light cycler primers ( Table S1 ) using sequence-specific detection probes ( Universal Probe Library; Roche , http://www . roche . com/ ) as described by the manufacturer on a LightCycler Instrument ( Roche ) . H19 and Snrpn expression assays were conducted on cDNA using the LightCycler Real Time PCR System ( Roche Molecular Biochemicals ) as described [23 , 44] . Gtl2 and Peg3 RT-PCR expression assays were conducted on cDNA from single blastocysts and included 0 . 1 μCi of [α-32P]-dCTP ( NEN ) . The data were quantified by phosphorimager analysis following allelic restriction digests [44] . Oocytes or preimplantation embryos were fixed in 3 . 7% paraformaldehyde , pH 7 . 5 , for 60 min at room temperature or overnight at 4 °C , washed two to three times in PBS/PVP , and stored at 4 °C in PBS/PVP . For staining , samples were permeabilized for 15 min in freshly prepared PBS containing 0 . 25% Triton-X100 , washed three times in PBS/PVP , then blocked for 1 h in 5% donkey serum/0 . 1% fish gelatin/0 . 2% Tween-20/PBS . Samples were then incubated overnight with polyclonal goat antibody against an MBD3 peptide found in both mouse and human MBD3 ( Santa Cruz Biotechnology , http://www . scbt . com/ ) diluted 1:50 in blocking solution . The specificity of the antibody was confirmed by loss of nuclear signal after blocking with the peptide the antibody was raised against ( Santa Cruz Biotechnology ) and further validated by the presence of a 34-kDa band on a Western blot of HeLa cell extracts . Blastocysts contain too little protein for visualization of MBD3 or MTA-2 by Western blotting . After three 15 min washes in 0 . 2% Tween-20/PBS , samples were transferred to a 1:500 dilution of donkey Cy3-conjugated anti-goat IgG ( Jackson ImmunoResearch Laboratories , http://www . jacksonimmuno . com/ ) and 2 μg/ml DAPI ( Sigma , http://www . sigmaaldrich . com/ ) in blocking solution for 60 min . Samples were washed three times for 15 min . Blastocysts were equilibrated in increasing amounts of Vectashield ( Vector , http://www . vectorlabs . com/ ) to maintain shape and maximal fluorescence . Finally , oocytes or embryos were mounted on a glass slide and visualized with a confocal microscope ( Leica , http://www . leica . com/ ) . All incubations were at room temperature unless otherwise noted . In order to count nuclei , z-stacks of the confocal images through each blastocyst were collected , with 2 . 5 μm between each z-plane . Stacks were assembled into movies in NIHImage ( http://rsb . info . nih . gov/nih-image/ ) , and nuclei were counted manually in each plane , taking care to note which nuclei had been counted in the previous plane . To determine mean fluorescence , ImageJ ( http://rsb . info . nih . gov/ij/ ) was used to draw a region of interest inside each of five nuclei per embryo in the middle slice of confocal stacks and to measure the mean pixel intensity in the region . The values for TG and NTG embryos were averaged and graphed in Excel ( http://office . microsoft . com/ ) to obtain the data in Figure 4 . Ten micrograms of isolated DNA were digested with StuI , separated on a 0 . 8% agarose gel , transferred to a nylon membrane , and probed as previously described [41] . The Gfp probe fragment was isolated from pEGFP-N1 ( Clontech ) and labeled with [α-32P]-dCTP ( NEN ) . Blots were analyzed using a Phosphorimager ( Molecular Dynamics ) . Pools of five blastocysts were embedded in approximately 10 μl of molten 2% low-melting point SeaPlaque agarose ( BioWhittaker Molecular Applications , http://www . lonzabioscience . com/ ) , and treated as previously described [21] . To prepare DNA for PCR , pellets were resuspended in an appropriate amount of water , and melted 5 min first at 65 °C and then at 80 °C . Blastocyst equivalents ( 2 . 5 ) of the mutagenized DNA were used for each PCR and products were cloned and sequenced as described previously [21] . Six or more clones were sequenced for each PCR . Strands from a PCR that contained an identical pattern of methylated CpGs and that could not be distinguished from other strands by polymorphisms were only counted once . ChIP assays were carried out using the Chromatin Immunoprecipitation Assay Kit ( Upstate , http://www . upstate . com/ ) according to the manufacturer's instructions , with a few modifications . The ES cells used in these experiments were derived from blastocysts generated from crosses between C57BL/6 ( B6 ) female mice and B6 ( CAST7P12X ) male mice . The ES cells were grown on MEF feeder layers in 60-mm dishes , trypsinized , and replated on gelatinized plates to allow MEFs to attach for 1 h . The supernatants containing the ES cells were then collected in 50-ml conical tubes , crosslinked with 1% formaldehyde ( Sigma ) for 15 min at room temperature , and then the reaction was quenched with 0 . 125 M glycine for 5 min at room temperature . Following lysis , sonication was carried out using a Branson Sonifier 250 ( http://www . sonifier . com/ ) at 30% output for four pulses of 10 s each . The sonicated cell supernatant was diluted 10-fold in ChIP dilution buffer ( Upstate ) and 1% of material was removed prior to the addition of antibodies ( input ) . 80–100 μg of chromatin was used for each immunoprecipitation reaction with the following antibodies: anti-acetyl-Histone H3 ( 06–599; Upstate ) and anti-MBD3 ( ab3755; abcam , http://www . abcam . com/ ) [18 , 20] . For the final step , samples were resuspended in 30 μl of TE and 1 μl was used for each PCR . Input DNAs were diluted 10-fold . Three separate ChIP experiments were performed with the antibodies described . For allele-specific analysis , input and immunoprecipitation DNAs were amplified with primers specific to different regions at H19 , Snrpn , and Oct4 ( see Table S1 ) . Briefly , all PCRs were carried out with Ready-To-Go PCR beads ( Amersham ) using 0 . 3 μM of each primer and 0 . 1 μCi of [32P]-dCTP . A fraction of the PCR was used for each digest ( see Table S1 ) . Products were resolved on 7% polyacrylamide gels and the relative band intensities were quantified using ImageQuant ( Molecular Dynamics ) . Real time PCR analysis was carried out on the ChIP samples using the LightCycler Real Time PCR System ( Roche ) to quantitatively assess the amount of chromatin bound in each ChIP experiment , and to determine the linear range of each ChIP PCR ( unpublished data ) . Briefly , reactions were set up in triplicate using the Ready-To-Go PCR beads ( Amersham ) , with a 5-min initial incubation with the TaqStart antibody ( Clontech ) , followed by addition of 0 . 3 μM primers ( see Table S1 ) , 1× SYBR Green ( Roche ) , and 1 . 5–3 . 0 mM MgCl2 . Data analysis was performed using the Light Cycler version 4 . 0 software ( Roche ) . Statistical values for each dataset , with the exception of the ChIP data , were determined by the Fisher exact test using the GraphPad Prism statistics software suite ( http://www . graphpad . com/prism/ ) . For the ChIP data , average percentages ( and standard deviations ) of bound material for the ChIP experiments were calculated , and these means were statistically compared using a one-tailed t-test .
The National Center for Biotechnology Information ( NCBI ) GenBank ( http://www . ncbi . nlm . nih . gov/sites/entrez ? db=Nucleotide ) accession number for Mbd3 is AF120995 . | Genomic imprinting is a specialized system of gene regulation whereby only one copy of a gene is used , either the maternal or the paternal copy . Misregulation of imprinting in humans results in developmental disorders such as Beckwith-Wiedemann Syndrome , and is implicated in many cancers . Study of imprinted genes in mice can lead to a greater understanding of these diseases as well as insight into gene regulation . Many imprinted genes are associated with methylation on the silenced allele . The imprinted gene H19 is maternally expressed and paternally methylated in a region upstream of the promoter known as the differentially methylated domain . This region is required for proper imprinted expression of H19 and its upstream imprinted neighbor Igf2 . Our studies have explored the requirement for methyl-CpG binding protein 3 ( MBD3 ) in silencing of the paternal allele . MBD3 is known to be part of a repressive complex that resides at silenced genes . In our experiments , we have shown that MBD3 is required for imprinting of H19 , and is also required for the maintenance of methylation on the paternal allele . Finally , the MBD3 protein can be found at the differentially methylated domain . The identification of a protein required for silencing of the paternal allele of H19 is an important step in understanding regulation of this gene . | [
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CaMdr1p is a multidrug MFS transporter of pathogenic Candida albicans . An over-expression of the gene encoding this protein is linked to clinically encountered azole resistance . In-depth knowledge of the structure and function of CaMdr1p is necessary for an effective design of modulators or inhibitors of this efflux transporter . Towards this goal , in this study , we have employed a membrane environment based computational approach to predict the functionally critical residues of CaMdr1p . For this , information theoretic scores which are variants of Relative Entropy ( Modified Relative Entropy REM ) were calculated from Multiple Sequence Alignment ( MSA ) by separately considering distinct physico-chemical properties of transmembrane ( TM ) and inter-TM regions . The residues of CaMdr1p with high REM which were predicted to be significantly important were subjected to site-directed mutational analysis . Interestingly , heterologous host Saccharomyces cerevisiae , over-expressing these mutant variants of CaMdr1p wherein these high REM residues were replaced by either alanine or leucine , demonstrated increased susceptibility to tested drugs . The hypersensitivity to drugs was supported by abrogated substrate efflux mediated by mutant variant proteins and was not attributed to their poor expression or surface localization . Additionally , by employing a distance plot from a 3D deduced model of CaMdr1p , we could also predict the role of these functionally critical residues in maintaining apparent inter-helical interactions to provide the desired fold for the proper functioning of CaMdr1p . Residues predicted to be critical for function across the family were also found to be vital from other previously published studies , implying its wider application to other membrane protein families .
In yeasts , including the pathogenic Candida , an up-regulation of multidrug transporter genes belonging to either ATP Binding Cassette ( ABC ) or Major Facilitator Superfamily ( MFS ) is frequently observed in the cells exposed to the drugs leading to the phenomena of multidrug resistance ( MDR ) [1] . Among the 28 putative ABC and 95 MFS transporter genes identified in the C . albicans genome , only ABC transporters CaCdr1p and CaCdr2p and MFS transporter CaMdr1p , are found to be the major determinants of azole resistance [2] , [3] . The reversal of the functionality of these multidrug efflux pump proteins represents an attractive strategy to combat azole resistance . The major ABC transporters such as CaCdr1p , CaCdr2p bear similar topology and exist as two homologous halves . These , like any other member of the ABC superfamily have four distinct modules: two transmembrane domains ( TMDs ) each consisting of six transmembrane segments ( TMSs ) and two nucleotide binding domains ( NBDs ) located on the cytosolic side of the membrane . Though similar in topology and promiscuity towards substrate specificity , these ABC multidrug transporters of C . albicans also display selectivity to the range of substrates they can export [4] . The transporters belonging to MFS , consists of membrane proteins from bacteria to higher eukaryotes and these are involved in symport , antiport or uniport of various substrates [5] , [6] . One of the 17 families of MFS transporters uses the proton motive force to drive drug transport and has been identified in both prokaryotes and eukaryotes [7] . Crystal structures of MFS proteins such as lactose permease ( LacY ) , glycerol-3-phosphate ( GlpT ) , EmrD and oxalate: formate antiporter ( OxlT ) , suggest high structural resemblance among this family of proteins [8] . These consist of 12 TMS , arranged with a similar predicted topology , strongly supporting a common structural architecture or fold across all the MFS transporters [9]–[12] . The fungal MFS members particularly those involved in drug transport are poorly explored in terms of their structure and function [13] . The multidrug MFS transporter CaMdr1p belongs to DHA1 family which is widely distributed and includes both drug-specific and multidrug efflux pumps [14] . Random and site-directed mutational strategies have been extensively used to understand the structure and function of these MDR efflux proteins . For example , random mutational analysis of an ABC transporter , ScPdr5p of budding yeast identified several amino acid residues that alter its substrate specificity and sensitivity to various inhibitors [15] , [16] . Tutulan-Cunita et al . observed that several point mutations led to significant changes in drug specificity of ScPdr5p which are distributed throughout the length of the protein [17] . Site-directed mutagenesis followed by an elegant screen done by Golin's group has revealed interactions between TMS 2 and the NBD which may help to define at least part of the translocation pathway for coupling ATP hydrolysis to drug transport mediated by ScPdr5p . Recently , Schmitt et al . have elucidated the role of H1068 in H-loop of ScPdr5p which couples ATP hydrolysis with drug transport [18] . Site-directed mutational analysis of multidrug ABC multidrug transporter CaCdr1p ( a close homologue of ScPdr5p ) has revealed insight into its drug binding and efflux properties . These studies have implicated some of the amino acid residues of TMS 5 , 6 , 11 and 12 as the components of the substrate binding pocket ( s ) of CaCdr1p [19] , [20] . Together , these studies suggest that the drug binding sites in CaCdr1p are scattered throughout the protein and probably more than one residue of different helices are involved in binding and extrusion of drugs . However , there is still insufficient information available to predict where and how exactly the most common antifungals such as azoles bind and how are they extruded by CaCdr1p . Site-directed mutational strategies rely on conservation of residues in a Multiple Sequence Alignment ( MSA ) . The conservation of a residue is calculated from the amino acid frequency distribution in the corresponding column of a MSA . However , the physicochemical conservation is not necessarily responsible for a protein's structure and function but could reflect a more general function such as membrane localization . Thus conservation alone is not sufficient to distinguish between residues responsible for the protein function and membrane localization . Membrane proteins differ from soluble proteins because of their inter-TM hydrophilic and TM hydrophobic propensities , which have allowed the development of efficient membrane protein TM prediction methods [21] and of membrane protein specific substitution matrices [22] . The quantification of residue conservation has evolved over the last few years to the use of information theoretic measures [23] . Relative entropy is a distance measure commonly applied to multiple alignments by comparing the observed frequency distribution with a background distribution . In the present study , we have developed and employed a new method using information theory to rationalize mutation strategies and also applied it to a MFS multidrug transporter CaMdr1p [24] . Relative Entropy ( RE ) or the Kullback-Liebler divergence is an information theoretic measure of the difference between two probability distributions and has been increasingly applied in bioinformatics to identify functional residues [24] , [25] . The use of RE with background frequencies [26] can improve the prediction of a protein's functional residues [27]–[32] as well as detect residues that determine the functional subtype of proteins [28] . Though the basic Kullback-Liebler equation has not changed , its intelligent application in our method calculates Relative Entropy ( REM ) relative to its context within the membrane . The REM scoring scheme has been improved by treating TM and inter-TM regions of MFS proteins separately which has drastically increased the credibility over the existing methods [23] . In this manuscript , we have compared traditional treatment of conservation , and standard RE , with our improved method . We validated our predictions by replacing the predicted highest REM positions of CaMdr1p with alanine by site-directed mutagenesis . We show that most of these residues when replaced with alanine showed decreased resistance to drugs which was corroborated by abrogated efflux of drugs . Additionally , we could further confirm the functional relevance of each of these high REM residues by predicting their location in deduced 3D model of CaMdr1p and their role in maintaining apparent inter-helical interactions . With this approach , our method enabled us to accurately predict MFS-wide function-specific residues , validated by using CaMdr1p .
A comprehensive non-redundant data set , sourced from all MFS sequences present in the 56 . 2 release , was generated . This data set was then aligned using a membrane-specific multiple alignment program , which stacked the helices appropriately . A highly conserved residue in a multiple alignment is predicted to have a functional significance . We calculated conservation values using the algorithm from Jalview . Residues shown to be conserved dominate the TM helices , and on closer evaluation are largely hydrophobic residues associated with membrane localization . The traditional relative entropy and our modified treatment of the method ( REM ) were calculated on the same alignment using scripts written in-house . ( Fig . 1 shows a representative section of the alignment along with the REM , RE and conservation scores; see supplementary Table S1 for the REM , RE and conservation scores for the entire MSA ) . The distribution curve generated on the basis of REM from the MSA is shown in Fig . 2A . Notably the dominant signal using a conventional conservation measure lies in the TM helices and traditional RE also issues high scores to these residues which are less frequent in nature . Our treatment using a REM dampens the membrane localization signals further increasing the signal from atypically occurring conserved residues ( Figure S1 ) . Since REM considers conservation as well as the background probability of a residue at a particular alignment position , it is an improved index of the functional significance of a residue . To emphasize this fact , thirty residues with highest values were short-listed each from the conservation list , RE list and REM list . On comparison , it was found that there are thirteen residues shortlisted as both conserved and with high RE while it was found that only nine out of these short-listed columns are both conserved and have high REM ( Supplementary Table S2 ) . The thirty alignment positions with high REM were further studied to assess their functional relevance . Expectedly , all residues predicted using REM , are not present in every protein in the family . In CaMdr1p , 16 residues were identical with the most frequently occurring residue in the thirty highest scored alignment columns , and were mutated to alanine to directly validate the prediction ( Fig . 2B ) . These sixteen out of the top thirty positions , wherein the residue in CaMdr1p matched with the most occurring residue across that alignment position in MSA were analyzed for further studies by site-directed mutagenesis . Interestingly , most of the sixteen residues with high REM turned out to be part of the well-known motifs of the MFS . These motifs are identified as Motif A ( GxLaDrxGrkxxl ) , Motif B ( lxxxRxxqGxgaa ) which are conserved throughout the MFS , Motif C ( gxxxGPxxGGxl ) only in 12 and 14-TMS family and Motif D2 exclusive to 12-TMS family [7] . Three out of the sixteen residues short-listed for CaMdr1p; E178 , G183 and R184 are a part of Motif A; residues L211 , R215 and G219 are a part of Motif B and G256 is a part of motif C . In addition to the known motifs mentioned above , two new motifs have been identified by our study . The residues in these stretches 273Wrxxf277 and 296Pespr300 have high REM scores . However , the known motif D2 does not appear to be highly conserved in our alignment and is thus not predicted to be family-wide function-specific . All the sixteen residues selected on the basis of high REM were mutated by employing site-directed mutagenesis and were replaced with alanine except G165 , G183 and G256 which were replaced by leucine . For functional analysis of the mutant variants , a heterologous hyper-expression system , where GFP-tagged CaMdr1p ( CaMDR1-GFP ) was stably over-expressed from a genomic PDR5 locus in a S . cerevisiae mutant AD1-8u− , was used [33] . The host AD1-8u− developed by Goffeau's group , was derived from a Pdr1-3 mutant strain with a gain-of-function mutation in the transcription factor Pdr1p , resulting in constitutive hyper-induction of the PDR5 promoter [34] . A single-copy integration of each transformant at the PDR5 locus was confirmed by Southern hybridization ( data not shown ) . Two positive clones of each mutant were selected to rule out clonal variations . These residues with high REM score showed increased drug susceptibility and abrogated efflux of substrates such as [3H] MTX and [3H] FLU ( Fig . 3 ) . Of note , there were few exceptions to the list of residues with high REM . For example residues T160 , L211 , W273 and R274 have high REM values but do not appear to be critical for function of CaMdr1p since the drug susceptibility and efflux are not affected upon replacement of these residues with alanine . To confirm that the change in susceptibility observed in the mutant variants was not due to their poor expression or mislocalization , we compared the localization of GFP-tagged version of CaMdr1p ( CaMDR1-GFP ) and its mutant variants by FACS and confocal imaging . A proper localization of all the mutant variants was confirmed by both these methods which showed proper rimmed appearance of GFP-tagged CaMdr1p . The Western Blot analysis further confirmed that the expression levels of CaMdr1p of all the mutant variants were similar thus corroborating FACS and confocal data ( Fig . 4 ) . For evaluating the relevance of high REM residues in CaMdr1p , a 3D homology model was constructed using available crystal structures of lac permease of E . coli ( 1pv6 ) , glycerol-3-phophate of E . coli ( 1pw4 ) and oxalate: formate transporter of O . formigenes ( 1zc7 ) as described in Materials and Methods . All the top thirty positions of highest REM are marked in the model which mostly lie in the N-terminal half of the protein ( Fig . 5A ) . Using the homology model , a symmetric contact map of CaMdr1p was generated as discussed in Materials and Methods ( Fig . 5B ) . We exploited this distance plot to ascertain the role of these residues with high REM . It is apparent that residues L211 , R215 and G219 in TMS 4 are within 8 A° distance to many residues of TMS 1 , 2 and 3 . For example , it can be seen that residue G219 on TMS 4 lie on the same face of the helix and is within 8 A° to the residues G165 and G169 on TMS 2 . Indeed , the mutation of predicted G165 and G169 on TMS 2 resulted in abrogated drug susceptibility and transport ( data not shown ) . All the predicted interactions are summarized in Fig . 6A and shown in a pictorial representation of the homology model in Fig . 6B .
The multidrug MFS transporter CaMdr1p harbors a conserved antiporter ‘motif C’ within TMS 5 . Our recent study has revealed that the conserved and critical residues of this motif and of TMS 5 are bunched together on the same face of its helical wheel projection and are critical in drug efflux [35] . However , the structure and function aspects of this major multidrug transporter remain poorly understood . To address some of these questions , in this study , we have rationalized conventional mutational strategy and applied computational approach to predict functionally critical residues of CaMdr1p . The sequence set described in this manuscript represents a comprehensive non-redundant coverage of annotated MFS sequences from SWISSPROT . Many methods have been developed to improve the MSA of membrane protein families for accurate predictions of residues critical for structure and function [36] . Membrane proteins have fold signals which are easily mapped to the primary sequence as TM and inter-TM stretches . Considering the differences in physico-chemical properties of these two regions , membrane protein specific substitution matrices have been developed [22] . However , we argued that a conservation score on the basis of identity or physico-chemical similarity still remains inadequate as the background frequencies of their immediate environmental milieu are radically different with respect to hydrophilic and hydrophobic propensities . This is also apparent from the conservation scores of the MSA wherein a large proportion of the conserved columns correspond to hydrophobic TM regions . Notably , two CaMdr1p residues ( F216 and L217 ) with high conservation but low REM were taken as controls , when replaced with alanine showed no change in the phenotype ( data not shown ) . One of the most basic fold specific signals is the hydrophobic core in globular proteins , and the TM region in membrane proteins . Unlike globular proteins , the hydrophobic TM region is continuous in the membrane protein's primary structure , and indeed this still remains one of the preferred methods to identify membrane proteins , and map their TM regions . While it is intuitive that the synchronous stretch of hydrophobic residues is responsible for membrane localization , the application of a scoring method that can distinguish these residues from family-wide alignment columns associated with other functions has not yet been deployed . In essence , we require a method that can objectively separate the TM signals from other signals . To overcome these limitations , we improved existing method ( s ) of information theory wherein REM was calculated on the basis of MSA of MFS proteins , keeping in mind the differences in the environmental milieus . We thus treated TM and inter-TM regions by different background probabilities for calculation of REM . These REM scores helped us to predict those sites which have amino acid distributions very different from the respective background distribution thereby statistically predicted to be functionally critical . Not all the residues predicted using REM , are present in every protein in the family . In CaMdr1p , 16 residues were identical with the most frequently occurring residue in the thirty highest scored alignment columns , and were mutated to directly validate the prediction ( Fig . 2B ) . Our results of drug susceptibility assays revealed that almost all of these matched residues with high REM when replaced with alanine displayed sensitivity to the tested drugs and showed abrogated drug transport ( Fig . 3 ) . Interestingly , when we mutated residues which had high conservation values but lower REM values ( negative control ) ; none showed alterations in drug susceptibilities and thus did not retain the functionally critical stringency as was evident from residues with higher REM . For example , analysis of a few conserved columns of the MSA , such as F216 , L217 and L171 having REM values between 0 . 57-0 . 44 revealed that their replacement with alanine did not affect the function of CaMdr1p ( data not shown ) . This strengthens the fact that our method takes into account the conservation along with the background frequency and thus lists out residues which affect the function . Also , to check the efficiency of the method , another negative control used was to mutate residues which are having low conservation and low REM values but lie in the vicinity of one of the 16 selected high REM residues . For example , when C225 which is closer to the critical G219 and D235 , was mutated to C225A , the functioning of the protein was not affected ( data not shown ) . Similarly , for critical G256 , when residues A248 , A253 and V254 which are within its vicinity were mutated as A248G , A253G and V254A , the mutant variants continued to behave as WT-CaMDR1-GFP [35] . To further elucidate the role of predicted residues in the functionality of CaMdr1p , a homology model based on the available crystal structures of lac permease , glycerol-3-phophate and oxalate: formate transporter was deduced [9]-[11] . The REM method predicts the relative importance of a residue purely from sequence analysis and is independent of the protein's structure . However , the role a residue plays in the protein's function is not readily apparent from its sequence . We exploited the protein's 3D model as a guide to reason why a residue is functionally critical . The deduced 3D model suggested that similar to other MFS structures , the 12 TM helices of the CaMdr1p span the membrane in such a way that they form the channel pore particularly aligned by residues of TMS 2 , 4 , 5 , 7 , 8 , 10 and 11 . From the deduced homology model of CaMdr1p , a symmetric contact map was generated to highlight the inter-helical interactions of the protein ( Fig . 5B ) . Based on the predictions from the distance map , we could show that many high REM residues are indeed a part of inter-helical interactions ( Fig . 6B ) . It is apparent that more than one residue pair is predicted to be involved in maintaining the interactions between helices ( Fig . 6A ) . Our aim in developing this method was to identify residues with high specificity which would play a critical role across this entire MFS protein family . Although signals associated with antiporter motifs have been identified using this method , a finer granularity in function such as substrate specificity determining residues is not expected , as these signals would not be family-wide . Since the enlisted residues with high REM values which are functionally critical for CaMdr1p are expected to be family-wide function-specific and thus critical for the entire MFS protein data set , we validated their relevance from the earlier published work . It is known that Motif A of the MFS transporters span an eight residue long loop between TMS 2 and 3 and is suggested to be involved in maintaining a β-turn linking the adjacent TM helices [14] . In the present study , G183 and R184 in the loop between TMS 2 and TMS 3 of CaMdr1p were picked up as family-wide function-specific residues thus corroborating that these residues are a part of Motif A ( GxLaDrxGrkxxl ) which holds importance throughout the MFS transporters . The hypothesized rocking motion in MFS presumably requires conformational changes in the TMS and the β–turns . In this , the transporter inter-converts between Ci ( inward facing ) and Co ( outward facing ) states for translocation of substrates . In glycerol-3-phosphate of E . coli , it was seen that D88 was involved in inter-conversion between these Ci and Co states of the protein [10] . Interestingly , D88 corresponds to E178 of CaMdr1p which also lies in Motif A which upon mutation to alanine turns out to be critical for drug susceptibility and efflux ( Table 1 ) . Motif B ( lxxxRxxqGxgaa ) of all MFS has a role in energy coupling which spans the N-terminal half of TMS 4 [7] . CaMdr1p contact map reveals that residues in Motif B interface with residues 165GxxxG169 on TMS 2 . Motifs rich in glycine and proline residues promote formation of special backbone conformation including kinks in TMS , tight interactions between TMS and very flexible β-turns . In human VAchT , Motif B and the adjacent sequences contain a total of nine notch signatures . A notch allows two helical TMS to approach each other unusually closely because small side chains are located at the interface . R124 of PcaK of Pseudomonas putida which is equivalent to high REM R215 of CaMdr1p of C . albicans is shown to be critical for helix packing [37] . Interestingly , G111 of LacY of E . coli which also occupies a position in the same alignment column is also critical and earlier shown to be a residue at a kink . Residues from Motif C ( gxxxGPxxGGxl ) which is exclusive to 12-TMS family are also picked up by our calculations [7] . G150 of LacY of E . coli which is equivalent to high REM G256 of CaMdr1p is function-specific for LacY protein [38] . A stretch of conserved residues 296Pespr300 , previously unidentified , at the end of TMS 6 were also predicted with high REM . We have mutated equivalent residues P296A , E297A and T298A of CaMdr1p that overlap with the consensus residues in the stretch and found that cells expressing these mutated variants displayed increased sensitivity to drugs . However , the functional significance of these residues is yet to be established . There are a few exceptions which emerged from our method . For example , our method did not pick up any residue of Motif D2 . This could be an artifact of the method used for alignments in earlier studies . In this study we have employed a membrane protein specific alignment method whereas earlier reports have used standard multiple alignments substitution matrices with smaller data sets . However , when we repeated the alignment using MUSCLE [39] and with the standard substitution matrix ( Blosum 62 ) [40] on the complete data set the motif still did not appear ( data not shown ) . Motif D2 is assumed to have a structural significance as it holds a major kink within TMS 1 but mutations in this motif do not alter the backbone conformation . As an example of the possibly insignificant role of the motif , in human VAchT , the mutation of L49G in this motif completely eliminates propensity for a kink or notch and abolishes activity while normally a glycine itself is expected to be present at this position and is supposed to be involved in maintaining a major kink in this motif [41] . Out of the 16 residues that were mutated , T160A , L211A , W273A and R274A did not lead to any phenotypic changes . It is known that for some of the positions in alignment , the most frequent amino acid does not match with the residue of CaMdr1p at that site . One reason for this could be that some of the functionally important residues co-evolve i . e . , these residues may mutate , with compensatory mutation occurring elsewhere in the protein to regain function [42] . T160 where the most frequent residue is a serine at that position may be one such case . Another reason may be that the alignment used in this study involved prediction of TMS with the possibility of errors in demarcating the edges of TM helices . Residues from columns lining the edges of the helices may be wrongly assigned to TM and inter-TM regions . This probably explains the lack of any effect of mutation on residues T160 of TMS 2 and L211 of TMS 4 which lie at the edge of the respective TMS . Other exceptions to our predictions are the mutation of W273 and R274 which though highly conserved and probably a part of the new motif but do not abrogate function upon mutation . Although a few tryptophans in an ABC transporter MRP1 , have been shown to be involved in substrate binding and transport [43] , generally , in a membrane protein tryptophan residues located on the surface of the molecule are mainly positioned to form hydrogen bonds with the lipid head groups while their hydrophobic rings are immersed in the lipid part of the bilayer [44] . We predict that W273 and R274 may be associated with membrane helix orientation and this function may not be perturbed by mutating them individually through alanine scanning . Alternatively , the tryptophan-arginine residues could be functionally critical in tandem and compensate each other for the loss of either one of them . Of note , in our predictions , substrate specific residues with high REM are not picked up which predominantly occur in C-terminal of MFS proteins . It should be mentioned that since our alignment considers the entire MFS , residues responsible for substrate specificity would only be selectively conserved within a subfamily and would not have sufficiently strong signals to be visible in this present family-wide study . For this , the same method may be applied to a data set classified on the basis of substrate selectivity to identify residues critical to the functioning of that subfamily . There are a number of conservation methods known but none has yet achieved both biological and statistical rigor . We have used REM to separate conserved residues from the background function of TM localization . The interpretations support the well-known fact that MFS has a conserved N-terminal half which has residues important for maintenance of a specific fold for this class of proteins while C-terminal half has a more specific role in substrate binding and recognition [7] . Taken together , our study provides an insight into the molecular details of MFS transporters in general and CaMdr1p in particular . Our method of scaled REM calculations improves its performance over other information theoretic methods . Additionally , this study also provides a method for rational mutational analysis not only for MFS proteins but can be applied to any class of membrane proteins and thus makes it possible to predict and locate family-wide functionally relevant residues .
Anti-GFP monoclonal antibody was purchased from BD Biosciences Clontech , Palo Alto , CA , USA . DNA modifying enzymes were purchased from NEB . The drugs cycloheximide ( CYH ) , 4-Nitroquinoline oxide ( 4-NQO ) , Methotrexate ( MTX ) and Protease inhibitors ( Phenylmethylsulfonyl fluoride , Leupeptin , Aprotinin , Pepstatin A , TPCK , TLCK ) and other molecular grade chemicals were obtained from Sigma Chemicals Co . ( St . Louis , MO , USA ) Fluconazole ( FLU ) was generously provided by Ranbaxy Laboratories , India . [3H] Fluconazole was custom prepared and [3H] Methotrexate ( MTX ) was purchased from Amersham Biosciences , United Kingdom . Plasmids were maintained in Escherichia coli DH5α . E . coli was cultured in Luria-Bertani medium ( Difco , BD Biosciences , NJ , USA ) to which ampicillin was added ( 100 µg/ml ) . The S . cerevisiae strain used was AD1-8u− ( MATa pdr1-3 his1 ura3 Δyor1::hisG Δsnq2::hisG Δpdr5::hisG Δpdr10::hisG Δpdr11::hisG Δycf1::hisG Δpdr3::hisG Δpdr15::hisG ) , provided by Richard D . Cannon , University of Otago , Dunedin , New Zealand [33] , [34] . The yeast strains used in this study are listed in the Supplementary Table S3 . The yeast strains were cultured in YEPD broth ( Bio101 , Vista , CA , USA ) or in SD-ura− dropout media ( 0 . 67% yeast nitrogen base , 0 . 2% dropout mix , and 2% glucose; Difco ) . For agar plates , 2 . 5% ( w/v ) Bacto agar ( Difco , NJ , USA ) was added to the medium . | Membrane proteins belonging to the Major Facilitator Superfamily ( MFS ) transport molecules , including drugs , across the membrane and are known to be associated with drug resistance . CaMdr1p is one such MFS major multidrug efflux pump whose over-expression is linked to frequently encountered azole resistance in hospital isolates of C . albicans . Amino acid residues critical for a protein's function are conserved across members of the protein family . However , the traditional measure of conservation is not a useful parameter in mapping a functionally important residue in membrane proteins e . g . , hydrophobically conserved stretches form helical transmembrane regions of the protein and are responsible for membrane localization , which individually have limited effect on binding and transport . We have developed a method that uses information theory to score the conservation of a residue relative to its context within the membrane and hypothesize that these residues would be critical for the protein's function . The relevance of predicted residues in the functioning of MFS is validated on CaMdr1p . | [
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] | 2009 | Rational Mutational Analysis of a Multidrug MFS Transporter CaMdr1p of Candida albicans by Employing a Membrane Environment Based Computational Approach |
Tick-borne rickettsiae are considered to be emerging , but data about their presence in western Europe are scarce . Ixodes ricinus ticks , the most abundant and widespread tick species in western Europe , were collected and tested for the presence of several tick-borne pathogens in western France , a region never previously explored in this context . There was a high tick abundance with a mean of 4 females , 4 . 5 males , and 23 . 3 nymphs collected per hour per collector . Out of 622 tested ticks , specific PCR amplification showed the presence of tick symbionts as well as low prevalence of Borrelia burgdorferi ( 0 . 8% ) , Bartonella spp . ( 0 . 17% ) , and Anaplasma phagocytophilum ( 0 . 09% ) . The most prevalent pathogen was Rickettsia helvetica ( 4 . 17% ) . This is the first time that this bacteria has been detected in ticks in this region , and this result raises the possibility that bacteria other than those classically implicated may be involved in rickettsial diseases in western France .
Ticks are one of the most important infectious disease vectors worldwide , and are second only to mosquitoes in the frequency of human pathogen transmission [1] . They are obligate blood feeding arthropods and transmit the largest variety of pathogens . Currently , the emergence of tick-borne diseases ( TBD ) is a growing concern , and their incidence is on the rise in many European countries favored by both socio-economic and environmental changes , and highlighting the need to increase surveillance of tick populations and associated pathogens [2–5] . Lyme disease caused by Borrelia species is unquestionably the predominant concern for the northern latitude [1] . However , ticks , and in particular Ixodes ricinus in Europe , which frequently bites humans , can transmit a large variety of other potentially dangerous human pathogens [3] including rickettsiae [6] . Rickettsiae are obligate intracellular alpha-proteobacteria distributed worldwide , and transmitted to humans and animals via arthropod vectors including insects , as well as ticks and mites [7] . Ticks are known vectors of rickettsiae responsible for spotted fever syndrome in humans , which is caused by at least 15 different Rickettsia species . The most life-threatening species are R . rickettsia , the agent of Rocky Mountain spotted fever and R . conorii , the causative agent of Mediterranean spotted fever , but several species of tick-borne rickettsiae that were considered non-pathogenic for decades are now associated with human infections [7] . Among potential emerging rickettsia species , Rickettsia helvetica is considered as an emerging tick-borne pathogen ( TBP ) , and has been first recognized in 1979 in I . ricinus as a new member of the spotted fever group of Rickettsia [8] . Later , in eastern France , following a human febrile infectious syndrome with specific seroconversion against R . helvetica , a 9 . 2% seroprevalence rate was reported in humans exposed to tick bites [9] . The bacteria has also been isolated from I . ricinus in central France , confirming its presence in this region , as suspected from a previous seroprevalence survey [10] . Since , R . helvetica has been associated with two cases of fatal perimyocarditis in Sweden , as the bacteria was detected by both Polymerase Chain Reaction ( PCR ) and immunohistochemistry in the pericardium , the pulmonary hilum , coronary artery and the heart muscle [11] . The bacteria has also been detected by PCR in samples obtained from two dead patients with sarcoidosis , and immunohistochemical examination showed presence of rickettsia-like organisms , suggesting that it may contribute to the granulomatous process , as is seen in sarcoidosis [12] . Antibodies against R . helvetica have been also associated with febrile illness after tick bite in several European and South-East Asian countries [13] , where immunohistochemical examination has confirmed the presence of the bacteria [14] . Lastly , in addition to positive serology , R . helvetica has been detected by PCR in two patients with acute febrile illness , rash and long-lasting myasthenia [15] , and subacute meningitis [16] . In Europe , R . helvetica is strongly suspected to be transmitted by I . ricinus , being detected in ticks in several European countries [14 , 17–28] . To confirm vector transmission , competence studies under controlled conditions are required . However , indirect proof of tick vector competency has been reported through detection of anti-R . helvetica antibodies in people exposed to tick bites [29] . In addition , the detection of R . helvetica in engorged I . ricinus found on unifected hosts as well as vertical transmission in ticks , strongly suggest this tick species is a reservoir of this pathogen [30 , 31] . Due to the broad host range of I . ricinus , many vertebrate species may also serve as potential reservoirs for the bacteria . R . helvetica has been found in blood from mice , wild rodents , roe deer , and wild boar , all without clinical signs of infection [23 , 30] , suggesting a zoonotic cycle , in which humans represent recent and accidental hosts . In the Netherlands , 24 . 7% of I . ricinus ticks collected from domestic animals were found to be infected with R . helvetica [19] when in Switzerland , 50% and 28% of ticks collected from cats and dogs respectively , were positive [32] . Interestingly , these infection rates were higher in ticks collected from these animals than in ticks collected from vegetation in the same region . A similar result was observed for ticks collected from roe deer , dogs and birds elsewhere in Europe [33–36] . Altogether , these results suggest that both domestic and wild animals may act as reservoirs for R . helvetica transmitted by I . ricinus . Fastidious epidemiological studies are still required in order to have a better understanding of the geographical distribution of TBP , and to increase public awareness of the potential danger represented by ticks . The aim of the present study was to obtain an overall picture of potentially high-risk TBP circulating in northern Brittany in western France , a region never previously explored in this context .
Ticks were collected in May 2014 in the “forêt de la Hunaudaye” ( 10 . 40km2 , 48 . 482945 N; 2 . 365779 W ) , Côtes d’Armor , Brittany , western France , located 15 km from the sea ( Fig 1A and 1D ) . Given that tick abundance is influenced by climate , vegetation , elevation , and host presence and densities , a complete description of these factors in the studied area was made to enable comparison with current and future studies ( Fig 1 ) . All maps were created using the WGS84 coordinate reference system; shapefiles were converted if needed . All spatial and geographical data were processed with R [37] . The climate map , created using data of the Köppen-Geiger climate classification [38] , showed a temperate climate with warm summer ( Fig 1B ) without dry season ( Fig 1C ) . Average monthly minimum and maximum temperatures and precipitation were downloaded from the WorldClim website ( version 1 . 4; http://www . worldclim . org ) with a resolution of 30 arc-seconds [39] . SRTM 90m Digital Elevation Data were downloaded from the CIAT-CSI SRTM website [40] and elevation ranged from 71–112 meters across the forest ( Fig 1D ) . Land cover data were taken from Broxton et al . [41] . Wood areas , rivers , roads , railway and buildings spatial data were downloaded from OpenStreetMap website ( http://www . openstreetmap . org ) . Crop types per plot were taken from the "Registre Parcellaire Graphique Bretagne 2013 Contours des îlots culturaux et leur groupe de culture majoritaire" , downloaded from GéoBretagne website ( geobretagne . fr/geonetwork/srv/fre/pdf ? id=18162 ) . The study site is mainly covered by croplands , natural vegetation and mixed forests ( Fig 1E ) , populated by beech ( Fagus sylvatica ) and oak ( Quercus sp . ) with conifers and holly ( Ilex aquifolium ) . The forest massif is comprised of both state forest and private plots representing an overall surface of 25 . 98 km2 ( Fig 1F ) . Fauna populating the forest include deer ( Cervus elaphus ) , roe deer ( Capreolus capreolus ) , wild boar ( Sus scrofa ) , diverse rodents , and birds . This forest is situated in a rural area and is surrounded by cattle farms and cultivated land ( Fig 1F , 1G and 1H ) . Number of cattle per district was extracted from 2010 agricultural census data ( Agreste database , data . gouv . fr ) and were normalized by district surface . Thanks to numerous paths enabling recreational activities , the forest is highly frequented by walkers with also a lot of hunting activity , including hunting with hounds . Questing ticks ( nymphs and adults ) were collected using the flagging method , whereby 1 m2 cotton cloths are dragged over the vegetation , from 16:30 to 19:30 on the 24th of May 2014 by four collectors and from 15:30 to 18:00 on the 25th by three collectors . The weather varied between overcast and sunny , ground vegetation remained wet , and the temperature remained 17–18°C . Tick activity was estimated as number of ticks per collector per hour , as previously calculated [17] . All specimens , returned alive to the laboratory , were then identified to the species level using taxonomic keys , categorized by sex and life stage , and frozen at -20°C prior to DNA extraction . Ticks were crushed , individually for adults and in pools of five for nymphs , by shaking with a bead beater ( mixer mill MM301 , Qiagen , Hilden , Germany ) as previously described [42] . DNA was extracted using the Nucleospin Tissue kit according to the manufacturer’s instructions ( Macherey-Nagel , Duren , Germany ) . Adults and nymph pools were eluted in a final volume of 50 μL . DNA extracts were then stored at -20°C until use . DNA extraction efficiency was confirmed in all samples with polymerase chain reaction ( PCR ) amplification of the 16S rRNA mitochondrial gene using tick-specific primers TQ16S+1F ( 5′-CTGCTCAATGATTTTTTAAATTGCTGTGG-3′ ) and TQ16S-2R ( 5′-ACGCTGTTATCCCTAGAG-3′ ) , as described [43] . Specific PCRs were used to detect the presence of B . burgdorferi s . l . , Anaplasma spp . /Candidatus Midichloria mitochondrii/Wolbachia spp . , SFG Rickettsia spp . , Babesia/Theileria spp . , F . tularensis and Bartonella spp . DNA in tick extracts as previously described [42] . All PCR reactions were performed in a MyCycler thermocycler ( Bio-Rad , Strasbourg , France ) . Each reaction was carried out in a 25 μL volume containing 0 . 5 μmol/μL of each primer , 2 . 5 mmol/L of each dNTP , 2 . 5 μL of 10X PCR Buffer , 1U of Taq DNA polymerase ( Takara Biomedical Group , Shiga , Japan ) , and 5 μL of each DNA extract . Negative ( sterile water ) and positive DNA controls were included in each run as previously described [42] . Qiagen ( Hilden , Germany ) performed sequencing on all positive samples , either directly on the PCR product or following extraction from agarose gel and purification using the NucleoSpin Extract II kit ( Macherey-Nagel , Duren , Germany ) . Sequences obtained were compared with known sequences listed in the GenBank nucleotide sequence databases via the National Center for Biotechnology Information Blast search option ( www . ncbi . nlm . nih . gov/BLAST ) , and sequence data were deposited in GenBank . Prevalence rates and exact binomial 95% confidence intervals were independently calculated for each microorganism in male and female adult ticks using Ecological Methodology software [44] . Prevalence rates were compared between males and females with the Fisher Exact test , using Genstat version 15 ( VSN International Ltd . , Hemel Hempstead , UK ) . For the pooled nymph samples , we employed the exact method of Hauck , assuming perfect sensitivity and specificity of our pathogen detection methods [45] . Hauck noted a one-to-one relationship between individual level prevalence , π , and the prevalence of positive pools , P . A point estimate for the prevalence rate can thus be obtained from the pool positive rate by π = 1- ( 1-P ) 1/k where k is the number of nymphs per pool . Exact 95% confidence intervals were then obtained by assuming a binomial distribution for the number of positive pools [46] . Nymph and adult female and/or male samples were then compared and considered to be significantly different if there was no overlap in 95% confidence intervals . In addition , the estimated nymph prevalence rates were used to estimate the number of individual nymphs infected . Prevalence rates of nymphs and adult ticks were then compared with the Fisher Exact test . The obtained sequences were submitted to Genbank with the following accession numbers: A . phagocytophilum: KU559922; R . helvetica: KU559920; and C . Midichloria mitochondrii: KU559921 .
A total of 622 ticks were collected from the vegetation , of which all were identified as I . ricinus . The collection comprised 78 females , 89 males , and 455 nymphs , which corresponded to 4 females , 4 . 5 males , and 23 . 3 nymphs collected per hour per collector . DNA was extracted from 258 samples: 91 pools with 5 nymphs each , and 167 single adults . The I . ricinus 16S rRNA gene was amplified in 231/258 samples ( 90% ) , which were then included in the study . No amplification products were obtained for 27 samples , corresponding to 18 females , 4 males , and 5 pools of nymphs , reflecting a probable failure of the DNA extraction , and were thus excluded from the analysis . PCR detection results are presented in Table 1 . Sequencing of Anaplasma spp . positive samples revealed only one pool of nymphs positive for Anaplasma phagocytophilum , whereas the remaining positive samples indicated the presence of the tick symbiont , C . Midichloria mitochondrii . The estimated point prevalence in nymphs of C . Midichloria mitochondrii and A . phagocytophilum was 13 . 4% and 0 . 2% respectively , with an overall prevalence in all ticks of 11 . 3% and 0 . 09% respectively . C . Midichloria mitochondrii prevalence rates were significantly higher in adult females ( 16 . 7% ) than males ( 4 . 7% ) ( P = 0 . 021 ) , whereas the estimated point prevalence in nymphs was not different to rates observed in adult females and males ( overlapping 95% confidence intervals , Fisher Exact p-value = 0 . 52 nymphs in comparison to adult females , and P = 0 . 068 with adult males ) . In contrast , the percentage of Rickettsia spp . positive samples was significantly higher in adult males ( 10 . 5% ) than either females ( 0% ) ( P = 0 . 007 ) or nymphs ( 1 . 9% , non-overlapping 95% confidence intervals , Fisher’s Exact p-value = 0 . 032 ) . Sequencing analysis demonstrated that all amplified sequences corresponded to R . helvetica with 100% identity with sequences present in databanks . B . burgdorferi and Bartonella spp . infection prevalence rates were very low , with no differences between adult males , females , and nymphs . Unfortunately , we were unable to sequence the corresponding amplicons for these two genera . No positive samples of Babesia , Theileria , or Francisella spp . were identified .
Relatively few epidemiological surveys have explored simultaneously the presence of multiple emerging human tick-borne pathogens considered to be important in France , as well as in Europe generally . To determine the presence of such pathogens in a french western region never previously investigated , 622 I . ricinus ticks were collected and screened for DNA of pathogens in a typical recreational Brittany forest . Tick abundance was higher than those previously obtained from the Sénart forest near Paris , France [17] . Thus this high tick abundance justifies increased surveillance for those TBP that could be transmitted to humans . Firstly , we detected the presence of C . Midichloria mitochondrii , an intra-mitochondrial symbiont bacterium detected in several tick genera including Ixodes spp . [47] . This bacterium may have a possible helper role in tick molting processes [48] , and despite believed to be harmless to mammals , it was recently suggested that it can be pathogenic for some vertebrate hosts [49] , and may have possible roles in the transmission of other tick-borne pathogens [47] . As for the known TBP , A . phagocytophilum was detected in only one nymph pool , leading to an overall prevalence of 0 . 09% , reflecting reported rates in France [17 , 50–53] . B . burgdorferi s . l . had an overall low prevalence ( 0 . 8% ) , similar to some rates previously reported in France , which can varied from 0 to 29% [17 , 51 , 53 , 54] . The absence of Babesia sp . in the study area was surprising considering the proximity of numerous bovine herds , which could act as Babesia divergens reservoirs ( Fig 1G ) [55] , and the presence of roe deer ( promoted by arable farmed areas ) in the forest , believed to be Babesia venatorum parasite reservoirs [56] . Regarding the increasing numbers of reports on the pathogenicity of R . helvetica in humans , the most significant result of the present study was the relatively high R . helvetica prevalence rate of 4 . 17% in questing I . ricinus . This is higher than the rate previously observed in 2006 in ticks from another area in western France 150 km from the current area ( 1 . 4% ) [53] , but is similar to rates reported near Paris ( France ) in 2008 [17] . In an extensive study evaluating the occurrence of Rickettsia spp . in the Netherlands from 2000 to 2008 , Sprong et al . reported prevalence rates from 6% to 66% in ticks depending on location , emphasizing the heterogeneous but increasing and persistent presence of this bacterium in Europe [23] . Indeed , the reported occurrence of this bacterium in ticks has varied from 3–14% in other European countries [18 , 57] . The recent reports presenting evidence of R . helvetica bacteraemia in birds , including migratory species , as well as R . helvetica presence in bird ticks , highlight the danger represented by avian populations for both enzootic maintenance and potentially vast distribution zones of the bacteria and infected ixodid ticks throughout Europe [33–35] . It was surprising that R . helvetica was not detected in female ticks in our study , when in the Netherlands , Sprong et al found no differences between tick life-stages [23] , and when usually infection prevalence in questing adults ticks exceeded infection rates in questing nymphs [58] . This discrepancy may suggest lowered transtadial transmission efficiency between nymphs and females and/or influence of tick microbiomes that may differ between tick life stages , and requires further investigation . Given that vertical bacterial transmission has been demonstrated in ticks under laboratory conditions , we should perhaps reconsider whether R . helvetica is predominantly a tick symbiont rather than a pathogen; this again highlights—for rickettsiae in particular and tick-borne microorganisms in general—the fine line between pathogenic and symbiont status [31] . Our findings contribute further knowledge to the geographic distribution of the studied pathogens , and to the significant risks of infection in people exposed to I . ricinus ticks , including R . helvetica , considered as an emerging TBP able to infect humans . Our results confirm R . helvetica’s reported wide distribution in Europe , emphasize that R . helvetica infection must be considered when diagnosing patients bitten by ticks in western France , where , although Lyme disease is now a recognized public health issue , it is not the case for the other TBD , such as the rickettsioses . Further studies are now required to improve pathogen characterization , to clarify R . helvetica’s pathogenicity in humans , and to evaluate the role of ticks as reservoirs and in the spread of infection . | Due to socio-economical and meteorological changes , the geographical distribution of several tick species is changing . As ticks are the most important vectors of pathogens in northern latitudes , performing epidemiological studies is essential to assess the extent of tick activity as well as the risk of pathogens transmission . The detection of the bacteria Rickettsia helvetica in questing ticks in western France , for the first time , raises the possibility that bacteria other than those classically implicated may be involved in rickettsial diseases in this region . | [
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... | 2017 | First identification of Rickettsia helvetica in questing ticks from a French Northern Brittany Forest |
The meiosis-specific chromosomal events of homolog pairing , synapsis , and recombination occur over an extended meiotic prophase I that is many times longer than prophase of mitosis . Here we show that , in mice , maintenance of an extended meiotic prophase I requires the gene Meioc , a germ-cell specific factor conserved in most metazoans . In mice , Meioc is expressed in male and female germ cells upon initiation of and throughout meiotic prophase I . Mouse germ cells lacking Meioc initiate meiosis: they undergo pre-meiotic DNA replication , they express proteins involved in synapsis and recombination , and a subset of cells progress as far as the zygotene stage of prophase I . However , cells in early meiotic prophase—as early as the preleptotene stage—proceed to condense their chromosomes and assemble a spindle , as if having progressed to metaphase . Meioc-deficient spermatocytes that have initiated synapsis mis-express CYCLIN A2 , which is normally expressed in mitotic spermatogonia , suggesting a failure to properly transition to a meiotic cell cycle program . MEIOC interacts with YTHDC2 , and the two proteins pull-down an overlapping set of mitosis-associated transcripts . We conclude that when the meiotic chromosomal program is initiated , Meioc is simultaneously induced so as to extend meiotic prophase . Specifically , MEIOC , together with YTHDC2 , promotes a meiotic ( as opposed to mitotic ) cell cycle program via post-transcriptional control of their target transcripts .
Meiosis is a specialized cell division program that results in the halving of parental genetic material and the production of haploid gametes . This reductive division depends on a series of chromosomal events that occur specifically during meiotic but not mitotic prophase , including the loading of meiosis-specific cohesins on sister chromatids , alignment and synapsis of homologous chromosomes , and generation of covalent linkages between homologs via recombination . These meiotic chromosomal events occur during meiotic prophase I , which takes much longer than mitotic prophase . In yeast , it has been shown that completion of these chromosomal events requires the extended prophase I: yeast meiotic prophase I lasts 3 . 5 hours , compared to 15 minutes for mitotic prophase [1] , and premature exit from prophase I results in recombination defects and chromosome missegregation [2] . Mammals similarly have an extended prophase I . In female mice , ovarian germ cells initiate meiosis around embryonic day 13 . 5 ( E13 . 5 ) , and arrest in the penultimate stage of prophase , diplotene , around the time of birth , one week after meiotic initiation [3 , 4] . In male mice , cohorts of testicular germ cells initiate meiosis continuously throughout post-pubertal life , each cohort taking two weeks from initiation to completion of meiotic prophase I [5] . In contrast , the typical mitotic prophase in mammalian cells lasts only minutes [6 , 7] . No mechanism for extension of meiotic prophase has yet been recognized in mammals . In other organisms , the extension of meiotic prophase is accomplished by meiosis-specific modifications of the cell cycle . In yeast , exit from meiotic prophase I is postponed via the suppression of mitotic cell cycle regulators by a meiosis-specific form of the anaphase-promoting complex [2] . In worm and fly , exit from meiotic prophase I is also actively suppressed by meiosis-specific factors via translational repression of , respectively , cyclins E and A [8 , 9] . Since meiotic initiation in both male and female mice is governed by the retinoic acid-induced gene Stra8 [10 , 11] , STRA8 activity might be at least indirectly related to prolonging prophase . Ovarian and testicular germ cells express Stra8 shortly before entering meiotic prophase I [12 , 13] , and Stra8 is required for the chromosomal events of meiotic prophase I , including cohesion , synapsis , and recombination [14 , 15] . Consistent with a pivotal role in meiotic initiation , most genes involved in meiotic prophase I depend on Stra8 for their expression . However , Stra8 is only transiently expressed at the time of meiotic initiation , and therefore is unlikely to be the factor responsible for maintaining meiotic prophase I . We previously identified a subset of early meiotic genes that are expressed independently or partially independently of Stra8 , and are induced concurrently or shortly after Stra8 [16 , 17] . This subset of partially Stra8-independent early meiotic genes includes cohesins and synaptonemal complex proteins with known meiotic functions , and also Meioc , an uncharacterized gene formerly named Gm1564 . We examined MEIOC expression and find that it is expressed throughout meiotic prophase I in both testicular and ovarian germ cells; this expression profile suggests that its function begins early and persists throughout meiotic prophase I in both sexes . We examined mice deficient for Meioc , and found that Meioc-deficient germ cells can initiate but do not complete meiotic prophase I . Instead , germ cells that have initiated meiosis proceed prematurely to an aberrant metaphase . Meioc-deficient germ cells that have initiated meiosis mis-express CCNA2 , which is typically expressed in mitotic spermatogonia , suggesting a failure to properly transition to a meiotic cell cycle program . We propose that Meioc functions continuously throughout meiotic prophase I to prevent premature exit from prophase I , likely by promoting a meiotic ( as opposed to mitotic ) cell cycle program . Further , MEIOC interacts with an RNA helicase , YTHDC2 , and binds a common set of germ cell transcripts , suggesting that MEIOC and YTHDC2 partner to regulate these transcripts . Our observations that Meioc-deficient germ cells fail to complete meiotic prophase I and instead produce numerous abnormal metaphases are concordant with a recent study [18] . However , whereas Abby and colleagues propose that this phenotype results from arrested meiotic progression , our molecular analyses of cyclin expression and MEIOC-bound transcripts lead us to an alternate interpretation of the phenotype–that the precocious metaphases observed are a result of cell cycle mis-regulation . We propose a model for meiotic prophase I as comprised of multiple subprograms: these include the chromosomal program , whereby chromosomes synapse and undergo recombination , and a meiosis-specific cell cycle program , whereby cells are maintained in an extended prophase I to allow completion of the chromosomal program .
We had previously identified Meioc ( Gm1564 ) as one of the earliest and most strongly induced transcripts upon meiotic initiation in the female germline [17] . In the study presented here , we identified full-length Meioc homologs in almost all vertebrate genomes examined . Furthermore , we found that Meioc’s conserved C-terminal domain , PF15189 ( previously DUF4582 ) , is present approximately once per genome in almost all metazoan genomes examined ( S1 Fig ) . We were unable to identify orthologs of Meioc or matches to PF15189 in Diptera , including Drosophila melanogaster , which hints at Meioc being replaced functionally by alternate proteins or pathways in this lineage . Next , we examined Meioc expression in adult tissue panels from human , mouse , rat , and chicken , and found its expression to be highly testis-specific ( S2 Fig ) . Thus , Meioc is a highly conserved gene whose expression pattern across diverse species is consistent with a role in meiosis . To determine the precise cell types in which MEIOC is expressed , we generated a rabbit polyclonal antibody to a C-terminal fragment of MEIOC , which we verified to be specific using subsequently generated Meioc-deficient mice ( S3 Fig ) . Immunohistochemistry for MEIOC on adult testis sections showed that MEIOC is expressed in spermatocytes , beginning in preleptotene and extending through most stages of meiotic prophase I , including leptotene , zygotene , and pachytene , but not during diplotene and diakinesis ( the final stages of meiotic prophase I ) or meiotic metaphase I ( Fig 1A ) . MEIOC is absent in spermatogonia , in post-meiotic spermatids , and in somatic cells . Subcellular localization of MEIOC during early to mid-prophase I was predominantly cytoplasmic , but by late pachytene a fraction of MEIOC was nuclear . The prolonged expression of MEIOC contrasts starkly with that of STRA8 , which is similarly induced in preleptotene cells , but then rapidly down regulated once cells enter leptotene ( Fig 1B ) . To determine if MEIOC is expressed at similar stages of meiotic prophase in the female , we immunostained for MEIOC on fetal ovary sections ( Fig 1C ) . MEIOC was detected by E13 . 5 , when germ cells are preparing to enter meiosis , and persists through leptotene , zygotene , pachytene stages of meiotic prophase , and dictyate arrest in the adult . In females , MEIOC is initially predominantly cytoplasmic , but becomes predominantly nuclear postnatally . Thus , MEIOC is similarly expressed in both sexes: beginning at meiotic initiation , and persisting through most of meiotic prophase in the male , and through to dictyate arrest in the female . To our knowledge , this combination of germ-cell-specific expression throughout most of meiotic prophase and predominantly cytoplasmic localization is unique to MEIOC . Our characterization of MEIOC expression broadly agrees with results obtained using antibodies generated against full-length MEIOC [18] , with a few exceptions: Abby and colleagues reported an exclusively cytoplasmic localization , whereas we observed MEIOC attaining nuclear localization towards the end of meiotic prophase . To explore the role of Meioc in germ cell differentiation and meiotic prophase , we generated Meioc-deficient mice using a targeting vector generated by the Knockout Mouse Project ( KOMP ) ( S4 Fig ) . Results reported here were performed in mice 5 to 7 generations backcrossed to the C57BL/6 background unless otherwise stated . Meioc-deficient mice ( Meioc -/- ) had markedly smaller ovaries and testes than did wild-type control ( Meioc +/- , and Meioc +/+ ) mice ( S5A Fig ) , and they were infertile . Meioc-deficient adult testes completely lacked post-meiotic germ cells ( S5B and S5C Fig ) and were dramatically depleted of cells in meiotic prophase I compared to littermate controls . To study progression through meiotic prophase I in a synchronous setting , we examined testes at 10 and 15 days after birth ( P10 and P15 ) to follow the meiotic development of the first cohort of spermatogenic cells ( Fig 2A ) . By P10 in wild-type testes , spermatogenic cells have initiated meiosis and progressed from preleptotene to the leptotene and zygotene stages of meiotic prophase . By P15 , the most advanced spermatogenic cells have transitioned through zygotene and progressed to the pachytene stage . No later stages of meiosis , namely diplotene and meiotic metaphases , are observed at P10 and P15 . In Meioc-deficient mutants , P10 and P15 testes contained cells with chromosomes condensed like those observed during metaphase . Meiotic metaphases are not expected until P20 , and were not observed in our control P10 and P15 wild-type testes . Meioc-deficient testes also contained cells with abnormal condensed nuclei , and apoptotic cells . Mutant testes also contained leptotene and zygotene-stage spermatocytes , but were devoid of pachytene spermatocytes . TUNEL-positive cells were rare in wild-type adult testes but were abundant in Meioc-deficient adult testes , specifically in cells with condensed or apoptotic nuclei ( S6 Fig ) . TUNEL staining was not observed in preleptotene , leptotene , zygotene-like , or metaphase-like cells of Meioc-deficient testes . To determine if similar defects occur in females , we examined Meioc-deficient and wild-type ovaries . In contrast to wild-type adult ovaries , which contain follicles at various stages of maturation , adult ovaries of Meioc-deficient females contain no oocytes or follicles ( S5D Fig ) . In wild-type fetal ovaries , germ cells progress from a premeiotic stage at E14 . 5 to zygotene or pachytene stages by E16 . 5 ( Fig 2B ) . In Meioc-deficient ovaries , metaphase-like cells were observed as early as E14 . 5 , and persist to E16 . 5 . Most remaining germ cells exhibited premeiotic morphology , and few reached the leptotene or zygotene stages of prophase , even by E16 . 5 . An independent Meioc knockout mouse line generated using the same KOMP vector on a mixed genetic background ( C57BL/6 crossed to NMRI ) exhibited similar histological phenotypes [18] . To pinpoint the timing of the primary defect in Meioc-deficient germ cells , we examined the time and stage at which aberrant metaphase-like cells first arise . In the testis , they are found adjacent to preleptotene , leptotene , and zygotene spermatocytes ( Figs 2A and S7 ) . The occurrence of metaphase-like cells adjacent to preleptotene cells in stage VIII tubules suggests that metaphase-like cells first arise shortly after the preleptotene stage , before recombination and synapsis would normally occur . Some germ cells proceed somewhat further , to the leptotene or zygotene stage , possibly because the primary defect that causes premature metaphase is not completely penetrant at the preleptotene stage . In the ovary , metaphase-like cells arise as early as E14 . 5 , when most wild-type germ cells are still in the pre-meiotic stage . Thus , in both sexes , the primary defect that causes premature metaphase occurs shortly after the decision to initiate meiosis , and prior to meiotic chromosomal events such as recombination and synapsis . To confirm that Meioc-deficient germ cells have initiated meiotic prophase I , we examined Meioc-deficient testes and ovaries for molecular markers of meiotic initiation and early meiotic prophase I . Meiotic initiation requires Stra8 , a retinoic acid-induced , germ cell-specific factor [14 , 15] . Germ cells in both wild-type and Meioc-deficient P10 testes and E14 . 5 ovaries express STRA8 ( Fig 3A ) . One of the first events following the decision to initiate meiosis is premeiotic DNA replication . To detect DNA replication , we injected the thymidine analog EdU into wild-type and Meioc-deficient postnatal male mice , or into pregnant mothers carrying wild-type and Meioc-deficient fetal female mice , and harvested gonads two hours later . Both wild-type and Meioc-deficient P10 testes and E14 . 5 ovaries had numerous EdU and STRA8 double-positive cells ( Fig 3A ) , indicating that they are able to undergo premeiotic DNA replication following the decision to enter meiosis . Next , we examined Meioc-deficient germ cells for markers of the chromosomal program of meiotic prophase I , including homologous chromosome synapsis , recombination , and loading of meiotic cohesins . We first stained for components of the synaptonemal complex: the axial element protein SYCP3 , and transverse filament protein SYCP1 ( Figs 3B–3D and S8A ) [19 , 20] . In wild-type P15 testis sections and spreads , we observed SYCP3 and SYCP1 staining indicative of the leptotene , zygotene , and pachytene stages of meiotic prophase: SYCP3 staining was thin and thread-like in the leptotene stage , and progressively thickened as chromosomes synapsed through the pachytene stage . SYCP1 localized to synapsed regions of the chromosomes in zygotene and pachytene stage spermatocytes . In Meioc-deficient P15 testes , some germ cells exhibited SYCP3 and SYCP1 localization on chromosomes similar to leptotene and zygotene stages , but which were often accompanied by dense aggregates of SYCP3 . Many germ cells displayed only SYCP3 aggregates . In the metaphase-like cells , SYCP3 localized to foci at the ends of chromosomes , likely the centromeres . This pattern of SYCP3 localization is similar to that observed in the first meiotic metaphases that normally appear beginning at P20 ( Fig 3E ) [21] . In wild-type E16 . 5 ovary sections , most germ cells were in zygotene and pachytene . In Meioc-deficient E16 . 5 ovary sections , no germ cells exhibited zygotene or pachytene-like SYCP3 staining . Instead , Meioc-deficient germ cells had either leptotene-like SYCP3 staining with some SYCP3 aggregates , or only SYCP3 aggregates ( Figs 3B and S8A ) . We next assayed Meioc-deficient cells for markers of meiotic recombination . Recombination is initiated by the formation of DNA double-strand breaks ( DSBs ) that are repaired by meiotic recombinase DMC1 [22 , 23] . Cells respond to DSBs by phosphorylating the histone variant H2AX , to yield γH2AX [24] . We assessed DSB formation by co-staining for DMC1 and γH2AX alongside SYCP3 in sections and spreads from wild-type and Meioc-deficient P15 testes and E16 . 5 ovaries ( Figs 3B , 3C and S8A ) . In wild-type P15 testes and E16 . 5 ovaries , we observed DMC1 foci and γH2AX staining indicative of leptotene , zygotene , and pachytene stages of meiosis . In both Meioc-deficient P15 testes and E16 . 5 ovaries , DMC1 foci and γH2AX were also present in leptotene/zygotene-like cells . In metaphase-like cells , DMC1 foci are absent , but γH2AX staining suggests these cells suffer DNA damage . Finally , we asked if cohesins are loaded onto chromosomes of Meioc-deficient germ cells . We immunostained for REC8 , a meiotic cohesin [21 , 25] , on spreads of meiotic cells from P15 testes ( Fig 3E ) . In the leptotene/zygotene-like Meioc-deficient cells , REC8 localized along the lengths of chromosomes , much as in wild type . In metaphase-like cells , REC8 localizes to the condensed chromosomes , similar to what is observed in meiotic metaphases found in wild-type adult testes ( Fig 3E ) [21] . Quantification of cell spreads reveals that in P15 Meioc-deficient testes , metaphase-like and other abnormal germ cells ( such as those with only clumpy SYCP3 staining ) comprised about half of all germ cells ( S8B Fig ) . In contrast , no meiotic metaphases were observed in wild-type P15 testes . Meioc-deficient testes also contained more leptotene stage germ cells but fewer zygotene stage germ cells than wild-type testes . In summary , Meioc-deficient metaphase-like cells express and correctly localize proteins associated with synapsis and sister chromatid cohesion , demonstrating that the primary defect driving these cells to premature metaphase occurs after they have initiated meiosis . A subpopulation of cells is able to proceed with synapsis , cohesion , and recombination up to the leptotene/zygotene stages . Abby and colleagues focused their attention on the defects in this leptotene/zygotene cell population [18] . However , given our earlier histological analyses showing that metaphase-like cells first arise prior to leptotene and zygotene , it is unlikely that problems in synapsis and recombination cause the premature metaphases . The failure to proceed past the zygotene stage of synapsis and recombination is more likely a secondary consequence of the primary defect driving premature metaphase . Analysis of germ cells spreads showed that in Meioc-deficient metaphase-like cells , SYCP3 localized to the centromeres , and REC8 to the condensed chromosomes , similar to wild-type meiotic metaphases ( Fig 3C and 3E ) . However , Meioc-deficient metaphase-like cells formed univalents instead of the bivalents formed in wild-type meiotic metaphases . Wild-type metaphase I cells form 20 bivalents , with 40 SYCP3 foci organized into 20 doublets , corresponding to 40 chromosomes organized into 20 homologous pairs . In contrast , Meioc-deficient metaphase-like cells retain 40 univalents , with 80 foci organized into 40 doublets , corresponding to 40 paired sister chromatids , with homologous chromosomes unpaired . The doublets of SYPC3 foci in the Meioc-deficient metaphase-like cells likely correspond to sister chromatid centromeres that have slightly separated , indicating a failure to maintain cohesion at sister centromeres . We did not observe any bivalents in the Meioc mutant amongst testis spreads from three P15 animals . We asked if the Meioc-deficient cells with univalent chromosomes undergo molecular events associated with metaphase . In germ cells undergoing meiotic metaphase I in adult wild-type testes , chromosomes , visualized via DAPI , align at the equator of the cell to form a metaphase plate . The chromosomes are aligned by a bipolar spindle , formed by α-tubulin-positive microtubules emanating from opposite poles of the cell and attaching to the centromeres , marked by centromeric histone H3 variant CENPA ( Fig 4 ) . These features of metaphase are patently absent in wild-type P15 testes and wild-type E16 . 5 ovaries , where the chromosomes are not yet condensed , and centromeres localize along the nuclear envelope , as previously described [26] . Metaphase-like cells from Meioc-deficient P15 testes and E16 . 5 ovaries assemble a spindle , albeit a disorganized one that appears to emanate from a single pole . Their chromosomes do not assemble on a metaphase plate , and are instead scattered throughout the nucleus . In addition , Meioc-deficient metaphase-like germ cells undergo histone H3 phosphorylation and nuclear envelope breakdown , two events associated with wild-type metaphase ( S9 Fig ) . In summary , metaphase-like cells from Meioc-deficient mice form spindles , phosphorylate histone H3 and undergo nuclear envelope breakdown much like wild-type meiotic metaphase cells . However , the chromosomes are in univalent rather than bivalent configuration , indicating a failure of the chromosomes to pair , likely as a result of prematurely proceeding to metaphase . To gain insight into the molecular pathways that Meioc may regulate so as to extend meiotic prophase I , we performed RNA-seq on whole ovaries from E14 . 5 wild-type and Meioc-deficient fetuses ( S1 Table ) . At this stage , Meioc-deficient ovaries did not exhibit TUNEL-positive apoptotic cells , which indicates that programmed cell death had not yet affected the size of the germ cell population ( S6 Fig ) . We observed , in Meioc-deficient ovaries , 465 genes expressed at higher levels than wild type ( q < 0 . 01 ) and 496 genes expressed at lower levels than wild type ( q < 0 . 01 ) ; the two sets of genes were enriched for distinct functions ( Table 1; S2 Table ) . Genes expressed at lower levels were enriched for involvement in the meiotic chromosomal program , which we interpreted as reflecting fewer cells entering meiotic prophase I in the mutant . Genes expressed at higher levels were enriched for factors typically associated with the mitotic cell cycle . Previously reported microarray analyses of Meioc-deficient gonads identified only 42 differentially expressed genes , of which 38 were expressed at lower levels [18] . Of these 38 genes , half were noted to be associated with meiosis . Those analyses failed to detect genes expressed at higher levels , and thus did not identify the misregulation of mitotic cell cycle factors . Given that RNA-seq provides more sensitivity than microarray analysis [27] , our RNA-seq analysis likely reveals a more complete snapshot of transcriptional changes in the absence of Meioc . We explored the possibility that the premature metaphase entry observed in Meioc-deficient germ cells was associated with misregulation of cell cycle factors . Progression through the cell cycle is tightly controlled by cyclical fluctuations in expression of cyclins , which induce oscillatory activation of cyclin-dependent kinases . We therefore examined cyclin expression , focusing on determining whether Meioc-deficient germ cells express cyclins typical of mitosis or meiosis . Cyclin A2 ( CCNA2 ) , which drives progression through mitotic S and G2-M [28] , is expressed in the male germline in mitotic spermatogonia and preleptotene spermatocytes , and is normally down-regulated upon entry into leptotene [29 , 30] . We immunostained wild-type and Meioc-deficient P15 testes for CCNA2 , as well as SYCP3 , to identify cells in meiotic prophase . We confirmed that in the wild-type , CCNA2 is expressed in mitotic spermatogonia , but not in germ cells that had entered meiotic prophase , as evident by thread-like SYCP3 staining ( Fig 5A ) . In contrast , in Meioc-deficient testes , CCNA2 is present in germ cells that exhibit SYCP3 staining typical of leptotene and zygotene . Thus , in Meioc-deficient testes , testicular germ cells in meiotic prophase aberrantly express CCNA2 . Cyclin A1 ( CCNA1 ) is thought to replace CCNA2 during the meiotic cell cycle: it is expressed in meiotic spermatocytes from late pachytene through metaphase , and is required to initiate meiotic metaphase [31 , 32] . Using single molecule fluorescent in situ hybridization ( smFISH ) , we observed Ccna1 mRNA expression in wild-type P15 testes in late pachytene cells , but not spermatogonia , which instead expressed Ccna2 ( Fig 5B ) . In Meioc-deficient P15 testes , we failed to observe Ccna1 expression in either meiotic or metaphase-like germ cells . Cyclin Bs are essential for the G2/M transition [28] . Cyclin B1 and B2 ( CCNB1 , CCNB2 ) are expressed in both mitotically and meiotically dividing cells . In contrast , cyclin B3 ( CCNB3 ) is expressed only during leptotene and zygotene in both males and females , and forms kinase-deficient complexes with CDK2 , raising the possibility that CCNB3 could be inhibiting precocious cell cycle progression during early meiotic prophase I [33 , 34] . Using smFISH , we observed Ccnb3 expression in meiotic cells of wild-type P15 testes and E16 . 5 ovaries as expected ( Fig 5C ) , and also in meiotic germ cells from Meioc-deficient testes and ovaries . In summary , we found that Meioc-deficient meiotic germ cells do not exclusively express either mitosis or meiosis-specific cyclins . They express meiosis-specific CCNB3 , suggesting that they have initiated the meiotic cell cycle program , but they also aberrantly express CCNA2 , which should be down-regulated during meiosis . Misexpression of CCNA2 , accompanied by the broad up-regulation of genes associated with the mitotic cell cycle , leads us to conclude that although Meioc-deficient germ cells can initiate the meiotic chromosomal program , they fail to properly transition from a mitotic to meiotic cell cycle program . Based on these novel findings , not reported by Abby et al . [18] , we propose that mis-regulation of the cell cycle is the primary cause of premature metaphases in the absence of Meioc . To gain insight into how MEIOC functions at the molecular level to prevent premature exit from meiotic prophase I , we determined MEIOC’s binding partners by performing an immunoprecipitation for MEIOC from testis lysates . Using quantitative mass spectrometry analysis , we identified one protein as interacting with MEIOC: YTHDC2 ( enrichment over MEIOC immunoprecipitation in Meioc-deficient testes > 1 . 5 , unique peptides >1; Table 2 , S3 Table ) . We confirmed the interaction between MEIOC and YTHDC2 by immunoprecipitating each protein from adult testes and immunoblotting for the other ( Fig 6A ) . MEIOC interaction with YTHDC2 was also previously observed [18] . YTHDC2 contains multiple domains that interact with nucleic acid—specifically , an R3H domain , an RNA helicase domain , and a YTH domain [35–37]—but its molecular function in mammalian cells remains poorly characterized . To gain insight into the function of YTHDC2 , we looked for YTHDC2 orthologs in other species . We identified YTHDC2 orthologs in almost all metazoans examined ( S10 Fig ) . In Drosophila melanogaster , the ortholog of mouse YTHDC2 is BGCN , which physically interacts with a partner , BAM , to regulate translation in germ cells [38] . Considering that we find no ortholog of MEIOC in the Drosophila genome ( S1 Fig ) , mouse MEIOC may be interacting with YTHDC2 to perform a role analogous to that of BAM with BGCN in Drosophila . Based on this hypothesis , we might expect similar phenotypes in Meioc-deficient and Ythdc2-deficient mice . Ythdc2-deficient male mice exhibit striking similarities to the Meioc-deficient mice: in both mutants , germ cells initiate but do not complete meiosis; instead , numerous abnormal metaphase-like cells are observed ( A . Bailey , D . de Rooij , and M . Fuller , personal communication ) . To determine if YTHDC2 protein expression is regulated by MEIOC , we immunostained wild-type and Meioc-deficient P15 testes for YTHDC2 ( Fig 6B ) . In both wild-type and Meioc-deficient testes , YTHDC2 was present in the cytoplasm of meiotic germ cells , including leptotene , zygotene , and pachytene cells in the wild-type , and leptotene/zygotene-like cells in the mutant . Thus , in contrast to previous reports [18] , we found that YTHDC2 expression is not dependent on Meioc . Given that YTHDC2 and MEIOC proteins localize to the cytoplasm , and that YTHDC2 contains multiple domains that interact with nucleic acid ( specifically , an R3H domain , an RNA helicase domain , and a YTH domain ) [35–37] , we hypothesized that a YTHDC2/MEIOC complex binds to and post-transcriptionally regulates mRNA , like the Drosophila BGCN/BAM complex . Based on the observations that Meioc-deficient germ cells exhibit precocious progression into a metaphase-like state and misexpress cell cycle transcripts and mitotic cyclin CCNA2 , we further hypothesized that this YTHDC2/MEIOC complex regulates transcripts involved in mitotic cell cycle progression . We therefore investigated the transcripts to which both MEIOC and YTHDC2 bind via RNA immunoprecipitation and sequencing ( RIP-seq ) . We performed MEIOC RIP-seq in wild-type P15 testes , along with the following controls: MEIOC RIP-seq in Meioc-deficient P15 testes , IgG RIP-seq controls in wild-type P15 testes , and RNA-seq from both wild-type and Meioc-deficient testes to control for changes in mRNA abundances in wild-type and Meioc-deficient testes . We performed YTHDC2 RIP-seq in P20 testes using two independent YTHDC2 antibodies , along with the following controls: IgG RIP-seq in wild-type P20 testes , and RNA-seq in wild-type testes . We identified 626 transcripts that were enriched in immunoprecipitation with MEIOC ( fold change > 3 , FDR < 0 . 05 , expressed at FPKM > 1 , S4 Table ) , and 80 transcripts enriched in immunoprecipitation with YTHDC2 ( fold change > 2 , FDR < 0 . 05 , expressed at FPKM > 1 , S4 Table ) . Of these , 67 transcripts were identified as both MEIOC and YTHDC2 targets ( a subset of results shown in Fig 6C ) . We validated a sampling of the MEIOC and YTHDC2 targets by RIP followed by quantitative PCR ( qPCR; S11 Fig ) . While Ccna2 was not a direct target of MEIOC or YTHDC2 , bound transcripts included other cell-cycle related transcripts such as Cdc27 , a component of the anaphase promoting complex [39] , as well as Creb1 and Atf2 , transcription factors that can upregulate the expression of Ccna2 [40–42] . In addition , both MEIOC and YTHDC2 interact with the Meioc transcript itself , but not with Ythdc2 transcript . In contrast to our model of the MEIOC/YTHDC2 complex as a regulator of meiotic prophase I exit , Abby and colleagues suggested that MEIOC and YTHDC2 function to stabilize transcripts involved in the chromosomal program of meiosis [18] . This conclusion was based , in part , on RIP data indicating that YTHDC2 bound four transcripts essential to the chromosomal program ( Spata22 , Spo11 , Meiob , and Rad21L ) [18] . This hypothesis predicts that MEIOC should also interact with these transcripts . We found no evidence , by either RIP-seq or RIP-qPCR , that MEIOC or YTHDC2 interacts with these transcripts ( Fig 6C , S11 Fig ) . Furthermore , we could not demonstrate enrichment for additional canonical transcripts in the meiotic chromosomal program , such as Dmc1 , Rec8 , and Sycp3 ( Fig 6C; S4 Table ) , which were not identified as YTHDC2 targets by Abby and colleagues [18] . To determine whether the MEIOC/YTHDC2 complex promotes or inhibits expression of its targets , we returned to our RNA-seq dataset from E14 . 5 wild-type and Meioc-deficient ovaries . Given the remarkable similarity of Meioc-deficient phenotypes in males and females , we hypothesized that MEIOC/YTHDC2’s targets from the testis would also be differentially expressed in the fetal ovary . We therefore compared MEIOC/YTHDC2’s shared targets to our RNA-seq dataset from E14 . 5 wild-type and Meioc-deficient ovaries ( S1 Table ) . Of the 67 MEIOC- and YTHDC2-bound mRNAs identified in the testis , 65 were expressed ( FPKM > 1 ) in the fetal ovary . Of these 65 MEIOC- and YTHDC2-bound transcripts , 28 ( 43% ) were expressed differentially between E14 . 5 wild-type and Meioc-deficient ovaries . With the exception of the Meioc transcript itself , all 27 of these differentially expressed mRNAs were present at higher levels in the absence of MEIOC ( S5 Table ) , suggesting that MEIOC and YTHDC2 destabilize their target mRNAs . These differentially expressed targets included the mitotic cell cycle regulators Atf2 , Cdc27 , and Creb1 . Not all MEIOC/YTHDC2-bound mRNAs were observed to be differentially expressed . This may be because most MEIOC/YTHDC2-bound mRNAs were expressed in gonadal somatic cells as well as in germ cells , which may obscure differential expression signals in RNA-seq data from whole gonads . Additionally , our MEIOC and YTHDC2 RIP experiments were performed using testis tissue , while our RNA-seq data was derived from fetal ovary; though they overlap , the sets of genes targeted by MEIOC and YTHDC2 in testis and ovary may not be identical . In summary , we found that MEIOC and YTHDC2 bind transcripts that regulate the mitotic cell cycle , likely resulting in their destabilization . These observations are consistent with our hypothesis that MEIOC facilitates the switch from a mitotic to a meiotic cell cycle program . We find no evidence that MEIOC interacts with transcripts of the meiotic chromosomal program , and thus no reason to believe that it directly stabilizes such transcripts , as recently proposed by Abby and colleagues [18] .
An extended prophase I is a conserved feature of meiosis , and is critical for enabling completion of meiotic chromosomal events in yeast [2] . Our analyses of Meioc-deficient mice identify Meioc as a critical factor required for this extended prophase I in mice: in the absence of Meioc , both testicular and ovarian germ cells can initiate meiosis and embark on meiotic prophase I , but fail to progress past the zygotene stage . Instead , Meioc-deficient cells proceed precociously to metaphase . Our studies demonstrate that Meioc is required for an extended meiotic prophase I in mice , and reveal the extended prophase I as a critical and actively regulated feature of meiosis in a vertebrate system . We posit that meiotic prophase I is comprised of various meiosis-specific subprograms , including a chromosomal program wherein chromosomes synapse and recombine , and a coordinately regulated cell cycle program that extends prophase I for the duration of the chromosomal program . We propose that when the chromosomal program of meiosis is initiated , the corresponding cell cycle program must be simultaneously implemented ( Fig 7 ) . Our previous findings demonstrated that Stra8 is required for the meiotic chromosomal program [14 , 15] . Our present findings lead us to propose that Meioc is simultaneously required to promote the meiotic cell cycle program . How can a germ cell ensure that it exits prophase into metaphase only when the meiotic chromosomal program is complete ? We reasoned that the germ cell must transition from a mitotic cell cycle program ( that of necessity is independent of the meiotic chromosomal program ) to a meiotic cell cycle program in which prophase exit is dependent on meiotic chromosomal checkpoints . We hypothesize that Meioc is required for this transition . This model predicts that in the absence of Meioc , a germ cell that has already expressed key meiotic regulators ( such as STRA8 ) and meiotic chromosomal proteins ( such as SYCP3 ) will continue to run a mitotic cell cycle program . Due to an active mitotic cell cycle program , the meiotic cell will proceed to metaphase on a mitotic schedule , independent of the meiotic chromosomal checkpoints . It was previously observed that leptotene/zygotene stage spermatocytes are not competent to enter metaphase upon stimulation with okadaic acid [43]; this is likely because in wild-type cells , exit from prophase into metaphase is strictly dependent on the meiotic chromosomal checkpoints . In contrast , in Meioc-deficient germ cells , a persistent mitotic cell cycle program renders the cell cycle independent of the meiotic chromosomal events , and drives cells into metaphase as early as preleptotene , or shortly thereafter . Consistent with this idea of cell cycle mis-regulation , Meioc-deficient spermatocytes that have initiated meiotic prophase I misexpress Cyclin A2 , which is normally expressed in mitotic spermatogonia and down-regulated by leptotene of meiotic prophase I . A second possibility is that Meioc functions to establish a checkpoint for exit from meiotic prophase I . However , if lack of a checkpoint led to premature resumption of the meiotic cell cycle , we might expect that the cell cycle resumed would be meiotic in nature , and thus primarily driven by cyclins typically expressed during the meiotic cell cycle , such as Cyclin A1 . Instead , we find that Cyclin A2 , not Cyclin A1 , is expressed in Meioc-deficient germ cells . Therefore , MEIOC appears to govern the transition from a mitotic to a meiotic cell cycle program , in part or in whole by suppressing the mitotic program . An alternate model has been proposed by Abby et al . , wherein Meioc is required for stabilization of meiotic transcripts , such as those required for the chromosomal program [18] . In their model , the failure to stabilize these transcripts leads to lack of sufficient proteins required for the chromosomal events of meiosis , thus forcing cells to switch prematurely to metaphase . We find this model unsatisfying for the following reasons . First , we find no evidence that MEIOC and YTHDC2 bind transcripts that function in the chromosomal program . The conditions used for RIP experiments may explain the difference between our results and those of Abby et al . For immunoprecipitation of RNA , we used lysis conditions without reducing agents in order to maintain proteins’ disulfide bonds . By contrast , Abby et al . used mild reducing conditions that could have relaxed disulfide bonds and potentially altered the proteins and transcripts with which YTHDC2 interacted . We propose that the non-reducing conditions used in this study are more likely to have captured the in vivo interactions of MEIOC and YTHDC2 . In addition , the model proposed by Abby et al . does not explain how a failure to stabilize transcripts of the meiotic chromosomal program results in premature metaphase . In the vast majority of knock-outs of genes required for the meiotic chromosomal program ( e . g . Dmc1 ) , germ cells arrest in meiosis and proceed to apoptosis , rather than attempting precocious metaphase [22 , 23] . Understanding the molecular function of MEIOC would aid in distinguishing these two alternative models . Our genetic and biochemical analyses suggest a role for MEIOC in post-transcriptional regulation of transcripts implicated in the cell cycle . First , the phenotype of Meioc-deficient mice is highly similar to that of male mice deficient for the mouse ortholog of Bgcn ( A . Bailey , D . de Rooij , and M . Fuller , personal communication ) . Drosophila BGCN is an RNA helicase that acts in concert with an interacting partner , BAM , to repress translation in Drosophila germ cells [38 , 44 , 45] . The shared phenotype between mouse Meioc and Ythdc2 suggests that they may act as interacting partners to regulate translation in the mouse germline , similar to BAM/BGCN in the fly . A putative mouse ortholog of fly bam had been previously identified , but mice lacking this gene exhibited no viability or fertility defects [46] . Meioc , while not orthologous to Drosophila bam , may be its functional analog in the mouse . Notably , we failed to identify an ortholog of Meioc in Drosophila , further supporting the notion that mouse MEIOC and Drosophila BAM substitute for each other in the two species . Consistent with the hypothesis that MEIOC and YTHDC2 function together , we find evidence that MEIOC physically interacts with YTHDC2 . Further , MEIOC and YTHDC2 interact with overlapping sets of transcripts . These transcripts include genes associated with the mitotic cell cycle , but not with meiosis , bolstering our model that a MEIOC/YTHDC2 complex post-transcriptionally regulates transcripts associated with cell cycle progression . Transcripts that interact with MEIOC and YTHDC2 are up-regulated in the absence of MEIOC , suggesting that MEIOC/YTHDC2 functions to destabilize their target mRNAs . While MEIOC’s PF15189 domain remains uncharacterized , YTHDC2 contains multiple domains that interact with nucleic acid . These domains include the R3H domain that binds single-stranded nucleic acid [36]; the DEAH box helicase domain that unwinds nucleic acids [35]; and the YTH domain that recognizes post-transcriptionally modified N6-methyladenosine ( m6A ) on RNA [37] . In particular , RNA helicases can regulate the stability of target transcripts by interacting with proteins that directly influence RNA stability/degradation , such as decapping enzymes , deadenylation complexes , and ribonucleases [35] . Helicases can further affect RNA stability by unfolding the RNA to make it accessible to these enzymes [35] . However , we do not yet know the extent to which these domains are active in the YTHDC2 protein , and how MEIOC may contribute to their activity . The precise molecular mechanism of MEIOC/YTHDC2 activity , and consequences for target transcripts , remain to be determined . Mouse MEIOC and YTHDC2 , and their Drosophila counterparts bam and bgcn , appear to have similar roles in gametogenesis based on post-transcriptional regulation of transcripts . However , the details of regulation differ between species , and even between sexes . Whereas MEIOC and YTHDC2 appear to regulate the meiotic cell cycle program , bam and bgcn function at earlier stages of Drosophila gametogenesis , prior to the decision to initiate meiosis . In Drosophila males , bam and bgcn are required for spermatogonia to cease proliferation and initiate spermatocyte differentiation and meiosis [47 , 48] . In the female , bam and bgcn function earlier to initiate the transit amplifying divisions [49 , 50] . Correspondingly , their target transcripts differ between the Drosophila sexes: BAM and BGCN repress mei-P26 translation in the male , but not in the female [38] . Conversely , BAM represses translation of nanos in the female but not the male [44] . Furthermore , mei-P26 and nanos are not components of the cell cycle program . Thus , while the involvement of the bam-bgcn and MEIOC-YTHDC2 complexes in gametogenesis via post-transcriptional regulation is conserved , their time of action , as well as the targets of translational repression , may vary according to sex and species . A common pathway induces both initiation of the chromosomal program of meiotic prophase I , as well as Meioc expression , thus genetically linking the meiotic chromosomal program with the meiotic cell cycle program . We previously demonstrated through an in vivo genetic knock-out mouse model that Stra8 is required for initiation of the meiotic chromosomal program in both ovarian and testicular germ cells [14 , 15] . More recently , further in vivo studies of Stra8-deficient ovaries showed that Stra8 is required for full induction of Meioc expression: Meioc expression is 4-fold higher in wild-type fetal ovaries than in Stra8-deficient ovaries [17] . Since Stra8 is induced by RA [10 , 11] , Meioc expression in fetal ovarian germ cells is thus also at least partially dependent on RA signaling . Contrary to these results , Abby et al . concluded that Meioc expression is completely independent of RA signaling in both ovarian and testicular germ cells , based on data from fetal gonads cultured with RA or an RAR inverse agonist as well as postnatal testes from pups exposed to the RAR inverse agonist [18] . This discrepancy in results in ovarian germ cells suggests that the in vivo genetic model may more accurately reflect the endogenous biology than a culture system , especially when dealing with a relatively modest ( 4-fold ) change in gene expression . Therefore , similar in vivo examination of whether RA and Stra8 contribute to Meioc expression in testicular germ cells is still needed . Our study leads us to propose that successful meiosis in mice requires coordination of a meiosis-specific cell cycle program with the elaborate chromosomal program of prophase I . Further studies will elucidate how Meioc , in partnership with Ythdc2 , promotes the transition to a meiosis-specific cell cycle program at the time germ cells initiate the meiotic chromosomal program .
All experiments involving mice were performed in accordance with the guidelines of the Massachusetts Institute of Technology ( MIT ) Division of Comparative Medicine , which is overseen by MIT’s Institutional Animal Care and Use Committee ( IACUC ) . The animal care program at MIT/Whitehead Institute is accredited by the Association for Assessment and Accreditation of Laboratory Animal Care , International ( AAALAC ) , and meets or exceeds the standards of AAALAC as detailed in the Guide for the Care and Use of Laboratory Animals . The MIT IACUC approved this research ( no . 0714-074-17 ) . A polyclonal antibody against MEIOC was raised in rabbits against C-terminal peptide CHESINSSNPMNQRGETSKH ( YenZym Antibodies , LLC ) , and affinity purified using the antigenic peptide ( SulfoLink Immobilization Kit for Peptides , ThermoScientific ) . The Meioc gene was targeted for homologous recombination in v6 . 5 embryonic stem ( ES ) cells with a targeting vector for a knockout-first allele of Meioc ( obtained from the Knockout Mouse Project Repository , vector PG00048_X_6_E03 ) ( S4 Fig ) . Resultant colonies were tested for correct integration by Southern blot analysis of a KpnI/XhoI restriction digest . Three independent , verified ES cell clones were injected into C57BL/6 recipient blastocysts , and germline transmission was obtained with all three clones . The ‘knockout-first’ allele is denoted 3lox or 3L as it retains 3 loxP sites . In the 3lox allele , the open reading frame is disrupted by the active lacZ reporter . The 3lox allele was subject to Flp recombination by breeding mice bearing the 3lox allele to ACTB:FLPe B6J mice ( Jackson laboratory no . 005703 ) . The resultant allele is a conditional allele , denoted 2lox or 2L . The lacZ and Neo genes are excised , leaving exon 3 flanked by loxP sites . The 2lox allele was subject to Cre recombination by breeding mice bearing the 2lox allele to MvhCre-mOrange mice [51] . The resultant allele is a knockout allele , denoted 1lox , 1L or Meioc - . Cre recombination excises exon 3 , and is predicted to result in a frame shift and generate a premature stop codon subsequent to exon 2 . All three alleles were genotyped by PCR ( detailed in S4 Fig ) . We analyzed both Meioc 3L/3L and Meioc-deficient ( Meioc -/-; Meioc 1L/1L ) mice . Meioc 3L/3L and Meioc-deficient mice or embryos were generated by heterozygote matings . For wild-type controls , we used littermates that were either heterozygote for the mutant and wild-type allele or homozygous for the wild-type allele . Meioc 3L/3L mice were of mixed 129S4 and C57BL/6 background . Meioc-deficient mice were backcrossed to the C57BL/6 strain for at least 5 generations; all data shown in figures are from mice 5 to 7 generations backcrossed . Mice , or pregnant mothers , were injected with 4μg/μl of EdU dissolved in PBS , for a final dose of 20μg/g . Samples were collected 2 h after EdU injection . Testes were fixed overnight in Bouin’s solution , embedded in paraffin , sectioned , and stained with hematoxylin and eosin . Sections were examined using a light microscope , and germ cell types were identified by their location , nuclear size , and chromatin pattern ( Russell et al . , 1990 ) . Postnatal or adult testes , or embryonic ovaries , were fixed one of three ways: in 4% paraformaldehyde ( PFA ) overnight followed by embedding in paraffin , in Bouins solution for 2 h followed by embedding in paraffin , or in 4% PFA for 1 h following by freezing in OCT ( Sakura Finetek , Torrance , CA ) . Paraffin or frozen blocks were sectioned . Paraffin sections were dewaxed , rehydrated , and subject to antigen retrieval by heating in citrate buffer ( 10mM sodium citrate , 0 . 05% Tween 20 , pH6 . 0 ) . Frozen sections were thawed and washed in PBS . Sections were then blocked in 5% normal donkey serum , incubated with primary antibodies at 4°C overnight , washed with PBS , incubated with the secondary antibody at room temperature for 1 h , and washed with PBS . Details for primary antibodies and their corresponding fixation and incubation conditions are detailed in S6 Table . For fluorescent detection , fluorophore-conjugated secondary antibodies were used at 1:250 ( Jackson Immunoresearch Laboratories or Invitrogen ) , and sections were mounted in ProLong Gold Antifade reagent with DAPI ( Thermo Fisher Scientific ) . For colorimetric detection , ImmPRESS peroxidase-conjugated secondary antibodies were used ( Vector Laboratories ) , followed by detection using DAB substrate ( Vector Laboratories ) . TUNEL staining was performed on PFA-fixed sections embedded in paraffin using the DeadEnd Colorimetric TUNEL System ( Promega ) according to the manufacturer’s intstructions . Slides were then counterstained with hematoxylin , dehydrated , and mounted in Permount ( Thermo Fisher Scientific ) . EdU was detected as per manufacturer’s protocol ( Click-iT EdU Alexa Fluor 488 Imaging Kit ) after secondary antibody incubation and wash . Spreads were prepared from male and female meiotic germ cells as previously described [52] with some modifications . Male germ cells in suspension were obtained by mechanically disrupting seminiferous tubules . Germ cells were spun down and resuspended in hypobuffer ( 30mM TrisHCl pH8 . 2 , 50mM sucrose , 17mM sodium citrate ) for 7 min at room temperature , then spun down again and resuspended in 100mM sucrose . Cell suspensions were placed on slides wetted with 1% PFA/0 . 15% TritonX-100 . Female germ cells were obtained by first incubating embryonic ovaries in hypobuffer for 15 min , then mechanically disrupting the ovaries in 100mM sucrose . Dispersed cells were then placed on slides wetted with 1% PFA/0 . 2% TritonX-100 . In both cases , slides were air dried , washed in 0 . 4% Photo-Flo , and stored at -80C until use . For immunofluorescence staining , frozen sections were thawed and washed in PBS . Sections were then blocked in 3% BSA/1% normal donkey serum/0 . 05% Triton-X , incubated with primary antibodies at 4°C overnight , washed with PBS , incubated with the secondary antibody at room temperature for 1 h , and washed with PBS . Detailed information on primary antibodies and incubation conditions is provided in S6 Table . Fluorophore-conjugated secondary antibodies were used at 1:250 ( Jackson Immunoresearch Laboratories or Invitrogen ) , and sections were mounted in ProLong Gold Antifade reagent with DAPI ( Life Technologies ) . Probe design , synthesis , and coupling were as previously described [53] . Probe sequences are provided in S7 Table . Samples were prepared and hybridization performed as previously described [17 , 53] . Germ cells were identified by smFISH for Dazl and/or nuclear morphology by DAPI staining . We performed RNA-seq on whole ovaries dissected away from mesonephros from E14 . 5 wild-type and Meioc 3L/3L fetuses . Each genotype was represented by three biological replicates of one pair of ovaries each . Total RNA ( ~1 μg ) was extracted from ovaries using Trizol ( Invitrogen ) according to the manufacturer’s protocol . Libraries were prepared using the Illumina TruSeq RNA Sample Preparation Kit . Libraries were multiplexed and sequenced on the Illumina HiSeq 2000 platform to obtain 40-base-pair single reads . RNA-seq data have been deposited in NCBI GEO under accession number GSE90702 and NCBI SRA under accession number SRP094112 . Reads were aligned to the mouse genome ( mm10 ) using TopHat v2 . 0 . 11 using default settings , and differential expression analysis was performed using Cufflinks v . 2 . 2 . 1 [54] with the RefSeq transcript annotation . Enriched GO categories were identified using DAVID [55] . To prepare lysates for immunoprecipitation followed by immunoblotting , one testis from a 3-month-old C57BL/6 male was homogenized in lysis buffer ( 25mM Tris-HCl pH7 . 5 , 150mM NaCl , 1 . 5mM MgCl2 , 1mM dithiothreitol ( DTT ) , 0 . 4% Triton X-100 ) supplemented with EDTA-free protease inhibitor ( Roche Diagnostics ) and 250U Benzonase nuclease ( EMD Millipore ) , incubated at 4°C with rotation for 30 min , and then centrifuged at 20 , 000 g for 15 min at 4°C . For immunoprecipitation , the soluble lysate from each testis was pre-cleared for 2 h at 4°C with Dynabeads Protein G ( Thermo Fisher Scientific ) prior to a 4°C overnight incubation with antibody-bound Dynabeads . Beads were prepared by three brief washes in PBS with 0 . 1% Tween 20 ( PBST ) followed by resuspension in PBST and incubation with 5 μg of anti-MEIOC antibody ( antibody generation described above ) or normal rabbit IgG ( Santa Cruz Biotechnology ) for 2 h at room temperature . Following the overnight incubation , beads were washed three times with lysis buffer containing 150mM NaCl and transferred to a new tube . To prepare lysates for mass spectrometry , immunoprecipitations were performed as described above with slight modifications: lysates were prepared from testes of P15 mice , and antibodies were crosslinked to the beads by a 30 min incubation with 5mM bis ( sulfosuccinimidyl ) suberate . Immunoprecipitations were performed in one of three conditions: wild-type ( C57BL/6 ) lysate with IgG antibody , wild-type lysate with MEIOC antibody , or Meioc-deficient lysate with MEIOC antibody . Each condition was represented by two biological replicates , with one testis pair per replicate . The immunoprecipitates were washed three times in wash buffer ( 25mM Tris-HCl pH7 . 5 , 150mM NaCl , 1 . 5mM MgCl2 , 1mM DTT ) , then washed twice with PBS . Immunoprecipitated proteins were denatured in sample buffer for 10 min at 70°C , resolved on a NuPAGE 4–12% Bis-Tris gel ( Thermo Fisher Scientific ) , and transferred to a nitrocellulose membrane . The membrane was blocked in 5% BSA/Tris-buffered saline containing 0 . 1% Tween-20 ( TBST ) for 1 h at room temperature , incubated overnight at 4°C with a primary antibody solution prepared in 5% BSA/TBST , and incubated for 1 h at room temperature with a 1:5 , 000 dilution of peroxidase-conjugated anti-rabbit IgG ( Jackson Immunoresearch ) prepared in 5% BSA/TBST . Proteins on the membrane were detected by the addition of Lumi-Light Western Blotting Substrate ( Roche ) . Antibodies used for immunoblotting were MEIOC ( 1:2 , 000 ) and YTHDC2 ( 1:1 , 000; Bethyl Laboratories A303-026A ) . Immunoprecipitates were washed with 100mM NH4HCO3 and reduced ( 10 mM DTT , 56°C for 45 min ) and alkylated ( 50 mM iodoacetamide , in the dark at room temperature for 1 h ) . Proteins were subsequently digested with trypsin ( sequencing grade , Promega , Madison , WI ) at an enzyme/substrate ratio of 1:50 at room temperature overnight in 100 mM NH4HCO3 pH8 . Trypsin activity was quenched by adding formic acid to a final concentration of 5% . Peptides were desalted using C18 SpinTips ( Protea , Morgantown , WV ) then vacuum centrifuged to near dryness and stored at −80°C . Peptide labeling with TMT 6plex ( Thermo Fisher Scientific ) was performed per manufacturer’s instructions . Samples were dissolved in 70 μL ethanol and 30 μL of 500 mM triethylammonium bicarbonate , pH8 . 5 , and the TMT reagent was dissolved in 30 μL of anhydrous acetonitrile . The solution containing peptides and TMT reagent was vortexed and incubated at room temperature for 1 h . Samples labeled with the six different isobaric TMT reagents were combined and concentrated to completion in a vacuum centrifuge . The peptides were separated by reverse phase HPLC using an EASY- nLC1000 system ( Thermo Fisher Scientific ) over a 140-min gradient followed by nanoelectrospray using a QExactive mass spectrometer ( Thermo Fisher Scientific ) . The mass spectrometer was operated in a data-dependent mode . The parameters for the full scan MS were: resolution of 70 , 000 across 350–2000 m/z , AGC 3e6 , and maximum IT 50 ms . The full MS scan was followed by MS/MS for the top 10 precursor ions in each cycle with a NCE of 32 and dynamic exclusion of 30 s . Raw mass spectral data files ( . raw ) were searched using Proteome Discoverer ( Thermo Fisher Scientific ) and Mascot version 2 . 4 . 1 ( Matrix Science ) . Mascot search parameters were: 10 ppm mass tolerance for precursor ions; 10mmu for fragment ion mass tolerance; 2 missed cleavages of trypsin . Fixed modifications were carbamidomethylation of cysteine and TMT 6plex modification of lysines and peptide N-termini; variable modification was oxidized methionine . Only peptides with a Mascot score greater than or equal to 25 and an isolation interference less than or equal to 30 were included in the quantitative data analysis . TMT quantification was obtained using Proteome Discoverer and isotopically corrected per manufacturer’s instructions . Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral . proteomexchange . org ) via the PRIDE partner repository [56] with the dataset identifier PXD005473 . MEIOC RIP-seq and IgG RIP-seq were carried out on P15 testes from wild-type C57BL/6 male mice ( N = 2 per RIP-seq type ) . MEIOC RIP-seq was also carried out on P15 testes from wild-type and Meioc-deficient littermates ( N = 2 per genotype ) . YTHDC2 RIP-seq and IgG RIP-seq were carried out on P20 testes from wild-type C57BL/6 male mice ( N = 2 per RIP-seq type ) . To prepare lysates , testis pairs were isolated and lysed under non-reducing conditions ( 50mM Tris-HCl , pH7 . 4 , 100mM NaCl , 1% NP-40 , 0 . 1% SDS , 0 . 5% sodium deoxycholate ) supplemented with 40U/mL RNAseOUT ( Thermo Fisher Scientific ) and EDTA-free protease inhibitor ( Roche Diagnostics ) . Lysates were incubated at 4°C with rotation for 15–25 min and cleared using Ultrafiltration Spin Columns , 0 . 45 μm cutoff ( EMD Millipore ) . Dynabeads Protein G were washed twice with lysis buffer and resuspended in lysis buffer at the original volume . The soluble lysate from each testis pair was pre-cleared for 1 h at 4°C with 100 μl of Dynabeads Protein G ( Thermo Fisher Scientific ) , and 40–80 μL was set aside as the input control . Beads were prepared by incubating 5 μg of anti-MEIOC antibody ( antibody generation described above ) , one of two anti-YTHDC2 antibodies ( Santa Cruz Biotechnology sc-249370 or Bethyl Laboratories A303-026A ) , or normal rabbit or goat IgG ( Santa Cruz Biotechnology ) per 100 μL Dynabeads with rotation for 45–60 min at room temperature . For immunoprecipitation , 570 μL lysate was incubated with 100 μL antibody-bound Dynabeads with rotation for 2 h at 4°C . The beads were then washed six times for 5 min with rotation in wash buffer ( 50mM Tris-HCl , pH7 . 4 , 300mM NaCl , 1mM EDTA , 1% NP-40 , 0 . 1% SDS , and 0 . 5% sodium deoxycholate ) . A subset of the immunoprecipitate was then set aside for immunoblotting to verify successful immunoprecipitation of MEIOC and YTHDC2 ( immunoblotting described above ) . The RNA from immunoprecipitates and input control was released by adding an additional 0 . 125% SDS and 250 mg/mL Proteinase K ( Thermo Fisher Scientific ) and incubating for 30 min with shaking at 37˚C . RNA was isolated via extraction with acid phenol:chloroform:IAA , pH4 . 5 ( Thermo Fisher Scientific ) using phase lock gel tubes ( 5 PRIME ) according to the manufacturer’s protocol . Extracted RNA was supplemented with GlycoBlue ( Thermo Fisher Scientific ) to 37 . 5 μg/mL and sodium acetate , pH5 . 5 , to 0 . 1M . RNA was precipitated overnight at -20˚C in two volumes of 100% ethanol , pelleted by spinning for 20 min at 16 , 000 g at room temperature , washed once with 80% ethanol , dried , and resuspended in 25 μl water . For each sample , 5 μL of RNA was kept for qPCR analysis and the remaining RNA was used for sequencing library preparation via the SMARTer Stranded RNA-Seq Kit ( ClonTech ) . Libraries from each RNA immunoprecipitation experiment ( MEIOC or YTHDC2 RIP , IgG control RIP , and input control ) were multiplexed and sequenced on the Illumina MiSeq platform . MEIOC RIP libraries were sequenced with 52-base-pair single-end reads . YTHDC2 RIP libraries were sequenced with 26-base-pair paired-end reads . Sequencing data have been deposited in NCBI GEO under accession number GSE90702 and NCBI SRA under accession number SRP094112 . For qPCR analysis , RNA was reverse transcribed using Superscript VILO Master Mix ( Thermo Fisher Scientific ) and analyzed in triplicate using Power SYBR Green PCR Master Mix ( Thermo Fisher Scientific ) according to the manufacturer’s protocol on a 7500 Fast Real-Time PCR System ( Applied Biosystems ) . Primers for qPCR analyses are listed in S8 Table . Results were analyzed using Actb expression as a non-target normalization control and calculating the fold change over the IgG control RIP . Prior to mapping , reads were trimmed for a minimum quality score of 20 and the first three bases of the first sequencing read , which were added during SMARTer Stranded library preparation , were removed using Cutadapt v1 . 8 . Reads were aligned to the mouse genome ( mm10 ) via TopHat v2 . 0 . 13 using default parameters and supplying the RefSeq transcript annotation . Alignments were converted to counts using HTSeq v0 . 6 . 1p1 , using the “–a” option to skip reads whose alignment quality indicated non-unique alignments ( i . e . , alignment quality <50 ) . DESeq2 v1 . 10 . 1 was then used to estimate RIP-seq enrichments resulting from MEIOC or YTHDC2 binding . DESeq2’s default procedure was applied to normalize read counts across all samples . Data were analyzed with multi-factor designs to estimate protein-specific binding over controls . For YTHDC2 RIP-seq data , log2 ( read counts ) for each gene was modeled as a linear combination of the gene-specific effects of three variables: binding to YTHDC2 ( “YTHDC2” ) , binding to IgG ( “IgG” ) , and batch ( “batch” ) ( S9A Table ) . The last variable captured differences due to the YTHDC2 antibody used and sequencing batch . This model identified transcripts that were enriched in YTHDC2 RIP-seq datasets generated using both antibodies . MEIOC analyses included RIP-seq experiments performed on wild-type and knockout samples . Read-count differences between wild-type and Meioc-deficient RIP-seq samples thus reflect both the effects of MEIOC protein binding and gene expression differences due to the Meioc genotype . To estimate the former independently of the latter , wild-type and Meioc-deficient RIP-seq and RNA-seq data were analyzed jointly , modeling log2 ( read counts ) as a linear combination of five variables: genotype , binding to MEIOC protein ( “Meioc . specific” ) , binding to MEIOC antibody ( “Meioc . nonspecific” ) , binding to IgG antibody ( “IgG” ) , and sequencing batch ( S9B Table ) . ( For this analysis , RNA-seq data were summarized as gene-level read counts obtained from HTSeq , processed identically to the RIP-seq samples . ) Enrichments ( FDR < 0 . 05; fold change > 3 for MEIOC; fold change > 2 for YTHDC2 ) are reported as the fold changes between samples with and without protein-specific binding , independent of the effects of non-specific binding and sequencing batch . These were obtained from the results function in DESeq2 supplying the argument: contrast = c ( “YTHDC2” , 1 , 0 ) or contrast = c ( “Meioc . specific” , 1 , 0 ) . For RIP-associated RNA-seq data , FPKMs were obtained using Cuffnorm v2 . 2 . 1 [54] . | Meiosis is the specialized cell division that halves the genetic content of germ cells to produce haploid gametes . This reductive division is preceded by a preparative phase of the cell cycle , meiotic prophase I , during which several meiosis-specific chromosomal events occur . Across sexually reproducing organisms , prophase of meiosis I is dramatically longer than mitotic prophase . However , it was not known in mammals how and why meiotic prophase I is extended . We have identified a mouse mutant in which this extended prophase I is disrupted: germ cells lacking Meioc initiate meiosis , but prematurely proceed to metaphase . Mutant male meiotic germ cells mis-express a cell cycle regulator that is normally expressed in mitotic male germ cells , suggesting that Meioc is required for germ cells to properly transition to a meiotic cell cycle program . Biochemical analyses of proteins and transcripts that associate with MEIOC protein suggest that MEIOC may promote the transition from a mitotic to meiotic cell cycle program by post-transcriptionally regulating target transcripts . Our studies indicate that in mammals , as in other sexually reproducing organisms , meiotic prophase I must be extended to allow time for meiotic chromosomal events to reach completion . | [
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"animal... | 2017 | Meioc maintains an extended meiotic prophase I in mice |
The Bolivian northern Altiplano is characterized by a high prevalence of Fasciola hepatica infection . In order to assess the feasibility , safety and efficacy of large-scale administration of triclabendazole as an appropriate public health measure to control morbidity associated with fascioliasis , a pilot intervention was implemented in 2008 . Schoolchildren from an endemic community were screened for fascioliasis and treated with a single administration of triclabendazole ( 10 mg/kg ) . Interviews to assess the occurrence of adverse events were conducted on treatment day , one week later , and one month after treatment . Further parasitological screenings were performed three months after treatment and again two months later ( following a further treatment ) in order to evaluate the efficacy of the intervention . Ninety infected children were administered triclabendazole . Adverse events were infrequent and mild . No serious adverse events were reported . Observed cure rates were 77 . 8% after one treatment and 97 . 8% after two treatments , while egg reduction rates ranged between 74% and 90 . 3% after one treatment , and between 84 . 2% and 99 . 9% after two treatments . The proportion of high-intensity infections ( ≥400 epg ) decreased from 7 . 8% to 1 . 1% after one treatment and to 0% after two treatments . Administration of triclabendazole is a feasible , safe and efficacious public health intervention in an endemic community in the Bolivian Altiplano , suggesting that preventive chemotherapy can be applied to control of fascioliasis . Further investigations are needed to define the most appropriate frequency of treatment .
Preventive chemotherapy , the large-scale administration of anthelminthic drugs to population groups at risk , is recommended by WHO for control and elimination of lymphatic filariasis , onchocerciasis , schistosomiasis and soil-transmitted helminth infections [1] . The aim of preventive chemotherapy is to regularly reduce worm load in infected individuals , thus controlling the associated morbidity and decreasing transmission rates . Biological and epidemiological similarities between Fasciola spp . and the helminths responsible for the diseases mentioned above , suggest that morbidity associated with fascioliasis could also be controlled through preventive chemotherapy by keeping intensity of infection at low levels among populations at risk [2] . Fascioliasis is a snail-borne zoonosis that can be transmitted to humans through the consumption of raw aquatic vegetables or fresh water contaminated with the cystic larval stages of the worms ( metacercariae ) [3] . Recent , conservative estimates on the burden of fascioliasis indicate that the number of individuals infected worldwide is at least 2 . 65 million , and more than 50% of them live in Latin America [4] . F . hepatica is the only liver fluke species transmitted in Bolivia [5] , where endemic communities face among the highest prevalence and intensity of F . hepatica infection in the world [6]–[9] . The area endemic for talp'a laqu , as fascioliasis is known in the local Aymara language , is limited to a relatively small region ( 60×60 km ) of the northern Altiplano ( i . e . the plain between Lake Titicaca and the capital city La Paz ) [8] , where transmission is linked to the presence of rivers and subsoil effluences inhabited by the intermediate snail host , Galba truncatula . In this region , the main reservoirs of infection are domestic animals , including ovines , bovines , porcines and equines [10] . In humans , fascioliasis is associated with an acute clinical phase resulting from the migration of the immature worms through the liver . Symptoms include fever , abdominal pain , respiratory disturbances and skin rashes . The chronic phase starts when the worms reach the bile ducts: progressive inflammation leads to fibrosis and thickening of the walls of the biliary system and of the surrounding hepatic tissue . Biliary colic pain due to blockage of the bile ducts and jaundice are possible complications . Severe infections may result in biliary cirrhosis with scarring and fibrosis of the liver [3] . Anaemia is a common finding in both acute and chronic fascioliasis [11]–[14] . Triclabendazole is the WHO-recommended essential medicine for treatment of fascioliasis [15] . The range of the cure rate produced by a single 10 mg/kg administration is 78–100% [16]–[21] , while information on egg reduction rate ( ERR ) is less abundant: three studies conducted in Egypt reported ERR of 73% and 100% based on arithmetic means [18] , [19] , and 63% based on geometric means [21] . Triclabendazole is generally regarded as a safe drug , although adverse events ( AEs ) can occur following treatment [16] , [17] . Such events are directly proportionate to intensity of infection and can be classified as systemic or mechanical . Systemic AEs are caused by biological substances released by the dying worms and include mild/transient dizziness , headache , nausea , and urticaria . Mechanical events are generally linked to the expulsion of dead worms from the biliary system towards the intestinal lumen , and include biliary colic pain , possibly associated with jaundice . Treatment with triclabendazole has usually been implemented in a clinical setting while its use in public health interventions is limited . In Egypt , however , a triclabendazole treatment programme based on selective chemotherapy ( test-and-treat ) of school-age children has been implemented in six districts in the Nile Delta area since 1998 [22] , [23] , while in Vietnam treatment has been decentralized since 2006 and is administered in peripheral hospitals and health posts based on a simplified diagnostic protocol [AF Gabrielli , personal communication] . In Bolivia , where prevalence of infection is higher than in Egypt or Vietnam , a more inclusive strategy offering treatment to entire population sectors without individual diagnosis might be appropriate in order to reduce costs and logistics related to the implementation of screening exercises . This approach would mirror the one currently recommended for schistosomiasis and soil-transmitted helminthiasis in areas of moderate and high risk [1]; the procurement of larger quantities of medicines needed for its implementation has been made possible via the donation of triclabendazole ( Egaten ) by Novartis Pharma AG through the WHO . Consequently , in 2008 , following suggestions by a panel of experts convened by the WHO [24] , the Ministerio de Salud y Deportes of Bolivia decided to opt for large-scale distribution of triclabendazole in endemic areas without individual diagnosis . Before this approach was widely implemented , a pilot study was conducted to test the safety and efficacy of such intervention . Safety assessments consisted of monitoring and recording any AEs occurring after treatment , and efficacy was assessed by monitoring prevalence and intensity of infection and by calculating cure and egg reduction rates . Safety , in particular , was considered as a key component of the protocol as AEs are known to limit the feasibility of preventive chemotherapy interventions for helminth infections , as their occurrence confines treatment to a clinical setting where proper management of cases is ensured by health personnel . AEs also have the potential to jeopardize compliance to the intervention as effects of treatment can be perceived as a greater health risk than the disease itself [25] .
The study protocol was approved by the Comisión de Etica de la Investigación ( CEI ) of the National Bioethics Committee of Bolivia on September 10 , 2007 . A written informed consent form explaining the purpose and the modalities of the study was developed , translated into Aymara and obtained from the parents/guardians of each participating child . The initiative was agreed by the civil ( sub-alcalde , head of the health post , director of the educational unit ) , and traditional ( jilakatas and malkus ) authorities of the community where the study was implemented . The study was conducted in Huacullani , a community in the Bolivian northern Altiplano , where prevalence of F . hepatica infection among school-aged children ranged between 31 . 2% and 38 . 2% in the 1990s [9] . Huacullani ( 16°26′0″S , 68°44′0″W ) is located at an altitude of 3 , 850 metres , approximately 500 meters from the shores of Lake Titicaca , in the municipality of Tihuanaku ( province of Ingavi , department of La Paz ) . At the time of the survey , the population of Huacullani was 2 , 472 . School-aged children ( 5–14 years ) were selected as the target group of the intervention , as they are at higher risk of infection and morbidity . Children are more likely than adults to become infected , as exemplified by their higher levels of prevalence of infection , and to develop mechanical AEs following treatment , because of the smaller size of their bile ducts and thus higher likelihood of blockage . Consequently , they are considered both the group at highest risk and the one most sensitive for detection of AEs following treatment . All children attending the primary school and the junior high school of Huacullani were considered eligible for enrolment in the study . A Scientific Committee formed by the Ministerio de Salud y Deportes , the Servicio Departamental de Salud of La Paz , the Universidad Mayor de San Andrés and the PAHO/WHO was established with the aim of developing a protocol and supervising the implementation of the pilot intervention . The protocol consisted of five consecutive study phases: baseline data collection; treatment; monitoring of AEs at day 0 , day 7 and day 30; first parasitological follow-up 3 months after treatment , with further treatment of any cases still positive; and second parasitological follow-up 2 months after the first follow-up ( Figure 1 ) . After a few preparatory meetings , field activities started in April 2008 , and were completed in November 2008 .
At the time of the baseline survey ( April 2008 ) , the school population of Huacullani consisted of 459 children aged 5 to 14 years , who were all considered eligible for treatment . 447 children returned the plastic container . In total , 437 faecal samples from an equivalent number of children were examined by the Kato-Katz thick smear technique – 4 children returned an empty plastic container , and 6 other children provided insufficient stool quantities to prepare a Kato-Katz slide . Stool samples were transported to the Faculty of Medicine of the Universidad Mayor de San Andrés in La Paz and processed . Slides were read within 24 hours of preparation . Overall , 95 children had positive and 342 had negative Kato-Katz smears . The parasitological prevalence of F . hepatica infection in this population was therefore 21 . 7% . Among the 95 children with positive Kato-Katz smears , 15 had an intensity of infection ≥300 epg ( 15 . 8% ) , and 11 a high-intensity infection ( ≥400 epg , 11 . 6% ) . The mean intensity of infection among all surveyed children ( including the ones with negative smears ) was 72 . 9 epg . Triclabendazole was administered in June 2008 to each child testing positive to the Kato-Katz test . Among the 15 children with an intensity of infection ≥300 epg , 10 were hospitalized before treatment , while 5 could not be treated as their parents refused hospitalization and/or treatment . By contrast , all the 80 Kato-Katz positive children with an intensity of infection <300 epg were treated as outpatients at school premises . In total , 90 children were administered triclabendazole: among them , the mean intensity of infection was 264 . 3 epg , and 7 had a high-intensity infection ( ≥400 epg , 7 . 8% ) . Among the 90 treated children , the number reporting one or more AEs on treatment day and one week after treatment ( June 2008 ) was 11 and 10 , respectively . One month after treatment ( July 2008 ) , only 82 children were interviewed , as 8 were neither at school nor could be traced in Huacullani; among them , only three children reported any AE . Details are provided in Table 2 . The number of reported AEs on treatment day , one week after treatment and one month after treatment was 15 , 13 and 3 , respectively . Headache was the most frequent event reported on treatment day , and abdominal pain was the most frequent one week later . All fevers were below 38°C . Only 3 of the children experiencing AEs on treatment day also reported an AE one week after treatment . Only 1 of the children with a high-intensity infection ( ≥400 epg ) reported an AE one week after treatment ( abdominal pain ) . Among children treated at school , only one girl requested to be taken to the local health post on treatment day , but after a medical examination , she did not require any specific medical attention , and all the signs and symptoms resolved spontaneously . None of the other children contacted the health post for medical assistance during the follow-up period . Overall , no medications were administered to treat AEs with the exception of antipyretics in case of fever . No SAEs occurred .
Overall , 21 . 7% of the children surveyed were found to be infected with F . hepatica at baseline . This was less than the prevalence of infection previously detected in Huacullani [9] , but was nevertheless high when compared with the usually low levels of F . hepatica in most endemic countries across the world , such as Egypt , Iran , Vietnam or Yemen for example , where prevalence of infection by faecal examination rarely exceeds 5% [10] , [22] , [23] , [30]–[32] . It is also likely that the true prevalence of infection is higher due to the low sensitivity of a single Kato-Katz smear . Treatment with a single administration of triclabendazole ( 10 mg/kg ) did not elicit frequent or considerable AEs , neither among children with a high intensity of infection , nor among the others . No significant medical attention was required in any case , as all symptoms resolved spontaneously without any appreciable consequence on the health status of treated individuals . The occurrence of AEs documented by our study contrasts with the absence of any event reported in Egypt [23] even though both the treatment regimen and the manufacturer of triclabendazole were the same . While comparison might not be fully appropriate , as in Egypt no active search of events was carried out , such discrepancy might be attributable to the lower mean intensity of infection observed in this country ( 12 . 2 epg at baseline ) . Frequency and severity of AEs are however expected to be less important at subsequent rounds of treatment in reason of the progressively decreasing intensity of infection , as shown by experiences from different helminth control interventions implemented across the world [1] , [21] , [33] . The parasitological cure rate achieved after a single administration of triclabendazole at 10 mg/kg was high ( 77 . 8% ) and consistent with previous reports in the scientific literature for this treatment course [27]–[29] . ERRs were also considerable , even though lower rates were observed among individuals with a higher intensity of infection at baseline ( Table 4 ) . The negative relationship between ERR and baseline intensity of infection has been described in the case of other helminth infections , such as those by Trichuris trichiura: both density-dependent fecundity and reduced bioavailability of triclabendazole per adult worm have been proposed as possible explanatory hypotheses [34] , [35] . Finally , only 1 . 1% of the 90 treated children still had high-intensity infections ( ≥400 epg ) at the first parasitological follow-up , compared to 7 . 8% at baseline . If we apply to fascioliasis the model described in other helminth infections , that intensity of infection is proportionate to morbidity [1] , [2] , it can be inferred that morbidity was under control in a very high proportion of children three months after a single administration of triclabendazole 10 mg/kg . Based on the results of the pilot intervention , we conclude that triclabendazole is a safe and efficacious drug when administered to a paediatric population living in a fascioliasis endemic area . These considerations suggest that a population-based drug distribution approach , without individual diagnosis and without direct medical supervision , in a manner comparable with the preventive chemotherapy interventions implemented worldwide against the four major helminth infections , is appropriate and feasible . Notably , triclabendazole was well tolerated across the population examined , including individuals with a high intensity of infection: AEs elicited were self-limiting , did not require any specialist medical attention and could be managed by the local health staff . In terms of efficacy , a single administration of triclabendazole was effective in reducing considerably the number of infected individuals , the mean intensity of infection and the proportion of high-intensity infections , and in keeping these indicators at low levels for a few months after treatment . Surveys with a longer follow-up are recommended in order to ascertain for how long a single administration of triclabendazole can sustain low prevalence and intensity of infection in endemic areas . Such a study would allow the most appropriate interval of re-treatment to be determined . Following the successful implementation of the pilot intervention , the health authorities of Bolivia decided to implement distribution of triclabendazole on a large scale . | Fascioliasis is highly prevalent in the northern Altiplano of Bolivia . We wanted to ascertain whether a preventive chemotherapy approach , involving the large-scale distribution of triclabendazole within endemic communities , would be feasible for controlling morbidity associated with this disease . Consequently , we implemented a pilot intervention among schoolchildren in a community near Lake Titicaca and assessed this intervention's safety ( by evaluating the occurrence of adverse events following treatment ) and its efficacy ( by measuring changes in prevalence and intensity of infection ) . Adverse events on treatment day , and one week and one month later were infrequent and mild , and no serious adverse events were reported . We observed cure rates of 77 . 8% after one treatment and 97 . 8% after two treatments , egg reduction rates of 74–90 . 3% after one treatment and 84 . 2–99 . 9% after two treatments , and a decrease in the proportion of high-intensity infections ( ≥400 epg ) from 7 . 8% to 1 . 1% after one treatment and to 0% after two treatments . We conclude that administration of triclabendazole is a safe and efficacious public health intervention for control of fascioliasis in an endemic community in the Bolivian Altiplano . Preventive chemotherapy with triclabendazole , without individual-level diagnosis and treatment , appears therefore as a feasible option . However , further investigation is needed to define the most appropriate frequency of treatment . | [
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] | 2012 | Administration of Triclabendazole Is Safe and Effective in Controlling Fascioliasis in an Endemic Community of the Bolivian Altiplano |
Vaccination with attenuated live varicella zoster virus ( VZV ) can prevent zoster reactivation , but protection is incomplete especially in an older population . To decipher the molecular mechanisms underlying variable vaccine responses , T- and B-cell responses to VZV vaccination were examined in individuals of different ages including identical twin pairs . Contrary to the induction of VZV-specific antibodies , antigen-specific T cell responses were significantly influenced by inherited factors . Diminished generation of long-lived memory T cells in older individuals was mainly caused by increased T cell loss after the peak response while the expansion of antigen-specific T cells was not affected by age . Gene expression in activated CD4 T cells at the time of the peak response identified gene modules related to cell cycle regulation and DNA repair that correlated with the contraction phase of the T cell response and consequently the generation of long-lived memory cells . These data identify cell cycle regulatory mechanisms as targets to reduce T cell attrition in a vaccine response and to improve the generation of antigen-specific T cell memory , in particular in an older population .
Herpes zoster , caused by the reactivation of the varicella zoster virus ( VZV ) , affects one in two to three adults over the course of life . By far the biggest risk factor for VZV reactivation is age with the annual incidence increasing from 0 . 3% in adults at the age of 50 to more than 2% in adults over the age of 75 years [1 , 2] . In 2006 , the FDA licensed a vaccine to protect against VZV reactivation , Zostavax . Several studies have described the vaccine in epidemiological terms of protection , safety , and efficacy [3–5]; in clinical trials , the efficacy of the vaccination to prevent VZV reactivation declines from 70% for 50–59 year-old adults to 38% for adults 70 years and older . Limited efficacy is not unique for the zoster vaccine but shared with other vaccinations in older adults , including the annual influenza vaccine . To develop a non-empiric , mechanistic approach to improve the success of vaccination , recent studies of several vaccines including yellow fever , live-attenuated influenza virus ( LAIV ) , inactivated influenza virus and two meningococcal polysaccharide vaccines have tried to identify system networks that orchestrate the vaccine response and can be targeted [6] . For influenza vaccination-induced responses , predictive models were developed based on cell population frequencies alone [7] . In many of these studies , transcriptional blood profiles early after vaccination were most informative and correlated with vaccine-induced antibody responses [8–16] . The underlying causes of VZV reactivation that zoster vaccination is supposed to mitigate are related to defects in cellular and not to those in humoral immunity . The strong correlation of herpes zoster with increasing age has been linked to a decrease in the frequency of VZV-specific T cells [17] . In contrast , antibody levels do not decrease with age and are not sufficient to prevent reactivation after stem cell transplantation [18] . Given the importance of cellular immunity in controlling infection , the objective of vaccination is to increase the frequency of VZV-specific long-lived memory T cells poised to produce IFN-γ , likely the best surrogate marker for protection against herpes zoster reactivation [19] . Antigen-specific T cells follow a typical sequence of events after vaccination or infection . First , they exponentially expand and differentiate into effector cells including follicular helper cells that control the activation of B cells and the generation of antibodies . While most of these effector cells undergo apoptosis , a small subset constitutes memory T cell precursors that differentiate into long-lived memory cells [20 , 21] . In addition to the extent of initial T cell expansion and help for B cell responses , success of vaccination therefore depends on effector T cells that escape cell death and survive as memory T cells . Elements of clonal expansion are affected by extrinsic factors such as antigen dose , strength and duration of stimulation and the initial inflammatory environment . Less is known about which cells survive contraction and by which mechanism . Initial stimulation strength appears to be important for the transition of CD4 effector into memory T cells [22 , 23] . Survival and transition into memory CD8+ T cells is supported by inhibition of mTORC1 activity [24] . However , systemic studies in particular for human vaccine responses have not been done . Here , we assessed transcriptional profiles of whole blood and of isolated activated CD4 T cells and serum concentrations of cytokines in individuals receiving zoster vaccination to build predictive models of T cell responses . Inclusion of adults between the age of 50 and 79 years and nine twin pairs allowed determining the influence of age and genetic factors . Vaccine-induced increases in VZV-specific antibody titers and IFN-γ–producing T cells did not correlate . Frequencies of VZV-specific T cells peaked between day 7 and day 14 after vaccination . The subsequent contraction was the major determinant of long-lived VZV-specific T memory cell frequencies and correlated with gene expression modules in activated CD4 T cells .
To identify host factors that influence the immunogenicity of the attenuated VZV pOka vaccine strain and the efficacy of VZV vaccination , we immunized 39 individuals aged 50 to 75 years , including 9 monozygotic twin pairs ( Table 1 ) . We measured VZV-specific T cell frequencies by IFN-γ–specific ELISpot , and VZV-specific antibody titers by ELISA . Experiments with PBMC depleted of either CD4+ or CD8+ T cells showed that the majority of VZV-specific T cells activated under these conditions were of the CD4+ T cell subset ( S1 Fig ) . Frequencies of T cells producing IFN-γ in response to in vitro VZV lysate stimulation increased approximately 10-fold to peak between day 8 and 14 after vaccination and then declined ( Fig 1A ) . By day 28 , frequencies were on average three fold higher than before vaccination . Frequencies of global CD4 and CD8 populations did not change over the course of the vaccine response ( S1 Fig ) . Concentrations of VZV-specific IgG antibodies consistently increased after vaccination although the increase was small in most individuals ( median increase of 28% , 95% CI: 21%-41% , p<0 . 01 ) ( Fig 1B ) . To determine whether the vaccine response was influenced by pre-existing VZV immunity , we ran linear regression analyses on the data ( Fig 1C–1G ) . Consistent with previous literature , we found that VZV-specific T cell frequencies decline with age prior to vaccination ( r = -0 . 46; p = 0 . 01 ) , whereas antibody concentrations did not show significant correlation with age ( p = 0 . 87 ) . The magnitude of the antibody response ( fold change from day 0 to day 28 ) inversely correlated with the initial antibody concentrations ( p < 0 . 001 , Fig 1C ) . A similar negative correlation has been observed with other vaccines and has been attributed to rapid clearance of the antigen by pre-existing antibodies [25] . Indeed , PCR for VZV DNA on days 1 to 3 in vaccinated individuals was always negative indicating the absence of viremia , despite the vaccine containing live virus . In contrast to antibodies , the magnitude of the T cell response ( fold change in frequency from day 0 to day 28 ) showed no correlation with initial antibody concentrations suggesting that T cell responses are not influenced by pre-existing immunity ( Fig 1E ) . Neither antibody nor T cell responses showed a correlation with initial T cell frequencies ( Fig 1D & 1F ) . Correlation between increases in VZV-specific antibodies and T cell frequencies between day 0 and day 28 did not reach significance ( p = 0 . 38 , Fig 1G ) indicating that these responses are relatively independent of each other . Following vaccination , the T cell response varied among individuals in terms of magnitude , but followed a consistent pattern , with frequencies of IFN-γ–producing VZV-reactive T cells peaking 8–14 days post-vaccination followed by a contraction in frequencies to a level that was higher than pre-vaccination frequencies . Using age alone to predict the increase in frequencies between day 0 and day 28 showed a negative trend , but did not reach significance ( p = 0 . 18 ) . To identify variables influencing the T cell response , we performed an unsupervised hierarchical clustering of frequencies of IFN-γ–producing VZV-specific T cells at day 0 , at the peak response at day 8 or 14 , and at day 28 . Individuals segregated into three clusters ( Fig 2A ) . Individuals of Cluster 1 started with relatively low frequencies , responded to vaccination with a strong effector cell expansion , but then had a more pronounced contraction phase than the individuals from the two other clusters ( Fig 2B ) . In contrast , individuals from Cluster 2 had the highest pre-vaccination frequencies , but only produced a small expansion from day 0 to peak frequencies . As a consequence , individuals from cluster 1 and 2 both gained the smallest benefit from vaccination with only a twofold increase in VZV-specific T cell frequencies between day 0 and day 28 ( Fig 2C ) , but for different reasons; failure of effector cell expansion for Cluster 2 ( Fig 2D ) and failure to develop long-lived memory for Cluster 1 ( Fig 2E ) . Cluster 3 showed the largest fold increase in specific T cell frequencies owing to effective effector cell generation in the first phase of the vaccine response ( Fig 2D ) , and less contraction in the second phase ( Fig 2E ) . When we compared the three clusters for demographic variables , we found that individuals in cluster 1 were older than those in clusters 2 and 3 ( Fig 2F , S1 Table ) . Conversely , when vaccinees were grouped into younger and older than 59 years , concordance between cluster C and older individuals was significant ( OR = 11 . 07 , 95% CI: 1 . 52–114 . 33 , p = 0 . 01 ) . Moreover , the T-cell frequency post-vaccination trajectories by age group ( Fig 2G ) showed a high resemblance of those of three clusters shown in Fig 2B . The average T-cell response of older vaccinees ( age>59 ) was similar to that in cluster 1 , while the average T-cell response of younger study participants was more comparable to those in clusters 2 and 3 . Gender and MHC class I or MHC class II types did not correlate with clustering . In particular , no variable was identified that distinguished individuals from clusters 2 and 3 . Our data suggest that a higher attrition of antigen-specific T cells after peak expansion accounts for the smaller overall vaccine responses observed in older individuals . To determine whether cell attrition generally is the critical determinant irrespective of age , we examined the relative contribution of expansion and contraction to the overall ( day 28 to day 0 ) gain in antigen-specific memory T cells after correction for age . The correlation of the overall T cell response with expansion showed a trend that did not reach significance ( r = 0 . 31 , p = 0 . 10; Fig 2H ) , while it inversely correlated with contraction ( r = -0 . 53 , p = 0 . 003; Fig 2I ) . Our cohort of vaccinees included 9 pairs of monozygotic twins . To examine the genetic influence on vaccine responses , we adjusted the response for age and performed pair-wise comparisons of participants . Each participant was compared with every other and the difference between each pair is plotted in Fig 3 . The locations of pair-wise differences between identical twins are shown as red vertical lines . In terms of the magnitude of the T cell response ( fold change from day 0 to day 28 ) , twin pairs were more similar than unrelated individuals; the average within-twin-pair difference was 36 . 5% of the average pair-wise difference between two unrelated individuals ( p = 0 . 008; Fig 3A ) . In terms of the magnitude of the antibody response ( fold change from day 0 to day 28 ) , identical twins were no more similar than unrelated individuals; the average within-twin-pair difference was 76 . 1% of the average pair-wise difference between two unrelated individuals ( p = 0 . 44; Fig 3B ) , suggesting that there is little genetic influence on antibody generation following zoster vaccination . When effector cell differentiation and contraction were assessed separately , the twin pairs only showed small trends toward similarity . The average within-twin-pair differences were 68 . 1% ( expansion ) and 69 . 6% ( contraction ) of the average pair-wise differences between two unrelated individuals . Either phase of the T cell response was not significantly more similar between twins than between unrelated individuals ( p = 0 . 28 for expansion , Fig 3C; p = 0 . 16 for contraction Fig 3D ) . Thus , although the genetic make-up influences the generation of memory T cells , the accelerated loss after the peak response is mainly a function of age . Gene expression arrays of whole blood were performed in vaccinees before ( n = 28 ) and one ( n = 18 ) or three days ( n = 10 ) after vaccination . Cell-specific gene expression profiles were generated by deconvolution using previously described algorithms [26] . Given that zoster vaccine is a live vaccine , it was surprising that gene expression before and one or three days after vaccination were highly similar . Only very few neutrophil- and lymphocyte-related genes changed in expression from day 0 to 1 ( Fig 4A ) . Significant changes for monocyte-related genes were found , but even here the number of probes with a significant change ( n = 341 corresponding to 326 genes , p<0 . 05 ) was low after adjusting for false discovery ( n = 9 , FDR<0 . 1 , S2 Table ) . When expression changes in monocyte-derived genes were analyzed for their correlation with T cell responses , we identified 493 probes corresponding to 479 genes that correlated with generation of VZV-specific effector T cells and 641 probes corresponding to 621 genes that correlated with the subsequent contraction phase with p<0 . 05 ( Fig 4B , S3 Table ) . Interestingly , these two sets of genes were significantly overlapping ( n = 268 , p<0 . 0001 ) , i . e . , the same changes that were positively ( n = 148 ) or negatively ( n = 120 ) correlated with expansion inversely predicted contraction ( Fig 4C ) ; their effects therefore cancelled out in determining net benefit in memory cell generation . Pathways associated with the overlapping genes centered on TNF-α and STAT3 ( S2 Fig ) . These data suggest that genes associated with monocyte activation influenced the expansion as well as apoptosis of short-lived effector T cells . To assess other potential predictors of vaccine responses , we quantified serum cytokine concentrations before and after vaccination using 51-plex fluorescent bead assay . Again , for the first 10 participants , serum was assessed on days 0 and 3 , for the subsequent 20 participants on days 0 and 1 . Changes in serum cytokine levels were small and variable . Most changes on day 1 were no longer evident on day 3 , therefore only changes from day 0 to day 1 were used in subsequent analyses . Only the serum concentration of resistin showed a significant increase ( p = 0 . 006 ) . Other inflammatory cytokines that would be expected to increase during a viral infection did not show a consistent pattern . In a principal component analysis , vaccination contributed to the variability captured by PC2 ( p = 0 . 017 ) , but not by PC1 ( Fig 5A ) . To determine whether serum cytokine concentrations or their vaccine-induced changes influenced the vaccine-induced T cell expansion or contraction , we fitted a linear regression with lasso regularization to the data and constructed predictive algorithms , where the penalty parameter was selected via cross-validation . Prediction algorithms were tested using leave-one-out prevalidation . We identified two different Lasso algorithms based on cytokine changes that predicted expansion and survival of antigen-specific T cells , respectively and that passed prevalidation . These two algorithms included different sets of cytokines consistent with the notion that different mechanisms are involved in determining the two phases of the T cell response ( Fig 5B–5E ) . Absolute cytokine concentrations by themselves were not predictive nor did their inclusion improve the Lasso predictors based on vaccine-induced changes in cytokine concentrations . The following algorithm yielded a correlation of 0 . 99 for predicting expansion compared to 0 . 54 for baseline frequencies alone: 3 . 98−0 . 60*log ( baseline T cell frequency ) +1 . 63* ( ΔLeptin ) −0 . 78* ( ΔMIG ) +0 . 06* ( ΔENA78 ) −0 . 11* ( ΔIP-10 ) +1 . 11* ( ΔIL-17 ) +1 . 29* ( ΔRANTES ) −1 . 43* ( ΔIFN-α ) −1 . 86* ( ΔFGF-basic ) − 1 . 70* ( ΔTRAIL ) −3 . 42* ( ΔResistin ) +4 . 23* ( ΔM-CSF ) , where “Δ” denotes the change in cytokine concentration from day 0 to day 1 ( Fig 5B ) . Following pre-validation , the correlation of predicted and observed increase in T cell frequencies was 0 . 64 compared with a correlation of 0 . 37 using initial T cell frequencies alone ( Fig 5C ) . Of note , several inflammatory cytokines had a negative influence on T cell expansion , most notably IFN-α and resistin , the latter showing a significant increase following vaccination in the univariate analysis . For predicting contraction , changes in cytokine distribution yielded an algorithm that correlated with the observed magnitude of the contraction and continued to outperform prediction by baseline frequencies alone ( 0 . 42 vs 0 . 20 ) after prevalidation ( Fig 5D and 5E ) . The algorithm for predicting contraction using changes in cytokine concentration was as follows: 2 . 00−0 . 41*log ( baseline T cell frequency ) +0 . 05* ( ΔLeptin ) −0 . 04* ( ΔTGF-β ) + 0 . 06* ( ΔIFN-α ) +2 . 12* ( ΔIL-1RA ) , where “Δ” denotes the change in cytokine concentration from day 0 to day 1 . While the Lasso predictor of effector cell expansion used 11 of 51 possible terms raising concerns of overfitting , the prediction model for contraction only included four cytokines . Moreover , three of four terms in the contraction model ( Leptin , IFN-α , IL-1RA ) , also appeared in a Lasso model predicting the increase in memory cell frequency from day 0 to day 28 , although the latter failing cross-validation . In contrast , only 2 out of the 11 terms in the day 0 to peak expansion model ( IFN-α and resistin ) contributed to the model predicting day 0 to day 28 increases in cell frequencies . These findings re-emphasize the importance of contraction in determining the increase in frequencies of long-lived memory T cells after VZV vaccination . Our analysis so far suggests that the initial T cell response in the first two weeks after vaccination is less important for memory cell formation than the cell survival after peak responses , in particular in older individuals . Extrinsic factors including monocyte activation appear to be more important for effector cell differentiation than for memory formation . We hypothesized that cell-intrinsic properties in activated T cells can be identified that correlate with T cell survival and memory cell formation . We focused on CD4 T cells since the majority of IFN-γ T cells in the ELISpot assays were CD4 T cells ( S1 Fig ) . In contrast to CD8 T cells , where HLA-A2 tetramers can be used to follow antigen-specific responses in about half of the population , human MHC class II molecules are too polymorph to monitor antigen-specific CD4 T cells using tetramers in a population study . For CD8 T cells , co-expression of HLA-DR and CD38 define activated T cells that include antigen-specific T cells at the time of the peak response after vaccination [27] . To determine whether the same phenotype defines activated CD4 T cells , we monitored the vaccine response using HLA-DR15 tetramers loaded with the VZV IE63 peptide 24 or gE peptide 54 . Results of three HLA-DRB1*15 individuals before and on days 8 and 14 after stimulation are shown in Fig 6A and 6B . Virtually all VZV tetramer-positive CD4 T cells stained for HLA-DR even before vaccination . Expression of CD38 was gained in a subset of about 30% of antigen-specific CD4 T cells after vaccination , suggesting that co-expression of HLA-DR and CD38 is a good surrogate marker to monitor T cell activation in CD4 T cells . CD4+HLA-DR+CD38+ T cells were sorted on days 8 and 14 in 17 of the original 39 individuals ( 8 from cluster 1 and 9 from clusters 2 and 3 ) and arrayed for gene expression . In 13 of the 17 individuals , we also obtained CD4+HLA-DR+CD38+ T cells prior to vaccination . Demographics and vaccination-induced increases in VZV-specific ELISpot frequencies of these 17 individuals are given in S4 Table . Expression data were correlated with the increase in ELISpot frequencies from day 0 to day 28 and with the decline from peak responses to day 28 . We used the set of 334 blood transcription modules that have been previously annotated according to their biological functions and/or tissue-specific expression patterns [13] . Significantly associated modules are shown in S5 Table . Fig 6C shows the distribution of the Pearson correlation coefficients for the modules . For both day 8 and day 14 samples , the distributions were highly skewed with many modules negatively correlating with contraction after peak responses and positively with gain in VZV-specific memory cells from day 0 to day 28 . This directionality is also illustrated in the circos plot in Fig 6D; the log2 ( p-value ) s shown in the plot , generally higher for days 8 and day 14 , signify preferentially negative correlation with contraction and positive correlation with long-term increase . Moreover , the network display in the circos plot shows that the same modules ( p<0 . 01 ) are frequently associated with contraction and long-term increase on days 8 and 14 . This high concordance is also supported by non-hierarchical clustering of correlation coefficients that is illustrated in the heat plot in Fig 6E . In this analysis , directionality of correlation coefficient for peak to day 28 contraction was inversed to have equal biological directionality as day 0 to day 28 increase . Association of day 8 and day 14 expression of gene modules for both outcomes co-clustered , while day 0 expression formed a separate cluster . A formal concordance statistics in S6 Table shows the pairwise correlation coefficients between the rho describing the correlation of module expression with outcome . S6B Table shows the concordance of p-values significant at 0 . 05 level measured by Cohen’s kappa . The highest number of gene modules significantly associated with attrition ( p<0 . 01 ) was found for the gene expression on days 8 and 14 ( 59 and 26 modules , respectively , Fig 6C ) . 50 modules on day 14 were identified that correlated with increase in VZV-specific T cells on day 28 . Dominant themes in these modules were that of cell cycle and cell division and that of DNA repair and mismatch repair ( S5 Table ) . One peculiarity was the finding of B cell-related modules exclusively on day 8 indicating contamination with B cell-derived transcripts although cell sorting was strictly gated on CD4 T cells after exclusion of duplets . As shown in Fig 6D , many of the significantly associated modules from days 8 and 14 arrays are shared and may be therefore particularly relevant . The heat plot in Fig 7A shows the correlation coefficients for those modules that were significantly associated with outcome at two time points and correlated with contraction of effector cells after peak responses as well as increase of long-term memory cells on day 28 compared to day 0 . With the exception of a module of ion transporters and two undefined modules that were associated with increased contraction , expression of all of these modules correlated negatively with contraction and positively with successful generation of long-lived T memory cells . As described in Fig 2 , attrition was the critical step determining the increase in antigen-specific T cells after vaccination irrespective of age ( Fig 2I ) , but was even more relevant in older individuals . The gene expression studies in activated CD4 T cells described in Figs 6 and 7 were done in a subgroup of 17 participants selected to include good and bad responders ( S4 Table ) . The average age of these selected participants was lower than the original group with 60 . 6 years ( inter-quartiles: 55 . 8–61 . 3 ) and only four individuals older than 70 years were included ( S4 Table ) . Given the limited sample size , we only explored whether the modules shown in Fig 7A correlated with age . Correlation coefficients are shown as heat plot in S3 Fig . The directionalities of correlation coefficients were concordant for age and effector cell attrition and correlations reached a significance level of p<0 . 1 for seven modules in spite of the small sample . These data suggest that , while the identified gene modules account for higher attrition in general , they at least in part also explain the higher attrition with age . The findings of the gene expression analysis suggested that regulation of cell division and DNA repair are critical processes determining effector cell survival and vaccination success . Three representative networks for day 14 are shown in Fig 7B , illustrating that most genes within one module contributed to the association and directionality of gene expression was highly uniform . In the cell cycle module M103 , TFDP1 that dimerizes with E2F1 was highly correlated with attrition ( r = -0 . 64 ) and d0 to d28 increase ( r = 0 . 72 ) suggesting a role for G1-S1 transcriptional activation . In contrast , members of the E2F family were not associated . For gene module 4 . 1 , BRCA1 had the highest association with attrition ( r = -0 . 71 ) , while Rad21 , ATR , CDC25 and CHK1 genes in module M103 were significantly correlated , stressing the importance of DNA replication and DNA damage checkpoints [28] . Survivin or BIRC5 , an anti-apoptotic molecule in the G2-M phase , was also negatively correlated with attrition as was the kinase CDK1 that functions as S and M phase regulator and phosphorylates survivin . Taken together , DNA structure checkpoints appear to be most important for the decision between apoptosis of effector cells and survival of long-lived memory cells .
Deciphering the molecular mechanisms that underlie the successful generation of immune memory after vaccination has remained a major challenge . Strategies to improve vaccine responses have so far been mostly empirical . To overcome these hurdles , systems biology approaches have been increasingly employed to identify pathways that influence the induction of vaccine-specific antibodies [8–15] . Here , we identify correlates of the induction of protective T cell immunity after vaccination with the live zoster vaccine . We found that the initial T cell expansion in the first days after vaccination and the subsequent cell death of effector T cells are at least in part independent processes contributing to the formation of long-lived memory T cells . Importantly , the extent of T cell contraction , determined by dysregulated cell cycle and DNA repair pathways , inversely correlated with memory cell generation and accounted for the compromised vaccine responses in older individuals . T cell immunity is important for the control of latent or acute viral infections such as with VZV and influenza virus , respectively [29–31] . Determinants of protective T cell immunity are less defined than humoral immunity [32] , though frequencies and repertoire breadth as well as polyfunctionality of antigen-specific T cells have been implicated [33 , 34] . In VZV vaccination studies in bone marrow recipients , the risk of zoster reactivation inversely correlated with the frequencies of VZV-specific CD4+ T cells [18] . In contrast , antibody titers did not correlate with reactivation . Similar to the T cell response to other herpes viruses , most of these CD4 T cells produce IFN-γ upon stimulation [35] . Consistent with previous studies , we found an age-associated decline in the frequencies of T cells that produced IFN-γ after in vitro restimulation with VZV while VZV-specific antibodies did not change [17] . It cannot be excluded that this decline reflects reduced ability to produce IFN-γ rather a decline in VZV-specific T cell frequencies; however , T cells in vitro expanded by VZV stimulation are mostly TH1 cells producing TNF-α and/or IFN-γ and only infrequently IL2 or TH2 cytokines [33] . We therefore decided to use the frequency of antigen-specific IFN-γ–producing T cells as a read-out for vaccination responses . By including identical twins in our vaccination cohort , we were able to show a genetic influence on vaccine-induced T cell responses . Heritable factors for several vaccine responses have been shown for children [36 , 37] . In contrast , a recent study could not find such an association for the antibody response to influenza vaccination in the adult [38] . The authors discussed that immune parameters influenced by heritable factors early in life become more variable with age . Our study participants were older; still inherited influence reached significance for T cell responses although the study population was small . It is possible that B cell responses are more influenced by non-heritable factors; indeed , we did not find any influence of twin status on the induction of VZV-specific antibodies ( Fig 3B and [39] ) . Systems analysis in previous vaccine studies that used live viruses such as live-attenuated yellow fever or influenza viruses identified the activation of a number of pathways of the innate immune system that were important for the induction of antibody responses [8 , 9 , 13 , 14] . Innate immune activation was not very obvious with zoster vaccination; resistin , a member of the adipokine family , was the only inflammatory cytokine that significantly increased in the serum . Resistin is known to induce the expression of inflammatory cytokines; however , changes in the expression of inflammatory genes in peripheral blood were minor . Resistin’s role in adaptive immunity has not been extensively studied , also because of species differences: resistin is produced by adipocytes in mice and rats whereas by leukocytes in humans . Interestingly , it has been shown to inhibit dendritic cell function and thereby favor the induction of Tregs , which could explain its negative effect on T cell expansion after vaccination [40] . T cell responses after vaccination include a period of rapid expansion upon antigen stimulation followed by a contraction phase , during which most of the responding T cells die from apoptosis and do not transit into long-lived memory cells . Antigen-specific T cell frequencies after zoster vaccination peaked at day 8 . This is earlier than described for the primary vaccine response to live viruses such as yellow fever , consistent with the vaccine response to VZV being a recall response [41] . We found expansion and effector cell differentiation to be poor predictors for the increase in frequencies of long-lived VZV-reactive memory CD4+ T cells . Monocyte activation and production of inflammatory markers favored T effector cell expansion . However , this gain was lost for T memory cell formation because the same genes were also associated with increased attrition . Overall , innate immune activation was minimal with zoster vaccination; resistin was the only inflammatory cytokine that significantly increased in the serum , and changes in gene expression were minor , which may explain why the influence of inflammatory genes on T cell responses was low . Surprisingly , T cell expansion was relatively independent of age . This finding was unexpected because most models of immunosenescence imply declining T cell responsiveness to stimulation with age and in particular reduced ability to proliferate due to telomeric erosion and expression of p16 [42–44] . The extent of contraction clearly determined the benefit gained from vaccination . Equally important , increased attrition accounted for the declining vaccine response to zoster vaccination with age . The factors that determine survival of T cells after peak responses and differentiation into long-lived memory cells are largely unknown . For CD4+ T cells , transition into memory cells is dependent on the strength of the initial TCR signal and only high affinity T cells survive [22 , 23] . At the same time , differentiation of CD4+ T cells has been shown to be substantially influenced by environmental cues [45] . Vaccine-induced changes in serum concentrations of cytokines yielded Lasso predictors that were different for the expansion and contraction , consistent with the notion that the two phases are regulated by different mechanisms . The prediction models for after-peak contraction and increase in antigen-specific T cell frequencies from day 0 to day 28 were highly inversely correlated ( r = 0 . 7 ) emphasizing the importance of effector cell attrition for the long-term benefit of vaccination . As least equal to environmental factors , T cell survival and effector cell attrition are determined by cell-intrinsic factors that have been only incompletely defined [22 , 23] . Gene expression analysis of activated CD4 T cells co-expressing CD38 and HLA-DR on days 8 and 14 identified several gene modules that correlated with the increase in frequencies of VZV-specific T cells on day 28 . Very strikingly , many of the associated modules were functionally related to cell cycle control or DNA repair . Interestingly , classical pro- or anti-apoptotic molecules that have been implicated in regulating cell attrition in the transition from effector to memory cells , like Bcl-2 , Bim or PUMA did not show up in this analysis . Also , typical markers of T cell senescence such as hTERT , p16 or CD57 did not correlate with the vaccination-induced increase in antigen-specific memory cells . CD85j , a negative regulatory receptor expressed on CD8 TEMRA cells with age [46] , but not other TEMRA- related molecules , was found to correlate with attrition . The finding of gene modules involved in the regulation of DNA replication and DNA damage checkpoints is not entirely surprising . Obviously , T cells exit from a stage of rapid proliferation at the time when frequencies peak . Proliferative responses require the integration of cell cycle progression and survival mechanisms with proper DNA replication and DNA repair . Cell cycle progression is monitored by checkpoints in the G1/S , intra-S and G2/M phases that respond to DNA damage and activate DNA repair mechanisms [47 , 48] . Our findings suggest that this process determines the extent of attrition of effector T cells and therefore the survival of long-lived memory cells . Since multiple related modules were implicated , it is unclear whether one particular checkpoint or a limited number of driver genes can be identified that account for the failure in cell cycle regulation and DNA repair and the associated cell death . The correlation between gene modules and attrition was maintained after correction for age , albeit with slightly lower significance . Gene modules identified here are therefore important for the survival of long-lived memory T cells in general independent of age . However , implicated gene expression modules also trended to correlate with age , and correlation coefficients for the correlation with attrition and age were generally concordant ( S3 Fig ) suggesting that these processes may be more frequently dysregulated in older individuals accounting for the increased effector cell attrition with age . CD38 and HLA-DR double-positive T cells include VZV peptide tetramer-positive cells only after vaccination , but they certainly also include specificities other than those for VZV . It is therefore possible that gene modules found to be correlated with formation of long-lived memory cells are common for CD4 T cell activation in general . However , many modules that were shared in day 8 and day 14 activated CD4 T cells were not found to be predictive in activated CD4 T cells harvested before vaccination ( Fig 6D ) suggesting that they reflect the population of VZV-specific T cells that were activated by vaccination . Our study on T cell responses after vaccination with the live zoster vaccine provides a unique opportunity to understand the constraints of a suboptimal T cell response as it occurs in an at least partially immunocompromised older population . One important conclusion is that the generation of effector T cells and of long-lived memory T cells are governed by at least in part different rulesets . Of particular interest is the finding that the contraction phase after the peak response is of utmost importance in memory cell formation , especially in older individuals . Contraction is mostly regulated by cell-intrinsic gene expression patterns and only to a lesser degree influenced by serum cytokines in the early stage of the immune response . It is currently unclear whether adjuvants that are commonly explored to promote T cell activation and expansion of effector cells also influence T cell memory cell differentiation . Interventions may be needed that improve the survival of expanded T effector cell populations [49] . A modulation of the cell cycle pathways implicated here may be the mechanism underlying the recent empirical observation that rapalogs can improve influenza vaccine response in the elderly [50] .
Healthy individuals between the age of 50 and 75 years ( 20 males and 19 females , mean age 61 . 7 years ) who had no history of shingles in the last five years and no prior vaccination with Zostavax were included in the study . Nine pairs of monozygotic twins were recruited from the SRI twin registry [51] . GoldenGate genotyping ( Illumina Inc . ) was performed to determine zygosity by IGenix ( Bainbridge Island ) . Twins were considered identical if DNA markers were above 99% identical . Participants were vaccinated with live-attenuated VZV vaccine Zostavax ( Merck & Co . Inc . ) . Peripheral blood was collected on the day of vaccination ( day 0 , prior to vaccine delivery ) , on day 1 or 3 , and on days 8±1 , 14±1 and 28±3 after vaccination . The studies were approved by the Stanford University Institutional Review Board , and all participants gave informed written consent . PBMCs were isolated by Ficoll gradient centrifugation . In selected experiments , PBMC depleted of CD4 or CD8 T cells using the Miltenyi autoMACS cell separator and magnetobeads were used . Four serial dilutions starting with one million cells/well were added to precoated IFN-γ–specific ELISpot plates ( Mabtech ) . Cell lysate from mock-infected ( control ) or pOKa-infected melanoma cells was added at a concentration of 3 μg/ml total protein , and cells were cultured for 16 h . Plates were developed according to the manufacturer’s instructions and analyzed using Immunospot software ( Cellular Technology Limited ) . Mean spot counts were calculated from the two serial dilutions that were closest to 50 spots per well , followed by subtracting the spots of uninfected melanoma cells . The average background in PBMC cultured with lysate from uninfected melanoma cells was 0 . 46 spots per 100 , 000 PBMCs . Serum was collected at days 0 and 28 . VZV-specific antibodies were determined using a commercially available kit ( Calbiotech ) . Antibody indices are given in arbitrary units . Serum samples from days 0 , day 1 or day 3 were stored at -80°C until processed . Human 51-plex fluorescent bead assays ( Affymetrix ) were performed in duplicates as published [38] . MFI data for cytokines were analyzed by regression for age and batch . The regression coefficient for batch was used to normalize the data for estimated batch effects . HLA-DRB1*15:01 monomers with thrombin-cleavable tethered CLIP peptide and Jun/Fos dimerization domains were produced in insect cells and purified by FPLC . The protein was biotinylated , peptide exchanged with VZV gE peptide 54 ( TSPLLRYAAWTGGLA ) and VZV IE63 peptide 24 ( QRAIERYAGAETAEY ) and tetramerized using PE- and APC-labeled streptavidin , respectively . Residual unbound streptavidin and biotinylated DR15 protein was removed from the tetramer product with biotin agarose ( Sigma ) and streptavidin agarose ( ThermoFisher ) . Prior to tetramer staining , PBMCs were pre-treated with 50 nM dasatinib ( CST ) for 30 minutes at 37°C . Tetramer staining ( 0 . 5μg/ml for each peptide-exchanged monomer ) was performed for one hour at room temperature with Fc block ( BioLegend ) in 100 μl final volume . During the last 30 minutes of tetramer staining , cells were stained with antibodies to CD38 FITC; CD19 , CD8 , and γδTCR PerCP-Cy5 . 5; CCR7 PE-Cy7; CD45RA APC-Cy7; HLA-DR Pacific Blue; CD3 BV605; CD4 BV650 ( all BioLegend ) ; and with live/dead aqua zombie . Samples were washed and data were collected using an LSRII flow cytometer ( Becton Dickinson ) . Data analysis was carried out with FlowJo software ( TreeStar ) gating on single tetramer-positive cells . On day 0 and day 1 , whole blood samples were collected into PAXgene tubes ( PreAnalytiX ) . Total RNA was extracted using PAX gene blood RNA kits ( Qiagen ) , labeled and hybridized to the Illumina HumanHT-12 V4 expression BeadChips . Microarray raw data were quantile normalized by using GeneSpring software ( Agilent ) . Deconvolution analysis was performed using a mixed effect regression model as previously described [26] . The analysis is built on the assumption that the observed gene expression level in the whole blood is the average cell-type specific gene expression levels weighted by their corresponding cell-type frequencies . Cell type frequencies were estimated by deconvolution from cell type-specific probes [52] . To identify significantly changed genes between day 0 and day 1 , deconvolution analysis was performed on the top 5000 genes with highest variability in expression . Genes with 1 . 5-fold or greater changes in expression and p< 0 . 05 were considered as significantly changed . For analyzing association of immune responses with cell type-specific gene expression , deconvolution regression analyses were performed with p< 0 . 05 as filtering criterion . CD4 T cells expressing CD38 and HLA-DR were purified on days 0 , 8±1 and 14±1 using an Aria cell sorter . Total RNA was extracted using RNeasy plus micro kit ( Qiagen ) . The quality and quantity of RNA were checked by a 2100 Bioanalyzer ( Agilent Technologies ) . Samples with RNA integrity values above 6 were used for further steps . cDNA was amplified from total RNA using Ovation PicoSL WTA System V2 ( Nugen ) , followed by labeling and hybridizing to the SurePrint G3 human gene expression 8x60k microarrays ( Agilent Technologies ) . Microarray raw data were quantile normalized using GeneSpring software . Module activity scores , statistical significance and Pearson correlation of modules to immune responses were calculated using the btm_tool program in Python with p<0 . 01 as filtering criterion [13] . The circos plot was generated using Circlize package in R [53] . Hierarchical clustering was performed based on correlation coefficients with the correlation coefficients between modules and day 0 to day 28 responses inverted . The networks showing gene coexpression relationships of individual modules were generated using Pajek software . The continuous variables were summarized with mean and standard error . The associations between continuous variables such as age and immune response were tested using linear regression analysis with and without adjustment of confounding factors . The similarity between twins was assessed by permutation test to compare the average within twin-pair distance with the corresponding distance between unrelated individuals . The prediction models for frequency expansion and contraction were constructed by employing lasso-regularized linear regression . The penalty parameters in lasso were selected via the leave-one-out cross-validation , and the final prediction performance was evaluated by comparing the predicted versus observed outcomes via the pre-validation procedure . The concordance among pairwise associations of gene module and changes in VZV-specific T cell frequencies were measured by the correlation between correlation coefficients of the corresponding associations as well as the kappa statistics for concordance in significant associations . For the latter metric , a kappa between 0 . 40 to 0 . 75 significantly different from zero indicated fair to good concordance . | Vaccination is one of the most successful medical interventions , but it loses its effectiveness in an older population that is of particular risk for infectious diseases . Shingles , caused by the reactivation of the chickenpox virus , is a prime example . Nearly every second individual has experienced shingles by the age of 80 years , and the shingles vaccine is only partially protective . Attempts to improve the vaccine response are mostly empiric . Vaccinations induce a rapid expansion of antigen-specific T cells with frequencies peaking after one to two weeks . Most expanded T cells die after the peak response , and only few T cells survive to provide protection from infection or , as in case of shingles , from reactivation of latent viruses . Most vaccine studies have focused on the early stages of the response; how T cells are activated and expand . Surprisingly , in our study with the shingle vaccine , T cell survival after the peak response was the major factor determining memory T cell frequencies . T cell attrition was increased with age , independent of genetic predisposition . Using systems biology tools we found several pathways involved in T cell division and DNA repair that could be targeted to improve T cell survival and thereby increase the effectiveness of vaccination . | [
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"immuno... | 2016 | Defective T Memory Cell Differentiation after Varicella Zoster Vaccination in Older Individuals |
Host and parasite gene expression in skin biopsies from Leishmania braziliensis-infected patients were simultaneously analyzed using high throughput RNA-sequencing . Biopsies were taken from 8 patients with early cutaneous leishmaniasis and 17 patients with late cutaneous leishmaniasis . Although parasite DNA was found in all patient lesions at the time of biopsy , the patients could be stratified into two groups: one lacking detectable parasite transcripts ( PTNeg ) in lesions , and another in which parasite transcripts were readily detected ( PTPos ) . These groups exhibited substantial differences in host responses to infection . PTPos biopsies contained an unexpected increase in B lymphocyte-specific and immunoglobulin transcripts in the lesions , and an upregulation of immune inhibitory molecules . Biopsies without detectable parasite transcripts showed decreased evidence for B cell activation , but increased expression of antimicrobial genes and genes encoding skin barrier functions . The composition and abundance of L . braziliensis transcripts in PTPos lesions were surprisingly conserved among all six patients , with minimal meaningful differences between lesions from patients with early and late cutaneous leishmaniasis . The most abundant parasite transcripts expressed in lesions were distinct from transcripts expressed in vitro in human macrophage cultures infected with L . amazonensis or L . major . Therefore in vitro gene expression in macrophage monolayers may not be a strong predictor of gene expression in lesions . Some of the most highly expressed in vivo transcripts encoded amastin-like proteins , hypothetical genes , putative parasite virulence factors , as well as histones and tubulin . In summary , RNA sequencing allowed us to simultaneously analyze human and L . braziliensis transcriptomes in lesions of infected patients , and identify unexpected differences in host immune responses which correlated with active transcription of parasite genes .
Leishmaniasis is characterized as a spectral disease , with clinical presentations ranging from a self-healing cutaneous form to a visceral form associated with high morbidity and mortality . The immune response to the various species is also spectral in nature , with L . donovani inducing minimal immune responses or actually inhibiting inflammation and immunity [1] , and L . braziliensis inducing immune activation and immune-mediated pathology [2] . L . braziliensis is the causative agent of tegumentary leishmaniasis in South America . Approximately 3–5% of patients infected with L . braziliensis will eventually develop mucocutaneous disease , a disfiguring manifestation involving the nasal and oropharyngeal mucosa [3] . L . braziliensis infections are typically associated with a strong Th1 response and a positive skin test response to soluble leishmanial antigens characterized by high levels of TNF and IFN-γ [4] . ‘Early’ L . braziliensis lesions frequently present as a small papule that can eventually progress to larger ‘late’ ulcerative lesions , typically containing small numbers of parasites . The more severe mucocutaneous forms of the disease are associated with increased cytokine responses and increased T cell proliferation responses to parasite antigens [4] . The robust immune response to this organism despite low or undetectable numbers of parasites in lesions , and the increased immune responses in mucocutaneous forms of the disease have led to the suggestion that exaggerated Th1 host immune responses contribute to the pathological tissue damage associated with L . braziliensis infections [5] . Recent studies suggest that a major factor in the development of disease caused by L . braziliensis is the recruitment of cytolytic CD8+ T cells that promote increased inflammation [6 , 7] . Immune responses in the skin of infected patients may not be accurately reflected by the type and magnitude of host responses in peripheral blood . Previous studies using immunohistochemistry have described the infiltration of T cells , macrophages , B cells , NK cells , and granulocytes into lesions [8–10] . Some research has implicated CCR2-positive monocytes in parasite killing [11 , 12] , while other studies have described the contribution of the skin micro-environment to the local immune response [13–16] . For these reasons , a meta-transcriptomic profiling of lesions may provide a more complete picture of host and parasite responses during infection . A microarray-based transcriptomic profiling of skin lesions from patients infected with L . braziliensis was recently reported [17] . This work uncovered an association between chemokines and cytotoxic T cell responses and disease-associated pathology . We extended this work using RNA-seq to simultaneously analyze host and parasite transcripts in these lesions . This approach allowed the detection of parasite transcripts expressed in the skin of infected patients , and revealed unexpected differences in the host response associated with the detectability of parasite transcripts in lesions . It also revealed that the parasite transcripts most abundantly produced in human lesions could not be predicted by in vitro cultivation of parasites in human macrophages .
RNA sequencing analysis was performed on biopsies from 10 healthy and 25 L . braziliensis infected patients . Principal Component Analysis ( PCA ) ( Fig 1A ) and hierarchical clustering ( S1A Fig ) of the human transcriptome data revealed distinct differences between healthy skin and lesions from infected patients , as expected . Lesions are clinically defined as either ‘early’ ( small papule , no ulceration , ≤ 30 days illness duration ) or ‘late’ ( ulcerated lesions , ≥ 30 days illness duration ) ( S1 Table ) . Within the group of biopsies from the 25 infected individuals , there were minimal appreciable differences in host responses between patients with early and late cutaneous leishmaniasis ( Fig 1B , S2 Table ) , as previously reported [17] . There was also no separation in host responses as a function of sex ( S1B Fig ) , lesion size ( S1C Fig ) , illness duration ( S1D Fig ) , or age ( S1E Fig ) . RNA-seq allows the simultaneous analysis of both host and parasite transcriptomes , and we sought to determine whether the presence or absence of detectable parasite transcripts in lesions could impact host responses . Lesions from six of the patients were considered parasite transcript positive ( PTPos ) when greater than 0 . 5% of the total reads mapped to the parasite genome ( Fig 1C , orange ) . Ten lesions were considered parasite transcript negative ( PTNeg ) because less than 0 . 01% of the total reads mapped to the parasite ( Fig 1C , blue ) . This is the same proportion of parasite reads that map non-specifically to highly conserved homologous sequences in the human genome . Nine lesions were considered parasite transcript intermediate ( PTInt ) when percent reads ranged from 0 . 01–0 . 2 ( Fig 1C , yellow ) . The number of parasite reads in this group was too low to provide a meaningful analysis of parasite gene expression , and therefore they were excluded from the PTPos group . By PCA , there was a clear separation in host responses of the PTPos ( Fig 1D , orange ) and PTNeg groups ( Fig 1D , blue , note dashed line separating blue and orange ) , with the PTInt group ( Fig 1D , yellow ) falling in between these two . This separation between groups becomes even more obvious with the addition of the third principal component , accounting for 11 . 89% of the variance , where there is a clear separation in host responses between PTPos and PTNeg lesions ( S1F Fig ) . Thus , there is a surprising separation of host responses , depending on the presence or absence of detectable parasite transcripts in lesions , allowing us to correlate host responses with parasite persistence or elimination . We found large differences in host transcripts between healthy controls and infected patients , as expected , with 4579 host genes being differentially expressed in lesions relative to normal skin using a cutoff fold change > 2 and P value < 0 . 05 ( S3 Table ) . A total of 3884 differentially expressed genes were common to PTNeg and PTPos , but there was a total of 477 differentially expressed genes unique to PTPos lesions ( Fig 2A , orange circle ) and 241 differentially expressed genes unique to PTNeg lesions ( Fig 2A , blue circle ) , relative to healthy controls . The intermediate ( PTInt ) samples shared 3817 differentially expressed genes with PTNeg and PTPos , as well as an additional 987 genes with PTPos and 214 genes with PTNeg . A total of 238 genes showed differential expression unique to the PTInt group ( Fig 2A , yellow circle ) . S4 Table shows direct quantitative comparisons between gene expression in PTPos and PTNeg lesions , totaling 719 differentially expressed genes . Twenty-five of the top 30 upregulated genes in PTPos lesions relative to PTNeg lesions encoded immunoglobulin fragments , including 9 of the top 10 genes ( Fig 2B , black bars ) . This group also included CXCL8 ( granulocyte migration ) , IL-21 ( B cell proliferation ) , and granulysin ( cellular cytotoxicity ) . The genes most highly expressed in PTNeg lesions relative to PTPos lesions included genes involved in skin defenses and epidermal cell development . The top 10 genes in this category included loricrin , filaggrin-1 , filaggrin-2 , and hornerin , all involved in skin development ( Fig 2B , light gray bars ) . Although immunoglobulin gene expression in PTNeg lesions increased slightly relative to uninfected healthy skin ( Fig 2C , blue ) , immunoglobulin gene expression in PTPos lesions was substantially higher ( 367 fold ± 261 compared to 56 fold ± 32 ) ( Fig 2C , orange ) . The level of immunoglobulin transcripts in the PTInt group ( Fig 2B , yellow ) fell between the PTPos and PTNeg groups ( 122 fold ± 75 ) . Thus , high immunoglobulin levels in lesions may portend a poor prognosis in this disease , as previously suggested ( 23 ) . An assessment of transcripts encoding cell-specific markers pointed to an increase in B cells and their products in the PTPos lesions . B cell transcripts encoding CD79A , CD19 , and CD20 , were upregulated in PTPos lesions , relative to healthy controls ( Fig 3A ) and relative to PTNeg lesions ( indicated by a # symbol ) . Transcript levels in the PTInt group fell between PTPos and PTNeg ( Fig 3A , yellow ) . Increases in transcripts encoding B cell markers were not observed when comparing between early and late cutaneous leishmaniasis lesions ( S2A Fig ) . T cell markers ( CD3e , CD4 , and CD8a ) demonstrated no significant differences between PTPos , intermediate and PTNeg lesions ( Fig 3A ) . No significant differences in T cell markers were detected between early and late cutaneous lesions ( S2A Fig ) as previously reported [17] . Transcripts for inflammatory molecules ( IFN-γ , TNF , IL-1β and FASLG ) , inhibitory molecules ( IL-10 , CTLA4 , PD1 , PDL1 , and LAG3 ) , and activation markers ( CD80 and CD38 ) were all higher in PTPos lesions compared to PTNeg lesions ( Fig 3B ) , with PTInt lesions exhibiting intermediate transcript levels ( Fig 3B ) . In contrast to the differences observed between PTPos and PTNeg lesions , we saw no significant difference in the levels of transcripts for cytokines or signaling molecules in early versus late lesions ( S2B Fig ) . We used the weighted gene co-expression network analysis ( WGCNA ) program to cluster human host gene expression through comparison of gene expression profiles using pair-wise correlations . Cluster profiles were then assessed as a function of parasite transcript abundance . This analysis yielded three modules of genes ( among a total of 51 modules in the network ) whose expression exhibited a significant correlation with parasite transcript abundance ( S3A Fig ) . The most highly correlated module ( see * in S3A Fig ) contained 100 genes ( Fig 3C , left ) . Eight of the top 14 genes and their normalized expression versus parasite transcript number are shown in Fig 3C , right . These host genes included IL-10 , IFN-γ , TNF , granzyme B and fas ligand . All of the genes in this module exhibited a higher expression in PTpos lesions ( Fig 3C , orange dots ) . The two additional modules that also showed a significant correlation between host gene expression and parasite transcript numbers contained immunoglobulin transcripts , chemokine genes , cytokines and growth factors ( S3 Fig ) . The Reactome FI plugin for Cytoscape was used to observe known interactions between the 719 genes that were differentially expressed between PTNeg and PTPos ( S4 Table ) . An additional 149 linker genes , known to influence or connect multiple genes in the gene set , were included in this analysis . A network of 558 genes generated numerous clusters showing a dense interaction of genes differentially expressed between PTNeg and PTPos . When clustering genes by functional interactions from the Reactome database , the top three clusters highlight specific immune and cellular pathways ( Fig 4 ) . The largest cluster ( Fig 4 , blue nodes ) consists of 137 genes associated with immune cell activation , costimulation , and cytokine and chemokine signaling , including TNF , IL-10 , and multiple C-C motif chemokines ( S4A Fig ) . Gene regulation in this category is associated with NF-κβ , CREB/STAT3 , JUN , and SP1 signaling . The second largest cluster ( Fig 4 , red nodes ) includes 103 genes involved in B and T cell activation and inhibition , regulated by SYK , SRC , FYN , PLCG1 and 2 , and PTPN11 ( S4B Fig ) . Cluster 3 ( Fig 4 , yellow nodes ) includes 72 genes associated with cell growth and proliferation signals ( S4C Fig ) . The transcriptomic differences between PTPos and PTNeg patients demonstrate the potential of these clusters to lead to the identification of new drug targets for treating cutaneous leishmaniasis . We analyzed parasite gene expression in the six PTPos patient samples and despite substantial differences in lesion size and duration ( three early and three late cutaneous PTPos lesions ) , there was a high degree of uniformity in L . braziliensis transcript expression in all six patient lesions . Pearson correlation analysis of the top 50 parasite genes in each sample quantitatively demonstrated the similarity between samples , and ranged from 0 . 83 to 0 . 92 ( Fig 5A ) . The 20 most highly-expressed parasite transcripts by average RPKM , listed in Table 1 , were fairly randomly dispersed across the parasite genome ( S5 Fig ) . Six of the parasite chromosomes are illustrated in Fig 5B and the parasite gene expression ( in RPKM ) is shown by vertical lines in the 6 patients ( each patient designated by a different color intensity ) . Again , there is remarkable uniformity in parasite gene expression from patient to patient . Within these six chromosomes , 12 of the top 20 parasite genes ( by RPKM ) are noted , including cysteine peptidases , cysteine synthase , a proteasome subunit , and various hydrolase-like and hypothetical proteins . All 20 of the most highly expressed parasite genes were expressed in all six patients , regardless of whether the lesions were early or late ( S5 Fig ) . The top parasite transcripts ( Table 1 ) consisted of cysteine peptidases , a phosphodiesterase , as well as kinases and transport proteins . The presence of hypothetical and putative proteins in this list illustrates our lack of understanding of parasite gene expression during natural infections . Several amastin family genes and known virulence factors including GP63 , heat-shock proteins 70 and 83 , and cysteine peptidases appear in the top 100 observed transcripts ( S5 Table ) . Our recent work demonstrated the benefits of dual-transcriptomic analyses , using in vitro infection of macrophages with L . major and L . amazonensis to observe expression pattern changes in the host cell and parasite gene expression , ranging from 4 to 72 hours [18] . To determine whether the L . braziliensis transcripts identified in lesions were the most highly expressed parasite genes expressed in vitro , we compared the top 40 L . braziliensis transcripts identified in lesions ( ranked by RPKM ) , to the homologous genes expressed by L . amazonensis or L . major 72 hours after an in vitro infection of human macrophages , as previously described by us [18] ( S6A Fig ) . A comparison of homologous genes by rank showed that more than 60% of the most highly expressed L . braziliensis genes in vivo ( S6A Fig , red ) did not match the most highly expressed parasite genes in cultivated macrophages following in vitro infection with L . amazonensis ( S6A Fig , green ) or L . major ( S6A Fig , blue ) . Only 15 of the top 40 genes expressed by L . braziliensis during in vivo infections appeared in the top 40 L . major genes expressed in vitro , and only 5 of the top 40 genes appeared in the top 500 L . amazonensis genes expressed in vitro . The converse of matching the most highly expressed genes was also true . The most highly expressed L . amazonensis genes expressed in vitro ( S6B Fig , green ) were not highly expressed by L . braziliensis in vivo or by L . major in vitro ( S6B Fig , red and blue ) . A direct comparison to L . braziliensis genes expressed in vitro was not possible because RNA-seq analysis of L . braziliensis transcripts following in vitro infection of human macrophages has not been performed . This work is underway . Therefore , definitive conclusions regarding differences in gene expression between in vitro and in vivo infections cannot be made . However the results from this inter-species comparison suggests that the transcripts identified in L . braziliensis lesions were not simply the most highly expressed leishmanial genes , but rather might be transcripts involved in promoting parasite persistence in the lesion microenvironment .
By applying RNA sequencing technology to skin biopsy material , we were able to capture in detail the transcriptomes of both the parasite and the human host during L . braziliensis localized cutaneous infection . This analysis allowed us to examine host responses as a function of parasite persistence . L . braziliensis infections have often been described as having low parasite numbers in lesions [19] . This is consistent with our observation that lesions from 10 of the 25 patients had no detectable parasite transcripts in them at the time of biopsy . This lack of detectable transcripts is consistent with an active elimination of parasites by the host . In contrast , six of the 25 patients had ample evidence of high confidence parasite transcripts in lesions ( averaging nearly 1M parasite reads/sample; see S1 Table ) . The presence of these transcripts indicates that viable parasites were persisting in lesions and continuing to produce transcripts that could contribute to their survival . Therefore , we compared host immune responses in lesions where parasites were producing detectable transcripts ( PTPos ) to those where parasite transcripts were undetectable ( PTNeg ) . The progression of L . braziliensis disease has been well-studied , and the small nodules of early tegumentary leishmaniasis typically progress to the larger sometimes necrotic lesions associated with late cutaneous leishmaniasis [20] . Studies have been done to examine differences in immune responses between these two groups [21] , and comparisons with regard to treatment efficacy between early and late disease have also been made [19 , 22] . A surprising result from the previous transcriptomic comparison between early and late L . braziliensis lesions was that the host immune response was initiated early during infection and persisted throughout the course of the disease [17] . Our RNA-seq confirms this previous observation . We could not separate the host response during early and late tegumentary leishmaniasis by Principal Component Analysis ( Fig 1 ) . However , when these responses were stratified by the presence or absence of parasite transcripts , a clear separation could be achieved between the host responses in lesions where parasite transcripts were detectable ( PTpos ) versus those where they were not detectable ( PTneg ) . We chose to contrast those two groups with an eye to understanding host responses that may be associated with parasite persistence or parasite elimination . One of the surprising observations from this work was the degree to which B cell transcripts were associated with lesions containing detectable levels of parasite transcripts . Of the top 100 genes that were differentially upregulated in PTPos lesions relative to healthy controls , 80 encoded immunoglobulin-related transcripts . Lesions in which parasite transcripts were not detectable ( PTNeg ) also showed some evidence of B cell activation , but not to the same extent . The quantity of immunoglobulin transcripts in PTPos lesions was higher than PTNeg lesions ( Fig 2C ) , as were the number of B cells as judged by transcripts encoding CD79A , CD19 , and CD20 ( Fig 3A ) . One interpretation of these results is that the persistence of actively transcribing parasites in the lesion prolongs the host immune response . However , we previously reported that in human visceral leishmaniasis , high levels of IgG were associated with on-going disease , and that IgG levels decreased as cell mediated immunity developed following treatment [23] . Furthermore , addition of parasite-specific IgG to L . major-infected JH mice exacerbated disease , increasing lesion size and inducing IL-10 production [23] . These previously published observations , along with the present association of high immunoglobulin transcripts in PTPos lesions , suggest that B cells and host IgG may be strong contributors to parasite persistence . We and others previously demonstrated that IL-10 could contribute to parasite survival , and a correlation between IgG levels and IL-10 production was identified in visceral leishmaniasis [23 , 24] . The present studies extend this correlation to American tegumentary leishmaniasis , and demonstrate that PTpos lesions had higher levels of IL-10 than PTNeg lesions ( Fig 3B ) . The association between parasite survival and cytokine production may be more complex than originally perceived , however , since PTPos lesions also expressed higher levels of IFN-γ and TNF , two cytokines that have well-established roles in classical macrophage activation and parasite elimination . In addition to an in depth look at the host response , RNA-seq also provides a picture of the parasite transcriptome during infection . Viewing parasite gene expression within the lesion provided some interesting surprises . First and foremost , the uniformity of parasite gene expression across all six patients , given the differences in lesion size , duration , and degree of necrosis among them was unexpected . It is possible that the most highly expressed parasite genes in lesions have the potential to contribute to parasite persistence . The identification of these gene products may lead to new candidates for vaccine development or new targets for diagnosis . In fact , recently published work from Khare et al . [25] targets the conserved kinetoplastid parasite proteasome , of which we observed a subunit highly expressed by L . braziliensis in patient lesions ( Table 1 ) . Also of note is that 8 of the 20 most highly expressed parasite genes encode “hypothetical proteins” of unknown function . The identification and characterization of these proteins may shed new light on how this parasite establishes infection , persists within mammalian cells , or escapes these cells to spread disease . We tested whether the parasite transcripts detected in vivo were a reflection of the most abundant transcripts expressed by amastigotes growing in human macrophages in vitro . The top 40 transcripts detected in lesions were different from the transcripts most highly expressed in vitro , and there was less than 40% ( L . major ) or essentially no ( L . amazonensis ) overlap between parasite gene expression in cultivated cells and in lesions . Although the parasite species and time post-infection may confound these findings , we believe that a contrast of the Leishmania genes expressed in vivo versus in vitro provides a useful baseline for future comparisons . We also observed a correlation between the prevalence of parasite transcripts in the lesion and the expression of a subset of host immune response genes . Using the weighted gene co-expression network analysis program ( WGCNA ) , we identified several key host response genes whose expression increased in lesions containing high levels of parasite transcripts ( Fig 3C ) . IL-10 has been previously associated with disease progression , so perhaps its inclusion in this category may not have been unexpected . However the association of TNF , granzyme B , and IFN-γ with parasite persistence was not expected . These observations may indicate that immunopathology is a driving factor in L . braziliensis infections . The second and third most significant modules ( S3 Fig ) correlate with several genes involved in B cell responses , immunoglobulin production and T cell interactions with parasite transcriptional activity . These observations are consistent with the hypothesis that B cells and IgG production may contribute to parasite survival .
This study was conducted according to the principles specified in the Declaration of Helsinki and under local ethical guidelines and this study was approved by the Ethics Committees of the Federal University of Bahia ( Salvador , Bahia , Brazil ) ( 010/10 ) , University of Maryland ( College Park ) ( 395840–3 ) and the University of Pennsylvania IRB ( Philadelphia , Pa ) ( 813390 ) . All patients provided written informed consent for the collection of samples and subsequent analysis . All cutaneous leishmaniasis ( CL ) patients were seen at the health post in Corte de Pedra , Bahia , Brazil , an area endemic to L . braziliensis . Diagnosis consisted of visual confirmation of a lesion characteristic of CL and parasite DNA detection and/or a positive delayed-type hypersensitivity response to Leishmania antigen . Biopsies were collected at the border of the lesions using a 4 mm punch before therapy . Patients consisted of 15 males and 10 females with illness duration ranging from 15 to 90 days and lesion sizes ranging from 4–960 mm2 ( S1 Table ) . Healthy ( uninfected ) skin samples were taken from volunteers living in a non-endemic area without a history of leishmaniasis , as described [17] . Samples were placed in RNA later and homogenized using a rotor-stator . Total RNA was isolated using the RNeasy Plus Kit from Qiagen . RNA integrity was assessed using an Agilent 2100 bioanalyzer . Poly ( A ) +-enriched cDNA libraries were generated using the Illumina TruSeq Sample Preparation kit ( San Diego , CA ) and checked for quality and quantity using bioanalyzer and qPCR ( KAPA Biosystems ) . Paired end reads ( 100 bp ) were obtained using the Illumina HiSeq 1500 platform . Trimmomatic [26] was used to remove any remaining Illumina adapter sequences from reads and to trim bases off the start or the end of a read when the quality score fell below a threshold of 20 . Sequence quality metrics were assessed using FastQC [http://www . bioinformatics . babraham . ac . uk/projects/fastqc/] . TopHat ( v 2 . 0 . 13 ) [27] was used to align reads to the applicable genome ( s ) with each genome alignment performed independently . Reads from healthy , early infection , and late infection skin samples were aligned to the human genome ( v . hg19/GRCh37 ) obtained from the UCSC genome browser ( http://genome . ucsc . edu ) . Reads from early infection and late infection samples were additionally aligned to the L . braziliensis ( v . MHOM/BR/75M2904 , Sanger Institute ) genome obtained from the TriTrypDB database ( www . tritrypdb . org ) . Two mismatches per read were permitted ( default TopHat parameter ) and reads were allowed to map only to a single locus ( TopHat option –g 1 ) . Additionally , gene model annotations were provided for the mapping ( TopHat option –G ) with limitations on the identification of novel splice junctions ( TopHat option –no-novel-juncs ) . The abundance of reads mapping to each gene feature in the aligned genome was determined using HTSeq [28] . Each resulting count table was restricted to protein-coding genes ( 20 , 956 genes for human and 8 , 556 genes for L . braziliensis ) . Non-expressed and weakly expressed genes , defined as having less than 1 read per million in n of the samples , where n is the size of the smallest group of replicates [29] ( here n = 8 for both human and parasite ) , were removed prior to subsequent analyses , resulting in count tables of 15 , 256 and 8 , 556 genes for human and L . braziliensis , respectively . Samples were classified as PTPos and PTNeg based on the percentage of reads mapping to the L . braziliensis genome . Parasite detectable-positive samples were defined as those with more than 0 . 5% of reads mapping to the parasite genome ( see Fig 1C ) . In the six PTpos samples , the average proportion of reads that mapped to the parasite genome was 0 . 98% ( see Fig 1C ) , for an average of 867 , 489 parasite reads per tissue sample sequenced to an average depth of 88 million reads ( S1 Table ) . The 10 PTNeg samples were defined as those with fewer than 0 . 01% reads mapping to the parasite; the proportion of reads in the PTNeg samples that mapped to the parasite genome was not different than the 10 healthy controls . We designated nine additional samples as PTInt because they expressed low levels of transcripts ( between 0 . 01–0 . 2% of total reads ) that could only tentatively be assigned to the parasite . The number of parasite reads in this group was too low to provide a meaningful analysis of parasite gene expression and therefore they were excluded from the PTPos group . Quantile normalization was applied to all samples [30] and data were log2-transformed . Multiple approaches were used to evaluate replicates and to visualize the relationships between samples , including Pearson correlation and Principal Component Analysis ( PCA ) . Limma ( a Bioconductor package ) was used to conduct differential expression analyses [31] . The voom module was used to transform the data based on observational level weights derived from the mean-variance relationship prior to statistical modeling [32] . Pairwise contrasts were done within limma to identify differentially expressed ( DE ) genes between conditions . Genes with a Benjamini-Hochberg ( BH ) multiple-testing adjusted P value of < 0 . 05 were defined as differentially expressed . Components of our statistical pipeline , named cbcbSEQ , can be accessed on GitHub ( https://github . com/kokrah/cbcbSEQ/ ) . | Leishmania spp are intracellular protozoan parasites that replicate primarily within host tissue macrophages . In this paper we simultaneously query host and parasite gene expression in human cutaneous L . braziliensis lesions . We observe an unexpectedly prominent role for B cells and immunoglobulins in lesions in which actively transcribing parasites reside . We also observe that parasite gene expression is surprisingly conserved among L . braziliensis lesions , and the genes that are expressed in lesions are not those that have been previously associated with parasite growth in vitro . This analysis of parasite and host gene expression in lesions may lead to the identification of new parasite virulence factors and may identify host responses that promote parasite persistence in lesions . | [
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"analysis"... | 2016 | Meta-transcriptome Profiling of the Human-Leishmania braziliensis Cutaneous Lesion |
Motile bacteria and archaea respond to chemical and physical stimuli seeking optimal conditions for survival . To this end transmembrane chemo- and photoreceptors organized in large arrays initiate signaling cascades and ultimately regulate the rotation of flagellar motors . To unravel the molecular mechanism of signaling in an archaeal phototaxis complex we performed coarse-grained molecular dynamics simulations of a trimer of receptor/transducer dimers , namely NpSRII/NpHtrII from Natronomonas pharaonis . Signaling is regulated by a reversible methylation mechanism called adaptation , which also influences the level of basal receptor activation . Mimicking two extreme methylation states in our simulations we found conformational changes for the transmembrane region of NpSRII/NpHtrII which resemble experimentally observed light-induced changes . Further downstream in the cytoplasmic domain of the transducer the signal propagates via distinct changes in the dynamics of HAMP1 , HAMP2 , the adaptation domain and the binding region for the kinase CheA , where conformational rearrangements were found to be subtle . Overall these observations suggest a signaling mechanism based on dynamic allostery resembling models previously proposed for E . coli chemoreceptors , indicating similar properties of signal transduction for archaeal photoreceptors and bacterial chemoreceptors .
Phototaxis in archaea is mediated by integral membrane protein complexes consisting of bacteriorhodopsin-like receptors , sensory rhodopsins , and tightly bound transmembrane signal transducers , Htrs . The latter are highly homologous to methyl-accepting chemotaxis proteins ( MCPs ) in bacteria [1] . Moreover , the archaeal genomes comprise homologs of the principal elements of bacterial two-component systems: the histidine kinase CheA , the response regulators CheY and CheB , and the methyltransferase CheR [2] . This homology suggests that the core properties of signal propagation are conserved and similar in archaeal phototaxis and bacterial chemotaxis [3] . This has been supported by studies of active fusion chimeras of archaeal transducers with bacterial receptors [4 , 5] . The phototaxis system of Natronomonas pharaonis , which is composed of sensory rhodopsin II , NpSRII , in a 2:2 complex with its cognate transducer , NpHtrII , and the Che proteins mentioned above , allows these archaea to avoid harmful blue-green light . It represents one of the best-studied archaeal sensor systems [6] that modulates the cell’s swimming behavior by means of a typical two-component cascade ( Fig 1 ) . Light absorption by NpSRII leads to activation of the cognate kinase CheA and , ultimately , to alteration of the flagellar rotation mode affecting cell mobility . Light induced conformational changes have been observed for NpSRII and NpHtrII . Upon photo activation NpSRII undergoes an outward motion of helix F [7–10] , which induces a clockwise rotation of helix TM2 of NpHtrII along with a displacement [8 , 11 , 12] , presumably similar to the piston-like motion in chemoreceptors [13–15] . These transient changes in the transmembrane part of NpHtrII lead to switching between conformational states of the membrane adjacent HAMP domain [16] . At the tip of NpHtrII in the vicinity of the CheA interaction site a thermodynamic equilibrium between conformations of different dynamics has been reported [17] . However , the mechanism of signal transduction from the HAMP domains to the tip and the transfer of this signal to the kinase CheA remain to be uncovered . In bacterial receptors HAMP domains are ubiquitously present separating the transmembrane domain from the cytoplasmic domain . Different models were suggested for HAMP activation and signaling [18 , 19] . The gearbox model [20] postulates either a relative rotation of helices in the HAMP domain or a combined rotation and tilting motion accounting for signal passage [21] . Alternatively , in the dynamic bundle model [22 , 23] alteration of HAMP domain dynamics due to a destabilizing effect of the transmembrane region is responsible for signaling . Signal propagation according to the latter model could be related to phase clashes in the heptad repeat pattern of the coiled-coil structure of receptor dimers . A mismatch between characteristic coiled-coil heptad repeats [24] of two domains results in the oppositional dynamic coupling between them: stabilization of the first domain destabilizes the following one downstream [23] . Finally , the possibility of allosteric mechanisms , including signal propagation along coiled coils , driven by dynamics rather than by conformational changes , has been previously discussed [25–27] . Despite the recent progress in the understanding of signaling by HAMP domains , the mechanisms of signal propagation along the cytoplasmic domain as well as kinase activation remain unclear [28] . The former implies changes in the helical packing of the cytoplasmic domain and hereby of its dynamics [29–31] . The latter has been shown to require an organization of the receptors into trimers-of-dimers [32] or even larger assemblies with CheW/CheA [33 , 34] and might involve local conformational changes in the tip region [35] . Larger arrays of receptors with an extended baseplate of bound CheA and CheW interconnect the trimers and are known to provide signal amplification by means of cooperative activation [34 , 36 , 37] . However , the actual mechanism of kinase control by receptor trimers is still unknown , though a recent comparison of receptor/CheA/CheW complexes in different methylation states by cryo-EM tomography indicates that activation of CheA may involve changes in the dynamics of two of the five CheA domains [38] . A reversible methylation/demethylation process of specific Glu/Gln residues located in the cytoplasmic adaptation domain of the receptors [39–41] provides tuning to different levels of input signal intensity at a constantly high level of sensitivity . Two enzymes carry out the modifications , CheR , methylating these sites , and CheB , which catalyzes the competing process of demethylation . The adaptation to the altering signal level is achieved by two mechanisms: activation of CheB by CheA-mediated phosphorylation and opposite propensities for the two modifications in the different methylation states [39] . The major effect of methylation is of electrostatic nature and comprises stabilization/destabilization of the adaptation domain [29] . While particular adaptation systems in other organisms can be more intricate with additional proteins and non-linear effects of intermediate methylation levels on the apparent kinase activity [40 , 42] , it is generally accepted that the two extreme methylation states correspond to different activating states of E . coli [28] and B . subtilis [40] chemoreceptors as well as of H . salinarum phototransducers [41] , and consequently to different kinase activity levels , though not necessarily to fully activated or deactivated states . These parallels between signaling and adaptation processes have been used for investigating different activating states of bacterial chemoreceptors via a change of their methylation level [31 , 43 , 44] . The relation between these two processes has also been shown for archaeal photorececeptor/phototransducer complexes [41] . Exploiting the analogy between chemo- and photoreceptors discussed above we have performed coarse-grained molecular dynamic simulations [45 , 46] to study the effects of methylation/demethylation in trimers of receptor/transducer dimers , NpSRII/NpHtrII , ( referred to as trimers-of-dimers further on ) . In the present study we have modeled differential activating states of trimers-of-dimers as the putatively fully methylated and demethylated states mimicked by charge addition and depletion . The simulations reveal that the removal of negative charges at the putative methylation sites , mimicking methylation [47] , causes the adaptation region to adopt a compact and static conformation . The neighboring domains , the kinase-interacting tip and HAMP2 , become more dynamic , whereas HAMP1 is again characterized by a static and compact conformation . These alternating dynamics are reversed by demethylation . Our results provide a model for the signaling of archaeal phototactic receptor/transducer complexes at the level of single trimer-of-dimers , that shows striking similarities to those proposed for the E . coli chemoreceptors [19 , 23 , 29–31 , 48–50] . The results presented here indicate that archaeal phototransducers and bacterial chemoreceptors share a general activation mechanism .
To reveal structural and dynamical effects of methylation/demethylation of the NpSRII/NpHtrII complex we have built a coarse-grained model of trimer-of-dimers based on a pre-equilibrated all-atom model of the 2:2 NpSRII/NpHtrII complex . The latter has been assembled by combining the crystal structure of the transmembrane part of NpSRII/NpHtrII with structures based on homology modeling as described in the Methods section ( see S1 Fig ) . The model dimer embedded in a model E . coli lipid membrane was equilibrated for in total 500 ns of all-atom MD until the root mean square deviation ( RMSD ) of the whole structure became stable . The equilibration led to two changes in the dimer structure ( see S1 Fig ) . First , the inter-HAMP region rapidly formed an asymmetric coiled-coil with one helix shifted with respect to the other by approx . 1 . 4 Å ( see S2 Fig ) . This shift caused a tilt of the whole dimer and corresponds to the minimum of the free energy for the isolated inter-HAMP region [51] . Second , both sensory rhodopsins underwent a motion in the transmembrane region resembling the U-V shape transition observed in recent X-ray structures [12] . This rearrangement preserves most contacts between NpSRII and NpHtrII and could provide a route for receptor cross-talk in the dense membrane lattice which might be physiologically relevant . Subsequently , we have constructed a model of the trimer-of-dimers as documented in detail in Methods . Briefly , using an appropriate snapshot from the dimer equilibration , we assembled a trimer-of-dimers based on the X-ray structure of the bacteriorhodopsin trimer ( pdb code 2NTU [52] ) . Due to the large system size of the complex of ~323 , 000 atoms , the all-atom model was subsequently converted into a coarse grain ( CG ) representation [45] and embedded into a CG-POPC bilayer ( S1 Fig ) . The solvated CG model of the methylated trimer-of-dimers was equilibrated for 2 μs with constraints on the inter-dimer interface in the highly conserved tip region ( positions 340–380 ) known from both X-ray crystallography ( pdb code 1QU7 ) [53] and NMR studies [54] . In a subsequent 6 μs equilibration step without any constraints the inter-dimer interface contacts remained stable . The equilibration simulation was followed by repeated production simulations of 2 μs each for the methylated and the demethylated systems ( the latter had been first equilibrated starting from the final structure of the unconstrained equilibration simulation of the methylated system until convergence of the measured observables was achieved in 2 μs , see Methods ) . During the equilibration the NpSRII/NpHtrII trimer induced a pronounced membrane curvature , which we quantified for both the methylated and the demethylated systems along two perpendicular directions within the membrane plane . As shown in Fig 2 , the membrane was highly and equally bent with average radii of curvature of 99 . 4±0 . 2 Å and 99 . 3±0 . 2 Å for the methylated and the demethylated systems , respectively . As indicated by the small standard deviations , the curvature radii in both systems do not change remarkably throughout the trajectories ( S3 Fig ) . Possible structural rearrangements within the NpHtrII dimers upon demethylation/methylation mimicked via charge reshaping in the adaptation region are analyzed in terms of the inter-backbone distances ( Fig 3 ) . Distance changes are most prominent in the membrane embedded part , in the two HAMP domains and in the CheA/CheW interaction region . We also compare the effects of methylation and demethylation with those induced by light activation . Fig 4 summarizes this comparison for the membrane embedded part of the phototaxis receptor/transducer complex demonstrating that methylation causes similar effects as observed experimentally upon illumination: The outward motion of helix F of NpSRII observed in the simulation is in agreement with the experimentally observed tilt triggered by the photoinduced cis-trans isomerization of the NpSRII retinal chromophore [7 , 8 , 10] ( for details of the trajectories see S4 Fig ) . The coupled transducer helix TM2 rotates by approx . 10–15° as shown experimentally by EPR spectroscopy [8] and by X-ray crystallography [11] . This rotation is accompanied by a piston-like motion of the TM2 helix by approx . 0 . 5–1 Å as also seen in the crystal structure [11] . Most intriguing , a recent study revealed an influence of the NpHtrII signaling domain on the NpSRII photocycle kinetics [17] , providing additional experimental evidence for the conformational coupling of receptor and transducer in these complexes . The agreement between light induced conformational changes for the transmembrane domain observed in the experiments and our simulations corroborate the present approach and calculations and gives confidence to the results obtained for alterations in the cytoplasmic domain . The opposite differences in the intra-dimer helix distances obvious for the two HAMP domains suggest that their packing is coupled in structural opposition ( Fig 3 ) . In the methylated state packing of HAMP1 is tighter whereas for HAMP2 it is significantly looser . This is in agreement with the dynamic-bundle model [18 , 23] according to which a phase stutter connection between the HAMP and the downstream bundles couples their packing stabilities oppositely in structurally adjacent segments . These phase stutters coincide with discontinuities in the coiled-coil heptad repeats [23] between HAMP1 and HAMP2 and between HAMP2 and the methylation sites ( see S5 Fig ) . Tight packing of the HAMP1 helices is thus coupled to a loose packing of HAMP2 and to a tight packing of the downstream bundle helices , and vice versa . Strikingly , the transition between methylation and demethylation does not lead to gross changes of the backbone packing density in the adaptation region ( labeled m . s . in Fig 3 ) . In contrast , larger differences in the intra-dimer backbone distances are again revealed in the regions which include the sites responsible for the interaction with the kinase CheA [54] . Here most prominent inter-backbone distance changes span more than 40 residues along the N-terminal ( positions 310–350 ) and nearly 20 residues along the C-terminal part of the tip ( positions 367–390 ) . This conformational rearrangement must result in a reorganization of the respective side chains which is beyond the resolution of the present method but may be the signal propagated to the bound kinase CheA . The inter-backbone distance change in the very tip region of the transducer including the helix turn ( positions 352–365 ) oscillates with negative as well as positive values which is evidence that the tip structure as a whole remains intact . A calculation of the relative inter-helical shifts in the tip region did not show any sliding upon demethylation; neither between the helices within one monomer nor between the two monomers in a dimer ( see S6 Fig ) , in accordance with a previous experimental study [55] . This is strong evidence that the tip presents stable interaction sites for association with the kinase proteins as previously suggested [56] . Methylated and demethylated trimer structures differ substantially in their conformations ( Fig 5 ) . The average distances between each dimer and the trimer central axis are primarily affected in the adaptation region as well as in both HAMP domains . In the adaptation region the additional charges generated by demethylation lead to strong electrostatic repulsion between the dimers causing deviations in the inter-dimer distances of up to 10 Å ( Fig 5 , S7 Fig ) . The first and second HAMP domains show an inverse response in their inter-dimer distances ( Fig 5 , S7 Fig ) . In contrast , inter-dimeric distances in the tip region do not significantly change ( S7 Fig ) . In spite of the observed detachment of the dimers in the adaptation regions , the total length of the complex did not change upon demethylation ( S8 Fig ) . Thus , the conformational rearrangements of the trimer observed upon methylation do not appear to cause a major change of the trimer conformation at the tip , which again corroborates the notion that the tip is a stable structural unit for interaction with the CheA/CheW kinase platform . In conclusion , intra- and inter-dimer conformational rearrangements are obvious upon demethylation . These conformational rearrangements are found to be in agreement with experimental data characterizing conformational changes of the transmembrane domains upon light activation [8 , 11 , 57] . The results are further in line with the prediction of the dynamic-bundle model and reveal conformational changes in the region of the CheA binding sites . The propagation of the signal along the cytoplasmic bundle to the tip , which is not obvious from the structural characterization , will be uncovered in the next section . The picture of the structural rearrangements in the NpHtrII/NpSRII trimer-of-dimers is incomplete without the description of the structural changes conveying the signal between the adaptation region and the transmembrane domain of the complex , where the conformational changes were found to resemble the light induced changes . The analysis of the CG MD trajectories points to an opposite relative longitudinal shift of the two helices in the inter-HAMP region as a putative structural factor of the observed dynamics conversion between the two HAMP domains ( S9 Fig and S10 Fig ) . The plausible role in signaling of this inter-HAMP region was previously highlighted by Gushchin and coworkers [51] . On the other hand , the large difference in the inter-dimer helix distances within the region connecting the transmembrane domain of the transducer and the HAMP1 ( Fig 3 ) implies the presence of decisive structural alterations in this region between the methylated and the demethylated systems . This may have parallels with the proposed change of the control cable helicity in chemoreceptors upon the kinase-off to kinase-on transition [58] . Adaptation clearly affects the dynamical characteristics of the complex . Analyses of the difference in the root mean square fluctuations ( RMSF ) per residue between the demethylated and the methylated complexes reveals regions with an alternating sign of ΔRMSF ( Fig 6 ) . In the demethylated state zones corresponding to the first HAMP domain and the adaptation region show higher mobility than in the methylated system , while zones of the transmembrane region of the complex , the second HAMP domain and the tip region exhibit lower mobility . Earlier experiments have shown that the first HAMP domain of NpHtrII is engaged in a thermodynamic equilibrium of two conformations , a dynamic and a more compact state [59] . Light activation of NpSRII and the propagation of the corresponding signal via rotation of helix TM2 shift this conformational equilibrium towards a more compact conformation [57] . This shift was found to be of opposite sign in HAMP2 [60] ( L . Li and M . Engelhard; C . Rickert et al . ; unpublished ) , making this domain more dynamic upon light activation . These observations are consistent with the sign inversion of the fluctuation differences observed here for the HAMP1 and HAMP2 domains ( Fig 6 ) . Again , the boundaries between two zones with different ΔRMSF coincide with discontinuities in the coiled-coil heptad repeats , termed phase stutters [23] , between HAMP1 and HAMP2 and between HAMP2 and the methylation sites ( Fig 6 and S5 Fig ) . The change of the dynamic pattern observed between the methylation sites and the tip is located close to the glycine hinge region ( positions 293 , 296 ) previously recognized as a structural element important for signal propagation [61] . This dynamics change is correlated to changes in the geometry of helix interaction: In the methylated state the helix conformation switches from a knobs-into-holes ( “da” ) residue packing to a complementary “x-da” packing close to position 240 , where ΔRMSF changes from negative to positive values , and back to “da” packing in the glycine hinge region ( 293 , 296 ) , where again ΔRMSF changes sign ( see also S11 Fig ) . These two packing states , related by axial rotation of the helices , were proposed to be associated with different signaling states of HAMP [20] and adaptation domains [56] in chemoreceptors . This observed pattern of dynamics is distinctly different from a globally altered flexibility , which would merely lead to a different extent of Brownian motion around the membrane anchor of the trimer . The observed domain-specific altered flexibilities reveal a tight control of the local dynamics in the coiled-coil transducer structure . In between the second HAMP domain and the CheA interaction sites close to the tip region , the small inter-helical distance changes ( Fig 3 ) indicate close structural similarities on the backbone level in the two states , while the dynamics ( Fig 6 ) clearly depend on the methylation state . Therefore , signal propagation via different dynamical states seems to provide the link between the CheA-activating region and the membrane-proximal HAMP domains .
To investigate signal transduction in archaeal phototaxis complexes we have studied a trimer-of-dimers model of the NpHtrII/NpSRII photosensoric complex exploiting the analogy ( see Introduction ) between bacterial chemoreceptors and archaeal phototaxis signal transducers as well as the relation between activation by native stimuli and adaptation established experimentally for chemoreceptors [3–5 , 31 , 41 , 43 , 44 , 47 , 62] . As it is the charge of the amino acid side chains undergoing methylation , rather than their size or shape , that modulates kinase activity [29] [30] , the effect of methylation and demethylation was mimicked by varying the charges of these amino acid positions . In the transmembrane region activation by light is known to result in a characteristic tilt of helix F of NpSRII [7 , 9] and a rotation of helix TM2 of the transducer NpHtrII [8 , 11] possibly accompanied by a piston-like motion [11 , 60] . Intriguingly , in our simulations the structural differences observed in the transmembrane part for the methylated and demethylated trimer resemble the experimentally determined behavior in response to light activation: methylation rotates TM2 by 10–15° and shifts it by 0 . 5–1 Å ( Fig 4 ) . In addition an outward movement of helix F of the NpSRII is revealed ( Fig 4 ) . In the HAMP domain region , activation by light leads to a more compact conformation of HAMP1 and equivocally to a more dynamic HAMP2 domain [57 , 59] similar to the changes observed in the present simulations ( Fig 4 ) . These observations support the conclusion that , at least for the transmembrane part of the NpSRII/NpHtrII complex and its HAMP domain regions , the mechanism for the signal propagation upon light activation strongly parallels the simulated changes by adaptation exerted through the methylation/demethylation of the transducer . Our results indicate that the cytoplasmic tip of the trimer does not undergo a gross structural rearrangement when methylation or demethylation occurs , in spite of the observed large scale opening of the adaptation domains of the dimers caused by electrostatic repulsion . Interestingly , the total length of the complex does not change significantly . Thus , the interface between the tip and the baseplate of CheA/CheW proteins remains largely intact , which might be an important factor for the integrity of signaling arrays . However we are aware of the fundamental limitation of the present model of the trimer-of-dimers , i . e . the omission of the baseplate proteins . Further computational and experimental studies are required to elucidate the role of the receptor-baseplate contacts and their dynamics in signaling . In contrast to the lack of conformational rearrangements in the tip discussed above , the dynamics show distinct differences between the methylated and demethylated states all over the transducer trimer . Demethylation induces a more dynamic behavior of the adaptation domain but leads to less dynamics of the tip ( Fig 6 ) . The two HAMP domains also respond in opposite ways , with HAMP1 adapting a looser dynamic state while HAMP2 adapts a more compact and static conformation . In the methylated trimer this pattern is inversed: static HAMP1—dynamic HAMP2—static adaptation domain—and dynamic tip . Notably , the glycine-rich hinge region seems to constitute the interface between methylation sites and tip and separates the two zones with different mobility ( Figs 6 and 7 ) . This dynamic pattern is coupled to structural rearrangements . Generally , the intra-dimeric distances ( Fig 3 ) increase in those regions of the trimer which experience a more dynamic behavior ( Fig 6 ) , and the changes in packing density are accompanied by changes in the axial rotation states ( S11 Fig ) as observed for different crystal structures of methyl-accepting domains [56] . A relation between signaling or adaptation and receptor dynamics as observed here for the phototaxis receptor complex has already been reported for chemoreceptors . B-factor analysis of X-ray structures of E . coli Tsr shows that methylation exerts a stabilizing structural influence by driving the HAMP domain towards a more compact and less dynamic state [43] . Also mobility changes of residues were observed upon signaling in chemoreceptors [43 , 63 , 64] . In particular , a yin-yang model has been proposed as a conceptual basis for signal transduction in chemoreceptors [30] . In this model a coupling region transmits each change in helix packing from the adaptation region to the tip , where it triggers a change in helix packing that is concerted but opposite in sign . Based on experiments [30] local transitions between a tightly packed , less mobile , “frozen” conformation and a more loosely packed , dynamic conformation of the adaptation domain were found rather than a global dynamical transition for the whole receptor as was suggested in [50] . Our findings are in agreement with and provide further evidence for this yin-yang model and show that the tip region undergoes a dynamic–“frozen” transition rather than a local conformational change between two relatively stable states . This model does not exclude the important role of the previously found conformational switches in the tip region [35] and of the HAMP1 domain [65] in the modulation of such dynamical behavior . In conclusion , the results from our CG calculations demonstrate that the dynamics of the archaeal transducer NpHtrII are modulated by methylation/demethylation and , likely , photostimulation in a cassette-like manner . The alternating differences in dynamics , which are characteristically coupled to structural rearrangements , are propagated to the kinase interactions sites . Thus , the coin of signal transmission along the rod-shaped cytoplasmic domain is represented by consecutive subdomains finely tuned by a cascade of alternating dynamics . The question remains how the signal is transferred from the transducer trimer to the kinase CheA , as we omitted the baseplate proteins in the current study . However , we can speculate here that a gross alteration of the interaction between the transducer tip and the CheA/CheW baseplate seems to be unlikely [56] . The observed changes of the local coiled-coil backbone packing of the transducer in the CheA-interaction region may alter the transducer surface epitope , which could propagate a local conformational change via the transducer—CheA interface and thereby modulate kinase activity . However , complete dissociation of the high-affinity binding interface has been excluded [66] . The other possible scenario might comprise a change in CheA dynamics that can be propagated within the five domains and consequently affect the internal dynamics of CheA . Evidence for this hypothesis stems from the importance of the hairpin residue flexibility situated on the tip of Tsr receptors [67] . A signaling mechanism based on an altered dynamics may also explain recent findings from cryo EM and proteolysis susceptibility experiments [38] which show that activation of CheA by Tsr leads to higher mobility of P1 and P2 domains of CheA . P1 and P2 carry the phosphorylation site and are responsible for CheY and CheB binding , respectively . Such scenario of a dynamical modulation of the CheA activity may also account for the signal spread across neighboring core complexes [28] . Similar models of dynamical allostery have been reported before for different systems [27 , 68] . CG molecular dynamics were essential to build and equilibrate this large system as well as to study the general mechanism of methylation and signal propagation . For a more detailed view of changes in side chain interactions within and between the involved proteins that give rise to the dynamical signaling , a more detailed investigation by all-atom molecular dynamics is required . Additionally , dissecting the role of dynamical and conformational changes of the transducer and CheA in a methylation state-dependent fashion will allow to distinguish different mechanisms of CheA activation . To this end , EPR spectroscopy on spin labels engineered into the transducer tip or the linkers between the five CheA domains could probe for effects of signal propagation . Alternatively , aiming for a fully detailed view , NMR spectroscopy combined with relaxation dispersion experiments can provide structural and dynamical information on the micro- to millisecond timescale , and show how the linker of the isolated CheA-P4 domain influences the phosphorylation activity of CheA [69] . The results reported here for the two extreme methylation states of the trimer-of-dimers suggest a mechanism for signal propagation in archaeal photoreceptor-transducer complexes similar to that in bacterial chemoreceptors . Accordingly , upon light activation of NpSRII the signal is transferred to NpHtrII via a movement of helix F and a concomitant screw-like motion of TM2 . The latter conformational change leads to alternating dynamics of HAMP1 , HAMP2 , the adaptation domain , and the CheA binding sites of the transducer . This mechanism is substantiated by experimental evidence such as rotary movement of TM2 [8] and observations concerning the dynamic pattern of the cytoplasmic domain [16 , 17] . The proposed mechanism provides an explanation how the seemingly subtle input stimulus actuated in the transmembrane part of the receptor can pass over its whole length and eventually affect the activity of the kinase bound on the opposite extremity of the receptor complex , which is 260 Å apart . The observation of subdomains along coiled-coil structural elements which can alter their structural and dynamical properties might be the basis for a universal mechanism in these structural elements not only found in chemo- and phototaxis .
An all-atom ( AA ) model of the NpsRII/NpHtrII dimer was prepared based on the available structures of different domains of the complex . The X-ray structure from 1H2S was used as a starting point for the transmembrane region of the complex ( consisting of the NpsRII dimer and the part of transducer from Gly 23 to Leu 82 ) , while models generated by homology ( the modeling scores are provided in S1 Table ) with the available structures were utilized to build the cytoplasmic region consisting of the two HAMP domains ( template: NMR structure 2ASX from Archeoglobus fulgidus ) and the cytoplasmic domain ( template: X-ray structure 2CH7 from Thermotoga maritima ) using Modeller [70] . The connecting region between the first and the second HAMP domains was predicted as a coiled-coil α-helix , and it was modeled as an ideal α-helix [51 , 71] . All the structures were aligned using the helical overlap between the adjacent domains . The model was embedded into a lipid bilayer containing 75% POPC and 25% POPG , which resembles a typical prokaryotic lipid composition using the CHARMM-GUI service [72] . After solvation and addition of Na+ and Cl- ( to neutralize the system at a salt concentration of 0 . 15 M ) the system contained 323 , 096 atoms in total . This all-atom model of the dimer in a prokaryotic model lipid membrane was subjected to an extensive equilibration simulation ( See main text and S12 Fig for RMSD plots ) . To construct a model of the trimer-of-dimers we took a snapshot from the dimer equilibration trajectory with the shift of the cytoplasmic domain securing an adequate mutual orientation ( i . e . , no overlaps ) of the dimers within the trimer . The model of the trimer itself was assembled using the X-ray structure of the bacteriorhodopsin trimer ( PDB code 2NTU ) [52] as a template for the structural alignment . We have chosen this relative orientation of the transmembrane domains of the dimers within the trimer-of-dimers based on the oligomer conformations predicted with the help of the protein-protein docking software M-ZDOCK [73 , 74] . Two oligomer conformations for the transmembrane part of the trimer-of-dimers were obtained ( S13 Fig ) , with the top-ranked one ( “triangle”-like ) featuring the inter-rhodopsin contacts similar to those in the bacteriorhodopsin trimer . The second ranked alternative conformation ( “ring”-like ) ( S13 Fig ) significantly differs within the transmembrane region of the complex but not in the cytoplasmic domain . According to these results we preferred to study the “triangle”-like conformation because its conformation corresponds to established inter-protein contacts . One NpSRII monomer of each NpSRII:NpHtrII dimer was aligned with one of the three monomers within the bacteriorhodopsin trimer using the Chimera [75] Match & Align tool ( combining sequence and 3D structures alignment ) . The built all-atom model was embedded into a lipid bilayer containing POPC using the VMD [76] Membrane plugin and subsequently converted into the Martini coarse grain ( CG ) representation [45] . This CG model of the trimer-of-dimers was placed into a simulation box filled with CG water particles and ions ( each resembling 4 water molecules , the system was neutralized at a salt concentration of 0 . 15 M , the total number of particles equaled 155 , 747 ) and equilibrated for 2 μs with the tip region first steered toward the known X-ray interface ( PDB code 1QU7 from Escherichia coli ) [53] and then constrained with the interface contacts preserved . This constrained MD simulation was followed by a further unconstrained equilibration of 6 μs . A number of production simulations were performed as listed in Table 1 after the equilibration simulations . The equilibration simulation of the demethylated system was started from the resulting structure of the unconstrained equilibration run of the methylated system . We performed an additional simulation starting from the equilibrated demethylated system , in which the methylation state was swapped to the fully methylated one . Over the course of this simulation we observed structural changes similar to the previously found differences between the methylated and the demethylated systems . In S14 Fig the time evolution of these changes during the demethylated-to-methylated transition is shown . These additional results proof statistical significance of our observations and indicate that the simulated systems are not trapped in local potential minima but explore the available phase space . Trajectories were produced with a total of 19 μs of CG time , in which sampling is 3–6 times faster compared to all-atom simulations due to the relative smoothening of interactions [77] . Classical molecular dynamics simulations were performed in the Gromacs 4 . 5 . 3 software package using the CHARMM36 forcefield [78 , 79] with CMAP corrections and the water model TIP3P [80] at ~150 mM NaCl ions to neutralize the system . Control simulations with 3 M NaCl resulted in an equal NpHtrII conformation in terms of its RMSD and RMSF values . For the retinal chromophore the set of parameters from [81] was used . The NPT ensemble was maintained with a Parrinello-Rahman barostat ( semi-isotropically with compressibility equals 4 . 5∙10−5 bar-1 ) and Nose-Hoover thermostat ( 323 K , τt = 2 ps ) . The cut-off for electrostatic interactions was set to 1 . 2 nm , while the long-range electrostatics was treated with PME . The time step of 2 fs was used for the all-atom simulations . Since large secondary structure alterations are expected not to occur during the signal propagation through NpHtrII [16 , 17 , 30 , 57] , we employed the MARTINI CG model , which is adjusted for a description of protein-protein interactions rather than for secondary structure formation or changes [82] . The standard MARTINI protocol was used for CG simulations in Gromacs as introduced in [45 , 83 , 84] . The retinal in NpSRII has been omitted without loss in stability of the receptor ( see S15 Fig for RMSD plot ) , and the common MARTINI approach for atoms-to-particles mapping was used with an average ratio of 4:1 . Inter-particle Lennard-Jones interactions are described in MARTINI in a form of four basic types of interacting particles ( polar ( P ) , charged ( Q ) , mixed polar/apolar ( N ) and hydrophobic apolar ( C ) , depending on their polarity or capability for H-bond formation ) subdivided further into 18 additional subtypes all interacting at 10 different levels ( from supra attractive 0 –through intermediate IV–to super repulsive IX ) . Electrostatics is treated in MARTINI according to Coulomb’s law based on the partial charges assigned in the force field . Protein secondary structure is preserved with constraints imposed to the regions with α-helical secondary structure , while coil regions are treated unconstrained allowing flexible regions of the complex to adjust their tertiary conformation . Though constraining of the structure by means of elastic network restraints could be used , we did not apply any additional restraints in the production simulations . In the course of the trimer-of-dimers assembling additional constraints were imposed to steer the tip regions of the three dimers towards the contacts established experimentally [53] . As a target for the steered and constrained MD simulations a homology model of the highly conserved tip region of the trimer-of-dimers was derived from the X-ray structure of the trimer-of-dimers ( PDB code 1QU7 [53] ) . Steered MD module of the PLUMED 1 . 3 plugin [85] was used to carry out these steered simulations using a harmonic potential with a force constant of 1000 kJ/ ( mol nm2 ) . To identify possible methylation sites we built a sequence alignment ( S2 Table ) for NpHtrII and two chemoreceptors for which the methylation sites were found experimentally , the Tsr receptor of E . coli [53] and TM1143 from Thermotoga maritima [86] . According to their homology or location with respect to these sites seven amino acid positions were admitted as putative methylation sites , namely Q259 , Q260 , E264 , E273 , E274 , E469 and Q470 . In addition , the sequences containing the residue pairs E273 , E274 and E469 , Q470 are identical or similar to the transferase recognition consensus sequence , respectively ( see S2 Table ) . Q259 , Q260 correspond to Tsr positions E274 , E275 . E264 is located close to identified positions E281 ( Tsr ) or Q297 ( TM1143 ) . As it was observed that it is the charge of the methylation sites side chains , rather than their size or shape , that modulates kinase activity [29 , 30] , the effect of methylation and demethylation was mimicked by modeling the side chains of residues corresponding to possible methylation sites with Qa beads ( charged particle , hydrogen acceptor ) in the demethylated system and with P1 beads ( uncharged polar particle , low polarity ) in the methylated system . The obtained MD trajectories were analyzed as described in the Supporting Information S1 File . | Achaea and bacteria can “see” and “sniffle” , they have photo- and chemosensors that measure the environment . On the cell poles , these sensor proteins form large arrays built of several thousands of different receptors . The receptors comprise extracellular or transmembrane sensory domains and elongated homodimeric coiled-coil bundles , which transduce the signals from the membrane across ~20 nm to a conserved cytoplasmic signaling subdomain in an unknown manner . In our study we performed coarse-grained molecular dynamics simulations of the phototactic receptor/transducer complex from Natronomonas pharaonis . Comparing fully methylated and demethylated complexes reveals an interconversion between states of different dynamics along the coiled-coil bundle , which might represent the essential characteristics of the signal transfer from the membrane to the binding sites of the downstream kinase CheA . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] | [] | 2015 | Signaling and Adaptation Modulate the Dynamics of the Photosensoric Complex of Natronomonas pharaonis |
Diffusion barriers are effective means for constraining protein lateral exchange in cellular membranes . In Saccharomyces cerevisiae , they have been shown to sustain parental identity through asymmetric segregation of ageing factors during closed mitosis . Even though barriers have been extensively studied in the plasma membrane , their identity and organization within the nucleus remains poorly understood . Based on different lines of experimental evidence , we present a model of the composition and structural organization of a nuclear diffusion barrier during anaphase . By means of spatial stochastic simulations , we propose how specialised lipid domains , protein rings , and morphological changes of the nucleus may coordinate to restrict protein exchange between mother and daughter nuclear lobes . We explore distinct , plausible configurations of these diffusion barriers and offer testable predictions regarding their protein exclusion properties and the diffusion regimes they generate . Our model predicts that , while a specialised lipid domain and an immobile protein ring at the bud neck can compartmentalize the nucleus during early anaphase; a specialised lipid domain spanning the elongated bridge between lobes would be entirely sufficient during late anaphase . Our work shows how complex nuclear diffusion barriers in closed mitosis may arise from simple nanoscale biophysical interactions .
Asymmetric segregation of ageing factors during cell division is essential to maintain parental identity between mother and daughter cells . This is an intense area of research – not only due to its applicability in disease models , but also due to the key role played by asymmetric cell division in the generation of eukaryotic diversity . Cell division is a highly dynamic process , starting at the establishment of polarity between the future mother and daughter cells , continuing via spatiotemporal coordination of lipids and structural proteins involved in membrane remodelling , and ending at cytokinesis . In contrast to most other unicellular eukaryotes , the yeast S . cerevisiae undergoes closed mitosis . That is , its nucleus remains intact at all times , and only breaks down right before cytokinesis . During anaphase , complex changes in the nuclear envelope ( NE ) result in a dramatic re-shaping of the nucleus: first , the mother lobe buds into a nascent daughter lobe , resembling joined ellipsoids; then , a dumbbell shape emerges , where both lobes remain connected by a long , narrow bridge . As anaphase progresses , cell fate factors become laterally compartmentalized along the cell division axis . Both the rapidly changing nuclear morphology and NE constitution are likely contributors to the compartmentalization of nuclear proteins . As a consequence , discerning their respective contributions has been the focus of much recent research . By using photobleaching techniques and computational simulations , it was recently shown that geometry changes may account for compartmentalization in the nucleoplasm , while lateral diffusion barriers located between nuclear halves could be responsible for protein segregation at the inner and outer nuclear membranes ( INM and ONM , respectively ) [1] , [2] . Even though the nature and structural organization of these diffusion barriers remains elusive , different lines of evidence suggest they depend on specialised lipid domains and scaffolding proteins , such as sphingolipids and septins , respectively . Sphingolipids are characterized by long , saturated hydrocarbon chains that favour their assembly into tightly-packed , thick bilayers . Sphingolipid-enriched domains are typically more viscous than other lipid phases of the membrane [3] , thus effectively reducing molecular diffusion [4] . As membrane proteins have specific affinities for lipid phases depending on their size , amphipathicity , and their membrane anchor , they may become differentially segregated from sphingolipid domains [5] . On a larger scale , morphological changes of the S . cerevisiae nucleus during anaphase are compatible with the hypothesis of sphingolipid domains being present at the NE between lobes [6] . This follows from the observation that sphingolipid domains modify the curvature index of membranes [7] , [8] , with a tendency towards negative curvatures such as those found between nuclear halves . Also , recent Fluorescence Loss In Photobleaching ( FLIP ) experiments suggest that nuclear membrane proteins experience distinct diffusion dynamics at the neck as compared with the lobes [2] . In another line of evidence , the diffusion barrier at the endoplasmic reticulum ( ER ) slows down the dispersion of membrane proteins therein , thus causing their asymmetric distribution [9] . Moreover , Sur2 , a sphinganine hydroxylase necessary for sphingolipid biosynthesis [10] , resides at the ER during anaphase [11] . Given the ONM and ER membrane are continuous during this stage [12] , the evidences above suggest such membranes' composition at the neck differ from those at the lobes [6] . Thus , distinct diffusion regimes could constitute the underlying protein segregation mechanism preventing free exchange between nuclear halves . However , one should note that irrespective of the presence of sphingolipid domains , other mechanisms such as protein preference for certain types of membrane curvatures [8] , [13] , [14] and electric potentials [15] , may also play a role in the lateral segregation of nuclear proteins . Separately , septins are a family of filament-forming , membrane-interacting cytoskeletal GTPases involved in many cellular membrane-remodelling events [16] , [17] . During mitosis , septin filaments organize into rings and other complex structures [18]–[20] . They are involved in processes requiring lateral compartmentalization and membrane sculpting into lobular enclosures , as is the case of mitosis and exocytosis [21]–[23] . In the plasma membrane , septins have been proposed as the main constituents of diffusion barriers [18] , [22] , [23] . In addition to their role in hindering protein mobility by forming rings that work as fences , septins possess a lipid-binding motif that enables them to interact with membranes [24] . Hence , it has been proposed that septins recruit and enrich specific phospholipids at the plasma membrane , locally affecting its fluidity [23] . In contrast , while septins have been observed on the inner leaflet of the plasma membrane at the bud neck [19] , [25] , their presence at the ER membrane and ONM is only supported by indirect evidence [1] . An interesting hypothesis is that septin filaments may constrain diffusion at the neck by recruiting and anchoring lipid microdomains [23] . However , it has yet to be shown whether they only recruit the machinery for assembling the barrier , or they are also components of the barrier itself . Notably , these two scenarios are not mutually exclusive , and recently have been referred to as the scaffold model vs . the diffusion-barrier model [25]–[27] . Lastly , recent experiments suggest that Bud6 [28] , a downstream effector of septins , is involved in the formation and maintenance of diffusion barriers that compartmentalize the NE [1] and the contiguous ER membrane [9] . Bud6 stimulates actin nucleation and assembly through the formin proteins Bni1 and Bnr1 during membrane remodelling events [29] , [30] . In particular , Bnr1 bundles actin filaments [29] and is localized at the bud neck during cell division [31] . In turn , actin polymerization into filaments and other structures is also required for proper nuclear membrane remodelling during anaphase [32] . Moreover , it has been shown that septins promote the assembly of actin filaments into rings [33] and that sphingolipids participate in cytoskeletal organization through actin dynamics during endocytosis [34] , as well as in membrane remodelling of other cell types at the dividing neck [35] , [36] . Taking these facts together , a synergy between sphingolipids and structural proteins at the bud neck emerges as a possible , efficient solution for the compartmentalization through diffusion barriers and simultaneous remodelling of membranes . In this work , we explore the roles of specialised lipid domains and structural proteins , organized as rings , in establishing nuclear lateral diffusion barriers in S . cerevisiae during anaphase . We postulate sphingolipids to form such specialised lipid domains , aligning to experimental evidence [5]–[8] . However , our analysis is not necessarily limited to them . Based on previous results from fluorescence microscopy techniques [2] and introducing spatial-stochastic simulations , we evaluate the plausibility that these molecular complexes constitute the diffusion barrier . As the nuclear morphology changes dramatically from early to late anaphase ( EA and LA , respectively ) , we studied these stages separately . Accordingly , we developed in silico models and simulated specialised lipid domains and protein rings using realistic nuclear geometries based on experimental measurements . Our results show that , in LA , a specialised lipid domain at the nuclear bridge is enough to compartmentalize the nucleus into different diffusion regimes . Moreover , we explored three different specialised lipid domain configurations in LA and found an optimum agreement with experiments when the domain spans the entire bridge [2] . In contrast , we found that a specialised lipid domain and a protein ring must act together at the neck to constrain diffusion between nuclear lobes in EA . Interestingly , the estimated necessary number of proteins at the ring to constitute the diffusion barrier suggests a polymeric , filamentous fence as the most likely scenario . Altogether , our results suggest that , even though the high viscosity and exclusion properties of specialised lipid domains are probable contributors to the diffusion barrier , additional mechanisms become necessary to fully explain asymmetric segregation . Namely , a protein ring-shaped ‘fence’ and an elongated nuclear morphology in EA and LA , respectively .
The compartmentalization effects of nuclear diffusion barriers are known to increase alongside anaphase , and are specific to the nucleoplasm , ONM and INM [1] , [2] . This was shown to be the case by a combination of FLIP assays and stochastic simulations tracking the fluorescence decay of diffusing marker proteins under continued photobleaching of a small region in the mother lobe ( Fig . 1 ) . There , the ratio of the daughter over mother lobe durations for losing 30% of their initial fluorescence defined the degree of compartmentalization ( °CP ) , where a higher ratio implies a slower bidirectional transmission of nuclear markers . Thus , the °CP is inversely proportional to the exchange rate between compartments , and it constitutes an indirect measure of the barrier strength . In this work , we used the FLIP profiles reported in [2] to study the compartmentalization of Nsg1-GFP ( ONM marker ) , GFP-Src1 ( INM marker ) , and the nuclear pore complex ( NPC , reported by Nup49-GFP ) . In all these cases , compartmentalization was not explained by geometry alone ( see Figs . 2A and 3A–C in [2] ) . However , such study utilized a single idealized nuclear geometry , and the variation bounds of numerical experiments were very small , as compared to those of FLIP profiles . So , it remained to be shown whether considering diverse cell geometries would significantly change model predictions , or better fit the data instead . To address this , we first developed realistic sets of 3D geometries in EA and LA from a heterogeneous sample of cell geometries ( Fig . S1 and Methods ) . Then , we used these geometries to carry out spatial-stochastic simulations of FLIP experiments , obtaining their corresponding decay profiles ( Movies S1 , S2 , S3 , S4 ) . Our simulations indeed show that considering distinct nuclear geometries may well account for the observed experimental variation bounds in FLIP experiments . Subsequently , we placed a virtual plane at the neck in EA , and midpoint of the bridge in LA , simulating a hypothetical barrier as in [2] . By fitting to experimental data , we estimated the probability of bidirectional particle transmission and used it as an indicator of barrier permeability . In agreement with previous findings [1] , [2] , our results show that only nuclear re-shaping during anaphase accounts for compartmentalization at the nucleoplasm , whereas diffusion barriers are responsible for compartmentalizing the NE ( Fig . S2 ) . Interestingly , the finding that barrier permeability is greater in LA than in EA for NPCs only ( Fig . S2 ) suggests that their compartmentalization is more sensitive to the changing geometry than that of other molecular species . For the particular case of the NPC , which constitutes the largest diffusing complex in the NE , we further tested whether volume exclusion could generate a crowding effect that hindered its exchange between nuclear lobes . In fact , this effect could easily arise given the narrow thickness of the perinuclear space at the neck and at the bridge in EA and LA nuclei , respectively ( Fig . S3 ) . Moreover , given the NPC constituted the largest molecule in our simulations ( Table S1 ) , but also the one with the lowest concentration ( ∼150 ) , so far it wasn't clear whether its size would hinder its diffusion at the joint between nuclear halves , thus explaining its compartmentalization . Upon running simulations , we did not find any difference in virtual FLIP profiles when NPC volume exclusion was accounted for or not , in a simplified scenario ( Fig . S4 ) . Nevertheless , the fact that many other proteins crowd the membrane did not escape us . However , considering their nanoscale volume exclusion effects is technically impossible at present . Not only are the sizes and diffusion coefficients of most of these crowders unknown , but also there is no guarantee all crowders have been identified already . Moreover , simulating such a huge amount of diffusing particles is computationally prohibitive . Thus , we relied on the previously estimated effective diffusion rates for the reporter proteins used in this study . These rates already account for crowding exerted by the highly inhomogeneous media where proteins diffuse . In addition , previous mathematical models suggest that factors other than excluded volume , such as protein-protein interactions , are contributors to the concentration dependence of lateral mobility [37] . Furthermore , considering time-varying diffusion coefficients didn't improve the fit of our simulations to FLIP data . Specifically , we could not fit our model to experimental observations in EA and LA by assuming a continuous range of varying diffusion coefficients . A proper fit was only possible when assuming independent ranges for EA and LA that would not make biological sense . Hence , the discrepancy found during the first 50 s ( Fig . S2 ) may be related to: a ) not considering the fluorophore maturation dynamics; or , most likely b ) the fact that the time span of the fluorescence decay profiles reported in [2] is a substantial fraction of the ∼500 s duration of the entire anaphase [38] , thus representing only a ‘snapshot’ of a highly dynamic process where the nucleus keeps growing while FLIP experiments take place . Overall , our spatial-stochastic simulations based on realistic 3D models of a heterogeneous sample of nuclear morphologies supported previous findings , and confirmed that missing variation bounds can be fully accounted for by considering distinct cell volumes away from an idealized average . Moreover , our simulations offer a suitable stage for testing diverse barrier compositions and configurations . In contrast to the majority of lipids constituting cellular membranes , the Van der Waals forces between sphingolipid larger backbones , and the associated sterols that stabilize them , result in tighter packing and thicker membranes ( Fig . 2 ) . This results in stabilized domains in a liquid ordered ( Lo ) rigid phase with decreased solubility for membrane proteins . Accordingly , sphingolipid domains are good candidates for constituting diffusion barriers by the direct contribution of two effects: an increased viscous drag that slows down protein diffusion within the Lo phase of the domain , and the exclusion of proteins coming from the liquid disordered phase ( Ld ) outside the domain . The latter originates from the hydrophobic matching between the protein amphipathic domains and the membrane where it diffuses . Moreover , in EA , partial depletion of NPCs has been observed at the bud neck; while in LA , loss of fluorescence at the bridge was markedly different from the lobes suggesting different diffusion dynamics ( Fig . 3A–C and original images in the Data Viewer , available online in [2] ) . These observations are compatible with the scenario of sphingolipid domains restricting protein exchange between nuclear halves by hindering their diffusion . Following this train of thought , we explored whether specialised lipid domains such as sphingolipid domains account for compartmentalization . To that end , we used the FLIP profiles mentioned above alongside NE dimensions measured by TEM ( Fig . S3 ) , and calculated the expected drop in the diffusion rate at the domain by following the Petrov-Schwille model [39] ( see Methods ) . Additionally , we modelled protein exclusion from the domain probabilistically: every time a protein's random walk finds the interphase of a domain when coming from other regions of the membrane , there is a percentage probability Pin that it will diffuse into the domain . This probability was estimated by fitting stochastic simulations to FLIP experiments . Conversely , the percentage probability of exiting the domain was fixed at Pout = 100% , reflecting the preferential protein solubility for ordinary lipids compared to sphingolipid domains . For the domain in EA , we followed observations of reporter proteins delocalization at the bud neck [1] ( Fig . 3A and original images in the Data Viewer , available online in [2] ) , and assumed a 300 nm wide ring shaped domain . Within the domain , we fixed a lower diffusion rate than at other regions of the membrane ( Table S2 ) and estimated Pin values by fitting simulations to FLIP data . Importantly , we first verified that a lower diffusion rate at the domain alone ( i . e . fixing Pin = Pout = 100% ) did not account for compartmentalization ( Fig . S5 ) . The estimated Pin values for each protein reporter ( Fig . S6 ) are listed in Table 1 , where a high Pin correlates to lower compartmentalization . As expected , relative Pin values reflect the exclusion from the specialised lipid domains experienced by each protein species . It is worth noting NPCs were insensitive to whether the domain is located at the ONM only ( Pin = 1 . 6% ) or at both the ONM and INM ( Pin = 1 . 5% ) . We then wondered if , during LA , a single specialised lipid ring domain would account for compartmentalization as it did in EA . To test this , we carried out simulations in LA nuclei , placing the domain at the centre of the bridge connecting the lobes ( Fig . 3B ) . However , this time we fixed Pin values to those previously found for EA and estimated the ring's width that would better fit the experimental data ( Fig . S7 ) . Surprisingly , a ring domain 300 nm wide fitted the FLIP profiles for Nsg1-GFP and GFP-Src1 in LA best , just as it was the case for EA . For the NPCs , a narrower ring 100 nm wide provided the best fit instead , regardless of whether the domain was assumed to be at the ONM only or at both the ONM and INM . The discrepancy between the rings' width necessary for compartmentalization in LA suggests that additional mechanisms must be accounted for . In what follows , we explore the spatial configuration of domains and the implications that nuclear elongation during anaphase has on them . Taken together , these results show the higher viscosity and exclusion properties of specialised lipid domains are suitable mechanisms for compartmentalizing nuclear lobes . The finding that , in LA , specialised lipid and single-ring domains of different widths compartmentalize our membrane markers is puzzling . Thus , we considered additional domain arrangements , assuming Pin reflects lipid-protein interactions causing protein exclusion and no other physical obstacles are present at the diffusion barrier . Accordingly , we developed two additional domain configurations in LA ( see Methods ) . On the one hand , a domain constituted by a series of parallel rings , each 300 nm wide , distributed along the entire bridge length ( Fig . 3C ) ; on the other , a continuous domain spanning the entire bridge length ( Fig . 3D ) . As before , we simulated FLIP experiments on these LA nuclei to estimate new effective Pin values . The estimations are shown in Fig . S8 and the resulting percentage probabilities Pin that showed the best fit are listed in Table 1 , where the probabilities for the single ring domain configuration are also shown for comparison . Notably , the estimated Pin values for these novel domain configurations are considerably larger than when assuming a single ring domain . Now , to quantitatively assess the strength of specialised lipid domains in a physically meaningful way , we calculated the transmission coefficient for each scenario in Table 1 , and their associated spatial configurations ( Fig . 3 ) . This coefficient , accounts for the permeability of protein across the barrier , which depends upon its mobility within the domain , its thickness , and the amount of protein available for moving ( see Methods ) . The results are shown in Fig . 4 , where we confirm that the diffusion barrier is stronger in the ONM ( Nsg1 marker ) than in the INM ( Src1 marker ) irrespective of the domain configuration . Moreover , experimental evidence shows an increase of the barrier strength as anaphase progresses [2] , which would imply a reduction on its transmission coefficient . From Fig . 4 , we see that this is only compatible with the scenario of a specialised lipid domain spanning the entire bridge length in LA . In general , NPCs are the species most strongly affected by the barrier , but their ability to permeate across it is independent of whether the specialised lipid domain lies at the ONM or at both the ONM and INM . To determine which domain configuration in LA better reproduces the biological reality , we simulated FLIP experiments as in Fig . 4C in [2] by placing the bleaching spot at different positions along the bridge ( see Methods ) . At each position , we calculated the °CP and its inverse ( °CP−1 ) , and correlated them with the position of the bleaching spot along the bridge relative to the normalized length of the entire nucleus . These experiments are aimed at identifying the position of putative diffusion barriers by observing the intersection of the °CP and °CP−1 curves . In particular , curves intersecting in a single point would suggest a narrow barrier , whereas curves intersecting at many points ( or overlapping ) would indicate a distributed barrier . Results from our simulations were compared against FLIP experiments performed in similar conditions [2] and are shown in Fig . 5 for each domain configuration , where the resulting curves from °CP and ( °CP ) −1 vs . the bleaching spot position are plotted . From Fig . 5 , one can see that a scenario where the domain is distributed along the entire bridge provides a better fit to the experimental data , when compared to a single central ring . In particular , the homogeneous domain configuration shows a slightly better fit than the multiple rings arrangement . Notably , localization data for Nup49-GFP shows NPCs are almost completely absent from the bridge during LA ( Fig . 3B in [2] ) , suggesting the diffusion barrier underlying its compartmentalization is also distributed along the entire bridge length . Overall , our results support specialised lipid domains to be plausible constituents of diffusion barriers , showing a spatial configuration compatible with nuclear morphological changes during anaphase . When considering a specialised lipid domain organized as one single ring at the neck in EA , the estimated Pin values were much lower than those of other domain configurations in LA ( Table 1 ) . As Pin accounts for protein exclusion effects originated from lipid-protein interactions at the Lo/Ld interphase , and these in turn depend on nanoscale properties that likely remain constant during anaphase , it is highly unlikely that Pin would undergo such high variations during anaphase ( Table 1 ) if the diffusion barrier were exclusively constituted of specialised lipid domains . On the other hand , the Pin values estimated for EA lie within the same order of magnitude than probabilities of membrane proteins passing through domains corralled by cytoskeleton structures in different cell types [40] . A number of proteins such as septins , Bud6 and actin filament bundles are known to be involved in establishing the diffusion barrier at the plasma membrane of S . cerevisiae , but their specific roles in nuclear compartmentalization remain largely unknown . For instance , FLIP experiments revealed a decreased compartmentalization of Nup49-GFP and Nsg1-GFP in the mutant bud6Δ [1] . However , whether a regulatory relationship between these nuclear proteins and lipids exists is yet to be seen . So , two very interesting open questions arise: ( 1 ) is the assembly of protein filaments promoted by lipids and membrane curvature ? Or , conversely , ( 2 ) do protein filamentous structures induce and stabilize nuclear membrane curvature in budding yeast ? [17] . Here , it is important to recall that the neck of the dividing nucleus during EA exhibits a large curvature index , and resembles that of the plasma membrane during mitosis . Additionally , evidence from septin organization at the plasma membrane offers a plausible scenario that may also be compatible with the organization of filamentous proteins at the NE [19] , [24] . In particular , previous studies hint at the possibility that specialised lipid domains may stabilize proteins in an immobile ring configuration [9] , [22]–[24] , [33] . Hence , it is natural to hypothesize that , in addition to the specialised lipid ring domain , a protein ring structure constitutes the diffusion barrier in EA . To test this hypothesis we assumed that , during EA , the diffusion barrier is constituted by a specialised lipid ring domain and a parallel , immobile protein ring , placed at its centre ( see Methods and Movies S1 and S3 ) . This configuration follows from the fact that some filamentous proteins have lipid-binding motifs that contribute to their stabilization within lipid microdomains [23] , [24] . For simulations , we fixed Pin values as in the homogeneous domain in LA ( Table 1 ) , and then estimated the necessary number of proteins at the ring to fit FLIP experiments ( see Methods ) . For this scenario , Fig . 6 shows the deviation of our simulations from experiments , where we indicate the numbers of protein at the ring that best fit the FLIP data . The small size of deviations estimated in Fig . 6 ( in comparison with Figs . S6 ) suggest that an immobile protein ring embedded at the centre of a specialised lipid ring domain could contribute to the diffusion barrier underlying nuclear compartmentalization in EA . Importantly , we assumed the domain was present at both the INM and ONM , and exhibiting the same biophysical properties in EA and LA . In fact , a scenario where Pin and Pout values remain constant during anaphase makes more biological sense . This follows from the value of the transmission coefficient associated to the specialised lipid domain alone being = 0 . 21±0 . 04 µm s−1 in EA ( recalling Pin now takes the same value as in LA ) , which is ten times higher than its corresponding value in LA ( Fig . 4 ) . Assuming the lipid-protein interactions governing the spatial distribution of diffusing proteins remain constant during anaphase , and given that the transmission coefficient is inversely proportional to the barrier thickness ( see Methods ) , only a tenfold increase in during anaphase would account for a similar drop in . Current evidence suggests this is exactly the case since ≈300 nm in EA [1] , [2] and the bridge length in LA averages ≈2 . 85±0 . 83 µm from a heterogeneous sample of 34 nuclei . The above results suggest that the NE in LA can well be compartmentalized by the combination of a homogeneous specialised lipid domain and an elongated nuclear shape ( Fig . 3D and Fig . 5 ) . In contrast , compartmentalization of the EA nucleus can be well achieved by a specialised lipid ring domain and an immobile protein ring acting as a semi-permeable fence , but not by the domain alone . Following this , we have estimated the necessary number of proteins at the ring to account for compartmentalization of Nsg1-GFP , GFP-Src1 and the NPC . Our results show that Nsg1-GFP compartmentalization at the ONM requires double the number of proteins at the ring than GFP-Src1 at the INM does . Moreover , these numbers are rather high ( ∼103 ) , which suggests a polymeric protein would be a good candidate for constituting the ring . Conversely , NPC compartmentalization at the NE requires much less proteins at the ring ( ∼80 ) . Notably , the latter was estimated when we assumed the ring to be exclusively located at the ONM , which follows from evidence suggesting the diffusion barrier is stronger at the ONM than at the INM [2] . Importantly , we also tested whether the high number of proteins at the ring compartmentalizing Nsg1 and Src1 would also compartmentalize NPCs . Assuming ∼103 proteins at the ring showed a 30% to 35% deviation of the simulations with respect to experimental data . However , a lower number of proteins ( ∼200 , deviation <15% in Fig . 6 ) already showed signs of a blocked exchange of NPCs between lobes . This could be due to a saturation of the ring at lower threshold concentrations for objects as large as NPCs . As protein ring structures tend to be formed by discontinuous sets of filaments [19] , and these in turn are formed by polymerized proteins , it may be that a large fraction of proteins at the ring is contained into filaments . By consequence , a scenario where rather high numbers of proteins at the ring are organized into a small number of filaments of different lengths would similarly compartmentalize large diffusing objects such as NPCs as a small number of non-polymerized proteins would . However , this scenario is not unique and other possible mechanisms are reviewed in the Discussion . For Nsg1-GFP , we followed recent findings regarding septin organization at the plasma membrane [19] and tested whether a septin double-ring ( each ring ∼4 nm wide and separated ∼8 nm from the other ) placed at the ONM could as well constrain lateral diffusion ( see Methods ) . We found that , though a protein double ring helps the lipid domains to compartmentalize the ONM , the fit is not better than that of a single ring made of polymeric proteins of a larger size ( Fig . 6 ) . Taken together , these findings suggest that specialised lipid domains likely constitute nuclear diffusion barriers; but also , that the observed compartmentalization arises from a synergistic relationship between such domains and other physical agents . Namely , a protein ring at the nuclear neck and an elongated geometry during early and late stages of anaphase , respectively .
In this work , we focused in compartmentalization of the S . cerevisiae nucleus during anaphase . Such compartmentalization is crucial for maintaining parental identity during mitosis , and it has long been questioned whether it is due to diffusion barriers or nuclear geometry changes alone . The study of diffusion barriers in cellular membranes is not only challenging from the experimental perspective , due to several technical constraints , but also theoretically . In such cases , computational simulations offer an important tool for exploring different working hypotheses . This holds specially true when data is scarce , or when studying an organelle that poses difficulties related to single-cell observation and manipulation in real time , such as the nucleus . In that spirit , we addressed the question of how specialised lipid domains and protein rings , two of the most plausible constituents of diffusion barriers , may organize during anaphase to compartmentalize nuclear proteins . We accounted for morphological changes of the nucleus by studying the early and late stages of anaphase . For this , we developed realistic in silico 3D models from heterogeneous samples of nuclear geometries , and carried out spatial-stochastic simulations with high spatial and temporal resolution . By means of computational modelling , we explored the properties of putative diffusion barriers in each nuclear enclosure and phase , and coupled them in a comprehensive biological picture . Our first round of simulations confirmed correctness of previous findings in [2] . Notably , this was concluded after carrying out more realistic simulations on a heterogeneous sample of nuclear morphologies , as opposed to an idealized , average geometry . It's important to emphasize that we relied on previously estimated effective diffusion rates for the reporter proteins considered in this study . These rates already account for crowding exerted by the inhomogeneous media where proteins diffuse . However , local variations of diffusion coefficients are perfectly possible when using free diffusion values . In fact , this is exactly what happens to proteins diffusing within membranes populated by lipid microdomains [41] , [42] . Given that sphingolipid domains are suitable candidates for constituting membrane diffusion barriers , we explored whether their physical properties could account for the observed compartmentalization . Since protein transitions between membrane phases depend on the protein's tertiary structure and amphipathicity , an exact determination of sphingolipid domains' protein exclusion values would require direct measurement of the lipid-protein dynamics at the interphase . Even though these measurements are beyond the scope of this manuscript , we can safely estimate Pin and compare its relative value among nuclear enclosures to extract useful information about the overall barrier strength . We did this by calculating the transmission coefficient of the barrier , which quantifies the barrier permeability in a physically meaningful way . Comparing this coefficient for the inner and outer nuclear membrane and during early and late stages of anaphase showed the sphingolipid domain hypothesis is in agreement with experimental evidence . From the different possible scenarios in which specialised lipid domains could organize in EA and LA , we chose those configurations that better match previous experimental observations . Our results suggest that , in LA , not only is the diffusion barrier present along the entire length of the nuclear bridge , but most likely it is constituted by a homogeneously distributed specialised lipid domain . On the other hand , while we showed that compartmentalization in EA can originate from a specialised lipid ring domain alone , the estimated Pin values suggested an additional mechanism must contribute to restrict lateral exchange at this stage . Then , by assuming a protein ring overlapped with the domain , subsequent simulations reproduced the observed compartmentalization between nuclear lobes . Furthermore , we estimated the number of proteins at the ring that were necessary to reproduce experimental FLIP profiles [2] . In what respects to Nsg1-GFP and GFP-Src1 compartmentalization ( ONM and INM , respectively ) , our estimations agree with the scenario of small proteins polymerizing to form stable , immobile ring structures . However , compartmentalization of NPCs required a much lesser amount of proteins at the ring . As the NPC is a much larger diffusing particle than the other markers , and it's diffusion occurs within a more complex environment ( ONM+INM+periplasm ) , other segregating mechanisms may be playing an important role . Constriction of the membrane at the neck , for instance , is likely to require the coordination of anchoring proteins that align the position of the nuclear neck with the mitotic neck of the cell . Thus , it may be that these scaffold proteins aggregate within the specialised lipid domain at the neck and hinder diffusion of NPCs , but not that of other smaller proteins . On the other hand , NPCs remain stable by inducing important membrane deformations in their vicinity [43] . The size of these local deformations ( spanning up to ∼100 nm ) is large enough to significantly affect the way the pore interacts with scaffolding protein rings at the membrane and with the narrow specialised lipid domain at the neck in EA ( ∼300 nm ) . This may constitute another exclusion mechanism of the barrier since the highly negative curvature index of the nuclear envelope at the neck may severely hinder the ingression of NPCs in the first place [13] , [14] . Unfortunately , not only are these mechanisms beyond the scope of our model but also , there is a generalized lack of high-resolution experimental studies tracking NPC segregation dynamics at this spatiotemporal scale . Overall , our results point to a plausible scenario where the diffusion barrier is composed of specialised lipids , but selectively requires additional biophysical mechanisms contributing to it during early and late anaphase stages . Namely , a protein ring that hinders molecular exchange between mother and daughter lobes in EA , and an elongated nuclear morphology that causes the same effect in LA . Among the proteins reviewed in our introduction that may work as fences , septins are already known to be involved in establishing a NE diffusion barrier [1] . Septins are a known component of the cytoskeleton , providing mechanical support to cellular membranes . During anaphase , an hour-glass shaped , gauze-like septin structure provides support at the neck of the S . cerevisiae plasma membrane [19] . It is yet to be seen whether such a similar structure exists at the level of the NE . In fact , this may be the case during EA , when both mother and daughter nuclear lobes have a prolate ellipsoid shape , just as the mother and budding cells that contain them . However , it is very challenging to experimentally assess how currently identified septin structures , as well as the other proteins they recruit , could support the morphological changes of the NE during anaphase . Another open , very interesting question relates to how septins are involved in shaping the junction between nuclear lobes during both anaphase stages and , at the same time , recruit the machinery for sphingolipid biosynthesis . While our manuscript was undergoing final revisions , an interesting study was published reporting that , in wild type anaphase yeast cells , the reduced abundance of NPCs in the NE at the bud neck is dependent on Bud6 and Sur2 [44] . In the same study , staining of lipid species other than sphingolipids was also reduced in the NE at the bud neck and was partially dependent on Bud6 and Sur2 function . These findings are consistent with our model of specialised lipid domains and protein rings as components of the barrier , where sphingolipids and septin-recruited proteins such as Bud6 are good candidates . On the other hand , it is also possible that sphingolipid domains are the sole agents shaping the nuclear bridge in LA , due to the local changes in membrane curvature induced by them [7] . For instance , loss of Spo7 , a protein part of a phosphatase complex that represses phospholipid biosynthesis , causes anomalous shaping of the nuclear membrane only in the cytosolic regions , leaving the bridges connecting lobes in LA intact [6] . Experimental evidence will determine whether septins actually constitute a physical obstacle for membrane-bound proteins . However , our simulations suggest this is rather unlikely for LA nuclei . Instead , our study suggests that a homogeneous specialised lipid domain alone may better explain a diffusion barrier spanning the entire bridge length in LA . Our model involving a protein ring in early , but not in late anaphase , is in agreement with other lines of evidence . Previous works showed that deleting Bud6 or the ring-promoting septin Shs1 [20] , which has been shown to decrease the °CP of Nsg1-GFP in EA [1] , have no effect on Nsg1-GFP compartmentalization in LA dumbbell nuclei [2] . This implies that , at least in the ONM , the diffusion barrier is regulated differently in EA , as compared to LA . Accordingly , our simulations show that the protein rings' contribution to compartmentalization is required in EA , but not in LA . Moreover , we also found that a lesser-populated ring is required at the INM than at the ONM , suggesting that effects on the former may indirectly arise from a scaffolding protein constituting a ring in the latter . The higher Pin estimated for GFP-Src1 ( INM ) , compared to Nsg1-GFP ( ONM ) , may be related to the former being a larger protein than the latter ( Table S1 ) . This follows from proteins embedding into membranes according to their size , tertiary structure and amphipathicity , with respect to the membrane thickness [45] . In addition , accumulation of scaffolding proteins at the junction of the lobes may effectively thicken the membrane and increase its exclusion properties [46] . On the other hand , the estimated Pin values for the NPC when the specialised lipid domain is assumed to exist only at the ONM or at both ONM and INM are strikingly similar . This suggests that the NPC is less sensitive to whatever barrier may exist at the INM , and that mostly the domain at the ONM ( or else , a cluster of proteins working as immobile obstacles ) determines its lateral exchange . On the other hand , compartmentalization of the INM has been shown to markedly occur during LA , and its unlikely that it is caused by INM proteins interacting with scaffolding proteins [2] . Thus , there exists the possibility that the NPC , because of its large dimensions , experiences the viscous drag caused by the specialised lipid domain , but not its protein exclusion properties . Hence , its lateral compartmentalization may originate from a slower diffusion rate at the barrier in addition to a blockage caused by protein fences in both stages of anaphase . Recently , however , a novel mechanism was discovered for controlling the redistribution of NPCs during anaphase that is compatible with a model of temporal release of the barrier [47] . Thus , further experiments are necessary to fully understand the complex relationship between dynamical diffusion barriers and its segregating effects on NPCs . In summary , we propose a plausible model for how diffusion barriers may be constituted and organized in the S . cerevisiae nucleus during closed mitosis . The model is based on the biophysical properties of two molecular complexes , sphingolipid domains and protein rings , which are known to be involved in diffusion barriers in other cellular membranes [35] , [36] , [48]–[51] . Importantly , we propose that , while compartmentalization during EA requires a synergy between a specialised lipid domain and a protein ring; the latter is not necessary in LA , where the elongated bridge supersedes this role . This represents a simpler , elegant way S . cerevisiae may achieve asymmetrical segregation of ageing factors during closed mitosis . Moreover , our model suggests novel experiments and provides quantitative predictions that may be further tested to better understand diffusion regimes in the nucleus . Additionally , it offers a suitable theoretical framework to explore diffusion barriers in other cellular membranes .
The Saffman-Delbrück model [4] supports the assumption of a decreased diffusion coefficient at the neck preventing free lateral mobility of membrane-bound proteins between the mother and daughter nuclear lobes . This model states that , for the typical scenario found in biological membranes , diffusion coefficients of membrane proteins depend mostly on the membrane thickness and viscosity , rather than in the size of the diffusing particle . The model is given by ( 1 ) where the diffusion rate of a cylindrical inclusion of radius , in a membrane with thickness , is determined by the bulk viscosities and of the membrane material and surrounding fluid , respectively . A logarithmic law to which the Euler-Mascheroni constant ≈0 . 577215 is subtracted governs the diffusion rate dependence on viscosities and particle-to-membrane dimensions . However , the Saffman-Delbrück model is valid only for membrane inclusions that are small compared to the characteristic length scale brought about by hydrodynamics . This hydrodynamic length scale is determined by the ratio ( 2 ) where is the surface viscosity of the membrane , measured in [Pa][s][m] , and , are the bulk viscosities of the fluid flanking each side of the membrane , measured in [Pa][s] . In equation ( 2 ) , we can assume that the fluids surrounding both sides of the membrane have bulk viscosities equal to that of cytoplasm ( ) . As we want to test diffusion in the ONM , INM and the whole NE , we will also assume that the nucleoplasm and periplasm have the same bulk viscosities than cytoplasm ( ) . To characterize the ratio of the membrane inclusion to the hydrodynamic length scale we use the non-dimensional reduced radius , which is given by ( 3 ) Thus , the Saffman-Delbrück model in equation ( 1 ) is valid on the condition that . Even though we can estimate , we don't know a priori the value of for the nuclear membranes . It is likely that for the small sizes of Nsg1-GFP and GFP-Src1 ( Table S1 ) , the Saffman-Delbrück model is still valid [57] , but we cannot assert the same for the NPC . Thus , we must retort to using a hydrodynamic model describing the mobility of a membrane inclusion of an arbitrary radius for arbitrary viscosities . This is readily available in the Petrov-Schwille generalization of the Saffman-Delbrück model , which is an approximation of an exact model developed earlier [58] and valid for a very wide range of values ( 10−3≤ε≤105 ) with a relative error below 0 . 015% with respect to the exact solution [39] . The Petrov-Schwille model is given by ( 4 ) where the parameters = 0 . 73761 , = 2 . 74819 , = 0 . 52119 and = 0 . 61465 were estimated by Petrov and Schwille to fit the exact solution [39] . From our TEM image analysis ( Fig . S3 ) , we estimate a membrane thickness of ≈4 nm for both ONM and INM . Topographic data from AFM of plasma membranes populated by sphingolipid rafts estimate up to a ∼7 Å increase in the membrane height where the domains are located [59]–[61] . For a lipid bilayer , this implies the membrane thickens up to ≈5 . 4 nm at the domains . On the other hand , a set of experiments using optical traps to track raft-associated proteins diffusing in the plasma membrane of mammalian cells estimate that they experience a three-fold higher viscous drag than non-raft proteins [3] . Starting from the effective diffusion rates previously estimated for the GFP-tagged proteins mentioned in this study [2] , is straightforward to calculate the drop in the diffusion rate at the sphingolipid domain for Nsg1-GFP and GFP-Src1 by using the Einstein-Smoluchowski relation ( 5 ) where the viscous drag is the inverse of the mobility . Thus , a drop to one third of the estimated effective diffusion rate is expected for a three-fold increase in the viscous drag at the sphingolipid domain . However , the scenario is not so simple for the NPC as it diffuses while embedded in the whole NE , which comprises three phases with different viscosities . Notably , the work of Pralle et al . [3] is the only available reference related to direct measurements of viscous drag of non-raft vs . raft-associated proteins . These experiments didn't address more complicated scenarios , as is the case for the NPC . Here , we approximated the viscous drag experienced by the NPC by using the Petrov-Schwille model and combining equations ( 4 ) and ( 5 ) into: ( 6 ) On the other hand , the bulk viscosity of the cytoplasm has been estimated to be ∼1 . 5 [62] , where is the bulk viscosity of water . At T = 30°C = 303 . 15 K , the temperature at which the FLIP experiments were carried out [2] , this viscosity is = 7 . 978×10−4 Pa s . Thus , the bulk viscosity of cytoplasm is = 11 . 967×10−4 Pa s . Given that we know the effective diffusion coefficients and sizes of the membrane-bound proteins diffusing in the membrane ( Table S1 ) , we can estimate by means of equation ( 4 ) the surface viscosities of the ONM , INM and the whole NE ( ONM+INM+perinuclear space ) experienced by Nsg1-GFP , GFP-Src1 and the NPC , respectively . In summary , we estimated the drop in the diffusion rate for the NPC at the sphingolipid domain by calculating the viscosities at the INM , ONM and periplasm using the Petrov-Schwille model . Estimations of all aforementioned parameters are listed in Table S2 . As expected , the drop of the NPC diffusion rate at the sphingolipid domain is more than three-fold , as it was the case for Nsg1-GFP and GFP-Src1 . From equation ( 6 ) , we can calculate the viscous drag experienced by Nsg1 and Src1 proteins ( Table S2 ) . This drag is one order of magnitude larger than measurements carried out before [3] . There are a number of possible explanations for this discrepancy: The permeability of a diffusion barrier with respect to a diffusing molecular species can be accounted by its transmission coefficient ( also known as the permeability coefficient [63] , [64] ) . This coefficient is given by ( 7 ) where is the partition coefficient , is the diffusion rate of protein within the barrier , and is the barrier thickness . The partition coefficient reflects the distribution of the diffusing protein inside and outside the specialised lipid domain . This can be calculated from the definition of chemical potential ( 8 ) where is the chemical potential of protein in the standard state and is its concentration . In our simulations , and before we set the bleaching reaction to start , the proteins diffusing in the specialised lipid domain phase reach a chemical equilibrium with the proteins diffusing outside the domain ( i . e . the net exchange between phases is zero ) . Thus , its chemical potential is the same in both phases , and we found the partition coefficient of the protein is given by ( 9 ) Importantly , in equation ( 8 ) we have ignored electrical and other less significant sources of work . While the electrical potential becomes important for diffusing ions and molecules with a high dipolar moment , it can be ignored for the case of uncharged proteins diffusing within a lipid membrane . The term in the right hand side of equation ( 9 ) depends on the Gibbs free energy to transfer the protein from one membrane lipid phase to the other , which in turn depends on how energetically favourable is the interaction of the protein with its surroundings . The details on these protein-lipid interactions are beyond the scope of our work , but we can get a fair estimation of the partition coefficient by calculating the ratio of protein surface concentrations inside and outside the specialised lipid domain . After computing the surface areas of our distribution of virtual nuclear hulls in EA and LA , and counting the number of proteins inside and outside the domain in equilibrium , we calculated for each of our in silico experiments where a specialised lipid domain was the sole component of the barrier . Taking into account the diffusion rates within the domain ( Table S2 ) and the geometry of the barrier ( Fig . 3 ) , we used equation ( 7 ) to calculate the transmission coefficients shown in Fig . 4 . For TEM analysis , yeast strain YYB5528 ( WT ) [2] was used . Single colonies from freshly streaked plates were incubated into 3 mL YPD media and grown overnight at 30°C . Cultures were diluted into 3 mL fresh YPD media and grown to OD600 ( optical density at 600 nm ) of ∼1 . 0 . Cells were then prepared for electron microscopy following the protocol in [65] . | Spatial segregation of molecular contents is often necessary for an accurate , timely accomplishment of cellular functions , such as signal transduction and cell-fate decisions . For instance , budding yeast division requires the asymmetric segregation of proteins to distinguish a newborn cell from its parent . However , the strategies to achieve this parental identity are poorly understood . This holds especially true for key proteins and molecular complexes involved in mitosis that diffuse within the nuclear envelope . In fact , segregation within the nuclear envelope has been experimentally verified , but both the nature and configuration of any plausible diffusion barrier remain unknown . In this work , we built virtual models of the nucleus and carried out simulations testing the plausibility of specialised lipid domains and protein rings constituting the diffusion barrier . Moreover , we explored distinct barrier configurations in early and late stages of cell division , and verified our simulation results match experimental observations . Our work shows that the biophysical properties of these molecules , coordinated with morphological changes in the nucleus , make them suitable components of the nuclear diffusion barrier . Importantly , our research approach offers a novel avenue to study diffusion barriers in other biological membranes . | [
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"physica... | 2014 | The Long and Viscous Road: Uncovering Nuclear Diffusion Barriers in Closed Mitosis |
Bolivia is one of the most endemic countries for Chagas disease . Data of 2005 shows that incidence is around 1 . 09‰ inhabitants and seroprevalence in children under 15 ranged from 10% in urban areas to 40% in rural areas . In this article , we report results obtained during the implementation of the congenital Chagas program , one of the biggest casuistry in congenital Chagas disease , led by National Program of Chagas and Belgian cooperation from 2004 to 2009 . The program strategy was based on serological results during pregnancy and on the follow up of children born from positive mothers until one year old; if positive , treatment was done with Benznidazole , 10 mg/Kg/day/30 days with one post treatment control 6 months later . Throughout the length of the program , a total of 318 , 479 pregnant women were screened and 23 . 31% were detected positive . 42 , 538 children born from positive mothers were analyzed at birth by micromethod , of which 1 . 43% read positive . 10 , 120 children returned for their second micromethod control of which 2 . 29% read positive , 7 , 650 children returned for the serological control , of which 3 . 32% turned out positive . From the 1 , 093 positive children , 70% completed the 30 day-treatment and 122 returned for post treatment control with 96% showing a negative result . It has been seen that maternal-fetal transmission rates vary between 2% and 4% , with an average of 2 . 6% ( about half of previously reported studies that reached 5% ) . In this work , we show that it is possible to implement , with limited resources , a National Congenital Chagas Program and to integrate it into the Bolivian health system . Keys of success are population awareness , health personnel motivation , and political commitment at all levels .
Chagas' disease or American trypanosomiasis , is an antropozoonosis caused by the protozoan Trypanosoma cruzi – a blood and tissue parasite . It currently affects 15 million people , produces more than 15 000 deaths each year and is the most socially and economically impacting parasitic disease in the Americas [1] [2] . The infection can be contracted by vector transmission – through the feces of haemophagic vectors of the reduvidae family and the triatominae sub-family , via blood transfusion or organ transplants , via vertical or congenital transmission from an infected mother to her new born child or fetus and by oral transmission . Other transmission mechanisms , with a minor epidemiological importance , include organ transplantation or laboratory accidents . The progressing vector control , along with the migration tendencies within and from endemic countries , has modified the distribution of the Chagas disease within the last years , and it has augmented the relative importance of the congenital transmission route . According to a report from the Chagas scientific work team in 2005 , the annual incidence of congenital Chagas would be of over 14 , 000 newborns . The disease is now present and has become a public health concern in the whole American continent , Europe , Japan and Australia [3] , [4] , [5] , [6] , [7] . The congenital transmission of Chagas disease was documented for the first time in 1911 , by Carlos Chagas , who had found parasites in the necropsies of two twins with convulsion episodes that died a few days after birth . Later , Dao in Venezuela ( 1949 ) , Jorg in Argentina ( 1953 ) , Howard in Chile ( 1957 ) and Bittencout and Rezende in Brazil ( 1959 ) , all describe the first cases in their respective countries [8] . Chagas congenital disease has also been reported in other endemic countries such as Uruguay , Paraguay , Colombia , Guatemala , Honduras and Mexico . Since then numerous articles have been published regarding congenital Chagas disease: epidemiological and clinical studies , evaluation of different diagnostic methods [9] , congenital Chagas as an imported disease ( cases reported in Spain , USA , Switzerland ) , and above all , highlighting the importance of the disease in terms of public health and the need for health programs concerned with its diagnosis and treatment in all the endemic countries [7] , [10] , [11] , [12] , [13] , [14] , [15] . However , the initiatives for control and management of congenital Chagas in endemic countries are far from achieving total geographic coverage [16] , [17] , [18] , [19] , [20] , [21] , [22] . Such initiatives are not only necessary in endemic countries , but should , in a targeted way , also be implemented in countries that receive or have received significant migration flows from Latin America such as USA , Spain , Switzerland and others [3] , [23] , [24] . The first congenital record in Bolivia is accredited to Azogue and col . in 1981 . They describe an 8% transmission rate in the “Percy Boland” maternity unit of Santa Cruz de la Sierra [25] . The same authors have published various articles on studies carried out in the 80s , in which they highlight the importance of detecting congenital Chagas in areas where the vector is controlled . Also they recommend the treatment of women in fertile age , they found hepatosplenomegaly as the most common sign in newborns and used the Strout technique in cord blood as a sensitive and less expensive diagnosis technique [26] , [27] . Between 1991 and 1994 , the Chagas control program of the National Health Department of Bolivia detected a maternal seroprevalence of 27 . 6% and a maternal-fetal transmission rate of 4 . 9% [28] , [29] . In 1998 , the Free University of Brussels ( ULB ) ( Brussels – Belgium ) and the IIBISMED of the faculty of Medicine of the UMSS ( State University of Cochabamba ) began a joint research project on mechanisms involved in congenital transmission of Chagas . The transmission rate in this study was 5% , and it was our reference at the beginning of the congenital Chagas program [28] . The results of this project , along with the investigations of international experts on congenital Chagas , were discussed in a conference which took place in Cochabamba , 2002 [30] , [31] . After that , the PAHO organized a technical meeting in Montevideo ( Uruguay , 2004 ) , where a sustainable and effective strategy for detection and treatment of congenital Chagas was designed . This strategy is currently still in effect and its main directive is: “To carry out intervention and control activities to prevent and control congenital infection by Trypanosoma cruzi , due to the importance that the latter has on children's health and the epidemiology of the parasitosis” , implying that such control measures have not yet been implemented in endemic countries [32] . In this article , we report results attained from 2004 to 2009 during the implementation of the congenital Chagas program in Bolivia , and present recommendations of the course to follow in order to maintain and improve this program .
The congenital Chagas component of the National Chagas program in Bolivia started in 2004 based on the recommendations of the PAHO [32] . The activities revolve around three axes of action: health personnel training , laboratory diagnosis and IEC . All children diagnosed with congenital Chagas were treated with Benznidazole; 10 mg/kg/day for 30 days , in two doses – complying with the current regulations in Bolivia . The first week was begun with 7 mg/kg/day in two doses . Considering that Benznidazole does not yet come in pediatric form , the medicine was dosed in the following manner: a tablet of 100 mg of Benznidazole was mixed into 10 milliliters of drinking water , producing a suspension of 10 mg/ml ( being a liposoluble medication , it cannot be diluted in water , but with a vigorous stir , a suspension is produced ) . Dosages were calculated according to the weight of the patient and were administered with the aid of a graduated syringe in order to improve the precision of quantity required . Children undergoing treatment were monitored on a weekly basis in order to adjust the dosage according to their weight , evaluate the compliance with treatment and rule out any possible adverse effects [37] . In order to avoid a second infection through the vector , it was advised that a vector control technician should visit the home address of the child undergoing treatment and spray the house if considered necessary . Six months after treatment was finalized , a serological test was performed to the child in order to certify the cure . In case of persistent positive serology , the test was repeated 6 months after and if still positive , the initial treatment ruled as “failed” and the treatment was repeated . In order to implement the strategy for congenital Chagas at a health facility , an evaluation of the conditions had to be carried out beforehand , particularly regarding the laboratory . The minimum equipment required was a microscope with a 40× lens , a hematocrit micro-centrifuge , a macro centrifuge , and adjustable-volume pipettes . Also , a reference laboratory with an ELISA reader is needed . The integration of the congenital Chagas program with the country health system has been gradual and steady , and with the financial support of the French Community of Belgium cooperation project ( WBI ) . At the beginning there were ten facilities supporting the departments of Cochabamba ( 3 ) , Tarija ( 4 ) and Chuquisaca ( 3 ) . The integration into the facility's routines started with an initial training of the health personnel and individuals in charge of gynecology , pediatrics , laboratory , and nursing sections . It was essential to form a solid work team , since the coordination between different sections was crucial for running the program . This initial training consisted of a theoretical part: The Chagas disease , diagnosis and treatment of Chagas and congenital Chagas followed by a practical part: laboratory procedures using the IHA , ELISA and micromethod techniques ( using 3T3 cells cultivated with T . cruzi ) [38] . Once the activities had begun , part of the health personnel was trained on using IEC components . Between three to six months later , the first supervision visits took place . These visits were made once or twice a year but more often if problems were detected at a particular facility . Monitoring indicators were designed and established in order to strengthen the program's integration into the health facilities . These allowed the facilities to grade their coverage according to their level of service . ( Table 1 ) During the development of the program , Chagas indicators were introduced into the national epidemiologic health information records . In 2008 , modifications were introduced into the monthly and weekly record forms so that the information recorded would reflect the country's epidemiological reality and the activities that were being carried out in the fight against the disease . The forms are available online in the Health and Sports Ministry website ( http://www . sns . gob . bo/snis/default . aspx ) . In 2009 , following the recommendations made at the meeting held in Montevideo in 2004 regarding the inclusion of basic congenital Chagas details within the Perinatal Information System or CLAP sheet [39] , the variables concerning diagnoses of Chagas in pregnant women and newborns were introduced into the Bolivian version of the CLAP sheet and into the child heath ID cards , in order to ensure the flow and availability of information . These modifications in the current forms have been supported by the ministerial resolution N° 1321 of 28/12/2009 . Towards the end of 2009 the goal of the program was almost achieved , with most facilities complying by sending monthly reports regarding congenital Chagas , in coordination with departmental National Health Information System ( SNIS ) personnel . Since 2004 , the laboratories included in the congenital Chagas program have gone through successive quality control tests in Chagas serodiagnosis , by means of serum panels and/or sample retesting . Between 2004 and 2006 , quality control was the responsibility of the University staff and the project staff . Since 2007 , this responsibility was delegated to the departmental reference laboratories . The national reference laboratory for Chagas CENETROP , in turn , performed quality checks on the departmental reference laboratories in 2008 and 2009 .
From June 2004 to December 2009 the program was implemented in 90 health facilities in 49 out of the 168 municipalities located in endemic areas ( Andean mesothermic valleys , tropical zones , the Chaco dry forest , and the Altiplano ) . As a result , 29% of all municipalities within endemic areas were covered . The 90 health facilities are comprised of 7 urban 3rd level hospitals , 35 2nd level hospitals ( 5 located in departmental capitals and 30 in province areas ) , and 48 1th level facilities ( 35 located in the capitals and 13 in provinces ) . These 90 facilities represent 73% of all health facilities with laboratory in the selected municipalities considering a denominator of 123 facilities ( data from Ministry of Health , Bolivia , http://www . sns . gob . bo/ ) . From the beginning of the project in 2004 until December 2009 , a total of 318 , 479 pregnant women were screened for Chagas and 74 , 228 were detected positive ( 23 . 3% ) . Considering that the project was comprised of 2 phases: the first from 2004 to 2006 , consisting of implementations in health facilities of Cochabamba , Tarija and Chuquisaca , and the second , in 2007 to 2009 , consisting of the expansion into the departments of Santa Cruz , La Paz and Potosi , we can appreciate an important increase in the number of women screened since 2007 . The average maternal seroprevalence shows a decrease with time as the program extends into more areas and it stabilizes around 22% since 2008 and after ( Figure 2 ) . Table 2 shows that , during 2009 , more than 100% of pregnant women registered in each of the 90 health facilities were screened ( source: data from SNIS and from Chagas congenital program ) . This data indicates bias in the prenatal screening and is due to repeated tests; i . e . same women tested in different facilities . This is one of the operational problems in the Bolivian health system that could not be solved . Between 2004 and 2009 , a total of 42 , 538 children born from positive mothers were analyzed at birth using the micromethod , 606 ( 1 . 4% ) of which read positive . Only 10 , 120 ( 24% ) returned for their second micromethod control , and 232 ( 2 . 3% ) of these read positive . For the serological control between 6 and 12 months , 7 , 650 ( 18% ) returned , with 254 ( 3 . 3% ) turning out positive . A number of 24 , 767 newborns from positive mother did not do the follow-up for multiples reasons such as failures in the reference system , failures in the communication with the mother , low health education levels , geographical barriers in the accessibility to the facilities , etc . In Figure 3 we can see that there is an increasing number of children controlled at birth according to the number of facilities participating in the program , which reflects a higher number of mothers screened . However , we cannot see a proportional increase in tests performed after birth . There were a total of 1 , 093 congenital Chagas diagnosed cases . As seen in Figure 4 , the number of cases diagnosed has continually increased as the number of health facilities entering the program has gone up , from 40 cases in 2004 ( 10 facilities ) to 303 cases in 2009 ( 90 facilities ) . If we calculate 5% of 42 , 538 newborns of positive mothers studied at birth ( 5% was our reference for congenital transmission before starting the program [28] ) , 51% of expected cases have been detected ( 1 , 093/2 , 127 ) . From the 1 , 093 cases diagnosed , 55% have been diagnosed at birth; a logical consequence considering the greater number of tests carried out at that time compared to the number of tests done after birth . The rest of the children have been diagnosed in relative proportion to the following tests , 21% in parasitological testing before 6 months and 24% in serological testing between 6 and 12 months . The maternal-fetal annual transmission rate varies between 2% and 4% , with an average of 2 . 6% , which is below the expected 5% ( Figure 5 ) . Those rates were calculated with the total cases diagnosed per year over the total newborns from positive mothers controlled at birth . No correlation has been seen between the total number of tests carried out and the detected transmission rate , however , we can observe that in the last two years the maternal-fetal annual transmission rate has stabilized at 2% . In Tables 3 through 8 , data is displayed by health facility/network from 2004 to 2009 . We can see that the seroprevalence found in pregnant women does not go through significant changes along the years of implementation in the various intervention areas . The variations in seroprevalence found were due only to the increase in health facilities within such geographical areas , with different epidemiological characteristics; and , above all , related to previous infestations rates [40] . Therefore , the seroprevalence in women is not related with transmission rates . From the 1 , 093 children diagnosed , 78% began the treatment ( 851 children ) and 70% completed the 30-day treatment ( 771 children ) . From the total of children treated , 122 underwent a first serological control test 6 months post-treatment , and 96% of these came out negative ( 118/122 ) . Unfortunately , the 4 children without negative serology at 6 months didn't have another serological test later , so we have no ways of knowing their final outcome . No child has received rescue treatment . As for the group that did not begin the treatment ( 242 children , 22% ) , the main reason was that mothers had already left the maternity unit before getting to know the test result . Five reported cases were not treated because the parents rejected the treatment . Six reported cases , mainly newborns with low weight , were not treated because they manifested another concomitant pathology , and therefore the neonatologist considered that the use of Benznidazole along with another medication was contraindicated in those newborns . Afterwards , the follow up on these children was disrupted or they in fact died ( hepatitis , toxoplasmosis , severe cardiopathy , intestinal malformation , etc . ) . Within the 10% of children who began the treatment and later abandoned it ( 80 cases ) , the main reasons evoked were: difficulty for the parents to attend to the weekly checkups at that facility , changes of address , travel to another department , difficulties in the reference to a lower level facility which was not in conditions to monitor the treatment , and death during the treatment ( 15 deaths reported due to reasons unrelated to the treatment ) .
Congenital Chagas disease is a recognized public health issue and must be included in the priorities of the countries with infected population . More awareness is needed on behalf of authorities in order to fight Chagas disease . It is also essential to continue studying the strategies which are most applicable to rural contexts in order to begin detection and treatment in less accessible and marginalized populations . We also recommend , as other authors , [19] , [46] , that special efforts must be made to achieve the detection of congenital Chagas in families with mothers who have Chagas , more so if a case of congenital Chagas has been proven within the family . In conclusion , it has been shown that it is possible , to implement a National Congenital Chagas Program following the PAHO recommendations of early detection and treatment of cases under 1 year old with limited resources . Yet it is necessary to continue the supervision and training activities in order to maintain the interest of health professionals , increase current coverage , and improve the overall quality of the program . | Congenital Chagas disease is the infection resulting of the transmission of T . cruzi parasites from an infected pregnant woman to her fetus during the pregnancy or at the time of delivery . This represents a relevant public health issue in endemic and non-endemic countries . The infected newborn detected at birth or before one year old , if treated , can be completely cured . In this work , we report results obtained from 2004 to 2009 during the implementation of a National Program based on PAHO recommendations; the implementation strategy has been adapted into the Bolivian context and integrated into the National Mother and Child Health program . Our conclusions show that it is necessary to test all newborns from positive mothers three times before one year of age . The infant , if positive and treated with a 30 day Benznidazole course , has excellent chances of cure . | [
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] | 2013 | Achievements and Challenges upon the Implementation of a Program for National Control of Congenital Chagas in Bolivia: Results 2004–2009 |
During the 2014 Ebola virus disease ( EVD ) outbreak , policy-makers were confronted with difficult decisions on how best to test the efficacy of EVD vaccines . On one hand , many were reluctant to withhold a vaccine that might prevent a fatal disease from study participants randomized to a control arm . On the other , regulatory bodies called for rigorous placebo-controlled trials to permit direct measurement of vaccine efficacy prior to approval of the products . A stepped-wedge cluster study ( SWCT ) was proposed as an alternative to a more traditional randomized controlled vaccine trial to address these concerns . Here , we propose novel “ordered stepped-wedge cluster trial” ( OSWCT ) designs to further mitigate tradeoffs between ethical concerns , logistics , and statistical rigor . We constructed a spatially structured mathematical model of the EVD outbreak in Sierra Leone . We used the output of this model to simulate and compare a series of stepped-wedge cluster vaccine studies . Our model reproduced the observed order of first case occurrence within districts of Sierra Leone . Depending on the infection risk within the trial population and the trial start dates , the statistical power to detect a vaccine efficacy of 90% varied from 14% to 32% for standard SWCT , and from 67% to 91% for OSWCTs for an alpha error of 5% . The model’s projection of first case occurrence was robust to changes in disease natural history parameters . Ordering clusters in a step-wedge trial based on the cluster’s underlying risk of infection as predicted by a spatial model can increase the statistical power of a SWCT . In the event of another hemorrhagic fever outbreak , implementation of our proposed OSWCT designs could improve statistical power when a step-wedge study is desirable based on either ethical concerns or logistical constraints .
The 2014 Ebola virus disease ( EVD ) epidemic is the largest recorded outbreak of any filovirus infection , primarily affecting three major countries in West Africa: Guinea , Liberia , and Sierra Leone . The three countries combined had a total of 15 , 901 cases ( confirmed , probable and suspected ) and 5 , 674 deaths as of November 26 , 2014 , when the epidemic peaked in the affected regions [1] . At that time , many candidate vaccines were proposed for Phase III trials in the affected countries , with different vaccine trial designs suggested for each region . Between April 1 , 2015 , and July 20 , 2015 , a Phase III trial in Guinea assessed the efficacy of a Zaire Ebolavirus vaccine ( rVSV-ZEBOV ) [2] . The design was a ring vaccination cluster-randomized trial , where the trial population was made up of clusters of all contacts and contacts of contacts of laboratory-confirmed Ebola cases . Thus , a robust contact tracing system was an essential component of the trial . Unfortunately , the 2014 Ebola outbreak has demonstrated that it requires valuable time to establish a reliable contact tracing system in the setting of damaged public health infrastructure , a severe shortage of health care workers , and community resistance , amongst other reasons [3] . The stepped-wedge cluster trial ( SWCT ) was another trial design proposed to test a candidate vaccine in Sierra Leone ( SL ) during the outbreak . In contrast with ring vaccination , the SWCT does not rely on a contact tracing system [4 , 5] . The trial population is made of geographically distinct clusters that are randomly and sequentially assigned to vaccination . This design is desirable when vaccination cannot be introduced to all clusters at once due to logistical or financial reasons , and has the ethical advantage of not intentionally withholding vaccines from unvaccinated clusters while they serve as control groups . However , when the vaccine is expected to be efficacious , the risk of infection is predicted to vary between clusters over time , and these different risks can be predicted , randomly choosing a cluster to be treated fails to prioritize those at highest risk ( which undermines the ethical advantage of the SWCT ) . Furthermore , Bellan et al . [6] have shown that spatiotemporal variation in infection risk undermines the statistical power of the SWCT . Here , we propose novel “ordered stepped-wedge cluster trial” ( OSWCT ) designs to address these limitations of the standard SWCT . OSWCT designs differ from SWCT when clusters are predicted to have different infection risks . Specifically , OSWCT designs use conditional randomization to assign clusters to vaccination . The cluster to vaccinate at a given time point is randomly selected from a subset of clusters that are likely to have a higher infection risk . We considered three strategies to identify the highest risk clusters: based on the order of first EVD case occurrence in each cluster as projected by a spatial model , based on the observed highest incident cases two weeks previously , or the highest projected weekly incidence . By prioritizing clusters , OSWCT mitigates the ethical dilemma of randomly assigning treatment to clusters when they are predicted to have low infection risk . In this study we assessed the statistical power of these novel trial designs . We simulated and estimated the statistical power of all the designs in the following steps . First , we constructed a metapopulation model that combines EVD transmission and individuals’ movements between regions in order to predict the spatiotemporal trends of the disease . Second , we used either the observed or modeled incidence data within districts of SL to assign clusters to receive vaccination for the OSWCT designs . Third , we used a stochastic model to simulate all trial designs , and finally we used a nonparametric method ( permutation test ) to analyze the simulated data and to estimate the statistical power of trial designs .
We tested the validity of the metapopulation model by comparing the model output to the epidemic trajectory in Liberia , assuming that the pre-intervention transmission model and human mobility parameters for EVD were the same as those in SL . We evaluated goodness of ordering and timing with the Spearman correlation coefficients for the observed versus expected order and timing of first case occurrence in each county . We also tested the extent to which the metapopulation model’s ordering of first case occurrence depended on the disease parameters used in the EVD transmission model . We therefore simulated a hypothetical outbreak of smallpox , varicella ( chickenpox ) , and measles in the same regions of SL , with model parameter values drawn from the published literature ( see S1 Appendix ) . We then coupled each of the new transmission models to the gravity model and reran the metapopulation model to obtain new ordering of first case occurrence . We measured the correlation between the metapopulation model’s ordering of the Ebola outbreak versus the hypothetical smallpox , varicella , or measles outbreaks by evaluating Spearman correlation coefficients . We first considered a standard stepped-wedge cluster trial ( SWCT ) [4 , 5] in which a single new cluster is randomly selected to receive vaccination at each pre-allocated time point during the trial period ( Fig 4 ) . In all of our simulations , a new cluster was vaccinated each week . Each cluster was considered part of the control group until it crossed over to the treatment arm of the trial , and all clusters were followed from the beginning of the trial until it ended . Vaccine efficacy was estimated by comparing incidence in the vaccinated and unvaccinated clusters at each time step [17] . While SWCTs are often used to evaluate the impact of interventions for chronic noncommunicable diseases , they encounter methodological problems when they are used to evaluate those for infectious diseases . Specifically , the force of infection over the course of an epidemic often varies widely between clusters and changes over time . To account for such variation between clusters , we proposed “ordered stepped-wedge cluster trial” ( OSWCT ) designs in which clusters are assigned to a treatment group based either on the observed incidence data or on a projection of cases derived from a spatially structured transmission model ( Fig 5 ) . The overarching purpose of this design is to randomly select for intervention a cluster from a more homogeneous subset of clusters . We simulated and assessed the statistical power of four vaccine study designs: a standard SWCT and three types of novel OSWCTs , one in which we ordered clusters according to the districts with the most observed cases in the two weeks prior ( data-OSWCT ) , a second in which we ordered clusters based on the ordering of first case occurrence in districts by our spatially structured EVD model ( first-OSWCT ) , and a final design in which we ordered clusters based on the highest predicted incidence at each implementation time step ( peak-OSWCT ) . For all OSWCTs , we randomly selected the cluster to be vaccinated at each time step from among the top N ( e . g . , 4 or 5 ) unvaccinated clusters ranked by the ordering strategy , and the remaining N-1 unvaccinated clusters served as the control group ( Fig 5 ) . Once vaccinated , clusters continued to contribute cluster time until the end of the trial . When there were N or fewer unvaccinated clusters remaining , OSWCT designs became analogous to the standard SWCT design . We chose these different ordering strategies because the first-OSWCT design does not rely on availability of epidemic data to be able to order clusters , therefore it can be used when one is planning an OSWCT early in the outbreak before all the regions have observed their first case . However , if a trial does not start until later in the outbreak after the disease has spread to all regions , then ordering based on first case occurrence is no longer an efficient strategy . Ordering strategies based on the highest observed incidence two weeks prior ( data-OSWCT ) or on a projection of weekly highest cases ( peak-OSWCT ) are applicable to trials starting early or late in the outbreak . In principle , a peak-OSWCT could be designed using a metapopulation model informed only by baseline geographic data and initial estimates of model parameters . However , a model that takes into account surveillance data up to the start of the trial as well as data on the implementation of other disease control interventions is likely to have greater predictive ability . Another advantage of the transmission model-driven ordering schemes ( first-OSWCT and peak-OSWCT ) is that the randomization can be undertaken before any cluster receives vaccination , compared to the data-driven ordering scheme ( data-OSWCT ) which relies on the availability of surveillance data and thus requires the randomization be updated as the trial progresses . Following Bellan et al . [6] , we used a stochastic model to simulate vaccine trials . We assumed that the trial would be conducted in geographically distinct clusters , with each drawn from the population of one of the 14 districts of Sierra Leone . In April 2015 , the U . S . Centers for Disease Control and Prevention ( CDC ) partnered with various institutions in SL including its Ministry of Health and Sanitation ( MOHS ) to conduct a Phase II and Phase III clinical trial named Sierra Leone Trial to Introduce a Vaccine against Ebola ( STRIVE ) [18 , 19] . The study enrolled HCWs and other frontline workers to assess the safety and efficacy of an Ebola vaccine candidate . Participants were randomized to receive vaccination immediately ( on the day of enrollment or within seven days ) or to receive delayed vaccination ( about six months later ) . We consequently assumed our simulated trials to be conducted in high-risk individuals , such as HCWs and burial team members , and that all the simulated clusters would be of equal size . We assumed that without effective vaccination , a proportion ( p ) of the reported incident cases in the district would occur in the corresponding cluster; using proportionality constant ( p ) , we derived the cluster-level hazard ( Hp ) to be directly proportional to the incidence of cases in the corresponding district . The force of infection risk for each individual within a specific cluster and time interval is HP * ε , where ε captures expected variation in individual infection that is log-normally distributed with mean 1 and standard deviation 1 . We assumed that a cluster could be fully vaccinated within one week and that the vaccine effect began after a delay of ( di ) . STRIVE was expected to enroll 6 , 000 participants [19] . We assumed that each of the 14 simulated clusters had 430 individuals in order to arrive at a simulated trial size of 6020 individuals , to approximately match the originally expected sample size of the STRIVE trial . In our baseline simulation , we assumed that a hypothetical study began early during the outbreak before all districts were reported to have cases ( between mid-May to late August 2014 ) . We assumed that 5 . 2% of the total number of cases in each district would have occurred within high-risk groups , consistent with CDC reports [20] . Based on the preliminary results of the rVSV ring vaccination trial in Guinea [2] , we assumed that vaccine efficacy ( ve ) was 90% and that the time from vaccination to the onset of vaccine-induced immunity ( di ) was one week . In sensitivity analyses , we varied trial start dates as well as values ( p ) , ( ve ) and ( di ) . When we simulated a trial that started late during the course of the outbreak ( between late November 2014 to mid-March 2016 ) , we dropped the ordering design based on first case occurrence in districts ( first-OSWCT ) , since all districts were predicted to have their first EVD case by late August 2014 . We used a nonparametric method ( permutation test ) described by Bellan et al . [6] to analyze the simulated data for all designs including SCWT as well as all types of OSWCT . For each cluster i during each week t , we calculated the simulated number of infected individuals ( Yit ) , their vaccine status ( Xit ) , their cluster-time status ( CTit ) ( that is , an indicator variable set to 1 for previously vaccinated clusters and the set of high-ranking clusters from which the vaccinated cluster was randomly drawn at time t ) , and the vaccinated and unvaccinated person-time ( PYit ) for all trial participants . We analyzed the data with a generalized estimating equation ( GEE ) , log ( E ( Yit ) ) = Ci + βvacXit + βct CTit + βtime t + log ( PYit ) , where Ci is a cluster-level random effect , and we estimated βvac , the log relative hazard of infection among vaccinated compared to the unvaccinated . We computed the estimated vaccine efficacy as V^e=1 – exp ( β^vac ) . We also computed the magnitude of bias in the vaccine efficacy estimate as V^e−Ve . Under the null hypothesis of no vaccine effect , the time at which a cluster received vaccination will have no impact on the number of cases that occur . We therefore permuted 1000 times the order in which clusters were vaccinated , keeping individuals’ final infection status unchanged , and re-estimated βvac for each permutation . We calculated a Wald statistic [21] for each permuted data set and tested the null hypothesis of no vaccine effect with a two-sided significance level α = 0 . 05 . To estimate the power to detect vaccine efficacy , we repeated this process 2000 times .
Table 1 gives the set of parameters that best fit the reported case counts for Sierra Leone in both the pre and post-intervention periods . Prior to the scale up of intervention measures we found the transmission coefficients of 0 . 48 in the community , 0 . 16 in ETUs , and 0 . 54 in funerals . After the implementation of intervention controls into the model , we estimated these transmission coefficients decreased by 81% in the community , 69% in ETUs and 52% at funerals . Fig 6 shows that the transmission model accurately fit the early disease trends reported in SL and Liberia through mid-September 2014 . However , without any change in the transmission model parameters to reflect intervention measures , the model predicted an abrupt increase in cases . To capture the impact of public health interventions on the Ebola epidemic after mid-September 2014 , we changed the transmission model parameters including transmission parameters , the case fatalities rates , the probability of hospitalization , and the probability of safe burial . In Fig 7 , we fit the transmission model with interventions to the reported data from the beginning of the outbreak up to mid-January 2015 , whereas the modeled trajectory without intervention measures deviated from the reported data after mid-September 2014 . The plot also shows a comparison of the model forecast with the reported data that we did not use for fitting the model from mid-January until October 2015 . Fig 8 plots the projected order and timing of the first cases in each district of SL against the reported order; Spearman Correlation coefficients were 0 . 84 ( P value <0 . 001 ) and 0 . 63 ( P value <0 . 01 ) , respectively . We obtained a similarly good fit for the projected order and timing of first county cases in Liberia with Spearman correlation coefficients of 0 . 95 ( P value <0 . 001 ) and 0 . 96 ( P value <0 . 001 ) , respectively ( Fig 9 ) . We found the metapopulation model’s projected ordering of first case occurrence within the districts to be consistent when we used different transmission model parameters for a hypothetical outbreak of smallpox , varicella , and measles in the same regions of SL . We obtained Spearman correlation coefficients of 0 . 88 ( P value <0 . 001 ) between the order for EVD versus smallpox , of 0 . 81 ( P value <0 . 001 ) for EVD versus varicella , and 0 . 78 ( P value <0 . 001 ) for EVD versus measles ( Fig 10 ) . We used the ordering of cases to simulate different vaccine trial designs in SL with clusters ordered to receive treatment based on random assignment ( SWCT ) , on observed highest incidence in the two weeks prior ( data-OSWCT ) , on the metapopulation model’s projected first case within districts ( first-OSWCT ) , or on the highest weekly projected incidence ( peak-OSWCT ) . For all of the OSWCT designs , the cluster to be vaccinated was randomly drawn from the 4 highest-ranked unvaccinated clusters ( once 4 or fewer clusters remained , the ordered designs effectively operate as standard SWCTs with random assignment ) . When the trial started early during the outbreak ( prior to September 2014 ) before all the districts observed their first case , we calculated the correlation between the different ways of ordering clusters , and we obtained similar correlation between first-OSWCT , data-OSWCT , and peak-OSWCT ( Fig 11 ) . When we estimated the bias in vaccine efficacy estimates at the end of the trial , we found that first-OSWCT underestimated vaccine efficacy by 3 . 0% , data-OSWCT overestimated vaccine efficacy by 1 . 4% , and peak-OSWCT overestimated vaccine efficacy by 0 . 7% . In contrast , SWCT underestimated vaccine efficacy by 0 . 87% . Fig 12 shows the change in bias over the course of the trial for all designs . We further investigated the bias of all designs when vaccine efficacy ( ve ) is set to zero ( see S1 Appendix ) , on average the bias was 0 . 7% for SWCT , 0 . 1% for first-OSWCT , -0 . 7% for data-OSWCT , and -0 . 4% for peak-OSWCT . We estimated the corresponding type I error for SWCT to be 3 . 1% , for first-OSWCT 2 . 8% , for data-OSWCT 3 . 3% , and for peak-OSWCT to be 3 . 6% . We found that the ordered study designs first-OSWCT , data-OSWCT , and peak-OSWCT had superior statistical power when compared to the standard SWCT design . In the baseline simulations , where we assumed the trial started 5 weeks after the onset of the outbreak , the ordered designs all had similar power to detect a vaccine efficacy of 90% with power ranging from 65% to 72% , compared to a power of 14% for the standard SWCT design . As we simulated later trial start dates , first-OSWCT design performed less efficiently compared to data-OSWCT and the peak-OSWCT designs ( Fig 13A ) . When we simulated trials with a start date after all clusters had observed their first case , we only simulated the SWCT , data-OSWCT , and peak-OSWCT designs , and no longer included the first-OSWCT design . The statistical power to detect a vaccine efficacy of 90% was 32% for SWCT , 84% for data-OSWCT , and 91% for peak-OSWCT ( Fig 13B ) . While all the OSWCT designs were more efficient than the SWCT design regardless of when the trial started , all designs lost efficiency when the trial start date was delayed . Similarly for trial start date set at 10 weeks after onset of outbreak , Fig 13C and 13D show that peak-OSWCT outperforms data-OSWCT , first-OSWCT , and SWCT regardless of the proportion ( p ) of cases that occur in high-risk groups , or the vaccine efficacy ( ve ) . In S1 Appendix we show similar results when the time from vaccination until vaccine-induced immunity ( di ) varies .
Here we demonstrate the superior efficiency of novel “ordered stepped-wedge cluster trial” ( OSWCT ) designs in detecting Ebola vaccine efficacy when compared to the standard stepped-wedge cluster trial ( SWCT ) . To our knowledge , we are the first to propose ordering clusters by timing of expected outcomes to increase the efficiency of a stepped-wedge cluster trial . Among the three ordered designs we evaluated , we found that ordering based on the highest weekly projected incidence ( peak-OSWCT ) was more efficient than ordering based on projected first case occurrence ( first-OSWCT ) or on observed highest cases in the prior two weeks ( data-OSWCT ) regardless of when a trial begins during the course of an Ebola outbreak . However , we also found that for trials starting within 5 weeks of the onset of an Ebola outbreak , the magnitude of the statistical power of all three ordered designs was similar . The preferred trial design may be influenced by available epidemiologic data . Both model-based approaches ( first-OSWCT and peak-OSWCT ) require knowing where the outbreak began . However , unlike the data-OSWCT , neither model-based approach requires surveillance in the two weeks before the trial begins . The peak-OSWCT is more dependent than the first-OSWCT on the parameterization of the model; hence without accurate estimates of model parameters from a priori knowledge , fitting to initial data on the outbreak , or both , the first-OSWCT approach is more desirable . We also found that if vaccine trials are delayed beyond the first 15 weeks of an outbreak then peak-OSWCT may be the optimal choice . Our findings support the importance of epidemiologic surveillance to inform vaccine trial design . Our transmission model is similar to other EVD models [11 , 23–25] in that we distinguish transmission that occurs from live patients in the community versus that which occurs in ETUs versus transmission during funerals . However , our model distinguishes between EVD patients with dry versus wet symptoms . We have shown that our model accurately fits the observed data when it takes into account metrics of disease control interventions such as ETU bed availability . This illustrates the importance of recording not only the incidence of disease in outbreak settings , but also tracking when and to what degree interventions are implemented . We stress that the efficacy of the first-OSWCT and peak-OSWCT depends on the accuracy with which the transmission model predicts the order of cases . We sought to address the question of model misspecification using the ordering derived from a simulation for infectious disease with different natural histories ( e . g . smallpox , varicella , and measles ) . Using our spatially explicit transmission model , we showed that the projected order of first case occurrence in each district of SL was robust to different transmission scenarios for smallpox , varicella , and measles . Fig 13A shows that when we derived the ordering from a smallpox model , we lost power compared to when we used the Ebola transmission model , but the power remained higher than that of a typical SWCT . This finding suggests that when planning a stepped-wedge vaccine trial for a filovirus outbreak in a resource-limited setting , one can approximately predict the order of first case occurrence in different regions and implement a first-OSWCT design by using current EVD transmission parameters combined with geospatial data from the affected country , even if the transmission parameters of the outbreak in truth differ substantially from those observed for EVD . We did not specifically explore the choice of optimal value for N ( the number of top clusters from which the cluster to vaccinate is randomly chosen ) , however the two extreme choices to avoid would be to set N as the total number of clusters ( which will be equivalent to the typical SWCT ) or set N to be 1 ( in which case there will not be any control group to compare to ) . More generally , a high value for N loses the advantage of an OSWCT while a very low value for N would lose efficiency because of the small number of controls to which to compare . Therefore , we anticipate the relationship between N and power to be non-monotonic . The specific shape of this relationship probably depends on a number of factors , including the distribution of number of cases per cluster . This aspect of optimal OSWCT design could be the subject of further investigation . Gravity models have been previously used to model human mobility in the context of various outbreaks . For instance , Ashleigh et al . [26] used a gravity model to describe the 2010 cholera epidemic in Haiti and to capture the ordering of first case occurrence among the departments of Haiti , Viboud et al . [27] used it to characterize seasonal influenza dynamics in the United States , and more recently it was used by Silva et al . [28] to capture EVD transmission dynamics within and between Guinea , Liberia , and SL . To our knowledge , our study is the first to link a gravity model to order a stepped-wedge cluster trial . For our ordering of first case occurrence , we found that the metapopulation model predicted cases to occur in the Western Area Rural and Urban districts sooner than actually occurred . This may be because these two districts ( which consist of Freetown and the surrounding area ) are more densely populated than the other districts of SL and that the gravity model gives more weight to more populated regions . This weight may be disproportionately large compared to their actual influence . Other limitations of the gravity model are that it does not take into account other factors that may influence migration such as weather conditions and social networks , both of which are known to impact migration in sub-Saharan Africa [29] . An alternative to measure migration between regions , thus improving the accuracy of predicted order of first case occurrence , could be to use data that directly capture human mobility , such as mobile phone data . Buckee et al . [30] described the use of call data records ( mobile phone calls or text messages ) to infer mobile phone users’ travel . These data can be used to estimate human mobility between regions and its impact on disease transmission . However , we reiterate that even the relatively simple gravity model can capture the spatiotemporal trends of an outbreak well enough that ordering clusters accordingly in a stepped-wedge cluster trial substantially increases the statistical power of the trial . When the vaccine being tested is expected to be efficacious and the risk of infection is predicted to differ between clusters at given time , the OSWCT design increases the probability that those at higher risk of infection will be vaccinated , and therefore we expect the OSWCT design to prevent more cases . In this way , the OSWCT is ethically advantageous , in addition to its statistical advantages . However , all types of SWCTs are ethically advantageous only to the extent the lags between implementing the intervention between clusters are due to genuine logistical constraints , not an intentional decision to increase the statistical power of the trial . In this study we did not consider other clinical trial designs such as a randomized clinical trial or a ring vaccination cluster-randomized trial . Because we also assumed the vaccine trial to be conducted in a small subgroup of the population ( healthcare workers ) , we did not consider the indirect effect ( herd immunity ) of the vaccination trial on the overall disease dynamics . Our main aim was to evaluate how ordering clusters when the risk of infection is heterogeneous among them may affect the statistical power of a SWCT design in settings where SWCT design may be desirable due to either ethical or logistical reasons . Our results support OSWCTs as more efficient designs than the standard SWCT . | When a vaccine is developed , it undergoes a series of tests to assess its safety and effectiveness . The last of these is called a Phase III clinical trial , in which the vaccine is tested on a subset of the population before it is approved for general use . A randomized controlled trial ( RCT ) in which individuals are randomized to receive vaccine or placebo is the most direct and efficient trial design to assess the efficacy of a vaccine . However , in circumstances where a disease has a very high mortality rate ( such as Ebola virus disease ) , the use of placebo is ethically questionable , especially when there is strong evidence that a vaccine will be safe and efficacious . Vaccine trials often must also address logistical constraints that prevent the introduction of the vaccine to the entire trial population in certain resource-poor settings . These issues were front and center in discussions about vaccine trials during the 2014 Ebola outbreak . The medical community faced questions on the clinical trial design that best balanced tradeoffs between ethical concerns , logistics , and statistical rigor . In this study , we propose and assess novel “ordered stepped-wedge cluster trial” designs as an alternative to mitigate these tradeoffs . | [
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... | 2016 | Novel Ordered Stepped-Wedge Cluster Trial Designs for Detecting Ebola Vaccine Efficacy Using a Spatially Structured Mathematical Model |
Mosquito-borne pathogens pose major public health challenges worldwide . With vaccines or effective drugs still unavailable for most such pathogens , disease prevention heavily relies on vector control . To date , however , mosquito control has proven difficult , with low breeding-site coverage during control campaigns identified as a major drawback . A novel tactic exploits the egg-laying behavior of mosquitoes to have them disseminate tiny particles of a potent larvicide , pyriproxyfen ( PPF ) , from resting to breeding sites , thus improving coverage . This approach has yielded promising results at small spatial scales , but its wider applicability remains unclear . We conducted a four-month trial within a 20-month study to investigate mosquito-driven dissemination of PPF dust-particles from 100 ‘dissemination stations’ ( DSs ) deployed in a 7-ha sub-area to surveillance dwellings and sentinel breeding sites ( SBSs ) distributed over an urban neighborhood of about 50 ha . We assessed the impact of the trial by measuring juvenile mosquito mortality and adult mosquito emergence in each SBS-month . Using data from 1 , 075 dwelling-months , 2 , 988 SBS-months , and 29 , 922 individual mosquitoes , we show that mosquito-disseminated PPF yielded high coverage of dwellings ( up to 100% ) and SBSs ( up to 94 . 3% ) . Juvenile mosquito mortality in SBSs ( about 4% at baseline ) increased by over one order of magnitude during PPF dissemination ( about 75% ) . This led to a >10-fold decrease of adult mosquito emergence from SBSs , from approximately 1 , 000–3 , 000 adults/month before to about 100 adults/month during PPF dissemination . By expanding breeding-site coverage and boosting juvenile mosquito mortality , a strategy based on mosquito-disseminated PPF has potential to substantially enhance mosquito control . Sharp declines in adult mosquito emergence can lower vector/host ratios , reducing the risk of disease outbreaks . This approach is a very promising complement to current and novel mosquito control strategies; it will probably be especially relevant for the control of urban disease vectors , such as Aedes and Culex species , that often cause large epidemics .
Mosquito-borne infectious diseases pose major public health challenges worldwide . Malaria and dengue are the most widespread , but other pathogens are also of concern , including viruses such as West Nile , chikungunya or Japanese encephalitis , and parasites such as those causing filariasis [1–5] . Urban vectors are especially problematic because they can transmit pathogens to large populations of susceptible humans , causing epidemics [1 , 2] . Since effective vaccines or treatments are available for only a few mosquito-borne diseases , prevention heavily relies on vector control; to date , however , mosquito control has proven difficult [1–3 , 6–9] . In particular with Aedes aegypti and Ae . albopictus ( the vectors of dengue , chikungunya , and yellow fever ) , current strategies depend on the ability of mosquito control staff to detect and eliminate mosquito breeding sites in and around human residences [3 , 8] . Unfortunately , both Aedes species breed in small water-holding containers that can be difficult to detect , leading to low breeding-site coverage in control campaigns; this partially explains why the performance of such campaigns can be so poor [9] . In general , mosquito control tactics that rely on source reduction via larval habitat management all face the challenge of low coverage , whereby cryptic or inaccessible mosquito breeding sites remain untreated [3 , 8] . Proof-of-concept research has shown that the egg-laying behavior of female mosquitoes can be exploited to have them disseminate tiny particles of pyriproxyfen ( PPF ) , a potent larvicide , from resting sites to nearby breeding sites [10 , 11] . This strategy relies on the innate ability of female mosquitoes to find and reach suitable breeding sites and on the ‘skip-oviposition’ behavior of some species , whose females visit several breeding sites to lay a few eggs in each [12 , 13] . The idea entails luring mosquitoes to ‘dissemination stations’ treated with PPF dust-particles that adhere to the insect’s body and are thus transferred to clean breeding sites subsequently visited for oviposition [10] . Although appealing , this approach has only been tested in small areas , with PPF dissemination measured at very short distances [10 , 11 , 14–16]; recently , a larger trial ( ref . [17] ) used an emulsifiable-PPF spray instead of dust-particle dissemination stations . Here , we investigate whether adult mosquitoes can transfer PPF particles from lure dissemination stations to sentinel breeding sites in a tropical neighborhood , and assess the impact of mosquito-disseminated PPF on juvenile mosquito mortality and adult mosquito emergence .
All field procedures were carried out with permission from dwelling owners . Sérgio LB Luz holds a permanent license ( 27733–1 ) from the Brazilian Institute for the Environment and Natural Resources ( IBAMA ) for sampling disease vectors . The study took place at the Tancredo Neves neighborhood in the city of Manaus , Amazonas , Brazil ( 3°6’S , 60°1’W; Fig 1 ) . Tancredo Neves is a lower middle-class residential neighborhood where most people live in single-family houses with a small yard; house/yard compounds will be referred to as ‘dwellings’ hereafter . Aedes aegypti and Ae . albopictus infest most dwellings in the study area , where dengue cases are common [9] . We set up a mosquito-surveillance network spanning about 50 ha of Tancredo Neves and comprising 55 randomly selected dwellings . The presence of egg-laying mosquitoes in each dwelling was sampled via ‘sentinel breeding sites’ ( SBSs ) . SBSs were 580-ml dark-brown plastic cups ( Fig 1 ) baited with 200 ml of hay infusion ( approximately 5g dry Zoysia sp . /liter of tap water , fermented for six days in a closed plastic container ) diluted in 250ml of tap water . As for standard ovitraps , SBSs offer artificial breeding habitats that can be promptly set/checked and from which larvae can be removed for further analysis; we used screw-capped cups to avoid any spillover of SBS contents ( hence possible cross-contamination ) during SBS retrieval and transportation to the laboratory . Mosquito surveillance was run monthly from January 2011 to September 2012 ( see study timeline in Fig 1 ) by simultaneously setting three SBSs in each dwelling during six days per month . We coded each SBS individually to ensure that each was set always at the same location; SBSs were thoroughly washed with water and soap between monthly sampling rounds . Any missing SBS was replaced by a new one with a different code in the next sampling round . Overall , we analyzed dwelling-level data from 1 , 075 dwelling-months ( excluding dwellings that were unavailable for sampling at certain months ) and breeding site-level data from 2 , 988 SBS-months ( excluding SBSs that did not produce data in a given month because they were overturned , went missing , or corresponded to dwellings that were unavailable for sampling ) . Dwelling-level data allowed us to investigate whether mosquitoes would effectively disseminate PPF over the whole study area , while SBS-level data provided insight on ( i ) breeding-site coverage ( as measured by the fraction of SBSs that became contaminated with mosquito-disseminated PPF ) and ( ii ) the effects of the trial on juvenile mosquito mortality and adult mosquito emergence ( see below ) . SBSs were retrieved after six days of operation to avoid emergence of adult mosquitoes in the study dwellings . Once in the laboratory , the contents of each SBS were transferred to a white plastic cup to ease the observation of mosquito juveniles; each cup received the same code as the corresponding SBS and was capped with gauze and kept for 8–16 days to monitor mosquito development . A pinch of TetraMin fish food ( Tetra , Melle , Germany ) was added every other day to each cup . Mosquito larvae found in each individual SBS-month were identified as Ae . aegypti , Ae . albopictus or Culex spp . [18] ( we ignore a few , rarer taxa in the present analyses ) , and were checked every two days to score juvenile mosquito death or adult mosquito emergence . For each SBS-month , juvenile mortality was estimated as the percent of individuals that died as larvae or pupae , and adult mosquito emergence as the sum of all individuals that emerged as adults . ‘Dissemination stations’ ( DSs; Fig 1 ) were two-liter , black plastic cups with 400 ml of tap water and the inner wall lined with black , velvet-like cloth dusted with 5 g/m2 of PPF ( Sumilarv 0 . 5 g granules , Sumitomo , London , UK ) ground to fine powder in a metal mortar . PPF is an insect juvenile-hormone analog that kills immature mosquitoes , especially pupae , at extremely low doses; it also reduces fertility in adult mosquitoes , but has no lethal or repellent effects on them [10 , 16] . PPF is recommended by the World Health Organization as a safe mosquito control means even in drinking water [3] , and is currently endorsed by the Brazilian Ministry of Health [8] . One hundred DSs were deployed in a sub-area of about 7 ha nested within the study area; DSs were 3 to 397 m from the nearest SBS ( Fig 1 ) . DS deployment ( ‘the trial’ hereafter ) took place from December 2011 ( month 11 ) to March 2012 ( month 14 ) , coinciding with the rainy season [9]; all DSs were removed from the field at the end of month 14 ( Fig 1 ) . DSs were placed in sheltered locations and checked fortnightly throughout the four months of the trial to refill water , re-dust cloth with PPF , and replace lost cups . In the laboratory , individual SBSs were scored each month by one of the investigators ( Elvira Zamora-Perea ) as contaminated or not contaminated with PPF . Contamination was inferred when mosquito juveniles in the SBS developed the abnormal morphology and coloration that characterizes PPF poisoning ( large bodies , blackish color; see S1 Fig ) . In addition to inducing these marked morphological abnormalities , PPF increases larval development time from the typical 8–9 days until adult emergence to 14–16 days until ( usually ) pupal death . We pre-tested the effectiveness of our PPF in a double-blind , randomized , controlled laboratory trial using 30 independent cohorts of 20 Ae . aegypti larvae each . All juveniles in the 15 cohorts treated with PPF ( 0 . 05 ppm a . i . ) died over three weeks of monitoring , whereas only one larva died in the 15 control cohorts ( see S1 Text ) . Juveniles in treated cohorts developed the morphological abnormalities typical of PPF poisoning ( see S1 Fig ) . After exploratory/descriptive analyses , we used generalized linear models ( GLMs; binomial family , logit link ) to analyze binary outcome data ( i ) at the dwelling level and ( ii ) at the breeding-site level . At the dwelling level , the binary outcome was 1 for dwellings with at least one SBS presenting evidence of contamination with mosquito-disseminated PPF and 0 otherwise . At the breeding-site level , the binary outcome was 1 for SBSs with evidence of contamination and 0 for those without . We investigated the effects of two key predictors: ( a ) time-period , comparing 10 months ‘before’ , 4 months ‘during’ , 3 months ‘early after’ , and 3 months ‘late after’ the trial; and ( b ) log10-distance ( in meters ) between each dwelling and the nearest DS , which was also used to approximate the distance between SBSs set in each dwelling and the nearest DS ( see Fig 1 for timeline and spatial arrangement of DSs and dwellings ) . Because no PPF was present in the environment before the trial , we used Firth’s correction [19] to estimate period effects with ‘before the trial’ as reference level . ‘Distance*period’ interactions were also tested . Dwelling-level models adjusted for the number of SBSs that were operational in each dwelling and month , which was specified as a three-level categorical covariate ( 1 , 2 , or 3 operational SBSs , with ‘1 SBS’ as the reference level ) . Relative model performance was assessed using second-order Akaike information criterion ( AICc ) scores and related metrics ( ref . [20] and S1 Text ) ; likelihood-ratio tests were used to evaluate covariate contribution to model fit . Categorical variables were analyzed with Pearson χ2 tests or conditional maximum-likelihood odds ratios [21] . Crude juvenile mortality rates were compared with nonparametric Kruskal-Wallis rank-sum and post hoc Tukey tests . Contour plots were built to spatially visualize GLM predictions and juvenile mosquito mortality data . We used linear regression to illustrate the effect of distance to the nearest DS on juvenile mosquito mortality . We analyzed the data using JMP 9 . 0 ( SAS Institute , Cary , NC ) .
All surveillance dwellings presented evidence of contamination with mosquito-disseminated PPF in ≥1 SBS at some time-point during DS deployment . There was evidence of contamination in 75 . 5% , 80% , 100% , and 94 . 4% of surveillance dwellings in months 11 , 12 , 13 , and 14 , respectively . Afterwards , dwelling-level PPF coverage fell from 79 . 2–81 . 5% ( months 15–16 ) to 1 . 9% ( month 20 ) of dwellings . Table 1 summarizes dwelling-level data over the four study periods . GLMs revealed strong period effects and a negative effect of distance; ‘distance*period’ interactions were not significant . According to the main-effects GLM ( Table 2 ) , the odds that a dwelling had evidence of contamination were 96 . 9 times higher during than before the trial ( likelihood-ratio test , χ2 = 692 . 8 , d . f . = 1 , P<0 . 0001; Table 2 , Fig 2 ) . A substantial decline in contamination odds was detected only 4–6 months after DSs were removed ( Table 2 , Fig 2 ) . The odds of contamination decreased at an average rate of 54 . 5% for each 10-fold increase in distance between dwellings and DSs ( Table 2 ) . A model with only period effects provided a poor fit to the data ( ΔAICc = 11 . 87 ) , but a distance-only model performed much worse and similarly to the intercept-only model ( see S1 Text ) ; thus , while both covariates substantially helped explain the data , period effects were more important than distance effects . Most of the SBSs that contained larvae ( i . e . , were visited by ≥1 egg-laying mosquito ) consistently became contaminated during the trial: from 67 . 9% in month 11 to 94 . 3% in month 13 . Afterwards , contamination fell back to 65 . 7% in month 15 and 1 . 7% in month 20 ( Fig 3 ) . Overall , we found evidence of PPF contamination in >85% of SBSs that were visited by egg-laying mosquitoes during the four-month trial period , with a steep decline afterwards ( Table 3 ) . GLMs revealed period and distance effects similar to those seen at the dwelling level ( see Tables 2 and 4 ) . ‘Distance*period’ interactions were , again , not significant; the main-effects model is presented in Table 4 , and its spatially-plotted predictions are shown in Fig 4 . AICc clearly favored this model over simpler versions ( S1 Text ) . Due to the abundance and oviposition behavior of Ae . aegypti , we hypothesized that , during the trial , SBSs with larvae of this species would have higher odds of presenting evidence of PPF contamination than SBSs without . Such odds were 329% higher in SBSs with Ae . aegypti larvae ( 91 . 2% contaminated ) than in those with only Ae . albopictus and/or Culex spp . larvae ( 70 . 5% contaminated; odds ratio 4 . 29 , 95%CI 2 . 47–7 . 54 ) . A similar effect was recorded when comparing SBSs with Ae . aegypti larvae only ( i . e . , probably visited only by Ae . aegypti ) vs . those without ( odds ratio 3 . 42 , 95%CI 1 . 89–6 . 41 ) ; see details in S1 Table . Juvenile mortality was assessed based on 29 , 922 mosquito larvae/pupae present in 2 , 287 SBS-months; overall , 9 . 2% of those mosquitoes ( 95%CI 8 . 9–9 . 5% ) died as juveniles . Before PPF dissemination , overall larval/pupal mortalities in SBSs were approximately 2 . 0/0 . 1% ( Ae . aegypti ) , 1 . 5/0 . 2% ( Ae . albopictus ) , and 6 . 9/0% ( Culex spp . ) . During the trial , these figures reached peak values of 27 . 9/80 . 7% , 43 . 6/70% , and 16 . 7/54 . 8% , respectively; pre-trial values were restored by months 15–16 ( see S1 Dataset ) . Before the trial , species-pooled mean juvenile mortality across SBSs was 4 . 2% ( SE = 0 . 5 ) ; 0% mortality was recorded in 87 . 8% of SBSs with ≥1 larva ( data from 1 , 124 SBSs and 11 , 970 mosquitoes ) . Mean juvenile mortality across SBSs rose to 75 . 1% ( SE = 1 . 8 ) during the four months of PPF dissemination , with 100% mortality recorded in 61 . 6% of SBSs with ≥1 larva ( data from 427 SBSs and 2 , 392 mosquitoes ) . Mean juvenile mortality progressively declined afterwards to 15 . 8% early after ( n = 365 SBSs ) and to just 0 . 6% late after the trial ( n = 371 SBSs ) . Mean monthly mortality of Ae . aegypti juveniles in SBSs rose from a 0–10% range before the trial ( median and inter-quartile range [IQR] all 0% ) to 62–94% during the trial ( median and IQR all 100% ) , and fell back to 0 . 3–1 . 4% ( median and IQR all 0% ) in the final 3-month period . Ae . albopictus mean monthly juvenile mortality was <2% ( range across months 0–4 . 6% ) before and about 64% ( range 29 . 5–84 . 2% ) during dissemination , and quickly fell back to baseline values after the end of the trial . Juvenile mosquito mortalities were significantly different across the four study periods: Kruskal-Wallis test of species-pooled mortality , χ2 = 1 , 140 . 5 ( d . f . = 3 , P<0 . 0001 ) . Tukey tests suggested , however , that Ae . aegypti mortality was comparable before and late after the trial , with a marginally significant difference when considering all species ( see details in S2 and S3 Tables ) . Juvenile mosquito mortality was over 20 times higher in SBSs with evidence of PPF contamination ( mean across months 67 . 3%; SE = 1 . 6 ) than in SBSs without such evidence ( 2 . 8%; SE = 0 . 3 ) ; 100% mortality was recorded in 287 of 564 contaminated SBSs ( 50 . 9%; 95%CI 46 . 8–55% ) and in 17 of 1 , 723 non-contaminated SBSs ( 1%; 95%CI 0 . 6–1 . 5% ) . During the four months of PPF dissemination , mean mortality in SBSs with evidence of PPF contamination reached 87 . 9% ( n = 364 SBSs ) , vs . 0 . 8% in SBSs without such evidence ( n = 63 ) . Fig 5A shows monthly mean juvenile mortality ( all species pooled ) in SBSs that contained ≥1 mosquito larva . Fig 5B shows results for Ae . aegypti ( n = 1 , 224 SBS-months ) : juvenile mortality reached 94 . 3% ( 95%CI 90 . 1–98 . 4% ) in month 13 , when 430 individuals in 124 SBSs were scored for mortality or emergence . Again , these large differences among periods were highly significant ( Kruskal-Wallis P<0 . 0001 ) . Finally , juvenile mosquito mortality decreased with increasing distance between DSs and SBSs during and , especially , early after the trial; on the contrary , no significant distance effects were evident before or late after DS deployment ( Fig 6 ) . Although larger and more persistent effects were apparent in and near the DS-deployment sub-area , the rise of mortality was evident throughout the study area , particularly for Ae . aegypti ( see S2 Fig ) . The median number of mosquitoes that completed development in SBSs each month before PPF dissemination was 1 , 177 ( IQR 851–1 , 427 ) , as compared to just 107 ( IQR 71 . 8–201 . 5 ) during the trial ( see S1 Dataset ) . Adult mosquito emergence rose back to 1 , 408 ( IQR 972–1 , 910 ) early after the trial and , somewhat surprisingly , peaked late after DS removal to 3 , 435 ( IQR 3 , 270–4 , 000 ) ( Kruskal-Wallis χ2 = 840 . 2 , d . f . = 3 , P<0 . 0001; see S1 Dataset ) . For a more general comparison , the median monthly number of immature Aedes spp . collected in SBSs in the same area and dwellings over the 28 months preceding the present study was 2 , 481 ( IQR 1 , 556–2 , 811 ) ( see S1 Text ) ; at a 4% typical baseline rate of juvenile mortality , monthly adult emergence can be estimated as about 2 , 400 ( IQR 1 , 500–2 , 700 ) . Overall , then , monthly adult mosquito emergence from SBSs was reduced by over one order of magnitude during the trial .
Our results provide evidence that urban mosquitoes can be very effective at transferring PPF dust-particles from simple dissemination stations to artificial breeding sites at the neighborhood scale . Maximum monthly coverage was 94 . 3% for SBSs and 100% for surveillance dwellings over 50 ha , and juvenile mosquito mortality reached 87 . 9% in SBSs contaminated by PPF-disseminating mosquitoes . This resulted in a >10-fold rise of juvenile mosquito mortality and a >10-fold fall of adult mosquito emergence; by lowering vector/host ratios , these strong effects can help reduce the risk of arboviral disease outbreaks [39] . We conclude that this approach is a very promising complement to current mosquito control strategies , which heavily rely on the difficult task of detecting vector breeding sites and therefore perform poorly . Mosquito-disseminated insecticides could profitably be combined both with current , standard control practices and with novel , more sophisticated tactics involving transgenic or Wolbachia-infected mosquitoes [40–43] . | Mosquito-transmitted diseases are among the most challenging infectious threats worldwide . Mosquito control is crucial for preventing infection and disease , particularly when effective vaccines or drugs are unavailable . A major drawback of current mosquito control strategies is that mosquito breeding sites are often overlooked , and therefore left untreated , during control campaigns . One appealing alternative proposes exploiting the innate breeding-site–finding ability of female mosquitoes to have them disseminate tiny insecticide particles that poison their offspring . Thus far , however , this idea has only been tested in small-scale trials . Here we show that mosquitoes effectively transferred insecticide particles from dissemination stations to sentinel breeding sites over distances between 3 and 400 m in a tropical urban neighborhood . This yielded high breeding-site coverage , with up to 94 . 3% of sentinel breeding sites presenting evidence of contamination with mosquito-disseminated insecticide . We recorded a 10-fold increase of juvenile mosquito mortality and a 10-fold decrease of adult mosquito emergence during the four-month dissemination trial . In combination with other tactics , this approach has the potential to considerably enhance mosquito-borne disease prevention , particularly in urban settings . | [
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"Results",
"Discussion"
] | [] | 2015 | Mosquito-Disseminated Pyriproxyfen Yields High Breeding-Site Coverage and Boosts Juvenile Mosquito Mortality at the Neighborhood Scale |
Chromosome pairing in meiotic prophase is a prerequisite for the high fidelity of chromosome segregation that haploidizes the genome prior to gamete formation . In the budding yeast Saccharomyces cerevisiae , as in most multicellular eukaryotes , homologous pairing at the cytological level reflects the contemporaneous search for homology at the molecular level , where DNA double-strand broken ends find and interact with templates for repair on homologous chromosomes . Synapsis ( synaptonemal complex formation ) stabilizes pairing and supports DNA repair . The bouquet stage , where telomeres have formed a transient single cluster early in meiotic prophase , and telomere-promoted rapid meiotic prophase chromosome movements ( RPMs ) are prominent temporal correlates of pairing and synapsis . The bouquet has long been thought to contribute to the kinetics of pairing , but the individual roles of bouquet and RPMs are difficult to assess because of common dependencies . For example , in budding yeast RPMs and bouquet both require the broadly conserved SUN protein Mps3 as well as Ndj1 and Csm4 , which link telomeres to the cytoskeleton through the intact nuclear envelope . We find that mutants in these genes provide a graded series of RPM activity: wild-type>mps3-dCC>mps3-dAR>ndj1Δ>mps3-dNT = csm4Δ . Pairing rates are directly correlated with RPM activity even though only wild-type forms a bouquet , suggesting that RPMs promote homologous pairing directly while the bouquet plays at most a minor role in Saccharomyces cerevisiae . A new collision trap assay demonstrates that RPMs generate homologous and heterologous chromosome collisions in or before the earliest stages of prophase , suggesting that RPMs contribute to pairing by stirring the nuclear contents to aid the recombination-mediated homology search .
Haploidization of the genome for sexual reproduction depends critically on homologous chromosome pairing early in meiotic prophase . How chromosomes pair is a long-standing and largely unanswered question , but pairing requires that chromosomes search for homology and then stabilize homologous interactions to the exclusion of heterologous associations . Given sufficient time , a random walk driven by diffusion , Brownian or metabolic motion might serve to foster homologous chromosome interactions . However , the complexity and efficiency of chromosome pairing has long suggested that the nuclear contents are actively stirred to bring homologous regions into proximity [1] . This notion is supported by the occurrence of well-conserved , rapid meiotic prophase chromosome movements ( RPMs; [2]–[7] ) . These movements are believed to be driven by SUN protein-mediated links through the intact nuclear envelope that connect telomeres to cytoplasmic motors [8] , [9] . Defects in SUN genes cause defects both in RPMs and in pairing ( reviewed in [10]–[12] and see [13]–[16] ) . Telomere-led chromosome positioning contributes to chromosome pairing in a variety of organisms that exhibit different styles of meiotic prophase . Organisms such as mouse , maize and S . cerevisiae exhibit a “canonical” meiosis in which synapsis ( synaptonemal complex formation [17] ) initiates along the paired chromosomes at multiple sites . Concomitant with pairing in these organisms , RPMs drive telomeres to cluster transiently in a limited region of the nuclear envelope , forming the chromosome bouquet [18]–[20] . In Caenorhabditis elegans , where synapsis is initiated only at specific telomere-proximal “pairing centers” [21] , RPMs similarly are present as the pairing sites accumulate at a common location [13] . In Schizosaccharomyces pombe , chromosomes do not synapse but the telomeres are drawn to the spindle pole and remain there throughout meiotic prophase as the whole nucleus is pulled back and forth from one end of the cell to the other by the spindle pole , a process which aligns homologs for recombinational interactions [22] . Thus , despite differing in detail , all these organisms share a stage in which telomeres are brought to a common site . A long-standing hypothesis is that the bouquet promotes pairing by aligning the chromosomes , but this continues to be a matter of debate [18]–[20] . Observations in several organisms challenge this hypothesis in its simplest form . The bouquet stage follows chromosome pairing in the fungus Sordaria macrospora [23] and follows synaptic initiation in female mice [24] and in cattle [25] . However , it remains possible that bouquet formation plays a subtle but important pairing role in these organisms , for example , in testing for and/or promoting pairing of relatively rare laggard chromosomes [20] . In S . cerevisiae , the bouquet is absent and pairing is delayed in the mutants ndj1Δ [26] , mps3-dNT ( deletion of N-terminal amino acids 2 through 64 of Mps3 [9] ) and csm4Δ [27]–[30] , consistent with a role for the bouquet in pairing . However , RPMs similarly depend on these proteins [27] , [29] , [31] , raising the possibility that RPMs aid pairing directly and , potentially , separately from any RPM role in bouquet formation . By a simple random stirring force model , RPM and pairing rates should be positively correlated , independent of bouquet formation . Here we examine the role of RPMs in homologous pairing . We extend recognition of RPMs to a period that coincides with pairing early in meiotic prophase and , using a novel chromosome collision trap , show that defects in RPMs decrease collisions between homologous as well as heterologous chromosomes . We find that RPM activities in two additional bouquet-defective mps3 mutants are intermediate between wild-type and ndj1Δ and provide evidence that the RPM reductions result from simple mechanical defects in the linkage between telomeres and cytoplasmic motors . Pairing kinetics in these and other bouquet-defective mutants indicate that pairing correlates with RPMs but not with canonical bouquet formation . We present a “stirring force” model for the role of RPMs in promoting homologous pairing .
We demonstrated previously that SUN protein Mps3 [32] , [33] forms a critical part of the link that connects telomeres with cytoplasmic motors to generate rapid chromosome movements in meiotic prophase in budding yeast [9] , [27] . Partial deletion allele mps3-dNT removes the intranuclear domain that binds Ndj1 and prevents normal accumulation of Mps3 at the telomeres , presumably largely abrogating a SUN protein-mediated link to the cytoskeleton and eliminating the RPMs . To further test the role of Mps3 , we made two additional deletions , of a coiled-coil region composed of residues 240–320 ( mps3-dCC ) which is in the perinuclear lumen and of an acidic region composed of residues 65–145 ( mps3-dAR ) which is intranuclear . Repeated attempts to delete the SUN domain failed to produce viable cells either in our standard laboratory strain ( unlike the results in [15] ) . Deletion mps3-dCC is reasonably predicted to eliminate dimerization of Mps3 [34] which we expect to influence RPMs directly . The impact on RPMs of deletion mps3-dAR is more difficult to predict as there is growing recognition of the roles of Mps3 and this domain in a wide variety of telomere and DNA double-strand break activities at the nuclear envelope in mitotic cells [35]–[42] . Surprisingly , the meiotic phenotypes of both alleles are relatively mild ( see below ) , but they have provided important insights into the role of RPMs and the meiotic bouquet . We assayed for the onset of pairing in strains with homologous loci marked by GFP-tagged spots ( concatemers of lacO bound by lacI-GFP fusion protein [43] ) where sufficiently close proximity of the two spots , <0 . 2 µm , causes them to appear as a single spot which is scored as “paired . ” A fraction of the population scores as paired prior to meiotic prophase due to somatic pairing in budding yeast [44] . This fraction decreases following induction of meiosis until meiotic pairing causes an increase in the fraction . In our wild-type strains , this increase in pairing starts between t3 and t4 ( where “t#” denotes the number of hours following induction of meiosis by transferring cells into sporulation medium; [9] , [27] and see below ) . At t3 , recombination is in its early stages , as induction of gene conversion has reached only 10–15% of its final levels ( see Supplemental Figure 7B in [9] ) . We looked for synapsis at t3 by using immunofluorescence to detect Zip1 protein in spread preparations of nuclei , where short lines of Zip1 mark early synapsis [45] . Among 300 nuclei , none had advanced beyond a spotty Zip1 pattern , indicating that extension of synapsis is largely absent at t3 . Having established t3 as an appropriately early time-point , we assayed for RPMs by acquiring thru-focus time-lapse images , where fluorescence signal is in essence projected onto a 2D plane orthogonal to the Z , or focusing , axis [46] . Previous results suggested that rapid movements were infrequent at t3 ( see Figure 2 in [27] ) , so we acquired images every 1 second for 120 seconds total , rather than our typical 60 seconds total , in a wild-type strain homozygous for a GFP spot adjacent to telomere 4R . We chose this locus because chromosome 4 and its right arm are relatively long , presumably buffering the movements of 4R telomeres from potential centromere effects and from opposing pulling by the 4L telomeres [27] . Movements were quantified in the time-lapse images using parameters that reflect the speed of the movements ( maximum and average speed ) and the tendency of the spots to move away from their starting positions ( bias and area ) . All measurements are made on spots projected along the Z ( focusing ) axis onto a plane , as required by thru-focus image acquisition [27] . Maximum speed indicates the single longest step taken by a spot during the time-lapse acquisition , and average speed indicates the mean for all steps , in units of ( projected ) microns per second . Bias , a unitless measure adapted from studies of bacterial motility [47] , is calculated as the average of the cosines of the angles made by the pairs of vectors representing successive movements ( bias is 0 for random movement , <0 for the tendency to remain in place and >0 for tendency to move away from the starting position ) . Area is the area of the minimum bounding box required to enclose all ( projected ) spot positions , in units of square microns , and represents the combined effects of average speed and bias . Except for average speed , all measures reveal significant increases in telomere mobility from t0 to t3 , indicating the presence of RPMs at t3 ( Figure 1A , 1B , 1D and Table 1; median values are compared for measurements of area , see [27] ) . The average speed per nucleus shows no increase from t0 to t3 ( Figure 1C , Kolmogorov-Smirnov test P = 0 . 623 ) , indicating that while these early movements broaden the range of travel of the telomeres , and occasionally are punctuated by faster movements , there is no net increase in average speed as compared with movements prior to induction of meiosis . Because RPMs are associated with increased average speeds at later time-points [27] , we asked whether the early movements represent bona fide RPMs by deletion of the known RPM gene CSM4 [27]–[30] and found that the early movements clearly are impaired ( Figure 1D , Table 1 ) . Thus , a CSM4-dependent increase in the range of telomere movement appears prior to the increase in average speed that has been reported to occur in leptotene cells [48] . Clearly , RPMs accompany the early stages of meiotic chromosome pairing . We developed a “collision trap” assay to ask whether RPMs could promote interactions between specific pairs of chromosome loci . In the absence of stabilization by recombination intermediates or synapsis , interactions between interstitial chromosome loci ( non-centromeric , non-telomeric ) are expected to be transient . To stabilize these interactions and enable their detection , we took advantage of the ability of tetramerizing lacI protein to link lacO DNA concatemers on separate chromatids [49] . We inserted lacO concatemers in the middle of the left arms of chromosomes 5 and 7 , in strains expressing either dimerizing-lacI-GFP , which does not connect lacO arrays on different chromosomes , or tetramerizing-lacI-GFP which establishes stable , strong interactions with other tetramerizing-lacI-GFP ( the principle of this assay is diagrammed in Figure 2A ) . Prior to the induction of sporulation , the strains were stored and grown in IPTG ( Isopropyl-βD-1-thiogalactopyranoside ) to block lacI binding to lacO sites . This prevented trapping chromosome-chromosome interactions during mitotic growth which destabilizes the marked chromosomes ( data not shown ) . Our collision trap is similar in principle to one based on Cre/loxP recombination in which transient collisions during meiotic prophase give rise to recombination products that are detected in viable spores [50] , but adds the capability to detect interactions in relation to specific stages in prophase . The pattern of Zip1 label was used to identify nuclei that were in early meiotic prophase ( Zip1 spots present ) but had not yet begun to extend synapsis ( Zip1 lines absent; Figure 2B–2D ) . Early meiotic prophase nuclei showing one rather than two GFP spots can arise from vegetative pairing , dimerizing-lacI-mediated association , chance overlap , undetected synapsis , loss of one of the lacO concatemers or stabilization of association by flanking early recombinational interactions . Nevertheless , in wild-type control cells , the fractions of single-spot nuclei in dimerizing-lacI-GFP was relatively low , 13% and 10% for chromosome 5 and chromosome 7 , respectively ( black bars in Figure 3A ) . The fractions of single-spot nuclei were considerably higher in tetramerizing-lacI-GFP , 30% and 57% for chromosome 5 and chromosome 7 , respectively ( gray bars in Figure 3A; chi square P values of 4 . 3×10−7 and 2 . 6×10−55 , respectively ) . Control genetic experiments ruled out higher levels of concatemer loss in the strains evaluated for tetramerizing lacI effects , and also excluded increased flanking recombination in tetramerizing versus dimerizing lacI-GFP in meiosis ( data not shown ) , leaving us to conclude that tetramerizing-lacI-GFP stabilizes collisions that otherwise would be unstable prior to synapsis . We next asked whether the levels of single-spot nuclei depended on Spo11 , which initiates recombination by generating DNA double-strand breaks , a requirement for the development of chromosome axial elements and synapsis [51] , [52] . The fractions of single-spot nuclei in the control dimerizing-lacI-GFP experiments for both loci are lower in spo11Δ than in wild-type ( dark blue bars in Figure 3B ) , and are lower in tetramerizing-lacI-GFP for chromosome 7 but not for chromosome 5 ( light blue bars in Figure 3B ) . These results suggest that recombination , axial element development ( which may contribute to chromosome stiffness ) and/or synapsis , which occur in wild-type but not in spo11Δ , play a role in generating stable interactions in early prophase; furthermore , one or more of these processes apparently affects mid-arm 5L and 7L differentially ( possibly because of their different lengths ) . Nevertheless , in spo11Δ single-spot fractions are higher in tetramerizing-lacI-GFP than in dimerizing-lacI-GFP for chromosomes 5 and 7 ( chi square P values of 3 . 5×10−40 and 2 . 5×10−14 , respectively ) , indicating trapped collisions . Additionally , in the presence of tetramerizing-lacI-GFP , but not of dimerizing-lacI-GFP , the fractions of single-spot nuclei in spo11Δ increase from earlier to later stages ( determined by increased Zip1 signal and presence of polycomplexes in later nuclei ) , as would be expected for ongoing collision trapping even in the absence of recombination and synapsis ( the same is seen for spo11Δ mps3-dAR; data not shown ) . With controls in place , we asked whether defective RPMs lowered the fractions of single-spot nuclei for homologous loci in tetramerizing-lacI-GFP . As expected , decreases in single-spot nuclei were seen for csm4Δ ( Figure 3C; for 5 and 7 , chi square P values of 8 . 0×10−2 and 1 . 2×10−16 , respectively ) and for mps3-dAR ( Figure 3D; for 5 and 7 , chi square P values of 5 . 0×10−2 and 9 . 9×10−16 , respectively ) . Similarly , in the spo11Δ mps3-dAR double mutant ( spo11Δ csm4Δ not tested ) , the single-spot fractions are lower than for either single mutant ( compare Figure 3E with Figure 3B and 3D; for 5 and 7 , comparing spo11Δ with spo11Δ mps3-dAR , chi square P values are 4 . 8×10−4 and 1 . 4×10−2 , respectively ) . It was possible that the weaker RPMs in mps3-dAR might allow tetramerizing lacI-stabilized collisions to accumulate to higher levels ( if RPMs disrupted these associations ) but the opposite was seen , consistent with mps3-dAR reducing the numbers of collisions . This result is consistent with the decreased homologous interaction measured for spo11Δ ndj1Δ as compared with either single mutant [53] . In each RPM mutant , levels for chromosome 7 are reduced below those for chromosome 5 , as for spo11Δ alone . Finally , we tested the impact of RPMs on single-spot nuclei for heterologous loci , with lacO concatemers on one chromosome 5 and one chromosome 7 , by comparing the single-spot fractions in wild-type versus csm4Δ ( mps3-dAR was not tested ) . As expected , background control levels are lower than for homologous loci ( compare black bars in Figure 3F and Figure 3A; chi square for 5/7 versus 7/7 P = 1 . 2×10−2 ) and fewer single-spot nuclei are found in csm4Δ than in wild-type ( light bars in Figure 3F; chi square P = 1 . 4×10−6 ) . These interaction measures are limited to early prophase nuclei where synapsis , specifically the extension of Zip1 spots into lines , has not begun and do not measure the interactions that presumably occur later during the extended prophase characteristic of RPM mutants . Thus , the observation of reduced heterologous interactions reported here do not contradict observations of increased ectopic recombination in ndj1Δ [54] , [55] and are consistent with the relatively late appearance of ectopic recombination products in ndj1Δ and csm4Δ [28] , [29] . These results are consistent with RPMs fostering homologous and heterologous chromosome interactions by moving chromosomes through the nucleus . We next quantified chromosome pairing rates and RPM activities and tested whether these are correlated . We examined pairing kinetics for a variety of homotopic sites ( diagrammed in Figure 4A , plus a 4R telomere-adjacent site , not illustrated ) tagged with ( dimerizing ) lacI-GFP on lacO concatemers , scoring for one spot ( paired ) or two spots ( unpaired ) . For each combination of genetic background and homotopic site , samples of 200 living cells were scored at hourly intervals following the shift into sporulation medium , in 3 or more independent experiments ( Figure 4B ) . The quantitative analysis of pairing kinetics is complicated by two major factors . First , progression through meiosis is not perfectly synchronous because asynchronous mitotic cells are shifted to sporulation medium . Second , some fraction of the single-spot cells represent chance overlap of homologous regions , mitotically paired homologous regions [44] , clustered centromeres [56] , [57] and/or clustered telomeres [57]–[59] , giving rise to large fractions of single-spot cells prior to meiosis and obscuring the lowest levels of meiotic pairing per se . These factors contribute to the variation indicated by the error bars ( Figure 4B ) . Another potential complicating factor is that the mutants could delay entry into meiotic prophase . However , since no such delay has been observed with ndj1Δ , mps3-dNT or csm4Δ [9] , [27] and pairing increases begin from t3 for all genotypes and loci , this possibility does not seem significant . In order to compare pairing rates of different genotypes and at different loci , rates were estimated simply by subtracting the t3 from the t5 fraction and dividing by 2 to give the rates in percent paired per hour ( Table 1 ) . RPM activity was measured in time-lapse movies at t4 in cells with Tub1-GFP marking the spindle pole body and GFP-spot markers adjacent to chromosome 4R telomere ( Video S1 ) . Telomere movements are fastest in wild-type , slightly slower in mps3-dCC , slower still in mps3-dAR and slowest in ndj1Δ . This is most apparent visually when comparing nuclei that have a single telomere spot . The differences are seen more clearly by quantifying the movements at t4 and , in order to allow the RPMs to develop fully , at t7 ( ndt80Δ was introduced to prevent wild-type , mps3-dCC and mps3-dAR strains from exiting prophase before 7 hours; Figure 5 ) . These data are inherently complex but , when the peaks and skewness of the histogram curves are compared , there is an evident trend to RPM activity where wild-type>mps3-dCC>mps3-dAR>ndj1Δ . We compared RPM activities at t4 to the rate of pairing between t3 and t5 . Since the RPM values in many of the datasets are not normally distributed , the median values of the RPMs ( which generally are very close to the mean values , data not shown ) are used in comparing RPMs to pairing rates . The median area measures for paired and unpaired 4R telomeres for the mutants are all significantly lower than wild-type with the exception of the paired telomere area for mps3-dCC ( Table 1 ) . RPM areas are graphed against pairing rates for each locus/genotype combination , separated into those nuclei where the chromosome 4R telomere spots used to measure the area are unpaired versus paired ( Figure 6 ) . These data are plotted to emphasize either the behavior of individual loci ( Figure 6A ) or the variation in the pairing rates by genotype ( Figure 6B ) . Paired ( synapsed ) telomeres generally have more robust RPMs than unpaired telomeres in wild-type cells but the reverse has been seen for RPMs in bouquet gene mutants , including mps3-dNT [27] . The partially defective mps3-dCC and mps3-dAR are like wild-type rather than mps3-dNT in having more robust RPMs when paired . Plots of the medians for each of the other RPM parameters also show a positive correlation with pairing rates ( Figure S1 ) . We expected that events that followed meiotic prophase would be similarly correlated with RPMs , and this is largely true . Each of the mutants makes fewer 4-spored asci with 4 viable spores than wild-type , and in mps3-dAR final sporulation is slightly lower but spore viability is slightly higher than in mps3-dCC or ndj1Δ ( Figure 7A–7B; measures combined in Table 1 to estimate viable spore production ) . Viable spore production in the different genotypes , approximated by multiplying the fraction of cells that make asci with 3 or 4 spores times the mean spore viability in 4-spored asci , correlates directly with RPM activities ( Table 1; values for csm4Δ and mps3-dNT are from published data [9] , [27] ) . Similarly , missegregation of chromosome 3 to give disomes in viable spores ( assay described in [60] ) correlates directly with increases in RPM defects ( Figure 7C; Table 1 ) . Genetic assays , requiring viable spores , have shown elevated premature sister chromatid separation ( PSCS ) in ndj1Δ [60] , [61] but not in csm4Δ [29] ) . In order to avoid the requirement for viable spores , we assayed PSCS cytologically in strains with one chromosome 7 marked with a centromere-adjacent lacO/lacI-GFP spot . Anaphase I cells were identified by DAPI staining and PSCS was scored if the sister GFP spots were clearly separated ( Figure 7D , Table 1 ) . PSCS is not elevated in mps3-dCC , is elevated equally over wild-type levels in mps3-dAR , ndj1Δ and csm4Δ , and is slightly higher still in mps3-dNT . By this measure , PSCS levels are only roughly correlated with RPMs and may be elevated in the mutants for reasons not directly related to RPMs , potentially because of defects in sister chromatid cohesion which has been reported for ndj1Δ and mps3-dNT [9] . The wild-type level of apparent PSCS ( 4 . 5% ) is higher than expected given >90% spore viability in 4-spored asci and may reflect crossing over between the centromere and the lacO/lacI-GFP marker . Progression past meiotic prophase was measured in DAPI stained cells by scoring for separation of the nucleus into two or more masses ( Figure 7E ) . Wild-type and mps3-dCC are indistinguishable but mps3-dAR shows a ∼1 hour delay and the more defective RPM mutants are still further delayed . With the exception of mps3-dCC , where the defects in sporulation and spore viability seem disproportionately strong with respect to the small increase in chromosome missegregation and no prophase delay , the mutant phenotypes outlined above generally trend with RPM defects . In ndj1Δ , completion of synapsis is delayed , with relatively short chromosomes being the last to synapse ( [26] , [60] . A similar observation has been made for recombination mutants dmc1Δ and rad51Δ [62] ) . We prepared silver-stained spreads of mutant meiotic nuclei at relatively late time-points that coincide with entry into the first meiotic division and examined 50 or more nuclei from each in order to determine synaptic configurations of late meiotic prophase nuclei at the electron microscope ( Figure 8 ) . As in ndj1Δ , short chromosomes lag in completion of synapsis , as demonstrated by the presence of relatively short single chromosome axes ( short arrows in Figure 8 ) , in nuclei composed mainly of synapsed axes ( long SCs pointed out by long arrows in Figure 8 ) . We searched for nuclei with the opposite pattern of synapsis in wild-type and in mutant cells , and found none . To do this , we identified spread nuclei with asynapsis either among the longest 3 chromosomes or among the shortest 3 chromosomes but not both . Strikingly , the asynapsed chromosomes were invariably among the shortest 3 chromosomes for wild-type ( 10 nuclei ) , mps3-dAR ( 9 nuclei ) , mps3-dNT ( 6 nuclei ) , csm4Δ ( 5 nuclei ) and ndj1Δ mps3-dAR ( 22 nuclei ) . This indicates that in budding yeast the shorter chromosomes synapse last , in contrast to larger organisms where the smaller chromosomes tend to synapse first [63] . In addition , single axes frequently are relatively distant from one another , 1 µm or more , suggesting that the failure to synapse is the result of a primary defect in pairing . Thus , in these mutants , pairing and synapsis are delayed without any other obvious abnormalities that might be detected in these preparations , e . g . , persistent nonhomologous associations or interlocks . The gradation of quantitatively different phenotypes among the mutants suggests a common mechanism with different degrees of impairment . Meiotic defects in ndj1Δ , mps3-dNT and csm4Δ generally have been attributed to defects in the connections between telomeres and motors present in the cytoskeleton . In ndj1Δ , proteins Mps3 and Csm4 associate with telomeres sufficiently to promote weak RPMs but in amounts that are difficult to visualize by immunolocalization [9] , [27]; in mps3-dNT , proteins Ndj1 and Csm4 are undetectable at telomeres and RPMs are absent [9] , [27] . In mps3-dCC , both Ndj1 and Csm4 accumulate apparently normally at the telomeres ( Figure 9A–9D ) , as expected given the nearly wild-type levels of RPMs in early prophase . The region of Mps3 that is absent in mps3-dCC lies between the nuclear membranes and thus shortens the telomere-cytoplasm bridge in the perinuclear lumen , possibly weakening the link to the cytoskeleton . In mps3-dAR , accumulations of Ndj1 occasionally are apparent at telomeres though more frequently are found in spots along the chromosome arms , and Csm4 is visualized only in association with the more prominent Ndj1 spots at telomeres ( Figure 9E–9H ) , suggesting that the telomere-cytoplasm link is frequently weakened or absent at telomeres . The significance of the non-telomeric accumulations of Ndj1 is not clear and is being pursued in independent work . Direct immunocytological examination of mps3-dCC and mps3-dAR proteins has been hampered by severe phenotypes caused by adding epitopes to the mutants ( data not shown , and S . Jaspersen , personal communication ) . We reported previously that telomere tethering to the nuclear envelope in meiotic prophase in wild-type cells is sufficiently stable during the spreading procedure to maintain telomere association with fragments of nuclear envelope that contain nuclear pores ( [9] Supplemental Information Figure 9 ) . We tested wild-type , ndj1Δ and csm4Δ for telomere-pore association and found the predicted results , a robust association in wild-type and csm4Δ ( Figure 9I–J ) that is lost in ndj1Δ ( Figure 9K ) . Telomere-pore association is apparent in mps3-dAR ( Figure 9L ) and requires Ndj1 ( Figure 9M ) , suggesting that telomeres are relatively well anchored . Telomere anchoring is defective in mps3-dAR vegetative cells [42] , and it seems likely that Ndj1 stabilization of telomere association with mps3-dAR protein overcomes this defect in meiotic prophase . Accumulation of Csm4 at telomeres is defective in mps3-dAR , for a reason that is not clear , and possibly accounts for the early RPM defects . By standard visual assays , the bouquet stage is essentially absent in the RPM mutants ndj1Δ [26] , mps3-dNT [9] and csm4Δ [27]–[29] , even though the RPMs and other parameters of meiosis are less defective in ndj1Δ than in the other two mutants ( as described in reference to Figure 7 and Figure 8 , above ) . Unrelated , pleiotropic effects of the mutants could account for bouquet failure , but it is possible that bouquet formation is particularly sensitive to RPM defects even while other meiosis parameters have a more graded response . To address this question , we analyzed bouquet formation in mps3-dCC and mps3-dAR . The standard assay for the bouquet configuration in budding yeast is to visualize the positions of ∼all telomeres with respect to the spindle pole , and to score as having a “tight bouquet” those nuclei with telomeres ( 1 ) tightly clustered and ( 2 ) in close proximity to the spindle pole . To accommodate the inevitable ambiguity in making this call , nuclei are scored as having a “loose bouquet” when telomeres are on the spindle pole side of the nucleus but not immediately adjacent to the spindle pole and are either loosely or tightly clustered [9] , [29] . Tight bouquet nuclei are nearly absent in mps3-dCC and mps3-dAR , as in ndj1Δ , and loose bouquet nuclei are similarly reduced in the three mutants as well ( Figure 10A ) . Thus , by the standard bouquet assay , mps3-dCC and mps3-dAR are as defective as ndj1Δ , the canonical bouquet-less mutant . We next looked more carefully at telomere behavior in the mutants to question whether the bouquet defect might arise from a defect in clustering or in accumulating at the spindle pole . We have shown previously that deletion of REC8 , which causes an arrest in meiotic prophase with tightly clustered telomeres [48] , provides a sensitized background that reveals an inability to form telomere clusters in ndj1Δ and mps3-dNT [9] . We quantified telomere distribution and spindle pole to telomere distance in 3D images of nuclei using software routines that automate the measurements . Briefly , image stacks composed of 64 slices at 0 . 2 micron intervals were deconvolved , smoothed by Gaussian blurring and thresholded to reduce background noise . The distribution of telomeres is estimated as the variance in Rap1-CFP signal per nucleus , the variance being higher when the signal is more wide-spread . The spindle pole to telomere distance is measured by finding the distance in microns between the centroids of the Spc42-dsRed and Rap1-CFP signals . Both these measures are normalized by dividing by the nucleus radius ( estimated by the distance between the Spc42-dsRed and DAPI centroids [9] ) . We found that these measurements generally correspond with visually-identified bouquet nuclei in wild-type samples when both measurements are less than 0 . 6 for a given nucleus ( data not shown ) . The results are shown with dashed lines at 0 . 6 for each axis and with the fractions of the populations that fall under each line ( Figure 10B; statistical analyses in Table 1 ) . In agreement with the visual scoring , mps3-dCC , mps3-dAR and ndj1Δ are defective in making “bouquets” in the rec8Δ background and , statistically , are indistinguishable from one another ( Table 1 ) . However , the phenotypes differ in detail . While telomere proximity to the spindle pole is similarly defective in the mutants , telomere cluster formation in rec8Δ mps3-dCC is significantly less defective , and in rec8Δ mps3-dAR is significantly more defective , than in rec8Δ ndj1Δ . Telomere clustering also is defective in vegetative cells in mps3-dAR [42] . The key observation is that chromosome pairing is less defective in mps3-dAR than in ndj1Δ even though telomere clustering appears more defective in mps3-dAR . Thus , telomere clustering and proximity to the spindle pole are , like canonical bouquet formation per se , poorly correlated with pairing rates .
Telomere-promoted rapid prophase movements in meiotic prophase first appear in early leptotene as an increase in translation of telomeres across the nuclear envelope , without a concomitant increase in the average speed of movement even though there are occasional , brief movements that are faster than seen in vegetative cells ( Figure 1 ) . These early RPMs foster interactions between heterologous as well as between homologous chromosomes , independent of meiotic recombination and prior to zygotene ( Figure 3 ) . Two mps3 mutants with defects in RPMs intermediate between wild-type and ndj1Δ also have intermediate chromosome pairing rates ( Figure 4 , Figure 5 , Figure 6 ) and in general have intermediate rates of sporulation and spore viability , disomic spore production , premature sister chromatid separation and , for mps3-dAR , delay prior to anaphase I ( Figure 7 ) . Among RPM mutants with a range of delays in completing synapsis , as in wild-type , we consistently observe that shorter chromosomes are the last to synapse ( Figure 8 ) . Given that ( 1 ) recombination generally is not reduced in RPM mutants , and ( 2 ) the normal mechanisms that lead to synapsis are likely intact [28] , [29] , [64] , [65] , we conclude that the delay in synapsis results primarily from a delay in pairing , consistent with prior work similarly supporting a role in pairing for Ndj1 [66] and Csm4 [30] . Immunocytological examination suggests that the common defect among the various RPM mutants is that telomeres do not engage cytoplasmic motors as in wild-type cells , either because attachments to the SUN-protein bridge across the nuclear envelope are weakened ( ndj1Δ , mps3-dAR , mps3-dNT; Figure 9 ) , the bridge itself is defective ( mps3-dCC ) or the motors associated with the cytoskeleton are somehow rendered ineffective ( csm4Δ ) . Surprisingly , as assayed here formation of the bouquet appears to be equally defective in the various RPM mutants ( Figure 10 ) . It is particularly informative that pairing in mps3-dCC appears only slightly delayed , ( Table 1 ) , suggesting that the bouquet makes at most a small contribution to pairing in budding yeast . Rather , the kinetics of pairing are strongly correlated with the ability of the telomere-led movements to change the locations of the chromosomes within the nucleus . We suggest that telomere translation along the nuclear envelope is the critical feature of the SUN protein-promoted movements , in part because deformations of the yeast nucleus reported by others [5] , [28] , [31] are relatively mild and infrequent in our strains . In mps3-dNT and csm4Δ , where movements are equivalent to the level in mitotic cells [27] , pairing and synapsis presumably are aided by movement from activities such as thermal motion , chromatin remodeling , DNA and RNA metabolism , and polymerization/depolymerization of intranuclear microtubules that might displace chromatin . We anticipated a pairing function for RPMs [27] and , with others , suggested that RPMs may in addition promote destabilization of inappropriate interactions , entanglements and/or interlocks [12] , [27] , [29] , [31] . We have not observed interlocked chromosomes in our spread preparations . Furthermore , longer chromosomes would seem more susceptible than short chromosomes to entanglements and interlocks , and a defect in resolving these problems could be expected to delay long chromosome pairing and/or synapsis disproportionately . However we have observed that smaller chromosomes are the last chromosomes to pair and synapse in the RPM mutants ( or perhaps never do synapse , as chromosomes that lack crossovers in ndj1Δ are the shorter ones - see Table S7 in [65] ) . The simplest interpretation is that RPMs primarily influence pairing and synapsis not by resolving interlocks but by extending the range of the homology search . Nevertheless , interlocks may be difficult to visualize in budding yeast and , furthermore , we cannot rule out the possibility that RPMs contribute to interlock formation by promoting telomere-proximal recombination at an early stage and then contribute to interlock resolution by continued movement at later stages . It also remains possible that RPMs disengage less cytologically evident entanglements , such as entanglements between chromosome axes which are resolved prior to the onset of synapsis or entanglements of loops of chromatin of different chromosomes , which would not involve the chromosome axes [27] . Conservation of the bouquet suggests conserved function and , given the timing of bouquet formation it is not surprising that a role for the bouquet in pairing is widely accepted . A complicating factor for earlier work is that prior to the recognition that telomere-led RPMs are well-conserved , bouquet formation appeared to be the primary defect in a variety of mutants with pairing and synapsis defects , a good example being ndj1Δ where it now appears that the RPM defect is primary , with bouquet and pairing defects being secondary . Fission yeast , which has provided the clearest data in support of a role for the bouquet in pairing and recombination , also has provided the clearest data for a role for the bouquet in SPB stability and spindle function in the first meiotic division [67] . The sporulation and spore viability defects in mps3-dCC could reflect a direct impact of the mutation on spindle pole body function per se , given that the defects in RPMs and chromosome segregation are mild or absent ( Table 1 ) . However , we have observed no defects in vegetative growth in mps3-dCC , and it is possible that absence of the bouquet in mps3-dCC specifically causes the later problems . Specifically how RPMs function to promote pairing in combination with the recombination-directed homology search is an open question . Following the DNA double-strand break ( DSB ) formation that launches meiotic recombination , resection creates single-stranded DNA that is coated with recA-like enzymes which then promote invasion of homologous DNA , this last step presumably insuring that pairing is homology-dependent . The farther the single-strand end can diffuse away from the axis of the chromosome , the less dependent on active whole-chromosome movement this part of the search would become , although presumably the potential for entanglements would increase as well . Reliance of timely pairing and synapsis on RPMs suggests that the single-strand extension search volume is limiting . A simple model for the role of RPMs early in prophase is that RPMs promote collisions by generating relatively random long-range chromosome movements , thus increasing the probability that a single-stranded end will encounter homologous DNA [45] , [68] , [69]; this process might be particularly important for short chromosomes that cannot reach across the nucleus when telomeres are tethered to the nuclear envelope ( Figure 11 ) . The meiotic delay associated with defective RPMs leads to negative consequences for the cell , although the mechanism is not certain . One possibility is that the pairing delay leads to continued resection which might help promote the homology search by extending the search radius but at a cost of increased entangling and/or possibly of increased ectopic recombination [54] . Alternatively , checkpoint adaptation before recombination is sufficiently complete could lead to chromosome missegregation , or depletion of energy stores during the prolonged prophase could prevent completion of sporulation . Whether RPMs play additional roles in meiotic chromosome metabolism remains to be determined but their conservation across phyla indicates that RPMs are critical for normal meiotic outcomes and fertility .
Strains and plasmids are described in Table S1 . For strains marked with GFP spots , pMDE798 and pAFS152 ( provided by A . Straight and A . W . Murray ) were transformed into MDY strains to generate DMC1 and CYC1 promoter-driven lacI-GFP expression respectively . A tandem array of 256 copies of the lac operator ( LacO256 ) was then targeted at the desired locus ( at chromosome 1L , 5L or 7L mid-arm; 1 , 5 or 7 centromere , 1R , 3L , 4R , 5L or 7L telomere ) into strains containing PDMC1- and/or PCYC1-lacI-GFP [9] , [27] , [70] . Standard genetic procedures were applied to generate various single and double mutants for movement analyses , and for bouquet and pairing assays , mainly by crossing appropriate single mutants followed by dissecting tetrads to identify further haploids in isogenic backgrounds . Isogenic haploid clones of opposing mating types were mated , and zygotes were selected on appropriate medium ( adenine- ) to get homozygous diploid clones which were then synchronized for sporulation . Partial deletion alleles of MPS3 , mps3-dAR ( residues 65–145 deleted ) and mps3-dCC ( residues 240–430 deleted ) were constructed by PCR and sequenced before use . The mutant alleles were cloned into the URA3 vector YIplac211 and integrated at the genomic site of MPS3 . Successful replacements of MPS3 with the deletion alleles were identified by PCR screening of 5-FOAR colonies . Fluorescence microscopy of living cells was carried out as described previously [9] , [27] , [46] . Briefly , an agarose pad was used to trap sporulating cells against the bottom of the coverslip for time-lapse microscopy [71] using a rapid , thru-focus method that produces images projected onto a single plane for each acquisition [46] . For bouquet analyses , cells were briefly fixed with 0 . 4% paraformaldehyde , mounted as for live cell microscopy , and imaged in high-resolution , deconvolved 3D stacks [9] . Fluorescent spot movements and distributions were analyzed using algorithms and software developed for the purpose . The efficiency of spot movements was estimated by measuring projected area over 20 , 60 or 120 second intervals [27] . Spread meiotic nuclei for immunofluorescence were prepared on poly-L-lysine coated slides [72] . Sporulations for all cytological assays were carried out in liquid medium using standard procedures [70] . Strains containing tetramerizing-lacI-GFP were kept in 20 mM IPTG through all stages of handling until shifting into sporulation , in order to prevent rearrangements and losses of the LacO concatemers . Bouquet assay strains labelled with Rap1-CFP to mark telomeres and Spc42-dsRED to mark the spindle pole body were fixed briefly with 50% ethanol containing DAPI to label DNA [9] . The significance of differences between non-normally distributed datasets was assessed using the Kolmogorov-Smirnov test , calculated at http://www . physics . csbsju . edu/stats/ , or the Mann-Whitney U test in Excel ( Microsoft ) , downloaded from http://udel . edu/~mcdonald/statkruskalwallis . html . Chi-square tests using Yates' continuity correction were calculated at http://udel . edu/~mcdonald/statchigof . html and Student's t-tests ( 2-tailed , 2-sample unequal variance ) were calculated using Excel . | Sexual reproduction involves the fusion of gametes , as of a sperm and an egg , to produce the next generation . Each gamete must carry half the number of chromosomes of each parent so that the correct number is restored at fertilization . In order to orient chromosomes properly so that the two chromosomes of each pair ( “homologs” ) separate to opposite poles in the first meiotic division , the chromosomes first must find one another and align in close proximity ( “pairing” ) and then be fastened together along their lengths ( “synapsis” ) in meiotic prophase . Pairing is a poorly understood process that involves movement coupled with a mechanism for recognizing homology . We examine the role played by telomere-promoted chromosome movements ( rapid prophase movements , or “RPMs” ) and find a correlation between movement and pairing rates , suggesting that RPMs contribute directly to pairing . RPMs cause collisions between nonhomologous as well as between homologous chromosomes , suggesting that RPMs stir the nuclear contents to stimulate recombination-dependent pairing . | [
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] | 2012 | Meiotic Chromosome Pairing Is Promoted by Telomere-Led Chromosome Movements Independent of Bouquet Formation |
Despite recent improvements in molecular techniques , biological knowledge remains incomplete . Any theorizing about living systems is therefore necessarily based on the use of heterogeneous and partial information . Much current research has focused successfully on the qualitative behaviors of macromolecular networks . Nonetheless , it is not capable of taking into account available quantitative information such as time-series protein concentration variations . The present work proposes a probabilistic modeling framework that integrates both kinds of information . Average case analysis methods are used in combination with Markov chains to link qualitative information about transcriptional regulations to quantitative information about protein concentrations . The approach is illustrated by modeling the carbon starvation response in Escherichia coli . It accurately predicts the quantitative time-series evolution of several protein concentrations using only knowledge of discrete gene interactions and a small number of quantitative observations on a single protein concentration . From this , the modeling technique also derives a ranking of interactions with respect to their importance during the experiment considered . Such a classification is confirmed by the literature . Therefore , our method is principally novel in that it allows ( i ) a hybrid model that integrates both qualitative discrete model and quantities to be built , even using a small amount of quantitative information , ( ii ) new quantitative predictions to be derived , ( iii ) the robustness and relevance of interactions with respect to phenotypic criteria to be precisely quantified , and ( iv ) the key features of the model to be extracted that can be used as a guidance to design future experiments .
There have been a number of success stories in macromolecular network modeling during the last decade . Special attention has been paid to dynamical modeling approaches . Among a broad spectrum of strategies , qualitative models and their associated methods have played a central role , allowing modelers to investigate the full space of possible discrete behaviors of several regulatory networks . To that end , a variety of methods for qualitative modeling , analysis and simulation of genetic regulatory networks ( GRN ) have been proposed since the seminal works of Kauffman [1] and Thomas [2] , [3] ( see [4] for a review ) . As they rely on discrete representations of both time and variables , these methods share two main advantages: first , the space of possible states is finite ( although possibly large ) , making it possible to hypothesize about the dynamics of biological regulatory systems despite the lack of kinetic information at transcriptional level . Second , regulatory networks can be built from local experimental observations or knowledge-based information ( gene-gene or gene-protein interactions ) . Although these approaches provide high-level insights into the functioning of gene networks , they often do not accurately reflect the real dynamics of GRN . Indeed , transitions between states in a GRN may exhibit a stochastic component as observed in [5] . This stochastic signal is closely related to population average behaviors [6] . Consequently , the dynamics of GRNs have a stochastic component which is difficult to observe in real time and to capture in discrete models . This has emphasized the need for probabilistic models and methods for analyzing and simulating GRN . Such probabilistic representations of gene networks are now widespread to complement discrete approaches . The Probabilistic Boolean Network ( PBN ) approach [7] , [8] is one of these . Due to its flexibility and the fact that it can be inferred directly from data , it has been extensively studied over the last decade . In [9] , finite state Markov chains are also proven to be useful in dealing with microarray data . It was established that the automatically reconstructed Markov chain gave rise to steady state distributions in accordance with some phenotypic biological observations . This suggests that Markov chain models are capable of mimicking biological behavior . More generally , Markov chain models are usually applied in the following way . First , a model that fits a given set of data is inferred [10] , [11] . Then , steady state distributions are computed , giving access to biological information , as they reflect some expected phenotypes [8] , [12] . In a final step , important product nodes are exhibited , as they control the steady-state distribution and the phenotype [5] , [13] , [14] . This latter task gives insights useful in designing new biological experiments , allowing both a better validation of the model and suggesting some therapeutic targets . Although those approaches are very efficient , they mainly rely on the quality of the network reconstruction process , that yields a two sides issue: inferring the “structure” of the gene regulatory network and computing transition probabilities that are consistent with the available data . In concrete terms , the lack of accurate observation datasets on the result of transition in a GRN usually makes the inference of the structure more accurate than the computation of the probabilities [5] . In a quite complementary way , [15] , [16] have proven that adding a probabilistic aspect to already qualitatively validated discrete models may help in determining parameters of the qualitative model . To do so , the authors add a probabilistic dimension to a discrete piecewise affine model . They introduce unknown transition probabilities between two states as the ratio of volumes defined by the qualitative parameters of the system . The main novelty of their approach is that they compute the whole set of transition probability matrices leading to given qualitative attractors of the system , instead of selecting a precise matrix as the above-mentioned approach does . This approach allows them to exhibit relations between transition probabilities and important coefficients of the system such as synthesis rates . However , as they use an analytic description of the set of accurate probability matrices , their method is limited to small networks composed of two or three genes . In the present work , we advance the idea of studying discrete knowledge-based transcriptional “intracellular” regulatory information given by qualitative models within a global probabilistic approach . The main novelty of our approach is that we compute the full set of probability transition matrices that correspond to quantitative “population scale” observations provided by protein time-series measurements . We rely on methods inspired by average-case analysis of algorithms theory [17] , [18] , making use of Markov chains coupled with transition costs to study statistical properties of pattern matching issues . We design a probabilistic framework allowing population scale observations to be integrated into a qualitative gene expression network assumed to be shared by several individual cells . Our approach should therefore be considered as a bridge between purely discrete modeling approaches and probabilistic simulations . We introduce three main novel features: first , we rely on a strong asymptotic property of Markov chains to fully describe the set of all possible weighted probabilistic networks matching with protein time-series observations . Second , we overcome computational problems as we drastically reduce the size of the model by focusing on slope changes ( switch from a variable increase to a variable decrease , for instance ) instead of changes in product levels . Third , we develop numerical methods to incorporate a set of suitable Markov chains – all those matching the numerical observations – rather than a single Markov chain that cannot be uniquely determined from the few quantitative observations we have at hand . These three novelties allow us to increase the robustness of our approach while reducing the set of data required to perform the analysis . Concretely , our approach involves first computing a discrete ( non-deterministic ) description of possible succession of slope variations . This can be deduced from knowledge-based transcriptional information , i . e . , either a logical graph or a qualitative event succession like those observed in novel generations of microarrays [19] . This provides us with a graph of transcriptional event transitions . The transcriptional events , arising on the scale of an individual cell , affect the protein concentrations , observed on a population scale . These two scales are related by adding an impact cost for each transition over a given protein concentration . This cost is easily deduced by fixing an arbitrary “natural” degradation rate and by applying an equilibrium principle as follows . Intuitively , in the absence of any information – when all the transition probabilities are chosen to be uniform – the expected protein concentrations will be constant . The next step consists of numerically determining the set of transition probability matrices that fit a global quantitative observed outcome . As an example , we expect the model to fit the time-series quantitative observations of the mean concentration of a single protein over a cell population - in this paper we focused on carbon starvation response in Escherichia coli . We have combined theoretical properties of Markov chains - inspired by symbolic dynamics - with reverse-engineering methods ( local inference methods ) to describe the full space of weighted Markov chains having the appropriate topological structure and whose global mean outcome fits the time-series curve . Then we investigate the geometric structure of the space of Markov chains to derive biological properties of the system: we derive a ranking of gene interactions with respect to their importance in achieving the considered protein variations . Such a classification is confirmed by the literature . We also accurately predict the quantitative time-series evolution of several non-observed population-cell protein concentrations using only knowledge of discrete gene interactions and very few quantitative observations on a single protein concentration . According to our modeling framework , variations in protein quantities appear to be driven by the dynamical behaviors , qualitatively described , that occur underneath at the gene regulatory scale .
As a major modeling contribution , and in the light of the above assumptions , this paper establishes a relationship between the concentration time series ( i . e . , quantitative knowledge ) and the qualitative behaviors of the biological system , as modeled by genetic regulatory networks . To that end , two matrices are considered ( see Figure 1 ) . Note herein that an exhaustive illustration of following features is proposed in the end of the Method section . The first matrix describes an event transition Markov chain which constitutes the core of the model . It depicts the probabilities ( latent variables of the model ) that the system will switch from one qualitative “basic behavior” to another , where a qualitative basic behavior means a constant slope for the variation of a product . The structure of the matrix is determined by the current extent of our knowledge of what regulates the system . Its numerical coefficients stand for the mean ratio of trajectories of the system that may cross a given transition . Our reverse engineering method aims at computing these numerical non-zero coefficients . As a companion matrix to this event description , a family of impact matrices is built for each protein involved in the system . Given a protein , the corresponding impact matrix will describe the global outcome of each transition between two events – corresponding to an arrow of the Markov chain – over the concentration of the protein . By way of example , if we assume that the system goes through a transition that activates the mRNA production of a gene , the effect ( or “impact” ) of this event may be modeled by an increase in the production rate of the protein encoded by , say 20% . Additionally , the effect of this event on all other proteins in the system may be modeled by a decrease in the production rate , a free parameter that we fix to 5% , since they undergo a natural degradation process and are not affected by the event transition . As detailed hereafter , the exact rates that are used are computed so that active and passive degradation have the same average impact during a random process . With these two matrices at hand , average-case analysis properties of Markov chains reveal a relationship between the event transition matrix , the impact matrices and the quantitative evolution of a protein concentration under given stimuli , allowing to establish some relations between observable variables of the model ( the observed growth ratio of given proteins ) and the latent variables of the model . Roughly , the time-series concentrations of a given protein make it possible to recover the main eigenvalue of the event transition matrix , which can be reformulated to infer times-series concentrations of other proteins , as well as global properties of the system . A Markov chain is a random process for which the next state depends on the current state only . It is described by a graph over the set of nodes , and edges labeled with probabilities in . Likewise , the random process can be described by a transition matrix . The Markov chain is described as minimal when this matrix is aperiodic and irreducible meaning that for sufficiently large and all vertices , there exists an -length cycle including . A stationary state of the Markov chain represents a numerical distribution of the nodes that does not evolve anymore , which corresponds to the eigenvector of the matrix . The main goal is to estimate the quantitative asymptotic impact of the Markov chain on a biological product quantity or a generic yield . Biologically , such a quantity is any of the phenotypic measurements that is impacted by the gene regulatory network , i . e . , any experimental bio-product concentration that might be inferred from either a cell growth rate or a protein concentration encoded by a gene that belongs to the system . To this end , an impact matrix is linked to the transition matrix of the Markov chain . The impact matrix is the same size as . Zero-coefficients in yield zero-coefficients in the impact matrix . Coefficients of the impact matrix are positive real values that describe the estimated cost of a transition on the change in the phenotypic quantity . Impact matrices simulate the effect of a Markov process over the global quantity as follows . Let , be two nodes of the Markov chain connected by an edge . Let denotes the probability of this transition and its impact . The elementary cost of the transition over the quantity is defined as . The induced elementary cost matrix is denoted by . The quantity is then said to evolve following a multiplicative accumulation rule from an initial distribution . Its mean value at time – that is , after iterations of the Markov process – ( i . e . , the average of the costs of all trajectories of length ) is strongly related to powers of elementary cost matrix , that is . In other words , to compute the mean value of the quantity at step , the elementary cost is multiplied along all paths of length – therefore introducing . Each path is weighted with the probability of starting from its initial node – information given by . The final impact is given by the sum of all these quantities – therefore multiplying by . In particular , as detailed below , such a multiplicative accumulation rule is useful to model the burst effect of a gene regulatory network on a metabolic scale , in which a single mRNA stochastically transcribed produces a burst of protein copy numbers [20]–[23] . When a Markov chain is fully determined and when an impact matrix is given , simple linear algebraic computations allow to calculate the growth rate of the corresponding quantity . The added value of a multiplicative law over a Markov chain relies on its asymptotic behavior , that is proved to be exponential , as stated in Theorem 1 . More precisely , a multiplicative accumulation rule follows an explicit log-normal law with explicit mean , variances and estimation of error terms . All these characteristics , such as the growth rate of the exponential , are related to dominant eigenvalues of the elementary cost impact matrix . It should be noted that when the Markov chain reaches a stationary state , the accumulation law itself enters a permanent regime , where its exponential rate is fixed . The error term is also exponential , but with a much smaller growth rate , ensuring that the stationary state of the Markov chain is quickly reached . ( Average case analysis theory for accumulation rules ) Let be a minimal Markov chain with transition matrix . A multiplicative accumulation rule with impact matrix asymptotically satisfies a normal law with mean and variancewhere is the dominant eigenvalue of the elementary cost matrix . The other quantities express by means of a generation of the elementary cost matrix , defined by . More precisely , express by means of the dominant eigenvalue of , and are constants corresponding to the dominant eigenvectors of and . There exists such that the error terms and verify and . Here , the minimality assumption restricts applications to a biological process such that its underlying Markov chain is aperiodic and irreducible; and ( ii ) for every considered cost matrix , there exists at most one aperiodic trajectory ( meaning that the cost evolution is aperiodic through times for this trajectory ) . Note that in the present work , these assumptions are those that will most restrict the biological referential . For instance , biological systems that display oscillatory behavior are outside the natural range of the approach . Nonetheless , one may overcome this weakness by modeling an input with oscillatory behavior and modeling the steps of the dynamics with independent Markov chains . This modeling device is particularly useful when one aims at modeling the circadian system . For a better illustration , please see below how to build such a Markov chain that describes the behaviors of a gene regulatory network . Given a set of impact rules and assuming that they all follow accumulation rules , optimization techniques were used to infer a Markov chain fitting all available experimental results – the growth rate of several biological quantities . The identification process was divided into two optimization problems . First , in the exact case , a Markov chain is computed which minimizes the euclidean distance between the growth rates and – see Theorem 1 above – of every impact rule associated with the Markov chain and the objective numerical values provided by the experimental results at hand . Local search algorithms are well suited to such an inference task ( see [24] for a review ) . Here , it is necessary to develop an ad-hoc local search algorithm capable of handling eigenvalues that have only an implicit definition . In order to take experimental errors into account , we considered a second optimization problem , in which the objective values were defined by an interval of validity . Our goal was to infer a Markov chain such that the growth rate of every impact rule belongs to its objective numerical interval , allowing some sets of valid Markov chains to be defined . These sets were approximated by using a polyhedra , defined as follows . First the local search algorithm was used to find a Markov chain whose growth rates were close to the middle of every objective intervals . This Markov chain defines a point , hereafter called the source point in the sequel , inside the solution set . Some points on the boundary of the solution set were then identified by setting a random direction and using a dichotomy method to find the intersection between the boundary and the line , starting from the source point with the expected random direction . As shown in the results section , the volume provides particularly meaningful information . In both cases , sensitivity analysis was performed by considering the following definition . The function was introduced , standing for the Euclidean distance between the growth rate of all impact rules and their objective numerical values . The sensitivity of a transition is then defined by the modification , in percent , when is modified by 1% . Note that it is closely related to the partial derivative according to variable of the function . The higher is the sensitivity of a transition , the more sensitive is the overall score to small variations of this variable . The previous theoretical framework can easily be adapted to the biological regulatory networks that display discrete dynamics [25] . Products of the system are gathered in a set and a relevant Markov chain summarizes the dynamics of the system . In order to handle computational issues of reverse engineering , the focus is on shapes of trajectories instead of graph states , formalized as follows . The main component of the modeling operation are transcriptomic events , i . e . , elements of . They describe the possible slopes in the variation of a bioproduct during a time unit ( i . e . increasing or decreasing ) . For instance , , also denoted by , stands for the increase in the transcriptional activity , or mRNA production , of the gene . The two events occurring over a product are denoted by and . It is sometimes useful to add some supplementary biological events such as a complex formation , when the information is available . This increases the accuracy of the model . The Event Transition Graph ( ETG ) encodes the possible successions of events . Its nodes are given by the set of events . An event targets if , in at least one trajectory of the system , varies with the slope and then 's slope changes to the sign . This graph may be derived easily from a state transition graph such as those produced by logical asynchronous multivalued Thomas mode piecewise linear models [26] . An Event transition Markov chain is an event transition graph endowed with a matrix probability . Biologically , considering a Markov chain means considering an average behavior of the system over a set of different cells . Since the focus is on events only ( i . e . successions of changes in the slope variations of products ) instead of states , the stationary states of the Markov chain correspond to cell populations where the proportion of cells with increasing/decreasing transcripts is fixed . Therefore , the stationary states of Markov chains do not correspond to stationary states of the biological system ( where all transcripts have a stable concentration ) . In order to avoid misunderstandings , a stationary state of an event transition Markov chain is called a permanent regime . The Initial state of the Event transition Markov chain depends on the biological process that is studied . Assuming that the cells within a population are not synchronized suggests that the initial distribution of events in the system is uniform . If the cells are forced to be synchronized at an early stage of the experiments , a dedicated initial state describing the forced condition must be taken into account . It was pointed out that the evolution of one – or several – protein concentrations resumes a multiplicative phenotypic impact of the gene regulatory network [21] , [23] . The multiplicative assumption was considered as relevant since the protein concentrations in a single cell follow standard evolution laws which are of exponential nature , similarly to the behaviors of systems governed by multiplicative laws [27] . Let be a gene in the system at hand and its encoded protein . The impact matrix describes the impact of the event transition Markov chain on the protein production . To define this matrix , an active impact scale and a passive impact scale must be introduced . If a given transition impacts a given gene via its mRNA production , we assume that its encoded protein production increases or decreases by the scale . Otherwise the protein rate is assumed to decrease via its natural degradation by the scale . Formally , let be an edge in the Markov chain ( can be any product and is either or ) . Reaching state means that the activity of gene changes leading to an active production or degradation of its associated protein . During all other transitions , where does not encode the protein , the system undergoes a natural degradation of protein . The production and degradation rate values are chosen as follows . The passive effect is set as equal to ( i . e . , a natural degradation of ) . The active degradation coefficient is defined according to the following equilibrium rule . Let ( resp . ) be the set of all events associated to an active degradation ( resp . production ) of the given protein . We first fix all the transitions to be uniform ( i . e . , all the probabilities of leaving a given state are equal ) , and denotes by the steady-state distribution of the associated Markov chain . Protein concentration is stable ifwhere and . This defines a degree two equation . Simple arguments prove that this equation has only one solution smaller than 1 that is assigned to . The active production coefficient is then defined as , the inverse of the active degradation coefficient . Eventually , the impact matrix associated to the protein is fulfilled thanks to the passive effect rate and the passive and active degradation rates . As the approach is dedicated to prokaryotic systems , a linear relationship between gene activities and their protein concentrations is assumed . This induced a standard evolution law to describe the quantitative evolution of the protein concentrations in the system in accordance with the qualitative events as described by the event transition Markov . More precisely , it was assumed that , as with other modeling studies [23] , [27] , a protein concentration evolves according to a succession of exponential laws , with . The cutting points are obtained using the available experimental data . The meaning of this succession is that the protein concentration at time is if . Then , for each , , and expresses byIt can be noted here that the concentration of a protein that is only degraded tends to , which is its basal concentration . Assuming it to be null leads to simpler formulas for and . According to the hypotheses discussed below , we assume that the protein concentration follows a multiplicative accumulation rule in each time interval . Let be the mean duration of a transition . In the permanent regime of , which is reached very quickly , the relation holds . According to Theorem 1 , this equation implies that the product is nothing but the dominant eigenvalue of the elementary cost matrix of . Additionally , introduced below equals the constant introduced in Theorem 1 . Taking all into account , the growth rates and required to apply our reverse-engineering methods described below , can be calculated from the protein concentration shape as soon as the mean duration time of a translation has been estimated . To that end , it is assumed that the duration is independent from the studied dynamics , allowing it to be computed from experimental knowledge on passive degradation . We introduce the shortest half-life of amino-acids of the protein of interest – usually available in the literature . According to the N-end rule , as depicted in [28] , fixing a passive degradation rate of entails that , which allows an explicit computation of and completes the inference of growth rates . For the sake of clarity , we propose to illustrate now the modeling method when applied on a simplistic Event Transition Graph ( core model ) . It is composed of two genes that monitor four events as depicted in Figure 2 . The graph is also depicted using a transition matrix in which one adds two unknowns ( latent variables ) for describing a Markov chain: and . To solve the problem in a biological context , one then considers the two following complementary informations: These informations are then used to infer and and relative probabilities . The inference task is performed by an adhoc matlab script ( The complete package and its corresponding tutorial are available in http://pogg . genouest . org ) . As a general result , several combinations of probabilities satisfy the given constraints . They are depicted in Figure 3 . Emphasizing a unique set of probabilities is therefore not possible . Unlike other Markov-like techniques , the Event Transition Markov chain models the impact of the Markov chain behaviors over the production of each protein of the system . We are thus able , for each combination of probabilities that satisfies the constraints , to estimate the protein growth rates in the permanent regime . Indeed , one can describe the distribution of Y protein growth rates for 10 , 000 probability combinations that satisfy the constraints ( Figure 4 ( A ) ) . This distribution is obviously sensitive to the probabilities . For illustration , the distribution of the protein Y growth rate for 10 000 probability combinations picked randomly is different , as attested when one depicts the difference of random and constrained distributions of Y protein growth rates in Figure 4 ( B ) , illustrating the close relations between protein X and Y concentration evolutions . Computing the distribution is not an easy task when one considers more than 3 genes or 6 events . In practice , we then overcome this problem by estimating the mean of each growth rate ( i . e . , ( prediction ) in the case of the Y protein growth rate as presented above ) , instead of each growth rate distribution . This provides some accurate predictions of protein concentration evolutions .
The original model [29] is given as a system of piecewise affine differential equations . It contains 6 genes and 37 constraints over inequalities and thresholds . This yields a state transition graph of 912 qualitative domains . The corresponding Event Transition Graph was automatically computed by applying the definition introduced in the method section and detailed in Supplementary Text S2 . The resulting graph , composed of 22 edges and 11 nodes , is depicted in Figure 6 . Note that for the sake of clarity , we manually introduced a component named “complex” that summarizes the effect of cAMP metabolite as depicted in [32] . This node , in accordance to the original model [29] , stands for a complexation of the Crp and Cya proteins and the carbon starvation signal . Following our formalization , this component is thus a natural product of , and the signal component . Although the event transition graph roughly summarizes the behaviors of the original qualitative model , it still highlights the major biological properties by its reading . For illustration , the repression of the crp gene by the Fis protein [33] is depicted by an active effect of on . However , the information about crp controlled by two distinct promoters is lost . As detailed above in the method section , we computed the impact matrices based on bacterial protein production growth rates . This setting appears to be suitable since E . coli can be viewed as a multi-scale system . Indeed , the change in protein concentration shall be considered as a protein scale amplification of events that occurs at the transcriptomic scale that are depicted as protein burst by experiments [20]–[22] . By way of illustration and following the equilibrium rule defined above , in the impact matrix over the Fis protein , the concentration of Fis , denoted by , undergoes a increase for each transition targeting . It suffers from a decrease for all transitions targeting . Finally , it goes through a decrease for all other transitions , reflecting a natural degradation for Fis ( see Supplementary Text S2 for a complete description of the impact matrix ) . This depicts the Event Transition Markov chain . We used quantitative information about changes in Fis protein concentration to reverse-engineer the transition matrix . Experimental evidence [30] shows that the Fis concentration multiplies by 10 in 80 minutes , during the stationary growth phase ( i . e . carbon starvation conditions ) and then decreases in the exponential phase ( see Figure 7 and Supplementary Text S2 for details ) . Therefore , the protein concentration curve was approximated by two successive steps ( stationary phase , from with until with ) and ( exponential phase , from with until with ) . The shortest half-life of amino-acids of the protein of interest is estimated as by the literature [28] , leading to a mean transition duration of . Applying our inference growth rate procedure – see method section – resulted in the computation of the growth rates for both the accumulation rules corresponding to the stationary phase ( , , i . e . , ) and the exponential phase ( , , i . e . , ) . Then , the reverse-engineering approach using , , , ( see Method section ) produced a probability transition matrix that fits the protein growth rates in both stationary and exponential growth phases . By repeating several times this procedure , one obtains a sampling of the set of all probability matrices that fits the given experimental protein growth rates . Using the transition matrix of the Event Transition Markov chain , we perform several simulations on protein concentrations , as impacted by the gene regulation network . First , the transition matrix was coupled with impact matrices on proteins Fis and Cya to simulate their permanent regimes during the stationary phase . Then , after minutes , it is assumed that the exponential phase is initiated , inducing a change in the structure of the gene regulatory network . This change takes place by adding 2 transitions from the “signal” box on the Event transition Markov Chain which activates and the “complex” compound . Because of the given initial conditions during the exponential growth phase , these transitions were neglected , but not in stationary phase conditions . Then , based on the same matrices ( impact and probability transition ) , new simulations are performed on the evolution of Fis and Cya concentrations . Figure 7 depicts the predicted variations of the Cya and Fis proteins during both phases . Compared to the available independent experimental results [30] , [31] , the simulations and experiments are overall significantly similar according to a Pearson correlation test . The transition matrix allows us to compute the quantitative behavior of Cya in both stationary and exponential phases . Based on sparse information about Fis only , the predicted Cya behavior is consistent with the experimentally observed behavior ( , p-value ) [31] , which is a quantitative validation of our model . Notice herein that we also predict the complete time series of Fis ( , p-value = ) , which confirms the exponential growth rate assumption . As a complementary result , the system remains for only a short time in the transient regime ( i . e . , the error made herein when one computes the mean is significantly lower than 1% after 7 minutes , or 20 iterations of the Markov Chain ) , which backs up our assumption of studying this microbial system in permanent regime in both growth conditions . This confirms the usefulness of our modeling approach for this specific biological system . In addition to the prediction feature , properties of the Markov chain provide insights into biological system behavior . According to the inference process , the proteins Cya and Crp have the same predicted behavior , as a posteriori confirmed by [34] . Furthermore , the sensitivities associated with the transitions of the Markov chain also represent an appreciation of the impact of a given biological compound . In particular , this demonstrates that , in stationary growth phase , transition is highly constrained . Interestingly , this transition implicitly monitors the CAMP-CRP complex that controls the metabolism of alternative carbon sources [33] . It is closely related to ability to the bacterial system to switch between both growth phases in function of the carbon starvation . Furthermore , Schneider and co-workers [35] suggest that fis is involved in a fine tuning of the homeostatic control of DNA supercoiling . A small change in the supercoiling drastically affects the expression of the gene fis , which is in total agreement with the constraints extracted from the Event Transition Markov chain . We performed a similar analysis over the whole system ( i . e . , in both stationary and exponential growth conditions ) . The most sensitive transitions are reported in Table 1 , in which we detail the biological meanings of such interactions . Not surprisingly , fis regulation is one of the corner stone genes of the system , but it might be a natural consequence of the inferring process in our modeling approach . However , with no specific transition matrix inference , gyrAB also emerges as one of the most , if not the most , important gene of the microbial system . Implicitly , this confirms the usefulness of the DNA topology for E . coli under carbon starvation conditions .
Our purpose was to illustrate the strength of coupling Markov models together with accumulation rules to study the dynamics of a gene regulatory network , by focusing on its effects at a larger scale – the quantitative protein scale . We assumed that the production of a protein by a gene that belong to a regulatory network , follows a multiplicative accumulation rule . This implies that a permanent distribution of the protein system will be reached in a very short time . In such a regime , each protein concentration follows an exponential dynamic . The permanent regime may be modified by external events , inducing a short transition to another permanent regime . This paper details why observing such a permanent distribution – possibly several – at the protein level allows us to recover the main probabilistic law that governs the gene regulatory network . The law is thus described by a Markov chain over the succession of transitions at the transcriptomic scale . Very general properties of this Markov chain – average case analysis ( see Theorem 1 ) – allow us to infer the Markov chain from a variety of heterogeneous information , such as qualitative behaviors based on existing models and partial quantitative data . We proposed an efficient algorithm based on this average case analysis to infer the Markov chain . In this method , it must be emphasized that the fundamental interest is to focus on transitions between biological events ( slope variations of products during a time unit ) instead of state variation as proposed by other state-of-the-art methods . Indeed , this abstraction of the system is required to reduce the size of the Markov chain in order to achieve the inference process . Having determined this Markov chain allows us to study the main asymptotic properties of the dynamic system: identifying the main transitions implied in the permanent regime and sorting the relevance of transition patterns . All these predictions may be quite easily checked with additional experimentation . Conversely , experimentation allows refinement of the Markov chain inference process . Taken together , mixing the properties of a Markov chain with accumulation rules , provides a tool to determine the quantitative and asymptotic properties of a dynamic system . For illustration and validation purposes , we computed a Markov chain for the event transitions of the Escherichia coli system in the carbon starvation . The computations were performed by using a gene regulatory network of this process and quantitative data about protein Fis production during the stationary phase . Our predictions of the behavior of Fis during the exponential phase and of Cya protein changes were confirmed by independent experimental observations , which emphasizes the ability of our approach to spread partial quantitative information through an Event Markov chain built from qualitative models . Moreover , our results produce various emerging properties such as ( i ) the sensitivity of a specific transition within the Markov chain or ( ii ) the quantitative prediction of gene products that are not directly optimized during the simulation . All these features reinforce our interpretation of the global quantitative behaviors of the natural system as modeled . From a technical viewpoint , the main interest of this approach is as follows: it is not necessary to build quantitative differential dynamic systems that need accurate and complex parameter estimations . Our method uses the results of several available observations to recover the main characteristics of the dynamics ( its exponential ratio ) and to export several dynamic and biological features . Such probabilistic-like reasoning shall be considered as complementary to formal verification techniques used for validating the qualitative properties of a system [29] . Other recent methods also use probabilistic techniques for studying gene regulatory networks [7] , [9] , [36] . However , their main purpose is to embed a deterministic model with probabilities . Their main analyses therefore focus on estimating impacts of variation . Probability matrices are computed to represent experiments accurately . Finally , transition probability matrices are used to compute permanent distributions . We argue that our approach is complementary since our average case analysis theory allows us to emphasize emerging properties of the system . Relations between the two scales of observations allow us to exhibit constraints between the gene regulatory network and protein observations . Eventually , this process elucidates transition probabilities that did not come to light with other available methods . A weakness of our approach relies on the fact that the Markov Chain inference process is based on knowledge of a full qualitative gene regulatory network [4] . This shortens the range of application of our method since , nowadays , relatively few biological systems are described at this level of abstraction . However , this flaw will be moderated by the fact that the gene regulatory network is used only in order to build a global frame of the event transition Markov chain , which is much more abstracted and smaller that the gene regulatory dynamics description . It is reinforced by our main approach which is to build the Markov chain automatically from biological assumptions – either from the literature or experiments such as microarrays . Another weakness lies in the assumption of a linear relationship between gene activity and the production of the corresponding protein ( relevant for a microbial system only ) . To avoid such a restriction , one must build novel accumulation rules based on other biological abstractions – metabolic and environmental phenotypes are the most natural candidates here . Extending the construction of event transition Markov chain to the models containing reactions instead of qualitative regulations – for instance , signaling networks – is also under study to extend the range of application of our approach . A final range of future works relies on extracting more precise properties from the Markov chain description of a given dynamic system . Such studies shall initially focus on the interpretation of the concentration joint law , standing as a correlation coefficient between time-series observations . They will also investigate the use of these Markov chains to isolate experimental noise from the noise inherent to the chaotic properties of the system . This would provide an estimation of measurement qualities . Finally , average case analysis can be performed on a class of probabilistic models that is much larger than Markov chains . This would allow us to deal with Markov chains that may handle slight variations over the course of times , eventually studying the adaptation of the model behaviors under given environmental variations . | Understanding the response of a biological system to a stress is of great interest in biology . This issue is usually tackled by integrating information arising from different experiments into mathematical models . In particular , continuous models take quantitative information into account after a parameter estimation step whereas much recent research has focused on the qualitative behaviors of macromolecular networks . However , both modeling approaches fail to handle the true nature of biological information , including heterogeneity , incompleteness and multi-scale features , as emphasized by recent advances in molecular techniques . The principle novelty of our method lies in the use of probabilities and average-case analysis to overcome this weakness and to fill the gap between qualitative and quantitative models . Our framework is applied to study the response of Escherichia coli to a carbon starvation stress . We combine a small amount of quantitative information on protein concentrations with a qualitative model of transcriptional regulations . We derive quantitative predictions about proteins , quantify the robustness and relevance of transcriptional interactions , and automatically extract the key features of the model . The main biological novelty is therefore the presentation of new knowledge derived from the combination of quantitative and qualitative multi-scale information in a single approach . | [
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] | 2011 | Integrating Quantitative Knowledge into a Qualitative Gene Regulatory Network |
Circadian clocks are endogenous oscillators that drive the rhythmic expression of a broad array of genes , orchestrating metabolism and physiology . Recent evidence indicates that post-transcriptional and post-translational mechanisms play essential roles in modulating temporal gene expression for proper circadian function , particularly for the molecular mechanism of the clock . Due to technical limitations in large-scale , quantitative protein measurements , it remains unresolved to what extent the circadian clock regulates metabolism by driving rhythms of protein abundance . Therefore , we aimed to identify global circadian oscillations of the proteome in the mouse liver by applying in vivo SILAC mouse technology in combination with state of the art mass spectrometry . Among the 3000 proteins accurately quantified across two consecutive cycles , 6% showed circadian oscillations with a defined phase of expression . Interestingly , daily rhythms of one fifth of the liver proteins were not accompanied by changes at the transcript level . The oscillations of almost half of the cycling proteome were delayed by more than six hours with respect to the corresponding , rhythmic mRNA . Strikingly we observed that the length of the time lag between mRNA and protein cycles varies across the day . Our analysis revealed a high temporal coordination in the abundance of proteins involved in the same metabolic process , such as xenobiotic detoxification . Apart from liver specific metabolic pathways , we identified many other essential cellular processes in which protein levels are under circadian control , for instance vesicle trafficking and protein folding . Our large-scale proteomic analysis reveals thus that circadian post-transcriptional and post-translational mechanisms play a key role in the temporal orchestration of liver metabolism and physiology .
Circadian clocks are endogenous self-sustained oscillators that drive daily rhythms of metabolism and physiology [1] , [2] . In mammals the molecular mechanism underlying circadian oscillations is based on interconnected transcriptional and translational feedback loops that ultimately regulate the rhythmic expression of clock controlled genes [2] . Gene expression studies in central ( suprachiasmatic nucleus of the hypothalamus ) and peripheral tissues have revealed thousands of rhythmic transcripts that are associated with daily control of metabolism [3]–[7] . In particular , the hepatic clock drives transcriptional oscillations of genes that are essential for local metabolism regulating glucose , cholesterol and bile acids homestostasis [8] , [9] . In this regard , daily rhythms of metabolites have been recently described in the mouse liver [10] , [11] . In human plasma and saliva metabolite cycles are reported to be independent of sleep and food intake [12] . In contrast to these investigations of the transcriptome and the metabolome , circadian protein oscillations have not been accessed at a large scale mainly due to the technological limitations in the measurement of protein abundance in a high-throughput and accurate manner . For instance , a protein expression study of mouse liver using two-dimensional ( 2D ) gel electrophoresis at four circadian time points detected 60 rhythmic spots , of which 39 could be identified as protein products [13] . Because recent evidence suggests that circadian metabolism is also influenced by post-transcriptional mechanisms [14]–[18] , it would be desirable to study the circadian dynamics of the proteome at a large scale . Mass spectrometry ( MS ) -based proteomics [19] has developed rapidly in recent years and its quantitative accuracy has improved dramatically [20] . It is increasingly applied not only to cell lines but also to more complex systems such as tissues , where accurate quantification with technologies like Stable Isotope Labeling by Amino acids in Cell culture ( SILAC ) has now become possible [21] , [22] . Here we aimed to identify global daily changes in protein abundance in the mouse liver by applying high resolution MS-based proteomics in combination with quantification via the in vivo SILAC mouse technology [23] . Mixing pooled livers from fully ‘heavy’ labeled SILAC mice with liver samples collected over two 24 h cycles enabled us to accurately quantify the abundance of thousands of proteins . Metabolic processes were particularly well covered and turned out to be under extensive circadian control by means of protein abundance . Bioinformatic analysis highlighted significant divergence between the circadian transcriptome and proteome , including protein abundance changes without corresponding message changes and large differences in the phase of abundance . We also focus on particular biological processes that seem to be tightly regulated at the post-transcriptional level and that may have practical implications such as rhythms of detoxifying enzymes that are relevant to chronotherapy .
Liver samples were harvested from wildtype C57BL/6 mice kept one day in constant darkness after being entrained to a 12–12 h light-dark schedule . Protein lysates were prepared from the mouse livers , which were collected at intervals of 3 h over two circadian cycles ( 4 mice per time point; n = 64 total mice ) . For each time point , protein extracts from the four livers were mixed in equal amounts to have a single sample per time point . We decided to use SILAC in an in vivo format for the proteomic quantification method [23] , [24] . An internal , spike-in standard mix was constructed by combining equal amounts of protein lysates from liver samples of two heavy SILAC labeled mice collected in anti-phase ( see Material and Methods ) . The pooled lysates of each of the 16 time points was mixed with equal amounts of the internal standard prior to digestion . Resulting peptides were then separated into six fractions and measured on a linear ion trap – Orbitrap mass spectrometer ( Figure 1A ) . The experiment was performed in technical triplicates resulting in 288 liquid chromatography ( LC ) -MS/MS files that were subsequently processed in the MaxQuant software environment [25] . The relative abundance of the liver proteome is calculated for each time point by taking the ratio of the mass spectrometric signal for individual proteins to the signal of the spiked in heavy SILAC standard . For circadian analysis we filtered out those protein groups with accurate quantification values in less than half of the samples measured; the resulting dataset contained 3132 protein groups ( Table S1 ) . Reproducibility was tested by comparing the measurements to each other and calculated Pearson correlation coefficients . For all of the comparisons we obtained high r values ( between 0 . 6 and 0 . 93 ) as illustrated for technical and biological replicates in Figure 1B . The stringently quantified proteome dataset of 3132 contains proteins from a broad range of metabolic and cellular processes . Coverage of transcription factors and cell cycle proteins in these post-mitotic cells was relatively low , as expected . We did not obtain quantification values for clock proteins , likely due to their low abundance particularly at some times of the cycle . Therefore proper entrainment of the mice was confirmed by assessing the expression profile of Bmal1 and Per2 mRNA as well as of PER2 protein in the collected liver samples ( Figure S1 ) . Nevertheless , the quantified liver proteome covered extensively many liver specific pathways like fatty acid and drug metabolism ( Figure 1C ) . In addition , more than 50% of coverage was observed for more general cellular processes and components such the ribosome , proteasome and spliceosome ( Figure 1C ) . To identify proteins with circadian rhythms of abundance in the mouse liver quantified dataset we adapted a statistical algorithm ( Materials and Methods ) . Specifically , we determined goodness-of-fit of the expression ratios to a cosine curve with a period of 23 . 6 hours , the circadian period reported for the used mouse strain [26] . To calculate the rate of false discovery the experimental data were repeatedly scrambled and fitted to the cosine curve , preserving the technical triplicates in the randomization . This allows an estimate of the frequency that a false observation matches the curve by chance . With this statistical analysis , at a false discovery rate ( FDR or q-value ) <0 . 33 , we identified 186 proteins ( Table S2 ) from the total 3132 dataset ( Table S1 ) that showed circadian rhythmic profiles of abundance in the mouse liver across two consecutive cycles . Based on this analysis , we thus estimate that at least 6% of our mouse liver proteome dataset shows circadian oscillations . We additionally compared the statistical analysis performed by our method to the JTK Cycle method , a standard method used in the circadian field to detect rhythmicity [27] . The results indicated an excellent correlation ( Pearson r>0 . 9 ) between the two methods for q-value estimation in the total quantified dataset ( Figure S1C ) as well as phase determination of statistical significant cycling proteins ( Figure S1D ) . This shows that the statistical algorithm to detect oscillations included in the Perseus software package may be widely applicable in circadian studies . We assessed the total abundance of each protein in the samples by using the added peptide intensity obtained in the MS analysis normalized to their molecular weight . The abundance profile of the cycling proteome was very similar to the one observed for the entire quantified proteome ( Figure 2A ) . This indicates that cycling proteins identified in our analysis are not biased against low abundance . Although the amplitude of oscillations has been studied extensively at the transcript level , there is currently no information about the accurate fold change of the cycling proteome . Using the logarithmic normalized expression ratios obtained for every data point , we calculated the amplitude of the abundance for each oscillating protein across the circadian day . The mean of the distribution of the total fold change for rhythmic proteins in the mouse liver is 1 . 38; therefore the large majority of rhythmic proteins change less than two fold ( Figure 2B ) . The abundance of cycling proteins does not correlate with the coefficient of variation of the oscillation ( Pearson r = 0 . 018 ) , indicating that circadian rhythms were equally detectable in high and low abundance proteins . Next we tested whether there were categorical protein annotations significantly different from the overall fold change distribution by applying a recently developed annotation enrichment algorithm [28] . Proteins cycling with small amplitude are statistically enriched in those annotated as being modified by acetylation as well as ubiquitin-like modifier proteins whereas glycosylated proteins show cycles with large amplitudes ( Benjamini Hochberg FDR<0 . 05 for all; Figure S1E ) . Enrichment analysis of the circadian proteome compared to the total quantified proteome revealed that several essential liver metabolic Kyoto Encyclopedia of Genes and Genomes ( KEGG ) pathways such as drug and bile acids metabolism were over-represented ( Fisher test p<0 . 05 ) ( Figure 2C , Table S3 ) . Interestingly , a significant fraction of the membrane bound proteome ( Figure 2D , Table S3 ) is under circadian regulation in the mouse liver . In addition , secreted proteins from both extracellular matrix as well as plasma- as those involved in blood coagulation- all synthesized in the liver , are also enriched ( Fisher test p<0 . 01 ) in the circadian proteome suggesting that the circadian clock in the liver may play a role in the generation of rhythms of plasma and extracellular proteins ( Figure 2C and 2D , Table S3 ) . Taken together , we report here the most comprehensive and accurately measured circadian proteome of the mouse liver to date and find that it is significantly enriched in essential protein categories whose temporal regulation has not been previously documented . We performed phase dependent hierarchical cluster analysis of the mouse liver circadian proteome using the normalized logarithmic expression ratios for all sampled circadian times ( CT ) . The heat map representation of the ratios showed cycling proteins with phases distributed across the two consecutive days ( Figure 3A ) . The overall pattern of these phases was similar to previously reported circadian profiles of transcripts in this organ [3]–[6] . The analysis resulted in two major branches in the dendrogram which mostly segregate proteins peaking during the day or during the night ( Figure 3A , 3B ) . Almost 2/3 of the cycling proteome displayed night phases ( CT12 to CT24 ) and 1/3 day phases ( CT0 to CT12 ) . Mice are nocturnal animals hence behavioral and metabolic activity is predominant during the dark phase , which may explain the larger number of night peaking proteins in the liver . We next asked if any protein category was statistically different in these two main clusters , using a Fisher exact test cut-off of p<0 . 02 . Secreted and extracellular proteins tended to be rhythmic with abundance peak during the day while proteins associated to membrane , to the endoplasmic reticulum ( ER ) as well as to the Golgi apparatus mainly peaked at night . Moreover , proteins from complement and coagulation cascades oscillated with exclusively day phases ( Figure 3B and S2A ) while many rhythmic proteins with nocturnal phases are involved in drug metabolism , bile secretion and protein processing in ER ( Figure 3B and S2B ) . To identify additional phase dependent enriched categories of cycling proteins in the mouse liver we directly tested for phase enrichment of categorical annotations in a wider set ( 838 ) of cycling candidates using a loser q-value cut-off ( <0 . 66 ) . To determine the significance of the cycling annotation distribution test , we employed a Fisher Exact Test cut-off of FDR<0 . 02 , resulting only in categories of proteins with phases highly significant clustered at specific time of the circadian cycle . Day-enriched Gene Ontology Cellular Component ( GOCC ) protein categories comprised mainly nuclear and extracellular proteins while mitochondrial associated proteins peaked at the day-night interphase ( Figure S2C ) . In contrast , proteins associated to Golgi displayed statistically enriched night phases . The cycling annotation distribution analysis also highlighted KEGG metabolic pathways enriched at specific times of the circadian cycle . Drug metabolism and protein processing in ER were remarkably enriched with nocturnal phases which agree with the exclusive presence of proteins from these pathways in the night hierarchical cluster described above ( Figure 3A , S2B and S2D ) . The complement and coagulation cascades showed , in contrast , day-enriched phases as mentioned above too ( Figure 3A , S2A and S2D ) . Together our enrichment analyses revealed that a broad range of cellular and metabolic components are subjected to temporal regulation by means of protein abundance . A considerable number of cycling proteins belong to categories that have not previously been described to be under circadian control in the mouse liver . Circadian rhythms of mRNA in the mouse liver have been widely studied , however it still remains unresolved whether and how the temporal changes in transcripts translate to global oscillations of protein abundance . To this end , we compared our circadian proteome to a microarray study of the mouse liver transcriptome from the Hogenesch group [3] . That experiment was performed with 1 h resolution and around 10% of the transcriptome was found to be cycling , depending on the statistical algorithm employed . We matched each protein group of our quantified dataset to its corresponding Affymetrix entries which gave us a final dataset of 3046 protein groups . Using the same statistical algorithm that we applied to the proteome we assessed both circadian oscillations for proteins and transcripts . By using a cut-off of q-value<0 . 33 we identified 181 protein groups ( 6% ) with significant circadian rhythmicity , among them 151 also showed rhythms at the mRNA level ( Figure 4A , Table S4 ) . Comparison of q-value distributions for the total dataset of protein and mRNA indicates that most of the cycling proteins with arrhythmic mRNA showed q-values for their transcripts far from the applied cut-off ( Figure S3A ) . Thus , around 20% of cycling proteins do not seem to be accompanied by statistical significant oscillations of their mRNA based on the gene array data . Circadian gene expression can also be assessed by analyzing cycling binding patterns of CLOCK and BMAL transcription factors in promoters . Therefore , in addition to this gene expression study we used a recently reported set of BMAL1 target genes found by chromatin immunoprecipitation ( ChIP ) [29] . In that data set , we found only four additional genes where the corresponding protein was cycling but the transcript did not show statistically significant oscillation . Together our statistical analyses suggest that around 20% of the cycling proteins do not show circadian regulation of their corresponding transcript , implying wide-spread circadian post-transcriptional control of protein abundance . Given that very recent evidence indicated that post-transcriptional mechanisms appear to largely determine the phase of mRNA oscillations [16] , [30]–[32] , we globally explored the distribution of phases for oscillating transcripts and proteins . We found that phases of rhythmic transcripts matching cycling proteins in the mouse liver were evenly distributed throughout the cycle with higher frequency during the night ( Figure 4B upper graph ) similarly to what was previously reported for the total circadian liver transcriptome [3]–[6] . In contrast , the phases of cycling proteins with rhythmic transcripts were distributed in two main clusters , a smaller one centered at the middle of the day and a larger one in the second part of the night . Surprisingly , very few proteins peaked in the first hours of the day ( Figure 4B lower graph ) . The divergence in the distribution of transcript and protein phase suggests that the cycles of protein abundance in the mouse liver do not necessarily reflect mRNA changes and instead are influenced by post-transcriptional mechanisms . For the clock genes themselves , a characteristic time delay ( usually 4–6 h ) between mRNA and protein expression has been described [17] . However , it is poorly understood whether this is a general feature of all cycling genes and proteins . To address this question using the quantitative nature of our circadian proteome data , we calculated the time delay between the peak of expression of each rhythmic transcript and its oscillating protein . The total distribution of time delays showed with a preferential window of 2 to 6 h between transcript and protein peaks for almost 50% of the cycling mRNA and proteins ( Figure S3C ) . Strikingly , around 40% of oscillating proteins peaked more than 6 h later than their corresponding transcripts . This data indicates that regulation of time delay between peaks of mRNA and protein is an important feature of circadian biology , implying general post-transcriptional mechanisms that define the overall phase-tuning of the proteome . Principal component analysis ( PCA ) of the cycling proteome and transcriptome showed excellent clustering of the technical triplicates and biological replicates in the two-dimensional graph ( Figure 4C ) . The graphical representation of the data on the basis of the two main PCA components resembles an analog clock ( Figure 4D ) , indicating that time is the main component accounting for the overall difference between both datasets . We then calculated the angles corresponding to the median value for each time point experimentally analyzed in the proteome and for the published transcriptome . These angles directly visualize ( Figure 4D ) the characteristic time delay between mRNA and protein cycles mentioned above ( Figure S3C ) . Very interestingly , the length of this time lag varies across the circadian cycle ( Figure 4D ) . Together the comparison of the phases of the transcriptome vs . the proteome clearly demonstrates that circadian protein oscillations are shaped post-transcriptionally . Having established that the circadian clock drives liver metabolism not only at the level of mRNA but also by precise regulation of the phase of protein abundance , we next examined individual metabolic processes . We found that rhythmic proteins associated to specific functions oscillated with similar phases regardless of whether their transcripts were cycling or , if so , when they were expressed . This implies that circadian post-transcriptional regulation coordinates individual metabolic pathways – something that became especially obvious for the metabolism of xenobiotics . Phases of abundance of crucial components of this pathway tightly clustered at the end of the night while there was no obvious coordination in the phases of the cycling mRNAs ( Figure 5A ) . Moreover , temporal coordination of protein abundance of drug metabolism in the liver was not restricted to enzymes involved in the three phases of the detoxification mechanism . Strikingly , it also extended to membrane transporters responsible for the intake of xenobiotic substances from the blood circulation into the liver and/or their subsequent secretion into bile after being conjugated ( Figure 5A and 5B ) . Supporting a metabolic function of this coordinated expression , we found that the abundance profiles of rhythmic enzymes involved in detoxification strongly correlated with the oscillations of xenobiotics levels reported recently in a liver circadian metabolome study [10] ( Figure 5C ) . A substantial number of proteins involved in complement and coagulation pathways oscillate in abundance , with their phases significantly clustered during the day . Their phases of abundance did not uniformly reflect the cycling patterns of their corresponding transcripts ( Figure S3D ) . Temporal regulation of gene expression mediated by the circadian transcription factor heat shock factor 1 ( HSF1 ) has been reported for some heat shock proteins [33] , however , our study shows for the first time that the protein folding machinery in the mouse liver is also regulated in a circadian manner at the protein level . We found many chaperones undergoing daily oscillations of abundance in the mouse liver with phases remarkably clustered at night . Heat shock proteins present an interesting contrast to the examples described above , in that protein cycles of most of them closely mirror their transcriptional profiles ( Figure S3E ) showing short time delays between mRNA and protein peaks . Thus , circadian regulation of protein abundance for this pathway seems to be extensively determined at the level of mRNA in contrast to what we observed for the metabolism of xenobiotics and coagulation cascades . In summary , our analyses indicate that post-transcriptional mechanisms have a large but diverse influence on the oscillation of the proteome essential for different cellular processes . Specifically , the contributions of this circadian post-transcriptional control appear to differ among diverse metabolic pathways .
For technical reasons , it has been difficult to attain high coverage at the level of the proteome . We thus know much more about genome-wide mRNA levels and have used these as proxies of protein abundance . However , recent advances in mass spectrometry and quantitative proteomics now allow studying proteins in a much more comprehensive and high-throughput manner that previously possible . As a result , it is becoming clear that the relationship between transcript and protein abundance is complex . For instance , a recent study quantifying transcripts and proteins in a mammalian cell line suggests that translation rate is the predominant mechanism that regulates cellular protein levels [34] . Circadian control of metabolism has been widely studied based on oscillations of transcripts with the general conclusion that approximately 10% of the transcriptome oscillate daily [3]–[7] . New proteomics methods now allow direct characterization of abundance changes of essential metabolic proteins across the day rather than inferring that from the corresponding RNA levels . Here we presented the first large scale quantitative proteomic approach , aimed at identifying circadian oscillations of protein abundance in the mouse liver and compare them to transcript rhythms . The proportion of the rhythmic liver proteome , around 6% , is notably similar to that reported for the circadian transcriptome in different mouse tissues . An earlier circadian proteome study using 2D gel electrophoresis reported that up to 20% of the assayed soluble proteins in the mouse liver were cycling [13] . This difference to our results , in which we accurately quantified more than 3 , 000 proteins , is likely due to technical limitations of 2D gel electrophoresis . This is also reflected in the fact that our study , but not the 2D gel study , identified a substantial part of the membrane proteome as cycling . Our results indicate that circadian clocks coordinate hepatic metabolism and other cellular processes not only by driving transcription but by orchestrating cycles of protein abundance . In particular , we observed significant differences in the phase distribution between cycling transcripts and corresponding proteins ( Figure 4B ) . This denotes a key contribution of circadian post-transcriptional regulatory mechanisms in tuning metabolism . The liver circadian proteome contains proteins involved in a broad range of metabolic processes . We performed a functional or physical interaction network analysis of cycling liver proteins in the STRING database [35] , the result of which is visualized with Cytoscape in Figure 6 ( see Material and Methods ) . One of the largest network clusters is comprised of interactions among essential components of xenobiotic metabolism with remarkably coordinated nocturnal phases as we described above . This data indicates that circadian regulation of hepatic xenobiotic detoxification is not only exerted by control of gene expression as previously reported [36]–[38] but moreover by a precise post-transcriptional control that ensures the presence of their essential components at the time of the day when the pathway is metabolically more active . By temporally driving cycles of abundance for detoxifying enzymes with higher levels during the night , the circadian clock can coordinate xenobiotics detoxification in the mouse liver to cycles of metabolic needs , ensuring proper detoxification during the nocturnal phase when mice are feeding and thus ingesting the majority of xenobiotics . This hypothesis is independently supported by recent metabolomics data [10] , which shows that the level of toxic metabolites in the liver cycle in accordance with the protein rhythms characterized here . Understanding abundance and activity cycles of detoxifying enzymes is an essential prerequisite for the determination of temporal variations of therapeutic responses and associated toxic effects both ultimately crucial for proper chronotherapy . Another prominent interaction network consists of extracellular proteins that are synthesized in the liver ( Figure 6 ) . Some studies have reported circadian variations in plasma levels of hemostatic factors that seemed to be preceded by liver oscillations of their respective mRNAs [39]–[41] . However it is not clear how circadian regulation of hepatic metabolism contributes to hemostasis . The fact that plasma components synthesized in the liver show hepatic protein cycles indicates that the daily oscillations of essential hemostatic components in the plasma may reflect , at least in part , hepatic protein rhythms . Supporting such observations , hemostatic variables have been shown to undergo circadian changes in humans [42] . For instance , predisposition towards clotting in the morning , due to increased levels of platelet aggregation and blood coagulation , has been associated with the higher incidence of myocardial infarctions . In contrast , fibrinolysis is enhanced in the evening concomitant with higher levels of thrombolytic factors [43]–[45] . An additional aspect of liver metabolism that seems to be shaped by circadian post-transcriptional control is the glucose and fatty acid metabolism . We see circadian rhythms of protein abundance in several essential enzymes of these pathways , many of them lacking temporal regulation at the level of transcription or showing protein phases almost in anti-phase to their cycling transcript ( Figure 6 ) . In addition to specific metabolic processes we found coordinated rhythms of abundance in liver proteins involved in more general cellular functions , such as protein folding , vesicle-mediated transport as well as DNA and RNA metabolism ( Figure 6 ) . Thus , an important node in our interaction network comprised a large number of chaperones oscillating with night phases , possibly indicating higher demand for protein folding or quality control at this time of day ( Figure S3E and Figure 6 ) . Similarly , many key components of vesicle trafficking oscillate in abundance in the mouse live with synchronized phases at night , unlike their transcripts ( Figure 6 ) . This is the case of several RAB GTPases such as RAB1 , an essential factor for ER-Golgi transport [46] which transcript is arrhythmic , as well as for RAB10 and RAB14 , both involved in the endocytic pathway and the late endosome-associated RAB7 protein ( Figure S4A , S4B and S4C ) . Protein rhythms with peak of abundance during the night were also observed for the small GTPases SAR1 and the ADP-ribosylation factor 5 , ARF5 ( Figure S4B and S4C ) . While SAR1 controls the association of COPII with ER membranes [47] , the conserved GTPase ARF5 associated with coatomer , constituting the minimal cytosolic machinery leading to COPI vesicle formation from Golgi membranes [48] . This data indicates that key proteins involved in both ER and Golgi vesicle formation are under circadian regulation . Moreover , rhythms of protein abundance can be found in the epsilon subunit of the coatomer , COPE ( Figure S4B ) , a coating complex crucial for intra-Golgi trafficking , retrograde Golgi-to-ER transport of dilysine-tagged proteins as well as for the processing , activity and endocytic recycling of LDL receptor ( LDLR ) [49] . Therefore rhythms of COPE and its associated GTPase ARF5 could determine cycles of recycling for essential hepatic receptors as LDLR as well as the liver specific C-type lectin asialoglycoprotein receptors ASGPR1 and ASGPR2 ( all rhythmic ) ensuring proper expression during the night when the liver receives most of the metabolic signals in mice . The liver is a key player in the regulation of cholesterol levels . It synthesizes cholesterol for export to other cells and removes cholesterol from the circulation by converting it to bile salts and excreting it into the bile . Additionally , the liver produces the various lipoproteins involved in transporting cholesterol and lipids throughout the body . While total cholesterol in plasma does not seem to oscillate daily , high and low density lipoprotein cholesterol ( HLD and LDL ) , show circadian rhythms in plasma with a through at the onset of the dark phase [50] , [51] . LDL is the major transporter of cholesterol in plasma and in humans proper LDLR-mediated hepatic cholesterol removal plays a crucial role in atherosclerosis prevention . Ldlr gene expression is reported to be circadian in rat liver [50] and in a human hepatocarcinoma cell line via SREBP as well as CLOCK/BMAL1 direct promoter activation [51] . Our study shows for the first time that the LDLR also exhibits daily cycles of protein abundance in the mouse liver with at least two-fold higher levels in the middle of the night ( Figure S4D ) . Interestingly the peak of the mouse hepatic LDLR correlates with lower LDL plasma levels , similar to what has been reported at the transcript level in rat livers [50] . The ABC transporter ABCA1 is crucial for maintaining plasma HDL levels due to its essential role in assembling cholesterol , phospholipids and APOA1 into HDL . It is also rhythmic in the mouse liver with maximum presence during the night ( Figure S4D ) correlating with high levels of lipids in plasma [52] . Two major lipoproteins APOA1 and APOOL also oscillated in abundance in the mouse liver ( Figure S4E ) while lack rhythms at the transcripts level . Their daytime peaks of expression are though in anti-phase to the reported peak of APOB in plasma [52] . In addition to dietary intake the other source of cholesterol is de novo synthesis in hepatocytes which is under negative feedback regulation: increased cholesterol in the cell decreases the expression and activity of HMG-CoA reductase ( HMGR ) , as well as the expression of the lanosterol 14 -demethylase , Cyp51 , both essential enzymes in cholesterol biosynthesis and intermediate metabolites [53] . Although we did not obtained quantitative values for HMGR our analysis identified circadian cycles of protein abundance in the mouse liver for several key enzymes involved in cholesterol and bile acid synthesis such as HMG-CoA synthase 1 ( HMGCS1 ) , isopentenyl-diphosphate delta-isomerase 1 ( IDI1 ) , CYP51 , CYP7A1 and CYP8B1 ( Figure 6 and S4F ) . The oscillation of CYP7A1 is concomitant with the rhythm of its mRNA , however , we observed relative long time delays between the peaks of abundance for the other proteins compared to their respective transcript ( Table S4 ) . In particular , CYP51 shows maximum levels at the onset of the night completely in anti-phase to its transcript . Together our data indicates that hepatic circadian control of cholesterol homeostasis and bile acids biosynthesis is not only driven at the level of transcription [9] but additionally defined post-transcriptionally . Moreover , circadian oscillations in the levels of these enzymes , most of them localized in the ER membrane , could be linked to the described circadian dilation of the ER in hepatocytes [54] which is an indication of ER stress . The ER responds to the stress by activating the unfolded protein response ( UPR ) to reduce the accumulation of unfolded proteins . Concordant with this the circadian proteome has an overrepresentation of proteins with nocturnal phases involved in the protein processing in ER ( Figure S2B and S2D ) . Moreover , it has recently been established that there is a connection between the metabolite-induced activation of the UPR , hepatic transformation of metabolites and the circadian clock controlled feeding behavior and all of these are essential for proper liver metabolism [55] . Another large node in our interaction network is comprised of cycling proteins involved in DNA and RNA metabolism ( Figure 6 ) . Among them many essential factors of the protein translation machinery oscillated in their abundance . For example the translation initiation factors EIF1 , E1F4A2 , EF4G1 and EIF5 showed concomitant rhythmic profiles of protein abundance across the two analyzed cycles ( Figure S5A ) . Furthermore , we observed cycles of abundance of two indispensable components of protein translation elongation , EEF2 and EEF1A1 , in phase with the translation initiation elements ( Figure S5B ) . Our data thus suggests rhythms of translation by means of protein abundance of indispensable pathway components . In support of this hypothesis , a study that appeared after our analysis was finished showed that the circadian clock influences the temporal translation of a group of mRNAs by regulating the expression and activation of essential translation factors [15] . In particular , we observed that the cycles of abundances of these factors with peaks at the middle of the night , between CT19 and CT20 ( Table S2 ) , strongly correlate with the time of the day when 70% of the circadian translationally regulated genes are found in the polysomal fraction [15] . Furthermore , most of the mRNAs temporal translated during the night are involved in ribosome biogenesis correlating with our phase enrichment results for cycling ribosome proteins ( Figure S2C and S2D ) . Taken together , our work highlights the importance of circadian post-transcriptional mechanisms in shaping the phase of daily protein oscillations in the liver thus determining cycles of metabolism and physiology . Although the overall role of these mechanisms has been remained elusive , recent studies have emphasized the contribution of temporal post-transcriptional regulation on the circadian transcriptome [16] , [18] , [32] . Only up to approximately 30% of cycling mRNAs also showed rhythms in transcription , implying that post-transcriptional regulation largely defines the oscillating mRNA pool . A potential mechanism for clock-regulated post-transcriptional regulation of mRNA is reported by Schibler and colleagues , showing a temperature dependent cycling of cold–inducible RNA binding protein ( CIRP ) binding to and regulating the amplitude of transcripts from several core circadian components , including Clock [18] . By assaying protein rhythms , we move a step further along the gene expression program . We conclude that although around 80% of the oscillating proteins are associated with rhythmic transcripts the phases of many of them are uniquely tuned post-transcriptionally , suggesting a temporal mechanistic heterogeneity in this molecular process . This study demonstrates that the mouse liver proteome is extensively regulated by the circadian clock , with about 6% of proteins in our dataset significantly oscillating with peaks at a variety of phases . The distribution of phases emphasizes the complexity of circadian post-transcriptional mechanisms . Protein oscillations ultimately govern circadian rhythms of cellular and metabolic processes essential for the fitness of the organism . Our findings point to clock regulation of many more individual proteins and entire pathways , elucidating new networks that may be conferring previously uncharacterized rhythms in metabolism and physiology . In addition to protein cycles of abundance , post-translational modifications are known to have pivotal roles in the clock molecular machinery . The same proteomics technologies employed here can also be used to quantify post-translational modifications and thereby investigate to what extent and how they also drive global circadian patterns .
All mice were bred and maintained in the animal facility of the Max Planck Institute of Biochemistry according to institutional guidelines and all animal experiments were approved by the government agencies of Oberbayern . Eight-week-old C57BL/6 mice were house in light-tight boxes with free access to food and water and entrained to a 12–12 h light-dark schedule for ten days before being transfer to complete darkness . After one day in constant darkness , mice were sacrificed at 3 h intervals over two days . Prior to liver excision , mice were perfused with ice-cold PBS to remove blood content . Livers were then quickly frozen in liquid nitrogen followed by storage at −80°C . SILAC mice [23] were kept in the same conditions; two animals were sacrificed in anti-phase at CT3 and CT15 , respectively , and livers excised as described above . Protein extracts from mouse livers were obtained as previously described [56] , [57] . Briefly , homogenization of 1 mg of liver was done in 1 ml of 0 . 1 mM Tris-HCl pH 7 . 6 supplemented with complete protease and phosphatase inhibitor cocktails ( Roche ) using an Ultra Turbax blender ( IKA ) at maximum speed at 4°C for 30–60 seconds . Sodium dodecyl sulfate ( SDS ) and dithiothreitol ( DTT ) were added to the homogenates to a final concentration of 4% and 0 . 1 mM , respectively , followed by brief sonication to reduce viscosity . After 5 min incubation at 95°C the mixture was then cleared by centrifugation at 16 , 000× g at room temperature for 10 min . Protein content was determined by measurements of tryptophan fluorescence as previously described [56] . Sixteen protein extract pools , corresponding to the samples collected at different circadian times , were obtained by mixing equal amounts of protein liver extracts from each of the four mice sacrificed at any given time point . Similarly the SILAC protein liver mix was obtained by adding equal amounts of the protein extracts obtained from the two SILAC liver samples collected in anti-phase . For sample preparation we used 100 µg of protein extract from each circadian time pool mixed with 100 µg of SILAC protein pool . The protein mixes were concentrated in 30 k Microcon filtration devices ( Millipore ) to a final volume of 30 µl and then processed by the FASP procedure [57] . Briefly , the samples were mixed with 0 . 2 ml of 8 M urea in 0 . 1 M Tris/HCl pH 8 . 5 ( UA ) , loaded into 30 k Microcon filtration devices ( Millipore ) and centrifuged at 14 , 000× g for 15 min . The concentrates were diluted in the devices with 0 . 2 ml of UA solution and centrifuged again . After centrifugation the concentrates were mixed with 0 . 1 ml of 50 mM iodoacetamide in UA solution and incubated in the dark at room temperature for 30 min . After centrifugation for 15 min the concentrate was diluted with 0 . 2 ml UA solution and concentrated again by centrifugation . This step was repeated twice . Next , the concentrate was diluted with 0 . 1 ml of 40 mM NaHCO3 and concentrated again twice . Subsequently , 2 µg of Lysyl Endopeptidase ( Wako Chemicals ) in 40 µl of 40 mM NaHCO3 was added to the filter and the samples were incubated at room temperature overnight . The peptides were collected by centrifugation of the filter followed by two additional 30 µl washes with 40 mM NaHCO3 . The concentration of peptides was determined by UV-spectrometry using an extinction coefficient of 1 . 1 for 0 . 1% ( g/l ) solution at 280 nm . Peptides were dissolved in 200 µL in Britton & Robinson buffer composed of 20 mM CH3COOH , 20 mM H3PO4 , and 20 mM H3BO3 , and NaOH , pH 11 . The peptides were separated by a pipette-based anion exchanger method [57] . Briefly , the pipette based column was assemble by stacking 6 layers of a 3M Empore Anion Exchange disk ( Varian , 1214-5012 ) into a 200 µl micropipette tip . For column equilibration and elution of fractions Britton & Robinson buffer titrated with NaOH to the desired pH was used . Peptides were loaded at pH 11 and fractions were subsequently eluted with buffer solutions of pH 8 , 6 , 5 , 4 , and 3 , respectively . All mass spectrometric ( MS ) experiments were performed on a nanoflow HPLC system ( Proxeon Biosystems , now Thermo Fisher Scientific ) connected to a hybrid LTQ-Orbitrap ( Thermo Fisher Scientific , Bremen , Germany ) , equipped with a nanoelectrospray ion source ( Proxeon Biosystems , now Thermo Fisher Scientific ) . Peptide mixtures were separated by reversed phase chromatography using in-house-made C18 microcolumns with a diameter of 75 µm packed with ReproSil-Pur C18-AQ 3-µm resin ( Dr . Maisch GmbH , Ammerbuch-Entringen , Germany ) in 4 hours LC gradient from 3% to 75% acetonitrile in 0 . 5% acetic acid at a flow rate of 200 nl/min and directly electrosprayed into the mass spectrometer . The LTQ-Orbitrap was operated in the positive mode to simultaneously measure full scan MS spectra ( from m/z 300–1650 ) in the Orbitrap analyzer at resolution R = 60 000 following isolation and fragmentation of the ten most intense ions in the LTQ part by collision-induced dissociation . Raw MS files were processed with MaxQuant ( version . 1 . 1 . 1 . 9 ) , a freely available software suite . Peak list files were searched by the ANDROMEDA a search engine , incorporated into the MaxQuant framework [58] , against the IPI-mouse ( version 3 . 68 ) containing both forward and reversed protein sequences . Initial maximum precursor and fragment mass deviations were set to 7 ppm and 0 . 5 Da , respectively , but MaxQuant achieved sub-ppm mass accuracy for the majority of peptide precursors . The search included variable modifications for oxidation of methionine , protein N-terminal acetylation and carbamidomethylation as fixed modification . Peptides with at least six amino acids were considered for identification specifying as enzyme LysC allowing N-terminal cleavage to proline . The false discovery rate , determined by searching a reverse database , was set at 0 . 01 for both peptides and proteins . Identification across different replicates and adjacent fractions was achieved by enabling matching between runs option in MaxQuant within a time window of 2 minutes . Quantification of SILAC pairs was performed by MaxQuant with standard settings using a minimum ratio count of 2 . All bioinformatic analyses were performed with the Perseus software ( http://www . perseus-framework . org/ ) . To determine the subset of cycling proteins , each protein expression profile is fitted to a cosine with a fixed period of 23 . 6 h and the amplitude and phase as free parameters . Profiles are ranked by their variance ratio . This is the part of the variance explained by the fit divided by the contribution to the variance that is not accounted for by the fit . Based on this ranking we determine a permutation-based false discovery rate by repeating the same procedure 1 , 000 times on the same profiles but with scrambled time labels , except for the technical replicates that were preserved . This permutation based procedure is similar to the one applied to FDR calculations for differential expression analysis in [59] . Hierarchical clustering was done in a phase-preserving way by restricting the order of elements to the one determined by the output of the cosine model-based fitting . During the growth of the tree in hierarchical clustering , only those links were permitted that conserve this order . While obviously the order of the terminal branches is not an outcome of the algorithm , the cluster structure is still a non-trivial result of the clustering . A standard PCA analysis was performed in the Perseus software . The expression data matrix has protein groups as rows and samples as columns and contains logarithms of ratios . Missing values were imputed by drawing random numbers from a normal distribution to simulate signals from low abundant proteins . The width parameter of this normal distribution was chosen as 0 . 3 of the standard deviation of all measured values and the center was shifted towards low abundance by 1 . 8 times this standard deviation . These parameters were empirically determined to result in good performance over many different proteomics data sets . The row means were subtracted from the matrix . Then the PCA was performed by singular value decomposition . Total cell protein extracts were prepared as mentioned above and 50 µg of protein was used for western blotting performed according to standard protocols . Antibodies used were PER21A from Alpha Diagnostics International , RAB1 from Sigma-Aldrich , SARA1; GAPDH and RAB10 from Cell Signaling . The density of the bands obtained in the western blots was calculated with the freely available gel analyzer program ImageJ . RNA extraction from liver was done using the RNeasy kit according to manufacturer's protocol ( Qiagen ) . cDNA was synthesized from 2 µg of liver total RNA using First Strand cDNA Synthesis kit following the supplier's instruction ( Fermentas ) . Quantitative PCR reactions were done by amplifying ten per cent of the cDNA with Sybr Green master mix ( Applied Bioscience ) on a CFX96 Real Time System ( BioRad ) . Mean values were calculated from triplicate PCR assays for each sample and normalized to those obtained for Gadph transcript . Interaction network analysis of the cycling proteome was performed with the STRING search tool ( version 9 . 0 ) using medium to high confidence ( 0 . 5–0 . 7 ) and with co-expression and experiments as active prediction methods . Using these parameters we obtained interaction scores for approximately 70% of the liver rhythmic proteins . Data visualization was done with Cytoscape 2 . 8 . 2 where we then combined interaction scores with phases of protein abundance . Data availability: The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium ( http://proteomecentral . proteomexchange . org ) via the PRIDE partner repository [60] with the dataset identifier PXD000601 . | The circadian clock is an evolutionary system that allows organisms to anticipate and thus adapt to daily changes in the environment . In mammals , the circadian clock is found in virtually every tissue regulating rhythms of metabolism and physiology . While a lot of studies have focused in how circadian clocks regulate gene expression little is known about daily control of protein abundance . Here we applied state of the art mass spectrometry in combination with quantitative proteomics to investigate global circadian oscillations of the proteome in the mouse liver . We found that approximately 6% of the liver proteins are cycling daily and interestingly the majority of these oscillations diverge from the behavior of their transcripts . Our data indicates that post-transcriptional mechanisms play an essential role in shaping the phase of rhythmic proteins downstream of transcription regulation to ultimately drive rhythms of metabolism . Moreover , the contribution of post-transcriptional regulation seems to differ among distinct metabolic pathways . Overall we not only found circadian oscillations in the abundance of proteins involved in liver specific metabolic pathways but also in essential cellular processes . | [
"Abstract",
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"proteomics",
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] | 2014 | In-Vivo Quantitative Proteomics Reveals a Key Contribution of Post-Transcriptional Mechanisms to the Circadian Regulation of Liver Metabolism |
Drosophila bithorax complex ( BX-C ) is one of the best model systems for studying the role of boundaries ( insulators ) in gene regulation . Expression of three homeotic genes , Ubx , abd-A , and Abd-B , is orchestrated by nine parasegment-specific regulatory domains . These domains are flanked by boundary elements , which function to block crosstalk between adjacent domains , ensuring that they can act autonomously . Paradoxically , seven of the BX-C regulatory domains are separated from their gene target by at least one boundary , and must “jump over” the intervening boundaries . To understand the jumping mechanism , the Mcp boundary was replaced with Fab-7 and Fab-8 . Mcp is located between the iab-4 and iab-5 domains , and defines the border between the set of regulatory domains controlling abd-A and Abd-B . When Mcp is replaced by Fab-7 or Fab-8 , they direct the iab-4 domain ( which regulates abd-A ) to inappropriately activate Abd-B in abdominal segment A4 . For the Fab-8 replacement , ectopic induction was only observed when it was inserted in the same orientation as the endogenous Fab-8 boundary . A similar orientation dependence for bypass activity was observed when Fab-7 was replaced by Fab-8 . Thus , boundaries perform two opposite functions in the context of BX-C–they block crosstalk between neighboring regulatory domains , but at the same time actively facilitate long distance communication between the regulatory domains and their respective target genes .
The three homeotic ( HOX ) genes in the Drosophila Bithorax complex ( BX-C ) , Ultrabithorax ( Ubx ) , abdominal-A ( abd-A ) and Abdominal-B ( Abd-B ) , are responsible for specifying cell identity in parasegments ( PS ) 5–14 , which form the posterior half of the thorax and all of the abdominal segments of the adult fly [1–3] . Parasegment identity is determined by the precise expression pattern of the relevant HOX gene and this depends upon a large cis-regulatory region that spans 300 kb and is subdivided into nine PS domains that are aligned in the same order as the body segments in which they operate [4–6] . Ubx expression in PS5 and PS6 is directed by abx/bx and bxd/pbx , while abd-A expression in PS7 , PS8 , and PS9 is controlled by iab-2 , iab-3 , and iab-4 [7–10] . Abd-B is regulated by four domains , iab-5 , iab-6 , iab-7 and iab-8 , which control expression in PS10 , PS11 , PS12 and PS13 respectively [6 , 11 , 12] . Each regulatory domain contains an initiator element , a set of tissue-specific enhancers and Polycomb Response Elements ( PREs ) and is flanked by boundary/insulator elements ( Fig 1A;[13] . BX-C regulation is divided into two phases , initiation and maintenance [14 , 15] . During the initiation phase , a combination of gap and pair-rule proteins interact with initiation elements in each regulatory domain , setting the domain in the on or off state . In PS10 , for example , the iab-5 domain , which regulates Abd-B , is activated by its initiator element , while the more distal Abd-B domains , iab-6 to iab-8 are set in the off state ( Fig 1B ) . In PS11 , the iab-6 initiator activates the domain , while the adjacent iab-7 and iab-8 domains are set in the off state . Once the gap and pair-rule gene proteins disappear during gastrulation , the on and off states of the regulatory domains are maintained by Trithorax ( Trx ) and Polycomb ( PcG ) group proteins , respectively [16 , 17] . In order to select and then maintain their activity states independent of outside influence , adjacent regulatory domains are separated from each other by boundary elements or insulators [18–24] . Mutations that impair boundary function permit crosstalk between positive and negative regulatory elements in adjacent domains and this leads to the misspecification of parasegment identity . This has been observed for deletions that remove five of the BX-C boundaries ( Front-ultraabdominal ( Fub ) , Miscadestral pigmentation ( Mcp ) , Frontadominal-6 ( Fab-6 ) , Frontadominal-7 ( Fab-7 ) , and Frontadominal-8 ( Fab-8 ) ) [6 , 17 , 19 , 20 , 22 , 23 , 25 , 26] . While these findings indicate that boundaries are needed to ensure the functional autonomy of the regulatory domains , their presence also poses a paradox [27 , 28] . Seven of the nine BX-C regulatory domains are separated from their target HOX gene by at least one intervening boundary element . For example , the iab-6 regulatory domain must “jump over” or “bypass” Fab-7 and Fab-8 in order to interact with the Abd-B promoter ( Fig 1A ) . That the blocking function of boundaries could pose a significant problem has been demonstrated by experiments in which Fab-7 is replaced by heterologous elements such as scs , gypsy or multimerized binding sites for the architectural proteins dCTCF , Pita or Su ( Hw ) [25 , 29–31] . In these replacements , the heterologous boundary blocked crosstalk between iab-6 and iab-7 just like the endogenous boundary , Fab-7 . However , the boundaries were not permissive for bypass , preventing iab-6 from regulating Abd-B . A number of models have been proposed to account for this paradox . One is that BX-C boundaries must have unique properties that distinguish them from generic fly boundaries . Since they function to block crosstalk between enhancers and silencers in adjacent domains , an appealing idea is that they would be permissive for enhancer/silencer interactions with promoters ( Fig 1B ) . However , several findings argue against this notion . For one , BX-C boundaries resemble those elsewhere in the genome in that they contain binding sites for architectural proteins such as Pita , dCTCF , and Su ( Hw ) [23 , 31–35] . Consistent with their utilization of these generic architectural proteins , when placed between enhancers ( or silencers ) and a reporter gene , BX-C boundaries block regulatory interactions just like boundaries from elsewhere in the genome [19 , 36–42] . Similarly , there is no indication in these transgene assays that the blocking activity of BX-C boundaries are subject to parasegmental regulation . Also arguing against the idea that BX-C boundaries have unique properties , the Mcp boundary , which is located between iab-4 and iab-5 , is unable to replace Fab-7 [31] . Like the heterologous boundaries , it blocks crosstalk , but it is not permissive for bypass . A second model is that there are special sequences , called promoter targeting sequence ( PTS ) , located in each regulatory domain that actively mediate bypass [43–45] . While the PTS sequences that have been identified in iab-6 and iab-7 enable enhancers to “jump over” an intervening boundary in transgene assays , they do not have a required function in the context of BX-C and are completely dispensable for Abd-B regulation [18 , 30] . A third model ( Fig 1C ) is suggested by transgene “insulator bypass” assays [46 , 47] . In one version of this assay , two boundaries instead of one are placed in between an enhancer and the reporter . When the two boundaries pair with each other , the enhancer is brought in close proximity to the reporter , thereby activating rather than blocking expression . Consistent with a possible role in BX-C bypass , these pairing interactions can occur over large distances and even skip over many intervening boundaries [48–51] . The transgene assays point to two important features of boundary pairing interactions that are likely to be relevant in BX-C . First , pairing interactions are specific . For this reason boundaries must be properly matched with their neighborhood in order to function appropriately . A requirement for matching is illustrated in transgene bypass experiments in which multimerized binding sites for specific architectural proteins are paired with themselves or with each other[52] . Bypass was observed when multimerized dCTCF , Zw5 or Su ( Hw ) binding sites were paired with themselves; however , heterologous combinations ( e . g . dCTCF sites with Su ( Hw ) sites ) did not support bypass . A second feature is that pairing interactions between boundaries are typically orientation dependent For example , scs pairs with itself head-to-head , not head-to-tail [52] . If both blocking and bypass activities are intrinsic properties of fly boundaries then the BX-C boundaries themselves may facilitate contacts between the regulatory domains and their target genes ( Fig 1C ) . Moreover , the fact that both blocking and bypass activity are non-autonomous ( in that they depend on partner pairing ) could potentially explain why heterologous Fab-7 replacements like gypsy and Mcp behave anomalously while Fab-8 functions appropriately . Several observations fit with this idea . There is an extensive region upstream of the Abd-B promoter that has been implicated in tethering the Abd-B regulatory domains to the promoter [53–56] and this region could play an important role in mediating bypass by boundaries associated with the distal Abd-B regulatory domains ( iab-5 , iab-6 , iab-7 ) . Included in this region is a promoter tethering element ( PTE ) that facilitates interactions between iab enhancers and the Abd-B promoter in transgene assays [57 , 58] . Just beyond the PTE is a boundary-like element , AB-I . In transgene assays AB-I mediates bypass when combined with either Fab-7 or Fab-8 . In contrast , a combination between AB-I and Mcp fails to support bypass [59 , 60] . The ability of both Fab-7 and Fab-8 to pair with AB-I is recapitulated in Fab-7 replacement experiments . Unlike Mcp , Fab-8 has both blocking and bypass activity when inserted in place of Fab-7 [30] . Moreover , its’ bypass but not blocking activity is orientation-dependent . When inserted in the same orientation as the endogenous Fab-8 boundary , it mediates blocking and bypass , while it does not support bypass when inserted in the opposite orientation . In the studies reported here we have tested this model by replacing the endogenous Mcp boundary with heterologous boundaries . Mcp defines the border between the set of regulatory domains that control abd-A and those that control Abd-B expression ( Fig 1A ) . Unlike the boundaries that are within the Abd-B regulatory domain ( e . g . Fab-7 or Fab-8 ) , Mcp is not located between a regulatory domain and its target gene . Instead , it defines the boundary between regulatory domains that target abd-A and those that target Abd-B . For this reason , we expected that it does not need bypass activity . Consistent with this expectation , we find that multimerized dCTCF binding sites fully substitute for Mcp . A different result is obtained for the Abd-B-associated boundaries , Fab-7 and Fab-8 . Both boundaries are ( for the most part ) able to block crosstalk between the abd-A regulatory domain iab-4 , which specifies A4 ( PS9 ) and the Abd-B regulatory domain iab-5 , which specifies A5 ( PS10 ) . Their blocking activity is orientation independent . However , in spite of blocking crosstalk , these replacements still inappropriately induce Abd-B expression in A4 ( PS9 ) , causing the misspecification of this segment . For the Fab-7 replacements , this occurred in both orientations , while for the Fab-8 replacement ectopic induction was only observed when it was inserted in the same orientation as the endogenous Fab-8 boundary . We present evidence showing that the boundary replacements activate the Abd-B gene in A4 ( PS9 ) by inappropriately targeting the iab-4 domain to the Abd-B promoter . In addition to altering the specification of A4 ( PS9 ) , the Fab-7 replacements induce novel transformations of A5 and A6 . These findings indicate that when Fab-7 is inserted into the BX-C in place of Mcp , it perturbs Abd-B regulation in several segments besides PS9 .
The Mcp boundary is defined by 340 bp core sequence that has enhancer blocking activity in transgene assays [36] and blocks crosstalk between iab-6 and iab-7 when substituted for Fab-7 [31] . Located just distal to the boundary is a PRE that negatively regulates the activity of the iab-5 enhancers [61] . We used CRISPR to delete a 789 bp DNA segment including the Mcp boundary and the PRE and replace it with an eGFP reporter flanked by two attP sites ( McpattP ) ( S1 Fig ) . The presence of two attP sites in opposite orientation allows integration of different DNA fragments by recombination mediated cassette exchange ( RMCE; [62] ) using the phiC31 integration system [63] . The Mcp boundary marks the division between the set of regulatory domains that control the abd-A and Abd-B genes ( Fig 1A ) . The iab-4 domain is just proximal to Mcp , and it directs abd-A expression in PS9 . The iab-5 domain is on the distal side and it regulates Abd-B in PS10 . A boundary in this position would be needed to block crosstalk between iab-4 and iab-5; however , neither of these domains would require the intervening boundary to have bypass activity . On the proximal side , iab-4 must bypass the putative Fab-3 and Fab-4 boundaries in order to activate the abd-A promoter , while on the distal side , iab-5 must bypass Fab-6 , Fab-7 and Fab-8 in order to activate Abd-B . If this expectation is correct , a generic boundary that has blocking activity but is unable to direct iab-4 to the abd-A promoter or iab-5 to the Abd-B promoter should be able to substitute for Mcp . To test this prediction ( Fig 2 ) , we introduced either the iab-5 PRE itself ( McpPRE ) or the PRE in combination with four dCTCF sites ( McpCTCF ) . In Fab-7 replacement experiments four dCTCF sites in combination with the iab-7 PRE blocked crosstalk between the iab-6 and iab-7 domains , but did not allow the iab-6 domain to regulate Abd-B expression in PS11 [30] . Abd-B is a master regulator of pigmentation in the male abdominal A5 and A6 segments and it controls the expression of the yellow and tan genes which are involved in melanin synthesis [64–66] . In flies carrying the null y1 allele , the tan gene is still expressed and the pigmentation in A5 and A6 is light brown-yellow not black [66 , 67] . In order to be able to recover recombinants and also to monitor the blocking activity of the replacement sequence and the on/off state of the iab-5 domain , we used a y1 genetic background and included a minimal yellow ( mini-y ) reporter in our Mcp replacement construct ( S1 Fig ) . The mini-y reporter consists of the cDNA and the 340 bp yellow promoter and lacks the wing , body and bristle enhancers of the endogenous yellow gene . As a result , activity of the mini-y reporter depends upon proximity to nearby enhancers . Expression of the mini-y reporter was examined in the y1 background . Based on previous studies [5 , 21 , 68] , the expression of this reporter should be determined by the activity state of the iab-5 domain . When iab-5 is shutoff by Pc-G dependent silencing in PS9 and more anterior parasegments , the mini-y reporter will also be silenced . When iab-5 is turned on in PS10 and more posterior parasegments , the mini-y reporter will be expressed . This parasegment-specific regulation of the reporter activity will be reflected in the segmental pattern of black melanin pigmentation in the adult cuticle . In replacements in which blocking activity is compromised , mini-y will be expressed in PS9 and in adults the A4 tergite will be black , just like the A5 and A6 tergites . In contrast , in replacements that have blocking activity mini-y will be silenced in PS9 , but active in PS10 and PS11 . In this case , A5 and A6 will have black pigmentation , while the stripe of pigmentation along the posterior edge of the A4 tergite will be light yellow brown , as only the tan gene will contribute to pigmentation in this segment . When we replaced the Mcp deletion by the iab-5 PRE alone ( McpPRE ) the mini-y reporter was active not only in A5 ( PS10 ) and more posterior segments , but also in A4 ( PS9 ) . As shown in Fig 2 , the pigmentation in A4 is black like that in A5 indicating that the reporter is expressed in both segments ( Fig 2 ) . This finding shows that , similar to classical Mcp deletions , the McpPRE replacement does not have blocking activity . In these Mcp deletions iab-5 is ectopically activated in PS9 by the iab-4 initiator . It then drives Abd-B expression in PS9 resulting in a gain-of-function ( GOF ) transformation of parasegment identity from PS9 to PS10 . We used two approaches to test whether this was true for the McpPRE replacement . In the first , we excised the mini-y reporter and introduced an X chromosome with a wild type yellow ( y+ ) gene . Since Abd-B directly regulates y+ expression in the abdomen [65 , 67] , a transformation of PS9 into PS10 should be accompanied by a PS10-like pattern of pigmentation . Fig 2 shows that this is the case . We also examined the pattern of Abd-B protein expression in the embryonic CNS . In wild type embryos Abd-B is not expressed is PS9 , while it is expressed at low levels in PS10 . As shown in Fig 3A , similar levels of Abd-B protein are detected in PS9 and PS10 in the McpPRE replacement . As predicted , a quite different result is obtained when we combined the iab-5 PRE with multimerized dCTCF sites . Expression of the mini-y reporter in the McpCTCF replacement was restricted to A5 ( PS10 ) and A6 ( PS11 ) as would be expected if the multimerized dCTCF sites block crosstalk between the iab-4 and iab-5 domains so that iab-5 is silenced by Pc-G factors in PS9 ( Fig 2 ) . The same pigmentation pattern is observed for the endogenous yellow in the Δmini-y derivative of McpCTCF , indicating that Abd-B is not turned on ectopically in PS9 . This conclusion is confirmed by antibody staining experiments ( Fig 3A ) . Thus , unlike replacements of Fab-7 , a generic boundary can fully substitute for Mcp . We next tested whether the Fab-7 boundary can substitute for Mcp . The Fab-7 region consists of a minor ( HS* ) and three major ( HS1 , HS2 and HS3 ) nuclease hypersensitive sequences [17 , 21 , 22 , 41 , 42] . Unlike Mcp or other known or suspected boundaries in BX-C , dCTCF does not bind to Fab-7 [33 , 69] . Instead , Fab-7 boundary function depends upon two BEN domain protein complexes , Elba and Insensitive , the C2H2 zinc finger protein Pita , and a large multiprotein complex , called the LBC [31 , 70–73] . In addition to a boundary function , the HS3 sequence can also function as a PRE ( iab-7 PRE; [73 , 74] . In previous studies , we found that a combination of HS1+HS2+HS3 can functionally substitute for the complete Fab-7 boundary in vivo and we used this sequence ( named for simplicity F7 ) for the Mcp replacements ( Fig 4 ) . Although Fab-7 has only limited orientation dependence in its endogenous context [30 , 73] , we inserted the HS1+HS2+HS3 sequence in both the forward ( same as endogenous Fab-7 ) and reverse orientations in the Mcp replacement platform as indicated in Fig 4 . The phenotypic effects of the Fab-7 replacement inserted in the forward orientation , McpF7 , are considered first . Like the McpCTCF replacement , the mini-y reporter is turned on in A5 ( PS10 ) and A6 ( PS11 ) in McpF7 males , and the tergites in both of these segments are black . However , McpF7 differs in two respects from McpCTCF . First , there are one or two small patches of darkly pigmented cuticle in the A4 tergite ( marked by the arrow ) . These patches are variable and appear to be clonal in origin . This finding indicates that the blocking activity of McpF7 is incomplete , and that the mini-y reporter and thus the iab-5 domain is ectopically activated by the iab-4 domain in a small number of PS9 cells . Second , instead of a stripe of yellow-brown pigmentation along the posterior margin , nearly the entire A4 tergite is covered in yellow-brown pigmentation . This pattern of pigmentation is not observed in A4 in y1 males carrying the McpCTCF replacement and the mini-y reporter ( Fig 2 ) or for that matter in control wild type y1 males ( see Fig 4 ) . The presence of the yellow-brown pigmentation throughout most of the A4 tergite suggests that cells in this segment ( PS9 ) are not properly specified . This is the case . When the mini-y reporter was excised and replaced by the endogenous X-linked y+ gene , the A4 tergite has a black pigmentation like A5 and A6 ( Fig 4 ) . Since expression of the yellow gene is controlled by Abd-B , this observation indicates that Abd-B must be ectopically activated throughout A4 . Antibody staining experiments of the CNS in McpF7 embryos indicate that this inference is correct ( Fig 3B ) . A simple interpretation of these findings is that McpF7 is unable to block crosstalk between iab-4 and iab-5 and , as a result , iab-5 is ectopically activated in PS9 cells and inappropriately drives Abd-B expression . However , such an interpretation is inconsistent with the expression pattern of the mini-y reporter; it is only activated in small clones in the A4 tergite and not in the entire A4 tergite . By way of comparison , the dark black pigmentation generated by the reporter in McpPRE , which has no boundary activity , is clearly quite different from the yellow-brown pigmentation observed for the reporter in McpF7 . In this respect , McpF7 resembles McpCTCF in that the iab-5 domain must be shut off by Pc-G silencing in ( most ) PS9 cells . This would imply that the iab-4 regulatory domain ( or one of the other abd-A domains that is turned on in PS9 cells ) must be responsible for ectopically activating Abd-B expression in PS9 . Moreover , this would mean that the mechanism underlying the misspecification of A4 ( PS9 ) in the McpF7 replacement differs from that in McpPRE or Mcp1 where iab-5 is not properly silenced in PS9 cells . There are other abnormalities in McpF7 replacement indicating that it has complicated and novel effect on Abd-B expression . In wild type males , the A6 sternite has a banana shape and no bristles , while the A5 and A4 sternites resemble isosceles trapezoids and are covered with bristles . While the A4 and A5 sternites in McpF7 males still have bristles , they are split into two connected lobes that resemble the banana shape of the A6 sternite . These morphological abnormalities indicate that the Fab-7 replacement induces a weak GOF transformation of both A4 ( PS9 ) and A5 ( PS10 ) towards an A6 ( PS11 ) identity . This type of transformation is not observed in Mcp boundary deletions , nor it is observed in the McpPRE replacement . Further evidence of A4/A5→A6 transformation can be seen in the pattern of trichome hairs in the tergites . In wild type flies , the A4 and A5 tergites are covered with trichomes , while trichomes are only found along the anterior and ventral margins of the A6 tergite ( see darkfield image in Fig 4 ) . In the McpF7 replacement , there are large regions of the A4 and A5 tergite that are devoid of trichomes . There are even anomalies in A6: the band of trichomes along the anterior margin is absent . Similar alterations in cuticular phenotypes are observed in McpF7 females ( S2 Fig ) . These findings indicate that the normal regulation of Abd-B is disrupted in several parasegments when Mcp is replaced by the Fab-7 boundary . In its endogenous context , the functioning of Fab-7 is weakly orientation dependent . For this reason , we anticipated that the reverse Mcp replacement , McpF7R , would give a similar though milder spectrum of phenotypic effects . Fig 4 shows that this is the case . In y+ background , large regions of the A4 tergite have a black pigmentation like A5 and A6 . The ectopic activation appears to be weaker than in the McpF7 replacement as there are regions in A4 in which the endogenous yellow gene is not turned on . Also , and unlike McpF7 , there are no bald patches in the A4 trichomes , while the sternite appears to have a normal isosceles trapezoid shape . However , the novel transformations seen in McpF7 in the more posterior segments A5 ( PS10 ) and A6 ( PS11 ) are still evident . The A5 tergite is not completely covered with trichomes , while the trichomes along the anterior margin of A6 are absent . The A5 sternite is also misshapen . Thus , like McpF7 , introducing a reversed Fab-7 boundary in place of Mcp disrupts Abd-B regulation in PS9 and also in other parasegments . Since the pattern of mini-y expression in McpF7R indicates that the iab-5 domain is silenced in ( most ) PS9 cells , the iab-5 regulatory domain can’t be driving Abd-B expression in this parasegment . Instead , misexpression of Abd-B in PS9 is likely driven by the iab-4 domain . This possibility will be considered further below . In previous Fab-7 replacement experiments we found that a 337 bp fragment ( F8337 ) spanning the Fab-8 boundary nuclease hypersensitive site is sufficient to fully rescue a Fab-7 boundary deletion [30] . In the direct ( forward ) orientation this fragment not only blocks crosstalk but also supports bypass . However , when the orientation of the Fab-8 boundary is reversed , bypass activity is lost , while blocking is unaffected . Since F8337 appears to have full boundary function , we inserted this fragment in both orientations next to the iab-5 PRE in the Mcp deletion ( McpF8 and McpF8R ) . The effects of the Fab-8 replacement in the reverse orientation , McpF8R , will be considered first . Like the McpCTCF replacement , McpF8R blocks crosstalk between iab-4 and iab-5 and the mini-y reporter is off in A4 ( Fig 5 ) . The fact that mini-y is not expressed in PS9 also means that iab-5 is silenced as it should be in PS9 cells . After the deletion of the mini-y reporter and introducing a wild type y+ allele , the pigmentation in the adult male abdomen is equivalent to that in wild type flies . The morphological features of McpF8R tergites and sternites also resemble those in wild type flies or the McpCTCF replacement and there is no indication of the other abdominal transformations seen in the Fab-7 replacements . Consistent with the phenotype of the adult cuticle , the pattern of Abd-B expression in the embryonic CNS resembles wild type ( Fig 3B ) . Thus , the McpF8R replacement fully substitutes for the endogenous Mcp boundary . A different result is obtained when the F8337 sequence is inserted in its normal forward orientation . Like the reverse orientation McpF8R , McpF8 efficiently blocks crosstalk between iab-4 and iab-5 and the mini-y reporter is not activated in A4 ( PS9 ) . On the other hand , like the Fab-7 replacements ( McpF7 and McpF7R ) most of the A4 tergite is covered in a yellow brown pigmentation instead of the normal stripe of yellow brown pigmentation along the posterior margin of the tergite that is seen in y1 males . Moreover , when the reporter is excised and the y1 allele replaced by the wild type y+ gene , nearly the entire A4 tergite is black . Consistent with the induction of y+ expression in A4 , Abd-B is active in PS9 in the embryonic CNS ( Fig 3B ) . The GOF transformation of A4 ( PS9 ) →A5 ( PS10 ) is not the only anomaly in McpF8 flies . While there does not seem to be any misspecification of the tergite or sternites in A5 ( PS10 ) , the line of trichomes along the anterior margin of the A6 tergite is disrupted or absent altogether indicating that there are some abnormalities in the temporal and/or special pattern of Abd-B expression in PS11 . In the Fab-7 replacement experiments , the relative orientation of the Fab-8 boundary was thought to be important because it determined whether the chromatin loops formed between the replacement boundary and the AB-I element and/or the PTE sequence upstream of the Abd-B transcription start site were circle loops or stem loops [30 , 75] . In the forward orientation circle loops are expected to be formed and in this configuration , the downstream iab-5 regulatory domain is brought into close proximity with the Abd-B promoter . In the reverse orientation , iab-6 and iab-7 are predicted to form stem loops , and this configuration would tend to isolate the iab-5 regulatory domain from the Abd-B promoter . It seemed possible that a similar mechanism might be in play in the Fab-8 replacements of Mcp . In the forward orientation ( McpF8 ) , the iab-4 regulatory domain would be brought into close proximity to the Abd-B gene , activating its ectopic expression in A4 ( PS9 ) . In the opposite orientation , the spatial relationship between the iab-4 domain and the Abd-B promoter would not be conducive for activation . In this case , Abd-B would be off in A4 ( PS9 ) . A strong prediction of this model is that the inappropriate activation of Abd-B in PS9 in the McpF8 replacement should depend on a functional iab-4 domain . To test this prediction , we used CRISPR ( see S3 Fig ) to delete a 4 , 401 bp sequence ( iab-4Δ ) that spans the putative iab-4 initiation element in flies carrying the McpF8 replacement . The iab-4Δ sequence was selected based on the clustering of multiple binding sites for transcription factors controlling segmentation of the embryo . [76] . Fig 5 shows that the ectopic activation of y+ in A4 in McpF8 flies was eliminated by the iab-4Δ deletion . Moreover , Abd-B was not activated in A4 ( PS9 ) in the embryonic CNS of iab-4Δ McpF8 embryos ( Fig 3B ) . Interestingly , the loss of trichomes along the anterior margin of the A6 tergite in McpF8 also seemed to depend on a functional iab-4 domain . As can be seen in Fig 5 , the trichome pattern in the A6 tergite of iab-4Δ McpF8 flies resembled that of wild type .
Boundaries flanking the Abd-B regulatory domains must block crosstalk between adjacent regulatory domains but at the same time allow more distal domains to jump over one or more intervening boundaries and activate Abd-B expression . While several models have been advanced to account for these two paradoxical activities , replacement experiments argued that both must be intrinsic properties of the Abd-B boundaries . Thus Fab-7 and Fab-8 have blocking and bypass activities in Fab-7 replacement experiments , while heterologous boundaries including multimerized dCTCF sites and Mcp from BX-C do not . One idea is that Fab-7 and Fab-8 are simply “permissive” for bypass . They allow bypass to occur , while boundaries like multimerized dCTCF or Mcp are not permissive in the context of Fab-7 . Another is that they actively facilitate bypass by directing the distal Abd-B regulatory domains to the Abd-B promoter . Potentially consistent with an “active” mechanism that involves boundary pairing interactions , the bypass activity of Fab-8 and to a lesser extent Fab-7 is orientation dependent . In the studies reported here we have tested these two models further . For this purpose we used the Mcp boundary for in situ replacement experiments . Mcp defines the border between the regulatory domains that control expression of abd-A and Abd-B . In this location , it is required to block crosstalk between the flanking domains iab-4 and iab-5 , but it does not need to mediate bypass . In this respect , it differs from the boundaries that are located within the set of regulatory domains that control either abd-A or Abd-B , as these boundaries must have both activities . If bypass were simply passive , insertion of a “permissive” Fab-7 or Fab-8 boundary in either orientation in place of Mcp would be no different from insertion of a generic “non-permissive” boundary such as multimerized dCTCF sites . Assuming that Fab-7 and Fab-8 can block crosstalk out of context , they should fully substitute for Mcp . In contrast , if bypass in the normal context involves an active mechanism in which more distal regulatory domains are brought to the Abd-B promoter , then Fab-7 and Fab-8 replacements might also be able to bring iab-4 to the Abd-B promoter in a configuration that activates transcription . If they do so , then this process would be expected to show the same orientation dependence as is observed for bypass of the Abd-B regulatory domains in Fab-7 replacements . Consistent with the idea that a boundary located at the border between the domains that regulate abd-A and Abd-B need not have bypass activity , we found that multimerized binding sites for the dCTCF protein fully substitute for Mcp . Like the multimerized dCTCF sites , Fab-7 and Fab-8 are also able to block crosstalk between iab-4 and iab-5 . In the case of Fab-7 , its’ blocking activity is incomplete and there are small clones of cells in which the mini-y reporter is activated in A4 . In contrast , the blocking activity of Fab-8 is comparable to the multimerized dCTCF sites and the mini-y reporter is off throughout A4 . One plausible reason for this difference is that Mcp and the boundaries flanking Mcp ( Fab-4 and Fab-6 ) utilize dCTCF as does Fab-8 , while this architectural protein does not bind to Fab-7 [33] . Importantly , in spite of their normal ( or near normal ) ability to block crosstalk , both boundaries still perturb Abd-B regulation . In the case of Fab-8 , the misregulation of Abd-B is orientation dependent just like the bypass activity of this boundary when it is used to replace Fab-7 [30] . When inserted in the reverse orientation , Fab-8 behaves like multimerized dCTCF sites and it fully rescues the Mcp deletion . In contrast , when inserted in the forward orientation , Fab-8 induces the expression of Abd-B in A4 ( PS9 ) , and the misspecification of this parasegment . Unlike classical Mcp deletions or the McpPRE replacement described here , expression of the Abd-B gene in PS9 is driven by iab-4 , not iab-5 . This conclusion is supported by two lines of evidence . First , the mini-y reporter inserted in iab-5 is off in PS9 cells indicating that iab-5 is silenced by PcG factors as it should be in this parasegment . Second , the ectopic expression of Abd-B is eliminated when the iab-4 regulatory domain is inactivated . Our results , taken together with previous studies [30 , 59 , 60] , support a model in which the chromatin loops formed by Fab-8 inserted at Mcp in the forward orientation brings the enhancers in the iab-4 regulatory domain in close proximity to the Abd-B promoter , leading to the activation of Abd-B in A4 ( PS9 ) . In contrast , when inserted in the opposite orientation , the topology of the chromatin loops formed by the ectopic Fab-8 boundary are not compatible with productive interactions between iab-4 and the Abd-B promoter . Moreover , it would appear that boundary bypass for the regulatory domains that control Abd-B expression is not a passive process in which the boundaries are simply permissive for interactions between the regulatory domains and the Abd-B promoter . Instead , it seems to be an active process in which the boundaries are responsible for bringing the regulatory domains into contact with the Abd-B gene . It also seems likely that bypass activity of Fab-8 ( and also Fab-7 ) may have a predisposed preference , namely it is targeted for interactions with the Abd-B gene . This idea would fit with transgene bypass experiments , which showed that both Fab-7 and Fab-8 interacted with an insulator like element upstream of the Abd-B promoter , AB-I , while the Mcp boundary didn’t [59 , 60] . Similar conclusions can be drawn from the induction of Abd-B expression in A4 ( PS9 ) when Fab-7 is inserted in place of Mcp . Like Fab-8 , this boundary inappropriately targets the iab-4 regulatory domain to Abd-B . Unlike Fab-8 , Abd-B is ectopically activated when Fab-7 is inserted in both the forward and reverse orientations . While the effects are milder in the reverse orientation , the lack of pronounced orientation dependence is consistent with experiments in which Fab-7 was inserted at its endogenous location in the reverse orientation . Unlike Fab-8 only very minor iab-6 bypass defects were observed . In addition to the activation of Abd-B in A4 ( PS9 ) the Fab-7 Mcp replacements also alter the pattern of Abd-B regulation in more posterior segments . In the forward orientation , A4 and A5 are transformed towards an A6 identity , while A6 is also misspecified . Similar though somewhat less severe effects are observed in these segments when Fab-7 is inserted in the reverse orientation . At this point the mechanisms responsible for these novel phenotypic effects are uncertain . One possibility is that pairing interactions between the Fab-7 insert and the endogenous Fab-7 boundary disrupt the normal topological organization of the regulatory domains in a manner similar to that seen in boundary competition transgene assays [77] . An alternative possibility is that Fab-7 targets iab-4 to the Abd-B promoter not only in A4 ( PS9 ) but also in cells in A5 ( PS10 ) and A6 ( PS11 ) . In this model , Abd-B would be regulated not only by the domain that normally specifies the identity of the parasegment ( e . g . , iab-5 in PS10 ) , but also by interactions with iab-4 . This dual regulation would increase the levels of Abd-B , giving the weak GOF phenotypes . Potentially consistent with this second model , inactivating iab-4 in the McpF8 replacement not only rescues the A4 ( PS9 ) GOF phenotypes but also suppresses the loss of anterior trichomes in the A6 tergite .
The backbone of the recombination plasmid was designed in silico and contains several genetic elements in the following order: [MCS5]-[attP]-[3xP3-EGFP-SV40polyA]-[attP]-[FRT]-[MCS3] . This DNA fragment was synthesized and cloned into pUC57 by Genewiz . The two multiple cloning sites MCS5 and MCS3 were used to clone homology arms into this plasmid . The orientations of the two attP sites are inverted relative to each other and serve as targets for фC31-mediated recombination mediated cassette exchange [62] . The 3xP3-EGFP reporter [78] was introduced as a means to isolate positive recombination events . The Flp-recombinase target FRT [79] was included for the deletion of the selectable mini-yellow marker after recombination mediated cassette exchange . Homology arms were PCR-amplified from y w genomic DNA using the following primers: CCTGCCGACTGAACGAATGC and ACGCCCTGATCCCGATACACATAC for the proximal arm ( iab-4 side; 3967 bp fragment ) , and GCGTTTGTGTGTAGTAAATGTATC and AAAGGCCAACAAAGAACACATGGACG for the distal arm ( iab-5 side; 4323 bp fragment ) . A successful homologous recombination event will generate a 789 bp deletion within the Mcp region ( Genome Release R6 . 22: 3R:16’868’830–16’869’619; or complete sequence of BX-C: 113821–114610 [4] ) . The recombination plasmid was injected into y w vas-Cas9 embryos together with two gRNAs containing the following guides: GCTGGCTTTTACAGCATTTC and GCTTTGTTACCCCTGAAAAT . Concentrations were as described in Gratz et al . [80] . The injected embryos were grown to adulthood and crossed with y w partners . Among the few fertile crosses , one produced many larvae with a clear GFP-signal in the posterior part of their abdomens . This observation suggested that these animals had integrated the recombination plasmid and that the 3xP3-EGFP reporter acts as an enhancer trap for Abd-B specific enhancers . GFP positive larvae were isolated and grown to adulthood . Emerging males showed the expected Mcp phenotype . Also , and as expected for a reporter located in the BX-C , no fluorescence signal could be detected in their eyes , indicating that the 3xP3-EGFP reporter is silenced in eye cells where the 3xP3 promoter is usually active . The planned homologous recombination event could later be verified by PCR and sequencing . We will refer to it as McpattP . 12 EGFP- and Mcp-positive candidate males were individually crossed with y w virgins . Only 2 were fertile . The sterility of others may be caused by presence of off-targets as a frequent non-specific result of CRISPR/Cas9 editing . Starting from the two fertile crosses , 2 independent balanced stocks could be obtained according to established crossing schemes . One of them was used to obtain a y w M{vas-integrase}zh-2A; McpattP/TM3 , Sb stock for recombination mediated cassette exchange . Because of poor survival rates in injection experiments , the McpattP chromosome was also temporarily combined with Dp ( 3;3 ) P5 , Sb ( y w M{vas-integrase}zh-2A; McpattP/ Dp ( 3;3 ) P5 , Sb ) . By selection we obtained homozygous McpattP line that was subsequently used for fly injections . For generating dsDNA donors for homology-directed repair we used pHD-DsRed vector that was a gift from Kate O'Connor-Giles ( Addgene plasmid # 51434 ) . The final plasmid contains genetic elements in the following order: [iab-4 proximal arm]-[attP]- [lox]- [3xP3-dsRed-SV40polyA]-[lox]- [iab-4 distal arm] . Homology arms were PCR-amplified from yw genomic DNA using the following primers: TTTGAATTCTTCCAGACACGCATCGGG and AAACATATGCTTGCTATCGACCCTCCTC for the proximal arm ( 846 bp fragment ) , and AATACTAGTCTCGGAAAGGGAAGAAGTTC and TACTCGAGCCGCTAAAGGACGTTCTGC for the distal arm ( 835 bp fragment ) . A successful homologous recombination event will generate a 4401 bp deletion within the iab-4 region ( Genome Release R6 . 22: 3R:16 , 861 , 368 . . 16 , 869 , 768; or complete sequence of BX-C [4]: 120073–115673 ) . Targets for Cas9 were selected using “CRISPR optimal target finder”–the program from O'Connor-Giles Lab . The recombination plasmid was injected into McpF8 vasa-Cas9 embryos together with two gRNAs containing the following guides: ATAGCAAGTAGGAGTGGAGT and GAACTTCTTCCCTTTCCGAGCGG . Concentrations were as described in Gratz et al . ( 2014 ) . Injectees were grown to adulthood and crossed with y w; TM6/MKRS partners . Flies with clear dsRed-signal in eyes and the posterior part of their abdomens were selected into a new separate line . The successful integration of the recombination plasmid was verified by PCR . 3 day adult flies were collected in eppendorf tubes and stored in 70% ethanol at least 1 day . Then ethanol was replaced with 10% KOH and flies were heated at 70°C for 1–1 . 5h . After heating flies were washed with dH2O two times and heated again in dH2O for 45min . Then the digested flies were washed with 70% ethanol and stored in 70% ethanol . The abdomen cuticles were cut from the rest of the digested fly using fine tweezer and a needle of an insulin syringe and put in a droplet of glycerol on a glass slide . Then the abdomens were cut longitudinally on the dorsal side through all of the tergites with the syringe . To spread the cuticles flat on the slides cuts may be done between the tergites . Than the cuticles were flattened with a coverslip . Photographs in the bright or dark field were taken on the Nikon SMZ18 stereomicroscope using Nikon DS-Ri2 digital camera , processed with ImageJ 1 . 50c4 and Fiji bundle 2 . 0 . 0-rc-46 . Primary antibodies were mouse monoclonal anti-Abd-B at 1:100 dilution ( 1A2E9 , generated by S . Celniker , deposited to the Developmental Studies Hybridoma Bank ) and polyclonal rabbit anti-Engrailed at 1:1000 dilution ( a kind gift from Judith Kassis ) . Secondary antibodies were goat anti-mouse Alexa Fluor 647 ( Molecular Probes ) and anti-rabbit FITC conjugated ( Jackson Research ) at 1:2000 dilution . Stained embryos were mounted in the following solution: 23% glycerol , 10% Mowiol 4–88 , 0 . 1M Tris-HCl pH 8 . 3 . Images were acquired on Leica TCS SP-2 confocal microscope and processed using GIMP 2 . 8 . 16 , ImageJ 1 . 50c4 , Fiji bundle 2 . 0 . 0-rc-46 . | Drosophila bithorax complex ( BX-C ) is one of a few examples demonstrating in vivo role of boundary/insulator elements in organization of independent chromatin domains . BX-C contains three HOX genes , whose parasegment-specific pattern is controlled by cis-regulatory domains flanked by boundary/insulator elements . Since the boundaries ensure autonomy of adjacent domains , the presence of these elements poses a paradox: how do the domains bypass the intervening boundaries and contact their proper regulatory targets ? According to the textbook model , BX-C regulatory domains are able to bypass boundaries because they harbor special promoter targeting sequences . However , contrary to this model , we show here that the boundaries themselves play an active role in directing regulatory domains to their appropriate HOX gene promoter . | [
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"recombination",
... | 2018 | Boundaries mediate long-distance interactions between enhancers and promoters in the Drosophila Bithorax complex |
Pea-comb is a dominant mutation in chickens that drastically reduces the size of the comb and wattles . It is an adaptive trait in cold climates as it reduces heat loss and makes the chicken less susceptible to frost lesions . Here we report that Pea-comb is caused by a massive amplification of a duplicated sequence located near evolutionary conserved non-coding sequences in intron 1 of the gene encoding the SOX5 transcription factor . This must be the causative mutation since all other polymorphisms associated with the Pea-comb allele were excluded by genetic analysis . SOX5 controls cell fate and differentiation and is essential for skeletal development , chondrocyte differentiation , and extracellular matrix production . Immunostaining in early embryos demonstrated that Pea-comb is associated with ectopic expression of SOX5 in mesenchymal cells located just beneath the surface ectoderm where the comb and wattles will subsequently develop . The results imply that the duplication expansion interferes with the regulation of SOX5 expression during the differentiation of cells crucial for the development of comb and wattles . The study provides novel insight into the nature of mutations that contribute to phenotypic evolution and is the first description of a spontaneous and fully viable mutation in this developmentally important gene .
In 1902 Bateson [1] reported the first examples of Mendelian inheritance in animals based on the genetic studies of four traits in chicken , one of these being the Pea-comb phenotype ( Figure 1 ) . The Pea-comb allele results in reduced comb and wattle size compared to wild-type individuals . Pea-comb shows incomplete dominance and as such the small comb shape can differ slightly between homo- and heterozygous birds . Homozygotes present three longitudinal rows of papillae , whilst heterozygotes can have a well-developed central blade ( still of reduced size compared to wild-type ) [2] . The wild-type has a single central blade of tissue and is therefore often denoted single comb . Bateson and Punnet [3] reported the first example of an epistatic interaction between genes when they showed that walnut comb is caused by the combined effect of Pea-comb and Rose-comb . Subsequent studies revealed that Pea-comb , besides its effect on comb and wattles , was also associated with a ridge of thickened skin that runs the length of the keel over the breast bone [4] . The Pea-comb mutation may have occurred early during domestication as the phenotype is widespread among both European and Asian breeds of chickens . Furthermore , it has been speculated that a reproduction in the tomb of Rekhmara at Thebes , Egypt , dated to ∼3 , 450 years before present depicts a rooster with the characteristic Pea-comb phenotype [5] . Chickens were domesticated from the red junglefowl with some contributions from the grey junglefowl [6] , two species adapted to subtropical or tropical environments . Chickens do not sweat , instead they dissipate up to 15 percent of their body heat through the comb and wattles [7] , making the Pea-comb phenotype adaptive to cold environments since it reduces heat loss . This phenotype has also been favoured in chickens bred for cock-fighting , as noted by Darwin [8] the smaller ornaments provided smaller targets for injury . In the present study we show that the classical Pea-comb phenotype in chickens is caused by a large expansion of a duplicated sequence in intron 1 of the gene for the SOX5 transcription factor .
Pea-comb has previously been assigned to chromosome 1 [9] , [10] . We refined the localization by linkage analysis using a dense set of genetic markers and a large segregating family . The interval harbouring Pea-comb was defined as 67 , 831 , 796–68 , 456 , 921 bp on chromosome 1 , based on flanking markers showing recombination with Pea-comb ( Table 1 ) . This interval contains a single gene , SOX5 , a member of the SRY-related HMG box family of transcription factors . SOX5 is located in a one Mb gene desert that is enriched for Evolutionary Conserved Non-coding Sequences ( ECNS; Figure 2A ) . This is a typical feature of developmentally important genes [11] , [12] . SOX5 was not an obvious candidate gene for Pea-comb but the comb is composed of extracellular matrix and SOX5 has a well-established role in chondrocyte development and production of extracellular matrix [13] . Mouse SOX5 knockouts die at birth from respiratory distress caused by a cleft secondary palate and narrow thoracic cage [13] . Mouse SOX5/SOX6 double knockouts die in utero with severe skeletal dysplasia , demonstrating that these two genes have critical , redundant roles during development [13] , [14] . To further refine the localization of Pea-comb we characterized SOX5 haplotype patterns among three breeds of chicken , a French experimental population , the Russian Orlov and the Chinese Hua-Tung . These breeds all carry Pea-comb and , to the best of our knowledge , there has been no exchange of genetic material between them for 100 generations or more . The Orlov and Hua-Tung are not fixed for Pea-comb , allowing recombination to reduce the size of the shared haplotype associated with the mutation . Initial IBD mapping using 12 samples from the three different populations revealed a completely shared haplotype between 67 , 961 , 701 bp and 68 , 061 , 854 bp ( Table 2 ) . SNP genotyping of all Hua-Tung and Orlov individuals available narrowed the shared haplotype further to a 50 kb region spanning positions 67 , 985 , 285 bp and 68 , 035 , 337 bp ( Figure 2A; Table 2 ) . The upstream break-point ( 67 , 985 , 285 bp ) was identified using a single Hua-Tung bird . The break was confirmed in two additional individuals from the same population which were homozygous at the six SNPs diagnostic of the Pea-comb haplotype , but heterozygous at this break-point . Downstream , the haplotype was broken at 68 , 035 , 337 bp in three Orlov birds ( Table 2 ) . This critical region is located upstream of the first annotated exon however a comparison with SOX5 from mammalian species indicated that exon 1 is missing from the chicken genome assembly and is expected to be found more than 200 kb upstream of exon 2 ( Figure 2A ) . We confirmed the existence of an upstream exon in chicken by 5′ RACE analysis . The obtained nucleotide sequence ( GenBank accession number FJ548639 ) showed 90% identity to human SOX5 exon 1 , but did not give a match in the chicken genome , implying a gap in the current chicken assembly . Resequencing the 50 kb region associated with Pea-comb from a set of Pea-comb and wild-type birds revealed a limited number of sequence polymorphisms , with fixed differences between genotypes . These potentially causative SNPs were interrogated using a larger set of wild-type birds from the AvianDiv panel [15] , however none of the alleles were found to be unique to the Pea-comb haplotype ( Table 2 ) . The failure to identify a causative point mutation led to a screen of the Pea-comb region for structural changes using Southern blot analysis . The SOX-85kb_SB probe ( Table S1 ) revealed a dramatic increase in the hybridization signal of a 3 . 2 kb BamHI fragment in Pea-comb birds ( Figure 2C ) whilst other probes from the region gave identical restriction fragment patterns for both alleles . The result implied that Pea-comb is associated with a large tandem array of a duplicated sequence containing a BamHI restriction site . PCR and sequence analysis revealed that this DNA fragment is also duplicated on wild-type chromosomes which have two copies ( Figure 2B ) , whereas the Pea-comb allele has a large number of copies . Quantification of the copy number of the duplicated fragment using both pulsed field gel electrophoresis ( PFGE ) and real-time PCR analysis confirmed that a massive amplification of a duplicated sequence is associated with the Pea-comb allele . PFGE analysis using the restriction enzyme PshA1 , which cuts outside the duplicated region , gave a 97 kb restriction fragment in Pea-comb birds in contrast to a predicted 10 kb fragment based on the reference genome sequence from a wild-type bird ( Figure S1 ) . The result indicates that the Pea-comb allele contains about 30 copies of the duplicated sequence . Real-time PCR analysis of Pea-comb birds from three breeds confirmed this finding and revealed a 20- to 40-fold sequence amplification ( Figure 2D ) . The real-time PCR analysis did not indicate two clear groupings corresponding to Pea-comb heterozygotes and homozygotes suggesting that the duplication may show further copy number variation among Pea-comb individuals . Interestingly , 100 years ago Bateson and Punnett [16] reported variable expression of the Pea-comb phenotype which may reflect a copy number variation of the duplicated sequence . Although the duplicated sequence is not evolutionary conserved , it is located close to two highly conserved ECNSs ( Figure 2A ) . The distance between these elements is about 10 kb on wild-type chromosomes in contrast to about 100 kb on Pea-comb chromosomes . The duplication includes a sequence repeated in two copies on wild-type chromosomes and each copy contains two partial LINE fragments ( Figure 2B ) . The expansion of this duplication must be the causative mutation because it was the only polymorphism showing complete association with the phenotype . A closer examination of the duplicated sequence shows that it is particularly GC-rich and contains a small CpG island ( Figure 2A and 2B ) . The wild-type chromosome contains two copies of this CpG island whereas the Pea-comb chromosome contains about 30 . This could be relevant for the mechanism of action of this intronic mutation . The Pea-comb phenotype is apparent at hatch and must therefore reflect altered gene expression during development . Tissue samples from the comb region were collected from both homozygous Pea-comb and homozygous wild-type birds at embryonic ( E ) days 6 , 7 , 8 , 9 , 12 and 19 for expression analysis . Quantitative RT-PCR analysis only revealed significant differences in SOX5 expression at stage E7 and E8 ( which were combined due to the low number of E8 samples ) . The results for E7+8 revealed significant upregulated SOX5 expression in the comb region in Pea-comb birds ( t = −5 . 0 , p = 0 . 002; Figure S2A ) . Expression analysis was also conducted using primers specific for each exon of SOX5 ( including the previously un-annotated exon 1 described above ) , however the results did not indicate any difference between genotypes in regards to differential splicing of SOX5 ( Figure S2B ) . Immunohistochemical staining with a human SOX5 antibody as well as in situ-hybridization with a chicken-specific cRNA probe was carried out to investigate SOX5 expression in both Pea-comb and wild-type embryos during development ( Figure 3 ) . Specific immunostaining of nuclei was seen in developing cartilaginous structures including the nasal septum , Meckel's cartilage and optic sclera ( Figure 3A and 3D ) . Scattered and rare SOX5 positive cells were seen in the surface ectoderm ( Figure 3B and 3M ) . All structures with SOX5 staining in wild-type embryos were also positive in Pea-comb embryos including the scattered cells in the ectoderm . However in Pea-comb embryos , striking ectopic SOX5 expression was observed in mesenchymal cells located just beneath the surface ectoderm where the comb and wattles will develop ( Figure 3A–3J ) . Differential expression was confirmed with in situ-hybridization ( Figure 3G–3H ) and quantitative real-time PCR ( see above ) . The ectopic expression is transient . Whereas few cells with ectopic expression are visible in the comb region by day E6 , they are prominent at E9 , and almost completely absent at E12 ( Figure 3K–3P ) . Thus , Pea-comb appears to be a spatiotemporal-specific , cis-acting regulatory SOX5 mutation .
A major challenge in current genome biology is to reveal the biological significance of the many Evolutionary Conserved Non-coding Sequences ( ECNS ) . The analysis of the functional significance of ECNS is hindered by a paucity of mutations in such regions which show an association with a phenotype . Here we demonstrate the first spontaneous SOX5 mutation associated with a phenotype , despite the rich abundance of ECNS in the SOX5 region ( Figure 2A ) . SOX5 is under complex regulation and as demonstrated here , mutations affecting its regulation can have very specific effects . It would be surprising if regulatory mutations in this gene do not to some extent contribute to phenotypic diversity present in humans . For instance , the human face shows a bewildering array of diversity . The nearly identical facial appearances of monozygotic twins imply that this diversity is nearly 100% genetically determined , but knowledge concerning the underlying molecular basis of this diversity is restricted to certain craniofacial abnormalities [17] . It is likely that regulatory mutations in developmentally important genes shape this type phenotypic diversity , and SOX5 may very well be one of the genes that contributes . The comb is a sexual ornament that shows strong sexual dimorphism in chickens and the fact that this sexual dimorphism is maintained in Pea-comb birds shows that the Pea-comb tissue maintains the response to the influence of sex hormones ( Figure 1 ) . That the comb is under sexual selection is evidenced by red junglefowl females showing mating preferences for males with large combs and reciprocally , males tend to favour females with larger combs [18] , [19] . The size of the comb is proportionally larger in many breeds of domestic chickens compared to their wild ancestors . In our previous study of a large intercross between White Leghorn chicken ( with larger combs ) and red junglefowl , we identified a number of Quantitative Trait Loci ( QTL ) affecting the size of the comb [20] . Interestingly , one of the QTL controlling the size of the female comb overlaps the SOX5 locus , which now becomes an obvious candidate gene for this QTL . However , the confidence interval for the QTL is large , as is usually the case in an F2 intercross , and the entire SOX5 region needs to be considered in a search for possible causative mutation ( s ) . SOX genes are defined by their high-mobility-group ( HMG ) domains and are divided into eight groups ( A to H ) based on protein sequence comparison [14] . SOX5 belongs to the D family of SOX genes , along with SOX6 and SOX13 . SOX5 has been termed an architectural transcription factor [21] , as binding to this protein will cause a sharp bend ( 80–135 degrees ) in the bound DNA and may lead to different regulatory regions of a target gene coming into closer proximity . SOX5 has been reported to have a co-operative role in chondrogenesis; during embryonic cartilage formation SOX5 and SOX6 assist SOX9 to activate specific genes [22] , and have a repressive role in oligodendrogenesis during neural development [23] . SOX5 is also expressed in the developing neocortex and cranial neural crest during the early stages of development . SOX5 postmitotically regulates migration , axon projection and postmigratory differentiation of certain neocortical neurons [24] but little is known about SOX5 function in neural crest derivatives [25] . With these different roles , the functional consequence of the transient ectopic SOX5 expression in Pea-comb birds is not clear . The comb is composed of layers of epidermis , dermis and central connective tissue , of which collagen and hyaluronan are the major components [26] . The ectopic SOX5 expression is first seen in E7 ( st28 ) mesenchyme ( Figure 3 ) . Previous studies with grafts of comb-primordia from different ages at various locations imply that cells giving rise to the comb are already determined by E4 ( st24 ) [27] , [28] and that the determination resides in the mesenchymal components and not in the ectoderm [27] . These experiments also revealed that the morphology of the comb was under control of the mesenchyme [27] , [29] . Heterotopic grafts of single-comb primordia to the neck region without beak mesenchyme , lost the serrated single ridge morphology and expanded laterally following the development , resembling that of complex comb types [29] such as the Pea-comb . Hence , changes in the underlying mesenchyme at the time of the ectopic SOX5 expression will not affect the determination and initial stages of the comb development but rather the development of comb shape . Our results indicate that ectopic SOX5 expression changes the modulating properties of the mesenchyme of the nasofacial region beneath the regions of the developing comb and wattles . The serration of a single comb is associated with loosely coherent clusters or points of proliferating mesenchymal cells [30] , [31] . Such clusters were not observed in the developing Pea-comb mesenchyme and this difference may be due to the ectopic SOX5 expression . Pea-comb is an additional example of a Copy Number Variation ( CNV ) associated with a phenotype . About 12% of the human genome contains tandem duplications that may show CNV [32] and a number of human diseases have been reported to be associated with CNVs [33] , [34] . It is important to distinguish CNVs that are due to duplications of single copy sequences ( de novo duplications ) and expansions or contractions of already duplicated sequences . We have previously reported three de novo duplications associated with phenotypic traits in domestic animals , Dominant white colour in pigs [35] , the Ridge phenotype in Ridgeback dogs [36] and Greying with age in horses [37] . In contrast , Pea-comb and most human diseases associated with CNVs involve expansions or contractions of existing duplications . Pea-comb is however an unusual CNV associated with a phenotype because it involves the amplification of a non-coding region located far from any coding sequence . Pea-comb therefore to some extent resembles the massive amplification of a trinucleotide repeat in intron 1 of Frataxin causing Friedrich ataxia [38] . However , the mechanism of action is probably very different since the expansion of the trinucleotide repeat in Frataxin leads to the formation DNA triplexes and “sticky DNA” causing transcriptional silencing [38] . The duplicated sequence in intron 1 of SOX5 is not evolutionary conserved between birds and mammals . This does not exclude the possibility that it contains regulatory elements which are important for SOX5 in birds , or in birds that develop combs and wattles . However , even if the duplicated sequence per se is not functionally important , the massive amplification of this sequence may disturb the action of regulatory elements in the region . For instance , tandem repeats may recruit DNA methylation which abolishes protein-DNA interaction at regulatory elements [39] . Our observation that the duplicated region is not only particularly GC-rich , but contains a small CpG island which becomes repeated about 30 times on the Pea-comb chromosome , suggests that DNA methylation maybe a plausible mechanism for Pea-comb as this effect may spread to neighbouring regulatory sites . Genetic studies of phenotypic diversity in domestic animals provide a strong case for the evolutionary significance of regulatory mutations . Other examples of cis-acting regulatory mutations underlying phenotypic traits in domestic animals include ( i ) a nucleotide substitution in intron 3 of IGF2 with a prominent effect on muscle growth in the pig [40] , ( ii ) regulatory mutations in the gene for microphtalmia-transcription factor ( MITF ) causing white spotting in dogs [41] , ( iii ) regulatory mutation ( s ) in BCDO2 causing the yellow skin phenotype in chicken [6] , ( iv ) a 4 . 6 kb duplication in intron 6 of STX17 causing Greying with age in horses [37] , ( v ) an 11 . 7 kb intergenic deletion causing intersexuality and lack of horns in goats [42] and ( vi ) a mutation creating an illegitimate microRNA target site in the sheep myostatin gene promoting muscle growth [43] . Furthermore , the ridge phenotype in dogs [36] and the dominant white colour in pigs [35] are caused by large duplications that most likely lead to dysregulated expression of some fibroblast growth factor genes and the KIT receptor , respectively . Most of these examples concern growth factors , growth factor receptors , or transcription factors that have important roles during development and for which null mutations are lethal or sub-lethal . The significance of regulatory mutations is also supported by the identification of mutations underlying morphological variation in Drosophila [44] , [45] and stickleback fish [46] . This wealth of data now demonstrates the prominent role of regulatory mutations , at least for morphological evolution , as predicted by King and Wilson more than 30 years ago based on the limited divergence in protein sequences between human and chimpanzee [47] .
DNA samples from a French pedigree consisting of 7 parental , 14 F1 and 244 F2 progeny were used for linkage analysis . The parentals consisted of four heterozygous Pea-comb birds and three homozygous wild-type birds . DNA samples from Pea-comb birds for identical-by-descent mapping came from a French experimental population kept by INRA , from a Chinese Hua-Tung population and from the Russian Orlov breed . DNA samples from various domestic breeds collected by the AvianDiv project [15] were used for real-time PCR analysis and to test whether candidate causal mutations from the Pea-comb region could be excluded since they were present among birds homozygous for the wild-type allele at the Pea-comb locus . Linkage analysis was conducted using the SNPs compiled in Table S1 . SNP genotyping was performed with Pyrosequencing ( See ‘Linkage primers’ , Table S1 for details ) . Fine-mapping was carried out on a small number of recombinant individuals that more exactly defined the Pea-comb region . In this case , one kb fragments were amplified and sequenced to detect SNPs ( see ‘1 kb fragment analysis’ , Table S1 for primers ) . IBD mapping was initially performed on a panel of 12 chickens; two Pea-comb and two wild-type birds from the linkage pedigree , four homozygous Pea-comb birds from the French pedigree , two Pea-comb birds from the Chinese Hua-Tung population and two Pea-comb birds from the Russian Orlov population . A collection of one kb regions spanning approximately 67 , 891 , 800 bp to 68 , 181 , 677 bp on chromosome 1 were sequenced for each animal to identify SNPs between lines ( See ‘SNPs used for IBD Mapping’ , Table S1 , for exact positions ) . In a similar way , the heterozygosity of chromosome 1 , fragment 68 , 181 , 600 bp to 68 , 335 , 500 bp , was determined by sequencing 16 homozygous Pea-comb birds belonging to the linkage pedigree ( Primers SOX+130 , SOX+140 , SOX+200 , SOX+260 in Table S1 ) . This re-sequencing effort revealed potential causative SNP that were differentially segregating between the Pea-comb and non-Pea-comb populations . These polymorphisms were subsequently tested in the non-Pea-comb individuals from the AvianDiv panel and used to define the Pea-comb region by six loci , positions 68 , 038 , 060 bp , 68 , 035 , 337 bp , 68 , 019 , 518 bp , 68 , 011 , 661 bp , 67 , 991 , 941 bp and 67 , 985 , 285 bp respectively . Pyrosequencing was used to assay these six variations in 34 Hua-Tung Pea-comb birds and 27 Orlov Pea-comb birds ( See ‘Pyro SNPs used for IBD mapping’ , Table S1 ) . Lastly , four of these loci were also genotyped for a variety of birds from the AvianDiv panel to check the frequency of the Pea-comb haplotype among wild-type chromosomes . The copy number of the SOX5 duplication was evaluated by comparing eight populations with wild-type phenotype ( red junglefowl , n = 5; commercial broiler , n = 5; Czech Golden Pencilled , n = 5; Friesian Fowl , n = 5; Finnish Landrace , n = 5; Red Villafranquina , n = 5; Transylvanian Naked Neck , n = 5; White Leghorn , n = 5 ) to three breeds segregating for Pea-comb ( French Pea-comb , n = 3; Hua-Tung , n = 13; Orlov , n = 13 ) . The real-time PCR assay contained TaqMan Gene Expression Master Mix ( Applied Biosystems ) , 900 nM of each primer combined with 250 nM of fluorometric probe and 30 ng of genomic DNA . The SOX5 assay was normalised using an assay designed to ribosomal protein S24 ( rps24 ) . Primer and probe concentrations of those reactions were 750 nM and 300 nM , respectively . Each assay was performed in triplicate , averaged and referenced to a wild-type red junglefowl . Details of primer and probe sequences are in Table S2 . Fold change was calculated using the equation 2− ( Normalized Ct peacomb assay−Normalized Ct rps24 assay ) and the range of this value was determined from the combined standard errors of both assays . Seventy kb on chromosome 1 from 67 , 969 , 741 bp to 68 , 041 , 242 bp were re-sequenced using a panel of ten birds; two wild-type parental birds from the linkage pedigree , two red junglefowl ( RJF ) birds , two homozygous Pea-comb from the French pedigree , two Pea-comb Hua-Tung birds and two Pea-comb Russian Orlov birds . Primers pairs were used to generate over-lapping PCR amplicons ranging from approximately 1200 bp to 1400 bp in size . Internal primers were used with each primer pair set . Primers were designed using Primer3 [48] . DNA sequences were analysed and edited in Codoncode Aligner ( CodonCode , Dedham , MA ) . The RJF genomic sequence used to generate the chicken genome sequence was used as a reference for alignment . The chicken genome reference sequence contained three gaps . Gap 1 spanned 67 , 981 , 199 bp–67 , 983 , 790 bp; gap 2 , 68 , 002 , 231 bp–68 , 003 , 557 bp and gap 3 , 68 , 006 , 200 bp–68 , 006 , 994 bp . Gaps 1 and 3 were closed using a PCR-based 2-step strategy [49] ( Primers Dynal-75_gap and Dynal-105_gap primers in Table S1 ) , whilst gap 2 was covered using long range PCR ( Primers LR_gap1 , Table S1 ) . Gap 2 was found to be a tandem duplication , part of the duplication linked to the Pea-comb mutation . Therefore sequencing was performed after the amplicon was cleaved with XhoI , and both halves sequenced independently . Southern blot analysis was performed using a set of six different probes ( SOX-55kb_SB to SOX-105kb_SB , Table S1 ) on a panel consisting of three homozygous Pea-comb birds from the linkage pedigree , three red junglefowls , two commercial broiler samples and two White Leghorn birds . The DNA was digested with BamHI and separated by 0 . 7% agarose gel electrophoresis . DNA plugs were prepared from nine chickens , three of each wild-type , Pea-comb heterozygous and Pea-comb homozygous birds . The plug preparation and restriction digest protocol follows that of Giuffra et al . [35] , with the following modifications . Whole blood stored in 0 . 5 M EDTA was used as starting material and resuspended to a concentration of 25×108 cells/ml in PBS after washing . Plugs were solidified at room temperature prior to digestion for 24 hours at 50°C in 0 . 5 mg/ml proteinase K , 1×NDS ( 0 . 5 M EDTA , 0 . 01 M Tris , 0 . 34 M N-Laurylsarcosine , pH 8 . 0 ) with constant shaking . Enzyme digestions were performed as described [35] . PshA1 ( New England BioLabs ) was selected for this experiment as this restriction enzyme was predicted to cut at position 67 , 998 , 520 bp and 68 , 005 , 614 bp , i . e . outside the duplicated region . PFGE of the PshA1 digested plugs was performed in a 1 . 0% agarose gel , 0 . 5% TBE at 14°C , 6 V/cm , switch times ramped from 1–25 seconds for 17 hours and fragment sizes were estimated using the MidRange I PFG Marker ( New England BioLabs ) . Southern blot analysis was performed as before , using the 986 bp product from the SOX-85kb_SB amplicon ( Table S1 ) as probe . The duplicated region was amplified with long-range PCR primers ( SOX-Duplication_LR1_F and R , Table S1 ) . In addition , internal primers were used to check the length of the potential duplication through nested PCR of the initial amplicon ( Primers SOX-Duplication_F , R11 , 12 and 13 , Table S1 ) . Heads from staged embryos were fixed in 4% paraformaldehyde in phosphate buffered saline ( PBS ) for one hour at 4°C . Fixed heads were incubated overnight in 30% sucrose in PBS at 4°C , embedded in OCT freezing medium ( Tissue-Tek , Sakura ) , frozen and sectioned in a cryostat . Cross sections and sagittal sections , 10 μm thick , were collected on glass slides ( Super Frost Plus , Menzel-Gläser ) . The sections were rehydrated in PBS for 15 min and then blocked in PBS containing 1% fetal calf serum , 0 . 1% Triton-X and 0 . 02% Thimerosal . The SOX5 antibody ( Abcam , a_6226041 ) was diluted 1∶500 in blocking solution and incubated on the slides over night at 4°C . The secondary antibodies ( Jackson Immunoresearch Laboratories ) were incubated at room temperature for two hours at a 1∶200 dilution in blocking solution . Samples were analysed using a Zeiss Axioplan2 microscope equipped with Axiovision software . Images were formatted , resized , enhanced and arranged for publication using Axiovision and Adobe Photoshop . A cRNA probe was made using a DIG RNA labeling kit ( Roche ) . The SOX5 probe was made from the chEST752i6 cDNA clone acquired from the BBSRC ChickEST Database [50] . The probe was hybridized to untreated sections over night at 66°C under conditions containing 50% formamide and 5×SSC in a humidified chamber . The DIG labeled nucleotides were detected using an alkaline-phosphatase coupled anti-DIG antibody ( Roche ) followed by incubation with BCIP/NBT developing solution ( Roche ) for 1–5 hours at 37°C . Images were captured using a Zeiss Axioplan2 microscope equipped with Axiovision software ( 3 . 0 . 6 . 1 , Carl Zeiss Vision GmbH ) . Tissue was collected from homozygous Pea-comb birds and homozygous wild-type birds . The ages of the birds sampled were embryonic ( E ) days 6 , 7 , 8 , 9 , 12 and 19 ( with hatching occurring at approximately day 21 ) . Two Pea-comb and two normal individuals were collected from each stage , with the exceptions of E7 , where nine samples ( four Pea-comb and five wild-type ) were used and two E8 samples ( one of each type ) . Tissues were initially stored in RNALater ( Ambion ) , with total RNA extracted from embryonic tissues using the Trizol reagent ( Invitrogen ) . The most central part of the presumptive beak and comb were dissected out . cDNA was made from 1 μg of RNA using GeneAmp ( Applied Biosystems ) . Samples were run in triplicate using IQ SyBr Green Supermix ( Biorad ) and normalized to β-actin and TATA-box binding protein ( TBP ) ; primers are given in Table S1 . SOX5 was amplified using primers SOX5_cDNA_1 crossing intron/exon boundaries . Control cDNA reactions containing primers but no RNA were performed in parallel . Samples were run on two separate machines: the ABI 7900HT and the Corbett Rotor-Gene 6000 . In addition to these primers , primers for each individual exon ( 2 to 15 ) were also used to analyse potential alternate SOX5 splicing in tissue from the comb . These were used on cDNA from two E7 samples ( Pea-comb and wild-type ) and two E9 samples ( Pea-comb and wild-type ) . Statistical analysis was performed by first correcting Ct values for batch effects caused by using two different machines , then conducting a two-sample t-test on the average of each set of triplicates . Information on the chicken genome sequence is available at http://www . genome . ucsc . edu . The sequence data presented in this paper have been submitted to GenBank with the following accession numbers FJ548629-FJ548639 | The featherless comb and wattles are defining features of the chicken . Whilst the Pea-comb allele was known to show a dominant inheritance and drastically reduce the size of both comb and wattles , the genetics underlying the mutation remained elusive . Chicken comb is primarily composed of collagen and hyaluronan , which are produced by chondrocytes . These cells are formed through the condensation and differentiation of mesenchyme cells during the chondrogenesis pathway , the early stages of which are regulated by SOX transcription factors . Here we pinpoint a massive amplification of a duplicated sequence in the first intron of SOX5 as causing the Pea-comb phenotype . By studying early embryos , we show that SOX5 is ectopically expressed during a restricted stage of development in the cells which underlie the comb and wattles of Pea-comb animals . We hypothesise that the sequence duplication alters the regulation of SOX5 expression when the differentiation of cells essential for comb and wattle development is taking place . Pea-comb adds to the growing list of phenotypic variation which is explained by regulatory mutations and so demonstrates the evolutionary significance of such events . | [
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] | 2009 | Copy Number Variation in Intron 1 of SOX5 Causes the Pea-comb Phenotype in Chickens |
Cell differentiation is typically directed by external signals that drive opposing regulatory pathways . Studying differentiation under polarizing conditions , with only one input signal provided , is limited in its ability to resolve the logic of interactions between opposing pathways . Dissection of this logic can be facilitated by mapping the system's response to mixtures of input signals , which are expected to occur in vivo , where cells are simultaneously exposed to various signals with potentially opposing effects . Here , we systematically map the response of naïve T cells to mixtures of signals driving differentiation into the Th1 and Th2 lineages . We characterize cell state at the single cell level by measuring levels of the two lineage-specific transcription factors ( T-bet and GATA3 ) and two lineage characteristic cytokines ( IFN-γ and IL-4 ) that are driven by these transcription regulators . We find a continuum of mixed phenotypes in which individual cells co-express the two lineage-specific master regulators at levels that gradually depend on levels of the two input signals . Using mathematical modeling we show that such tunable mixed phenotype arises if autoregulatory positive feedback loops in the gene network regulating this process are gradual and dominant over cross-pathway inhibition . We also find that expression of the lineage-specific cytokines follows two independent stochastic processes that are biased by expression levels of the master regulators . Thus , cytokine expression is highly heterogeneous under mixed conditions , with subpopulations of cells expressing only IFN-γ , only IL-4 , both cytokines , or neither . The fraction of cells in each of these subpopulations changes gradually with input conditions , reproducing the continuous internal state at the cell population level . These results suggest a differentiation scheme in which cells reflect uncertainty through a continuously tuneable mixed phenotype combined with a biased stochastic decision rather than a binary phenotype with a deterministic decision .
Consider a general cell differentiation process in which precursor cells can respond to two external signals , each driving differentiation into a specific lineage ( Figure 1A ) . Such processes are common , for example , in stem-cell differentiation in the early embryo [1] , [2] and in the hematopoietic system in which more specialized cells are generated from earlier progenitors through cascades of binary cell fate decisions [3] . Under mixed conditions , when both driving signals are present , several hypothetical outcomes may occur . If the two differentiated states are mutually exclusive , cells will make a definite decision and will differentiate into one state or the other ( Figure 1B ) . Most experimental and theoretical studies of cell differentiation show occurrence of such mutually exclusive steady states [4]–[6] . Another scenario shown by other models is that of multistability ( Figure 1C ) , where some input conditions give rise to a third steady state in which genes specific to both lineages are co-expressed . Tri-stability was observed in a number of systems in which low-level co-expression of lineage-specific transcription factors occurs in progenitor cells [7]–[9] . In both scenarios the transition between states is sharp . In contrast , cell state can also shift continuously from one extreme to the other ( Figure 1D ) . Such gradual transition at the population level can be realized in qualitatively different ways at the single cell level . Each cell on its own can make a definite decision , resulting in a heterogeneous population with cells showing either one or the other phenotype ( Figure 1Ei ) . Alternatively , cells can show a mixed phenotype at the single cell level , with individual cells co-expressing specific genes of both lineages simultaneously ( Figure 1Eii ) . Notably , all scenarios presented in Figure 1 are indistinguishable under polarized input conditions—that is , when applying only one input at a time . The understanding of binary cell fate decisions in various cellular systems has advanced in the last decade through combination of experimental investigations with mathematical modelling [3] , [5] , [6] , [9] . Dynamical systems theory was used for describing how gene regulatory networks ( GRNs ) that control cell differentiation influence cell state over time . This type of analysis provides a framework for defining differentiated states as attractors ( stable steady states ) of a dynamical system that describes the GRN [3] , [10] . A simplified GRN motif was identified in most studied binary cell fate systems , in which two fate determining transcription factors ( corresponding for the two differentiated lineages ) cross-inhibit each other , while each factor also positively regulates its own level ( Figure 1A ) . This motif was investigated , for example , in the PU . 1-GATA1 system controlling erythroid/myeloid cell differentiation [9] , [11]–[14] , and also in the Th1–Th2 system [5] , [6] , [15]–[17] , which is the subject of the current study . It was shown by these studies and others that this network motif , under various conditions , can give rise to either a bi-stable or a tri-stable system . In the latter case , two steady states correspond to the differentiated lineages , and another steady state corresponds to the progenitor cell state , in which both TFs are expressed at intermediate levels . While cell differentiation was traditionally considered as a binary process , recent studies of hematopoietic cells reveal existence of a continuum of cell states bridging previously described subsets [18] , [19] . However , it is still not well understood how such intermediate cell states are generated from progenitor states , nor how their existence complies with prevailing theoretical models of cell differentiation . In order to gain better understanding of the logic employed by the GRN governing cell differentiation , it is beneficial to study its responses under mixed input conditions [2] , [20]–[25] at the single cell level . In this study we use differentiation of naive CD4+ T cells towards the Th1 and Th2 lineages as a model system to study this question . Antigen-activated CD4+ T cells can differentiate into various cell types depending mainly on the cytokines present in their environment during activation [26] , [27] . Differentiation of CD4+ T cells towards the Th1 lineage is driven by the cytokine IL-12 , while IL-4 drives differentiation towards the Th2 lineage ( Figure 1A ) . Th1 cells , involved in protection against intracellular pathogens , are characterized by the expression of the lineage-specific transcription factor ( TF ) T-bet , and by production and secretion of effector cytokines such as IFN-γ and TNFα [26] . Th2 cells express the lineage-specific TF , GATA3; secrete the cytokines IL-4 , IL-5 , and IL-13; and are involved in protection against extracellular pathogens [26] . Existence of cells co-expressing IFN-γ and IL-4 was observed in both mouse and human [28] , [29] , but the input conditions and the status of expression of transcription factors leading to their formation are not clear .
In order to characterize the differentiation decision logic , naïve CD4+ T cells were activated in the presence of a combinatorial matrix of the two external signals IL-12 and IL-4: increasing levels of IL-12 ( signal A ) in rows and of IL-4 ( signal B ) in columns ( see also Text S1 ) . Following 7 d of culture , cells were restimulated through their T cell receptor , and their responses were measured . We characterize each cell by four parameters: the levels of the two lineage-specific TFs , T-bet and GATA3 , and the levels of the two lineage characteristic cytokines , IFN-γ and IL-4 . Levels of these four proteins were measured for each cell by intracellular staining using fluorescently labelled monoclonal antibodies , followed by flow cytometry . First , we mapped the population average response of the TFs to a matrix of input conditions . Histograms of the levels of T-bet ( Figure 2A ) and GATA3 ( Figure 2B ) show unimodal distributions that change continuously with input signals . As expected , we measure high levels of T-bet and low levels of GATA3 in a region of inputs that corresponds to Th1 driving conditions ( Figure 2C , D , region 1 ) , and the opposite pattern for a region of Th2 driving conditions ( Figure 2C , D , region 2 ) . However , we find that expression of T-bet and of GATA3 are not mutually exclusive: both TFs are co-expressed at a relatively high level in response to a large variety of mixed input conditions ( Figure 2C , D , region m ) . These results are supported by measurements of mRNA levels , which also show co-expression of T-bet and GATA3 at high levels under mixed input conditions ( Figure S1B ) . Co-expression of T-bet and GATA3 arises at early time points of the differentiation process under mixed input conditions and can be observed already at day 3 after activation ( Figure S2 ) . Our results show that mouse CD4+ T cells can be driven into a mixed Th1–Th2 state directly from the naïve state , in addition to reprograming of Th2 cells as was recently shown [30] . Moreover , we reveal that this mixed state is continuously tuneable , showing varying levels of the lineage-specific TFs in response to different mixtures of driving signals . Next , we characterized the response to mixed inputs at the single cell level . In Figure 2E–G , we present scatter plots showing T-bet and GATA3 levels of cells cultured under Th1 ( E ) , mixed ( F ) , or Th2 ( G ) conditions . In all cases cells cluster as a single unimodal population , and no evidence of bi-stability is observed . Importantly , under mixed conditions most cells co-express T-bet and GATA3 . To better visualize single-cell patterns of expression , we define a parameter α for each cell , which is related to the ratio between its T-bet and GATA3 expression levels: α = atan ( T-bet/GATA3 ) —that is the angle that the cell forms with the x-axis ( Figure 2F , see Materials and Methods ) . α is a robust measure of the ratio between T-bet and GATA3 that is not prone to noise in low-value denominators . Using this ratiometric parameter reduces effects of extrinsic factors such as cell size , which influence levels of both proteins in a similar way . Plotting the distribution of this parameter ( Figure 2H ) reveals that external signals continuously shift the cell population from a GATA3 dominant state ( α = 0° ) to a T-bet dominant state ( α = 90° ) while exhibiting intermediate values for input mixtures . This is indicative of a population of cells that co-express the two master regulators at levels that continuously tune with inputs , though with a relatively large cell-to-cell heterogeneity . These results are supported by analysis using flow microscopy , which shows co-expression of T-bet and GATA3 at varying levels in nuclei of T cells driven under mixed input conditions , spanning the range between Th1 and Th2 states ( Figure 2I ) . Now that we have demonstrated a unimodal continuous output behaviour , we inquired how both inputs combine to determine this output . We find that each input ( IL-12 , IL-4 ) influences the outputs in an independent manner . The expression level of the two TFs can be described as: F1 ( IL-12 ) ×F2 ( IL-4 ) , where the functions F1 and F2 represent the dependence on each input separately ( Figures 2J , S3 ) . A similar property was previously observed for input functions describing bacterial promoters [31] . The resulting one-dimensional dependencies ( F1 , F2 ) are gradual , consistent with a continuous tuneable state . This separation of variables simplifies description of the system's response under mixed conditions and restricts possible mathematical models of the system . We note that levels of T-bet increase with increasing level of IL-12 and slightly decrease with increasing levels of IL-4 , as can be expected . On the other hand , we find that levels of GATA3 increase with IL-4 but also slightly increase with levels of IL-12 when IL-4 is present ( Figures 2D and S3 ) . This finding indicates that IL-12 does not strongly repress GATA3 , and may even have some net indirect weak positive effect on its level of expression . To gain a theoretical understanding of our findings of continuously tuneable cell states , we analyzed a functional network motif that is widely used for describing systems of binary cell fate decisions ( Figure 3A ) . The motif consists of two lineage-specifying transcription factors , X and Y ( corresponding to T-bet and GATA3 in the case of Th1–Th2 differentiation ) , which cross-inhibit each other and positively autoregulate their own expression . This network motif has been used to model Th1–Th2 differentiation [5] , [6] , [15] , [17] , as well as other cell differentiation systems [9] , [11]–[14] , [32] . In the case of the Th1–Th2 system , it represents a simplified view of more complex biological interactions ( Figure S13 ) , offering a tractable system that can provide general principles , rather than quantitatively explaining fine details of the data , which may require a refined model . Each link in the simplified network effectively describes more than one regulatory link of the full network . For example , T-bet autoregulation is mediated by at least two parallel pathways: IFN-γ secretion , which is up-regulated by T-bet , and in turn drives T-bet expression via STAT1 signalling; and up-regulation of IL12R by T-bet , which drives IFN-γ expression via STAT4 ( Figure S13 and Table S1 for related references ) . GATA3 autoregulation involves both a direct regulatory effect , as well as an autocrine/paracrine loop through GATA3-mediated secretion of IL-4 , which drives GATA3 expression via STAT6 ( Figure S13 and Table S1 ) . Of note , the full network of Figure S13 is coherent with respect to the links of the simplified GRN; each path in the full network that starts and ends at the T-bet node , without going through GATA3 , has a positive sign; and similarly for all paths that start and end at GATA3 without going through T-bet . Each path that starts at T-bet and ends at GATA3 has a net negative sign; and similarly for all paths that start at GATA3 and end at T-bet ( as detailed in Figure S13 ) . Previous analyses [5] , [6] , [9] , [15] , [33] showed that the motif of Figure 3A induces bi- or tri-stability ( Figure 1B , C ) through a toggle switch mechanism . In these studies , the regulatory links in the GRN are usually described by a steep function , e . g . a Hill function , xn/ ( xn+kn ) , with a Hill parameter n>1 . We will now show how the same motif , with different parameterization , can recapitulate our findings of a continuous transition with one stable state . Based on our observations of graded responses , we analysed the steady states of the GRN of Figure 3A under conditions of gradual regulatory links , using a Hill parameter of n = 1 . We find that for given levels of the two inputs , β1 and β2 , the system's behaviour depends on the ratios between the threshold levels of the cross-inhibitory and the autoregulatory arrows ( , , respectively; see Figure 3A , B , Figure S12 , and Text S1 ) . If these parameters correspond to the area below the dashed hyperbola in Figure 3B ( ) , the system is either mono-stable at one of the extreme phenotypes ( one TF highly expressed and the other at zero , Figure 3B regions I and II ) , or bi-stable ( region IV ) . However , if the parameters correspond to the area above the hyperbola , the system always has only one stable state . This state continuously tunes between the extreme phenotypes through region III , by changing the ratio of the two inputs , β1/β2 . Inside region III , both TFs are expressed at intermediate levels . Changing inputs without changing internal parameter values for the GRN cannot shift a bi-stable system into a mono-stable one and vice versa . Our experimental observations support the low-n model in a number of ways . First , we plot T-bet and GATA3 levels for a trajectory in input-space that corresponds to gradually changing the ratio β1/β2 . Expression levels of both TFs continuously shift from a pure Th1 state into a pure Th2 state , without sharp transitions ( Figure 3C ) , in accordance with model predictions ( Figure 3D ) . Moreover , experimental results concur with the model over the entire measured input-space ( Figure 3E , F ) . Finally , multistability is expected to result in either a multimodal population at transition points , or increased levels of noise in intermediate expression levels [34] , [35] . Analysis of expression-level distributions of T-bet and GATA3 does not support bi-modality of the population ( Figures 2A and S1A ) . Additionally , the noise level , calculated as SD/mean , does not considerably change with varying input conditions , for both T-bet and GATA3 ( Figure S4 ) . We thus conclude that the accepted core model for the GRN controlling cell differentiation can comply with our observations for a mixed and mono-stable tuneable state under mixed conditions , provided that the effective regulatory links gradually depend on the levels of the regulatory proteins . In particular , a low hill parameter of the autoregulatory links is sufficient , under most parameter values , to account for this behaviour ( see Text S1 ) , while cross-inhibition can be steep . Additionally , we predict that the effective positive autoregulatory links in the network motif of Figure 3A are dominant over cross-inhibition so that the system resides “above the hyperbola” of Figure 3B . We further characterized cells' phenotype by mapping the levels of the two lineage characteristic cytokines IFN-γ and IL-4 over the entire input space , asking to what extent do they follow our findings regarding the TFs . In contrast with the TFs , the expression-level distributions of these cytokines are bimodal ( Figure 4A , B ) , which is a well-known characteristic of cytokine gene expression [36] . The fraction of cytokine-expressing ( positive ) cells varies with input level , while the level of cytokine expression for these positive cells remains almost constant ( Figure 4A , B ) . Despite this difference , the population mean follows a pattern similar to that of the TFs over the different input mixtures as observed both by internal staining ( Figure 4C , D and Figure S2 , Pearson correlation 0 . 56 ( 0 . 91 ) between IFN-γ and T-bet ( IL-4 and GATA3 ) , respectively ) and ELISA ( Figures S2 and S5 , Pearson correlation 0 . 75 ( 0 . 65 ) ) . A mixed phenotype is observed also here , as co-expression of IFN-γ and IL-4 is evident under mixed conditions at the protein ( Figure 4C , D ) and mRNA ( Figure S1D ) levels . As with the master regulators , cytokine input functions can also be described as separable functions of the two inputs ( Figure S3 ) . Notably , IFN-γ protein levels show a sharper negative response to external IL-4 compared with that of T-bet . This might reflect the more direct repression of IFN-γ by GATA3 , which then indirectly down-regulates T-bet [37] . Single-cell analysis ( Figure 4E–G ) reveals a highly heterogeneous expression of IFN-γ and IL-4 under mixed input conditions , with subpopulations of cells expressing only IFN-γ , only IL-4 , both cytokines or neither , as shown in Figure 4F . Consistent with the tuneable state observed at the TF level , input signals also continuously modulate the percentage of cells in each subpopulation of cytokine co-expression ( Figure S6 ) . Similar to the analysis above , we define for each cell a parameter α′ , which is related to the ratio between its IFN-γ and IL-4 expression levels: α′ = atan ( IFN-γ/IL-4 ) . Under Th1 and Th2 driving conditions , α′ is peaked around 90° and 0° , respectively , as expected . However , under mixed input conditions α′ shows a very broad distribution , significantly overlapping with both Th1 and Th2 populations , reflecting the large heterogeneity in levels of cytokine expression ( Figure 4H ) . To investigate the behaviour of other lineage-specific cytokines , we repeated these experiments measuring also levels of the Th2 cytokines IL-5 and IL-13 ( total of six parameters for each cell ) . Under our experimental conditions we observed only a small fraction of cells expressing IL-5 ( ∼10% under Th2 conditions versus ∼1% under Th1 conditions ) , which didn't allow us to significantly analyze its co-expression patterns . IL-13 showed a very similar behaviour to that of IL-4 . Under mixed conditions there is a subpopulation of cells co-expressing IL-13 and IFN-γ ( Figure S17 ) , and the mean level of both cytokines continuously increases by shifting input conditions from Th1 to Th2 through various mixtures ( Figure S18 ) . These observation support a model of stochastic expression of IFN-γ and IL-4 , as was previously observed for IL-4 and other cytokines [38]–[41] . Hence , a population of cells cultured under the same conditions is heterogeneous , with some cells expressing a cytokine while others do not . We set to characterize properties of stochasticity of those two cytokines , with respect to levels of expression of the two master regulators . As we measure the levels of the two TFs and two cytokines for each cell , our data allow for characterization of mutual dependencies between these proteins . Thus , we binned cells cultured under Th1 , mixed , or Th2 conditions according to their level of T-bet or GATA3 , and evaluated the chance of IFN-γ or IL-4 expression , respectively , in each bin . We find that the probability of cytokine expression monotonically grows with expression level of the corresponding TF ( Figure 5A–B ) . Of note , although the probability of making IFN-γ in the entire population of cells grown under Th1-inducing conditions is ∼60% ( Figure 5A top , green line ) , it reaches ∼85% for those cells expressing the highest levels of T-bet . Similarly , the probability of making IL-4 is ∼20% in the entire population of cells grown under Th2 conditions , while it reaches ∼40% in cells that highly express GATA3 ( Figure 5B , bottom ) . Under mixed input conditions , both cytokines show a gradual monotonic increase in their probability of expression with the levels of their corresponding TF ( Figure 5A , B , middle ) . Note that for Th1 ( Th2 ) -inducing conditions , the GATA3 ( T-bet ) signal is mainly due to background ( predominantly cell autofluorescence and nonspecific staining ) and is uncorrelated with the secretion probability of the downstream cytokine , as expected . The results of Figure 5A , B suggest that stochastic expression of IFN-γ and IL-4 is biased by the level of expression of T-bet and GATA3 , respectively . Next , we checked for dependence between expression of the two cytokines in the same cell; for example: if a cell is expressing IFN-γ , does it have a higher or lower chance to express also IL-4 ? We find that expression of IFN-γ and IL-4 occurs by two independent stochastic processes ( median mutual information MI = 0 . 023 over all input conditions , see also Figures S7 and S8 ) . Similar results were obtained also for independence of IFN-γ and IL-13 ( median MI = 0 . 005 ) . These results are in accordance with previous studies that evaluated mRNA expression in T cell clones and in individual cells [42] . The Th2 cytokines IL-4 and IL-13 show somewhat larger dependence , though still at a low level ( median MI = 0 . 04 ) . It was previously shown that IL-4-expressing and nonexpressing Th2 cells have similar levels of GATA3 [40] . We extend this analysis to include both the Th1 and Th2 axes , comparing levels of T-bet and GATA3 in the four subpopulations of cytokine expression . While absolute levels of the TFs somewhat vary between these subpopulations , we find that they all have a similar GATA3/T-bet ratio ( Figures 5C and S9 ) . Moreover , we observe that different cells that express the same levels of T-bet and GATA3 may show all four patterns of cytokine expression . Hence , although when cultured under mixed conditions some cells behave for example like Th1 cells ( expressing IFN-γ but not IL-4 ) and others like Th2 cells ( expressing IL-4 but not IFN-γ ) , their internal state , as defined by levels of expression of the master regulators , is mixed and similar . The distinction between cell state under mixed versus polarizing conditions is evident when comparing , for example , the subpopulation of cells that express IFN-γ but not IL-4 . If taken from a population of cells that were cultured under Th1 conditions , the IFN-γ+–IL-4− cells have high levels of T-bet and low levels of GATA3 ( normalized GATA3/T-bet ratio ≪1 , Figure S16 ) . However , if taken from a population of cells cultured under mixed input conditions , both factors are expressed at high levels ( normalized GATA3/T-bet ratio ∼1 , Figure S16 ) . To further check stochasticity of cytokine production and stability of the mixed state , we viably sorted cells that were cultured under mixed conditions into four subpopulations , according to their expression pattern of IFN-γ and IL-4: −/− , +/− , −/+ , and +/+ . Each sorted subpopulation of cells was cultured for another week under mixed input conditions including T cell receptor stimulation . We find that all initial subpopulations are able to repopulate all four combinations of cytokine secretion following restimulation after the second week of culture ( Figure 5D ) . In addition , all four sorted subpopulations retained their similar GATA3/T-bet ratio also after the second week of growth under mixed conditions , at an intermediate level ( ∼1 ) , between those obtained for cells grown under Th1 and Th2 conditions ( ∼0 . 4 and ∼20 , respectively , Figure S16B ) . We find some differences in the patterns of cytokine expression after the second week , between subpopulations of cells that expressed IFN-γ after the first week ( +/− , +/+ ) and those that did not express it ( −/− , −/+ ) . The first show a higher tendency toward IFN-γ expression following the second week ( Figure 5D ) . This difference may be attributed to the influence of the higher amounts of IFN-γ available for these cultures in the beginning of the second week , as it is expressed by the cells . This is different than the situation in the first week , where the cells only express lower amounts of IFN-γ upon primary activation . The excess amount of IFN-γ can drive cells stronger toward a Th1 phenotype , resulting in a higher fraction of IFN-γ-expressing cells and a lower fraction of IL-4-expressing cells . Nevertheless , the ability of cells sorted from the four subpopulations to repopulate all four states and the stability of the T-bet/GATA3 ratio provide evidence for further stability of the mixed state , for at least 2 wk of culture , though we cannot exclude convergence into the polarized states at longer times .
Our findings can be explained by a two-stage model based on continuous , analogue expression of TFs that then bias a binary stochastic cytokine secretion . This model is shown schematically in Figure 6 . First , input signals are mapped through the GRN in an analogue way , into continuously variable expression levels of the two master regulators . We show that the observed pattern of TF co-expression and their continuous tuning in response to levels of the input signals can be explained using the accepted simplified model for the network motif controlling the system , provided that the effective autoregulation on TF levels is graded , and dominates cross-inhibition . The second stage of the process is probabilistic in nature . During restimulation , cytokines are expressed stochastically in each cell with probabilities that are biased by the level of the relevant master regulator in that cell . These two stochastic processes are independent , as if the cell is throwing two biased coins , one determining whether to express IFN-γ or not , and the other determining whether to express IL-4 or not ( see Figure S7 ) . As each cytokine can be either “on” or “off , ” under mixed conditions four subpopulations of cells arise , each with a different expression pattern: IFN-γ−/IL-4− , IFN-γ+/IL-4− , IFN-γ−/IL-4+ , and IFN-γ+/IL-4+ . The model presented in this work can account for previous observations that challenged the Th1–Th2 dichotomy [43] . It can also reconcile the recent observations of continuums of hematopoietic cell states [18] , [19] with prevailing theoretical models of cell differentiation . While the model predicts that cross-inhibition does not necessarily lead to multistability , it shows that it still restricts the state of the system . Thus , cells can be found only in a subset of the large multidimensional space defined by combinatorial protein expression , as restricted by the GRN . Within this allowed region , cell state can continuously tune , in response to levels of input signals . While simplified models as we use here do not capture the full complexity of the regulatory network controlling cell differentiation , their main value is in their ability to reveal general classes of behaviour of these systems . Our analysis identifies conditions under which the widely used network motif of Figure 3A does not produce a bi- or tri-stable switch , but a mono-stable system , whose steady state continuously tunes from one extreme phenotype to the other in response to varying input levels . Our model can be refined when more quantitative data about the regulatory links controlling Th1–Th2 differentiation become available , together with detailed dynamical data on system state over time . Such approach was used , for example , to reveal the interplay between TCR and cytokine signalling during Th1 differentiation [44] . The core regulatory network that we studied ( Figure 3A ) was used previously to model the Th1–Th2 system [5] , [6] , [15] , [17] as well as other systems of binary cell fate decisions [9] , [11]–[14] , [33] . A notable example is the PU . 1–GATA1 system that controls erythroid/myeloid differentiation . Motivated by experimental results , various models of that network were proposed , which can generate tri-stability [9] , [11]–[14] . Two of the steady states correspond to the two differentiated cell states ( expressing either PU . 1 or GATA1 ) and the third state corresponds to the progenitor cell state , in which both factors are expressed at intermediate levels . The levels of the two TFs in the progenitor state are highly variable between cells , and it was shown that this variability in turn biases the differentiation potential of progenitor cells [45] , [46] . It is interesting to compare our results with those studies . While our data are better explained by one steady state when compared to a bi-stable system ( Figure S14 ) , we cannot exclude tri-stability with our current data . This is mainly due to the relatively high level of noise in the measurements of the expression of T-bet and GATA3 in single cells by flow cytometry . While part of this noise is technical , as can be seen from the T-bet knockout data ( Figure 2A ) , the observed noise also reflects real biological variability between cells due to stochastic gene expression . A study in which mRNA molecules of GATA3 in Th2 cells were counted by single-molecule RNA fluorescence in-situ hybridization ( FISH ) supports our findings of highly heterogeneous GATA3 levels in the cell population [47] . Moreover , no bistability was observed for GATA3 mRNA levels in that study , supporting our observations at the protein level . Although we cannot formally exclude tri-stability of the Th1–Th2 system , several differences between our observations and those of the PU . 1–GATA1 system support a mono-stable tuneable solution in the case of Th1–Th2 differentiation . First , the observed mixed state is distinct from the progenitor state of the system , the naïve CD4+ T cell . Naive T cells show very low levels of expression of both T-bet and GATA3 [48] , while in the mixed state both factors are expressed at levels similar to their levels in the polarized states ( Figure 2C , D , Figure S1A , B ) . In addition , the mixed state requires simultaneous presence of the two inputs in order to up-regulate the expression of both transcription factors , unlike a progenitor state that is independent of the differentiation driving signals . Second , we observe stability of the mixed state: under mixed conditions , we detect cells that are co-expressing T-bet and GATA3 already 3 d after activation of naïve cells , and the levels of the two proteins at that time correlate with their levels at day 7 ( Figure S2 ) . Under mixed conditions , cells can be kept in culture for at least 2 wk while keeping expression of both transcription factors , and do not seem to resolve towards a more Th1 or Th2 like phenotype ( Figure S16B ) . Finally , when binned into four subpopulations based on patterns of expression of the cytokines IFN-γ and IL-4 , all four subpopulations ( −/− , +/− , −/+ , +/+ ) show similar GATA3/T-bet ratios after 1 wk ( Figure 5C ) and 2 wk ( Figure S16B ) of culture under mixed conditions . If the mixed population that we observe was a combination of cells in three stable states of a tri-stable system , one would expect to see a lower GATA3/T-bet ratio for cells that express IFN-γ but not IL-4 ( +/− ) , and a higher ratio for cells that express IL-4 but not IFN-γ ( −/+ ) . Based on these observations , we conclude that the tuneable mono-stable model better explains our observations for the Th1–Th2 system compared to a tri-stable case . These results suggest that different cellular systems may use a similar gene circuit topology but have different dynamic properties , depending on the quantitative parameters of the regulatory network . We have demonstrated how a gene regulatory circuit controlling cell fate decision can be designed for plasticity and robustness , to handle complex mixtures of signals to which cells are exposed . The continuously tuneable mixed states identified here can allow for a higher flexibility of the immune response under complex conditions when various counteracting signals may simultaneously occur . For example , in a recent study [49] it was shown that after infection causing a Th1 or Th2 response , most T cells in a draining lymph node were exposed to activating amounts of IFN-γ or IL-4 , respectively . If Th1 and Th2 responses were mutually exclusive , T cells would lose their ability to respond to an unrelated challenge of the opposite nature that occurs simultaneously within the same lymph node . However , the mechanism described here allows cells to be in a mixed state , representing the actual levels of both signals . In this way , the system keeps the two options viable , at least until further information becomes available for these cells . Thus , cells can assess the two inputs and make a decision that is not binary but is gradual or fuzzy [50] . We suggest that this model of continuum differentiation combined with biased stochasticity is advantageous for differentiating systems that encounter uncertainty , such as most functions of the immune system [39] , [51] . The classic bi-stable switch model is more suitable for rigid developmental programs such as embryonic development . We expect that similar continuously tuneable mixed phenotypes exist also in other differentiation pathways of CD4+ T cells ( such as Th17 , or induced regulatory T cells [52] , [53] ) and in other cell types in the hematopoietic system . We note that direct differentiation of naive CD4+ T-cells into a mixed Th1–Th2 phenotype was also observed concurrently by two other groups , using different experimental approaches [54] , [55] .
All animal work was approved by the Weizmann Institute's Institutional Animal Care and Use Committee ( IACUC ) and was conducted according to relevant national and international guidelines . Female 5–8-wk-old C57BL/6 mice were obtained from Harlan Laboratories ( Rehovot , Israel ) and housed at the Weizmann Institute . All mice were kept in small cages and fed sterile food and acid water . Naïve CD4+ T-Cells were purified from C57BL/6 splenocytes by magnetic beads separation ( CD4+CD62L+ MACS , Milthenyi biotech ) . As a control , we also sorted CD4+CD62L cells by FACS ( FACSARia , BD ) , which provides higher purity of this population , avoiding potential memory phenotype cells . Similar results were obtained by both cell selection methods . Cells were cultured in a complete RPMI 1640 medium . CD4+ T cells were stimulated using plate coated with anti CD3 ( 1 µg/ml ) and anti-CD28 ( 3 µg/ml ) monoclonal antibodies in the presence of various external levels of IL-4 ( 0 to 540 ng/ml ) and IL-12 ( 0 to 540 ng/ml ) or the corresponding antibody ( 10 µg/ml ) , as indicated . After 4 d cells were removed from stimulations and transferred to a new plate for an additional 3 d . On day 7 cells were restimulated by a plate-bound anti-CD3 ( 2 µg/ml ) for 4 h before addition of Brefeldin A and Monensin for an additional 2 h . Refreshing the media before restimulation was shown to have no significant effect on the results ( Figure S10 ) . Levels of T-bet and GATA3 somewhat increase upon restimulation , but are well-correlated before and after re-stimulation ( Figure S15 ) . Cells were cultured under mixed cytokines condition ( 100 ng/ml IL12 , 4 ng/ml IL4 ) as described above . On day 7 cells were stained with Miltenyi's cytokine secretion assay for IL-4 secretion ( PE ) and IFN-γ secretion ( FITC ) , according to the manufacturer's instructions . Stained cells were sorted into four subpopulations of secretors/nonsecretors , using BD FacsAria ( BD Biosciences ) . Secondary culture of the sorted groups was carried out for an additional 7 d as described above , while being exposed to mixed cytokine environment ( 100 ng/ml IL-12 , 4 ng/ml IL-4 ) . Cells were stained with Invitrogen live/dead fixable dead cell stain kit . Subsequently cells were fixed and permeabilized , and were stained with various antibodies for 1 h at 4°C . The antibodies used were: FITC anti-IL-4 , PerCP–Cy5 anti-IFN-γ , PacificBlue anti-T-bet , PE anti-CD4 ( Biolegend ) , and Alexa647 anti-GATA3 ( eBioscience ) . FACS analysis was performed using BD LSRII ( BD Biosciences , Mountain View , CA ) . Quantitative evaluation of protein levels in supernatant was done using an extension of the ELISA assay , with spectrally distinguishable beads ( Spherotech PAK ) as the solid phase . Primary antibodies were covalently linked to the beads , with spectrally different beads linked to antibodies against a unique cytokine ( IFN-γ and IL-4 ) . The various coated beads were incubated simultaneously with the supernatant and secondary biotinylated antibodies for 2 h , washed , and stained with streptavidin-PE . The beads' fluorescence level was used to separate the different cytokines . A standard curve for each cytokine was generated and fitted using a four-parameter hill function and was used to quantify fluorescent results ( Figure S5 ) . Beads were analyzed using BD LSRII . Cells were stained with PacificBlue anti-T-bet , Alexa647 anti-GATA3 , and APC/CY7 anti-CD4 . The cells were run on the Imagestream X , an imaging flow cytometer that acquires up to six channels of imagery including brightfield , darkfield , and four channels of fluorescent imagery using a CCD camera . Images were analyzed using the Imagestream Data Analysis and Exploration Software ( IDEAS 4 ) . Flow cytometry data were analyzed using “EasyFlow , ” a dedicated in-house tool-set written in MATLAB , allowing graphical analysis of flow cytometry data , including gating , compensation control , histogram fitting , and statistical analysis , while providing a natural interface with native MATLAB algorithms . Cells were gated on four parameters: lymphocytes were selected using an FSC-SSC gate and live CD4+ cells were selected based on live/dead and anti-CD4 staining . The resulting cells were analyzed for protein levels . For population analysis , median fluorescence levels were calculated for each growth condition . Histograms were drawn using a logicle rescaling in such a way as to keep linearity for low fluorescence values while approaching log scale for high values [1] . For single cell analysis of secretion probabilities ( Figure 5A , B ) , cells were binned according to their measured level of the transcription factor , and the percentage of cytokine-producing cells was calculated for each bin . Data collected from three different experiments performed on different days are qualitatively similar ( see Figure S11 ) . The angular parameters ( see Figures 2G and 3F ) were calculated in the log space . We shifted each parameter such that its isotype median is set to be at the origin ( 1 , 1 ) and then rescaled values such that the 95th percentile is normalized to 10 . The angle was then calculated using the equation α = atan ( x/y ) . Total RNA was extracted using RNeasy mini kit ( Qiagen Valencia , CA ) , from the four sorted cell subpopulations ( IL-4+ , IFN-γ+ , IL-4+ IFN-γ+ , and nonsecreting cells ) . RNA concentration was measured on Nanodrop . 1 µg of total RNA was converted into cDNA using superscript II ( Invitrogen ) The ABI PRISM 7900HT Sequence Detection System was used for qt-RT-PCR analysis . Custom designed primer and probe sets were validated by serially diluting cDNA isolated from cells expressing the target gene and verifying the slope . Taqman PCR Master Mix was purchased from Applied Biosystems ( NJ ) . Amplification was carried out in a total volume of 25 µl for 40 cycles of 15 s at 95°C , 1 min at 60°C . Initial denaturation was performed for 10 min at 95°C . Target gene expression was normalized relative to expression of the Abelson ( ABL ) gene . | During cell differentiation , progenitor cells respond to external signals that drive the expression of genes that are characteristic of the differentiated cell states . This process is controlled by gene regulatory networks that typically involve positive autoregulation and cross-inhibition between master regulators of the two differentiated states . Mapping the system's response to mixtures of external signals can help us to understand the operational logic of these binary cell fate decisions . Here , we study differentiation of CD4+ T cells into Th1 and Th2 lineages under mixed-input conditions , at the single cell level . We reveal that cell state is not restricted to a small number of well-defined phenotypes , but rather tunes through a continuum of mixed-phenotype states in which levels of lineage-specifying transcription factors gradually change with the levels of the two inputs . Using mathematical modeling we establish the conditions under which the system has one stable steady state that continuously tunes in response to changes in levels of the inputs . Results of this model qualitatively explain our experimental observations . We further characterize expression patterns of downstream lineage-specific genes—cytokines that are driven by the two master regulators upon cell re-stimulation . We find a highly heterogeneous population with cells expressing either one of the cytokines , both cytokines , or neither . Of note , the fraction of cells in these subpopulations continuously tunes with input levels , thus reproducing a tunable state at the cell population level . Our results can be explained by a two-stage scheme in which the gene regulatory network is responsible for a continuously tunable cell state , which is translated into a heterogeneous cytokine expression pattern through uncorrelated and biased stochastic processes . | [
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] | 2013 | Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates |
HIV-1 enters target cells by virtue of envelope glycoprotein trimers that are incorporated at low density in the viral membrane . How many trimers are required to interact with target cell receptors to mediate virus entry , the HIV entry stoichiometry , still awaits clarification . Here , we provide estimates of the HIV entry stoichiometry utilizing a combined approach of experimental analyses and mathematical modeling . We demonstrate that divergent HIV strains differ in their stoichiometry of entry and require between 1 to 7 trimers , with most strains depending on 2 to 3 trimers to complete infection . Envelope modifications that perturb trimer structure lead to an increase in the entry stoichiometry , as did naturally occurring antibody or entry inhibitor escape mutations . Highlighting the physiological relevance of our findings , a high entry stoichiometry correlated with low virus infectivity and slow virus entry kinetics . The entry stoichiometry therefore directly influences HIV transmission , as trimer number requirements will dictate the infectivity of virus populations and efficacy of neutralizing antibodies . Thereby our results render consideration of stoichiometric concepts relevant for developing antibody-based vaccines and therapeutics against HIV .
To infect cells , HIV-1 virions need to fuse their membrane with the target cell membrane , a process triggered by the viral envelope ( env ) glycoprotein trimer [1] , [2] . Due to its key function in the virus life cycle and as prime target for neutralizing antibodies and entry inhibitors , analyses of env trimer structure and function remain in the focus of current HIV vaccine and drug research [3]–[5] . Each env trimer consists of three heterodimeric protomers , composed of the non-covalently associated gp120 surface and gp41 transmembrane subunits . Binding of gp120 to the primary receptor CD4 on target cells triggers conformational changes in gp120 that expose the binding site of a co-receptor , most commonly CCR5 or CXCR4 [6] . Subsequent co-receptor binding activates the gp41 transmembrane subunits , which triggers a prototypic class I fusion process via insertion of the N-terminal fusion peptides into the target cell membrane . Refolding of the gp41 N- and C-terminal heptad repeat regions into six-helix bundles drives approximation and fusion of viral and target cell membranes [1] , [7] , [8] . While the HIV entry process has been defined in considerable detail , we currently lack information on the stoichiometric relations of interacting molecules . Likewise , the thermodynamic requirements of membrane fusion pore formation and pore enlargement , enabling passage of the viral core into the target cell cytoplasm , are only partially understood [9]–[11] . The energy required for the entry process is generated by structural rearrangements of the envelope trimer that follow receptor binding [7] , [8] , [12] . How many trimers must engage in receptor interactions ( a number referred to as stoichiometry of entry ) [13]–[15] in order to elicit the required energy to complete fusion has not been conclusively resolved . Whether HIV needs one or more trimers to complete entry will strongly influence virion infectivity and efficacy of neutralizing antibodies targeting the trimer . Previous studies resulted in contradicting stoichiometry estimates , suggesting that either a single trimer is sufficient for entry [13] or that between 5 to 8 trimers are required [14] , [15] . In comparison , for Influenza A virus , which achieves membrane fusion through the class I fusion protein hemagglutinin ( HA ) , postulated necessary HA trimer numbers range from 3 to 4 [16]–[18] to 8 to 9 [13] . Calculations based on the energy required for membrane fusion suggested that indeed the refolding of a single HIV envelope trimer could be sufficient to drive entry [7] , [8] . Numerous lines of evidence however suggest that several env-receptor pairings are commonly involved in the HIV entry process . Electron microscopy analysis of HIV entry revealed the formation of an “entry claw” consisting of several putative env-receptor pairs [19] , which is supported by biochemical analyses indicating that the number of CCR5 co-receptors needed for virus entry differs among HIV-1 isolates and requires up to 6 co-receptors [20] , [21] . Precise delineation of the stoichiometry of entry , as we present it here , substantially contributes to our understanding of HIV pathogenesis by defining a viral parameter that steers virus entry capacity , potentially shapes inter- and intra-host transmission by setting requirements for host cell receptor densities , and by defining stoichiometric requirements for virion neutralization . The latter is of particular importance considering the ongoing efforts to generate neutralizing antibody based therapeutics and vaccines targeting the HIV-1 entry process [3] , [4] , [22] .
To estimate the stoichiometry of entry ( in the following referred to as T ) we employed a previously described combination of experimental and modelling analyses [13]–[15] . Our strategy centers on the analysis of env pseudotyped virus stocks carrying mixed envelope trimers consisting of functional ( wt ) and dominant-negative mutant env , where a single dominant-negative env subunit incorporated into a trimer renders the trimer non-functional . We included envs of 11 HIV-1 strains in our analysis covering subtypes A , B and C and a range of env characteristics such as primary and lab-adapted strains , different co-receptor usage and different neutralization sensitivities ( Table 1 ) . To derive estimates of T from mixed trimer experiments two key parameters need to be considered: the mean virion trimer numbers and the distribution of virion trimer numbers across a virion population [14] , [15] . To assess virion trimer numbers , we determined p24 and gp120 content of purified virus stocks by ELISA . Although only an approximation , as also partially shed and non-functional trimers are accounted for , this analysis yielded upper limits of virion trimer content . We observed between 6 to 20 trimers per virion among the 11 strains probed ( Table 1 ) , which is in close agreement with previous estimates of HIV-1 virion trimer content [23]–[28] . While trimer incorporation into pseudotyped particles may be lower than on replication-competent virus [29] , this does not preclude estimation of T as trimer content is controlled for in our mathematical analysis . Since available experimental data do not provide information on the distribution of trimers across virion populations ( i . e . frequencies of virions with a given trimer number in the population ) , we utilized previously determined trimer number variation across virions in our modeling [26] . Key in our experimental design are dominant-negative env mutants . To obtain robust estimates of T we performed the experiments with two individual env mutations that both lead to a complete loss in entry capacity , either by a mutation of the Furin cleavage site ( R508S/R511S ) [13] , [30] , [31] or a mutation of the gp41 fusion peptide ( V513E ) [13] , [32] . Importantly , expression levels of the mutant envs were in the range of 80 to 100% of the corresponding wt envs ( Table 1 ) , ascertaining that during mixed trimer experiments env expression levels on virions follows the ratio of wt and mutant env plasmids transfected into virus producer cells . To assess T , mixed trimer expressing pseudovirus stocks of each strain were generated by transfecting producer cells with different ratios of mutant and wt env plasmids ranging from 0 to 100% of the dominant-negative mutant env . The resulting virus stocks were probed for infectivity on TZM-bl reporter cells and infectivity was plotted as function of mutant env content ( Fig . 1A and B ) . Based on our model [15] , differences in T result in different infectivity plots in this graphical analysis ( Fig . 1A ) . Intriguingly , the 11 envs showed variable patterns suggesting that the strains differ in T ( Fig . 1B ) . The mixed trimer infectivity data ( Fig . 1B ) and the mean virion trimer numbers ( Table 1 ) were then used to infer T of each strain by our model . This resulted in estimates of T ranging from 1 to 7 trimers for the 11 HIV strains tested ( Fig . 1C , S1 Table and S1 Fig . ) , with the majority of strains requiring 2 to 3 trimers for entry . Of note , the data derived with the V513E and R508S/R511S mutant returned closely matching results with identical estimates for T ( n = 4 ) or estimates differing only by 1 ( n = 6 ) ( Fig . 1C ) . The only higher discrepancy was observed for the highly neutralization sensitive , T cell line adapted strain NL4-3 where the two env mutations appeared to have individual effects on the readout but both yielded estimates of T that were amongst the highest in the env panel ( T = 7 with V513E and T = 4 with R508S/R511S ) . To verify that the mathematical approach ( in the following referred to as “basic model” ) provides a valid estimate of T , we probed several alternative analyses of the data shown in Fig . 1B . These analyses incorporate previously described extensions of the basic model that account for additional parameters that could potentially influence data acquisition and analysis [15] . The model extensions showed for the majority of strains a significantly improved curve fit to the experimental data ( S1 Table ) . However , these analyses frequently yielded highly divergent values of T and the additional model parameters included in the model extensions for the two dominant-negative mutants of the same strain ( S1 Table and exemplified for the CAP88 Env in S2 Fig . ) . This was in stark contrast to the basic model where the two independent T estimates of each strain were with few exceptions in close agreement ( Fig . 1C and S1 Fig . ) . As we can safely assume that the parameter estimates for the two different mutations of the same strain should be similar we can reject the model extensions . A further indication that the model extensions we probed are not valid in the context of our analysis was that the derived estimates of T were in many cases implausibly high whereas the basic model generated estimates that fit the described range of trimer levels on HIV virions . We are thus confident that the basic model we utilize for the estimation of T is valid and provides robust estimates . The mean virion trimer number of the probed virus stocks is an important model parameter in our analysis of T and fluctuations in the mean virion trimer number may therefore influence the estimates . To test the influence of mean virion trimer number variation on our estimates of T we performed additional data analyses where instead of the measured individual trimer numbers ( Table 1 ) , identical mean trimer numbers for all 11 strains were assumed . We chose 3 values for this comparison that covered the range of trimer numbers measured across our panel: mean trimer numbers of 5 and 26 ( representing the lowest and highest trimer contents measured in individual experiments ) and a mean trimer number of 13 , the mean of trimer numbers measured across our virus panel . Applying these trimer numbers to our data set we obtained estimates of T ranging from 1 to 17 trimers ( S3A Fig . ) . To determine the mean virion trimer numbers of the virus stocks we measured gp120 and p24 contents . This allows to derive virion numbers based on previously reported estimates of 1200 to 2500 p24 molecules per virion [23] , [24] , [33] . We chose an average estimate of 2000 p24 molecules per virion to derive the mean trimer numbers shown in Table 1 . To investigate the influence of p24 assumptions on our analysis we also tested a higher p24 content estimate of 2400 molecules per virion as recently reported [33] , consequently yielding 20% higher mean virion trimer numbers across all viruses . Employing these 20% higher trimer numbers in our analysis had only a modest effect on the T estimates yielding identical or slightly higher ( mostly by one trimer ) estimates of T ( S3B Fig . ) . While these analyses confirm that absolute values of T vary depending on the mean virion trimer number assumed for the analysis , the differences in T among the 11 strains persisted , highlighting that they reflect qualitative entry properties of the respective envs . Hence , independent of the absolute mean trimer numbers , differences in T between viral strains can be detected by our approach . As a further assay verification we tested the influence of target cells on our estimation of T . We reasoned that if our experimental approach truly measures the stoichiometry of entry , then the obtained data should be a sole function of the envelope trimer and not be influenced by target cell type and receptor density . We thus chose TZM-bl cells as target cells for their known reproducible performance and good signal to noise ratio in the luciferase reporter readout . Since these engineered cells overexpress the entry receptors of HIV and thus do not reflect features of physiological relevant target cells , we sought to verify that the obtained T estimates are indeed independent of the target cells used . To this end we chose two envelopes which yielded a low T estimate ( primary isolate JR-FL , T = 2 ) and a high T estimate ( lab-adapted strain SF162 , T = 4 to 5 ) on TZM-bl cells and repeated the estimation of T on PBMC as target cells ( Fig . 1D and E and S4 Fig . ) . As anticipated , we obtained for both viruses almost identical curves and T estimates as with the TZM-bl reporter cells , confirming that estimates of T are truly independent of the target cell type . Hence , use of TZM-bl cells for our assay setup is appropriate and the estimated T values are valid for physiologically relevant target cells of HIV-1 . The entry stoichiometry of a strain can be expected to influence virus population infectivity as strains with a low T will benefit from a higher proportion of the virus population carrying the required minimum trimer number ( Fig . 2A ) . To directly probe the influence of T on virus infectivity we assessed the in vitro infectivity of the 11 HIV-1 strains in our panel . Of note , in the context of pseudoviruses infectivity is solely determined by the Env genes . Intriguingly , infectivity proved to be inversely correlated with T ( r = −0 . 635 , p = 0 . 036; Fig . 2B ) indicating that strains that accomplish entry with low T are more infectious than strains with high T . Of note , we observed very divergent infectivities also for strains with very similar estimates of T ( Fig . 2B ) . This is likely caused by different mean trimer numbers of the strains , as the mean trimer number in conjunction with T dictates virion population infectivity ( Fig . 2A , C and D ) . For instance , amongst the viruses with T = 2 strain P3N has the highest infectivity and highest mean virion trimer number ( 20 . 3 ) whereas ZM214 , the strain with lowest infectivity also has the lowest mean virion trimer number ( 6 . 7 ) measured across these viruses ( Table 1 ) . It can expected that additional factors beyond T and trimer numbers , such as propensity to shed gp120 or differential affinity for CD4 , which are not covered by our analysis , may further contribute to different infectivity of the strains . To investigate the interplay between entry stoichiometry and infectiousness of a virus population in more detail , we performed mathematical analyses of the relation between entry stoichiometry and trimer numbers per virion of a virus population . We found that indeed the entry stoichiometry steers virus population infectivity , with a higher entry stoichiometry resulting in a lower fraction of potentially infectious virions ( Fig . 2C and D ) . Hence , the T of a strain and the therewith linked entry capacity may potentially contribute to the infectious to non-infectious particle ratio which is known to be low for HIV-1 [24] . To further explore the relation between virus infectivity and T we analyzed envs with deletions of the gp120 variable loops 1 and 2 ( V1V2 ) and compared them to the matching wildtype envs . As we and others have previously shown , V1V2 deletion causes a dramatic reduction of virus infectivity through impairment of trimer integrity ( Fig . 3A and [34]–[38] ) . When we probed T and compared the infectivity curves of the wt and V1V2-deleted env pairs , we observed distinct curve shifts of the V1V2-deleted envs across the majority of strains ( Fig . 3B and C ) . Indeed , T of the V1V2-deleted envs proved significantly increased compared to the matching wt envs ( Fig . 3D; mean T of 3 . 1 for wt envs versus mean T of 6 . 85 for V1V2-deleted envs; paired t test p = 0 . 0069 ) . Importantly , this reduction in entry efficiency and the ensuing high estimates for T upon V1V2 deletion are not simply caused by reductions in env content of these virions , as V1V2-deleted env is expressed to similar levels on virions as the corresponding wt env ( 80–100% of wt , S5 Fig . ) . While expression levels of trimers certainly influence the estimates of T , we verified that the observed env content reduction of V1V2 deleted viruses was too low to inflict an overestimation of T ( S5 Fig . ) highlighting that indeed functional properties and not quantity of the respective wt and ΔV1V2 envelopes are decisive in defining T . To further investigate the interplay between trimer numbers and T we produced pseudoviruses which expressed JR-FL wt and JR-FL ΔV1V2 with a deletion of the gp41 cytoplasmic tail ( CT ) as this is known to lead to an increased incorporation of trimers into virions [39] , [40] . Indeed , CT deletion resulted in approximately 2-fold increased levels of trimers on virions ( S6A–S6B Fig . ) . In support of the strong interplay between virion trimer numbers and infectivity thresholds defined by T , the infectivity of both viruses upon CT deletion was increased ( S6C Fig . ) . Intriguingly , the increase in infectivity upon CT deletion was higher for JR-FL ΔV1V2 ( 9-fold ) compared to JR-FL wt ( 2-fold ) , highlighting that envelopes with a reduced entry capacity , as here JR-FL ΔV1V2 , benefit more if virions carry higher trimer numbers and thus meet the stoichiometric requirements for entry ( S6D–S6E Fig . ) . The number of trimers required for HIV entry likely influences virus infectivity in many ways . Besides determining a threshold trimer content that renders virions infectious , different T's could also manifest in different kinetics of the entry process as viruses with higher T may require more time to recruit and engage the necessary number of trimer-receptor pairings . To determine virus entry kinetics we employed a time-of-inhibitor addition experiment to derive the time required per virus strain to reach 50% of entry into target cells ( Fig . 4A and S7A–S7B Fig . ) . Synchronized infection following spinoculation and temperature arrest in this assay setup allows assessment of entry kinetics solely as factor of envelope function post attachment to the target cells . When comparing the entry kinetics of the wt and V1V2-deleted strains we found that V1V2-deleted envs showed significantly delayed entry into target cells ( Fig . 4B; mean times to 50% entry 19 . 9 minutes for wt envs and 37 . 7 minutes for V1V2-deleted envs; paired t-test p = 0 . 0002 ) . As stated above , a potential explanation for this is that more time is required for V1V2-deleted strains to assemble a sufficient number of trimers in the virus-target cell contact zone to achieve entry . Indeed , we found a strong correlation between the estimated T and half-maximal entry time when all viruses , wt and V1V2-deleted strains , were analyzed ( Fig . 4C; r = 0 . 568 , p = 0 . 0073 ) but also for wt envs alone ( r = 0 . 649 , p = 0 . 0307 ) , highlighting that entry stoichiometry and entry kinetics are tightly linked . The fact that we observe a significant correlation between estimated T and half-maximal entry time does however not exclude that additional processes beyond the recruitment of the necessary number of trimer-receptor pairings also influence the entry kinetics . Rates of CD4 and co-receptor binding and speed of the ensuing conformational rearrangements may differ between strains [41] and thereby contribute to the overall variance in entry kinetics . Additionally , for virions with a high T it must be considered that formation of the required number of contacts with the target cell may need longer time periods during which virions may detach again or decay before entry is completed [42] . To further explore the relationship between the entry stoichiometry and infectivity we performed additional studies with the subtype C strain CAP88 [43] , which had the highest T and lowest infectivity within our panel ( Fig . 1B , 1C and 2B ) . CAP88 is a transmitted/founder virus which carries a lysine ( K ) at position 160 of gp120 , a site frequently targeted by neutralizing antibodies [44] . Among 4894 Env sequences deposited in the Los Alamos HIV Sequence Database asparagine ( N ) at position 160 , as part of an N-linked glycosylation sequon , is with 93 . 3% the most prevalent residue at position 160 [45] . Loss of this glycosylation site is both associated with escape from PG9/PG16-like antibodies and decreased entry capacity [38] , [46]–[48] . Supporting this we found that reconstitution of the N-linked glycosylation site ( K160N ) in CAP88 results in a 4-fold increase in virus infectivity ( Fig . 5A and [38] ) highlighting the importance of N160 for env functionality . To probe if the increased infectivity of the CAP88 K160N mutant may be due to changes in trimer structure and function that result in a reduction of T , we analyzed T of CAP88 wt and CAP88 K160N ( S8A–S8B Fig . ) . Indeed , we found that the increased infectivity of CAP88 K160N is reflected by a decreased T ( Fig . 5B and S8C Fig . ) . As this example highlights , changes in trimer structure inferred by naturally occurring mutations , possibly due to antibody escape , can result in a decreased entry capacity of the respective env , which in turn is reflected by an increase in the stoichiometry of entry . The finding that a single point mutation in the CAP88 env could dramatically alter entry fitness and entry stoichiometry prompted us to further explore the influence of point mutations on env entry phenotype . To this end we selected three JR-FL variants mimicking resistance mutants as they may occur in vivo during neutralization escape: the JR-FL D664N escape mutant resistant to the MPER antibody 2F5 , the JR-FL V549M N554D mutant which has a highly increased resistance against the entry inhibitor T-20 [49] , and a JR-FL env with point mutations N332S P369L M373R and D664N rendering it resistant against the broadly neutralizing antibodies ( bnAbs ) PGT128 , 2G12 , b12 and 2F5 ( S9 Fig . ) . While all three JR-FL variants showed similar mean virion trimer numbers as JR-FL wt , we observed differences in env infectivity with the 2F5 escape mutant infecting equally well as JR-FL wt whereas the T-20 and the multiple bnAb escape mutant env showed strongly reduced infectivity at 9% and 19% of JR-FL wt , respectively ( Fig . 6A ) . When we compared the three escape mutant envs and JR-FL wt in the mixed trimer assays we observed a distinct curve shift for both dominant negative mutants of the T-20 escape variant ( Fig . 6B ) , while the other three envs gave almost identical curves . Mathematical analysis of the data indeed revealed that the T-20 escape mutant requires 4 to 6 trimers for entry while all other mutants , like JR-FL wt , require 2 trimers ( Fig . 6C ) . Hence , the T-20 escape mutant showed both , an increase in T and a loss in infectivity while the bnAb escape mutant env maintained T despite showing infectivity loss , confirming our earlier findings that viruses with the same T can still show a wide variation of infectivities ( Fig . 2B ) . This can possibly be attributed to increased trimer decay rates or variations in CD4 and co-receptor engagement , especially since the bnAb escape mutant carried mutations in the CD4 binding site . Interestingly , the increased demand of the T-20 escape mutant for trimers during entry was also reflected in delayed entry kinetics of this env variant ( Fig . 6D ) [50] . These entry characteristics of the T-20 escape mutant are intriguing as the resistance mutations lie in the heptad repeat region of gp41 and interfere with six-helix bundle formation , which is a key step providing energy for membrane fusion during the entry process [51] . Thus , it is mechanistically plausible that the mutant env may require more trimers for entry to compensate for the disturbed six-helix bundle formation and generate sufficient energy to achieve membrane fusion .
Fusion of biological membranes , as required for entry of enveloped viruses , occurs in a plethora of cellular processes . In the case of HIV , fusion is executed by envelope glycoprotein trimers upon interaction with adequate receptor molecules on the target cell membrane [1] , [8] . While the principle steps are known and thought to be shared across different biological systems and membrane types [7] , [52] , [53] , the exact mechanisms and stoichiometric and thermodynamic requirements of most membrane fusion processes are not completely resolved [1] , [8] , [10] , [11] , [54] , [55] . Definition of the components involved in HIV entry and the membrane fusion process is of particular interest as improved understanding of the determinants of HIV entry bears the promise to funnel the development of enhanced strategies to prevent and treat viral infections [1] , [7] . The efficacy of HIV entry shapes inter- and intra-host transmission and determines the vulnerability to a range of therapeutic and preventive strategies such as neutralizing antibodies , entry inhibitors and antibody based vaccines . Considering that the stoichiometry of entry defines the number of trimers required for a virus to infect , in turn it also defines the number of trimers on a virion that need to be blocked by neutralizing antibodies . Depending on the stoichiometry of entry the quantities of antibody needed for effective neutralization can therefore vary substantially [56] ( S10 Fig . ) . To resolve molecular requirements of HIV membrane fusion , we explored in the present study the stoichiometry of HIV-1 entry ( T ) , which defines the number of envelope trimers required per virion to fuse with the target cell membrane and thereby initiate infection [13]–[15] . Our estimates of T are based on a combined strategy of experimental data acquisition and mathematical modelling . We analyzed envelopes from 11 HIV-1 strains including different HIV subtypes , CCR5 and CXCR4 users , and envelopes with open ( lab adapted strains ) and closed ( primary isolates ) trimer conformation . We found that T differs substantially between individual strains with measurements ranging from 1 to 7 trimers that are required for entry . While a previous study suggested that HIV −1 isolates generally require only a single trimer for entry [13] our analysis retrieved values for T which were , with one exception ( strain REJO ) , greater than 1 , supporting the findings of alternate modelling approaches by us and others [14] , [15] . Of note , only one of the two dominant-negative env mutants we probed recorded T = 1 for the strain REJO while the other mutant yielded an estimate of T = 2 . Thus , while our data cannot exclude that T = 1 for some strains , based on our observations a range of different entry stoichiometries as we describe here seems more plausible . We postulate two potential underlying causes for the variations in T we observe across strains . Our estimates are based on virion trimer content measurements by ELISA and are therefore a composite of functional and non-functional trimers present on virions . Considering this , a high estimate of T may be derived as the consequence of premature trimer inactivation through rapid trimer decay [38] , [57] , or a spontaneous adoption of the CD4-bound conformation [57] , [58] . In both cases high estimates of T would reflect a decreased proportion of functional trimers on virions . Alternatively , a high T may be required by envelopes which have adopted a trimer conformation with a low energetic state as described for the open conformation of lab adapted strains [58] , [59] . There , the lack in energy released upon trimer conformational rearrangements may be compensated by higher trimer numbers roped into the entry process . Most notably , for all three wildtype strains that yielded high estimates of T as well as the V1V2-deleted env variants , the high T was associated with a low infectivity ( Fig . 2B , 3A and 3D ) . The latter is particularly intriguing as it supports the possibility that env deficiencies in entry can be partially overcome by higher numbers of trimers engaged during the entry process . Interesting insights also stem from the SF162/P3N env pair: P3N was isolated from a rhesus macaque after successive rapid transfer following initial challenge with SHIV-SF162 [60] . While SF162 has a high estimate of T of 4 to 5 , P3N has a T of 2 and is the most infectious env in our panel ( Fig . 2B ) . Thus , HIV ( or SIV ) has the potential to evolve from a less fit to a highly transmissible env in vivo . The exact mutations responsible for the different phenotypes remain to be determined; as we previously showed , the V1V2 domains of SF162 and P3N appear to play an important role in this regard [38] . A high T was also linked with slower virus entry kinetics ( Fig . 4C ) suggesting that engagement of multiple trimer-receptor pairings requires prolonged time periods . A similar relationship between kinetics of membrane fusion and the number of involved fusion proteins has been previously demonstrated for SNARE ( Soluble NSF Attachment protein Receptor ) complex mediated membrane fusion [61] , [62] and Influenza virus membrane fusion [16] , [17] . The interplay between HIV-1 entry stoichiometry and entry kinetics our study reveals thus underscores the general finding that the kinetics of membrane fusion processes are governed , at least in part , by the number of participating fusion proteins . We rate the tight association of T with functional properties of the envelopes , namely entry capacity and entry kinetics , as a strong indicator of the validity of our analysis . Nevertheless , such estimates of T can only be an approximation as certain parameters which influence the estimates cannot be determined experimentally and assumptions need to be made for the mathematical analysis . In the literature different approaches towards modelling of mixed trimer experiments have been described and led to partially deviating results , highlighting the importance of validating the models and parameters used [13] , [14] , [63] . Virion trimer numbers are commonly estimated from gp120 and p24 ELISA data ( Table 1 ) [23] , [24] . These analyses yield values for the average envelope content of virions but do not provide information on the frequency distribution of viruses carrying different trimer numbers across a virion population . The latter is a factor that impacts on the interpretation of mixed trimer experiments [14] , [15] , [64] . Additionally , env content estimates by ELISA do not deliver information on env functionality , hence functional and non-functional trimers will be accounted for [65] . A further potential limitation stems from the nature of the mixed trimer experiments which require that all combinations of envelopes probed lead to a random trimer formation . Since preferential formation of homotrimers could strongly influence results obtained from mixed trimer experiments , we controlled for equal expression levels of the co-expressed env variants . In addition , previous studies from us and others indicate that related env variants indeed form randomly mixed trimers [13] , [66] , [67] . As outlined in our previous work , we have incorporated in our mathematical model several functions to capture these parameters involved in HIV entry and carefully verified the validity of our approach in the current study both in vitro and in silico ( S1–S2 Fig . , S1 Table and [15] , [63] , [68] ) . Nevertheless , this does not exclude that additional parameters beyond T contribute to the variation in entry phenotype between individual HIV-1 strains . For instance , differences in trimer stability or affinities for CD4 and co-receptors could significantly impact on virus entry efficacy without direct influence on T . Membrane fusion via the stalk-pore mechanism [11] is a multi-step process that ultimately depends on energy provided by fusion proteins [9] , [11] , [52] , [69] . Approximation of two membranes is followed by fusion of the two proximal membrane leaflets , forming the hemifusion stalk intermediate . Subsequent fusion of the distal membrane leaflets creates a fusion pore , which may either expand or close again depending on the forces exerted on the membranes . Viral envelope glycoproteins , such as the HIV-1 env or Influenza virus HA trimer , are metastable structures that undergo a series of conformational changes following receptor engagement which releases energy utilized in the fusion process [7] , [8] . Although the energy required for hemifusion stalk formation could potentially be recovered from refolding of a single envelope glycoprotein trimer [8] , [55] , subsequent formation of the fusion pore and pore enlargement are thought to require higher energy levels [9] , [11] , [70] . Growing evidence suggest that only concerted action of several trimers leads to membrane fusion and maintenance of a fusion pore large enough to allow passage of the HIV capsid [1] , [9] , [10] , [20] . Our estimates that , for the majority of HIV-1 primary isolates , 2 to 3 env trimers are required to mediate infection are thus in accordance with these studies on the mechanisms of membrane fusion . Interestingly , our estimates of the HIV entry stoichiometry resemble those made for Influenza A virus [16]–[18] , postulated to require 3 to 4 HA trimers for entry , and vesicular membrane fusion via SNARE complexes , which generate energy during refolding at levels comparable to viral fusion proteins [71]–[73] . In high similarity to HIV and Influenza , also 1 to 3 SNARE complexes appear to be required to induce membrane fusion [61] , [62] , [74] . To further explore this relationship , we compared reported values of energy required for membrane fusion and energies released by fusion proteins ( S11 Fig . ) . Membrane fusion requires an energy input of 40 to 120 kbT [9] , [55] , [75] , [76] . Refolding of HA trimers into the six-helix bundle conformation was estimated to release 30 to 60 kbT [8] , [9] , [77] while SNARE complex assembly into 4-helix bundles releases an estimated 19 to 65 kbT [71]–[73] , [78] . Considering that 3 to 4 HAs and 1 to 3 SNARE complexes were estimated to participate in entry , this yields total energies of 20 to 240 kbT released during the respective membrane fusion processes . In analogy , assuming a total energy of 40 to 120 kbT required for membrane fusion , our estimates that 2 to 7 trimers are required for HIV entry indicate that each trimer releases between 6 to 60 kbT during the entry process . Of note , the calculated total energies released by both HA trimers and SNARE complexes appear to be higher than the reported energy required for membrane fusion . This could potentially be due to inefficient coupling of the energy generated through protein conformational rearrangements into membrane deformation , or divergence between artificial membranes employed in biophysical experiments to measure membrane fusion energies and naturally occurring membranes containing proteins and having varying lipid composition . In sum , the strong agreement in the estimated energies across biological systems is intriguing and suggests that the overall energy requirements of membrane fusion and principles of energy elicitation by fusion proteins are closely related . Our estimates that HIV strains typically require 2 to 7 trimers for entry are also consistent with the observations that env trimers cluster on virions and that HIV establishes a contact zone where several env-receptor pairings between virus and target cell are formed , the so-called “entry claw” [19] , [28] . When defining molecular requirements for HIV entry , it is important to consider that envelope trimer activities may reach beyond solely providing the energy for membrane fusion . For example , binding of HIV trimers to their target cell receptors triggers an array of intracellular signals , which amongst other processes is thought to govern intracellular actin re-arrangements [79] and may be required to support membrane fusion and pore enlargement as previously proposed [10] , [11] . In summary , our estimates of the HIV entry stoichiometry are in strong accordance with requirements found in other membrane fusion processes . Importantly , we show that capacities of individual HIV envelopes in mediating entry can vary substantially which is likely due to differences between trimers in soliciting energy required for membrane fusion . Our data strongly suggest that viruses overcome these envelope limitations by increasing the number of envelope-receptor pairings involved in the entry process . Hence , the stoichiometry of HIV entry is an important parameter steering virion infectivity and its assessment provides a relevant contribution towards a refined understanding of HIV-1 entry and pathogenesis . Knowledge obtained from the quantitative assessment of trimer-receptor interactions during HIV entry is a prerequisite in unravelling the stoichiometry of trimer interactions with target cell receptors or virus inactivation by neutralizing antibodies and entry inhibitors and thus may aid future approaches in HIV vaccine or entry inhibitor design .
293-T cells were obtained from the American Type Culture Collection ( ATCC ) and TZM-bl cells [80] from the NIH AIDS Research and Reference Reagent Program ( NIH ARP ) . Both cell types were cultivated in DMEM ( Gibco ) containing 10% heat inactivated FCS and penicillin/streptomycin . Plasmids encoding the envelopes of strains JR-FL , SF162 , NL4-3 , RHPA , AC10 , REJO , BG505 and ZM214 were obtained from the NIH ARP . Envelope ZA110 was described previously [67] . Envelope clone P3N [60] was a gift from Dr . Cecilia Cheng-Mayer , Aaron Diamond AIDS Research Center , New York , USA . Envelope clone CAP88 [81] was a gift from Dr . Lynn Morris , National Institute for Communicable Diseases , Johannesburg , South Africa . All envelope point mutations were generated by site-directed mutagenesis ( Agilent QuikChange II XL ) according to the manufacturer instructions . All point mutant envelopes were sequenced by in-house Sanger sequencing to confirm presence of the desired mutations and absence of unintended sequence changes . V1V2-deleted envelopes were previously described [67] . The Luciferase reporter HIV pseudotyping vector pNLLuc-AM was previously described [67] . T-20 [82] was purchased from Roche Pharmaceuticals . To estimate the stoichiometry of entry we employed a previously described approach [13] . To produce HIV-1 pseudotype virus stocks expressing mixed trimers with varying ratios of functional to dominant-negative env , 293-T cells in 12 well plates ( 100 . 000 cells per well in 1 ml complete DMEM , seeded 24 h pre-transfection ) were transfected with 1 . 5 µg pNLLuc-AM and 0 . 5 µg env expression plasmids , using polyethyleneimine ( PEI ) as transfection reagent . The ratio of functional to dominant negative env expression plasmids was varied to yield combinations with 100 , 90 , 70 , 50 , 30 , 10 and 0% of functional env . Total env plasmid content was always at 0 . 5 µg . After overnight incubation the transfection medium was replaced with 1 ml fresh complete DMEM and virus-containing supernatants were harvested 48 h post transfection . All mixed trimer combinations of an individual virus strain were always generated in parallel , excluding influences of producer cells and transfection procedure . To determine virus infectivity , serial dilutions of virus stocks starting with 100 µl of undiluted virus supernatant were added to TZM-bl reporter cells in 96-well plates ( 10 . 000 cells per well ) in DMEM medium supplemented with 10 µg/ml DEAE-Dextran . TZM-bl infection was quantified 48 h post-infection by measuring activity of the firefly luciferase reporter . For each functional to dominant negative env ratio series , the infectivity of the stock containing 100% functional ( wt ) env was taken as reference ( 100% infectivity ) and the relative infectivity of the stocks with increasing percentages of dominant-negative env were calculated in relation to that infectivity value . The resulting data of relative infectivity were plotted over the fraction of dominant-negative env of each stock and the data were analyzed with mathematical models [15] as described below . To produce HIV-1 pseudotype virus stocks for comparisons of virus infectivity and determination of virus entry kinetics , 293-T cells in T75 flasks ( 2 . 250 . 000 cells in 15 ml complete DMEM , seeded 24 h pre-transfection ) were transfected with env and pNLLuc-AM plasmids ( 7 . 5 µg and 22 . 5 µg , respectively ) using PEI as transfection reagent . The medium was exchanged 12 h post-transfection and virus-containing supernatants were harvested 48 h post-transfection . The supernatants were cleared by low speed centrifugation ( 300 g , 3 minutes ) , aliquoted and stored at −80°C . Virus infectivity was quantified on TZM-bl reporter cells as described above . To determine gp120 and p24 content of HIV-1 pseudotype virus preparations , 293-T cells in T25 flasks ( 750 . 000 cells in 5 ml complete DMEM , seeded 24 h pre-transfection ) were transfected with env and pNLLuc-AM plasmids ( 2 . 5 µg and 7 . 5 µg , respectively ) using PEI as transfection reagent . The medium was exchanged 12 h post-transfection and virus-containing supernatants were harvested 48 h post-transfection . The supernatants were cleared by low speed centrifugation ( 300 g , 3 minutes ) , then ultracentrifugated ( SW28 rotor , 2 h , 28 . 000 rpm , 4°C ) , the supernatant removed and viral pellets resuspended in 0 . 3 ml cold PBS and stored at −80°C . Virion associated p24 and gp120 antigens were quantified by ELISA as previously described [67] . Briefly , virus preparations were dissolved in 1% Empigen ( Fluka Analytical , Buchs , Switzerland ) and dilutions of each sample probed for gp120 and p24 . Gp120 was captured on anti-gp120 D7324 ( Aalto Bioreagents , Dublin , Ireland ) coated immunosorbent plates and detected with biotinylated CD4-IgG2 and Streptavidin-coupled Alkaline Phosphatase ( GE Healthcare , Chalfont St Giles , UK ) . P24 was captured on anti-p24 D7320 ( Aalto Bioreagents , Dublin , Ireland ) coated plated and detected using Alkaline Phosphatase-coupled antibody BC1071 ( Aalto Bioreagents , Dublin , Ireland ) . Concentrations of gp120 and p24 in the samples were calculated in relation to standard curves obtained with gp120 and p24 proteins of known concentration . To derive virion numbers from p24 concentrations , we assumed 2000 ( for the data in main manuscript and figures ) or alternatively 2400 ( S3B Fig . ) p24 molecules per virion [23] , [24] , [33] . Trimers per virion were then calculated from the obtained gp120 concentration in relation to the number of virions per sample . To determine virus entry kinetics , TZM-bl cells were seeded in 96-well plates ( 20 . 000 cells per well ) in complete DMEM supplemented with 10 µg/ml DEAE-Dextran . 24 h post-seeding , cells were first cooled at 4°C for 5 minutes , then the medium was removed . HIV-1 pseudotype virus stocks adjusted to 50 . 000 RLU in 100 µl DMEM at 4°C were added per well and plates centrifuged for 70 minutes at 1200 g and 10°C . The low temperature was chosen to allow virus attachment during spinoculation but no entry . Following spinoculation the supernatant with unbound virus was removed and 130 µl of DMEM , pre-warmed to 37°C , were added per well to initiate infection ( timepoint zero ) and plates were incubated at 37°C . At defined timepoints post-infection , 20 µl of T-20 ( 375 µg/ml in DMEM; yielding a final assay concentration of 50 µg/ml ) were added per well to stop the viral entry process . To obtain a measure for infectivity across different experiments , the wells with the last T-20 addition at 120 min after infection start were used as 100% reference infectivity value and the infectivity of all other T-20 treated wells were set in relation to it . In addition , a mock-treated well ( addition of 20 µl DMEM at timepoint zero ) was evaluated to assess absolute infectivity in absence of T-20 . Isolation and infection of PBMCs was performed as previously described [85] . Briefly , PBMCs were isolated from pooled buffy coat of 3 healthy blood donors , stimulated with anti-CD3 and PHA in presence of 100 U IL-2 for 2 days and then seeded in 96-well plates at a density of 100 . 000 cells per well in 100 µl RPMI medium supplemented with 10% FCS , antibiotics , 100 U IL-2 and 2 . 5 µg/ml ( final assay concentration ) polybrene . Serial dilutions of mixed trimer virus stocks ( 100 µl per well ) were added to the PBMCs and cells were incubated at 37°C for 72 h before determining luciferase reporter activity . Correlation analyses according to Pearson and multiple unpaired t-tests to derive the p-values for the comparisons of wt and V1V2-deleted envs were performed in GraphPad Prism 6 . | Our estimates of the HIV-1 entry stoichiometry , that is the number of envelope glycoprotein trimers needed to mediate fusion of viral and target cell membrane , close an important gap in our understanding of the HIV entry process . As we show , stoichiometric requirements for envelope trimers differ between HIV strains and steer virus entry efficacy and virus entry kinetics . Thus , the entry stoichiometry has important implications for HIV transmission , as demands on trimer numbers will dictate the infectivity of virus populations , target cell preferences and virus inactivation by trimer-targeting inhibitors and neutralizing antibodies . Beyond this , our data contribute to the general understanding of mechanisms and energetic requirements of protein-mediated membrane fusion , as HIV entry proved to follow similar stoichiometries as described for Influenza virus HA and SNARE protein mediated membrane fusion . In summary , our findings provide a relevant contribution towards a refined understanding of HIV-1 entry and pathogenesis with particular importance for ongoing efforts to generate neutralizing antibody based therapeutics and vaccines targeting the HIV-1 envelope trimer . | [
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"... | 2015 | Different Infectivity of HIV-1 Strains Is Linked to Number of Envelope Trimers Required for Entry |
Clathrin-mediated endocytosis ( CME ) proceeds through a series of morphological changes of the plasma membrane induced by a number of protein components . Although the spatiotemporal assembly of these proteins has been elucidated by fluorescence-based techniques , the protein-induced morphological changes of the plasma membrane have not been fully clarified in living cells . Here , we visualize membrane morphology together with protein localizations during CME by utilizing high-speed atomic force microscopy ( HS-AFM ) combined with a confocal laser scanning unit . The plasma membrane starts to invaginate approximately 30 s after clathrin starts to assemble , and the aperture diameter increases as clathrin accumulates . Actin rapidly accumulates around the pit and induces a small membrane swelling , which , within 30 s , rapidly covers the pit irreversibly . Inhibition of actin turnover abolishes the swelling and induces a reversible open–close motion of the pit , indicating that actin dynamics are necessary for efficient and irreversible pit closure at the end of CME .
Cells communicate with the extracellular environment via the plasma membrane and membrane proteins . They transduce extracellular signals and substances into the cellular plasma via cell surface receptors , channels , and pumps , as well as by various endocytic processes [1–3] . Cells also disseminate their intracellular contents to the extracellular space via exocytosis . These dynamic cellular processes are largely dependent on the assembly and catalytic function of various proteins in the plasma membrane . Clathrin-mediated endocytosis ( CME ) is conducted by more than 30 different proteins . Extensive studies using fluorescence imaging techniques revealed the spatiotemporal dynamics of individual proteins in living cells [4–6] . In addition , a number of in vitro studies revealed unique functions of these proteins in deforming the plasma membrane [7] . For instance , Bin-amphiphysin-Rvs17 ( BAR ) domain proteins bind to the surface of the lipid bilayer and induce membrane curvature and tubulation and are therefore presumed to be involved in membrane deformation in an early stage of CME [8] . Dynamin also induces membrane tubulation with a smaller diameter and vesiculation via a nucleotide-dependent conformational change , and therefore has been considered to be involved in the vesicle scission process [9 , 10] . Despite our increasingly detailed knowledge regarding the cellular dynamics of these proteins in vivo and their catalytic activity in vitro , the morphological changes of the plasma membrane during CME in living cells have not been studied . This has mainly been due to a lack of imaging techniques for visualizing the membrane . Electron microscopy ( EM ) has made a substantial contribution to the study of CME , owing to its high spatial resolution . The detailed morphological changes of the plasma membrane , together with the assembly of proteins , such as clathrin , have been imaged and analyzed in a series of images to understand the entire process of CME [11–15] . However , aligning a thousand EM snapshots still suffers from a large limitation in the time resolution . In contrast to EM , fluorescence labeling and imaging techniques are powerful tools for studying protein dynamics in living cells . Recent advances in these techniques allow time-lapse imaging of a single protein molecule in a living cell with subsecond time resolution . However , it is not suitable for imaging morphological changes of the plasma membrane in a living cell at a submicrometer scale . Scanning probe microscopies , including atomic force microscopy ( AFM ) , are powerful approaches for characterizing the surface of a specimen at nanometer resolution . Notably , high-speed AFM ( HS-AFM ) has been utilized to visualize various molecular structures and reactions at subsecond resolution in vitro [16–19] . We recently developed an HS-AFM for live-cell imaging and successfully visualized structural dynamics of the plasma membrane in living cells [20 , 21] . In this study , we utilize this HS-AFM to analyze the morphological changes of the plasma membrane during CME . To understand the role of specific proteins during the morphological change , HS-AFM is combined with confocal laser scanning microscopy ( CLSM ) so that we could simultaneously visualize membrane structures and protein localizations during CME in living cells . Overlaying AFM and fluorescence images reveals the dynamics of protein assembly and concomitant morphological changes of the plasma membrane with high spatial resolution . In particular , we elucidate the role of actin in the closing step of CME .
To reveal protein-induced membrane deformation during CME in a living cell , we first established a hybrid imaging system with HS-AFM and CLSM . We previously reported the development of a tip-scanning AFM unit and its combination with an inverted optical microscope with a fluorescence illumination unit [20 , 21] . In this study , we combined the HS-AFM unit with an inverted optical microscope equipped with a confocal laser scanning unit to increase the optical resolution . To obtain stable imaging , the stage was redesigned . The detailed configuration of the stage is described in Fig 1A and 1B . A cross-shaped movable XY-stage is mounted on the base plate of the inverted optical microscope stage , which allows the specimen to move independently of the AFM unit and the objective lens . The AFM scanning unit now has a 6 . 0 × 4 . 5 μm2 scanning area , which is larger than the previous unit ( 4 . 0 × 3 . 0 μm2 ) , to enable imaging of a larger area of the cell surface . We then established a procedure for aligning confocal fluorescence and AFM images . The details of the alignment method are described in S1 Fig . In brief , the probe was brought to approach and attach on the glass surface without scanning . The z-position of the probe tip and confocal plane was also aligned by setting the glass surface as a reference position ( z = 0 ) . The x-y position of the probe tip on the optical axis was determined by imaging an autofluorescence signal of the probe ( S1A and S1B Fig ) . The center position of the probe fluorescence was defined as an origin ( x , y = 0 , 0 ) , and the x-y position of the AFM image also refers to this scale ( S1B Fig ) . Once the reference position of the probe tip and optical axis was determined , the cross-shaped sample stage allowed the specimen ( cell ) to move in x-y directions without changing the relative position between the HS-AFM and CLSM . The spatial accuracy of the hybrid imaging described above was tested by observing a chemically fixed cell . CV-1 in origin with SV40 gene line 7 ( COS-7 ) cells expressing enhanced green fluorescent protein ( EGFP ) -fused clathrin light chain a ( EGFP-CLCa ) were fixed and observed by hybrid imaging . Several different membrane structures could be identified in the AFM image of the cell surface ( Fig 1 ) : membrane invaginations of different sizes ( diameters ) ; ruffle-like short narrow protrusions; and large swellings . When the AFM image was overlaid on the confocal fluorescence image of EGFP-CLCa based on the x-y position alignment , several clathrin spots readily colocalized with membrane invaginations identified in the AFM image ( Fig 1C ) . The section profile analysis revealed that the diameter of the membrane invaginations ( pits ) identified in the AFM images ranged between 150 and 400 nm , whereas those colocalized with a clathrin spot ranged between 150 and 350 nm ( 237 ± 78 nm [mean ± SD]; n = 4 ) ( the details of the AFM image analysis are provided in S2 Fig ) . Because this size range of clathrin-coated pits ( CCPs ) was larger than the previously reported ones ( 20–175 nm ) [11] , we examined the size of the entire CCP by observing the cytoplasmic side of the plasma membrane . COS-7 cells were unroofed by the procedure previously described [22] , fixed , and then observed by HS-AFM . As shown in Fig 1D , CCPs with hexagonal network of clathrin were clearly observed . The diameter ranged between 150 and 400 nm ( 225 ± 49 nm [mean ± SD]; n = 45 ) , which approximately corresponds to 130 to 380 nm after subtracting the tip curvature . Therefore , we concluded that the membrane invaginations with clathrin fluorescence spot observed by our hybrid imaging system were CCPs , and the size of the pit ranged from 150 to 400 nm . This result matches well to previous EM observations of the CCP in COS-7 cells , in which CCPs larger than 150 nm were often observed [23] . To evaluate the accuracy of the image alignment , the position ( x , y ) of the membrane invagination in the AFM image was compared with that of the corresponding fluorescence spot in the confocal image . The x-y offset between the centroid of the membrane invagination in the AFM image and the fluorescent spot of clathrin was 27 ± 20 nm ( mean ± SD; n = 7 ) . Considering the diameter of the membrane invaginations ( 150–400 nm ) , we concluded that our image-overlaying procedure is accurate enough to merge the clathrin spot with the AFM image . It should be noted that the number of membrane invaginations in the AFM image was less than that of the fluorescent clathrin spots—i . e . , there were some clathrin spots that did not colocalize with membrane invaginations in the AFM image . Such clathrin signals could be due to either clathrin-coated vesicles ( CCVs ) that had already budded from the plasma membrane or CCPs that formed in the basal surface of the cell . To follow the entire process of CME , we then performed time-lapse hybrid imaging of living cells . A live COS-7 cell expressing EGFP-CLCa was observed by HS-AFM and CLSM in culture medium containing fetal bovine serum ( FBS ) . The temperature was kept constant at 28 °C , at which the entire CME process—including scission step—was demonstrated to occur [24] . Both AFM and CLSM images were obtained every 10 s , and overlaid . Small membrane invaginations as described in Fig 1 were colocalized with EGFP-CLCa spots over several minutes ( Fig 2A and 2B ) ( see video in S1 Movie ) . The average x-y offset of the fluorescent spot from the corresponding membrane pit in the AFM image was 38 ± 13 nm ( mean ± SD; n = 8 ) ( S3 Fig ) , which was slightly larger than that in the fixed cell ( 27 ± 20 nm ) . This is partly due to the time lag between AFM and CLSM scanning; although AFM and CLSM images were taken with the same frame rate ( 10 s/frame ) , the exact timing of data recording at a certain position in the scanning area was not perfectly synchronized because 2 different scanning systems are operated by 2 independent controllers . Considering the fact that CCPs are diffusing on the membrane ( S1 Movie ) , the time lag between 2 scanning systems results in a small offset of each CCP spot between 2 different images . However , because this offset is much smaller than the size of the CCP ( 150–400 nm ) , we concluded that our hybrid time-lapse imaging procedure has spatial resolution high enough for assigning individual clathrin spots to the membrane invaginations in the AFM image . Because the plasma membrane of a living cell is always fluctuating in x , y , and z directions , we carefully examined the effect of scanning parameter on the morphologies of CCPs . When the amplitude of the cantilever was increased by 5% , cortical actin network beneath the plasma membrane became more visible ( see S4A Fig ) , which sometimes occurred during our time-lapse observation . In such a condition , the CCP ( an arrow in S4A Fig ) was still clearly observed , and the difference in the diameter was less than 9% , indicating that this amount of fluctuation had little effect on the morphological analyses of the CCP during CME ( see later sections ) . In addition , “tip skipping” sometimes occurred near the CCP ( S4B Fig ) . However , the frequency of tip skipping was very low ( 12 out of 272 CCPs ) , and tip skipping occurred on 1 or 2 consecutive scanning lines , which corresponds to approximately 37 nm . From these analyses , we concluded that fluctuation of tip–sample interaction does not affect the morphological analyses of the CCPs . The fluorescence intensity of the clathrin spot and the diameter of the pit in the AFM image were plotted against time ( Fig 2B ) . The clathrin signal appeared 20 to 30 s before the membrane started to deform . The clathrin signal then increased ( growing phase ) until it reached a stable phase as demonstrated by a previous study [25] ( see S5A Fig for the definition of individual phases ) . During the growing phase , the aperture of the pit in the AFM images also increased ( Fig 2B ) , suggesting that the size of the pit also enlarged during this period . During the stable phase , the aperture also remained almost constant . There were large variations in the duration of the growing and stable phases; the growing phase ranged between 40 and 280 s , and the subsequent stable phase lasted between 0 and 260 s ( n = 35 ) ( Fig 2C ) . Following the stable phase , the closing phase proceeded over a short period of time ( 20–50 s ) ( Fig 2B and 2C ) . Some pits closed in a single frame ( <10 s ) . When these fast-closing events were observed at a higher scanning rate ( 1 s/frame ) , the pit closed in as fast as 3 s ( S5B Fig ) . Notably , the clathrin spot remained for another 20 to 30 s after the pit closed and then suddenly disappeared . This could indicate that either the clathrin coat remains on the vesicle and is eventually disassembled by G-associated kinase ( GAK ) [26 , 27] , or the vesicle eventually moves out of the focal plane of the CLSM . We obtained a similar result when the pit area , instead of the pit diameter , was plotted against time ( S5C Fig ) . The total lifetime of the CCP ranged from 40 to 330 s ( n = 113 ) . Assemblies of other CCP-related proteins were also investigated by time-lapse hybrid imaging . Epsin is known to add bending stress to the lipid bilayer at an early stage of CME , thus changing the membrane curvature [28] , and it recruits clathrin to the pit surface . COS-7 cells simultaneously expressing mCherry-fused epsin and EGFP-CLCa were subjected to time-lapse hybrid imaging . Epsin started to assemble on the plasma membrane prior to the membrane invagination , which was similar to clathrin ( Fig 3A and 3B; S2 Movie ) ( see also S6 Fig for other observations ) . However , statistical analysis of the timing of assembly and membrane invagination revealed that epsin assembles prior to clathrin; fluorescence spots of epsin and clathrin appeared 47 ± 9 ( mean ± SD; n = 8 ) and 34 ± 13 s ( mean ± SD; n = 35 ) , respectively , before the membrane started to invaginate ( Fig 3E ) . During the growing phase , fluorescence signals of both proteins increased ( Fig 3B ) . Epsin and clathrin signals peaked at 14 ± 5 s ( n = 8 ) before and 3 ± 7 s ( n = 35 ) after the pit closed , and they disappeared at 13 ± 5 s ( n = 8 ) and 39 ± 13 s ( n = 35 ) after the closure , respectively . All of these results demonstrated that CCPs in COS-7 cells show a large variation in the lifetime ( 40–280 s ) , and their opening and closing events are tightly coupled with protein assembly . Dynamin localizes at the neck of the pit and plays a role in the vesicle scission [15] in the last step of CME . COS-7 cells expressing mCherry-fused dynamin 2 ( an isoform ubiquitously expressed in a variety of cells ) together with EGFP-CLCa were subjected to the time-lapse hybrid imaging . Although dynamin plays a role in the vesicle scission , a dynamin signal started to assemble on the CCP in an early stage of CME as demonstrated in previous studies [4 , 29]; in our observations , it assembled 25 ± 12 s ( mean ± SD; n = 13 ) before the membrane invagination began ( Fig 3C , 3D and 3E , S3 Movie ) ( see S7 Fig for other observations ) . The signal gradually increased in the growing phase , but during the stable phase , the dynamin signal was not clearly defined compared to clathrin . The dynamin signal peaked with the same timing as pit closure and gradually decreased thereafter ( it completely disappeared 38 ± 9 s [mean ± SD; n = 13] after the closure ) , consistent with the notion that it is involved in the last step of CME . We further confirmed that the membrane pits observed were indeed CCPs and not other types of endocytic structures . Caveolae are found in another endocytic pathway that is mediated by other sets of proteins ( caveolin , etc . ) but also includes invagination of the plasma membrane . mCherry-fused caveolin1 , a major component of caveolae , was expressed in COS-7 cells together with EGFP-CLCa , and live cells were subjected to time-lapse hybrid imaging . The clathrin spots did not colocalize with caveolin1 spots during the observation . Overlaying 3 images ( AFM , EGFP-CLCa , and mCherry-caveolin1 ) clearly revealed morphological differences between CCP and caveolae ( Fig 4A , see also S4 Movie ) . The aperture of caveolae ranged from 80 to 120 nm , whereas that of CCPs ranged from 150 to 400 nm ( Fig 4B ) . Similar to the CCP , the aperture of the caveolae observed by AFM was slightly larger than that observed by EM [30] , probably for the same reason as described above . Caveolae had longer lifetimes than CCPs; the average lifetime of a CCP was 81 ± 55 s , whereas caveolae remained open for over 400 s . They also showed different lateral movements in the plasma membrane; the diffusion coefficient of CCPs was 7 . 3 × 10−9 ( cm2 s−1 ) , whereas that of caveolae was 2 . 1 × 10−9 ( cm2 s−1 ) ( Fig 4C and 4D ) . Taken together , these results indicate that time-lapse hybrid imaging could identify and distinguish the different aperture openings and diffusion kinetics of the 2 types of invaginations . In contrast to the growing and stable phases , which continue for more than 1 min , the closing and disassembly phase was completed relatively rapidly ( <30 s ) . In many cases , the membrane aperture suddenly disappeared ( Fig 2 ) . However , the detailed image analyses revealed several unique membrane structures and dynamics in the closing step of the CCP , which include ( i ) capping , ( ii ) two-step , and ( iii ) re-opening ( Fig 5A–5C ) . The capping motion was frequently observed in more than 50% of the CME events ( 54 . 9% , Fig 5D , S1 Table ) . A small membrane region adjacent to the CCP swelled and eventually covered over the pit ( Fig 5A ) . The section profile analysis ( S2 Fig ) revealed that the swelling region was 378 ± 62 nm in diameter and 38 ± 10 nm in height ( mean ± SD; n = 13 ) , which is comparable to the pit size . The entire closing motion took 23 ± 13 s ( mean ± SD; n = 48 ) . This structure is very similar to the membrane protrusion observed by ion-conductance microscopy [31] ( see Discussion section for details ) . Two-step closing was observed in approximately 20% of the CME events ( Fig 5D ) . The pit aperture first decreased to approximately 120 nm and then disappeared ( Fig 5B ) . The duration of the small-aperture step was <40 s . AFM imaging with higher time resolution ( 2 s/frame ) revealed two-step motions with faster small-aperture steps ( approximately 10 s ) . These results indicate that many CCPs close with a two-step motion , but the duration of the smaller-aperture step varied from several seconds to 40 s . A re-opening motion was also observed in more than 10% of the total CME ( Fig 5D ) . A pit once closed completely then re-opened after several frames of the closure ( Fig 5C ) . The duration of the closed state varied between 10 and 70 s ( 30 ± 24 s , n = 8 ) . During the closed state , clathrin signal decreased to between 14% and 70% ( 39 ± 18% , n = 8 ) and re-increased after the pit re-opened ( Fig 5C ) . The position of re-opening was within 13 to 113 nm ( 52 ± 31 nm , n = 8 ) from the closed position . These observations were similar to what was previously described as “hot spot , ” in which multiple cycles of assembly and disassembly of endocytic proteins such as clathrin or dynamin occurred in a limited area of membrane [32–34] ( see Discussion section for details ) . There was a clear distinction between the capping and re-opening motions: pits that closed with capping did not tend to re-open ( Fig 5E ) , suggesting that capping plays a role in irreversible closing . In contrast , the two-step motion was not mutually exclusive to other motions so that we sometimes observed a two-step motion that finally culminated with capping ( S8 Fig ) . The comparison of the total lifetime revealed a wide distribution in capping-ended pits , whereas two-step and re-opening motions showed a narrow distribution of about 100 s ( Fig 5F ) . Actin and actin-related proteins are also known to contribute to CCP assembly , although their exact role is not fully understood [14 , 35] . We previously observed and reported the dynamic turnover of the cortical actin network [36]; actin filaments are polymerized near the plasma membrane and descend into the cytoplasm . Therefore , we first examined the effect of actin inhibitors on the CME process . The analysis of the CCP lifetime in the presence of actin inhibitors revealed an inhibitory effect of the cortical actin network on the progress of CME . Cytochalasin B ( an inhibitor of actin polymerization ) and CK666 ( an inhibitor of the Arp2/3 complex , which binds to F-actin and generates a branching point ) both shortened the CCP lifetime , whereas jasplakinolide—which inhibits actin depolymerization and stabilizes the cortical actin network [36]—prolonged the lifetime ( Fig 6A , S2 Table , S5–S7 Movies ) . In the presence of cytochalasin B , both the growing and stable phases shortened from 45 ± 34 s to 7 ± 12 s and from 88 ± 29 s to 59 ± 33 s , respectively , whereas there was little effect on the duration of the closing step ( 31 ± 9 to 22 ± 7 s ) ( Fig 6B , S3 Table , S8 Movie ) . This indicates that the collapse of the actin network accelerates CCP assembly and maturation , whereas the stabilization of the network inhibits the process . Dissecting the two-step closing motion also revealed that cytochalasin B and CK666 shortened the duration of the large aperture , whereas jasplakinolide prolonged it ( Fig 6A ) , implying that actin dynamics accelerate the assembly of CCP-related proteins . In addition to the lifetime of the CCP , actin dynamics are involved in the closing motion of the CCP . The most striking effect of the inhibitors was a reduction of the capping motion and an increase in re-opening motions; cytochalasin B and CK666 drastically reduced the frequency of capping motions ( from 56% to 0 . 4% by cytochalasin B and from 56% to 3% by CK666 ) and increased the frequency of re-opening motions ( from 20% to 67% by cytochalasin B and from 20% to 47% by CK666 ) ( Fig 7A and 7C , S4 Table ) ( see S9 Fig for other examples ) . The involvement of actin polymerization in membrane swelling was also demonstrated by ion-conductance microscopy [31] ( see Discussion section for details ) . Jasplakinolide also showed a similar effect but to a smaller extent ( Fig 7A and 7C ) . These observations are in good agreement with the result that the capping and re-opening motions are inversely related ( Fig 5E ) and that actin polymerization plays a role in an efficient and irreversible closing of the vesicle . In addition to the re-opening motions , two-step motions were also increased by cytochalasin B and CK666 treatments ( Figs 7A and 6A ) , suggesting that two-step motions and actin polymerization are tightly coupled . Blocking actin depolymerization , but not polymerization , affected the frequency of CCP formation as previously reported [35] , implying that the cortical actin layer also has an inhibitory effect on CCP formation . To confirm that membrane swelling in the capping motion was induced by actin , we followed the localization of actin during CME . COS-7 cells simultaneously expressing Lifeact-GFP and mCherry-CLCa were subjected to time-lapse hybrid imaging . There were several variations in the actin signal depending on the basal level of actin around the CCP ( Fig 7D and 7E , S9 Movie ) ( see S10 Fig for other examples ) . When the basal level was low , a burst of actin assembly was observed when the CCP closed . More precisely , it started to increase before the pit closed ( −59 ± 18 s [mean ± SD]; n = 14 ) , and peaked slightly after ( 2 . 5 ± 7 s [mean ± SD]; n = 15 ) the pit closure ( Figs 3E and 7D ) . The burst of actin signal intensity was tightly correlated with the membrane swelling in the capping motion; the actin signal peaked when the membrane swelled . This is in good agreement with the result obtained with cytochalasin B ( Fig 7A ) , in which addition of cytochalasin B reduced the frequency of the capping motion . On the other hand , when the basal actin level around the CCP was high , the signal first decreased during the growing and stable phases , then increased again toward the end of the CME ( Fig 7E ) . In this case , membrane swelling was not observed . Taken together , these results demonstrate that actin depolymerization occurs in the growth and maturation phases of CME , and active actin assembly is required for the irreversible scission of the vesicle from the plasma membrane . Furthermore , the swelling of the membrane sometimes developed into a ruffle-like protrusion , even after the pit closure ( S10 Fig ) , demonstrating that active polymerization of actin occurs around the CCP and generates local forces on the membrane . The involvement of other CCP-related proteins in morphological changes of the membrane was further investigated using RNA interference . Knockdown of dynamin 2 ( Fig 8A ) —in which 88% and 92% of dynamin 2 expression was suppressed after 24 and 48 h of transfection , respectively—did not affect the frequency of pit formation ( Fig 8B ) but reduced the occurrence of the capping motion from 59% to 35% ( Fig 8C and 8D , S5 Table , S10 Movie ) . The effect was similar to what was observed in the presence of cytochalasin B ( Fig 7A ) . However , in contrast to cytochalasin B and CK666 , which increased both re-open and two-step motions , dynamin knockdown markedly increased the two-step motion but only slightly increased the re-open motion ( Fig 8C ) . The detailed analysis of the two-step motion revealed a prolonged duration of the small aperture ( Fig 8E , S3 Table ) . These results suggest that dynamin is involved in the capping formation as well as the complete closing of the CCP . In addition to the prolonged duration of the small aperture ( two-step motion ) , the duration of the large aperture was also slightly prolonged in the dynamin-knockdown cells , which resulted in a significant increase of the CCP population with total lifetime longer than 100 s ( 13% in control knockdown and 54% in dynamin knockdown ) ( Fig 8E , S6 Table ) . This is in good agreement with our observation that dynamin started to appear at the CCP during the growing phase ( Fig 3C and 3D ) . These results suggested that dynamin is involved not only in the closing step but also in the assembly and maturation phases of the CCP , as was suggested in a previous study [25] . It should be noted that dynamin knockdown did not completely block the progress of CME , implying that a small amount of dynamin 2 slowly catalyzes the closing reaction or dynamin 1 is induced to compensate for the reduction of dynamin 2 , as was demonstrated in the previous study [37 , 38] ( see Discussion section for details ) .
Imaging the shape of the plasma membrane together with localization of proteins has been technically challenging . Although AFM was first applied to a living cell in 1992 [39] and a hybrid system of AFM and fluorescence microscope was available [40 , 41] , their low scanning rate did not allow visualization of the endocytic process . Many other microscopic techniques such as high-frequency microrheology [42] and high-speed ion-conductance microscopy ( HS-ICM ) [31] have also been utilized for cell surface imaging and characterization . The recent development of HS-AFM with a sample-scanning system overcame the problem of limited time resolution and visualized the dynamics of cell surface structures with subsecond time resolution [43] . However , AFM with a sample-scanning configuration is not suitable for combining with high-resolution live-cell imaging of CLSM because this configuration moves the specimen ( living cells ) while the position of the cantilever is fixed . In this study , we utilized a tip-scanning type of HS-AFM coupled with CLSM and were able to successfully visualize morphological changes of the plasma membrane during CME in a living cell . Our AFM images revealed unique membrane structures at the end stage of CME as well as the role of CME-related proteins , especially actin and dynamin , in such morphological dynamics . Notably , some of these structures are similar to what were previously observed by different approaches ( TIRF , ion conductance , etc . ) , demonstrating the fidelity of our imaging technique . Recent advances in fluorescence microscopy enabled highly precise spatiotemporal analyses of proteins involved in CME in yeast [44 , 45] and animal cells [4] . They revealed the timings of protein assembly at the CCP and disassembly after the pit closure . On the other hand , the morphological changes of the plasma membrane during CME were mainly drawn based on EM snapshots of fixed and stained cells . The localization of a specific protein on the CCP has also been revealed by immune EM and by fluorescence EM . For instance , clathrin exists at the place where the membrane is slightly bent [11 , 12] . However , the time resolution of the snapshot analysis is limited and is not suitable for following membrane dynamics . In time-lapse fluorescence imaging , a complete pit closure was detected by a pH-sensitive fluorescent dye combined with fast exchange of the external medium between high- and low-pH solutions [4 , 46 , 47] , but other morphological changes of the membrane—such as invagination and protrusion—could not be detected . Our time-lapse hybrid imaging of HS-AFM and CLSM rendered both the membrane morphology and protein localization with a time resolution of several seconds , which was particularly suitable for tracking the morphological changes of the CCP together with the assembly of specific proteins . Our observations of fixed cells , unroofed cells , and living cells all revealed that the size of the CCPs varied between 150 and 400 nm , which is slightly larger than the CCPs in other cell lines . This is somehow similar to clathrin plaque , which is larger than CCPs and undergoes a different mechanism of endocytosis ( 50–500 nm in HeLa cells and 50–300 nm in skin melanoma cell line 2 [SK-MEL-2] ) [12] . However , we believe that what we observed in this study were CCPs and not clathrin plaques because of the following two reasons . First , although the clathrin plaque is involved in endocytosis , it initially forms a flat sheet of clathrin on the flat plasma membrane without significant membrane invaginations . On the other hand , in our observations , the membrane started to invaginate approximately 30 s after clathrin started to assemble ( Figs 2 and 3E ) . Second , a previous study reported that COS-7 cells contain clathrin plaques at the basal plasma membrane but not at the apical ( nonadhering ) surface [33] . Our image analysis of CME revealed several unique membrane dynamics at the end of the process , which is coupled with the function of related proteins ( Fig 5 ) : capping , a two-step motion , and re-opening . Capping occurred in most of the CME events ( approximately 60% ) and was mediated by actin ( Fig 7 ) and dynamin ( Fig 8 ) . A region of the adjacent membrane swelled and covered over the CCP ( Figs 5 and 7 ) . The peak actin signal ( GFP-Lifeact ) corresponded with the timing of membrane swelling ( Fig 7D ) , and the inhibition of actin polymerization by inhibitors ( cytochalasin B and CK666 ) perturbed the capping motion ( Fig 7A ) , indicating that the membrane swelling is caused by rapid and local actin polymerization . These characteristics of capping seem to be similar to membrane protrusions observed by HS-ICM combined with CLSM [31]; small membrane protrusions ( caps ) were frequently ( 101 out of 145 at 28 °C ) observed beside the pit at the end of CME . The cap was abolished when actin polymerization was inhibited by latrunculin B [31] . Because such membrane protrusion was reported in several other studies [31 , 43] , this could be a general mechanism of CME . In addition to this observation , we found that this capping motion was related to other closing motion ( re-open ) ( Fig 7 ) and also to the function of dynamin ( Fig 8 ) , suggesting functional interaction between actin and dynamin at the closing step of CME ( see later section for further discussion on dynamin ) . It is noted that most of the membrane swelling occurs at one side of the CCP and moves across the pit towards the opposite side ( Figs 5 and 7 ) , which is reminiscent of actin comet tails formed behind the motile Listeria monocytogenes [48 , 49] . We could not find any preference in the direction of the capping . It might be the case that a sudden burst of actin polymerization at a certain point on the CCP induces membrane swelling . These results clearly support the idea that a short burst of actin polymerization produces membrane swelling beside the pit . In addition to capping , re-open motion was frequently observed in our AFM images . There are 3 possible interpretations of this events: ( i ) a new CCP was formed at the same position of the membrane after the previous vesicle was budded off ( re-formation ) , ( ii ) the CCP is still connected to the plasma membrane with an unresolvable small aperture and reversibly changes the aperture size ( re-widening ) , or ( iii ) the CCP is completely separated from the plasma membrane and then reversibly fuses back to the original position ( re-fusion ) . In the first case ( re-formation ) , the motion has several similarities to what was previously reported as the “hot spot , ” which is approximately 400 nm in diameter and sequentially produces CCPs [34] with multiple cycles of dynamin polymerization [32] . It can be speculated that clathrin and other proteins remained in the hot spot and efficiently recruited proteins necessary for another round of CME . In our observation , clathrin also remained after the closure of the first CCP and started to increase again during the second growing phase ( Fig 5C ) . The rate of the clathrin assembly was very similar between first and second CCP assembly ( Fig 5C ) . These results strongly suggest that the re-open motion we observed in AFM could be re-formation of the CCP at the same spot soon after the preceding pit is closed . However , this could not totally exclude the possibility of re-fusion and re-widening models . Further analyses with higher spatiotemporal resolutions of both AFM and fluorescence signal will provide a clearer answer to this question . It is noted that inhibition of actin dynamics not only decreased the capping motion but also increased the occurrence of re-opening motions ( Fig 7A ) . There could be several possible explanations for this observation . If the re-open motion is a fusion of the vesicle back to the plasma membrane ( re-fusion ) as we discussed before , this result suggests that actin dynamics are required for irreversible detachment of the CCV from the plasma membrane . In one possible scenario , actin polymerizes around the vesicle and provides a driving force to push the vesicle into the cytoplasm by interacting with myosin in the cell cortex . There are a number of reports of nonmuscle myosins in the cell cortex [50–52] , which are involved in various molecular events at the cell surface . Another scenario is that actin assembles near the plasma membrane but does not attach to the vesicle . Newly assembled actin filaments between the plasma membrane and the vesicle may spatially hinder the reversible fusion of the vesicle to the plasma membrane , which consequently pushes the vesicle into the cytoplasm , ultimately leading to endosome fusion , as was suggested by previous studies [53 , 54] . If the re-open motion is re-formation of a new vesicle ( hot spot ) , this result suggests that actin dynamics have a negative effect on the formation of a new vesicle . However , our result that the inhibition of actin did not affect the number of newly formed CCPs ( Fig 7B ) does not support this possibility . To clarify the mechanism of the re-open motion by actin inhibitors , further studies will be required . In addition to the closing motion , we found a role of actin dynamics in the growing phase of the CCP . Our observation that destruction of the actin network by cytochalasin B or CK666 accelerated the CME process ( shortened the lifetime of open apertures ) and stabilization of the network by jasplakinolide prolonged the lifetime ( Fig 6 ) suggests that the cortical actin layer has an inhibitory effect on the progress of CME . This could be explained by the following 3 mechanisms . The first possibility is that the assembly of CCP protein components at the plasma membrane is inhibited by the cortical actin layer . Because the cortical layer is supposed to be similar to a hydrogel state , the diffusion of cytoplasmic proteins through the cortex is also supposed to be slow . The second possibility is that the cortical actin layer spatially inhibits the growth of the CCP . As the size of the CCP grows , neighboring actin filaments must be excluded . Although it is not clear whether the exclusion is mediated by physical force or an enzymatic process [55 , 56] , the progress of CME is tightly coupled with the dynamics of the cortical actin network . The third possibility is that the actin cortex generates membrane tension that inhibits the progress of CME . Disruption of actin cortex reduces the membrane tension and accelerates the pit formation by clathrin coating . The role of actin dynamics in CME has been debated in previous studies [53 , 57] . The cortical actin layer is known to be actively involved not only in endocytosis but also exocytosis . In a secretion process of neuronal cells , the cortical actin network is involved in ( i ) tethering secretory vesicles [58–63] , ( ii ) providing a platform for directed movement toward the plasma membrane [64] , and ( iii ) facilitating the generation of new release sites [65–68] . In endocytosis , actin is known to localize to the CCP in both yeast and mammalian cells [44 , 45] . Although several models have been proposed for the function of actin in CME , many details remain obscure . Our results demonstrate not only the involvement of actin in capping formation but also functional interaction of actin and dynamin in the closing step of CME . In addition to actin , we demonstrated the role of dynamin in the closing motion ( Fig 8 ) . Knock down of dynamin reduced the capping and re-opening motions and increased the two-step motion ( Fig 8 ) , which is partly similar to the effect of actin inhibition ( Fig 7 ) . This effect can be partially explained by a feedback mechanism between actin and dynamin recruitment , which was previously reported [5]; dynamin knockdown decreased the accumulation rate of actin , which resulted in the decrease of the capping motion . In addition , the loss of dynamin apparently prolonged the duration of a smaller aperture size in the two-step motion ( Fig 8 ) , suggesting that dynamin plays a role not in the initial narrowing of the aperture but in the complete scission of the vesicle . This is compatible with the known function of dynamin: it binds to the neck of the CCP and catalyzes membrane scission [69 , 70] . On the other hand , a recent in vitro study reported that actin and BAR domain proteins , but not dynamin , are essential for membrane scission [71] . Therefore , it might be the case that the initial narrowing of the pit aperture is mediated by actin and BAR domain proteins , and the final scission step might be accelerated by a catalytic function of dynamin . In such a case , the inhibition of individual proteins would not completely abolish the complete process but would only slow down the progress . We found that knock down of dynamin also prolonged the assembly and/or maturation phases of CME ( Fig 8 ) . Indeed , dynamin appeared on the CCP just prior to the initiation of membrane invagination and kept accumulating as the pit grew ( Fig 3 ) [72] . The role of dynamin in the assembly and maturation phases has been debated in previous studies [5 , 25] . Because the catalytic activity of dynamin is regulated by nucleotide-based mechanisms [70] , the assembly of dynamin at the CCP may not , in itself , induce any membrane deformations . Considering the fact that the dynamin knockdown slowed down the progress of CME , it might be the case that dynamin is necessary for recruiting other protein machineries to the CCP . Further analyses are required for elucidating the role of dynamin in the whole process of CME . Another possibility might be an activation of dynamin 1 , which was recently reported in non-neuronal cells [38 , 39]; the dynamin 1—which is presumed to be a neuron-specific isoform of dynamin but was recently found to be expressed in many non-neuronal cell lines—was activated in non-neuronal cells when CME was dysregulated with the expression of a truncated mutant of adaptor protein . Therefore , at nonphysiological low temperature , dynamin 1 might be activated and compensate for the reduction of dynamin 2 . Further investigation is required to reveal functional interaction between 2 isoforms of dynamin in CME . All of the observations described in this study were conducted at 28 °C . As demonstrated in previous studies , endocytic activity is largely affected by temperature , especially in neuronal cells [73–75] . This is partly due to the reduction of membrane fluidity and of catalytic activity of proteins involved . Actin polymerization is reduced in cultured cells at nonphysiological temperature [76 , 77] . Because the actin polymerization promoted CME ( inhibition of actin dynamic elongated the lifetime , Fig 6 ) , imaging at nonphysiological temperature may elongate the lifetime of CCPs , as was discussed before [31] . Therefore , it is highly intriguing to observe dynamic morphologies of CCPs at physiological temperature and reveal the involvement of membrane and protein dynamics in the CME process . For this purpose , the establishment of a stable imaging system of HS-AFM at 37 °C , as well as a higher scanning rate , is necessary .
COS-7 cells were purchased from DS Biopharma Medical ( EC87021302-F0 ) . Cytochalasin B was purchased from Sigma-Aldrich ( St . Louis , MO ) , and jasplakinolide was purchased from Abcam ( Cambridge , United Kingdom ) . The reagents were added to the culture medium at final concentrations of 2 μM for cytochalasin B and 1 μM for jasplakinolide . HEPES-NaOH ( pH 7 . 0–7 . 6 ) , which was used to maintain a constant pH of the medium during AFM observation , was purchased from Sigma-Aldrich . The mammalian expression vectors encoding Dyn2-pmCherryN1 and epsin 2-pmCherryC1 were gifts from Christien Merrifield ( Addgene #27689 and #27673 , respectively ) , and the vector for EGFP-Lifeact expression was a kind gift from Dr . Mineko Kengaku ( Kyoto University , Kyoto , Japan ) . Silencer select siRNA targeting DNM2 ( #s4212 ) and siRNA targeting Luciferase ( #12935–146 ) were purchased from Ambion ( Waltham , MA ) and ThermoFisher Scientific ( Waltham , MA ) , respectively . Transfection reagents Lipofectamin2000 and Effectene were purchased from ThermoFisher Scientific and Qiagen ( Hilden , Germany ) , respectively . Anti-dynamin 2 antibody was from Cell Signaling Technology ( Danvers , MA ) , and PVDF membrane was from Bio-Rad Laboratories ( Hercules , CA ) . At 1 or 2 d before AFM imaging , COS-7 monkey kidney–derived fibroblast-like cells were seeded on a poly-L-lysine–coated glass slide and grown at 37 °C with 5% CO2 in Dulbecco’s Modified Eagle Medium ( DMEM ) supplemented with 10% FBS . AFM imaging was performed in DMEM supplemented with 10% FBS and 10 mM HEPES-NaOH ( pH 7 . 0–7 . 6 ) . cDNAs encoding human CLCa ( CLTA , NM_007096 ) and caveolin1 ( CAV1 , NM_00172895 ) were amplified by RT-PCR and cloned into the vector pEGFP-C3 ( Clontech , Fremont , CA ) to create fused proteins with EGFP . The plasmids were introduced into cells using Effectene Transfection Reagent according to the manufacturer’s protocol . At 24 to 48 h after transfection , the cells were used for AFM imaging . Expression of the fusion protein was confirmed by fluorescence signals from the cells . For experiments with fixed cells , the cells were fixed with 5% paraformaldehyde in PBS for 15 min at room temperature and washed with PBS . For unroofing cells on a cover glass , cells were mildly sonicated , as described in the previous study [22] , and then fixed . Cells were transfected with siRNAs using Lipofectamine 2000 ( Invitrogen , Carlsbad , CA ) following the manufacturer’s instructions and were harvested after 24 to 48 h for analysis by SDS-PAGE and immunoblotting , using PVDF membranes . Each membrane was cut into 2 halves at the protein size of 60 kDa; the upper half was blotted with anti-dynamin 2 antibody , and the bottom half was detected with anti-β-actin antibody . BIXAM ( Olympus Corporation , Tokyo , Japan ) —which is a tip-scan–type HS-AFM unit combined with an inverted fluorescence/optical microscope ( IX83; Olympus ) equipped with a phase contrast system and a confocal unit ( FV1200; Olympus ) —was used for this study . The tip-scan HS-AFM imaging system was developed based on a previous study [20] . In brief , the modulation method was set to phase modulation mode to detect tip–sample interactions . An electron beam–deposited sharp cantilever tip with a spring constant of 0 . 1 N m−1 ( USC-F0 . 8-k0 . 1 , a customized cantilever from Nanoworld [Neuchâtel , Switzerland] ) was used . All observations were performed at 28 °C . The AFM tip was aligned with confocal views as described in the Results section . The images from the confocal microscope and AFM were simultaneously acquired at a scanning rate of 10 s/frame . The captured sequential images were overlaid by using AviUTL ( http://spring-fragrance . mints . ne . jp/aviutl/ ) based on the tip position . The fluorescence intensity was quantified by Image J software ( http://rsbweb . nih . gov/ij/ ) . The lifetime of the pit was analyzed with Metamorph imaging software ( Molecular Devices , San Jose , CA ) . The diameter/height of the pit or membrane swelling region was obtained using AFM Scanning System Software Version 1 . 6 . 0 . 12 ( Olympus ) . For the measurement of the x-y offset between pit and clathrin fluorescence spots , the distance between the centroids of the AFM invagination and the fluorescence spot were measured in ImageJ . A diffusion coefficient of the pit was calculated based on the position of the pit . In the calculation , the effect of drift defined as a displacement of caveolin fluorescence spots in the fluorescence movie was corrected by subtracting a drift from pit position . For time-lapse analysis of AFM and fluorescence images of the CCPs , the time when the plasma membrane started to invaginate and when the pit completely closed on AFM images are defined as t = 0 , and the fluorescence signal intensity was plotted against this time scale . Data presented as graphs are from 3 independent experiments . The number of total CCPs analyzed for each analysis is specified in figure legends or the main text . Statistical analysis was performed by two-way analysis of variance followed by Student t test . | Cells communicate with their environments via the plasma membrane and various membrane proteins . Clathrin-mediated endocytosis ( CME ) plays a central role in such communication and proceeds with a series of multiprotein assembly , deformation of the plasma membrane , and production of a membrane vesicle that delivers extracellular signaling molecules into the cytoplasm . In this study , we utilized our home-built correlative imaging system comprising high-speed atomic force microscopy ( HS-AFM ) and confocal fluorescence microscopy to simultaneously image morphological changes of the plasma membrane and protein localization during CME in a living cell . The results revealed a tight correlation between the size of the pit and the amount of clathrin assembled . Actin dynamics play multiple roles in the assembly , maturation , and closing phases of the process , and affects membrane morphology , suggesting a close relationship between endocytosis and dynamic events at the cell cortex . Knock down of dynamin also affected the closing motion of the pit and showed functional correlation with actin . | [
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"analysis",... | 2018 | Morphological changes of plasma membrane and protein assembly during clathrin-mediated endocytosis |
The proopiomelanocortin gene ( POMC ) is expressed in the pituitary gland and the ventral hypothalamus of all jawed vertebrates , producing several bioactive peptides that function as peripheral hormones or central neuropeptides , respectively . We have recently determined that mouse and human POMC expression in the hypothalamus is conferred by the action of two 5′ distal and unrelated enhancers , nPE1 and nPE2 . To investigate the evolutionary origin of the neuronal enhancer nPE2 , we searched available vertebrate genome databases and determined that nPE2 is a highly conserved element in placentals , marsupials , and monotremes , whereas it is absent in nonmammalian vertebrates . Following an in silico paleogenomic strategy based on genome-wide searches for paralog sequences , we discovered that opossum and wallaby nPE2 sequences are highly similar to members of the superfamily of CORE-short interspersed nucleotide element ( SINE ) retroposons , in particular to MAR1 retroposons that are widely present in marsupial genomes . Thus , the neuronal enhancer nPE2 originated from the exaptation of a CORE-SINE retroposon in the lineage leading to mammals and remained under purifying selection in all mammalian orders for the last 170 million years . Expression studies performed in transgenic mice showed that two nonadjacent nPE2 subregions are essential to drive reporter gene expression into POMC hypothalamic neurons , providing the first functional example of an exapted enhancer derived from an ancient CORE-SINE retroposon . In addition , we found that this CORE-SINE family of retroposons is likely to still be active in American and Australian marsupial genomes and that several highly conserved exonic , intronic and intergenic sequences in the human genome originated from the exaptation of CORE-SINE retroposons . Together , our results provide clear evidence of the functional novelties that transposed elements contributed to their host genomes throughout evolution .
The proopiomelanocortin gene ( POMC ) is expressed mainly in pituitary corticotrophs and melanotrophs , as well as in a population of ventral hypothalamic neurons of all jawed vertebrates [1 , 2] . POMC encodes a prohormone that gives rise to several bioactive peptides that include ACTH ( adenocorticotropic hormone ) , α- , β- , and γ-MSH ( melanocyte-stimulating hormone ) and β-endorphin . A large body of evidence implicates POMC-derived peptides in evolutionarily conserved functions as diverse as the stress response , skin and hair pigmentation , analgesia , and the regulation of food intake and energy balance [3 , 4] . Pituitary and brain transcriptional regulation of POMC are controlled by modular cis-acting elements present in the 5′ flanking region [5] . Whereas pituitary-specific POMC expression depends on proximal sequences located within 400 bp upstream of the transcription start site , neuronal expression is independently controlled by distal sequences located several kb further upstream [6] . Combining the use of phylogenetic footprinting analysis and transgenic mouse studies , we have recently determined that POMC expression in the hypothalamus is conferred by the action of two enhancers , nPE1 and nPE2 , located at −12 kb and −10 kb of the mouse POMC gene , respectively [5] . Although nPE1 and nPE2 are unrelated at the sequence level , their regulatory functions seem to overlap , since only the removal of both elements from transgenic constructs leads to the loss of reporter gene expression in hypothalamic POMC neurons [5] . One of the most compelling observations derived from the recent completion of several genome projects is the overwhelming contribution of transposed elements to mammalian genome composition . For example , 45% of the human genome and 33% of the mouse genome are derived from insertions of transposed elements , the vast majority of which have lost transposable activity [7 , 8] . Although historically viewed as genome parasites , mobile elements may also participate in gene evolution by providing a large collection of distinct sequences that may contribute to novel functional elements in their host genomes [9 , 10] . For instance , the number of SINE ( short interspersed nucleotide elements ) of the primate-specific Alu family in the human genome exceeds one million , and many of them have been reported to function as novel splicing sites , cis-regulatory elements , polyadenylation sites , or protein domains [11–13] . A random transposition event may be useful for the host genome if the inserted mobile element directly interacts with neighboring genes or becomes functional after accumulating advantageous mutations . If the novel function improves fitness , the transposon-derived element may become fixed into the genome by purifying selection , a process called exaptation [14 , 15] . Recently , several high-throughput studies performed in different mammalian genomes detected the presence of thousands of transposed elements that are likely to have been exapted since they are under purifying selection , although their functional properties have not yet been tested [16–20] . Last year , the discovery was reported of several ultraconserved functional sequences in terrestrial vertebrate genomes that originated from ancient exaptation events of SINEs , which were active until recently in the living fossil fish coelacanth [21] . Using an in silico paleogenomic strategy , we demonstrate here that the neuronal POMC enhancer nPE2 originated from the exaptation of a CORE-SINE retroposon in the lineage leading to mammals and has remained under purifying selection for the last 170 million years . Functional studies performed in transgenic mice show that two nonadjacent 45-bp regions of nPE2 , which are derived from an exapted CORE-SINE retroposon , are essential for enhancer activity in POMC hypothalamic neurons . In addition , we demonstrate the existence of other highly conserved sequences in the human genome that originated from the exaptation of CORE-SINE retroposons . Together , our results provide clear examples of the functional novelties that transposed elements contributed to their host genomes throughout evolution .
In previous work , we identified nPE2 enhancers in the genomes of several placental mammals , but not in chicken , the frog Xenopus tropicalis , or in teleost fishes [5] . To trace the evolutionary history of nPE2 , we performed further BLAST searches in more recently available mammalian and nonmammalian draft genomes of the Ensembl database and the trace archives of the National Center for Biotechnology Information ( NCBI ) . We retrieved additional ortholog nPE2 sequences from placental mammals ( Eutheria ) of all available orders ( Figures 1 and 2 ) except Xenarthra ( armadillo and sloth ) , probably due to insufficient sequencing coverage . In addition , we identified nPE2 sequences from the marsupials ( Metatheria ) short-tailed opossum , and wallaby , as well as from the monotreme ( Prototheria ) egg-laying platypus . Based on these findings and the absence of nPE2 in nonmammalian vertebrates , we conclude that nPE2 is a mammalian novelty that appeared in an ancestor to all extant mammals , prior to 170 million years ago ( MYA ) [22] . We next determined the nPE2 regions under stronger selective constraint by performing an evolutionary divergence test that calculates the substitution rate along every branch of a phylogenetic tree constructed with all species analyzed ( Figure 1 ) . A sliding-window plot based on the alignment and phylogenetic tree of all ortholog nPE2 mammalian sequences showed an extremely conserved central region within a stretch of approximately 160 bp with substitution rates lower than five nucleotides per site ( segment between black arrows in Figure 1 ) . Upstream and downstream of this 160-bp region the aligned sequences are highly divergent , an indication of more relaxed evolutionary constraint ( Figure 1 ) . A ClustalX alignment of nPE2 sequences along the most conserved region is shown in Figure 2 . The overall conservation of nPE2 sequences among these 16 different mammals is remarkably high , with the most divergent sequences contributed by the nonplacental mammals platypus , wallaby , and opossum ( Figure 2 ) . The most striking aspect of this alignment is that the middle part of nPE2 is ultraconserved in Mammalia whereas the 5′ and 3′ extremes appear to be under less strict purifying selection . For instance , near the 5′ part of the enhancer , the sequence ATA/GAAAGC ( 20–27 , numbers refer to the mouse nPE2 ) , which is almost identical in all species including nonplacental mammals , has been changed to ATGCCCGC in the rabbit only . Similarly , the consensus element CATTAG ( 11–16 ) , which contains a potential recognition site for homeodomain transcription factors , is changed to CAGGAG in the dog only . The number and length of insertions and deletions ( indels ) in nPE2 across species is quite small . However , some of the indels are phylogenetically informative , such as an A residue in the sequence CCCCATTC ( 82–90 ) , which is present in all placental nPE2s but missing in basal groups ( monotremes and marsupials ) . A 1 . 4-kb fragment of the mouse Pomc gene containing nPE2 , but not nPE1 , was ligated upstream of the chicken β-globin minimal promoter followed by the lacZ reporter gene and used to generate transgenic mice ( Figure 3 , top ) . The ability of nPE2 to drive transgenic expression to POMC neurons was determined by X-Gal staining followed by ACTH immunohistochemistry in coronal brain sections of adult F1 transgenic mice of five independently generated pedigrees ( Figures 4A [wild type ( WT ) ] and S1 ) . The reporter gene was eutopically expressed in a high percentage of POMC arcuate neurons in three of the five lines ( 68% , 61% , and 45% , respectively ) . Variable patterns of ectopic reporter gene expression were also observed in nonhypothalamic brain neurons of these three transgenic lines ( unpublished data ) . The other two transgenic lines did not express lacZ in the brain or other tissues analyzed . To investigate whether the developmental onset of nPE2-driven lacZ expression coincides with that of endogenous Pomc , we subjected 9 . 5 , 10 . 5 , 12 . 5 , and 14 . 5 days post coitum ( dpc ) transgenic embryos to X-Gal staining followed by ACTH immunohistochemistry . Figure 3 shows that both signals are detected in the ventral diencephalon at 10 . 5 dpc , consistent with the onset of endogenous Pomc expression in this brain region [23] . As expected , β-gal activity was not seen in corticotrophs or melanotrophs of the pituitary [5] , which start expressing POMC at 12 . 5 and 14 . 5 dpc , respectively [23] . Thus , reporter gene expression driven by a 1 . 4-kb genomic fragment containing nPE2 ligated to a heterologous promoter is able to recapitulate the spatial and temporal expression patterns of Pomc in mediobasal hypothalamic neurons . To identify the critical sequences of nPE2 for enhancer activity , we created an additional series of five transgenes carrying discrete overlapping deletions of approximately 45 bp each that we named regions 1 to 5 ( Figures 1 , 2 , and 4 ) . Each deletion was designed by taking into consideration the most conserved segments within nPE2 and the location of potential transcription factor binding sites , as determined using the MatInspector program [24] . For each transgene , we generated multiple transgenic mouse lines and analyzed lacZ expression in coronal brain sections from F1 adult animals and sagital sections from F1 or F2 whole embryos at 14 . 5 dpc . Like the WT construct , transgenes carrying deletions 2 , 4 , or 5 reliably targeted reporter gene expression to hypothalamic POMC neurons in adults ( Figures 4 and S1 ) and embryos ( Figures S2 ) , and the overall expression levels were similar to WT nPE2 transgene . Colocalization of β-galactosidase activity within ACTH immunoreactive hypothalamic neurons was observed in two out of five nPE2-Δ2 , three out of five nPE2-Δ4 , and eight out of 12 nPE2-Δ5 transgenic mouse lines . In contrast , all five nPE2-Δ1 and six nPE2-Δ3 transgenic pedigrees failed to direct lacZ expression to POMC neurons ( Figure 4; Table S1 ) . Together , these results indicate that the nPE2 regions encompassing deletions 1 and 3 are essential for enhancer activity , as summarized in Figure 4B . Interestingly , region 3 is the most phylogenetically conserved sequence within nPE2 ( Figures 1 and 2 ) and concentrates the vast majority of potential transcription factor binding sites identified by the MatInspector program ( unpublished data ) . To investigate the molecular evolutionary process underlying the emergence of nPE2 as a novel mammalian enhancer of POMC , we performed BLAST searches for nPE2 paralogs in all available mammalian genomes . In addition to the anticipated 100% hit at its POMC locus , the opossum nPE2 sequence matched three short high identity instances on different chromosomes of the opossum genome . These four related sequences were used as queries to reexamine the opossum genome , resulting in additional significant hits that were annotated as SINE-derived sequences in the University of California Santa Cruz ( UCSC ) Genome Browser . Similar results were obtained from BLAST searches of trace sequences from the wallaby genome . To determine the family of SINE retroposons with greatest similarity to nPE2 , we aligned opossum and wallaby nPE2 to representative consensus sequences of the various SINE families obtained from Repbase [25] using ClustalX [26] . Sequence alignments and identity values indicated that nPE2 is most similar to members of the CORE-SINE retroposon superfamily and , particularly , to members of the MAR1 family ( Figures 5 and S3; Table 1 ) . CORE-SINEs , V-SINEs , and Deu-SINEs are the three superfamilies of tRNA-like SINEs that were identified among a wide range of species and characterized by a highly conserved central region [18 , 27–29] . In particular , CORE-SINEs carry a 65-bp “core” sequence and were first described as mammalian-wide interspersed repeats ( MIRs ) in mammalian genomes [30–32] and later found in nonmammalian vertebrates and invertebrates [27 , 28] . Sequence comparisons between marsupial nPE2s and consensus MAR1s revealed greatest similarity in the core region , and somewhat less similarity in the 5′ pol III promoter-like region and the 3′ variable region ( Figure 5 ) . For example , the identity between opossum nPE2 and MAR1 is 59% along the entire 70 bp of the core and this value rises to 71% in the 45 bp of the core's 3′ end . Table 1 shows the percentage of identity and evolutionary distance between opossum and wallaby nPE2 and core sequences of different CORE-SINEs . Within MAR1s , the values are highest for MAR1a , followed by MAR1 and somewhat lower for MAR1b , which is the most divergent member of this family . Other members of the CORE-SINE superfamily , including Ther1 and Ther2 from placental mammals , Mon1 from platypus , and MIRs from all mammals , display lower levels of identity with nPE2 ( Table 1; Figure S3 ) . This sequence divergence explains our initial failure to identify the nPE2 enhancer as a SINE-derived element in nonmarsupial genomes . Interestingly , some MAR1 instances present in the opossum genome have two adjacent cores that probably originated from a duplication event . These particular elements , which we named “nested” MAR1s ( Figure S4 ) , are present in marsupial genomes and share an even higher identity with nPE2 at the core level ( Figure 5; Table 1 ) . Thus , using this in silico paleogenomic approach , we were able to determine that nPE2 is an evolutionary mammalian novelty derived from the exaptation of a CORE-SINE retroposon . Our discovery that the mammalian neuronal POMC enhancer nPE2 derives from an ancient CORE-SINE was possible because several MAR1 instances similar to nPE2 are present in marsupial genomes . In fact , we found more than 5 , 000 recognizable nearly full-length copies of MAR1 in the opossum genome and several hundred of them are low-divergence sequences with identity scores higher than 80% . Table S2 and Figure S5 show the top 20 instances most identical to the consensus MAR1 that are , on average , more than 85% identical to the consensus MAR1 sequence from Repbase . The identity further increased at the level of the core ranging from 89% to 100% ( Figure 6A ) . In addition , we found evidence of target site duplications in many MAR1 instances of the opossum genome , another indication that the retroposition events have occurred recently ( Figure 6B ) . Within the tRNA-like promoter region the conservation is higher in Box B , ranging from 90% to 100% , than in Box A , where the level of conservation is lower in the majority of the instances and much more variable , ranging from 10% to 100% . CORE-SINEs are nonautonomous retroposons that use the enzymatic machinery of active partner long interspersed nucleotidic elements ( LINEs ) to create new instances in the genome [33 , 34] . It has been shown that the interaction between a CORE-SINE and its associated LINE depends on sequence identity between their 3′ ends [27 , 28] . Therefore , identification of an active MAR1 partner in the opossum genome is an important indication of its current activity . MAR1 mobilization has been suggested to occur through the interaction with the retroposition machinery of members of the Bov-B LINE family [27 , 28] . We found several Bov-B LINE instances throughout the opossum genome; however , none of them appear to be full-length elements . Interestingly , we found another non-long terminal repeat ( LTR ) retrotransposon named RTE-3 MD showing 100% conservation along the first 51 bases of the 3′ sequence of the consensus MAR1 ( Figure 6C ) . Bov-B LINEs and RTE-3s belong to the same LINE clade [35] and their consensus sequences are 71% identical . BLAST results indicated that nearly full-length RTE-3 instances are widespread in the opossum genome , which may explain the high copy number of MAR1s detected ( Figure 6; Table S2 ) . We obtained very similar results when analyzing trace sequences of the wallaby genome . Altogether , these data indicate that MAR1s are still active mobile elements in marsupial genomes or have been active until very recently . The suggestion that MAR1s and their partner LINE RTE-3 are still active in the opossum genome was also proposed recently [20] . We also found that other members of the CORE-SINE superfamily , such as Ther1 and Ther2 , are widely present in the opossum genome . Using consensus sequences of these elements ( RepBase ) we found approximately 5 , 000 and 2 , 500 recognizable copies of Ther1 and Ther2 , respectively . Compared to opossum MAR1s , Ther1 and Ther2 displayed lower levels of identity and showed no evidence of recent activity . To our knowledge , nPE2 constitutes the first functionally documented example of a CORE-SINE–derived sequence that was exapted in the mammalian lineage . To investigate whether other phylogenetically conserved instances are derived from CORE-SINE retroposons , we searched for related sequences across orthologous mammalian loci . The MAR1 MD core sequence was compared against the human genome using BLAT ( http://genome . ucsc . edu/cgi-bin/hgBlat ? command=start ) and several thousand similar sequences were detected . Subsequently , we used UCSC Table Browser ( http://genome . ucsc . edu/cgi-bin/hgTables ) to select the most highly conserved hits across mammals , according to the Most Conserved Elements database ( phastCons; [36] ) . Figure 7A shows the only nine highly conserved hits that we found between the MAR1 MD core and exonic , intronic , or intergenic regions of the human genome . The high conservation of these CORE-SINE–derived sequences in orthologous mammalian loci indicates they have been under strong purifying selection for at least 150 million years . Until experimental proof uncovers their functional role , the presence of these highly conserved CORE-SINE–derived sequences in mammalian genomes will remain a mystery . Figure 7B shows the putative CORE-SINE exaptation event into a highly conserved mammalian sequence present between exons 4 and 5 of the zinc finger transcription factor gene ZNF384/CIZ/NMP4 , which is thought to be involved in the regulation of bone metabolism and spermatogenesis [37] .
The aim of the present study was to determine the evolutionary history of the POMC neuronal enhancer nPE2 . We first demonstrated that nPE2 orthologs are highly conserved in their nucleotide sequence in all placental and nonplacental mammals , but absent in other vertebrates . We then performed a systematic search for nPE2 paralogs in all available mammalian genomes and identified three short sequences similar to opossum nPE2 within the opossum genome . The use of these four sequences as queries in further BLAST searches revealed that they are highly similar to various members of the marsupial CORE-SINE retroposon family MAR1 . We named the use of progressive searches of genome databases to reconstruct the origin of functional novelties from evolutionary relics “in silico paleogenomics” to distinguish it from the term “paleogenomics , ” which is more commonly used in genomic research involving DNA samples obtained from fossil specimens . Our findings are consistent with the hypothesis that an ancient CORE-SINE retroposon was mobilized into the POMC locus and exapted as a neuronal enhancer in the lineage leading to mammals more than 170 MYA . Around 30 to 40 million years later , after the split that led to marsupials [22] , a group of CORE-SINEs now known as MAR1s started to colonize the marsupial genomes , remaining active until very recently ( see Results and Figure 6; also [20 , 28] ) . This is in clear contrast to the evolution of CORE-SINEs in placental mammals , which lost transposable activity around 100 MYA and remain now as fossil sequences [28] . The fact that nPE2 is more similar to MAR1s seems to be fortuitous , and suggests that MAR1s are more similar to the ancestral CORE-SINE that was exapted into nPE2 than all other members of the superfamily . The abundance of similar copies of MAR1s within marsupial genomes was key to uncovering the evolutionary origin of nPE2 and indicates that marsupial genomes represent a uniquely positioned source from which to trace the evolutionary origin of mammalian genes . Evidence that nPE2 derives from the exaptation of a CORE-SINE is based on the relatively high percentage of identity between opossum nPE2 and MAR1s ( Figure 5 ) . The similarity is especially remarkable in the core region ( 59% ) and even higher along the 45 bp of its 3′ end ( 71% ) . This level of identity is comparable to that reported between different MAR1s ( MAR1a and MAR1b cores are 63% identical ) and to an ancient LF-SINE exapted as a cell-specific enhancer of ISL1 , which are 61% identical in their most similar region [21] . To our knowledge , the ISL1 enhancer and nPE2 are the sole functionally proven examples of enhancers whose sequences are derived from ancient retroposons , and nPE2 is the first one discovered to have originated from a member of the CORE-SINE family . To dissect the regions of nPE2 involved in POMC neuronal enhancer function , we performed a deletional analysis in transgenic mice and identified two essential nonadjacent 45-bp sequences: regions 1 and 3 . Region 3 is almost absolutely conserved among all species ( Figures 1 and 2 ) , suggesting that the array of transcription factors binding to it has probably been constant since the origin of mammals . Interestingly , the 5′ and 3′ halves of region 3 seem to be mutually redundant , since they can be independently removed without impairing reporter gene expression in hypothalamic POMC neurons ( deletion of regions 2 or 4 ) . The presence of two A + T-rich motifs ( AATTAAAA and AATTGAAA ) with potential binding sites for homeodomain transcription factors in each half of region 3 is provocative . In contrast to region 3 , the essential region 1 admits many base substitutions , microinsertions , and microdeletions ( Figures 1 and 2 ) . However , it is well known that cis-acting elements can differ in sequence and still play similar functions , either due to degeneracy in binding site specificity [38] or compensatory mutations in other sites [39] . Region 1 is derived from the 5′ tRNA-like portion of the consensus MAR1 , whereas region 3 is derived from the core . This observation is in agreement with other examples of exaptation showing that functionally relevant SINE-derived sequences may come from different portions of the original retroelement [17–19 , 21] . Based on our findings , it is difficult to know if the CORE-SINE inserted upstream of POMC functioned as an enhancer immediately upon its insertion , as proposed for some Alu elements that carry potential binding sites for nuclear receptors [40–42] . Alternatively , the retroposon insertion initially provided adequate raw material for the accumulation of favorable mutations until it evolved into a novel neuronal POMC enhancer and became fixed in the lineage leading to mammals , before 170 MYA [22] . Although nPE2 is a mammalian novelty , all jawed vertebrates studied to date , including birds , amphibians , and fishes , express POMC in ventral hypothalamic neurons , suggesting that an nPE2-independent regulatory mechanism must control neuronal POMC expression in other vertebrates . This is consistent with our recent findings showing that the entire 5′ flanking region of POMC from the pufferfish Tetraodon nigroviridis is capable of directing the expression of a reporter gene to POMC pituitary cells but not to POMC hypothalamic neurons of transgenic mice ( unpublished data ) . The ability of nonmammalian vertebrates to express POMC in ventral hypothalamic neurons suggests that the appearance of nPE2 probably replaced the function of an earlier POMC neuronal enhancer . This puzzle will be resolved when neuronal POMC regulatory elements and their cognate trans-acting factors from other vertebrates are identified . Another important conclusion from our study is that exaptation of CORE-SINEs is probably not restricted to nPE2 . From several thousand exonic , intronic , and intergenic sequences that we found in the human genome to be derived from the core region of CORE-SINE retroposons , nine of them constitute strongly suggestive examples of exaptation since they are highly conserved among all mammalian ortholog loci . There is a growing list of SINE retroposition events that may have contributed to evolutionary novelties in mammals [9 , 11 , 14 , 43 , 44] , but the vast majority of reported examples correspond to lineage-specific SINEs like Alu and B1 elements present in the primate and rodent genomes , respectively . Since Alu and B1 retrotransposition events are relatively modern , their derived sequences are likely to be easily recognized . However , not all these cases should be considered examples of exaptation until novel adaptive functions followed by purifying selection are confirmed . More recently , several high-throughput studies detected the presence of transposed element sequences that are likely to have been exapted since they are under purifying selection , although their functional properties have not yet been tested [16–18] . For example , an ancient SINE family that was active in amniotes ( mammals , birds , and reptiles ) was discovered and named AmnSINE [18] . More than 1 , 000 AmnSINE-derived instances were found in the human genome and around 10% of them have been under purifying selection in mammals and likely contributed to adaptive novelties in this class . Another recent study demonstrated the existence of thousands of human transposed element fragments under strong purifying selection mostly located near developmental genes [16] . Last year , the discovery was reported of several ultraconserved functional sequences in terrestrial vertebrate genomes that originated from ancient exaptation events of a LF-SINE , which had been active until recently in the living fossil fish coelacanth [21] . Unlike the case of nPE2 , recognition of those elements as derived from a LF-SINE was facilitated by the remarkably high level of conservation between the functional tetrapod sequences and the coelacanth retroposon , which must have diverged around 410 MYA . In summary , our study documents the evolutionary history of a mammalian regulatory element that originated from an ancient retroposition event . The difficulty in detecting the origin of nPE2 as an exapted CORE-SINE retroposon illustrates the underestimation of this phenomenon and encourages the finding of the many more thousands of examples of retroposon-derived functional elements still hidden within the genomes and whose discovery will help us to better understand the dynamics of gene evolution and , at a larger scale , the origin of macroevolutionary novelties that led to the appearance of new species , orders , or classes .
To find nPE2 ortholog and distant paralog sequences we performed BLAST searches using human or mouse nPE2 sequence as queries against whole-genome assemblies from the Ensembl website ( http://www . ensembl . org ) ; we also searched the Trace Archive ( http://www . ncbi . nlm . nih . gov/Traces ) using megaBLAST [45] . Species used were Mus musculus ( mouse ) , Rattus norvegicus ( rat ) , Cavia porcellus ( guinea pig ) , Oryctolagus cuniculus ( rabbit ) , Homo sapiens ( human ) , Macaca mulatta ( macaque ) , Callithrix jacchus ( common marmoset ) , Tupaia belangeri ( tupaia ) , Canis familiaris ( dog ) , Felis catus ( cat ) , Myotis lucifugus ( microbat ) , Equus caballus ( horse ) , Bos taurus ( cow ) , Tursiops truncatus ( bottlenose dolphin ) , Sorex araneus ( shrew ) , Loxodonta africana ( African elephant ) , Monodelphis domestica ( South American short-tailed opossum ) , Macropus euge nii ( tammar wallaby ) , and Ornithorhyncus anatinus ( platypus ) . Trace archives of Dasypus novemcinctus ( nine-banded armadillo ) and Choloepus hoffmanni ( two-toed sloth ) were also searched for nPE2 with negative results . nPE2 sequence accuracy was determined by comparing all trace reads spanning the regions and deducing a consensus . Sequences were aligned with ClustalW ( http://www . ebi . ac . uk/clustalw ) [26] . The alignments were manually refined and edited using GenDoc ( http://www . cris . com ) . Transposed element sequences were obtained at Repbase ( http://www . girinst . org ) . Sequence identity was calculated between aligned pairs of sequences as the number of residues that matched exactly ( identical residues ) . Each sequence was compared to every other sequence . Evolutionary distances were calculated using MEGA version 3 . 1 [46] . Evolutionary distance between a pair of sequences was measured by the number of nucleotide substitutions occurring between them . We calculated the distance using the Tamura 3-parameter distance model with Rate Uniformity and Pattern Homogeneity . Sliding windows analysis of substitution rates was performed using HYPHY [47] . We estimated the number of substitution through the best-fit maximum likelihood model K80 ( determined by a model test analysis ) , which corrects for multiple hits , taking into account transitional and transversional substitution rates and differences in substitution rates among sites . Evolutionary rates among sites were modeled using the Gamma distribution and equilibrium nucleotide frequencies were considered to be equal . Transgenes with deletions of nPE2 subregions were made by PCR with megaprimers [48] . Outer primers 1 and 2 amplified a 1 . 4-kb fragment spanning a region from −10 . 4 to −9 kb of the 5′ flanking region of mouse POMC gene that includes nPE2 . Primer 1: 5′-ATACGCGTCGACTAGGCAAGAGATGCCAGCTAGACCTTAC-3′ ( SalI site underlined ) ; primer 2: 5′-ATACGGGGTACCTCCAGAAGGCATCCTTGCATAGTGCCTC-3′ ( KpnI site underlined ) . The 1 . 4-kb amplified fragment was cloned into the SalI and KpnI sites of the pTrap vector [49] to obtain construct WT-nPE2 ( Figure 3A ) . A series of internal primers was designed to perform successive overlapping deletions within nPE2: primer 1a , 5′-CCAAAGGGCCCTTTAGCACAGTAGCCCACC-3′;1b , 5′-CTACTGTGCTAAAGGGCCCTTTGGCTGTAA-3′; 2a , 5′-CCTTTGGATGGGCCCTTGAGACGGCTTTCATCCAC-3′; 2b , 5′-CCGTCTCAAGGGCCCATCCAAAGGTCAATTGAAATC-3′; 3a , 5′-AGAAGAAGAATGTTACAGCCAAAGGGCCCTGGTGA-3′; 3b , 5′-CTTTGGCTGTAACATTCTTCTTCTCCACACAAATTGA-3′; 4a , 5′-ATCAATTTGTGTGGGGTTTTAATTTGCTTTATTAC-3′; 4b , 5′-AATTAAAACCCCACACAAATTGATTCCTCTTTGCCCTTGA-3′; 5a , 5′-CTTTATGGCATTGAAGAATGAAAGAGATTTCAATTGA-3′; and 5b , 5′-CTTTCATTCTTCAATGCCATAAAGGGGCCCAAC-3′ . Underlined sequences are complementary within each pair of primers and flank each region to be deleted . In the first step , the outer primers were used in combination with the internal primers carrying each deletion ( Figure 3B ) . The following combinations of primers were used: 1/2 ( WT nPE2 ) ; 1/1a and 1b/2 ( Δ1 ) ; 1/2a and 2b/2 ( Δ2 ) ; 1/3a and 3b/2 ( Δ3 ) ; 1/4a and 4b/2 ( Δ4 ) ; 1/5a and 5b/2 ( Δ5 ) . The PCR fragments produced in the two different sets of reactions performed with primers 1 and 2 were used in a second step as template and megaprimers , which are complementary around the deleted region . A final PCR amplification with outer primers 1 and 2 was performed to generate a −10 . 4/−9 kb fragment carrying each of the nPE2 deletions . To reduce sequence errors , PCRs were performed with a low number of cycles , high concentration of template , and the turbo Pfu polymerase ( Stratagene ) in a PTC-200 Peltier Thermal Cycler ( MJ Research ) . Cycling conditions: initial denaturation 94 °C 5 min; ten cycles of 94 °C 5 min , annealing-ramp 60 °C-55 °C 1 min , 72 °C 2 min; ten cycles of 94 °C 2 min , annealing 55 °C 1 min , 72 °C 2 min; final extension at 72 °C 10 min . PCR products were subcloned into pZErOTM-2 ( Zero BackgroundTM/Kan Cloning Kit , Invitrogen ) and deletions were confirmed by sequencing before cloning of inserts into the SalI and KpnI sites of pTrap . Prior to microinjection , all transgenes were digested with NotI , eluted from an agarose gel , and purified with the Elutip-D system ( Schleicher & Schuell ) . After precipitating with 3 M sodium acetate ( pH 5 . 2 ) and 100% ethanol , the DNA was washed with 70% ethanol and resuspended with microinjection buffer ( 5 mM Tris-HCl , pH 7 . 4; 0 . 1 mM EDTA ) . Transgenic mice were generated by pronuclear microinjection of B6CBF2 zygotes as described previously [6] . Microinjected zygotes were transferred to the oviduct of B6CB pseudopregnant females . Transgenic pups were identified by tail genomic DNA PCR with the following primers: LPZ ( 5′-TCCCAGTCACGACGTTGTAAAACG-3′ ) and P ( 5′-GGTACCGCATGCGATATCGAGCTC-3′ ) , which amplify a transgenic-specific 166-bp fragment . The deletions were detected with primers flanking the element nPE2: delta 2 . 5 ( 5′-TGATTTTACTTGGGCCTC-3′ ) and delta 2 . 3 ( 5′-TCAGGCTTGTTCCCATCC-3′ ) that amplify 340-bp fragments from the endogenous gene and 300-bp fragments from the transgenes with the deletions , respectively . Animals were kept in a ventilated rack ( Thoren Caging Systems ) under a 12-h light/dark cycle and 20–22 °C room temperature ( RT ) . All procedures using live animals were approved by the respective Institutional Animal Care and Use Committees and followed the Public Health Service guidelines for the humane care and use of experimental animals . Transgene expression was determined in F1 adult mice of each independently generated pedigree . Mice were deeply anesthetized , perfused with 4% paraformaldehyde ( PFA ) in KPBS ( 0 . 9% NaCl , 16 mM K2HPO4 , 3 . 6 mM KH2PO4 , pH 7 . 4 ) , and brains were excised , postfixed in 4% PFA-KPBS 1 h 20 min at 4 °C , and sectioned ( 50 μm ) in a Vibratome 1000 ( Ted Pella ) . Brain slices were stained with 1 mg of 5-bromo-4-chloro-3-indolyl-β-D-glucuronic acid ( X-Gal ) /ml in staining solution ( PBS [pH 7 . 3] containing 2 . 12 mg of potassium ferrocyanide/ml , 1 . 64 mg of potassium ferricyanide/ml , 2 mM MgCl2 , 0 . 01% sodium deoxycolate , and 0 . 02% NPO-40 ) for 4 h at 37 °C . After X-Gal staining , brain slices were treated with 1% H2O2 in KPBS for 40 min , washed twice with KPBS , and incubated overnight at 4 °C with a rabbit polyclonal anti-ACTH-IC-1 ( National Hormone and Peptide Program , Harbor-UCLA Medical Center Research and Education Institute , Torrance , California ) diluted 1:1 , 000 in KPBS-0 . 3 % Triton X-100 and 2% normal goat serum . The next day slices were washed in KPBS and incubated with biotinylated anti-rabbit immunoglobulin G antibody ( Vector ) diluted 1:200 in KPBS-0 . 3 % Triton X-100 for 2 h at RT . After washing in KPBS , slices were incubated with avidin/biotin-horseradish peroxidase complex ( Vectastain Elite ABC kit , Vector ) for 1 h at RT , washed in KPBS , and developed with 2 . 5% of diaminobenzidine ( DAB , Sigma ) and 0 . 05% H2O2 in TBS ( 150 mM NaCl , 50 mM Tris-HCl , pH 7 . 5 ) . Stained slices were then mounted onto 1% gelatin-coated slides ( in 0 . 1% KCr ( SO4 ) 2 ) . X-Gal/ACTH analysis was performed in at least ten different sections per hypothalamus of at least two different independent lines carrying the same transgene . Generally , two transgenic siblings per pedigree were analyzed ( see other details in Table S1 ) . Developmental studies for each transgene were performed in timed pregnant dams obtained by mating B6CBF1 stud males with F0 or F1 transgenic females from a representative transgenic pedigree . After killing the pregnant dam at defined dpc , embryos were removed immediately , washed with KPBS , and fixed with 4% PFA-KPBS for 20 min ( E9 . 5–E12 . 5 ) or 30 min ( E14 . 5–E16 . 5 ) . Fixed embryos were stained whole-mount with X-Gal ( 37 °C for 4 h ) , dehydrated with sucrose 30% in KPBS at 4 °C overnight and , the next day , sliced in a cryostat ( 20 μm ) . Sections were air dried at RT overnight and postfixed with cold 4% PFA 10 min and washed with KPBS . X-Gal staining was performed at 37 °C for 4 h , followed by anti-ACTH immunohistochemistry as described above , with some modifications . Briefly , the slides were incubated in 1% H2O2-KPBS for 30 min at RT with light shaking , washed twice with KPBS , and incubated with anti-ACTH ( 1:300 ) antibody for 4 h at 37 °C . After washing with KPBS , slices were incubated with secondary antibody ( 1:200 ) 1 h at 37 °C , washed with KPBS , and incubated with the Vectastain Elite ABC kit ( Vector ) or 1 h at RT . Finally , slices were developed with DAB . To find more examples of exapted CORE-SINE sequences , we used BLAT to search the Core sequence of MAR1 MD depicted in Figure 6 against the human genome assembly ( hg18 ) at http://genome . ucsc . edu . From the obtained output we selected only those hits that overlapped with the Most Conserved ( phastConsElements17way ) Track using the Table Browser at http://genome . ucsc . edu/cgi-bin/hgTables . This track contains predictions of conserved elements that were obtained by running phastCons [36] on the multiple alignments generated using multiz on best-in-genome pairwise alignments generated for each species using BLASTZ , followed by chaining and netting . | One of the most striking observations derived from the genomic era is the overwhelming contribution of transposed elements to mammalian genomes . For example , 45% of the human genome is derived from mobile element fragments . Although historically viewed as “junk DNA , ” transposed elements could also contribute to novel advantageous functional elements in their host genomes , a process called exaptation . Functionally proven examples of exaptation derived from ancient retroposition events are rare . Using an in silico paleogenomic strategy , we unraveled the evolutionary origin of nPE2 , a neuronal enhancer of the proopiomelancortin gene that participates in the production of hypothalamic peptides involved in feeding behavior and stress-induced analgesia . We demonstrate that nPE2 originated from the exaptation of a SINE retroposon in the lineage leading to mammals and remained under purifying selection for the last 170 million years . The difficulty in detecting nPE2 origin as an exapted retroposon illustrates the underestimation of this phenomenon and encourages the finding of the many thousands of retroposon-derived functional elements still hidden within the genomes . Their discovery will contribute to a better understanding of the dynamics of gene evolution and , at a larger scale , the origin of macroevolutionary novelties that lead to the appearance of new species , orders , or classes . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"evolutionary",
"biology",
"genetics",
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"(mouse)"
] | 2007 | Ancient Exaptation of a CORE-SINE Retroposon into a Highly Conserved Mammalian Neuronal Enhancer of the Proopiomelanocortin Gene |
We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model . To implement slow-adapting power-law dynamics of the gating variables of the potassium , n , and sodium , m and h , conductances we used fractional derivatives of order η≤1 . The fractional derivatives were used to solve the kinetic equations of each gate . We systematically classified the properties of each gate as a function of η . We then tested if the full model could generate action potentials with the different power-law behaving gates . Finally , we studied the patterns of action potential that emerged in each case . Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of η . In comparison with the classical model , the action potential shapes for power-law behaving potassium conductance ( n gate ) showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance ( m gate ) , the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance ( h gate ) the spikes had wider peak that for low values of η replicated pituitary- and cardiac-type action potentials . With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and η , such as square wave bursting , mixed mode oscillations , and pseudo-plateau potentials . Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable . As a consequence , power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and , we propose , expand the computational capacity of the neuron .
The Hodgkin and Huxley model is [1] CdVdt=− ( gm ( V−El ) +gK¯n4 ( V−EK ) +gNa¯m3h ( V−ENa ) ) +I ( 1 ) where C is the membrane capacitance; V is the membrane voltage; gm is the passive conductance; El is the leak reversal potential; gK¯ and gNa¯ are the maximum potassium and sodium conductances , respectively; EK and ENa are their reversal potentials , and I is the input current . The gating variables n , m , and h are defined by the general equation dxdt=αx ( V ) ( 1−x ) −βx ( V ) x ( 2 ) where x = [n , m , h] , the function α is the forward rate , and β is the backward rate . The gating variables n and m are known as activation variables while h is an inactivation variable . The functional forms of n , m , and h are [1]: αn ( V ) =0 . 1−0 . 01 ( V−V0 ) e1−0 . 1 ( V+V0 ) ) −1 ( 3 ) βn ( V ) =0 . 125e− ( V−V0 ) /80 ( 4 ) αm ( V ) =2 . 5−0 . 1 ( V−V0 ) e2 . 5−0 . 1 ( V−V0 ) ) −1 ( 5 ) βm ( V ) =4e− ( V−V0 ) /18 ( 6 ) αh ( V ) =0 . 07e− ( V−V0 ) /20 ( 7 ) βh ( V ) =11+e ( 3−0 . 1 ( V−V0 ) ) ) ( 8 ) In this work we systematically study the effects on the spiking activity of the Hodgkin-Huxley model to the implementation of fractional dynamics on each of the gating variables: dηxdtη=αx ( V ) ( 1−x ) −βx ( V ) x ( 9 ) where we use the Caputo definition [21] of the fractional derivative for η<1 dηfdtη=1Γ ( 1−η ) ∫0tf′ ( t ) ( t−u ) ηdu ( 10 ) where Γ is the Gamma function . The fractional derivative value is the result of integrating the activity of the function over all past activities weighted by a function that follows a power-law . The weighted past values are called the memory trace . As opposed to the first derivative , the fractional derivative provides information over all past activity . We numerically integrate the fractional derivative using the L1 scheme [22] , dηx ( tN ) dtη≈ ( dt ) −ηΓ ( 2−η ) [∑k=0N−1[x ( tk+1 ) −x ( tk ) ][ ( N−k ) 1−η− ( N−1−k ) 1−η]] ( 11 ) where 0<η≤1 , tk = k dt , N = tN/dt , and dt = 0 . 001 ms . By combining this equation and the gating dynamic equation and solving for x at time tN we obtain the equation that we use to integrate the function x ( tN ) ≈dtηΓ ( 2−η ) [αx ( V , tN−1 ) ( 1−x ( tN−1 ) ) −βx ( V , tN−1 ) x ( tN−1 ) ]+x ( tN−1 ) −[∑k=0N−2[x ( tk+1 ) −x ( tk ) ][ ( N−k ) 1−η− ( N−1−k ) 1−η]] ( 12 ) Where , again , x = [n , m , h] . The first two components of the right hand side of the equation are the solution of the classical differential equation . The last component of the equation is the memory trace . We have recently developed efficient ways to computationally solve these equations [19] . The memory trace is the last part of Eq 12 −[∑k=0N−2[x ( tk+1 ) −x ( tk ) ][ ( N−k ) 1−η− ( N−1−k ) 1−η]] ( 13 ) The large number of simulations performed for this study were managed using our recently developed simulator workflow manager ( NeuroManager ) [23] . In brief , NeuroManager is an object-oriented application written in MATLAB ( Natick , MA ) that automates the workflow of submitting neuroscience simulations . The simulations in this paper were run by NeuroManager using a heterogeneous set of resources ranging from local UNIX servers ( multi-core XEON processors ) , institutional clusters ( Cheetah cluster at the UTSA Computational Biology Initiative , www . cbi . utsa . edu ) , and national resources ( Stampede Cluster at the Texas Advanced Computing Center , www . tacc . utexas . edu ) . NeuroManager allows the user to isolate the free parameters of the simulations and define them as an Input Parameter Vector and organizes the results and products of each simulation . All code is available at GitHub ( https://github . com/SantamariaLab/PowerLawHH ) , and the ModelDB database ( https://senselab . med . yale . edu/ModelDB accession number 187600 ) . Unless otherwise indicated the simulations use the following parameter values assuming 1 cm2 of membrane: C = 1 μF , gNa¯ = 120 mS , gk¯ = 36 mS , gm¯ = 0 . 3 mS , ENa = 50 mV , EK = -77 mV , and EL = -54 mV . For all the simulations we used the same initial conditions: m = 0 . 0529; h = 0 . 5960; n = 0 . 3177; and V0 = -65 mV , which produced a zero change in voltage in the classic case . To calculate the value of the Mittag-Leffler function ( see below ) we used the algorithm developed by I . Podlubny and M . Kacenak ( www . mathworks . com/matlabcentral/fileexchange/8738-mittag-leffler-function ) . The value of the power-law behaving gate was calculated using Eq 12 and the value of all other variables in Eq 1 were calculated using a Runge-Kutta method of 4th order .
Traditionally , a single ion channel is described to have an open and closed states . The closed state can be composed of multiple ‘hidden’ states . Under Markovian assumptions states are independent and their residence times follow exponential dynamics . To produce power-law dynamics of the open-close transitions one can assume the existence of a large number of hidden states . Under such a model the state of the channel can be described as a diffusion process over a large number of traps . These types of models are well known to produce anomalous diffusion , a power-law behavior [24] and have been shown to replicate single channel dynamics [25] . It is also possible that the residence times do not follow exponential dynamics , due to internal state interactions or temporal correlations [25] . A purely power-law process does not have a mean residence time [26] . This would result in the absence of a stationary response . Since it is possible to get stationary responses when measuring conductance dynamics , it is necessary to assume that a channel can have a normal and power-law transitions . As such , we develop our model by expanding the Hodgkin-Huxley gating dynamics ( Eq 2 ) to have both classical and power-law components dxdt=r0[αx ( V ) ( 1−x ) −βx ( V ) x]+∑i=1mrid1−ηidt1−ηi[αx ( V ) ( 1−x ) −βx ( V ) x] ( 14 ) The sum on the right hand side of the equation describes multiple gating processes with different fractional order dynamics that describe memory dependent activity . We chose to use the same reaction rates ( αx ( V ) and βx ( V ) ) for simplicity and then scale them with the factors ri , i = 0 to m . This is similar to the fractional relaxation equation of [26] . This full model describes a system that has a finite mean residence time ( classical component ) with the perturbation from power-law processes . We can write the same equation in a compact form dxdt=∑i=0mrid1−ηidt1−ηi[αx ( V ) ( 1−x ) −βx ( V ) x] ( 15 ) We define η0 = 1 and r0 = 1 so in the case when m = 0 the model reduces to Eq 2 . For m = 1 the system models a mixture of the classical and a single fractional order process . In our case , we assume that the rate of transition of the classical model is much smaller than the rate of the fractional model ( r0 ≪ r1 ) . This means that the fractional dynamics occur much faster than the classical process . Thus we can approximate the dynamics as ( r1→1 ) dxdt≈d1−ηdt1−η[αx ( V ) ( 1−x ) −βx ( V ) x] ( 16 ) Re-arranging the fractional order operator yields our model d−1+ηdt−1+ηdxdt=[αx ( V ) ( 1−x ) −βx ( V ) x] ( 17 ) dηxdtη=[αx ( V ) ( 1−x ) −βx ( V ) x] ( 18 ) The solution of this linear fractional differential equation can be obtained using the Laplace transform technique ( see also [15] ) ℒ ( dηxdtη ) =ℒ[x∞ ( V ) −xτx ( V ) ] ( 19 ) Where x∞ ( V ) = αx ( V ) / ( αx ( V ) +βx ( V ) ) , τx ( V ) = 1/ ( αx ( V ) +βx ( V ) ) . Resulting in: sηX ( s ) ˜−sη−1x ( 0 ) =x∞ ( V ) τx ( V ) s−X ( s ) ˜τx ( V ) ( 20 ) Where X ( s ) ~ is the Laplace transform of x and s the Laplace space variable . Re-arranging X ( s ) ˜=x∞ ( V ) τx ( V ) s ( sη+1τx ( V ) ) +sη−1x ( 0 ) sη+1τx ( V ) ( 21 ) Using the method of partial fractions and re-arranging: X ( s ) ˜=x∞ ( V ) s+[x ( 0 ) −x∞ ( V ) ]sη−1sη+1τx ( V ) ( 22 ) Note that the inverse Laplace transform of ℒ−1 ( sη−1sη−1τx ( V ) ) =∑n=0∞znΓ ( ηn+1 ) =Eη ( z ) ( 23 ) Eη is the Mittag-Leffler function or the generalized exponential function [27] . Therefore , taking the inverse Laplace transform of the entire equation results in [17] x ( t ) =x∞ ( V ) +[x ( 0 ) −x∞ ( V ) ]Eη ( −[tητx ( V ) ] ) ( 24 ) We characterized the response of each one of the power-law behaving activation gates ( Eq 18 with x = [n , m , h] ) to fixed voltage step commands . The simulations consisted of a period of 20 to 30 ms at a voltage V = 0 followed by the target voltage for up to 100 ms , with target voltages varying from -100 to 120 mV . For a given value of the input voltage command we varied η from 0 . 2 to 1 . 0 . We compared the results of the numerical ( Eq 12 , Fig 1A dotted line ) and analytical ( Eq 24 , Fig 1A solid line ) solutions for all the traces , values of η , and voltage commands . The average mean squared error ( m . s . e . ) between the numerical and analytical solutions for the n gate was 8 . 2x10-7 , for the m gate was 2 . 7x10-4 , and for the h gate was 9 . 2x10-7 . The relatively higher m . s . e . in the m gate traces could be due to the very fast kinetics of this variable which results in deviations from the analytical solution at very short periods of time . In fact , the simulations were unstable for the power-law m gate for values of η≤0 . 2 , even when using time steps as small as 10−5 ms . In any case , for the large majority of cases our numerical integrations are well matched by the analytical solutions . In order to quantify the effect of power-law dynamics on each gate we calculated the instantaneous long term response function ( x∞η , with x = [n , m , h] , see definition in explanation of Eq 19 ) . The values of x∞η were obtained from the responses of the respective gates to all combinations of voltage commands and values of η . Specifically , to calculate x∞η we measured the value of the power-law behaving gates at t = 90 ms for the n gate , 40 ms for the m gate , and 110 ms for the h gate . These times were chosen because the value of the traces changed by less than 0 . 01% from the previous millisecond . For η = 1 the n , m , and h gates reproduced the classic Hodgkin-Huxley sigmoidal functions ( Fig 1B ) . However , as the value of η decreases the slope at the inflection point of the n and h gates become shallower , but not for the m gate ( arrows in Fig 1B ) . Comparing the values of x∞η using our numerical ( dotted ) and analytical ( solid ) models shows a very good match . Therefore , power-law dynamics affects the long term response of the n and h gates but has little effect on the fast activating m gate . A hallmark of a power-law process is that the temporal response of the system cannot be characterized with a single time constant . To illustrate this property we fitted a dual-exponential process to the temporal response of each power-law gate over time windows of up to 100 ms . This fitting process resulted in the calculation of a fast and slow time constant ( τxη , x = [n , m , h] , see explanation of Eq 19 for a definition ) . For all the gates when η = 1 the τxη were identical for the fast and slow time constants and to the classic Hodgkin-Huxley model . For the n and h gates as η decreases the fast time constant accelerates while the slow time constant slows down , consistent with power-law dynamics . In comparison , the effect of the fractional order derivative on the m gate was fitted with a single exponential process that decreased with lower values of η . This suggested that the fast m gate kinetics were only affected over very short periods of time . In summary , power-law dynamics have a strong effect on τxη and x∞η for the n and h gates , while only having an effect on the fast time constant of the m gate . The shapes of the kinetic curves for each of the gate variables as a function of η do not allow to predict whether the complete Hodgkin-Huxley model could produce spikes . In order to test this hypothesis we implemented a full Hodgkin-Huxley model in which a gating variable is governed by fractional dynamics while the other two remained normal . In all simulations we injected a constant current step , from 1 to 24 nA , for 500 ms . We found that action potentials were generated for all values of η for each one of the power-law dynamic gates . As is well known , the classical Hodgkin-Huxley model can respond with a single spike before it generates a sustained train of action potentials , with this first shape of the spike being slightly different than the rest [28] . For this reason , we characterized the second generated spike at the minimum input current to elicit spiking for the different values of η for each of the activation gates ( Fig 2A ) . In the case of power-law n as η decreased the width at half-height of the action potential broadened , from 1 . 18 ms for η = 1 . 0 to 1 . 86 ms for η = 0 . 2 . There was also a decrease in the minimum value of the repolarization . A similar analysis for the m gate shows that for lower values of η the action potential narrows ( Fig 2A , m gate ) . The effect of power-law behavior on the h gate shows a strong effect on the repolarization phase of the action potential ( Fig 2A , h gate ) . As the value of η decreases the spike width increases . For η = 0 . 2 the voltage seems to reach a fixed steady state , known as depolarization block . However , as we will show later , this is not the case . Instead , the spiking activity transitions to a pseudo-plateau action potential . Using the same data we calculated the current threshold to generate at least one action potential ( Fig 2B ) . This analysis shows that for power-law dynamics in the n gate the current threshold initially increases and then decreases as a function of decreasing η . In contrast , for both power-law dynamic m and h , the current threshold increases . Overall , this analysis shows that fractional order dynamics of the individual gating variables results in the generation of action potentials . Depending on the gate being modified the current threshold of the action potential changes with respect to the classic Hodgkin-Huxley model . We performed a phase plane study of the action potentials generated at the current threshold . The phase plane analysis is commonly used in experimental work to determine changes in intrinsic excitability [29 , 30] . In the case of implementing power-law dynamics in the n gate the overall trajectory of the action potential remains intact with the largest change being the repolarization phase ( Fig 2C , n gate ) . A similar analysis when the m gate has power-law dynamic shows that the speed of the action potential increases as a function of η [30 , 31] ( Fig 2C , m gate ) . Similar to the n gate , the effect of power-law dynamics on the h gate affects the repolarization phase of the action potentials ( Fig 2C , h gate ) . Phase plane plots are also used in experimental work to determine the voltage threshold by determining the voltage when the speed of the voltage crosses a determined value [30] . In our case we determined the voltage threshold as the value of the voltage when dv/dt>20 mV/ms . This analysis shows that when n has power-law dynamics the voltage threshold increases up to 2 . 14 mV . In contrast , when the power-law dynamics is in the m gate the threshold decreases by 1 . 68 mV . As expected from its kinetic properties , power-law dynamics in the h gate has no effect on the voltage threshold ( Fig 2D ) . The overall analysis of single action potentials shows that spikes can be generated with a wide range of values of η . Whenever an action potential is generated the amplitude is similar to the classical Hodgkin-Huxley model . The types of action potentials generated in all cases resemble various types of spikes reported in the literature [29 , 32–35] , including those from non-neuronal cells [36–38] . Thus , conductances with power-law properties can generate a wide range of action potentials shapes observed in multiple cell types . After analyzing the effects of power-law dynamics on individual gates and on the shape of single action potentials we characterized the spiking patterns that emerge from this process . For this purpose we simulated the response of the full model to constant current injection for periods of time between 1 , 500 to 3 , 000 ms . For the different combinations of values of η and injected current the model showed multiple spiking patterns . For example , for a constant input current of 18 nA we varied the power-law dynamics of the n gate while keeping the m and h gates normal . For η = 1 . 0 the model generated the typical repetitive spiking pattern with a constant firing rate of 84 Hz ( Fig 3A ) . For a value of η = 0 . 8 the number of spikes decreased by almost half and resulted in an average firing rate of 43 Hz . However , the spiking pattern transitioned from repetitive to increasing inter-spike intervals ( Fig 3B ) . For η = 0 . 6 the firing rate dropped to 13 Hz with sub-threshold oscillation between each spike ( Fig 3C ) . Further decrease to η = 0 . 4 also showed sub-threshold oscillations and an increasing inter-spike interval with an average firing rate of 28 Hz ( Fig 3D ) . Another example shows that the effect of power-law dynamics on the h gate also changes the spiking patterns generated by the model in response to constant input . In this case , for a fixed input current of 11 nA and values of η ≤ 0 . 6 the model generates bursts of action potential and sub-threshold oscillations ( Fig 3E–3G ) . These examples show that the power-law behaving conductances results in complex spiking patterns that evolve over time . We classified the spiking patterns generated by the effect of implementing power-law dynamics in individual gates . Since the models could produce non-stationary patterns we decided to classify the spiking activity based on their short ( <500 ms ) and long term ( >1000 ms ) responses . We classified the spiking responses as: resting state ( RS ) , no spikes or only one spike at the onset of the stimulus; tonic spiking ( TS , Fig 4A ) ; phasic spiking ( PS ) , a few spikes within the first 500 ms ( Fig 4B ) ; mixed-mode oscillations ( MMO ) , single spikes surrounded by sub-threshold oscillations ( Fig 4C ) ; square-wave bursting ( SWB ) , a group of spikes surrounded by sub-threshold oscillations ( Fig 4D ) ; and pseudo-plateau bursting ( PPB ) , long lasting spikes more commonly seen in non-neuronal cells ( Fig 4E and 4F ) [36–38] . We manually classified the spiking patterns generated by the model for a range of input currents from 0–20 nA and η = 0 . 2–1 . 0 . We then produced a spiking pattern phase transition diagram for each of the power-law behaving gating variables ( Fig 5 ) . In the case of modeling power-law activation of the potassium channel the phase diagram shows that the spiking activity transitions from RS→ PS → MMO → TS for η = 0 . 3–0 . 8 ( Fig 5A ) . In all cases , when large input current is applied to the model , this overcomes the dynamics imposed by the fractional derivative and recovers the repetitive firing of the Hodgkin-Huxley model . The same analysis applied to the activation and inactivation variables of the sodium channel results in very different behaviors . The spiking activity of the model to fractional dynamics of the activation variable , m , results in increased threshold as η decreases . After the threshold is crossed tonic spiking results for the duration of the simulation ( Fig 5B ) . When power-law dynamics is applied to the inactivation variable , h , there are multiple spiking patterns that emerge . After the spiking threshold is crossed and for values of η <0 . 8 the system presents SWB and PPB ( Fig 5C ) . For very strong input the neuron spikes regularly ( TS ) except for values of η ≤ 0 . 2 . In summary , the presence of power-law activation dynamics results in an increase in the diversity of spiking patterns , from tonic spiking to mixed mode oscillations and bursting . The numerical solution of the fractional derivative ( Eq 12 ) can be described as a negative feedback mechanism to the value of the gate being computed . The value of the gate at time t is equal to the normal integration of the equation of differences plus a factor that is called the memory trace ( Eq 13 ) . When the power-law dynamics of a gate is integrated into the entire Hodgkin-Huxley model then the memory trace acts as a balance between gate activation and action potential generation . To illustrate this point we analyzed the membrane voltage , gate values , and memory traces of several simulations when they generated different spiking patterns . As shown before , the MMO patterns are obtained when implementing power-law dynamics in the n gate . We compared the voltage trace of the power-law n , with η = 0 . 7 , ( Fig 6A , black line ) and classic ( Fig 6A , gray line ) models under the same current input conditions . This shows that the sub-threshold oscillations are not just a process in which the action potential threshold of the classic model is not reached , but that affects the underlying firing rate and spike shape ( Fig 6A , right ) . The memory trace of the n gate shows a negative contribution to the activation of the gate during the action potential depolarization and positive during the repolarization phase ( Fig 6B ) . The negative feedback effect during the generation of the action potential results in a peak value of n smaller than in the classic Hodgkin-Huxley ( Fig 6C ) . As a result the dynamics of the normally activated m and h gates are also modified ( Fig 6D and 6E ) . As shown in Fig 1 , the time constant of the potassium conductance decreases over short periods of time . This is due to the positive feedback contribution of the memory trace as the action potential repolarizes . Then this conductance compensates faster for the influx of sodium current , thus blocking the generation of an action potential , instead , producing a sub-threshold oscillation . As the effect of the memory trace vanishes on the n gate then the two currents behave closer to the classical case and an action potential is produced . This dynamics is better understood with a phase plane of the currents involved ( Fig 6F–6H ) . We plotted the value of the sodium current ( INa ) versus Iw = potassium + leak + input currents ( Fig 6F , black line ) . We compared this phase plot to the classic Hodgkin-Huxley model under the same conditions ( Fig 6F , gray line ) . As a reference , we plotted the balanced current between INa and Iw ( Fig 6F , red line ) . Trajectories above this line tend to generate an action potential , while trajectories under this line show that the repolarizing currents are stronger than the INa . At the base of the phase plot we found an attractor that corresponded to the sub-threshold oscillations ( red square in Fig 6F and 6G ) . This attractor has a trajectory around the line of balanced current . To better visualize the attractor we plotted the value of the imbalance current ( INa+Iw ) vs Iw ( Fig 6H ) . This plot shows that the balance point is around -10 nA . After an action potential is generated then the Iw is faster to compensate for Ina ( * in Fig 6H ) , bringing the trajectory close to the center of the attractor and oscillate outwards until the potassium conductance returns to a normal state , which then allows the generation of a new action potential . We performed a similar analysis of the PS spiking pattern ( Fig 7 with the corresponding voltage trace in Fig 4B ) . As in the MMO spike pattern the phase plane plot of the INa vs Iw also shows the presence of an attractor at the base of the trajectory ( Fig 7A , red square ) . The current balance point between INa and Iw is close to -6 nA ( Fig 7B ) . As the model generates spikes ( S1 to S4 in Fig 7B ) the positive imbalance current decreases until the model generates a first sub-threshold oscillation ( labeled missed spike in the figure ) , then a forth spike ( S4 ) is generated , then the trajectory settles into the attractor ( RS in the figure ) . For the duration of this simulation ( 1 , 500 ms ) no more action potentials were generated; however , it is possible that after the effect of the memory trace on the n gate vanishes the model could start spiking again . The attractors for the MMO and PS patterns are very similar ( Figs 6H and 7B ) . In both cases , the generation of a new action potential is suppressed by a faster compensation of the INa by the potassium current , which is consistent with an acceleration of the time constant due to power-law dynamics . Power-law dynamics in the h gate can generate SWB and PPB spiking patterns ( Fig 8A ) depending on the combination of input currents and values of η ( see Fig 5C ) . There are two types of PPB patterns produced by the model . The first one resembles pituitary cell action potentials , which are characterized by a spike followed by high voltage oscillations [36] . The second PPB spiking pattern resembles cardiac myocyte action potentials with a sharp spike followed by a high voltage plateau [37] . Pituitary-type action potentials were generated with higher input currents than cardiac-type action potentials ( Fig 8A ) . In all cases , including the SWB , the amplitude of the memory trace was more than an order of magnitude larger than in the case of the power-law n gate ( Fig 8B ) . In the case of the SWB pattern the spiking activity is slowed down and , as in the case of power-law n gate dynamics , the sub-threshold oscillation do not correspond to just missing spikes from the classic model ( Fig 8A , square wave bursting column , black and gay plots , respectively ) . The effect of the memory trace on the activation of the h gate is to slow down its response when compared to the classic model ( Fig 8B and 8C ) . This slowdown allows the action potential to broaden ( cf Fig 2A ) and , as a consequence , the maximum value of the n gate is higher than in the classic model ( Fig 8D ) , with the m gate not being affected ( Fig 8E ) . As the effect of the memory trace vanishes from the dynamics of the h gate then the system can again generate a series of action potentials . In the case of pituitary-type PPB patterns ( Fig 8A pituitary-type column ) the memory gate also results in a slower activation of the h gate . In this case , this allows the sodium current to remain open for longer periods of time , which compensates for the potassium current , causing an oscillation at a voltage higher than the action potential threshold ( Fig 8B–8E Pituitary-type column ) . As mentioned above , the cardiac-type PPB patterns are generated with lower input currents than the pituitary-type ( Fig 8A cardiac-type column ) . This results in a sharper initial spike and avoids the oscillatory behavior seen for the pituitary-type spiking ( Fig 8B–8E cardiac type column ) . Note that the voltage traces of the pituitary- and cardiac-type spiking patterns show oscillations in different parts of the action penitential . While the pituitary-type has the oscillations in the decaying supra-threshold section of the action potential the cardiac type show sub-threshold oscillations . The phase plane analysis of the SWB and PPB spiking patterns confirms that attractors generated by the power-law h gate can appear in different sections of the action potential . In all cases the amplitude of the current generated by the power-law model was larger than in the classic case ( Fig 9A ) . In each one of the trajectories generated we identified the location of the attractors ( red boxes ) . Analyzing the imbalance current phase plane shows that for the SWB pattern the activity is similar to the one of the PS pattern , in which the action potentials during a burst decrease their positive current until the trajectory enters close to the attractor and then spirals out until generating another burst of action potentials . In contrast , in the pituitary-style pattern the attractor is located in the early repolarization of the action potential . Finally , the cardiac-type has a similar trajectory to the SWB and PS patterns , except that the time course of the action potential spreads over a long window of time . In summary , the effect of the negative feedback of the memory trace on each of the gates variables of the Hodgkin-Huxley model results in the emergence of temporal attractors that balance the depolarizing and repolarizing currents . As a results power-law dynamics of membrane conductances can give rise to a wide range of spiking patterns .
The standard model of a membrane conductance is based on the independence of the open , closed , and inactive states . This assumption is based on a Markov model of protein function . The rate at which a state changes is determined by the voltage and temperature , but not by the previous history of the channel . At the stochastic level this implies that the probability of transition between states depends exclusively on the present state of the system . As a result , the dynamics of the conductance is characterized with an integer order differential equation ( η = 1 ) . The Markov model of a voltage or calcium activated conductance is represented by a single open state and multiple closed or inactive states . Power-law activation of such channels can emerge when the number of closed/inactive states is large [5] . In those cases , the state of the channel is assumed to diffuse over the multiple closed states . The time between open episodes depends on the trajectories through the close/inactive states . Under conditions in which the probability of staying in the same state is similar across all states ( trapping probability ) then the open states follow a power-law distribution . This behavior is equivalent to a random walk with random waiting times , which results in anomalous diffusion , a well-known power-law process [39] . Under this model , each closed/inactive state is still independent and , formally , the process is memory-less . While the transition between states only depends on the present state the emergent behavior is imposed by the complex interactions of a large number of closed states . Thus , the memory trace from the fractional derivative represents the complexity of the distribution in internal states of the channel . An alternative mechanism to generate a power-law behavior is that there is a small number of internal states that interact with each other . In this case , the transition rates between states not only depend on the present state but on some memory of where the state has been in the past , such as in allosteric processes [40] . At the stochastic level this would mean that the probability of transition changes depending on the previous trajectory of the state . A transition state going from C2→C1→0 with a rate between C1→0 of x would be different if the trajectory were C3→C1→0 . The slow power-law activation ( η < 1 ) emerges because a state that is closed increases the probability of the next state to remain closed , slowing down the opening of the channels . The memory trace of the fractional derivative represents then how much internal states influence each other , thus deviating from classical Markovian dynamics . Power-law voltage dynamics could also be possible without the sum of multiple membrane conductances but because of actually having fractional order capacitance properties [41] . Thus , a neuron could have independent sources of power-law dynamical properties in the voltage and membrane conductances . While only using a sodium and potassium conductances our power-law conductance models replicate action potential shapes and activity patterns of multiple cell types . However , some of these patterns are generated by the combination of several conductances . In this context the effect of the power-law dynamics captures the combination of multiple conductances or the different expression of sub-units , which could provide more internal-states or states that interact more strongly . Our results suggest that it is the potassium or inactivating variables that provide the increase in spiking shape and pattern richness , which is consistent with recent experimental results . For example , different potassium sub-units allow cortical cells to generate firing rate adaptation [13 , 42 , 43] , which we have suggested follows power-law dynamics [14]; the recovery from inactivation of some calcium and sodium channels has been shown to be history dependent [6 , 7]; and extended recordings of neurons also show history dependence [8 , 44] . In our previous work we implemented power-law dynamics in the membrane voltage of a LIF model . In this model our aim was to replicate the firing rate adaptation reported in multiple types of cortical cells . Instead of increasing the complexity of the model by adding different types of conductances operating in different time domains we proposed that their cumulative effect results in power-law behavior . We showed that with fixed parameters ( threshold and membrane resistance ) our model replicated a wide array of experimental results by only changing the input current and the value of η . Most experiments were replicated with values of η < 0 . 2 [14] . In the present study , the power-law dynamics of the sodium and potassium conductances resulted in changes of the spike shape and spiking patterns that again only depended on the input current and the order of the fractional derivative . The model was consistent with experimental results that suggest that it is the potassium conductances and the recovery from inactivation that allows neurons to generate complex spiking patterns [6 , 7 , 13 , 42 , 43] . As such , fractional derivatives can capture the complexity of the combination of multiple conductances or the intrinsic dynamics of individual channels . A recent study , analyzed the spiking and network properties of a fractional order voltage dynamics Hodgkin-Huxley model [15] . This work showed that applying the fractional order derivative to the voltage reproduces spiking properties not seen in the original model , such as the fast time-to-peak and spike time adaptation . However , this model did not generate complex patterns such as MMO or SWB . This could be due to the effect of the memory trance only on the membrane voltage without affecting the kinetics of the gating variables . In this study it was also found that the range of current inputs that elicit spiking is reduced as a function of the value of decreasing η . Although , we find that in our model the threshold to generate spiking varies we found spiking over the entire range of tested values of η . Furthermore , whenever action potentials were generated their amplitude was very similar to the classic model . There are two studies close to our work in which the authors generalized the Hodgkin-Huxley model by applying fractional order dynamics to all the gates [16 , 17] . However , these studies were more focused on the application of fractional dynamic analytical and numerical techniques and only analyzed the generation of a single action potential over a narrow range of parameters and values of η > 0 . 65 . In contrast , our work systematically studied the response of the model to individual changes of each gate to power-law dynamics over a broad range of input currents and values of η . In any case , the numerical techniques used in these and our studies could be incorporated into standard neuronal simulation packages [45] . The detection of power-law dynamics is a topic of growing interest across the biological sciences [46] . While in stochastic processes detection of a power-law could be complicated by noise , in mesoscopic phenomena , such as in ionic currents in neurons , the measurements can be done more easily; however , experiments have to be designed to be able to detect the existence of power-laws . Isolating single conductances in neurons is experimentally challenging , thus there has to be combination of steps to conclude the existence of power-law behavior: The number of spiking patterns a neuron can generate in relation to its input determines its information capacity [48] . In a Markov process , the spiking activity of a neuron is history dependent as a function of its slowest time constant . This implies that the spiking response , such as firing rate , measures the amplitude or timing of the input . However , if a neuron is constantly integrating inputs and its condition reflects the integration over temporal scales then the spiking activity can vary . Our results show that if conductances follow power-law dynamics then the spiking activity of the neuron will reflect not only the amplitude of the input but how long this input has been delivered , as this would be reflected in the changing spiking pattern . Thus , power-law adaptation increases the computational capacity of neurons . Taking our previous and present results together suggest that power-law dynamics in the voltage or membrane conductances increases the spiking repertoire of a neuron and provides constant adaptation to encode information even in the case of having a small number of conductances . | There is increasing evidence that the activity of individual membrane ion channels , conductances , and the firing rate of neurons are history dependent . In this work we studied how history dependent activation of membrane conductances affect the action potential activity of the Hodgkin-Huxley model , a widely used model of action potential generation . In order to implement history dependent activation , we made use of fractional order differential equations . This type of history dependent differential equations are increasingly being used in biomedical sciences to simulate complex phenomena . We use fractional order derivatives to model the kinetic dynamics of the gate variables for the potassium and sodium conductances of the Hodgkin-Huxley model . Our results show that power-law dynamics of the different gate variables result in a wide range of action potential shapes and spiking patterns , even in the case where the model was stimulated with constant current . As a consequence , power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and , we propose , expand the computational capacity of the neuron . | [
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"a... | 2016 | Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model |
The obesity epidemic is responsible for a substantial economic burden in developed countries and is a major risk factor for type 2 diabetes and cardiovascular disease . The disease is the result not only of several environmental risk factors , but also of genetic predisposition . To take advantage of recent advances in gene-mapping technology , we executed a genome-wide association scan to identify genetic variants associated with obesity-related quantitative traits in the genetically isolated population of Sardinia . Initial analysis suggested that several SNPs in the FTO and PFKP genes were associated with increased BMI , hip circumference , and weight . Within the FTO gene , rs9930506 showed the strongest association with BMI ( p = 8 . 6 ×10−7 ) , hip circumference ( p = 3 . 4 × 10−8 ) , and weight ( p = 9 . 1 × 10−7 ) . In Sardinia , homozygotes for the rare “G” allele of this SNP ( minor allele frequency = 0 . 46 ) were 1 . 3 BMI units heavier than homozygotes for the common “A” allele . Within the PFKP gene , rs6602024 showed very strong association with BMI ( p = 4 . 9 × 10−6 ) . Homozygotes for the rare “A” allele of this SNP ( minor allele frequency = 0 . 12 ) were 1 . 8 BMI units heavier than homozygotes for the common “G” allele . To replicate our findings , we genotyped these two SNPs in the GenNet study . In European Americans ( N = 1 , 496 ) and in Hispanic Americans ( N = 839 ) , we replicated significant association between rs9930506 in the FTO gene and BMI ( p-value for meta-analysis of European American and Hispanic American follow-up samples , p = 0 . 001 ) , weight ( p = 0 . 001 ) , and hip circumference ( p = 0 . 0005 ) . We did not replicate association between rs6602024 and obesity-related traits in the GenNet sample , although we found that in European Americans , Hispanic Americans , and African Americans , homozygotes for the rare “A” allele were , on average , 1 . 0–3 . 0 BMI units heavier than homozygotes for the more common “G” allele . In summary , we have completed a whole genome–association scan for three obesity-related quantitative traits and report that common genetic variants in the FTO gene are associated with substantial changes in BMI , hip circumference , and body weight . These changes could have a significant impact on the risk of obesity-related morbidity in the general population .
There is a worldwide epidemic of obesity and type 2 diabetes across all age groups , especially in industrialized countries [1] . In the United States alone , over two-thirds of the population has a body mass index ( BMI ) of 25 kg/m2 or greater and is thus overweight [2 , 3] . Being overweight is a well-established risk factor for many chronic diseases , such as type 2 diabetes , hypertension , and cardiovascular events [4] , and increases in BMI are associated with higher all-cause mortality [5 , 6] . The economic cost attributable to obesity in the United States has been estimated to be as high as $100 billion/yr [7] , and includes not only direct health care costs but also the cost of lost productivity in affected individuals [8] . Individual susceptibility to obesity is thought to be determined by interactions between an individual's genetic make-up and behavior and the environment . Thus , the increased prevalence of obesity likely reflects the exposure of genetically susceptible individuals to unhealthy secular trends in environmental and behavioral factors , such as diet and exercise [9] . In industrialized countries , between 60%–70% of the variation in obesity-related phenotypes appears to be heritable [10 , 11] . The traditional approach for mapping disease genes relies on linkage mapping followed by progressive fine-mapping of candidate linkage peaks [12] . While the approach has been extremely successful at identifying genes that predispose carriers to rare Mendelian disorders [13] , it has met only limited success when applied to complex traits such as obesity . We have taken advantage of recent advances in genotyping technology that enable detailed assessment of entire genomes [14 , 15] . These advances have already allowed the identification of genes that influence quantitative variation in heart disease–related phenotypes [16] and of susceptibility genes for age-related macular degeneration [17] , inflammatory bowel disease [18] , and type 2 diabetes [19] . We recruited and phenotyped 6 , 148 individuals , male and female , ages 14–102 y , from a cluster of four towns in the Lanusei Valley in the Sardinian province of Ogliastra [20] . By studying an isolated population , we expected to increase the genetic and environmental homogeneity of our sample , increasing power [21 , 22] . Our cohort included >30 , 000 relative pairs and represents >60% of the population eligible for participation in the study; a detailed account of the family structures we examined is available elsewhere [20] . We took advantage of the relatedness among individuals in our sample to substantially reduce study costs [23] . Specifically , because our sample includes many large families , we reasoned that genotyping a relatively small number of markers in all individuals would allow us to identify shared haplotype stretches within each family . We could then genotype a subset of the individuals in each family at higher density to characterize the haplotypes in each stretch and impute missing genotypes in other individuals in the family [23 , 24] . For the analyses presented here , we genotyped 3 , 329 individuals using the Affymetrix 10 , 000 SNP Mapping Array and we genotyped an additional 1 , 412 individuals using the Affymetrix 500 , 000 SNP Mapping Array Set . The genotyped individuals were selected to represent the largest families in our sample , without respect to phenotype . The high-density arrays were generally used to genotype both parents and one child ( in larger sibships ) or just the parents ( in smaller sibships ) ; the lower density arrays were used to genotype everyone else . Except when parents and offspring were genotyped in the same family , we tried to ensure that individuals genotyped with the high-density array were only distantly related to one another . For the 2 , 893 individuals that were genotyped with the 10 , 000 SNP arrays only , we used a modified version of the Lander-Green algorithm [25 , 26] to probabilistically infer missing genotypes [24] . Our approach for estimating missing genotypes is implemented in MERLIN ( http://www . sph . umich . edu/csg/abecasis/MERLIN/ ) and described in detail elsewhere [24] . Our initial analysis focused on evaluating the additive effects of 362 , 129 SNPs ( Table S1 ) that passed quality control checks [27 , 28] . The remaining SNPs failed quality checks ( ∼2 . 9% of SNPs failed checks for data completeness , Hardy–Weinberg equilibrium , and Mendelian incompatibilities ) or had a minor allele frequency of <5% ( ∼25 . 7% of SNPs had low minor allele frequencies ) .
We tested 362 , 129 SNPs for association with three obesity-related quantitative traits ( BMI , hip circumference , and weight ) . Height was included as a covariate in analysis of hip circumference and weight . In addition , we included age and sex as covariates in every analysis . The genomic control parameter [29] for our initial analysis of each trait ranged from 1 . 07 to 1 . 09 , indicating that our estimated test statistics might be slightly inflated . This is likely due to unaccounted-for distant relationships among the sampled individuals . All results presented in our tables have been adjusted using the method of genomic control [29] . After adjustment , we observed no significant excess of results exceeding liberal significance thresholds . For example , the proportion of test statistics that were significant at α = 0 . 001 was 0 . 00098 . Results of our initial association analysis are summarized in Figure 1 and in Table 1 . We used the false-discovery rate ( FDR ) to select a small set of very promising trait SNP associations for rapid replication . Using an FDR [30] of 20% highlighted a small set of SNPs for each trait . This set include the top eight SNP association results for hip circumference and weight ( FDR = 0 . 013 and FDR = 0 . 16 , respectively ) and the top nine SNP association results for BMI ( FDR = 0 . 20 ) . Eight of the SNPs listed in Table 1 overlap among the three traits . In particular , SNP rs9930506 and a cluster of nearby SNPs on Chromosome 16 show strong association with BMI ( p = 8 . 6 × 10−7 ) , hip circumference ( p = 3 . 4 × 10−8 ) and weight ( p = 9 . 1 × 10−7 ) . Two of the associated SNPs in the cluster , rs9939609 and rs9926289 , fall within an intronic region where sequence is strongly conserved across species . For comparative purposes , using a conservative Bonferroni correction aimed at an overall type I error rate of 0 . 05 ( one false positive per 20 genome-scans ) , would result in a significance threshold of 1 . 4 × 10−7 . This cluster of SNPs on Chromosome 16 overlaps the FTO [31] gene , an extremely large gene whose exons span >400kb ( Figure 2 ) . KIAA1005 , a gene of unknown function , also maps nearby . The FTO gene has not been previously implicated in obesity , but it maps to a region where linkage to BMI has been reported in two previous genome-wide linkage scans ( LOD = 3 . 2 in the Framingham Heart Study [32] and LOD = 2 . 2 in the families with white ancestry from the Family Blood Pressure Program [33] ) . Furthermore , a syndrome that results from deletion of this region of Chromosome 16q includes obesity as one of its features [34] . Although multiple SNPs within FTO show evidence for association , these do not point to multiple independently associated SNPs—rather , it is likely they are all in disequilibrium with the same causal variant ( s ) . In a sequential analysis in which we selected the best SNP for each trait and then conditioned on it to successively select the next best SNP , only one FTO SNP was selected ( results presented in Table S2 ) . This result is consistent with the fact that the SNPs fall in a region of strong linkage disequilibrium , both in Sardinia and in the HapMap ( Figure 2B ) . Our FDR analysis of BMI selected one additional SNP outside this cluster , rs6602024 ( Figure 3 ) . This SNP maps to Chromosome 10 and shows association with BMI ( p = 4 . 9 × 10−6 ) , weight ( 1 . 6 × 10−5 ) , and hip circumference ( p = 0 . 00047 ) . The SNP maps to the platelet-type phosphofructokinase ( PFKP ) gene , which acts as a major rate-limiting enzyme in glycolysis , converting D-fructose-6-phosphate to fructose-1 , 6-bisphosphate [35] . Alterations in the structure or regulation of PFKP could alter the balance between glycolysis and glycogen production , ultimately leading to obesity . Table 2 shows the phenotypic effects associated with each of the two SNPs in our sample . Because rs9930506 is more common , it shows more significant association despite being associated with smaller phenotypic effects ( the two homozygotes differ , on average , by ∼1 . 5 BMI units ) . A rarer polymorphism , such as rs6602024 , impacts only a smaller proportion of the population and shows less significant association , despite a larger difference between homozygote means ( which differ , on average , by ∼2 . 9 BMI units ) . In each case , a more accurate estimate of the effect is provided by the regression model with age , sex , and ( where appropriate ) height as covariates . In a study , such as ours , that estimates effect sizes for many SNPs , statistical fluctuation means that some estimates will be slightly high and others will be low . SNPs that reach statistical significance are likely to include those for which effect size estimates are inflated ( this is the winner's curse phenomenon ) [36] , and thus we proceeded to replicate our top association signals in additional large samples . To further investigate the association between rs9930506 and rs6602024 and obesity-related traits , we genotyped these SNPs in the GenNet study [37] . The study includes a series of families recruited through probands with elevated blood pressure . The families included in this analysis comprise 3 , 467 individuals in total ( 1 , 101 African Americans [AA] in 369 families , 839 Hispanic Americans [HA] in 223 families , and 1 , 496 European Americans [EA] in 457 families ) . Overall , individuals in GenNet are heavier than those in our original Sardinian sample . Nevertheless , our findings strongly confirm evidence for association between rs9930506 and the three BMI-related traits ( weight , hip circumference , and BMI ) . Specifically , rs9930506 showed association with all three traits among EA and HA in the GenNet study ( meta-analysis of the EA and HA samples results in a p-value between 0 . 0005 and 0 . 001 , depending on trait; see Table 3 ) . The association is significant and in the same direction as in our original sample . The allele frequencies are also similar in all three samples , with a frequency of 0 . 46 in our Sardinian sample for allele “G” of rs9930506 and of 0 . 44 and 0 . 33 in the GenNet EA and HA samples , respectively . In the GenNet sample , homozygotes for the two rs9930506 alleles differ in weight by ∼1 . 0 BMI units on average . We also examined the relationship between rs9930506 and the three traits in AA , but did not observe evidence for association within that group . In AA , allele “G” of marker rs9930506 has a somewhat lower frequency of 0 . 21 . In addition , AA show quite distinct patterns of linkage disequilibrium ( LD ) and thus it is not surprising that the association does not replicate . For example , in the HapMap sample of Utah residents with ancestry from northern and western Europe ( CEU ) , the eight SNPs that show association with obesity-related traits in our sample are strongly associated with each other and tag a total of 38 different variants ( r2 > 0 . 80 ) . In contrast , in the HapMap Yoruba in Ibadan , Nigeria ( YRI ) the strength of LD in the region is greatly reduced such that rs9930506 is not in strong LD ( r2 < 0 . 3 ) with any of the other Chromosome 16 SNPs that show association in Sardinia . In an attempt to fine-map association in the region , we decided to genotype the region of strong association in greater detail . In general , the study of samples from AA participants can afford an opportunity to fine-map association signals and even facilitate identification of the causal variants [38] . As noted above , a total of 38 different variants are in LD ( r2 > 0 . 8 , HapMap CEU ) with the eight SNPs that are associated with obesity-related traits in our Sardinian sample . We selected an additional seven SNPs in the region to tag these 38 variants in samples with reduced LD . Together with rs9930506 , these seven variants capture the other 30 SNPs with r2 > 0 . 58 ( average r2 = 0 . 87 , HapMap YRI ) . The results are summarized in Table 4 and show that , whereas all the variants show association in EA and HA , none of the variants shows association in AA . One possible explanation is that obesity in AA has a different genetic architecture . Alternatively , it is possible that because some of the variants are quite common in EA and HA but rare in AA , much larger sample sizes will be required to adequately gauge their effects ( for example , rs1421085 and rs3751812 have minor allele frequencies >0 . 25 in these first two populations , but <0 . 11 in AA ) . In contrast to rs9930506 , we did not replicate association between SNP rs6602024 in the PFKP gene and the three obesity-related traits . The “A” allele was rare in all populations , with a frequency of 0 . 12 in our Sardinian sample , 0 . 11 in the HA and EA GenNet subsamples and 0 . 25 in the AA GenNet subsample . The results are summarized in Table 5 and show that , although homozygotes for the rare “A” allele at rs6602024 were on average heavier by ∼1 . 0–3 . 0 BMI units than homozygotes for the “G” allele at the SNP , these homozygotes were rare and , overall , there was no significant association . Corroborating evidence that PFKP and rs6602024 are associated with BMI is the observation that a region of ∼120 kb including the Pfkp gene has been implicated in a mouse model of obesity [39] ( see Discussion ) . A definite assessment of the impact of PFKP on obesity-related quantitative traits in human populations will likely require examination of much larger sample sizes . Our genotyping results also hint at the possible importance in Sardinia of other genes previously investigated as candidates influencing obesity and related traits ( Tables S3–S5 ) . When we evaluated evidence for association across previously identified candidate genes , we observed a small excess of nominally significant p-values . ( We tested 837 candidate SNPs in 74 candidate genes against three traits and found that 145 tests were significant at p < 0 . 05 , corresponding to 5 . 8% of the 2 , 511 tests . We observed no such excess when the whole genome was considered . ) Among the interesting candidates that show association in our sample are the two adiponectin receptor genes [40] ADIPOR1 ( best single SNP p-value = 0 . 013 , 0 . 027 , and 0 . 016 for BMI , hip circumference , and weight ) and ADIPOR2 ( best p-values = 0 . 018 , 0 . 019 , 0 . 013 ) and the lipoprotein lipase gene , LPL [41] ( best p-values = 0 . 014 , 0 . 006 , 0 . 018 ) . Nevertheless , all the association signals observed in any of these previous candidate genes are far less significant than those in FTO or PFKP .
FTO association provides an example of how genome-wide association studies can point to previously unsuspected candidate genes . An interstitial deletion overlapping the region produces human syndromic obesity [34] and a hint that the gene might be involved in stress responses stems from the observation that it is down-regulated when the heat shock response transcription factor Htf1 is inhibited [42] . Because the gene has no recognizable functional domains and has not been studied in detail in experimental models , no putative function can be currently imputed . The fact that FTO is associated not only with BMI but also with hip circumference and weight is consistent with previous analyses of heritability in our cohort [20] . The analyses suggested that 80% of the genetic variance of these traits is determined by common loci ( individually , the traits have heritabilities between ∼30%–45% ) . Although the three traits examined here are correlated ( all pairwise correlations were >0 . 73 ) , it is important to note that apart from the SNPs that overlap FTO , other strongly associated SNPs differed among the traits ( see Tables 1 and S2 ) . In contrast to FTO , PFKP is a critical enzyme within the well-studied pathway of glucose metabolism but , to our knowledge , has not been previously implicated in obesity in humans . PFKP is one of the three phosphofructokinase subunit proteins that show partially overlapping patterns of expression and form hetero-tetramers in diverse cells and tissues . The subunits are encoded by different genes . One form is highly expressed in muscle ( PFKM ) ; a second , in liver ( PFKL ) ; and the third , PFKP , is the only form in platelets and is also highly expressed in subregions of the brain [42] . None of the forms has been previously implicated in obesity in humans , although PFKM is mutated in some cases of impaired glycogen synthesis ( glycogen storage disease VII; see Online Mendelian Inheritance in Man , http://www . ncbi . nlm . nih . gov/entrez/dispomim . cgi ? id=232800 ) [35] . It is of considerable interest that compared to the other isozymes , PFKP has lower affinity for fructose-6-phosphate and decreased inhibition by ATP [43] . Consequently , PFKP is the most stringently regulated , responding to small changes at typical metabolic levels of effectors [44] . Genetic variants in the enzyme could thus adjust the rate of glycolysis , shifting the balance of metabolism between gluconeogenesis and glucose assimilation—a possible step in the etiology of obesity . Additionally , it is intriguing that in mice a locus associated with obesity has been mapped to a 127-kb interval that includes Pfkp [39] . The mouse locus shows strong evidence of interaction with diet , with different effects in mice fed high-fat and low-fat diets . One possibility is that greater homogeneity of diet in Sardinia facilitated mapping , but made replication in other populations more difficult . How significant are the associations observed ? The replication of the FTO association in two different populations indicates that it is likely important not only in Sardinia , but in many different populations . In contrast , the failure to replicate the PFKP association in other populations suggests that ( a ) the association we identified may refer to rarer , population-specific variants; ( b ) the effects of the locus may depend on genetic or environmental background; or ( c ) the association identified in our original sample is due to the statistical fluctuations inherent in testing hundreds of thousands of SNPs . As for the public health impact of the observed associations , a 1-unit increment in BMI has been associated with an 8% increase in the risk of coronary heart disease [45] and excess weight in middle life is associated with increased overall risk of death [46] . Thus , the alleles reported here , which shift BMI by 1–1 . 5 units , have effects that are not only statistically significant but could also have important health consequences . Furthermore , apart from the direct contribution of these gene variants , they provide an entrée to the analysis of genes and pathways that contribute additionally , and open new routes to possible eventual intervention . Note: After completing this manuscript , we became aware of additional evidence that supports our report of association between FTO and obesity-related traits . First , genotyping of 1 , 780 individuals from the SUVIMAX study [47 , 48] replicated association of allele rs9930506 with increased BMI ( p = 0 . 006 ) . Combined evidence from SUVIMAX , GenNet EA , and GenNet HA resulted in a replication p-value of 1 . 5 × 10−5 . In addition , two other large independent studies also show association of SNPs in FTO with increased BMI [49 , 50] . Genotyping of the SUVIMAX sample did not provide evidence for association between rs6602024 and BMI .
We recruited and phenotyped 6 , 148 individuals , male and female , ages 14–102 y , from a cluster of four towns in the Lanusei Valley [20] . During physical examination of each individual , a blood sample was collected ( for DNA extraction ) and anthropometric traits were recorded . Here , we report analyses of hip circumference , weight , and the derived quantity BMI ( which is calculated from a combination of height and weight ) . Genotyping was carried out using the Affymetrix 10K and 500K chips ( http://affymetrix . com/ ) using standard protocols . Summary assessments of genotype data quality are provided in the Results section and in Table S1 . To follow up on SNPs rs9930506 and rs6602024 , we genotyped and examined the association between these two SNPs and BMI , hip circumference , and body weight in the GenNet study . The study comprises 3 , 467 individuals in total , recruited between 1995 and 2004 ( 1 , 101 AA , 839 HA , and 1 , 496 EA ) . Individuals were recruited at two field centers: EA were recruited from Tecumseh , Michigan , and AA and HA were recruited from Maywood , Illinois . Participants were recruited from families starting from a proband with high blood pressure . DNA was available for 3 , 205 individuals ( 968 AA , 824 HA , and 1471 EA ) . SNP genotyping was performed using the 5′-nuclease–based assay ( TaqMan; ABI , http://www . appliedbiosystems . com/ ) analyzed on an ABI Prism 7900 Real Time PCR System . Within each ethnic group , genotype completeness rates exceed 98% and there was no evidence for deviation from Hardy–Weinberg equilibrium ( p > 0 . 05 ) . To ensure adequate control of type I error rates , we applied an inverse normal transformation to each trait prior to analysis [20] . The inverse normal transformation reduces the impact of outliers and deviations from normality on statistical analysis . The transformation involves ranking all available phenotypes , transforming these ranks into quantiles and , finally , converting the resulting quantiles into normal deviates . We included sex , age , and age2 as covariates in all analysis . Height was significantly associated with weight and hip circumference and was included as an additional covariate in analysis of those traits . We fitted a simple regression model to each trait and used a variance component approach to account for correlation between different observed phenotypes within each family . For individuals who had genotype data available , we coded genotypes as 0 , 1 , and 2 ( depending on the number of copies of the allele being tested ) . For individuals with missing genotype data , we used the Lander–Green algorithm to estimate an expected genotype score ( between 0 and 2 ) for each individual [24] . Briefly , to estimate each genotype score we first calculate the likelihood of the observed genotype data . Then , we instantiate each missing genotype to a specific value and update the pedigree likelihood . The ratio of the two likelihoods gives a posterior probability that the instantiated genotype is true , conditional on all available data . Due to computational constraints , we divided large pedigrees into subunits with “bit-complexity” of 19 or less ( typically , 20–25 individuals ) before estimating missing genotypes . Our analytical approach considers all observed or estimated genotypes ( rather than focusing on alleles transmitted from heterozygous parents ) and thus is not immune to effects of population stratification . In homogenous populations , this type of analysis is expected to be more powerful [51 , 52] . To adjust for the effects of population structure and cryptic relatedness among sampled individuals , we used the genomic control method to adjust our test statistics for each trait separately [29] . FDRs were calculated with R's p . adjust ( ) procedure using the method of Benjamini and Hochberg [30] . Since the initial analysis often identified clusters of nearby SNPs that all showed similar levels of association , we also carried out a sequential stepwise analysis . In this analysis , we selected the best SNP for each trait , and then conditioned on it to successively select the next best SNP . This sequential analysis can help identify regions with multiple independent association signals . The stepwise analysis was repeated for five rounds . We selected 74 candidate genes previously tested for association with obesity in humans [53] . For each gene , we first evaluated the ability of the Affymetrix SNPs to tag common SNPs ( MAF > 0 . 05 ) within +/− 5 kb of the gene ( r2 > 0 . 50 or r2 > 0 . 80 ) using the HapMap CEU database [54] . We then evaluated evidence for association using all Affymetrix SNPs within each gene as well as neighboring Affymetrix SNPs that could be used to improve coverage ( r2 > 0 . 5 ) . For each gene , we report coverage statistics as well as the SNP that showed strongest evidence for association . We selected 74 genes that were previously targeted in associations studies aiming to identify genetic determinants of obesity in humans [53]: ACE , ACTN , ADIPOQ , ADIPOR1 , ADIPOR2 , ADRB1 , ADRB2 , AGER , AHSG , APOA2 , APOA4 , APOA5 , AR , BDNF , CASQ1 , COL1A1 , COMT , CRP , CYP11B2 , DIO1 , ENPP1 , ESR1 , ESR2 , FABP2 , FOXC2 , GAD2 , GFPT1 , GHRHR , GNAS , GNB3 , GPR40 , H6PD , HSD11B1 , HTR2C , ICAM1 , IGF1 , IGF2 , IL6 , IL6R , KCNJ11 , KL , LEP , LEPR , LIPC , LPL , LTA , MC4R , MCHR1 , MKKS , MTHFR , MTTP , NMB , NOS3 , NPY , NPY2R , NR0B2 , NTRK2 , PARD6A , PLIN , PPARG , PPARGC1A , PRDM2 , PTPN1 , PYY , RETN , SCD , SELE , SERPINE1 , TAS2R38 , TNF , UCP1 , UCP2 , UCP3 , and VDR . We did not consider genes associated with drug-induced body weight gain or mitochondrial genes [53] . The following genes have previously been investigated for their role in obesity and related traits but are not well tagged by SNPs in the Affymetrix array: ADRB3 , DRD4 , INS , and APOE . | Although twin and family studies have clearly shown that genes play a role in obesity , it has proven quite difficult to identify the specific genetic variants involved . Here , we take advantage of recent technical and methodological advances to examine the role of common genetic variants on several obesity-related traits . By examining >4 , 000 Sardinians , we show that a specific genetic variant , rs9930506 , and other nearby variants on human Chromosome 16 are associated with body mass index , hip circumference , and total body weight . The variants overlap FTO , a gene with poorly understood function . Further studies of the region may implicate new biological pathways affecting susceptibility to obesity . We also show that the association is not restricted to Sardinia but is also seen in independent samples of European Americans and Hispanic Americans . This finding is particularly important because obesity is associated with increased risk of cardiovascular disease and diabetes . | [
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] | 2007 | Genome-Wide Association Scan Shows Genetic Variants in the FTO Gene Are Associated with Obesity-Related Traits |
It is not currently possible to measure the real-world thought process that a child has while observing an actual school lesson . However , if it could be done , children's neural processes would presumably be predictive of what they know . Such neural measures would shed new light on children's real-world thought . Toward that goal , this study examines neural processes that are evoked naturalistically , during educational television viewing . Children and adults all watched the same Sesame Street video during functional magnetic resonance imaging ( fMRI ) . Whole-brain intersubject correlations between the neural timeseries from each child and a group of adults were used to derive maps of “neural maturity” for children . Neural maturity in the intraparietal sulcus ( IPS ) , a region with a known role in basic numerical cognition , predicted children's formal mathematics abilities . In contrast , neural maturity in Broca's area correlated with children's verbal abilities , consistent with prior language research . Our data show that children's neural responses while watching complex real-world stimuli predict their cognitive abilities in a content-specific manner . This more ecologically natural paradigm , combined with the novel measure of “neural maturity , ” provides a new method for studying real-world mathematics development in the brain .
Naturalistic thought is an important phenomenon to understand in children who spend most of their time absorbing new information from complex scenes such as homes , schools , computers , and televisions . There is recent interest in neural activity that occurs spontaneously when people watch a natural scene or movie [1]–[3] . Naturalistic neuroimaging studies open up opportunities to collect neural measurements of children's unconstrained thoughts during real-world stimulus viewing . In this study we ask whether children's neural activity during unconstrained natural viewing of educational videos statistically predicts their performance on mathematics and verbal tests . Advances in developmental functional magnetic resonance imaging ( fMRI ) have been rapid considering that the practice of scanning children in fMRI studies began less than 20 y ago [4] . Traditional fMRI studies of category and concept development often test neural processes under conditions of maximal stimulus control ( e . g . , isolated pictures , tones , words , letters , or digits ) with short-duration stimuli and equally short response times ( i . e . , 2 s ) . These types of studies are critical for understanding brain development , and considerable progress has been made toward understanding all aspects of brain development using a diverse array of controlled tasks in children; see [5]–[7] for review . However , the general approach of using stripped down experimental designs could present a limitation on a broad understanding of child development , as the types of thoughts that a child has in a 2-s time window with uncomplicated tasks and stimuli may not be as diagnostic of their cognitive development as how they think over long periods of time with more complex stimulation . The more traditional neuroimaging approach of using highly controlled , simple stimuli and tasks could be complemented by an approach that tests children's neural responses under more complex real-world conditions . As a first step toward interpreting children's real-world neural activity , we tested the relationship between children's natural viewing neural activity and their school-based knowledge . We focused on mathematics development because substantial progress has been made in characterizing the neural profile of calculation in adults [8] , [9] , children [10]–[12] , and non-human primates [13] . The data consistently indicate that regions of intraparietal cortex are more responsive during numerical processing compared to processing of other stimulus classes such as colors [14] , shapes [15] , [16] , faces , and words [16] , [17] , as well as actions such as grasping and saccadic eye movements [17] . Moreover disruption of normal functioning in intraparietal cortex through cortical lesions [18] and genetic disorders [19] is associated with selective impairments for numerical processing . Here we ask whether children show number-specific neural responses during a typical early childhood educational experience by testing the maturity of children's neural timecourses as they view educational videos . In addition , we test for a dissociation between the neural correlates of children's school-based mathematics and verbal test scores in order to examine whether there are dissociable , content-specific patterns in children's brain activity during natural viewing . Finally , we compare the neural measure derived from natural viewing with a neural measure from a traditional fMRI task to determine the relative strengths of those measures as statistical predictors of children's math performance .
As described in Materials and Methods , we used an intersubject correlation method [2] to measure the similarity of children's neural responses to those of adults after both groups watched the same 20-min Sesame Street video ( Figure 1A ) . Our version of the intersubject method correlated the whole neural timecourse at every voxel in the brain between each child and a group of adults . From this correlation we derived a measure for each child of how “adult-like” or , mature , his/her pattern of neural activation was at each voxel . These maps are designated “neural maturity” maps . We performed group statistics ( Fisher-transformed one sample t-tests ) over the children's “neural maturity” maps ( Figure 1B ) . The map shows regions where the similarity in neural timecourses between the children and adults while watching the video was consistently high at the group level . Broadly speaking , the children showed group-level similarity to adults in cortical regions associated with vision ( occipital cortex ) , auditory processing ( lateral temporal cortex ) , language ( frontal and temporal cortex ) , visuo-spatial processing and calculation ( intraparietal cortex ) , and several other functions . For comparison , Figure 1B also shows the mean intersubject correlation within the group of children ( middle panel ) and within the group of adults ( right panel ) . The intersubject correlations among subjects reinforce claims that there are certain universals in the way that the human brain processes information [2] . We found that the intersubject correlations of children-to-adults , which we have termed “neural maturity , ” increased with age . Neural maturity increased with age across large sections of the brain including basic sensory and motor cortices as well as areas of association cortex such as the intraparietal sulcus ( IPS ) and Broca's area ( Table 1 ) . A whole-brain analysis comparing the intersubject correlations of children-to-adults with adults-to-adults revealed statistically higher intersubject correlations in adults-to-adults than children-to-adults predominantly in left hemisphere cortex including the left IPS , left Broca's area and the inferior and middle frontal gyri , left superior temporal sulcus , and the left fusiform and inferior temporal gyri ( Figure 1C ) . These results complement the age-related increases in neural maturity that we report in Table 1 and indicate that neural responses that are universal among adults are still maturing in children , particularly in the left hemisphere . Interestingly , the statistical comparison of intersubject correlations for children-to-children versus children-to-adults indicated that children exhibit statistically higher intersubject correlations with other children than with adults in superior temporal cortex , predominantly in Brodmann area 22 . This is an interesting finding because it suggests that there are brain regions in which children's neural responses are still immature but the pattern of neural responses is systematic among children . We also tested a whole-brain analysis comparing children-to-children intersubject correlations with adult-to-adult intersubject correlations . Children showed significantly higher intersubject correlations than did adults in superior temporal cortex along Brodmann 22 . We note that the higher intersubject correlations among children in superior temporal cortex were bilateral at a slightly lower threshold than shown in Figure 1C . The fact that children show significantly higher intersubject correlations than adults in this region indicates that it not only exhibits a higher correlation among children than between children and adults but it also exhibits less between-subject variability in neural activity in childhood than in adulthood . Maps of the statistical differences in intersubject correlations between children and adults are presented in Figure 1C . We next explored the relation between the child-to-adult intersubject correlations , which we are calling “neural maturity , ” and behavior . Children were administered the TEMA-3 and KBIT-2 , standardized tests for childhood mathematics and verbal/non-verbal IQ , respectively . In a voxelwise whole brain analysis , test scores were correlated with children's neural maturity values . This analysis returned a map of correlation coefficients relating individual variability in test scores with individual variability in neural maturity for the whole brain ( Figure 2 ) . These brain-behavior correlation maps represent the correlation of one standardized test while controlling for the other standardized test score in a partial correlation . Figure 2A shows the resulting maps of the whole brain analysis of children's neural maturity correlated with their performance on the TEMA-3 mathematics test ( controlling for their KBIT-2 scores ) . The data show that in bilateral regions of the IPS , children's neural maturity predicted their performance on the standardized math test , independently of how they performed on the KBIT-2 test . That is , children who performed better specifically on the math test exhibited more similar IPS responses to adults while watching the educational videos . The bottom panel of Figure 2A illustrates the average correlation between children's neural maturity values and math test scores for the left and right IPS regions of interest ( ROIs ) . The ROI-averaged neural maturity values were calculated by taking each subject's average timecourse for the whole ROI and correlating that timecourse with the group average timecourse from the adults . The group-level correlation between the natural viewing neural timecourses of children and adults can be seen in Figure 3 , which shows the raw ROI-averaged timecourses for the left and right IPS , for each group . The timecourses show comparable patterns of peak responses for children and adults across the video series in the IPS . The residual timecourses after framewise displacement ( FD ) correction are shown in Figure S1 for both subject groups along with the average child-to-adult correlation for each brain region . We tested whether our brain-behavior correlation between math test scores and IPS neural maturity could be explained by some other variable related to the scanning session . We found that the correlation between math test scores and IPS neural maturity is not explained by general memory and attention because neural maturity in those same IPS voxels did not correlate with children's scores on a general memory test about the video ( left: R = −0 . 07 , p = 0 . 41; right: R = 0 . 32 , p = 0 . 12 ) . In addition , the relationship between neural maturity and math test scores was not attributable to individual differences in head motion as the correlation remained significant when motion ( translation and rotation ) and KBIT-2 scores were simultaneously controlled ( right: R = 0 . 67 , p<0 . 01; left: R = 0 . 76 , p<0 . 01 ) . We performed a parallel analysis with the standardized verbal IQ test scores ( KBIT-2 verbal ) to test for a functional dissociation between the mathematics and verbal domains . Figure 2B shows regions where children's neural maturity was correlated with their performance on the KBIT-2 verbal test , controlling for TEMA-3 performance . This analysis yielded a different pattern of regions including Broca's area and ventral temporal cortex . Figure 2B ( bottom panel ) illustrates the relationship between neural maturity and verbal test scores in Broca's area calculated from individual subjects' ROI-averaged timecourses . The correlation between verbal test scores and neural maturity in Broca's area remained significant when math test scores and motion parameters were simultaneously controlled ( R = 0 . 68 , p<0 . 01 ) . Figure 4 shows the ROI-averaged timecourses for children and adults in Broca's area across the natural viewing movie sequence . Broca's area has been previously reported to respond during picture naming and verb generation tasks , consistent with our finding that it relates to children's formal verbal abilities [20]–[23] . However , the main finding is that the relationship between natural viewing neural maturity and math test scores is dissociable from the relationship between neural maturity and verbal test scores , implicating content-specific processing during natural viewing . Table 1 reports all of the brain regions that exhibited content-specific correlations between neural maturity during natural viewing and the math and verbal test scores in the whole brain analyses . Figure 5 shows a summary of the mean natural viewing intersubject correlation values for children-to-adults ( neural maturity ) and adults-to-adults in the left and right IPS and Broca's area . Adults showed a significantly higher intersubject correlation with other adults than did children with adults in all three regions , providing further evidence that the intersubject correlation during natural viewing strengthens over development ( all p-values<0 . 001 ) . In order to test whether the neural responses in the IPS during the natural viewing session were driven primarily by the numerical content portions of the Sesame Street video , we analyzed changes in response amplitude over the timecourses relative to the content of the movie . As mentioned earlier , Figures 3 and 4 show changes in percent signal change over the course of the movie as well as the timing of the movie content . The baseline ( the zero ordinate ) in Figures 3 and 4 is the mean timecourse value . We found that the right IPS region exhibited a significantly greater response amplitude during the numerical content than the non-numerical content from the video ( Figure 6; t ( 22 ) = 3 . 58 , p<0 . 01 ) . The left IPS exhibited greater responses to numerical content compared to non-numerical content , but the difference was not significant . Broca's area did not exhibit a significant difference in percent signal change between numerical and non-numerical clips . Figure 6 shows the response amplitude differences between numerical and non-numerical clips for the right IPS , left IPS , and Broca's area . Importantly , we observed that the IPS intersubject correlations were not driven exclusively by these differences in amplitude between the numerical and non-numerical content because the intersubject neural maturity correlation was significant during both the numerical content and non-numerical content in both IPS regions ( Fisher transformed r versus zero; left: numerical t ( 22 ) = 3 . 14 , p<0 . 005 , non-numerical t ( 22 ) = 4 . 40 , p<0 . 001; Right: numerical t ( 22 ) = 5 . 23 , p<0 . 001 , non-numerical t ( 22 ) = 4 . 97 , p<0 . 001 ) . This shows that blood oxygen level dependent ( BOLD ) amplitude and intersubject correlation are distinct measures of brain development because neural responses are systematic and temporally correlated between subjects even during the presentation of stimuli for which the IPS does not show a selective , high-amplitude BOLD response . The implication is that there is a systematic temporal pattern even in the low-amplitude BOLD responses . In a second experiment , we tested the same children in a more traditional fMRI paradigm to validate our natural-viewing method . In this traditional paradigm , the children were tested on a matching task with isolated pairs of faces , numbers , words , and shapes . We tested whether the ROIs that emerged from the neural maturity correlations also elicit content-specific responses during a more controlled , traditional fMRI paradigm . Figure 7A shows the children's neural response amplitudes for each of the four stimulus classes from the traditional paradigm inside the IPS ROIs that were defined as showing a relationship between children's neural maturity and math test scores during natural viewing . The data from this more traditional fMRI paradigm indicate that the IPS responded more strongly to numerical stimuli than to the three classes of non-numerical stimuli during the traditional matching task . This result accords with our finding that the maturity of children's neural responses in the IPS during educational video viewing has a biased relation to mathematics processing and is not generically related to intelligence . As described earlier , the bias for numerical processing in the IPS has been well established by several previous traditional fMRI studies with adults as well as children [8] , [10] , [11] , [14]; see [9] , [12] for review . So far no study has demonstrated a relationship between young children's neural amplitudes during traditional fMRI tests of numerical processing and their formal school-based math test performance . We tested whether children's number-related BOLD amplitudes from the traditional paradigm would predict their math test scores . We did not find a correlation between number-related amplitudes and children's math test performance in the traditional paradigm ( left IPS: R = −0 . 17 , p = 0 . 53; right IPS: R = −0 . 29 , p = 0 . 25 ) . This result contrasts with our findings from the natural viewing neural measures that showed a significant correlation between children's IPS activity and math performance . One explanation of the difference in results is that the content of the math-related material in the educational video is more closely related to children's school-based math skills , which results in a better correlation between the natural viewing neural data and the math test scores . This raises the possibility that neural responses to real-world stimuli might be better predictors of full-blown math development than neural responses from a simpler traditional fMRI task . As a cross-validation of our results , we tested whether our natural viewing intersubject correlation results are maintained when the IPS is defined by activation during the traditional numerical tests . We selected the clusters of parietal voxels that elicited a statistically greater response during the traditional number task than the face , shape , and word-matching tasks ( whole-brain , random effects analysis; n = 22 children; number matching > face , word , and shape matching; false discovery rate [FDR] corrected , q<0 . 05 ) . These intraparietal ROIs , now defined by activation during a traditional fMRI task , showed the same partial correlation between children's math IQ scores and their neural maturity correlations during the Sesame Street video , controlling for KBIT scores ( Figure 7B ) . Moreover , the spatial distribution of children's neural responses to numbers from the traditional task overlapped with the brain regions that showed a correlation between children's natural viewing neural maturity measures and their formal math test scores . Figure 8 shows the spatial overlap of the natural viewing and traditional task results in the IPS ( whole-brain results are plotted for both datasets in Figure 8 ) . The IPS overlap between these two maps is impressive given that one result ( natural viewing neural maturity ) represents the relation between children's math test scores and their child-to-adult timecourse correlations from watching Sesame Street while the other result ( traditional task ) represents children's neural responses to numbers over other stimulus categories from a matching task . Finally , we used the traditional task number-related ROIs as an independent localizer to test the relationship between math test scores and the natural viewing neural responses during the numerical versus non-numerical content of the video . We tested the correlation between children's math test scores and ( 1 ) natural viewing neural maturity for the numerical versus non-numerical video content , and ( 2 ) response amplitude for the numerical versus non-numerical video content . We controlled for KBIT-2 test scores and motion in these analyses . We found that the right IPS , defined by the traditional numerical task , showed a significant correlation between math test scores and the neural maturity natural viewing correlation only for the numerical video content ( one-tailed tests; numerical: R = 0 . 52 , p<0 . 05; non-numerical: R = 0 . 37 , p = 0 . 11 ) . In addition , response amplitude during only the numerical content of the video was significantly correlated with children's math test scores ( R = 0 . 63 , p<0 . 05 ) . Response amplitude to the non-numerical video content was negatively correlated with math test scores because of its negative correlation with response amplitude to numerical content ( R = −0 . 63 ) . The left IPS showed less of a distinction between numerical and non-numerical content in the correlation with math test scores as correlations for both content types were significant or marginally significant ( numerical: R = 0 . 39 , p = 0 . 09; non-numerical: R = 0 . 55 , p<0 . 05 ) . Response amplitude for numerical video content was also marginally correlated with math test scores in the left IPS ( R = 0 . 37 , p = 0 . 10 ) . Reviewing the neural measures from both the natural viewing and traditional paradigm , we found that the right IPS appears to be more mature than the left IPS in children . The right IPS showed a higher neural maturity score than left IPS in regions defined both by the traditional task and the natural viewing task ( Fisher transformed paired t-tests; traditional: t ( 22 ) = 2 . 41 , p<0 . 05; natural: t ( 22 ) = 2 . 74 , p<0 . 01 ) . The general pattern is that the temporal response pattern in the right IPS in children shows more similarity to the adult IPS than does the left IPS . Although both regions showed strong correlations between neural maturity and mathematics performance , the right IPS correlation between neural maturity and math test scores was specific to the numerical content of the video while the left IPS response was not . Compared to the left IPS , the right IPS is considered to play a greater role in the early stages of numerical development [12] . Our natural viewing data suggest that both the left and right IPS are important for mathematics development in early childhood but that the right IPS matures faster than the left IPS and its response is more selectively modulated by numerical content in early childhood . In summary , the results from the natural viewing paradigm demonstrate that the whole timecourse of neural activation from fMRI ( not just the neural amplitude ) carries important information about cognitive and brain development . The data indicate that in early childhood the IPS ( particularly in the right hemisphere ) responds in a content-specific manner to numerical information presented naturalistically and that both the amplitude and temporal pattern of the neural response are related to children's school-based math performance . A comparison of the natural viewing and traditional paradigms shows that number-selective responses from the two paradigms overlap in parietal cortex . Both paradigms indicate content-specificity in the IPS for numerical processing in children . Yet , only the neural measures from the natural viewing paradigm correlated with children's formal school-based math performance . This suggests that some aspect of the stimulus content or measurement from the natural viewing paradigm is better able to represent the neural basis of children's early math performance than the traditional paradigm .
We used a novel fMRI method to show that naturalistic neural activity is related to school-based mathematics knowledge in children . Specifically , the similarity in children's IPS neural timecourse to that of adults during natural viewing predicts their mathematics test performance . The relationship between naturalistic IPS activity and math performance is dissociable from the relationship between natural viewing activity in Broca's area and children's verbal IQ performance . In addition to showing a mathematics-related temporal pattern in the IPS , children's IPS responses during the numerical segments of the Sesame Street video were also higher in amplitude than during the non-numerical video segments . Together these findings demonstrate content-based neural responses during natural viewing of educational videos in children . Although both the left and right IPS showed a strong relationship between neural maturity and children's math test scores , the right IPS showed an overall higher neural maturity score in the natural viewing paradigm and a more mathematics-specific neural response profile . As mentioned earlier , this finding is consistent with prior reports of the development of numerical processing in the brain [10] , [11]; see [12] for review . Young children show number-related activations that are often stronger in the right hemisphere . Some have argued that right hemisphere IPS activations reflect more fundamental , early-developing numerical functions such as the comparison of analog quantities whereas the left IPS represents formal symbolic numerical content [8] , [10] , [11] , [12] , [15] . For example , Piazza and colleagues [8] showed that in adults , the left IPS is more involved in processing precise symbolic representations of numerical values than the right IPS . The precision of symbolic numerical representations increases throughout childhood . These findings thus predict that the left IPS will show a more protracted developmental trajectory than the right IPS . Our study confirms that prediction and expands the evidence to include both the amplitude and temporal pattern of children's neural responses in the IPS . In addition , our data provide novel evidence of a relationship between children's formal , school-based mathematics abilities and their neural responses in the IPS to a naturalistic education stimulus . A traditional fMRI numerical task confirmed that both the left and right IPS regions responded selectively during basic numerical judgments compared to judgments of other categories in children . The IPS regions that exhibited number-related responses during the traditional paradigm overlapped math-related activations from the natural viewing paradigm . The overlap between number-related IPS activations across these tasks is impressive given that the tasks were quite different: the natural viewing task involved watching Sesame Street whereas the traditional task was numerical matching . Despite the fact that the stimulation and demands of the two tasks are very different , both tasks elicited activation patterns in the IPS that were selectively related to numerical processing . However , despite the overlap between the natural viewing and traditional paradigms , the naturalistic and traditional fMRI measures differed in their ability to explain variability in children's math performance . In both hemispheres , the natural viewing timecourse correlation of children-to-adults , or “neural maturity , ” was more closely related to children's math performance than the traditional neural measure of BOLD amplitude from numerical stimuli in the IPS . The implication is that the natural viewing paradigm is better suited for predicting children's mathematics development than the traditional paradigm . There are several reasons that the naturalistic stimuli and the measure of “neural maturity” could be ideal for predicting children's math performance . One reason is that the naturalistic neural maturity measure might better account for the richness of the whole BOLD timecourse , such as small and large scale fluctuations in activity . Brain regions that selectively respond to preferred information types could show subtle variation in the temporal response pattern to preferred information as well as to non-preferred information , e . g . , [24] . Our finding of significant and sometimes math-specific intersubject correlations within both the numerical and non-numerical video content is consistent with that conclusion . That finding shows evidence of systematic neural responses to stimuli that elicit both high- and low-amplitude BOLD activity . Previous studies with adults [25] also have shown that natural viewing paradigms can reveal aspects of neural functioning that are not captured by the traditional measure of response amplitude , including variation in the temporal response windows of different brain regions and the sensitivity of different brain regions to temporal order in event sequences see [3] for review . Thus the naturalistic “neural maturity” measure has the potential to pick up a different set of neural response characteristics than the traditional measure of BOLD amplitude , particularly in the temporal dimension . Another possible advantage of the natural viewing paradigm for studying children is that the natural viewing stimuli more fully engage the faculties that are used to learn in the real world . There is evidence that children's performance on reading , school readiness , and creativity tests improve after viewing educational programs such as Sesame Street [26] . Thus the content of educational videos , such as those used in the current study , can interact with children's school-based knowledge . The content of a real-world video might be a better stimulus for eliciting the suite of cognitive and neural processes that children likely recruit in school . These advantages of the natural viewing stimuli over a more traditional task with simple stimuli suggest that naturalistic studies of brain activity with real-world stimuli could serve as an important complement to highly controlled fMRI experiments on mathematics development . We have reported a new set of analytic procedures for studying children's developing brain responses to complex real-world scenes . Complex real-world scenes simultaneously present multiple types of meaningful information across multiple modalities . The broad goal of this research is to understand how children's brains reflect signatures of the knowledge they have acquired , with the long-term goal of linking brain development to children's experiences and school performance [27] . We conclude that early in development , children exhibit dissociable , content-specific patterns of brain activity when left to view and think about educational material on their own . The degree to which children's brains elicit adult-like temporal patterns in their neural timecourses during natural viewing is a statistical predictor of their formal , real-world academic performance . Our data indicate that these complex stimuli can be used to identify individual differences in the brain mechanisms underlying children's real-world knowledge . The use of complex , real-world neuroimaging paradigms has the potential to advance our understanding of brain development in its natural context .
Twenty-seven typically developing children ( ages 4 . 3 to 10 . 8 y , mean age = 7 . 1 y , SD = 1 . 6 , 16 female ) and 20 adults ( ages 18 . 9 to 25 . 4 y , mean age = 20 . 7 y , SD = 1 . 7 , 13 female ) successfully participated in one or more of the experimental conditions ( 26 children and 20 adults in the natural viewing fMRI paradigm , 23 children and 20 adults in the traditional fMRI paradigm , and 19 children in the behavioral standardized testing ) . Children were excluded from conditions due to excessive head motion ( >5 mm ) , opting-out , or experimenter error . The mean motion deviations for the remaining children ( after online motion correction ) were 0 . 39 mm translation ( σ = 0 . 37 ) and 0 . 36 degrees rotation ( σ = 0 . 24 ) in the natural viewing paradigm and 1 . 26 mm translation ( σ = 1 . 33 ) and 1 . 5 degrees rotation ( σ = 1 . 38 ) in the traditional paradigm . There was significantly less child motion in the natural viewing paradigm compared to the traditional paradigm ( translation: t ( 21 ) = 3 . 5 , p<0 . 005; rotation: t ( 21 ) = 3 . 97 , p<0 . 005 ) . The difference in child head motion between tasks is noteworthy considering that the natural viewing task was almost twice as long as the traditional task . Anecdotally , children seemed calmer and more engaged by the natural viewing task than the traditional task and this observation is empirically supported by the motion data . All participants were screened for neurological abnormalities . All procedures were approved by the Research Subjects Review Board . Prior to the MR scanning session , children were given a 30-min training session in a mock scanner to practice the experimental task , and remaining motionless during scanning . In the actual MR scanner , headphones , foam padding , and medical tape were used to secure the children's heads . Adults received verbal instructions and a brief session of task practice . During the MR scanning session , we measured participants' neural activity ( BOLD ) during ( 1 ) a natural viewing paradigm and ( 2 ) a traditional fMRI paradigm with faces , shapes , numbers , and words . Whole brain BOLD imaging was conducted on a 3-Tesla Siemens MAGNETOM Trio scanner with a 12-channel head coil at the Rochester Center for Brain Imaging . High-resolution structural T1 contrast images were acquired using a magnetization prepared rapid gradient echo ( MP-RAGE ) pulse sequence at the start of each session ( TR = 2 , 530 ms , TE = 3 . 44 ms flip angle = 7 degrees , FOV = 256 mm , matrix = 256×256 , 160 or 176 [depending on head size] 1×1×1 mm sagittal left-to-right slices ) . An echo-planar imaging pulse sequence with online motion correction was used for T2* contrast ( TR = 2000 ms , TE = 30 ms , flip angle = 90 degrees , FOV = 256 mm , matrix 64×64 , 30 sagittal left-to-right slices , voxel size = 4×4×4 mm ) . The first six TRs of each run were discarded to allow for signal equilibration . The “movie” run of the natural viewing paradigm was one functional run of 610 volumes . The traditional fMRI paradigm was distributed over two to four functional runs of 132 volumes each . Total scanning time was approximately 40 min . fMRI data were analyzed with the BrainVoyager 2 . 1 software package and in-house scripts drawing on the BVQX toolbox in MATLAB . Preprocessing of the functional data included , in the following order , slice scan time correction ( sinc interpolation ) , motion correction with respect to the first ( remaining ) volume in the run , and linear trend removal in the temporal domain ( cutoff: two cycles within the run ) . Functional data were then registered ( after contrast inversion of the first remaining volume ) to high-resolution de-skulled anatomy on a participant-by-participant basis in native space . For each individual participant , echo-planar and anatomical volumes were transformed into standardized space [30] . Data from adults and children were normalized into the same Talairach space . The functional data from the traditional fMRI paradigm were not smoothed . A Gaussian spatial filter with an 8 mm full-width at half-maximum was applied to each volume for the natural viewing paradigm . We spatially smoothed the natural fMRI data because of the precedent set by Hasson and colleagues [2] for inter-subject correlations; we used the more conservative smoothing kernel ( 8 mm ) of the two kernels tested in that prior study ( 8 mm and 12 mm ) . Functional data from the traditional fMRI paradigm were analyzed using the general linear model ( random effects analysis ) . Experimental events ( duration = 10 s ) in the traditional fMRI paradigm were convolved with a standard dual gamma hemodynamic response function . There were four regressors of interest ( corresponding to the four stimulus types ) , one regressor for the button press , and six regressors of no interest , corresponding to the motion parameters obtained during preprocessing . For the natural viewing fMRI paradigm , the data were pre-processed as described above for the traditional paradigm , and the resulting timecourses formed the basis for the intersubject correlation analyses . FD [31] was regressed out of each subject's timecourse to control for frame-to-frame head motion . FD is calculated by summing the absolute values of the derivatives from the six motion estimates of translation and rotation . Rotational displacements are converted to millimeters by projecting radians onto a sphere with a 50 mm radius ( following Power et al . [31] ) . Subsequent analyses were performed on the residual timecourses after FD was regressed out . An additional control for signal intensity changes ( DVARS following Power et al . [31] ) is presented in Figure S2 . That method removes volumes ( 1 back and 2 forward ) surrounding timepoints where signal intensity changes by 0 . 5% or greater . We implemented a developmental intersubject correlation method by correlating the timecourse of each voxel in the brain ( for the whole 20-min video ) for each child with the corresponding voxel in each adult ( paired r-maps ) . In these children-to-adults correlations , we correlated each subject with every other subject ( rather than correlating each child's data with an adult average ) in order to be able to carry out parallel analyses for adults-to-adults and children-to-children without including the subjects' own data in the average . Additionally , this approach of correlating each subject with every other subject preserves the variability from individual subjects . After obtaining paired r-maps for each child paired with each adult , we then calculated a mean image across the paired r-maps ( a mean r-map ) for each child . Each mean r-map represented that child's average similarity to adults . Each child thus had one r-map representing their mean similarity in neural activity to a group of adults at every voxel in the brain . These maps provide an index of how “adult-like” or , mature , each child's neural responses are across the brain , and are referred to as “neural maturity maps . ” We performed group-level statistics ( one sample t-test on Fisher-transformed r values ) over the children's “neural maturity maps” to plot the average group-level similarity of children's natural viewing BOLD timecourses to those of adults ( whole brain ) . That analysis is shown in Figure 1 . The same intersubject correlation method was used within-groups for the children-to-children and adults-to-adults correlation maps shown in the right two panels of Figure 1 . A whole brain analysis was conducted to measure the correlation between the children's chronological ages and their neural maturity maps ( i . e . , one correlation per voxel , between the vector of children's ages and their neural maturity values ) . Similarly , whole brain partial correlations between behavioral tests ( TEMA , KBIT ) and neural maturity were conducted over the children's neural maturity maps . The whole-brain partial correlations were conducted by regressing one test score out of the other and then correlating the residuals with neural maturity for each voxel , across the subject group . In addition , we also conducted ROI analyses . The regions tested in all ROI analyses were defined with independent data from their statistical tests . Note that some additional ROI data are shown from whole-brain analysis to illustrate individual subject scores . Percent signal change was calculated to illustrate the timecourses from the natural viewing paradigm . Percent signal change was calculated for each subject on the raw timecourse data by dividing each timepoint's intensity value by the mean intensity of the whole timecourse , then multiplying by 100 and subtracting 100 . Statistical tests over response amplitudes from ROIs were conducted on the residual timecourses after the FD regression . | In the real world , children learn new information by participating in classrooms , interacting with their family and friends , and watching educational videos . While previous neuroimaging research has typically used simple tasks and short-lasting stimuli , in this study we examined brain development using a more complex and naturalistic educational stimulus . Children and adults all watched the same Sesame Street video as we measured their neural activity using functional magnetic resonance imaging ( fMRI ) . We examined the timecourses of neural activity over the length of the video for children and adults . We found that the degree to which children showed adult-like brain responses was correlated with their math and verbal knowledge levels . In the intraparietal sulcus , children's neural correlation with adults depended on their mathematics knowledge whereas in Broca's area , it depended on their verbal knowledge . Additional experiments showed that children's neural responses in the intraparietal sulcus are selectively driven by numerical content both when children are watching Sesame Street and when they engage in a number matching task . These convergent results highlight the broad role of the intraparietal sulcus in processing numerical information . In addition , our study validates the use of naturalistic stimuli and child-to-adult neural timecourse correlations for studying brain development . We suggest that this new approach can enrich our understanding of how children's brains process information in the real world . | [
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] | 2013 | Neural Activity during Natural Viewing of Sesame Street Statistically Predicts Test Scores in Early Childhood |
Geometry of the heart adapts to mechanical load , imposed by pressures and volumes of the cavities . We regarded preservation of cardiac geometry as a homeostatic control system . The control loop was simulated by a chain of models , starting with geometry of the cardiac walls , sequentially simulating circulation hemodynamics , myofiber stress and strain in the walls , transfer of mechano-sensed signals to structural changes of the myocardium , and finalized by calculation of resulting changes in cardiac wall geometry . Instead of modeling detailed mechano-transductive pathways and their interconnections , we used principles of control theory to find optimal transfer functions , representing the overall biological responses to mechanical signals . As biological responses we regarded tissue mass , extent of contractile myocyte structure and extent of the extra-cellular matrix . Mechano-structural stimulus-response characteristics were considered to be the same for atrial and ventricular tissue . Simulation of adaptation to self-generated hemodynamic load rendered physiologic geometry of all cardiac cavities automatically . Adaptation of geometry to chronic hypertension and volume load appeared also physiologic . Different combinations of mechano-sensors satisfied the condition that control of geometry is stable . Thus , we expect that for various species , evolution may have selected different solutions for mechano-adaptation .
Mass and area of the cardiac walls vary with changing mechanical load . We regard development and preservation of cardiac geometry as a result of homeostatic control that keeps mechanical load of the cardiac tissue in the physiologic range . Following this principle , we think that cellular mechanisms sense mechanical load imposed by pressure and volume in the cavities of the heart . Obtained information about this load is used to change tissue mass and structure locally . Subsequently , macroscopic geometry of the cardiac cavities and walls changes , thus affecting circulation hemodynamics . As a result , mechanical load of the cardiac tissues changes , implying closure of the control loop for adaptation of cardiac geometry . Several mechano-sensing structures have been identified in cardiac tissue ( Fig . 1 ) [1] , [2] , [3] , [4] . The major part of cardiac mass consists of myocytes , being the cells that are responsible for cardiac contraction . Myocytes contain myofilaments , which are composed of a serial repetition of sarcomeres , representing the basic contractile units . Sarcomere length decreases during contraction from about 2 . 2 µm to 1 . 8 µm by sliding of the thin actin filaments past the thick myosin filaments . In the sarcomere , the Z-disc forms a planar network of cross-connections between the actin filaments . The tips of the myosin filaments are connected to the Z-disk by strands of titin parallel to the actin filaments . The Z-discs extend over the different filaments in the cell and across the cell membrane with integrin connections to the extracellular matrix ( ECM ) . The integrins respond to stress between myocyte and ECM by activating protein-mediated chemical pathways , leading to structural changes of the tissue [5] , [6] , [7] . Connections of actin and titin to the Z-disk are mechano-transductive too [4] , [8] , [9] , [10] . G-protein coupled receptors appear sensitive to strain of the cellular membrane [2] , [11] . Stretch-activated ion channels modulate intracellular Ca-concentration [12] , [13] , [14] , [15] . Tissue stretch elevates the number of fibroblasts and formation of ECM-related proteins [16] , possibly leading to fibrosis . Mechano-transductive signals modify tissue structure , leading to change of macroscopic cardiac geometry . In Fig . 2 , the configuration of mid-wall surfaces forms the core of cardiac geometry . We consider the heart to be composed of four cavities enclosed by five walls . Both atrial cavities are enclosed by curved walls with holes for valves and blood vessel connections . For simplicity we neglected the atrial septum . The ventricular unit is composed of three curved walls , enclosing two cavities . The openings at the base are covered by a non-contractile basal sheet , harboring the four cardiac valves . For each wall , volume and mid-wall area are defined by unfolding the wall to a flat surface . In this representation , wall thickness equals wall volume divided by wall area . Micro-structurally , a cardiac wall is composed of a network of connected myocytes and a network of connective tissue ( Fig . 2 ) . For the latter networks we use the terms myocyte matrix ( MyoM ) and extra-cellular matrix ( ECM ) , respectively . With a change of cavity volume the size of matrices changes with that of the walls . We need an objective indication of the macroscopic size of these matrices relative to ultra-structural features . Thus , the reference size of the MyoM in a wall is defined as the area of the mid-wall surface , when having stretched the sarcomeres in the myocytes to a length of 2 . 3 µm . For the ECM , in absence of clearly visible ultra-structural length markers , the reference size is referred to the strain level at which ECM stress equals the target value of adaptation . Cardiac wall function is determined mainly by three parameters , i . e . , wall volume , reference area of the MyoM and that of the ECM , respectively . Considering the myocardial tissue to be healthy and working optimally , wall volume is proportional with the maximum amount of contractile work that can be delivered . The reference area of the contractile myocyte matrix MyoM determines enclosed cavity volume at which sarcomere length is optimal for power generation . The reference area of the ECM determines the diastolic pressure-volume relation . An increase of this area causes a shift of this relation towards higher volumes . Within a single cardiac wall , reference areas for MyoM and ECM are generally different . For the ventricles , volume is maximal at end-diastole , after which the ventricles contract immediately . So , for ventricular walls , ECM and MyoM reference areas are similar . In the atria , volume is maximal at the beginning of ventricular diastole . In the subsequent ventricular fast filling phase , the atria empty . Next , the atria are activated , causing further emptying of the atria . So , for atrial walls , maximum wall area is considerably larger than wall area at the beginning of atrial contraction . Thus , in contrast with ventricular walls , for the atrial walls ECM reference area is considerably larger than MyoM reference area . With eccentric hypertrophy , both wall mass and MyoM wall area are elevated , resulting in proportional increase of wall thickness and cavity diameter [17] , [18] , [19] , [20] . With concentric hypertrophy , wall mass is elevated , but MyoM wall area remains about the same , thus causing the wall to thicken while cavity diameter is maintained [21] . ECM reference wall area is a major determinant of diastolic filling . With diastolic dysfunction , ECM area is small relative to MyoM area , implying that stiffness of passively elastic structures in the wall hamper proper diastolic filling . As a result , in the end-diastolic state , the sarcomeres of the MyoM are not sufficiently lengthened for a forceful contractile stroke . For proper adaptation of cardiac wall geometry to mechanical load , information gathered by the various mechano-sensors in that wall is processed to a well-tuned blend of structural changes ( Fig . 1 ) . Specific sensors detect specific types of mechanical load by altering the configuration of mechano-sensitive molecules , thus initiating cascades of chemical reactions . The various pathways of these reactions mutually overlap and interact [1] , [3] , [22] , thus forming a complex network of information processing . The output of this network determines changes in myocardial structure , macroscopically leading to adaptation of wall mass , MyoM area and ECM area . It is not clear yet , how the different mechano-sensed signals contribute to the various types of structural adaptation . The present computational modeling study focuses on how mechano-sensed signals affect tissue structure , and thereby determining global cardiac geometry . We used a novel approach by considering general principles of control systems theory . A first requirement is stability of homeostatic control of cardiac geometry , i . e . , perturbations in mechanical or hemodynamic load must be properly compensated . Secondly , the system should take care for the myocytes to operate in a range of sarcomere length and mechanical load that is optimal for mechanical performance .
The CircAdapt model ( Fig . 3 , upper left ) simulates beat-to-beat hemodynamics and mechanics of the closed circulation [23] . The source code and short manual are added as supporting files . In this model , the left ( LA ) and right ( RA ) atrial cavities of the heart are encapsulated by their respective left ( LAW ) and right ( RAW ) atrial walls . The left ( LV ) and right ventricular ( RV ) cavities are encapsulated by the left ( LVW ) and right ( RVW ) ventricular lateral walls , and separated by the ventricular septum ( SVW ) . Ventricular interaction ( TriSeg model ) is derived from the equilibrium of forces in the three walls , acting on their common junction [24] . As seen from the heart , the large arteries are simulated by a non-linear representation of the arterial characteristic impedance in series with an arterial compliance [25] , [26] . The large veins are represented similarly , as seen from the atria , consisting of a venous characteristic impedance and a venous compliance . The systemic peripheral resistance connects the systemic arterial compliance with the systemic venous compliance . Similarly , the non-linear pulmonary peripheral resistance connects pulmonary arterial and venous compliances . Myofiber stress depends on myofiber strain , strain rate and time , as reported in experiments on isolated cardiac muscle fibers [27] , [28] . A unique and useful feature of the CircAdapt model is that the walls of heart and blood vessels can be shaped by adaptation signals , derived from mechanical load of the constituting tissues . The core of the CircAdapt model is a set of differential equations with about 30 state variables , being 8 volumes ( arteries and veins of left and right circulation and 4 heart cavities ) , 6 inertias ( 4 valves and 2 atrial inlet ducts ) , 5 contractilities and 5 sarcomere lengths ( 5 cardiac walls ) , and 2 geometric measures related to ventricular interaction . Starting with some reasonable initial state , about 30 beats ( time resolution 2 ms ) are simulated to reach steady state . In that steady state , hemodynamics of moderate exercise ( Table 1 ) is satisfied . Blood pressure is controlled by adjustment of circulating blood volume . This steady state is used as reference state for the simulations . The model is simulated in Matlab ( MathWorks , www . mathworks . com ) . Calculation time is 4 seconds per heart beat on a HP-Pavilion laptop computer ( www . hp . com ) . The source code is added as supplementary file . The CircAdapt model is used to calculate myofiber stress and strain in the myocardial walls , given their geometry ( Fig . 3 ) . We investigated by simulation how local tissue structure responds to local changes in mechano-sensed variables . A set of candidate variables has been proposed based on known physiological mechanisms of mechanotransduction . Below we show how these variables are calculated from available stress and strain signals . Within the tissue , total stress Stot along the fiber direction is a summation of stress in the various substructures . Stress Sact is generated by the activated cross-bridges , connecting actin and myosin . This stress depends on the degree of activation which is a function of sarcomere length Ls and time t after activation . Stress Stit is born by the passive elastic structures inside the myocyte such as titin . By neglecting viscous effects , this stress depends directly on Ls . Stress Secm is born by the passive elastic structures outside the myocyte . This stress depends on time varying myofiber strain ef ( t ) , and has no direct relationship with sarcomeres inside the myocyte . However , since sarcomeres and the surrounding tissue components share the same deformation field , for mathematical convenience ef ( t ) is referred to reference sarcomere length Ls , ref . Thus , for total stress Stot we used ( 1 ) The candidate mechano-sensed variables are listed in Table 2 . Variable Secm , max represents maximum stress in the ECM during the cardiac cycle . The signal may result from an increasing chance of rupturing collagen struts in the ECM , causing dilation of the ECM directly . Moreover , near locations of rupture , severe deformations may induce mechano-transductive signals through integrins , connecting cell interiors to the ECM . Variable Sact , max indicates maximum stress in the actin filaments , possibly detected by molecules in the Z-disks . The signal is closely linked to contractility . Variable Sint refers to stress between myocyte interior and myocyte exterior , possibly born by the integrins . Integrins are known to connect Z-disks of myocytes to the collagen filaments of the ECM . If either stress in the myocytes or stress in the ECM , or both , is low , stress in the integrin connection is low . Variable SZtit refers to stress within the Z-disc between actin and titin filaments . Complex mechano-sensitive molecules are found near the connection sites of these filaments within the Z-disc . Variable Ls , act indicates working sarcomere length during active contraction , obtained by weighing sarcomere length with actin stress . Similarly , Ls , int represents sarcomere length , weighted with stress between cell interior and exterior . Variable eact represents strain changes during the active state , implemented by weighing with actin stress . Finally , variable wstroke represents stroke work density . Deviations of sensed variables from their target value results in a structural response with a change in geometry so that the sensed variables approach their target value . To facilitate the analysis , the components of vector LnSens represent the logarithm of calculated sensed variables , stored in vector Sens ( Table 2 ) , normalized to the corresponding target value , stored in vector SensRef: ( 2 ) Sensed signals induce structural changes in the myocardial tissue , resulting in change of wall volume Vwall , MyoM wall area Amyom and ECM wall area Aecm , respectively , as defined in relation to Fig . 2 , and forming together the geometric vector Geom . The network of mutually overlapping chemical pathways ( Fig . 1 ) is represented by a currently unknown matrix M of transfer coefficients ( Fig . 3 ) . The change of Geom , induced by the sensed signals , is obtained by the following matrix multiplication: ( 3 ) The dot represents matrix multiplication . If Sens equals SensRef , LnSens equals zero ( Eq . 2 ) , implying no change of Geom ( Eq . 3 ) . First we consider the open loop relation , in which cardiac geometry determines an adaptive change of tissue structure ( Fig . 3 ) . Adaptation is assumed to occur in steps , numbered t . Vector Geom5t defines geometry of five walls , providing three components each ( Eq . 3 ) , thus consisting of 15 components . The addition of number 5 indicates handling of all walls together in one vector . The CircAdapt model is used as a function , named CircAdapt , to calculate the associated vector Sens5t by: ( 4 ) After logarithmic conversion of Sens5 to LnSens5 ( Eq . 2 ) , Eq . ( 3 ) is used to calculate the increment of vector ln ( Geom5t ) . Thus , for vector Geom5t+1 after the tth adaptation step it is found: ( 5 ) Transfer matrix M5 for all walls together determines feedback properties , and should therefore be optimized for best control characteristics . Assume that vector Geom5 equals Geom5Ss in steady state . With perfect control , any vector Geom5t in Eq . ( 5 ) , should be followed by vector Geomt+1 being equal to Geom5Ss , resulting in relation: ( 6 ) Matrix M5 is solved by choosing a sufficiently large set of geometric vectors Geom5m near Geom5Ss , where subscript m indicates vector number . Simulations with CircAdapt ( Eq . 4 ) renders the related set of vectors Sens5m , resulting with Eq . ( 2 ) in vector sets LnSens5m . M5 is found by solving Eq . ( 6 ) : ( 7 ) The −1 exponent indicates matrix inversion . Superscript T indicates matrix transposition . When using the above estimated transfer matrix M5 , any change of geometry is perfectly controlled in a single step ( Eq . 6 ) back to steady state . This principle of control is however not physiologic . Matrix M5 contains many cross-terms where sensed signals in one wall ( e . g . the LV wall ) are used for feedback in another wall ( e . g . an atrial wall ) . We assume that locally sensed signals can affect local structure only . So , no cross-terms are allowed between different walls . Furthermore , we assume that adaptation characteristics are the same for all walls since they all consist of comparable myocardial tissue . So , a new matrix M5u is defined , satisfying the latter conditions . In this matrix , all cross-terms between different walls are set to zero , leaving 5 similarly shaped sub-matrices along the diagonal , each of which corresponding to a particular wall . Using the condition that adaptation characteristics are the same for all walls , all sub-matrices are set identical to the universal matrix M according to Eq . ( 3 ) . The much smaller matrix M is designed to calculate local increments of 3 geometric parameters from the set of locally sensed variables . The matrix is found as the best fit matrix , using the method of Eq . ( 7 ) , but now substituting vector lists of LnSensm and ln ( Geomm ) for all walls and all m . The latter vectors are 5 times shorter than vectors LnSens5m and Geom5m , respectively , while the list of vectors is 5 times longer . It is used: ( 8 ) Matrix M5u is composed of 5 copies of matrix M on the diagonal and zeroing all other elements . In simulating an adaptation step , from a certain geometry vector Geom5t , the new vector Geom5t+1 is found by replacement of matrix M5 in Eq . ( 5 ) with M5u . Because M5u differs from M5 , vector Geom5t+1 will not be equal to the steady state vector Geom5Ss ( Eq . 6 ) . To analyze the way that adaptation approaches steady state , in a linearized form we write an adaptation step for vector Geom5 as a multiplication with matrix H5: ( 9 ) Symbol I5 represents the 15×15 identity matrix . In Eq . ( 9 ) vector divisions are component by component . Principles of control systems theory require all Eigen values of the 15×15 square matrix H5 to be less than 1 . If the latter condition is not satisfied , the related Eigen vector of Geom5t will diverge from the target vector Geom5Ss step by step by a factor equal to that Eigen value , implying instability of control by adaptation . We also have used a more qualitative approach to judge quality of control by checking that any deviation of geometry from the stable end solution should highly correlate with the calculated compensation . Besides feedback coefficients , for the candidate sensed variables , target values ( SensRef in Eq . 2 ) have to be determined . Target values are chosen so that cardiac geometry is physiological in the steady state of adaptation . Whenever possible , for atrial and ventricular tissue the same target values are used . If however calculated steady state geometry of the atria appears non-physiologic , while ventricular geometry is physiologic , target values of the atria are adjusted so that atrial geometry becomes physiologic too . Matrix Mu will however be the same for atria and ventricles under all circumstances .
Because the heart must withstand increased levels of hemodynamic load , a state of moderate exercise was assumed for simulation of adaptation . In Table 2 , the stable reference state of geometric parameters of the five myocardial walls is presented together with some hemodynamic parameters . MyoM reference area indicates mid-wall area at a sarcomere length of 2 . 3 µm . ECM reference area indicates maximum mid-wall area during the cardiac cycle with exercise . Though adaptation is simulated with exercise , hemodynamic data are also presented for the state of rest . In the ventricular walls ECM size is about equal to MyoM size , whereas in the atria , ECM size is considerably larger than MyoM size . The latter finding is in agreement with earlier statements in the introduction . ECM size is mostly determined by the state of maximum stretch which occurs at the beginning of ventricular diastole , when the atria are not active . During the ventricular fast filling phase atrial volumes decrease . Thereafter , at the beginning of atrial contraction , atrial volumes are lower than at the end of systole . Since MyoM size is mostly determined by the phase of contraction , MyoM area is lower than ECM area . In Fig . 4 , simulated time courses of LV and RV hemodynamics are shown for moderate exercise and for the resting state . Besides , for all 5 cardiac walls , stress in the actin filaments of the myocytes ( Sact ) and in the ECM ( Secm ) are shown as a function of time . From the time courses expressing mechanical load , for each wall the set of 8 sensed variables ( Table 2 ) was calculated for 10 , 000 random fractional increments in tissue volume , reference wall area of the MyoM and that of the ECM , respectively representing hypertrophy , dilation of the MyoM and dilation of the ECM . In Fig . 5 , all available locally sensed variables were used to reconstruct the latter fractional increments . Reconstructed increments are plotted as a function of the true increments of these parameters for all myocardial walls . Most correlations are high . Correlations for mass of the RA wall ( RAW ) and area of the septal wall ( SVW ) are somewhat lower . Note also that various slopes differ from the line of identity . In Table 3 , the quality of control was quantified for the best performing combinations of sensed variables . Column 1 represents the minimum correlation coefficient cormin among the 15 graphs , examples of which are shown in Fig . 5 . Column 2 represents maximum Eigen value EVmax of matrix H ( Eq . 9 ) , characterizing convergence to steady state by closed loop control . In column 3 svd3 represents the third Eigen value of matrix Mu ( Eq . 8 ) normalized to the sum of Eigen values . If this Eigen value equals zero , the matrix is singular , implying that corrections by control cannot be complete . So , we used svd3 as a measure of the distance to matrix singularity . When using a minimum of 3 sensed variables , the combination 134 , i . e . , Secm , max , Sint and SZtit , rendered best correlation , but EVmax exceeded unity . For combination 126 , i . e . , Secm , max , Sact , max and Ls , int , correlation was somewhat lower , but EVmax was lower than 1 . Combinations 125 and 124 were nearly as good , showing that sensed signals SZtit , Ls , act and Ls , int contributed similarly to the quality of control . When using 4 sensed variables , the combination 1236 rendered best correlation . With combination 1346 correlation was slightly lower , but EVmax was better . Adding redundancy by using a 4th sensed variable elevated correlation , lowered EVmax and increased svd3 , thus indicating a clear improvement of control by adaptation . By using 8 variables , correlation did not improve much further . Strikingly , signal Secm , max was needed in all well performing combinations , while Sact , max was also often used . The signals systolic strain eact and stroke work density wstroke were of minor importance . In Table 4 coefficients of transfer matrix Mu are shown for the combinations 134 , 126 , 1236 , and 1346 , respectively ( printed bold in Table 3 ) . In all combinations , the effect of ECM-stress Secm , max was found to increase tissue mass ( hypertrophy ) and dilate both MyoM and ECM . Increase of working length Ls , int of the sarcomere , weighted with integrin stress ( 126 , 1236 , 1346 ) , dilated MyoM so that sarcomere length returned to normal . Increase of active stress ( 126 , 1236 ) increased wall mass and shrank both MyoM and ECM . Integrin stress Sint ( 134 , 1236 , 1346 ) shrank the MyoM . Internal stress SZtit in the Z-disk ( 134 , 1346 ) increased wall mass and shrank the ECM . On average , coefficients related to Ls , int are relatively high , indicating that this length was controlled in a relatively small range . In the right columns of Table 4 , target values of the applied candidate variables are shown . In Fig . 6 , reconstructed fractional increments of the structural parameters wall volume , MyoM area and ECM area are plotted as a function of the true increments , following the format of Fig . 5 . Here , however , instead of using all available information the reconstruction was based on combination 126 with only 3 sensed variables , being ECM stress Secm , max , active stress Sact , max , and integrin stress weighted sarcomere length Ls , int . Compared to Fig . 5 , in Fig . 6 correlations were less , and the slopes varied more . For selected combinations of sensed variables ( 134 , 126 , 1346 , 1236 ) , control of wall mass was simulated by closing the control loop , as indicated in Fig . 3 . Next , cardiac output was increased by 20% , followed by carrying out 240 adaptation steps . In Fig . 7 fractional increments in tissue mass are shown as a function of the number of adaptation steps for all 5 myocardial walls . Since adaptation occurs on a much larger time scale than hemodynamic variations , in all simulations control of geometry was slowed down by multiplication of the calculated feedback correction ( Eq . 3 , 5 ) with a factor of 0 . 1 . Adaptation with combination 134 is not shown , because it did not converge due to the high value of EVmax . With combination 126 , control was stable , but there was a component with slow convergence , clearly shown by left atrial wall mass . After 240 control steps , steady state was still not reached . By adding feedback with Sint ( 1236 ) , convergence of control was much faster , as shown by convergence of all wall masses already after 180 steps . After replacing feedback of Sact with that of SZtit ( 1346 ) convergence was slightly faster , but there was more overshoot early in the response after about 40 steps . Apparently , when using 4 instead of 3 feedback factors , convergence was faster . Corresponding data on adaptation of MyoM and ECM area are not depicted , but convergence behavior was similar for those variables . Sensitivity to changes in target variables has been investigated by variation of the target values of combination 1236 , i . e . , Secm , max , Sact , max , Sint by 20% , and Ls , int by 5% . In the simulations , relative changes in wall volume , MyoM area and ECM area were recorded . In Table 5 , data are presented as a sensitivity matrix of ratio of logarithmic changes . Values of 1 . 0 , 2 . 0 and −1 . 0 represent linear , quadratic and reciprocal dependency , respectively . Apparently , geometric parameters are sensitive to changes in sarcomere length . Increase of the target value of working sarcomere length implies decrease of needed mass ( except for the RAW ) and MyoM area , while ECM area also decreases , albeit somewhat less . Most magnitudes of dependency exponents are smaller than 1 , implying that these dependencies are moderate and non-critical . Increase of ECM stress implies general shrinkage of the heart . Decrease of active stress ( Sact , max ) causes wall mass to increase and cavities to decrease in size , i . e . , known as concentric hypertrophy . The effect of integrin stress is about opposite to that of ECM stress . Finally , we simulated adaptation for several clinically relevant phenomena , i . e . chronic volume load , hypertension and decreased contractility of the left ventricle ( LV ) . In Fig . 8 , wall volumes and thicknesses are averaged over the cardiac cycle , and indicated by numbers in ml and mm , respectively . Furthermore , LV end-diastolic pressure is indicated in mmHg . With a 20% increase of volume load , cavity volumes increase and walls thicken , while end-diastolic pressure slightly rises . These changes represent eccentric hypertrophy . With a 20% increase of mean aortic pressure , wall thicknesses of LVW and SVW increase , while other changes in geometry are minor . LV end-diastolic pressure rises . These changes represent concentric hypertrophy . With a decrease of contractility , changes in geometry are similar to those of concentric hypertrophy . LV end-diastolic pressure rises further , indicating loss of pump function . Note that in these cases , geometry is fully adapted . With pathology , adaptation is often incomplete , resulting in more severe signs of function decrease .
Adaptation of cardiac geometry to mechanical load is considered a homeostatic system . In the CircAdapt model of cardiovascular mechanics and hemodynamics , geometry of left and right atrial wall and left , right and septal wall of the ventricles were used to simulate pressures and volumes in heart and blood vessels as a function of time . In atrial and ventricular walls , a group of 8 mechano-sensed signals was calculated . A matrix of transfer coefficients simulated the network of mechano-transductive pathways , leading to structural changes , i . e . , changes in wall mass , in area of the myocyte matrix ( MyoM ) and in area of the extra-cellular matrix ( ECM ) . The control loop was closed by using the calculated structural changes to determine a new geometry for all cardiac walls . Various combinations of mechano-sensed signals were evaluated on quality of control . One combination of 3 sensed signals was found , resulting in high stability of control of geometry for all cardiac walls ( Fig . 5 , 7 ) , using the criterion that the highest Eigen value of matrix H ( Eq . 9 ) should be smaller than one . By adding a 4th signal , several combinations were found to satisfy the latter condition ( Table 3 , Fig . 5 ) . With four signals , control appeared faster and more accurate; showing that introduction of redundancy in feedback improves control properties . In our search , the combination 1236 ( Table 3 , Fig . 5 , 7 ) appeared best . Sensed signals were maximum ECM stress ( Secm , max ) , maximum active stress ( Sact , max ) , integrin stress ( Sint ) between ECM and MyoM and integrin stress weighted sarcomere length ( Ls , int ) . With this combination , Secm , max is predicted to induce hypertrophy and dilate both MyoM and ECM . The latter finding is in agreement with that in a longitudinal study ( 12 weeks ) by Donker et al . [29] on dogs , subjected to volume overload after reduction of heart rate by atrio-ventricular conduction blockade . The rate of increase of LV mass appeared synchronous and proportional with the level of end-diastolic stress , which finding agrees also with the finding that stretch of myocardial cells decelerates breakdown of contractile proteins [30] . In the Donker et al . study , end-diastolic volume increased gradually while end-diastolic stress decreased , thus indicating that the ECM dilated just as predicted . During the phase of LV dilation , peak-systolic stress did not change , suggesting that the MyoM dilated with increase of end-diastolic volume . However , complete agreement could not be proven because information about integrin stress and sarcomere length was not reported . Dilation of ECM after volume load has been reported in other studies [17] , [31] as well . We predicted that peak systolic stress Sact , max should also induce hypertrophy and shrinkage of the MyoM and ECM . In patients , pressure overload is found to induce systolic stress overload , followed by concentric hypertrophy [32] , [33] , just as found in our simulation ( Table 5 ) . Furthermore , we predicted that Sint should shrink both wall mass and MyoM area , and that sarcomere stretch , as indicated by a long Ls , int , should induce hypertrophy , dilation of MyoM and shrinkage of ECM . About the effect of integrin stress ( Sint ) little is known quantitatively , because this stress cannot be measured directly . Several combinations of sensed variables were found with stable control of geometry in all walls ( Table 3 ) . In nearly all well-performing combinations sensing stress in the ECM and MyoM ( Secm , max , Sact , max ) appears important . Necessary information about sarcomere length may be obtained from one variable out of the group SZtit , Ls , act and Ls , int . Information about stress Sint between MyoM and ECM , which is likely sensed by integrins , appeared to improve stability of control . Information on active stress weighed strain ( eact ) and stroke work ( wstroke ) seemed of minor importance . As mentioned in the introduction , in the real heart , more than three sensing mechanisms are reported to exist , indicating substantial redundancy in mechano-sensing . Redundancy improves robustness of control by use of alternative pathways if one of the primary pathways is blocked . The advantage of robustness is shown by comparing case 1236 and 126 in Fig . 7 . By missing of sensed signal Sint the system remains stable , albeit performance of the system is not as good , as shown by a substantially slower component in corrective control . Numerous reports [1] , [3] , [11] , [22] have been presented about the intricate network of many different and partly overlapping chemical pathways , converting sensed information to structural change . We think that redundancy of the network renders so many degrees of freedom in design that various network solutions satisfy the condition of stable control of geometry . Thinking in terms of the evolution of species , it is to be expected that survival rate improves by proper control of cardiac geometry . So , in different species , the network of chemical pathways and mutual connections may be different with the restriction that the condition of stable control is satisfied . In the model , adaptation may interfere with beat-to-beat changes of hemodynamics . In the real situation , adaptation is on a much slower time scale than cardiac beat-to-beat dynamics . Instead of using the best estimate of the steady state in a single , fast control step , the speed of step by step control was diminished by multiplication with a factor 0 . 1 . Thus , adaptation effects became slow relative to the beat to beat changes of hemodynamics in order to avoid unphysiological dynamic interference . After all , real adaptation is very slow as compared to beat to beat changes . Further reduction of the speed of adaptation did not affect the simulated result anymore . Comparing the speed of control for case 1236 in Fig . 7 with measurements by Donker et al . [29] , we deduced that 3 controls steps are about equivalent with 1 day of adaptation . In Fig . 8 , changes in geometry are shown after adaptation to changes in load . Wall volumes and thicknesses are averaged over the cardiac cycle , and indicated by numbers in ml and mm , respectively . Furthermore , LV end-diastolic pressure is indicated in mmHg . With a 20% increase of volume load , cavity volumes increase and walls thicken , while end-diastolic pressure slightly rises . These changes represent eccentric hypertrophy . With a 20% increase of mean aortic pressure , wall thicknesses of LVW and SVW increase , while other changes in geometry are minor . LV end-diastolic pressure rises somewhat . These changes represent concentric hypertrophy . With a decrease of contractility , changes in geometry are similar to those of concentric hypertrophy . LV end-diastolic pressure further rises , indicating loss of pump function . Note that in these cases , geometry is fully adapted . With pathology , adaptation is often incomplete , resulting in more severe signs of function decrease . Pathologic decrease of contractility of the myocardial tissue rendered concentric hypertrophy ( Table 5 ) . Apparently , more myocardial mass was needed to satisfy the needs for pumping . A side effect of the increase of wall thickness is stiffening of the wall , thus hampering diastolic filling . Clinically , the resulting elevation of diastolic pressure is often interpreted as a sign of maladaptation . When simulating a further decrease of contractility , the LV appeared not able to contract sufficiently , leading to ECM dilatation . The latter state is clinically interpreted as dilated cardiomyopathy . In summary , we succeeded in simulating adaptation of whole heart geometry to mechanical load , using a computer model . A system of local mechano-feedback is found that is universal to atrial and ventricular myocardium , resulting in stable control of geometry . Mechano-sensing by the myocardial cells induces local changes in tissue mass ( hypertrophy ) , myocyte matrix ( MyoM ) extent and extra cellular matrix ( ECM ) extent . Macroscopically , these changes indicate control of wall mass , wall area of the MyoM and wall area of the ECM . At least three mechanical variables should be sensed for proper control of geometry . Redundancy , introduced by additional variables , improves accuracy and reliability of control . A best-fit matrix of coefficients , quantifying transfer of sensed signals to structural change , was estimated by computer simulation . In a search to find best control properties with the use of just four sensed variables , we found a solution that closely resembles physiology of adaptation . With this solution , sensed maximum ECM stress induces hypertrophy and dilates both MyoM and ECM . Sensed maximum myocyte stress induces hypertrophy also , but shrinks MyoM and ECM . Sensed stress in the integrins , bridging the ECM to the MyoM , shrinks both wall mass and MyoM . Elongation of sarcomeres induces increase of wall mass , MyoM dilation and ECM shrinkage . Different combinations of mechano-sensors were found that satisfied the condition of stable control of geometry . Thus , we expect that for the various species evolution may have selected different solutions of mechano-adaptation . | The heart is known to adapt size of the cavities and thickness of the walls to the pumping requirements set by blood pressure and blood flow . We think that mechanical load of the cardiac tissue provides feedback signals for adaptation of mass and thickness of the cardiac walls . Many cellular mechanisms are known where mechanical load initiates a cascade of chemical reactions , eventually affecting structure and mass of the tissue . Because these mechanisms interact intricately , understanding of the system of adaptation as a whole is tremendously complicated . We present a novel approach by considering adaptation as a control system . Using the principle that control should converge to a stable end state , general rules are found that should be satisfied on transfer of mechanical load to structural adaptation in the cells of the tissue . We think that deeper understanding of the mechanism of adaptation requires that knowledge on mechano-transductive pathways is placed in the context of regarding adaptation as a system . Knowledge on adaptation of cardiac geometry to mechanical load is crucial in predicting long term effects of pathologic disorders or therapeutic interventions that chronically affect blood pressure or blood flow . | [
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"... | 2012 | Control of Whole Heart Geometry by Intramyocardial Mechano-Feedback: A Model Study |
A robust , bistable switch regulates the fluctuations between wakefulness and natural sleep as well as those between wakefulness and anesthetic-induced unresponsiveness . We previously provided experimental evidence for the existence of a behavioral barrier to transitions between these states of arousal , which we call neural inertia . Here we show that neural inertia is controlled by processes that contribute to sleep homeostasis and requires four genes involved in electrical excitability: Sh , sss , na and unc79 . Although loss of function mutations in these genes can increase or decrease sensitivity to anesthesia induction , surprisingly , they all collapse neural inertia . These effects are genetically selective: neural inertia is not perturbed by loss-of-function mutations in all genes required for the sleep/wake cycle . These effects are also anatomically selective: sss acts in different neurons to influence arousal-promoting and arousal-suppressing processes underlying neural inertia . Supporting the idea that anesthesia and sleep share some , but not all , genetic and anatomical arousal-regulating pathways , we demonstrate that increasing homeostatic sleep drive widens the neural inertial barrier . We propose that processes selectively contributing to sleep homeostasis and neural inertia may be impaired in pathophysiological conditions such as coma and persistent vegetative states .
Inherent in the design of robust and bistable switches is hysteresis , which prevents small or random fluctuations from triggering a state change in the system [1] . Arousal states display bistable behavior and are regulated by a biologic switch that possesses hysteretic properties [2]–[5] . Inhaled general anesthetics offer the opportunity to study the molecular and neuroanatomical pathways essential for the aroused , conscious state as well as the orderly transition to and from the unconscious state [6] , [7] . General anesthetics are known to exert their hypnotic properties in part by interacting with endogenous systems that regulate arousal state [8]–[10] . Functionally these interactions include modulation of ion channels to suppress neuronal excitability [11] . Behaviorally the effects of these interactions are described by various endpoints that correspond to different depths of general anesthesia including ( in order ) amnesia , hypnosis , and ultimately immobility [12] . Although historically most studies of anesthetics have been performed on mammals , similar endpoints have been described for invertebrates . Furthermore , in vertebrates and invertebrates similar concentrations of anesthetics induce those endpoints [13] . Phylogenetically and functionally related classes of genes also alter anesthetic sensitivity across multiple phyla [7] , [14]–[16] . Collectively these data suggest that mechanisms of arousal control have been conserved throughout evolution , even if gross brain anatomy has diverged . We previously established in both mice and fruit flies that different concentrations of anesthetics are required for induction of and emergence from general anesthesia , and that this hysteresis cannot be explained solely by pharmacokinetics [7] . Hysteretic dissociation of anesthetic induction from emergence is consistent with the existence of a barrier termed “neural inertia” that separates and stabilizes behavioral states . The inertial barrier leads to maintenance of wakefulness or anesthesia , and presumably exists to oppose rapid and potentially catastrophic transitions between these states . The effective size of the inertial barrier can be estimated by measuring the area between the induction and emergence curves . Switching between wakeful and anesthetized states would thus be difficult with high neural inertia but would occur easily with low neural inertia . Here we sought insight into the mechanisms underlying this behavioral state barrier by studying its genetic and anatomical bases as well as its relation to other arousal-regulating processes such as circadian clock function and sleep . Previous studies have demonstrated that the concentration-response curve for induction of anesthesia can be manipulated genetically , particularly by mutations that alter excitability [7] , [17] . In the present study we demonstrate that the inertial barrier can be collapsed by loss-of-function mutations in genes that have opposing effects on induction of isoflurane anesthesia . These genes encode the hyperpolarizing Shaker potassium channel ( Sh ) and its positive modulator SLEEPLESS ( SSS ) , the loss of which causes resistance to anesthesia induction , as well as the depolarizing cation channel , narrow abdomen ( NA ) and its positive modulator UNC79 , the loss of which increases sensitivity to anesthesia induction . The requirement of all four genes for maintenance of neural inertia by isoflurane is consistent with a model in which these genes contribute to mutual inhibition by arousal-promoting and arousal-suppressing loci to create a bistable system in which either the waking or anesthetized state predominates , similar to the “flip-flop” switch that has been proposed to stabilize waking and sleep in mammals [2] . Indeed , we find that the sss gene acts in different sets of neurons to influence induction of and emergence from anesthesia . We also find that arousal per se does not control neural inertia since the inertial barrier is unaffected by certain hyperaroused mutants . Instead , as in previous studies with other anesthetics [18]–[20] we report that emergence from anesthesia becomes more difficult in sleep-deprived animals . Consequently , the neural inertial barrier to reversing the anesthetized state is broadened with sleep deprivation . Collectively our data suggest that some molecular and anatomical arousal pathways that underlie sleep homeostasis also contribute to neural inertia .
We undertook the present study to determine whether distinct mechanisms control induction of and emergence from anesthesia . To establish baseline levels of hysteresis for wildtype animals we first established dose-response curves for induction and emergence using isoflurane . As in mammals [7] the two curves are distinct in flies ( Figure 1a ) , suggesting that induction and emergence are not caused by identical processes operating in reverse . However , unlike mammals some flies do not resume movement during the stepwise , downward anesthetic titration . These animals are not dead , but rather exhibit a slower pattern of emergence not amenable to plotting on this time scale ( Figure 1b , c , f ) . The failure of a Drosophila population to fully emerge when anesthetic levels are reduced below the limit of detection is a property subject to genetic regulation and consequently contributes to the measurement of neural inertia [7] . Next we examined induction and emergence curves for animals bearing lesions in genes that have previously been implicated in anesthetic sensitivity . In agreement with published studies [16] , [21] , [22] we found that disruption of na dramatically increased sensitivity to induction of the anesthesia state by isoflurane , as did disruption of unc79 , a gene that is believed to act in the same pathway ( Figure 1b ) . Since wildtype NA is thought to underlie a leak sodium current that promotes excitability [23] , we asked whether the correlation between change in excitability and anesthesia induction would apply to other genes that regulate excitability . We began by examining the contribution of Shaker ( Sh ) potassium channels , which decrease excitability , and confirmed our recent finding that a loss of function mutation in Sh decreases sensitivity to induction ( Figure 1c ) . The phenotypes of animals bearing mutations in na/unc79 and Sh suggest that excitability is positively correlated with resistance to induction of isoflurane anesthesia . The Sh mutation increases excitability and also increases resistance to induction of anesthesia by isoflurane . We hypothesized that a similar positive correlation would exist between excitability and ease of emergence from isoflurane anesthesia . Indeed , Sh mutants readily emerged from anesthesia . In fact , in these flies emergence is impacted much more than induction and occurs at relatively high concentrations of isoflurane , thereby leading to a collapse of neural inertia ( Figures 1c , e ) . The same reduction in neural inertia can be observed for animals with disrupted expression of the sleepless ( sss ) gene , which positively regulates Sh K channels [24] , [25] . Like Sh mutants , sss mutants show resistance to anesthesia induction ( Figure 1f ) . And as with Sh mutants , the emergence curve for strong sss mutants is compressed against the induction curve , leading to a collapse of neural inertia ( Figures 1e , f ) . The ability of sss mutants to reduce the neural inertial barrier is correlated with the strength of the underlying mutation . sssP1 mutants , with no detectable SSS protein , have a more extreme phenotype than hypomorphic sssP2 mutants in which SSS expression is reduced by ∼30% ( Figure 1e , Figure S1a and [25] ) . However , a surprising result arises from analysis of na/unc79 mutants . Although these mutants have decreased excitability and therefore would be predicted to resist emergence from anesthesia , they exit the anesthetized state at doses of isoflurane similar to or greater than those required for induction . Thus , na/unc79 mutations reduce the barrier to changing behavioral states in both directions ( Figures 1b , d ) . That is , they promote transitions from the aroused to the anesthetized state and also from anesthesia back to the aroused state . Consistent with this observation , na mutants have highly fragmented bouts of waking and sleep ( Figure S2a ) . sss is known to regulate Shaker K channels [24] , [25] , so we combined sss and Sh mutants to determine if the two genes act in the same pathway to affect neural inertia . Consistent with this interpretation , the EC50 for induction in Sh;sss double mutants was similar to or only slightly higher than that in Sh or sss single mutants ( Figure S1b–d; Table S1 ) . We also found that Sh loss of function heterozygotes have reduced neural inertia , whereas sssP1 heterozygotes do not , indicating that anesthetic sensitivity is more responsive to reductions in Sh than in sss ( Figure S1e ) . Having determined that anesthesia induction and emergence are controlled by different genes , we next asked whether different types of anesthetics act on the same or different arousal-regulating pathways . To address this question , we measured dose-response curves for induction and emergence in the presence of halothane , another common volatile anesthetic , using both wildtype and sssP1 mutants . As with isoflurane , halothane exposure revealed a neural inertial barrier between the awake and anesthetized states in control animals . In contrast to what was observed with isoflurane , however , the halothane induction curve was unaffected and the emergence curve was slightly left-shifted in sssP1 mutants , leading to expanded neural inertia ( Figure 1g ) . The failure of isoflurane and halothane to elicit qualitatively similar shifts in induction and emergence in sss mutants is consistent with published reports suggesting different anesthetics act on different molecular or neuroanatomical pathways [26] , [27] . The neural pathways underlying the actions of volatile anesthetics are not well understood in mammals , and in invertebrates even less is known . Progress has been stymied in part by an inability to identify and study the roles of the different circuits that control arousal , each of which may be affected to different degrees by a given anesthetic . Our ability to collapse neural inertia with mutations that have opposing effects on isoflurane induction suggests that induction can be genetically dissociated from processes that stabilize the anesthetized state and prevent emergence from it ( Figures 1b–g ) . Genetic dissociation of neural inertia and anesthesia induction raises the possibility that these phenomena may also be anatomically separable . Because sleep phenotypes of sss mutants are effectively rescued by localized expression of a sss transgene , we used this approach to determine if the induction and neural inertia phenotypes of sssP1 mutants arise from distinct anatomic loci . We coupled various promoters driving the GAL4 transcription factor to a transgene encoding wildtype sss in a homozygous sssP1 mutant background , then determined correlations between expression patterns and rescue of the two sssP1 phenotypes: ( a ) right-shifting of induction and ( b ) a more dramatic right-shifting of emergence with consequent collapse of neural inertia . As expected , the native sss promoter rescued these phenotypes robustly ( Figure 2a , b ) . SSS expression is high in the head and particularly in the brain compared to the body [25] , so we asked whether sss expression in the nervous system is sufficient to regulate transitions between the anesthesia and waking states . Importantly , the pan-neuronal driver elav-GAL4 rescued induction , emergence , and neural inertia whereas the glial driver repo-GAL4 had no effect on these phenotypes ( Figures S3a–c ) . These results are consistent with the idea that a barrier between the waking and anesthetized states is generated by neurons in the brain . Another driver , vglut-GAL4 , which expresses in glutamatergic neurons , phenocopied the rescue of the induction phenotype observed with sss-GAL4 in a sssP1 mutant background ( Figure 2c; Table S1 ) . Restoring wildtype SSS protein to glutamatergic neurons also significantly altered the EC50 for emergence ( Table S1 ) , shifting the emergence dose-response curve roughly 20% , in parallel with the induction rescue . However , unlike the sss promoter , the vglut promoter could not rescue the collapse of neural inertia in sssP1 mutants ( Figure 2d ) . Importantly , this result illustrates that glutamatergic expression of sss is insufficient to restore the barrier between the waking and anesthetized states . Unlike vglut , another promoter , D42 , failed to rescue the induction phenotype of sssP1 mutants . However , restoration of sss expression in D42-expressing neurons of sssP1 mutants rescued the concentration-response curve for emergence , leading to wildtype levels of neural inertia ( Figures 2e , f ) . Together , the results of rescuing the sssP1 anesthesia phenotypes with vglut-GAL4 and D42 suggest that different sets of neurons are involved in entry into , as well as exit from and stabilization of , the anesthetized state . Promoters with broad expression patterns such as cha and C309 rescued both induction as well as emergence to varying degrees . For emergence , significant partial or full rescue was observed with cha-GAL4 , MZ1366 , Mai301 , Sep54 , 30y and C309 . However , neural inertia was only rescued by a subset of these promoters , namely Mai301 , Sep54 and 30y . Importantly , induction was not rescued by any of these drivers . Moreover , the majority of drivers failed to alter any phenotype ( Figures S3a–c ) . These data suggest that large but divergent populations of neurons separately control induction and emergence and consequently the stability of the anesthesia state , although we cannot exclude the possibility that small subsets of cells labeled by the positive drivers are responsible for the rescue . Anesthesia and sleep may both involve suppression of arousal [9] , [10] , an idea that is supported by the effects of mutations in Sh and sss on these behavioral states [7] , [24] , [25] , [28] . We next addressed whether anesthesia and sleep are regulated by similar biological processes . Sleep drive has been modeled as the combined output of the circadian clock and a homeostatic process of unknown composition [29] . To test whether the same processes modulate the arousal circuitry affected by isoflurane we first attempted to measure concentration-response relationships at different times of day . Measuring the transition from the awake to the anesthetized state in our assay requires that animals be active prior to exposure to drug . This waking activity could not be achieved during long time periods including ZT3-9 and ZT14-22 since at these times animals have a high probability of being immobile due to their natural propensity to sleep . Thus , we addressed circadian regulation by assaying effects of circadian clock mutants . We restricted all measurements described herein to ∼2 hrs starting just after ZT10 , near one of the two daily peak activity times . During this period we addressed the circadian contribution to anesthetic sensitivity using a mutant in which the output signal from the clock is abolished , pdf01 , and two core clock mutants , cyc01 and Clkjrk . We found that the induction and emergence profiles , and hence neural inertia , were unaffected in all three mutants ( Figures 3a–c; Figure S4a ) , indicating that the circadian clock is not required for isoflurane-dependent anesthesia . In addition to abolishing circadian clock cycling , cyc01 and Clkjrk mutations cause reductions in sleep [30] , [31] , much like Sh and sss loss of function mutations [25] , [28] . Sh and sss mutants , however , display both sleep and isoflurane anesthesia phenotypes , whereas cyc and Clk mutants do not exhibit the latter . We wondered how common it is to find mutations like cyc01 and Clkjrk that lead to dissociation of the anesthesia and sleep phenotypes . It has been suggested that general anesthetics co-opt arousal pathways that have evolved to regulate the sleep/wake cycle [9] , [10] . We thus hypothesized that anesthesia involves an overlapping set , or even a subset , of arousal pathways normally utilized to regulate sleep . If this were the case then non-circadian mutants might also be identifiable that reduce sleep without affecting the anesthetized state . To test this hypothesis , we examined the effects of DATfmn mutants , which have impaired dopamine transporter function , on the concentration-response relationships of induction of and emergence from isoflurane-dependent anesthesia . Like cyc01 and Clkjrk mutants , DATfmn mutants show normal anesthetic sensitivity but abnormally low sleep ( Figures 3c , d; Figure S4b , and [30]–[32] ) . Thus , not all arousal pathways are shared between sleep and anesthesia . cyc01 , Clkjrk , Datfmn Shmns and sssP1 reduce daily sleep , and we show here that a mutation in na causes an increase in sleep as well as fragmentation of sleep and wake bouts ( Figure S2a , b ) . Thus , all these mutations alter levels of daily sleep , but only sss mutants are known to reduce sleep homeostasis , the process that promotes sleep in response to prolonged wakefulness . To address directly whether the homeostatic component of sleep contributes to the response to anesthesia , we tested whether sleep deprivation could alter sensitivity to isoflurane . In wildtype animals , 6–24 hrs of sleep deprivation elicits robust homeostatic recovery sleep [25] , [33] , a reflection of increased sleep drive and depressed arousal . We exposed experimental animals to mild mechanical agitation for 24 hrs , up to and including times at which animals were treated with isoflurane . Control animals were similarly agitated only during isoflurane treatment and for 15 minutes beforehand . We have previously observed that such agitation is sufficient to awaken sleeping flies but not those that are anesthetized . Consistent with the hypothesis that the anesthesia state may use pathways underlying sleep homeostasis , we found that increasing homeostatic sleep drive led to a small but significant shift in the EC50 for emergence . Although no change was observable in the EC50 for induction of the anesthesia state relative to controls , the net effect was a significant increase in neural inertia for sleep-deprived animals ( Figures 3e , f; Table S1 ) .
We previously demonstrated an evolutionarily conserved property of the brain , resistance to changes in arousal state , which we have termed neural inertia [7] . One hallmark of this observed phenomenon , hysteresis of anesthetic action , has been described in mathematical simulations of cortical activity in response to anesthetics as well [5] , [34] . In these models and in various biological systems , bistability and ultimately feedback are required for hysteresis . By bistability we mean that a system can exist in either of two stable states . In our case these are the anesthetized and waking states . Other examples of bistability abound in nature , such as metabolic adaptations [1] , [35] , [36] and cell fate decisions [1] , [37] . In these situations , changes in concentration of a biochemical signal lead to positive or negative feedback , resulting in a subsequent change in sensitivity to the initial signal . Consequently , exit from the particular state must proceed along a different concentration-response curve than led to entry into the state . Another way to think about bistability is in terms of state diagrams . In the simplest example , an inducer ( a drug in our case ) provides the binding energy to initiate the transition from the awake state to a state of anesthesia . Once the transition is complete and the state change has occurred , a feedback mechanism is initiated that increases the sensitivity of the system to the drug , thus requiring an even greater opposing shift in concentration of drug to reverse the process . Feedback can come at the single cell level , as we have outlined above , but it can also derive from recruitment of other cell types into a unified circuit . A relevant example of this phenomenon can be found in the mutual excitation of thalamic and cortical neurons required for waking . Excitation of thalamic nuclei by arousal systems leads to a switch from the burst firing state characteristic of sleeping or anesthesia to the tonic firing state characteristic of waking [38] , [39] . The result is recruitment of cortical neurons into a positive feedback loop that maintains excitation of both sets of neurons , thus stabilizing the waking state . It has been hypothesized that anesthetics recruit sleep circuitry , perhaps by suppressing arousal systems [9] , [10] . But what is the nature of this circuitry ? One possibility is that anesthetics could act on a bidirectional neuronal pathway that regulates both induction and emergence . In this scenario , initial anesthetic exposure would alter activity in the pathway such that upon emergence , the population would behave differently and thus produce hysteresis . Alternatively , anesthetics could affect two separate ( or partially non-overlapping ) pathways: one whose function is disrupted to permit induction and a second whose function must recover to permit emergence . We cannot say for certain where general anesthetics such as isoflurane or halothane act in the fly brain . However , we find that different drivers can separately rescue the shifts in induction and emergence caused by the sssP1 mutation . Thus , our results support a role for distinct anatomical circuits in control of bistability of the waking and unconscious states . Notably , neural inertia is distinct from sensitivity to induction of the anesthesia state since we can collapse hysteresis both with mutations that profoundly inhibit and those that facilitate induction of anesthesia . Most strikingly , na/unc79 mutations facilitate induction of anesthesia , which might be predicted based upon their decreased neural excitability . But they also promote emergence from anesthesia , indicating that they more generally destabilize behavioral states . na mutants also show frequent transitions between sleep and waking ( i . e . fragmentation of sleep and wake bouts ) and provide perhaps the best genetic evidence for the existence of molecules that stabilize behavioral states . Collectively our findings suggest the existence of certain features of a minimal neural circuit that underlies neural inertia . First , components must exist to stabilize the waking vs the anesthesia state . This requirement is illustrated in the following example . In the absence of bistability , a simple kinetic model describes the transitions between two states , one unbound and the other bound to drug ( Figure 4a ) . The resulting dose-response curves for the forward and reverse reactions are coincident ( Figure 4b ) . In a bistable situation such as waking and anesthesia , we propose that upon entry into either state , distinct feedback mechanisms are activated to shift drug sensitivity toward stabilization of the state ( Figure 4c ) . As a result the dose-response curves for induction and emergence show hysteresis ( Figure 4d ) . At a circuit level , feedback could take the form of mutual inhibition or positive reinforcement by neurons that facilitate each state ( Figure 4e ) . Next , we can assign additional components based on measured effects of mutations on induction and emergence . Since loss of excitatory NA facilitates both entry into anesthesia ( induction ) and exit from this state ( emergence ) , we suggest that na/unc79 is expressed in both arousal-promoting and arousal-inhibiting cells ( Figure 4e ) . If Sh/sss were expressed in the same neurons , mutations in these genes should have opposing effects to those in na/unc79 . However , while mutations in Sh/sss retard entry into anesthesia , they do not retard exit from this state . Thus , we place Sh/sss in arousal-promoting but not arousal-inhibiting cells ( Figure 4e ) . Lastly , there appear to be at least 2 subpopulations of neurons that have distinct effects on induction and emergence when sss is present . Thus , we divide the arousal-promoting portion of our circuit into two parts that reinforce each other's activity as well as suppress the arousal-inhibiting side of the circuit ( Figure 4e ) . Now we can assess how well our simple 3-cell model explains our data ( Figure 4e ) . During isoflurane anesthesia , activity in the wake-suppressing side of the circuit ( blue , A ) dominates . Once activated , A cells impede emergence by inhibiting the wake-promoting system ( red , W ) . As a result , exiting the anesthetized state requires that anesthetic be lowered substantially below the level required to enter this state . This effect is responsible for the leftward shift of the emergence curve relative to the induction curve ( contrast Figure 4b with Figure 4d ) . During waking the situation reverses . Activity within W cells dominates and is stabilized by mutual reinforcing connections ( red vertical arrows ) . This positive feedback increases the amount of anesthetic required to overcome the waking state and induce anesthesia . This effect leads to a rightward shift of the induction curve relative to the emergence curve in Figures 4b , d . Additional stability in the waking state is provided by inhibition of the A cells . This model also explains the effects of our mutants . We propose that loss of na in cell 1 leads to reduced activity in the W circuit , thus left-shifting the induction curve . We also propose that loss of na in cell 3 leads to reduced activity in the A circuit , thus right-shifting the emergence curve . The net effect is collapse of hysteresis . For sss mutants we propose that activity is increased in cells 1–2 of the W circuit , which results in two changes . The first is a right-shift of the induction curve . The second is inhibition of the A circuit even during anesthesia , which destabilizes this state and right-shifts the emergence curve . Again , the net effect is collapse of hysteresis . Our model also explains how restoration of sss expression in distinct cells can rescue the induction , emergence and neural inertia phenotypes of sss mutants . We propose that sss in cell 1 reduces suppression of the A side of the circuit during waking , thus restoring the position of the right-shifted induction curve . In contrast , sss in cell 2 reduces suppression of the A side of the circuit during anesthesia , thus restoring the position of the right-shifted emergence curve . We have also addressed a long-standing hypothesis about the means by which anesthetics are thought to modulate arousal - that is , by co-opting existing sleep-regulatory mechanisms [9] , [10] . We have demonstrated that of 8 genes we tested that have been reported to contribute to control of baseline ( daily ) sleep in flies , only a subset affect induction and stability of isoflurane-dependent anesthesia . Among the genes that have no effect are 3 that are essential to timekeeping by the central circadian clock , suggesting that circadian control of arousal is not required for normal isoflurane sensitivity . Similarly , reduced dopamine transporter function does not affect induction of or emergence from isoflurane-dependent anesthesia , despite leading to a profound reduction in sleep . If these distinct arousal pathways do not contribute to circuits underlying anesthesia , then which ones do ? A recent study suggests that dopaminergic inputs to the fan-shaped body contribute to sensitivity to isoflurane anesthesia , but this study did not distinguish between effects on induction and emergence [40] . Notably we find that D42-driven expression of sss , which rescues altered emergence and neural inertia but not induction in sssP1 mutants , does not appear to express in the fan-shaped body [24] , so it is likely that other neurons contribute to the circuitry underlying isoflurane anesthesia as well . D42 is a promoter that is known to express in mixed populations of central neurons as well as some neurons of the peripheral nervous system [24] . D42 was derived from an enhancer trap screen , rather than a cloned gene regulatory element , and the site of insertion of its Gal4-containing P-element is unknown . Thus , the fly gene that it is associated with and any corresponding mammalian gene , including the neurons that express the latter , are also unknown . Due its broad expression pattern , it is difficult to say which neurons are mediating the effects of the D42 driver . However , one possibility is the mushroom bodies , where D42 is known to express [24] and which we have previously shown to participate in sleep regulation [41] . Like our own work , several studies also indicate that mechanisms underlying sleep homeostasis may contribute to the anesthetized state [18]–[20] ( though unlike ours , these studies suggest that sleep deprivation impacts both induction and emergence ) . Consistent with this hypothesis , we find that elevated homeostatic pressure to sleep suppresses arousal and increases neural inertia . This hypothesis is also supported by our finding that sssP1 mutants , which show reduced sleep homeostasis , exhibit reduced neural inertia . This effect is likely to be confined to specific brain circuitry since the promoters that rescue collapsed neural inertia represent a subset of the promoters that rescue sleep loss in sss mutants [24] . However , our hypothesis does not explain why mutants such as cyc01 , Clkjrk and DATfmn have normal neural inertia . These mutants sleep substantially less than controls [30] , [31] , [32] and thus might be expected to have accumulated homeostatic drive to sleep . We hypothesize that these two effects - reduced sleep and increased sleep drive - counteract each other in terms of neural circuit activity , thus leading to no net effect on isoflurane sensitivity . In contrast , in the absence of intact sleep homeostatic mechanisms , such as we find in sss mutants [25] , the resulting imbalance in neural circuit activity unmasks changes to the induction and emergence processes . To extend this hypothesis further , mutations that alter induction , emergence or neural inertia may lead to the identification of genes that contribute to sleep homeostasis . Interestingly , the relationship between sleep homeostasis and neural inertia cannot necessarily be generalized to all anesthetics . Indeed , our data show that although isoflurane-dependent neural inertia is collapsed in sss mutants , neural inertia resulting from halothane-induced anesthesia is not . Taken alongside our rescue of anesthesia induction and neural inertia in sss mutants using different promoters , these data strongly suggest that different anesthetics utilize different arousal pathways to render animals unresponsive . That is , whereas anesthesia has often been treated as a whole-brain phenomenon , our data support actions for different anesthetics in specific circuits that govern arousal . Interestingly , of the mutations that been shown to affect general anesthesia , those with the biggest impact in flies ( our data ) and mammals [42] cause impairment of ion channel function . Whether these effects are due to loss of drug binding sites in the proteins affected by these mutations , or whether the resulting changes in membrane potential alter anesthetic efficacy [43] remains to be determined . Pharmacokinetics do not appear to be a factor , however , since at the EC50 for emergence in both flies and mammals , isoflurane concentrations are similar in controls and mutants that have altered neural inertia [7] . In any case , specific molecular and neuroanatomical changes clearly alter the state of anesthesia , thus supporting the idea that general anesthetics act on selective targets [11] . In summary , we have provided further evidence that neural inertia represents a barrier to changes in arousal state . We have also shown that this barrier can be genetically and anatomically dissected , and that it is distinguishable from the processes that control induction of anesthesia , at least when this state is studied with isoflurane . While these conclusions are based on studies of Drosophila , it is worth noting that we previously demonstrated genetic control over neural inertia in mammals as well , including mice deficient in noradrenaline production [7] . The commonality of neural inertia in such disparate organisms argues for conserved basic circuit design underlying control of arousal throughout evolution . It should be noted that although we have emphasized the possibility that circuit-based feedback mechanisms underlie bistability in our system , it is also possible that post-translational modifications contribute to this property . In either case , the clinical importance of our findings is particularly notable for two reasons . First , our results confirm that the sensitivity to induction of anesthesia cannot be used to reliably predict how easily a patient will exit from the anesthesia state . Second , feedback and bistability may be impaired in coma or persistent vegetative states such that the neural inertial barrier separating waking from unconscious states is widened beyond the range of reversibility by normal physiological processes . The conservation of mechanisms underlying waking and anesthesia among distantly related phyla suggest that extension of our current work in Drosophila will continue to shed light on the genetic and anatomical processes underlying behavioral state stability , an issue of fundamental importance to both neuroscience and clinical medicine .
All mutant and transgenic flies were outcrossed 4–7 times into an isogenic w1118 ( iso31 ) background . Unless otherwise stated , controls for mutant animals were outcrossed siblings . GAL4 lines were generated or obtained as previously described [24] , except for Gr21a and nos , which were obtained from the Bloomington Stock Center ( Bloomington , IN ) . The Shmns and ShDf lines were obtained from D . Bushey , C . Cirelli and B . Ganetzky ( University of Wisconsin ) , and DATfmn flies were obtained from K . Kume ( Kumamoto University ) . nae04385 and unc79f03453 were obtained from Bloomington , and unc79c04794 was obtained from Exelixis ( Harvard ) . sssP1 , sssP2 , and UAS-sss were described previously [24] , [25] . 3–4 male and 5–8 female flies were combined on standard molasses-yeast-cornmeal food and allowed to mate at 21–23°C for 7–10 days . Adults were then discarded , and newly eclosing flies were collected over a 4 day period . 1–5 day-old females were loaded into 65×5 mm cylindrical tubes containing 5% sucrose and 2% agarose and entrained to a 12-hr∶12-hr light∶dark cycle for at least 2 d before being assayed for anesthetic sensitivity or sleep at 25°C using the Drosophila Activity Monitoring System ( Trikinetics , Waltham , MA ) . Anesthetics dissolved in air were delivered to flies in parallel , and final concentrations and flow rates were measured as previously described [7] . With flow rates set at 15 ml/min/tube , we calculate that gas concentrations inside our . 75 ml tubes will reach equilibrium within 18 seconds . For anesthesia measurements , individual flies were exposed to increasing and then decreasing dosages of isoflurane using a previously described protocol [7] . The anesthetic endpoint that was used was immobility , with induction being defined as the lowest concentration at which movement ceased for five or more minutes , whereas emergence was defined as the highest concentration at which movement resumed . Locomotor counts over 5 min periods for each individual fly were converted to a value of 1 , signifying activity , or 0 , indicating no movement . Flies that did not move for 15 minutes prior to the start of anesthesia or during the first 5 minutes at the lowest anesthetic dose were excluded from subsequent analysis . Flies that did not recover activity during the 24 hours following anesthesia were also excluded from analysis ( <2% for the genetic background for all our experiments , w1118 iso31 ) . Behavior was analyzed using custom software written in MATLAB ( MathWorks , Natick , MA ) where sleep was identified as periods of inactivity lasting at least 5 min [44] . Concentration-response curves were fit to the Hill equation using Prism 4 ( GraphPad , La Jolla , CA ) , in which the top constant , degree of cooperativity ( Hill coefficient ) and EC50 were allowed to vary and only the bottom constant was constrained to zero . Anesthetic experiments were conducted during the evening locomotor activity peak ( ZT10:20 to ZT12:40 ) . During this period , flies show consolidated activity and wakefulness . Responses to anesthetics are thus unlikely to be confounded by inactivity due to normal sleep . To calculate neural inertia , the area between the induction and emergence concentration-response curves was integrated over the range of the induction curve's EC1 to the emergence curve's EC99 , as previously described [7] . Neural inertia for each set of induction and emergence curves is expressed as the mean ± standard error . To elicit sleep homeostasis , mechanical stimulation was applied to iso31 animals for 1 second every min for 24 hrs , ending at the last dose of applied isoflurane , using DAMS monitors mounted to a platform vortexer . Control iso31 animals received identical mechanical stimulation throughout dosing of anesthetic , but were not sleep-deprived prior to this time . Specifically , controls were placed on a vortexer with experimental animals beginning 15 minutes before the first dose of isoflurane and mechanically perturbed for 1 second every minute until the final dose of isoflurane at ZT12:40 . Pilot studies were used to find the appropriate strength of mechanical stimulation to awaken sleeping but not anesthetized flies . Differences in neural inertia and sleep , as well as log ( EC50 ) s for induction and emergence , were analyzed with one-way ANOVAs followed by Bonferroni correction for multiple comparisons or Student's t-tests ( unpaired , two-tailed ) where applicable . | An annual 234 million surgical procedures are performed worldwide , making general anesthetics among the most common drugs administered to humans . Remarkably , however , we still do not understand the mechanisms by which general anesthetics render patients unconscious or the processes that re-establish consciousness upon emergence from anesthesia . We previously showed that the brain resists transitions between the wakeful and anesthesia states by generating a barrier to such transitions in both directions . We also showed that the existence of this barrier is conserved from invertebrates to mammals . In our present work , we use the genetic tractability and the simplified nervous system of the fruit fly Drosophila melanogaster to show that four genes are required to maintain this barrier . We also show that , as in mammals , there is overlap between pathways regulating natural sleep and general anesthesia . We propose that some of these shared pathways are impaired in conditions such as coma and persistent vegetative states , in which the barrier to transitioning to the waking state appears to be insurmountable . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [] | 2013 | Genetic and Anatomical Basis of the Barrier Separating Wakefulness and Anesthetic-Induced Unresponsiveness |
Fasciola gigantica ( Digenea ) is an important foodborne trematode that causes liver fluke disease ( fascioliasis ) in mammals , including ungulates and humans , mainly in tropical climatic zones of the world . Despite its socioeconomic impact , almost nothing is known about the molecular biology of this parasite , its interplay with its hosts , and the pathogenesis of fascioliasis . Modern genomic technologies now provide unique opportunities to rapidly tackle these exciting areas . The present study reports the first transcriptome representing the adult stage of F . gigantica ( of bovid origin ) , defined using a massively parallel sequencing-coupled bioinformatic approach . From >20 million raw sequence reads , >30 , 000 contiguous sequences were assembled , of which most were novel . Relative levels of transcription were determined for individual molecules , which were also characterized ( at the inferred amino acid level ) based on homology , gene ontology , and/or pathway mapping . Comparisons of the transcriptome of F . gigantica with those of other trematodes , including F . hepatica , revealed similarities in transcription for molecules inferred to have key roles in parasite-host interactions . Overall , the present dataset should provide a solid foundation for future fundamental genomic , proteomic , and metabolomic explorations of F . gigantica , as well as a basis for applied outcomes such as the development of novel methods of intervention against this neglected parasite .
Liver flukes are socio-economically important parasitic flatworms ( Platyhelminthes: Trematoda: Digenea ) affecting humans and livestock in a wide range of countries . Two key representatives are Fasciola gigantica and F . hepatica . These parasites are the main cause of fascioliasis , a significant disease in ungulates [1]–[3] and humans , which is usually contracted via the ingestion of contaminated aquatic plants [4] . Fascioliasis due to F . gigantica is recognized as a neglected tropical disease and is estimated to affect millions of people , mainly in parts of Africa , the Middle East and South-East Asia [2] , [5]–[10] . Fasciola gigantica and F . hepatica share common morphological , phylogenetic and biological characteristics , most clearly inferred by the evidence of sustained F . gigantica x F . hepatica ( i . e . hybrid or introgressed ) populations [11]–[13] . Fasciola spp . have di-heteroxenous life cycles [2] , [14] which involve ( freshwater ) lymnaeid snails as intermediate hosts and mammalian definitive hosts . The pathogenesis of fascioliasis in the definitive host is characterized by two main phases: ( i ) the acute/subacute phase begins with the ingestion of the metacercarial stage on herbage and is characterized by tissue damage , caused by the migration of immature worms through the duodenal wall , and then the liver capsule and parenchyma ( usually 2–6 weeks ) [1] . Clinical signs can include abdominal pain , fever , anaemia , hepatomegaly and weight loss; ( ii ) the chronic phase commences when adult worms have established in the biliary ducts ( ∼7–8 weeks after infection ) [1] . In addition to hepatic fibrosis ( following acute/subacute infection ) and anaemia , the chronic phase is characterized by progressive cholangitis , hyperplasia of the duct epithelium and periductal fibrosis , which can result in cholestatic hepatitis [15] , [16] . The onset of clinical signs can be variable , slow and typically include anaemia , jaundice , inappetence , oedema/ascites and/or diarrhoea [17] , [18] . Fascioliasis can also sometimes be associated with complications , such as co-infections with anaerobic bacteria [1] , [10] . Despite their substantial morphological and biological similarities , differences in host specificity between F . gigantica and F . hepatica appear to define the aetiology and clinical manifestation of disease in the definitive host [2] . A well-characterized difference between these parasites is their adaptation to different intermediate snail hosts . Fasciola gigantica usually prefers snail species ( e . g . , Radix natalensis and R . rubiginosa ) that live in warm climates , whereas F . hepatica often utilizes snails ( e . g . , Lymnaea tomentosa and Galba truncatula ) that are widespread in cool climates [19] . This difference in intermediate host-preference appears to affect the distribution of the parasites , with F . gigantica being the most common cause of fascioliasis in the tropics and F . hepatica being more common in temperate regions . In sub-tropical regions , where both species of Fasciola can co-exist , fascioliasis is reported to be associated with F . gigantica , F . hepatica and/or F . gigantica x F . hepatica hybrid populations [2] , [19] . The clinical manifestation of fascioliasis in definitive hosts can also depend on parasite factors ( e . g . , species/strain of worm , infective dose and/or intensity of infection ) and host factors ( e . g . , species of host , immune response and phase/duration of the infection ) [1]–[3] , [20]–[23] . Some studies seem to suggest that F . gigantica may be better adapted to parasitize cattle , with higher levels of resistance being observed in sheep and goats [20] , [21] , [24] . In contrast , most breeds of sheep are highly susceptible to fascioliasis caused by F . hepatica [20] . Current evidence [2] , [20] , [24] , [25] suggests differences in biology between F . gigantica and F . hepatica as well as the disease ( s ) that these parasites cause; yet , our understanding of the molecular biology of these parasites and of fascioliasis , particularly in humans , is in its infancy [16] , [26] . Recent developments in high-throughput sequencing [27]–[30] and bioinformatics [31] are now providing researchers with the much-needed tools to explore the fundamental biology of digeneans [32] , [33] . To date , molecular biological research of socioeconomically important trematodes has been dominated by a focus on Schistosoma mansoni and S . japonicum , culminating , recently , in the sequencing of their nuclear genomes [34] , [35] . These two genome sequences provide an invaluable resource to support fundamental explorations of the biology and evolution of flukes as well as their interactions with their hosts [35] . However , the biology of schistosomes , which live en copula ( i . e . as male/female pairs ) in the blood stream of mammalian hosts , is distinct from that of hermaphroditic liver flukes , such as F . gigantica and F . hepatica . Recently , the transcriptomes of several foodborne liver flukes , including F . hepatica , Clonorchis sinensis and Opisthorchis viverrini , were determined [36] , [37] . Although this progress has improved our understanding of the molecular biology of these worms and has paved the way toward the discovery of new intervention targets , almost nothing is known about F . gigantica . This paucity of knowledge is clearly illustrated by the comparison of >60 , 000 transcripts currently available for F . hepatica [36] , [38] , [39] with a total of 39 for F . gigantica in public databases ( National Center for Biotechnology Information , NCBI ) . In the present study , we characterized the transcriptome of the adult stage of F . gigantica and provide an essential resource for future explorations of this socioeconomically important parasite . We used massively parallel nucleotide sequencing of a non-normalized cDNA library to provide a deep insight into this transcriptome as well as relative transcription levels in this developmental stage . In addition , comparative analyses of the dataset predicted a range of proteins that are conserved among trematodes , providing an invaluable resource to underpin future efforts toward developing new approaches for the intervention against and control of fascioliasis .
Adults of F . gigantica were collected ( at an abattoir in Khon Kaen , Thailand ) , from the large bile ducts of a liver from a water buffalo ( Bubalus bubalis ) with a naturally acquired infection . All work was conducted in accordance with protocols approved by the animal ethics committee of the Department of Anatomy , Faculty of Veterinary Medicine , Khon Kaen University , Thailand . Adult worms were washed extensively in physiological saline and then transferred to and maintained in culture in vitro for 2 h [36] to allow the worms to regurgitate caecal contents . Subsequently , all worms were washed extensively in physiological saline , snap-frozen in liquid nitrogen and then stored at −80°C . The specific identity of each individual worm was verified by isolating genomic DNA [40] and conducting PCR-coupled , bidirectional sequencing ( ABI 3730xl DNA analyzer , Applied Biosystems , California , USA ) of the second internal transcribed spacer ( ITS-2 ) of nuclear ribosomal DNA [36] . In addition , the reproductive state and ploidy of each of three adult worms used for transcriptomic sequencing were examined histologically [41]; the presence of mature eggs and sperm confirmed that all three worms represented F . gigantica and not F . gigantica x F . hepatica hybrids ( see [11] ) . A full poly ( A ) -selected transcriptome sequencing approach ( RNA-seq ) was employed . DNase I-treated total RNA was extracted from three adult worms of F . gigantica using the TriPure isolation reagent ( Roche ) , according to manufacturer's protocol . The amounts of total RNA were determined spectrophotometerically , and RNA integrity was verified by agarose gel electrophoresis and using a 2100 BioAnalyzer ( Agilent ) . Polyadenylated ( polyA+ ) RNA was purified from 10 µg of total RNA using Sera-Mag oligo ( dT ) beads , fragmented to a length of 100–500 nucleotides , reverse transcribed using random hexamers , end-repaired and adaptor-ligated , according to the manufacturer's protocol ( Illumina ) . Ligated products of ∼200 base pairs ( bp ) were excised from agarose and PCR-amplified ( 15 cycles ) . Products were cleaned using a MinElute column ( Qiagen ) and sequenced on a Genome Analyzer II ( Illumina ) , according to the manufacturers' instructions . The short-insert , single reads , generated from the adult F . gigantica cDNA library , were assembled using the computer program SOAPdenovo v1 . 04 [42] . Briefly , short-insert , single-end reads filtered for adapter sequences and suboptimal read quality ( i . e . with PHRED quality scores of <28 ) were used to construct and store a De Bruijn-graph using a k-mer value of 29 bp . Sequence reads were trimmed , and links with low coverage were removed before contig sequence k-mers were conjoined in an unambiguous path . To reduce apparent redundancy , sequences of >200 nucleotides were clustered using the contig assembly program ( CAP3 ) [43] , employing a minimum overlap length of 40 nucleotides and an identity threshold of 95% . Using BLASTn and then BLASTx analyses , all nucleotide sequences ( n = 12 ) with significantly higher identity ( based on the E-value ) to those of any potential contaminants ( including bacteria , fungi and/or the bovid host ) than to digeneans or any other eukaryotes ( for which sequence data are currently available ) were removed . The raw sequence reads derived from the non-normalized adult F . gigantica cDNA library were then mapped to the non-redundant transcriptomic data using the program SOAP2 [44] . Briefly , raw sequence reads were aligned to the non-redundant transcriptomic data , such that each raw sequence read was uniquely mapped ( i . e . to a unique transcript ) . Reads that mapped to more than one transcript ( designated “multi-reads” ) were randomly allocated to a unique transcript , such that they were recorded only once . To provide a relative assessment of transcript abundance , the number of raw reads that mapped to each sequence was normalized for length ( i . e . reads per kilobase per million reads , RPKM ) [45] . The non-redundant transcriptomic dataset for adult F . gigantica was annotated ( April 2010 ) based on BLASTx for protein sequence homology at permissive ( E-value: <1E−05 ) , moderate ( <1E−15 ) and/or stringent ( <1E−30 ) search strategies against sequences in available databases , including: ( i ) NCBI non-redundant sequence database ( GenBank , http://www . ncbi . nlm . nih . gov/ ) ; ( ii ) non-redundant genome-wide sequence databases for eukaryotic organisms [ENSEMBL ( http://www . ensembl . org/ ) , SchistoDB for S . mansoni ( http://schistodb . net/schistodb20/ ) [46] and the Chinese Human Genome Center at Shanghai database for S . japonicum ( http://lifecenter . sgst . cn/schistosoma ) [47]; ( iii ) transcriptomic datasets available for F . hepatica , Clonorchis sinensis and Opisthorchis viverrini [36] , [37]; and ( iv ) manually curated information resources for peptidases ( MEROPS database ) [48] and kinases ( European Molecular Biology Laboratory kinase database , http://www . sarfari . org/kinasesarfari/ ) . Proteins were conceptually translated from the predicted coding domains of individual nucleotide sequences . Protein-coding sequences were classified functionally using the program InterProScan [49] , employing the default search parameters . Based on their homology to conserved domains and protein families , predicted proteins of F . gigantica were assigned gene ontology ( GO ) categories and parental ( i . e . level 2 ) terms ( http://www . geneontology . org/ ) . Inferred proteins with homologues/orthologues in other organisms were mapped to conserved biological pathways utilizing the Kyoto encyclopedia of genes and genomes ( KEGG ) orthology-based annotation system ( KOBAS ) [50] . Orthologues in KEGG ( i . e . metabolic ) pathways were displayed using the tool iPath2 ( http://pathways . embl . de/ipath2 ) [51] . Signal peptides were also predicted using the program SignalP 3 . 0 , employing both the neural network and hidden Markov models [52] , and transmembrane domains using TMHMM [53] , a membrane topology prediction program . Proteins inferred to be classically excreted and/or secreted from F . gigantica , based on the presence of a signal peptide , absence of any transmembrane domain ( s ) as well as sequence homology to one or more known excretory/secretory ( ES ) proteins listed in databases for eukaryotes [54] , F . hepatica [39] , S . mansoni [55] and the nematode Brugia malayi [56] , [57] were identified and collated .
More than 20 million , short-insert Illumina reads were generated for the adult stage of F . gigantica ( Table 1 ) . Raw sequence data were deposited in the sequence read archive ( SRA ) database of NCBI ( http://www . ncbi . nlm . nih . gov/sra ) under accession number SRA024257 . BLASTn searches ( E-value: 1E−05 ) revealed that all 39 expressed sequence tags ( ESTs ) available in public databases for this parasite were contained within the present , assembled sequence dataset ( available via http://gasser-research . vet . unimelb . edu . au/; contact corresponding authors ) ; thus , only the sequence data from the present study were assembled ( see Table 1 ) . Short reads clustered into 30 , 525 unique sequences with a mean length of 524 nucleotides ( range: 201–18 , 098 ) and with a G+C content of 46 . 0±4 . 2% . More than 25% of the raw reads were re-mapped ( sequence length of ≥200 nucleotides ) to the transcriptomic data , with a mean depth of coverage of 188±469 reads per sequence . The transcriptomic dataset was used to interrogate genomic/transcriptomic databases ( i . e . F . hepatica , C . sinensis , O . viverrini , S . mansoni , S . japonicum and NCBI non-redundant sequence databases ) using BLASTx . The majority of F . gigantica sequences ( 27 , 755 of 30 , 513 sequence matches , equating to 91 . 0% ) matched previously identified molecules at an E-value threshold of 1E−05 ( Table 1 ) . Proteins inferred from the transcriptome of F . gigantica were compared with those predicted from transcriptomic data for the adult stages of F . hepatica , C . sinensis and O . viverrini [36] , [37] and complete proteomic datasets for selected organisms , including Saccharomyces cerevisiae ( yeast ) , S . mansoni and S . japonicum ( trematodes ) , Caenorhabditis elegans ( ‘elegant worm’ ) , Drosophila melanogaster ( vinegar fly ) ; Danio rerio ( zebra fish ) , Gallus gallus ( chicken ) , Xenopus tropicalis ( frog ) ; Bos taurus ( cattle ) , Homo sapiens and Mus musculus ( mouse ) ( Table 2 ) . As expected , proteins predicted for F . gigantica ( n = 30 , 513 ) had the highest sequence homology to F . hepatica using permissive ( 27 , 354 sequences; 89 . 6% ) , moderate ( 25 , 390 sequences; 83 . 2% ) and stringent ( 20 , 798 sequences; 68 . 2% ) search strategies . Amino acid sequences inferred for F . gigantica had the greatest similarity to those of other members of the class Trematoda included herein , resulting in 10 , 752 to 27 , 354 sequence matches ( 35 . 2–89 . 6% ) or a total of 27 , 745 sequences matches ( 90 . 9% ) at an E-value of 1E−05 . In agreement with findings for other trematodes [34]–[37] , proteins inferred for F . gigantica had a higher sequence similarity to those of mammals ( 30 . 1–30 . 2% ) than C . elegans ( 23 . 8% ) . Comparative protein sequence analysis was carried out between or among key members of the Trematoda ( Table 3 ) . Despite significant differences in biology and life history , representatives of the family Fasciolidae ( i . e . F . gigantica and F . hepatica ) shared greater protein sequence homology ( 38 . 3%; E-value: 1E−05 ) with sequences encoded in the genomes of S . japonicum and S . mansoni ( blood flukes; family Schistosomatidae ) than to those encoded by transcripts from the adult stages of C . sinensis and O . viverrini ( liver flukes; family Opisthorchiidae; 26 . 8%; E-value: 1E−05 ) . Only a small number of proteins predicted for F . gigantica ( i . e . 253 and 705 sequences at an E-value of 1E−30 and 1E−05 , respectively ) were homologous among the representatives of the families Fasciolidae , Schistosomatidae and Opisthorchiidae , but absent ( based on a similar level of sequence homology ) from the other eukaryotic organisms included in the present study ( see Table S1 ) . These molecules included proteases ( mastin and leucine amino peptidase ) , membrane transporter proteins ( aquaporin 3 , multidrug resistance-associated protein-type ATP-binding cassette transporter and oxalate:formate antiporter ) and proteins involved in cellular signalling ( i . e . calcium binding proteins and an epidermal growth factor-like peptide ) . Proteins inferred from the transcriptome of F . gigantica were predicted to contain signal peptide domains ( 1 , 543 sequences ) and/or transmembrane domains ( 3 , 599 sequences ) ( Table 1 ) . Based on the presence of signal peptide domains in and absence of transmembrane motifs from the predicted proteins as well as the presence of one or more homologues in current ES protein databases , 255 putative ES proteins , including cysteine proteases , cathepsins B and L , legumain and cystatin ( a cysteine protease inhibitor ) were inferred ( Table S2 ) . Predicted proteins were also categorized according to their inferred molecular function , cellular localization and association with biological pathways , and compared with those encoded in the transcriptomes of the adult stages of other liver flukes , including F . hepatica ( Table 1 and Table S3 ) . A significant proportion ( 30 . 6% ) of the transcriptome of F . gigantica was inferred to encode 3 , 535 conserved protein domains or family signatures . Based on this annotation , 1 , 124 GO terms were inferred . The transcriptome of F . gigantica contained most of the parental ( i . e . level 2 ) terms assigned previously to F . hepatica ( 87% ) [36] , C . sinensis and O . viverrini ( 80% ) [37] , based on analyses of sequence data generated previously from normalized cDNA libraries representing adult worms . Predicted proteins assigned to the term ‘biological process’ ( 3 , 461 sequences; 401 GO terms ) were associated predominantly with: ( i ) cellular processes ( 3 , 322 sequences; 64 . 1% ) , such as protein amino acid phosphorylation and transmembrane transport; ( ii ) metabolic processes ( 2 , 686 sequences; 51 . 8% ) , such as protein amino acid phosphorylation and translation; and ( iii ) localization ( 863 sequences; 16 . 7% ) , such as the directed movement of substances within or between cells including the transport of solutes across a membrane . Proteins assigned to the term ‘molecular function’ were mainly linked to: ( i ) binding ( 3 , 362 sequences; 70 . 1% ) , such as the binding of ATP , zinc ion and protein; ( ii ) catalytic activities ( 2 , 736 sequences; 52 . 8% ) of enzymes , including protein kinases; and ( iii ) transporter activity ( 342; 6 . 6% ) , including ATPase activity , coupled to the transport of molecules through membranes . Predicted proteins for F . gigantica were also linked to cellular components , such as membranes , nucleus , protein complexes or ribosomes ( Table S3 ) . Significant similarity ( E-value: 1E−05 ) between protein sequences predicted for F . gigantica and those in the KOBAS database allowed 4 , 466 sequences to be assigned to 1 , 981 KO terms and 225 standardized KEGG pathway terms ( Table 1 ) . A significant proportion of amino acid sequences were associated with: ( i ) metabolic pathways ( 1 , 259 sequences; 549 KO terms ) , including carbohydrate , amino acid and lipid metabolism; ( ii ) cellular processes ( 919 sequences; 324 KO terms ) , including those linked to cell communication as well as the endocrine and/or immune systems; ( iii ) environmental information-processing pathways ( 738 sequences; 278 KO terms ) , including signal transduction , membrane transport and signaling molecules; ( iv ) genetic information processing pathways ( 661 sequences; 355 KO terms ) , including folding , sorting and degradation , translation and replication and repair; and ( v ) pathways linked to human diseases ( 341 sequences; 165 KO terms ) , including cancers , neurodegenerative disorders and infectious diseases ( Table 4 ) . Inferred proteins of F . gigantica ( 2097 sequences; 892 KO terms ) were mapped to conserved , orthologous KEGG metabolic pathway terms , with a high degree of confidence based on protein sequence homology , employing moderate ( 785 KO terms; 88 . 0%; E-value , 1E−15 ) and stringent ( 589 KO terms , 66 . 0%; E-value , 1E−30 ) search strategies ( Figure S1 ) . Proteins predicted for F . gigantica that shared highest homology to conserved metabolic enzymes of eukaryotes ( listed in the KEGG database ) were associated predominantly with carbohydrate , lipid and/or energy metabolism . A high degree of similarity in metabolic pathways was evident between F . gigantica and F . hepatica [36] ( Figure S2 ) , regardless of whether the data were derived from a non-normalized cDNA library sequenced by Illumina ( F . gigantica ) [present study] or a normalized library sequenced using 454 technology ( F . hepatica ) [36] . Interestingly , in F . gigantica , there was no evidence of any transcripts encoding 3-oxoacyl-[acyl-carrier-protein] synthase II [EC:2 . 3 . 1 . 179] , which , in eukaryotes , is usually linked to the fatty acid biosynthesis pathway ( KEGG pathway map00061 ) . Although this molecule was encoded in F . hepatica ( Figure S2 ) and S . mansoni [34] , it is the only enzyme representing this particular pathway in these organisms . Current evidence ( cf . [58] , [59] ) indicates that digeneans lack the repertoire of enzymes required for the de novo synthesis of fatty acids and that they are highly dependent on complex fatty acid precursors from their host ( s ) . Most abundantly transcribed genes ( as assessed based on RPKM ) in adult F . gigantica were those linked to reproductive processes , antioxidant molecules ( thioredoxin , peroxiredoxin and fatty acid-binding proteins ) , molecular chaperones ( heat shock proteins 70 and 90 ) , proteins involved in the glycolytic pathway ( fructose-bisphosphate aldolase , fructose-16-bisphosphatase-related protein , glutamate dehydrogenase and glyceraldehyde phosphate dehydrogenase ) , translation ( elongation factor-1 alpha , RNA-binding protein 9 and cytosolic 80S ribosomal protein L39 ) , cytoskeletal proteins ( alpha-tubulin and dynein ) and cysteine ( calpain , cathepsin B , legumain-1 and legumain-2 ) and metallo ( prolyl carboxypeptidase ) proteases ( Table S4 ) . A detailed examination of the data revealed that a full complement of proteins required to degrade carbohydrates to phosphoenolpyruvate via the glycolytic pathway [58] was present ( Figure S3 ) . Proteins predicted for F . gigantica were assigned to major families ( 2 , 214 sequences; 998 terms ) based on homology to annotated proteins in the KEGG protein family database . Sequences encoded in the transcriptome were almost equally subdivided into three major categories: ‘genetic information processing’ ( 704 sequences; 31 . 8% ) , ‘cellular signaling’ ( 704 sequences; 31 . 8% ) and ‘metabolism’ ( 676 sequences; 30 . 5% ) ( Figure 1A ) . Putative proteins were further categorized into various sub-categories , including: ( i ) protein kinases ( 364 sequences; 16 . 4% ) ; ( ii ) cytoskeleton proteins ( 338 sequences; 15 . 3% ) ; ( iii ) ubiquitin enzymes ( 224 sequences; 10 . 1% ) ; and ( iv ) proteases ( 214 sequences; 9 . 7% ) ( Figure 1B ) . A further in silico analysis assigned most of the protein kinases ( 308 sequences ) to eight structurally-related classes , inferred to be crucial for normal cellular processes ( Figure 1C and Table S5 ) , such as: ( i ) CMGC ( 66 sequences; 21 . 4% ) cyclin-dependent ( CDKs ) , mitogen-activated protein ( MAP kinases ) , glycogen synthase ( GSK ) and CDK-like serine/threonine kinases; ( ii ) CAMK ( 55 sequences; 17 . 9% ) , Ca2+/calmodulin-dependent serine/threonine kinases; ( iii ) AGC ( 44 sequences; 14 . 3% ) , cAMP-dependent , cGMP-dependent and protein kinase C serine/threonine kinases; ( iv ) STE ( 35 sequences; 11 . 4% ) , serine/threonine protein kinases associated with the mitogen-activated protein kinase cascade; and ( v ) tyrosine kinases ( 34 sequences; 11 . 0% ) . Kinases that were abundantly transcribed included cAMP-dependent protein kinases ( AGC ) and casein kinases ( CK1 ) , essential for cell signal transduction; Ca2+/calmodulin-dependent serine/threonine kinases ( CAMK ) involved in calcium signalling [60]; and dual-specificity tyrosine- ( Y ) -phosphorylation regulated kinase 2 ( CMGC ) involved in the regulation of cellular growth and/or development [61] . Similarly , further of the F . gigantica dataset inferred 304 proteases ( linked to 247 MEROPS terms ) and 137 protease inhibitors ( 122 MEROPS terms ) , including representatives of five of the seven protease catalytic types defined within the MEROPS database [48] ( Figure 1D and Table S6 ) . The ratio ( aspartic:cysteine:metallo:serine:threonine ) of catalytic types of proteases represented in the MEROPS database [48] and present in the of transcriptome of F . gigantica was 5:34:34:22:5 , which was comparable with those inferred from the genomes of S . japonicum ( 4:32:35:21:8 ) and S . mansoni ( 6:29:35:23:7 ) [34] , [35] . In F . gigantica , genes encoding the metalloproteases ( 82 MEROPS terms; 33 . 5% ) , leucyl aminopeptidases , cytosolic exopeptidases , which cleave N-terminal residues from proteins and peptides , were abundantly transcribed . Cysteine proteases ( 82 MEROPS terms; 33 . 5% ) inferred included those involved in the digestion of host proteins ( legumain/asparaginyl endopeptidase and cathepsins ) and calcium-induced modulation of cellular processes ( calpain ) ( Table S4 ) . Like all eukaryotes , F . gigantica was inferred to possess a rich diversity of serine proteases ( 55 MEROPS terms; 22 . 4% ) , including an abundantly transcribed serine carboxypeptidase , which are presumably important for fundamental cellular processes . Threonine proteases ( 13 MEROPS terms; 5 . 3% ) which were abundantly represented included enzymes required for the assembly and activation of the proteasome complex [62] . Aspartic proteases encoded ( 13 MEROPS terms; 5 . 3% ) included cathepsin D , an aspartyl lysosomal peptidase which , in trematodes , is suggested to play a role in the degradation of host tissues [63] . Cathepsins representing families B and L were inferred ( Table S7 ) from the present dataset by annotating and re-mapping sequences of ≥200 nucleotides ( ‘stringent conditions’ ) . Inspection of the annotated data identified 18 and two sequences with homology to cathepsin B ( including clades B1 and B2 ) and cathepsin L ( clades 1 and 2 ) , respectively . As cathepsin L is reported to be a dominant family of proteins of F . gigantica and F . hepatica [39] , [64]–[67] , the relative levels of transcription of genes encoding members of cathepsins B and L were explored . The re-mapping of raw sequences ( Illumina ) to previously published transcripts ( n = 15 ) encoding cathepsins from F . gigantica ( see Table S8 ) [66]–[68] revealed high ( RPKM of 2 , 543–214 , 634 ) and low ( RPKMs of 14–21 ) levels of transcription for 10 and two representatives , respectively , of 12 distinct members of the cathepsin L family , and low and moderate ( RPKMs of 0 . 9 and 300 ) levels for two of the three representatives of cathepsin B , respectively ( Table S8 ) .
A number of trematodes are of major socioeconomic importance; yet , they cause some of the most neglected diseases of humans and livestock worldwide . Until recently , there has been a reliance on data and information available for schistosomes ( blood flukes ) [34] , [35] to infer aspects of the molecular biology of key trematodes . The recent characterization of the transcriptomes of the liver flukes F . hepatica , C . sinensis and O . viverrini [36] , [37] has provided the first insights into the molecular biology of these foodborne trematodes . Extending this work , the present study provides a deep exploration of the transcriptome of the adult stage of F . gigantica . With only 39 transcripts previously available in public databases , the >30 , 000 sequences characterized here are novel for this species and constitute a significant contribution to current databases [36] , [37] , [69]–[73] and an invaluable resource to advance our understanding of the fundamental biology of F . gigantica , its interplay with its hosts and the disease that this parasite causes . Importantly , the present transcriptomic data set will also be an essential resource for the future assembly of the nuclear genome of F . gigantica , assisting in the determination of gene structures , prediction of alternative transcript splicing and the characterization of regulatory elements . The present transcriptomic dataset should , in the future , assist significantly in identifying genes linked specifically to parasitism and also to our understanding of the evolution of trematodes [74] . Based on current similarity searches , 80% ( BLASTx , E-value 1E−15 ) to 90% ( BLASTx , E-value 1E−05 ) of the predicted protein sequences of F . gigantica and F . hepatica were inferred to be homologues , reflecting their close biological and phylogenetic relationships [75] . More broadly , 253 protein sequences inferred for F . gigantica were homologous ( BLASTx , E-value <1E−30 ) to proteins identified in other trematodes but divergent from those predicted for a range of other eukaryotes , including human , mouse , cattle , zebrafish , vinegar fly , ‘elegant worm’ and/or yeast . Although there is a paucity of data on the function of the majority of such molecules , their characterization could lead to the discovery of new targets for the design of safe trematocidal drugs and/or vaccines . Massively parallel nucleotide sequencing from a non-normalized cDNA library and the subsequent assembly of sequence data have produced a high quality draft of the transcriptome of adult F . gigantica and provided invaluable insights into the relative abundance of transcripts . The assignment of molecules encoded in the transcriptome to molecular functions and biological pathways has revealed a substantial diversity of terms , comparable with those predicted for other liver flukes , including F . hepatica [36] , C . sinensis and O . viverrini [37] , and the blood fluke S . mansoni ( http://amigo . geneontology . org/; http://schistodb . net/schistodb20/ ) . Proteins known to be expressed in adult F . hepatica [39] , [76] , [77] were compared with those inferred from the transcriptome of F . gigantica . Molecules well represented in the adult transcriptomes of both F . gigantica [the present study] and F . hepatica [39] included antioxidants , heat shock proteins and cysteine proteases . Antioxidants have been suggested to play a role in host immune modulation and shown to be highly expressed throughout the life history of F . hepatica [39] , including peroxiredoxin , thioredoxin and glutathione transferases , whose expression has been suggested to protect fasciolids from harmful , host-derived reactive oxygen species [78]–[80] . A similar protective role has also been reported for protein chaperones , such as heat shock protein-70 , which have been inferred to play an important role in relation to protein folding and whose expression is proposed to be induced by one or more host immune responses to F . gigantica or F . hepatica [81] . Therefore , within the definitive host , adult stages of F . gigantica and F . hepatica appear to express repertoires of molecules that are directed toward the protection of cellular processes from the host response to liver fluke infection , including the protection from reactive oxygen species ( ROS ) [82] . Protection from damage caused by ROS is important , since juveniles of F . gigantica are susceptible ( in vitro ) to antibody-dependent cell-mediated cytotoxicity involving ROS [83] . A diverse array of proteases were abundantly represented in the transcriptome of the adult stage of F . gigantica , as expected based on previous proteomic studies [63] , [84] . Cysteine proteases constituted a significant proportion of catalytic enzymes encoded in this species ( Figure 1; Table S6 ) , which appears to reflect their crucial roles in parasite feeding and/or immuno-modulation in the definitive host [85] , [86] . A cathepsin B-like molecule ( B1 ) was also well represented in the present transcriptome ( Table S7 and Table S8 ) . Evidence of abundant transcription of one or more homologues in the tegument and/or digestive and reproductive tracts [68] and their absence from ES products [39] , [64] , [66] , [87] suggests one or more key functions for cathepsin Bs within the tissues of this parasite . A detailed analysis also revealed that transcripts encoding cathepsin Ls ( including members of clades 1 , 2 and 5; [66] ) were abundant in the present dataset ( Table S8 ) , consistent with their dominance in ES products from adult F . hepatica [39] , [66] , [88] . The complexity of the cathepsins and the close relatedness of some of them were reflected in a technical challenge in the assembly of ( short-read ) Illumina sequence data . The abundance of many related and , apparently , paralogous and/or alternatively spliced transcripts encoding cathepsin Ls ( cf . [66] ) prevents accurate assemblies from short transcripts , even under stringent conditions ( as used herein ) . This point emphasizes a limitation of the de novo-assembly of single-end sequences produced using short-read sequencing platforms , such as Illumina [27] and SOLiD [30] , in the absence of a reference genome sequence . This limitation should be overcome in the future through the combined assembly and annotation of paired-end sequence data with medium to long sequences ( e . g . , of 350–1000 nucleotides ) produced using alternative sequencing technology , such as 454 ( Roche ) [89] . Such an integrated sequencing approach , preferably in conjunction with proteomic analyses , could be used to quantitatively study transcription/expression profiles in key developmental stages and distinct phenotypes ( or hybrids ) of F . gigantica [11] , [13] , [90] . Although the transcriptome of the adult stage of F . gigantica has been defined here , there is no information on differential transcription among miracidial , sporocyst , redial , cercarial , juvenile and adult stages of this parasite . Clearly , exploring transcription among and also within all developmental stages of this parasite will have important implications for understanding development , reproduction , parasite-host interactions as well as fascioliasis at the biochemical , immunological , molecular and pathophysiological levels . Detailed knowledge of the transcriptome of F . gigantica will also assist in the study of developmental processes and metabolic pathways through functional genomics . Gene perturbation assays are available for S . mansoni and F . hepatica [91]–[95] , suggesting that they could be adapted to F . gigantica for functional genomic explorations . The integration of data from comparative and functional analyses could pave the way for the development of new intervention methods against F . gigantica , built on the identification and of essential genes or gene products linked to key biological or biochemical pathways . For instance , phosphofructokinase ( a glycolytic enzyme ) is a known metabolic “choke-point” in S . mansoni [96] , because trivalent , organic antimony compounds can inhibit worm growth in vitro [97] . The genes encoding phosphofructokinase and other key enzymes in the glycolysis pathway were abundantly transcribed in adult F . gigantica ( Figure S3 ) . Also a thioredoxin-glutathione reductase ( a multifunctional detoxifying enzyme ) might represent a novel drug target in F . gigantica , because a gene encoding a homologue of this enzyme in S . mansoni has been shown to be essential for life , based on functional genomic analyses [98]–[100] . Clearly , future structural and functional explorations of molecules ( including kinases , proteases and their inhibitors , neuropeptides and selected structural proteins ) , which are recognized to be conserved among fasciolids and schistosomes and/or predicted to be essential and druggable [34] , [101]–[104] , should assist in the design and development of entirely new classes of potent trematocidal compounds . | Fasciola gigantica ( Digenea ) is a socioeconomically important liver fluke of humans and other mammals . It is the predominant cause of fascioliasis in the tropics and has a serious impact on the lives of tens of millions of people and other animals; yet , very little is known about this parasite and its relationship with its hosts at the molecular level . Here , advanced sequencing and bioinformatic technologies were employed to explore the genes transcribed in the adult stage of F . gigantica . From >20 million raw reads , >30 , 000 contiguous sequences were assembled . Relative levels of transcription were estimated; and molecules were characterized based on homology , gene ontology , and/or pathway mapping . Comparisons of the transcriptome of F . gigantica with those of other trematodes , including F . hepatica , showed similarities in transcription for molecules predicted to play roles in parasite-host interactions . The findings of the present study provide a foundation for a wide range of fundamental molecular studies of this neglected parasite , as well as research focused on developing new methods for the treatment , diagnosis , and control of fascioliasis . | [
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"medicine",
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] | 2011 | A Portrait of the Transcriptome of the Neglected Trematode, Fasciola gigantica—Biological and Biotechnological Implications |
The contribution of rare coding sequence variants to genetic susceptibility in complex disorders is an important but unresolved question . Most studies thus far have investigated a limited number of genes from regions which contain common disease associated variants . Here we investigate this in inflammatory bowel disease by sequencing the exons and proximal promoters of 531 genes selected from both genome-wide association studies and pathway analysis in pooled DNA panels from 474 cases of Crohn’s disease and 480 controls . 80 variants with evidence of association in the sequencing experiment or with potential functional significance were selected for follow up genotyping in 6 , 507 IBD cases and 3 , 064 population controls . The top 5 disease associated variants were genotyped in an extension panel of 3 , 662 IBD cases and 3 , 639 controls , and tested for association in a combined analysis of 10 , 147 IBD cases and 7 , 008 controls . A rare coding variant p . G454C in the BTNL2 gene within the major histocompatibility complex was significantly associated with increased risk for IBD ( p = 9 . 65x10−10 , OR = 2 . 3[95% CI = 1 . 75–3 . 04] ) , but was independent of the known common associated CD and UC variants at this locus . Rare ( <1% ) and low frequency ( 1–5% ) variants in 3 additional genes showed suggestive association ( p<0 . 005 ) with either an increased risk ( ARIH2 c . 338-6C>T ) or decreased risk ( IL12B p . V298F , and NICN p . H191R ) of IBD . These results provide additional insights into the involvement of the inhibition of T cell activation in the development of both sub-phenotypes of inflammatory bowel disease . We suggest that although rare coding variants may make a modest overall contribution to complex disease susceptibility , they can inform our understanding of the molecular pathways that contribute to pathogenesis .
The inflammatory bowel diseases ( IBD ) , Crohn’s disease ( CD ) and ulcerative colitis ( UC ) are chronic inflammatory disorders of the gastrointestinal tract that can cause diarrhoea , abdominal pain , bleeding and weight loss . Collectively they affect approximately 827 per 100 , 000 individuals in European populations and their incidence is rising [1] . CD may affect any part of the gut with discontinuous penetrating lesions , whereas in UC the disease is limited to the colon and rectum and the lesions are continuous but superficial [2] . Both diseases are multi-factorial , with a complex aetiology that involves a combination of an underlying genetic predisposition and environmental triggers . A variety of factors have been proposed to contribute to the pathogenesis including changes within the intestinal microbiota , a defective mucosal barrier , and / or dysregulation of the immune response [3] . A meta-analysis of genome-wide association studies ( GWAS ) in CD and UC by the International IBD Genetics Consortium ( IIBDGC ) , followed by extensive confirmation of association signals in more than 75 , 000 individuals has increased the number of IBD-associated loci to 163 [4] . The majority of these loci are associated with both CD and UC , which suggests that there is extensive overlap in the biological mechanisms involved in their pathogenesis . However , although our understanding of the aetiology of IBD has been substantially advanced by GWAS-based approaches , only a modest proportion of total disease variance can be explained by current genetic findings ( <15% ) [4] . It has been proposed that rare coding sequence variants may make a substantial contribution to disease variance , and confer disease risks large enough to warrant use in preventative screening [5] . Such variants would not be detectable by a conventional GWAS approach because they are not well tagged by the common SNPs on which GWAS panels are based [6] . New high throughput DNA sequencing technologies have made it feasible to investigate the contribution of rare variants to complex disease . In CD , it has long been known that low frequency coding variants in NOD2 make a substantial contribution to disease risk [7–9] , and more recent high-throughput sequencing strategies have discovered several independent IBD associated rare variants in NOD2 and other genes from GWAS loci including IL23R , CARD9 , IL18RAP , CUL2 , C1orf106 , PTPN22 , RNF186 and MUC19 [10–12] . However , a recent large-scale sequencing study of the coding regions of 25 autoimmune candidate genes in more than 40 , 000 individuals yielded little evidence that rare variants drive the associations observed at susceptibility loci for common immune disorders , including CD [13] . Thus the exact contribution of rare coding variants to IBD and other immune disorders remains unknown . Here we describe a targeted high throughput sequencing approach in pooled DNA samples from 474 CD patients and 480 population controls to screen all exons , splice sites , and proximal promoter regions in 531 positional and functional candidate genes . We sequenced CD patients with early-onset disease and/or strong family history to enrich for functional causal variants with stronger effects , and we looked beyond common loci using functionally-derived bioinformatics data such as pathway and protein network analysis to identify additional candidate genes involved in key processes such as the immune-response and autophagy . Potential functional variants and those with evidence of association with CD underwent validation genotyping in a follow up study including 6507 IBD cases and 3064 controls with replication of the top hits in an additional 3662 IBD cases and 3639 controls giving a total of over 10 , 000 IBD cases and over 7 , 000 controls for the final combined analysis . We discovered significant novel association of a rare coding variant in BTNL2 and suggestive associations of additional variants in potentially novel IBD genes .
An overview of our strategy for the discovery of rare variants associated with CD is shown in Fig . 1 . We selected 531 candidate genes for sequencing in phase I based on 5 selection criteria ( Table 1 and described in Materials and Methods ) . A total of 6 , 249 exons , together with associated splice sites and proximal promoter regions , were sequenced in 474 CD cases and 480 population controls . Samples were sequenced in case-only or control-only pools of 12 , 18 or 24 individuals using the Illumina Genome Analyzer II platform . An average of 98 million sequence reads were generated per pool , of which 87% could be aligned to the reference genome and 64% passed subsequent quality control steps ( Materials and Methods ) . Of these , an average of 25 . 7 million reads mapped to the targeted genomic regions , which corresponded to a capture efficiency of 40 . 5% . We observed a mean read depth of >1000x per pool across the 1 . 57 million bases captured . Taking into consideration the number of individuals per pool , on average 90% of all bases had coverage greater than 4x per haploid genome ( S1 Fig . ) . In order to reduce false positives calls due to sequencing errors , we applied a stringent filtering procedure ( Materials and Methods ) , after which the number of variants was approximately constant across all pools for all types of variants ( S2 Fig . ) . Next , variant allele frequencies in each pool were estimated from base-call counts . We assessed the accuracy of this approach by comparing these estimates to minor allele frequencies ( MAFs ) derived from genotyping data generated by the Wellcome Trust Case Control Consortium ( WTCCC ) ; genotypes were available for 153 SNPs located in the captured genomic regions in 66 . 5% ( 388 controls , 246 cases ) of the individuals sequenced in this study [14] . We observed a very strong correlation ( Spearman Rank Correlation r = 0 . 977 ) between MAFs for the WTCCC genotypes and the pooled sequencing data ( Fig . 2 ) . After filtering , 3 , 749 single nucleotide variants ( SNVs , here used to refer to any single nucleotide variation regardless of minor allele frequency ) were retained , of which over half were low frequency ( <5% , S1 Table ) . Just over half of the SNVs were located in exons ( 51 . 1%; 1914 SNVs ) , with the remainder located in introns , untranslated regions ( UTRs ) , putative splice sites and intergenic regions . We considered 106 of the SNVs ( 3% ) to be novel because they were not present in dbSNP138 ( http://www . ncbi . nlm . nih . gov/SNP/ ) . Analysis of all SNVs yielded a transition/transversion ratio ( Ti/Tv ) of 2 . 41 , which is expected given the bias toward coding sequences in our target regions and is in agreement with previous studies [11] . In addition to SNVs we identified 183 deletions and 117 insertions . Only 14 of these insertion/deletions ( indels ) were located in an exon ( S1 Table ) . A high rate of true positives in our sequencing data was corroborated by the presence of 97% of our variants in dbSNP138 , and the strong correlation between MAFs for the pooled sequencing data and the WTCCC genotype data . Regarding sensitivity of variant detection , the regions captured in our sequencing contain 1 , 599 variants with a MAF >5% in the phase I release of the 1000 Genomes project , 1 , 291 of which ( 80 . 7% ) were detected in our pooled sequencing data . Our strategy relied on the necessity of sequencing individuals in case-only or control-only DNA pools which could potentially inflate any biases that would arise due to sequencing batch effects . We therefore used principal component analysis to control for this and identify any outlier pools . Examination of PC axes 1 and 2 revealed pools 7 and 8 to be outliers . Both were case pools , although each represented a single lane of flow-cell data from two different runs of the GAII sequencer . Once these pools were removed the data showed reasonable separation of points , but there was a clear tendency for case and control pools to be separated along PC axis 1 ( S3 Fig . ) , which led to an overall genomic inflation of 1 . 3 ( Fig . 3 ) . The extent of the systematic bias in the data meant that PC axes could not be used as covariates in a logistic regression to correct for it , as previously noted [15] , nor could we apply methods designed to correct for overdispersion but not bias [16] . We therefore applied a genomic control method for downstream association analysis plus additional QC measure for removal of SNVs with strong over dispersion among pools ( Materials and Methods ) . We note that it is possible that the high systematic bias reflects genuine causal influences given the candidature of all the genes sequenced , but equally we cannot exclude the possibility of experimental sources of bias . Variant level association with case-control status of pools was performed using logistic regression on 3 , 442 SNVs after exclusion of 307 SNVs that were too rare ( had zero count in case or controls ) , or only had allele counts in excluded pools ( Materials and Methods ) ( Fig . 3 ) . Encouragingly , several known common and low frequency CD susceptibility variants were detected including variants in ATG16L1 , IRGM , IL23R , CARD9 and NOD2 , and rare variants in IL23R and NOD2 [7 , 8 , 10 , 11 , 17] , all of which showed the expected CD odds ratios and allele frequencies in both cases and controls ( S2 Table ) . We noticed that 1 , 099 of 3 , 442 SNVs tested for association in our sequencing data were either included in the IBD Immunochip project directly ( 803 ) or by a suitable tagging SNP ( r2≥0 . 8 , n = 296 ) [4] . The IBD Immunochip dataset was therefore considered as an independent replication study for these 1099 variants . We found that 43 of the 141 variants ( 30 . 5% ) that were at least nominally associated in our sequencing data ( p<0 . 05 ) were also associated in the CD Immunochip data ( p<5x10−8 ) , resulting in a significant correlation between the two datasets ( r = 0 . 446 , p = 4 . 46x10−32 ) . The majority of variants identified by our study were rare , resulting in modest statistical power for the SNV-wise tests of association . We therefore applied gene-level association tests to investigate whether the burden of predicted functional variants non-synonymous and stop-gain variants ) was different in cases compared to controls ( Materials and Methods ) . In our discovery sequencing we identified 341 genes containing one or more functional variants . Thus the gene-burden test provided >90% power to detect a gene-level association where the cumulative MAF is 5% and the cumulative risk ( OR ) is 2 . 5 at an alpha level of 0 . 00015 ( allowing for Bonferroni correction based on 341 genes/tests ) . We identified significant gene-level associations for BTNL2 ( no . of variants = 18 , p = 8 . 15x10−5 ) and NOD2 ( no . of variants = 10 , p = 9 . 03x10−6 ) ( S3 Table ) . Since both genes contained substantially more functional variants than other genes that were tested we controlled for LD by permutation analysis ( Materials and Methods ) , which resulted in loss of significance for BTNL2 ( p = 0 . 022 ) , whilst NOD2 remained significant ( p<0 . 001 ) . Repeating the analysis to include all intragenic variants ( functional and non-functional ) gave a similar outcome , although neither gene survived permutation testing ( p>0 . 001 ) . In Phase II we selected 85 variants for validation of disease association by Sequenom ( 84 SNVs ) or Taqman ( 1 SNV ) genotyping in 6 , 335 IBD cases from the UK IBD Genetics Consortium ( 3 , 715 CD and 2 , 619 UC ) and 2 , 974 controls ( Materials and Methods ) . UC cases were included in the validation because of the extensive overlap in known associated loci for these two related phenotypes [4] . SNVs were selected based on at least nominal evidence of association in the pooled sequencing experiment ( p < 0 . 05 ) , and we prioritised those predicted to be functionally relevant ( S1 Text ) . SNVs already genotyped as part of the IBD Immunochip experiment [4] were excluded . Post-genotyping quality control revealed that two SNVs failed to genotype , two were non-polymorphic and one was not in Hardy Weinberg equilibrium ( p < 1x10−6 in controls ) leaving a total of 80 SNVs ( S4 Table ) . The genotyping call rate for all remaining SNVs was >90% . To allow validation of our variant calling analysis pipeline we genotyped an additional subset of 368 individuals previously included in our sequencing experiment and were able to show strong correlation between predicted and actual allele frequencies for all 80 SNVs ( r = 0 . 94 , p = 2 . 42x10−38 ) and low frequency SNVs ( MAF<5% , r = 0 . 86 , p = 1 . 69x10−24 ) . In addition , allele frequencies derived from the pooled sequencing experiment were compared to those derived from all individuals in the phase II genotype data and revealed a highly significant correlation ( r = 0 . 971 , p < 6 . 58x10−48 ) , further supporting the validity of the pooled sequencing approach . We followed up 3 insertion deletion polymorphisms by Taqman genotyping in 2 , 532 IBD cases and 3 , 545 controls ( rs58682836/COBL frameshift delTTC , rs71297581/TYK2 upstream insC , and rs3833864/PIK3C upstream insC ) . The indel rs71297581 failed genotyping quality control , producing poor genotype clusters , and neither rs58682836 nor rs3833864 were associated with IBD ( p > 0 . 5 ) . There was some evidence of association ( p < 0 . 05 ) for 16 SNVs across 12 genes , CHTOP , ARIH2 , NICN1 , PLSCR1 , IL12B , BTNL2 , QRSL1 , CALML5 , GLT1D1 , RTEL1 , ATG4B and TBX21 ( S5 Table ) . These were associated with either CD ( 11 variants ) , UC ( 5 variants ) or IBD ( 12 variants ) , with 4 of these variants located in BTNL2 . BTNL2 and IL12B map to established UC and IBD risk loci and have previously been implicated in UC and IBD respectively ( 6p21/HLA class II/UC and 5q31/IBD respectively ) , whilst ARIH2 and NICN1are within the same previously described IBD locus ( 3p21 . 3/IBD ) but the genes themselves have not been implicated . Association of the other 10 genes and their respective variants with IBD has not been reported previously . Since BTNL2 is within the MHC region and close to the common IBD associated locus in the HLA class 2 region we investigated the extent of LD across the 4 variants and their independence from the known risk locus using haplotype and conditional analysis within a set of cases and controls previously genotyped in both the Immunochip study and our follow up genotyping study ( Materials and Methods ) . The analysis showed that the rare BTNL2 variants p . G454C and p . D336N ( rs28362675 and rs41441651 ) were in almost complete LD with each other ( r2 = 0 . 99 ) and remained associated with IBD even when the effect at the common SNPs was accounted for ( p < 0 . 049 ) , as did BTNL2 c . -118G>T ( rs28362684 , p = 0 . 039 ) but not the missense variant ( p . S334L ) . Regarding association of the 80 variants with IBD , only the two highly correlated variants in BTNL2 ( p . 454C and p . D336N ) surpassed the Bonferroni threshold for multiple testing ( p < 0 . 0006 for 79 independent SNVs tested ) . However there was significant enrichment for association signals among the 79 variants , with nearly 3 times the number of significant results than would be expected by chance , with 14% of p-values for association with IBD ( i . e . 11/79 ) being less than 0 . 05 ( p = 0 . 00189 ) . Recognising the relatively low power of the validation panel to detect significant association of rare variants with disease , we next carried out extended genotyping ( Phase III ) of the 5 top SNVs that had a p < 0 . 01 ( and in the case of BTNL2 were independent of each other and the known common risk variants ) in an additional panel of 3 , 662 IBD cases and 3 , 639 controls ( Materials and Methods ) , and then performed a combined case-control analysis of all 10 , 147 IBD cases and 7 , 008 controls that were either sequenced or genotyped ( Table 2 ) . We confirmed a genome-wide significant association with BTNL2 p . G454C and increased risk of IBD at ( p = 9 . 65x10−10 , OR = 2 . 3 [95%CI = 1 . 75–3 . 04] ) . We detected association for 3 other variants of the 5 tested in phase III ( p < 0 . 005 ) . Notably , in the combined analysis the direction of the effect for each of the 5 SNPs is consistent with the effect in the validation panel ( p < 0 . 031 ) . However , the 3 additional associations do not meet correction for 79 independent tests ( P<0 . 00063 ) and are therefore suggestive . They include two low frequency missense variants IL12B p . V298F and NICN1 p . H191R associated with a reduced risk for IBD and one noncoding variant ARIH2 c . 338-6C>T which was associated with an increased risk ( Table 2 ) . Two of the 3 missense variants associated with IBD ( IL12B p . V298F and BTNL2 p . G454C ) were predicted to be damaging or non-tolerated by Polyphen2 [18] and/or SIFT ( sorts intolerant from tolerant ) or Provean [19] . IL1B encodes the p40 subunit common to both the interleukin-12 and interleukin-23 heterodimeric cytokines . The p . V298F variant is not in LD with the common risk variant at this locus ( r2 = 0 . 001 , D’ = 0 . 079 ) , and is predicted to disrupt the structure of the p40 protein by the mCSM structure prediction tool [20] , with a predicted stability change ΔΔG of −0 . 917 . We also used the available structure of the IL12B ( p40 ) and IL23A ( p19 ) proteins to model the effect of the V298F mutation in IL12B ( S4 Fig . ) . This indicated an altered conformational state of a region of p40 which is important for binding to its partner proteins IL23A ( p19 ) and IL12A ( p35 ) [21] . BTNL2 is located on chromosome 6p21 . 3 , which contains two common and independent risk loci for IBD . The closest ( approximately 200Kb proximal to BTNL2 ) is within the HLA class II region and is associated with UC ( rs477515 , p = 5x10−133 ) . The other locus is much further away ( approximately 1 . 1Mb distal of BTNL2 ) within the HLA-class I region , and associated with CD ( rs9264942 , p = 5x10−28 ) [4] . We observed that BTNL2 p . G454C was associated very strongly with UC ( p = 3 . 5x10−12 , Table 2 ) and also associated with CD but to a lesser extent ( p = 3 . 6x10−5 , Table 2 ) . In view of the extended LD in this region , it is possible that these associations could be due to LD with the known common risk variants in the HLA class I or class II regions . We investigated this by further conditional logistic regression analysis using 1 , 638 IBD cases and 1 , 243 controls genotyped in both the Immunochip study and both our genotyping studies . We confirmed that BTNL2 p . G454C was not in LD with either of the two common IBD risk variants ( r2 < 0 . 001 , D’ < 0 . 7 ) . Conditional analysis showed that BTNL2 p . G454C remained significantly associated with IBD when the effect at the common UC associated SNP ( rs477515 ) was accounted for ( p = 0 . 0045 , S6 Table ) , or the common CD associated SNP ( rs9264942 ) was accounted for ( p = 4 . 83x10−5 , S6 Table ) . Haplotype analysis showed that the risk “A” allele for the rare variant occurred on haplotypes containing either the non-risk or the risk allele for both of the common variants , further suggesting their independence . Given the strength of the effect of p . G454C in UC individuals in particular ( Table 2 ) we carried out specific haplotype analysis using this and the common UC GWAS SNP in the class II HLA region and showed that haplotype A-A containing the risk allele at the rare variant ( p . G454C ) and the non-risk “A” allele at the common UC GWAS SNP ( rs477515 ) respectively , although very rare , was increased in frequency in cases , ( 0 . 2% ) compared to controls ( 0 . 07% ) ( S7 Table ) , and the haplotype G-A containing the risk allele at both the common and the rare variant had a much higher risk for disease ( OR = 6 . 51 [95%CI = 1 . 87–22 . 72] ) than the haplotype G-C that only had the risk allele at the common SNP and lacked the rare risk allele ( OR = 1 . 38 [95%CI = 1 . 20–1 . 57] ) .
In this study we investigated the contribution of rare variants to susceptibility to inflammatory bowel disease in a large set of candidate genes . Use of targeted next generation sequencing in combination with a DNA pooling strategy allowed us to screen over 500 genes for variants in more than 900 individuals , which is ten-fold more than were investigated in previous studies of IBD [10–12] . The results demonstrate that this is a cost-effective strategy for identifying low frequency variants that may be associated with disease . We were able to validate our approach by accurate estimation of the minor allele frequencies of 153 SNPs previously genotyped in individual case and control samples by the Affymetrix 500K SNP array , and by successfully reproducing the effect sizes ( odds ratios ) and allele frequencies of multiple common and low-frequency variants previously associated with IBD . We also demonstrated highly significant overlap of association for 1 , 099 SNPs that were common to our study and the recent GWAS/Immunochip meta-analysis for IBD [4] , and showed a strong correlation between the allele frequencies and odds ratios of 80 SNVs that were genotyped by both pooled DNA sequencing and genotyping in our follow up study . Strong correlations between allele frequency estimates from pooled sequencing and genotyping have also been reported in previous studies of Crohn’s disease [10 , 11] , although read counts tended to underestimate actual frequencies for rare variants in one study [10] . However this approach could prove useful when supported by stringent quality control and validation measures . Sequencing of coding and potential regulatory regions of 531 genes in a discovery set of 954 individuals , followed by genotyping in 17 , 131 individuals has allowed us to identify a novel disease associated genetic variant within a gene that maps to a region previously associated with IBD , and suggestive associations of other variants in a known IBD susceptibility gene and in other genes not previously implicated in IBD . The association of the rare variant p . G454C in BTNL2 reached genome-wide significance , and was independent of the known common risk variants for IBD in the HLA region in both a conditional and haplotype analysis . However , this is a complex region of the genome with extensive allelic variation and linkage disequilibrium , and additional as yet unknown IBD risk variants at this locus may exist that are independent of the two main HLA signals previously described but correlated with our rare variant . The glycine residue is highly conserved across all mammals and the cysteine substitution is predicted to be damaging by SIFT ( score = 0 . 01 ) and probably damaging by PolyPhen2 ( score = 0 . 997 ) . This variant was in almost complete LD with another missense variant D336N which is not predicted to be damaging . BTNL2 codes for the butyrophilin like protein 2 , which is a member of butyrophilin family that shares sequence homology with the B7 co-stimulatory molecules . The butyrophilins are implicated in T cell inhibition and the modulation of epithelial cell-T cell interactions [22] . BTNL2 negatively regulates T-cell activation independently of CD28 and CTLA-4 , is predominantly expressed in gastrointestinal tissues including human terminal ileum ( www . gtexportal . org ) , and is overexpressed in mouse models of colitis [23] . Recently it has been shown that BTNL2 promotes the expression of Foxp3 , which is a transcription factor required for regulatory T cell development and function [24] . In view of its important role in immune modulation and homeostasis and an expression pattern restricted to intestinal epithelial and immune cells , mutations in BTNL2 may affect its ability to regulate T cell activation in response to mucosal inflammation . Common variants at the BTNL2 locus , have been previously shown to be associated with ulcerative colitis whilst being independent of the nearby known HLA susceptibility alleles [25] . Additional coding and loss-of-function variants in BTNL2 , have been associated with susceptibility to other immune related disorders including adult-onset sarcoidosis [26 , 27] and rheumatoid arthritis [28] . Although no variants other than the two rare and highly correlated missense mutations in BTNL2 surpassed the Bonferroni threshold for testing the 79 independent variants for association with IBD , there was significant enrichment for association signals among these 79 variants , and our extension study and combined analysis showed that the direction of the effect for all 5 SNVs tested was consistent with the initial finding . This suggests that there are likely to be additional true positives within phase II and III of our study that have not met the stringent Bonferroni threshold . This emphasises the difficulty in obtaining statistically robust evidence for association of rare variants even with a combined sample of 17 , 000 tested here and a relatively large effect size such as , for example , ARIH2 c . 338-6C>T , OR = 2 . 39 . The association of common variants at the IL12B locus with both CD and UC is well established [4] , although no obvious causal variant has yet been found . The association of the low frequency IL12B variant V298F with IBD which was detected in our sequencing experiment was retained in the combined analysis of 10 , 146 IBD and 7 , 008 controls , ( p = 0 . 00183 , OR = 0 . 82 [95%CI = 0 . 72–0 . 93] ) . IL12B encodes the IL12p40 subunit common to both IL12 and IL23 , both of which are produced by activated dendritic cells and macrophages and lead to activation of distinct subsets of T-cells . We found that the minor allele of V298F is associated with a reduced risk of both CD and UC and is independent of the common risk variants at this locus . The variant is predicted to have a damaging or destabilizing effect on protein function or structure , and modeling of the effect of the mutation on the structure of the p40 subunit predicted an altered conformational state which could affect binding to its partner proteins . Thus the rare ( Phe ) allele may reduce the risk of IBD by attenuating the activation of T cell populations by IL12 and IL23 . We found two additional suggestive associations in ARIH2 and NICN1 . Ariadne homolog 2 ( ARIH2 ) is a member of an unusual family of E3 ubiquitin-protein ligases . Loss of ARIH2 has been shown to cause degradation of IκBβ in dendritic cells leading to dysregulated activation of NFκB . The SNP rs200140527 is associated with IBD , and is 6bp upstream of the splice acceptor site for exon 9 of ARIH2 , although the C>T change is not predicted to affect the strength of the splice site [29] . Nicolin 1 ( NICN1 ) is a nuclear protein and part of the neuronal tubulin polyglutamate complex [30] although very little else is known about its function . It is expressed in multiple tissues including the human terminal ileum and transverse colon ( www . gtexportal . org ) . The nonsynonymous SNP p . H191R is associated with a protective effect for CD and UC in this study . NICN1 is on chromosome 3 at 49 . 46Mb , i . e . approximately 460kb proximal to ARIH2 on 3p21 and within a 2Mb locus previously associated with IBD that contains multiple independent genome-wide significant SNPs [4] . Previous sequencing studies have reported that rare coding variants make a limited contribution to the genetics of immune disorders and hypertriglyceridaemia , explaining 1–2% of their genetic variance [10–13 , 31 , 32] . However , these studies have generally sequenced a limited number of genes located in regions derived from the association of common variants with the disease . Our study highlights the challenges in identifying rare variant association for a polygenic complex trait like IBD . In sequencing more than 500 genes from both GWAS and pathway or network analysis combined with follow up genotyping in over 17 , 000 individuals we found genome-wide significant association of a rare variant in one gene and suggestive association of 3 SNVs in 3 other genes . However , our follow up studies were powered to detect associations of rare variants with relatively strong effects . For example , our phase II validation panel had 57% power to detect association of a low frequency variant with an allele frequency of 2 . 5% and OR = 1 . 3 at alpha level of 0 . 01 ( to flag candidate associations ) , and 75% power to detect a rare variant with an allele frequency of 1% and OR of 1 . 6 . In the combined analysis of 10 , 147 cases and 7 , 008 controls , we had 69% power to confirm association of a variant with a MAF of 0 . 025 and OR of 1 . 3 at alpha level of 0 . 0006 ( correction for 79 SNV tests ) , but 89% power to confirm association for a variant with MAF 0 . 01 and an OR of 1 . 6 . It is therefore likely that some rare variants with effect sizes of less than 1 . 6 remain undiscovered in these genes . It is also possible that a proportion of variants that are recognised as being suggestive of association in this study may turn out to be false positives , so further replication and subsequent functional studies will be required to prove causality . If our 4 newly discovered associations were added to the 26 low frequency SNVs identified in 13 other genes from previously published studies of IBD [7 , 10–12 , 17 , 33] this would total 30 IBD associations with low frequency SNVs in 17 of 548 sequenced genes . However , these screens have predominantly interrogated the coding regions of less than 3% of all known genes . Our study has targeted <25% ( 198 ) of all the known genes that map to the 163 IBD associated regions identified by the most recent mapping efforts of the International IBD Consortium [4] . A comprehensive evaluation of the true extent of the contribution of rare coding variants to IBD will have to await whole exome sequencing of very large numbers of case and controls [34] , and whole genome sequencing to capture rare regulatory variants in non-coding regions . The value of studies of rare variants in IBD lies not only in the discovery of additional risk variants which may aid future genetic profiling in at risk populations , but also in their potential to discover further genes and pathways involved in IBD . Our study provides additional evidence of the importance of the regulation of T cell activation and mucosal T cell responses involving BTNL2 , and the potential role of proteosomal degradation in the pathogenesis of IBD .
A total of 531 candidate genes were selected based on: ( a ) Crohn’s disease GWAS hits; ( b ) GWAS hits from other immune disorders; ( c ) Pathway analysis based on Gene-set enrichment analysis; ( d ) IBD related literature; and ( e ) Network Analysis ( Table 1 ) . Details of these selection criteria are provided in S1 Text . Exon coordinates from RefSeq [35] and Ensembl [36] were combined to include all potentially coding regions . Proximal promoters were included by selection of genomic regions from 200 bp upstream to 50 bp downstream of the transcription start site . Putative splice sites were included by addition of five bp each side of coding exons . In total 6 , 290 genomic intervals were successfully synthesized for the Agilent SureSelect DNA Capture Array . Capture probes ( 120 bp; 60bp tiling ) corresponding to 1 , 569 , 003 bp of target sequence . Crohn’s disease patients for the sequencing experiment ( n = 474 ) were recruited from specialist IBD clinics in London and Newcastle [37] after informed consent and ethical review ( REC 05/Q0502/127 ) . Population controls for sequencing ( n = 480 ) were obtained from the 1958 British Birth Cohort [38] . All individuals were of European ancestry . The chances of detecting rare variants with large effects in the sequencing stage was increased by selection of Crohn’s disease ( CD ) patients with an early age of onset <20 years ( n = 204 ) , or with a family history of IBD ( n = 174 ) or both early onset and family history ( n = 96 ) . Additionally , 178 ( 86% ) of those individuals with a family history also had at least one affected first degree relative . DNA samples were quantified in triplicate ( Qubit , Life technologies ) prior to pooling in equimolar amounts to a total of 3 μg of DNA . Pools of 24 CD case DNA samples or 24 control DNA samples were made with a total of 44 pools , 474 cases and 480 controls ( including 9 pilot/test pools of 12 and one test pool of 18 CD cases; S1 Text ) and libraries were prepared following standard protocols . The validation panel for phase II , consisted of 3 , 799 unrelated CD and 2 , 708 unrelated UC , patients recruited by the UK IBD Genetics Consortium [4] and the replication panel consisted of an additional 1644 CD cases and 2018 UC cases recruited from London and Newcastle ( as described above ) . Additional population controls ( n[validation] = 3 , 064; n[replication] = 3622 ) were from the 1958 British Birth cohort and the National Blood Donor Service [14] . All cases and controls analysed in the replication phase III were independent and unrelated to those sequenced in the phase I and phase II discovery cohort . Sequencing reads were aligned to the hg18 ( NCBI 36 ) reference genome using Novoalign ( version 2 . 07 . 09 , Novocraft Technologies ) . We performed quality control using SAM tools [39] and removed PCR duplicates using Picard tools [40] . SNVs and indels were called using SAM-tools and filtered based on the following criteria: i ) Phred base quality score ≥ 20 , ii ) any allele to have at least two base calls on each strand , iii ) minimum base call count for any allele to be the equivalent to at least one expected chromosome count ( N allele-specific base calls / N total base calls * 2 * N individuals in pool ) , with at least 0 . 3 expected chromosome counts attributable to each strand , iiii ) criteria to be met in at least three different pools from at least two different batches . These parameters were optimized to reduce biases across all 44 pools ( S2 Fig . ) . After filtering , base call counts were normalised to allele frequencies for each pool based on the total number of base calls that passed the filtering criteria . Variants were annotated using ANNOVAR [41] . Further details of read alignment , quality control and variant calling are provided in S1 Text . After excluding variants previously implicated with IBD and variants analysed in the IBD Immunochip project [4] , we selected 96 SNVs for follow up in phase II using the Sequenom iplex genotyping platform . We chose variants that a ) surpassed multiple testing in the pooled sequencing based case-control comparison ( p < 10−5 ) , b ) were modestly significant in the pooled sequencing based case-control comparison ( p < 0 . 05 ) and had a low allele frequency ( MAF < 5% ) , c ) had functional consequence ( within 20bp of a splice acceptor or donor site or non-synonymous variant ) , and were novel or low frequency ( < 1% ) , d ) were absent from one group ( either controls or cases ) and had a functional consequence ( within 20bp of a splice acceptor or donor site or non-synonymous variant ) . In total 84 SNVs passed design and were genotyped via Sequenom iplex in 2 , 974 controls , 3 , 715 Crohn’s disease and 2 , 620 ulcerative colitis cases . Individuals for which more than 20% of SNVs could not be called were excluded from further analysis . One additional SNV ( rs138274580/ATG4B ) and 3 indels ( rs58682836/COBL frameshift delTTC , rs71297581/TYK2 upstream insC , rs3833864/PIK3C upstream insC ) , that failed iplex design , were genotyped using the TaqMan chemistry ( Life Technologies ) ; SNP since they were ranked as high priority in all categories of our variant selection criteria ( S1 Text ) . Finally we selected 5 SNVs with p<0 . 01 in any one phenotype ( CD , UC or IBD ) and , in the case of multiple SNVs in BTNL2 , were indicated by LD and conditional regression analysis to be independent of each other and the known common risk variants , for replication genotyping via KASPTM chemistry at LGC Genomics ( Hoddesdon , Herts , UK ) in 3666 additional IBD cases and 3622 additional controls . In order to validate previous phases we also included a further 858 individuals who had been sequenced and/or undergone sequenom iplex genotyping . To investigate LD and independence of the BTNL2 variants from the known IBD GWAS hits within the MHC we used Immunochip data supplied by the UKIBD Genetics Consortium that was available for 1 , 638 of our genotyped IBD cases and 1 , 243 of genotyped controls . Allele frequencies for each SNV in each pool were standardized and subjected to principal components analysis ( PCA ) to identify outlier pools and investigate systematic bias between cases and controls . PCA revealed considerable bias , such that cases and control pools could be largely separated by PC axis 1 alone . Various statistical methods for dealing with pooled SNV data have been proposed [16] . In light of the PCA results , we adopted a genomic control approach because it can correct for both overdispersion ( additional variance that is distributed equally among pools ) and bias ( a consistent tendency for allele frequencies in case pools to be different from controls pools ) . For each SNV , a logistic regression across pools was performed using expected chromosome counts for the two most common alleles to form the dependent variable , and case-control status as the independent variable . The reversal of the conventional functional from allows for different pool sizes to be readily accounted for , and also appropriately reflects the study design ( pool status is fixed by the experimenter , not pool allele frequencies ) . Genomic control was performed by dividing the chi-square statistic for association by the median chi-square statistic across all SNVs . We used evidence for especially strong SNV-specific overdispersion among pools ( via a test of residual deviance from the logistic regression for association , p < 1 . 5x10−5 ) as an additional QC measure for removal of suspect SNVs . Burden tests for significant association of a group of SNVs ( e . g . all SNVs in a gene ) were also performed taking in account both the pooled design and the presence of case-control bias . For a given set of n SNVs , genomic-control-corrected z2 values were summed and tested against the chi-squared distribution with n degrees of freedom . Significant sum-statistics were further tested via permutation of case-control status among pools , to correct for false positives that could be caused from linkage disequilibrium distributing the same signal among multiple SNVs . Note that our burden test allows SNV groups containing a mixture of both risk and protective variants to be tested appropriately . Statistical analyses of pooled sequencing data was performed using R project for statistical computing ( http://www . r-project . org/ ) . Cases-control analysis of validation and replication genotyping data was performed with PLINK version 1 . 07 [42] using Armitage Trend Test . Additional conditional regression , linkage disequilibrium and haplotype analysis at known common IBD loci was performed using UNPHASED v3 . 0 . 12 [43] . The effect of the mutation Val298Phe on IL12B ( p40 ) protein stability was examined using the tool mCSM , which predicts the effect of mutations in proteins using graph-based signatures [20] . The structure of the complex of human IL12B ( p40 ) and IL23A ( p19 ) is available in the RCSB Protein Data Bank [44] ( PDB entry 4GRW ) , and was used as the template to model the structure of mutant IL12BV298F . The modelling procedure first generated the sequence alignment between the target ( IL12BV298F ) and the template structure ( 4GRW chain B ) by running the tool T-Coffee [45] . The aligned sequences were then used as an input to the structure modelling package Modeller 9v8 [46]to generate 200 structures of IL12BV298F . Among these , only the one with the best Discrete Optimized Protein Energy score was selected for inspection of the mutation Val298Phe . The structure representation tool PyMol ( Version 1 . 5 . 0 . 4 , Schrödinger , LLC ) was used for visual inspection and structural analysis . The interaction between IL12BV298F and IL23A was modelled by superimposing the IL12BV298F structure onto the human wild-type IL12B . | Crohn’s disease and ulcerative colitis are two forms of inflammatory bowel disease which cause chronic inflammation of the gastrointestinal tract . Common genetic variants in more than 160 regions of the human genome have been associated with an altered risk of these disorders , but leave much of the estimated genetic contribution to disease risk unexplained . We sought to establish whether rare genetic variants which alter the structure or function of the proteins encoded by genes also contribute to disease susceptibility . We used high throughput DNA sequencing to screen over 500 genes for such variants in nearly 500 patients and controls , and validated interesting variants in about 10 , 000 patients and 7 , 000 controls . We detected association of a limited number of rare variants from coding regions with disease , suggesting that they do not account for a large proportion of genetic susceptibility . However , they highlight the involvement of genes of potential importance in the development of inflammatory bowel disease , including those involved in the activation of immune cells , the regulation of immune response genes , and the degradation of proteins in cells . | [
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] | [] | 2015 | Pooled Sequencing of 531 Genes in Inflammatory Bowel Disease Identifies an Associated Rare Variant in BTNL2 and Implicates Other Immune Related Genes |
Enamel-renal syndrome ( ERS ) is an autosomal recessive disorder characterized by severe enamel hypoplasia , failed tooth eruption , intrapulpal calcifications , enlarged gingiva , and nephrocalcinosis . Recently , mutations in FAM20A were reported to cause amelogenesis imperfecta and gingival fibromatosis syndrome ( AIGFS ) , which closely resembles ERS except for the renal calcifications . We characterized three families with AIGFS and identified , in each case , recessive FAM20A mutations: family 1 ( c . 992G>A; g . 63853G>A; p . Gly331Asp ) , family 2 ( c . 720-2A>G; g . 62232A>G; p . Gln241_Arg271del ) , and family 3 ( c . 406C>T; g . 50213C>T; p . Arg136* and c . 1432C>T; g . 68284C>T; p . Arg478* ) . Significantly , a kidney ultrasound of the family 2 proband revealed nephrocalcinosis , revising the diagnosis from AIGFS to ERS . By characterizing teeth extracted from the family 3 proband , we demonstrated that FAM20A−/− molars lacked true enamel , showed extensive crown and root resorption , hypercementosis , and partial replacement of resorbed mineral with bone or coalesced mineral spheres . Supported by the observation of severe ectopic calcifications in the kidneys of Fam20a null mice , we conclude that FAM20A , which has a kinase homology domain and localizes to the Golgi , is a putative Golgi kinase that plays a significant role in the regulation of biomineralization processes , and that mutations in FAM20A cause both AIGFS and ERS .
Enamel-Renal Syndrome ( ERS; OMIM #204690 ) is a recessive syndrome characterized by severely hypoplastic ( thin ) or aplastic enamel on both the primary and secondary dentitions , pulp stones , and failed or delayed eruption of much of the permanent dentition , particularly the posterior teeth . Coronal dentin is sometimes resorbed and replaced by lamellar bone and there is often hypercementosis on root surfaces . These dental symptoms are associated with nephrocalcinosis , although blood chemistry analyses are typically normal [1]–[3] . Gingival enlargement is sometimes noted [4] , [5] . The initial patient complaint is the lack of enamel and failed eruption of many permanent teeth . Nephrocalcinosis is typically discovered by a renal ultrasound scan ordered because of the known association between this rare pattern of dental defects and renal dysfunction , rather than due to a patient complaint or history of renal problems [4]–[6] . In the original report of ERS one of the two affected individuals died of renal failure [1] . Another report described the results of a series of renal evaluations on an ERS patient that observed minimal renal calcifications at age 5 that became progressively denser in roentgenograms taken at ages 8 , 11 , and 14 years , and then stabilized [2] . Subsequent reports found the kidney calcifications in patients with ERS to be benign [3] , [4] , [7] . The literature describes patterns of recessive tooth defects similar to that observed in enamel renal syndrome ( ERS ) , but without evidence of nephrocalcinosis [8]–[23] . As in ERS , the patient's initial chief complaint relates to the lack of enamel and failure of permanent tooth eruption . Dental radiographs reveal that most if not all of the teeth lack an enamel layer and have extensive pulp calcifications . The unerupted teeth show pericoronal radiolucencies delimited by a sclerotic margin . The teeth are usually smaller than normal , often with misshapened roots [12] . A common observation on radiographs is resorption of the occlusal surface ( sometimes all the way to the pulp ) of unerupted teeth [20] . When the malformed teeth are characterized histologically , they lack dental enamel , but show normal-looking dentin with well-formed dentinal tubules [13] , [14] . The minimal “enamel” has no prismatic structure . On some teeth there is extensive localized root and/or crown resorption with partial replacement of the resorbed dentin by lamellar bone or in some places by globular structures comprised of incompletely coalesced concentric calcifications [12] . The thin roots are often covered by an abnormally thick layer of what appears to be cellular cementum [11] , [19] . Recently , advanced genetic methods involving targeted exome capture , next generation DNA sequencing , and bioinformatics computer analyses implicated FAM20A ( family with sequence similarity 20 , member A ) located on chromosome 17q24 . 2 as the defective gene in a recessive disorder manifesting the same oral features as described above [22] , [24] and designated “Amelogenesis Imperfecta and Gingival Fibromatosis Syndrome” ( AIGFS; OMIM #614253 ) . The association between FAM20A and a syndrome that included severe enamel hypoplasia and gingival hypertrophy was confirmed by mutational analyses in four additional families that identified three homozygous FAM20A mutations ( c . 34_35delCT; c . 813-2A>G; c . 1175_1179delGGCTC ) and compound heterozygous mutations ( c . 590-2A>G with c . 826C>T ) in four families [25] . In none of the families with FAM20A mutations were teeth available for microscopic examination or were renal ultrasounds performed . We have characterized three families with a recessive syndrome caused by FAM20A mutations . All affected individuals in these families had mutations in both FAM20A alleles . Extracted molars were characterized histologically and shown to have hypercementosis and dentin replaced by lamellar bone . All findings were consistent with a diagnosis of AIGFS; however , we were intrigued by the similarity of AIGFS with ERS and inquired further about whether or not our probands had kidney problems .
The proband's parents were born in the Caribbean . Oral photos and a panoramic radiograph were obtained for the proband and DNA was collected from nine family members ( Figure 1 ) . The proband ( III:1 ) , his younger sister ( III:4 ) , and niece ( IV:1 ) were affected . According to the proband , when his adult teeth finally came in they were the same size as his baby teeth and very short . As far as he remembers , his gums have always been large and bumpy . He reported that he was otherwise healthy with average height and weight . Intraoral photographs of the proband showed a mixed dentition , small dental crowns with generally thin enamel , and yellow discoloration . Over-retained primary molars in the mandibular arch and partially erupted maxillary premolars were observed . The proband had a deep anterior overbite , a posterior cross-bite , and a class III molar relationship . The vertical dimension appeared to be reduced . Radiographically , the proband had the full complement of permanent teeth , but eruption of the canines , mandibular premolars and molars was failed or delayed . No radiopaque enamel layer was apparent on any of the teeth . Even unerupted teeth with completed root formation lacked enamel . Pericoronal radiolucencies outlined by sclerotic borders were observed around unerupted teeth , symptomatic of a slow expansion of the dental follicle covering the crown . In some cases the dentin occlusal surface appeared concave and close to the pulp chamber , suggesting pre-eruptive crown resorption . Most teeth showed intrapulpal calcifications . The gingiva appeared to be hyperplastic . Family 1 was one of the original 24 AI families that we recruited for genetic studies [26] . No disease-causing mutations in the proband's DNA were identified during mutational analyses of the proven AI candidate genes ( AMELX , ENAM , FAM83H , WDR72 , KLK4 , and MMP20 ) and AMBN [27] . FAM20A analyses however identified a G to A transition resulting in a missense mutation in exon 7 ( c992G>A; g . 63853G>A; p . Gly331Asp ) that is homozygous in the proband ( III:1 ) , his sister ( III:4 ) and niece ( IV:1 ) , heterozygous in proband's parents ( II:1 and II:2 ) and unaffected sister ( III:3 ) , and absent from his three first cousins ( III:6 , III:7: , and III:8 ) . This sequence variation has not been previously identified in the dbSNP database or in 1000 Genomes Project Pilot Data [28] . The glycine ( G331 ) that is replaced by aspartic acid is conserved throughout vertebrate evolution ( Figure S1 ) and the substitution was predicted to be probably damaging by PolyPhen-2 analyses [29] . This family was recruited almost 10 years ago and we have not been able to obtain any information concerning kidney calcifications . Family 2 was a consanguineous family from Jordan ( Figure 2 ) . A panorex radiograph of the proband showed the retention of primary teeth and delayed eruption of permanent cuspids , premolars , and second molars . No radiopaque enamel was detected and expanded peri-coronal radiolucencies were evident on all unerupted teeth . The pulp chambers were typically calcified and nearer the occlusal surface than expected . On some unerupted teeth the crown occlusal to the pulp chambers had disappeared , as if by resorption . An ultrasound of the proband's kidneys revealed bilateral medullary nephrosis with small calcifications in both kidneys causing acoustic shadowing . Otherwise both kidneys were normal in size and corticomedullary differentiation , each measuring about 11 cm in bipolar length . The proband ( V:5 ) was the only affected person . FAM20A mutation analyses of the proband and his parents ( IV:1 and IV:2 ) identified an A to G transition that altered the splice acceptor site at the end of intron 4 ( c . 720-2A>G; g . 62232A>G ) that is homozygous in the proband and heterozygous in both parents . This sequence variation has not been previously identified in the dbSNP database or in 1000 Genomes Project Pilot Data . Several possible aberrant RNA splicing outcomes could occur [30] . Skipping of exon 5 would delete 31 amino acids ( p . Q241_R271del ) . No normal transcript variants skipping exon 5 are listed in GenBank . Retention of intron 4 would introduce a premature termination codon , and likely cause mutant FAM20A transcripts to be degraded by nonsense-mediated decay . Human Splice Finder version 2 . 4 . 1 [31] suggests there could be activation of a 5′ cryptic splice site that would add one nucleotide to exon 5 and lead to a frameshift and subsequent nonsense-mediated decay . Family 3 was a large kindred from Iran with two affected cousins ( Figure 3 ) . All of the proband's teeth were extracted ( and some saved ) prior to recruitment . A pre-surgical panoramic radiograph showed no radiopaque enamel , delayed tooth eruption , intrapulpal calcifications , and pericoronal radiolucencies . DNA was obtained from the proband and her younger , unaffected brother . Only FAM20A was characterized by mutational analyses . The proband was a compound heterozygote for two C>T transitions that both resulted in premature translation termination ( TGA ) codons . The nonsense mutations were in exon 2 ( c . 406C>T; g . 50213C>T; p . R136* ) and in exon 11 ( c . 1432C>T; g . 68284C>T; p . R478* ) . The unaffected younger brother was heterozygous for the nonsense mutation in exon 2 , but his exon 11 sequence was normal on both alleles . The exon 2 nonsense mutation was previously reported to cause AIGFS when found on both FAM20A alleles [24] . Four extracted secondary teeth from the proband of family 3 were provided to us for analyses ( Figure S2 ) . The “enamel” on the crowns was thin , soft and crusty , and only evident near the cervical margins . The teeth were smaller than normal and showed irregularities of root form . The area coronal to the root furcation was sometimes expanded , while the roots themselves were short , thin , and sometimes fused . Some parts of the roots showed pronounced concavities resembling a row of bites from an apple . A mesial-distal cut was made through #18 , the mandibular left second molar ( Figure 4A , 4C ) and compared to the normal tooth ( Figure 4B ) . Much of the mesial half of the crown had been resorbed and was only partially replaced by mineral , producing “hollow” areas on 3-D μCT images ( Figure 4D–4F ) , while much of the pulp chamber was calcified . SEM analysis of the occlusal surface of the FAM20A−/− mandibular second molar ( tooth #18; Figure 5A ) revealed a variety of surface features , including rough , knob-like calcifications ( Figure 5B ) , dentin with exposed dentinal tubules ( Figure 5C ) , some relatively smooth mineral near the dentin surface ( Figure 5D ) , and pitted “enamel” mineral superficially resembling volcanic rock on the lateral aspect of the crown ( Figure 5E–5F ) . SEMs of deliberately fractured areas showed no mineral organization characteristic of true enamel ( Figure 6 ) . SEMs of dentin looked the same as normal dentin ( Figure 7 ) . SEMs of the mandibular third molar ( tooth #32; Figure 8A ) showed a relatively smooth root surface ( Figure 8B ) perforated by small holes ( dentinal tubules ) and larger craters suggestive of resorption lacunae ( Figure 8C–8E ) . Backscatter SEMs of tooth #18 revealed that there was no enamel on the occlusal surface of the crown , although a thin , crusty material more highly mineralized than dentin covered part of the lateral coronal surfaces ( Figure 9 ) . Remarkably , only a small remnant of dentin , with apparent resorption lacunae at its edges , was evident in the mesial half of this FAM20A−/− molar crown . In its place was a laminated , bone-like material with osteocyte lacunae . Based upon the patterns of the growth lines , this lamellar bone had undergone repeated cycles of resorption and deposition . The calcifications in the pulp were unevenly mineralized . The most highly mineralized pulp material was comprised of incompletely coalesced spherical structures ( calcospherites ) . Thus there appeared to be at least two types of pathological mineralization within the crown: lamellar-like bone and calcospherites that apparently formed by a different mechanism . Backscatter SEMs of the root region revealed thin roots , with normal-looking root dentin covered by a thick layer of laminated , cementum-like mineralized tissue similar to the lamellar mineralized tissue that had replaced dentin in the crown but with fewer osteocyte lacunae ( Figure 10 ) . The dentin was often separated from this thick cementum-like layer by a hypermineralized line that might represent the original cementum . This line was sometimes interupted at places where the root surface had been resorbed locally and replaced . An unerupted third molar ( #32 ) was also characterized by bSEM and showed a somewhat different pattern of pathological resorption and mineralization ( Figure 11 ) . The “enamel” layer was very thin , rough , and discontinuous . It was more highly mineralized than dentin in most places , and appeared to have coalesced from multiple mineral foci . The dentin had well-organized dentinal tubules . The pulp was partially calcified . Besides major resorption of dentin from part of the root surface , the bulk of the pathology was in the furcation area , which included a large hypermineralized region of coalesced calcospherites surrounded by lamellar bone that extended coronally to the pulp and apically beyond the furcation so that the bone-like material seems to have entirely replaced the dentin at the furcation but retained the original morphology of the furcation . Human embryonic kidney ( HEK ) 293 cells were cotransfected with two plasmid constructs that expressed Flag-tagged FAM20A and a Golgi-GFP ( green fluorescent protein ) marker , respectively ( Figure 12 ) . In cells that received both constructs , the FAM20A signal superimposed upon that of the Golgi marker .
Our SEM analyses of FAM20A−/− molars detail an assortment of developmental malformations juxtaposed with secondary modifications . Developmentally , the enamel failed to form and the roots were small and misshapened . The crown and roots were susceptible to secondary resorption and turnover . Lamellar bone replaced parts of the resorbed crown , while a thick material resembling cellular cementum covered the roots . Highly mineralized , coalesced spherical calcifications were observed in the pulp and/or radicular area . We expect that the secondary root resorption and pathological mineralization occurred during the period of impaction . These unusual dental changes are rare in patients with non-syndromic amelogenesis imperfecta [27] , [37] , but are hallmark features of Enamel-Renal Syndrome ( ERS ) and were observed in the teeth from our probands with FAM20A mutations and nephrocalcinosis . These distinctive dental histological changes have previously been described in persons diagnosed as having AI without checking for nephrocalcinosis [8] , [11] , [12] , [15] . The similarities between our histological observations and those reported for extracted teeth with a comparable dental phenotype are remarkable and increase our suspicion that ERS has been historically under-diagnosed . FAM20A is part of a small gene family that in human and mouse has three members: FAM20A , FAM20B , and FAM20C . All three proteins have signal peptides of 21 amino acids and appear to be secreted [38] . The FAM20 genes encode proteins of similar size with a conserved C-terminal putative kinase domain ( cd10469 ) . FAM20A ( 17q24 . 2; cDNA reference sequence NM_017565 . 3 ) encodes a 541 amino acid protein . FAM20B ( 1q25; NM_014864 . 3 ) encodes 409 amino acids and FAM20C ( 7p22 . 3; NM_020223 . 2 ) 584 amino acids . Analysis of human expressed sequence tags ( ESTs ) suggests that the FAM20 family is expressed in many tissues . The National Center for Biotechnology Information ( NCBI ) human EST database currently has 5 , 779 , 625 entries for 45 healthy tissues . Among these are 103 EST entries for FAM20A ( Hs . 268874 ) from 22 tissues , with larynx ( 82/million ) , testes ( 60/million ) , and kidney ( 47/million ) showing the highest proportion of FAM20A transcripts . Mice homozygous for a defined 58-kb deletion in the 5′ region of Fam20a showed growth-cessation and growth-delay [39] . Recently , Fam20a null mice were characterized with severe ectopic calcifications in the kidneys [35] . FAM20B is a xylose kinase in the Golgi required for the efficient addition of glycan attachments on secreted proteins . [40] . FAM20C has recently been identified as Golgi casein kinase [41] , the enzyme that phosphorylates the secretory calcium binding phosphoproteins critical for biomineralization [42] . FAM20C mutations cause autosomal recessive lethal osteosclerotic bone dysplasia ( Raine syndrome; OMIM #259775 ) [43] , as well as non-lethal osteosclerotic bone dysplasia [44] , [45] . FAM20A localizes to the Golgi , so perhaps FAM20A is a Golgi kinase like FAM20B and FAM20C , and its deficiency results in altered post-translational modifications of secreted proteins . In the absence of FAM20A , the dental follicle does not support tooth eruption , slowly expands , and generates psammomatous calcifications . The connective tissue of the gingiva also slowly expands and psammomatous calcifications are deposited within the hyperplastic gingiva [22] . Similar calcifications occur in the dental pulp and possibly in the kidneys , causing nephrocalcinosis . This pattern of ectopic mineralization might be explained by failure to catalyze appropriate post-translational modifications on extracellular matrix molecules that inhibit mineralization when FAM20A is absent . The nine novel , disease-causing FAM20A mutations so far reported are obviously destructive of protein structure and function . Three are nonsense mutations ( c . 406C>T , p . Arg136*; c . 826C>T , p . Arg276*; c . 1432C>T , p . Arg478* ) . Three are splice junction mutations at the borders of exon 3 ( c . 590-2A>G , p . Asp197_Ile214delinsV ) , exon 5 ( c . 720-2A>G , p . Gln241_Arg271del ) and exon 6 ( c . 813-2A>G , p . Arg271Serfs*70 ) . Two are frameshifts ( c . 34_35delCT , pLeu12Alafs*67; c . 1175_1179delGGCTC , p . Arg392Profs*22 ) , and one is a missense mutation ( c . 992G>A , p . Gly331Asp ) at a highly conserved site . Only one of the nine FAM20A disease-causing mutations was found in more than one family ( c . 406C>T ) . These data strongly implicate FAM20A in the etiology of this recessive disorder that combines enamel defects , retention of primary teeth , delayed and failed eruption of permanent teeth with pericoronal radiolucencies , pulp calcifications , small and misshapened teeth , gingival hyperplasia , and now , nephrocalcinosis . Only further studies can show if defects in other gene ( s ) can cause this pattern of malformations and if FAM20A defects were not previously associated with nephrocalcinosis due to a lack of penetrance , subclinical presentation , or delayed onset .
Peripheral whole blood ( 5 cc ) or buccal swabs were obtained from participating family members . Genomic DNA was isolated using the QIAamp DNA Blood Maxi Kit ( Qiagen Inc , Valencia , CA ) and ( 50 ng ) from affected individuals was amplified using the Platinum PCR Supermix ( Invitrogen , Carlsbad , CA ) , and the amplification products were purified using the QIAquick PCR Purification Kit ( Invitrogen , Carlsbad , CA ) . The primer pairs and polymerase chain reaction conditions for the amplification of the coding regions were previously described for AMBN [26] , AMELX [46] , ENAM [47] , FAM83H [48] , WDR72 [49] , KLK4 [50] , and MMP20 [51] . Twelve primer pairs were synthesized to amplify the eleven FAM20A exons ( Figure S3 ) , which covered all coding sequences and intron/exon borders . These FAM20A reactions were annealed at 57°C for 60 s , extended at 72°C for 90 s , and run for 35 cycles . A full-length mouse Fam20a cDNA clone ( BC029169 ) in pCMV-SPORT6 was obtained from Thermo Scientific Open Biosystems ( Lafayette , CO , USA ) . Restriction sites were introduced before the Fam20a translation initiation codon ( NotI ) and replacement of the translation termination codon ( SalI ) by PCR , which generated a 1645-bp amplicon ( primer set: gcggCCGCTTGGGCCATGCCCG , agtcgacGCTCGTCAGATTAGCCTG ) . The amplification product was extracted from the gel and ligated into pCR2 . 1-TOPO ( Invitrogen ) . The Fam20a coding region was excised by double digestion with NotI and SalI and ligated into pCMV-Tag 4 ( Agilent ) , which had been restricted with NotI and SalI . Proper construction of the recombinant expression plasmid was verified by DNA sequencing . HEK293 cells were cultured with 2 mL Dulbecco's Modified Eagle Medium ( DMEM ) with 10% fetal bovine serum ( FBS ) in a Lab-Tek chamber slide ( 1 chamber ) with cover ( 70360-12 , Electron Microscopy Sciences , Hatfield , PA , USA ) to reach 60% confluence on the day prior to transfection . Four µg of pCMV-Tag 4-Fam20a plasmid in 10 µL of Lipofectamine2000 ( Invitrogen ) was diluted with 500 µL of Opti-MEM® I reduced serum media ( Invitrogen ) , and incubated for 20 min at room temperature . The pCMV-Tag 4-Fam20a/Lipofectamine2000 complexes were then added to the culture media . After 6 h , the culture media with complexes were replaced with 2 mL fresh media containing 20 µL of Golgi marker , CellLight Golgi-GFP BacMan 2 . 0 ( C10592 , Invitrogen ) . After 18 h , the cells were fixed with 4% paraformaldehyde for 15 min at room temperature , washed with PBS buffer 3 times , and then permeabilized with PBST for 15 min at room temperature . Following blocking with 5% sheep serum in PBST for 30 min at room temperature , anti-Flag antibody ( 1∶200 , F7425 , Sigma-Aldrich ) was applied . After over-night incubation of primary antibody at 4°C , the cells were washed with PBS buffer for 15 min and then incubated for 30 min at room temperature in solutions containing anti-rabbit IgG secondary antibody conjugated with Alexa Fluor 594 ( 1∶500 , A-11012 , Invitrogen ) . The slides were then rinsed in PBS buffer for 15 min , mounted with ProLong Gold antifade reagent with DAPI ( P-36931 , Invitrogen ) , and examined under a Leica DM5000B fluorescence microscope . The teeth were secured on petri-dish containing 1% agarose , scanned and analyzed using a SCANCO μCT-100 series micro-computed tomography system at the University of Michigan School of Dentistry micro-CT core . Tooth specimens were glued to a stub and sputter coated with gold for 75 s and then imaged using a Field Emission Gun Scanning Electron Microscope ( FEG-SEM; Amray 1910 Field Emission Scanning Electron Microscope ) at the Microscopy and Image Analysis Laboratory at the University of Michigan . Teeth were cut using a slow diamond saw ( Model 650 , South Bay Technology , Inc , San Clemente , CA , USA ) , infiltrated by 1∶1 , 1∶2 and 1∶3 acetone∶Epon for 12 h , degassed twice , infiltrated overnight with pure Epon , polymerized at 60°C for 48 h with the cut surface placed face down in a 25 mm SeriForm mounting cup ( Struers , Ballerup , DK ) . The blocks were sequentially polished with successively finer grades ( 400 , 800 and 1200 ) of silicone carbide paper ( South Bay Technology , Inc ) followed by 4 h of polishing with 1 . 0 micro alumina abrasive with Multitex Polishing Cloth using a Buehler Supermet 2 Position Polisher ( Lake Bluff , IL ) , sonication and rinsed with water . The finely polished tooth surface was coated with carbon and imaged using the Cameca SX-100 Electron Microprobe Analyzer ( CAMECA , 92622 Gennevilliers Cedex , FR ) at the University of Michigan Electron Microbeam Analysis Laboratory ( EMAL ) using the backscatter mode at a beam current of 15 kV and 10 nA . | FAM20A belongs to a family of 3 genes ( FAM20A , FAM20B , and FAM20C ) that encode kinases ( phosphorylating enzymes ) that modify proteins within the secretory pathway . FAM20C phosphorylates secretory calcium-binding phosphoproteins ( SCPPs ) that are critical for bone , dentin , and enamel biomineralization , and other calcium-binding proteins in milk and saliva . The function of FAM20A is unknown , but defects in the FAM20A gene have recently been shown to cause dental enamel defects along with enlarged gingiva ( amelogenesis imperfecta and gingival fibromatosis syndrome or AIGFS; OMIM #614253 ) . We identified three families with disease-causing mutations in FAM20A . All of the symptoms of AIGFS are also found in enamel-renal syndrome ( ERS , OMIM #204690 ) , which in addition features kidney calcifications known as nephrocalcinosis . We were only able to acquire a kidney ultrasound from one of our patients with FAM20A mutations , and it showed these kidney calcifications . We conclude that FAM20A mutations cause ERS and that persons diagnosed with AIGFS should have their kidneys examined . We also were able to obtain teeth from a patient with defined FAM20A mutations and to characterize the unusual mineral deposits that replace and add to normal tooth structures and may provide clues to the function of FAM20A . | [
"Abstract",
"Introduction",
"Results",
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"Methods"
] | [
"biology"
] | 2013 | FAM20A Mutations Can Cause Enamel-Renal Syndrome (ERS) |
Rabies is an important neglected disease , which kills around 59 , 000 people a year . Over a third of these deaths are in children less than 15 years of age . Almost all human rabies deaths in Africa and Asia are due to bites from infected dogs . Despite the high efficacy of current rabies vaccines , awareness about rabies preventive healthcare is often low in endemic areas . It is therefore common for educational initiatives to be conducted in conjunction with other rabies control activities such as mass dog vaccination , however there are few examples where the efficacy of education activities has been assessed . Here , primary school children in Zomba , Malawi , were given a lesson on rabies biology and preventive healthcare . Subsequently , a mass dog vaccination programme was delivered in the same region . Knowledge and attitudes towards rabies were assessed by a questionnaire before the lesson , immediately after the lesson and 9 weeks later to assess the impact the lesson had on school children’s knowledge and attitudes . This assessment was also undertaken in children who were exposed to the mass dog vaccination programme but did not receive the lesson . Knowledge of rabies and how to be safe around dogs increased following the lesson ( both p<0 . 001 ) , and knowledge remained higher than baseline 9 weeks after the lesson ( both p<0 . 001 ) . Knowledge of rabies and how to be safe around dogs was greater amongst school children who had received the lesson compared to school children who had not received the lesson , but had been exposed to a rabies vaccination campaign in their community ( both p<0 . 001 ) indicating that the lesson itself was critical in improving knowledge . In summary , we have shown that a short , focused classroom-based lesson on rabies can improve short and medium-term rabies knowledge and attitudes of Malawian schoolchildren .
Of the estimated 59 , 000 people who die from rabies annually [1] , the vast majority result from a bite from a rabid dog . Children are at greater risk of suffering dog bites than adults [2 , 3] and as a result approximately 40% of all human rabies deaths occur in children aged under 15 years old [4 , 5] . Elimination of the rabies virus can be achieved through annual vaccination of 70% of the dog population and human exposures to rabies virus will continue to occur until elimination has been achieved . Prompt post-exposure treatment is effective at preventing rabies , however incomplete adherence to recommended protocols has resulted in many deaths . School based rabies education is an efficient way of reaching large numbers of children . Lessons containing simple messages can improve rabies prevention through appropriate behaviour , such as immediately washing bite wounds and seeking post-exposure vaccination . Whilst many governments and NGOs advocate the integration of education components in rabies elimination programmes [6] , few have published studies documenting the effectiveness of their interventions [7–9] . Knowledge , attitudes and practices ( KAP ) studies can be used to assess how effective education initiatives are by comparing responses prior to , and after , an intervention . Studies have shown that knowledge of rabies and rabies prevention can vary greatly across rabies endemic countries . For example , in one rabies KAP study in Tanzania only 5% of those interviewed knew of the importance to thoroughly wash dog bite wounds [10]; over 35% of respondents in Ethiopia did not know the symptoms of rabies in people [11]; and in Cambodia only 48% of people knew that vaccination could protect dogs from rabies [4] . A lack of understanding about the risk of rabies and preventive measures reduces the perceived need for control measures . It also reduces engagement with elimination efforts and the likelihood of taking appropriate action to prevent rabies in the event of exposure . Most rabies KAP studies have focused on adult populations [4 , 10–20] despite the disproportionally high incidence of rabies in children [8] . Only two studies have evaluated the efficacy of lessons to improve rabies KAP in children; Kanda et al . in Sri Lanka [9] and Dzikwi et al . in Nigeria [21] . Furthermore , studies assessing longer term knowledge retention after a short lesson and comparison with control populations exposed to mass dog vaccination campaigns alone are lacking . The current study was therefore conducted to investigate the immediate and medium-term impact of short lessons on primary school children’s understanding of rabies prevention in Zomba City , Malawi .
Zomba city , a rabies education naïve area in Southern Malawi , was chosen as the study site ( Fig 1 ) . Zomba City , located in Zomba District of southern Malawi is the fourth largest city in Malawi , with a human population of 114 , 000 ( based on 3 . 2% annual growth rate since the 2008 census [22] ) . Mission Rabies is an international NGO working to establish effective rabies control activities in Malawi , including mass dog vaccination , community rabies education and enhanced canine rabies surveillance initiatives . Mass dog vaccination and education campaigns were conducted in Blantyre city in May 2015 and May 2016 . No previous education or vaccination activities had taken place in Zomba city prior to this study . Local reports of high rates of canine and human rabies and an absence of rabies vaccination or education activities prompted requests from local authorities for expansion of Mission Rabies activities to the Zomba region . This study was undertaken during the initial stages of work in Zomba . The 17 public primary schools in Zomba City had a total of 25 , 824 registered school children in 2016 . All schools were included in the study . Fifteen received rabies education classes shortly before a mass dog vaccination campaign in the region . Two control schools did not receive education classes prior to the vaccination campaign . The control schools were closed during the period before vaccination so were not available to conduct the rabies education classes before the campaign . To investigate whether the choice of control introduced a bias , being a convenient sample , demographics of children in the intervention schools and the controls schools were compared . Lessons were delivered in the national language , Chichewa , by trained Malawian Mission Rabies education officers to children in the seventh school year ( standard 7 ) . The interactive lessons lasted for approximately 45 minutes and included school children participation , requiring volunteers to join in short demonstrations and question-answer sessions . Learning points included which animals can transmit rabies , rabies symptoms and prevention , and safety around dogs . Education officers received four days of training in how to deliver a standardised lesson before commencing the study . The Mission Rabies school education campaign teaches all year groups , with lessons ranging from 20 minutes for younger years to 60 minutes for older year groups . A single year group was included in this study to standardise the type of lesson delivered . The education programme was evaluated using self-administered paper questionnaires . Rabies lessons and initial questionnaires were conducted between 11th and 17th July 2016 . In the 15 schools where rabies lessons were given , the same standardised questionnaire was completed by school children at three time-points; a “pre” questionnaire prior to receiving the lesson , a “post” questionnaire by the same children immediately following the lesson and a “retention” questionnaire , in the same class at the same schools 7 . 5 to 10 . 5 weeks later . The pre-questionnaire was used to assess children’s baseline knowledge and attitudes; the post-questionnaire assessed instant impact of the lesson on school children’s knowledge and attitudes; and the retention questionnaire assessed longer-term learning . The control group completed the same questionnaire only once ( “control” questionnaire ) , after the vaccination campaign that took place between 6th and 17th August 2016 . This approach allowed us to assess the impact of the rabies lesson itself rather than just the exposure to the wider dog rabies vaccination programme . Retention and control questionnaires were completed between 8th and 29th September 2016 . The initial draft of the questionnaire was designed using input from NGO staff and publications conducting similar questionnaires directed at adults , followed by a process of informal feedback and refinement through application in two schools in Blantyre city . The questionnaire was written in English and translated to Chichewa . It was independently back translated to English for comparison with the original version to ensure question integrity was maintained . The questionnaire consisted of four sections . Section A identified demographic information . Section B assessed dog ownership practices and understanding of animal welfare ( to be reported outside of this study ) . Section C contained questions about rabies . Section D asked about prior rabies education and dog vaccination history . This section was used by the NGO and did not form part of this study . A copy of the questionnaire can be found in S1 Appendix . To minimise age related bias the questionnaire was given to standard 7 school children only , as described above . Sample size was determined using a sample size calculator [24] with the following parameters: 95% confidence level; 5% margin of error; and a response distribution of 50% . These parameters were chosen to give the most conservative sample size . The target population contained 2 , 844 standard 7 school children registered with the education department in Zomba , therefore a sample size of 339 was required . This was the equivalent of 22 . 6 school children per school for the educated group and 169 per school for the control group . To account for incomplete questionnaires and varying school attendance this was increased to 30 per school for the educated group and 190 per school for the control group . Even though each year group has an average of 169 registered students , which would lend itself well to systematic random sampling of every fifth student from the class register , attendance was much lower . For this reason , the teacher selected 30 children to take part , or as many as were present in the class where there were less than 30 school children . The teacher was instructed to select school children at random and not to choose by ability . All school children had the opportunity to decline taking part at each stage of the study . Anonymity was maintained throughout the study by allocating each participant a unique identification code ( UIC ) consisting of a school code followed by a sequential number . The UIC was entered on the questionnaire , a consent letter for school children’s parents/guardians and on the data entry smartphone application . Less than half of the school children could be reliably matched between the pre and retention questionnaire due to school children absence at the time of the retention questionnaire or because school children forgot their UIC . Where there were fewer school children present for the retention than for the pre/post questionnaire , additional school children were invited to take the questionnaire with the provision that they had been present for the rabies lesson . The UIC was adapted to take this into consideration . All questionnaires were presented to children on paper . Results from the questionnaires were entered into digital versions of the questionnaire created in a smartphone application ( the Mission Rabies App ) that allows remote data collection through smartphones [25] . Where possible , data were entered through single- and multi-select options to minimise transcription error [26] . Data were uploaded to a Microsoft SQL database on a secure cloud based server , from which it could be downloaded remotely via a password protected website as a csv file , which was imported into Excel 2013 ( Microsoft Inc . , Redmond , WA ) and R Studio [27] for analysis . To allow statistical analysis numerical scores were allocated to answers based on accuracy of response for questions which had correct or incorrect answers . A completely correct answer scored 2 , a mostly correct answer 1 , a missing or wrong answer 0 and an incorrect answer -1 . For example , the latter includes answers that could have deleterious consequences for the child or an animal , or if an incorrect species susceptible to rabies was identified . Scores of 0 were given to answers that are not correct but would not have deleterious consequences . For example , the risk of getting rabies from milk is theoretical but people are not encouraged to drink milk from a rabid animal [28 , 29] . Therefore , responses that milk could transmit rabies were given a score of 0 , as whilst it is wrong it is not harmful . Scores allocated to each question response can be found in S1 Table . When questions allowed for multiple answers the score was the sum of all answers selected . Questions were grouped into categories that assessed knowledge and attitudes towards rabies and safety around dogs ( S2 Table ) . Data were analysed using the R statistical software version 3 . 3 . 2 [30] with paired t-tests comparing matched pre and post questionnaire responses [31] . Results from 13 pre-questionnaires could not be matched to post questionnaires and these data was excluded when performing paired t-tests between these questionnaires . Two tailed two sample t-tests [31] were used to compare data that could not be matched: pre to retention , pre to control and control to retention scores . To deal with the issue of multiple testing , the threshold cut-off for significance was adjusted to a p-value < 0 . 003 based on the Bonferroni correction [31] . Mixed effects multiple linear regression [31] was used to determine the effect of demographics on baseline questionnaire scores . Children who had not replied to any of the questions considered in the model were removed from the regression analysis . Children’s age , gender , religion and dog ownership status were considered in the model as fixed effects . The school each student studied at was introduced in the model as a random effect . Variables selection was carried out using manual backward elimination and variables retained in the final regression model were chosen based on their effect on the Akaike information criterion ( AIC ) . The study and questionnaire were approved by the University of Edinburgh’s Human ( Research ) Ethical Review Committee ( HERC ) . In Malawi , we obtained permission from the Department of Education . Signed consent was granted by head teachers of all schools prior to commencing the study . Consent letters were given to school children to give to their parents/guardians with clear instructions on how to remove their child’s data from the study if desired .
The number of school children who completed the questionnaire at each stage is shown in Fig 2 . Only 122 out of 386 school children who completed the pre-questionnaire and could remember their UIC completed the retention questionnaire . School children who had been present for the lesson but did not complete the questionnaire and those who could not remember their UIC comprised the remainder of school children completing the retention questionnaire ( Fig 2 ) . S1 Fig shows the distribution of missing data in the questionnaire responses . The mean age of school children was 13 years old with a range between eight and 15 years old for pre , post and control groups , and nine and 15 years old for retention groups . Though approximately equal , slightly more females than males completed the questionnaire . Approximately 50% of the educated school children were Catholic , with other Christian denominations accounting for at least 30% . This differed in the control group where more school children belonged to other Christian denominations followed by Catholicism . Across all groups between nine and 15% of school children were Muslim . Across all questionnaires the majority of school children either owned or had contact with dogs ( pre 78 . 8% , n = 304; post 79 . 3% , n = 302; retention 83 . 6% , n = 317; control 82 . 3% , n = 284 ) . The main reason for dog ownership was guarding ( pre 86 . 1% , n = 242; post 86 . 3% , n = 276; retention 81 . 8% , n = 233; control 86 . 1% , n = 223 ) . Most dogs were kept tied up or in a cage outside ( combined results: pre 76 . 2% , n = 294; post 74 . 5% , n = 284; retention 68 . 9% , n = 261; control 58 . 6% , n = 202 ) . S2 Fig shows graphical comparisons of means/proportions and confidence intervals of demographic characteristics between intervention ( educated school children ) and control ( school children not exposed to the rabies lesson ) . Pre-questionnaire results were used to determine baseline knowledge and attitudes . Overall , this was assessed by pooling the scores for all questions assessing knowledge or attitudes . Accounting for school differences as a random effect , the mixed effects multiple linear regression model showed that male students had higher baseline scores . Similarly , when compared to Catholic students , students who said they did not belong to a religious group had higher scores . School children that did not own or have contact with dogs had lower overall baseline scores when compared to those who owned dogs . Results of the regression model are presented in Table 1 . The variables selection process used is explained in Table 2 . Safety around dogs was assessed by asking school children how to behave around dogs to avoid being bitten . Of all responses , 86 . 6% identified an appropriate behaviour ( stand still , be calm , cover face and play with friendly dogs ) ( S3 Fig ) . School children achieved a mean score of 19 . 23 ( sd 7 . 89 ) out of 71 for rabies knowledge . Seven out of the eight questions assessing rabies knowledge allowed multiple responses , yet between 43 . 0% and 72 . 5% of school children gave single answers to these questions . Most school children knew that people can get rabies ( 86 . 5% , n = 334 ) . 91 . 0% ( n = 628 ) of responses for the question ‘Which animals can get rabies ? ’ were correct with 49 . 0% ( n = 338 ) of school children selecting dogs , whilst only 83 . 3% ( n = 685 ) of responses were correct for ‘Which animals can give rabies to people ? ’ . Being bitten was the most commonly selected option for how people can get rabies with 59 . 4% ( n = 318 ) of responses . Many school children could identify symptoms of rabies ( 95 . 6% , n = 723 correct responses ) . Of the responses for ‘What do you do if bitten by a dog ? ’ 24 . 2% ( n = 172 ) of responses were correct and 68 . 4% ( n = 486 ) were completely correct . Over half of the responses ( 54 . 0% n = 249 ) given to the question asking children to identify ways to prevent dogs from getting rabies were that a dog should be vaccinated annually . School children also identified that vaccinating dogs and people would prevent people from getting rabies ( 32 . 2% n = 170 and 39 . 2% , n = 207 responses respectively ) . S3 Table details rabies knowledge responses across all questionnaires . Most school children believed that rabies was serious and that dogs should be vaccinated giving an overall rabies attitude score of 3 . 81 , out of 4 . Overall score results , as well as scores for each category are presented in Fig 3 . Furthermore , results of t-test comparisons are shown in Table 3 . Results for overall score , safety around dogs and rabies knowledge all demonstrated a significant improvement in score immediately after the lesson . This reduced over time but remained significantly greater than baseline at the retention questionnaire . Control scores were significantly different to retention scores but not significantly different to pre-questionnaire scores . Some question responses illustrated an improvement in knowledge more than others . For example , 32% of children identified that they should inform an adult if they were bitten by a dog after the lesson compared to 17% before the lesson . They also knew to clean the wound for 15 minutes ( proportion of responses per questionnaire: pre 1 . 8% n = 13 , post 9 . 9% n = 81 , retention 8 . 8% n = 65 ) and apply antiseptic ( proportion of responses per questionnaire: pre 4 . 5% n = 32 , post 10 . 3% n = 84 , retention 9 . 3% n = 69 ) . This knowledge diminished over time but remained greater than baseline levels 9 weeks after the lesson . Conversely school children failed to identify that they should go for 5 vaccines and go to the hospital ( S4 Fig ) . Following the lesson there was an increase in the number of animals that school children identified could get rabies . The number of responses increased for all mammal species , though the greatest difference was in cats , bats and monkeys . This knowledge waned overtime though remained greater than baseline ( for all mammal species other than dogs ) and was greater than control groups ( except for mongoose ) . Equally , there was a very similar increase in the number of animals school children identified that could transmit rabies to people . Again , the number of responses increased for all mammals with the greatest increase for cats , bats and monkeys . Children’s responses to the question assessing how rabies can be transmitted to people followed a similar pattern with an increase in correct responses that diminished over time but remained greater than baseline and the control group for all but one of the correct answers . There was an increase in response rate to saliva and scratches , and to a lesser extent licking wounds , after the lesson . However , this was coupled with a reduction in children’s responses indicating that bites can transmit rabies to people . There were no significant changes between school children groups or questionnaires for attitudes towards rabies ( Table 3 ) as most school children had initially shown strong attitudes . Similarly , over 80% of responses to all questionnaire types indicated that children knew that people could get rabies .
This study assessed the impact of a rabies lesson on an education naïve population and demonstrated that this is an effective way to improve knowledge in primary school children in an urban setting . Some aspects of the lesson were more effective than others in teaching children about rabies and its prevention . Knowledge remained greater than baseline suggesting the lesson allowed mid-term learning , though research to determine long-term knowledge retention is warranted . The educational component of this study took place alongside a rabies vaccination campaign and it was demonstrated that the vaccination campaign did not alter children’s knowledge about rabies or how to be safe around dogs . Rabies attitudes did not alter after the lesson but this was because children had already identified that rabies was serious prior to the lesson . The lesson format presented in this study was effective at teaching school children about rabies and its prevention using few resources and training . This study was conducted in the city of Zomba . We therefore believe that this lesson could be successfully used throughout urban Malawi providing children with effective techniques to reduce child mortality from this fatal disease . Future research is needed to assess the efficacy of this lesson in the rural setting and whether the increase in knowledge correlates to reduced risk of rabies . | Rabies is a fatal disease that claims the lives of approximately 59 , 000 people every year . Children under the age of 15 make up 40% of all human rabies deaths yet this is preventable through a combination of vaccinating dogs against rabies and education . Numerous studies have shown that people in rabies endemic areas lack sufficient knowledge about rabies , and there are many misconceptions about its treatment and prevention . Whilst many organisations run vaccination and education campaigns , few have assessed their impact on rabies knowledge , attitudes or practices ( KAP ) . Fewer still have assessed the impact on children . This study investigated the impact of a rabies lesson on school children’s knowledge and attitudes about rabies in conjunction with a rabies vaccination campaign in Zomba , Malawi . We found that a rabies lesson improved school children’s knowledge about rabies and how to be safe around dogs . We observed that knowledge remained higher several weeks later . Knowledge about both canine rabies and bite prevention was greater amongst school children who had received the lesson compared to school children who had not received the lesson , but had been exposed to a rabies vaccination campaign in their community . This indicates that the lesson itself was critical in improving knowledge . | [
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"mammals"... | 2018 | A rabies lesson improves rabies knowledge amongst primary school children in Zomba, Malawi |
In order to inform the rational design of HIV-1 preventive and cure interventions it is critical to understand the events occurring during acute HIV-1 infection ( AHI ) . Using viral deep sequencing on six participants from the early capture acute infection RV217 cohort , we have studied HIV-1 evolution in plasma collected twice weekly during the first weeks following the advent of viremia . The analysis of infections established by multiple transmitted/founder ( T/F ) viruses revealed novel viral profiles that included: a ) the low-level persistence of minor T/F variants , b ) the rapid replacement of the major T/F by a minor T/F , and c ) an initial expansion of the minor T/F followed by a quick collapse of the same minor T/F to low frequency . In most participants , cytotoxic T-lymphocyte ( CTL ) escape was first detected at the end of peak viremia downslope , proceeded at higher rates than previously measured in HIV-1 infection , and usually occurred through the exploration of multiple mutational pathways within an epitope . The rapid emergence of CTL escape variants suggests a strong and early CTL response . Minor T/F viral strains can contribute to rapid and varied profiles of HIV-1 quasispecies evolution during AHI . Overall , our results demonstrate that early , deep , and frequent sampling is needed to investigate viral/host interaction during AHI , which could help identify prerequisites for prevention and cure of HIV-1 infection .
Despite the success of human immunodeficiency virus type 1 ( HIV-1 ) preventive campaigns [1] and the use of combined antiretroviral therapy ( cART ) in managing the disease [2] , there is still a great need for safe , effective , and scalable strategies to prevent and cure HIV-1 infections worldwide [3] . In order to inform the rational design of new interventions it is critical to understand the events occurring during acute HIV-1 infection ( AHI ) , which play a central role in determining the course of the disease [4 , 5] . The pathogenesis of AHI is attended by profound virological and immunological changes: viral trafficking from the portal of entry to lymphatic tissues [6] , extensive viral expansion followed by partial contraction [7 , 8] , massive CCR5+ CD4+ T-cell depletion in the gut-associated lymphoid tissue ( GALT ) [9 , 10] , activation of innate immunity effectors with increased cytokine secretion ( i . e . , “cytokine storm” ) [11] , and development of the first adaptive immune responses [12–14] . Early during AHI the latent viral reservoir -the major obstacle for HIV-1 eradication [15 , 16]- is seeded [17 , 18] , and the initial and irreversible injury to the immune system occurs [19] . Acute HIV infection has lasting significance , as the peak plasma viral load ( pVL ) is correlated with pVL set point [5]–a strong correlate of prognosis [20] . HIV-1 sequences sampled during acute and early HIV-1 infection have been used to study the viral population bottleneck following transmission [21–27] , viral demographic processes occurring during AHI [28] , and early viral adaptation to host immune responses [12 , 14 , 29] . Single genome sequencing ( SGS ) has been an important advance in the field , allowing for the discrimination between infections established by single or multiple transmitted/founder ( T/F ) viruses and for subsequent evolution of viral genomes [21 , 24] . More recently , next-generation sequencing ( NGS ) has increased the sampling capacity [27 , 30 , 31] , allowing identification of low frequency variants and providing a more precise characterization of early viral dynamics . These molecular techniques applied to simian immunodeficiency virus ( SIV ) /macaque models ( reviewed in [32] ) , and to human cases [22–24 , 28 , 30 , 31] consistently showed selection of viral variants escaping some of the early cytotoxic T-lymphocyte ( CTL ) responses . However , since sampling in humans is typically temporally sparse and generally begins after peak viremia , our understanding of viral dynamics during AHI is still incomplete . Moreover , most of our knowledge derives from infections established by a single T/F virus; little is known about viral evolution in infections established by multiple T/F viruses , which account for 20–60% of new infections [22 , 23 , 27 , 33] . To bridge this gap , early-capture HIV-1 infection cohorts have been developed [5 , 34] . Among the recent findings from these studies are the inverse correlation between CD8+ T cell activation and pVL set point [34] , and the establishment of pVL set point at viremia nadir within the first 42 days of detectable viremia [5] . The aim of the present study was to characterize the complexity and dynamics of viral quasispecies during AHI . Targeted deep sequencing ( TDS ) of HIV-1 [35] was performed on plasma specimens collected twice weekly , with sampling starting prior to peak viremia and extending through nadir . We evaluated 6 participants: one with an infection established by a single T/F virus , and five with infections established by multiple T/F viruses . The five cases with multiple T/F viruses revealed unique viral profiles , with the minor T/F variants displaying different dynamics for each participant . Rapid shifts in frequencies of T/F variants were observed over the course of 1–3 weeks during AHI , beginning prior to viremia nadir and , in one case , prior to peak viremia . In most participants , variation at CTL epitopes implying escape was first detected at the end of peak viremia downslope , proceeded at higher rates than previously measured in HIV-1 infection , and usually occurred through the exploration of multiple mutational pathways . In some participants with multiple T/F viruses , the processes of CTL escape and outgrowth of minor T/F variants occurred concurrently . These results , combining early , deep , and frequent sampling , allowed us to investigate AHI with unprecedented timing and resolution , and support a dynamic model of AHI with rapidly changing viral lineages , likely in response to both CTL and possibly other host responses .
Six participants ( 3 male and 3 female ) from the RV217 AHI cohort were studied . The 6 study participants presented here constitute a subset of the larger RV217 cohort , and were selected for longitudinal analysis based on criteria explained in detail in Materials and Methods . Briefly , participants 20225 , 40100 , and 40061 had been initially selected for a longitudinal study of CTL responses [36] and participants 40436 , 10463 and 40265 were selected based on longitudinal FL SGS-based analysis from pre-peak viremia through 6 months post-infection ( p . i . ) , which showed homogeneous viral populations at pre-peak viremia and detected the presence of additional T/F variants at viremia nadir ( participants 40436 and 10463 ) or at 6 months p . i . ( participant 40265 ) . All of the selected participants had at least one sample before HIV-1 infection , and had 2 or more HIV ELISA-negative/HIV-1 NAT-reactive samples . Their socio-demographic characteristics and reported risk factors are shown in Table 1 . Four participants came from Thailand , one from Kenya , and one from Uganda , with 5/6 participants reporting transactional sex . All participants had at least one HIV-1 RNA negative sample , and nucleic acid testing ( NAT ) -conversion was documented within a median window of 3 . 5 days ( range: 2–14 days ) ( Table 2 ) . By employing twice-weekly sampling ( Table 3 ) , virological and serological markers were followed with very high resolution . The pVL and CD4+ cell counts are shown for each case in Fig 1A . Peak viremia ( range: 5 . 79–7 . 99 log10 copies/ml ) occurred 9–18 days from the onset of viremia . Participants from Thailand were infected with CRF01_AE , while participants from Kenya and Uganda were infected with unique recombinant forms of subtype A1 with either subtype C ( participant 20225 ) or subtype D ( participant 10463 ) . For all of the studied participants , full-length ( FL ) SGS-derived sequences sampled at pre-peak viremia ( d2-d7 ) showed homogeneous quasispecies ( Fig 1B ) . After excluding genomes containing G-to-A hypermutation , sequences followed a star-like phylogeny with computed mean Hamming distances and estimated times to most recent common ancestors ( tMRCA ) ( S1 Table ) in agreement with previous reports of Fiebig stage II subjects [21 , 24] . Sampling depth of this initial analysis was only 10–12 sequences per subject , giving a statistical likelihood of only 0 . 65–0 . 72 for the detection of minor sequence variants present at 10% frequency [21] . Thus , to obtain a more sensitive and precise estimate of the number of T/F viruses that were responsible for productive clinical infection in these very high-risk subjects , we performed TDS at the earliest AHI samples ( i . e . , from the first positive NAT through peak viremia ) . For subject 20225 , we confirmed the presence of a single T/F sequence lineage , but for the other five participants , we identified one or more additional sequence lineages whose members were present in low abundance ( 0 . 3–4 . 3% of sequences ) ( Fig 1C ) . The maximum within-subject diversity in the five subjects infected with multiple lineages ranged 0 . 7–2 . 2% . The comparison of within- and between individual HIV-1 genetic distances in East Africa and Thailand supports that these five RV217 participants had acquired their multiple variant infections from single sexual partners ( S1 Fig ) . The frequency of new HIV-1 infections in the RV217 cohort established by multiple lineages is the subject of ongoing investigation ( see Materials and Methods ) .
Five of the six participants considered for the current analysis presented infections established by multiple T/F viruses , with minor T/F variants circulating at low levels during pre-peak/peak viremia . To date , initial imbalance in the frequencies of T/F viruses has been reported in one human case [49] and in the SIV/macaque model [50] , but limited information is available about viral evolution during acute infection . The current work represents the first in-depth report of the evolution of minor T/F viruses during AHI with frequent sampling before and after peak viremia to document changes prior to establishing viral load set point . What are the mechanisms responsible for the initial imbalance between major and minor T/F viruses ? The low frequency of minor T/F viruses at pre-peak/peak viremia observed here could represent a combination of selective and stochastic processes occurring during the eclipse phase . Recent experiments in the SIV/macaque model using mixtures of phenotypically identical FLIMCs distinguishable by molecular barcodes showed that some of these infections could be established by multiple T/F viruses with the minor variant present at pre-peak viremia at 4–6% [50] . Potential mechanisms could involve: 1 ) different rates of expansion of the initial infectious foci at the portal of entry , limited either by local availability of target cells or by partially effective inhibitory innate immune mechanisms , 2 ) initial infection of a target cell that became quiescent for some days and later reactivated , or 3 ) viral sequestration in dendritic cells [51] . In the current work , the genetic distance among cognate T/F viruses could translate to marked phenotypic differences; thus , it is possible that the major T/F viruses may have presented advantages regarding early selective mechanisms compared to cognate minor T/F viruses . What are the mechanisms responsible for the shifts in major and minor T/F viruses observed during AHI ? Why do these shifts occur at different timing and rates in different individuals ? It is possible that the diverse viral profiles observed here may reflect the interplay between viral and host factors . In vitro , the major 40100 T/F virus presented a lower sensitivity to type I IFNs , a higher dependence on α4β7 integrin , and a lower replication capacity compared to the cognate minor T/F virus . It is well established that the levels of type I IFNs [11 , 52] and the availability of target cells expressing α4β7 integrin [9 , 10 , 47] show dramatic changes in the host over the first weeks of HIV-1 infection . Thus , a possible interpretation of the results of participant 40100 is that , among other mechanisms , the major T/F had a selective advantage over the minor T/F virus in the early host environment -where type I IFNs levels were high and α4β7-expressing target cells were abundant- while the minor T/F had a selective advantage in the post-peak viremia host environment , where type I IFNs levels were lower and target cells in the GALT had been depleted . According to this model , as profound changes occur within the host environment during AHI , the relative benefit accompanied by a particular viral phenotypic trait may erode or even become a detriment . The abovementioned interpretation derives from ex vivo experiments performed on cognate T/F viruses from a single participant . Thus , it is important to note that the in vivo viral dynamics in the other studied participants may be due to mechanisms other than IFN sensitivity and integrin dependence , especially considering: 1 ) the diversity in profiles , and 2 ) the possibility that each pair of cognate T/F viruses may differ in other sets of biological properties . Moreover , it is possible that even small differences between hosts and between infecting viral swarms may act in synergy to result in marked different inter-individual profiles of viral dynamics during AHI . For instance , in 40061 , major and minor T/F viruses differed in the sequence within epitope Gag SM9 , which opens the possibility that the early replacement of major T/F by the minor T/F could be associated with CTL responses . The kinetics of replacement of the major T/F show a profile consistent with other CTL escapes ( Fig 5 , lower panel ) , and the early presence of ex vivo responses towards the major but not the minor T/F sequence would support this hypothesis . However , the low level of the measured response could also indicate that the observed dynamics was only marginally impacted by CTLs , and could be due to other phenotypic differences between cognate 40061 T/F viruses , as indicated for 40100 . The biological characterization of cognate T/F viruses from other participants is in progress . Importantly , many of the changes in the host environment occurring during AHI take place at a time or anatomical location that are logistically hard to systematically sample [47 , 53] . Consequently , in infections established by multiple T/F viruses , the combination of the study of viral dynamics and the biological characterization of cognate T/F viruses may help reveal important host phenomena that are not apparent in infections established by single T/F viruses . The use of IMCs from infections established by multiple T/F viruses could be applied in ex vivo and animal models to test alternative hypotheses for selective pressures contributing to the variation reported here . In the cases of participants 40061 , 40100 and 40436 , the analyzed time points allowed us to study the dynamics of the outgrowth of the minor T/F lineage with great detail . However , in the cases of participants 10463 and 40265 , the outgrowth of the minor T/F occurred during a period that was not sampled . Nevertheless , the latter two cases remain important as they illustrate: a ) that minor T/F viruses can persist at low levels during AHI , and b ) that minor T/F lineages can represent a source of genetic material that can accelerate the viral genetic divergence , with the potential to allow for more rapid viral escape from immune effectors . In most study participants , the emergence of HIV-1 CTL escape in plasma was first detected during the downslope of peak viremia . In 10463 , viral evolution consistent with CTL escape was first detected around peak viremia , a timing consistent with non-human primate ( NHP ) models [53 , 54] , recent results confirming pre-peak viremia T cell response in HIV-1/humans [34 , 36] , and HIV-1 phylogenetic analysis of diversity post-peak viremia [28 , 30] . In all cases , CTL escape proceeded rapidly . In several CTL escapes , WT frequency fell from >90% to <50% within one week . The fast rate of replacement of WT by escape mutants supports strong selective pressure exerted by the CTLs [43 , 55] . The rates of CTL escape we measured are high relative to previous escape rates measured in HIV-1/humans: we find rates >0 . 8 d-1 while previous rate estimates are <0 . 5 d-1 [12 , 28 , 30 , 31 , 56] . However , the escape rates we measured are in the same range as escape rates found in acute infection for NHP models [43] . Furthermore , recent results characterizing T cell response in AHI suggest the strongest CTL-mediated selective pressure may occur during the downslope of peak viremia [34 , 36] , a period we capture through multiple time points within each individual but that previous studies have missed or captured with a single time point [12 , 28–31 , 56 , 57] . The use of TDS provided evidence of epitope shattering [37] as a major mechanism of escape from early CTL responses during AHI , highlighting the genetic plasticity of the virus and indicating the development of a diverse viral genetic pool during the first weeks following viral transmission . Interestingly , while in some participants CTL escape proceeded as the pVL remained virtually unchanged ( e . g . , 40100 ) , in other cases we noticed a transient and modest increase of pVL , coinciding with a change in dominance of escape variants ( e . g . , 20225 ) . Among the possible causes for this are: a ) differences in fitness between WT and escape variants , and among escape variants [58] , and b ) the emergence of new CTL clonotypes with broader variant recognition [59] . The underlying mechanism linking pVL dynamics with CTL escape and the change in escape dominance warrant further study . In the current analysis , the strength of the selective pressure exerted by early CTLs was assessed through the timing and rate of viral escape from these responses . It is important to note that viral escape was detected in some but not all of the initially targeted epitopes . This phenomenon has been previously described and studied by several groups ( e . g . , [29 , 58 , 60] ) , and is likely influenced by host ( e . g . , genetic background , magnitude/quality of CTL response ) and viral factors ( e . g . , genetic barrier to escape , viral fitness , capacity of targeted protein accommodate sequence change ) . The study of multiplicity of infection has potential epidemiological [33] and clinical [61] implications . A recent in-depth analysis of HIV-1 superinfection during chronic infection suggests that minor viral variants may play an important role in the development of breadth of humoral adaptive immune responses [62] . It will be important to explore if the low-level circulation of minor T/F variants from the onset of the infection could also affect the later development of breadth of humoral immune responses . The current study has several limitations , including the number of study participants , the examination of viral variation in subgenomic regions , and the sampling of viral quasispecies in the plasma compartment only , in lieu of genital or gastrointestinal tract sampling . As previously recognized [30 , 31] , the intensive technical approach followed in the current paper can only be used in a small number of study participants . Thus , it is possible that other individuals infected with multiple T/F viruses might present additional profiles of viral evolution , including the persistence of minor T/F lineages at low-level beyond 6 months p . i . Moreover , the participants analyzed in the current paper came from a high-risk cohort study [5] , and were intentionally selected to represent infections established by a ) a single T/F virus or b ) multiple T/F viruses where the minor variants circulated at low levels during pre-peak/peak viremia . The frequency of new infections with the latter profile is presently unknown and is the focus of ongoing investigation in the RV217 and other AHI cohorts . The current results support the need to revisit previous estimates of multiplicity of infection , by using more sensitive techniques , which may help improve current models of HIV-1 transmission and AHI . The majority of the data from the current paper was acquired using TDS , a validated NGS technique that provides deep sampling of the viral quasispecies while preserving linkage among polymorphisms [35] that is required for the definition of haplotypes representing the different T/F variants . However , in TDS , the size of the targeted subgenomic regions is constrained by the reading length of current , accurate , and established NGS technologies ( ~400 bp ) . While we studied multiple subgenomic regions per participant , TDS did not provide linkage among them . Generally , at the early time points examined by TDS in each participant , there was a high concordance in the frequencies of the T/F variants among the different regions . Overall , these data support that in these participants the frequency of inter-variant recombinants was relatively low during the early time points . The study of recombination between major and minor T/F viruses was further assessed by FL SGS , which preserves linkage across the entire viral genome , and the results were in agreement with TDS . However , due to the limited sampling depth of SGS , we cannot rule out the presence of recombinants arising between different T/F within a participant . Also , a new protocol for next-generation sequencing , the “primer-ID” , has been recently introduced in the field , which controls for “unrecognized sequence resampling” and “differential amplification” that “can skew allele frequency” [63] , thus providing higher sampling accuracy . The TDS protocol applied in the current work did not incorporate primer-ID , and thus we cannot rule out resampling . Nevertheless , high correlation in allele frequencies among technical replicates ( see Materials and Methods ) and concordant frequencies of major/minor T/F lineages in contemporary genotyping of different subgenomic regions ( e . g . , participant 40061 ) support the current TDS results . As new sequencing technologies with longer reading length , deep , and accurate sampling become available , it will be important to further explore these cases . The current study revealed early and rapid replacement between major vs . minor T/F viruses and wild type vs . CTL escape variants during AHI . Due to the frequent nature of sample collection , the examined compartment was peripheral blood plasma . Thus , it is possible that local viral replication of different viral variants at mucosal sites and lymphoid tissues -each containing different frequencies and densities of target and effector cells- may follow different dynamics than the ones measured in plasma [53] . The systematic sampling of additional body compartments during AHI will be necessary to address these questions . In HIV-1 infection , the “eclipse phase” ( i . e . , the period between the time of the infection of the first cell in body to the moment when the virus is detectable in blood plasma ) lasts 7–21 days [4 , 64] . Thus , with currently available methods , it is not possible to rule out that the presence of multiple T/F lineages in plasma at Fiebig stages I/II may not be derived from multiple exposures from a common source during the eclipse phase ( i . e . , rapid superinfection ) . However , several lines of evidence strongly support that it is possible for a single risk event to result in the transmission of multiple T/F lineages: a ) the reported viral diversity in donor fluids ( e . g . , [65] ) , b ) the documentation in the literature of at least 82 cases of HIV-1 infections established by multiple T/Fs in heterosexuals , men who have sex with men , and intra-venous drug users ( see metadata analysis in [27] based on [21 , 23 , 27–29 , 33 , 66–68] ) , and c ) the data from the SIV/macaque models [69] . In conclusion , the current data show that HIV-1 populations can present rapid and dramatic changes before the establishment of the viral nadir . Understanding the dynamic interplay between host innate and adaptive responses and viral phenotype during AHI may be critical to the identification of the prerequisites for prevention and cure of HIV-1 infection .
Study RV217/WRAIR#1373: All subjects were adults and all provided written consent . For subjects that were unable to read , the consent document was read to them with an impartial witness present; the volunteer , the witness and the study staff obtaining consent signed the affidavit with a signature or mark . All procedures and documents were reviewed and approved by local and US Army IRBs . Study RV229/WRAIR#1386: All individuals participating in this study were adults and provided written informed consent . All studies were reviewed and approved by the human subject ethics and safety committees in each country as well as by the Walter Reed Army Institute of Research ( Silver Spring , MD , USA ) , in compliance with all relevant federal guidelines and institutional policies . The current analysis focuses on six participants from the early capture AHI infection cohort RV217 , which has been described in detail elsewhere [5 , 36] . Briefly , the multi-center prospective observational RV217 study enrolled high-risk consented adults at four clinical research sites: Walter Reed Project , Kericho , Kenya; Makerere University Walter Reed Project , Kampala , Uganda; Mbeya Medical Research Center , Mbeya , Tanzania; and Armed Forces Research Institute of Medical Sciences , Bangkok , Thailand . During the initial surveillance phase ( phase Ia ) , HIV-uninfected participants were evaluated twice weekly with NAT ( Aptima HIV-1 RNA Qualitative test , Hologic Inc . , San Diego , CA ) on a small blood volume sample collected via finger-stick . NAT was performed within 24–48 hours of sample collection , and participants with reactive results were recalled to initiate the next phase of the study ( phase Ib ) , which included the twice-weekly sampling of larger blood volumes for one month ( Table 3 ) . Upon HIV-1 confirmation by standard serological methods , HIV acute cases were offered participation in long-term follow up phase . All HIV-1 positive participants were referred to care providers for management of the infection , based on national guidelines . Treatment was generally made available at no cost through host nation care and treatment programs . The cases presented in the current manuscript represent a selected subset drawn from a group of n = 40 RV217 participants for which pre-peak , immediate post-peak , and 6 months p . i . SGS sequences were available ( all of which are part of the n = 50 RV217 participants included in the principal analysis by [5] ) . To date , cross-sectional analysis of ~10 full-length SGS from pre-peak viremia per participant ( or pairs of half-length SGS equivalents ) showed that 9/40 participants had evidence of multiple T/Fs . In the current manuscript we have presented evidence that 5 individuals that had a homogeneous viral profile by SGS at pre-peak viremia had multiple T/Fs by NGS . The remainder cases are the focus of ongoing analysis . pVL was measured using the Abbott Real-Time HIV-1 Assay ( m2000 RealTime System , Abbott Laboratories , Abbott Park , IL ) , with a lower limit of detection of 40 copies/ml . Peripheral blood CD4+ cell counts were determined by flow cytometry on FACSCalibur by BD Multitest ( Becton Dickinson , Franklin Lakes , NJ ) . The day of first positive viremia is defined as day zero ( d0 ) and the nadir viremia is defined as the lowest viral load after the peak viremia through d42 . In the current paper , AHI is defined as the period from the advent of viremia to the early nadir/set-point occurring within 42 days of the advent of viremia [5] . Early AHI refers to the interval from the first positive NAT through peak viremia , while late AHI refers to the period from peak viremia to early nadir/set point . The staging system employed throughout the current paper was as described by Fiebig et al . [70] . None of the participants included in the present paper initiated antiretroviral treatment within the timeframe of the current analysis . Viral RNA ( vRNA ) was extracted from plasma using the QIAamp Viral RNA Mini Kit ( QIAGEN , Valencia , CA ) . Near FL or half-length HIV-1 genomes were amplified and sequenced through SGS , as published [24 , 71] . For the six studied participants , 10–12 FL amplicons were sequenced at pre-peak viremia , and 7–11 ( median: 10 ) FL or FL-equivalent amplicons ( i . e . , pairs of 5’- and 3’-half genomes ) were sequenced at nadir and at 6 months p . i . Due to low pVL , the 6 months p . i . SGS sequences from participant 40061 were obtained from PBMC-derived proviral DNA . For two participants , SGS-derived FL amplicons were also obtained for d14 , d21 , and d24 ( participant 40100 ) , and for d14 and d21 samples ( participant 40061 ) . Sequences were deposited in the GenBank under accession numbers KY580473—KY580727 . For each participant , subgenomic areas of interest were selected for TDS based on the comparison of SGS-derived sequences from pre-peak viremia , nadir , and 6 months p . i ( S15 Fig ) . In order to preserve linkage ( i . e . , phasing ) among polymorphisms within an area , the targeted regions were constrained in size to fit within the reading length of the NGS platform ( i . e . , <400 bp ) . When multiple candidate regions were available , we selected those areas that encompassed both , differences between T/Fs and CTL epitopes . Reverse transcription and amplification primers were tailored for each participant ( S4 Table ) , and exploited sites of high conservation among SGS-derived sequences from different time points . TDS was performed as previously described [35] . Briefly , using the protocol mentioned in the SGS section ( see above ) , vRNA was extracted from plasma and cDNA was generated through reverse transcription with SuperScript III First Strand Synthesis System ( Invitrogen , ThermoFisher Scientific , Carlsbad , CA ) . cDNA was titrated ( see below ) and 2 , 000 copies were distributed into separate sets of tubes ( Titanium Taq kit , Clontech , Mountain View , CA ) for nested PCR to avoid saturation . PCR products were visualized by electrophoresis on a 1 . 5% agarose gel . Amplicons were separated using electrophoresis on a 2 . 0% agarose gel stained with crystal violet and were purified with the NucleoSpin Extract II kit ( Machery-Nagel , Düren , Germany ) . Ion Xpress barcodes and adapters ( Life Technologies , ThermoFisher Scientific , Carlsbad , CA ) were ligated to purified amplicons using the Ion Plus Fragment Library Kit ( Life Technologies , ThermoFisher Scientific ) according to the manufacturer’s instructions , followed by quantification using a 2100 Bioanalyzer ( DNA 1000 kit , Agilent Technologies , Sunnyvale , CA ) . Emulsion PCR ( ePCR ) and enrichment were carried out on OneTouch/ES or IonChef instruments with the Ion One-Touch Template or Ion PGM Hi-Q Chef kits , respectively ( Life Technologies , ThermoFisher Scientific ) . Sequencing was carried out on PGM instruments using Ion 316v2 BC chips with the Ion PGM Hi-Q sequencing kit ( LifeTechnologies , ThermoFisher Scientific ) . cDNA used for TDS was titrated by endpoint dilution [35 , 72] followed by nested PCR in the same conditions that are used for library preparation . Reactions were run in quadruplicate and were visualized by electrophoresis on a 1 . 5% agarose gel . Technical strategies were adopted to preclude contamination across samples from the same participant , other participants , or laboratory strains . vRNA extraction was performed in biosafety hoods with laminar flow , which had been decontaminated with RNase AWAY ( Invitrogen , ThermoFisher Scientific , Carlsbad , CA ) , CONFLIKT ( Decon Labs , King of Prussia , PA ) , copper-bis- ( phenanthroline ) -sulfate/H202 solution ( i . e . , “CoPA solution” ) and UV light . Each biosafety hood had dedicated pipettes , filtered sterile tips , and personal protective equipment ( PPE ) . One plasma sample from each participant was extracted at a time . Technicians were only allowed to extract vRNA , perform reverse transcription or first round PCR if on that day they had not been in contact with high-copy number DNA . Different technicians were assigned different samples in a sequential fashion i . e . , d5 sample from participant 20225 was processed before the d9 sample from the same participant , and each sample was managed by a different technician . Reverse transcription of vRNA , first round and second round PCR , amplicon purification , and ligation of barcodes/adapters were performed by a single technician , who analyzed one sample at a time . Each technician handled reagents and samples in biosafety hoods assigned for their use , i . e . the use of hoods was controlled and monitored . Reverse transcription , first round and second round PCR had individual negative controls , to guarantee that none of the steps were compromised . In order to preserve the integrity of all of the samples , unique and distinguishable barcodes were ligated , one sample at a time; the NGS reads obtained after each sequencing run were interrogated bioinformatically for the whole array of barcodes , to ensure the absence of contamination . SSP-PCR was used to retrieve the sequences of the virtually FL genomes of minor T/F variants from participants 40100 ( d2 ) , 10463 ( d7 ) , and 40265 ( d12 ) using plasma samples at early AHI . Using the information from available SGS-derived FL sequences and from TDS , primers that were specific for the minor T/F variants were designed . SSP-primers were then utilized to amplify 5–6 overlapping fragments ( size range: 1 , 000–5 , 000 bp ) with high fidelity Taq polymerase ( Expand High Fidelity PCR System , Roche Applied Sciences , Indianapolis , IN ) . When needed to fill gaps in the genomes , new SSP primers were designed based on the SSP-PCR-derived amplicons , in a new iteration . Finally , contigs were generated in Sequencher ( version 5 . 3 , Gene Codes , Anne Arbor , MI ) and were compared to cognate SGS-derived FL sequences . SGS-derived FL sequences from pre-peak viremia were studied with the Poisson-Fitter v2 tool ( http://www . hiv . lanl . gov/content/sequence/POISSON_FITTER/pfitter . html ) to assess the Hamming distance , to estimate the tMRCA using a Poisson model , and to test for star phylogeny [73] , with mutation rate set at 2 . 16e-05 and with removal of sequences that presented Fisher exact p-values < 0 . 1 in the built-in hypermutation test . Alignments of SGS-derived FL sequences were generated using Geneious 3 ( http://www . geneious . com ) [74] , with manual editing . The Highlighter for Nucleotide Sequences v2 . 2 . 3 online tool ( http://www . hiv . lanl . gov/content/sequence/HIGHLIGHT/highlighter_top . html ) [21] was used to generate highlighter plots and to determine the mosaic structure of recombinants between cognate major and minor T/F variants . The genetic distance between cognate major and minor T/F viruses was computed as the nucleotide or amino acid p-distance in MEGA6 . 06 [75] ( www . megasoftware . net ) . All references to HIV-1 codon are based on the HXB2 coordinate system [76] . In order to calculate the epitope entropy , published amino acid HIV-1 sequences for each epitope were downloaded from the Los Alamos HIV-1 Database using QuickAlign ( http://www . hiv . lanl . gov/content/sequence/QUICK_ALIGNv2/QuickAlign . html ) and were filter for the corresponding subtype/clade ( CRF01_AE for 40100 Env LV9 , 40061 Gag SM9 , 40061 Vif QY9 , 40061 Vpr NY9 , and 40061 Vpr WL9; subtype A1 for 10463 Nef EQ11 , 20225 Pol SP10 , and 20225 Rev VL9 ) . Then , the Shannon entropy for each position and their means were computed using Entropy ( http://www . hiv . lanl . gov/content/sequence/ENTROPY/entropy_one . html ) as previously described [29] . Fastq files were exported from the PGM using Torrent Suite 4 . 4 software ( LifeTechnologies , ThermoFisher Scientific ) . Quality control was performed using FastQC ( courtesy of Dr . Simon Andrews , Babraham Institute , Cambridge , UK ) . Fastq files were imported into CLC Genomics Workbench version 7 . 0 . 3 ( Aarhus , Denmark ) to remove sequencing adapters and trim sequences based on quality , as previously described [35] , followed by barcode-based demultiplexing . Alignment to reference was performed using tmap version 3 . 2 . 2 ( by Nils Homer , distributed through https://github . com/iontorrent/TMAP ) using the following parameters: command = map2; match score = 1; mismatch penalty = 3; gap open penalty = 5; gap extension penalty = 2; and soft-clip only the right portion of the read . For each alignment , the corresponding major T/F sequence was used as a reference . Quality control of Sequence Alignment/Map ( SAM ) alignments was performed using Samstat version 1 . 08 [77] . Nautilus [78] was used to analyze SAM files in order to tally the frequency of each base at each sequence position and to determine the frequency of haplotypes . Alignments were also visualized using Tablet [79] . Based on previous assay validation [35]: 1 ) alignments were required to present a minimum coverage of 50 , 000 reads per position to be admissible for analysis; 2 ) polymorphisms had to be supported by bi-directional sequencing; and 3 ) the lower limit of quantification for single nucleotide substitutions was set to 0 . 5% ( though complex variants , distinguishable by multiple polymorphisms , were evidenced with a lower detection limit , under the assumption of sequencing error being independent at each position ) . The reproducibility of TDS was assessed though three independent technical replicates of a highly diverse sample from participant 20225 ( i . e . , d20 rev region ) ( S16 Fig ) . High correlation was observed among replicates ( R2 = 0 . 882–0 . 972; Spearman’s ρ: 0 . 956–0 . 970 ) , comparable with previous reports on NGS of HIV-1 primary samples [80] . Next-generation sequencing data has been deposited in the Sequence Read Archive ( SRA ) , National Center for Biotechnology Information , under BioProject PRJNA371358 ( BioSamples: 6297991 , 6298007–6298208 ) . FLIMCs corresponding to 40100 major and minor T/F viruses were constructed based on the corresponding sequences , as previously described [81] . Fluorescent reporter genes , eGFP and mCherry [82] , with distinct excitation/emission spectra were used for discrimination in co-infection experiments ( see below ) . Viral stocks were produced and titrated as previously reported [81] . FLIMCs from 40100 major and minor T/F viruses utilized CCR5 but not CXCR4 based on the GHOST coreceptor assay . For participants 20225 , 40100 , 40061 and 40265 a matrix of overlapping peptides spanning the T/F virus for each subject were used to stimulate PBMCs in ex vivo IFN-γ ELISpot assays . ELISpot against reactive 18mers were mapped to all HIV-1 specific T cell responses . Optimal epitopes were then defined and confirmed for participants 20225 , 40100 , and 40061 . To better understand cellular responses in participant 40100 , five representative variant peptides spanning Env LV9 epitope were used to stimulate PBMCs , in ex vivo IFN-γ ELISpot assays . Responses against all variants were detected but differed in magnitude across the tested peptides . IFN-γ ELISpot plates ( Mabtech , Cincinnati , OH ) were used for detection of reactive responses and plates were read on the Cellular Technology Limited ( LTD ) ELISpot reader ( Shaker Heights , OH ) . Image capture software version 6 . 5 . 4 ( Shaker Heights , OH ) was used to view and count spot forming cells per well . In the case of participant 40061 , for the assessment of T-cell function to Gag SM9 variants by flow cytometry as previously described [36] . Briefly , cryopreserved specimens were thawed , washed , stimulated in the presence of 3μg/ml HIV variant peptides , co-stimulatory antibodies CD28/CD49d , CD107a-FITC ( clone H4A3 ) ( BD Biosciences ) and incubated at 5% CO2/37°C for 6 hours . Brefeldin A and monensin were added two hours after stimulation . Cells were then stained with Live/Dead Aqua Dead Cell Stain Kit ( Invitrogen , Eugene , OR ) and surface stained with the following antibodies: anti-CD127 ( clone A019D5 ) PerCP-Cy5 . 5 , antiCD45-RO ( clone UCHL1 ) eFluor-650 , anti-CD38 ( clone HIT2 ) conjugated to Brilliant Violet 711 , anti-CD27 ( clone CLB-27/1 ) APC-Alexa750 , anti-CD14 ( clone Tuk4 ) PE-Cy5 , anti-CD19 ( clone SJ25-C1 ) PE-Cy5 , anti-CD56 ( clone B159 ) PE-Cy5 , CD3 ( clone SK7 ) PE-Cy5 , and anti-CD197 ( clone 3D12 ) PE-Cy7 . Cells were then stained intracellularly with the following antibodies: anti-CD3 ( clone UCHT1 ) BV421 , anti-CD8 ( clone 3B5 ) Qdot605 , anti-CD4 ( clone SK3 ) BV786 , anti-IFN-γ ( clone B27 ) APC , anti-TNF-α ( clone MAb11 ) Alexa Fluor 700 , perforin ( clone D-B48 ) PE , and anti-granzyme B ( clone GB11 ) PE-CF594 . Cells were acquired on a LSRII flow cytometer ( BD Biosciences ) and analysis was performed using FlowJo software , version 8 . 5 ( Tree Star , Ashland , Oregon ) . The selection criteria for IFN-γ ELISPOT vs . intracellular cytokine staining assays were the number of cells available and number of variants tested for each participant . High resolution HLA class I genotyping was performed as previously described [86] ( S5 Table ) . Escape rates were calculated as previously described [43 , 55] . Between two time points , ta and tb , the average escape rate is given by: ε=ln[fMT ( tb ) /fWT ( tb ) ]−ln[fMT ( ta ) /fWT ( ta ) ]tb−ta , where fWT and fMT are the wild type and mutant associated frequencies , respectively . In the current analysis , we grouped all mutants together , so that the escape rate represented an average over different mutant variants . Prism , version 6 . 0e ( GraphPad Software ) and JMP10 ( SAS Institute , Cary , NC ) were used for statistical analyses . | The development of safe , effective , and scalable vaccines and cure strategies to control the HIV-1 pandemic is a major public health concern . The rational design of these preventive and treatment measures requires a profound knowledge of the interaction between HIV-1 and its host during the first weeks that follow viral infection ( i . e . , acute infection ) . Here we performed a systematic and in-depth study of individuals whose infection was detected before peak viremia and before the emergence of the first antibody responses . Plasma samples were collected twice weekly during acute infection and we performed next-generation sequencing of the viral swarms . In most participants , we first detected viral escape from the initial adaptive cellular immune responses at the end of peak viremia downslope . Viral escape proceeded at higher rates than previously measured in HIV-1 infection and usually through the exploration of multiple mutational pathways . The analysis of sequences from infections established by multiple viral lineages revealed dramatic shifts in the frequencies of the viruses that composed the HIV-1 population within each host . These results , using early , deep , and frequent sampling , support rapidly changing viral lineages likely in response to both adaptive cellular immunity and possibly other host responses during acute HIV-1 infection . | [
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"retrov... | 2017 | Rare HIV-1 transmitted/founder lineages identified by deep viral sequencing contribute to rapid shifts in dominant quasispecies during acute and early infection |
Dengue is a rapidly emerging vector-borne Neglected Tropical Disease , with a 30-fold increase in the number of cases reported since 1960 . The economic cost of the illness is measured in the billions of dollars annually . Environmental change and unplanned urbanization are conspiring to raise the health and economic cost even further beyond the reach of health systems and households . The health-sector response has depended in large part on control of the Aedes aegypti and Ae . albopictus ( mosquito ) vectors . The cost-effectiveness of the first-ever dengue vaccine remains to be evaluated in the field . In this paper , we examine how it might affect the cost-effectiveness of sustained vector control . We employ a dynamic Markov model of the effects of vector control on dengue in both vectors and humans over a 15-year period , in six countries: Brazil , Columbia , Malaysia , Mexico , the Philippines , and Thailand . We evaluate the cost ( direct medical costs and control programme costs ) and cost-effectiveness of sustained vector control , outbreak response and/or medical case management , in the presence of a ( hypothetical ) highly targeted and low cost immunization strategy using a ( non-hypothetical ) medium-efficacy vaccine . Sustained vector control using existing technologies would cost little more than outbreak response , given the associated costs of medical case management . If sustained use of existing or upcoming technologies ( of similar price ) reduce vector populations by 70–90% , the cost per disability-adjusted life year averted is 2013 US$ 679–1331 ( best estimates ) relative to no intervention . Sustained vector control could be highly cost-effective even with less effective technologies ( 50–70% reduction in vector populations ) and in the presence of a highly targeted and low cost immunization strategy using a medium-efficacy vaccine . Economic evaluation of the first-ever dengue vaccine is ongoing . However , even under very optimistic assumptions about a highly targeted and low cost immunization strategy , our results suggest that sustained vector control will continue to play an important role in mitigating the impact of environmental change and urbanization on human health . If additional benefits for the control of other Aedes borne diseases , such as Chikungunya , yellow fever and Zika fever are taken into account , the investment case is even stronger . High-burden endemic countries should proceed to map populations to be covered by sustained vector control .
Dengue is a rapidly emerging disease endemic in more than 100 countries , with evidence of transmission reported in 128 countries [1] . Today , hundreds of thousands of severe dengue cases arise every year , including about 20 000 deaths . The economic cost of the illness in the Americas and South-East Asia is already measured in the billions of dollars annually , including costs such as work and school days lost [2][3][4] . In the Western Pacific , between half and two-thirds of affected households have incurred debt as a result of the care they received [5][6] . Environmental change and unplanned urbanization are conspiring to raise the cost of dengue infection further beyond the reach of health systems and households . In 2014 , Southern China suffered the worst outbreak of dengue fever in more than two decades; Japan saw autochthonous transmission in its first outbreak of the disease since 1945 [7 , 8] . In the absence of a fully effective vaccine or any treatment , dengue control has depended solely on the control of the Aedes aegypti and Aedes albopictus vectors . Current strategies include personal protection or biological , chemical , and environmental measures . In 2006 , the second edition of the Disease Control Priorities Project ( DCP2 ) put the cost per disability-adjusted life year ( DALY ) averted by vector control at US$ 1992–3139 ( presumably in 2005 US$ ) . Since then , the dengue economics literature suggests lower cost-effectiveness ratios ranging from 2005 US$ 227 ( 2013 US$ 344 ) per DALY averted by larval control in Cambodia to 2009 US$ 615–1267 ( 2013 US$ 802–1652 ) per DALY averted by adult mosquito control in Brazil [9][10] . A recent systematic review concluded that results were not easily comparable due to differences in methodological assumptions , and that combined control strategies remained largely unexplored [11] . The authors note that “there is growing interest in combining vector control with vaccination once a dengue vaccine becomes widely available , which recognizes that one intervention is insufficient to effectively reduce the burden of disease . ” They cite results from studies with malaria and lymphatic filariasis that support the impact of simultaneously targeting vectors and pathogens . Prior to 2015 , the absence of a dengue vaccine did not preclude efforts to model the conditions under which such an immunization strategy might be cost-effective [12][13][14][15] . Prospects for a viable immunization strategy have since improved , though not without complications [16] . In December 2015 , the first-ever dengue vaccine , known as chimeric yellow fever virus-dengue virus tetravalent dengue vaccine or CYD-TDV ( Dengvaxia ) , was approved for use in the Philippines . A strategy consisting of one year of catch-up vaccinations targeting children 9–15 years of age , followed by regular vaccination of 9-year-old children , may be cost-effective at costs up to $72 from a health-care perspective and up to $78 from a societal perspective [17] . However , a review of the results of eight independent modelling groups concluded that “the potential risks of vaccination in areas with limited exposure to dengue as well as the local costs and benefits of routine vaccination are important considerations” [18] . It has been argued that , even in the era of a vaccine , the health sector response to dengue is expected to continue to depend in large part on vector surveillance and control [19 , 20] . In this paper , we undertake a comprehensive review and synthesis of the evidence on the cost and cost-effectiveness of dengue control interventions from the health system perspective . We appraise the cost-effectiveness of sustained vector control ( including outbreak response ) in the presence of a ( hypothetical ) highly targeted and low-cost immunization strategy using a ( non-hypothetical ) medium-efficacy vaccine . We consider also outbreak response and/or medical case management alone . We model a zero-cost or null scenario in which no intervention at all is implemented ( not even medical case management ) . We generate cost and cost-effectiveness estimates for six middle-income countries: Brazil , Columbia , Malaysia , Mexico , the Philippines , and Thailand . These countries were the focus of recent systematic reviews of epidemiological trends and make up an estimated 15% of the global burden of dengue [21] . Our approach is deliberately conservative . As the focus of the study is assessing the cost-effectiveness of vector control in the age of a dengue vaccine , a conservative approach means making generous assumptions about the efficacy and cost of the vaccine and ungenerous assumptions about the efficacy and cost of vector control .
The model runs two probabilistic Markov chains in parallel–one each for vector and human populations . The model is depicted in Supporting Information S1 Fig . In every cycle , the probability of a vector being infected with the dengue virus , thus moving from susceptible ( SV ) to infected ( IV ) , is dependent on the number of infected humans in the previous cycle . Likewise the probability of a susceptible human ( SH ) being infected is dependent on the number of infected vectors . However , susceptible humans are separated into two categories , denoted SH1 and SH2 , representing susceptibility to first and second infections respectively , or DH1 and DH2 . New-borns enter the model in state NBH . New-borns have maternal immunity and therefore remain immune from dengue infection for a period of time . As maternal immunity wanes , new-borns enter the susceptible population ( SH1 ) . When a human recovers from their first infection ( DH1 ) , they enter a state of recovery from first infection ( RH1 ) . In state RH1 , humans are assumed to have cross-immunity and cannot contract dengue , after which they become susceptible to a second infection ( SH2 ) from a different dengue serotype . If a human contracts dengue for a second time ( DH2 ) –that is , contracts a different dengue serotype–they may progress to severe dengue ( SDH ) . In SDH , there is an elevated probability of mortality ( Death ) . If an individual recovers from DH2 or SDH they are assumed to no longer be susceptible ( RH2 ) . The baseline model therefore allows for the effect of herd immunity ( and loss thereof ) . As individuals move through the states , contracting dengue , the proportion of individuals that are still susceptible decreases and the population as a whole builds up herd immunity . If new individuals entering the model are less likely to contract dengue for reasons other than acquired immunity ( for example , the introduction of sustained vector control , as described below ) , herd immunity wanes over time , at the rate of population replacement , and the probability of transmission increases . We model symptomatic or apparent cases only . The number of cases of asymptomatic or unapparent cases is potentially much larger [22] . Several studies have demonstrated high levels of asymptomatic infection in endemic countries [23][24] , leading to more severe primary symptomatic infections in those individuals [25] . Recent evidence suggest that asymptomatic viraemic individuals can infect Aedes aegypti [26] , although there is no evidence as to whether these are then able to infect humans . We have not estimated the effect of asymptomatic infection in our models due to the limited literature available . The ( biting ) female vector population is assumed to be proportional to the human population . The average number of female vectors per host is 1 . 0–6 . 0 , with a sine function of period length 2π and a standard deviation of 20% [27] . In addition to seasonal variation , there is also a probability in every cycle of switching to an outbreak model of the vector population . In defining dengue outbreaks , previous studies have used moving means of the number of human cases and standard deviations of those means , but these differ between countries and even at subnational level [28] . We model outbreaks as a 100–200% increase in the vector population over a period of 2–9 weeks [29] . The increase is relative to the baseline vector population of any given intervention . This choice allows us to more directly model the effect of outbreak response . The Markov model is structured such that the increase in vector population results , through a higher probability of infective bites , in a higher number of human cases . During the burn-in period , the probability of outbreak is set such that frequency of outbreak is once every 3–5 years [30] . This range is consistent with the country reviews , although somewhat conservative with regard to Malaysia [31] . Urbanization is reflected in part by increases in the new-born and susceptible human populations , given by the crude birth rate and the urban population growth rate minus the crude birth rate , respectively . We model the combined effect of both unplanned urbanization and climate change after the burn-in period by allowing for the frequency of outbreak to drop to as low as once every 2 years ( that is , frequency follows a triangular distribution with 2 and 5 as minimum and maximum , respectively , and 3 as the most likely value ) . Alternatively , we could have modelled climate change as an increase in the average number of female vectors per host and/or standard deviation of the sine function . However , this alternative would suggest predictable rather than unpredictable variation over time in the number of vectors and , by extension , a different type of outbreak response than we have defined below . A list of the baseline model parameters and distributions of the probabilities underpinning the model are listed ( with sources ) in Table 1 . Additional parameters for the intervention models are listed in Table 2 , and described here . Whenever possible , we used country-specific parameter values; in practice , these were mainly available for population at risk estimates , unit cost estimates , and basic health statistics such as population , birth rates , death rates ( adult and child ) , life expectancy , and urban growth rates . Of note , we used country-specific probabilities of death among severe dengue cases receiving care . These were based on country-reported data , and varied considerably: from a low of 0 . 65% in Mexico to a high of 7% in Brazil [32] . In the presence of medical management , the fatality rate of severe dengue cases is assumed to be 0 . 7–7% ( range of best estimates from the six countries ) . The fatality rate is the same regardless of whether there is vector control or immunization . In the absence of any medical case management , however , the fatality rate for severe dengue is assumed to be 5–20% [40][41] . Recent reviews reveal that many existing vector control technologies have not been robustly evaluated for impact on reducing human dengue cases [57 , 58] . By technology , we refer to any combination of strategies and intervention tools that have been costed in the economics literature; these include biological ( e . g . copepods ) , chemical ( e . g . insecticides ) and environmental ( e . g . screens ) interventions . To our knowledge , only one study has measured the epidemiological effect of such vector control technologies . A randomized controlled trial ( RCT ) of community mobilization for dengue prevention demonstrated a lower risk of infection with dengue virus in children ( relative risk reduction 29 . 5% , 95% confidence interval 3 . 8% to 55 . 3% ) and fewer reports of dengue illness ( 24 . 7% , 1 . 8% to 51 . 2% ) [59] . Several trials have , however , evaluated entomological impact . The above-mentioned RCT found a 51 . 7% ( 36 . 2% to 76 . 1% ) reduction in the number of pupae per person ( pupae found/number of residents ) . An earlier systematic review and meta-analysis found that the most effective method ( integrated vector control ) resulted in decreases of 67–88% ( best estimates ) in the following indices: the number of containers with larvae per 100 houses ( Breteau index ) , the percentage of water containers positive for larvae or pupae ( Container index ) and the percentage of houses with water containers containing larvae or pupae ( House index ) [60] . The value of larval indices has been challenged , however; pupal indices may be more valuable given lower pupal mortality and higher correlation with adult densities [61] . All evidence considered , the systematic review concluded that “dengue vector control is effective in reducing vector populations , particularly when interventions use a community-based , integrated approach , which is tailored to local eco-epidemiological and sociocultural settings and combined with educational programmes to increase knowledge and understanding of best practice” . We opted to model the effect of vector control as a reduction of the vector population ( or , more generally , a reduction of the vector population capable of transmitting the virus ) . Entomological effects may vary significantly between settings , not least because the choice of vector control technology is country- if not community-specific [62] . We therefore considered two broad categories of vector control technology: a medium-efficacy technology that reduces vector populations by 50–70%; and a high-efficacy technology that reduces vector populations by 70–90% . We acknowledge that such reductions in adult vectors may not have been conclusively demonstrated for long-term use of existing technologies . These above reductions are applied to periods of outbreak also , such that sustained vector control limits the increase in vector populations during the initial phases of the outbreak , before the outbreak response is deployed . We define sustained vector control as vector control activities undertaken routinely throughout the year , usually monthly , regardless of changes in the number of vectors or human cases . Outbreak response activities , in comparison , are undertaken only in response to spikes in the number of vectors or human cases and only for as long as those spikes last . During outbreaks , increases in vector populations still occur in the presence of vector control , but from a lower baseline level . In the baseline model of outbreak , the vector population remains high for 2–9 weeks . The effect of outbreak response is modelled by a switch of the vector population to pre-outbreak levels after a lag of 1–2 weeks–the minimum time assumed to be required to detect the increase in vector populations and deploy the outbreak response . Since we are assuming that any sustained vector control programme includes also an outbreak response component , this same effect is modelled for both sustained vector control and outbreak response alone . This assumption is optimistic with regard to outbreak response and therefore conservative with regard to demonstrating the cost-effectiveness of sustained vector control relative to outbreak response . In the absence of sustained control , vector populations return to baseline values , whereas in the presence of sustained control , vector populations return to a level determined by the vector control technology ( e . g . 70–90% below baseline values , in the case of the high efficacy technology ) . We assume that when sustained vector control and outbreak response are introduced , it is the susceptible ( rather than infected ) vector population that is immediately reduced . The number of infected vectors decreases more gradually ( over their 28-day life span ) . This assumption is pessimistic with regard to vector control and therefore conservative with regard to demonstrating the cost-effectiveness of vector control versus immunization . The first-ever dengue vaccine has now been licensed for use in persons aged 9–45 by countries in Asia and Latin America and is under regulatory review by others . Phase III clinical trials of CYD-TDV ( Dengvaxia ) have measured efficacy over 25 months from the first dose [63][64] . Pooled results ( age ≥ 9 ) suggest efficacy of 38% ( 3–63% ) among seronegatives and 78% ( 65–86% ) among seropositives [16] . Unfortunately , these same trials reveal high rates of hospitalization among those who were vaccinated when seronegative . This result suggests that the immunization strategy should target the seropositive population [55] . In our model , we consider an optimistic scenario in which the immunization strategy is perfectly effective in targeting the susceptible seropositive population ( further described below ) . The effect of immunization is modelled as a probability among individuals susceptible to a second infection ( SH2 ) of moving directly to the state of recovery from a second infection ( RH2 ) . The same percentage reductions are applied to both outbreak and non-outbreak cycles because outbreaks are modelled as changes in vector population rather than in serotype distribution . The entire at risk human population is targeted for vector control . The broadest definition of the at-risk human population includes the urban population of the six countries . We also considered the subset living in urban slum areas . Poor urban communities typically have environmental characteristics that facilitate Aedes spp . breeding , including presence of refuse deposits and containers for water storage [65][66] . We also extracted from DengueMap a list of all locations from which alerts of dengue cases or deaths had been issued in 2013 [67] . We obtained the latitude and longitude coordinates for these locations using the geocode program of the R package mapproj . We then merged this dataset with the g-econ database [33] . We counted populations living within gecon-coded areas satisfying at least one of the following two conditions: 1 ) population density of more than 250 per km2 ( non-rural settings ) ; AND 2 ) average minimum temperature of no less than 5 degrees Celsius AND an average maximum temperature of no more than 36 degrees Celsius; AND 3 ) gross domestic product ( GDP ) per capita ( 2005 purchasing power parity ) of less than US$ 10 000 ( excludes areas with a level of development equivalent to a high income country ) ; OR occurrence of a dengue alert within the 1-degree latitude by 1-degree longitude cell . These cut-offs were based on studies identified in a recent systematic review of dengue risk mapping models [68] . In PSA , the susceptible human population was represented by a triangular distribution using g-econ , urban slum and urban populations as the most likely , minimum and maximum values , respectively . Mathematical models suggest that , to achieve significant reduction in the disease burden , immunization is most effective if it includes only individuals that have been already exposed to at least one dengue virus [69] . Some countries have decided to roll out immunization by targeting specific age groups , such as all 9–14 year olds in the first year and then all ( new ) 9 year olds in the second year onwards . Targeting strategies may be adapted to local settings . In theory , age or other individual characteristics could be selected to either minimize the number of seronegative people or maximize the number of seropositive people vaccinated . We assume optimistically that the susceptible seropositive population is so effectively targeted that 0% of the people that receive the vaccine are seronegative ( 100% are seropositive ) . In other words , we assume that targeting strategies are perfectly effective in avoiding vaccine-enhanced disease in vaccinated seronegative people . Furthermore , we assume that 70–80% of the population that is seropositive ( and still susceptible to second infection ) is contained in the population of people that are targeted over a period of 52 weeks . Of these , 80–90% are ( again , optimistically ) assumed compliant with vaccination ( comparable to yellow fever vaccination coverage among children in Brazil ) . It bears emphasizing here that the objective of this paper is to assess the cost-effectiveness of vector control in the presence of a ( hypothetical ) highly targeted and low-cost vaccination strategy using a ( non-hypothetical ) medium-efficacy vaccine; it is not to assess the cost-effectiveness of the medium efficacy vaccine itself . The cost of medical management of dengue cases is based on the utilization of general health services only , or the “hotel cost” of hospital bed days and ambulatory visits excluding any laboratory tests or drugs . The hospitalization rate , duration of hospitalization , and number of ambulatory visits are provided in Table 1 . We assume a hospitalization rate of 14% for non-severe dengue and 100% for severe dengue ( references are provided in Table 1 ) . Hospitalized non-severe cases are hospitalized in primary hospitals , and severe cases are hospitalized in specialist hospitals . Unit costs ( best estimates and standard errors ) were obtained using data and methods from WHO-CHOICE [70] . These unit cost estimates are summarized in Table 3 . All symptomatic cases were assumed to receive medical management in all scenarios , regardless of whether there was sustained vector control , outbreak response and/or immunization . We conducted a search of the literature on the cost of sustained vector control interventions and identified eight studies with primary data , from 12 countries , considering different biological , chemical , and environmental measures . A subsequent systematic review revealed no additional studies [11] . We extracted data on costs as well as populations or households covered . Costs were converted to per capita terms and inflated to 2013 US$ using GDP deflators . These unit costs were then modelled in a multivariate log-log regression on population covered and GDP per capita . More than 50% of the variation in unit cost between the studies was explained by these two variables alone , driven by a strongly negative relationship with population . In addition to economies of scale , it is likely that higher cost biological and environmental measures were only implemented at smaller scale , in targeted communities . A plot of the data and regression model results are available in Supporting Information S2 Fig and Supporting Information S1 Table . We calculated means and standard errors for the predictions for the six countries considered in this study , using their urban populations and GDP per capita . For each country , 1000 values of ( log ) unit cost were drawn from a normal distribution , and then exponentiated . These unit cost estimates are summarized in Table 3 . The best estimates are in the range of about 2013 US$ 0 . 04–0 . 05 per person per month–similar to the cost of larviciding and/or adulticiding programs described in studies from Brazil , Cambodia , Venezuela , and Thailand . The confidence intervals are wide , however , with highs of up to 2013 US$ 0 . 06–0 . 09 allowing for smaller scale biological and environmental measures . These unit costs are similar to those described in studies from Guatemala , Kenya , Mexico , Myanmar , and the Philippines , published prior to 2013 . No costing studies have been undertaken for technologies under development using Release of Insects with Dominant Lethality ( RIDL ) or Wolbachia symbiont infection of vectors . The cost of outbreak response was taken from a study from Panama [53] . That study found that the cost of vector control during an outbreak , including larviciding and adulticiding , was about 2005 US$ 0 . 035 per person per week . The Panama costs are conservative relative to a more comprehensive costing of outbreak response in Cuba , including health education and/or replacement of defective water tanks [71][72] . Similarly , the Panama costs are conservative relative to environmental and/or chemical interventions triggered by case reports in Malaysia and Thailand [73][74] . We considered adjustments for our six countries using the proportion of labour in costs ( 71% ) and GDP per capita relative to that of Panama in the year of the study . Minimum , most likely , and maximum values were obtained considering adjustments for GDP per capita only , or GDP per capita and the labour proportion , or no adjustment at all . We assumed that a sustained vector control programme would implement extraordinary outbreak response interventions in the midst of an outbreak; therefore , during an outbreak , the total cost of sustained vector control includes the cost of both the sustained vector control and outbreak response interventions . To the cost of both sustained vector control and outbreak response alone we also added the cost of sustained surveillance , based on a study from Brazil , at a cost of 2013 US$ 0 . 014 per person per week . Again we generated country-specific minimum , most likely , and maximum values using the proportion of labour in costs ( 39% ) and relative GDP per capita [56] . Since the cost of a future vaccine is unknown , the cost per person vaccinated was assumed to be about 2013 US$ 20 . This unit cost is assumed to include the cost of screening for seropositivity , or whatever the cost of the perfectly effective targeting strategy , as well as administration of the vaccine itself . It is purposefully optimistic . An earlier study considered a range of US$ 10–300 per unit for the cost of vaccine production alone [12] . In most countries , US$ 20 is less than the cost of the 1–3 outpatient visits that would be required for administration alone ( Table 3 ) . It is also considerably lower than the maximum of the median willingness-to-pay results ( US$ 70 ) from Vietnam , Thailand , and Colombia [75] .
The number of cases of symptomatic dengue in the baseline model ( medical case management alone ) is depicted in Fig 1 , for each of the six countries . The waves represent seasonal variation . Outbreaks are not visible in the best estimate or uncertainty intervals obtained from the 1000 simulations but are visible in individual simulations , only one of which is depicted for illustration . We compare these baseline model results to recent published estimates to ensure that we are not overstating the potential effects of intervention in terms of cases or DALYs averted [22] . For most countries our estimates in year one are towards the lower bound of those published estimates , and for all countries the uncertainty intervals overlap . Our estimates trend slightly upward over time . Note that the published estimates take into account current ( but variable ) efforts to control dengue whereas the estimates presented in Fig 1 are for our baseline model ( medical case management alone ) . The effect of sustained vector control on the total number of dengue cases ( including severe cases ) is depicted over time in Fig 2 . Different sustained vector control technologies ( medium and high efficacy ) are considered , and compared to the baseline ( medical case management only ) . Vector control technologies of low efficacy ( <50% reduction in vector populations ) have limited longer-term impact on transmission , and are therefore not depicted . Initially , medium- and high-efficacy vector control technologies result in a significant decrease in the number of cases . The combination of vector control and acquired immunity push the basic reproduction number to below one . In time , the number of susceptible people waxes and herd immunity wanes . With high-efficacy vector control , the period of low transmission lasts from four to seven years , varying between countries . Differences between countries are explained by differences in the country-specific parameters , namely birth rates , death rates , and urban growth rates , which together determine the rate at which susceptible people are introduced into the model . The number of cases of severe dengue follows a similar pattern , but with a somewhat longer-term effect of high efficacy vector control , as can be verified in Supporting Information S3 Fig . The effect of highly targeted immunization using a medium-efficacy vaccine on the number of dengue cases is depicted over time in Fig 3 , alone and in combination with sustained vector control ( high efficacy ) . The total number of dengue cases is largely unaffected by immunization alone , because it is targeted at seropositives only and the number of severe dengue cases is small relative to the total . Only the combination of sustained vector control and immunization maintains the number of cases at very low levels , for between four and nine years depending on the country . Again , differences between countries can be explained by differences in the country-specific epidemiological parameters . The number of cases of severe dengue , however , exhibits a different trend , as can be seen in Supporting Information S4 Fig . Here , because of our optimistic assumptions about how effectively an immunization strategy could target the seropositive population , we have a large and sustained effect of immunization , alone and in combination with sustained vector control ( high efficacy ) . This figure should be interpreted as confirmation that our assumptions about the immunization strategy have been optimistic . Table 4 summarizes the average annual number of DALYs in the period 2015–2030 under different intervention scenarios , compared to WHO Global Health Estimates for the year 2012 . Our model suggests that medical case management alone would result in an average of 22 . 3–232 . 3 thousand DALYs per year ( range of best estimates across the six countries ) . The uncertainty intervals on these estimates lie just above WHO estimates for the year 2012 , with the exception of the Philippines ( for which our uncertainty interval overlaps with the WHO estimate ) . Life expectancy is lower in the Philippines ( 69 years ) , than for any of the other countries ( 74–78 years ) . Note that WHO Global Health Estimates reflect variable levels of intervention across countries; they are also based on highest observed life expectancy globally . Outbreak response has a negligible impact on the average burden , given that outbreaks occur relatively infrequently on average and that the response is triggered only with a lag after the increase in the vector population . The introduction of sustained vector control ( medium efficacy ) reduces the burden to an average of 18 . 5–167 . 0 thousand DALYs per year . Sustained vector control ( high efficacy ) reduces the burden to an average of 12 . 0–89 . 6 thousand DALYs per year . Average annual costs over the 15-year period are reported in Table 5 . At current prices of existing technologies , the total cost of sustained vector control ( using a high efficacy technology ) including outbreak response and medical case management ( 2013 $US 58 . 0–377 . 6 million , range of best estimates across the six countries ) is comparable to what would have to be spent on outbreak response and medical case management ( 2013 $US 57 . 7–368 . 7 million ) . This result is driven by differences in the cost of treating the cases that would not be averted by outbreak response , given a minimum one week lag between the increase in vector populations and deployment of the outbreak response . In Table 5 , the best estimates of cost are expressed also as a percentage of government health expenditure ( GHE ) in 2013 . Sustained vector control ( high efficacy ) including medical case management would cost 0 . 4–1 . 2% of GHE in five of the six countries . In the Philippines , where GHE in 2013 was much lower in per capita terms than for the other five countries , it would cost 4 . 0% of GHE . Average cost-effective ratios ( ACERs ) and incremental cost-effectiveness ratios ( ICERs ) are presented for each country in Tables 6 and 7 , considering high- and medium-efficacy vector control technologies , respectively . If sustained vector control is effective in reducing mosquito populations by 70–90% ( high-efficacy , Table 6 ) , the average cost per DALY averted would be US$ 679–1331 ( range of best estimates across the six countries ) relative to the null scenario . The combination of high-efficacy sustained vector control and a highly targeted and low-cost immunization strategy using a medium-efficacy vaccine dominates all other interventions except immunization alone . However , immunization alone averts far fewer DALYs . In five of the six countries , the combination of vector control and immunization is very cost-effective , with an ICER well below one times GDP per capita . In the Philippines , it is cost-effective at a threshold just above one times GDP per capita . Recall that the Philippines is the country with the lowest life expectancy among the six; it is also the country with lowest GDP per capita , and therefore the country with the ( presumed ) lowest willingness-to-pay ( WTP ) . If vector control is effective in reducing mosquito populations by only 50–70% ( medium-efficacy , Table 7 ) , the average cost per DALY averted would be US$ 808–1907 ( range of best estimates across the six countries ) relative to the null scenario . Again , the combination of medium-efficacy sustained vector control and a highly targeted and low-cost immunization strategy using a medium-efficacy vaccine dominates all other interventions except immunization alone . However , again , immunization alone averts far fewer DALYs . In four of the six countries , the combination of medium-efficacy vector control and immunization is very cost-effective ( the ICER is below one times GDP per capita ) . In Mexico and the Philippines it is cost-effective at thresholds between two and three times GDP per capita . Mexico is the country with the lowest reported death rate among severe dengue cases receiving medical management . Uncertainty around cost-effectiveness is reflected in the cost-effectiveness acceptability curves of Figs 4 and 5 . The combination of high-efficacy sustained vector control and a highly targeted and low-cost immunization strategy using a medium efficacy vaccine ( Fig 4 ) has the highest probability of being most cost-effective at WTP thresholds as low as one quarter of GDP per capita ( per DALY averted ) . At a WTP threshold of one times GDP per capita , the probability that this is the most cost-effective strategy exceeds 85% in four of the six countries .
We estimated the cost and cost-effectiveness of sustained vector control in six high-burden countries . Our model suggests that , at current prices , the cost of sustained vector control and medical case management is comparable to what would otherwise have to be spent on outbreak response and medical case management . In December 2015 , in its decision to approve Dengvaxia , Mexico reported that it was spending about 2 . 5% of its health budget on medical treatment alone [77] . Many countries have abandoned or lack effective surveillance systems to respond rapidly enough to outbreaks to make much of a dent in the number of cases requiring medical management [78] . Our results on cost are nonetheless conservative relative to an earlier review and qualitative synthesis of the evidence from single settings , which found that the cost of outbreak response exceeds that of sustained vector control [79] . We considered only direct medical costs and control programme costs–we did not consider direct non-medical costs faced by patients during their care ( e . g . food , transportation ) nor any productivity losses ( time spent away from work ) of the patients or their caretakers . Our estimates of the cost per DALY averted by sustained vector control ( related to doing nothing ) are lower than that of the DCP2 , but higher than those from studies of single settings . In those studies , cost-effectiveness ratios ranged from 2005 US$ 227 ( 2013 US$ 344 ) per DALY averted by larval control in Cambodia to 2009 US$ 615–1267 ( 2013 US$ 802–1652 ) per DALY averted by adult mosquito control in Brazil [9][10] . Taking a broader societal perspective including productivity losses , the Cambodia programme cost only 2005 US$ 37 ( 2013 US$ 56 ) per DALY averted . Again , we did not consider these productivity losses–their measurement remains controversial , although few would argue that they are zero . We have not ( nor to our knowledge has any earlier study ) considered the additional benefits of vector control targeted at dengue for the prevention of other diseases , such as Chikungunya , yellow fever and Zika fever , transmitted by the same vectors and ( for all but yellow fever ) lacking effective vaccines and specific treatment . These additional benefits of sustained vector control would increase its cost-effectiveness . Even with conservative assumptions , our results suggest that sustained vector control can be highly cost-effective . Our model suggests that the introduction of highly targeted and low-cost immunization strategy using a medium-efficacy vaccine does not alter the conclusion that sustained vector control can be highly cost-effective . On the contrary , vector control may complement a medium-efficacy vaccine , or a vaccine that is highly effective but against only secondary infections or only one of the four dengue serotype , or whose production is highly constrained . In these instances , sustained vector control may compensate to some extent for lower levels of coverage by immunization . There are as yet no costing studies for dengue vaccine delivery , nor even a known price for the vaccine itself [77][80][81] . Indeed , there are still many unknowns about immunization for dengue . Overall , our assumptions for immunization can be considered conservative from the perspective of the cost-effectiveness of sustained vector control . That is , we have been optimistic in our assumptions about immunization with regard to both effects and costs , in order to have the most conservative estimate of the cost-effectiveness of vector control in the presence of immunization . We have been particularly optimistic in assuming that the targeting strategies for immunization are perfectly effective in avoiding seronegative people . This analysis should not , therefore , be used to draw conclusions about the cost-effectiveness of the current vaccine . When new information or new vaccines become available , these model parameters should be adapted . We accounted for many of the known sources of uncertainty within a probabilistic framework . In spite of considerable uncertainty , sustained vector control emerged with a high probability of cost-effectiveness . Some sources of uncertainty , however , could not be accounted for and remain as more serious limitations to our study . First , there is remaining uncertainty around particular parameter values . The serotype immunity variable ( serotype composition ) is assumed to be constant over time . The probability that dengue fever following a second infection develops into severe dengue is based on clinical paediatric data from four hospitals [39] . While the probability of death among severe dengue cases receiving medical management was based on country-reported data , the considerable disparity across countries raises questions . The availability of better data for these parameters would improve the precision of our model . Second , there is remaining uncertainty about the structure of the model itself , namely with respect to outbreaks , which are modelled as increases in the vector population . Vector indices have been correlated to increases in dengue cases during outbreaks , but the strength of evidence is limited by a lack of well-controlled studies [82][83] . Other studies have considered spatial , meteorological , epidemiological , and entomological factors , and dengue serotype [84] . Although this study could benefit from a more sophisticated outbreak model , uncertainty remains in the choice of variables and availability of data . Another limitation of the model structure is that we have not modelled heterogeneity in transmission . The probability of contracting dengue assumes an even distribution of vectors to humans throughout the susceptible population . However , a study from Armenia and Colombia found that 95% of Ae aegytpi pupae were concentrated in only 5% of houses [85] . More detailed risk mapping could improve our analysis and lead to better targeting and enhanced cost-effectiveness of vector control . Third , we have made no assumptions about the relative costs of the different vector control technologies–in fact , we have assumed the same unit cost for both medium- and high-efficacy technologies . Obviously , this analysis does not help us to choose amongst available vector control technologies . Further research is needed to understand the drivers of cost and effect across current and future vector control technologies . Indeed , it is unclear whether reductions in excess of 70–90% can be sustained using existing technologies; more research on the long-term impact of both existing and upcoming technologies is needed . Nonetheless , the result on the cost-effectiveness of high-efficacy vector control is fairly robust to higher unit costs . Comparing ICERs to WTP , we find that in most settings , the cost of sustained vector control using high-efficacy technologies could be considerably higher than that assumed in our model ( about US$ 0 . 05 per capita per month ) before it would no longer be considered cost-effective . At a WTP threshold of three times GDP per capita , the cost could be as much as 2 . 9–13 . 9 times higher without affecting our conclusions for any of the settings considered . At a WTP threshold of one times GDP per capita , the cost could be as much as 2 . 2–4 . 6 times higher without affecting our conclusions for Brazil , Colombia , Malaysia , and Thailand . Fourth , our model does not reflect uncertainty around the impact of resistance to insecticides in a scenario of sustained vector control . The data on this are limited , as a result of poor routine resistance monitoring [86] . New vector control technologies , such as genetic modification or symbiont infection of vectors , once developed , may help in mitigating the risk of resistance [87] . Our results are favourable to sustained vector control in general ( not to any one technology in particular ) and should not discourage the development of new technologies with demonstrated efficacy and safety . Indeed , our results apply to any technology that reduces the number of vectors able to transmit disease , rather than the number of vectors per se . Finally , our results may not be generalizable to other settings , for which we do not have good epidemiological or cost data . Low-income countries , especially those in Africa , are arguably more vulnerable and less prepared for the effects of unplanned urbanization and climate change , including the spread of vector-borne diseases [88] . These same countries have the poorest quality data on dengue , starting with the frequent misclassification of dengue as malaria . A regional study of the cost and cost-effectiveness of sustained vector control in low-income countries of Africa is needed . This paper focussed on the cost-effectiveness of sustained vector control in six middle-income endemic countries representing 15% of the estimated global burden of dengue . We have shown that sustained vector control will be cost-effective in most of these countries if it succeeds in reducing mosquito populations by more than 50% . Importantly , we show that the introduction of a highly targeted and low-cost immunization strategy using a medium-efficacy vaccine does not weaken the investment case for sustained vector control . Middle-income endemic countries should proceed with mapping the populations to be covered by sustained vector control . | Transmitted by the Aedes mosquito , dengue affects more than 100 countries and is rapidly emerging as the leading vector-borne disease . There has been a 30-fold increase in the number of cases reported since 1960 . The cost of the illness to the health system and to society at large is estimated at several billions of dollars annually . The health sector response has depended in large part on controlling mosquito populations during outbreaks . Recently , the first-ever dengue vaccine received regulatory approval for use in several countries . However , its roll-out and long-term impact still needs to be evaluated in the field . In this paper , we examine how the introduction of this vaccine might alter the investment case for sustained effort to control mosquitoes . To our knowledge , this is the first economic evaluation of mosquito control in the era of the dengue vaccine . We model the cost and effects of mosquito control in Brazil , Columbia , Malaysia , Mexico , the Philippines , and Thailand . We evaluate the cost-effectiveness of mosquito control in the presence of a vaccine that does not offer full protection to all individuals . Our results suggest that sustained mosquito control will continue to be cost-effective , even if roll-out of the current vaccine is highly targeted and low-cost . These results support current global policies and strategies for the prevention and control of dengue . | [
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"med... | 2017 | An economic evaluation of vector control in the age of a dengue vaccine |
Multigenic traits are very common in plants and cause diversity . Nutritional quality is such a trait , and one of its factors is the composition and relative expression of storage protein genes . In maize , they represent a medium-size gene family distributed over several chromosomes and unlinked locations . Two inbreds , B73 and BSSS53 , both from the Iowa Stiff Stock Synthetic collection , have been selected to analyze allelic and non-allelic variability in these regions that span between 80–500 kb of chromosomal DNA . Genes were copied to unlinked sites before and after allotetraploidization of maize , but before transposition enlarged intergenic regions in a haplotype-specific manner . Once genes are copied , expression of donor genes is reduced relative to new copies . Epigenetic regulation seems to contribute to silencing older copies , because some of them can be reactivated when endosperm is maintained as cultured cells , indicating that copy number variation might contribute to a reserve of gene copies . Bisulfite sequencing of the promoter region also shows different methylation patterns among gene clusters as well as differences between tissues , suggesting a possible position effect on regulatory mechanisms as a result of inserting copies at unlinked locations . The observations offer a potential paradigm for how different gene families evolve and the impact this has on their expression and regulation of their members .
Sequencing entire genomes of several plant species has shown that a prominent feature is the extensive duplications of genes [1] , [2] . Because the duplication of genes is frequently associated with a change in gene regulation [3] , it has been suggested that copying genes could represent a response to the environmental challenge that plants have to meet because of their immobility [4] . Therefore , it has been of great interest to determine the timing and mechanisms of gene duplications and the role of each copy in gene expression . Variation in gene copy number has also been observed between closely related species because of syntenic alignments of chromosomal regions . If a gene were copied before a progenitor of two species splits , one would expect that both copies would be present in progeny genomes . For instance , in the comparison of maize , sorghum , and rice , the fie gene homologs were duplicated in tandem before their progenitor split . After maize arose by a whole-genome duplication event , the sorghum and rice lineages retained both tandem copies , while the duplicated regions of the maize genome lost one of the two gene copies [5] . Indeed , a genome-wide analysis of tagged genes linked to a physical map indicated that polyploidization of maize led to massive losses of one of the duplicated gene copies [6] . Therefore , alignments between orthologous chromosomal segments of duplicated regions in maize with those of sorghum and rice have been used to examine how a single gene family has expanded before and after the polyploidization of maize [5] . In this case , the expansion and shrinkage of gene copies are tied to an important quantitative trait . Such a trait in cereal crops is the nutritional quality of their grain . The grain is the source of essential amino acids for the diet of animals and humans . Because cereal grain contains very little free amino acids , the bulk is derived from its protein content . Therefore , the relative proportion of each protein and its amino acid composition in the mature seeds of cereal crops dictates its nutritional quality [7] . Indeed , seeds accumulate proteins during maturation that have no known enzymatic function but specifically store amino acids , which are hydrolyzed during germination . These proteins are therefore called storage proteins . In cereals like rice , sorghum , and maize , they are referred to as prolamines because of their high content in proline and glutamine . They fall into four groups based on amino acid sequence homology , the α- , β- , γ- , and δ-prolamines . While in rice γ-prolamines have been extensively amplified and placed in unlinked locations [8] in sorghum and maize α-prolamines have undergone a similar expansion [9] . Because of these differences between rice and sorghum , they can act as an excellent reference to maize in respect to differential gene amplification . Interestingly , the first copying event that occurred in the progenitor of sorghum and maize resulted into a new locus , containing prolamines of two sizes: 19-kDa and 22-kDa . Additional copying occurred in each genome , but gene copies were also lost , either entirely deleted or damaged through premature stop codons . It is interesting that damaged gene copies have been quite stable over a long period of time and there is evidence that in some cases transcripts of genes with stop codons accumulate at low levels [10] , [11] . The low level could be explained by turnover of aborted translation of mRNA [12] . Nevertheless , transcription per se could be the reason that the life of genes is extended despite the fact that no full-length proteins are produced . Furthermore , alleles have been found that differ only by a premature stop codon , indicating that gene conversion might counteract gene silencing [13] . Whereas mechanisms of generating paralogous gene copies are poorly understood , syntenic alignments have shown that genes can insert at close and unlinked distances . Therefore , it is likely that copying involves also an extrachromosomal copy that includes some of the flanking sequences as well , which contain common target sites for transcriptional activators [14] . In other cases , where genes are tandemly duplicated , unequal crossing over between flanking direct repeats ( DR1 , DR2 , DR3 ) might have been an alternative route early in evolution [15] . To examine the role of gene copies within a multigene family , we took advantage of the haplotype variability of the α-zein gene family between two different inbred lines of maize . This family has been subdivided based on sequence homology and chromosomal location in z1A , z1B , z1C , and z1D [16] . The differential abundance is mostly based on tandem gene amplification , except that z1A and z1C gene copies are present in two locations on chromosome 4S . Interestingly , there are also allelic differences of individual gene copies between different inbred lines . Indeed , amino acid sequence heterogeneity has been used to map individual genes by IEF-gel analysis of segregating hybrids [17] . These observations could also be explained by the presence or absence of gene copies . There are a total of six different loci: one on chromosome 1 , four on chromosome 4 ( two of them physically linked ) , and one on chromosome 7 . At each locus , copies can be spread apart by several 100 kb , requiring the cloning of overlapping chromosomal fragments . Here , we took advantage of a BAC library made from inbred BSSS53 [15] as well as a supplementary BSSS53 BAC library and screened for clones comprising the allelic chromosomal regions of B73 . The BSSS53 clones were also sequenced and their content analyzed . Annotated sequences of both inbreds were aligned via their genes . Because these regions refer to a unique set of allelic differences that can be inherited as a linked unit , we consider them haplotypes of these loci . The prominent feature of these haplotypes is that they can differ in the content of sequences rather than simply single nucleotide polymorphism ( SNPs ) . Consequently , haplotypes have diverged in intergenic spacing and gene content , mostly in recent times of less than 2 mya , but long before domestication . There is also an interesting chronological order of events , where gene insertions are followed by retrotransposition into intergenic regions and even sometimes into genes . Variability has also an impact on the accumulation of transcripts , illustrating that quantitative traits could be directly linked to non-allelic gene copies within the same species . Interestingly , when endosperm is cultured , transcription of some gene copies can be induced , indicating that their expression was epigenetically regulated .
Taking advantage of the available FPC map [18] we positioned the z1D locus to FPC33 on chromosome 1S , the z1A1 and z1C1 locus to FPC156 on chromosome 4S , the z1C2 locus to FPC160 on chromosome 4S , the z1A2 locus to FPC163 on chromosome 4S , the z1B1 and z1B2 loci on FPC297 on chromosome 7S ( Figure 1 ) . These placements are consistent with previous mapping experiments [11] . However , z1B1 and z1B2 were closer than expected from the genetic map . While hybridization experiments have failed to link clone c0492M16 representing z1B1 and clone c0531H07 representing z1B2 [19] , both clones could be connected based on the FPC map with a single overlapping clone . From FPC297 , clone b397H03 , was chosen and sequenced ( accession GQ214221 . 1 ) because at the time the study was underway the reference genome sequence had not been available . Furthermore , although the B73 maize genome-sequencing project provided an excellent tiling path of overlapping BAC clones , individual clones lacked contiguous sequences . Here , one contiguous sequence was formed from the three overlapping clones comprising the entire z1B locus . It turned out that the bridging clone added one additional member of the z1B gene cluster , which was not present on the flanking BAC clones . As a result , we now have a complete set of α-zein copies in B73 . Another Stiff Salk Synthetic line that is of great interest is BSSS53 because of its high methionine content [20] . Because both lines are derived from the same breeding experiment , one could arguable use them as a model for haplotype variability of common inbreds . We used two BAC libraries ( see Materials and Methods ) of this inbred to isolate the complete set of α-zein genes . Because allelic gene copies are more conserved than tandemly duplicated copies , each chromosomal region of the two inbred lines was aligned with conserved sequences to illustrate sequence variability ( Figure 2 ) . In total , 41 α-zein genes , in B73 , and 48 in BSSS53 , are positioned on three chromosomes ( 1S , 4S , and 7S ) and form five distinct loci ( three on chromosome 4S ) . Differences in gene copy number seem not to be a general feature for all α-zein loci . Besides the z1C1 cluster the z1A1 cluster is the only other one that varies in gene copy number , both being physically linked to the z1C1 by a 300 kb segment . Three overlapping clones of BSSS53 were aligned to the z1A1 B73 allelic region ( Figure 2A ) ( accession GQ214222 . 1 ) . The z1A1 locus differed by only one copy , with 8 in BSSS53 and 9 in B73 . Taken together , within about half a megabase , BSSS53 has 30 α-zeins and B73 has 23 , but haplotype variability and gene content is very uneven over the entire length . The degree of variability is best illustrated by the differential expansion of these closely linked regions . Deletion or insertion of gene copies is more dramatic in the z1C1 region than in the z1A1 region and its gene density is higher ( Table 1 ) . The allelic regions of the z1C1 cluster differ in size between 169 kb in BSSS53 and 111 kb in B73 , while the allelic regions of the z1A1 cluster are nearly the same with 107 and 104 kb , respectively . We used nucleotide synonymous substitution rates ( Ks values ) to determine the chronology of tandem amplification events . The first z1C1 and z1A1 genes arose before the split of the Andropogoneae tribe 11 . 9 mya , probably by the insertion of one gene copy followed by unequal crossing over within the coding region because the resulting two tandem gene copies encode a 19-and 22-kDa zein with different number of internal repeats . These two new gene copies each seeded tandem clusters now separated by 300 kb of chromosomal DNA containing non-related genes and transposable elements ( TEs ) [9] . As a result the z1A1 region has mostly 19-kDa zein and the z1C1 region mostly 22-kDa zein genes . When the two allelic z1A1 regions are aligned , the only non-allelic 19-kDa zein gene present in B73 arose about 0 . 5 mya . This event , however , did not result from unequal crossing over within the coding region , but is rather a tandem duplication of ∼5 kb ( Figure 2A ) . Another tandem duplication resulted into the z1A1-3 and z1A1-4 copies also about 0 . 5 mya , present in both inbreds . It is not surprising that older copies became damaged because the newer ones could assume the role of providing storage proteins for the seed . The older copies at the z1A1 locus ( i . e . z1A1-1 , -8 , and -9 ) are either severely truncated at the 3′ end ( z1A1-9 is only 332 bp long ) , missing the A from the start codon ( z1A1-8 ) or have a premature stop codon ( z1A1-1 ) in both inbred lines ( Figure 2A ) ( phylogenetic trees constructed for each locus are available as Figure S1 ) . The z1A2 locus ( Figure 2B ) ( BSSS53 accessions GQ214223 . 1 and GQ214224 . 1 ) , which arose 2 . 2 mya after allotetraploidization ( Table S1 ) is populated by three zein gene copies , in both haplotypes , spread over 120 kb in B73 . In BSSS53 , we isolated two BAC clones that provided us with the genomic sequence of the three zein genes , but it proved to be difficult to design a PCR probe based on the large fragment between z1A2-2 and z1A2-3 , which is mainly composed of retroelements ( REs ) . Interestingly , the oldest copy for this locus , z1A2-2 , appears to be present within a helitron element , characterized by the 5′-TC , CTAG-3′ , hairpin sequences upstream of 3′ end , and a host nucleotide that is a G instead of the regular A . Because it lacks a gene encoding the helicase , it would be classified as a non-autonomous element [21] . No other genes besides the zein are present in the helitron . To determine whether the helitron has copied the zein gene , we screened 32 inbred lines . However , there appear to be no haplotypes lacking the z1A2 locus , different to the cytosine deaminase gene , for example , linked to z1C1 locus that was also a paralogous copy in some inbred lines but not others [22] . This might suggest that the z1A2 insertion occurred after and independent of the helitron movement . The other two zein copies at this locus are either intact ( z1A2-1 ) or have a premature stop codon ( z1A2-3 ) . The z1B locus has nine tandem copies that are spread over ∼200 kb . It took three overlapping BACs from BSSS53 ( accession GQ214225 . 1 ) with a total length of more than 260 kb to cover the allelic complement of the B73 z1B locus ( Figure 2C ) . Over a length of more than 260 kb , except for a 1 kb indel , the two haplotypes are nearly identical , with more than 97% sequence homology . This is twice the length of the z1A1 locus for the same number of zein genes , illustrating a rather large expansion of intergenic space . The original zein gene copy that inserted before allotetraploidization is z1B3 . Through subsequent copying events the other eight copies were generated . Six of them do not have additional insertions between them , one example being the z1B1 and z1B2 genes . Another is the duplication of a pair resulting in z1B4 , -5 , -6 and -7 . It is interesting that the pair z1B4 and z1B6 that arose from the most recent amplification ( Figure S1 ) appears to be intact . All the other seven copies have accumulated premature stop codons but none of them is truncated . The z1D locus is characterized by a massive expansion of intergenic regions ( Figure 2D ) . In B73 , three overlapping clones generated a total of ∼480 kb contiguous chromosomal sequence , where the five zein gene copies are spread over more than 300 kb [19] . Overlaps , however , had to take advantage of the deep coverage of the B73 BAC libraries ( 30x ) , which were not available for BSSS53 ( 3 . 5x ) . Indeed , the low gene-density in this region made it impossible to isolate a complete allelic complement from BSSS53 . However , two BSSS53 BAC clones containing all the z1D genes were isolated and sequenced ( accessions GQ214226 . 1 , and GQ214227 . 1 ) . Both clones were aligned with the B73 sequence based on the zein gene copies and TEs that inserted into this region before different haplotypes emerged . The most recently amplified zein copies , z1D2 and z1D4 ( Table S1 ) , are intact , while all other copies are damaged . They have either been truncated like the z1D1 gene or accumulated stop codons like the z1D3 and z1D5 gene copies . In addition to the stop codons , these two copies acquired RE insertions in B73 . The insertion in the latter copy occurred as recently as 0 . 12 mya , while the first one has its reading frame disrupted by a solo LTR . The major force of DNA mobility in the maize genome has been retrotransposition of LTR retrotransposons . When a retrotranscript inserts into a chromosomal region , it generates LTRs , which are identical at the time of insertion . One can assess the relative times of RE insertion events in each chromosomal region based on the Ks values of LTRs , which is two fold higher than that of a gene [23] . Such an approach is very helpful to gain insights into how these insertions relate to the insertion of gene copies . For instance , does the z1D locus harbor the oldest REs and the z1A2 the newest , respectively , according to the young age that the genes in this region have ? We looked at REs that are shared between the two inbreds , others that are specific to one or the other and also the nested ones , with a subcategory for nested elements that are also haplotype-specific ( Table S2 ) . One can immediately notice the contrast between the z1A2 and the z1D loci . The latter one is the site for very old insertions , some that even precede the allotetraploidization event: the Gypsy35 element that is 6 . 2 million years old being a good example ( Figure 2D* ) . Insertions like this are a good indicator that the two progenitors of maize had already undergone some retrotranspositions of the same elements , a phenomenon that became so active after allotetraploidization . Although the z1D locus is characterized by other insertions as old as 4 mya ( not found in any other loci ) , it is still prone to acquiring additional insertions as shown by the nested haplotype-specific Gypsy73 ( Figure 2D** ) element , whose LTRs are identical . In fact , the z1D5 zein gene in B73 has another one of these recent haplotype specific insertions , with a Zeon2 element ( Figure 2D*** ) inserting 0 . 12 mya , as described above . On the other hand , the z1A2 locus has no REs older than 1 . 5 mya , no nesting and a very recent haplotype specific insertion: the Zeon2 , with both LTRs identical ( Figure 2B* and Table S2 ) . In general , haplotype specific insertions are younger than 2 mya , with the oldest ones present at the z1C and z1D loci . Also , the age of the nested REs among all loci is less than 1 mya , with the exception of z1D locus with two REs that are close to 2 mya . It is here that the biggest cluster of nested elements was identified having about 60 kb in size . Nesting is almost absent for the z1A1 and z1A2 loci , with only one insertion ( Zeon2; 1 . 19 mya ) in the oldest RE ( Prem2; 2 . 04 mya ) ( Figure 2A* and ** , respectively ) of the first locus . An interesting question that arises is whether haplotype divergence might have an impact on gene expression . We previously used abundant EST resources to determine which genes are expressed [9] . However , these resources did not provide comparable quantitative levels and did not include BSSS53 . To determine which gene copy is expressed , at what level , in which inbred , we created cDNA libraries from immature endosperm at 18 days after pollination ( DAP ) from B73 and BSSS53 and their reciprocal crosses . We designed three universal primer pairs to amplify nearly full-length zein sequences specifically for the z1A1 and z1A2 loci , z1B and z1D , and z1C loci , respectively , and then randomly sequenced several 96-well plates ( enough to detect a zein gene expressed at a threshold of 0 . 3% ) for each sample and compared the results with genomic sequences . It is quite striking that in the case of the four 19-kDa zein gene clusters only two out of 26 gene copies are expressed at high levels ( Figure 3A and 3B ) . They are the same copies in both inbreds with no quantitative differences of expression in reciprocal crosses . Moreover , based on Ks values , these two copies would represent the most recently amplified gene copies . That is not say the older gene copies are not expressed , but at very reduced levels or not at all . In case of the two z1A loci a single gene copy , the transcripts of z1A2-1 , account for more than 90% of the total pool ( Figure 3A ) . This result differs from a recent expression study of α-zein genes [24] . Although the study also shows that z1A2-1 has the highest level of expression among the z1A gene copies , it seems to exhibit less specificity for individual copies . Indeed , primer selection for PCR seems to be the critical difference in respect to the length of primers , mismatches of primers to different clusters , and length of PCR products covering polymorphisms . Our study also used deeper sequencing of samples and was done for two inbreds with known genomic sequences and their reciprocal crosses . On the other hand , it was important to see that developmental expression does not seem to switch the relative contribution of each gene copy [24] , which permitted us to simplify our study by sampling a single developmental time point . The z1A2 locus is derived from z1A1 ( paralogous ) and z1A2-1 copy is the most recent tandem amplification , having the same age as z1A2-3 , 1 . 4 mya ( Table S1 ) . Despite an in-frame stop codon , the latter one is expressed although to very low levels at least in B73 . There is one non-allelic gene copy , z1A1-6+B73 , that is absent in BSSS53; it is expressed in B73 and in its hybrid with BSSS53 although at very low levels . The other 19-kDa zein gene clusters - z1B and z1D - also have only one gene copy expressed at high levels , which is again the most recent tandem amplification , z1B4 , accounting for more than 80% of the total transcripts ( Figure 3B ) . It together with z1B6 are the only ones that have an intact ORF , while the others have all accumulated in-frame stop codons , and are expressed at low levels , if expressed at all . Another inbred that we have analyzed ( W22 ) has a slightly different pattern of gene expression with the z1B1 transcripts ranking second after z1B4 , with more than 20% of total ( not shown ) . Analyzing its sequence we found that it has an intact ORF , unlike the alleles of B73 and BSSS53 , indicating allelic variations of stop codons as shown previously for z1C1 gene copies [13] . The z1D zein genes appear to be silenced , with only two copies ( z1D2 and -4 ) having intact ORFs; the others have either been truncated , accumulated stop codons , or had REs inserted on top of them . Given the expression potential of gene copies and the variability within the same gene family , we investigated whether gene expression could be changed by induction . A simple device for doing so is to culture differentiated cells . Indeed , tissue cultures have been shown to be responsible for turning on genes that are normally silent in vivo . For example Tos17 retrotransposon is activated in tissue culture of rice and this is due to cytosine demethylation [25] . It also has been shown that demethylation occurs at high frequency in tissue cultures of maize . Therefore , demethylation has been proposed as the main source of tissue culture-induced variation [26] . Previously , it was shown that one specific inbred , A636 , can be used to initiate maize endosperm cultures that faithfully maintain expression of storage protein genes [27] . A new culture was initiated as described previously and A636 endosperm was cultured for several weeks as described under Materials and Methods . RNA was then isolated from the callus cultured on solid and liquid media and cDNA libraries were created from RNA of A636 immature endosperm and A636 cultured endosperm cells . Sequencing of random cDNAs followed the same protocol as for the B73 and BSSS53 inbreds . The sequences generated were then compared to the genomic sequences of those two . There is clearly a difference in the expression pattern between normal and cultured endosperm ( Figure 4 ) . For some gene copies , expression appears to be induced like z1B3 and azs22 . 12 . While z1B3 has a premature stop codon , azs22 . 12 is a complete and intact gene copy in A636 , B73 , and BSSS53 that arose very recently ( 0 . 6 mya ) . Therefore , azs22 . 12 represents an example of a gene copy that was reactivated through the tissue culture process . The more common changes are expression levels . Expression is reduced for z1B4 and azs22 . 19 , but enhanced for z1B1 , azs22 . 4 , azs22 . 7 , and azs22 . 9 . In contrast to the z1C and z1B loci , z1A loci do not seem to be significantly affected under tissue culture conditions ( Figure 4A ) . Genes at the z1D locus remain silent , although two of them have intact ORFs . On the other hand , presence of a premature stop codon in the gene's ORF does not prevent enhanced expression after tissue culture treatment . For example z1B1 has an in-frame stop codon but is expressed at higher levels under tissue culture conditions . On the other hand , genes that have intact coding regions can be down regulated , like it is the case for z1B4 and z1B6 , for example . Therefore , changes in the expression levels of trans-acting factors through tissue culture might also play a role in quantitative levels of expression . Because even genes with premature stop codons are still transcribed , it appears that selection for conserved gene sequences also extends to the promoter regions of the α-zein genes . Although little is known about specific transcriptional activators of α-zein genes , they share a sequence motif with many other storage protein genes , GTGTAAAG , which occurs about 300 bp upstream of the translation start site and is called the −300 element or the P-box ( prolamine-box ) [28] . This element acts as an enhancer in a transient expression system and binds to the prolamine-binding factor ( PBF ) , which has been identified as a maize domestication locus [29] , [30] , [31] . A second trans-acting factor that is known is opaque2 ( o2 ) . However , in o2 mutants some α-zein gene copies are still expressed , indicating a redundant system of factors . We therefore compared the upstream region of all α-zein gene copies to identify sequence motifs within a window of 500 bp that might deviate from a consensus sequence using the PLACE database [32] . Indeed , functional genes have the P-box core motif and some of the non-expressed genes have mutations in this motif ( Figure S2 and Table S1 ) , consistent with the role of PBF as a regulator of zein gene transcription . Besides the P-box , we can also find a sequence motif for the o2 transcriptional activator ( Table S1 ) . It is present 171 bp upstream of the start codon , on the lower strand , for the z1A loci , 181 bp for the z1B locus , and 178 bp for the z1D locus . Because the methylation status of the promoters plays an equally important role in gene regulation , along with the presence of binding sites for various transcription factors , we analyzed the three expressed loci ( z1A , z1B , and z1C ) by bisulfite-sequencing 500 bp upstream of the start codon in three different tissues: normal and tissue-cultured endosperm , and leaf . Due to the high sequence homology in the promoters of paralogous gene copies it would be virtually impossible to analyze individual promoter sequences . Therefore we used universal primers for the three loci mentioned above to get an insight into their cytosine methylation status . The differences are striking and hint towards a possible different gene regulation mechanism that is locus-specific ( Figure 5 and Figure S4 ) . The z1A locus is characterized by five highly methylated cytosines , in leaf tissue ( Figure S4A , black arrows ) , whereas the rest of the promoter maintains a very low methylation level . The pattern is very similar to that of the z1C promoters , where there are four highly methylated cytosines , one of them representing the binding site for O2 ( Figure S4C; black and dark purple arrows , respectively ) . All five peaks at the z1A loci are in CG context , whereas only the one mentioned above for the z1C loci is in CG context , among the four . Very basal methylation is present along all the other cytosines in leaf DNA . Surprisingly , the endosperm grown under tissue-culture conditions has higher methylation levels than its normal counterpart , for z1A . This is not the case for z1B , where it behaves as an average between high and low peaks detected in leaf and normal endosperm , a unique pattern characteristic to this locus . Another striking difference is the high methylation patterns of the z1A and z1B loci in endosperm , when compared to z1C , all cytosines being less than 10% methylated here; another possible indication of the different transcriptional regulation of the 19- versus the 22-kDa zeins . Furthermore , the overall methylation of the z1C promoters is lower in all contexts and tissues analyzed . Interestingly , all cytosines at the z1B locus are in CHH context only , while the other two loci are characterized by CG , CHG and CHH methylation .
An unexpected result in the analysis of plant genomes has been that haplotype variability extends beyond nucleotide polymorphism to large-scale insertions and deletions , including genes , and could extend up to 2 . 6 Mb segments that are present in one but not the other haplotype [33] . Here , we investigated how this variability extends throughout the entire α-zein gene subfamily , which spreads over three of the ten maize chromosomes in six distinct locations . Our study gives an in-depth view of haplotype variability at the level of a specific medium-size gene family in maize rather than global [33] or single loci [34] . Because each location of the α-zeins , except for the z1C2 locus , contains tandem arrays of gene copies , they occupy large chromosomal regions . The largest region comprises two loci , z1C1 and z1A1 , about 540 kb in size on B73 chromosome 4S . We do not know the size in BSSS53 because of the lack of a fingerprinted map , but each cluster by itself is quite variable in size . While the z1A1 region is about 100 kb for each haplotype , the z1C1 region is 111 in B73 and 169 kb in BSSS53 . The second largest is the z1D region with 300 kb , followed by the z1B region with 200 kb , and the z1A2 with 120 kb in B73 . Although expansion of intergenic regions by retrotransposition is common to all zein gene loci , it seems to have been most active at the z1D locus . Although retrotransposition occurred before allotetraploidization , it was rather infrequent compared to recent times . Based on comparison with sorghum , maize had a greater activity of transposition , with additional copying of prolamine genes in tandem and also to unlinked positions as exemplified by the z1A2 and z1C2 loci on chromosome 4S and z1B on 7S , respectively . Diploidization possibly set in motion further divergence of homoeologous regions of maize as we can see from the z1B locus . Although this locus formed already before allotetraploidization , most retrotranspositions occurred between 0 . 1–3 mya . Interestingly , Ks values vary for common insertion events albeit not drastically . This variation indicates that haplotypes differ in Ks values if no apparent selection applies . However , it seems to be more parsimonious to suggest that rates changed after establishing different haplotypes because of a change in recombination rates , which could counteract nucleotide substitutions by sequence conversion . Consistent with this assumption is that haplotype variability occurred more recently . For instance , 52% of the z1A1 cluster in BSSS53 is composed of REs , but in B73 only 35% . The difference is due to elements that inserted only recently in one of the two haplotypes . We can clearly see that after chromosome expansion , additional retrotransposition resulted into segregating genotypes that remained stable . These genotypes constitute haplotypes that mainly differ in the intergenic space of these gene clusters . Interestingly , the percentage of TEs at all α-loci is significantly lower than the maize genome average , estimated at almost 80% for Maize 4a . 53 release . Although we can find examples of REs that inserted into zein gene copies ( e . g . z1D3 and z1D5 in B73 ) the reverse is not true despite that some zein genes were copied very recently . However , insertion into a gene might be favored if it is already damaged because both z1D genes had already accumulated stop codons in both inbred lines . While the nesting effect is also very recent it does not extend to zein gene copies to insert into other zein genes . Although we can observe some variability in the copy number of zein genes , particularly in the z1C1-z1A1 region , it is surprising how low this is compared to other gene clusters like the rp1 locus on chromosome 10 [35] . For instance , unequal crossing over could result in a change of gene copies . However , reconstruction of all gene clusters indicates a different mechanism of gene amplification . Although it is tempting to speculate that because of chromatin structure recombination would occur within actively expressed copies , we actually do not know whether recombination occurs preferentially within certain copies of a gene cluster . One would even expect gene conversion to reduce allelic diversity . However , chromosome alignments would have to be quite precise because of selection against unequal crossover between two conserved REs , which otherwise would lead to loss of gene copies . One interesting feature , common to all α-zein genes is that the most recently amplified gene copies contribute the most to mRNA accumulation . The older copies either accumulated premature stop codons or are truncated ( Table S1 ) . Presumably , older gene copies accumulate more mutations , gene truncation , even gene loss , and chromosomal rearrangement because the younger ones can complement a loss of function [36] , [37] , [38] , [39] . In the case of the 22-kDa zeins , the younger zein copies ( Zp22/6 and Zp22/D87 ) , which arose by a segmental duplication , are responsible for nearly 40% of the total z1C transcripts and this causes a shift to 65% of the transcripts being attributed to the new genes [11] . The transcriptional regulator O2 was no longer regulating their expression , which is true for the other α-zein gene loci and possibly could be explained by the interaction of other transcription factors with DNA binding motifs in the upstream promoter regions . Here , we also can show that , based on the phylogenetic data , the youngest zein gene copies are the ones that accumulate mRNA to detectable levels while the older ones have either accumulated stop codons or have been truncated . The methylation status of the promoters and the gene bodies themselves also seem to play an important role in the regulation of this family . Gene bodies of storage protein genes have been shown to be undermethylated in endosperm when compared to different somatic tissues and embryo , where a common methylation pattern was reported [40] . A more recent study corroborates the undermethylation in gene bodies with a CG depletion of duplicated sequences and speculates that the higher the expression of a gene is , the more CG depleted its sequence will be [41] . Our study confirms the observations but also extends the analysis to promoters of the different 19-kDa zein loci . In addition , we could show that other cytosines than the one inside the ACGT core sequence of the promoter of the 22-kDa zeins are highly methylated in the leaf and not in the endosperm . These could potentially play a role in the regulation of the newer 22-kDa zein copies that are not under the control of O2 [11] . Analysis of expression levels for members of the α-zein gene family has provided us with evidence that indeed the epigenetic state of each copy is an important factor in reviving older copies from a silenced state . We hypothesize that the longer the endosperm cells are cultured in liquid media the more likely it is that gene copies will return to their original methylation state ( Figure S3 ) . It is interesting to note the difference between gene copies at the z1A loci and the rest of the α-zeins . Whereas all the others show obvious effects on the expression levels when grown under tissue culture conditions , these genes are less affected . This would suggest that they might be under control of different regulators than the rest of the family members . Just like the O2 transcription factor does not extend its influence over younger zein copies at the z1C1 locus ( Zp22/6 and Zp22/D87 ) [11] the gene copies at the z1A2 locus , which arose after allotetraploidization from a translocation event originating at the z1A1 locus , might be under the control of different transcription factors . It is also interesting to note that among the 19-kDa zeins , the z1A copies are the only ones that have the position of both the P-box and the Opaque2ZMB32 motifs shifted by 10 bp , closer to the start codon ( Table S2 ) . Each of the loci analyzed for their methylation patterns in the promoter region differs from one another , probably due to position-specific influence caused from insertion into unlinked locations in respect to the donor copies . A recent study in rice endosperm shows that methylation is lower in all sequence contexts with a drastic decrease in CG methylation ( 93% of embryo level ) , 2x decrease for CHG and 5x decrease for CHH contexts [42] . This does not seem to be the case for maize endosperm , with fluctuations unique to each of the loci analyzed . For example , the z1B promoters are characterized by neither CG nor CHG contexts . Only CHH methylation is possible and the pattern is distinct , but overall having a decrease in methylation of the endosperm , compared to leaf . The z1C promoters , on the other hand , show significant drops in CHH methylation , not just CG or CHG , whereas z1A is characterized by significant decreases in CG methylation , as observed for the five highly methylated cytosines , but overall higher methylation for the others . Therefore , differential methylation patterns could be a position effect and result in differential expression of members of a multigene family .
B73 and BSSS53 plant material came from our lab stocks while A636 seeds were a gift from Dr . Hugo K . Dooner at the Waksman Institute . Initial screening of the BAC library already available in BSSS53 [15] was done by PCR with degenerated primers based on zein gene sequences . Once a BAC pool was identified as positive , it went through several rounds of dilutions until a single colony was identified and grown on LB plates . We later switched to screening the pooled BACs by filter hybridization . New primers were developed , that were specific for each of the zein loci and they were used to screen the BAC pools by PCR . Once a pool was identified as positive we diluted an aliquot of the stock in LB medium and directly streaked it on LB agar plates . Single colonies were then picked and grown on filters that were later hybridized with a PCR-generated probe specific for each locus . Due to the low coverage of the existing BAC library , a new one was created in order to isolate the BAC that contains the first two zein copies at the z1A2 locus . We partially digested genomic DNA with HindIII enzyme and cloned the fragments into the pINDIGO-BAC-5 vector from Epicentre . Both libraries combined had an average insert size of 100 kb and a genome coverage of 4 . 5x . All positive BACs were sequenced in a 3730xl DNA sequencer using the BigDye terminator chemistry ( Applied Biosystems ) by “shotgun” strategy up to 8x coverage . BAC sequences were assembled using PhredPhrap and then went through a first round of annotation using a series of software available on-line: BLAST suite from NCBI was used for homology searches and sequence comparison between the two inbreds , RepeatMasker for TE searches , and SoftBerry for gene prediction models . Sequences were then manually annotated , false gene predictions were eliminated , TSDs ( target site duplications ) and LTRs were identified for the TEs , and then the two haplotypes were aligned . A threshold of at least 95% homology was set when comparing the two sequences . To estimate the insertion time for the REs we used the left and right LTRs sequences that we input in the Mega4 software to calculate the nucleotide synonymous substitution rates ( Ks values ) . Default settings were changed to Distance and Std . Err . , Pairwise deletions and Kimura 2-parameter . We then used the Ks value reported for LTRs [23] to calculate the insertion time . To avoid any bias , we removed any indels or sequencing gaps from the LTRs before comparing their sequence . For the cDNA analysis in B73 and BSSS53 we used immature endosperm tissue harvested 18 DAP . RNA was extracted using the Spectrum Plant Total RNA Kit from Sigma , which was reverse-transcribed using the SuperScript III First Strand Synthesis Kit from Invitrogen . The cDNA was PCR-amplified with three primer pairs: one for the z1A loci ( 5′ primer: CTCTTAa/gATTAGTAGCTAATAt/cATC; 3′ primer: CTGGGAAGCCACAAACATCA ) , one for z1B and z1D loci together ( 5′ primer: ATTAGTCGGTAATCCATCAACC; 3′ primer: CTAGAAGATGGCACCACCAATG ) , and one primer pair that had been previously used for the 22 kDa zeins [11] . PCR products were ligated in the pGEM-TEasy vector from Promega and then transformed into E . coli cells ( ElectroMAX , DH10B; Invitrogen ) . We then randomly sequenced cDNA clones with universal primers from both ends . The consensus sequence of the two reads was obtained using SeqMan software ( part of DNASTAR Lasergene package ) . The consensus sequences were then blasted against a database containing the genomic sequences of all the zein gene copies from the two inbreds , and the hit with the highest score was recorded . All the hits for each individual gene were summed and the value converted in percent of total transcripts . The tissue culture experiment followed the same steps as above with the only difference that 13 DAP immature endosperm was used instead and the inbred line was A636 . The endosperm harvested from the same ear that was used to isolate RNA for immediate analysis of transcript levels in vivo , was used to induce the tissue culture . Same culture medium and conditions were applied as in [27] . One month later callus was regenerated from the endosperm . This was transplanted on a fresh solid medium and grown for two more weeks and then used to isolate RNA . After that , the tissue culture was maintained in liquid media , having the same composition as the solid one , minus the agar . A new batch of fresh callus was collected after two months of sub-culturing and used to isolate RNA , which was later used in the analysis presented as Figure S3 . 500 bp upstream of the start codon were used to search for motifs that are either shared or unique in all the members of the α-zein gene family . The search was done using PLACE [32] . Genomic DNA of A636 from normal endosperm , tissue culture-grown endosperm and leaf tissue was treated according to the protocol of Epitect Bisulfite Kit from Qiagen , for bisulfite conversion . Universal primers were manually designed for amplifying the z1C zein copies ( 5′ primer: ACATGTGTAAAGGTGAAGAG; 3′ primer: GGTCATTACTAATACACTTCAC ) . For the z1A and z1B clusters regions of high conservation among all zeins at the specific cluster were analyzed and then primers were designed in those regions using the z1A2-1 and z1B4 promoters as reference , respectively . Methyl Primer Express Software , freely available from Applied Biosystems , was used for the design . z1A 5′ primer: AGTGATTTTTTAAATYGATTATTAT , z1A 3′ primer: TATTTATACACATATCAATCCTTATACTT . z1B 5′ primer: TATGTGGTTAATGTTATATATGTGTAA , z1B 3′ primer: TTATTACTACTAAATTCCACTTTCTATATT . After PCR amplification the products were cloned into pGEM-TEasy vector from Promega and one 96 well plate was sequenced for each of the samples; i . e . , one for leaf DNA , one for normal endosperm and one for tissue culture-generated endosperm , with each of the three primer combinations , respectively . The consensus was obtained using SeqMan . Then the sequence was scanned for all the cytosines , to look for site of conversion .
We would like to thank Moisés Cortéz-Cruz and Amy Nelson for initial BAC library screening and sequencing in BSSS53 and Galina Fuks and Rémy Bruggmann for their help with the bioinformatics analysis . We also appreciate advice and help from members of the Dooner Lab at Waksman Institute . | We present here how the structure and function of a multigene family has shaped the architecture of the maize genome in a haplotype-specific manner , before and after allotetraploidization . The alpha zein gene family , the main component of storage protein genes , provides us with a model of how multicopy gene families evolve and are regulated in the plant kingdom . Indeed , gene copying might be the mechanism that helps plants adapt to variable environmental conditions . In this context , the alpha zein genes have evolved from a common ancestral copy , located on the short arm of chromosome 1 , to become a 41-member gene family in the reference maize genome , B73 . Different haplotypes can vary , though , as we show here , both in gene copy number and in their sequence context , the latter one being the result of the tremendous transposable element activity that the maize genome has undergone after its allotetraploidization . That had impact not only on the expression patterns of the gene family members , with newest copies contributing the most of the mRNA pool , but also on the mechanisms employed in their regulation , such as methylation of promoter sequences , which seems to be locus-specific . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods",
"Acknowledgments"
] | [
"genomics",
"gene",
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"genetics",
"plant",
"genetics",
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"biology",
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"genetics",
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] | 2011 | Differential Gene Expression and Epiregulation of Alpha Zein Gene Copies in Maize Haplotypes |
Since 1960 , magnetic fields have been discussed as Zeitgebers for circadian clocks , but the mechanism by which clocks perceive and process magnetic information has remained unknown . Recently , the radical-pair model involving light-activated photoreceptors as magnetic field sensors has gained considerable support , and the blue-light photoreceptor cryptochrome ( CRY ) has been proposed as a suitable molecule to mediate such magnetosensitivity . Since CRY is expressed in the circadian clock neurons and acts as a critical photoreceptor of Drosophila's clock , we aimed to test the role of CRY in magnetosensitivity of the circadian clock . In response to light , CRY causes slowing of the clock , ultimately leading to arrhythmic behavior . We expected that in the presence of applied magnetic fields , the impact of CRY on clock rhythmicity should be altered . Furthermore , according to the radical-pair hypothesis this response should be dependent on wavelength and on the field strength applied . We tested the effect of applied static magnetic fields on the circadian clock and found that flies exposed to these fields indeed showed enhanced slowing of clock rhythms . This effect was maximal at 300 μT , and reduced at both higher and lower field strengths . Clock response to magnetic fields was present in blue light , but absent under red-light illumination , which does not activate CRY . Furthermore , cryb and cryOUT mutants did not show any response , and flies overexpressing CRY in the clock neurons exhibited an enhanced response to the field . We conclude that Drosophila's circadian clock is sensitive to magnetic fields and that this sensitivity depends on light activation of CRY and on the applied field strength , consistent with the radical pair mechanism . CRY is widespread throughout biological systems and has been suggested as receptor for magnetic compass orientation in migratory birds . The present data establish the circadian clock of Drosophila as a model system for CRY-dependent magnetic sensitivity . Furthermore , given that CRY occurs in multiple tissues of Drosophila , including those potentially implicated in fly orientation , future studies may yield insights that could be applicable to the magnetic compass of migratory birds and even to potential magnetic field effects in humans .
Endogenous clocks help organisms to adapt to the 24-h cycle of the earth . One of the main characteristics of endogenous clocks is that they continue to oscillate even in the absence of external time cues . Under such conditions , they “free-run” with their endogenous inherited periods that are slightly different from 24 h . Therefore , they are also called circadian clocks . Under natural conditions circadian clocks are synchronized to the 24-h cycle on Earth by different Zeitgebers . The most important Zeitgeber is the light–dark cycle , but temperature , humidity , and the social environment can also serve as Zeitgebers . Additionally , magnetic fields have been discussed as agents that synchronize circadian clocks since 1960 , when Brown et al . [1] found that the circadian rhythms of fiddler crabs and other organisms are influenced by small changes in the intensity of Earth's magnetic field . In particular , the electromagnetic field with a frequency of 10 Hz is known to show a prominent 24-h oscillation [2]; thus , the field could serve as geophysical synchronizer of the circadian clock . Although electromagnetic fields are not consciously perceived by humans , Wever [3] demonstrated that the circadian activity rhythm of humans can be entrained by an artificial 10 Hz electromagnetic field , mimicking the natural oscillations on Earth . The activity of mice , house flies , and fruit flies could also be synchronized or phase-shifted by 10 Hz electric fields applied for 12 h daily [4 , 5] . Cyclically changing intensities of a static magnetic field can also work as weak synchronizers of the circadian clock , as shown by Bliss and Heppner [6] for house sparrows . The latter study was motivated by the idea that birds perform magnetic compass orientation and that this ability depends on a functional clock . Thus , the authors speculated about a link between the magnetosense and the circadian clock . Despite the compelling effects of ( electro ) -magnetic fields on the circadian clock , the mechanisms by which the magnetic field is perceived and how it manipulates circadian clocks are unknown . Two main models of magnetic field sensing are currently under discussion: ( 1 ) the magnetite model proposing a primary process involving tiny crystals of permanently magnetic material [7] , and ( 2 ) the radical-pair model suggesting a “chemical compass” based on singlet–triplet transitions in photopigments [8–10] . The radical-pair mechanism , in which the magnetosensor is a photopigment , was first suggested by Schulten and coworkers [8–10] . The first step of the reaction is the absorption of a photon of light energy by the pigment molecule , leading to the transient formation of radical pairs ( unpaired electrons ) with opposite electron spin states ( singlet electrons ) . The model predicts that these electrons are close enough together that the unpaired electron spin states can undergo transition , at some frequency , from antiparallel to parallel spin states ( singlet–triplet intersystem crossing ) . Such singlet–triplet intersystem crossing is a natural feature of many photochemical reactions that , in fact , may have nothing to do with magnetosensitivity and is the result of the system reaching the most energetically favorable state subsequent to absorption of photon energy . The critical feature of a magnetosensitive radical-pair reaction is that the radicals have a sufficiently long lifetime and are oriented in such a way that singlet–triplet transitions are modified by their alignment in the presence of an applied magnetic field . In such a case , product yield or rate of formation would be different depending on the relative amount of singlet or triplet formation , and these changes in output would provide a means for the organisms to detect the effects of even very weak applied fields . This presumed radical-pair mechanism has indeed been observed experimentally in reactions involving organic compounds [11] and in biological systems such as photosynthetic reaction centers [12 , 13] . Since the radical-pair mechanism of magnetosensitivity depends on the activation of the relevant photopigment by light , it is wavelength dependent . Magnetosensitivity is maximal at the absorbance maximum of the photopigment , and it depends on the strength of the magnetic field . Ritz et al . [10] calculated the yield of the triplet products in relation to the strength of the magnetic field and the angle of the field with respect to the alignment of the radical pair in space . They found that there is a small window of the magnetic field strength at which the radical-pair mechanism furnishes magnetosensory capacities . In agreement with these calculations , Wiltschko and Wiltschko found that the magnet compass orientation of birds works only at a certain range of magnetic intensities [14] . That magnetoperception is wavelength dependent has been shown not only in birds but also in newts and fruit flies . As photopigments , cryptochromes ( CRYs ) have been proposed [9 , 15 , 16] . CRYs are UV- and blue-absorbing photoreceptors , found in plants and animals , that contain flavin adenine dinucleotide ( FAD ) as chromophore [17] and have been shown to form radical pairs subsequent to light activation [18] . CRYs are expressed in the eyes of mammals [19] and migratory birds [20 , 21] , where crucial magnetoreceptors have been localized [22 , 23] . However , CRYs are also expressed in the circadian clock neurons of mice and flies [24–27] . This raises the possibility that magnetic fields can interact directly with the circadian clock . The fruit fly Drosophila melanogaster is best suited to reveal a putative role of CRYs in the magnetosensitivity of the circadian clock , because there is only one CRY , and because mutants are available that either knock out the binding domain of the FAD ( cryb mutants , [28] ) or the entire CRY molecule ( cry0 mutants , [29]; cryOUT mutants , [25]; cryΔ mutants , [30] ) . The molecular mechanisms of the circadian clock are largely unraveled . The main players of the clock are the clock genes period , timeless , Clock , and cycle , which participate in complex molecular feedback loops to generate the circadian oscillation [31 , 32] . CRY is known to play an important role in the photoreception pathway to Drosophila's endogenous clock [27] . Light promotes the binding of CRY to TIMELESS ( TIM ) , leading to the degradation of TIM [33 , 34] . Under light–dark cycles the action of CRY leads to a “reset” of the clock every morning . However , under constant light ( LL ) , TIM-mediated degradation by CRY occurs continuously . Therefore , the amount of TIM is reduced and the clock slows down ( i . e . , the free-running period of the locomotor rhythm is lengthened ) . When the constant illumination exceeds a critical intensity ( > 10 lx ) , TIM disappears completely and the clock stops running ( the flies become arrhythmic [35] ) . On the basis of the background described above we speculated that a magnetic field might influence the circadian clock via CRY under blue light but not red light conditions . To investigate this possibility , we measured the free-running periods of the flies' locomotor rhythms before and during exposure to a magnetic field under constant weak blue light or red light illumination . The blue and red lights were adjusted to such low intensities that in the absence of the magnetic field the flies showed only a slightly longer period than when they were under constant dark ( DD ) conditions . We found that 40% of the flies further lengthened the free-running periods during the exposure to a magnetic field of 300 μT under blue light illumination . This effect was completely absent under red light conditions . We also demonstrate that the response of fruit flies to the magnetic field is not only wavelength dependent , but also dependent on the strength of the magnetic field , as is implied by the radical-pair model . Furthermore , the magnetic field could not induce any period lengthening in cryb and cryOUT mutant flies; but period lengthening was enhanced after overexpressing CRY in the clock neurons . Our results indicate that the magnetic field influences the circadian clock via CRY .
The illumination of the flies was a critical parameter during our experiments , because we intended to activate CRY , but not to such an extent that arrhythmic behavior would occur . We recorded the flies under different intensities of blue light ( 465–470 nm ) and found that 0 . 18 μW/cm2 was a suitable light intensity for the experiments , at which the flies did not become arrhythmic , but showed a significant period lengthening . The mean period at 0 . 18 μW/cm2 blue light was 25 . 8 ± 0 . 14 h ( mean ± standard error of the mean [SEM] , n = 25 ) . This period was 1 . 7 h longer than under DD conditions [36] , indicating that CRY was activated by the constant weak blue light and that it provoked a constant weak TIM degradation . Our rationale was that we should see further changes in the free-running period of the flies as soon as they were exposed to the magnetic field affecting the singlet–triplet intersystem crossing of FAD in light-activated CRY . In a first experiment we tested the influence of magnetic fields of different intensities ( Figure 1 ) . The radical-pair mechanism predicts that there should be an optimal magnetic strength and that the effects would be smaller under too low or too high magnetic fields [9] . To enable comparison with previous experiments [37] we chose static magnetic fields of 0 μT , 150 μT , 300 μT , and 500 μT; excepting the control of 0 μT these are , respectively , 3 , 6 , and 10 times stronger than natural magnetic fields . The free-running periods of the flies were determined before and during the application of the constant magnetic fields and the changes in period were calculated ( independent of their direction ) . We found that even flies without exposure to a magnetic field exhibited period changes over the recording time , but that these changes were significantly smaller than those occurring under the influence of a magnetic field of 300 μT ( Figures 2A , 2B and 3A ) . ANOVA revealed that the period changes depended significantly on the strength of the magnetic field ( Figure 3A ) . Most flies lengthened their periods in response to the magnetic field ( Figure 2A ) ; but there are also some flies with shortened periods ( Figure 2B ) . To quantify the number of flies with shortened and lengthened periods we defined the following categories ( Table 1 ) : Flies exhibiting period changes smaller than 0 . 5 h ( as most wild-type flies did ) were defined as flies showing no effects . Flies with shortened or lengthened periods by 0 . 5 h or more were defined as flies showing period shortening or lengthening , respectively . The third category consisted of flies in which periodogram analysis could not detect any significant period . These were defined as arrhythmic . χ2 analysis revealed that the number of flies with lengthened periods was significantly higher when a magnetic field of 300 μT was applied ( Table 1 ) . Next we tested whether the effect of the magnetic field depends on the wavelength of the constant light . For that purpose the experiments were performed under a constant red light ( 625–630 nm ) of 0 . 18 μW/cm2 , with or without applying a field of 300 μT . We found no difference in period changes between the two groups ( Figure 3B; Table 1 ) . This result clearly indicates that the lengthening of the periods by the magnetic field is dependent on the wavelength . So far our results are consistent with the idea that magnetoreception involves a radical-pair mechanism occurring in a photopigment . In the next step we wanted to know whether CRY might be the relevant photopigment , so we tested cryb mutants in which FAD binding is impaired [38] and cryOUT mutants that lack CRY completely [25] . As expected , both mutants show short periods under blue light of 0 . 18 μW/cm2 ( Figure 2C ) , because TIM is not degraded due to the mutation in the CRY protein ( e . g . , [34] ) . The free-running periods of cryb and cryOUT mutants were 23 . 4 ± 0 . 05 h ( n = 25 ) and 23 . 7 ± 0 . 04 h ( n = 26 ) , respectively . After exposure to the 300 μT magnetic field , 88 . 0% of cryb flies and 76 . 9% of cryOUT flies did not show any period changes ( Figure 2C; Table 1 ) , and when changes occurred these were significantly smaller than those in wild-type flies ( Figure 4 ) . This indicates that CRY is involved in the magnetically sensitive response of the fly . Next we wanted to test whether we could not only diminish the responses to the magnetic field by knocking out CRY , but also increase the responses by overexpressing CRY in the clock neurons . This test could be done by expressing CRY under the control of the strong clock-gene promoter of the timeless gene ( in timgal4/uas-cry flies ) . The circadian clock of such flies shows increased light sensitivity [38] , and accordingly we found that CRY-overexpressing flies showed significantly longer free-running periods than wild-type flies under blue light . This result indicates that the higher amount of light-activated CRY degrades TIM to a greater extent . The average period of the 24 tested CRY-overexpressing flies was 26 . 9 ± 0 . 33 h , and one fly showed arrhythmicity . After exposure to the magnetic field , most CRY-overexpressing flies became arrhythmic ( Figure 2D; Table 1 ) and the remaining ones showed large period changes ( Figure 4 ) . All flies except one lengthened their free-running periods ( Table 1 ) . These results show that the degree of responsiveness to the magnetic field is directly linked to the levels of CRY present in the fly , and that the site of action of the magnetic field effect can be directly localized to the clock neurons , in which CRY is active in setting the circadian clock .
We show here that Drosophila's clock is magnetosensitive , and that this sensitivity depends on CRY . When the first studies were performed that showed magnetosensitivity of the circadian clock , CRY had not been detected yet and the mechanism of how the clock is influenced by magnetic fields was completely elusive . Now , we know that CRY is expressed in the clock neurons of Drosophila [25 , 30] , that CRY absorbs UV and blue-green light [39] , and that CRY consecutively binds to TIM and induces its degradation [34] . Under constant conditions this process leads to period lengthening or arrhythmicity [35] . The magnetic field effect that we observed in our study led to further lengthening of the period , in the same way as an increased intensity of blue light would lead to enhanced CRY function . The effect of a magnetic field was particularly pronounced in CRY-overexpressing flies , in which small differences in CRY function should be significantly amplified . Additionally , we could show that this reaction depends on the strength of the magnetic field and on the wavelength of the light . Red light that is outside of the absorption and action spectra of CRY [39–42] did not provoke significant responses . Consistent with the involvement of cryptochrome , cryb and cryOUT mutants respond neither to blue-green light nor to the magnetic field . Our results strongly support the radical-pair model suggesting light-activated flavin-based photoreceptors as sensors for magnetic fields . In several animals magnetoreception has been shown to depend preferentially on blue light [43–47] . Ritz et al . [10] stated first that CRYs are suitable molecules for such photoreceptor-based magnetoreception . CRYs are blue-green photoreceptors in plants and flies [17 , 28 , 48 , 49] and have been shown to form radical pairs upon photoexcitation [18] as is true for the closely related DNA photolyases [50] . Other classes of photoreceptors , such as phototropins [51] and chlorophylls [52] found in plants , also undergo radical-pair reactions . However , rhodopsins , which are the major photosensory pigments in flies and other animals , are not able to form radical pairs because photoexcitation leads to cis–trans isomerization of retinal rather than an electron transfer [53] . Thus , CRYs are the only currently known class of molecules found in animals that are likely to fulfill the physical and chemical characteristics that are required for functioning as the primary magnetic sensor . The first evidence that CRYs may work as magnetosensors comes from the plant Arabidopsis thaliana , where it was shown that CRYs mediate magnetic field-dependent hypocotyl growth inhibition under blue light [37] . In these experiments , the effect of the magnetic field was to increase the plant's response to perceived blue light by enhancing cryptochrome activity , similarly to the presently observed magnetic field effect in flies . The second evidence comes from the fly D . melanogaster ( that paper was published during the preparation of the present manuscript [54] ) . Gegear et al . [54] showed clearly that fruit flies need functional CRY to perceive a magnetic field that was paired with a sugar reward during a classical learning experiment . Although this study did not analyze the effect of magnetic field strength on CRY activity , these data are highly consistent with our present observations that the magnetic field intensifies the effects of blue light on the clock . The radical-pair mechanism predicts that the rate or yield of product formation from such a reaction will be altered by an applied magnetic field . Therefore , the pairing of blue light and the magnetic field may lead to the perception of more intense ( brighter ) light as compared to blue light of the same intensity presented alone . Consequently , hypocotyl growth was inhibited to a higher degree in Arabidopsis [37] , and flies learned to correlate a more intense light signal under magnetic field presence with the sucrose award [54] . In our experiments the circadian clock lengthened its period or became arrhythmic as if it experienced blue light of a higher intensity . All three phenomena clearly do not depend on directional effects of the magnetic field . That flies can also perform magnetic compass orientation was shown first by Wehner and Labhart [55] and further elaborated by Phillips and Sayeed [43] and Dommer et al . [47] . The latter two studies showed that Drosophila adult males and larvae are capable of learning the alignment of important environmental gradients ( i . e . , light , food , and humidity ) with respect to the geomagnetic field [43 , 47] . These responses were maximal under UV light , but the involvement of CRY was not tested . The ability of animals to detect geomagnetic fields has substantial biological importance , because it is used by invertebrate and vertebrate species for orientation and navigation [56] . Almost certainly CRY also plays a crucial role in this process ( see [10 , 16 , 20 , 21 , 57] for discussion ) . Fruit flies may be well suited to further unraveling the involvement of CRY in the mechanisms of magnetic compass orientation . Since CRY is also present outside the clock neurons in tissues such as the compound eye and the ellipsoid body [25] , in which it might show a directional alignment , it will be most rewarding to test the roles of these structures in magnetic compass orientation . In conclusion , we provide the first evidence of a mechanistic link between CRY and magnetic sensitivity in the circadian clock of Drosophila . Our data provide powerful support for the radical-pair hypothesis , because they show the predicted relationship between strength of the applied field and the reaction ( here period of the clock ) . Furthermore , the fact that the magnetic field leads to enhanced photosensitivity of CRY in both plants and flies argues for a similar mechanism of magnetoperception in all organisms that contain CRYs , including humans . Finally , given the recent observations that sun compass orientation in the monarch butterfly is dependent on the circadian clock and on CRY [57] , this effect of magnetic field on the circadian clock may in fact play a role in fly orientation .
CantonS was used as wild-type strain and compared with the cry mutants cryb [28] and cryOUT [25] . For CRY overexpression in all clock neurons , w;tim ( UAS ) gal4 flies [58] and w;UAS-cry flies [38] were crossed . All flies were reared under 12:12 light-dark cycles on Drosophila medium ( 0 . 8% agar , 2 . 2% sugar beet syrup , 8 . 0% malt extract , 1 . 8% yeast , 1 . 0% soy flour , 8 . 0% corn flour , and 0 . 3% hydroxybenzoic acid ) at either 20 °C or 25 °C . Only adult male flies at an age of 3–6 d were used for the experiments . Locomotor activity of individual male flies was recorded automatically with infrared light beams at constant 20 °C as described previously [59 , 60] . Briefly , the flies were confined to photometer cuvettes that were placed with one end in an infrared light beam . The number of light beam crosses by a walking fly was monitored during consecutive 1-min intervals . Monochromatic LEDs ( Lumitronix LED-Technik , Jungingen , Germany ) emitting blue ( 465–470 nm ) and red ( 625–630 nm ) light were used as light sources to illuminate the flies . Light intensity of both LEDs was adjusted to 0 . 18 μW/cm2 . Under constant blue light of 0 . 18 μW/cm2 , flies displayed slightly longer free-running periods than those in DD , but did not become arrhythmic . To expose the flies to magnetic fields , Helmholtz coils of sufficient diameter ( 20 cm ) to cover the locomotor recording device were placed above and below the recording device , generating a perpendicular static magnetic field ( DC mode of current ) ( Figure 1 ) . The magnetic field intensity was controlled by changing the voltage of the power supply that connects to the coils and was placed outside the recording device . The magnetic intensities were measured by a gaussmeter ( 475 DSP Gaussmeter , Lake Shore Cryotronics , Ohio , United States ) . To generate magnetic fields of 150 , 300 , and 500 μT , 6 , 12 , and 20 volts were given to the coils , respectively . For comparison , the natural magnetic field of the Earth ranges between 30 μT ( equator ) and 60 μT ( poles ) . We used static magnetic fields of 150–500 μT instead of oscillating magnetic fields of natural strength to compare our results directly with former results on A . thaliana [37] . The free-running rhythms were first recorded for 10–15 d without a magnetic field , and then fields of four different intensities ( 0 , 150 , 300 , 500 μT ) were applied . All experiments were performed in the same recording device that was positioned in a climate chamber . The climate chamber was surrounded by a metal case shielding the flies from the local geomagnetic field . In the absence of the applied artificial magnetic fields the Gaussmeter could not detect any magnetic field strength in the chamber containing the flies . Furthermore , the climate chamber was tightly temperature controlled . Ventilators close to the Helmholtz coils immediately blew away any heated air . A maximum of 0 . 5 °C temperature fluctuations were measured close to the Helmholtz coils . Such small temperature changes are known not to influence the circadian clock . The raw data were displayed as actograms using the program El Temps ( Antoni Diez-Noguera , Barcelona , 1999; http://www . el-temps . com ) . To evaluate the periods of the free-running rhythms , data for 10 d before and during the exposure to the magnetic field was chosen and the periods were calculated by the χ2 periodogram analysis [61] . This is an objective method making a double-blind experimental approach needless . In the control flies that did not experience any magnetic field , exactly the same subjective days of the flies were used for period determination . If a peak above the 0 . 05 confidence level appeared in the periodogram , the period was designated as statistically significant . A one-way ANOVA was used to test for significant influences of the magnetic fields or of CRY on the amount of period changes . A subsequent post hoc test with Bonferroni's adjustment was applied for pairwise comparisons ( Systat 11; SPSS , Chicago , Illinois , United States ) . χ2 analysis was used to test whether the number of arrhythmic flies or the number of flies showing period lengthening increased significantly upon exposure to the magnetic field . Values were regarded as significantly different at p < 0 . 05 . | Magnetic fields influence endogenous clocks controlling the sleep–wake cycle of animals , but the underyling mechanisms are unclear . Birds that can do magnetic compass orientation also depend on light , and the blue-light photopigment cryptochrome was proposed to act as a navigational magnetosensor . Here we tested the role of cryptochrome as a light-dependent magnetosensor of the clock in the fruit fly Drosophila melanogaster . In wild-type flies we found that constant magnetic fields slowed down the speed of the clock in a dose-dependent manner—but only in the presence of blue light . In mutants lacking functional cryptochrome , the magnetic fields had no significant effects on the endogenous clock , whereas the effects were enhanced after overexpression of cryptochrome . Our data suggest that cryptochrome works as a magnetosensor in the endogenous clock when it is excited by blue light . Our work supports previous data showing that fruit flies need functional cryptochrome to perceive a magnetic field , demonstrating that the interaction of cryptochome and magnetic fields are not just for the birds . | [
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] | [
"neuroscience"
] | 2009 | Cryptochrome Mediates Light-Dependent Magnetosensitivity of Drosophila's Circadian Clock |
Arboviral diseases are an important public health concerns . Vector control remains the sole strategy to fight against these diseases . Because of the important limits of methods currently used to assess human exposure to Aedes mosquito bites , much effort is being devoted to develop new indicators . Recent studies have reported that human antibody ( Ab ) responses to Aedes aegypti Nterm-34kDa salivary peptide represent a promising biomarker tool to evaluate the human-Aedes contact . The present study aims investigate whether such biomarker could be used for assessing the efficacy of vector control against Aedes . Specific human IgG response to the Nterm-34kDa peptide was assessed from 102 individuals living in urban area of Saint-Denis at La Reunion Island , Indian Ocean , before and after the implementation of vector control against Aedes mosquitoes . IgG response decreased after 2 weeks ( P < 0 . 0001 ) , and remained low for 4 weeks post-intervention ( P = 0 . 0002 ) . The specific IgG decrease was associated with the decline of Aedes mosquito density , as estimated by entomological parameters and closely correlated to vector control implementation and was not associated with the use of individual protection , daily commuting outside of the house , sex and age . Our findings indicate a probable short-term decrease of human exposure to Aedes bites just after vector control implementation . Results provided in the present study indicate that IgG Ab response to Aedes aegypti Nterm-34kDa salivary peptide could be a relevant short-time indicator for evaluating the efficacy of vector control interventions against Aedes species .
Chikungunya and dengue fevers are diseases caused by chikungunya ( CHIKV ) and dengue ( DENV ) viruses , respectively . These viruses are transmitted to the human host by the bite of an infected Aedes mosquito , especially Aedes aegypti and Aedes albopictus mosquitoes [1 , 2] . During the past three decades , the range of the mosquito vectors has increased and dengue and chikungunya have become endemic in areas where they previously were not creating major public health problems in tropical and subtropical regions [1] . Currently , no specific therapeutic drugs or commercial vaccine are available and vector control remains the sole method for reducing transmission . Vector control strategies commonly used are based on: i ) reduction of larval habitats by physical elimination of water-holding container and/or using larvicides and ii ) control of adult mosquitoes by insecticide spraying . However , some recent techniques could be also effective Aedes mosquito control strategies such as: i ) lethal ovitraps used for killing eggs , larvae , and female mosquitoes when they alight to oviposit , ii ) transgene system such as RIDL RIDL , i . e . “Release of Insects carrying a Dominant Lethal which induce repressible female-specific lethality , iii ) the use of Wolbachia-induced cytoplasmic incompatibility which can reduce mosquito life span and reproduction . The successful control of CHIKV and DENV transmission remains then linked to the efficacy of such anti-vector strategies . The evaluation of vector control against CHIKV and DENV transmission , and other arboviruses such as Zika , is based on entomological methods , such as the identification and numbering of larval habitats , the collection of adult mosquitoes ( by traps , pyrethrum spray or human lading catches ) [3] . The indices of Breteau , Adult Productivity , House and Adult density are the most common indicators for evaluating the abundance of Aedes population [4] . Unfortunately , these indicators present numerous limitations regarding large-scale follow-up . The identification of larval habitats is very labor-time consuming . Indices based on Aedes immature stages are a poor proxy for measuring adult abundance and are not efficient for assessing transmission risk [4] . Estimation of adult mosquitoes abundance is most appropriate to assess transmission risk [4] , but adults collection is fastidious and ethical concerns related to human lading catches may arise . In addition , these methods are mainly applicable at the community level and are not applied for evaluating the heterogeneity of the individual exposure to Aedes bites Much effort is needed to develop new , sensitive and complementary indicators for measuring individual exposure to Aedes bites and efficacy of vector control , and are highlighted by the recent Zika virus epidemic . The measure of human antibody ( Ab ) response to Aedes salivary proteins represents a novel approach . Previous studies have shown that bioactive molecules in arthropod saliva , injected in human skin during the vector bites , could induce host immune reactions [5–7] . Recent studies have demonstrated the usefulness of anti-saliva Ab response for measuring exposure of humans to arthropods bites , such as , ticks [8] sand fly [9 , 10] , Glossina [11] and mosquitoes [12–19] . Additionally , human Ab IgG response to whole saliva was identified as pertinent tool for evaluating efficacy of vector control against Anopheles [20 , 21] , Phlebotomus [22] and Aedes albopictus [23] . However , the use of whole saliva is not an ideal indicator , because of: i ) potential cross-reactivity with other vectors; ii ) weak stability / fast degradation of proteins in wholes saliva and iii ) poorly reproducible batches produced for large-scale studies . Optimization of this tool would be the identification of specific and antigenic salivary proteins and/or peptides . For example , only one gSG6-P1 salivary peptide , derived from gSG6 salivary protein , has been validated as specific biomarker of human exposure to An . gambiae and An . funestus bites [17 , 24–27] , and has been used to evaluate the efficacy of insecticide treated nets against malaria transmission [28] . In regard to Aedes species , the 34kDa salivary protein appears to be antigenic and specific to Aedes genus [29–31] . One peptide ( the Nterm-34kDa ) from this protein in Aedes aegypti saliva has been recently validated , by several studies , as appropriate candidate biomarker of specific exposure to Aedes bites [32 , 33] . The present study investigated whether individuals from La Reunion Island ( Indian Ocean ) , who are exposed to Aedes albopictus and not to Aedes aegypti species bites [34] , presented specific Ab responses to Aedes aegypti Nterm-34kDa salivary peptide , and whether the level of this specific IgG response could be influenced by the implementation of vector control strategies against Aedes mosquito species . For this purpose , human IgG responses to the Nterm-34kDa peptide were measured in individuals , before and after vector control implementation ( VCI ) .
This study followed ethical principles recommended by the Edinburgh revision of the Helsinki Declaration . The protocol was approved by a French Ethics Committee ( the Sud-Ouest , Outre Mer Ethics Committee; 25th February of 2009 ) and authorized by French Drug Agency ( AFFSAPS , French Ministry of Health; 12th January of 2009 ) . Written informed consent was obtained from all subjects included in the study . The study was conducted in two urban districts of Saint-Denis , the largest city in La Reunion Island , situated in the Indian Ocean ( 21 , 8160 S; 55 , 8310 E ) , ( 23 ) . During the massive chikungunya outbreak that occurred on 2005 , about 36% of the inhabitants of this island were infected [35] . In 2009 and 2010 , moderate outbreaks of chikungunya were also reported . Recently , autochthonous cases of DENV infection have been reported [36] . A longitudinal study was carried out during the peak of Ae . albopictus abundance , from the 2nd of May to 9th of July 2010 . Overall , 75 households and 102 individuals aged from 18 to 65 years , were randomly included by a “door to door” approach and according to the agreement of studied population to participate to the study . Each household was visited four times: before ( T0 ) VCI and 15 ( T0+15 ) , 30 ( T0+30 ) , 45 ( T0+45 ) days after VCI . The vector control implemented ( VCI ) was performed by the vector control unit of the Agence Régionale de la Santé ( ARS ) few days after the T0 visit and included: i ) physical elimination of Aedes positive breeding sites combined with ii ) spatial spraying of deltamethrin insecticide at 1g/ha concentration , twice two days apart , as previously described [23] . At each visit , artificial Ae . albopictus aquatic habitat were also physically eliminated by ARS team during each visit after VCI . A dried blood spot was collected from every individual at each visit ( for immunological analysis ) . Standardized questionnaires were individually administered to collect information about individual protection against mosquito bites ( use of bednets , mosquito repellent , mosquito coils , daily commuting out homes; i . e: getting out of the house every day for a professional activity ) . Dried blood spots ( n = 10; “not exposed individuals” ) were also collected during winter ( February ) from people who had not been out of France during the last four months before blood sampling , to serve as non-exposed control . The densities of Aedes albopictus adult mosquito were monitored every two days using four ( two for each district ) Mosquito Magnet® traps baited with CO2 and octenol . At each visit , all Ae . albopictus breeding sites were then physically eliminated . During the visits , larval indices were also calculated: i ) House index ( HI ) : percentage of houses infested with Aedes larvae and/or pupae and ii ) Breteau index ( BI ) : number of positive containers per 100 houses inspected . The Nterm-34 kDa salivary peptide has been selected as previously described [32] and synthesized and purified ( >95% ) by Genepep SA ( St-Jean de Vedas , France ) . The peptides were shipped in lyophilized form and then suspended milliQ water and stored in aliquots at -20°C . Enzyme-linked immunosorbent assay ( ELISA ) was performed as previously described [32] . Briefly , the peptide ( 20μg/mL in 100 μl of Phosphate Buffer Saline , i;e . PBS ) was coated for 150 minutes at 37°C into Maxisorp plates ( Nunc , Roskilde , Denmark ) . Plates were blocked by Protein-Free Blocking-Buffer ( Pierce , Thermo Scientific , France ) . Each eluate was incubated in triplicate at 4°C overnight at 1/20 dilution in PBS-Tween 1% . Mouse biotinylated Ab to human IgG ( BD Biosciences , San Diego , CA ) was incubated at a 1/1000 dilution in PBS-Tween 1% and peroxidase-conjugated streptavidin ( GE Healthcare , Orsay , France ) was added ( 1/1000 dilution in PBS-Tween 1% ) . Colorimetric development was carried out using 2 , 2’-azino-bis ( 3-ethylbenzthiazoline 6-sulfonic acid ) diammonium ( ABTS; Thermo Scientific , France ) and absorbance ( OD ) was measured at 405 nm . Individual results were expressed as the ΔOD value calculated according to the formula ΔOD = ODx − ODn , where ODx represented the mean of individual OD values in the two wells containing antigen and ODn the OD value in well without antigen . A subject was considered as an “immune responder” if ΔOD was higher than the cut-off ( Cut-off = mean ( ΔODunexposed ) + 3SD = 0 . 181 ) calculated from specific IgG level in negative “not exposed” controls ( n = 10 ) . After verifying that ΔOD values were not normally distributed , non-parametric tests were performed using GraphPad Prism5 software ( San Diego , CA ) to compare the ΔOD . The Mann–Whitney test was used for comparison of Ab levels of two independent groups and the Wilcoxon matched-pairs test was used for comparison of two paired groups . The non-parametric Kruskal–Wallis test was used for comparison of more than two groups and the Pearson’s Chi-squared test was used to compare two proportions . Moreover , a linear mixed effect regression ( with individual as random effect ) using R-software with the ‘nlme’ package , was performed for multivariate analysis of the specific IgG Ab response . All differences were considered significant at P < 0 . 05 .
The usefulness of measuring a human antibody response against Ae . albopictus using the Nterm-34kDa peptide was validated in individuals that were exposed to the bites of Ae . albopictus and in individuals that were not exposed ( P < 0 . 0001 , Mann-Whitney , Fig 1 ) . Overall , 88 . 23% ( 90/102 ) of Reunionian individuals presented specific IgG responses which were higher than the cut-off . The median value ( ΔOD = 0 . 510 ) of the specific IgG response from exposed individuals was 3 fold higher than the cut-off ( ΔOD = 0 . 181 ) . These results indicated the existence of high specific Ab response to Ae . aegypti Nterm-34kDa salivary peptide in human populations exclusively exposed to Ae . albopictus bites . The effectiveness of the VCI was evaluated by examining changes in IgG Ab response to Nterm-34kDa salivary peptide ( Fig 2 ) and prevalence of “immune responders” ( in % , Table 1 ) before and after the intervention . The median ΔOD value for IgG level decreased significantly until 30 days after VCI ( P <0 . 0001 from T0 to T0+15 and P = 0 . 0002 from T0+15 to T0+30; Wilcoxon matched paired test ) . No significant difference was observed between T0+30 and T0+45 time-periods ( Fig 2 ) . The proportions of immune responders ( Table 1 ) , also decreased from T0 ( 88 . 23% ) to T0+30 ( 67 . 64% ) , however a slight increase was observed at T0 + 45 ( 71 . 56% ) . The impact of VCI was also assessed according to the initial level of IgG Ab response in individuals ( T0 , i . e . before VCI ) . The “Immune responders” at T0 ( i . e: those with ΔOD ≥ 0 . 181; n = 90 ) were divided into three groups according to the values of the tercile at T0 ( Fig 3 ) : “lower responders” ( 0 . 181 ≤ ΔOD ≤ 0 . 4225; n = 30; Fig 3A ) , “medium responders” ( 0 . 4225 < ΔOD ≤ 0 . 7301; n = 28; Fig 3B ) and “higher responders” ( 0 . 7301 < ΔOD≤ 1 . 765; n = 32; Fig 3C ) . The changes in IgG Ab response in each group was assessed from T0 to T0+45 . For all groups , the IgG level to the Nterm-34kDa peptide decreased progressively . This decrease was significant until T0+30 for “medium” and “higher” groups ( Fig 3B and 3C ) . Interestingly , for the “lower responders” group , the median value of the IgG response was very low from T0+15 and below the cut-off until the end of the follow-up , whereas this point was never observed for the other immunological groups . Multivariate analysis was performed to assess the influence of potential confounders factors on the IgG Ab response , including: VCI , use of individual protection against mosquito bites , daily commuting out of dwelling house , sex and age . The results presented in Table 2 showed that the level of IgG Ab response to Nterm-34kDa peptide was significantly ( P <0 . 05 ) influenced by only VCI factor ( decrease of the IgG Ab level ) whereas , no significant influence of the other factors was observed . The presence of Ae . albopictus mosquitoes was estimated through adult mosquito density , BI ( Breteau Index ) and HI ( House Index ) . The evolution of these entomological parameters during the follow-up are presented in the Table 1 and in the Fig 4 . Overall , entomological indicators appeared to decrease during the study . Indeed , except the increase of adult mosquito density observed for a short period at T+15 and the slight increase of HI and BI at T0+30 , the values of these parameter remained decreasing until T0+45 after VCI . This decreasing trend appeared to be significant associated to the evolution of the median level of IgG response to Nterm-34kDa salivary peptide ( Fig 4 ) and to the proportion of “immune responders” ( Table 1 ) until the T0+30 time-point . From T0+30 to T0+45 , entomological parameters decreased whereas anti-Nterm-34kDa IgG response remained unchanged .
The present study reported , for the first time , the detection of IgG Ab response to Ae . aegypti Nterm-34kDa salivary peptide in human adult individuals exclusively exposed to Ae . albopictus bites . It shows that the level of specific Ab response and the proportion of immune responders significantly decreased after VCI . This decrease was detected earlier ( 2 weeks ) and persisted until 4 weeks after VCI . During the first week after VCI , the decrease of the IgG Ab response may be associated to the drop of adult mosquito density . This similar evolution of adult mosquito density and Ab response to the Nterm-34kDa salivary peptide appeared to persist until the end of study . Analysis of the evolution of HI showed a similar pattern with the level of IgG Ab response to the Nterm-34kDa peptide from T0 to T0+30 . Firstly these results showed the existence of Ab response to Ae . aegypti Nterm-34kDa salivary peptide in adult population exposed to Aedes mosquito bites , as previously observed in children [32] . In addition and as major point of the present study , these results validate the usefulness of the IgG Ab response to one salivary antigen for evaluating human exposure to Aedes bites and for monitoring vector control strategies against arboviral diseases . Two previous studies had validated IgG Ab response to Ae . aegypti Nterm-34kDa salivary peptide as pertinent candidate biomarker of Aedes bites in African and Asian individuals [32 , 33] . Here , the IgG Ab response to the Nterm-34kDa peptide was detected in 82 . 23% at T0 and relevant at least 67 . 74% ( at T0+30 ) of individuals living in urban area in La Reunion Island where Ae . aegypti is virtually absent [34] and Ae . albopictus is highly anthropophilic [37 , 38] . This highlights cross-reactivity between Ae . aegypti and Ae . albopictus salivary peptides . Such cross-reactivity was already reported in one study describing immunogenic proteins in Ae . albopictus saliva [39] . Indeed , in this former study , sera from individuals exclusively exposed to Ae . albopictus or Ae . aegypti bites , showed similar antigenic profiles by immunoblotting especially for the 34kDa family proteins . In contrast to the high proportion of immune responders observed in our study , another recent study reported only 19% of immune responders for both Ae . aegypti and Ae . albopictus SGE [18] . This difference could be probably explained by the fact that the present study specifically targeted Ab response to only one antigen ( antigenic peptide ) instead of overall antigenic proteins contained in the SGE . It can be hypothesizes that , among this cocktail of proteins , antigenic sequences located of the 34kDa putative protein , are probably low detected by specific Ab in the sera . The present findings highlight the pertinence of the IgG Ab response to Ae . aegypti Nterm-34kDa salivary peptide to detect human exposure to Ae . albopictus bites . The human Ab response to vectors’ salivary proteins is a pertinent tool for evaluating the efficacy of vector control strategies , against malaria [20–22 , 28 , 40] . In the present study , a significant decrease of human IgG response to Nterm-34kDa peptide was observed just one week after VCI . As it has been clearly reported an association between the level of human IgG Ab response to salivary proteins and vector densities [15 , 17 , 18 , 40 , 41] , we can hypothesize that the decrease of specific IgG response could be linked to the drop of Ae . albopictus density and by consequence , to a decrease of human exposure to Aedes mosquito bites . The rapid decrease of IgG Ab response to Nterm-34kDa peptide associated to the rapid reduction of entomological indices ( adults’ density , HI , BI ) emphasized the potential of the IgG response to the Nterm-34kDa peptide to detect rapid variations in human exposure to Aedes bites . This indicates that this peptide could be an accurate biomarker for evaluating the short-time efficacy of VCI on human-vector contact . The early but not long ( until 30 days ) significant decrease of IgG level to Nterm-34kDa , suggests a rapid but not sustained impact of the VCI done during the present study ( combined insecticide treatment and physical elimination of breeding sites ) , on the density of Aedes population . Others studies , reported same quick reduction in entomological parameters after VCI against Aedes mosquito [42–45] . It indicated that the IgG response to Nterm-34kDa peptide rapidly dropped after the interruption of human exposure to Aedes . Doucoure and his colleagues observed same decrease 6 weeks after VCI , using Ae . albopictus SGE as antigens [23] . The IgG response to the Nterm-34kDa peptide may be therefore more sensitive than IgG response to SGE for detecting early variations in human exposure to Aedes bites . This biomarker property appears appropriate for evaluation of emergency interventions during outbreaks , targeting adult vectors , especially in endemic areas of Aedes mosquito . Related to the three groups of immune responders defined according to the initial level of immune response at T0 , specific IgG response rapidly ( 15 days after VCI ) lowered below the cut-off in the “lower immune responders” group . Interestingly , this disappearance of specific IgG response was not observed in “medium” and “higher responders” groups . As previously observed for Ae . albopictus SGE [23] , this result emphasized the short half-life of IgG Ab response to Aedes salivary antigen after interruption of human-vector contact , especially in case of low exposure to mosquito bites ( i . e . low immune responder at T0 ) . No significant difference of specific IgG level was observed between T0+30 and T0+45 days after VCI . This seems to indicate that vector control strategies implemented in the study area have became ineffective after 30 days . A range of time from 15 to 30 days could be then selected for adequate evaluation of VCI by using such salivary biomarker . Future investigations should precise this timing to improve the operational evaluation of VCI efficacy . The multivariate analysis showed no influence of sex , age and professional activity in the level of IgG Ab response to Nterm-34kDa after VCI . As previously reported for Anopheles salivary biomarker [28] , it indicated that this salivary biomarker could be useful whatever sex and age of individuals . However , the exclusion of youngest population ( <18 years ) in our study represents a significant limitation . This biomarker should be validated for all age groups to demonstrate its full potential . In conclusion , the results suggest that the Ab response to the Aedes Nterm-34kDa peptide represent a relevant tool for evaluating human exposure to Ae . albopictus vector . This candidate biomarker can detect the short-time variations of human exposure to Aedes mosquito bite after vector control implementation . This immuno-epidemiological tool appears relevant to assess the efficacy of vector control against arboviruses vectors . | In absence of effective treatment and vaccine , vector control is the main strategy against arboviral diseases such as dengue , Zika and chikungunya . Given the limitation of entomologic tool currently used , news tools are urgently needed to assess the efficacy of vector control against arboviral diseases . The present study aimed to investigate whether human IgG antibody specific response to only one Aedes salivary peptide could be useful for assessing the efficacy of vector control against arboviral diseases . For this purpose , IgG response to Nterm-34kDa peptide was assessed from 102 individuals living in urban area at La Reunion Island , Indian Ocean , before and after the implementation of vector control against Aedes albopictus mosquito species . A significant decrease of this specific IgG level was noticed after vector control implementation . The decrease was associated to the decline in Aedes mosquito density estimated by entomological parameters , such as adult mosquito density , House and Breteau indices . The results of the present study indicated that human IgG response to the Aedes Nterm-34kDa salivary peptide could be a useful tool to evaluate the efficacy of vector control strategies against arboviruses . | [
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... | 2016 | Human IgG Antibody Response to Aedes Nterm-34kDa Salivary Peptide, an Epidemiological Tool to Assess Vector Control in Chikungunya and Dengue Transmission Area |
Rhodopsins ( Rhs ) are light sensors , and Rh1 is the major Rh in the Drosophila photoreceptor rhabdomere membrane . Upon photoactivation , a fraction of Rh1 is internalized and degraded , but it remains unclear how the rhabdomeric Rh1 pool is replenished and what molecular players are involved . Here , we show that Crag , a DENN protein , is a guanine nucleotide exchange factor for Rab11 that is required for the homeostasis of Rh1 upon light exposure . The absence of Crag causes a light-induced accumulation of cytoplasmic Rh1 , and loss of Crag or Rab11 leads to a similar photoreceptor degeneration in adult flies . Furthermore , the defects associated with loss of Crag can be partially rescued with a constitutive active form of Rab11 . We propose that upon light stimulation , Crag is required for trafficking of Rh from the trans-Golgi network to rhabdomere membranes via a Rab11-dependent vesicular transport .
The Drosophila phototransduction pathway has been extensively studied [1] , and most known members of the pathway were isolated in forward genetics screens by means of electroretinogram ( ERG ) and phototactic assays [2]–[6] . Since vision is not an essential sense , previous screens were performed on homozygous viable mutants . This strategy was highly successful and led to the characterization of numerous proteins that play a critical role in light responses . However , the phototransduction cascade is likely to also rely on components that are shared with other processes that are essential for viability . We therefore initiated a large mosaic ERG screen of lethal mutations on the X chromosome . Here , we report the characterization of Calmodulin-binding protein related to a Rab3 GDP/GTP exchange protein ( Crag ) . The Crag gene was first identified in a biochemical screen for photoreceptor Calmodulin ( CaM ) –binding proteins [7] . Crag contains a CaM binding site and interacts with CaM in a calcium-dependent manner [7] , [8] . Mutations in the Crag gene were later shown to affect the epithelial architecture and polarized localization of basement membrane proteins including Perlecan , Laminin , and Collagen IV [8] . Based on sequence homology , the N-terminus of Crag contains three conserved domains: uDENN , DENN , and dDENN , and belongs to the DENN ( differentially expressed in neoplastic and normal cells ) protein superfamily [9] . The first DENN family member was identified in a screen for variable mRNA expression in neoplastic cells [10] , and 18 genes encoding DENN domain proteins are present in the human genome . DENND4A , DENND4B , and DENND4C are the human homologs of Crag , and their cellular function in mammalian systems has not been characterized . Many DENN-domain-containing proteins have been found to interact directly and function as guanine nucleotide exchange factors ( GEFs ) of various Rab proteins [11] . The DENN/MADD protein was found to be a GEF for Rab3 and Rab27 [12] , [13] , whereas Connecdenn was found to be a GEF for Rab35 [14] . Furthermore , a genome-wide survey revealed that most DENN proteins are GEFs [15] . However , none of the DENN proteins were identified as GEFs for Rab11 . Rabs are localized to distinct intracellular membranes [16] , [17] , switch between the inactive ( GDP-bound ) and active ( GTP-bound ) conformational state , and respond to various signaling cues . In the active state , Rab proteins interact with their effectors and regulate vesicle trafficking at numerous different steps [16] . GEFs bind to inactive Rabs and facilitate the exchange of GDP for GTP , thereby activating the Rabs . Rab11 has been shown to affect many cellular processes . It mediates protein recycling by regulating membrane transport from recycling endosomes [18] . It is present in the trans-Golgi network ( TGN ) and post-Golgi vesicles , where it is required for membrane transport from the TGN to the plasma membrane [19] . In polarized MDCK cells , Rab11 is required for apical recycling and basolateral-to-apical transcytosis of immunoglobulin receptors [20]–[22] . In motile cells , Rab11 is required for transport of integrin to the leading edge [23] . During cellularization of Drosophila embryos , Rab11 is required for basolateral membrane growth [24] . Rab11 has been shown to bind to a subunit of the exocyst complex , Sec15 , which regulates polarized vesicle transport in epithelial cells and neurons [25]–[27] . However , despite Rab11's important cellular functions , no GEF for Rab11 has been identified to date . Rhodopsins ( Rhs ) are light sensors in Drosophila and vertebrate photoreceptor cells . Rh1 is the major Rh in Drosophila and is present in R1–R6 photoreceptor cells . Upon absorption of a photon ( 580 nm ) , Rh1 undergoes a conformational change to an active form , metarhodopsin ( metaRh ) , which in turn signals through a G-protein-coupled cascade that triggers the opening of the transient receptor potential ( TRP ) channel and leads to the depolarization of photoreceptor cells [1] . Besides its sensory role , Rh1 is required to form a rhabdomere terminal web , a meshwork of F-Actin cables , which is proposed to play a supporting role in the highly stacked rhabdomeric membranes [28] , [29] . A complete loss of Rh1 causes a collapse of the rhabdomere membrane at ∼90% of pupal development [30] . During development of photoreceptors , Rh1 is synthesized and matures in the endoplasmic reticulum , after which it is transported to the rhabdomeres via the Golgi . Impairment of its maturation process leads to severe photoreceptor degeneration [31] , [32] . Rab11 has been shown to be required for the post-Golgi trafficking of Rh1 to the apical rhabdomere membrane during the development of the photoreceptors . Rab11 colocalizes with Rh1 in the sub-rhabdomere region in vesicles , and reducing Rab11 activity causes defects in rhabdomere morphogenesis and accumulation of Rh1-positive vesicles in the cytosol [33] , [34] . However , a role for Rab11 in adult photoreceptor cells and the phototransduction pathway has not been documented . In adult flies , the conversion of metaRh1 back to Rh1 upon light exposure mainly occurs on the rhabdomere membrane upon absorption of a second photon ( 580 nm ) [35] . In addition , some Rh1 is endocytosed and degraded through a lysosomal pathway , to scavenge spontaneously activated or phosphorylated metaRh , thereby preventing photoreceptor degeneration [34] , [36] . As a consequence , newly synthesized Rh1 is delivered back to rhabdomeres to maintain Rh1 homeostasis as well as the overall rhabdomere morphology . However , it is unclear how this process is regulated in response to light exposure . Our data indicate that Crag and Rab11 play an essential role in the regulated transport of Rh1 to the rhabdomere membrane upon light stimulation and Ca2+ influx .
To isolate novel genes that are involved in visual transduction , we performed an F3 forward genetic screen on the Drosophila X chromosome [37] . Mutations were induced using low concentrations ( 7 . 5–15 mM ) of ethylmethane sulfonate on an FRT-containing X chromosome , and 33 , 887 stocks were screened for lethal mutations . A collection of 5 , 859 X-linked lethal mutations was established , and mutant clones in the eye were generated with ey-FLP [38] . We then performed ERG recordings on mutant photoreceptor cells of 3-wk-old flies and screened for aberrant ERGs . Hundreds of mutations were isolated , and rough mapping was performed through rescue of the lethality using X-chromosome duplications [39] . Mutations rescued by the same duplication were crossed inter se , and complementation groups were established . Here we report the characterization of one of these complementation groups . This complementation group , XE10 , consists of three alleles ( A , C , and D ) , and homozygous animals die as second or third instar larvae . ERGs of 3-wk-old XE10 mutant eye clones exhibit a reduction in both the amplitude of depolarization and the size of “on–off” transients when compared to control flies ( Figure 1A and 1B ) . To identify the gene that is mutated in the XE10 complementation group , we performed duplication and deficiency mapping [39] , [40] and narrowed the candidate region to a ∼120-kb interval . We performed complementation tests between the XE10 alleles and known lethal mutations in the region , and found a lethal insertion PBac{WH}CG12659f07899 [41] inserted in the first exon of the Crag gene that fails to complement all three XE10 alleles for larval lethality ( Figure 1C ) . The XE10 mutations also fail to complement a previously isolated null allele of Crag , CragCJ101 [8] . A Crag genomic construct rescues the lethality and phenotypes associated with all XE10 mutations ( blue in Figure 1D ) , showing that we have identified novel alleles of Crag . We identified a missense mutation in CragA ( C1371S ) , a nonsense mutation in CragC ( R798STOP ) , and a point mutation affecting a splice donor site in CragD ( Figure 1F ) . Lethal phase analyses indicate that the A and C alleles are severe loss-of-function or null alleles , whereas D is a hypomorphic allele ( Figure 1E ) . We observe no immunoreactivity in CragC clones of L3 larval eye imaginal discs using an antibody recognizing Crag , whereas in wild-type cells , the antibody reveals cytoplasmic punctae ( Figure 1G ) . Crag is a homolog of the human DENND4A , DENND4B , and DENND4C proteins . Expression of a UAS–human DENND4A construct using the ubiquitous daughterless-GAL4 driver rescues the lethality caused by loss of Crag ( Figure 1E ) , indicating that Crag and DENND4A have conserved functions . Since Crag mutant clones exhibit reduced ERG amplitude in 3-wk-old flies , we first examined whether the development of the visual system is affected by Crag mutations . To assess the morphology of photoreceptors , we performed cross-section and transmission electron microscopy ( TEM ) analysis of the eyes of newly eclosed flies . The data show that Crag mutant photoreceptors have normal rhabdomere morphology at day 1 ( Figure 2B ) , indicating that the photoreceptors develop properly . To examine axonal targeting of the photoreceptors , we immunohistochemically stained the terminals of R7 and R8 in the medulla for Chaoptin , a photoreceptor-membrane-specific protein [42] . As shown in Figure S1 , the two-layer projection patterns of R7 and R8 photoreceptors in the medulla in control and Crag mutant cells are indistinguishable . We then performed TEM in the lamina to determine whether R1–R6 photoreceptors target properly . Crag mutant photoreceptors form cartridges with the normal complement of photoreceptor terminals and synapses ( Figure S2A–S2D ) . These data indicate that the photoreceptors display proper axonal guidance and synapse formation . Hence , the aberrant ERGs of Crag mutant clones are not likely due to developmental defects . To determine whether Crag mutations cause photoreceptor degeneration , flies were kept either in a 12-h on/off light ( ∼1 , 800 Lux ) cycle or in constant darkness , and ERGs were recorded at different time points . Flies carrying eye-specific clones of the CragC or CragCJ101 null alleles ( Figure S3A and S3B ) exhibit normal ERG responses at day 1 when kept in the dark , indicating that the photoreceptors develop and function properly ( Figure 2A , 2C , and 2D ) . To further examine whether phototransduction is affected by Crag mutations in dark-raised flies , we performed intracellular recording of single photoreceptor cells . The data show that Crag mutant photoreceptors exhibit normal response to light stimulation when compare to controls ( Figure S3C–S3E ) . However , both the ERG amplitude and the size of on–off transients become smaller with age when the flies are reared in a 12-h on/off light cycle , whereas the ERGs of wild-type controls are unaffected in aged flies ( Figure 2A , 2C , and 2D ) . Interestingly , Crag mutant clones still exhibit a normal ERG response when kept in the dark for 2 wk . The phenotypes of aged flies kept in a 12-h on/off light cycle are fully rescued by the genomic rescue construct ( Figure 2A ) . Moreover , when the flies are exposed to constant light , ERG amplitudes of Crag mutant clones are severely affected , and a 90% reduction of ERG amplitude is observed in Crag clones after only 5 d . After 2 wk in constant light , the ERG responses are completely abolished in Crag mutant cells ( Figure 2A ) . Since Rh1 is the major light sensor of Drosophila photoreceptors , we also measured the Rh1 levels of flies exposed to light for different periods of time ( Figure S4 ) . The data show that the Rh1 levels of Crag mutant clones gradually decrease when the flies are aged in a light/dark cycle but not when they are kept in the dark . These data show that mutations in Crag cause a light- and age-dependent disruption of photoreceptor cell function . To determine whether the photoreceptor cells undergo neurodegeneration , we performed cross-section and TEM analysis of the fly eye . Crag mutant photoreceptors have normal rhabdomere morphology in newly eclosed animals , but upon 2 wk of light exposure , the rhabdomeres become severely damaged ( Figure 2B and 2E ) . Since the sizes of the on–off transients are reduced in Crag mutant cells upon light exposure , we also examined the morphology of the photoreceptor terminals after 14 d of incubation in the light/dark cycle . The morphology of the terminals is also disrupted in Crag mutant clones , and subcellular organelles such as capitate projections , mitochondria , and active zones are barely recognizable ( Figure S2F ) . The wild-type controls , as well as mutant flies that carry a genomic rescue construct ( data not shown ) , maintain a normal morphology of photoreceptors upon light exposure ( Figures 2B , 2E , and S2E ) . These data show that Crag mutations indeed cause light-induced photoreceptor cell degeneration . Since loss of Crag causes a light-dependent degeneration , we hypothesized that Crag in photoreceptor cells may play a role in phototransduction . Upon light stimulation , Rh1 undergoes a conformational change to metaRh , which consequently triggers the opening of TRP channels through G-protein-coupled signaling and leads to the depolarization of photoreceptor cells [1] . InaD contains five PDZ domains and serves as a scaffold protein to allow many players in the pathway to form a signaling complex [43]–[45] . We performed immunostainings of major phototransduction proteins with a whole mount protocol to assess whether Crag is required for their subcellular distribution . Rh1 , TRP , and InaD are all properly localized in Crag mutant photoreceptors in flies kept in dark , as the staining patterns are similar to those of wild-type cells ( Figure 3A ) . These data are in agreement with the functional data , as Crag mutant clones exhibit normal ERG responses in newly eclosed flies or flies kept in darkness for several weeks . Upon 5 d of exposure to a light/dark cycle , these proteins are still properly localized in control photoreceptors . However , in Crag mutant cells , Rh1 is massively accumulated in the cytosol ( Figure 3B ) . In contrast , a cytosolic accumulation of TRP and InaD is not observed ( Figure 3B ) . Notably , with the whole mount staining protocol , a crescent shaped distribution pattern of these proteins is observed at the base of the rhabdomeres , which is consistent with previous studies [36] , [46]–[48] . In contrast , a uniform distribution of these proteins in the rhabdomeres has been observed in immunostainings of thin sections [49]–[52] . We therefore also performed immunostainings in cross-sections of photoreceptors and observed a rhabdomeric localization of Rh1 , InaD , and TRP in flies kept in dark ( Figure 3C ) . Similarly , the data also show that light stimulation induces a cytosolic accumulation of Rh1 , but not TRP and InaD , in Crag mutant cells ( Figure 3D ) . The difference in localization between the two protocols is probably due to a different accessibility of the antibodies , since rhabdomeres are highly packed membrane stacks . These data suggest that Crag is involved in Rh1 transport upon light stimulation but not during photoreceptor development . Since Rh1 is a transmembrane protein , its accumulation in the cytosol of Crag mutant cells upon light stimulation would imply an accumulation of membrane structures . We therefore examined the ultrastructure of the light-stimulated photoreceptor cells by TEM . After 5 d of 12-h on/off light stimulation , Crag mutant cells exhibit a massive accumulation of vesicles in the cytosol ( 25 . 2±8 . 4 in wild type; 186 . 8±21 . 3 in Crag mutants: n = 10 , p<0 . 001 ) when compared to controls ( Figure 3E and 3F ) . The accumulation of Rh1 as well as vesicles in the cytosol indicates a defect in vesicular trafficking of Rh1 in Crag mutant cells . During photoreceptor development , Rh1 is delivered into rhabdomeres through a Rab11-mediated vesicle transport [34] . In adult flies , a subpopulation of metaRh is endocytosed and degraded [36] , [46] , and this Rh1 loss should be replenished with newly synthesized Rh1 to maintain homeostasis . The accumulation of vesicles in Crag mutant photoreceptors could be due to an increase in endocytosis , a decrease in the clearance of the endocytosed vesicles , or a failure to secrete newly synthesized vesicles . With white light stimulation , Rh1 is internalized at a very slow rate , as determined by Western blots and immunostainings ( Figure S5A and S5B ) . Therefore , we kept flies for a 6-h period in blue light to determine which aspect of Rh1 trafficking is affected by Crag . Blue light converts Rh1 to metaRh , and metaRh requires orange light ( 580 nm ) to be converted back into Rh1 . In the absence of orange light , blue light triggers massive endocytosis and degradation of metaRh [53] ( Figure 4A ) . Western blot data show that exposure for 6 h to blue light leads to a similar decrease of Rh1 in both wild-type and Crag mutant cells ( Figure 4B , upper panel ) , indicating that endocytosis and degradation of metaRh is not significantly affected in Crag mutant photoreceptors . Moreover , upon 24 h of recovery in the dark , there is a significant increase in Rh1 levels in both wild-type and Crag mutant cells , indicating that the de novo synthesis of Rh1 is also not affected in Crag mutant cells . Previous studies have shown that internalized Rh1 may form insoluble aggregates that are not detectable in Western blot [36] . We therefore homogenized fly heads in SDS buffer containing 1 M urea and performed dot blots to assess total Rh1 levels . The data confirm that Rh1 is indeed degraded upon 6 h of blue light stimulation in both wild-type and Crag mutant cells ( Figure 4B , lower panel ) . In summary , Crag does not appear to affect endocytosis , degradation , or synthesis of Rh1 . We further examined Rh1 dynamics upon blue light stimulation with the whole mount staining protocol . As shown in Figure 4C , 6 h of blue light stimulation triggers massive endocytosis of Rh1 in both wild-type and Crag mutant photoreceptors . After 18 h of recovery in the dark , cytosolic Rh1 is reduced and the crescent shaped pattern is re-formed in the wild-type photoreceptors , whereas in Crag mutant photoreceptors , Rh1 remains accumulated in the cytosol . Furthermore , the accumulation of Rh1 in Crag mutants persists even after 36 h of recovery . Blue light exposure drives photoreceptors into prolonged depolarizing afterpotential ( PDA ) [35] . To test whether the accumulation of Rh1 in Crag mutant cells is caused by a PDA , we exposed the flies to blue light followed by orange light for 20 min to terminate the PDA . We then kept the flies for 18 h in the dark and examined the Rh1 distribution ( Figure S5C ) . Rh1 still accumulates in the cytosol of Crag mutant photoreceptors but not in controls , indicating that Rh1 localization defects are not caused by PDA . We next performed TEM analysis of photoreceptors during the course of the blue light treatment and the recovery ( Figure S6 ) . The data indicate that electron densities in the cytosol correlate with the Rh1 levels in the cytosol . Interestingly , after 36 h of dark recovery , the rhabdomeres start to break down in Crag mutant cells , as revealed by Actin staining and TEM analyses ( Figure S6 ) . The data suggest that persistent accumulation of Rh1 in the cytosol leads to the breakdown of rhabdomeres and the degeneration of photoreceptor cells . Since the degradation of Rh1 is unaffected in Crag mutant cells , we hypothesized that the accumulation of Rh1 in the cytosol may be the result of a defect in transport of newly synthesized Rh1 to the rhabdomeres . We therefore stained the photoreceptors with a trans-Golgi marker , Peanut agglutinin [54] , and observed an expansion of the TGN in Crag mutant photoreceptors ( Figure 4D ) . Moreover , a portion of the accumulated Rh1 colocalizes with the trans-Golgi marker . These data , along with the vesicle accumulation observed by TEM , suggest that Crag is required for post-Golgi trafficking of Rh1 to the rhabdomeres . In summary , loss of Crag leads to accumulation of newly synthesized Rh1 in post-Golgi vesicles , a breakdown of rhabdomeres , and , eventually , photoreceptor degeneration . Crag possesses three DENN domains and may serve as a GEF for a Rab protein . To identify its potential target ( s ) , we performed a screen using a collection of 31 Rab dominant negative ( DN ) transgenic lines [55] ( Figure S7 ) . To bypass the requirement of some Rabs for the development of photoreceptor cells , we used Rh1-GAL4 , which is expressed late in photoreceptor formation and in adult photoreceptors , to drive expression of the Rab-DNs . To lower expression levels we also raised the flies at 18°C prior to eclosion . We then aged the flies in a 12-h light/dark cycle or darkness for 21 d and performed ERGs . Expression of only Rab11-DN ( S25N ) causes a light-dependent reduction of ERG amplitude in 3-wk-old flies exposed to light , similar to that in Crag mutants ( Figures 5A , 5B , and S7 ) . To further confirm that loss of Rab11 activity in the adult eye leads to a light-dependent photoreceptor degeneration , we expressed two Rab11 double-stranded RNA constructs [34] , [56] in the adult eye using Rh1-GAL4 and found that they also cause a reduction of ERG amplitude upon light stimulation ( Figure 5A and 5B ) . In contrast , expression of wild-type or constitutively active ( CA ) Rab11 does not cause a phenotype ( Figure 5A and 5B ) . Next , we performed TEM after light and dark exposure of photoreceptors with Rab11 knockdown . Light exposure disrupts the photoreceptor cell morphology in these cells , whereas dark incubation causes no obvious effects after 3 wk ( Figure 5C ) . These data indicate that knockdown of Rab11 leads to a light-induced photoreceptor degeneration similar to that of loss of Crag . Hence , Rab11 is a potential target of Crag . To assess whether Crag and Rab11 physically interact , we generated tagged Crag ( FLAG ) and Rab11 ( HA ) expression constructs and first examined their protein localizations in Drosophila S2 cells . Crag colocalizes with Rab11 when both are co-expressed ( Figure 6A ) . When Rab7 and Crag are co-expressed , the large overlapping punctae observed when Rab11 and Crag are co-expressed are not obvious ( Figure 6B ) . We then performed co-immunoprecipitation ( co-IP ) using an antibody against HA to pull down Rab11 , and found that Crag is co-precipitated with Rab11 , indicating that Crag is a binding partner of Rab11 ( Figure 6C ) . GEFs bind to the GDP-loaded Rabs to promote the release of GDP , whereas binding of GTP to Rabs diminishes the binding of the GEFs . Hence , GEFs have a higher affinity for GDP-bound forms of Rabs than for GTP-bound forms . We therefore performed co-IP between Crag and the DN ( mostly GDP-bound ) or CA ( mostly GTP-bound ) form of Rab11 . The results show that Crag binds preferentially to the Rab11-DN , and weakly to Rab11-CA ( Figure 6C and 6D ) . We next mapped the Rab11 binding domain of Crag by generating a series of deletion constructs . The constructs containing the three DENN domains co-immunoprecipitate with Rab11 , whereas those lacking DENN domains do not co-immunoprecipitate with Rab11 ( Figure 6E ) . These data suggest that Crag may be a GEF for Rab11 . To determine whether Crag possesses GEF activity , we performed in vitro activity assays . Unfortunately , expression of the 180-kDa Crag protein in Escherichia coli produces insoluble protein in inclusion bodies . We therefore purified the protein using a baculovirus expression system in insect cells . For the GEF assay we preloaded the Rab proteins with fluorescence-labeled BODIPY-GDP , and then added excessive unlabeled GDP with or without the potential GEF and measured the release rate of BODIPY-GDP from the Rabs . Since the human homolog of Crag , DENND4A , was shown to exhibit GEF activity against Rab10 [15] , we first performed the GEF assay with Rab10 . As shown in Figure 6F , Crag strongly promotes GDP release from Rab10 , indicating that Crag and DENND4A are functionally conserved and that the purified Crag protein is a GEF in vitro . However , Rab10 is not expressed in the adult eye [57] , and expression of Rab10-DN does not cause a light-dependent degeneration ( Figure 5 ) . Hence , the degeneration phenotypes associated with Crag mutations are unlikely to be caused by defects in Rab10 activation . Next , we performed the GEF assay for Crag against Rab11 and Rab5 . As shown in Figure 6G and 6H , Crag indeed facilitates the GDP dissociation from Rab11 but not from Rab5 . However , the kinetics of the reaction with Rab11 is slow compared to that of Crag against Rab10 . We hypothesized that the slow kinetics is due to the molecular properties of purified Rab11 . We therefore added 10 mM EDTA to GDP-preloaded Rab11 to examine its release kinetics . EDTA absorbs Mg2+ from the Rab proteins and induces a rapid release of GDP [58] . Although the kinetics for GDP release is significantly accelerated , it is relatively slow when compared to EDTA-triggered GDP release of Rab5 ( Figure 6G and 6H ) . We concluded that the kinetics of Rab11 in vitro is generally slow . Since Crag binds to CaM in a calcium-dependent manner [7] , [8] , and since cellular Ca2+ levels increase during photoactivation , we next examined whether CaM regulates Crag activity . In the presence of CaM and Ca2+ , the exchange rate is indeed increased ( Figure 6G ) . Hence , it's possible that Crag activity is enhanced during light stimulation upon a Ca2+ influx in photoreceptor cells . These data , together with our in vivo observations , indicate that Crag is a GEF for Rab11 . We next examined the interactions between Crag and Rab11 in vivo . We first performed immunostaining of Crag and Rab11 . Crag and Rab11 colocalize in punctate structures ( Figure 7A ) , indicating that they may physically interact in vivo . The subcellular localization of Rab proteins often depends on their activation status . When bound to GTP , Rab proteins bind to various effectors that help target the proteins to the proper membrane compartments . Hence , many GEFs regulate the subcellular localization of Rabs . To determine whether Crag is required for the proper subcellular localization of Rab11 , we exposed the flies to 12 h of light stimulation and performed immunostaining of Rab11 and Rh1 . In control photoreceptors , Rab11 colocalizes with Rh1 in numerous punctae , and many of the punctae are in close vicinity to the rhabdomeres , providing further evidence for a role for Rab11 in regulating Rh1 transport . In Crag mutant photoreceptors , Rab11 exhibits a more diffuse pattern , less punctae are observed , and the punctae are rarely localized in the vicinity of rhabdomeres ( Figure 7B ) . These data show that Crag is indeed required for the proper localization of Rab11 in adult photoreceptors . To determine whether Rab11 functions downstream of Crag , we overexpressed a CA form of Rab11 ( Rab11-CA ) , which does not require a GEF for its activation , in Crag mutant cells . If Crag functions as a GEF for Rab11 , expression of Rab11-CA may rescue the defects associated with the loss of Crag . Hence , we aged the flies for 3 wk in a 12-h light/dark cycle and examined photoreceptor function with ERGs . As shown in Figure 7C–7E , Rab11-CA expression in Crag mutant cells partially rescues the ERG phenotypes . We also expressed Rab10-CA in the Crag clones and observed no significant rescue ( data not shown ) . To further determine whether the Rab11-CA is able to also suppress the light-induced morphological defects of photoreceptors associated with the loss of Crag , we performed TEM . The data show that the rhabdomeres are much better preserved upon light stimulation in Crag mutant cells expressing Rab11-CA than in those expressing a GFP control ( Figure 7F ) . In addition , we compared the time course of photoreceptor degeneration using ERG recordings in flies that contained Crag mutant clones and/or expressed a DN mutation for Rab11 in the eyes ( Figure S8 ) . The data show that no significant additive or synergetic effect is observed when Crag and Rab11 are impaired , suggesting that they are involved in the same pathway required for photoreceptor maintenance . In summary , these data indicate that Rab11 functions downstream of Crag genetically , and suggest that Crag regulates Rh1 transport via Rab11 in Drosophila photoreceptors .
During development of photoreceptors , Rh1 and other phototransduction proteins are synthesized in the endoplasmic reticulum and transported to the rhabdomeres to build functional photoreceptors . Some molecular players , including Rab11 and XPORT , have been shown to play a role in this process [32] , [34] . Upon light activation Rh1 is converted to metaRh ( Figure S10A ) . MetaRh is then converted back into Rh1 on rhabdomere membranes via absorption of another photon , allowing the maintenance of Rh1 levels in the rhabdomere [35] . In wild-type photoreceptors , a portion of metaRh is phosphorylated and endocytosed [46] , and it has been proposed that internalization of metaRh promotes the clearance of dysfunctional proteins and serves as a proofreading mechanism ( Figure S10B ) . Internalized Rh1 is then degraded through an endosomal/lysosomal pathway [36] . Obviously , the gradual loss of Rh1 in wild-type photoreceptors leads to the necessity to constitutively synthesize Rh1 and replenish the rhabdomeric pool . This is nicely illustrated with the loss of retinol dehydrogenase ( RDH ) , which is required for the regeneration of the chromophore of Rh1 . Loss of RDH leads to progressive reduction in rhabdomere size and light-dependent photoreceptor degeneration [50] . Our data show that Crag is required to maintain homeostasis of Rh1 upon light stimulation . Loss of Crag leads to Rh1 accumulation in the cytosol and , eventually , retinal degeneration in the presence of light . Mutations in genes that affect metaRh1 turnover , such as Calmodulin and arrestin 2 [52] , [59] , lead to prolonged deactivation time of the photoresponse . Since both ERGs and single-cell recordings of Crag mutant photoreceptors are normal , it is unlikely that Crag is involved in the recycling of metaRh1 to Rh1 . To test whether Crag is required for transport of newly synthesized Rh1 in adult photoreceptors , we exposed the flies to blue light to trigger massive endocytosis and degradation of Rh1 , and then measured the new synthesis and transport of Rh1 back to the rhabdomeres over time . Crag is not required for the synthesis of Rh1 . However , in Crag mutants , the newly synthesized Rh1 accumulates in the cytosol . We propose that Crag is required for the delivery of newly synthesized Rh1 to the rhabdomeres and that loss of Crag leads to a gradual reduction in the size of rhabdomeres and to degeneration of the photoreceptor cells ( Figure S10C and S10D ) . Indeed , the time course and morphological features of degeneration associated with loss of Crag are very similar to the phenotypes observed in RDH mutants , further supporting that Crag is involved in the Rh1 synthesis/delivery pathway . Rab11 has been implicated in various intracellular membrane trafficking processes . Its diverse functions in different membrane compartments are mediated through its downstream effectors in a context-specific manner; many of these functions have been identified in previous studies [60] . However , GEFs for Rab11 in any context have not yet been identified . Our in vivo and in vitro data provide compelling evidence that Crag is a GEF for Rab11 . First , in Drosophila S2 cells , Crag colocalizes and physically interacts with Rab11 . Second , Crag preferably binds to the GDP-bound form of Rab11 , and the DENN domains are required for binding . Third , Crag is required for the proper localization of Rab11 in photoreceptors upon light stimulation . Fourth , loss of Crag or Rab11 leads to a similar light-induced photoreceptor degeneration . Fifth , expression of Rab11-CA partially rescues the degeneration caused by Crag mutations . Finally , an in vitro GEF assay shows that Crag facilitates the release of GDP from Rab11 . It has been previously established that Rab11 is essential for photoreceptor cell development and Rh1 transport during pupal stages [34] . However both rhabdomere morphology and Rh1 localization are normal in Crag clones in newly eclosed flies . Similarly , initial deposition of TRP is also not affected by Crag mutations , in agreement with previous findings that Rh1 and TRP are co-transported to the rhabdomeres during their development [32] . Interestingly , cytosolic localization of TRP is not observed in Crag mutant photoreceptor cells exposed to light , suggesting that during light stimulation , Rh1 and TRP dynamics are distinct . Indeed , internalization of TRP upon light stimulation has not been reported in previous studies . Our data therefore indicate that other GEFs must exist for Rab11 during photoreceptor development , and that Crag is specifically required for Rab11 GDP/GTP exchange during light activation in adult flies . In addition , Crag may function as a GEF for Rab10 in other processes and cells , such as polarized deposition of basement membrane proteins in follicle cells . The biochemical assay shows that the kinetics of Crag GEF activity is slow when compared to the GEF activity of other DENN-domain-containing proteins such as the Rab35 GEF [14] . Crag exhibits GEF activity against Rab10 with much faster kinetics than against Rab11 , indicating that the slow kinetics may be due to properties of Rab11 . This is further supported by the slow kinetics of EDTA that triggers GDP release of Rab11 . It's possible that the GDP/GTP exchange of Rab11 requires other co-factors besides its GEF , as , for example , documented for Rab6 [61] , [62] . CaM is a ubiquitously expressed calcium sensor [63] . In the Drosphila photoreceptor cells , photoactivation leads to influx of Ca2+ and activation of CaM . It has been shown that CaM is required for the termination of the photoresponse in several steps , including TRP inactivation and conformational change of metaRh [59] , [64] . Crag contains a CaM binding site and interacts with CaM in a calcium-dependent manner [7] , [8] . In our in vitro GEF assay , the presence of CaM and Ca2+ indeed enhances the GEF activity of Crag . Hence , it is possible that a light-induced increase of intracellular Ca2+ level enhances Crag activity via CaM binding . The activation of Crag/Rab11 then may serve to replenish rhabdomeric Rh1 , whose loss is also induced by light stimulation . In vertebrate rod cells , polarized transport of Rh is mediated by post-Golgi vesicles that bud from the TGN and fuse with the base of the outer segment [65] , [66] . Rab11 has been detected on rhodopsin-bearing post-Golgi vesicles in photoreceptors [67] , [68]; however , it has not yet been shown that Rab11 is required for Rh trafficking . DENND4 proteins are highly similar to Crag . Here we showed that expression of the UAS–human DENND4A construct not only rescues the lethality but also rescues the light-induced photoreceptor degeneration caused by loss of Crag ( Figure S9 ) , showing that the molecular function of DENND4A is also conserved . Moreover , three different subtypes of Usher syndrome , an inherited condition characterized by hearing loss and progressive vision loss , have been mapped to the vicinity of the DENND4A locus at 15q22 . 31 [69]–[71] . Hence , DENND4A may also function through Rab11 in human photoreceptors , and loss of DENND4A may lead to photoreceptor degeneration .
ERG recordings were performed as previously described [38] . In brief , adult flies were glued to a glass slide , a recording probe was placed on the surface of the eye , and a reference probe was inserted in the thorax . A 1-s flash of white light was given , and the response was recorded . Single-photoreceptor recordings were performed as previously described [72] . In brief , a small hole was cut on the cornea of the eye , and the hole was sealed by Vaseline . A reference probe was placed at the back of the head , and a recording probe was inserted into the retina through the previously cut hole . The membrane potential was monitored by AXOCLAMP-2B ( Axon Instruments ) . When the membrane potential dropped to below −60 mV , the fly was given a 10-ms white light stimulation ( white LED , 7 , 000 mcd , super bright LEDs ) , and the response was recorded . Electron microscopy of the photoreceptor and the lamina was performed as described [73] . In brief , fly heads were dissected and fixed at 4°C in 4% paraformaldehyde , 2% glutaraldehyde , 0 . 1 M sodium cacodylate , and 0 . 005% CaCl2 ( pH 7 . 2 ) overnight , postfixed in 1% OsO4 for 1 h , dehydrated in ethanol and propylene oxide , and then embedded in Embed-812 resin ( Electron Microscopy Sciences ) . Thin sections ( ∼50 nm ) of the photoreceptor and lamina were stained in 4% uranyl acetate and 2 . 5% lead nitrate , and TEM images were captured using a transmission electron microscope ( model 1010 , JEOL ) with a digital camera ( US1000 , Gatan ) . For quantification , the sizes of rhabdomeres are determined in Image J . For cross-sections , fly heads were bisected , fixed in 4% paraformaldehyde for 3 h , dehydrated in acetone , embedded in LR white resin ( Polysciences ) , and sectioned . Immunostaining was performed as described by [51] . For whole mount staining of fly heads , heads were fixed in 4% formaldehyde upon removal of the proboscis . The photoreceptors were dissected and fixed for 15 min . Standard immunostaining procedures were then performed , as previously described [74] . Images were obtained with a Zeiss LSM 710 confocal microscope . Antibodies were as follows: rat anti-Crag [8] , 1∶200; mouse monoclonal anti-Rab11 ( BD Biosciences ) , 1∶20; rabbit anti-Rab11 [34] , 1∶1 , 000; mouse anti-Rh1 [75] , 1∶50; rabbit anti-TRP [76] , 1∶100; rabbit anti-InaD [45] , 1∶200; rabbit anti-Arr2 [52] , 1∶200; biotin-conjugated Peanut agglutinin ( Vector Labs ) , 1∶1 , 000; Alexa 488–conjugated phalloidin ( Invitrogen ) , 1∶200; and Alexa 405– , Alexa 488– , Cy3- , or Cy5-conjugated secondary antibodies ( Jackson ImmunoResearch ) , 1∶200 . Flies were kept in a box with a blue LED ( 465 nm ) light panel ( ∼1 , 000 lux ) for 6 h with or without 24 h recovery in dim white light . For Western blots , fly heads were separated , homogenized , and incubated with SDS loading buffer ( 50 mM Tris-HCl [pH 6 . 8] , 2% SDS , 10% glycerol , 1% β-mercaptoethanol , 12 . 5 mM EDTA , and 0 . 02% bromophenol blue ) for 20 min at room temperature . For dot blots , fly heads were homogenized in SDS buffer with 1 M urea , as previously described [36] . Sample buffer was applied to natural cellulose membrane and air dried , followed by antibody detection . A polyclonal rabbit anti-Rh1 antibody ( 1∶2 , 000 ) was used to detect Rh1 [34] . S2 cells were cultured in Schneider's media with 10% fetal bovine serum at room temperature and transfected using Lipofectamin LTX ( Invitrogen ) . For immunostaining , mouse anti-FLAG ( M2 , Sigma ) and rat anti-HA ( Roche ) antibodies were used to detect Crag ( FLAG ) and Rab ( HA ) proteins . For co-IP , cells were harvested 40 h after infection and lysed with lysis buffer ( 50 mM Tris-HCl [pH 8 . 0] , 100 mM NaCl , 1 mM EDTA , 1% Nonidet P-40 , and Complete Protease Inhibitor Cocktail Tablets [Roche] ) . For comparing the binding affinity of Rab11-DN and Rab11-CA with Crag , EDTA was removed from the lysis buffer . Then the cell lysates were incubated with anti-HA affinity gel ( Sigma ) for 3 h in lysis buffer at 4°C . The anti-HA gel was pelleted and analyzed by Western blot using anti-HA ( Roche ) , 1∶2 , 000 , or anti-FLAG ( M2 , Sigma ) , 1∶1 , 000 , followed by HRP-conjugated secondary antibodies ( Jackson ImmunoResearch ) , 1∶5 , 000 . Crag cDNA tagged with GST ( GE Healthcare ) was cloned into a pOPINJ vector [77] . The construct was transfected into SF9 cells ( Invitrogen ) , along with Bsu36I-digested BacPAK6 DNA . Recombined viruses were harvested from the medium and amplified with a second round of infection . To express Crag protein , Hi-5 cells ( Invitrogen ) were infected with the virus and cultured for 40 h before they were harvested and lysed . Crag protein was then purified from the cell lysates with glutathione sepharose 4B ( GE Healthcare ) . Rab5 , Rab10 , Rab11 , and CaM proteins were generated using the same protocol . GEF assays were performed as described previously [61] . For preloading with GDP , Rab5 , Rab10 , and Rab11 were incubated in 110 mM NaCl , 50 mM Tris-HCl ( pH 8 . 0 ) , 1 mM EDTA , 0 . 8 mM DTT , 0 . 005% Triton X-100 , and 50 µM BODIPY-GDP ( Invitrogen ) for 60 min at 30°C . 10 mM MgCl2 was then added to stop the reaction . 0 . 1 uM preloaded Rab proteins were then incubated with or without 0 . 05 µM Crag in a 110 mM NaCl , 50 mM Tris-HCl ( pH 8 . 0 ) , 12 mM MgCl2 , 0 . 8 mM DTT , and 2 mM GDP solution . To test whether CaM regulates Crag function , 0 . 05 µM CaM and 1 mM CaCl2 were added to the above reaction . As a positive control , preloaded Rab11 was incubated with 10 mM EDTA in the above solution . The fluorescence intensity was recorded automatically by a FLUOstar OPTIMA plate reader ( BMG Labtech ) every 30 s over a 2-h period . | Animals sense light through receptors called Rhodopsins . These proteins are typically localized to stacked membranes in photoreceptors . In flies , upon light exposure , Rhodopsin undergoes conformational changes and becomes active as metarhodopsin . Metarhodopsin then initiates a signaling cascade that activates the photoreceptor cell . To deactivate the light response , metarhodopsin is converted back into Rhodopsin by absorption of another photon of light . Under certain conditions , metarhodopsin cannot be converted back to Rhodopsin , and it is then endocytosed and degraded . Rhodopsin then needs to be synthesized and delivered back to the membrane stacks . Here , we show that the Calmodulin-binding protein Crag is required for the delivery of newly made Rhodopsin to the membrane stacks . Loss of Crag leads to the accumulation of Rhodopsin in the cytosol , followed by shrinkage of membrane stack volume , and , eventually , photoreceptor cell degeneration . We also show that Crag activates a target protein , Rab11 , which mediates the vesicular transport of Rhodospin to the membrane . Finally , we document that the human homolog of Crag , DENND4A , is able to rescue the loss of Crag in flies , suggesting that DENND4A functions in a similar process in vertebrates . | [
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"cel... | 2012 | Crag Is a GEF for Rab11 Required for Rhodopsin Trafficking and Maintenance of Adult Photoreceptor Cells |
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